r/EPA
            United States
            Environmental Protection
            Agency
               Office of Research and
               Development
               Washington DC 20460
 EPA/600/P-95/001aF
 April 1996
Air Quality
Particulate Matter
for
            Volume I  of III

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                                     EPA/600/P-95/001aF
                                     April 1996
 Air Quality Criteria for
    Particulate  Matter
        Volume I of III
                  P-otection
National Center for Environmental Assessment
    Office of Research and Development
   U.S. Environmental Protection Agency
    Research Triangle Park, NC 27711
                                     Printed on Recycled Paper

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                                   DISCLAIMER

     This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication.  Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
                                        I-ii

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                                     PREFACE

     On April 30, 1971 (Federal Register, 1971), in accordance with the Clean Air Act
(CAA) Amendments of 1970, the U.S. Environmental Protection Agency (EPA) promulgated
the original primary and secondary National Ambient Air Quality Standard (NAAQS) for
paniculate matter (PM).  The reference method for measuring attainment of these standards
was the "high-volume" sampler (Code of Federal Regulations, 1977), which collected PM up
to a nominal size of 25 to 45 /xm (so-called "total suspended paniculate," or "TSP").  Thus,
TSP was the original indicator for the PM standards.  The primary standards for PM,
measured as TSP, were 260 /*g/m3, 24-h average not to be exceeded more than once per
year, and 75 jug/m3, annual geometric mean.  The secondary  standard was 150 ^g/m3, 24-h
average not to be exceeded more than once per year.
     In accordance with the CAA Amendments of 1977, the  U.S. EPA conducted a re-
evaluation of the scientific data for PM, resulting in publication of a revised air quality
criteria document (AQCD) for PM in December  1982 and a later Addendum to that
document in 1986.  On July 1,  1987, the U.S. EPA published final  revisions to the NAAQS
for PM.  The principle revisions to the 1971 NAAQS included (1) replacing TSP as  the
indicator for the ambient standards with a new indicator that includes particles  with an
aerodynamic diameter less than or equal to a nominal 10 jum  ("PM10"), (2) replacing the
24-h primary TSP standard with a 24-h PM10 standard of 150 /ng/m3, (3) replacing the
annual primary TSP standard with an annual PM10 standard of 50 ptg/m3, and (4) replacing
the secondary TSP standard with 24-h and annual PM10 standards identical in all respects to
the primary standards.
     The present PM AQCD has been prepared in accordance with the CAA, requiring the
EPA Administrator periodically to review and revise, as appropriate, the criteria and
NAAQS for listed criteria pollutants.  Emphasis has been place on the presentation and
evaluation of the latest available dosimetric and health effects data; however, other scientific
data are also presented to provide information on the nature,  sources, size distribution,
measurement, and concentrations of PM in the environment and contributions of ambient PM
to total human exposure.  This document is comprised of three volumes,  with the present one
(Volume I) containing Chapters 1 through 7.
                                         I-iii

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                                 PREFACE (cont'd)

     This document was prepared by U.S. EPA's National Center for Environmental
Assessment-RTP, with assistance by scientists from other EPA Office of Research and
Development laboratories (NERL; NHEERL) and non-EPA expert consultants. Several
earlier drafts of the document were reviewed by experts from academia, various U.S.
Federal and State government units, non-governmental health and environmental
organizations, and private industry.  Several versions of this AQCD have also been reviewed
in public meetings by the Agency's Clean Air Scientific Advisory Committee (CASAC).
The National Center for Environmental Assessment (formerly the Environmental Criteria and
Assessment Office) of the U.S. EPA's Office of Research and Development acknowledges
with appreciation the valuable contributions made by the many authors, contributors, and
reviewers, as well as the diligence of its staff and contractors in the preparation of this
document.
                                         I-iv

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                  Air Quality Criteria for Participate Matter


                         TABLE OF CONTENTS


                               Volume I

 1. EXECUTIVE SUMMARY  	1-1

 2. INTRODUCTION	2-1

 3. PHYSICS AND CHEMISTRY OF PARTICULATE MATTER	3-1

 4. SAMPLING AND ANALYSIS METHODS FOR PARTICULATE MATTER
   AND ACID DEPOSITION  	4-1

 5. SOURCES AND EMISSIONS OF ATMOSPHERIC PARTICLES  	5-1

 6. ENVIRONMENTAL CONCENTRATIONS	6-1
   Appendix 6A:  Tables of Chemical Composition of Paniculate Matter	6A-1

 7. HUMAN EXPOSURE TO PARTICULATE MATTER:  RELATIONS TO
   AMBIENT AND INDOOR CONCENTRATIONS	7-1


                               Volume II

 8. EFFECTS ON VISIBILITY AND CLIMATE	8-1

 9. EFFECTS ON MATERIALS	9-1

10. DOSIMETRY OF INHALED PARTICLES IN THE RESPIRATORY
   TRACT	10-1
   Appendix 10A:  Prediction of Regional Deposition in the Human
                Respiratory Tract Using the International Commission
                on Radiological Protection Publication 66 Model	10A-1
   Appendix 10B:  Selected Model Parameters	10B-1
   Appendix IOC:  Selected Ambient Aerosol Particle Distributions  	10C-1

11. TOXICOLOGICAL STUDIES OF PARTICULATE MATTER	11-1
                                  I-v

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                  Air Quality Criteria for Particulate Matter


                       TABLE OF CONTENTS (cont'd)


                               Volume III

12. EPIDEMIOLOGY STUDIES OF HEALTH EFFECTS ASSOCIATED
   WITH EXPOSURE TO AIRBORNE PARTICLES/ACID AEROSOLS  	12-1

13. INTEGRATIVE SYNTHESIS OF KEY POINTS: PARTICULATE
   MATTER EXPOSURE,  DOSIMETRY, AND HEALTH RISKS  	13-1

   Appendix 13A: References Used To Derive Cell Ratings in the
                Text Tables 13-6 and 13-7 for Assessing Qualitative Strength
                of Evidence for Particulate Matter-Related Health Effects  .... 13A-1
                                  I-vi

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                           TABLE OF CONTENTS
LIST OF TABLES  	I-xix
LIST OF FIGURES	I-xxvii
AUTHORS, CONTRIBUTORS, AND REVIEWERS  	I-xlvii
U.S. ENVIRONMENTAL PROTECTION AGENCY SCIENTIFIC ADVISORY
BOARD, CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE   	I-lv
U.S. ENVIRONMENTAL PROTECTION AGENCY PROJECT TEAM
 FOR DEVELOPMENT OF AIR QUALITY CRITERIA FOR
 PARTICULATE MATTER	Mix
1.   EXECUTIVE SUMMARY  	1-1
    1.1   INTRODUCTION 	1-1
         1.1.1    Purpose of the Document  	1-1
         1.1.2    Organization of the Document  	1-1
    1.2   AIR QUALITY AND EXPOSURE ASPECTS	1-2
         1.2.1    Chemistry and Physics of Atmospheric Particles  	1-2
         1.2.2    Sources of Airborne Particles in the United States	1-3
         1.2.3    Atmospheric Transport and Fate of Airborne Particles	1-5
         1.2.4    Airborne Particle Measurement Methods	1-5
         1.2.5    Ambient U.S. Paniculate Matter Concentrations:
                 Regional Patterns and Trends	1-7
         1.2.6    Human Particulate Matter Exposure	1-8
    1.3   DOSIMETRY	1-10
    1.4   PARTICULATE MATTER HEALTH EFFECTS  	  1-11
         1.4.1    Epidemiology Findings	1-12
         1.4.2    Toxicology Findings  	1-14
         1.4.3    Population Groups at Risk	  1-16
    1.5   WELFARE EFFECTS	1-17
         1.5.1    Visibility Effects	1-18
         1.5.2    Climate Change	1-19
         1.5.3    Materials Damage	1-19
    1.6   KEY CONCLUSIONS	1-20

2.   INTRODUCTION	2-1
    2.1   LEGISLATIVE REQUIREMENTS	2-1
    2.2   REGULATORY BACKGROUND  	2-2
    2.3   SCIENTIFIC BASIS FOR  THE EXISTING PARTICULATE MATTER
         STANDARDS	2-4
         2.3.1    Rationale for the Primary Standards	2-4
         2.3.2    Pollutant Indicator	2-5
         2.3.3    Averaging Time and Form of the Standards	2-7
                 2.3.3.1  24-Hour  Standard  	2-7
                 2.3.3.2  Annual Standard  	2-7
                                   I-vii

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                          TABLE OF CONTENTS (cont'd)
                                                                              Page

          2.3.4   Level of the Standards	2-8
                  2.3.4.1  Assessment of the Quantitative Epidemiological
                          Studies	2-8
                  2.3.4.2  Identification of Margin of Safety Considerations  	2-9
                  2.3.4.3  24-Hour Standard  	2-10
                  2.3.4.4  Annual Standard	2-15
          2.3.5   Welfare Effects  	2-17
    2.4   TOPICS/ISSUES OF CONCERN FOR CURRENT CRITERIA
          DEVELOPMENT  	2-17
          2.4.1   Air Quality and Exposure	2-18
                  2.4.1.1  Physics and Chemistry of Atmospheric Aerosols	2-18
                  2.4.1.2  Measurement Methodology	2-20
                  2.4.1.3  Ambient Levels	2-20
                  2.4.1.4  Cut Points	2-20
                  2.4.1.5  Exposure	2-21
          2.4.2   Health Effects  	2-22
                  2.4.2.1  Respiratory Tract Dosimetry	2-23
                  2.4.2.2  Epidemiology Studies  	2-24
                  2.4.2.3  Toxicology of Paniculate Matter Constituents  	2-26
                  2.4.2.4  Sensitive Groups	2-27
          2.4.3   Welfare Effects  	2-28
                  2.4.3.1  Effects on Materials	2-28
                  2.4.3.2  Visibility Effects	2-28
                  2.4.3.3  Climate Change	2-29
                  2.4.3.4  Vegetation and Ecosystem Effects	2-29
    2.5   DOCUMENT CONTENT AND ORGANIZATION	2-29
    REFERENCES	2-32

3.   PHYSICS AND CHEMISTRY OF PARTICULATE  MATTER	3-1
    3.1   INTRODUCTION  	3-1
          3.1.1   Overview	3-1
          3.1.2   Atmospheric Aerosol Size Distributions	3-4
          3.1.3   Definitions	3-7
                  3.1.3.1  Definitions of Particle Diameter	3-7
                  3.1.3.2  Definitions of Particle Size  Fractions	3-9
                  3.1.3.3  Other Terminology	3-14
          3.1.4   Major Chemical Constituents	3-14
          3.1.5   Chemical Composition and Its Dependence on Particle Size  .... 3-16
          3.1.6   Particle-Vapor Partitioning  	3-18
          3.1.7   Single Particle Characteristics	3-20
          3.1.8   Dry Deposition  	3-21
          3.1.9   Atmospheric Scavenging or Wet Deposition	3-21
    3.2   PHYSICAL PROPERTIES AND PROCESSES   	3-22
          3.2.1   Aerosol Size Distributions	3-22

                                       I-viii

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                       TABLE OF CONTENTS (cont'd)
               3.2.1.1  Particle Size Distribution Functions	3-22
               3.2.1.2  Log-Normal Size Distributions	3-22
               3.2.1.3  Ambient Aerosol Size Distributions	3-23
               3.2.1.4  Coagulation of Spherical Particles	3-23
       3.2.2    Particle  Formation and Growth	3-25
               3.2.2.1  Equilibrium Vapor Pressures	3-25
               3.2.2.2  New Particle Formation	3-26
               3.2.2.3  Particle Growth	3-27
               3.2.2.4  Equilibria with Water Vapor	3-28
               3.2.2.5  Particle Growth in Fogs and Clouds  	3-31
       3.2.3    Resuspension of Particulate Matter   	3-32
               3.2.3.1  Resuspension Mechanics  	3-32
               3.2.3.2  Applied Studies	3-33
               3.2.3.3  Aerodynamic Resuspension	3-34
               3.2.3.4  Mechanical Resuspension	3-34
               3.2.3.5  Physical and Chemical Properties of Resuspended
                       Particles 	3-35
               3.2.3.6  Levels of Production and Transport of
                       Resuspended Aerosols	3-36
       3.2.4    Particle  Removal Mechanisms and Deposition	3-38
3.3    CHEMICAL COMPOSITION AND PROCESSES   	3-41
       3.3.1    Acid Aerosols and Particulate Sulfates  	3-41
               3.3.1.1  Aerosol Acidity	3-41
               3.3.1.2  Sources of Sulfate  	3-43
               3.3.1.3  Gas-Phase Oxidation of Sulfur Dioxide  	3-44
               3.3.1.4  Aqueous-Phase Oxidation of Sulfur Dioxide   	3-45
       3.3.2    Particulate Nitrates  	3-64
               3.3.2.1  Sources	3-64
               3.3.2.2  Major Gas-Phase Reaction	3-64
               3.3.2.3  Major Aqueous-Phase Reaction  	3-65
               3.3.2.4  Other Reaction Mechanisms  	3-66
               3.3.2.5  Ammonium Nitrate Vaporization Equilibria	3-67
               3.3.2.6  Sulfate/Nitrate Interaction	3-68
               3.3.2.7  Ammonium Chloride Vaporization Equilibrium   	3-69
       3.3.3    Carbon-Containing Particulate Matter	3-70
               3.3.3.1  Elemental Carbon  	3-70
               3.3.3.2  Organic Carbon	3-73
               3.3.3.3  Semi-Volatile Organic Compounds	3-83
       3.3.4    Metals and Other Trace Elements  	3-89
3.4    FIELD STUDIES OF TRANSPORT AND TRANSFORMATIONS	3-96
       3.4.1    Field Studies of Transport Processes   	3-97
               3.4.1.1  Field Measurements  Related to Transport Modeling ....  3-99
               3.4.1.2  Field Measurements  Related to Dispersion  Modeling  ..  3-104
       3.4.2    Field Studies of Transformations	3-106

                                    I-ix

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                         TABLE OF CONTENTS (cont'd)
                                                                            Page

                  3.4.2.1 Gas-to-Particle Conversion	3-106
                  3.4.2.2 Field Studies of Water Uptake By Atmospheric
                         Aerosols  	3-118
                  3.4.2.3 Pertinent Results of the Southern California Air
                         Quality Study	3-123
    3.5    DRY DEPOSITION	3-126
          3.5.1    Theoretical Aspects of Dry Deposition	3-126
          3.5.2    Field Studies of Dry Deposition  	3-131
          3.5.3    Measured Deposition Velocities  	3-133
    3.6    WET DEPOSITION	3-135
          3.6.1    Introduction   	3-135
          3.6.2    Field Studies of Wet Deposition	3-137
          3.6.3    Overview of Sulfur Dioxide and Nitrogen Oxide Wet
                  Scavenging	3-143
    3.7    PHYSICAL AND CHEMICAL CONSIDERATIONS IN
          SELECTING A SIZE CUT POINT FOR SEPARATING FINE
          AND COARSE PARTICULATE MATTER  	3-144
          3.7.1    Background	3-146
          3.7.2    Size Measurements  	3-147
          3.7.3    Appropriate Display of Size-Distribution  Data	3-148
          3.7.4    Comparison of Particle-Counting and Particle-Collection
                  Techniques	3-153
          3.7.5    Review of Size-Distribution Data  	3-156
                  3.7.5.1 Early Studies   	3-156
                  3.7.5.2 Recent Work   	3-156
          3.7.6    Intermodal Region	3-162
                  3.7.6.1 Coarse Mode   	3-162
                  3.7.6.2 Fine Mode	3-168
          3.7.7    Conclusions	3-187
    3.8    SUMMARY	3-187
    REFERENCES	3-194

4.   SAMPLING AND ANALYSIS METHODS FOR PARTICULATE MATTER
    AND ACID DEPOSITION  	4-1
    4.1    INTRODUCTION  	4-1
    4.2    SAMPLING FOR PARTICULATE MATTER	4-5
          4.2.1    Background	4-5
          4.2.2    Large Particle Separators  	4-6
                  4.2.2.1 Cutpoint Considerations	4-6
                  4.2.2.2 Total Suspended Particulates	4-11
                  4.2.2.3 Total Inhalable Particles	4-12
                  4.2.2.4 PM10	4-12
          4.2.3    Fine Particle Separators  	4-21
                  4.2.3.1 Cutpoint Considerations	4-21

                                       I-x

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                       TABLE OF CONTENTS (cont'd)
                                                                            Page

               4.2.3.2  Virtual Impactors	4-22
               4.2.3.3  Cyclones    	4-24
               4.2.3.4  Impactors   	4-25
      4.2.4    Sampling Considerations	4-27
               4.2.4.1  Siting Criteria	4-27
               4.2.4.2  Averaging Time/Sampling Frequency   	4-28
               4.2.4.3  Collection Substrates	4-30
               4.2.4.4  Chemical Speciation Sampling	4-31
               4.2.4.5  Data Corrections/Analyses  	4-34
      4.2.5    Performance Specifications  	4-35
               4.2.5.1  Approaches  	4-35
               4.2.5.2  Performance Testing	4-37
      4.2.6    Reference and Equivalent Method Program  	4-41
      4.2.7    Determination of Size Distribution	4-43
               4.2.7.1  Cascade Impactors	4-43
               4.2.7.2  Single Particle Samplers  	4-49
      4.2.8    Automated Sampling	4-51
               4.2.8.1  Smoke Shade  	4-52
               4.2.8.2  Coefficient of Haze	4-54
               4.2.8.3  Tapered Element Oscillating Microbalance* Sampler  . .  . 4-55
               4.2.8.4  Beta Gauge  	4-58
               4.2.8.5  Nephelometer	4-60
      4.2.9    Specialized Sampling	4-65
               4.2.9.1  Personal Exposure Sampling	4-65
               4.2.9.2  Receptor Model Sampling  	4-68
               4.2.9.3  Particle Acidity	4-69
      4.2.10   Measurement Methods Comparisons	4-71
               4.2.10.1 Nitrate	4-71
               4.2.10.2 Carbonaceous  Particulate Matter  	4-75
4.3   ANALYSIS OF PARTICULATE MATTER  	4-75
      4.3.1    Mass Measurement Methods   	4-79
      4.3.2    Physical Analysis	4-80
               4.3.2.1  X-Ray Fluorescence of Trace Elements  	4-81
               4.3.2.2  Particle Induced X-Ray Emission of
                       Trace Elements	4-87
               4.3.2.3  Instrumental Neutron Activation Analysis of
                       Trace Elements	4-90
               4.3.2.4  Microscopy Analysis of Particle Size, Shape,
                       and Composition	4-91
      4.3.3    Wet Chemical Analysis  	4-93
               4.3.3.1  Ion Chromatographic Analysis for Chloride,
                       Nitrate, and Sulfate   	4-94
               4.3.3.2  Automated Colorimetric Analysis for Ammonium,
                       Nitrate, and Sulfate   	4-97

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                        TABLE OF CONTENTS (cont'd)
                 4.3.3.3  Atomic Absorption Spectrophotometric and
                        Inductive Coupled Plasma Atomic Emission
                        Spectrophotometry Analyses for Trace Elements	4-99
         4.3.4   Organic Analysis	4-100
                 4.3.4.1  Analysis of Organic Compounds   	4-100
                 4.3.4.2  Analysis of Organic and Elemental Carbon	4-103
                 4.3.4.3  Organic Aerosol Sampling Artifacts  	4-105
         4.3.5   Methods Validation	4-113
    4.4   BIOAEROSOLS  SAMPLING AND ANALYSIS	4-114
         4.4.1   Analytical Methods	4-114
         4.4.2   Sample  Collection Methods	4-115
    4.5   SUMMARY	4-116
         4.5.1   PM10 Sampling  	4-118
         4.5.2   Fine Particle Sampling	4-119
         4.5.3   Concentration Corrections to Standard Conditions  	4-119
         4.5.4   Performance Versus Design Specifications for Sampling
                 Systems	4-120
         4.5.5   Automated Sampling	4-120
         4.5.6   Particulate Matter Samplers for Special Applications	4-121
    REFERENCES	4-122

5.   SOURCES AND EMISSIONS OF ATMOSPHERIC PARTICLES  	5-1
    5.1   INTRODUCTION  	5-1
    5.2   SOURCES OF PRIMARY PARTICULATE MATTER	5-4
         5.2.1   Wind Erosion and Fugitive Dust	5-4
         5.2.2   Stationary Sources	5-14
         5.2.3   Mobile  Sources  	5-19
         5.2.4   Biomass Burning  	5-25
         5.2.5   Sea-Salt Production and Other Natural Sources of Aerosol	5-27
    5.3   SOURCES OF SECONDARY  PARTICULATE MATTER
         (SULFUR DIOXIDE, NITROGEN OXIDES, AND
         ORGANIC CARBON)	5-29
    5.4   EMISSIONS ESTIMATES  FOR PRIMARY PARTICULATE
         MATTER AND  SULFUR DIOXIDE, NITROGEN OXIDES,
         AND VOLATILE ORGANIC COMPOUNDS IN THE
         UNITED STATES	5-34
    5.5   APPLICATIONS AND LIMITATIONS OF EMISSIONS
         INVENTORIES AND RECEPTOR MODELS	5-47
         5.5.1   Uncertainties in Emissions Estimates  	5-47
         5.5.2   Receptor Modeling Methods   	5-50
         5.5.3   Source Contributions to Ambient Particles Derived
                 by Receptor Models  	5-59
    5.6   SUMMARY AND CONCLUSIONS	5-66
    REFERENCES	5-70

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                          TABLE OF CONTENTS (cont'd)
                                                                             Page

6.   ENVIRONMENTAL CONCENTRATIONS	6-1
    6.1   BACKGROUND, PURPOSE, AND SCOPE	6-1
          6.1.1   Dimensionality and Structuring of the Aerosol Data Space	6-2
          6.1.2   Spatial Pattern and Scales	6-3
          6.1.3   Temporal Pattern and Scales  	6-3
          6.1.4   Space-Time Relationships  	6-4
          6.1.5   Particle Size Distribution  	6-7
          6.1.6   Aerosol Chemical Composition	6-8
    6.2   GLOBAL AND CONTINENTAL SCALE AEROSOL PATTERN	6-9
    6.3   U.S. NATIONAL AEROSOL PATTERN AND TRENDS	6-14
          6.3.1   Nonurban National Aerosol Pattern	6-14
                  6.3.1.1  Nonurban PM2 5 Mass Concentrations	6-15
                  6.3.1.2  Nonurban Paniculate Matter Coarse Mass
                          Concentrations  	6-15
                  6.3.1.3  Nonurban PM]0 Mass Concentrations  	6-17
                  6.3.1.4  PM25/PM10 Ratio at Nonurban Sites	6-20
                  6.3.1.5  Nonurban Fine-Particle Chemistry   	6-20
                  6.3.1.6  Seasonality of the Nonurban Chemistry  	6-25
                  6.3.1.7  Background Concentrations of Particle Mass
                          and Chemical Composition	6-32
          6.3.2   Urban National Aerosol Pattern—Aerometric Information
                  Retrieval System	6-46
                  6.3.2.1  National Pattern and Trend of Aerometric
                          Information Retrieval System PM10	6-48
                  6.3.2.2  Eastern U.S. PM10 Pattern and Trend	6-52
                  6.3.2.3  Western U.S. PM10 Pattern and Trend	6-54
                  6.3.2.4  Short-Term Variability of PM10 Concentrations  	6-57
                  6.3.2.5  Aerometric Information Retrieval System PM2 5
                          Concentrations	6-60
                  6.3.2.6  Other National Surveys	6-60
          6.3.3   Comparison of Urban and Nonurban Concentrations	6-63
    6.4   REGIONAL PATTERNS AND TRENDS	6-67
          6.4.1   Regional Aerosol Pattern in Eastern New York,
                  New Jersey,  and the  Northeast	6-68
                  6.4.1.1  Nonurban Size and Chemical Composition in the
                          Northeast	6-70
                  6.4.1.2  Urban Aerosols in the Northeast  	6-71
          6.4.2   Regional Aerosol Pattern in the Southeast  	6-73
                  6.4.2.1  Nonurban Size and Chemical Composition in the
                          Southeast	6-73
                  6.4.2.2  Urban Aerosols in the Southeast	6-76
          6.4.3   Regional Aerosol Pattern in the Industrial Midwest	6-78
                  6.4.3.1  Nonurban Size and Chemical Composition in the
                          Industrial Midwest	6-81

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                       TABLE OF CONTENTS (cont'd)
               6.4.3.2  Urban Aerosols in the Industrial Midwest	6-82
      6.4.4    Regional Aerosol Pattern in the Upper Midwest   	6-84
               6.4.4.1  Nonurban Size and Chemical Composition in the
                       Upper Midwest	6-84
               6.4.4.2  Urban Aerosols in the Upper Midwest	6-88
      6.4.5    Regional Aerosol Pattern in the Southwest	6-90
               6.4.5.1  Nonurban Size and Chemical Composition in the
                       Southwest   	6-90
               6.4.5.2  Urban Aerosols in the Southwest	6-90
      6.4.6    Regional Aerosol Pattern in the Northwest	6-94
               6.4.6.1  Nonurban Size and Chemical Composition in the
                       Northwest	6-96
               6.4.6.2  Urban Aerosols in the Northwest	6-96
      6.4.7    Regional Aerosol Pattern in Southern California  	6-100
               6.4.7.1  Nonurban Size and Chemical Composition in
                       Southern California  	6-100
               6.4.7.2  Urban Aerosols in Southern California	6-102
6.5   SUBREGIONAL AEROSOL PATTERNS AND TRENDS  	6-105
      6.5.1    Subregional Aerosol Pattern in the Northeast  	6-105
               6.5.1.1  Shenandoah National Park  	6-105
               6.5.1.2  Washington, District of Columbia	6-108
               6.5.1.3  Comparison of Nonurban to Urban Aerosols	6-110
               6.5.1.4  New York City, New York  	6-112
               6.5.1.5  Philadelphia, Pennsylvania	6-116
               6.5.1.6  Whiteface Mountain, New York	6-119
      6.5.2    Subregional Aerosol Pattern in the Southeast  	6-119
               6.5.2.1  Atlantic Coast States	6-119
               6.5.2.2  Texas and Gulf States	6-120
               6.5.2.3  Atlanta	6-124
               6.5.2.4  Great Smoky Mountains	6-124
      6.5.3    Subregional Aerosol Pattern in the Industrial Midwest	6-124
               6.5.3.1  Pittsburgh, Pennsylvania  	6-125
               6.5.3.2  St. Louis, Missouri  	6-128
               6.5.3.3  Chicago, Illinois   	6-132
               6.5.3.4  Detroit, Michigan  	6-134
      6.5.5    Subregional Aerosol Pattern in the Southwest	6-135
               6.5.5.1  El Paso, Texas  	6-135
               6.5.5.2  Phoenix and Tucson, Arizona  	6-137
               6.5.5.3  Grand Canyon National Park	6-140
      6.5.6    Subregional Aerosol Pattern in the Northwest	6-140
               6.5.6.1  South Lake Tahoe  	6-141
               6.5.6.2  Salt Lake City, Utah, Subregion	6-143
               6.5.6.3  Denver, Colorado  	6-145
               6.5.6.4  Northern Idaho-Western  Montana Subregion	6-145

                                    I-xiv

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                          TABLE OF CONTENTS (cont'd)
                                                                             Page

                  6.5.6.5  Washington-Oregon Subregion	6-148
                  6.5.6.6  Other Northwestern Locations	6-151
          6.5.7   Subregional Aerosol Pattern in Southern California  	6-151
                  6.5.7.1  San Joaquin Basin   	6-151
                  6.5.7.2  Los Angeles-South Coast Air Basin-Southeastern
                          Desert Air Basin	6-154
6.6 CHEMICAL COMPOSITION OF PARTICULATE MATTER
    AEROSOLS AT URBAN AND NONURBAN SITES	6-163
6.7 ACID AEROSOLS  	6-168
          6.7.1   Introduction  	6-168
          6.7.2   Geographical Distribution	6-169
          6.7.3   Spatial Variation (Regional-Scale)	6-169
          6.7.4   Spatial Variation (City-Scale)	6-172
          6.7.5   Seasonal Variation  	6-173
          6.7.6   Diurnal  Variation	6-174
          6.7.7   Indoor and Personal Concentrations	6-176
    6.8   NUMBER CONCENTRATION OF ULTRAFINE PARTICLES	6-177
          6.8.1   Introduction  	6-177
          6.8.2   Ultrafine Particle Number-Size Distribution	6-177
          6.8.3   Relation of Particle Number to Particle Mass	6-182
          6.8.4   Conclusion	6-184
    6.9   AMBIENT CONCENTRATIONS OF ULTRAFINE METALS	6-186
          6.9.1   Introduction  	6-186
          6.9.2   Formation of Ultrafine Particles   	6-187
          6.9.3   Techniques for Collecting and Analyzing Ultrafine Metals  .... 6-190
          6.9.4   Observations of Very Fine Metals	6-193
                  6.9.4.1  Stack and Source-Enriched Aerosols	6-193
                  6.9.4.2  Ambient Aerosols   	6-194
          6.9.5   Conclusions	6-205
    6.10  FINE AND COARSE PARTICULATE MATTER
          TRENDS AND PATTERNS	6-206
          6.io.l   Daily  and Seasonal Variability in PM2-5 and PM10	6-207
          6.10.2   Fine and Coarse Paniculate  Matter
                  Trends and Relationships  	6-216
                  6.10.2.1  Visual Range/Haziness	6-216
                  6.10.2.2  Interagency Monitoring of Protected Visual
                            Environments  	6-219
                  6.10.2.3  Philadelphia 	6-221
                  6.10.2.4  Harvard Six-Cities Study	6-223
                  6.10.2.5  Aerometric Information Retrieval System	6-228
                  6.10.2.6  California Sites  	6-228
          6.10.3   Interrelations and Correlations  	6-228
                  6.10.3.1   Upper Range of Concentration for Various
                            Paniculate Matter Size Fractions	6-231

                                       I-xv

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                         TABLE OF CONTENTS (cont'd)
                  6.10.3.2  Relationships Among PM25, PM(10_25), PM10,
                           and Total Suspended Particles in Philadelphia	6-242
                  6.10.3.3  Correlations Between PM2 5, PM(10_2 5), and PM10 .. 6-249
                  6.10.3.4  Fine Fractions	6-249
    6.11   SUMMARY AND CONCLUSIONS	6-251
    REFERENCES	6-259

    APPENDIX 6A: TABLES OF CHEMICAL COMPOSITION OF
                   PARTICULATE MATTER	6A-1

7.   HUMAN EXPOSURE TO PARTICULATE MATTER: RELATIONS
    TO AMBIENT AND INDOOR CONCENTRATIONS  	7-1
    7.1    INTRODUCTION  	7-1
          7.1.1    Ambient Paniculate Matter Concentration as a Surrogate
                  for Paniculate Matter Dosage	7-3
          7.1.2    General Concepts for Understanding Paniculate Matter
                  Exposure and Microenvironments  	7-5
          7.1.3    Summary of State-of-Knowledge in the 1982 Criteria
                  Document	7-9
    7.2    INDOOR CONCENTRATIONS AND SOURCES OF
          PARTICULATE MATTER	7-10
          7.2.1    Introduction   	7-10
          7.2.2    Concentrations of Particles in Homes and Buildings	7-12
                  7.2.2.1  Particle Concentrations in Homes: Large-Scale
                         Studies in the United States  	7-12
                  7.2.2.2 Other Studies of Paniculate Matter Indoors	7-44
                  7.2.2.3  Personal Exposures to Environmental
                         Tobacco Smoke	7-55
                  7.2.2.4 The Fraction of Outdoor Air Particles
                         Penetrating Indoors  	7-56
                  7.2.2.5  Studies of Particulate Matter in Buildings	7-61
          7.2.3    Indoor Air Quality Models and Supporting Experiments	7-67
                  7.2.3.1  Mass Balance Models	7-67
          7.2.4    Summary of Indoor Particulate Matter Studies  	7-68
          7.2.5    Bioaerosols	7-70
                  7.2.5.1  Plant Aerosols	7-71
                  7.2.5.2 Animal Aerosols  	7-74
                  7.2.5.3  Fungal Aerosols  	7-76
                  7.2.5.4 Bacterial Aerosols  	7-78
                  7.2.5.5  Viral Aerosols  	7-79
                  7.2.5.6 Ambient and Indoor Air Concentrations of
                         Bioaerosols   	7-80
                                     I-xvi

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                      TABLE OF CONTENTS (cont'd)
7.3   DIRECT METHODS OF MEASUREMENT OF HUMAN
      PARTICULATE MATTER EXPOSURE BY PERSONAL
      MONITORING	7-81
      7.3.1   Personal Monitoring Artifacts	7-81
      7.3.2   Characterization of Paniculate Matter Collected by
              Personal Monitors	7-82
      7.3.3   Microscale Variation and the Personal Cloud Effect	7-82
7.4   NEW LITERATURE ON PARTICLE EXPOSURES SINCE 1981	7-83
      7.4.1   Personal Exposures in U.S. Studies	7-83
              7.4.1.1  The Particle Total Exposure Assessment
                      Methodology Study  	7-88
      7.4.2   Personal Exposures in International Studies   	7-97
              7.4.2.1  Personal Exposures in Tokyo (Itabashi Ward),
                      Japan	7-99
              7.4.2.2  Personal Exposures in the Netherlands	7-100
              7.4.2.3  Reanalysis of Phillipsburg, New Jersey, Data  	7-103
              7.4.2.4  Overview of Comparison of Personal Exposure to
                      Ambient Paniculate Matter Concentrations 	7-105
      7.4.3   Personal Exposures to Constituents of Paniculate Matter	7-105
7.5   INDIRECT MEASURES OF EXPOSURE  	7-109
      7.5.1   Time-Weighted-Averages of Exposure  	7-109
      7.5.2   Personal Exposure Models Using Time-Weighted Averages of
              Indoor and Outdoor Concentrations of Paniculate Matter   .... 7-110
7.6   DISCUSSION	7-114
      7.6.1   Relation of Individual Exposures to Ambient
              Concentration  	7-114
      7.6.2   Relation of Community Paniculate Matter Exposure to
              Ambient Paniculate Matter Concentration  	7-119
              7.6.2.1  Methodology  	7-120
      7.6.3   U.S. Environmental Protection Agency Analysis  of Data
              Sets  	7-134
              7.6.3.1  Tokyo, Japan, Data Set	7-134
              7.6.3.2  Phillipsburg, New Jersey, Data Set	7-134
              7.6.3.3  Beijing, China, Data Set  	7-138
              7.6.3.4  Riverside, California, Data Set  	7-139
              7.6.3.5  Azusa, California, Data Set  	7-140
      7.6.4   Discussion of Statistical Analyses:  Mean Personal  Exposure
              Monitor Versus Mean SAM	7-144
7.7   IMPLICATIONS FOR PARTICULATE MATTER AND
      MORTALITY MODELING  	7-149
      7.7.1   Relative Toxicity  of Ambient Paniculate Matter and
              Indoor Paniculate Matter  	7-151
      7.7.2   Summary: Linkage of Ambient Concentrations of Paniculate
              Matter to Personal Exposures to Paniculate Matter	7-154

                                  I-xvii

-------
                  TABLE OF CONTENTS (cont'd)
7.8   SUMMARY AND CONCLUSIONS	7-160
REFERENCES	7-166
                           I-xviii

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                                  LIST OF TABLES
Number                                                            -             Page

3-1       Lognormal Parameters for Ambient Aerosols	3-24

3-2       Henry's Law Coefficients of Some Atmospheric Gases Dissolving in
          Liquid Water	3-47

3-3       Some Secondary Organic Compounds Identified in Ambient Particles in
          Urban Air	3-77

3-4       Predicted Percent Contribution to Secondary Organic Aerosol
          Concentrations at Los Angeles	3-79

3-5       Amount of Secondary Aerosol Produced in a Typical Los Angeles Smog
          Episode According to Functional Groups  	3-79

3-6       Reactivity Scale for the Electrophilic Reactions of Polycyclic Aromatic
          Hydrocarbons	3-82

3-7       Values of Log P£ f°r Various Polycyclic Aromatic Hydrocarbons
          at 20  °C	3-87

3-8       mp Values for Polycyclic Aromatic Hydrocarbons Sorbing to UPM in
          Osaka, Japan  	3-87

3-9       Effects of Three Types of Artifacts on Volume-Averaged Values of 0
          Measured Using a Filter/Adsorbent Sampler	3-88

3-10      Concentration Ranges of Various Elements Associated with Particulate
          Matter in the Atmosphere  	3-91

3-11      Compounds Observed in Aerosols by a Roadway at Argonne National
          Laboratory	3-93

3-12      Compounds Observed in Aerosols in a Forested Area, State College,
          Pennsylvania  	3-93

3-13      Recent Field Studies of a-Mesoscale Transport and Trajectory Model  . . .  3-102

3-14      Scavenging Ratios	3-142

3-15      Comparison of Ambient Fine- and Coarse-Mode Particles	3-145

3-16      Relative Humidity of Deliquescence and Crystallization for Several
          Atmospheric Salts   	3-170
                                        I-xix

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                              LIST OF TABLES (cont'd)
Number                                                                         Page

3-17      Summary of Hygroscopic Growth Factors	3-175

3-18      Comparison of Sulfate Concentration and Mass Mean Diameters of
          Aerosols for Days with Higher and Lower Relative Humidity   	3-181

4-1       U.S. Environmental Protection Agency-Designated Reference and
          Equivalent Methods for PM10	4-44

4-2       Instrumental Detection Limits for Particles on Filters	4-77

4-3       Minimum Detectable Limits for X-Ray Fluorescence Analysis of
          Air Filters	4-84

4-4       Instrumental Neutron  Activation Analysis Counting Scheme and
          Elements Measured   	4-91

4-5       Overview of Analytical Methods  	4-114

5-1A      Constituents of Atmospheric Fine Particles and Their Major Sources	5-2

5-1B      Constituents of Atmospheric Coarse Particles and Their Major Sources	5-3

5-2       Average Abundances  of Major Elements in Soil and Crustal Rock	5-8

5-3       Composition of Fine Particles Released by Various Stationary Sources
          in the Philadelphia Area  	5-15

5-4       Fractional  Organic and Elemental Carbon Abundances in Motor Vehicle
          Emissions   	5-21

5-5       Phoenix PM2 5 Motor Vehicle Emissions Profiles	5-22

5-6       Nationwide Primary PM10 Emission Estimates from Mobile and Stationary
          Sources, 1985 to 1993  	5-36

5-7       Miscellaneous and Natural Source Primary PM10 Emission Estimates,
          1985 to  1993   	5-37

5-8       Nationwide Sulfur Oxides Emission Estimates, 1984 to 1993	5-38

5-9       Nationwide Nitrogen  Oxide Emission Estimates,  1984 to 1993	5-39

5-10      Nationwide Volatile Organic Compound Emission Estimates, 1984 to
          1993  	5-40

                                        I-xx

-------
                              LIST OF TABLES (cont'd)
Number

5-11      Projected Trends in Paniculate Matter, Sulfur Dioxide, and Oxides
          of Nitrogen Emissions	5-43

5-12      Receptor Model Source Contributions to PM10	5-62

6-1       Spatial Regions and Scales	6-3

6-2       Annual Average Concentrations and Chemical Composition from Interagency
          Monitoring of Protected Visual Environments Monitoring Sites	6-38

6-3       Annual Summer and Winter Concentrations from Interagency Monitoring
          of Protected Visual Environments Monitoring Sites	6-40

6-4       Summary of Annual and Seasonal Average Ranges of Background
          Concentration Levels of PM10 and PM2 5	6-44

6-5       Maximum SO 4 and Hydrogen Ion Concentrations Measured at North
          American Sites	6-170

6-6       Regulated Metals and the Volatility Temperature  	6-188

6-7       Composition of the Aerosols Present at Grand Canyon National Park in
          the Summer of 1984 for the Sulfate Episodes of August 15 and 16   	6-197

6-8       Measurements of Fine and Very Fine Metals	6-198

6-9       Measurements of Fine and Very Fine Metals (Lead and Nickel)	6-201

6-10      Comparison of Selected Species at Shenandoah National Park;
          Washington, District of Columbia; San Gorgonio Wilderness, California;
          and Grand Canyon National Park During Summer 1993	6-204

6-11      Maximum Value; Second, Third, Fourth, and Fifth Highest Values;
          98th and 95th Percentile Values; 50th Percentile Value; and the
          Difference Between the Median and the Maximum Values and the Number
          of Measurements Available  from Eight California Air Resources
          Board Sites:  PM2 5, PM(10_2.5), and PM10  	6-240

6-12      Maximum Value; Second, Third, Fourth, and Fifth Highest Values;
          98th and 95th Percentile Values; 50th Percentile Value; and the
          Difference Between the Median and the Maximum Values and the Number
          of Measurements Available  for Sites in Philadelphia from 1979 to 1995:
          PM2 5, PM(10.2 5), PM10,  and Total Suspended Particles	6-241
                                        I-xxi

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                             LIST OF TABLES (cont'd)
Number                                                                        Page

6-13      Relationships Between PMX (PM2 5 or PM10) and Total Suspended
          Particles as a Function of Total Suspended Particle Concentration
          Levels for Several Sites in Philadelphia:  Ratio of PMX to Total
          Suspended Particles and Coefficient of Determination  	6-248

6-14      Means and Standard Deviations for PM2 5, PM10_2 5), and PM10 and
          Coefficients of Determination Between Pairs for Eight California
          Air Resources Board Sites During the Period 1989 to 1990	6-250

6-15      Means and Standard Deviations for PM2 5, PM(10_2 5), PM10, and
          Total Suspended Particles and Coefficients of Determination
          Between Pairs for Several  Sites in Philadelphia During Periods
          from 1979 to 1995	6-251

6-16      PM25/PM10 (Fraction of PM10 Contributed by PM2 5) .  .	6-252

6A-la     Summary of PM2 5 Studies	6A-2

6A-lb     Summary of Coarse Fraction Studies	6A-3

6A-lc     Summary of PM10 Studies	6A-4

6A-2a     PM2 5 Composition for the United States  	6A-13

6A-2b     Coarse Particle Composition for the United States	6A-21

6A-2c     PM10 Composition for the United  States	6A-29

6A-3      Selected Ratios of Paniculate Matter Composition by Geographic
          Region	6A-37

6A-4a     Site-to-Site Variability of PM2 5 Concentrations 	6A-38

6A-4b     Site-to-Site Variability of PM10 Concentrations  	6A-39

7-1       Concentrations of Particles in Homes of Children Participating in the
          Harvard Six-City Study	7-14

7-2a      Reconstructed Source Contributions to Indoor PM2 5 Mass for Steubenville,
          Ohio  	'	7-19

7-2b      Reconstructed Source Contributions to Indoor PM2 5 Mass for Portage,
          Wisconsin	7-19
                                        I-xxii

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                              LIST OF TABLES (cont'd)
Number                                                                         Page

7-3       Weighted Summary Statistics by New York County for Respirable
          Suspended Paniculate Concentrations  	7-21

7-4       Weighted Analysis of Variance of Respirable Suspended Paniculate
          Concentrations in the Main Living Area of Homes Versus Source
          Classification	7-22

7-5       Respirable Suspended Paniculate Concentration in Homes by
          Source Category  	7-22

7-6       Regressions of Indoor on Outdoor PM10 and PM2 5 Concentrations:
          Particle Total Environmental Assessment Methodology Prepilot
          Study	7-25

7-7       Weighted Distributions of Personal, Indoor,  and Outdoor Particle
          Concentrations  	7-27

7-8       Weighted Distributions of PM2 5/PM10 Concentration Ratio	7-28

7-9       Stepwise Regression Results for Indoor Air Concentrations of PM10 and
          PM2.5 Coefficients	7-34

7-10      Penetration Factors,  Decay Rates, and Source Strengths: Nonlinear
          Estimates	7-36

7-11      Indoor-Outdoor Mean Concentrations of Fine Particles in Three
          Large-Scale Studies  	7-42

7-12      Influence of Recent Cigarette Smoking on Indoor Concentrations of
          Particulate Matter   	7-45

7-13      Indoor Average PM2 5 and PM10 by  Reported Smoking in the Home
          and Evaporative Cooler Use During Sampling Week for Tuscon, Arizona,
          Study	7-47

7-14      Regression of Indoor on Outdoor PM10 Concentrations:  THEES Study,
          Phillipsburg, New Jersey	7-52

7-15      Median Values for Environmental Tobacco Smoke Markers	7-56

7-16      Fraction of Concentration of Outdoor Particles Estimated To Be Found
          Indoors at Equilibrium:  Results from the Particle Total Exposure
          Assessment Methodology Study	7-62
                                       I-xxiii

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                              LIST OF TABLES (cont'd)
Number                                                                         Page

7-17      Smoking, Nonsmoking, and Outdoor RSP Concentrations and Ratios .....  7-64

7-18      An Overview of Organisms, Aerosols, and Disease Agents  ...........  7-71

7-19      Quantile Description of Personal, Indoor, and Outdoor PM3 5
          Concentrations, by Location in Two Tennessee Communities ..........  7-85

7-20      Regression Equation of Those Individuals Having Statistically Significant
          Relationships of Exposure with Outdoor Air Concentrations ...........  7-87

7-2 la     Particle Total Exposure Assessment Methodology Prepilot Study:
          24-Hour PM10 Concentrations ............................   7-90

7-2 Ib     Particle Total Exposure Assessment Methodology Prepilot Study:
          24-Hour PM2 5 Concentrations  ............................ 7-91

7-22      Regressions of Personal Exposure on Indoor and Outdoor PM10 and PM2 5
          Concentrations:  Particle Total Exposure Assessment Methodoloy
          Prepilot Study ....................................... 7-92

7-23      Population- Weighted Concentrations and Standard Errors, Particle Total
          Environmental Methodology Study  .......................... 7-94

7-24      Summary of World Health Organization/United Nations Environment
          Programme Global Environment Monitoring System/Personal Exposure
          Pilot Study Results  .................................... 7-99

7-25      Summary of Correlations Between PM10 Personal Exposures of Seven
          Tokyo Residents ant the PM10 Measured Outdoors Under the Eaves of
          Their Homes,  and the Particulate Matter Measured at the Itabashi
          Monitoring Station  ...................................  7-102

7-26      Comparison of Personal Exposure Monitor Exposure of Individuals
          to the Simultaneous Ambient Particulate Matter Concentration in
          Several U.S. and Foreign Cities ...........................  7-106
7-27      Forty-eight-Hour Personal Exposure to PMjo ...................  7-121

1-28      Parameter Estimates for 48-Hour PM10 Personal Exposure Monitor
          Data Taken by Subjects Living Near a Main Road in Tokyo  .........  7-124

7-29      Parameter Estimates for 48-Hour PM10 Perosonal Exposure Monitor
          Data Taken by Subjects Living Farther from the Same Tokyo Main
          Road Described  in Table 7-28  ............................  7-125

                                       I-xxiv

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                              LIST OF TABLES (cont'd)
Number

7-30      Average Personal Exposure Data Compared with Itabashi Site Monitor . .  .  7-126

7-31      Results of Linear Regression Analysis, Assuming a Normal Error
          Using the Exposure Data from Japan	7-126

7-32      Results of Linear Regression Analysis, Assuming a Lognormal Error
          Using the Exposure Data from Japan	7-127

7-33      Results of an Orthogonal Regression Analysis to the Exposure Data
          from Japan   	7-129

7-34      Results of an ANOVA Analysis of the Exposure Data from Japan	7-133

7-35      Covariance and Correlation Matrix for Average Personal Exposure
          and Ambient Exposures from Japan   	7-134

7-36      Summary of Results of the Analysis  of the  Exposure Data from Japan  ...  7-135

7-37      Personal Exposure Suspended Paniculate Matter Data from
          Phillipsburg, New Jersey	7-136

7-38      Results of an ANOVA Analysis of the Personal Exposure Data of
          Phillipsburg, New Jersey	7-137

7-39      SAM Site Concentrations, PM10 Data, from Phillipsburg, New Jersey  . . .  7-137

7-40      Results of an ANOVA Analysis of the Site Exposure Data of
          Phillipsburg, New Jersey	7-138

7-41      Average Personal PM10 Exposure Data Compared with the Site Exposure
          Data for Phillipsburg, New Jersey  	7-138

7-42      Results of the Analysis of the Exposure Data from Phillipsburg,
          New Jersey  	7-140

7-43      Personal and Ambient Exposure Data for Beijing, China	7-141

7-44      Results of the Linear Regression Analysis for the Beijing, China,
          Exposure Data  	7-141

7-45      Estimated Mean Vector, Covariance Matrix, and Correlation Matrix of
          Personal Exposure PM10 Data from Riverside, California	7-142
                                        I-xxv

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                              LIST OF TABLES (cont'd)
Number                                                                         Page

7-46      Results of an ANOVA Analysis of the Personal Exposure Data of
          Riverside, California   	7-143

7-47      Results of the Analysis of the Exposure Data from Riverside, California .  .  7-143

7-48      Average 24-Hour PM10 Personal Exposure Data Compared with the
          Personal Exposure Monitor-SAM Site Exposure Data for
          Riverside, California   	7-145

7-49      Results of the Linear Regression Analysis of the Exposure Data from
          Azusa, California   	7-146
                                        I-xxvi

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                                   LIST OF FIGURES
Number                                                                              Page

3-1       Number of particles as a function of particle diameter	3-5

3-2       Particle volume distribution as a function of particle diameter  	3-6

3-3       An idealized distribution of ambient paniculate matter showing fine- and
          coarse-mode particles and the fractions collected by size-selective
          samplers	3-10

3-4       Specified particle penetration through an ideal inlet for four different
          size-selective sampling criteria	3-13

3-5       Particle size related to relative humidity	3-15

3-6       Ion concentration as a function of particle size, measured in
          Claremont, California	3-18

3-7       Model dust emissions for the United States	3-37

3-8       Diffusion constants and settling velocities for particles	3-39

3-9       Particle deposition from wind tunnel studies  	3-40

3-10      Sedimentation and inertia effects on large particle deposition	3-40

3-11      Comparison of observed hydrogen peroxide depletions and observed
          sulfate yields  	3-61

3-12      Extrapolations from correlations of wind-tunnel-measured deposition
          velocities for z  = 1 m,  densities of 1, 4, and 11.5 g cm"3	3-130

3-13      An example of histogram display and fitting to log-normal functions for
          particle-counting size-distribution data	3-149

3-14      An example of an effective display of impactor data  	3-150

3-15      Size distributions of sulfate, Long  Beach, June 1987, showing use of
          fitted log-normal distributions to describe diurnal variations in size
          and concentration	3-151

3-16      Effect of changing endpoints	3-152
                                          I-xxvii

-------
                              LIST OF FIGURES (cont'd)
Number                                                                            Page

3-17      These size distributions, obtained during a U.S. Environmental
          Protection Agency study of the Denver brown cloud, represent one
          of the few efforts to compare particle-counting and particle-collection
          size-distribution measurements	3-154

3-18      Grand average volume-size distributions from the Aerosol
          Characterization Experiment in 1972	3-157

3-19      Volume-size distribution taken in the midwestern United States near the
          Cumberland Power Plant in Tennessee	3-158

3-20      Examples of size distribution histograms for total mass, sulfate, and
          iron obtained at two visibility levels using an Andersen impactor  	3-159

3-21      Impactor size distribution measurement generated by Lundgren et al.
          with the Wide Range Aerosol Classifier:  Philadelphia and Phoenix	3-160

3-22      Example of aged and fresh coarse-mode particle-size distributions	3-161

3-23      Size distributions reported by Noll and co-workers from the Chicago
          area using an Andersen impactor for the smaller particles and a Noll
          Rotary Impactor for the larger particles	3-163

3-24      Size distribution of dust generated by driving a truck over an unpaved
          test track	3-165

3-25      Size distribution of emissions from a pulverized-coal power plant and
          the particle size distributions remaining after several types of control
          devices	3-166

3-26      Size distributions from a fluidized-bed, pulverized coal combustor,
          after initial cleanup by a cyclone collector and after final cleanup
          by a baghouse	3-167

3-27      Particle growth curves showing fully reversible hygroscopic growth
          of sulfuric acid particles, deliquescent growth of ammonium sulfate
          particles at about 80%  relative humidity, hygroscopic growth of
          ammonium sulfate solution droplets at relative humidity greater than
          80%, and hysteresis until the crystallization point is reached  	3-170

3-28      Theoretical predictions and experimental measurements of growth
          of NH4HSO4 and ammonium sulfate particles at relative humidity
          between 95 and 100%	3-172
                                         I-xxviii

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                               LIST OF FIGURES (cont'd)
Number                                                                           Page

3-29      Tandem Differential Mobility Analyzer measurements of the sensitivity
          of particle size to relative humidity at Claremont, California	3-174

3-30      Example of growth in particle size due primarily to increases in
          relative humidity from Uniontown,  Pennsylvania  	3-176

3-31      Mass size distribution of nonvolatile aerosol material	3-178

3-32      Example of particle-counting volume distribution obtained in
          Claremont, California	3-180

3-33      Relative humidity versus sulfur, during the 1986 Carbonaceous
          Species Methods Comparison Study, for particles with Dae greater
          than 0.56 urn	3-182

3-34      Data from the South Coast Air Quality Study  	3-184

3-35      Log-log plot of sulfate mode concentration versus aerodynamic mode
          diameter from Claremont, California, during the summer South Coast
          Air Quality Study   	3-185

3-36      Typical results of size-distribution measurements taken with a Berner
          impactor in a Vienna street with heavy automotive traffic	3-186

4-1       Characteristics of aerosol measurement instruments	4-3

4-2       American Conference of Governmental Industrial Hygienists,
          British Medical Research Council, and International Organization
          for Standardization size-selective sampling criteria   	4-7

4-3       Sampling efficiency of Institute for Occupational Medicine ambient
          inhalable aerosol sampler for three different types of test aerosol  	4-9

4-4       Liquid particle sampling effectiveness curves with solid particle
          points superimposed for the Wedding IP10 and the Andersen Samplers
          Model 321A inlets at eight kilometers per hour 	4-14

4-5       Two-stage  Sierra Andersen PM10 sampler	4-15

4-6       Sampling characteristics of two-stage size-selective inlet  for liquid
          aerosols  	4-16
                                         I-xxix

-------
                              LIST OF FIGURES (cont'd)
Number

4-7       Penetration of particles for 16.67-liters-per-minute dichotomous sampler
          PM10 inlets   	4-18

4-8       Collection performance variability illustrating the influence of wind
          speed for the Andersen 321A PM10 inlet  	4-19

4-9       Aerosol separation and internal losses for a 2.5-micrometer dichotomous
          sampler virtual impactor  	4-23

4-10      Percent collection as a function of aerodynamic diameter for the
          U.S. Environmental Protection Agency enhanced method glass cyclone .... 4-26

4-11      Performance of glass fiber filters compared to greased substrate	4-28

4-12      Schematic diagram of an annular denuder system	4-33

4-13      Measured calibration of the Andersen Cascade Dupactor as compared
          to that supplied by the manufacturer	4-47

4-14      Internal losses for the Micro-Orifice Uniform Deposit Impactor  	4-48

4-15      Rupprecht and Patashnick Tapered Element Oscillating Microbalance®
          sampler	4-56

4-16      Andersen beta gauge sampler  	4-59

4-17      Integrating  nephelometer	4-61

4-18      Particle-scattering coefficient per volume concentration as a function of
          particle size for spherical particles of refractive index 1.5 illuminated by
          550-nanometer light	4-62

4-19      Correlation of b«,n and fine fraction mass  	4-64
                         !>p

4-20      Collection efficiency of the MSP personal aerosol sampler inlet	4-67

4-21      Modified dichotomous sampler  	4-70

4-22      Comparison of PM2 5 nitrate mass measurements from Teflon* filter versus
          denuded nylon filter sample collection for Los Angeles, California  	4-73

4-23      Comparison of PM2 5 nitrate mass measurements from Teflon* filter
          versus denuded nylon filter sample collection for Claremont,  California  . .  . 4-74
                                         I-xxx

-------
                              LIST OF FIGURES (cont'd)
Number

4-24      Schematic of a typical X-ray fluorescence system	4-83

4-25      Example of an X-ray fluorescence spectrum	4-86

4-26      Schematic of a particle induced X-ray emission/PESA analysis system  .... 4-89

4-27      Schematic representation of an ion chromatography system	4-95

4-28      Example of an ion chromatogram showing the separation of fluoride,
          chloride, nitrite, nitrate, phosphate, and sulfate ions  	4-96

4-29      Schematic of a typical automated colorimetric system	4-98

4-30      Percent correction for vapor adsorption on quartz fiber filters
          for submicrometer particle sampling at a face velocity of 40 cm s-1
          for 13 samples in Portland,  Oregon  	4-108

4-31      Two types of filter series used for adsorption artifact corrections  	4-109

4-32      Schematic of the BYU Organic Sampling System	4-110

5-1       Size distribution of particles generated  in a laboratory
          resuspension chamber	5-9

5-2       Size distribution of California source emissions, 1986  	5-10

5-3       Chemical abundances for PM2 5  emissions from paved-road dust in
          Denver, Colorado  	5-12

5-4       Chemical abundances for PM2 5  emissions from wood burning in
          Denver, Colorado  	5-26

5-5       Estimates of primary PM10 emissions by U.S. Environmental
          Protection Agency region for 1992	5-46

5-6       Estimates of sulfur dioxide emissions by U.S.  Enviromental
          Protection Agency region for 1992	5-46

6-1       Time scales for particle emissions   	6-4

6-2       Relationship of spatial and temporal  scales for coarse and fine
          particles  	6-5
                                        I-xxxi

-------
                              LIST OF FIGURES (cont'd)
Number                                                                           Page

6-3       Residence time in the lower troposphere for atmospheric particles
          from 0.1 to 1.0 /im  	6-6

6-4       Space-time relationship in urban and mountainous areas  	6-7

6-5       Continental scale pattern of aerosols derived from visibility observations
          over land and satellite monitoring over the oceans:  North America	6-10

6-6       Global pattern of oceanic aerosols derived from satellite observations	6-11

6-7       Seasonal pattern of oceanic aerosols derived from satellite observations  ....  6-13

6-8       Fine mass concentration derived from nonurban Interagency Monitoring of
          Protected Visual Environments/Northeast States for Coordinated Air Use
          Management networks	6-16

6-9       Coarse mass concentration derived from nonurban Interagency Monitoring
          of Protected Visual Environments/Northeast States for Coordinated Air
          Use Management networks	6-18

6-10      PM10 mass concentration derived  from nonurban Interagency Monitoring of
          Protected Visual Environments/Northeast States for Coordinated Air Use
          Management networks	6-19

6-11      Fine fraction of PM10 derived from nonurban Interagency Monitoring of
          Protected Visual Environments/Northeast States for Coordinated Air Use
          Management networks	6-21

6-12      Yearly average absolute and relative concentrations for sulfate and
          nitrate   	6-23

6-13      Yearly average absolute and relative concentrations for organic carbon
          and elemental carbon  	6-24

6-14      Seasonal pattern of nonurban aerosol concentrations for the entire  United
          States: monitoring locations; PM10, PM2 5, and PMCoarse; sulfate, soil,
          organic carbon, and elemental carbon fractions; and tracers   	6-26

6-15      Seasonal pattern of nonurban aerosol concentrations for the eastern United
          States: monitoring locations; PM10, PM2 5, and PMCoarse; sulfate, soil,
          and organic carbon, and elemental carbon fractions; and tracers	6-29
                                         I-xxxii

-------
                              LIST OF FIGURES (cont'd)
Number                                                                         Page

6-16      Seasonal pattern of nonurban aerosol concentrations for the western United
          States: monitoring locations; PM10, PM2 5, and PMCoarse; sulfate, soil,
          organic carbon, and elemental carbon fractions; and tracers	6-31

6-17      Trend of valid PM10 monitoring stations in the Aerometric Information
          Retrieval System database  	6-47

6-18      Aerometric Information Retrieval System PM10 quarterly concentration
          maps using all available data	6-49

6-19      Aerometric Information Retrieval System PM10 and PM2 5 concentration
          patterns for the conterminous United States	6-50

6-20      Aerometric Information Retrieval System concentration data for east
          of the Rockies: monitoring locations; PM10 concentration trends;
          PM10 and PM2 5 relationship; and PM10, PM2 5, and PMCoarse
          seasonal pattern   	6-53

6-21      Aerometric Information Retrieval System concentration data for west
          of the Rockies: monitoring trends; PM10 concentration trends;
          PM10 and PM2 5 relationship; and PM10, PM2 5, and PMCoarse
          seasonal pattern	6-55

6-22      Short-term PM10 concentration time series for Missoula, Montana, and
          Knoxville, Tennessee  	6-58

6-23      Geographic variation of the standard deviation of the lognormal
          distribution of PM10 concentrations from the Aerometric Information
          Retrieval System	6-59

6-24      Annual PM2 5 concentration pattern obtained from Interagency
          Monitoring of Protected Visual Environments/Northeast States for
          Coordinated Air Use Management and Aerometric Information Retrieval
          System networks	6-61

6-25      Monthly mean concentrations in micrograms per cubic meter of PM15,
          PM2 5, PM15-PM2 5, and total  sulfate as (NH4)2SO4 in Portage, Wisconsin;
          Topeka, Kansas; Harriman, Tennessee; Watertown,  Massachusetts;
          St. Louis, Missouri; and Steubenville, Ohio   	6-62

6-26      Spatial maps of PM10 concentration difference between Aerometric
          Information Retrieval System and Interagency Monitoring of Protected
          Visual Environments/Northeast States for Coordinated Air Use
          Management networks	6-64

                                       I-xxxiii

-------
                              LIST OF FIGURES (cont'd)
Number                                                                           Page

6-27      Urban excess concentrations for the United States, the eastern
          United States, and the western United States	6-66

6-28      Aerosol regions of the conterminous United States  	6-68

6-29      Interagency Monitoring of Protected Visual Environments/Northeast States
          for Coordinated Air Use Management concentration data for the Northeast:
          monitoring locations; PM10, PM2s, and PMCoarse; sulfate, soil,
          organic carbon, and elemental carbon fractions; and tracers	6-69

6-30      Aerometric Information Retrieval System concentration data for the
          Northeast:  monitoring locations; regional PM10 concentration trends;
          PM10 and PM2 5 relationship; and PM10, PM2 5, and PMCoarse seasonal
          pattern	6-72

6-31      Short-term variation of PM10 average for the Northeast   	6-74

6-32      Urban excess concentration for the Northeast	6-74

6-33      Interagency Monitoring of Protected Visual Environments/Northeast States
          for Coordinated Air Use Management concentration data for the Southeast:
          monitoring locations; PM10, PM2 5, and PMCoarse; sulfate, soil,
          organic carbon, and elemental carbon fractions; and tracers   	6-75

6-34      Aerometric Information Retrieval System concentration data for the
          Southeast: monitoring locations; regional PM10 concentration trends;
          PM10 and PM2 5 relationship; and PM10, PM2 5, and PMCoarse
          seasonal pattern	6-77

6-35      Short-term variation of PM10 average for the Southeast   	6-79

6-36      Urban excess concentration for the Southeast	6-79

6-37      Interagency Monitoring of Protected Visual Environments/Northeast States
          for Coordinated Air Use Management concentration data for the industrial
          Midwest:  monitoring locations; PM10, PM2 5, and PMCoarse; sulfate,
          soil, organic carbon, and elemental carbon fractions; and tracers	6-80

6-38      Aerometric Information Retrieval System concentration data for the
          industrial Midwest:  monitoring locations; regional PM10 concentration
          trends; PM10 and PM2 5 relationship; and PM10, PM2 5, and PMCoarse
          seasonal pattern	6-83

6-39      Short-term variation of PM10 average for the industrial Midwest	6-84

                                        I-xxxiv

-------
                              LIST OF FIGURES (cont'd)
Number                                                                          Page

6-40      Urban excess concentration for the industrial Midwest	6-85

6-41      Interagency Monitoring of Protected Visual Environments/Northeast States
          for Coordinated Air Use Management concentration data for the Upper
          Midwest:  monitoring locations; PM10, PM2 5, and  PMCoarse;  sulfate,
          soil,  organic carbon, and elemental  carbon fractions; and tracers	6-86

6-42      Aerometric Information Retrieval System concentration data for the
          Upper Midwest:  monitoring locations; regional PM10 monitoring trends;
          PM10 and PM2 5 relationship; and PM10,  PM2 5, and PMCoarse seasonal
          trends	6-87

6-43      Short-term variation of PM10 average for the Upper Midwest	6-89

6-44      Urban excess concentration for the Upper Midwest	6-89

6-45      Interagency Monitoring of Protected Visual Environments/Northeast States
          for Coordinated Air Use Management concentration data for the Southwest:
          monitoring locations; PM10, PM2 5, and PMCoarse; sulfate, soil,
          organic carbon, and elemental carbon fractions; and tracers	6-91

6-46      Aerometric Information Retrieval System concentration data for the
          Southwest:  monitoring locations; regional PM10 monitoring trends;
          PM10 and PM2 5 relationship; and PM10,  PM2 5, and PMCoarse seasonal
          trends   	6-92

6-47      Short-term variation of PM10 average for the Southwest	6-93

6-48      Urban excess concentration for the Southwest  	6-94

6-49      Interagency Monitoring of Protected Visual Environments/Northeast States
          for Coordinated Air Use Management concentration data for the Northwest:
          monitoring locations; PM10, PM2 5,  and PMCoarse; sulfate, soil,
          organic carbon, and elemental carbon fractions; and tracers	6-95

6-50      Aerometric Information Retrieval System concentration data for the
          Northwest: monitoring locations; regional PM10 monitoring; PM10 and
          PM2  5 relationship; and PM10, PM2 5,  and PMCoarse seasonal trend   	6-97

6-51      Short-term variation of PM10 average for the Northwest	6-99

6-52      Urban excess concentration for the Northwest	6-99
                                       I-xxxv

-------
                              LIST OF FIGURES (cont'd)
Number                                                                           Page

6-53      Interagency Monitoring of Protected Visual Environments/Northeast States
          for Coordinated Air Use Management concentration for Southern
          California:  monitoring locations; PM10, PM2 5, and PMCoarse; sulfate,
          soil,  organic carbon, and elemental carbon fractions; and tracers	6-101

6-54      Aerometric Information Retrieval System concentrations for Southern
          California:  monitoring locations; regional PM10 monitoring trends;
          PM10 and PM2 5 relationship; and PM10, PM2.5, and PMCoarse
          seasonal trend	6-103

6-55      Short-term variation of PM10 average for Southern California  	6-104

6-56      Urban excess concentration for Southern California	6-104

6-57      Interagency Monitoring of Protected Visual Environments/Northeast States
          for Coordinated Air Use Management concentration for Shenandoah
          National Park:  PM10, PM2 5, and PMCoarse;  chemical fraction of
          sulfate, soil, organic carbon,  and elemental carbon; and tracers  	6-106

6-58      Interagency Monitoring of Protected Visual Environments/Northeast States
          for Coordinated Air Use Management concentration for Washington,
          District of Columbia:  PM10, PM2 5, and PMCoarse; chemical fraction of
          sulfate, soil, organic carbon,  and elemental carbon; and
          tracer concentrations	6-109

6-59      Excess aerosol concentration  at Washington, District of Columbia,
          compared to Shenandoah National Park:  PM10, PM2 5, and PMCoarse
          and concentration of sulfate, soil, organic carbon, and
          elemental carbon	6-111

6-60      Daily concentration of fine mass and fine sulfur at Washington, District
          of Columbia, and Shenandoah National Park	6-112

6-61      New York City region:  aerosol concentration  map, trend, and seasonal
          pattern	6-113

6-62      Fine, coarse, and PM10 particle concentrations at three
          New York City sites	6-115

6-63      Philadelphia region:  aerosol  concentration map, trend, and seasonal
          pattern	6-117

6-64      Seasonal particle concentrations at four Philadelphia sites	6-118
                                        I-xxxvi

-------
                               LIST OF FIGURES (cont'd)
Number                                                                            Page

6-65      PM10 concentration seasonality at Whiteface Mountain and neighboring
          low-elevation sites	6-120

6-66      Aerosol concentration patterns for Southeast Atlantic Coast states
          and sites in North Carolina and Florida:  monitoring sites, trends,
          seasonal pattern, North Carolina sites, Florida sites, and seasonal
          pattern for Winston-Salem	6-121

6-67      Aerosol concentration pattern in Texas and Gulf states	6-122

6-68      Pittsburgh subregion:  aerosol concentration map, trends, and seasonal
          pattern	6-126

6-69      Fine, coarse, and PM10 concentration at sites in or near Pittsburgh	6-127

6-70      St. Louis  subregion:  aerosol concentration map, trends, and seasonal
          pattern	6-129

6-71      Fine, coarse, and PM10 seasonal concentration patterns in or near
          St. Louis	6-131

6-72      Chicago subregion:  aerosol concentration map, trends, and seasonal
          patterns	6-133

6-73      El Paso subregion:  aerosol concentration map, trends, and seasonal
          pattern	6-136

6-74      Fine, coarse, and PM10 concentration patterns in El Paso and
          San Antonio   	6-138

6-75      Phoenix-Tucson subregion:  aerosol concentration map, trends,  and
          seasonal pattern	6-139

6-76      Excess aerosol concentration and composition at South Lake Tahoe
          compared to  Bliss State Park	6-142

6-77      Salt Lake City region: aerosol concentration map, trends, seasonal
          pattern, and seasonal  patterns at sites  in or near Salt Lake City	6-144

6-78      Northern Idaho-Northwestern Montana subregion:  aerosol concentration
          map, trends,  and  seasonal pattern   	6-146

6-79      PM10 concentration patterns at sites in Northern Idaho-Northwestern
          Montana subregion	6-147

                                        I-xxxvii

-------
                              LIST OF FIGURES (cont'd)
Number

6-80      Aerosol concentration patterns in Washington State and Oregon	6-149

6-81      San Joaquin Valley:  aerosol concentration map, trends, and seasonal
          pattern	6-152

6-82      Fine, coarse, and PM10 seasonal patterns in the San Joaquin Valley	6-153

6-83      Los Angeles:  aerosol concentration map, trends, and seasonal pattern  .  . .  6-155

6-84      Fine, coarse, and PM10 seasonal patterns near Los Angeles	6-157

6-85a     Major constituents of particles measured at sites in the eastern
          United States, as shown in Tables 6A-2a, 6A-2b, and 6A-2c	6-165

6-85b     Major constituents of particles measured at sites in the central
          United States, as shown in Tables 6A-2a, 6A-2b, and 6A-2c	6-166

6-85c     Major constituents of particles measured at sites in the western
          United States, as shown in Tables 6A-2a, 6A-2b, and 6A-2c	6-167

6-86      Mean air pollutant concentrations for days when winds were from the
          southerly direction, plotted versus population density	6-173

6-87      Average monthly aerosol  strong acidity for Year 1 sites of the Harvard
          24-City Study	6-174

6-88      Diurnal pattern of sulfate and hydrogen ion at Harriman, Tennessee,
          weekly pattern and daily average	6-175

6-89      Aerosol number and  volume size distributions from an urban site at
          Long Beach, California	6-178

6-90      Aerosol number and  volume size distributions from a background site in
          the Rocky Mountains, Colorado	6-179

6-91      Number concentrations as a function of time of day at Long Beach,
          California  	6-180

6-92      Number and volume  size  distributions at the Rocky Mountain site,
          showing an intrusion of urban air	6-181

6-93      Number and volume  and  mass size distributions from Los Angeles,
          California, showing comparison of three measurement techniques	6-183
                                       I-xxxviii

-------
                              LIST OF FIGURES (cont'd)
Number                                                                          Page

6-94      Relationship between particle number and particle volume	6-185

6-95      Impact of treatment temperature on the enrichment of metals in the fly
          ash after the thermal treatment of soils from a Superfund site	6-189

6-96      Average normalized concentrations as a function of stage number, for
          selenium, sulfur, calcium, aluminum, silicon, potassium, molybdenum,
          tungsten, nickel, and chromium for five BLPI samples from a coal-fired
          power plant	6-194

6-97      Fine and very fine sulfur at Grand Canyon National Park, summer
          1984  	6-196

6-98      Concentration, in micrograms per cubic meter, of fine and very fine
          metals (nickel, selenium, and lead) in Long Beach, California,
          December 10 through  13,  1987, in four-hour increments  	6-199

6-99      Patterns of zinc, arsenic, sulfur, and selenium in the United  States   	6-203

6-100     Apparent deposition of automotive lead aerosol in the respiratory tract
          of one of the authors as determined by cascade impactor and Proton-
          Induced X-ray Emissions as a function of aerodynamic diameter for
          greater than 4, 4 to 2, 2 to 1, 1 to 0.5, 0.5 to 0.25, and less than
          0.25-/nm particles of size classes  1 through 6, respectively   	6-205

6-101     Concentrations of PM2 5 measured at the PBY site in southwestern
          Philadelphia	6-208

6-102     Concentrations of PM10 measured at the PBY site in southwestern
          Philadelphia	6-209

6-103     Frequency distribution of PM2 5 concentrations measured at the PBY
          site in southwestern Philadelphia   	6-210

6-104     Frequency distribution of coarse mode mass derived by difference
          between PM10 and PM2 5	6-210

6-105     Frequency distribution of PM10 concentrations measured at the PBY
          site in southwestern Philadelphia  	6-211

6-106     Frequency distribution of PM2 5 concentrations  measured at the
          Riverside-Rubidoux site	6-212
                                       I-xxxix

-------
                              LIST OF FIGURES (cont'd)
Number                                                                           Page

6-107     Frequency distribution of PM(10_2 5) concentrations measured at the
          Riverside-Rubidoux site	6-213

6-108     Frequency distribution of PM10 concentrations calculated as the
          sum of PM2 5 and PM(10_2 5) masses measured at the Riverside-Rubidoux
          site  	6-213

6-109     Concentrations of PM2 5 measured  at the Riverside-Rubidoux site	6-214

6-110     Concentrations of PM(10_2 5) measured at the Riverside-Rubidoux site  ....  6-215

6-111     Concentrations of PM10 measured at the Riverside-Rubidoux site  	6-215

6-112     United States trend maps for the  75th percentile extinction coefficient,
          Bext for winter, spring, summer,  and fall	6-218

6-113     Secular haze trends  (1960 to 1992) for six eastern U.S. regions,
          summer and winter   	6-220

6-114     Eastern U.S. regional background trend of sulfate indicated by
          seasonal trend data from Shenadoah and Great Smoky Mountains
          National Parks   	6-222

6-115     Total suspended particle and PM2 5 trend data for the city of
          Philadelphia from the Aerometric Information Retrieval System, IPN,
          and Harvard database	6-223

6-116     Comparison of fine  and coarse particle parameters in Philadelphia
          in 1983 and 1993:  PM2 5 and PM(10_2 5) at  South Broad Street site,
          1983; PM2 5/PM10 at South Broad  Street site, 1983; PM2 5 and
          PM(io-2.5) at Presbyterian Home site, 1993;  and PM2 5/PM10 at
          Presbyterian Home Site, 1993	6-224

6-117     Trend data from the  Harvard Six-Cities Study:   Steubenville, fine, coarse,
          PM15, and total suspended particle  means; Steubenville, fine, coarse,
          PM15, and total suspended particle  90th percentiles;  St. Louis, fine,
          coarse, PM15, and total suspended  particle means; and St. Louis, fine,
          coarse, PM10, and total suspended  particle 90th percentiles	6-225

6-118     Trend data from Harvard Six-Cities Study:  Harriman,  fine, coarse,
          PM15, and total suspended particle  means; Harriman, fine, coarse,
          PM15, and total suspended particle  90th percentiles;  Watertown, fine,
          coarse, PM15, and total suspended  particle means; and Watertown, fine,
          coarse, PM15, and total suspended  particle 90th percentiles	6-226

                                         I-xl

-------
                              LIST OF FIGURES (cont'd)
Number                                                                            Page

6-119     Trend data from Harvard Six-Cities Study: Portage, fine, coarse,
          PM15, and total suspended particle means; Portage,  fine, coarse,
          PM15, and total suspended particle 90th percentiles; Topeka, fine,
          coarse, PM15, and total suspended particle means; and Topeka, fine,
          coarse, PM15, and total suspended particle 90th percentiles	6-227

6-120     Trend data from Aerometric Information Retrieval System:
          New York City, Site 69, fine, coarse, and PM10 means; New York City,
          Site 69, fine, coarse, and PM10 90th percentiles; New York City,
          Site 71, fine, coarse, and PM10 means; and New York City, Site 71,
          fine, coarse, and PM10 90th percentiles	6-229

6-121     Trend data from Aerometric Information Retrieval System: Detroit,
          fine, coarse, and PM10 means; Detroit, fine, coarse, and PM10
          90th percentiles; St. Louis, fine, coarse,  and PM10 means; and St.  Louis,
          fine, coarse, and PM10 90th percentiles	6-230

6-122     Trend data from Aerometric Information Retrieval System: Philadelphia,
          fine, coarse, and PM10 means and Philadelphia fine, coarse, and PM10
          90th percentiles	6-231

6-123     Trend data from San Jose from California Air Resources Board:
          fine, coarse, and total means; fine, coarse, and total 90th percentiles;
          every sixth-day fine and coarse mass for 1991; and  fine  and coarse mass
          as a fraction of PM10  	6-232

6-124     Trend data from Stockton-Hazelton from California Air  Resources  Board:
          fine, coarse, and total means; fine, coarse, and total 90th percentiles;
          every sixth-day fine and coarse mass for 1991; and  fine  and coarse
          mass as a fraction of PM10	6-233

6-125     Trend data from Visalia from California Air Resources Board:  fine,
          coarse, and total means; fine, coarse, and total 90th percentiles;
          every sixth-day fine and coarse mass for 1991; and  fine  and coarse
          mass as a fraction of PM10	6-234

6-126     Trend data from Bakersfield from California Air Resources Board:  fine,
          coarse, and total means; fine, coarse, and total 90th percentiles;
          every sixth-day fine and coarse mass for 1991; and  fine  and coarse
          mass as a fraction of PM10	6-235
                                          I-xli

-------
                             LIST OF FIGURES (cont'd)
Number                                                                         Page

6-127     Trend data from Azusa from California Air Resources Board: fine,
          coarse, and total means; fine, coarse, and total 90th percentiles;
          every sixth-day fine and coarse mass for 1991; and fine and coarse mass
          as a fraction of PM10 	6-236

6-128     Trend data from Riverside-Rubidoux from California Air Resources
          Board:  fine, coarse, and total means; fine, coarse, and total 90th
          percentiles; every sixth-day fine and coarse mass for 1991; and fine
          and coarse mass as a fraction of PM10	6-237

6-129     Trend data from Lone Pine from California Air Resources Board:  fine,
          coarse, and total means; fine, coarse, and total 90th percentiles;
          every sixth-day fine and coarse mass for 1991; and fine and coarse
          mass as a fraction of PM10	6-238

6-130     Trend data from El Centra from California Air Resources Board: fine,
          coarse, and total means; fine, coarse, and total 90th percentiles;
          every sixth-day fine and coarse mass for 1991; and fine and coarse
          mass as a fraction of PM10	6-239

6-131     PM2 5 and total suspended particle (TSP) relationships in Philadelphia,
          IPN Average, March 1979 to December 1983:  comparison of PM2 5
          with TSP,  frequency distribution of PM2 5/TSP, comparison of
          PM2 5/TSP with PM2 5,  and comparison of PM2 5/TSP with TSP 	6-243

6-132     PM2 5 and total suspended particle (TSP) relationships in Philadelphia,
          IPN, South Broad Site, March 1982 to December 1983:  comparison of
          PM2 5 with TSP, frequency distribution of PM2 5/TSP, comparison of
          PM2 5/TSP with PM25,  and comparison of PM25/TSP with TSP 	6-244

6-133     PM2 5 and total suspended particle (TSP) relationships in Philadelphia,
          Aerometric Information Retrieval System, 1987 to 1990: comparison of
          PM2 5 with TSP, frequency distribution of PM2 5/TSP, comparison of
          PM2 5/TSP with PM2 5,  and comparison of PM2 5/TSP with TSP 	6-245

6-134     PM10 and total suspended particle (TSP) relationships in Philadelphia,
          IPN, South Broad Site, March 1982 to December 1983:  comparison of
          PM10 with TSP, frequency distribution of PM10/TSP, comparison of
          PM10/TSP with PM10, and comparison of PM10/TSP with TSP  	6-246

6-135     PM10 and total suspended particle (TSP) relationships in Philadelphia,
          Aerometric Information Retrieval System, 1987 to 1990:  comparison of
          PM10 with TSP, frequency distribution of PM10/TSP, comparison of
          PM10/TSP with PM10, and comparison of PM10/TSP with TSP  	6-247

                                        I-xlii

-------
                              LIST OF FIGURES (cont'd)
Number                                                                            Page

7-1       Sizes of various types of indoor particles	7-6

7-2       An example of personal exposure to respirable particles	7-11

7-3       The annual mean concentration of respirable particles for the
          highest and lowest site from the network of indoor and outdoor monitors
          in each city  in the Harvard Six-City Study  	7-14

7-4       Distribution percentiles for annual average  concentrations of indoor
          respirable paniculate matter by household smoking status and
          estimated number of cigarette packs smoked in the home during Phase 2
          of the  Harvard Six-City Study	7-17

7-5       PM2.5  in smoking and  nonsmoking  homes in three of the
          Harvard  Six-City Study sites	7-18

7-6       Cumulative frequency distribution of 24-hour personal, indoor, and
          outdoor PM10 concentrations in Riverside,  California	7-29

7-7       Cumulative frequency distribution of 24-hour indoor and outdoor  PM2 5
          concentrations in Riverside, California	7-30

7-8       Forty-eight-day sequence of PM10 and coarse paniculate matter in
          Riverside, California, Particle Total Exposure Assessment
          Methodology study	7-30

7-9       Average  indoor and outdoor 12-hour concentrations of PM10 during the
          Particle Total Exposure Assessment Methodology study in
          Riverside, California   	7-32

7-10      Sources of fine particles and thoracic  particles in all homes
          (Riverside, California)   	7-38

7-11      Sources of fine particles and thoracic  particles in homes with
          smokers  (Riverside, California)	7-39

7-12      Sources of fine particles and thoracic  particles for homes with
          cooking during data collection (Riverside, California)  	7-40

7-13      Results of six penetration experiments in a test home	7-53

7-14      The change  in suspended particle mass concentration versus tune,
          as measured by optical particle counter, assuming spherical
          particles  of unit density	7-53

                                          I-xliii

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                              LIST OF FIGURES (cont'd)
Number                                                                           Page

7-15      The ratio of the suspended particle concentration after a resuspension
          activity to the indoor concentration before that activity, by particle
          size	7-54

7-16      Fraction of indoor paniculate matter from outdoor airborne
          particulate matter, under equilibrium conditions, as a function
          of air-exchange rate, for two different size fractions 	7-61

7-17      Comparison of respirable particles in smoking and nonsmoking areas
          of 38 buildings  in the Pacific Northwest	7-65

7-18      Chart of pollen prevalence	7-72

7-19      Chart of fungal spore prevalence in Kalamazoo, Michigan, for  1994  	7-78

7-20      Residential outdoor  monitors versus central-site mean of two
          dichotomous samplers in Riverside, California	7-95

7-21      Personal exposures versus residential outdoor PM10 concentrations in
          Riverside, California  	7-95

7-22      Increased concentrations of elements in the personal versus the
          indoor samples  	7-97

7-23      Source apportionment of Particle Total Exposure Assessment
          Methodology PM10  personal monitoring data	7-98

7-24      The relationship between  PM10 in outdoor air and  indoor air at
          each house in the study	7-101

7-25      Correlations between PM10 at the Itabashi monitoring station and
          PM10 in outdoor and personal exposure	7-102

7-26      Example of difference between serial correlation and cross-sectional
          correlation of personal exposure monitor (PEM) and SAM,  showing
          how pooling of individuals can mask an underlying relationship
          of PEM and SAM	7-104

7-27      Personal versus outdoor SO4=	7-107

7-28      Estimated ("best fit" model)  versus measured personal SO4=	7-107

7-29      Personal activity cloud and time-weighted average exposure	7-111
                                         I-xliv

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                              LIST OF FIGURES (cont'd)
Number                                                                           Page

7-30      Components of personal exposure	7-116

7-31      Plot of 48-hour average personal PM10 exposure and ambient PM10
          data from Japan—linear regression	7-127

7-32      Plot of relationship between average personal PM10 exposure versus
          ambient PM10 monitoring data from Phillipsburg, New Jersey, and
          regression line calculated by U.S. Environmental Protection Agency  ....  7-139

7-33      Plot of means of personal exposures and ambient PM10 from Beijing,
          China, and regression line calculated by U.S. Environmental Protection
          Agency	7-142

7-34      Particle Total Exposure Assessment Methodology mean 24-hour
          PM10 data compared for personal exposure monitor and SAM  	7-144

7-35      Ambient and personal monitoring PM10 data from Azusa, California,
          and calculated (slightly negative slope) regression line, which becomes
          positive if single outlier value is deleted	7-146

7-36      Comparison of indoor and outdoor concentrations of lead in a
          home in Denver, October 1976, for one week, starting at 1600 hours ....  7-153

7-37      Venn diagram showing focusing of information to more completely
          specify toxicity of a given particulate matter mixture	7-154

7-38      Fraction of ambient particulate matter to which people are exposed as a
          function of fraction of time outdoors and air-exchange rate for fine and
          coarse particles	7-157

7-39      Conceptual representation of potential contributions of particulate
          matter of ambient origin and particulate matter generated  indoors to
          total human exposure of a hypothetical individual	7-161
                                         I-xlv

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                  AUTHORS, CONTRIBUTORS, AND REVIEWERS
                       CHAPTER 1. EXECUTIVE SUMMARY
Principal Authors

Dr. Robert Chapman—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Beverly Comfort—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. William Ewald—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Lawrence J. Folinsbee—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Lester D. Grant—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Annie M. Jarabek—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Dennis Kotchmar—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. David Mage—National Exposure Research Laboratory (MD-56), U.S. Environmental
Protection Agency, Research Triangle Park, NC 27711

Dr. Allan Marcus—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. James McGrath—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Joseph P. Pinto—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. William E. Wilson—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
                                      I-xlvii

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              AUTHORS, CONTRIBUTORS, AND REVIEWERS (cont'd)
Contributors and Reviewers

Dr. Judith Graham—National Exposure Research Laboratory (MD-75), U.S. Environmental
Protection Agency, Research Triangle Park, NC  27711

Dr. Jeanette Wiltse—Office of Research and Development (8601), Waterside Mall,
401 M St. S.W., Washington, DC  20460
                          CHAPTER 2. INTRODUCTION
Principal Authors

Dr. Lester D. Grant—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC  27711

Dr. Dennis Kotchmar—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC  27711
      CHAPTER 3. PHYSICS AND CHEMISTRY OF PARTICULATE MATTER
Principal Authors

Dr. Paul Altshuller—National Center for Environmental Assessment, U.S. Environmental
Protection Agency, Research Triangle Park, NC  27711

Mr. William Ewald—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC  27711

Dr. Noor Gillani—Earth Systems Science Laboratory, University of Alabama, Huntsville,
AL 35899

Dr. Dale Gillette—National Exposure Research Laboratory (MD-81), U.S. Environmental
Protection Agency, Research Triangle Park, NC  27711

Dr. Susanne Hering—Aerosol Dynamics, Inc., 2329 Fourth Street, Berkeley, CA 94710

Dr. Paul J.  Lioy—Environmental Occupational Health and Science Institute, Exposure
Measurement and Assessment Division, 681 Frelinghuysen Road, Piscataway,
NJ  08855-1179
                                     I-xlviii

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              AUTHORS, CONTRIBUTORS, AND REVIEWERS (cont'd)


Principal Authors (cont'd)

Dr. Kenneth Noll—Illinois Institute of Technology, Chicago, IL  60616

Dr. Spyros Pandis—Carnegie-Mellon University, Pittsburgh, PA 15146

Dr. James Pankow—Oregon Graduate Center, Beaverton, OR  97229-3678

Dr. Steven Schwartz—Brookhaven National Laboratory,  Upton, NY  11934

Dr. William E. Wilson—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711


Contributors and Reviewers

Dr. Michael Barnes—National Exposure Research Laboratory (MD-46), U.S. Environmental
Protection Agency, Research Triangle Park, NC 27711

Dr. Thomas Cahill—University of California—Davis, Crocker Nuclear Lab., Davis,
CA 95616

Ms. Beverly Tilton—National Center for Environmental  Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Sheldon Friedlander—University of California, 5531 Boelter Hall, Los Angeles,
CA 90095

Dr. Peter McMurray—University of Minnesota, Department of Mechanical Engineering,
111 Church Street, S.E., Minneapolis, MN 55455-0111

Dr. Sidney  Soderholm—NIOSH, 1095  Willowdale Rd., Morgantown, WVA  26505

Dr. Barbara Turpin—Rutgers University, Box 231, New Brunswick, NJ  08903
            CHAPTER 4.  SAMPLING AND ANALYSIS METHODS FOR
                PARTICULATE MATTER AND ACID DEPOSITION
Principal Authors

Dr. Judith Chow—Desert Research Institute, P.O. Box 60220, Reno, NV 89506-0220


                                      I-xlix

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              AUTHORS, CONTRIBUTORS, AND REVIEWERS (cont'd)


Principal Authors (cont'd)

Dr. Steven McDow—University of North Carolina at Chapel Hill, Chapel Hill, NC  27599

Dr. Susanne Hering—Aerosol Dynamics, Inc., 2329 Fourth Street, Berkeley, CA  94710

Dr. Charles Rodes—Research Triangle Institute, Center for Aerosol Technology,
P.O. Box 12194, Research Triangle Park, NC 27709-2194


Contributors and Reviewers

Dr. Robert Burton—National Exposure Research Laboratory (MD-56), U.S. Environmental
Protection Agency,  Research Triangle Park, NC  27711

Dr. Thomas Cahill—University of California—Davis, Crocker Nuclear Laboratory, Davis,
CA 95616

Dr. Delbert Eatough—Brigham Young University, Department of Chemistry, Provo,
UT 84602

Mr. William Ewald—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Sheldon Friedlander—University of California, Department of Chemical Engineering,
5531 Boelter Hall, Los Angeles, CA  90095

Dr. Peter McMurry—University of Minnesota, Department of Mechanical Engineering,
111 Church Street,  S.E., Minneapolis, MN 55455-0111

Dr. Sidney Soderholm—National Institute of Occupational Safety and Health,
1095 Williowdale Road, Room 111, Morgantown, WV 26505

Dr. Barbara Turpin—Rutgers University, Environmental Sciences Building, Box 231,
College Farm  Road, New Brunswick, NJ  08903

Dr. Russell Wiener—National Exposure Research Laboratory (MD-77), U.S. Environmental
Protection Agency,  Research Triangle Park, NC  27711

Dr. Nancy Wilson—National Exposure Research Laboratory (MD-56), U.S. Environmental
Protection Agency,  Research Triangle Park, NC  27711
                                        1-1

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              AUTHORS, CONTRIBUTORS, AND REVIEWERS (cont'd)


     CHAPTER 5.  SOURCES AND EMISSIONS OF ATMOSPHERIC PARTICLES


Principal Authors

Dr. Joseph P. Pinto—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711


Contributors and Reviewers

Dr. Paul Altshuller—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Judith Chow—Desert Research Institute, P.O. Box 60220, Reno, NV 89506-0220

Mr. William Ewald—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Timothy Larson—University of Washington, Department of Civil Engineering, Seattle,
WA 98195

Dr. John Seinfeld—California Institute of Technology, Department of Chemical Engineering,
Pasadena, CA 91125

Dr. Barbara Turpin—Rutgers University, Environmental Sciences Building, Box 231,
College Farm Road, New Brunswick, NJ  08903

Dr. John Watson—Desert Research Institute, P.O. Box 60220, Reno, NV 89506-0220


               CHAPTER 6.  ENVIRONMENTAL CONCENTRATIONS


Principal Authors

Dr. Paul Altshuller—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Thomas Cahill—University  of California—Davis, Crocker Nuclear Laboratory, Davis,
CA 95616

Dr. Susanne Hering—Aerosol Dynamics, Inc., 2329 Fourth Street, Berkeley, CA 94710
                                       I-li

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              AUTHORS, CONTRIBUTORS, AND REVIEWERS (cont'd)
Principal Authors (cont'd)

Dr. Rudolf Husar—Washington University, Center for Air Pollution and Impact and Trend
Analyses, St. Louis, MO 63130

Dr. Joseph P. Pinto—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC  27711

Dr. Robert Stevens—National Exposure Research Laboratory (MD-47), U.S. Environmental
Protection Agency, Research Triangle Park, NC  27711

Mr. Robert Willis—ManTech Environmental Technology, Inc., P.O. Box 12313, Research
Triangle Park, NC 27709.

Dr. William E. Wilson—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC  27711

Dr. Michael Zelenka—National Exposure Research Laboratory (MD-75), U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
Contributors and Reviewers

Dr. John Core—WESTAR Council, 1001 SW 5th Avenue, Suite 1100, Portland, OR 97204

Mr. William Ewald—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC  27711

Dr. Timothy Larson—University of Washington, Department of Civil Engineering, Seattle,
WA 98195

Dr. Brian Leaderer—John B. Pierce Laboratory, 290 Congress Avenue, New Haven,
CT 06519

Dr. Helen Suh—Harvard University, School of Public Health, 665 Huntington Avenue,
Boston, MA 02115
                                       I-lii

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              AUTHORS, CONTRIBUTORS, AND REVIEWERS (cont'd)
          CHAPTER 7.  HUMAN EXPOSURE TO PARTICULATE MATTER:
           RELATIONS TO AMBIENT AND INDOOR CONCENTRATIONS
Principal Authors

Dr. Harriet Burge—Harvard School of Public Health, Environmental Science and
Engineering,  Boston, MA 02115

Dr. Dave Mage—National Exposure Research Laboratory (MD-56), U.S. Environmental
Protection Agency, Research Triangle Park, NC  27711

Dr. Lance Wallace—National Exposure Research Laboratory, U.S. Environmental Protection
Agency, Wallington, VA  22186

Dr. Robert Ziegenfus—Kutztown University, Department of Geography, Kutztown, PA
19550
Contributors and Reviewers

Dr. Gerald Akland—National Exposure Research Laboratory (MD-56), U.S. Environmental
Protection Agency, Research Triangle Park, NC  27711

Dr. Timothy Buckley—National Exposure Research Laboratory (MD-56),
U.S. Environmental Protection Agency, Research Triangle Park, NC  27711

Dr. Robert Burton—National Exposure Research  Laboratory (MD-56), U.S. Environmental
Protection Agency, Research Triangle Park, NC  27711

Mr. William Ewald—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC  27711

Dr. Paul J.  Lioy—Environmental Occupational Health Science Institute, Exposure
Measurement and Assessment Division, 681 Frelinghuysen  Road, Piscataway, NJ
08855-1179

Dr. Peter McMurry—University of Minnesota, Department of Mechanical Engineering,
111 Church Street, SW, Minneapolis, MN  55455

Dr. William E. Wilson—National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC  27711
                                       I-liii

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                 U.S. ENVIRONMENTAL PROTECTION AGENCY
                          SCIENCE ADVISORY BOARD
                CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE

             PARTICULATE MATTER CRITERIA DOCUMENT REVIEW

Chairman

Dr. George T. Wolff—General Motors Corporation, Environmental and Energy Staff,
General Motors Bldg., 12th Floor, 3044 West Grand Blvd., Detroit, MI  48202


Members

Dr. Stephen Ayres—Office of International Health Programs, Virginia Commonwealth
University,  Medical College of Virginia, Box 980565, Richmond, VA  23298

Dr. Philip Hopke—Clarkson University, Box 5810, Pottsdam, NY  13699-5810

Dr. Jay Jacobson—Boyce Thompson Institute, Tower Road, Cornell University, Ithaca,
NY 14853

Dr. Joseph Mauderly—Inhalation Toxicology Research Institute, Lovelace Biomedical and
Environmental Research Institute, P.O. Box 5890, Albuquerque, NM 87185

Dr. Paulette Middleton—Science and Policy Associates, 3445 Penrose Place, Suite 140,
Boulder, CO 80301

Dr. James H. Price, Jr.—Research and Technology Section, Texas Natural Resources
Conservation Commission, P.O. Box 13087, Austin, TX  78711-3087


Invited Scientific Advisory Board Members

Dr. Morton Lippmann—Institute of Environmental Medicine, New York University Medical
Center, Long Meadow Road, Tuxedo, NY  10987

Dr. Roger O. McClellan—Chemical Industry Institute of Toxicology, P.O. Box 12137,
Research Triangle Park, NC 27711


Consultants

Dr. Petros Koutrakis—Harvard School of Public Health, 665 Huntington Avenue, Boston,
MA 02115
                                       I-lv

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                 U.S. ENVIRONMENTAL PROTECTION AGENCY
                          SCIENCE ADVISORY BOARD
                CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE
                                     (cont'd)
Consultants (cont'd)

Dr. Kinley Larntz—Department of Applied Statistics, University of Minnesota, 352 COB,
1994 Buford Avenue, St. Paul, MN 55108-6042

Dr. Allan Legge—Biosphere Solutions, 1601 llth Avenue, N.W.,  Calgary, Alberta
T2N 1H1,  Canada

Dr. Daniel Menzel—Department of Community and Environmental Medicine, University of
California—Irvine, 19172 Jamboree Boulevard, Irvine, CA 92717-1825

Dr. William R. Pierson—Energy and Environmental Engineering Center, Desert Research
Institute, P.O. Box 60220, Reno, NV 89506-0220

Dr. Jonathan Samet—Johns Hopkins University, School of Hygiene and Public Health,
Department of Epidemiology, 615 N. Wolfe Street, Baltimore, MD 21205

Dr. Christian Seigneur—Atmospheric and Environmental Research, Inc., 6909 Snake Road,
Oakland, CA  94611

Dr. Carl M. Shy—Department of Epidemiology,  School of Public Health, University of
North Carolina, CB #7400 McGravran-Greenberg Hall, Chapel Hill, NC  27599-7400

Dr. Frank  Speizer—Harvard Medical School, Charming Laboratory, 180 Long wood  Avenue,
Boston, MA 02115

Dr. Jan Stolwijk—Epidemiology and Public Health, Yale University, 60 College Street,
New Haven, CT  06510

Dr. Mark J. Utell—Pulmonary Disease Unit, Box 692, University of Rochester Medical
Center, 601 Elmwood Avenue,  Rochester, NY 14642

Dr. Warren White—Washington University, Campus Box 1134,  One Brookings Drive,
St. Louis, MO  63130-4899
Designated Federal Official

Mr. Randall C. Bond—Science Advisory Board (1400), U.S. Environmental Protection
Agency, 401 M Street, S.W., Washington, DC 20460
                                      I-lvi

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                 U.S. ENVIRONMENTAL PROTECTION AGENCY
                          SCIENCE ADVISORY BOARD
                CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE
                                    (cont'd)
Designated Federal Official (cont'd)

Mr. A. Robert Flaak—Science Advisory Board (1400), U.S. Environmental Protection
Agency, 401 M Street, S.W., Washington, DC  20460
Staff Assistant

Ms. Janice M. Cuevas—Science Advisory Board (1400), U.S. Environmental Protection
Agency, 401 M Street, S.W., Washington, DC  20460
Secretary

Ms. Lori Anne Gross—Science Advisory Board (1400), U.S. Environmental Protection
Agency, 401 M Street, S.W., Washington, DC  20460

Ms. Connie Valentine—Science Advisory Board (1400), U.S. Environmental Protection
Agency, 401 M Street, S.W., Washington, DC  20460
                                     Mvii

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                  U.S. ENVIRONMENTAL PROTECTION AGENCY
        PROJECT TEAM FOR DEVELOPMENT OF AIR QUALITY CRITERIA
                          FOR PARTICULATE MATTER
Scientific Staff

Dr. Lester D. Grant—Director, National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC  27711

Dr. Michael A. Berry—Deputy Director,  National Center for Environmental Assessment,
(MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC  27711

Dr. Dennis Kotchmar—Project Manager,  Medical Officer, National Center for
Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research
Triangle Park, NC  27711

Ms. Beverly Comfort—Deputy Project Manager/Technical Project Officer, Health Scientist,
National Center for Environmental Assessment (MD-52), U.S. Environmental Protection
Agency, Research Triangle Park, NC 27711

Dr. Lawrence J. Folinsbee—Chief, Environmental Media Assessment Group, National
Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency,
Research Triangle Park, NC  27711

Dr. A. Paul Altshuller—Technical Consultant, Senior Atmospheric Scientist, National Center
for Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research
Triangle Park, NC  27711 (Retired)

Dr. Robert Chapman—Technical Consultant, Medical Officer, National Center for
Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research
Triangle Park, NC  27711

Mr. William Ewald—Technical Project Officer, Health Scientist, National Center for
Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research
Triangle Park, NC  27711

Mr. Norman Childs—Chief, Environmental Media Assessment Branch, National Center for
Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research
Triangle Park, NC  27711 (Retired)

Dr. Judith A. Graham—Associate Director for Health, National Exposure Research
Laboratory  (MD-77), U.S. Environmental Protection Agency, Research Triangle Park,
NC 27711
                                       Mix

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                  U.S. ENVIRONMENTAL PROTECTION AGENCY
        PROJECT TEAM FOR DEVELOPMENT OF AIR QUALITY CRITERIA
                           FOR PARTICULATE MATTER
                                     (cont'd)
Scientific Staff (cont'd)

Ms. Annie M. Jarabek—Technical Project Officer, Toxicologist, National Center for
Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research
Triangle Park, NC  27711

Dr. Allan Marcus—Technical Project Officer, Statistician, National Center for Environmental
Assessment (MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC
27711

Dr. James McGrath—Technical Project Officer, Visiting Senior Health Scientist, National
Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency,
Research Triangle Park, NC  27711

Dr. Joseph P. Pinto—Technical Project Officer, Physical Scientist, National Center for
Environmental Assessment, U.S.  Environmental Protection Agency, Research Triangle Park,
NC 27711

Ms. Beverly Tilton—Technical Project Officer, Physical Scientist,  National Center for
Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research
Triangle Park, NC  27711 (Retired)

Dr. William E. Wilson—Technical Consultant, Physical Scientist, National Center  for
Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research
Triangle Park, NC  27711
Technical Support Staff

Mr. Douglas B. Fennell—Technical Information Specialist, National Center for
Environmental Assessment (MD-52), U.S. Environmental Protection Agency, Research
Triangle Park, NC  27711

Ms. Emily R. Lee—Management Analyst, National Center for Environmental Assessment
(MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC  27711

Ms. Diane H. Ray—Program Analyst, National Center for Environmental Assessment
(MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
                                        I-lx

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                 U.S. ENVIRONMENTAL PROTECTION AGENCY
        PROJECT TEAM FOR DEVELOPMENT OF AIR QUALITY CRITERIA
                          FOR PARTICULATE MATTER
                                     (cont'd)
Technical Support Staff (cont'd)

Ms. Eleanor Speh—Office Manager, Environmental Media Assessment Branch, National
Center for Environmental Assessment (MD-52), U.S. Environmental Protection Agency,
Research Triangle Park, NC 27711

Ms. Donna Wicker—Administrative Officer, National Center for Environmental Assessment
(MD-52), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Richard Wilson—Clerk, National Center for Environmental Assessment (MD-52),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Document Production Staff

Ms. Marianne Barrier—Graphic Artist, ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park, NC  27709

Mr. John R. Barton—Document Production Coordinator, ManTech Environmental
Technology, Inc., P.O. Box 12313, Research Triangle Park, NC  27709

Mr. Donald L. Duke—Project Director, ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park, NC  27709

Ms. Shelia H. Elliott—Word Processor, ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park, NC  27709

Ms. Sandra K. Eltz—Word Processor, ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park, NC  27709

Ms. Sheila R. Lassiter—Word Processor, ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park, NC  27709

Ms. Wendy B. Lloyd—Word Processor, ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park, NC  27709

Ms. Carolyn T. Perry—Word Processor, ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park, NC  27709

Ms. Terri D. Ragan—Personal Computer Technician, ManTech Environmental Technology,
Inc., P.O. Box 12313, Research Triangle Park, NC 27709

                                      I-lxi

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                 U.S. ENVIRONMENTAL PROTECTION AGENCY
        PROJECT TEAM FOR DEVELOPMENT OF AIR QUALITY CRITERIA
                          FOR PARTICULATE MATTER
                                     (cont'd)
Document Production Staff (cont'd)

Mr. Derrick Stout—Local Area Network System Administrator, ManTech Environmental
Technology, Inc., P.O. Box 12313, Research Triangle Park, NC  27709

Ms. Cheryl B. Thomas—Word Processor,  ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park,  NC 27709
Technical Reference Staff

Ms. Ginny M. Belcher—Bibliographic Editor, ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park, NC  27709

Mr. Robert D. Belton—Bibliographic Editor, Information Organizers, Inc.,
P.O. Box 14391, Research Triangle Park, NC  27709

Mr. John A.  Bennett—Bibliographic Editor, ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park, NC  27709

Ms. S. Blythe Hatcher—Bibliographic Editor, Information Organizers, Inc.,
P.O. Box 14391, Research Triangle Park, NC  27709

Ms. Susan L. McDonald—Bibliographic Editor, Information Organizers, Inc.,
P.O. Box 14391, Research Triangle Park, NC  27709

Ms. Deborah L. Staves—Bibliographic Editor, Information Organizers,  Inc.,
P.O. Box 14391, Research Triangle Park, NC  27709

Ms. Patricia  R. Tierney—Bibliographic Editor, ManTech Environmental Technology, Inc.,
P.O. Box 12313, Research Triangle Park, NC  27709
                                      I-lxii

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                     1.  EXECUTIVE SUMMARY
1.1  INTRODUCTION

1.1.1  Purpose of the Document
     The purpose of this document, Air Quality Criteria for Particulate Matter, is to present
air quality criteria for particulate matter (PM) in accordance with Clean Air Act (CAA)
Sections 108 and 109,  which govern establishment, review, and revision of U.S. National
Ambient Air Quality Standards (NAAQS).

•  Section 108 directs the U.S. Environmental Protection Agency (EPA) Administrator to list
   pollutants that may reasonably be anticipated to endanger public health or welfare and to
   issue air quality criteria for them. The air quality criteria are to reflect the latest scientific
   information useful in indicating the kind and extent of all exposure-related effects on
   public health and welfare expected from the presence of the pollutant in ambient air.

•  Section 109 directs the EPA Administrator to set and periodically revise, as appropriate,
   (a) primary NAAQS to protect against adverse health effects of listed criteria pollutants
   among sensitive population groups, with an adequate margin of safety, and (b) secondary
   NAAQS to protect against welfare effects (e.g.,  impacts on vegetation, crops, ecosystems,
   visibility, climate, man-made materials, etc.).

•  To meet these CAA mandates, this document assesses the latest scientific information
   useful in deriving criteria as scientific bases for decisions on possible revision of current
   PM NAAQS.  A  separate EPA PM Staff Paper draws upon assessments in this document,
   together with other information, in delineating key information used to develop and
   present appropriate options for consideration by the EPA Administrator with regard to
   review of the PM NAAQS.

1.1.2   Organization of the Document
•  This Executive Summary (Chapter 1) summarizes key points from ensuing chapters.

•  Chapter 2 provides a general introduction, including an overview of the rationale
   underlying the current PM NAAQS, i.e.,  150 ptg/m3 (24-h) and 50 /ig/m3 (annual
   average) as PM10 (particles < 10 /xm aerodynamic diameter, dae).

•  Chapters 3 through 7 provide background information on air quality and exposure aspects,
   to help to place the succeeding discussions of PM effects into perspective.

•  Chapter 8 deals with visibility and climate effects; and Chapter 9 assesses materials
   damage, as key types of welfare effects of concern for the current PM  NAAQS review.
   Welfare effects of PM on vegetation,  crops, and ecosystems are not assessed in the
   document.
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•  Chapters 10, 11, and 12, respectively, discuss PM dosimetry, toxicology and community
   epidemiology information.  Chapter 13 provides an integrative synthesis of key points
   from those health chapters and other preceding air quality and exposure chapters.
1.2  AIR QUALITY AND EXPOSURE ASPECTS

     The document's discussion of air quality and exposure aspects considers chemistry and
physics of atmospheric PM; analytical techniques for measuring PM mass, size, and chemical
composition; sources of ambient PM in the United States; temporal/spatial variability and
trends in ambient U.S. PM levels; and human exposure relationships.

1.2.1   Chemistry and Physics of Atmospheric Particles
•  Airborne PM is not a single pollutant, but rather is a mixture of many subclasses of
   pollutants with each subclass containing many different chemical species. Atmospheric
   PM occurs naturally as  fine-mode and coarse-mode particles  that, in addition to falling
   into different size ranges,  differ in formation mechanisms, chemical composition, sources,
   and exposure relationships.

•  Fine-mode PM is derived from combustion material that has  volatilized and then
   condensed to form primary PM or from precursor gases reacting in the atmosphere to
   form secondary  PM.  New fine-mode particles are formed by the nucleation of gas phase
   species,  and  grow by  coagulation (existing particles combining) or condensation (gases
   condensing on existing particles). Fine particles are composed of (a) freshly generated
   particles, in an ultrafine or nuclei mode, and (b) an accumulation mode, so called because
   particles grow into and  remain in that mode.

•  Coarse-mode PM, in contrast, is formed by crushing, grinding, and abrasion of surfaces,
   which breaks large pieces of material  into smaller pieces. They are then suspended by the
   wind or by anthropogenic activity. Energy considerations limit the break-up of large
   particles and small particle aggregates generally to a minimum size of about 1  ^tm in
   diameter. Mining and agricultural activities are examples of anthropogenic sources of
   coarse-mode particles.  Fungal spores, pollen, and plant and  insect fragments  are
   examples of  natural bioaerosols also suspended as coarse-mode particles.

•  Within atmospheric particle modes, the distribution of particle number, surface, volume,
   and mass by diameter is frequently approximated by lognormal distributions.
   Aerodynamic diameter,  dae, which depends on particle density and is defined as the
   diameter of a particle with the same settling velocity as a spherical particle with unit
   density (1 g/cm3) is often used to describe particle size. Typical values of the mass
   median aerodynamic diameter (MMAD) and geometric standard deviation (ag) of each size
   mode of an aerosol are:

          - Nuclei mode:              MMAD=0.05 to 0.07 jan     ag = 1.8
          - Accumulation mode:       MMAD= 0.3 to 0.7 ptm       crg = 1.8
          - Coarse mode:             MMAD= 6 to 20  m         o  = 2.4
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   At high relative humidities or in air containing evaporating fog or cloud droplets, the
   accumulation mode may be split into a droplet mode (MM AD = 0.5 to 0.8 ^irn) and a
   condensation mode (MM AD =  0.2 to 0.3
•  Research studies use (a) impactors to determine mass as a function of size over a wide
   range and (b) particle counting devices to determine number as a function of size.  Such
   studies indicate an atmospheric bimodal distribution of fine and coarse particle mass with
   a minimum in the distribution between 1 and 3 /*m dae. Routine monitoring studies,
   however, is generally limited to measuring TSP (total suspended particles) including both
   fine and coarse particles up to 40 or more jum dae; thoracic particles or PM10 (upper size
   limited by a 50% cut at 10 /xm dae); fine particles or PM2 5 (upper size limited by a 50%
   cut point at 2.5 /im dae), and the coarse fraction of PM10, i.e., the difference between
   PM10 and PM2 5 (PM10_2 5).  Cut points are not perfectly sharp for any of these PM
   indicators; some particles larger than the cutpoint are collected and some smaller-particles
   smaller than the cutpoint are not retained.

•  The terms "fine" and "coarse" were originally intended to apply to the two major
   atmospheric particle distributions which overlap in the size range between 1  and 3 /xm
   diameter.  Now, fine has come to be often associated with the PM2 5 fraction and coarse
   is often used to refer to PM10_2 5. However, PM2 5 may also contain, in addition to the
   fine-particle mode, some of the lower-size tail of the coarse particle mode between about
   1 and 2.5 /xm dae.  Conversely, under high relative humidity conditions, the larger
   particles in the accumulation mode may also extend into the  1  to 3 (j,m dae range.

•  Three approaches are used to classify particles by size:  (1) modes, based on formation
   mechanisms and the modal structure observed in the atmosphere, e.g., nuclei and
   accumulation modes which comprise the fine particle mode and the coarse particle mode;
   (2) cut point, based on  the 50%  cut point  of the specific sampling device, e.g.,  PM2 5,
   PM10_2 5, and PM10; and (3) dosimetry, based on the ability  of particles  to enter certain
   regions of the respiratory tract.

1.2.2   Sources of Airborne Particles in the United States
•  The chemical complexity of airborne particles requires that the composition and sources of
   a large number of primary and secondary  components be considered.  Major components
   of fine particles are: sulfate,  strong acid, ammonium, nitrate, organic compounds, trace
   elements (including metals), elemental carbon, and water.  Major sources of these fine
   mode substances are fossil  fuel combustion by electric utilities,  industry  and motor
   vehicles;  vegetation burning; and the smelting or other processing of metals.

•  Sulfur dioxide (SO2), nitrogen oxides (NOX),  and  certain organic compounds are major
   precursors of fine secondary  PM.  NO reacts with ozone (O3) to form NO2.  SO2 and
   NO2 react with hydroxy radical (OH) during the daytime to form sulfuric and nitric acid.
   During the nighttime NO2 reacts with ozone and forms nitric acid through a sequence of
   reactions involving the nitrate radical  (NO3).  These acids may react further with ammonia
   to form ammonium sulfates and nitrates.  Some types of higher molecular weight organic
   compounds react with OH radicals,  and olefinic compounds also react  with ozone, to form
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   oxygenated organic compounds which can condense onto existing particles. SO2 also
   dissolves in cloud and fog droplets where it may react with dissolved O3, H2O2,  or, if
   catalyzed by certain metals, with O2, yielding sulfuric acid or sulfates, that lead to PM
   when the droplet evaporates.

•  The formation of secondary PM depends on reactions involving OH, O3, and H2O2,
   species which are normally present in the atmosphere but which are generated in higher
   concentrations during the photochemical  smog formation process. Since smog formation
   increases with sunlight and temperature,  secondary PM peaks during the summer in most
   U.S. areas.

•  Background geogenic and biogenic emission sources  include:  wind blown dust from
   erosion and reentrainment; the long-range transport of dust from the Sahara desert; sea
   salt; particles formed from the oxidation of sulfur compounds emitted from oceans and
   wetlands; the oxidation of NOX from natural forest fires and lightning; and the oxidation
   of hydrocarbons (such as terpenes) emitted by vegetation.

•  Major components of coarse particles are aluminosilicates and other oxides of crustal
   elements (e.g., Fe, Ca, etc.) in soil dust; fugitive dust from roads, industry, agriculture,
   construction and demolition; fly ash from combustion of oil and coal; and additional
   contributions from plant  and animal material.

•  Fugitive dust constitutes  about 90% of estimated PM10 emissions in the United States.
   Emissions  are sporadic and  widespread.  Only a small percentage of this material is
   emitted in  the fine particle size fraction.

•  Uncertainties in emissions inventory estimates could  range from about 10% for well
   defined sources (e.g.,  for SO2) to an order of magnitude for widespread and sporadic
   sources (e.g., fugitive dust).

•  There  has  been no clear  trend in estimated emissions of fugitive dust and emissions from
   natural sources from 1984 to 1993. Estimated primary PM10 emissions  from combustion
   sources have decreased by about 10%; estimated SO2 emissions have decreased by about
   6%; and there was no significant change in estimated NOX emissions from 1984 to 1993.

•  Receptor modeling has proven to be a useful method for identifying contributions of
   different types of sources especially for the primary components of ambient PM.
   Apportionment of secondary PM is more difficult because it requires consideration of
   atmospheric reaction processes and rates. Results from western U.S. sites indicate that
   fugitive dust, motor vehicles, and wood  smoke are the major contributors  to ambient PM
   samples there, while results from eastern U.S. sites indicate that stationary combustion
   and fugitive dust are major contributors to  ambient PM samples in the East.  Sulfate and
   organic carbon are the major secondary components  in the East, while nitrates and organic
   carbon are the major secondary  components in the West.
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•  Fine and coarse particles have distinctly different sources, both natural and anthropogenic.
   Therefore different control strategies are likely to be needed, depending on whether fine
   or coarse particles (or both) are selected for control.

1.2.3   Atmospheric Transport and Fate of Airborne Particles
•  Dry deposition of fine particles is slow. Nuclei-mode (ultrafine) particles are rapidly
   removed by coagulation into accumulation-mode particles.  Accumulation-mode particles
   are removed from the atmosphere primarily by forming cloud droplets and falling out in
   raindrops.  Coarse particles are removed mainly by gravitational settling and inertial
   impaction.

•  Primary and secondary fine particles have long lifetimes in the atmosphere (days  to
   weeks) and travel long distances (hundreds to thousands of kilometers). They tend to be
   uniformly distributed over urban areas and larger regions, especially in the eastern United
   States. As a result,  they are not easily traced back to  their individual sources.

•  Coarse particles normally have shorter  lifetimes (minutes to hours) and only travel short
   distances (< 10's of  km). Therefore, coarse particles tend to be unevenly distributed
   across urban areas and tend to have more localized effects than fine particles.  (Dust
   storms occasionally cause long range transport of the smaller coarse-mode particles.)

1.2.4   Airborne Particle Measurement Methods
•  Measurement of ambient PM  mass and chemical composition is  important for:  source
   attribution; inventories of the  observed  mass;  health and welfare effects studies; and
   determination of compliance with standards.  A comprehensive approach requires a
   combination of analytical techniques to  assess: (1) mass; (2) elemental composition;
   (3) water-soluble ionic species; and (4) organic compounds.

•  Various sampling systems based on gravimetric (weight) measurements of collected
   particles yield direct measurements of airborne particle mass.  The high volume (hi-vol)
   sampler, used extensively in the United States before establishment of PM10 as the
   indicator for the PM  standard, collects and measures the mass of total  suspended
   particulates (TSP), including both fine and coarse particles.  Certain other samplers (e.g.,
   dichotomous samplers or impactors) use one or more sampler heads or other separator
   devices to selectively collect and measure the mass of  various size fractions of PM.

•  There are no calibration standards for suspended particle mass; therefore, the accuracy of
   particle mass measurements cannot be determined.  The precision of particle mass
   measurements can be estimated by comparing results from collocated samplers. When
   using different measurement techniques, samplers of different design or manufacturer,
   and, in some cases, when using identical systems of different age or cleanliness,
   substantial biases of 50% or more have been observed. Mass  concentration measurements
   with a precision close to 10%  have been obtained with collocated samplers of identical
   design and same time since cleaning.
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•  Available technology allows accurate (±10 to 15%) measurement of several of the major
   components of coarse and fine particles (minerals, sulfates, strong acids, and ammonium).
   However, collection and measurement technologies for elemental carbon, organic carbon,
   and nitrates are not as well established.

•  Field studies of EPA Equivalent PM10 Reference Methods and reviews of field data from
   collocated PM10 samplers show substantial biases under certain conditions. These biases
   result from: (a) allowing a tolerance of ± 0.5 /zm for the 10 /xm cutpoint; (b) inadequate
   restrictions  on internal particle bounce; (c) soiling of certain types  of PM10 inlets; and the
   losses of semivolatile components.

•  Semivolatile organic compounds and semivolatile ammonium compounds (such as
   NH4NO3) may be lost by volatilization during sampling. Such losses may be very
   important in woodsmoke impacted areas for organic compounds  or in agricultural and
   other areas  where low sulfate and high ammonia lead to high NH4NO3 concentrations.

•  Beta attenuation, tapered element oscillating microbalance (TEOM), and optical
   monitoring  methods have been extensively field tested.  Although acceptable comparisons
   with EPA reference sampling methods have been reported in some collocated field studies,
   significant losses of semivolatile components may occur during and after sample
   collection.  The presence of significant amounts of semivolatile particles at sampling
   locations in the  western United States is a major concern.

•  Certain older optical methods, which provided estimates of ambient PM levels used in
   epidemiology studies  of the 1950s to 1970s, are still employed in some countries.  These
   include (a)  the black smoke (BS)  method, based on light reflectance from particle stains
   on sample collection filters and extensively used in Britain and elsewhere in Europe; and
   (b) the coefficient of haze (COH) method, based on light transmission through the filter
   stain and used in some U.S. areas.  Neither method directly measures the mass of
   collected particles; so credible estimates of particle concentrations (in /xg/m3) can only be
   made via site-specific calibration  against mass measurements from collocated gravimetric
   sampling devices.  BS and COH sampling devices typically have «4.5 /mi cut points,
   collect mainly fine particles but also some coarse particles up to  «10 jum, and are more
   comparable to PM2 5  than PM10 or TSP measurements.  BS and  COH readings  are
   especially sensitive to elemental carbon particle concentrations.

•  Personal PM exposure samplers are desirable for evaluating individual exposures.
   Relatively unobtrusive personal samplers have been designed for several particle size
   cutpoints, and recent studies suggest that acceptable precision is possible, covering the
   size range from at least 0.1 to 10 pun dae.

•  Physical elemental  analysis methods for metals  and other elements include x-ray
   fluorescence (XRF), particle-induced x-ray emission (PIXE), and instrumental neutron
   activation analysis (IN A A). Atomic absorption spectrometry (AAS) is used for soluble
   ions such as sodium, magnesium, potassium,  and calcium.  Ion chromatography (1C) is
   used for nitrate and sulfate. Automated colorimetry (AC) is used to measure ammonium,
   chloride,  nitrate, and  sulfate.

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•  Accurate chemical speciation of organics, nitrates, and acidity requires comprehensive
   sampling system components, including gas stream denuders and sequential filter packs.
   Sampling artifacts can cause significant errors in measurement of organic PM.  Some
   disagreement exists, however, about whether adsorption or volatilization artifacts are most
   important.  Sampling artifacts may be introduced by changes in temperature or organic
   vapor concentration during sampling and/or storage.  Organic aerosol sampling artifacts
   can cause large errors in particle mass measurements in areas where a large fraction of the
   PM mass is organic.

•  Chemical analysis of the organic fraction of airborne PM is very costly and difficult
   because  of the complex mixture of hundreds of compounds.  Analyses of organic
   compounds have utilized high-performance liquid chromatography (HPLC) and gas-
   chromatography combined with mass spectroscopy (GC/MS), but only 10  to 20% of the
   organic mass has been identified as  specific chemical compounds.  It is now known that
   some organic vapors are adsorbed on quartz-fiber filters and that some semivolatile
   material evaporates during and after sampling.  However, most information on organic,
   elemental, and carbonate carbon comes from samples collected on quartz-fiber filters.

•  A variety of methods are now available for sampling or analysis of all types of
   bioaerosols, including fungal spores, bacteria, pollen, and plant or animal  fragments.
   Analytical methods include:   cell culture, microscopy, immunoassay, other bioassay
   methods, chemical assays, and molecular techniques for DNA/RNA-containing particles.

•  Light and electron-microscopy analysis of particle morphology (size and shape) can also
   be used to help identify sources and transport mechanisms for airborne particles.

1.2.5   Ambient U.S.  PM Concentrations:  Regional Patterns and Trends
•  Particle mass data have been collected  at a number of rural, suburban, and urban sites
   across the United States by various  local, state, and  national programs.  The data have
   been stored in the Aerometric Information Retrieval  System (AIRS).  Data have also been
   collected at remote sites as part of the IMPROVE and NESCAUM networks.

•  Estimates of annual average biogenic and geogenic PM,0 concentrations range from 5 to
   11 /zg/m3 for the eastern United States and 4 to 8 /xg/nr  for the  western United States.
   Annual average PM10 concentrations in national parks, wilderness areas, and national
   monuments in the western United States range  from  5 to 10 jug/m3  (based  on data from
   IMPROVE). The lowest values in AIRS, obtained at remote sites,  range from 4 to
   10 /ig/m3.  Annual average PM10 values representative of relatively clean  suburban and
   rural  areas reported in AIRS for 1993 ranged from 9 to 13 /-ig/m3.

•  The five cities with the highest annual mean PM10 concentrations for urban sites in the
   western United States, found in AIRS  from 1990 to  1994, were Southern California cities
   in agricultural regions:  Visalia, CA; Bakersfield, CA; Fresno, CA; Riverside, CA; and
   Stockton, CA.  The average concentration in these five areas ranged from 44.8 to
   60.4
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•  Annual average PM10 concentrations for most urban areas in the United States are
   typically greater than about 20 /^g/m3.  Highest annual mean PM10 concentrations in the
   western United States are significantly higher than corresponding five year annual mean
   values of about 34 /ig/m3 in eastern U.S. urban areas (Atlanta, GA;  Paterson, NJ;
   Roanoke, VA; Philadelphia, PA; and Atlantic City, NJ) and 36 |ug/m3 in central U.S.
   urban areas (St. Joseph, MO; Steubenville, OH; Cleveland, OH;  Omaha,  NE; and
   Chattanooga, TN).  The  lowest annual mean PM10 concentrations  found at sites in U.S.
   populated areas (Penobscot Co., ME; Marquette, MI; and Lakeport, CA)  averaged about
   12 /xg/m3 during the period from 1990 to 1994.

•  PM10 mass concentrations averaged over regions or by city, using sites in operation
   during  1988 to 1994, show a significant year to year decrease at most sites.  Exceptions
   are Philadelphia and some locations in Southern California.  The regional  decreases at
   urban sites between 1988 and 1994 were:  20% for the contiguous United States; 18% for
   the eastern United States; and 28% for the western United States.

•  Information on trends of PM2 5 (fine) and PM10_2.5 (coarse) have been examined.
   However, the data from Philadelphia, several AIRS sites, the Harvard Six-City sites, and
   California sites is generally not sufficient either in number of sites or number of years
   (2.5 to  10 years per site) to demonstrate differential trends in coarse PM and fine PM.

•  Long time series for PM2 5'and PM10 are available from a relatively few sites in
   Philadelphia and California.  Typically,  PM2 5 to PM10 ratios are  greater than 0.5 at these
   sites on an annually averaged basis.  However, values of the ratio of PM2 5 to PM10 are
   highly variable and can be much smaller than 0.5 on individual days.  Because of these
   considerations, values of PM2 5 should be inferred from PM10 only where some site-
   specific information is  available.  Seasonal or yearly estimates will be more reliable than
   daily estimates.

•  Sulfate (SO^) and strong acidity (H2SO4 plus HSO^) are regional pollutants distributed
   relatively evenly over areas of the eastern United States during the summertime.
   However,  in high density livestock areas and the centers of large urban areas, ammonia
   neutralizes part of the acidity.
•  Data for assessing day-to-day variability in PM2 5 and PMio are only available from one
   site in Philadelphia. These data can be used to indicate the potential for daily changes in
   24-hour average PM2 5 and PM10 levels for risk analyses.  During this study,  average
   day-to-differences in PM2 5 were 6.8 ± 6.5 ^g/m3 and 8.6 + 7.5 ptg/m3 for PM10.
   Maximum day-to-day differences were 54.7 /xg/m3 for PM2.5 and 50.4 for PM10.

1.2.6  Human PM  Exposure
•  The total personal exposure to PM consists of outdoor (ambient) and indoor exposures.
   Nonambient conditions, mainly indoors at home or at work, occupy the vast majority of a
   person's time. In the U.S., the average daily time spent indoors is 20 h/day,  or 85% of
   the day.  Some additional time,  about 1.0 to 2.0 h (5%) of the day, is also spent in other
   nonambient microenvironments (e.g.,  in vehicles in transit)
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•  PM10 in ambient air penetrates into residential microenvironments and reaches an
   equilibrium approaching outdoor concentrations.  Once indoors, PM of ambient origin
   decreases due to deposition on surfaces through gravitational settling and electrostatic
   attraction. The coarse PM has a much higher deposition rate than the fine PM.

•  Human indoor activity (e.g., walking on carpets) tends to resuspend previously deposited
   PM >  5 fim and to stir up or suspend other material (such as tracked-in soil and a variety
   of biological material such as mold spores and insect debris).  Such activity increases
   indoor PM10 but not indoor PM2 5.

•  In residential and occupational  indoor microenvironments,  PM is generated by indoor
   sources (cooking, smoking, vacuuming, dusting, etc.) and  is  also removed from the indoor
   air by gravitational deposition and mechanical means (air cleaners, filters, etc.).

•  When a cross-sectional analysis is performed  that compares ambient PM10 to individual
   personal exposures to PM10 for a group of subjects, the correlation often goes towards
   zero, because of the large  influences of indoor sources  and sinks that vary between the
   individuals.  However, other types of analyses, as follow,  indicate significant
   contributions of ambient particles to total human exposure.

•  Because of the relative day-to-day consistency within any given residence  for the indoor
   sources and sinks of PM10, the longitudinal (time-series) correlation of personal exposure
   of a specific individual to total indoor PM10 (from outdoor and indoor sources) and
   ambient PM10 can be very high.  Two analyses conducted  to date on a limited number of
   subjects have yielded R2 values above 0.9 when indoor sources are consistent from day-to-
   day.

•  Experimental data on a cohort of elderly housewives (N=5) and  retirees (N=2),
   purposefully chosen to have minimal sources of PM at home, shows that their personal
   exposures to PM10 are highly correlated both with the ambient PM10 immediately outside
   their homes (0.77 <  r <  0.96) and at a nearby monitoring station (0.75  < r < 0.96).
   For the identical cohort of elderly housewives and retirees, their  personal exposures to
   PM >  10 /*m (TSP - PM10) had virtually no  correlation with the ambient PM > 10 /mi
   (r = -0.03; R2 = 0.00).

•  Experimental data on personal exposures to sulfates, which are predominantly of outdoor
   origin and submicron size, show consistently high correlations of total personal exposures
   with ambient sulfate (0.78 
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•  Because indoor sources typically do not generate fine mode particles of the same chemical
   composition as the most common fine particles of ambient origin (e.g., sulfates, metals,
   etc.), the presence of indoor PM sources will not change the relationships noted in the
   immediately preceding  bullet.  Also, the production of indoor-generated particles is
   controlled by daily indoor activities. Therefore,  the exposure to indoor-generated particles
   will not be correlated with the concentration of ambient (outdoor-generated) particles, and
   time-series epidemiology based on ambient measurements will not identify health effects
   of indoor-generated particles.

•  Therefore, the measurements of daily variations of ambient PM concentrations, as  used in
   the time-series epidemiology studies of Chapter  12, have a plausible linkage to the daily
   variations of human exposures to PM from ambient sources, for the populations
   represented by the ambient monitoring stations.   This linkage should be better for
   indicators of fine particles (PM2 5) than for indicators of fine plus  coarse  particles (PM10
   or TSP), which, in turn,  should be better than indicators of coarse particles (PM10-PM2 5).
1.3    DOSIMETRY

     For risk assessment purposes, exposure-dose-response models are useful in examining
the effects of different host characteristics, activity patterns,  and exposures on biological
responses.  Development of a comprehensive biologically based exposure-dose-response
model  to aid risk assessment requires more information on mechanisms of action of particles
on respiratory tract tissues, including behavior of particles once inhaled or deposited (e.g.,
hygroscopic growth, disaggregation of aggregated particles), pathological processes affecting
deposition and clearance of particles, and factors which influence the response of tissue to
particle burden.  Deposition and clearance models are useful in estimating average regional
particle deposition and regional clearance as a  function of major particle characteristics.
Dosimetry models have also been useful to characterize average PM deposition patterns for
humans as a function of age, gender, and activity pattern and may also ultimately be useful
for extrapolating laboratory animal data to estimate concentrations that might be expected to
be associated with effects in humans.

•  Particles  may be deposited in (a) the extrathoracic airways (i.e., mouth, nose, and larynx);
   (b) in airways of the tracheobronchial region; and (c) in the alveolar region where gas
   exchange  occurs.  There are differences in deposition mechanism and dose distribution in
   each of these areas that are dependent on particle size and airway geometry.  The major
   mechanisms of particle deposition on airway surfaces in the respiratory tract are
   impaction, sedimentation, diffusion, interception, and electrostatic precipitation.

•  Respiratory tract deposition patterns  are primarily dependent on particle size and
   distribution (as indicated by the mass median aerodynamic diameter and the geometric
   standard deviation) within the inspired air.  Biologic effects may be a function not only  of
   particle mass deposition but also particle number or the total surface area of the particles.
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 •  Various host factors have been shown to influence predicted particle deposition patterns
   including age, ventilation pattern, and the presence of obstructive or inflammatory airway
   disease.  Higher overall ventilation increases total  deposition.  Increased mouth breathing
   increases the deposition of coarse particles in the tracheobronchial region.  Obstructive
   airway disease, such as asthma, emphysema, and chronic bronchitis, results in increased
   deposition of particles  in the lower respiratory tract.

 •  Acute effects of PM are probably best related to deposited dose, whereas chronic effects
   may be related to cumulative or retained dose. Retention of particles is a function of
   deposition site, clearance of particles by macrophages or the mucociliary system, and
   particle characteristics, especially solubility. Chronic effects may also arise from
   recurring cycles of pulmonary injury and repair.

 •  There are substantial differences among laboratory animal species with regard to the
   inhalability of different sized particles as well as quantitative and qualitative differences  in
   airway geometry.

 •  With regard to PM, extrapolation of responses from laboratory animals to humans is
   hampered by limited development of models due to the lack of data characterizing
   differences in inhalability, airway geometry, and clearance mechanisms among species.
   In humans, some  inhalable particles can exceed 15 jum dae, while  in small laboratory
   animals, inhalable particles seldom exceed 4 ^m dae.
   Respiratory tract dosimetry supports the choice of PM10 as an index of thoracic particles.
   However, dosimetric considerations do not provide insight into the selection of a size cut
   to characterize a fine particle mode.
1.4  PARTICULATE MATTER HEALTH EFFECTS

     Many epidemiologic studies have shown statistically significant associations of ambient
PM levels with a variety of human health endpoints, including mortality, hospital admissions,
respiratory symptoms and illness measured in community surveys, and changes in pulmonary
mechanical function.  Associations of both short-term (usually days)  and long-term (usually
years) PM exposure with most of these endpoints have been consistently observed.  The
general  internal consistency of the epidemiologic data base enhances  the confidence accorded
the reported results and has contributed to increasing public health concern.  However, there
remains uncertainty regarding the shapes of PM exposure-response relationships; the
magnitude and variability of risk estimates for PM; the ability to attribute observed health
effects to specific PM constituents; the time intervals over which PM health effects
(e.g., shortening of life) are manifested; the extent to which findings in one  location can be
generalized to other locations; and the  nature  and magnitude of the overall public health risk
imposed by ambient PM exposure.  While the epidemiology data provide support for the
associations mentioned above, understanding of underlying biologic mechanisms has not yet
emerged.
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1.4.1  Epidemiology Findings
     The findings from the epidemiology studies are often expressed in terms of relative risk
(RR), indicating the ratio of the probability of occurrence of a given effect between two
different exposure conditions or exposure groups, or as an odds ratio, which is similar to RR
for conditions that occur relatively infrequently (such as PM-mortality).  Relative risks are
often expressed for a specific increase in a PM indicator (e.g.,  a 50 /*g/m3 increase in PM10)
and provide an estimate of percentage increase  in risk above baseline mortality or morbidity
rates in the lowest exposure time periods or location per the stated increment of PM indicator
concentration.  For example, a RR = 1.05 per 50 ^g/m3 PM10 increase  implies that an
approximate 5 % increase over background risk level is associated with a 50 /^g/m3 increase
in PM10  over baseline in the particular study area, assuming linearity of  dose-response
relationships and the absence of a threshold.

Ambient PM Mortality Effects
•  Early  studies of severe air pollution episodes in Europe and the U.S. from the 1930's to
   1950's indicated that exposure to high ambient levels of urban air pollution can produce
   marked increases above the expected numbers of daily deaths during severe episodes
   (e.g.,  in the Meuse Valley in 1930, in Donora in  1948, and  in London in 1952).  These
   observations left little doubt that exposures to ambient air containing high concentrations
   of particles and other copollutants such as SO2 can be lethal, although underlying
   mechanisms have not yet been delineated.

•  More  than 20 time-series analyses published in the past 10 years demonstrate positive
   associations between daily mortality and 24-h concentrations of ambient particles indexed
   by various measures  (BS, COH, TSP, PM10, PM25, H+, SOp in numerous metropolitan
   areas  of the U.S. and other countries.  Relative risk (RR) estimates for daily mortality in
   relation to daily ambient PM concentration are generally positive and  statistically
   significant across a variety of statistical modeling  approaches and  methods of adjustment
   for effects of relevant covariates such as season, weather, and co-pollutants.

•  Numerous time-series analyses of TSP-mortality relationships have explored many
   methodological issues related to use of specific types of models (e.g., parametric,
   non-parametric, Poisson, GLM, LOESS,  etc.), model specification (e.g., inclusion of only
   PM in analytical models or other copollutants as well), control for impacts of weather
   variables (temperature, humidity,  synoptic weather patterns), and  adjustments for other
   potentially confounding covariates.  Several  analyses of data from Philadelphia by various
   investigators have proven to be especially useful in confirming significant positive
   relationships between 24-h TSP concentrations and daily mortality, while also clarifying
   season-specific variations in the PM-mortality RR and the impacts of weather adjustments
   or other copollutants on the  RR attributed to PM.  Recent Health  Effects Institute-
   sponsored analyses underscore the great complexity inherent in simultaneous  statistical
   adjustment for health effects of multiple air pollutants. Overall, the analyses have
   produced basically robust results indicative of significant PM effects on mortality.

•  RR estimates for total non-accidental mortality associated with a 50 /*g/m3 increase  in
   24-h average PM10 range from 1.015 to 1.085. With PM10  as the only pollutant  index in
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   the model, RR = 1.025 to 1.085.  In the studies testing multiple pollutant models (with
   copollutant(s)  in the model), PM10 RR = 1.015 to 1.025.  Higher relative risks are
   indicated for the elderly and for those with pre-existing respiratory conditions.

•  The new time-series analyses clearly substantiate significant associations between daily
   mortality or morbidity and ambient 24-h PM10 concentrations typical of U.S. urban
   airsheds.  Less extensive evidence points toward fine particles as likely being important
   contributors to the observed PM-associated mortality,  based on studies showing positive
   associations of daily mortality with various fine particle indicators  (e.g., PM2 5, SO^  ,
   H+, etc.).

•  Fine particles  (PM2 5) showed a consistent and statistically  significant relationship to acute
   mortality in six U.S. cities, with relative risks ranging form 1.02 to 1.06 per 25 jug/m3
   PM2 5.  Coarse particles showed no significant relationship to excess mortality in five of
   the six cities.  In Steubenville, PM15_2 5 was more strongly related to mortality  than was
   PM2 5, but these two particle indicators were highly correlated.

•  Recent chronic (annual average) PM exposure studies  also indicate statistically significant
   positive associations between excess mortality and fine particle indicators.  Relative risk
   estimates derived from such studies suggest greater percentage increases in mortality risk
   than do the short-term mortality RR estimates.  However, the chronic exposure RR
   estimates are based on PM concentration during the 5  or  15 to 20 year study periods and
   do not necessary reflect the full  impacts of longer past PM exposures  (likely much higher
   in the most highly polluted cities).  Thus, lower RR estimates than the published ones for
   mortality associated with chronic PM exposure are apt to apply.

Ambient PM Morbidity Effects
      Numerous epidemiologic studies in the United States and elsewhere have also
demonstrated significant associations between ambient PM exposures indexed by a variety of
indicators (BS, TSP, PM10,  PM2 5, SOJ,  H+) and various acute and chronic morbidity
outcomes.  Such  outcomes include, for example, hospital  admissions,  increased respiratory
symptoms, and decreased lung function.

•  Studies of hospitalization for chronic obstructive pulmonary disease (COPD) and
   pneumonia show moderate, but statistically significant RR's in the  range of 1.06 to 1.25
   per increase of 50 /xg/m3 in PM10 (24-h). Most studies of hospitalizations for respiratory
   illnesses typically include patients >65 years of age.  Increased hospital admissions for
   respiratory causes during London episodes suggested an association with sulfuric acid
   aerosols, as well as with BS and SO2 levels.  Recent studies also show associations
   between ambient acidic aerosols  and summertime respiratory hospital admissions.

•  Studies of PM associations with  lower respiratory disease yielded odds ratios (OR) which
   ranged from 1.10 to 1.28, and studies of cough yielded odds ratios ranging from 0.98 to
   1.29 for a 50 /*g/m3 increase in PM10 (24-h). Limited data were available relating PM
   exposure to asthma or respiratory symptoms in adults.  Chronic cough, chest illness, and
   bronchitis showed positive associations  with annual average PM concentrations.
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•  Pulmonary function studies of children suggest that short term effects result from PM
   exposure.  Peak expiratory flow rates  were decreased 30 to 40 ml/sec per 50 /xg/m3
   increase in PM10 (24-h).  Somewhat larger effects occurred in symptomatic groups, such
   as asthmatics.  An estimate of the effect of PM on lung function in adults found a 29
   (±10) ml  decrease in FEVj per 50 /xg/m3 increase in PM10, similar in magnitude to
   changes found in children.  The chronic pulmonary function studies are less  numerous
   than the acute studies and the results are inconclusive.

•  Bronchitis symptoms and prevalence rates in children were found to be somewhat more
   closely associated with  annual average H+ concentrations than with other PM indicators.
   However,  in studies demonstrating these effects, the acid levels  were highly  correlated
   with other fine-particle indicators.

•  While numerous studies of PM related respiratory morbidity have been conducted using
   PM10 as an indicator, only a few studies have examined the effects of fine particle
   indicators, e.g., PM2 5.  Although different studies have suggested that one of these
   exposure indicators is a better predictor than the other for  a specific endpoint, this  issue  is
   as yet unresolved.  The PM2 5 studies  do show effects related to exposure to the fine
   fraction, but high correlations among PM2 5, PM^, and acid aerosols  make it very
   difficult to attribute the effects to a single specific exposure indicator.

1.4.2  Toxicology Findings
     The data on the potential toxicity of PM are derived from controlled human exposure,
laboratory animal, and occupational studies.  Each type of study has its own strengths and
weaknesses.  The studies vary in the populations examined (i.e., by age, health status, etc.),
the duration of the study,  and the exposure atmospheres (i.e., size  distribution, concentration,
chemical composition).  The studies indicate that inhaled PM causes effects on  the
respiratory tract.  The magnitude and character of the effects are dependent  on the particle
size distribution and  exposure dose, as well as on the physiologic status of the host.

Acidic Aerosols
     Most of the toxicology data on PM  are derived from controlled exposure studies in
humans and laboratory animals.  These studies have most extensively focused on acidic
aerosols, namely sulfuric acid aerosol and various  sulfates and nitrates, and have included
characterization of acid aerosol effects on pulmonary mechanical function, lung particle
clearance mechanisms, and other lung defense mechanisms.

•  Healthy subjects experience few, if any, decrements in lung function or altered  airway
   responsiveness following  single exposures to inhaled acid aerosols (H2SO4) at levels up to
   2,000 /xg/m3 for 1 h.  Mild lower respiratory symptoms (such as cough) occur at exposure
   concentrations in the >500 /xg/m3 range.

•  A substantial portion of inhaled acid aerosols may be neutralized by airway ammonia or
   buffered by airway surface liquids.
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•  Acid aerosol exposures ( > 100 /ig/m3) can cause changes in mucociliary clearance, in
   healthy or asthmatic humans. Mucociliary clearance in laboratory animals is initially
   increased and then ultimately decreased by repeated exposures to 125 jug/m3 H2SO4
   aerosol. Chronic exposure of laboratory animals to higher acid levels (~ 250 jug/m3) for
   52 weeks alters clearance and is also associated with changes in the bronchial tree
   indicative of mucus hypersecretion.

•  Asthmatic subjects are more sensitive than healthy subjects to the effects of acid aerosols
   on lung function. Responses in asthmatics are generally observed with acute ( < 3 h)
   exposures  at concentrations of ~  350 /ig/m3 and higher.  Exposures in the 450 to
   1000 /ig/m3 range in asthmatics can result in changes in airway responsiveness to
   bronchoconstrictor agents.

•  Adolescent asthmatics may be more sensitive than adults and may experience small
   decrements in pulmonary mechanical function in response to acute exposures ( < 3 h) to
   H2SO4 at levels between 70 and 100 /*g/m3.

•  Acute exposure ( < 24 h) of healthy  laboratory animals to H2SO4 at concentrations above
   1000 jug/m3 can alter pulmonary mechanical function largely due to bronchoconstriction.
   In guinea pigs,  100 /*g/m3 of acid aerosol may produce small transient effects. Chronic
   exposure (weeks/months) to  500 /zg/m3 H2SO4 is also associated with alterations in
   pulmonary  mechanical function.

•  Submicron droplets of H2SO4 are  effective in altering pulmonary mechanical function in
   laboratory animals.  Aerosols larger than 2 to 4 /mi have a low inhalability  in small
   laboratory animals (e.g., mice, rats, etc.)  However, acid aerosol studies in humans do
   not permit a clear distinction between responses to aerosols in the range of 0.1 to 20
•  Lung defense mechanisms and resistance to bacterial infection may be altered by exposure
   to H2SO4 concentrations of about 1000 j*g/m3 in laboratory animal species; alveolar
   macrophage function may be affected at levels as low as 500 /*g/m3 H2SO4.  Human
   exposure to acid aerosol (1000 /ig/m3) did not affect macrophage function.

•  Low levels of H2SO4  (100 /xg/m3) have been shown to react synergistically with O3.
   Exposure of healthy and asthmatic  subjects to a mixture of H2SO4 and O3 suggests  that
   100 ptg/m3 H2SO4 may slightly exacerbate  O3 lung function effects.

•  Acid coating of ultrafine zinc oxide (ZnO) particles appears to enhance the effects of acid
   on some responses in the guinea pig, including permeability, inflammation, and diffusing
   capacity.  Larger impacts on such endpoints occurred at lower concentrations of H2SO4
   and ZnO with combined exposure than with separate exposures to each alone.

Other PM Constituents
     Controlled human exposures to PM constituents other than acid aerosols are  limited.
Laboratory animal studies and occupational exposure studies provide information on other
PM substances, including metals, diesel emissions, crystalline silica,  and other miscellaneous
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particles.  Human studies of particles other than acid aerosols provide insufficient data to
draw confident conclusions regarding health effects.

•  Acute inhalation exposures of humans and laboratory animals to high levels (mg/m3) or
   chronic exposures to lower concentrations of metal particles can have effects on the
   respiratory tract.  The effective exposure levels in such studies are markedly higher than
   metal concentrations now generally present in the ambient U.S. atmosphere.

•  Ultrafine particles occur  in the ambient atmosphere in high numbers and have a high
   collective surface area.  The presence of ultrafine particles in human alveolar macrophages
   suggests human exposure to ambient ultrafines or aggregates of ultrafine particles.
   Limited human studies indicate slower clearance of ultrafine  than of larger inhalable
   particles.  Laboratory animal studies suggest potential toxic effects of inhaled insoluble
   ultrafine particles, but the limited available data preclude  quantitative estimates of any
   effective concentrations or doses for ambient ultrafine particle species.

•  At very high concentrations (> 1,000 ju-g/m3) chronic exposures to diesel particles cause
   inflammatory, histological, and biochemical changes in laboratory animals.  The toxicity
   of diesel emissions is considered to be associated with the particle rather than the gas
   phase.  A no-adverse-effect level  for chronic diesel particle exposure has been estimated at
   155 /ig/m3.  Thus, at current concentrations (< 10 /^g/m3) typical for U.S. ambient air,
   diesel PM is not likely to exert significant health effects on healthy humans.

•  Chronic exposure to crystalline silica has been shown to cause inflammation of the lung
   followed by silicosis, a fibrotic lung disease, in occupationally-exposed workers. Using a
   high estimate of 10% for the crystalline  silica fraction in PM10, current data suggests that,
   for healthy individuals not compromised by other respiratory ailments, maintenance of  the
   50 /zg/m3 annual NAAQS for PM10 would be adequate to protect against silicotic effects
   from ambient crystalline  silica exposures.

1.4.3  Population Groups at Risk
     Susceptibility can be affected by factors  which influence dosimetry or the response of
tissues to particle burdens.  The mechanisms by which the various sizes and constituents of
ambient PM could exert or  modify health effects are not understood.  Mechanistic studies to
date have mainly focused attention on deposition and clearance mechanisms and less on the
biological response to PM.  Host factors that may increase the susceptibility to PM include
both changes in physiologic factors affecting respiratory tract deposition and pathophysiologic
factors affecting response.  For example,  asthmatics show increased response to acid aerosols
or bioaerosols; COPD patients show increased PM deposition and impaired clearance;  and
airway inflammation or compromised  immune status may alter tissue response to inhaled
particles.

•  Susceptible groups most  clearly at special risk for PM effects include the elderly and those
   with cardiopulmonary disease, based on available epidemiology findings.
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•  Epidemiology studies indicate that mortality and hospitalization for respiratory causes are
   strongly related to ambient PM exposures.  Several hypotheses have been advanced for
   possible underlying mechanisms.  For example, PM may impair ventilation in COPD
   patients by causing airway narrowing and increasing the work of breathing.  In addition,
   PM may  lead to increased secretion and/or increased viscosity of mucus, possibly
   exacerbating airway narrowing.  Also,  some types of PM can cause inflammatory
   responses and epithelial cell damage in people with chronic respiratory disease.

•  Epidemiologic findings indicate that ambient PM exposures are also associated with
   increased risk for mortality and hospitalization due to cardiovascular causes.  Cardiac
   arrhythmia has been hypothesized as being  involved in mortality due to acute PM
   exposure.

•  Epidemiology findings  indicate that risk of  mortality and morbidity due to  lower
   respiratory disease (e.g. pneumonia) is increased by ambient PM exposure.  This may be
   due to exacerbation, by PM, of already existing respiratory disease. PM may also
   increase susceptibility to infectious disease by decreasing clearance, impairing macrophage
   function, or through other specific and nonspecific effects on the immune system.  The
   epidemiologic findings  also indicate that individuals with preexisting infectious respiratory
   disease (e.g. pneumonia) are at increased risk for PM effects.

•  Smokers  constitute a significant fraction (ca. 80%) of individuals with COPD and a
   smaller but notable portion of cardiovascular disease patients.  Therefore, smokers are
   another population group at likely increased risk for PM health effects.

•  Asthmatics are more responsive than non-asthmatics to acid aerosols in controlled
   exposure studies.  Asthma exacerbations are well  known to be associated with ambient and
   indoor bioaerosols. In epidemiological studies, asthma exacerbations,  sometimes requiring
   medical attention have also been associated with ambient coarse PM dominated PM10
   exposure.

•  Children  and  adolescents may also be potentially susceptible to ambient PM effects due to
   their increased ventilatory  frequency resulting in greater respiratory tract PM deposition.
   In children, epidemiologic studies reveal associations of PM exposure  with increased
   bronchitis symptoms and small decreases in lung function.
1.5  WELFARE EFFECTS

     Chapter 8 discusses visibility and climate change impacts of airborne particles as two
key types of welfare effects associated with ambient airborne particulate matter.  Chapter 9
discusses damage to materials due to PM and related pollutants.  PM-related effects on
vegetation, crops, and ecosystems are not covered in this document.
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1.5.1   Visibility Effects
•  Chapter 8 of this document includes information supplementary to several other significant
   reviews of the science of visibility, including the 1991 report of the National Acid
   Precipitation assessment  Program, the National Research Council's Protecting Visibility in
   National Parks and Wilderness Areas (1993), and EPA's 1995 Interim Findings on the
   Status of Visibility Research.  The following points are made in Chapter 8, and/or in the
   above referenced documents.

•  The relationships between air quality and visibility are well understood.  Ambient fine
   particles are the major cause of visibility impairment.  Significant scientific evidence
   exists showing that reducing fine particle concentrations will improve visibility.

•  The National Research Council  defines visibility qualitatively as "the  degree to which the
   atmosphere is transparent to visible light."  This definition may be expressed
   quantitatively in terms of contrast transmittance.  The EPA has defined visibility
   impairment as a reduction in visual range and atmospheric discoloration.

•  Light, as it passes through the atmosphere from a scene to an observer, is both scattered
   and absorbed.  The rate  of loss  of transmitted light intensity with distance is measured by
   the light-extinction coefficient which may be expressed as the sum of the coefficients for:
   (a) light scattering due to gases; (b) light scattering due to particles; (c) light absorption
   by gases,  and; (d) light absorption by particles.

•  Light scattering efficiency depends on particle size, falling off rapidly for particles below
   0.3 or above 1.0 /mi in diameter.  Therefore,  particles in the accumulation mode  (of the
   fine particle mode) are most effective in scattering light and are more important in
   visibility degradation than either nuclei mode or coarse mode particles.  Light absorption
   is not a strong function of particle size.   Under exceptional circumstances, such as dust
   storms,  coarse particles can dominate scattering.

•  In addition to reducing the intensity of light carrying information about a scene
   (transmitted radiance), particles also scatter light into the observer's view. This
   extraneous light, called air light or path radiance, carries no information about the scene.
   The competition between these two sources of light,  expressed as the ratio of transmitted
   radiance from the scene  to path radiance, determines the contrast transmittance and the
   visual quality of the view.

•  Visibility  at any location is  affected by air quality and non-air quality related effects.  The
   visibility effects of atmospheric constituents are dependant upon not just the mass of
   pollutants, but on the size distribution and refractive  index of particles, which are strongly
   influenced by relative humidity.  Non-air quality effects include the angle between the sun
   and the observer's sight  path, location of clouds, and reflectivity  of the ground.  These
   effects are independent of effects due to changes in atmospheric constituents.  Lighting
   and scene effects can be accounted for by defining a range of these effects when
   estimating visibility changes due to air quality influences.
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•  The relationship between air pollution and the appearance of a scenic view is well
   understood.  Models exist that, given an adequate description of the air quality and non-air
   quality variables, can produce a simulated photograph that accurately depicts a cloud-free
   scene as it would appear to a human observer.

•  There are several potential quantitative indicators of visibility.  Indicators such as contrast
   transmittance, which provide the most information on the appearance of a scenic view,
   provide little information that is helpful in developing control strategies to improve or
   protect visibility.  Indicators such as fine particle mass and composition provide more
   information useful for control strategies but provide less  information on visibility.
   Potential indicators include:  (a) fine particle mass  and composition (fine particle mass
   alone provides less of both types of information); (b) scattering by dried ambient particles;
   (c) scattering by particles under ambient conditions; (d) extinction (calculated from
   measurements of scattering plus absorption); (e)  light extinction measured directly;  and (f)
   contrast transmittance.

1.5.2  Climate Change
•  Particles suspended in the atmosphere  affect the earth's energy  budget and thus exert an
   impact on climate:  (a) directly by increasing  the reflection of solar radiation by cloud-free
   portions of the atmosphere,  and (b)  indirectly by affecting cloud microphysical properties
   in ways that increase  the brightness  and stability of clouds.

•  Estimates of atmospheric  sulfate aerosol solar radiation effects (expressed as radiative
   forcing) range from -0.3 W m"2 to -1.1 W m"2 for  direct effects and range from -0.4 to
   -1.6 W m"2 for indirect effects.  These values may be compared to the estimated radiative
   forcing of +2.4 W m"2 due to the increase in concentrations of greenhouse gases from the
   pre-industrial era to 1994.

•  Therefore,  on a globally averaged basis, radiative cooling due to anthropogenic particles
   may have substantially offset the radiative heating due to increases in atmospheric
   concentrations of greenhouse gases such as carbon  dioxide, methane,  and
   chlorofluorocarbons.

•  Aerosol lifetimes are also much shorter than the time required for global mixing,
   therefore, aerosol radiative effects are  most likely to exert their influence on a regional
   rather than on a global basis.

•  The lifetimes of particles in the troposphere are short (days to weeks) compared to the
   above greenhouse gases (years to over 100 years).  Therefore, aerosol concentrations will
   respond more rapidly to variations in emissions than will the greenhouse gases.

1.5.3  Materials Damage
•  Particle exposure results in the soiling  of painted surfaces and other building materials,
   increasing the cleaning frequency for exposed surfaces  and possibly reducing their useful
   lifetimes.
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•  Evidence suggests possible effects of particles on fabrics, electronics, and works of art.

•  Building materials (metals, stones, wood, paints) undergo wear even in the absence of
   pollutant exposure through physical, chemical, and biological interactions involving
   moisture, temperature, oxygen, and various types of biological agents.

•  Deposition of acid aerosols may increase the corrosion of metals by reacting directly with
   the metal or the metal corrosion film.

•  Deliquescent or hygroscopic salts, deposited on metals, accelerate corrosion by decreasing
   the critical relative humidity. The decrease in the critical relative humidity results in an
   increase in the amount of moisture on the metal surface.  Acid forming gases dissolve in
   the moisture layer, causing generation of corrosive acids  and ionic species.  Exposure to
   acid forming gases may also limit the life expectancy of paints and  may damage various
   building stones and cement products beyond that resulting from natural weathering
   processes.

•  There are insufficient data available to accurately estimate economic impacts of exposure
   of building materials to acid forming aerosols and particles.
1.6  KEY CONCLUSIONS

•  Epidemiologic studies show consistent positive associations of exposure to ambient PM
   with health effects, including mortality and morbidity.  The observed associations of
   ambient PM exposure with health effects must be adjusted for the effects of other
   environmental or demographic factors, depending on whether the effects are acute or
   chronic, in order to quantitatively assess the role that may be attributed to PM exposure.
   Estimates of PM health effects have shown reasonable quantitative consistency in different
   studies,  with only modest sensitivity to different methods of analysis.  However,  a clear
   understanding  of specific biologic mechanisms remains to be established.

•  Individuals with cardiovascular or pulmonary disease, especially if they are elderly, are
   more likely to suffer severe health effects (mortality or hospitalization) related to PM
   exposure than  are healthy young adults.  Children and asthmatics are also susceptible to
   certain PM effects, e.g.,  increased respiratory symptoms and decreased lung function.
   Smokers also constitute a population group at increased risk for ambient PM exposure
   effects.

•  Recent analyses continue to support the use of PM10 as an indicator of ambient particle
   exposures associated with human health effects.  The consistent association of mortality
   and various morbidity end points with PM10 exposure clearly substantiates the earlier
   rationale underlying selection of this indicator and PM10 standard for protection of public
   health.
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•  Additional consideration of the subdivision of PM10 into fine and coarse components is
   also warranted.  Indices of PM exposure that have been most consistently associated with
   health endpoints are by PM10 or PM15 and fine particle indicators.  Less consistent
   relationships have been observed for TSP and the coarse fractions of PM10_2 5.

•  In human populations, the daily variation in the personal exposure to ambient fine
   particles is reflected by daily variation in ambient fine particle concentration measured at a
   central monitoring site better than total exposures to coarse particles are reflected by
   measurements of ambient coarse particles at community monitoring sites. This is
   consistent with the observed high correlations of personal  sulfate exposures  with  ambient
   sulfate concentrations found experimentally.  Therefore, central  site measurements of fine
   particle indicators can be useful in PM epidemiology studies.

•  Development of a comprehensive biologically-based exposure-dose-response model to aid
   health risk assessment requires further data characterizing  differences in inhalability,
   airway geometry, and clearance rates among species.  Information is also required on
   mechanism(s) of action,  pathological processes affecting deposition and clearance of
   particles, and factors which influence the response(s) of respiratory  tract tissues to particle
   burden.

•  Estimation of public health impacts of ambient airborne particle  exposures in the  United
   States would most credibly require use of relative risk estimates  derived for particular
   U.S. urban areas,  in combination with estimates of exposures to ambient particle
   concentrations for the general population and/or specific susceptible subgroups (e.g., the
   elderly) within those particular areas.  In view of geographic differences in ambient  PM
   mixtures and demographics, broad generalization and  application of some single  "best
   estimate" of relative risk for a given increment in concentration  of a given particle
   indicator (e.g., PM10, PM2 5,  etc.) would be subject to much uncertainty.

•  Epidemiological studies indicate increased health risks associated with exposure to PM,
   alone or in combination with other air pollutants.  PM-related increases in individual
   health risks are small, but likely significant from an overall public health perspective
   because of the large numbers of individuals in susceptible  risk groups that are exposed to
   ambient PM. PM10 and indicators of fine particles are more consistently associated  with
   health risks than indicators of coarse particles.

•  Aerosol effects on visibility and climate,  through light scattering and changes in cloud
   microphysics, primarily arise from fine particles.

•  Based on points discussed above, fine and coarse particles should be considered as
   separate subclasses of pollutants.  Consideration of formation, composition,  behavior,
   exposure relationships, and sources argue for monitoring fine and coarse particles
   separately.  Because fine and coarse particles are derived from different sources,  it is also
   necessary to quantify ambient levels of fine and coarse particles  separately in order to plan
   effective control strategies.
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                           2.  INTRODUCTION
     This document is an update revision of "Air Quality Criteria for Particulate Matter and
Sulfur Oxides" published by the United States Environmental Protection Agency (EPA) in
1982, an Addendum to that document published in 1986, and an Acid Aerosols Issue Paper
published in 1989, and it will serve as the basis for reevaluating the current National
Ambient Air Quality Standard (NAAQS) for paniculate matter (PM) set in 1987.  The
present document, Air Quality Criteria for Particulate Matter, critically assesses the latest
scientific information relative to determining the health and welfare effects associated with
exposure to various concentrations of PM in ambient air.  The document is not intended as a
complete and detailed  literature review, but it does evaluate thoroughly information relevant
to PM NAAQS criteria development based on pertinent literature available through
February, 1996.
2.1   LEGISLATIVE REQUIREMENTS
     Two sections (Sections 108 and 109, U.S. Code, 1991) of the U.S. Clean Air Act
(CAA) govern the establishment, review, and revision of National Ambient Air Quality
Standards (NAAQS).  Section 108 directs the Administrator of the U.S. Environmental
Protection Agency (EPA) to list pollutants that may reasonably be anticipated to endanger
public health or welfare and to issue air quality criteria for them.  The air quality criteria are
to reflect the latest scientific information useful in indicating the kind and extent of all
exposure-related effects on public health and welfare that may be  expected from the presence
of the pollutant in ambient air.
     Section 109  directs the Administrator of EPA to propose and promulgate "primary" and
"secondary" NAAQS for pollutants identified under Section 108.  Section 109(b)(l) defines a
primary standard as a level  of air quality, the attainment and maintenance of which, in the
judgment of the Administrator, based on the criteria and allowing for an adequate margin of
safety, are requisite to protect the public health.  Section 109(d) of the CAA requires the
periodic review and, if appropriate, revision of existing criteria and standards.  Under

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Section 109(b) of the CAA, the Administrator must set secondary NAAQS that are based on
the criteria and are requisite to protect the public welfare from any known or anticipated
adverse effects associated with the presence of such pollutants. Welfare effects are impacts
of air pollution not directly affecting human health, such as effects on vegetation, crops,
soils, water, animals, manufactured materials, weather, visibility, and climate, as well as
damage to and deterioration of property, hazards to transportation, and effects on economic
value and personal  comfort and well-being.
2.2  REGULATORY BACKGROUND
     "Particulate matter" is the generic term for a broad class of chemically and physically
diverse substances that exist as discrete particles (liquid droplets or solids) over a wide range
of sizes.  Particles originate from a variety of stationary and mobile sources.  They may be
emitted directly or formed  in the atmosphere by transformation of gaseous emissions such as
sulfur oxides (SOX), nitrogen oxides (NOX), and volatile organic substances.  The chemical
and physical properties of PM vary greatly with time, region,  meteorology, and source
category, thus complicating the assessment of health and welfare effects.  Particles in
ambient air usually occur in two somewhat overlapping bimodal size distributions:  (1) fine
(diameter less than 2.5 /-im) and (2) coarse (diameter larger than 2.5 ^m). The two  size
fractions tend to have different origins and composition.
     On April 30,  1971  (Federal Register, 1971), EPA promulgated the original primary and
secondary PM NAAQS under Section 109 of the CAA.  The reference method for measuring
attainment of these standards was the  "high-volume"  sampler (Code of Federal Regulations,
1986), which collects PM up to a nominal size  of 25 to 45 pm (so-called "total suspended
paniculate" or "TSP").   Thus, TSP was the original indicator  for the PM standards.  The
primary standards for PM (measured as TSP) were 260 jug/m3, 24-h average not to be
exceeded more than once per year,  and 75 /«g/m3, annual geometric mean.  The secondary
standard (measured as TSP) was 150 /xg/m3, 24-h average not to be exceeded more than once
per year.
     On October 2, 1979 (Federal Register, 1979a), EPA announced that it was in the
process of revising the Air Quality Criteria Document (AQCD) and reviewing the existing
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PM NAAQS for possible revisions.  External review drafts of that revised AQCD were made
available for public comment and peer review by the Clean Air Scientific Advisory
Committee (CASAC) of EPA's Science Advisory Board (SAB).  CASAC prepared a
"closure" memorandum to the Administrator indicating its  satisfaction with the final draft of
the AQCD.  After closure, minor technical and editorial refinements were made to the
AQCD (U.S. Environmental Protection Agency, 1982).  The final draft of the document was
issued simultaneously with the proposal of revisions to the PM standards.
     On March 20,  1984 (Federal Register, 1984), EPA proposed a number of revisions to
the primary and secondary PM standards. Following publication of the proposal, EPA held a
public meeting in Washington, DC on April 30, 1984, to receive comments on the proposed
standards revisions.  After the close of the original public comment period (June 5, 1985),
CASAC met on December 16 and 17, 1985, to review the proposal and to discuss the
relevance of certain  new scientific studies on the health effects of PM that had emerged since
CASAC completed its review of the AQCD and staff paper in January 1982.  Based on its
preliminary review of these new studies, CASAC recommended  that EPA prepare separate
addenda to the AQCD and staff paper to evaluate relevant  new studies and to discuss  their
potential implications for standard-setting. The EPA announced  acceptance of these
recommendations on April 1, 1986 (Federal Register,  1986a). On July 3, 1986, EPA
announced (Federal Register, 1986b) the availability of an  external review draft document,
entitled Second Addendum to Air Quality Criteria for Paniculate Matter and Sulfur Oxides
(1982):  Assessment of Newly Available Health Effects Information (U.S. Environmental
Protection Agency, 1986).  At the same time (on July 3 1986), EPA announced a
supplementary  comment period to provide the public an opportunity to comment on the
implications of the new  studies and addenda for the final standards.  On October 15 and 16,
1986, the CASAC held a public meeting to review the AQCD addendum, at which time
CASAC members and representatives of several organizations provided critical review
comments on the subject addendum.
     The CASAC sent a closure letter on the EPA AQCD addendum to the Administrator
dated December 15,  1986, which stated that the 1986 addendum and the 1982 AQCD,
previously reviewed  by CASAC, represented a scientifically balanced and defensible
summary of the extensive scientific literature on PM and SOX (Lippmann, 1986b).
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     On July 1, 1987 (Federal Register, 1987), EPA published final revisions to the NAAQS
for PM. The principal revisions in 1987 included (1) replacing TSP as the indicator for the
ambient standards with a new indicator that includes only particles with an aerodynamic
diameter less than or equal to a nominal 10 /*m ("PM10"), (2) replacing the 24-h primary
TSP standard with a 24-h PM10 standard of 150 /zg/m3, (3) replacing the annual primary TSP
standard with an annual PM10 standard of 50 pig/m3, and  (4) replacing the secondary TSP
standard with 24-h and annual PM10 standards identical in all respects to the  primary
standards.
2.3   SCIENTIFIC BASIS FOR THE EXISTING PARTICIPATE
      MATTER STANDARDS1
     The following discussion describes the bases for the existing PM NAAQS set in 1987.
The discussion includes the rationale for the primary standards, the pollutant indicator for
paniculate matter, the averaging time and form of the standard, and finally a discussion of
EPA's assessment that led to the standard set in 1987.

2.3.1   Rationale for the  Primary  Standards
     In selecting primary standards for PM, the Administrator must specify (1) the particle
size fraction that is to be used  as an indicator of paniculate pollution, (2) the appropriate
averaging tunes and form(s) of the standards, and (3) the numerical levels of the standards.
Based on the assessment of relevant scientific and technical information in the earlier 1982
PM AQCD and addenda, the staff paper and staff paper addendum outlined a number of key
factors considered in making decisions in each of these  areas. The following discussion of
the 1987 revisions of the standards focuses mainly on the considerations that were most
influential in the Administrator's selection of particular  options.
1 Adapted from Federal Register (1987).

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2.3.2   Pollutant Indicator

     Based on the assessment of the available scientific information, EPA concluded in 1987
that (1) a separate PM standard (as opposed to a combination standard for PM and SOX)
remained a reasonable public health policy choice, and (2) given current scientific knowledge
and uncertainties, a size-specific (rather than chemical-specific) indicator should be used.
In assessing the information in the AQCD,  EPA reached several conclusions summarized as

follows:
  (1)  Health risks posed by inhaled particles are influenced by both the penetration and
      deposition of particles in the various regions of the respiratory tract and the biological
      responses to these deposited materials. Smaller particles penetrate furthest in the
      respiratory tract.  The largest particles are deposited predominantly in the
      extrathoracic (head) region, with somewhat smaller particles depositing in the
      tracheobronchial region; still smaller particles can reach the deepest portion of the
      lung, the pulmonary region.

  (2)  The risks of adverse health effects associated with deposition of typical ambient fine
      and coarse particles in the thoracic region (tracheobronchial and pulmonary deposition)
      are markedly greater than  those associated with deposition in the extrathoracic region.
      Maximum particle penetration to the thoracic region occurs during oronasal or mouth
      breathing.

  (3)  The size-specific indicator for primary standards should represent those particles small
      enough to penetrate to the thoracic region.  The risks of adverse health effects from
      extrathoracic deposition of typical ambient PM are sufficiently low that particles
      depositing only in that region can safely be excluded from the indicator.
     Considering the above conclusions, other information on air quality composition, the

need to provide protection for sensitive individuals who may breathe by mouth or oronasally
and the similar convention on particles penetrating the thoracic region adopted by the

International Standards Organization (1981), EPA staff recommended that the size-specific
indicator include particles of diameters less than or equal to a  nominal  10 /xm  "cut point"

generally referred to as "PM10". In terms of collection efficiency, this represents a 50% cut

point or diameter (D50),  the aerodynamic particle diameter for which the efficiency of

particle collection is 50%. With such a cut point,  larger  particles are not excluded entirely

but are collected with substantially decreasing efficiency,  and smaller particles are collected

with increasing (up to 100%) efficiency.  Ambient samplers with this cut point provide a

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reliable estimate of the total mass of suspended PM of aerodynamic size less than or equal to
10 fjim. Such an indicator (PM10) is conservative with respect to health protection in that it
includes all of the particles small enough to penetrate to the sensitive pulmonary region and
includes approximately the same proportion of the coarse-mode fraction that would be
expected to reach the tracheobronchial region.  It places substantially greater emphasis on
controlling smaller particles than does a TSP indicator,  but it does not completely exclude
larger particles from all control.
     The assessment of then-available information on respiratory tract deposition in the 1986
AQCD and staff paper addenda reinforced the conclusions reached  in the original EPA
assessment. In particular, (1) the data did not provide support for an indicator that excluded
all particles larger than  10 pirn in diameter; (2) the analysis used to support an alternative
indicator with a nominal size cut point of 6 /^m (Swift and Proctor,  1982) significantly
underestimated thoracic deposition of particles larger than 6 /^m in diameter under natural
breathing conditions; (3) the PM10 indicator generally included a similar or larger fraction of
the range of particles that can deposit in the tracheobronchial region, although it appeared to
be somewhat less conservative in this regard than previously thought with respect to large
(> 10 /im) particle deposition under conditions of natural mouthbreathing; and (4) the studies
of tracheobronchial deposition generally involved adult  subjects (other information indicating
even greater tracheobronchial deposition of particles in  children than in adults provided an
additional reason for an indicator that includes particles capable of penetration to the
tracheobronchial region).  Consideration of these and the earlier conclusions led EPA to
reaffirm its recommendation for a PM10 indicator.  The CAS AC  also restated its support for
PM10 in its review of the proposal and the closure letter to the Administrator  (Lippmann,
1986a,c).
     In 1987  the Administrator accepted the recommendations of the staff and CAS AC, as
well as their underlying rationale,  and decided to replace TSP as the particle indicator for the
primary standards with a new indicator that included only those particles  less than a nominal
10 fim in diameter (PM10) as  specified in the Federal Reference Method.
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2.3.3   Averaging Time and Form of the Standards
     The EPA's assessment at that time of scientific information available prior to 1987
confirmed the need for both short- and long-term primary standards for PM.  The alternative
of a single averaging time would not provide adequate protection against potential effects
from both long- and short-term exposures without being unduly restrictive.  The forms for
the 24-h and annual standards are discussed below.

2.3.3.1  24-Hour Standard
     The Environmental Protection Agency decided in 1987 that the 24-h standard should be
stated in a statistical form that uses more than  1 year of data and accounts for variations in
sampling frequency in order to predict the actual number of exceedances to be expected in an
average year. When used with an appropriate  standard level, the statistical form can provide
improved health protection that is less sensitive to changes in sampling frequency than the
deterministic form and can also offer a more stable target for control programs. Recognition
of the limitations of the deterministic form also led EPA to promulgate a statistical form for
the ozone standard (Federal Register,  1979b).

2.3.3.2  Annual Standard
     The EPA Administrator decided to change the form of the annual standard in 1987
from the previous annual geometric mean form to a statistical form expressed as an expected
annual arithmetic mean.  The expected annual  arithmetic mean is equivalent to the long-term
arithmetic average concentration level, assuming no changes in underlying emissions.  The
expected arithmetic mean is more directly related to the available health effects information
than is the annual geometric mean, which was  the previous form of the standard.  Because
the arithmetic mean concentration is proportional to the sum of the daily means, it reflects
the total  cumulative exposure of PM to which an individual is exposed. Thus, it is an
appropriate indicator to protect against any health effect that depends on chronic, cumulative
PM exposure.  It is also a reasonable indicator for protecting against health effects that
depend on repeated short-term high concentrations (short-term peaks have an influence on the
arithmetic mean that is proportional to their frequency,  magnitude,  and duration).  The
geometric mean, on the other hand, deemphasizes the effects of short-term peak
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concentrations and is heavily influenced by days of relatively clean air.  For these reasons,
EPA staff and CASAC recommended the change to an arithmetic mean.
     Under the statistical form, the expected annual arithmetic average is determined by
averaging the annual arithmetic averages from 3 successive years of data. The prior
deterministic form of the standard did not adequately take into account the random nature of
meteorological variations.  In general, annual mean PM concentrations vary from year to
year, even if emissions remain constant, due to the random nature of meteorological
conditions that affect the formation and dispersion of particles in the atmosphere. If only
1 year of data is considered, compliance with the standard and, consequently,  emission
control requirements, may be determined on the basis of a year with unusually adverse or
unusually favorable weather conditions.  The problem of year-to-year variability is, however,
reduced by averaging 3  years of data.

2.3.4   Level of the Standards
     The original Office of Air Quality Planning and Standards (OAQPS) PM Staff Paper
and CASAC recommendations set forth a framework for determining the levels for the
proposed PM standards  that would protect public health with an adequate margin of safety.
The Administrator's decision in 1987 relied heavily on that framework and on the supporting
material  in the staff paper and its addendum, as well as the CASAC closure letters.  The
essential steps in this framework are summarized here.

2.3.4.1  Assessment of the Quantitative Epidemiological Studies
     The 1982 AQCD and its 1986 addendum identified a small number of community
epidemiological  studies that are useful in determining concentrations at which  PM is likely to
adversely impact public health. The EPA staff used these quantitative studies  to examine
concentration-response relationships and to develop numerical "ranges of interest" for
possible  PM10 standards.
     A number  of uncertainties associated with the use of these studies had to be considered
in selecting an appropriate margin  of safety. As discussed in the staff paper,  the AQCD, and
the addenda to those documents, epidemiological studies are generally limited  in sensitivity
and are subject to inherent difficulties involving control for covariates or confounders.
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Moreover, many of the quantitative studies were conducted in times and places where
pollutant composition may have varied considerably from current U.S. atmospheres. Also,
most of the studies used British Smoke—British Smoke (BS) is a pseudo-mass indicator
related  to small particle (aerodynamic diameter less than a nominal 4.5 /mi) darkness—or
TSP as particle indicators.  None of the published  studies used the proposed PM10 indicator.
Thus, assumptions had to be used to convert the various results to common (PM10) units.

2.3.4.2 Identification of Margin of Safety Considerations
      The 1982 AQCD and its addendum identified an additional substantial body of scientific
literature that,  although it did not provide reliable concentration-response relationships  for
ambient exposures, did provide important qualitative insights into the health risks associated
with human exposure to  particles.  This literature included both quantitative and qualitative
epidemiological studies, controlled human exposure experiments, and animal toxicological
studies.  The EPA staff assessed this literature to identify additional factors and uncertainties
that should be considered in selecting the most appropriate margin of safety.
      Experience had shown that it was difficult to  identify, with confidence, the lowest
pollution level  at which an adverse effect would occur. Furthermore, in cases such as  the
present one, the evidence suggested that there is  a  continuum of effects, with the risk,
incidence, or severity of harm decreasing, but not necessarily vanishing, as the level of
pollution is decreased.
      The requirement for an adequate margin of safety for primary standards addresses
uncertainties associated with inconclusive scientific and technical information available  at the
time of standard setting.  It also aims to provide  a  reasonable degree of protection against
hazards that research has not yet identified. Both kinds of uncertainties are components of
the risk associated with pollution at levels below those at which human health effects can be
said to occur with reasonable  scientific certainty.  Thus, by selecting primary standards that
provide an adequate margin of safety,  the Administrator sought not only to prevent pollution
levels that have been demonstrated to be harmful, but also to prevent lower pollutant levels
that may pose an unacceptable risk of harm, even if that risk is not precisely identified as to
nature or degree.
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     In the absence of clearly identified thresholds for health effects, the selection of a
standard that provides an adequate margin of safety requires an exercise of informed
judgment by the Administrator.  The level selected will depend on the expected incidence
and severity of the potential effects and on the size of the population at risk, as well as on
the degree  of scientific certainty that the effects will  in fact occur at any given level of
pollution.
     The 1986 EPA staff paper recommended a range of potential standards for the
Administrator's consideration.  The recommended range was below the levels  at which EPA
staff, with  the concurrence of CAS AC, had  concluded from the available data that adverse
health effects were "likely", but in the domain where the data suggested that such effects
were "possible".  The Administrator proposed refined ranges of standard levels that were
based on the  1984 staff and CAS AC recommendations.   After consideration of the then new
scientific evidence contained in the AQCD addendum,  the staff revised its recommended
range of standards.  The Administrator considered the revised EPA assessments and the
CASAC recommendations (Lippmann, 1986b) in making the final decision on the standard
levels.  The rationales for the levels of the 24-h and  annual standards are presented below.

2.3.4.3 24-Hour Standard
     The 1987 assessment of the short-term epidemiological data expressed PM levels in
both the BS or TSP and PM10 units.  The term "effects likely" denoted concentration ranges
derived from the 1982 AQCD and its addendum at or above which a consensus judgment
suggested the greatest certainty that the effects studied would occur, at least under the
conditions  that occurred in the original studies.  In the "effects possible"  range, EPA found
credible scientific evidence suggesting the existence of adverse health effects in sensitive
populations, but substantial uncertainty existed regarding the conclusions to be drawn from
such evidence.
     The 1987 review of the data did  not provide evidence of clear thresholds in exposed
populations.  Instead, they suggested a continuum of response for a given number of exposed
individuals, with both the likelihood (risk) of any effects occurring and the extent (incidence
and severity) of any potential effect decreasing with  concentration (this was particularly true
for the statistical analyses of daily mortality in London).  Substantial agreement existed that
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 wintertime pollution episodes produced premature mortality in elderly and ill populations, but
 the range and nature of observed associations provided no clear bases for determining lowest
 effects-likely levels or for defining a concentration below which no association remained.
 The lung function studies in children also provided evidence of effects at concentrations  over
 a range, but the relationships were not certain enough to derive  effects-likely levels for
 PM10.  The lung function studies did, however, suggest levels below which detectable
 functional changes were unlikely to occur in exposed populations. Following CAS AC
 recommendations, EPA used the combined range of effects-possible  studies as a starting
 point for developing alternative standards.
      The original range proposed by the Administrator, drawn from the 1984 staff analysis,
 was 150 to 250 jiig/m3 PM10 24-h average, with no more than one expected exceedance per
 year.  The lower bound of this range was derived from the original assessment of the London
 mortality studies.  As a result of reanalyses of the London mortality data and the findings
 from the then current U.S. morbidity studies, the staff reduced the level of the lower bound
 of the range of interest to 140 pig/m3, and noted that the difference between it and the
 original lower bound (150 /*g/m3) was within the range of uncertainty associated with
 converting the  morbidity study results from TSP to PM10.
      At that time, the  study  of Lawther et al. (1970) was judged to provide evidence that
 health effects were likely at PM concentrations above 250 jig/m3 (as BS).  The effects
 observed in this study (related to aggravation of bronchitis) were of concern because of both
 their  immediate impact and their potential for inducing longer  term deterioration of health
 status in a significant sensitive group.  Based on the uncertain  conversion between smoke and
 PM10, the lowest effects likely level derived from the Lawther study (250 j«g/m3 as BS) was
judged to be in the range  of 250 to 350 jug/m3 in PM10 units.
      The 1987 assessment of the Lawther et al.  (1970) study formed the basis for the upper
bound of the range of PM10 standards proposed by the Administrator in 1984.  Considering
this study alone, a PM10 standard of 250 ^g/m3 might have appeared to contain some margin
of safety, even for the  sensitive bronchitics studied, because it incorporated a conservative
PM10 conversion factor and because of differences between exposure conditions in the British
study and current U.S. air quality.  Because persons with chronic bronchitis were identified
as a group particularly  sensitive to particulate pollution, a  standard of 250 /Lig/m3 (as PM10)
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also might have provided some margin of safety for other, less sensitive groups.
Nevertheless, this study of bronchitics in London had inherent limitations in sensitivity that
precluded derivation of unequivocal "effects thresholds" at 250 /*g/m3 as BS and, by
extension, PM10. The 1982 AQCD noted that associations between pollution and health
status persisted at lower BS concentrations in selected, more sensitive individuals.   Although
the lead author of the study objected to attaching any importance to these latter findings
(Lawther, 1986), EPA, with CASAC concurrence,  found no basis for asserting that this
study demonstrated a population threshold at 250 /tg/m3.
     In evaluating the margin of safety for a 24-h standard, it was also important to consider
the London mortality studies.  A standard at the upper portion of the proposed range
(250 /ig/m3) would be well below the levels (500 to 1,000 pig/m3 as BS) of the historical
London episodes in which  the scientific consensus indicated that pollution was responsible for
excess mortality.  The portions of the population at greatest risk of premature mortality
associated with PM exposures in those episodes  included the elderly and persons with
preexisting respiratory or cardiac disease.  Although the extent of life shortening could not
be specified,  the seriousness of the effect strongly justified a margin of safety for it (below
the consensus effects levels) that was larger than that warranted for the effects on bronchitis.
     The staff assessment at that time of several reanalyses of London mortality suggested,
however, that the risk of premature mortality  for sensitive individuals extended to
concentrations substantially lower than those that occurred in the "episodes".  Other analyses
(Mazumdar et al., 1982; Ostro,  1984; Shumway et al., 1983) provided no objective support
for a population threshold below which such a risk no longer existed.  Although the risk to
individuals may be  small at concentrations of 250 ^g/m3 and below, the number of people
exposed to lower concentrations, given U.S. levels, was substantially larger than the number
exposed to higher levels.  The increased number of individuals exposed increased the risk
that effects would occur in the total population exposed.
     Differences in the composition of particles and gases among U.S. cities and between
U.S. conditions and London at the time that the mortality and morbidity data were  gathered
added to the complexity of assessing risk associated with PM in the United States.  In the
case of the mortality studies, however, the staff found that at least one study (Ozkaynak and
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Spengler, 1985) provided qualitative support for an association between daily mortality and
particle concentrations in then nearly contemporary U.S. atmospheres.
     The 1982 assessment of the mortality studies and related factors prompted the EPA
Administrator to consider standard levels that extended from 250 /xg/m3 to the lower bound
of the original staff range (150 />tg/m3) and even lower.  Reanalyses of the London mortality
data prior to 1987 provided additional evidence that serious adverse health effects may occur
at PM concentrations below 250 /zg/m3.  These analyses addressed a number of the
uncertainties associated with the earlier studies and reinforced the Administrator's concern
that a 24-h standard  at the upper end of the proposed range may not provide an adequate
margin of safety.  However, given the uncertainties in converting from BS to PM10
measurements, particularly at lower concentrations and the possible differences in particle
composition between London at the time the data were gathered and the contemporary United
States, it was difficult to use these studies to set a precise level for a PM10 standard.
     Given these difficulties, it was  important to examine studies contemporary with the
other studies that utilize gravimetric  measurements of particulate concentrations.  The staff
found the studies of  Dockery et al. (1982) and Dassen et al. (1986) to be useful.  The
Dockery study observed physiologically small but statistically significant decreases in lung
function in a group of children exposed to peak PM10 levels of 140 to 250 /ig/rn3. The
decrements persisted for 2 to 3 weeks following the exposures.  The study also suggested the
possibility of larger responses in a subset of the  children,  including those with existing
respiratory symptoms.  The Dassen study recorded similar decrements in children in the
Netherlands following exposure to PM10 levels estimated at 200 to 250 /xg/m3, but no
observable effects 2  days  after exposure to PM10 levels estimated at 125 /ig/m3.  The particle
composition, at least in the Dockery  study, was more representative of contemporary U.S.
cities in that time period,  and the associated aerometry provided a more reliable estimate of
PM10 levels than did the measurements used in the London studies. It was reasonable  to
expect the endpoints  observed (small reversible reductions in lung function in children) to be,
in most cases, more  sensitive to air pollution than those observed in the London studies.
These effects, per se, are of uncertain significance to health, but they may be associated with
aggravation of respiratory symptoms in children with preexisting illness.  Long-term
examination of respiratory health in the same community studied by Dockery et al. (1982)
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suggested that the children in that community had a higher incidence of respiratory illness
and symptoms than children in communities with lower particle levels,  but the data showed
no evidence for any persistent reduction in lung function (Ware et al., 1986).  Uncertainties
with respect to the effects of other pollutants (e.g., sulfur dioxide), the consistency of the
changes, and exposures precluded specifying unequivocal "effects likely"  levels based on this
study.  The EPA assessment therefore suggested that short-term lung function effects in
children were possible across a range of 140 to 250 />ig/m3 or more  as PM10.
     In making a decision on a final standard level, the Administrator also considered
information from the more qualitative studies of PM.  These studies suggested increased risks
for sensitive groups (asthmatics) and risks of potential effects (morbidity in adults) not
demonstrated in the more quantitative epidemiological literature. The qualitative studies did
not provide clear information on  effect levels but did justify  consideration of effects of PM
that have  not been sufficiently investigated.
     Based on the  1982 assessment  of the available scientific data, in 1984, the EPA
Administrator expressed an inclination to select a 24-h level  from the lower portion of the
proposed  range of 150 to 250 /ug/m3.  The addendum to the  1982 assessment supported the
original findings  and, if anything, suggested  an even wider margin of safety was warranted.
The Administrator, therefore, decided to set  the final standard at the lower bound of the
range originally proposed (i.e., 150 /-ig/m3).   This standard provided a substantial margin of
safety below the  levels  at which there was a  scientific consensus that PM caused premature
mortality  and aggravation of bronchitis. Such a margin was judged to be necessary because
of the seriousness of the effects and because the analyses of  daily mortality studies suggested
that adverse effects may occur at PM levels  well below the consensus levels.  The standard
was in the lower portion of the range where  sensitive, reversible physiological responses of
uncertain health significance had been possibly, but not definitely, observed in children.
Using a conservative  assessment  of  the lung  function/particle relationship from Dockery
et al. (1982), a change  in concentration from background levels («20 /-ig/m3) to 150 jug/m3
would produce lung function changes of at most 10 to  15%  in less than 5% of exposed
children.  Based  on the results of Dassen et  al. (1986), it appeared unlikely that any
functional changes would be detected 1 or 2 days following  such exposures.  Thus, the
maximum likely  changes in lung function appeared to present little risk of significant adverse
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responses. Standards set at a somewhat higher level, however, would have presented an
unacceptable risk of premature mortality and would have allowed the possibility of more
significant functional changes.  Furthermore, a standard level of 150 ptg/m3 was fully
consistent with the recommendations of CASAC on the 24-h standard (Lippmann,  1986c).

2.3.4.4 Annual Standard
     The long-term  epidemiological studies examined in 1987 were subject to confounding
variables that reduced the studies'  sensitivity  and made their interpretation difficult.  No clear
thresholds could be identified for the effects-likely levels, and evidence existed for effects at
lower levels (the effects-possible levels); however, the evidence was inconclusive,  and the
effects were difficult to detect.
     Based on an EPA assessment of PM10/TSP ratios in areas with elevated TSP levels,  the
effects-likely levels from the Ferris et al. (1973) study were revised to 80 to 90 /xg/m3 as
PM10.  Because of limitations in sampling duration and the conversion to PM10,  this  estimate
was particularly uncertain, with effects possible at lower concentrations.  Of greatest concern
was the possibility of long-term deterioration of the respiratory system in exposed
populations, the potential for which was indicated by lung function (mechanical pulmonary)
changes and increased  incidence of respiratory disease. One set of studies (Ferris et  al.,
1973, 1976) provided some evidence for a  "no-observed-effect level" for those effects at or
below 60  to 65 /*g/m3 as PM10 (130 /tg/m3 as TSP), whereas another study (Bouhuys et al.,
1978) suggested some possibility of symptomatic responses in adults at long-term median
levels at or below about 50 to 55 jig/m3 as PM10.  The importance of these symptomatic
responses, which were unaccompanied by lung function changes, to long-term respiratory
health was unclear.
     The  most important study of  long-term effects at that time was an ongoing examination
of six U.S. cities (Ware et al.,  1986).  The study indicated the possibility of increased
respiratory symptoms and illnesses in children at multiyear levels across a range  of 40 to
more than 58 pig/m3  as  PM10 but found no evidence of reduced lung function at these
concentrations.  This study did  not find similar gradients in symptoms and illness within
some of the cities,  which had somewhat smaller localized pollution gradients. The results of
a separate series of studies of long- and intermediate-term (2- to 6-week) exposures in a
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number of U.S. metropolitan areas (Ostro, 1987; Hausman et al.,  1984) were more
supportive of the possibility of effects within cities (respiratory-related activity restrictions in
adults) at comparable U.S. exposure levels.  The results of these studies were generally
consistent with the earlier U.S. studies. In particular, the finding of symptomatic responses
in children with no change in lung function (Ware et al., 1986) was consistent with similar
findings in adults (Bouhuys et al., 1978) at estimated long-term PM10 levels down to
50 ^ig/m3. However, the information available to support the existence of significant adverse
effects at  annual PM10 levels below 50 ^ig/m3 (especially when 24-h levels are maintained
below 150 jig/in3) was quite limited and uncertain.
     Because of the uncertainties and the  limited scope  and number of long-term quantitative
studies available for review in 1987, it was important to examine the results of qualitative
data from a number of epidemiological, animal, and ambient particle composition studies in
determining what would  constitute an adequate margin of safety for an annual standard.
These studies justified concern for serious effects not directly evaluated in the above studies.
Such effects included damage to lung tissues  contributing to chronic respiratory disease,
cancer, and premature mortality.  Substantial segments of the population may be  susceptible
to one or  more of these effects.  Although the qualitative data did not provide evidence for
major  risks of these effects at the annual PM levels in most U.S. cities at that time,  the
Administrator believed that the seriousness of the potential effects and the large population at
risk  warranted caution in setting the standard.
     Based on  findings discussed in the 1982 AQCD, the EPA Administrator proposed in
1984 to select an annual  standard level from  a range of 50 to 65 pig/m3.  In the proposal, the
Administrator favored a standard  in the lower portion of the range. The evidence discussed
in the 1986 addendum, although subject to substantial uncertainty,  reinforced this inclination.
In light of the 1986 assessment, and in accordance with CASAC recommendations, the
Administrator decided to set the level of the annual standard at the lower bound of the
original range (50 jig/m3, expected annual arithmetic mean).  This standard provided  a
reasonable margin of safety against long-term degradation in lung function, which was judged
likely to occur  at estimated PM10 levels above 80 to 90  /*g/m3 and for which there was some
evidence at PM10 levels above 60 to 65 jug/m3.  Such a  standard also provided reasonable
protection against the less serious symptomatic effects (bronchitis)  for which only
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 inconclusive evidence was available.  Moreover, the staff and CAS AC recommended that the
 combined protection afforded by both 24-h and annual NAAQS be considered in selecting the
 final standard level.  In this regard, analyses of air quality data showed that implementation
 of the 24-h standard would reduce substantially the annual levels in many U.S. areas to
 below 50 /itg/m3, adding to the protection afforded by the annual standard in areas with
 higher 24-h peak-to-mean ratios. Based on the then available information on risks associated
 with annual exposures, the EPA Administrator believed that the annual and 24-h standards
 provided an adequate margin of safety.

 2.3.5   Welfare  Effects
     No convincing evidence existed  indicating significant adverse soiling and nuisance at
 TSP levels below 90 to 100 /*g/m3, and,  on that basis, the Administrator concluded that
 secondary standards different from the primary standards  were not requisite  to protect the
 public welfare against soiling and nuisance.  This conclusion was supported  by CASAC's
 determination that there was no scientific support for a TSP-based secondary standard
 (Lippmann, 1986c).  Therefore, the Administrator decided to set 24-h and annual secondary
 PM10 standards that are equal in all respects to the primary standards.
     The other welfare effects of principal interest were impairment of visibility, potential
 modification of climate, and contribution to acidic deposition.   All three of these effects were
 believed to be related to regional-scale levels of fine particles, and control programs designed
 to ameliorate them would likely involve region-wide reductions in emissions of sulfur oxides.
 The Administrator also concurred with the staff suggestions that a separate secondary particle
 standard was not needed to protect vegetation or to prevent adverse effects on personal
 comfort and well-being.
2.4   TOPICS/ISSUES OF CONCERN FOR CURRENT CRITERIA
      DEVELOPMENT
     Based on the available scientific evidence, several critical topics and associated issues
are addressed in this document, as part of the current CAA-mandated periodic review of
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criteria and NAAQS for PM. Some of the most critical topics and issues addressed are as
follows.

2.4.1    Air Quality and Exposure
2.4.1.1  Physics and Chemistry of Atmospheric Aerosols
     The atmospheric aerosols of interest because of their potential health and welfare effects
consist of two principal components:  a gas phase ("air" in this case) and a solid or liquid
particle phase in suspension.  Fine particles in the atmosphere consist mainly of (1) sulfate,
nitrate, ammonium ions, and water; (2) photochemically formed organic  aerosols; and
(3) carbon, organic matter, and metallic components emitted directly into the atmosphere.
Coarse particles in the atmosphere are composed mainly of silica,  calcium carbonate, clay
minerals, soot,  and, sometimes, organic substances.  A general relationship exists between
chemical composition and  particle diameter, with particles of  <2.5 /xm in diameter
containing most of the SO42", H+, and NH4+, as well as a significant fraction of the NO3"
and Cl".  The particle volume (mass) frequency  function is often multimodal.  The fine-
volume fraction may have two or more modes below 1.0.  The coarse fraction generally has
one mode within the range « 5  to 50 /mi.  The  particle volume frequency functions for the
fine and coarse fractions often behave independently, (i.e., vary in relative proportion of the
total ambient particle mix  from location to location or from one time or season to another at
the same location).
     Previous documentation has shown that hydroxy, hydroperoxy, and alkoxy radicals are
probably important in the  oxidation of SO2 to SO3", although the rate  constants for some of
these reactions  are not well established.  The  hydroxy radical  dominates the gas-phase
oxidation of SO2 in the clean troposphere,  and H2O2 is effective in the formation of SO42" in
particles, mists, fogs, and  rain.   Transition metals and soot have been shown to be effective
catalysts  for atmospheric oxidation of SO2.  Oxidation rates for NO and NO3" are known but
have been considered too low to be important.  The oxidation rate for NO2~ is known, but
the tropospheric concentration of HNO2 is probably too low for this reaction to be important.
Except for reactions  of carbon (soot), solid surface reactions do not appear to be effective
pathways for H2SO4 formation in the troposphere.
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      The physical properties of particles are physical configuration, bulk material properties,
 and surface properties.  The bulk material properties that affect aerosol behavior include
 chemical composition, vapor pressure, hygroscopicity and deliquescence,  and index of
 refraction.  These properties control the physical state and growth of particles and result in
 scattering and absorption of light by tropospheric particles.  Hygroscopicity, deliquescence,
 and efflorescence are critical properties in the  growth of particles, but there  is a paucity of
 thermodynamic data to permit prediction of deliquescence and hygroscopic behavior and
 vapor pressures of multicomponent systems, especially for relative humidities below about
 90%.  Few studies of desorption under atmospheric conditions  have been reported; of more
 concern, desorption may prove to be important in biological systems.  Shape, structure, and
 density are physical configuration properties that are important  parameters in the equations of
 motion for particles.  Because of irregularities in particle geometry or because the particles
 are agglomerates, the three configuration properties are usually defined in terms of an
 aerodynamic diameter.   Surface properties of importance include electrostatic  charging,
 adhesion, and the influence of surface films.
      The physical properties  of particles  and their modal distributions are important
 considerations (1) in the sampling and analysis of atmospheric particles and  (2) in predicting
 or determining the flux to biological and  nonbiological materials and deposition in the human
 and laboratory animal respiratory tracts.
      Advances in understanding the properties and behavior of atmospheric paniculate matter
 have been made since publication of the previous criteria document (U.S.  Environmental
 Protection Agency, 1982).  In the current revision of the document, newer literature and data
 on the above topics are reviewed and discussed.  For example,  chemical pathways and rates
 of atmospheric particle formation and  of removal from the atmosphere,  by dry deposition and
 by precipitation scavenging, are examined.  Likewise, the physical processes of nucleation,
 condensation, and coagulation by which condensible material is converted into particles are
 discussed, along with the size distribution of the resulting particles.  The physical properties
 relevant to sampling considerations and deposition on surfaces,  including those of the
 respiratory tract,  are also discussed, including coverage  of several newer areas of expanded
 research: aerosol equilibria, the unique properties of semi-volatile aerosols, and the role of
particle-bound water.
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2.4.1.2  Measurement Methodology
     Techniques available for measurement of mass and specific components of aerosols are
examined.  Special attention is given to the suitability of current technology for measurement
of aerosol mass with sufficient accuracy and precision to determine compliance with one or
another possible type of a new PM standard (i.e., a PM10 standard with a lower level or a
fine-particle standard).  The need for continuous or daily PM measurements, the difficulty of
removing particle-bound water without losing NH4NO3 or semivolatile organic matter, and
problems in defining and maintaining a precise cut at 10 /mi or lower (e.g., at 2.5 fjm) are
also assessed.

2.4.1.3  Ambient Levels
     The present draft of the revised PM AQCD describes ambient PM data for the United
States, with characterization as available by size (fine/coarse) and chemical composition.
Data that focus on the current U.S. PM10 standard are emphasized, but information is also
provided on PM2 5,  PM2 5.10, and other similar cut points, as data are available.  Ambient
patterns are discussed,  to include daily, seasonal, regional, etc. Acid aerosol data are also
described as above as a separate aspect of PM.  Key questions addressed include:  What
information is available on the distribution of PM with regard to:  geographic,  seasonal,
diurnal, size, composition, sources, and trends?  How important are uncertainties introduced
by variations in the position and shape of the 10-/im cut point in various PM10 monitors?
How important are measurement uncertainties due to volatilizable/condensible components
(e.g., loss of ammonium nitrate and, possibly, other ammonium salts) or to the loss of
semivolatile organics or retention of particle-bound water? How do these uncertainties  vary
geographically and seasonally?  How do these uncertainties differ  for filter collection and
subsequent weighing as compared to continuous indicators?

2.4.1.4 Cut Points
     Information helpful in evaluating the possible need for a new fine particle standard in
addition to or instead of a PM10 NAAQS is presented.  This information includes discussion
of sources, sampling problems, composition,  lung deposition, epidemiology, biochemistry,
and toxicology of fine and coarse particles.  Other considerations include techniques for
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separating fine particles from coarse particles.  Can fine and coarse particles be separated
adequately by a single size cut-point in all areas of the country or will the optimal cut point
differ in time and space, especially between very dry areas where coarse particles may be
found below  2.5 /mi and very humid areas  where fine particles occur above 1.0 /im?  If a
single fine-particle cut point is chosen, which is best:  2.5 pm; 1.0 jtim; or something in
between?  Is  separation  by size adequate or will chemical composition measurements also be
needed?

2.4.1.5 Exposure
     Paniculate matter exposure estimates  for most epidemiology studies are based on data
from ambient monitoring sites. Relationships between such measurements and personal
exposure are  of interest in evaluating and interpreting epidemiology studies. Aspects
assessed in the present document include:  urban scale PM exposure models, indoor/outdoor
PM characteristics and relationships, and the validity of ambient measurements  to provide
appropriate estimates to relate to health effect endpoints.  Two exposure estimates are of
concern, individual and  population estimates of PM exposure.   The type of epidemiology
study determines which  estimate is appropriate.  Additionally,  other factors (such as exposure
durations) that may determine health effects are considered. Human exposure patterns to
ambient and indoor air particles, including  consideration of activity patterns and various
microenvironments, are also characterized.
     Actual human exposure differs from outdoor concentrations due to:  the infiltration of
ambient aerosols indoors; indoor sources; and human activity patterns. Human exposure can
be determined through measurements and models.  For PM, indoor and personal monitoring
data show both higher than ambient and lower than ambient PM concentrations in indoor
settings as a function of varying particle size and human activity patterns.
     Coarse-mode particles (>2.5 /xm), which are generally of nonanthropogenic origin
(windblown dust, etc.),  require turbulence to provide vertical velocity components greater
than their settling velocity to  allow them to remain suspended  in the air.  Outdoor particles
enter into an  indoor setting either (1) by bulk flow (e.g., through an open window) in which
all particles can enter at the inlet condition or, (2)  by diffusional flow (e.g., through cracks
and fissures in the barrier of the building envelope).  Current investigations suggest that both
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fine and coarse particles penetrate indoors with high efficiency.  However, indoor settings
are usually quiescent, and the larger ambient particles that do enter indoors quickly settle
out, leading to the presence of the familiar dust layers  that require indoor settings to be
cleaned constantly.  Fine particles, which enter indoors, however, are not easily removed by
settling or impaction and are more reflective of ambient fine particle concentrations than are
coarse particles.  Human activity in indoor settings does generate fine particles (<2.5 fj,m)
from smoking, vacuuming, cooking, etc., and resuspends  coarse particles that previously had
settled out.  Thus, indoor PM consists of both:  (a) ambient particles  which have penetrated
indoors and remain suspended, and (b) particles  generated indoors.
     Two major factors influencing the relationship of ambient to indoor PM air quality are
(1) the variability of indoor concentrations of PM compared to outdoor concentrations as a
function of particle size (e.g., fine indoor > fine outdoor and coarse indoor < coarse
outdoor) and (2) the variation of exposures of individuals  related to the different activities
that are involved  with the local generation of particles  in their immediate surroundings
(smoking, traffic, dusting and vacuuming at home,  etc.).
     Long-term personal exposures to coarse-fraction PM (>2.5 jum) can be less than half
the ambient concentrations.  Long-term personal exposures to fine-fraction PM (<2.5 ftm)  of
ambient origin may be estimated by ambient measurements of the <2.5 /xm PM fraction.
However, the concentration of particles generated indoors or due to personal activities would
not be expected to vary in concert with ambient concentrations.  Therefore, variations in
ambient concentrations can serve as an indicator of variations in total exposure to ambient
particles, experienced both outdoors and in various microenvironments.

2.4.2  Health Effects
     A rapidly growing body of epidemiologic data examines associations  between PM
concentrations and human health effects, ranging from respiratory function changes and
symptoms to exacerbation of respiratory disease and excess mortality. These effects appear
to lie along an increasing gradient of severity of effects in different subpopulations.
Although the exact biological mechanisms underlying such effects are poorly understood,  the
emerging pattern  of findings points toward the plausibility that the observed associations
likely reflect real relationships between ambient PM exposures and human health impacts.
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This revised PM criteria document assesses evidence suggesting that this overall pattern of
effects may extend to concentrations of PM10 below the current NAAQS or may be
associated with other PM size fractions (e.g., fine particles  < 2.5 /*m).  Controlled human
exposure  and laboratory animal studies are also evaluated, and the overall coherence and
consistency of findings in relationship to the epidemiologic database is assessed.  These
include, for example:  (1)  studies of respiratory tract deposition and clearance of particles;
(2) experimental studies (animal and human) evaluating mechanisms of action of various
particles (by size, chemical composition, etc.) in order to evaluate biological plausibility of
effects reported by epidemiology studies; and (3)  other experimental studies that demonstrate
various toxic effects of PM constituents in humans or in  animal models.

2.4.2.1 Respiratory Tract Dosimetry
     The biological endpoint or health effect of an aerosol  exposure is likely more directly
related to the quantitative pattern of deposition within the respiratory tract than just to the
external exposure concentration. The regional deposition pattern determines not only the
initial respiratory tract dose but also the specific pathways and rates by which the  inhaled
material is cleared and redistributed. Thus, in order to evaluate different toxic responses to
inhaled particles across species  and to accurately extrapolate such laboratory animal data to
humans, or to evaluate differences that sex, age, or disease may have on human variability,
the various physicochemical, anatomic, and physiologic factors described must be integrated
to estimate a deposited dose or perhaps a retained dose (deposition  — clearance = retention).
Delineation of the dose to each  respiratory tract region (extrathoracic, tracheobronchial, and
pulmonary) is desired because each region has different dominant factors controlling
deposition and clearance, and different defense mechanisms.  A theoretical model  to describe
particle deposition and clearance would require detailed information on all the influential
parameters mentioned above (e.g., respiratory rates, exact airflow patterns,  complete
measurements of the branching  structure of the respiratory tract, pulmonary region
mechanics) for men, women, children,  and across the various species used in toxicity
studies. An empirical model (i.e., equations fit to experimental data) may adequately
describe regional deposition and require much less data to develop the model structure.
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     Within the dosimetry chapter, the anatomy of the respiratory tract and the
physicochemical, anatomical, and physiological factors controlling particle deposition,
clearance, and retention are reviewed.  Other factors that modify deposition, including sex,
age, disease state, and exposure to  irritants also are discussed.  The available human and
laboratory data on deposition and clearance and their positive and negative  attributes for use
in quantitative model development are discussed.  Available validated model structures to
estimate deposition and clearance in humans and laboratory animals are described and
evaluated.  The application of these models to quantitative extrapolation of the human and
animal toxicity data also are discussed. Consideration is given to uncertainties in input
parameters and the variability of model predictions when evaluating the usefulness of models
for quantitative dose extrapolation.

2.4.2.2 Epidemiology Studies
     Epidemiologic analyses are expected to provide some of the most crucial information
useful in deriving health criteria upon which to base Agency decisions regarding possible
revision of the current PM standards,  and such studies are accorded extensive attention in
this document.
     One useful distinction is to separate short- and long-term PM exposure effects. The
short-term effects include changes in respiratory function,  symptom indicators, hospital
admissions associated with exacerbation of respiratory or cardiovascular disease, and
excesses of daily death rates in urban areas associated with concurrent 24-h PM
measurements on the same or preceding few days.   The short-term effects studies are
typically longitudinal in nature and are specific to a community or metropolitan area with
reasonably homogeneous PM exposures.  The analyses of  data in short-term studies use time-
series analysis methods. The  long-term or chronic exposure effects studies typically use
annual PM concentrations and  annual symptom or death rates and are more likely to involve
comparisons across several communities rather than within a single community.  Although
both kinds of  epidemiologic analysis are useful, it is important to assess the consistency of
conclusions based on different kinds of studies. Coherence of effects at lower concentrations
is  a useful criterion for assessing diverse  studies with different endpoints or effects, different
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 populations, and different exposure metrics (Bates et al., 1990) and is considered as part of
 the evaluation of the available epidemiology literature.

 Mortality Studies
      Studies examining the relationship between ambient measures of PM and mortality were
 examined during the last review process (U.S. Environmental Protection Agency,  1982,
 1986) and contributed to the key scientific bases underlying the current PM10 NAAQS.
 However,  given the uncertainties in converting from British Smoke to PM10 measurements,
 particularly at lower concentrations, and the possible differences in particulate composition
 between London at the time the data were gathered  and the contemporary United States, it
 was difficult to  determine a precise level for a relationship between PM10 and mortality.
 Since that  time, numerous contemporary U.S.  mortality studies using either PM10  or TSP
 measurements have been published that examine short-term measurements. Also,  long-term
 PM ambient measurements and mortality have been examined in some recent studies.  These
 and other newly emerging PM-mortality studies are  summarized and critically evaluated.
      Issues of greatest concern so far relate primarily to the use and interpretation of the
 short-term mortality studies.  Almost all analyses of the relationship between PM and excess
 mortality require statistical adjustment for mortality  excesses associated with other potential
 confounding factors,  including other environmental stressors such as  temperature and relative
 humidity or other pollutants (co-pollutants) associated with PM and with mortality. For
 example, weather-related effects may be directly related to excess mortality, but may also be
 indirectly related when weather affects PM emissions and  atmospheric concentrations.
 Statistical and conceptual approaches to estimating the direct and indirect effects of covariates
 or confounders and specification of statistical adjustments for  possible confounding factors
 are evaluated  in interpreting the PM effects on mortality calculated from each study. Studies
using different exposure metrics are considered and  differences in particle size distribution or
particle composition between cities are considered as the data allow.
      Specification of "exposure-effect" relationship(s) between mortality and PM is also
important.  A number of studies have reported no evident  threshold for effects,  even at
relatively low concentrations,  but the ability to carry out meaningful threshold evaluations
may be greatly limited by the  statistical power  of the available studies.  Estimates of the
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relationship between PM and mortality may depend on differences in model specification.
Even with similar model specifications (exposure-response relationship, adjustment for
weather, copollutants, and other factors) there may be differences in the effects of PM at a
given concentration, possibly related to differences in particle size/composition and/or
climate or demographics among different cities.  An important component of the health
effects assessment in the criteria document is identification of susceptible subpopulations and
other variables such as weather, climate, or other pollutants, potentially contributing to
increased mortality  risk.

Morbidity Studies
     Decreased pulmonary function in predominantly healthy children was been reported in
some earlier epidemiology studies.  More recent studies add to this database.  Earlier long-
term exposure studies provided no evidence for an effect from PM  exposure on level of
pulmonary function, whereas some recent studies report reductions  in pulmonary function
associated with chronic exposure to particulate pollution.  An evaluation of the epidemiologic
database relating  short-term (24-h) and long-term (annual) ambient measurement of PM10 and
other measures of PM to changes in pulmonary function test results in children and adults is
presented.  The strength and consistency of epidemiologic databases that relate short-term
(24-h)  and long-term (annual) PM10 and other ambient PM indicator measurements to
changes in the rate  and/or severity of respiratory symptoms and disease are also critically
reviewed.  Studies examining exacerbation of respiratory  (i.e., COPD and asthma) and
cardiovascular diseases that lead to increased medical care utilization (such as emergency
room visits and hospital admissions) in relation to ambient PM exposure are also evaluated.
As appropriate, other factors and copollutants are  also examined  in relation to findings on
each of the above types of health endpoints.

2.4.2.3   Toxicology of Particulate Matter Constituents
     In addition to assessing epidemiologic studies of PM differentiated mainly in terms of
various size indicators (TSP, PM10, etc.), the toxicology of various major subclasses of PM
constituents is also  evaluated.  That evaluation focuses on acid aerosols, metals, ultrafine
particles, diesel particles, silica, bioaerosols, and other types of particles that make up
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ambient air mixes of particles in the broad class designated in toto as "paniculate matter".
Animal inhalation toxicology and other types of studies are reviewed to ascertain information
on several  key health issues, e.g.:  (1) the influence of particle size, number, and mass on
health responses; (2) the differential influence of varying particle chemistry on the health
effects observed; (3) the array of health effects that can be caused by specific PM
constituents; (4) exposure-response relationships for various exposure durations (acute and
chronic); (5) mechanisms of toxicity; and (6) pollutant interactions.  Information from these
studies relates to evaluation of the biological plausibility of the mortality and morbidity
associations reported in epidemiological studies. The data on relationships among particle
size, mass, number, and toxic effects may aid in determining the appropriateness of various
exposure indicators of potential human effects.
     Evaluation of the controlled human exposure (clinical) studies database concerning PM
and health  outcomes is presented as a subsection of the overall PM constituent toxicology
chapter.  This includes critical review  of PM effects on pulmonary function in healthy and
asthmatic individuals, pulmonary clearance mechanisms, airway reactivity, and immunologic
defense especially in relation to particle size but only to a limited extent in relation to
chemical composition.  There remains an almost complete absence of controlled experiment
data on exposure of humans to particles other than acid aerosols.
     Human clinical studies of PM constituents have been almost completely limited to
measuring  effects on symptoms, lung function, and airway reactivity, in addition to  a few
studies of effects on mucociliary clearance.  Few have used bronchoalveolar lavage  to study
PM effects on airway inflammation and host defense; nor have many examined effects of
acid aerosols or other particle exposures  on airway inflammation in asthmatic subjects or on
exacerbation of effects of antigen challenge in allergic or asthmatic subjects.

2.4.2.4  Sensitive Groups
     Available data are also evaluated for insight concerning human population groups
potentially  having increased susceptibility to ambient PM exposure.  Preexisting respiratory
or cardiovascular disease, in conjunction with advanced age,  appear to be important factors
contributing to increased susceptibility to PM mortality. For morbidity health endpoints,
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children and asthmatic individuals may display increased sensitivity to PM exposure, and, as
such, this topic is discussed.

2.4.3   Welfare Effects
2.4.3.1   Effects on Materials
     All manmade materials  exposed to the outdoor environment undergo degradation by
heat, moisture, and some bacteria and fungi.  For many years, air pollution has been
suspected of accelerating the  natural degradation processes.  For example, acidic pollutants
have been associated with accelerated degradation of paints  such as water-based paint and
alkyd coatings containing titanium dioxide, lead minium, or ferric oxide red.  Other
researchers have reported acidic pollution-related effects on automotive paint and steel
coating.  Paniculate matter has also been reported to produce paint soiling. Also, acid
aerosols and other particles containing acids also have  been reported to affect building stones,
cement, and concrete.  Acidic or acid-forming aerosols change the physical characteristics of
some stones,  cement, and concrete by changing the chemical composition.  Studies
examining the effects on materials of PM pollution (primary and secondary particles and
aerosol precursor gases)  are reviewed and summarized; where possible, changes in material
damage are correlated with changes in PM concentrations.

2.4.3.2   Visibility Effects
     There are several definitions for visibility; however, visibility is generally defined as
the degree to which the atmosphere is transparent to visible light or a reduction in visual
range and atmosphere  discoloration.  In 1977, Congress amended the Clean Air Act (CAA)
to address problems with visibility impairment resulting from manmade air pollution,
particularly in Class I  Federal areas (national  parks and wilderness areas). Airborne PM in
the form of varying amounts of sulfates, ammonium and nitrate ions, elemental carbon and
organic carbon compounds, water and smaller amounts of soil dust, lead compounds, and
other trace species reduce visibility, thereby affecting transportation safety and creating a loss
in aesthetic appeal. The fundamentals of visibility impairment,  including the effects of PM
concentration, aerosol composition, and size and pollutant emission trends on visibility are
evaluated. Indicators of visibility and air quality are also discussed.
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2.4.3.3 Climate Change
     It has been suggested that fine particles released into the atmosphere may alter the
climate through a reduction in the amount of solar radiation reaching the earth's surface, thus
cooling the surface while heating the aerosol layer.  The scattering and absorbing properties
of aerosols and their vertical distribution are briefly reviewed and reference made to other
assessments of their effects on radiative balance and how changes in radiative balance may
affect weather and climate. Aerosols also affect weather and climate through their role as
cloud condensation nuclei.  The concentration, composition,  size,  and number of aerosols can
influence the structure, stability, and albedo of clouds,  possible changing the location and
amount of  rainfall and  the rate of global  and  regional warming due to greenhouse gases.
Airborne particles also play an important role in influencing  the penetration of ultraviolet
light (e.g., UV-B) to the surface of the Earth due to stratospheric ozone depletion, as is also
briefly discussed.

2.4.3.4 Vegetation and Ecosystem Effects
     Extensive information also exists which indicates that ambient PM (especially  wet and
dry deposition of acidic particles) can damage both terrestrial and aquatic vegetation and
ecosystems.  Such information is thoroughly evaluated elsewhere (Irving, 1991; U.S.
National Acid Precipitation Assessment Program, 1991) but is not assessed in the present
document.
2.5   DOCUMENT CONTENT AND ORGANIZATION
     The present document critically reviews and assesses relevant scientific literature on
PM through February, 1996.  The material selected for review and comment in the text
generally comes from the more recent literature published since 1982, with emphasis on
studies conducted at or near PM pollutant concentrations found in ambient air.   Older
literature cited in the previous 1982 EPA PM AQCD and its Addenda (U.S.  Environmental
Protection Agency, 1982, 1986) is generally not discussed. However, as appropriate,  some
limited discussion is included of older studies judged to be significant because of their
potential usefulness in deriving a NAAQS.  An attempt has been made to discuss key
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literature in the text and present it in tables as well.  Reports of lesser importance for the
purposes of this document  are typically only summarized in tables.
     Generally, main emphasis is placed on consideration of published material that has
undergone scientific peer review.  However,  in the interest of admitting new and important
information, some material not yet fully published in the open literature but meeting other
standards of scientific reporting may be included as reviewed by CAS AC.  As noted earlier,
emphasis has been placed on studies in the range of current ambient levels.  On this basis,
studies in which the lowest concentration employed exceeded this level have been included if
they contain unique data, such as documentation of a previously unreported effect or of
mechanisms of effects, or if they were multiple-concentration studies designed to provide
information on concentration-response relationships.  In reviewing and summarizing the
literature, an attempt is made to present alternative points of view where scientific
controversy exists.  As warranted, considerations bearing on the quality of studies are noted.
     The present document consists of 13 chapters.  The Executive Summary  for the entire
document is contained in Chapter 1, followed by this general introduction in Chapter 2.
Chapters 3 through 7  provide background information on physical and chemical properties of
PM and related compounds; sources and emissions; atmospheric transport, transformation,
and fate of PM; methods for the collection and measurement of PM; and ambient air
concentrations and factors affecting exposure of the general population.  Chapter 8  describes
effects on visibility and climate, whereas Chapter 9 describes damage to materials
attributable to PM. Chapters 10 through 13 evaluate information concerning the health
effects of PM.  More specifically, Chapter 10 discusses dosimetry of inhaled particles in the
respiratory tract,  and  Chapter 11 summarizes information on the toxicology of specific types
of PM constituents, including laboratory animal studies  and controlled human exposure
studies.  Chapter  12 discusses epidemiological studies, and Chapter 13  integrates key
information on exposure, dosimetry, and critical health risk issues derived from studies
reviewed in the prior  chapters.
     Neither control techniques nor control strategies for the abatement of PM are  discussed
in this document, although some topics covered may be incidentally relevant to abatement
strategies.  Technologies for controlling PM emissions are discussed in  other documents
issued by EPA's  Office of  Air Quality Policy and Standards (OAQPS).  Likewise, issues
                                          2-30

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germane to the scientific basis for control strategies, but not pertinent to the development of
criteria, are addressed in numerous  other documents issued by OAQPS.
     In addition, certain issues of direct relevance to standard setting are not explicitly
addressed in this document, but are instead analyzed in documentation prepared by OAQPS
as part of its regulatory analyses materials.  Such analyses include (1) discussion of what
constitutes an  "adverse effect" and delineation of particular adverse effects that the primary
and secondary NAAQS are intended to protect against, (2) exposure analyses and assessment
of consequent  risk, and (3) discussion of factors to be considered in determining an adequate
margin of safety.  Key points and conclusions from such analyses are summarized in a Staff
Paper prepared by OAQPS and reviewed by CAS AC.  Although scientific data contribute
significantly to decisions regarding the above issues, their resolution cannot be achieved
solely on the basis of experimentally acquired information.  Final decisions on items (1)  and
(3) are made by the Administrator,  as mandated by the Clean Air Act.
     A  fourth issue directly pertinent to standard setting is  identification of populations at
risk, which is  basically a selection by EPA of the subpopulation(s) to be protected by the
promulgation of a given standard. This issue is addressed only partially in this document.
For example, information is presented on factors, such as preexisting disease, that may
biologically predispose individuals and subpopulations to adverse effects from exposures  to
PM.  The identification of a population at risk, however, requires information above and
beyond  data on biological predisposition, such as information on levels of exposure, activity
patterns, and personal habits.  Such information is included in the Staff Paper developed  by
OAQPS and reviewed by CASAC as a separate  item from this document.
                                          2-31

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REFERENCES

Bates, D. V.; Baker-Anderson, M.; Sizto, R. (1990) Asthma attack periodicity: a study of hospital emergency
       visits in Vancouver. Environ. Res. 51: 51-70.

Bouhuys, A.; Beck, G. J.; Schoenberg, J. B. (1978) Do present levels of air pollution outdoors affect respiratory
       health? Nature (London) 276: 466-471.

Code of Federal Regulations. (1986) Appendix B - reference method for the determination of suspended
       paniculate matter in the atmosphere  (high-volume method). C. F. R. 40: §50.

Dassen, W.; Brunekreef,  B.; Hoek, G.; Hofschreuder, P.; Staatsen, B.; De Groot, H.; Schouten, E.;
       Biersteker, K. (1986) Decline in children's pulmonary function during an air pollution episode. J. Air
       Pollut. Control Assoc. 36: 1223-1227.

Dockery, D. W.;  Ware, J. H.; Ferris, B. G., Jr.; Speizer, F. E.; Cook, N. R.; Herman, S. M. (1982) Change
       in pulmonary function in children associated with air pollution episodes.  J. Air Pollut. Control Assoc.
       32: 937-942.

Federal Register.  (1971) National primary and secondary ambient air  quality standards. F. R. (April 30)
       36: 8186-8201.

Federal Register.  (1979a) National ambient air quality standards; review of criteria and standards for paniculate
       matter and sulfur oxides. F. R. (October 2) 44: 56730-56731.

Federal Register.  (1979b) National primary and secondary  ambient air quality standards: revisions to the  national
       ambient air quality standards for photochemical oxidants. F. R. (February 8) 44: 8202-8221.

Federal Register.  (1984) Proposed revisions  to the  national ambient air quality standards for paniculate matter.
       F. R. (March 20) 49:  10408-10435.

Federal Register.  (1986a) National ambient air quality standards; review of criteria and standards for paniculate
       matter and sulfur oxides. F. R. (April 1) 51: 11058.

Federal Register.  (1986b) National ambient air quality standards: review of criteria and standards for paniculate
       matter and sulfur oxides. F. R. (July 3) 51: 24392-24393.

Federal Register.  (1987) Revisions to the national ambient air quality standards for paniculate matter. F. R.
       (July 1) 52: 24634-24669.

Ferris, B. G., Jr.; Higgins, I. T. T.;  Higgins, M. W.; Peters, J. M.  (1973) Chronic nonspecific respiratory
       disease in Berlin, New Hampshire, 1961 to 1967: a follow-up study. Am. Rev. Respir.  Dis.
       107: 110-122.

Ferris, B. G., Jr.; Chen,  H.; Puleo, S.; Murphy, R. L.  H., Jr.  (1976) Chronic nonspecific respiratory disease in
       Berlin, New Hampshire, 1967 to 1973: a further follow-up study. Am. Rev. Respir. Dis. 113: 475-485.

Hausman, J. A.; Ostro, B. D.; Wise, D. A. (1984) Air  pollution and lost work. Cambridge, MA: National
       Bureau of Economic Research; NBER working paper no. 1263.

International Standards Organization.  (1981) Size definitions for panicle sampling: recommendations of ad hoc
       working group appointed by Committee TC 146 of the International Standards Organization. Am. Ind.
       Hyg. Assoc.  J. 42: A64-A68.
                                                  2-32

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Irving, P., ed. (1991) Acidic deposition: state of science and technology: volumes I-IV. Washington, DC: The
       U.S. National Acid Precipitation Assessment Program.

Lawther, P. J. (1986) [Letter to John Bachmann]. Washington, DC: Office of Air Quality Planning and
       Standards; August 22. Available for inspection at: U.S. Environmental Protection Agency, Central
       Docket Section, Washington, DC; docket no. A-82-37, IV-D-319.

Lawther, P. J.; Waller, R. E.; Henderson, M. (1970) Air pollution and exacerbations of bronchitis. Thorax
       25: 525-539.

Lippmann, M. (1986a) [Letter to EPA Administrator Lee Thomas]. Washington, DC: U.S. Environmental
       Protection Agency, Clean Air Scientific Advisory Committee; January 2. Available for inspection at:
       U.S. Environmental Protection Agency, Central Docket Section, Washington, DC; docket no.
       A-82-37, IV-D-315.

Lippmann, M. (1986b) [Letter to EPA Administrator Lee Thomas]. Washington, DC, U.S. Environmental
       Protection Agency, Clean Air Scientific Advisory Committee; December 15. Available for inspection at:
       U.S. Environmental Protection Agency, Central Docket Section, Washington, DC; docket no.
       A-82-37, IV-D-339.

Lippmann, M. (1986c) [Letter to EPA Administrator Lee Thomas]. Washington, DC: U.S. Environmental
       Protection Agency, Clean Air Scientific Advisory Committee; December 16. Available for inspection at:
       U.S. Environmental Protection Agency, Central Docket Section, Washington, DC; docket no.
       A-82-37, IV-D-338.

Mazumdar, S.; Schimmel, H.; Higgins, I. T. T. (1982) Relation of daily mortality to air pollution: an analysis of
       14 London winters, 1958/59-1971/72. Arch. Environ. Health 37: 213-220.

Ostro, B. (1984) A search for a threshold in the relationship of air pollution to mortality:  a reanalysis of data on
       London winters.  Environ. Health Perspect. 58: 397-399.

Ostro, B. D. (1987) Air pollution and morbidity revisited: a specification test. J. Environ. Econ.  Manage.
       14: 87-98.

Ozkaynak, H.; Spengler, J. D. (1985) Analysis of health effects resulting from population exposures to acid
       precipitation precursors. Environ. Health Perspect. 63: 45-55.

Shumway, R. H.; Tai, R. Y.; Tai, L. P.; Pawitan, Y. (1983) Statistical analysis of daily  London mortality and
       associated weather and pollution effects.  Sacramento, CA:  California Air Resources Board;  contract no.
       Al-154-33.

Swift, D. L.; Proctor, D. F. (1982)  Human respiratory deposition of particles during oronasal breathing. Atmos.
       Environ. 16:  2279-2282.

U.S. Code. (1991) Clean  Air Act, §108, air quality criteria and control techniques,  §109, national ambient air
       quality standards.  U. S.  C. 42: §§7408-7409.

U.S. Environmental Protection Agency. (1982) Air quality criteria for paniculate matter and sulfur oxides.
       Research Triangle Park, NC: Office of Health and Environmental Assessment, Environmental Criteria
       and Assessment Office; EPA report no. EPA-600/8-82-029aF-cF. 3v. Available from: NTIS, Springfield,
       VA;  PB84-156777.
                                                  2-33

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U.S. Environmental Protection Agency. (1986) Second addendum to air quality criteria for paniculate matter and
       sulfur oxides (1982): assessment of newly available health effects information. Research Triangle Park,
       NC: Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office;
       EPA report no. EPA-600/8-86-020F. Available from: NTIS, Springfield, VA; PB87-176574.

U.S. National Acid Precipitation Assessment Program. (1991) The U.S. National Acid Precipitation Assessment
       Program 1990 integrated assessment report. Washington, DC: The U.S. National Acid Precipitation
       Assessment Program.

Ware, J.  H.; Ferris, B. G., Jr.; Dockery, D. W.; Spengler, J. D.; Stram, D. O.; Speizer, F. E. (1986) Effects
       of ambient sulfur oxides and suspended particles on respiratory health of preadolescent children.
       Am. Rev. Respir. Dis. 133: 834-842.
                                                  2-34

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               3.   PHYSICS AND CHEMISTRY OF
                      PARTICULATE MATTER
3.1   INTRODUCTION
3.1.1   Overview
     Atmospheric particles originate from a variety of sources and possess a range of
morphological, chemical, physical, and thermodynamic properties.  Examples include
combustion-generated particles such as diesel soot or fly ash, photochemically produced
particles such as those found in urban haze, salt particles formed from sea spray, and soil-
like particles from resuspended dust. Some particles are liquid, some are solid; others
contain a solid core surrounded by liquid.  Atmospheric particles contain inorganic ions and
elements, elemental carbon, organic compounds, and crustal compounds.  Some atmospheric
particles are hygroscopic and contain particle-bound water. The organic fraction is especially
complex, containing hundreds of organic compounds.
     Particle diameters span more than four orders of magnitude, from a few nanometers to
one hundred micrometers.  Combustion-generated particles, such as those from power
generation, from automobiles, and in tobacco smoke,  can be as small as 0.003 ^m and as
large as 1 /xm. Particles produced in the atmosphere by photochemical processes range in
diameter  from 0.003 to 2 jim.  Fly ash produced by coal combustion ranges from 0.1  to
50 fan. or more. Wind-blown dust, pollens, plant fragments, and cement dusts are generally
above 2 /xm in diameter.  Particles as small as a few nanometers  (Covert et al., 1992;
Clarke, 1992) and as large as 100 /*m have been measured in the atmosphere (Lin et al.,
1993).
     Particles are ubiquitous in the atmosphere.  The lowest concentrations are found in
background marine environments, where particle number concentrations range from 100/cm3
to 400/cm3.  In background continental environments, particle  concentrations vary from
100/cm3 to 5,000/cm3; while in urban areas of the United States concentrations may be as
high as 4,000,000/cm3 (Willeke and Whitby, 1975; Whitby and Sverdrup, 1980).  Particles
account for a mass of a few /xg/m3 near the surface over dry continental areas to several
hundred /ig/m3 in polluted urban areas.
                                       3-1

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     The composition and behavior of airborne particles are fundamentally linked with those
of the surrounding gas.  Aerosol is defined as a suspension  of solid or liquid particles in air
and includes both the particles and all vapor or gas phase components of air.  However, the
term aerosol is often used to refer to the suspended particles only.  Particles may be solid or
liquid or a  mixture of both phases.  Paniculate is an adjective and should only be used as a
modifier, as in particulate matter.
     Particulate material can be primary or secondary.  Primary particles are  composed of
material  emitted directly into the atmosphere.  This includes material emitted in particulate
form such as wind-blown  dust, sea salt, road dust, mechanically generated particles and
combustion-generated particles such as fly ash and soot.  It  also includes particles  formed
from the condensation of high temperature vapors such as those formed during combustion.
The concentration of primary particles depends on their emission rate, transport and
dispersion, and removal rate from the atmosphere.
     Secondary particles form from condensable vapors  formed by chemical reaction
involving gas-phase precursors or by other processes involving chemical reactions of free,
adsorbed, or dissolved gases.  Secondary formation processes  can result in either the
formation of new particles (Wiedensohler et al., 1994; Covert et al.,  1992; Clarke et al.,
1991, 1993; Frick and Hoppel, 1993; Hoppel et al., 1994; Weber et  al., 1995) or the
addition  of particulate material to preexisting particles  (Andreae et al., 1986; Wall et al.,
1988; Wu and Okada, 1994).  Most atmospheric sulfate  particles are  formed from
atmospheric oxidation of sulfur dioxide.  Atmospheric nitrate is also essentially secondary.
Oxides of nitrogen react in the atmosphere to form nitric acid vapor which in turn may react
with ammonia gas to  form particulate ammonium nitrate. Nitric acid may also react with
particles containing sodium chloride or calcium carbonate, releasing hydrochloric acid or
carbon dioxide, and forming sodium nitrate or calcium nitrate which  remains in the particle.
A portion of the organic aerosol is also attributed to secondary processes  (Hildemann et al.,
1994a,b; Turpin and Huntzicker, 1991; Mylonas et al., 1991; Pickle  et al., 1990; Gray
et al., 1986).  Secondary  aerosol formation can depend on concentrations of other gaseous
reactive  species such  as ozone, hydroxyl radical, or hydrogen peroxide; atmospheric
conditions  including solar radiation and relative humidity; and the interactions  of precursors
and preexisting particles within cloud or fog droplets (Meng and Seinfeld, 1994; McMurry
                                           3-2

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 and Wilson, 1983; Hoppel and Frick,  1990).  As a result, it is considerably more difficult to
 relate ambient concentrations of secondary species to sources of precursor emissions than it
 is to identify the sources of primary particles.
     Airborne paniculate matter can be anthropogenic or natural in origin. Both
 anthropogenic and natural particulate material can occur from either primary or secondary
 processes.   Anthropogenic refers to particulate matter which is directly emitted, or formed
 from precursors which are emitted, as a result of human activity.  Primary anthropogenic
 sources include  fossil fuel combustion, fireplace emissions, and road dust. Secondary
 anthropogenic particulate material can be  generated photochemically from anthropogenic
 SO2, NOX,  or organic gases.  Primary natural sources include wind blown dust from soil
 undisturbed by man, sea-salt, natural forest fires and biogenic sources  such as pollen, mold
 spores,  leaf waxes and fragments from plants (Simoneit  and Mazurek, 1982).  In addition,
 plants emit  gaseous species  such as terpenes (Lamb et al., 1987).  Terpenes are
 photochemically reactive. In the presence of ozone or hydroxyl radicals they react to form
 secondary organic particles  (Kamens et al., 1981; Pandis et al., 1991,  1993).
     Volatilization and sorption processes also affect concentrations and compositions of
 airborne particles. Some aerosol constituents  are semivolatile and exist in both gas and
 particle phases.  Their gas-particle distribution depends on  atmospheric conditions such as
 temperature, the concentrations of other aerosol species including water vapor, and the  vapor
 pressure of  the constituent.  Some inorganic compounds such as ammonium nitrate (Stelson
 and Seinfeld,  1982a,b; Bassett and Seinfeld, 1983, 1984) and organic compounds, including
 many polycyclic aromatic hydrocarbons (Yamasaki et al., 1982; Ligocki and Pankow, 1989;
 Pankow, 1987, 1994a,b), are semivolatile.  Diurnal temperature fluctuations can cause
 substantial changes in the particle-phase concentrations of semivolatile  constituents as a result
 of gas-particle redistribution.  Evidence exists suggesting that this volatilization-sorption
 cycle results in the redistribution of semivolatile material among particles of differing origins
 (Venkataraman and Hildemann, 1994).
     A  complete description of the atmospheric aerosol would include an accounting of the
chemical composition, morphology, and size of each particle and the relative abundance  of
each particle type as  a function of particle size (Friedlander, 1970).  However, most often
the physical and chemical characteristics of particles are  measured  separately.  Number size
                                           3-3

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distributions are often determined by physical means,  such as electrical mobility or light-
scattering.  Chemical composition is determined by analysis  of collected samples. The mass
size distribution and the average chemical composition of the aerosol as a function of size
can be determined by collection of size-segregated samples (Countess et al., 1980; Hering
and Friedlander, 1982; John et al.,  1990;  Sloane et al., 1991).  Recent developments in
single particle analysis  techniques coupled with multivariate  classification methods (Van
Grieken and Xhoffer, 1992; Germani and Buseck,  1991; Mansoori et al.,  1994) are bringing
the description envisioned by Friedlander closer to reality.  This introductory section
describes some of the measurements that have been made on atmospheric particles, and the
insights thus provided on the nature, origins, and atmospheric processes that affect particle
composition.

3.1.2   Atmospheric Aerosol  Size Distributions
     Size is one of the most important parameters in determining the properties, effects and
fate of atmospheric particles.  The atmospheric deposition rates of particles, and therefore,
their residence time  in the atmosphere, are a strong function of particle size.  Size also
influences deposition patterns of particles  within the lung. Light scattering is strongly
dependent on particle size.  Particle size distributions,  therefore, have a strong  influence  on
atmospheric visibility and through their effect on radiative balance on climate.
     Atmospheric size distributions for averaged continental background,  urban-influenced
background, averaged urban, and freeway-influenced urban  aerosols are shown in
Figures 3-1.  (Whitby  and Sverdrup, 1980).  Figure 3-1 describes  the number of particles as
a function of particle diameter.  For the same data, the particle volume distribution is shown
in Figures 3-2.  Note that for the particle number distribution both the diameter and the
number of particles are shown on a logarithmic scale.  For the volume distribution,  the
volume is shown on an arithmetic scale and the distribution  is plotted such that the volume of
particles in a specified size range is proportional to the corresponding area under the curve.
These distributions show that most  of the particles are quite small, below 0.1 fim, while  most
of the particle volume  (and therefore most of the mass) is found in particles > 0.1.
     An important feature of atmospheric aerosol  size distributions is their multimodal
nature.  Volume distributions, measured in ambient air in the United States, are almost
                                           3-4

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  1,000,000-
    10,000-
 r-    loo-
 •a
 z
 T3
      0.01-
    0.0001-
   0.000001-
                                       a
 Clean Background
 Urban Influenced
  Background
 Average Urban
 Urban + Freeway
^	1	1
                                              200,000
                             150,000
                                            g-100,000
                                            D>
                                            o
                                               50,000--
0.01
0.1
1
             10
                                     100
             Particle Diameter, Dp (u,m)
0.01        0.1          1         10
      Particle Diameter, Dp (urn)
Figure 3-1.  Number of particles as a function of particle diameter: (a) data are shown
             on a logarithmic scale to display the wide range in number concentrations
             from different sites; (b) averaged urban distribution are shown on a linear
             scale for which the area under the curve is proportional to particle
             number.
Source: Whitby and Sverdrup (1980).
always found to be bimodal, with a minimum between 1.0 and 3 pm.  The distribution of
particles that are mostly larger than the minimum is termed "coarse".  The distribution of
particles that are mostly smaller than the minimum is termed "fine".  Whitby and Sverdrup
(1980) and Willeke and Whitby (1975) identified three modes:  nuclei, accumulation, and
coarse.  The three modes are most apparent in the freeway-influenced size distribution of
Figure 3-2b.  The smallest mode, corresponding to particles below about 0.1 /mi, is the
nuclei mode. The middle mode, from 0.1 to 1 or 2 /xm, is the accumulation mode.  Fine
particles include both the accumulation and the nuclei modes.  The largest mode, containing
particles larger  than 1 or 2 /un, is the coarse particle mode. Whitby and coworkers observed
                                          3-5

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    70
    65-
    60-
    55-
        a
Clean
 Background
Urban Influenced
 Background
South-Central
 New
70
65
60--
55-
Average Urban
Urban + Freeway
     0.01
               0.1        1        10
             Particle Diameter, Dp (urn)
            100      0.01      0.1       1        10
                          Particle Diameter, Dp (u,m)
                                   100
Figure 3-2.
                 Particle volume distribution as a function of particle diameter: (a) for
                 the averaged background and urban-influenced background number
                 distributions shown in Figure 3-1 and a distribution from south central
                 New Mexico, and (b) for the averaged urban and freeway-influenced
                 urban number distributions shown in Figure 3-1.
Source: Whitby and Sverdrup (1980) and Kim et al. (1993c).


that continental background aerosols not influenced by sources have a small accumulation
mode and no nuclei mode.  For urban aerosols, the accumulation and coarse particles modes
are comparable in volume.  The nuclei mode is small in volume but, as discussed further in
Section 6.8, dominates the  number distributions of urban aerosols.
     Many measurements indicate that the chemical compositions of coarse and fine particles
are distinct.   The processes that affect the formation and removal of these two size fractions
of atmospheric  aerosols are also distinct.  Coarse particles are generated by mechanical
processes and consist of soil dust, fly ash, sea  spray, plant fragments, particles from tire
wear, and emissions from rock-crushing operations.   These particles are removed primarily
by impaction and settling.  Nuclei and accumulation mode particles contain primary particles
                                          3-6

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from combustion sources and secondary particles that result from condensation of low-
volatility vapors formed from chemical reactions.  Particles in the nuclei mode may be
transferred into the accumulation mode by coagulation.  Cloud coalescence and
transformations in  cloud droplets, followed by evaporation, are other processes that are
important in atmospheric formation of accumulation mode particles.  Accumulation mode
particles do not ordinarily grow  into  the coarse mode, because number concentrations are too
low for coagulation to be effective.  Nuclei are readily removed  by diffusion to surfaces.
However, accumulation mode particles are not easily removed from the airstream.  They
have long atmospheric lifetimes and are able to penetrate deep into the lungs.  The nuclei and
accumulation modes are fairly independent of the coarse mode, both  in formation and
removal (Willeke and Whitby, 1975; Whitby and Sverdrup, 1980).
     Fine and coarse particles are best differentiated by their formation mechanism (Wilson
and Suh, 1996). Fine particles are formed by nucleation with gases while coarse particles
are formed by mechanical processes  from larger particles or bulk materials.  The most
appropriate size cut for separating fine from coarse particles is in the range of 1  to 3 fim in
particle diameter; however, a precise size cut cannot be determined because of some size
overlap between the fine and coarse particle modes.

3.1.3    Definitions
3.1.3.1   Definitions of Particle Diameter
     The diameter of a particle may  be determined geometrically,  from optical or  electron
microscopy; by light scattering and Mie theory, or by its behavior, such as its electrical
mobility, its settling velocity, or its aerodynamic behavior. Although atmospheric  particles
are often not spherical, their diameters are described by an "equivalent" diameter,  that of a
sphere which would have the same physical behavior.  Two parameters that are often used
are the Stokes diameter and the aerodynamic diameter.  The Stokes diameter, Dp, describes
particle size based on the aerodynamic drag force imparted on a  particle when its velocity
differs from that of the surrounding fluid.  For a smooth,  spherically shaped particle, Dp
exactly equals the  physical diameter of the particle.  For irregularly shaped particles, Dp is
the diameter of an equivalent sphere  that would have the same aerodynamic resistance.
Particles of equal Stokes diameters that carry the same electric charge will have the same
                                          3-7

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electrical mobility.  Particles of equal density and equal Stokes diameter have the same
settling velocity.
     Aerodynamic diameter, Da, depends on particle density and is defined as the diameter
of a spherical particle with equal settling velocity but a material density of 1 g/cm3.
Particles with the same physical size and shape but different densities will have the same
Stokes diameter but different aerodynamic diameters.  For particles greater than about
0.5 pirn, the aerodynamic diameter is generally the quantity of interest because it is the
parameter that is important to particle transport,  collection, and respiratory tract deposition.
Respirable, thoracic, and inhalable particle sampling are based on particle aerodynamic
diameter.
     Aerodynamic diameter, Da, is related to the Stokes diameter, Dp, by:
                                                 1/2
                                                                                     (3-1)
                                                    IX
where p is the particle density, and C and Ca are the Cunningham slip factors evaluated for
the particle diameters Dp and Da respectively.  The slip factor is a function of the ratio
between particle diameter and mean free path of the suspending gas; it is given by the
expression (Hinds, 1982):
                      C  = 1 +  — {2.514 + 0.800 exp(-0.55  —E)}                  (3-2)
                                Dp                             X
where X is the mean free path of the air. C is an empirical factor that accounts for the
reduction in the drag force on particles due to the "slip" of the gas molecules at the particle
surface.  It is important for particles less than 1 /im in diameter, for which the surrounding
air cannot be modeled by a continuous fluid.  At normal atmospheric conditions
(temperature = 20 °C, pressure =  1 atmosphere) X  = 0.066 pm. For large particles
(Dp > 5 fj,m) C =  1; while for smaller particles C >  1.
      For particles with diameters greater than the mean free path, the  aerodynamic diameter
given by equation (3-1) is approximated by:
                                            3-8

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                                   DP                  (°P
This expression, which shows that aerodynamic diameter is directly proportional to the
square root of the particle density, is often used for particles as small as 0.5 /mi.  For
particles with diameters much smaller than the mean free path, the slip factor must be taken
into account.  In this case the aerodynamic diameter is directly proportional to the particle
density [Da  = (p) Dp for Dp«X].

3.1.3.2   Definitions of Particle Size Fractions
     In the preceding discussion several subdivisions of the aerosol size distribution were
identified.  The  aerosol  community uses three different approaches or conventions in the
classification of  particles by size:  (1) modes, based on the observed size distributions and
formation mechanisms; (2) dosimetry, based  on the entrance into various compartments of
the respiratory system; and (3) cut point, based on the 50% cut point of the specific sampling
device.
Modal
     The  modal classification was first  proposed by Whitby (1978).  An idealized version is
shown in Figure 3-3.  A number of actual  distributions are shown in Section 3.7.  The
observed modal  structure is frequently approximated by several log-normal distributions.
     Coarse Mode:  The distribution of particles with diameters mostly greater than the
     minimum in the particle mass distribution, which generally occurs between 1 and 3 tiro..
     These particles are usually mechanically generated.
     Fine Mode: The distribution of particles with diameters mostly smaller than the
     minimum in the particle mass distribution, which generally occurs between 1 /on and
     3 /on.  These particles are usually formed from gases.
     Accumulation Mode:  That portion of the fine particle fraction with diameters above
     about 0.1 /im.  Secondary particulate matter, formed from chemical reactions in the
     atmosphere, often "accumulates" in this  size range.  Accumulation-mode particles
     normally do not grow into the coarse mode.
     Nuclei Mode:  That portion of the fine particle fraction with diameters below about
     0.1 jLtm. The nuclei mode can be observed as a separate mode only in clean or remote
                                           3-9

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           I
           o>
           o
           to
           to
70
60
50
40

30
20
10
                 0.1
                       Fine-Mode Particles
     Coarse-Mode Particles
        0.2     0.5   1.0     2      5    10     20
            Aerodynamic Particle Diameter (Da), \im
       	Total Suspended Particles (TSP)	*
                     PM-io     •
                              PM2.5
<-PM(
                                                                     TSP
                                                                    HiVol
                       50   100
                                   '(10-2.5p
Figure 3-3. An idealized distribution of ambient particulate matter showing fine-mode
            particles and coarse-mode particles and the fractions collected by
            size-selective samplers.
Source: Adapted from Wilson and Suh (1996).
     areas or near sources of new particle formation by nucleation.  Nuclei-mode particles
     rapidly grow into the accumulation mode.
     Over the years, the terms fine and coarse,  as applied to particle sizes, have lost the
precise meaning given in Whitby's (1978) definition.  In any given article, therefore, the
meaning of fine and coarse must be inferred from the author's usage.  In this document, the
term mode is  used with fine and coarse when it is desired to specify the distribution of
fine-mode particles or coarse-mode particles as shown in Figure 3-3.
     Modes Within the Accumulation Mode:  Aqueous-phase reactions may occur within
cloud droplets, fog droplets or particles at very high relative humidity.  The partial drying
out of these particles may lead to a mode which is larger than the accumulation mode formed
under drier conditions.  This has been called the droplet mode.  A smaller mode, perhaps
                                          3-10

-------
formed from non-hygroscopic material or formed after the relative humidity has decreased,
may also be observed. This has been called the condensation mode (Hering and Friedlander,
1982; John et al., 1990).  This phenomenon is discussed in greater detail in Section 3.7.
     Modes Within the Nuclei Mode:  Measurements over clean, remote areas (Hoppel et al.,
1986; Hoppel and Frick, 1990; Covert et al., 1992; Wiedensohler et al., 1994) indicate that
under some  conditions two modes may be observed within the nuclei mode. Aerosol
physicists distinguish these as:
     Aitken Nuclei:  That portion of the nuclei mode which exhibits a local maximum in the
     number distribution above 15 nm; and the
     Ultra-fine Nuclei:  That portion of the nuclei mode which exhibits a local maximum in
     the number distribution below 15 nm.
     Ultra-fine Panicles in a Biological Context:  In the terminology of health scientists
ultrafine is often used to characterize any size distribution, natural or laboratory-generated,
which, under dry conditions, has  a mass median diameter below about 0.1  /mi.  One
hypothesis holds that such particles may cause inflammation and other health effects due to
their physical size in addition to any chemically-induced effects (Oberdoster, 1995).
Ultrafine particles, in the health effects usage, are closely related to the nuclei mode.  In this
document ultrafine will be  used in the biological context and may include particles from the
minimum size of about 3 nm to about 100 nm (100 nm = 0.1 /mi).

Dosimetry
     In a second approach, size fraction definitions are based on human health significance.
This convention classifies particles into inhalable, thoracic, and respirable particles  according
to their entrance into the various  compartments of the respiratory system.   In a general  sense,
inhalable particles refer to  those that enter the respiratory tract, including the head airways
region. Thoracic particles  refer to particles that reach the lung airways and the gas-exchange
region of the lung, and respirable particles  are those that reach the gas-exchange region.  In
the past exact definitions of these terms have varied among organizations.  As of 1993 a
unified set of definitions was adopted by the American Conference of Governmental
Industrial Hygienists (ACGIH) (1994), the International Standards  Organization (ISO), and
the European Standardization Committee (CEN).
                                          3-11

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Sampler Cut Point
     Another set of definitions of particle size fractions arises from considerations of
size-selective sampling. Size-selective sampling refers to the collection of particles below or
within a  specified aerodynamic size range, usually defined by the 50% cut point size, and has
arisen in an effort to measure particle size fractions with some special significance, e.g.,
health, visibility, source apportionment, etc. The PM10 standard set by the U.S.
Environmental Protection Agency in 1987 is an example of size-selective sampling (Federal
Register, 1987).  The PM10 size cut was designed to focus regulatory concern on those
particles  small enough to enter the thoracic region. PM10 samplers, as defined in Appendix J
to 40 Code of Federal Regulations (CFR) Part 50 (Federal Register,  1988), collect all of the
fine particles and part of the coarse particles.  The upper cut point is defined as having a
50% collection efficiency at 10 ±0.5 j«m diameter.  The slope of the collection efficiency
curve is defined in amendments to 40 CFR, Part 53.  The curve which defines PM10, and the
curves which define inhalable, thoracic, and respirable particles, are shown in Figure 3-4.
     Prior to the establishment of the PM10 standard, the particulate matter standard was
based on total suspended particulate matter (TSP).  TSP is defined by the design of the High
Volume Sampler (hivol) which collects all of the fine particles but only part of the coarse
particles.  The upper  cut off size  of the hivol depends on the wind speed and direction, and
may vary from 25 to  40 /*m.  Heroic measures, such as were undertaken with the  Wide
Range Aerosol Classifier (WRAC), are required to collect the entire coarse mode (Lundgren
and Burton, 1995). Samplers  with  upper cut-points of 3.5, 2.5, 2.1 and  1.0 /nm are  also in
use.  Dichotomous samplers split the particles into smaller and larger fractions,  which may
be collected on separate filters.
     An idealized distribution showing the normally observed division of ambient  aerosols
into fine-mode particles and coarse-mode particles, and the size fractions collected by TSP,
PM10, PM2 5 and PM(10_2 5) samplers,  is shown in Figure 3-3.
     In an analysis reported in 1979, EPA scientists endorsed the need to measure fine and
coarse particles  separately (Miller et al., 1979). Based on the availability of a dichotomous
sampler with a separation  size of 2.5 /mi, they recommended 2.5 /^m as the cut point
between fine and coarse particles.  Because of the wide use  of this cut point, the PM2 5
fraction is frequently  referred  to as "fine" particles.  However, while the PM2 5 sample
                                          3-12

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            100
                                                                PM10
                                                             •  IPM
                                                             •  TPM
                                                             O  RPM
                                                                      100
                            Aerodynamic Particle Diameter (urn)
Figure 3-4.  Specified particle penetration through an ideal inlet for four different size-
            selective sampling criteria. PM10 is defined in the Federal Register (1988).
            Curves for inhalable particulate matter (IPM), thoracic particulate matter
            (TPM) and respirable particulate matter (RPM) size cuts are computed
            from definitions given by American Conference of Governmental Industrial
            Hygienists (1994).
contains all of the fine particles it may, especially in dry areas or during dry conditions,

collect a small fraction of the coarse particles.  A PM10-PM2 5 size fraction may be obtained

from a dichotomous sampler or by subtracting the mass on a PM2 5 sampler from the mass
on a PM10 sampler.  The resulting PM10-PM2 5 mass, or PM(10.2 5), is sometimes called

"coarse" particles.  However, it would be more correct to call PM2 5 an indicator of

fine-mode particles,  PM10 an indicator of thoracic particles, and PM(10-2.5) an indicator of

the thoracic component of coarse-mode particles.
                                        3-13

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3.1.3.3  Other Terminology
     Other terminology that has been introduced in this section is summarized below:
     Primary Particles: material emitted into the atmosphere, either directly as particles or a
           vapor which rapidly forms particles by nucleation and/or condensation, from
           either natural sources or sources derived from human activity;
     Secondary Paniculate Material:  material formed in the atmosphere as the  result of
           chemical conversion of precursor gaseous species;
     Internal Mixture:  an aerosol  for which the chemical composition of each individual
           particle is the  same, that is, equal to the bulk composition;
     External Mixture: an aerosol for which different chemical species comprise separate
           particles;
     Anthropogenic:  derived from human activities;
     Biogenic:  derived from plants;
     Bioaerosols:  airborne microorganisms  and aeroallergens;
     Fossil:  derived from fossil fuel combustion; and
     Contemporary carbon:  derived from non-fossil fuel sources such as plants, wood
           burning, and cooking oils.

3.1.4   Major Chemical Constituents
     The major constituents of atmospheric aerosol are sulfates, nitrates, carbonaceous
compounds, water, hydrogen ions,  ammonium ions, and materials of crustal origin.  Average
compositions vary with particle size, geographic location and  season.  Inorganic ions,
including sulfate and nitrate,  are typically analyzed by ion chromatography. Crustal elements
are analyzed by x-ray fluorescence and/or proton-induced x-ray emission.  The  equilibrium
models for inorganic ions predict that water  is an important constituent of atmospheric
particles, but measurements of particle-associated water are limited. McMurry  and
coworkers (McMurry and  Stolzenburg, 1989; Zhang et al., 1993) measured the sensitivity of
particle size to relative humidity for Los Angeles and Grand Canyon aerosols.   They found
that atmospheric particles of a single size exhibited two distinct hygroscopicities.  These were
described as "more" and "less" hygroscopic, as shown in Figure 3-5.  For example, the
diameters of more hygroscopic  0.2 /j,m particles humidified to approximately 90% relative

                                          3-14

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1.60
1.55
1.50
1.45
1.40
1.35
1.30
1.25
1.20
1.15
1.10
1.05
1.00
f\ HC
0.95
• Los Angeles
o More Hygroscopic Particles
• * Less Hygroscopic Particles
-
-
-
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fl£l °A 5^^° 1 *V
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-
-
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60 70 80 90 10
            O
                                  DMA2 Relative Humidity
1.60
1.55
1.50
1.45
1.40
1.35
1.30
1.25
1.20
1.15
1.10
1.05
1.00
0.95
                       Grand Canyon
                       o More Hygroscopic Particles
                       * Less Hygroscopic Particles
                         10  20   30   40   50   60  70   80   90  100
                                  DMA2 Relative Humidity
Figure 3-5. Particle size related to relative humidity.

Source: Zhang et al. (1993).



humidity increased by factors of 1.23  ± 0.08 and 1.49 ± 0.11 for Los Angeles and Grand
Canyon particles, respectively.  For relative humidities above 85 or 90%, water was the
most abundant particulate species both in Los Angeles and at the Grand Canyon.
     Because of the multitude of carbonaceous compounds present in atmospheric aerosols,
carbonaceous material is often categorized as either organic carbon or elemental carbon.
                                           3-15

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Most measurements of aerosol carbon are made using one of a variety of thermal techniques
that report particulate  organic and elemental carbon concentrations (Johnson et al.,  1981;
Huntzicker et al., 1982;  Mueller et al.,  1982; Turpin et al.,  1990).  The split between
organic and elemental carbon is somewhat operationally defined, but the term elemental
generally refers to the nonvolatile,optically absorbing (black) portion of the carbon  aerosol.
Elemental carbon is associated with soot emissions from combustion.  The remaining, more
volatile portion is termed organic.  Various methods of further classifying the organic
fraction include: selective solvent extraction (Lioy and  Daisey, 1986),  functional group
identification (Allen et al., 1994; Gordon et al., 1988), and division into neutral and acidic
fractions (Hildemann et al., 1994a). Radiocarbon dating techniques have been used to
distinguish fossil and contemporary carbon (Currie et al.,  1994; Kaplan and Gordon,  1994;
Hildemann et al., 1994a).

3.1.5    Chemical Composition and Its  Dependence on Particle  Size
     Since the work of Whitby  (1978), many studies have been conducted that provide
chemical or elemental composition data on the coarse and fine fractions of the atmospheric
aerosol.  This has been done in several  ways.   The  dichotomous sampler collects a PM2 5
and a PM10-PM2 5 or  coarse faction of PM10.   Alternately, a PM10 and a PM2 5 sample may
be collected and the PM2 5 composition subtracted from the  PM10 composition. Results from
many such studies are presented in Section 6-6. More detailed information may be obtained
by analysis of smaller size fractions obtained with cascade impactors (Figures 3-6,  20,
Section 6-9). Studies conducted in most parts of the U.S. indicate that sulfate, ammonium,
and hydrogen ions; elemental carbon and secondary  organic carbon; and certain transition
metals are found predominantly in the fine particles. Crustal materials such  as calcium,
aluminum, silicon, magnesium,  and iron are found predominately in the coarse particles.
Some organic material such as pollen, spores, and plant and animal debris is also found
predominantly in the coarse mode.  Some components  such  as potassium and nitrate may be
found in both fine and coarse particles but from different sources or mechanisms.  Potassium
in coarse particles comes from soil, and in fine particles, comes from combustion of wood.
Nitrate in fine particles comes primarily from the reaction of gas-phase nitric acid with gas-
                                         3-16

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phase ammonia to form paniculate ammonium nitrate.  Nitrate in coarse particles comes
primarily from the reaction of gas-phase nitric acid with pre-existing coarse particles.
     In the presence of cloud or fog droplets, or when sodium chloride particles from ocean
spray or other sources are present, a mechanism is available for sulfate, nitrate, and
ammonium ions  to occur in the coarse mode.  Detailed size distributions of the inorganic
ions in Los Angeles are shown in  Figure 3-6 (Wall et al., 1988; John et al., 1990). These
data show two modes for sulfate and nitrate aerosols between  0.1 and 1 /xm.  Similar results
for sulfate  aerosols were reported  by Hering and Freidlander (1982). The smaller mode,
corresponding to particles near 0.2 /xm in diameter, is attributed to gas-phase formation of
condensible species and is referred to as the condensation mode.  The larger mode has  a peak
near 0.6 jrni and is called the droplet mode (Hering and Freidlander, 1982).  Its existence is
attributed to secondary formation through heterogeneous, aqueous-phase transformations.
McMurry and Wilson (1983) found 0.6 /xm sulfate particles in power plant plumes and
attributed their existence to formation by heterogeneous processes.  Further analysis of the
data by Meng and Seinfeld (1994) indicates that these aqueous reactions most likely occur in
cloud or fog droplets.
     The data of Figure 3-6  in Los Angeles show that paniculate nitrate is found in both
coarse and fine particles.  Nitrate  near the coast was predominantly in the coarse mode.
Coarse mode nitrate was less prominent  for inland sites. Several investigators (Wall et al.,
1988; John et al.,  1990; Andreae et al.,  1986) proposed that the coarse particle nitrate results
from the heterogeneous reaction of nitric acid with sea salt. On the basis of single particle
analysis by electron microscopy-energy dispersive XRF spectroscopy, Wu and Okada (1994)
concluded that coarse-particle nitrate in a coastal region of Japan formed on sea salt. Coarse
nitrate collected  at an inland  site was associated with  soil dust. These data suggest that a
heterogeneous chemical reaction on the surface of a mechanically generated, primary particle
may provide a mechanism for adding secondary material to the coarse particle mode.  They
also show that secondary particulate material can be formed by the interaction of a natural
constituent (sea salt) with a species derived from anthropogenic emissions (nitric acid).
                                          3-17

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                  Legend
                    Ammonium, NH4
                    Nitrate,
                    Sulfate.SO
                    Hydrogen Ion, (-T
                    Sodium, Na+
                    Chloride, Cl
                                                    I	1  I  rT'TT'f]
             0.01
0.1               1
 Aerodynamic Diameter, Dae (urn)
Figure 3-6.  Ion concentration as a function of particle size, measured in Claremont,
            CA.
Source:  Wall et al. (1988).
3.1.6   Particle-Vapor Partitioning
     Several atmospheric aerosol species, such as ammonium nitrate and certain organic
compounds, are semivolatile and are found in both gas and particle phases.  The gas-particle
distribution of semivolatile organic compounds depends on compound vapor pressure, total
particle surface area, particle composition, and atmospheric temperature (Pankow, 1987;
Junge,  1977; Bidleman, 1988).  Junge (1977) modeled this relationship using a linear form of
a Langmuir adsorption isotherm. Measurements of semivolatile organic compounds show
that gas-particle distributions are highly correlated with total suspended paniculate matter,
temperature, and the sub-cooled liquid vapor pressure of the pure compound (Foreman and
Bidleman, 1990; Ligocki and Pankow, 1989; Yamasaki et al., 1982).  Yamasaki et al. (1982)
used this information to model an empirical  relationship between the gas-particle distribution,
total suspended paniculate matter and temperature. Pankow showed that the expressions  of
                                         3-18

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Junge (1977) and Yamasaki et al. (1982) are consistent and continued the theoretical
development of equilibrium gas-particle partitioning (Pankow, 1987; 1991; 1994a,b).
     Although it is generally assumed that the gas-particle partitioning of semivolatile
organics is in equilibrium in the atmosphere, the kinetics of redistribution are not well
understood.  Gerde and Scholander (1989) and Rounds and Pankow (1990) predicted  that
redistribution in the ambient air could take minutes to hours.   Since changes in atmospheric
conditions (i.e., temperature) will drive redistribution,  it is not clear whether equilibrium
conditions are maintained.  However, the gas and particle data agree reasonably well  with
equilibrium theories.  Hampton et al. (1983) report that the gas-particle partitioning of semi-
volatile hydrocarbons from motor vehicle emissions can be described by Raoult's Law,  i.e,
the hydrocarbon species behave  as solutes.  The development of an understanding of gas-
particle partitioning of semivolatile organic compounds is hampered by the difficulty
associated with measuring the multitude of compounds, all present in small concentrations.
Diurnal temperature fluctuations, which cause gas-particle partitioning to be dynamic  on a
time scale of a few hours, add to the measurement problems.
     Stelson and Seinfeld (1982a) developed a thermodynamic model to predict the
temperature and relative humidity dependence of the ammonium nitrate  equilibrium
dissociation constant.  The model is  supported by ambient data at inland sites in the Los
Angeles Basin (Hildemann et al., 1984; Doyle et al., 1979).  Bassett and Seinfeld extended
the equilibrium model to include sulfates (1983) and the effect of particle size (1984).  With
the inclusion of sodium chloride in the equilibrium model,  Pilinis and Seinfeld (1987) were
able to predict observations at coastal sites.  Atmospheric models based on equilibrium
considerations have been successful in accounting for the gas-particle partitioning of
inorganic species measured in Phoenix, Arizona (Watson et al., 1994a), and Uniontown,
Pennsylvania (Saxena et al., 1993).  Wexler and Seinfeld (1992) found that under some
atmospheric conditions, such as cool, cold, or very clean air, the  size distributions of
ammonium ion and nitrate are not accurately predicted by equilibrium considerations alone,
and that transport kinetics can be important.  The dynamic changes in gas-particle
partitioning, caused by changes in temperature or total concentration, both in the atmosphere
and after collection, cause sampling problems which are discussed in Chapter 4.
                                          3-19

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3.1.7   Single Particle Characteristics
     The "mixing characteristics" of the aerosol describes the distribution of chemical
species among particles. An aerosol in which all particles contain the same homogeneous
blend of chemical species is internally mixed.  In an externally mixed aerosol each chemical
species is found in a distinct set of particles. Experiments measuring atmospheric aerosol
properties for single-particle size ranges (Hering and McMurry, 1991; Covert et al., 1990;
Zhang et al., 1993) and single-particle analyses (De Bock et al.,  1994; Sheridan et al., 1993;
Van Borm et al., 1989; Anderson et al., 1988) indicate that atmospheric aerosols are to some
degree both internally and externally mixed. Single particle analyses provide descriptions of
individual particle compositions.  These are then categorized either manually or through
multivariate methods such as cluster analysis (Kim and Hopke, 1988) to give an accounting
of the relative number of particles of each chemically  defined particle type.  Morphological
information can also be included in particle type definitions.
     Single-particle composition and morphology provide insights into the sources and
atmospheric processes  affecting airborne particles.  For example,  a priori one expects that
particles emitted from  different sources would  in fact be distinct.  However, Andreae et al.
(1986) observed that over remote ocean areas between 80 and 90% of silicon-rich particles
(presumably originating from  silicate mineral particles) were also rich in sodium, chlorine,
and variable amounts of potassium,  magnesium, calcium, and sulfur  (attributed to sea salt
particles).  The  internal mixing of silicates with sea salt, particles originating from different
sources and externally mixed  when emitted into the atmosphere,  suggested the processing of
aerosol  particles within clouds (see Section 3.2.2.5).  The hypothesis was  that a single cloud
droplet  could take up two or more particles and that these particles would remain together
after droplet evaporation.  Other mechanisms of particle coalescence, such as differential
settling, Brownian coagulation, and  electrostatic attraction,  were considered too slow to
account for the large fraction  of  internal mixing observed.  Andreae  et al. (1986) also found
enrichment of sulfur (presumably sulfate) on sea salt particles.  This also was attributed to
the interaction of clouds with particles.  Gas-to-particle conversion in cloud droplets or by
condensation can also lead to  mixtures of aerosol species.
      Particle morphology has many effects on atmospheric particle properties and processes.
Chain agglomerates, for example, have much larger surface areas on which adsorption and
                                           3-20

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chemical reactions can take place than spherical particles of identical volumes.  In addition,
the atmospheric lifetime is longer, and the optical absorption per unit mass is greater for
chain agglomerates than for more compact particles.  Combustion-generated soot particles are
often chain agglomerates composed of a large number of small primary spherules.
Laboratory experiments conducted by Huang et al.  (1994) and Colbeck et al. (1990)
demonstrated that condensation-evaporation processes can cause chain agglomerates to
become more compact.  Colbeck et al. (1990) also  showed that the collapse of the soot
aggregates resulting from humidification results in a decrease in both the optical scattering
and extinction of the particles.

3.1.8   Dry Deposition
      Dry deposition is the process whereby,  in the absence of precipitation, airborne gases
and particles are transported down to the surface of the earth where they are removed.
Atmospheric turbulent mixing continually  brings airborne  gases and particles into close
proximity to the earth's surface, where they may diffuse across a thin layer of stagnant air to
the surface itself.  Actual removal at the surface depends on the affinity between the airborne
substance and the surface element (ground, body of water, vegetation surface, etc.).  Dry
deposition is a complex process but it is represented as occurring in three steps: (1) transport
down to the vicinity of the earth by turbulent mixing processes;  (2) diffusion across a thin
quasi-laminar layer of air; and (3) attachment to the surface itself.  Dry deposition of
particles is a strong function of particle size,  atmospheric conditions and terrain
physiography.   For large particles  (e.g., above 10 /xm in diameter), gravitation also
contributes significantly to the overall dry deposition process.

3.1.9   Atmospheric Scavenging or Wet Deposition
      Atmospheric gases are scavenged directly by absorption in droplets and by chemical
reactions in clouds.  The direct absorption of gases in falling droplets depends on the
solubility of the gas in water, and may be affected by the presence  of other species in
solution (Seinfeld, 1986).  Particles are scavenged in clouds when they  serve as nuclei for
the formation of cloud droplets (cloud condensation nuclei). This process is especially
important for fine particles. Particles are  also scavenged below clouds when they are

                                          3-21

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intercepted by falling hydrometeors, e.g., rain, snow, etc.  This process is more important
for coarse particles than fine particles.  Because fine particles tend to follow air motions,
they  move out of the way and are not impacted by falling rain drops.  The wet removal of
particles depends on the air trajectories  through clouds, the supersaturation to which the air
mass is exposed, and the time for which droplets are present before arriving at the ground.
3.2   PHYSICAL PROPERTIES AND PROCESSES
3.2.1    Aerosol Size Distributions
3.2.1.1   Particle Size Distribution Functions
     The distribution of particles with respect to  size is perhaps the most important physical
parameter governing their behavior. The concentration of the number of particles as a
function of their diameter is given by a particle number distribution.
     Because atmospheric particles cover several orders of magnitude in particle size, size
distributions are often expressed in terms  of the logarithm of the particle diameter, on the
X-axis, and the differential  concentration  on the Y-axis:  dN/d(logDp) = the number of
particles  per cm3 of air having diameters  in the size range from log Dp to log(Dp  4- dDp).
It is not proper formally to  take the logarithm of  a dimensional quantity. However, one can
think of the distribution as a function of log(Dp/Dp0), where the reference diameter
Dp0 = 1  /nm is not explicitly stated. If dN/d(logDp) is plotted on a linear scale, the number
of particles between Dp and Dp + dDp is proportional to the area under the curve of
dN/d(logD ) versus logDp.  Similar considerations apply to distributions of surface, volume,
and mass.

3.2.1.2   Log-Normal Size Distributions
     Under some conditions, atmospheric aerosol size distributions may be approximated by
a sum of log-normal distributions.  Although such log-normal representations are not always
an accurate description of the actual aerosol size  distributions, they have been found, in
many cases, to be convenient mathematical constructs to represent aerosol size distributions.
The use of log-normal approximations to  aerosol  size distributions was first introduced by
Foitzik (1950) and later expanded to a wide range of atmospheric data by Whitby and

                                         3-22

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co-workers (e.g., Whitby and Sverdrup, 1980; Willeke and Whitby, 1975). A log-normal
distribution is a specific form of the size distribution function for which the population of
particles follows a Gaussian distribution function with  respect to the logarithm of the particle
diameter.  The logarithm of the geometric standard deviation, ag, is the standard deviation of
the quantity logDp and defines the width of the distribution.  For a monodisperse aerosol,
that is, one for which all particles are the same diameter, ag = 1.  For polydisperse aerosols,
ffg >  1.  Typical values for one of the modes of the atmospheric aerosol, such as the
accumulation mode discussed above,  are 1.8 <  ag  <2.8.   For log-normal distributions,
84.1% of the particles are below the size Og-Dgn, 84.1% lie above the size Dgn/ag, and 95%
of the particles lie within two standard deviations of the mean, that is, the range from
Dgn/2ag to Dp-2ag.
     One of the properties of the log-normal distribution is that if the number distribution is
log-normal, the surface and volume distributions are also log-normal, and their geometric
standard deviation a   is the same as for the number  distribution.
                  fe

3.2.1.3  Ambient Aerosol Size Distributions
     Log-normal parameters which describe ambient aerosol size distributions are listed  in
Table 3-1.  These parameters are the geometric number mean diameter, Dgn, geometric
standard deviation, ag, and number concentration, N, for each mode.  Also given  are the
parameters of the lognormal volume distributions, geometric mean diameters, Dgv, and the
corresponding total particle volume  for each mode, V.  The tables include data from
Sverdrup and Whitby  (1980) and results from more  recent measurements in a nonurban area
of New Mexico (Kim et al., 1993c).  Note that the volume geometric mean diameters for the
accumulation mode vary from 0.2 ^m to 0.4 /xm and those for the coarse mode from 5 to
12 /im. The  standard deviations for the coarse particle mode tend to be larger than  for the
accumulation mode.

3.2.1.4  Coagulation of Spherical Particles
     Many processes  affect the size distribution of an  aerosol, including addition of volume
by gas-to-particle conversion, and losses by deposition. Even without these processes, under
conditions in  which the total volume of an aerosol is conserved, the number of particles  will
                                          3-23

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                    TABLE 3-1. LOGNORMAL PARAMETERS FOR AMBIENT AEROSOLS
A. Parameters of the Number


Site of Measurement
Clean continental background
Average continental background
Urban influenced background
Urban average
Urban and freeway
South central NM - February 1989
South central NM - July 1989



N
N
N
N
N
N
N
Nuclei
Num.
(cm3)
: 1,000
: 6,400
: 6,600
: 106,000
: 2,120,000
: not reported
: not reported
Mode
Dgn
(Mm)
0.016
0.015
0.014
0.014
0.013


Accumulation Mode

ffg
1.6
1.7
1.6
1.8
1.74


Num.
(cm3)
800.00
2,300.00
9,600.00
32,000.00
37,000.00
706.00
253.00
Dgn
(/mi)
0.067
0.076
0.120
0.054
0.032
0.13
0.13

ag
2.1
2.0
1.84

1.98
1.72
1.71
B. Parameters of the Volume


Site of Measurement
Clean continental background
Average continental background
Urban influenced background
Urban average
Urban and freeway



V
V
V
V
V
Nuclei
Volume
(/tm3 cm3)
: 0.01
: 0.04
: 0.03
: 0.63
: 9.20
Mode
Dgv
(Mm)
0.030
0.034
0.028
0.038
0.032
Accumulation Mode

ffg
1.6
1.7
1.6
1.8
1.74
Volume
(p,m3 cm3)
1.50
4.45
44.00
38.40
37.50
Dgv
(Mm)
0.35
0.32
0.36
0.32
0.25

ag (
2.1
2.0
1.84
2.16
1.98
Distribution



Coarse Mode
Num.
(cm3)
0.72
3.2
7.2
5.4
4.9
0.42
0.72
Distribution
Dgn
(Mm)
0.93
1.02
0.83
0.86
1.08
2.45
1.59


ffg
2.2
2.16
2.12
2.25
2.13
1.91
2.27


Reference
(1)
(1)
(1)
(1)
(1)
(2)
(2)

Coarse Mode
Volume
;/im3 cm3)
5.0
25.9
27.4
30.8
42.7
Dgv
(Mm)
6.0
6.04
4.51
5.7
6.0

ffg
2.0
2.16
2.12
2.25
2.13

Reference
(1)
(1)
(1)
(1)
(1)
Sources: (1) Whitby and Sverdrup (1980), (2) Kim et al. (1993c).

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decrease by coagulation while the average volume per particle increases. The coalescence of
two particles always reduces the total surface area and therefore is favored
thermodynamically.  Thus, in this sense,  aerosols are inherently unstable.  In some cases
coagulation leads to the formation of chain agglomerates, such as for soot and some metals.

3.2.2   Particle Formation and Growth
     A significant portion of the fine atmospheric aerosol is secondary, i.e., material added
to the particle phase as the result of gas-to-particle conversion processes.  For example, fine
sulfate and nitrate particles are mostly formed by secondary processes.  One mechanism of
gas-to-particle conversion is homogeneous gas-phase chemical reactions to form a
condensible species, such as  the oxidation of sulfur dioxide to form sulfuric acid.
Condensible species can either nucleate to form a new particle (nucleation), or can condense
onto the surface of an existing particle (condensation).  Another important class of
gas-to-particle conversion mechanisms is heterogeneous chemical reactions, which are
chemical reactions involving both  gas-phase  and particle-phase constituents.   Transformation
on the surface of particles, such as the uptake of nitric acid on the surface of calcium
carbonate particles to produce calcium nitrate, is one type of heterogeneous reaction.
Aqueous-phase chemical reactions, such as the dissolution of sulfur dioxide into a
hygroscopic particle or fog or cloud droplet, followed by oxidation of the dissolved sulfur
dioxide to sulfate  and evaporation of the fog or cloud droplets back to aerosol size, provide
an important mechanism for conversion of gases to particles.  Heterogeneous reactions lead
to addition of aerosol material to existing particles.  Nucleation results in an increase in
particle number as well as an increase in particle mass.  Condensation leads only to an
increase of aerosol mass and surface area, but does not affect the total number  of particles.
In this section the physical aspects of these gas-to-particle conversion mechanisms, and their
effects on the particle size distribution, are discussed.

3.2.2.1  Equilibrium Vapor Pressures
     An important parameter in particle nucleation and in particle  growth by  condensation is
the saturation ratio S, defined as the ratio of the partial pressure of a species, p, to its
equilibrium  vapor pressure above a flat surface, p0:  S = p/p0.  For either condensation or
                                          3-25

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nucleation to occur, the species vapor pressure must exceed its equilibrium vapor pressure.
For particles, the equilibrium vapor pressure is not the same as p0.  Two effects are
important:  (1) the Kelvin effect, which is an increase in the equilibrium vapor pressure
above the surface due to its curvature; thus very small particles have higher vapor pressures
and will not be stable to evaporation until they attain a critical size and (2) the solute effect,
which is a decrease in the equilibrium vapor pressure  due to the presence  of other
compounds.
     For an aqueous solution of a nonvolatile salt, the presence  of the salt decreases the
equilibrium vapor pressure of the drop.  This effect is in the opposite direction as the Kelvin
effect,  which increases the equilibrium vapor pressure above a droplet because of its
curvature.

3.2.2.2  New Particle Formation
     When the vapor concentration of a species exceeds its equilibrium concentration
(expressed as its equilibrium vapor pressure), it is considered condensible.  Condensible
species  can either condense on the surface of existing particles or can form new particles.
The relative importance of nucleation versus condensation depends on the  rate of formation
of the condensible species and on the surface area of existing particles.  An analytical
relation for the relative importance of each pathway is dependent on the ratio of the square of
the available surface area to the rate of formation (McMurry and Friedlander, 1979).
In urban environments, the available  particle surface area is sufficient to rapidly scavenge the
newly formed condensible species.  New particle formation is usually not  important except
near sources of condensible species.  Wilson et al. (1977) report observations of the nuclei
mode in traffic.  New particle formation  can also be observed  in cleaner,  remote regions.
Bursts of new particle formation in the atmosphere under clean conditions correspond to low
aerosol surface area concentrations (Covert et al., 1992). The highest concentrations of
volatile ultrafine particles occur in  regions corresponding to the  lowest particle mass
concentrations, indicating that new particle formation is inversely related to the available
aerosol surface area (Clarke, 1992).  In contrast to continental aerosols, where sulfate
particles are the result of conversion  of sulfur dioxide, the sulfate particles over the oceans
                                           3-26

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are the result of the conversion of dimethylsulfide emitted by phytoplankton (Charlson et al.,
1987).

3.2.2.3   Particle Growth
     When material is added to the particle phase by condensation or by particle-phase
chemical reactions, particles of different sizes may grow at different rates, depending on the
mechanism involved.  Condensational growth can have a different effect on the size
distribution of the aerosol than the effect of heterogeneous conversion through chemical
reactions within a droplet.  The relative rates at which the size of particles change depend on
whether the rate-limiting step in the growth process is transport to the particle, chemical
reactions at the  surface of the particle, or chemical reactions within the particle.  These are
referred to as transport-limited, surface-reaction rate-limited or volume-reaction rate-limited.
These  different physical mechanisms give rise to a different form of the growth law for the
particle. Growth  laws are the expressions for dy/dt or dDp/dt as a function of particle size
(where v is single particle volume and Dp is particle diameter).
     For condensational growth, the rate-limiting step relevant to the rate at which particles
of different size grow is transport of condensible species to the particle  surface.  For
particles much smaller than the mean free path of air (free molecule regime), transport is
governed by single molecular bombardment of the surface, and the volume (or mass) of these
particles grows in proportion to their surface area.  For particles larger than the mean free
path (continuum regime), transport is governed by diffusion. In this regime  the loss  of
diffusing species at the surface of the particle causes a gradient in the concentration of the
diffusing species near the surface of the particle such that the volume of the  particle grows in
proportion to particle diameter rather than surface area.
     For heterogeneous chemical reactions, the rate  limiting step for growth may not be the
transport of the  reacting species to the particle, but the rate of reaction  on or within the
particle. For reactions at the surface of the particle, the rate of growth is controlled  by the
particle surface area; for droplet-phase reactions, it depends on the volume of the particle.
In summary, the aerosol growth laws show that in the continuum regime the particle  size
dependence of the rate of change of particle volume  varies from a dependence on D  for
condensation by diffusion, to D 2 for surface reactions, to Dp3 for droplet reactions.
                                           3-27

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Condensation by diffusion varies as Dp2 in the free molecule regime, in the transition
regime, intermediate between the free molecule and continuum regimes, condensation by
diffusion varies  between a dependence on Dp2 for small particles to a dependence on Dp for
larger particles.
3.2.2.4   Equilibria with Water Vapor
     The principal equilibrium of concern pertinent to ambient aerosols is that with water
vapor.  This equilibrium is important as it influences the size of the particles and in turn
their aerodynamic properties (important for deposition to the surface,  to airways, following
inhalation, and to sampling instrumentation) and their light scattering  properties.  This
section reviews recent work describing this equilibrium as it pertains to ambient aerosols.
     Briefly the interaction of particles with water vapor may be described as follows.
As relative humidity increases, crystalline soluble salts in aerosol particles undergo a phase
transition to become aqueous solution aerosols.   According to  the phase rule, for particles
consisting of a single component, this phase transition is abrupt, taking place at a relative
humidity that corresponds  to the  vapor pressure of water above the saturated solution (the
deliquescence point).  With further increase in relative humidity the particle growth is such
that the vapor pressure of  the solution (concentration of which decreases as additional water
is accredited) is maintained equal to that of the  surrounding relative humidity; the particle
thus tends to follow the equilibrium growth curve. As relative humidity decreases, the
particle follows the equilibrium curve to the deliquescence point. However, rather than
crystallizing at the deliquescence relative humidity, the particle remains a solution
(supersaturated solution) to considerably lower relative humidities. Ultimately the particle
abruptly loses its water vapor (efflorescence), returning  typically to the initial,  stable
crystalline form.   This behavior has been amply demonstrated in numerous laboratory studies
(Tang  and Munkelwitz, 1977; Tang, 1980).  Recently Tang and Munkelwitz (1994) have
presented data for water activity  (equilibrium relative humidity) as a function of composition
for several sulfate salts.
     For particles consisting of more than one component, the solid  to liquid transition will
take place over a range of relative humidities, with an abrupt onset at the lowest
deliquescence point of the  several components,  and with subsequent growth as  crystalline
                                           3-28

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material in the particle dissolves according to the phase diagram for the particular
multicomponent system.  Under such circumstances a single particle may undergo several
more or less abrupt phase transitions until the soluble material is fully dissolved.
At decreasing relative humidity such particles tend to remain in solution to relative
humidities well below the several deliquescence points; such behavior has been amply
demonstrated.  In the case  of the sulfuric acid-ammonium sulfate-water system the phase
diagram is fairly completely worked out.  Mixed anion systems containing nitrate are more
difficult due to the equilibrium between particulate NH4NO3 and gaseous NH3 and HNO3
(Tang et al., 1978,  1981; Spann and Richardson, 1985). Spann and Richardson also give the
compositional dependence of the relative humidity of efflorescence.  For particles of
composition intermediate between NH4HSO4 and (NH4)2SO4 this transition occurs in the
range from  40%  to below 10%, indicating that for certain compositions the solution cannot
be dried in the atmosphere. At low relative humidities particles of this composition would
likely be present in the atmosphere as supersaturated solution droplets (liquid particles) rather
than as  solid particles, thus they would exhibit  hygroscopic rather  than deliquescent behavior
during relative humidity cycles.
     Evidence of the interaction of ambient aerosol particles with  water vapor has been
obtained by several investigators.  Koutrakis et al. (1989) found systematically  increasing
aerosol  mean diameter with increasing relative  humidity, which they attributed to water
accretion on sulfates. Rood et al. (1989) examined the response of light scattering coefficient
of ambient aerosols to increase in temperature and attributed the decrease in light scattering
upon heating to loss of liquid water  associated with the particles.  However, heating can also
cause the loss of NH4NO3 and semi-volatile organic material.  More detailed information
regarding the size dependence of hygroscopic properties has been obtained by examining the
change  in particle size of a monodisperse size cut selected with a mobility analyzer,
subjecting that aerosol to an increase or decrease in relative humidity, and reanalyzing the
size  at the new humidity.  Studies of this phenomenon in the Los Angeles area indicate this
phenomenon, but also frequently indicate the presence  of externally mixed aerosol, in which
some of the aerosol exhibits the growth expected of soluble salts, where another, apparently
hydrophobic, fraction does  not exhibit such growth (McMurry and Stolzenburg, 1989).  Such
bimodal growth with relative humidity was exhibited by particles present at Hopi Point,
                                          3-29

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Arizona (Pitchford and McMurry (1994). In the latter study the relative humidity
dependence of the size of the more hygroscopic fraction was found to be consistent with that
expected for sulfate salts. Such external mixtures have also been commonly observed in
European aerosols (Svenningsson et al.,  1994).  Saxena et al. (1995) have shown that the
presence of organic compounds in particles may lead to more water condensation on particles
(as shown by data collected in Arizona) or to less water condensation, probably because of
the formation of an organic film that isolates inorganic salts from the ambient gas-phase (as
shown by data collected in Los Angeles).
     The tune constant that characterizes the rate of exchange of water vapor between the
gas phase and a solution droplet is of interest relative to the rate of response of particles to
changes in relative humidity in the  ambient environment,  especially in the vicinity of
surfaces, and relative to changes experienced by particles following inhalation or during
sampling.  It is generally assumed that the rate of this  water exchange is rapid.  The
characteristic time for diffusional growth in response to a change in relative humidity was
calculated by Pilinis et al. (1989) to be about 1  X 10~7 s. However Khlystov et al. (1993)
noted that this estimate was erroneously  low by several orders of magnitude.  The latter
investigators examined the characteristic  time for establishment of phase equilibrium in
response to a change in relative humidity for (NH4)2SO4 aerosol particles (dry radius
0.5 /im).  The characteristic time increases from ca  1 ms at 8% relative humidity to 1.6 s at
99% relative humidity.  Above 99% relative humidity the characteristic time can become
much longer because of the large change in droplet radius at such relative humidities. These
calculations indicate that the water equilibrium can be  expected to be rapidly achieved in the
ambient environment.  A possible but important exception is near  100% relative humidity,
pertinent to dry deposition of particles to vegetation or to water, where the  equilibrium size
might not be reached in the time required for the particle to traverse the diffusive layer
adjacent to the surface.
     The lability of water associated with ambient aerosol has been evidenced in
comparisons by Malm et al. (1994) of measured paniculate light scattering coefficient
obtained with an  integrating nephelometer with values reconstructed from aerosol
composition, taking  into account the relative humidity  dependence of light scattering
coefficients of the aerosol components.   The reconstructed  values were found to
                                          3-30

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systematically exceed the measured value.  However when in the reconstruction the relative
humidity was taken as that in the nephelometer chamber (invariably lower than ambient
because of heating in the chamber) the reconstruction was markedly improved.

3.2.2.5  Particle Growth in Fogs and Clouds
     Several measurements of the aerosol mass distributions in urban areas have shown that
two distinct modes can exist in the 0.1 to 1 /urn diameter range (Hering and Friedlander,
1982; McMurry and Wilson, 1983; Wall et al., 1988; John et al., 1990).  These are referred
to as the condensation mode (approximate aerodynamic diameter 0.2 ^m) and the droplet
mode (aerodynamic diameter around  0.7 ^m).  Hering and Friedlander (1982) and John et al.
(1990) postulated that the larger  mode could result from aqueous-phase chemistry.  Meng and
Seinfeld (1994) proposed that growth of condensation mode particles by accretion of water
vapor or by gas-phase or aerosol-phase sulfate production cannot explain the existence of the
droplet mode. Activation of condensation mode particles, formation of cloud/fog drops
followed by aqueous-phase chemistry, and aqueous droplet evaporation was shown by these
authors to be a plausible mechanism  for formation of the urban and  regional aerosol droplet
mode.  The sulfate formed during fog/cloud processing of an air mass favors the aerosol
particles that had access to most  of the fog/cloud liquid water content, which are usually the
particles with dry diameters around 1 fim (Pandis et al., 1990b).  These two submicron
mass-distribution modes have been also observed in non-urban continental  locations
(McMurry  and Wilson,  1983; Hobbs et al.,  1985; Radke et al., 1989),  but the frequency of
their co-existence remains unknown.   Thus,  cloud processing of an air parcel can clearly
affect the scattering efficiency and in general the radiative properties of the corresponding
aerosol (Hegg et al., 1992; Bower and Choularton,  1993).
     The aerosol distribution is also  modified during in-cloud processing by collision-
coalescence of droplets and impaction scavenging  of aerosols (Pruppacher and Klett, 1980).
The aerosol scavenging by droplets is a relatively  slow process, and collision coalescence
among droplets of different sizes causes a redistribution of aerosol mass in such a manner
that the main aerosol mass is associated with the main water mass (Flossmann et al., 1985).
The processing of the remote aerosol distribution by clouds has been clearly demonstrated in
a series of field studies (Frick and Hoppel,  1993).  This multiple processing of remote
                                         3-31

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aerosol by nonprecipitating clouds results in an extra mode in the aerosol number distribution
(Hoppel et al.,  1986; Frick and Hoppel, 1993).
     Clouds and fogs can influence the atmospheric aerosol number and mass concentration
and chemical composition, the shape of the aerosol size distribution, the aerosol acidity and
radiative properties.  These effects can be important in all environments (urban, rural and
remote) and all seasons.  Our qualitative understanding of the aerosol-cloud interactions has
improved significantly, but, with few exceptions, the quantification of these effects remains
uncertain (Altshuller,  1987; Kelly et al., 1989; Pandis et  al., 1992b).

3.2.3    Resuspension of Particulate Matter
     The resuspension of deposited material as well as the suspension of material which has
not been previously airborne can be an important source of paniculate contamination
(Gillette, 1980). This discussion will use  "resuspension"  to include both resuspension and
suspension.  Surface contamination may result from the atmospheric deposition of a number
of materials; for some of these (e.g., plutonium), resuspension has been considered to be the
most important exposure pathway.  Likewise, resuspended soil particles have the greatest
atmospheric mass over continents of any single particle type (Peterson and Junge, 1971).
Despite this importance,  the literature shows relatively few experimental or theoretical
studies for the resuspension mechanism compared to other aerosol generation mechanisms.
The following summarizes work on the physics of resuspension,  physical/chemical properties
of resuspension generated particles, and levels of production and transport of resuspended
particles.

3.2.3.1  Resuspension Mechanics
     Resuspension studies may be divided into applied research and detailed studies of
mechanisms.  Applied studies are usually motivated by atmospheric deposition of hazardous
substances (i.e., radionuclides from the Chernobyl  1982 accident [Cambray, 1989]) and the
need to predict the spreading of contamination and the lifetime of hazardous air
concentrations. Resuspension experiments have been conducted over a wide range of surface
types.  Many experiments have been conducted in dry or arid regions, simply because many
contamination events have occurred in such locations (i.e., the Nevada Test Site). Of the
                                          3-32

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experiments conducted over vegetation, most have been related to short grass.  Alternately,
applied studies may be motivated by mitigation efforts for soil erosion by wind or by need
for measurement of high atmospheric paniculate concentrations caused by resuspension,
so-called "fugitive dust".  Experiments concerning wind erosion have largely occurred in
locations where  wind erosion is prevalent,  e.g., in the "Dust Bowl" area of the central
United States.

3.2.3.2  Applied Studies
     Resuspension can occur due to the action of wind or by mechanical stresses.  Applied
research considers resuspension factors, K (air concentration divided by surface
concentration) (units of length"1) and resuspension rates (flux of contaminant divided by
surface concentration) (unit of time"1).  Mechanical stresses, such as disturbances by traffic
or agricultural operations, might result in large amounts of resuspension over  short intervals
in specific  localities.  For example, Sehmel (1984) quotes K values of 4 x  10 m"1 (for
beryllium particles by vigorous sweeping in an unventilated room) to 7 x 10"3 m"1 for
plutonium particles in extensive traffic at the Nevada Test Site to  3 X 10"7 m"1 for gamma-
radioactive-fallout by walking on the deposit in an Australian desert.
     Wind generated resuspension is considered to be of major importance  because  it can be
relatively continuous and can occur over large regions. Resuspension has been found to
increase as a power of wind speed (with the resuspension rate being related to the second or
third power of wind speed).  Examples of resuspension factors from wind stresses quoted by
Sehmel (1984) range  from 3 x 10"4 m"1 for uranium at Maralinga trials to 9 X  10"11 m"1 for
yttrium chloride on a cleared, sandy soil.   Part of the range of Ks quoted above might be
caused by the aging of deposits, although a lack of understanding of the  mechanisms
dominant in the  resuspension process has precluded identifying any reasons  for the wide
range of results.
     Nicholson's (1988) data verify previous  work,  giving an approximate  I/time decrease  of
the resuspension rate.  Makhon'ko's (1986) data for resuspension from grass suggest a
relationship between relative resuspension rate K' versus phytomass m in grams per square
meter, K'  = 2.9 x  10"8 m"1-4  [sec"1].
                                          3-33

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3.2.3,3   Aerodynamic Resuspension
     Aerodynamic models include (1) balance of forces models and (2) statistical
mechanisms. Balance of forces models account for forces holding the particles to the
surfaces versus those forces acting to remove the particles from the surfaces.  Experimental
studies of particle motions show that particles being entrained into a turbulent fluid tend to
move vertically into  the stream with unsteady motions (Sutherland, 1967).  Braaten et al.
(1989) and Braaten and Paw U (1992)  stressed  the importance of bursts of a sweeping eddy
having the characteristics  of large shear stress near the wall where particles are sparsely
deposited, breaking up the viscous sublayer and transporting fluid forces to the particles.
This mechanism removes  particles from a surface in short bursts  followed by periods of little
resuspension activity.  Observations of Lycopodium spores placed on the flat floor of a wind
tunnel were used to verify the model.
     Reeks et al. (1988) proposed a different aerodynamic mechanism that would account for
sudden random injections of particles into the air, the injections taking place more randomly
in time than in the above  force balance model.   Their mechanism calls for the individual
particles to  accumulate energy from the turbulent stream (most efficiently at  a resonant
frequency for the particle).  Accumulation of energy takes place because energy dissipation is
limited by the local fluid and substrate. Once sufficient energy has accumulated to overcome
the potential energy well holding it in place, the particle  is resuspended. Slow motion
movies of saltating sand surfaces showed such a vibrating motion of a particle before it
becomes airborne (Willetts, 1992).

3.2.3.4   Mechanical Resuspension
     The importance of mechanical disturbance is seen in the differences of resuspension
factors given by Sehmel (1984) for mechanical activities  over contaminated soil versus those
for wind.   Another example of the comparison of resuspension by mechanical disturbance
with resuspension by the wind  was given by Garland (1979) as a two-order-of-magnitude
increase of the resuspension factor for the  mechanical disturbance of a full 5-liter bottle
dragged along the grass 20 times in 5 minutes in wind compared to the 10 m/s wind alone.
Sehmel (1984) conducted experiments to determine the fraction of tracer particles
resuspended by driving cars and trucks through the deposited tracer or near the deposited
                                          3-34

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tracer.  The fraction increases with speed and size of vehicle.  The fraction resuspended per
vehicle pass increased as the first power of vehicle speed for the truck driven through the
tracer, the fourth power of vehicle speed for  a car driven through the tracer, and the third
power of the vehicle speed for a car driven near the tracer.
      The emission of PM10 particles in wind erosion is driven by the mechanical process of
sandblasting, although Shinn et al. (1983) have pointed out the importance of direct
aerodynamic emission for  low emission rates  below erosion threshold.  Threshold velocities
for particles smaller than 10 micrometer diameter are several times greater than that for
100 micrometer particles (Bagnold, 1941).  Nonetheless, one observes  submicrometer to
10-micrometer particles  in wind erosion events for winds very much below the threshold
velocity for the above  mentioned particles.  Gillette and Walker (1977) interpreted this to be
caused by the mechanical suspension (sandblasting) of fine particles by more-easily-eroded
sand particles.   Shao et al. (1993) showed that sand-grain bombardment (saltation) is the
overwhelmingly dominant  mechanism in maintaining  fine particle emissions from the surface.
To derive an expression for the emission of dust, Shao et al. (1993) assumed that the number
of dust particles dislodged from a surface per sand grain impact was proportional to  the ratio
for the kinetic energy loss  of the impacting  sand grain to the binding potential energy holding
a dust particle to the surface.  This assumption led to the prediction that the dust flux is
proportional to the sand  grain mass flux, which was in turn proportional to the  friction
velocity cubed.  Dust emission is highly sporadic.  After the wind stress threshold is
surpassed, the vertical flux increases with the third power of friction velocity.

3.2.3.5   Physical and Chemical Properties of Resuspended Particles
     The physical and chemical properties of resuspended particles  depend partly on the
properties of the particles that were deposited on the surface in the initial stage  of
resuspension.  But, "the  deposited particles probably lose their individual identity by
becoming attached to host  (soil) particles. When the pollutant particle is transported
downwind, it is usually attached (aggregated)  to this host particle" (Sehmel, 1973).
Furthermore, the host particle is most likely an aggregate itself.  Studies of the  cross section
of particles, mineralogy, and scanning electron microscope analysis  of dust samples show
                                          3-35

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that particles suspended from the soil are aggregated.  For these reasons, this section
describes physical properties of the aggregated (host plus pollutant) particles.
     The size distribution of resuspended soil particles may be described as lognormal
bimodal with one mode at 2 to 5 micrometers and another mode at 30 to 60 micrometers
(Sviridenkov et al., 1993; Patterson and Gillette, 1977a,b; Gillette and Nagamoto,  1992;
Gillette, 1974).  Because the mass mode of the distribution for particles  smaller than
10 micrometers is roughly at 2.5 micrometers, a rough approximation is that half the PM10
mass is smaller than 2.5 micrometers and half is larger.  The ratio


                                       ^  <0.1                                   (3-4)
defines the upper size of suspended dust, where vsed is the sedimentation velocity of the
upper size limit, and u* is friction velocity. Data from Pinnick et al. (1985) show that very
similar size distributions result from resuspension by traffic.
     The mineralogical components of dust collected in Texas dust storms, given in order of
abundance,  are: for particles 1 to 10 micrometers:  quartz, mica, kaolinite, mixed layer
phyllosilicates and feldspars; for particles smaller than 1 micrometers: mica, kaolinite,
quartz, and mixed layer phyllosilicates (Gillette et al., 1978).  Studies of elemental
composition show that composition of the resuspended material, compared to that of the total
sediment, is enriched in elements associated with the smallest particles (i.e., titanium) and
impoverished in elements associated with the coarsest materials (i.e., silicon).

3.2.3.6   Levels of Production and Transport of Resuspended Aerosols
     Airborne  dust measurements in the southern and central Great Plains states of the
United States were made in the early 1970's.  The total mass of dust produced by individual
dust storms was 0.3 to  0.5  X 1012g (Gillette et al., 1978).  Individual dust storm production
rates may be compared to the global production rate estimated by d'Alameida (1989) of
1,800  to 2,000 X  1012g per year.  The Great Plains study, part of a severe storm study,
showed that the dust storms were typically associated with vigorous frontal activity, and that
the dust travels great distances (many 100's of km) as tracked by jet aircraft. Estimates of
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transport distance for dust of well over 1,000 km (from West Texas dust storms to deposition
sites in northern Minnesota)  were supported by isentropic trajectories, positions of rainclouds
and elevated concentrations of calcium in collections of rainwater in the National Acid
Deposition Program/National Trends Network.  Even greater transport distances of
resuspended dust are shown by oxygen isotopic 18 to 16 ratios (518) in quartz (parts per
thousand). By matching the  618 value for deposited quartz and source areas for the quartz
(wind erodible soils) the following long-range transport paths were  found:  Asian deserts to
Hawaii; Sahara desert to the Caribbean, South America, and Florida; and U.S. sources to
Greenland and northern Europe (Jackson et al., 1973).
     A model developed for national acid rain and desertification/paleoclimate studies
(Gillette and Passi, 1988) expressed the emission of dust for a given study area as an integral
over friction velocity (expressing the forcing function),  and the threshold friction velocity
(expressing the resistance of the soil and environment to ablation).  Results from the model
for the contiguous United States (Figure 3-7) show a strong agreement of the model dust
emissions with known emissions from dusty areas (Gillette and Hanson, 1989).  Predicted
alkaline emissions also agree in many respects with observed wet deposition patterns of
alkaline elements (Gillette  et al.,  1992).  A considerable fraction of wind emitted dust is
from dust devils (Gillette and Sinclair, 1990).
                  45

                  40

                  35

                  30

                  25

                  20

                  15

                  10

                   5

                   0
                            1015202530354045505560
    KT4-
    10*- 10-*a(cm2
    < 10-8g/crn2
,,.. i,,.. i,,,, i,,,, i
45

40
35
30
25

20
15

10

5
                    0    510152025303540455055
Figure 3-7. Model  dust emissions for the United States.
Source: Gillette and Hanson (1989).
                                                60
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3.2.4   Particle Removal Mechanisms and Deposition
     Particles in the air are in constant motion.  They are subject to Brownian motion, which
is the constant random movement along an irregular path caused by the bombardment by
surrounding air molecules.  This process is most important for small particles,  and is related
to the particle diffusion coefficient. Particles are also subject to the earth's gravitational
force, as characterized by a sedimentation velocity. Gravitational settling is most  important
for  larger particles.  Both of these processes involve the motion of the particle  relative to  its
surrounding air medium.
     A particle subject to any constant external force  will reach a terminal velocity called the
drift velocity. The proportionality between the particle drift velocity (Vdrift) and the external
force (Fext) is called  the particle mobility, B, and is defined by:
                                       V         n
                                  B = _** =    c                                (3-5)
                                        Fext     371 uDp

where C is the Cunningham correction factor and \i is the particle velocity.  The
Cunningham correction factor must be introduced because when the particle diameter,  Dp,
approaches the same order as the mean free path of air, the resistance  to particle motion
becomes less than that predicted by continuum theory.  The Cunningham correction factor
increases as the  particle size decreases. When the external force is that due to gravity
(Fext = mg), the drift  velocity is the settling (sedimentation) velocity (Vdrift =  VTS =  Bmg).
If the external force  is an electric field, then the drift velocity equals qEB where q is the
electric charge on the particle and E is the electric field.  Small particles are the most
mobile.  For particle diameters much smaller than the mean free path (Dp «  0.066 /mi for
air  at standard conditions), B varies as the inverse square of the particle diameter.  For large
particles, B varies inversely with particle diameter. The particle diffusion coefficient is
related to particle mobility by D = BKT.
      Brownian diffusion is important for small particles, whereas gravitational settling is
important for large ones.   During  a time period of 1 s a 0.1 jum particle will travel a total
distance of about 40 /mi due to Brownian motion, while it will fall about  1 /mi due to
gravity.  In the  same 1 s time period,  a 1 /mi particle will travel a total distance of about
8 /im due to Brownian motion and will fall 35 /mi due to gravity. Note that the diffusion
                                          3-38

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constant is directly proportional to the particle mobility B, while the settling velocity depends
on the product of particle mass and mobility, mB.  Diffusion constants and settling velocities
are plotted in Figure 3-8.
                      10
                      10
                   «: io2
                        -7
                      10
                      10
                        0.01
Setting Velocity (cm/s)
Diffusion Coefficient (cm2/s)
0.1           1           10
     Particle Diameter (urn)
                                     100
Figure 3-8.  Diffusion constants and settling velocities for particles.
     The deposition of particles in the atmosphere is not easily modeled, and is characterized
by a deposition velocity, which is defined as the ratio of the flux of particles to the surface to
the ambient concentration.  Results from wind tunnel studies,  shown in Figure 3-9, show
characteristic minima.  Small particles are collected by diffusion, larger particles are
collected by impaction and sedimentation. Deposition models which account for these
mechanisms are given by Sehmel (1980), Fernandez de  la Mora and Friedlander (1982) and
Fernandez de la Mora (1986). Atmospheric data from Lin et  al.  (1994), shown in
Figure 3-10, show that inertial mechanisms, as well as sedimentation, are important for the
deposition of large particles.  As can be seen in Figure  3-9, these various removal
mechanisms are least effective for particles in the 0.1 to 1.0 ^m diameter size range.
Therefore, accumulation mode particles, which occur mainly in this size range, have long
                                           3-39

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                         10
                      o
                      :s
                      8.10"
                         10
                                     Grass about 10cm high
                                     (Chamberlain, Clough, Little)
                                     Filter paper (Clough)
                                     Smooth surface (Sehmel)
                           10
                             -2
                                 _!	1 I~><-1 I !... „-!"''I	1 I I II ll		I
10           1

    Particle Diameter
                         10
Figure 3-9.  Particle deposition from wind tunnel studies.


Source:  Chamberlain (1983).
                       101
                    9 10
                    i
                         -2.
                       10'
                    X

                    E
                       10"
                       10
                               o Flux       • Mass
                               v Calculated deposition velocity
                                                                           10
                                  102°|
                                       o>

                                     ^  or
                                  io1  S
                                                                              o>
                                       I
                                  10"2I
                                                                              'in
                          0.1
  1             10
Particle diameter, urn
                            100
Figure 3-10.  Sedimentation and inertia effects on large particle deposition.


Source:  Lin et al. (1994).
                                               3-40

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 lifetimes in the atmosphere.  Such particles act as nuclei for the formation of cloud droplets
 and are removed when the cloud droplets grow large enough to fall as rain.  Falling rain
 drops also remove larger particles  by impaction and smaller particles by diffusion.  More
 details on removal processes  may be found in Sections 3.5 and 3.6.

 3.3   CHEMICAL COMPOSITION AND PROCESSES
 3.3.1   Acid Aerosols  and Particulate  Sulfates
     Sulfuric acid and its neutralization products with ammonia constitute a major
 anthropogenic contribution to fine particle aerosol. This section reviews recent advances in
 understanding of the sources, removal processes, loadings and properties of tropospheric
 sulfate aerosols.  Emphasis is given to properties and processes pertinent to these aerosols in
 regions influenced by anthropogenic  emissions as distinguished from remote locations
 influenced primarily by natural sources.

 3.3.1.1 Aerosol Acidity
     Aerosol acidity can occur in suspended paniculate matter (liquid or solid) or in the gas
 phase.  The concept of aerosol acidity includes both the actual acid dissociation and the H+
 potentially available for reaction when the gas  or particle  contacts a liquid or solid surface
 (Waldman et al., 1995).  With respect to pulmonary surfaces and fluids, many components in
 the air are acidic. The extent and  location of acid  deposition in the airways is greatly
 affected by whether exposure is to gaseous or paniculate acids and also varies according to
 the size of particles.
     The  principal  acids found in the atmosphere are related to mineral acids: particulate
 sulfuric acid  (H2SO4) and bisulfate (HSCXj) and gaseous nitric (HNO3), nitrous (HNO2), and
 hydrochloric  (HC1)  acids.  Organic acids (such as  carboxylic and dicarboxylic acids) can also
 be found in the particulate and gaseous forms.  Formic and  acetic acids are the most
 abundant of organic acid compounds. As weak acids,  these tend to exist in the
 nondissociated form and will  often be volatile.  In the  atmosphere, the magnitude of particle
 acidity contributed from organic acids is generally found to  be minor compared to particle
 strong acids (Lawrence and Koutrakis, 1994).  However,  organic and inorganic acids may be
generated  indoors (Zhang et al., 1994).
                                         3-41

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     Particle strong acidity (PSA) has recently been reviewed by Waldman et al. (1995).
The primary source of PSA is sulfuric acid, formed largely by oxidation of SO2. When the
acid is formed in the gas phase, it rapidly condenses to very small particles; these grow in
the atmosphere by condensation and coagulation.  When formed within cloud or fog droplets,
the acidic sulfates are also found in the accumulation mode after the droplets evaporate.
Measurements in southern Ontario for aerosols in several size ranges indicated that  the vast
majority of PSA was in the range of 0.2-2 /^m (Koutrakis et al., 1989).   The contribution to
PSA from nuclei mode (<0.1 /xm) particles is not substantial; acidic particles in this size
range either grow or are neutralized rapidly.   Particles larger than 2 /*m contain little sulfate,
but include wind-blown materials,  in which there is often an abundance of alkaline materials.
     The form of PSA is rarely pure  sulfuric  acid; H2SO4 can be partially neutralized to
forms that are still acidic. A variety of crystalline forms intermediate in acidity between
H2SO4 and (NH4)2SO4 may be observed in the solid state.  However, in  the atmosphere,
sulfate salts will usually be present in solutions containing H+, NH4+, HSO4, and SO4=. PSA
is defined,  in terms of molar or equivalent concentrations, as the sum of H+  and HSO4.
Some writers have recommended that PSA be defined as the concentration of free H+ ions in
the particles as they exist in the atmosphere (Saxena et al., 1993).   In this definition HSO4
would not be counted as contributing to the PSA.  There are  several disadvantages to this
definition.  The water associated with a particle increases as the  relative humidity increases;
as the solution becomes more dilute, more HSO4 dissociates and the PSA increases.  Thus
for this  definition PSA varies with relative humidity and must be calculated from equilibrium
theory.  As a particle enters the lung the relative humidity increases to near  100% relative
humidity and the PSA will also increase.  If deposited in the lung all of the H+  in HSO4 will
be available for reaction. Therefore,  it  seems most useful for health effects  purposes to
define PSA as [H+] + [HSO^ (Schlesinger,  1994; Saxena, 1994).
     In practice PSA is measured by extracting collected particles and determining  H+ from
a measurement of the pH of the solution.  One technique uses a  pH=4 solution  of perchloric
acid (HCLO4), which prevents dissociation of weak acids but dilutes the  solution sufficiently
to allow dissociation of HSO4" (Koutrakis  et al., 1988a,b;  Suh et al., 1994a).  PSA  may also
be measured by extracting the collected particles with water, titrating with base, and using
                                          3-42

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 the Gran titration technique to determine strong acidity separate from weak acidity (Brosset
 etal., 1975).
    Theory predicts a very fast neutralization reaction between PSA and atmospheric
 ammonia, which limited laboratory experiments with pure compounds appear to confirm
 (Huntzicker et al.,  1980).  However, measurements seem to indicate that neutralization is
 slower under field conditions and that some amount of PSA may persist even with ammonia
 present (Brauer et al.,  1991; Liang and Waldman, 1992; Harrison and Kitto,  1992).
 Measurements of PSA and ammonia are normally averages over time periods of several to
 24 h.  Thus,  it is possible that nonzero concentrations occur in different time intervals and
 only appear to be coincident.  Recent research has evaluated the possibility that organic
 compounds retard  the rate of neutralization (Daumer et al., 1992).  Attenuation of regional
 PSA levels in central city locations has been observed to varying degrees. People and their
 activities generate  ammonia, and in areas with higher population densities, ambient ammonia
 concentrations are generally higher.  A study in Philadelphia showed daily decreases, in the
 city center relative  to the suburbs, as high as 60%, although these were during a summer
 with pollution levels notably lower than the previous and subsequent  summers (Suh et al.,
 1995).  PSA levels are also attenuated indoors due to ammonia generated by people (Suh et
 al., 1992, 1994b).
    The recent articles by Spengler et al. (1989) and Thompson et al. (1991)  on the Harvard
 24-cities study PSA results and Thurston et al. (1992) report data for daily (or alternate-day)
 sampling over the entire year at fixed sites.  These have shown  that the largest PSA
 exposures occur in the warmer months.  The  highest levels are specifically associated with
 summertime, regional stagnation periods.  Frequently, PSA episodes  are coincident with
 photochemical smog and high ozone levels, although the converse is not always the case.
 Simultaneous measurements on a regional scale have confirmed  the spatial homogeneity in
 PSA levels over large areas.  Good correlations for daily PSA concentrations  among
 suburban sites 100 km  apart were observed in New York (Thurston et al., 1992).

3.3.1.2  Sources of Sulfate
     Knowledge of the sources of sulfates is important to understanding the processes
responsible for the observed loading, composition, and size distribution of sulfates and to
                                         3-43

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developing effective methods to control sulfate concentration.  Ambient sulfate may be either
primary or secondary.  Primary refers to material emitted into the atmosphere as participate
sulfate or as gas-phase SO3 or H2SO4, species which readily nucleate and condense to form
particles.  Secondary refers to material that is formed by gas to particle conversion following
the chemical reaction of SO2, or other sulfur containing gases, to form SO3, H2SO4, or SOJ
in solution.  Most sulfate in the troposphere is secondary sulfate formed from SO2.
     Atmospheric oxidation of SO2 takes place both by gas-phase reaction and by  aqueous-
phase reaction. The principal  gas-phase mechanism is thought to be the OH-initiated
reaction.  The principal aqueous-phase reactions are thought to be oxidation by H2O2, O2
(when catalyzed by trace metals), and O3.   Aqueous-phase reactions followed by cloud
evaporation can result in formation of aerosol. Evaporation can be a major production route
for atmospheric sulfate aerosols.  The relative proportion of sulfate aerosol produced by the
aqueous and gas-phase routes is not well established.

3.3.1.3   Gas-Phase Oxidation of Sulfur Dioxide
     Gas phase oxidation of SO2 is thought to occur largely, if not entirely, by  a sequence of
reactions initiated by the reaction of OH with SO2.

                             SO2 + OH  + M ^ HSO3  + M                        (3-6)

                                 HSO3 + O2 -*  SO3 + HO2                           (3-7)

                                  S03+H20 -* H2S04                             (3-8)

The gaseous H2SO4 subsequently adds to existing particles or may nucleate to form new
particles.
     Until recently the evidence for the occurrence of this reaction in the atmosphere has
relied  on modeled OH concentrations and on laboratory-determined reaction rate coefficient
(Gleason et al., 1987)  for the  OH +  SO2 reaction.  However, recent measurements of OH
and H2SO4 in the atmosphere  provide empirical evidence for this mechanism (Eisele and
Bradshaw, 1993; Eisele and Tanner,  1993; Eisele et al., 1994).   Simultaneous measurements
                                          3-44

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of OH and SO2 allow the gas-phase reaction production rate of H2SO4 to be calculated at the
time and location of the measurement. Likewise, measurements of particle size distribution
allow the effective first-order rate coefficient for diffusive uptake of H2SO4 monomer by
aerosol particles to be calculated, and measurement of the concentration of H2SO4 monomer
allows the loss rate by this mechanism to be calculated.  Comparison of the calculated
production and loss rates of H2SO4 monomer show them to be equal,  consistent with the
observed steady  state concentration of this species.  This study lends substantial confidence to
the applicability  of the laboratory mechanism and rate to evaluation of the rate of sulfuric
acid formation in the ambient atmosphere.
     The reaction of SO3 has recently been reexamined by Kolb et al. (1994), who  find that
the reaction is second order in water vapor and propose that the reaction takes place by
interaction of SO3 with water vapor dimer:

                              SO3 + (H2O)2 -> H2SO4 + H2O                         (3-9)

     The investigators note that it is probable that sufficient water dimer exists in the
atmosphere to allow  the reaction to efficiently form sulfuric acid vapor.

3.3.1.4  Aqueous-Phase Oxidation of Sulfur Dioxide
Aqueous-Phase Equilibria
     The liquid  water content of the atmosphere,  VVL, is usually expressed either in  g of
water per m3 of air or  as a dimensionless volume fraction L  (e.g., m3  of liquid water per
m3  of air).   Typical liquid water content values are 0.1 to  1 g m"3  (L = 10'7- 10"6) for
clouds, 0.05 to 0.5 g m'3 (L= 5 x 10'7 - 5  x  10'6) for fogs, and  only lO'5  to 10~4g m'3
(L = 10~n-10~10) for aerosols. Cloud chemistry has been reviewed by Schwartz (1984a,
1986a).  Aqueous-phase oxidation of SO2 has recently been reviewed by Martin (1994).
     For dilute solutions the equilibrium distribution of a reagent gas A between the gas and
aqueous phases is given by Henry's law

                                      [A] =  H$A                                 (3-10)
                                          3-45

-------
where pA is the partial pressure of A in the gas-phase,  [A] is the equilibrium aqueous-phase
concentration of A and HA is the Henry's law coefficient for species A.  The customary units
of H^ are mole I"1 arm"1 .  HA can be viewed as the equilibrium constant of the reaction

                                      A(g) *=> A(aq)                                (3-11)

     Table 3-2 gives the Henry's law coefficients of some atmospheric gases in liquid water
at 298 K.  The values  given reflect only the physical solubility of the gas regardless of the
subsequent fate of the dissolved  species A.  Some of the species included in Table 3-2
dissociate after dissolution or react with water. Henry's law constants do not account for
these processes, and the modifications necessary will be discussed in the next paragraph.
Henry's law coefficients  generally  decrease for increasing temperatures, reflecting the lower
solubilities of gases at higher temperatures (Seinfeld, 1986).
     Several gases, after dissolving in the aqueous-phase, ionize and establish an
aqueous-phase chemical equilibrium system. For example for SO2,

                                  SO2(g)  ^ SO2- H2O                            (3-12)
                               S02- H20 *? HSO3" + H+

                                                                                   (3-14)
                                  HSOj  *+  SO4= + H +
with
                       [S02-H20]         [HS03-][H+]         [S04=][H+]
                       	»  J^-cM = 	» rL-el  ~ 	
                          Pso2       Sl    [S02-H20]'   s2
Ksi and Ks2 are the first and second dissociation constants for SO2. It is convenient to
                                          3-46

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             TABLE 3-2.  HENRY'S LAW COEFFICIENTS OF SOME
            ATMOSPHERIC GASES DISSOLVING IN LIQUID WATER
Species
°2
NO
C2H4
N02
°3
N20
CO2
H2S
SO2
CH3ONO2
OH
HN02
NH3
HO2
HCOOH
HCHO
CH3COOH
H202
HNO3
H (M/atm) (298 K)
1.3X10'3
1.9X10'3
4.8X10'3
l.OxlO'2
1.2X10'2
2.5 XIO'2
3.4X10'2
0.12
1.23
2.6
55.
49.
57.
2.0X103
5.6 XIO3
6.3 xlO3
8.7 XIO3
IxlO5
2.1X105
Source:  Schwartz (1986a).
consider the total dissolved sulfur in oxidation state IV as a single entity and refer to it as
                             = [S02- H20]  +  [HS0]  +  [S04=]
(3-16)
     The three sulfur species are in rapid equilibrium and therefore [S(IV)] changes only
when SO2 is transferred between the gas and aqueous phases. The total dissolved sulfur,
S(IV), can be expressed as a function of only the pH and the partial pressure of SO2 over the
solution by:
                                       3-47

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                       [S(IV)] = Hs02ps02
                                                          [H+]
                                                                                   (3-17)
The above equation can be expressed in a form similar to Henry's law as
                                                                                   (3-18)
where Hg^ is the effective (or modified) Henry's law coefficient given for S(IV) by

                                               1C       K Tf
                                         J +  	 +  	
                                                                                   (3-19)
The modified Henry's law coefficient relates the total dissolved S(IV) (not only SO2-H2O)
with the SO2 vapor pressure over the solution.  The effective Henry's law coefficient always
exceeds the Henry's law coefficient, indicating that the dissociation of a species enhances its
solubility in the aqueous phase.
     Several of the species that are in rapid equilibrium can be also considered as single
entities:
                                                                                   (3-20)
                                = [H2S04(aq)] +  [HSO41 + [SO4=]
                                     = [HN03(aq)] + [NO3~]
                             [N02 ]  = [HN02(aq)] + [NO2]
                           [HCHOT] =  [HCHO]  + [H2C(OH)2]
                                                                                   (3-21)
                                                                                   (3-22)
                                                                                   (3-23)
Equations relating the total concentrations of these aqueous-phase species with the
corresponding equilibrium concentrations of the gas-phase species can be derived similarly to
those for S(IV).
                                          3-48

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Aqueous-Phase Reaction Mechanisms
     The aqueous-phase conversion of dissolved SO2 to sulfate is thought to be the most
important chemical transformation in cloudwater. Dissolution of SO2 in water results in the
formation of three chemical species: hydrated SO2 (SO2 • H2O), the bisulfite ion (HSOJ) and
the sulfite ion (SO^).  At the pH range of atmospheric interest (pH =2-7) most of the S(IV)
is in the form of HSO^, whereas at low pH (pH <2),  all of the S(IV) occurs as SO2 • H2O.
At higher pH values (pH >7), (SO^) is the preferred S(IV) state (Seinfeld, 1986). The
individual dissociations are fast, occurring on timescales of milliseconds or less (Martin,
1984; Schwartz and Freiberg, 1981; Seinfeld, 1986).  Therefore, during a reaction
consuming one of the three species, SO2 • H2O, HSO^, or  SO^, the corresponding
aqueous-phase equilibria are  re-established instantaneously.  The dissociation of dissolved
SO2 enhances  its aqueous solubility. The total amount of dissolved S(IV)  is quite pH
dependent but always exceeds that predicted by Henry's law for SO2 alone.  The Henry's
law coefficient for SO2 alone, Hso , is 1.23 M  arm"1 at 298 K, while for the same
temperature, the effective Henry's law coefficient for S(IV), HS(IV), is 16.4 M arm"1 for
pH=3,  152 M atnv1 for pH=4 and 1,524 M atnT1 for pH=5.  Equilibrium S(IV)
concentrations for SO2 gas-phase concentrations of 0.2-200  ppb, and over  a pH range of 1 to
6 vary approximately from 0.001 to 1000 mM.
     Several pathways for S(IV) transformation to S(VI) have been identified involving
reactions of S(IV) with O3, H2O2, O2 (catalyzed by Mn2+ and Fe3+), OH, SO5",  HSO5~
S04-, PAN, CH3OOH, CH3C(0)OOH,  HO2, NO3, NO2, N(III), HCHO and Cl^  (Pandis
and Seinfeld, 1989a; Martin, 1994).
     Although ozone reacts very slowly with SO2 in the gas phase, the  aqueous-phase
reaction is rapid. The possible importance of O3 as an aqueous-phase oxidant for S(IV) was
first suggested by Penkett (1972) and the kinetics of

                              S(IV)  + O3 -> S(VI)  + O2                         (3-24)

have been studied by several  investigators (Erickson et al., 1977; Penkett et al., 1979;
Maahs, 1983). Hoffmann and Calvert (1985), after a detailed investigation of existing
                                        3-49

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experimental kinetic and mechanistic data, suggested the following expression for the rate of
the reaction of S(IV) with dissolved ozone:
                                       -  H20]  +  k1[HS031 + k2[SO3=])[O3]        (3-25)
recommending the values k0 = 2.4 x 104 M'1 s'1, k{ = 3.7 x 105 M'1 s'1 and, k^ = 1.5 x
109 M"1 s"1. They also proposed that  this reaction proceeds by nucleophilic attack on ozone
by SO2 • H2O, HSOJ,  and SO^.  An increase in the aqueous-phase pH results in an increase
of all three, [SO2 • H2O],  [HSO^] and [SO^], equilibrium concentrations and therefore  in an
increase of the overall reaction rate.  For an ozone gas-phase mixing ratio of 30 ppb, the
reaction rate varies from less than 0.001 mM h"1 (ppb SO^"1 at pH=2 (or less than 0.01%
SO2 (g) h'1 (g water /m3 air)'1) to 3,000 mM h'1 (ppb SO;,)'1 at pH=6 (7,000% SO2 (g) h'1
(g water /m3 air)"1).  The gas-phase SO2 oxidation rate  is of the order of 1% h"1 and
therefore the S(IV) heterogeneous oxidation by ozone is significant for pH values greater
than 4. The strong positive dependence of the reaction rate on the pH renders this reaction
self limiting. The production of sulfate by this reaction  lowers the pH and effectively
decreases the rate of further reaction. The availability of atmospheric ozone guarantees that
this reaction will play an important role both as a sink of gas-phase SO2 and as a cause  of
cloudwater acidification as long as the pH of the atmospheric aqueous phase exceeds 4.
     Hydrogen peroxide, H2O2,  is one of the most effective oxidants of S(IV) in clouds and
fogs (Pandis and Seinfeld, 1989a; Jacob and Hoffmann, 1983; Chameides, 1984;  Schwartz,
1986a; Seigneur and Saxena, 1988; Nair and Peters,  1989; Bott and Carmichael,  1993).
H2O2 is very soluble in water and under typical ambient conditions its aqueous-phase
concentration is approximately six orders  of magnitude  higher than that of ozone.  This
reaction has been studied in detail by several investigators (Hoffmann and Edwards, 1975;
Penkett et al., 1979; Martin and Damschen,  1981; Cocks et al., 1982; Kunen et al., 1983;
McArdle and Hoffmann, 1983) and the reproducibility of the measurements suggests a lack
of susceptibility of this reaction to influence of trace constituents.   The proposed rate
expression is (Hoffmann and Calvert, 1985)
                                         3-50

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                        R  = _d[SqV)]  = k[H+][H202][HS03-]                   (3.26)
                          2        dt            i  + K[H+]

with £=7.45 X  107 M'1 s4 and #=13 M'1 at 298 K. Noting that  H2O2 is a very weak
electrolyte; that  [H+]  [HSO^] = Hso Kslpso (Equation 3-15); and that for pH> 2,
1 + K [H+] - 1, one concludes that the rate of this reaction is practically pH independent in
the pH range of atmospheric interest.  For a H2O2(g) mixing ratio of 1 ppb the rate is
roughly 300 mM h'1  (ppb SO;,)'1 (700%  SO2(g)h'1 (g water  /m3 air)'1).  The near pH
independence can also be viewed as the result of the cancellation of the  pH dependence of
the S(IV) solubility and the reaction rate constant.   The reaction is very  fast and indeed both
field measurements (Daum et al., 1984a) and theoretical studies (Pandis  and Seinfeld, 1989b)
have suggested that H2O2(g) and SO2(g)  rarely coexist in clouds and fogs.  The species with
the lowest concentration before the cloud or fog formation is the limiting reactant, and is
rapidly depleted  inside the cloud or fog layer.
     The fastest, aqueous-phase, atmospheric reaction of SO2  is believed to be with
hydrogen peroxide, and with ozone at higher pH values. However, results of a study by
Hofmann and Jacob (1984) show that in heavily polluted atmospheric water droplets, such as
those found in urban fogs, metal-catalyzed  oxidation by O2 contributes significantly to
formation of sulfate in the liquid phase, and in such situations  may be more important than
oxidation by hydrogen peroxide.  Organic peroxides have been also proposed as potential
aqueous-phase oxidants of dissolved sulfur  (Graedel and Goldberg, 1983; Lind and Lazrus,
1983; Hoffmann and Calvert, 1985).  However,  simulations  for typical continental clouds
suggest that these reactions are of minor  importance for the  S(IV) oxidation and represent
only small sinks  for the gas-phase methylhydroperoxide (0.2% CH3OOH h"1) and peracetic
acid (0.7% CH3C(O)OOH h'1).
     The S(IV) oxidation by O2 is known to be catalyzed by Fe3+ and Mn2+

                                       Mn2+,Fe3 +
                       S(IV) + 1/2 02	> S(VI)                    (3-27)
                                         3-51

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This reaction has been the subject of considerable interest (Hoffmann and Boyce, 1983;
Martin, 1984, 1994; Martin et al., 1991; Hoffmann and Jacob, 1984; Hoffmann and Calvert,
1985; Clarke and Radojevic, 1987) and significantly different measured reaction rates, rate
laws and pH dependencies have been reported (Hoffmann and Jacob, 1984). Martin and Hill
(1987a,b) have demonstrated that this reaction is inhibited as ionic strength increases.  They
explained most of the literature discrepancies by differences in these factors during the
various laboratory studies.
     In the presence of oxygen, iron in the ferric state, Fe(III), catalyzes the oxidation of
S(IV) in aqueous solutions.  Fe(II) appears not to catalyze the reaction directly but must be
first oxidized to Fe(III) before S(IV) oxidation can begin (Huss et al., 1982a,b).  The recent
review by Martin (1994) gives a comprehensive discussion of the oxidation of SO2 by O2 in
the presence of iron.
     For pH values from 0 to 3.6 the iron-catalyzed S(IV) oxidation rate is first order in
iron, first order in S(IV) and  is inversely proportional to [H+] (Martin and Hill, 1987a),
                            R  = _          = k   e                                (3.28)
This reaction is inhibited by ionic strength and sulfate and these effects are described by:
                                                  -2 I1'2
                                                                                    (3-29)
                                  k{ = k*  x  101 +1
and
                                                      1/2
                                kl = kt*	-	_                          (3-30)
                                         1 + 150[S(VI)]2/3
where / is the ionic strength of the solution and [S(VI)] is in M.  A rate constant fc = 6 s"
has been recommended by Martin and Hill (1987a). Sulfite appears to be almost as equally
inhibiting as sulfate.
      The rate expression for the same reaction changes completely above pH 3.6.  This
suggests that the mechanism of the reaction differs in the two pH regimes, and is probably a
                                           3-52

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free radical chain at high pH and a non radical mechanism at low pH (Martin et al., 1991).
The low solubility of Fe(III) above pH 3.6 presents special experimental problems. At high
pH the reaction rate depends on the amount of iron in solution, rather than on the total
amount of iron present.  At this range the reaction is second order in dissolved iron (zero
order above the solution iron saturation point) and first order in S(IV).  The reaction is still
not very well understood and Martin et al. (1991) proposed the following phenomenological
expressions (in M  s"1)
                        pH4.0: -          = lxl09[S(IV)][Fe3+]2                   (3-31)
                                    dt
                         PH5.0-6.0: d[S(IV)1 =  lxl(T3[S(IV)]                    (3-32)
                                        dt
                           pH7.0: -          = 1X1(T4[S(IV)]                      (3-33)
                                       dt
for the following conditions:

       [S(IV)] ^  10/*M,[Fe3+]>0.1/xM, K0.01M, [S(VI)] < 100M, and T=298K.

Note that iron does not appear in the pH 5-7 rates because it is assumed that a trace of iron
will be present under normal atmospheric conditions.  This reaction is important in this high
pH regime (Pandis and Seinfeld, 1989a,b; Pandis et al.,  1992b).
     Martin et al.  (1991) also found that non-complexing organic molecules (e.g., acetate,
trichloroacetate,  ethyl alcohol,  isopropyl alcohol, formate, allyl alcohol, etc.) are highly
inhibiting at pH values of 5 and above, and are not inhibiting at pH values of 3 and below.
They calculated that, for remote clouds, formate would be the main inhibiting organic, but
by less than 10%.  In contrast, near urban areas formate could reduce the rate of the
catalyzed oxidation by a factor of 10-20 in the high pH regime.
     The manganese catalyzed S(IV) oxidation was initially thought to be inversely
proportional to the H+ concentration.  Martin and Hill (1987b) suggested that ionic strength,

                                         3-53

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not hydrogen ion, accounts for the pH dependence of the rate. These authors were also able
to explain some unusual behavior described in the literature on this reaction and to partially
reconcile some of the literature rates.  The manganese catalyzed reaction obeys zero-order
kinetics in S(IV) in the concentration regime above 100 mM S(IV),
                                           = k0[Mn2+]2                           (3-34)
                                                -4 07 I"2
                                               TT^r                           (3'35)
                                ^o  ~ ko  X 10
with k*0 = 680 M'1 s'1 (Martin and Hill, 1987b). For S(IV) concentrations below 1 mM the
reaction is first order in S(IV),
                                         =  k0[Mn2+][S(IV)]                        (3-36)
                                                -4 07 1 1/2
with k0 = 1,000 M'1 s'1 (Martin and Hill, 1987b).  It is still not clear which rate law is
appropriate for use in atmospheric calculations, although Martin and Hill (1987b) suggested
the provisional use of the first order, low S(IV) rate.
     When both Fe3+  and Mn2+ are present in atmospheric droplets, the overall rate of the
S(IV) reaction is enhanced over the sum  of the two individual rates.  Martin (1984) reported
that the rates measured were 3  to 10 times higher than expected from the sum of the
independent rates.  Martin et al. (1991) obtained at pH 3.0 and for [S(IV)] <  10 mM the
following rate law
            = 750[Mn(II)][S(IV)]  + 2600[Fe(III)][S(IV)]  +  1.0xl010[Mn(II)][Fe(IH)][S(IV)]
      dt
                                                                                  (3-38)
and a similar expression for pH 5.0 in agreement with the work of Ibusuki and Takeuchi
(1987).
                                          3-54

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     Free radicals, such as OH and H02, either heterogeneously scavenged by the aqueous
phase or produced in the aqueous phase, participate in a series of aqueous phase reactions
(Graedel and Weschler, 1981; Chameides and Davis, 1982; Graedel and Goldberg,  1983;
Schwartz,  1984b; Jacob, 1986; Pandis and Seinfeld, 1989a).
     Pandis and Seinfeld (1989a) proposed that under typical remote continental conditions
there are two main radical pathways resulting in the conversion of S(IV) to  S(VI):

               S(IV)(+OH) -> SO5~ (+O2~  ) -» HSO5~  (+HS03- ) -> S(VI)         (3-39)
                  S(IV)(+OH) -» SO5~ -» SO4~ (+Cr, HSO3- -* S(VI)            (3-40)

with the first of these two pathways typically being faster that the second.
     Nitrogen dioxide has a low water solubility and therefore its low resulting
aqueous-phase concentrations suggests that its oxidation of S(IV)
                    2N0  + HS0- - ?-> 3H+ + 2N02-  + SO4=              (3-41)
should be of minor importance in most cases.  This reaction has been studied by Lee and
Schwartz (1983) at pH 5.0, 5.8 and 6.4 and was described as a reaction that is first order in
NO2 and first order in S(IV), with a pH-dependent rate constant.  The evaluation of this rate
expression (3-41) was considered tentative by Lee and Schwartz (1983), in view of evidence
for the formation of a long-lived  intermediate species.  The apparent rate constant was found
to increase with pH.  This reaction is considered of secondary importance at the
concentrations and pH values representative of clouds.  However, Pandis and Seinfeld
(1989b) reported that for fogs occurring in urban polluted areas with high NO2
concentrations this reaction could be a major pathway for the S(IV)  oxidation, if the
atmosphere has enough neutralizing capacity, e.g. high NH3 (g) concentrations.
     Sulfite and bisulfite can form complexes with various dissolved aldehydes. One
important example is the reaction of sulfite or bisulfite with formaldehyde to produce
hydroxymethanesulfonate ion (HMS) (Boyce and Hoffmann, 1984; Munger et al.,  1984,
1986; Olson and Hoffman,  1989; Facchini et al., 1992).
                                         3-55

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     The HMS formed acts as a S(IV) reservoir protecting it from further oxidation, and its
formation has been advanced to explain high S(IV) concentrations that have been observed in
clouds, fogs, and dew (Pierson and Brachaczek, 1990).  The rates of S(IV) complexation and
oxidation are highly dependent on cloud pH and on the concentrations of HCHO and
oxidants. Characteristic times for S(IV) depletion through complexation and oxidation can be
compared for typical ranges of HCHO,  H2O2, and pH.  At pH values below about 4, the rate
Of complex formation is several orders of magnitude Slower than the reaction of S(IV) with
dissolved H2O2. Thus, in this range oxidation predominates over complexation.  The
characteristic times of the two reactions become approximately comparable at pH around 5 so
that complexation with HCHO becomes competitive with oxidation by H2O2.  When pH
exceeds 6, the reactions of S(IV) with HCHO became more important than reaction with
H2O2.  HMS formation can inhibit S(IV) oxidation if the S(IV) complexation rate is
comparable to,  or greater than, the S(IV) oxidation rate and the rate of SO2 mass transport
into the drop controls the rate of S(IV)  oxidation.  The effectiveness of HMS as a S(IV)
reservoir depends critically on its resistivity to OH attack.

Formation of Sulfates in Clouds
     The atmospheric aqueous phase (clouds,  fogs) can be  viewed as a processor of the
aerosol size/composition distribution (Pandis et al., 1990a,b). Precipitating clouds are well
known to be the major removal mechanism of aerosol particles from the atmosphere.  At the
same time,  the  liquid droplets provide the reacting medium for aqueous-phase reactions
(Graedel and Weschler,  1981; Chameides and Davis, 1982; Graedel and Goldberg, 1983;
Jacob and Hoffmann, 1983; Munger et  al.,  1983; Chameides, 1984; Seigneur and Saxena,
1984; Hoffmann and Jacob, 1984; Fuzzi et  al., 1984; Hong and Carmichael, 1986a; Hill et
al., 1986; Jacob, 1986; Jacob et al., 1985,  1986a,b;  Johnson et al., 1987;  Fuzzi et al., 1988;
Dlugi, 1989; Pandis and Seinfeld, 1989a,b; Munger  et al.,  1990;  Forkel et al.,  1990; Bott,
1991; Joos  and Baltensperger, 1991; Earth, 1994; De Valk, 1994).  Several gaseous species
dissolve in cloudwater and react giving  products that remain in the aerosol phase after the
cloud dissipates; for example, the dissolution of S02, its ionization, and subsequent oxidation
to sulfate.  These species can attract additional gaseous species, such as ammonia and water
into the aerosol phase and thereby increase  further the aerosol mass.  Therefore, aerosol
                                         3-56

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processing by nonprecipitating clouds represents a mechanism by which atmospheric particles
can grow during their residence time in the atmosphere.  A detailed review of the state of
science in 1990 has been presented by United States National Acid Precipitation Assessment
Program (U.S. NAPAP) (1991).
     A cyclical relationship between the occurrence of smog and fog in polluted areas has
been proposed by Munger et al.  (1983) and was termed the smog-fog-smog cycle.  In a
polluted atmosphere with high aerosol  concentration, the formation of late night and early
morning fogs is augmented enhancing smog production, visibility reduction,  and aerosol
sulfate the next day (Cass,  1979;  Cass and Shair, 1984; Pandis  et al., 1990a,b). Processing
of aerosol by clouds can result in similar cyclical relationships and enhanced contribution of
the aerosol produced in clouds to ground-level particulate concentrations (Altshuller, 1987).
This processing cycle accelerates  the production of  atmospheric acidity through aqueous-
phase reactions (Schwartz,  1989).

Cloud Effects on Particle Number Concentration
     There has been a series of observations  of enhanced aerosol number concentrations in
the vicinity of clouds (Saxena and Hendler, 1983; Hegg et al., 1990; Radke  and Hobbs,
1991; Hegg et al., 1991).  Saxena and Hendler (1983) suggested that the observed high
aerosol number concentrations near clouds could be due to the shattering of rapidly
evaporating droplets.  Hegg et al.  (1991) proposed that the high actinic radiation fluxes near
cloud tops could lead to high OH concentrations and nucleation of new H2SO4/H2O particles.
The high relative humidity  areas around clouds often have total  particle number
concentrations about twice those in the air at  the same level but well removed from  the cloud
boundaries (Radke and Hobbs, 1991).  Kerminen and Wexler (1994a,b) have demonstrated
that there is high nucleation probability associated with these high relative  humidity  areas,
especially near relatively clean clouds.   All these speculated mechanisms for production of
new particles produce negligible new aerosol  mass,  but may influence the shape of the
aerosol distribution, especially in remote regions. Aqueous-phase reactions producing sulfate
and nitrate increase the aerosol mass, but do not influence directly the aerosol number
concentration, unless some  cloud droplets shatter into  many smaller droplets.  The removal
                                          3-57

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of gas-phase SO2, H2SO4, and NH3, due to their transfer to the aqueous-phase, indirectly
slows down the production of new particles in the vicinity of the cloud.

Cloud Effects on Aerosol Mass Concentration
     Significant production of sulfate has been detected in clouds and fogs in different
environments (Lazrus et al., 1983; Hegg and Hobbs, 1987, 1988; Pandis and Seinfeld,
1989b; Husain et al., 1991; Swozdziak and Swozdziak, 1990; Pandis et al., 1992b; De Valk,
1994; Liu et al., 1993). The detection of sulfate-producing reactions is often hindered by the
variability of cloud liquid water content and the temporal instability and  spatial variability in
concentrations of reagents and product  species (Kelly et al.,  1989).  The production of
sulfate has also been detected and investigated in laboratory clouds (Hansen et al., 1991).
Aqueous-phase oxidation of HSO3" by H2O2 is particularly fast, as illustrated by the mutual
exclusivity of SO2 and H2O2 observed in clouds (Daum et al.,  1984a,b,  1987).  Other
reactions, including oxidation of dissolved SO2 by ozone  and oxidation by O2 catalyzed by
Fe3+ and Mn2+, may also contribute, significantly in some cases, to sulfate production
(Pandis et al., 1990b; Earth et al., 1992; Earth, 1994). During aqueous-phase sulfate
production the reactants including SO2, H2O2, O3, and OH are transferred from the gas
phase to the cloud droplets. This transport includes a series of steps (gas-phase diffusion,
transport across the gas-liquid interface, dissociation and  aqueous-phase diffusion) that
ultimately couple the gas and aqueous phases and in some cases control the overall sulfate
production rate (Schwartz, 1988).
     The formation  of sulfate in raining and non-raining  clouds has been modeled (Seigneur
et al, 1984; Seigneur and Saxena,  1988; Seigneur and Wegrecki, 1990).  The results have
been compared to experimental measurements of cloud chemistry.  Contributions to sulfate
formation from gas-phase reactions and from various aqueous phase mechanisms during
daytime and nighttime can be compared.
     Hydrogen peroxide is the most important oxidant for the  conversion of SO2 in cloud
water at pH 4.5 or lower (Calvert et al., 1985) and dominates  the aqueous sulfate formation
pathways (McHenry and Dennis,  1994) in the northeastern United  States.  The measured
H2O2 gas-phase mixing ratio  over the northeastern and central  United States  has been
reported to vary from 0.2 to 6.7 ppb (Sakugawa et al., 1990) with the highest values during
                                          3-58

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the summer and the lowest during the winter months.  The H2O2 concentrations usually
increase with decreasing latitude and increasing altitude (Sakugawa et al., 1990). The
availability of hydrogen peroxide may be the limiting factor in sulfate formation in clouds.
This limitation is more pronounced near SO2 sources  and during the  winter months.  The
seasonal contribution of clouds to sulfate levels depends on both the  availability of oxidants
and on the cloud cover.  In cases where the sulfate cloud production is oxidant limited,
changes in aerosol sulfate levels will be less than proportional to SO2 emission changes, with
the relationship being more nonlinear in winter than in spring or summer (U.S. NAPAP,
1991).
     Evaluations of the rate of the SO2-H2O2 reaction in cloudwater indicate that the
characteristic time for this reaction is a  few minutes to an hour, depending on conditions
(Schwartz, 1984a; Meagher et al., 1990).  Since such  a reaction time is shorter than the
lifetime of stratiform clouds in the troposphere  it is anticipated that the reaction of SO2 and
H2O2 will proceed to completion in liquid water stratiform clouds. Evidence of this
occurring  would be that only one or the other of these species would be present in such
clouds, but not both at the  same time.  This expectation has been borne out in field
measurements supporting the inference of rapid reaction given by the model estimates.
Daum  and colleagues (Daum et al., 1984a; Daum, 1988) have presented results of
simultaneous aircraft measurements of H2O2 in collected cloudwater  samples and SO2 in air
(filter pack measurements)  in nonprecipitating stratiform clouds indicating that in almost all
instances either one or the other species was at  very low concentrations, and by inference
that the reaction has proceeded essentially to completion in the clouds.  A rather different set
of results was reported by Husain et al.  (1991)  who conducted measurements of gas-phase
SO2 and H2O2  during cloud events at Whiteface Mountain, NY.  Although a general negative
correlation between the two species concentrations was exhibited, the data indicated
substantial periods of apparent coexistence of these species.
     There is the possibility of spatial inhomogeneities in the clouds  that are not resolved in
the sampling period (typically 30 min in the Daum studies; an hour or more for the Husain
studies), in which one region was H2O2 rich and another SO2 rich.  In such instances  a lack
of coexistence of the two species would be masked by the extended duration of sampling.
Such spatial inhomogeneities might also account for the few  instances reported by Daum in
                                          3-59

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which SO2 and H2O2 apparently coexisted in clouds.  Additionally, local patches of
subsaturated air in the clouds during the sampling period might also account for these
observations, although Daum took efforts to exclude such instances from their data base.
Yet another possible explanation of the Husain results is that the cloud was relatively newly
formed, and the material had not had time to react.  An obvious improvement in this
approach is to measure the species, as well as cloud liquid water content, with greater time
resolution.  Burkhard et al. (1994) have reported aircraft measurements of gas-phase SO^ and
H2O2 during in-cloud flights; traces of liquid water content are also shown.  These data
support a strong anticorrelation of SO2 and H2O2 in clouds on various time (location) scales,
with numerous instances  of peaks of SO2 coincident with valleys of H2O2 and vice versa.
     A quantitative estimate of the amount of cloudwater sulfate that is formed by in-cloud
reaction can be gained by inferring the amount of cloudwater sulfate that derives from
preexisting sulfate aerosol. Husain et al. (1991) has used selenium as a tracer to allow such
inferences to be drawn.   By measuring the sulfate to selenium ratio in clear air aerosol that is
representative  of the aerosol that is the pre-cloud aerosol of the clouds under investigation,
and by assuming that the fractional incorporation of the sulfate and selenium into cloudwater
is identical (and/or by measuring this ratio), it is possible to infer the amount of cloudwater
sulfate derived from preexisting sulfate aerosol and, by difference,  the amount formed by
in-cloud reaction.  A series of such studies carried out at Whiteface Mountain, NY, indicates
that the assumption of identical scavenging of sulfate and  selenium  is valid (1.04 ± 0.29;
1.04 ± 0.19 in two  separate cloud systems).  Evidence of enhanced sulfate in cloudwater,
attributed to sulfate formed by in-cloud reaction, was found in five of six cloud systems
studied; amounts formed were consistent with ambient SO2 concentrations.  Examination of
the pH dependence of the concentration of in-cloud produced sulfate inferred by this
technique indicated that sulfate was produced by  in-cloud reaction only at pH values below
4.0,  consistent with  oxidation by H2O2, but not with oxidation by O3.
     Recently Snider and Vali (1994) reported studies of oxidation of SO2 in winter
orographic clouds  in which SO2 was released and the extent of increased concentrations of
sulfate in cloudwater (relative  to the unperturbed cloud) were compared to decreased
concentrations of H2O2 (sum of gaseous plus aqueous, inferred from aqueous concentrations).
Despite considerable scatter, the data fall fairly close to the one-to-one line, indicative of the
                                          3-60

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expected stoichiometry of reaction, Figure 3-11.  The investigators also modeled the reaction
kinetics.  The rate of reaction is sensitive to the liquid water content (LWC) of the cloud
during the time between the point of cloud condensation to the point of sampling.  Since this
profile was not known the investigators assumed a linear profile for LWC versus time.  The
resulting model predictions agreed closely with the extent of reaction inferred from changes

in H2O2 and sulfate concentrations, supporting the applicability of the model.
                   0.25
               2  0.20
               £  0.15
S  0.10
               Jf
                   0.05
                                                          a 66
                                       0.10
                                       0.05
                   094
                                       0.00
                                                 8F
                           0.00
                                                      .
                                                      8J

                                                      1
                                                     1
                                          Q94 -
                                                             1
                                                   0.05
0.10 -
                       W                                   :
                   000h  .-ik  i.... i.... i.... i.... i  ..."
                      OOO   | 0.05    O10    O15    O20    O25
                                     (ppbv), OBSERVATION

Figure 3-11. Comparison of observed hydrogen peroxide (H2O2) depletions (DH o ,
             abscissa) and observed sulfate yields (YSO4, ordinate). Errors associated
             with experiments 84, SB, 8F, and 8J are indicated and data values from
             these experiments are labeled in the inset figure.  Data values
             corresponding to experiments 94 and 66 are also labeled. The slope of the
             best fit line, forced through the origin, and calculated using only those
             data values indicated by circles is 1.21 (±0.13).

Source: Snider and Vali (1994).
     In contrast to the H2O2 reaction, oxidation of SO2 by O3 exhibits a strong pH
dependence.  The reaction is quite rapid at high pH (~6) but is expected to greatly slow
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down as strong acid is produced in the course of the reaction.  However, if concentrations of
NH3 or other basic materials are sufficiently high to maintain a pH above 5, the reaction can
continue to proceed at a high rate.
     Walcek et al. (1990) calculated that, during the passage of a midlatitude storm system,
over 65 % of the sulfate in the troposphere over the northeastern United States was formed in
cloud droplets via aqueous-phase chemical reactions.  The same authors indicated that,
during a 3-day springtime period, chemical reactions in clouds occupying 1 to 2% of the
tropospheric volume were responsible for sulfate production comparable to the gas-phase
reactions throughout the entire tropospheric volume under consideration.  McHenry and
Dennis  (1994) estimated that annually more than 60% of the ambient sulfate in Central and
Eastern United States is produced in clouds with the non-precipitating cloud production
dominating over precipitating cloud production.  Spatial variability of emissions and ambient
H2O2 concentrations induces spatial variability in the contribution of in-cloud sulfate
production, making it highest in the south. These  conclusions are in quantitative agreement
with similar calculations of Dennis et al. (1993) and Karamchandani and Venkatram (1992).
Aqueous-phase oxidation in  clouds is also the most important pathway for the conversion of
SO2 to sulfate on the global scale (Hegg,  1985; Langner and Rodhe, 1991).
     Clouds could under some conditions also be a significant source of aerosol nitrate
during the night.  Choularton et al.  (1992) and Colvile et al. (1994) observed production of
around  0.5 /ig m"3 of nitrate during the processing of an air parcel by a hill cap cloud.  They
speculated that the sources of this nitrate were gaseous N2O5 and NO3.
     Chemical heterogeneities in the droplet population affect  significantly the overall sulfate
production rate and the produced sulfate size distribution (Seidl, 1989; Twohy et al., 1989;
Lin and Chameides,  1991; Pandis et al., 1990a,b;  Ayers and Larson,  1990; Hegg and
Larson, 1990; Bower et al., 1991; Ogren and Charlson,  1992; Roelofs, 1992a,b; 1993;
Carter and Borys, 1993; Bott and Carmichael, 1993; Collett et al., 1993b).  Neglecting these
chemical concentration differences could result in significant underestimations of the sulfate
production rates in some cases (Hegg and Larson,  1990; Roelofs, 1993).  Ice-related
microphysical processes can also have a significant impact on cloud chemistry (Taylor,  1989;
Wang and Chang, 1993; Collett et al., 1993a).
                                          3-62

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     Fogs in polluted environments have the potential to increase aerosol sulfate
concentrations by droplet phase reactions but at the same time to cause reductions in the
aerosol concentrations of nitrate, chloride, ammonium and sodium, as well as in the total
aerosol mass concentration, because of the more rapid deposition of larger  fog droplets
compared to smaller particles (Pandis et al., 1990a). Pandis et al.  (1992b) calculated that
more than half of the sulfate in a typical Los Angeles air pollution episode  was produced
inside  a fog layer the previous night.  This heterogeneously produced sulfate  represented 5 to
8% of the measured PM10  mass.

Aqueous-Phase Oxidation  of Sulfur Dioxide in Aerosols
     Until recently it was thought that the low amount of liquid water associated with
particles  (volume fraction on the order of 1 x 10"10, compared to clouds, for  which the
volume fraction is the order of 1 x 10"7) precluded significant aqueous-phase  conversion of
SO2 in such droplets. However, field studies (McMurry, et al., 1981; McMurry and
Wilson, 1982,  1983) of aerosol growth as a function of size suggest the occurrence of
aqueous-phase reactions. Model studies (Saxena and Seigneur, 1987) indicate that conversion
of SO2 to sulfate in aerosols at 90% relative humidity can contribute perhaps  10%  to the total
sulfate formation (90% due to the  gas-phase reaction of SO2 with OH).  At night the
conversion rate is lower, 10% of the daytime rate, and is almost all due to aqueous-phase
reactions.  At higher relative humidities and/or lower temperatures the aqueous-phase
contribution would be expected to  increase.
     Sievering and colleagues (1991) have also called attention to the possibility of rapid
oxidation of SO2 by O3  in aqueous sea-salt aerosols, which are buffered by the  alkalinity of
sea salt particles,.  Indeed, it appears that such a rate may initially be quite rapid,  1 /iM s"1
corresponding to 8% h"1, in the example given by Sievering et al. (1991) for  liquid water
content 50 ptg m"3 and SO2 concentration 2 n mol m"3 (mixing ratio 0.05 ppb).  Despite this
rapid initial rate, it would appear that the extent of such oxidation may be quite limited.
For the example given by Sievering et al.  (1991), the sea-salt sodium concentration is given
as 100 n mol m"3.  Based on the concentrations of (HCO^ + CO3= ) and Na+ in seawater
(2.25 and 454 m mol kg"1,  respectively), the  alkalinity of the sea salt aerosol  is expected to
be 0.5  n mol m"3.  Consequently, after only 0.25 n  mol m"3 of SO2 is taken up in solution
                                          3-63

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and oxidized (i.e., 12% of the initial SO2), the initial alkalinity would be exhausted, and the
reaction rapidly quenched.
     Sievering et al. (1994) have presented field measurements over Lake Michigan of
coarse-mode sulfate (diameter 5-20 pirn), which they ascribe at least in part to oxidation of
SO2 in such particles derived from wind driven spray of lake water,  in which the pH is
maintained high by alkalinity present in the lake water.  Calculations were carried out for
liquid water volume fraction of 13 X  10~12 (13 pg m"3).  The  alkalinity was inferred from
the measured cation minus anion difference (cations NH^  , Mg+ + , Ca+ + ; anions SO^ ,
NO~3) in the coarse mode,  which averaged 26 neq m"3, corresponding to an aqueous
alkalinity of 2 x  10"5 M.  In the absence of mass  transport limitation the rate of the aqueous -
phase O3-SO2 reaction was calculated to be 7  ± 3 x  10~4 Ms"1; however, mass transport
limitation reduced this rate by a factor of 20 to 40 at pH 7.  The conversion rate referred to
gas-phase SO2 was calculated as 0.5 to 1.7% h"1.  The investigators concluded that this
mechanism is a significant contributor  to the SO2 oxidation under these conditions.  Again,
however, concern may be raised with that conclusion, namely that the indicated oxidation
rate, 2 x 10"5 M s"1 after taking mass transport limitation into account, would quickly
produce an acidity equal to the initial  alkalinity, thereby quenching the reaction.

3.3.2   Particulate Nitrates
3.3.2.1  Sources
     By analogy  to the sulfur system,  sources of aerosol nitrates might be distinguished  into
primary, gas-phase, and aqueous-phase.  However, as primary nitric acid emissions are
considered to be small, the present discussion focuses on in situ production mechanisms  in
the atmosphere.   Once nitric acid has been formed its reaction with ammonia in the gas
phase may lead to the formation of paniculate ammonium nitrate.  Nitric acid may also react
with salts of chloride or carbonate, releasing the corresponding acid, and forming a
paniculate  salt or a solution.

3.3.2.2  Major Gas-Phase Reaction
     The principal mechanism  for gas-phase production of nitrates is reaction of OH with
NO2 to form HNO3.
                                          3-64

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                                OH + NO2 + M -> HNO3                           (3-42)

Here, as with SO2, the mechanism and rate of the gas-phase reaction is well established from
laboratory studies (see Hicks et al.,  1991), and the principal source of uncertainty in
describing the reaction rate is the concentrations of the reagent species, mainly OH.
     The reaction of OH with NO2  is approximately 10 times as fast as the reaction of OH
with SO2 (Equation 3-6) (Finlayson-Pitts and Pitts, 1986).  Therefore, NO2 is preferentially
converted to HNO3 and the conversion of SO2 to H2SO4 is delayed until much of the NO2
has reacted (Gillani and Wilson, 1983).

3.3.2.3  Major Aqueous-Phase Reaction
     A second key pathway for formation  of nitric acid is the reaction sequence:
                                 NO2 + 03 ^N03+O2                            (3-43)

                                  NO3+NO2 *± N2O5                            (3-44)

                              N205 +H20(1)  -* 2HNO3(aq)                         (3-45)

The reaction of N2O5 with water vapor is thought to be slow, but reaction with condensed
water, in cloud or fog droplets, or in or on the surface of wet particles, is thought to be fast
(Tuazon et al.,  1983).  Other reactions  of NO3 and/or N2O5, for example N2O5 with
aromatics (Pitts et al., 1985a,b) must also be considered.  Reaction of N2O5 with liquid
water appears to be rapid and irreversible. Studies of the uptake of N2O5 on aqueous
sulfuric droplets give mass accommodation coefficients of about 0.1 (Mozurkewich and
Calvert, 1988; Van Doren et al., 1990; Fried et al., 1994) .  Thus the  overall rate and yield
of this reaction can be evaluated from the pertinent gas-phase rate constants and the  mass
transfer rate constant for uptake of N2O5 by aqueous aerosol or cloud droplets (Finlayson-
Pitts and  Pitts, 1986).
                                         3-65

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3.3.2.4  Other Reaction Mechanisms
     Reactions  of NO3 other than Equation 3-39 must be considered.  In daytime NO3 may
undergo photolysis.
                                                                                (3-46)
However, during the night NO3 concentration can build up sufficiently that formation of
HNO3 by hydrogen abstraction from alkanes and aldehydes may become significant
(Finlay son-Pitts and Pitts, 1986).
     The aqueous-phase reactions of NO and NO2 to yield HNO3 also need to be considered.
Field measurements comparing the chemical composition of cloud droplets and rain with that
of the surrounding air suggest the conversion of nitrogen oxides to nitric acid in the aqueous
phase (Lazras et al., 1983; Colvile et al., 1994).  The aqueous-phase conversion of NO2 to
nitric acid,
2NO2 + H2O(1)  -  2H+ + NO
                                                     2
has been proposed.  However, laboratory studies indicate that this reaction in pure water is
too slow to be an important source of HNO3 in clouds (Schwartz, 1986b).  Measurements  in
smog chambers and indoor environments, however, suggest that a heterogeneous analog of
Equation 3-43 may be occurring.
     Aqueous phase reactions of NO2 with O2, O3, and H2O2 are also though to be
insignificant under representative atmospheric conditions (Schwartz, 1986b).  The chemical
kinetics of the aqueous-phase oxidation of NO by O2 has been reexamined by two groups
(Lewis and Deen, 1994; Pires et al.,1994). Evaluation of the rate of this reaction in
cloudwater confirms that the reaction rate is negligible under atmospheric conditions, as
indicated earlier by Schwartz and White (1983).
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3.3.2.5   Ammonium Nitrate Vaporization Equilibria
     In the sulfate system the vapor pressure of H2SO4 is negligible, so that all sulfate may
be considered present in the particles.  Also, at least for acidic sulfates (that is, not fully
neutralized) the vapor pressure of NH3 is likewise negligible. Even for fully neutral
(NH4)2SO4 any hydrolysis of NH/  to form NH3 that might escape to the vapor phase is
suppressed by the resultant acidity.  In contrast, nitrates in aerosols are distinguished from
sulfates because of the volatility  of NO3" (as HNO3) and of NH4NO3 (as NH3 + HNO3).
The equilibrium

                        NH4N03 (s) or (aq) <=*  NH3(g)+HNO3(g)                  (3-48)

is such that at ambient conditions the partial pressures of NH3 and/or HNO3 are appreciable
above  crystalline NH4NO3 and likewise above solutions containing NH4+ and NO3" ions (of
not necessarily equal concentrations).  It is thus necessary to consider these equilibria not just
for the crystalline material but also for solutions, in the latter case  as a function of
concentration or, equivalently, water activity.  Such a treatment has been given in detail by
Stelson and Seinfeld (1982a,b), and that study is the basis for much subsequent interpretation
of field measurements.
     As an example of such a study, Harrison and Msibi (1994) compare the measured
concentration product of HNO3 and NH3 versus the equilibrium constant for the reaction.
Agreement is found roughly within a factor of 2 or so based on assumption of equilibrium
with pure NH4NO3  (crystal or solution).  However, when the observations were stratified by
relative humidity, no strong trend of measured concentration product with relative humidity
was evidenced.
     As noted above, the time scale of reaching this equilibrium is of interest,  for example
as it may  influence dry deposition or accommodation to changing gaseous environments, as
in human  airways.  Wexler and Seinfeld (1990) modeled the time dependence of achieving
this equilibrium and concluded that equilibrium is  generally reached within seconds to
minutes for typical aerosol loadings.  By evaluating the time scales for equilibrium of vapor-
phase species with a population of aerosol  particles, Wexler et al. (1992) found that
ammonium salts in the gas and aerosol phases are not always in equilibrium, especially under

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less polluted and cooler conditions. Thus, both transport and thermodynamic properties of
the aerosol population govern the distribution of ammonium salts.  At low temperatures and
low aerosol loadings the time constant for achieving this equilibrium could be a day or more.
     An important implication of the  high vapor pressure of ammonium nitrate
(as NH3 + HNO3) is that ammonia will distill from any ammonium nitrate if there is an
acidic site present, for example acidic sulfate that is less than fully neutralized by ammonia.
As a consequence ammonium nitrate aerosol cannot exist indefinitely in the presence of
acidic sulfate aerosol (Gebhart et al.,  1994).
     A further consequence of this equilibrium is the influence it may exert on dry
deposition. Sievering et al. (1994) reported steep gradients of NH4NO3 concentration with
height above forest canopies, and inferred high rates of deposition of paniculate nitrate, 2 to
9 cm s"1, comparable to those of gaseous HNO3.  They attribute this to the large particle size
of the nitrate, 2 to 2.5 j«m mean diameter, citing calculation of Peters and Eiden (1992).
An alternative explanation of the observations, which does not appear to be ruled out, is that
the deposition is actually of HNO3. The deposition of HNO3 may perturb the equilibrium of
NH4NO3 with NH3 + HNO3, leading to a decrease of NH4NO3 in the vicinity of the surface
and giving the appearance of enhanced deposition of the paniculate species.

3.3.2.6   Sulfate/Nitrate Interaction
     In the eastern United States enough H2SO4 is usually formed to react with the available
NH3. Indeed, the sulfate is frequently acidic, the average  composition in the summer being
approximately NH4HSO4.  Since appreciable concentrations of NH3 and HNO3 are present in
equilibrium with NH4NO3, while the  vapor pressure of NH3 in equilibrium with (NH4)2SO4
or NH4HSO4 is very low, NH4NO3 is not stable in the presence of NH4HSO4 and
transformations produce (NH4)2SO4 and HNO3.  However, if SO2 emissions are reduced and
less H2S04 is formed, some  NH3 may be left over after all H2SO4 has been converted  to
(NH4)2SO4. Paniculate NH4NO3 will form if the concentrations of HNO3 and excess NH3
are sufficient to exceed the equilibrium constant of Equation 3-38, Kp =  [HNO3][NH3],
which at 17°C, is 4 ppb2 over the solid and 1 ppb2 over the solution droplet at 85% relative
humidity (Harrison and Msibi, 1994).
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     If the H2SO4 formed in the atmosphere is insufficient to react with all available NH3,
i.e. 1/2[H2SO4] <[NH3], the concentration of sulfate plus nitrate may be controlled by the
amount of NH3 available (until the concentration of sulfate plus nitrate is less than the
amount needed to react with NH3,  i.e., 1/2[H2SO4] + [HNO3]  < [NH3]. Thus, as SO2
emissions are reduced, NH4NO3 may replace (NH4)2SO4.
     NH4NO3 would not be expected to have as long a lifetime in the atmosphere as
(NH4)2SO4.  It is likely that HNO3 will have a very high dry deposition rate. As HNO3 is
removed by dry deposition, NH4NO3 will evaporate to maintain the [HNO3][NH3]
concentration product. Modeling studies have not addressed this issue, perhaps because of
lack of certainty in the necessary parameters:  the NH4NO3 equilibrium constant, the
NH4NO3 evaporation rate, the HNO3 dry deposition rate, and the atmospheric concentrations
of NH3.
     Sampling problems caused by the volatility of NH4NO3 are discussed in Chapter 4,
Section 4.2.10.1.  Reliable measurements of NH4NO3 require special techniques,
e.g. denuders to remove gas-phase HNO3 and nylon filters to absorb any HNO3 vapors that
evaporate from collected NH4NO3 (Benner et al., 1992; Koutrakis et al., 1992).  Large
concentrations of NH4N03, observed in areas of California where NH3 is  high and SO2
emissions are low (Hering et al., 1988; Benner et al., 1991), suggest that  replacement of
(NH4)2SO4 by NH4NO3 as SO2 emissions are reduced is a possibility.

3.3.2.7  Ammonium Chloride Vaporization Equilibrium
     Although paniculate chloride  is not a  major component of the atmospheric ambient
aerosol, it is of interest because it is involved in some paniculate formation processes. For
example, sea  salt contains NaCl that may react with HNO3 to lead to NaNO3 coarse particles
and a release  of HC1.  HC1 could react with NH3 to form paniculate ammonium chloride
(NH4C1).  However, the concentrations of NH3 and HC1 are  typically too  low and the
volatility of NH4C1 too high, to lead to NH4C1 condensation.  However, in stack plumes with
high concentrations of HC1 and NH3 (NH3  may be emitted from a stack with a selective
catalytic reduction system), NH4C1 particles could be formed.  Therefore,  it  is important to
include NH4C1 in formulations of aerosol equilibria (see e.g., Wexler and  Seinfeld, 1990,
1991; Seigneur and Wu, 1992).
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3.3.3    Carbon-Containing Particulate Matter
     The carbonaceous fraction of ambient paniculate matter consists of both elemental (EC)
and organic carbon (OC).  Elemental carbon, also called black carbon or graphitic carbon,
has a chemical structure similar to impure graphite and is emitted directly into the
atmosphere predominantly during combustion.  Organic carbon is either emitted directly by
sources (primary OC) or can be formed in situ by condensation of low  volatility products  of
the photooxidation of hydrocarbons (secondary OC). Soot is sometimes used to refer to the
primary carbonaceous aerosol (sum of primary EC and OC) but soot has  no firmly
established definition. Small additional quantities of aerosol carbon may exist either as
carbonates (e.g., CaCO3) or CO2 adsorbed onto particulate matter (Appel et  al., 1989;
Clarke and Karani, 1992).

3.3.3.1  Elemental Carbon
     Elemental carbon is a strong absorber of visible radiation and is the major species
responsible for light absorption by atmospheric particles (Novakov,  1984; Goldberg, 1985;
Finlayson-Pitts and Pitts, 1986; Japar et al., 1986; Sloane et al., 1991;  Hamilton and
Mansfield, 1991).  Elemental carbon found in atmospheric particles is a complex three
dimensional array of carbon with small amounts of other elements such as oxygen, nitrogen,
and hydrogen incorporated in its graphitic hexagonal structure (Chang et  al.,  1982).
     Wood-burning fireplaces and diesels are major sources of EC (Mulhbaier and Williams,
1982; Dasch and Cadle, 1989; Brown et al., 1989;  Dod et al.,  1989; Hansen and Rosen,
1990; Burtscher, 1992). In areas  where  wood burning is significant, more particulate
graphitic carbon is expected in winter than in summer. Tracer techniques have been
developed for the calculation of the source contribution to the EC concentrations, including
use of K as a woodsmoke  tracer (Currie  et al., 1994) and use of the carbon isotopic tracers
14C and 12C (Lewis et al., 1988; Klouda et al., 1988; Currie et al., 1989).  Around 47%  of
the EC in Detroit, 93%  in Los Angeles and 30 to 60% in a rural area in Pennsylvania has
been attributed to motor vehicle sources  (Wolff and Korsog,  1985; Pratsinis  et al., 1988;
Keeler et al., 1990).  The corresponding contribution of diesel emissions to EC
concentrations in Western Europe is estimated to be 70 to 90% (Hamilton and Mansfield,
1991). Elemental carbon was also a major constituent of the Kuwait oil fires, with
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concentrations as high as 178 mg m"3 inside the plume (Cofer et al., 1992; Daum et al.,
1993; and references therein). Global emissions of EC were estimated by Penner et al.
(1993) to be 12.6 to 24  Tg C yr'1, while the EC emission for the US was 0.4 to 1.1 Tg yr'1
and for the rest of North America 0.2 Tg yr"1.
     Elemental carbon also scatters light (Appel et al., 1985) although its light scattering
efficiency is smaller than the efficiencies of the other aerosol principal components (Sloane
et al.,  1991).  Because EC both absorbs and scatters light, its contribution to total light
extinction exceeds its  contribution to fine particle mass.  For example, in Los Angeles, EC
was found to represent 8.5 to 10% of the fine paniculate mass, but to account for 14 to 21%
of the total light extinction (Pratsinis et al.,  1984).  A significant fraction of the dark colored
fine EC particles is able to penetrate the indoor atmosphere of buildings and may constitute a
soiling hazard of objects like works of art (Ligocki et al.,  1993).
     The concentration  of EC varies with location and season. Elemental carbon
concentrations in rural and remote areas usually vary from 0.2 to 2.0 /ig m"3 (Wolff, 1981;
Clarke et al., 1984; Goldberg, 1985; Cadle and Dasch, 1988; Japar et al., 1986; Shah et al.,
1986; Pinnick et al., 1993) and from 1.5 to  20 /ig m'3 in urban areas (Wolff,  1981;
Delumyea and Kalivretenos, 1987; Pratsinis et al., 1984, 1988; Grosjean,  1984a;
Heintzenberg and Winkler, 1984; Goldberg, 1985; Shah et al.,  1986; Rau, 1989).  The
concentration of EC over the remote oceans is approximately 5 to 20 ng m"3 (Clarke, 1989).
Average EC concentration values are around 1.3 and 3.8 jug m"3 for U.S.  rural and urban
sites respectively (Shah  et al., 1986).  Average PM10EC values exceeding 10 /*g m"3 are
common for some urban locations (Chow et al.,  1994).  The ratio of EC to total carbon has
been observed to vary from 0.15  to 0.20 in rural areas, to 0.2 to 0.6 in urban areas (Wolff et
al., 1982; Gray et al., 1984; Grosjean, 1984a; Pratsinis et al., 1984; Chow et al., 1993a).
The annual mean of this ratio was approximately 0.4 for the Los Angeles basin  in 1982
(Gray et al., 1986), while this ratio  in the same area decreases to 0.2 during summer midday
periods (Larson et al., 1989; Wolff et al., 1991).  Aging of an air mass results in lowering
of the EC fraction of the aerosol due to its mixing with non-combustion particles or by
condensation of material from the gas phase (Burtscher et al., 1993).
     The distribution of EC emitted by automobiles is unimodal with over 85%  of the mass
in particles smaller than 0.12 fim aerodynamic diameter (Venkataraman et al., 1994).  The
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ambient distribution of EC is bimodal with peaks in the 0.05 to 0.12 ^m (mode I) and 0.5 to
1.0 /xm (mode II) size ranges (Nunes and Pio,  1993; Venkataraman and Friedlander, 1994).
The creation of mode II is mainly the result of accumulation of secondary aerosol products
on primary aerosol particles.
     The degree of mixing of EC particles with other aerosol components is controversial.
Particles emitted from spark-ignition engines have been found to consist of a core of EC
covered with a layer of PAHs and an outermost shell of volatile compounds (Steiner et al.,
1992). Ambient carbonaceous aerosol in urban areas has been found to consist of aggregated
spherules, with a range of carbon structures from amorphous (OC) to graphitic (EC) within
aggregates (Katrinak et al., 1992). These aggregates are  often (especially during summer
months) coated with sulfates and nitrates (Katrinak et al., 1992, 1993).  However, often
sulfate and EC are externally mixed (Covert and  Heintzenberg, 1984). Coating of EC with
organic compounds may alter its hygroscopicity and its lifetime in the atmosphere (Andrews
and Larson,  1993).   Noone et al. (1992a) reported  that the interstitial aerosol inside urban
fogs is enriched in EC, something that would tend  to increase its lifetime in the atmosphere
with respect to other species like sulfate or OC (Nunes and Pio, 1993).  However, the degree
of incorporation of EC in droplets is highly variable (0 to 80%) and its behavior  appears to
vary from hygroscopic to hydrophobic (Hansen and Novakov,  1989).  Our lack of
understanding  of the processes,  where by EC changes from hydrophobic to hygroscopic,
makes a quantitative estimate of the atmospheric  lifetime of EC problematic.
     The participation of EC and soot in atmospheric chemical reactions with SO2, O3 and
NO2 has been the subject of a series  of studies (Baldwin, 1982; Dlugi and Giisten, 1983;
Akhter et al.,  1984,  1985; Jassim et  al., 1986; Sergides et al., 1987; Gundel et al., 1989;
Chughtai et al.,  1991).  The strong dependence of  the often conflicting results of these
studies on the  nature of the samples inhibits the extrapolation of their conclusions to the
atmosphere. Chughtai et al. (1991) reported that oxidation and hydrolysis of accessible
reactive sites on the soot surface result in particle solubilization and  accelerated particle
removal from  the atmosphere.   DeSantis and Allegrini (1992) suggested  that NO2 reactions in
the presence of SO2 on carbon-containing particles could  be a source of HNO2 in the urban
environment.  The reaction of soot with ozone is faster than its reaction with NO2, which in
turn is faster than its reaction with SO2 (Smith et al.,  1989).  The review by Hoffmann and
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Calvert (1985) concludes that the reaction of soot with SO2 is not a major atmospheric
pathway for sulfate formation.

3.3.3.2   Organic Carbon
     The organic component of ambient aerosol both in polluted and remote areas is a
complex mixture of hundreds of organic compounds (Cass et al., 1982; Seinfeld, 1986;
Rogge et al., 1993d; Hahn,  1980; Simoneit and Mazurek,  1982;  Zafiriou et al., 1985;
Graedel, 1986; Gray et al.,  1986).  Only 10 to 20% of the organic material has been
characterized in terms of molecular structure.  Compounds identified in the  ambient aerosol
include n-alkanes, n-alkanoic acids, n-alkanals, aliphatic dicarboxylic acids,  diterpenoid acids
and retene, aromatic poly carboxy lie acids, polycyclic aromatic hydrocarbons, poly cyclic
aromatic ketones and quinones, steroids, N-containing compounds, regular steranes,
pentacyclic triterpanes, iso- and anteiso-alkanes,  etc.  (Graedel,  1986; Mazurek et al.,  1989;
Hildemann et al., 1991, 1993, 1994; Rogge et al., 1993d).  OC does not strongly absorb
light, but its light scattering  efficiency in urban hazes is  similar to that of nitrate and sulfate
(McMurray et al.,  1995;  Lowenthal et al., 1995).
     Aerosol OC measurements are often subject to sampling artifacts due to adsorption of
organic vapors on the filters used or evaporation of the  collected mass.  These sampling
problems are discussed in Section 3.3.3.1.  Wolff et al.  (1991) found that this sampling error
represented roughly 20% of the measured OC under urban polluted conditions.  McMurry
and Zhang (1989) observed in ambient and smog chamber measurements that a consistently
large fraction of the OC (40 to 70%) was collected on the  quartz filters following their
impactors.  The  strong possibility of sampling artifacts in the laboratory and field
measurements presented below, increases the uncertainty of our current knowledge about
aerosol OC.  Most of the investigators report the OC concentration as concentration of
carbon.  These values neglect the contribution to the aerosol mass of the other elements
(namely oxygen, hydrogen and nitrogen) of the organic  aerosol compounds.  Measured OC
values  have been multiplied by 1.5 (Wolff et al.. 1991)  or  1.4 (White and Macias, 1989) to
estimate the total organic mass.
     The concentration of OC is around 3.5 ^gC m'3 in rural locations (Stevens et al.,
1984) and 5 to 20 /*g C m"3  in polluted atmospheres (Grosjean, 1984a; Wolff et al., 1991).
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Wolff et al. (1991) and Chow et al. (1994) summarizing measurements during the summer
and fall of 1987 in the Los Angeles basin, reported that OC represented on average 10 to
18% of the PM10 mass and 11 to 24% of the PM2 5 mass during the summer and 15 to 25%
of the PM10 and 16 to 25% of the PM2 5 during the fall.  Wolff et al. (1991) suggested that
these values should be reduced by roughly 20% to correct for the sampling bias and then
multiplied by 1.5 to account for the non-carbon mass of the organic aerosol compounds (an
overall increase by roughly a factor of 1.3).  In rural areas of the western U.S. particulate
OC concentrations are comparable to  sulfate (White and Macias,  1989).  In other areas OC
contributes roughly 10 to 15% of the  PM2 5 and PM10 mass (Stevens et al., 1984).  Organic
compounds accumulate mainly in the  submicrometer aerosol size range (Finlayson-Pitts and
Pitts,  1986; McMurry and Zhang, 1989) and their mass distribution is typically bimodal with
the first peak around diameter of 0.2  ptm and the second around 1 jum (Pickle et al., 1990;
Mylonas et al., 1991).
     The  contribution of the primary  and secondary components of aerosol OC have been
difficult to quantify.  The lack of a direct chemical analysis method for the identification of
either of these OC  components has led researchers to the  employment of several indirect
methods.  These methods include the  use of tracer compounds for either the primary or the
secondary OC (Larson et al.,  1989; Turpin and Huntzicker, 1991, 1995; Turpin et al.,
1991), the use of models describing the emission and dispersion of primary OC (Gray, 1986;
Gray et al., 1986; Larson et al.,  1989; Hildemann, 1990) and the use of models describing
the formation of secondary OC (Pilinis and Seinfeld, 1988; Pandis et al., 1992a; Pandis et
al., 1993). The above studies concluded that the secondary OC contribution  is maximized in
the early afternoon of summer days, varying from 30 to 60%  of the total OC depending on
location.  The yearly averaged contribution of secondary  OC is smaller,  10 to 40%.
     The  interactions of the OC compounds with each  other and the inorganic aerosol
species are poorly understood. The compounds have the  potential to form organic films
around the inorganic and EC core of  the aerosol (Gill et al., 1983).  Goschnick et al. (1993)
provided evidence for such formation by reporting that carbon compounds and organic
hydrogen  were enriched within the particles' outer layer,  while inorganics like NH4NO3 were
enriched inside the particles.  The presence  of such films can inhibit the transport of water
and other  inorganic components between the gas and aerosol phases (Otani and Wang, 1984;
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Rubel and Gentry, 1984). However, atmospheric OC may be water-soluble and hygroscopic
as well as water-insoluble (Saxena et al., 1995) and organic particles may serve as cloud-
condensation-nuclei (Novakov and Penner, 1993).

Primary  Organic Carbon
     Primary carbonaceous particles (OC) are produced by combustion (pyrogenic), chemical
(commercial products), geological (fossil fuels), and natural (biogenic) sources.  The
complexity of the molecular composition of OC is such that tracer compounds are still
necessary to decouple the contributions of the various sources.  Rogge et al.  (1991)  suggested
that fine  aerosol cholesterol could be used as a tracer for meat  smoke.  An alternative
proposed meat smoke tracer set consists of myristic acid (n-tetradecanoic acid), palmitic acid
(n-hexadecanoic acid), stearic acid (n-octadecanoic acid), oleic  acid (cis-9-octadecenoic  acid),
nonanal and 2-decanone (Rogge et al.,  1991).  Benzothiazole has been used as a tracer  for
tire wear contributions to ambient aerosol (Kim et al., 1990; Rogge  et al.,  1993b).   Steranes
and pentacyclic triterpanes (hopanes) can be used as tracer compounds for the vehicular
sources (Rogge et al., 1993a). The odd carbon number n-alkanes ranging from C27  to C33
can serve as a molecular tracer assemblage for biogenic primary OC (green,  dead, and
degraded plant wax material directly emitted or resuspended from soil and road dust)
(Mazurek and Simoneit, 1984; Simoneit, 1989; Rogge et al., 1993c).  The iso- and anteiso-
alkanes can be used to trace the cigarette smoke contribution to the outdoor atmosphere
(Rogge et al.,  1994),
     Primary biogenic organic matter consists predominantly of lipids, humic and fulvic
acids, and often represents a major fraction of the carbonaceous aerosol mass (Duce et  al.,
1983; Gagosian et al., 1987; Mazurek et al., 1989, 1991; Simoneit,  1984,  1986, 1989).
Mamane  et al. (1990) reported that most coarse OC in the Great Lakes region is of biologic
origin while most fine OC is anthropogenic.

Secondary  Organic Carbon
     Secondary organic  aerosol material is formed in the atmosphere by the condensation on
already existing particles of low vapor pressure products of the oxidation of organic  gases.
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As the hydrocarbons are oxidized in the gas-phase by species such as the hydroxyl radical
(OH), ozone (O3) and the  nitrate radical (NO3), their oxidation products accumulate in the
gas phase.  If the concentration of such a product is smaller than its saturation concentration,
the species remains mainly in the gas phase.  Small amounts of the species can be adsorbed
on aerosol surfaces or dissolved in the aerosol phase at this stage (Yamasaki et al., 1982;
Pankow, 1987; Ligocki and Pankow, 1989; Pankow and Bidleman, 1991; Pankow, 1994a,b;
Pandis et al., 1992a).  If the gas-phase concentration of a species exceeds its saturation
concentration, the species  condenses on the available aerosol surface so that at equilibrium its
gas-phase concentration equals its saturation concentration.  If this gas-phase concentration is
reduced to less than the saturation value as a result of dilution, deposition or chemical
reaction, the aerosol species evaporates in an effort to maintain thermodynamic equilibrium
(Pilinis and  Seinfeld, 1988).  Many volatile organic compounds (VOC) do not form aerosol
under atmospheric conditions due to the high vapor pressure of their products (Grosjean and
Seinfeld, 1989). These VOC include all alkanes with up to six carbon atoms (from methane
to hexane isomers), all alkenes  with up to six carbon atoms (from ethylene to hexene
isomers), benzene  and  many low-molecular-weight carbonyls, chlorinated compounds and
oxygenated  solvents.
     Organic aerosols  formed by gas-phase photochemical reactions of hydrocarbons,  ozone
and nitrogen oxides have been identified in both urban and rural atmospheres (Grosjean,
1977).  Most of these species are di- or poly-functionally substituted alkane derivatives.
These compounds  include  aliphatic organic nitrates (Grosjean and Friedlander, 1975),
dicarboxylic acids  (adipic  and glutaric acids) (O'Brien et al.,  1975), carboxylic acids derived
from aromatic hydrocarbons (benzoic and phenylacetic acids), poly substituted phenols  and
nitroaromatics from aromatic hydrocarbons (Kawamura et al., 1985; Satsumakayashi et al.,
1989, 1990).  Some species that have been identified in ambient aerosol and are believed to
be secondary in nature are depicted in Table 3-3. Despite the above studies, the available
information about  the molecular composition of atmospheric secondary OC and about the
composition of the OC produced during the oxidation of specific hydrocarbons remains
incomplete. The reaction mechanisms leading to the observed products are to a great  extent
speculative at present (Finlayson-Pitts and Pitts,  1986).
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    TABLE 3-3. SOME SECONDARY ORGANIC COMPOUNDS IDENTIFIED IN
	AMBIENT PARTICLES IN URBAN AIR	
Compound                                                             n
HOOC(CH2)nCOOH                                                   1-8
HOOC(CH2)nCHO                                                     3-5
HOOC(CH2)nCH2OH                                                   3-5
HOOC(CH2)nCH2ONO or CHO(CH2)nCH2ONO2                           3-5
CHO(CH2)nCH2OH                                                    3-5
CHO(CH2)nCHO                                                      3-5
HOOC(CH2)nCOONO or CHO(CH2)nCOONO2                             3-5
CHO(CH2)nCOONO                                                   3-4
HOOC(CH2)nCOONO                                                  3-4
HOOC(CH2)nCOON02                                                 4-5
HOOC(CH2)nCH2ONO2                                                3-4
(C6H5)-(CH2)nCOOH                                                   1-3
HOOC-(C6H4)-(CH2)nCH3	1-2
Source:  Schuetzle et al. (1975), Cronn et al. (1977).
     Natural hydrocarbons like the monoterpenes (C10H16) and isoprene (C5H8) are emitted
by various types of trees and plants.  In the United States the biogenic hydrocarbon sources
are estimated to produce 30 to 60 Mt of carbon per year (isoprene and monoterpenes
combined) whereas anthropogenic hydrocarbon sources have  been estimated to account for
27 Mt of carbon per year (Lamb et al., 1987; Zimmerman, 1979; Altshuller, 1983).
Laboratory investigations have indicated that biogenic hydrocarbons are very reactive under
typical atmospheric conditions (Arnts and Gay, 1979).  The  aerosol forming potential of
biogenic hydrocarbons has been investigated in a series of smog chamber studies (Kamens et
al., 1981, 1982; Hatakeyama et al., 1989; 1991; Pandis et al., 1991; Zhang et al.,  1992).
These studies demonstrate that isoprene photooxidation does not contribute to the production
of secondary aerosol under ambient conditions.  On the other hand, pinenes and other
monoterpenes form secondary aerosol in their reactions with  O3 and OH and have the
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potential to contribute significantly to aerosol in areas with high vegetation coverage.
Monoterpenes were estimated to contribute around 15%  of the secondary organic aerosol
(SOA) in urban areas with low vegetation like Los Angeles,  while they are expected to
dominate the SO A in areas with high vegetation coverage like Atlanta (Pandis et al., 1991,
1992a).
     The chemical composition of the majority of the aerosol products of the monoterpene
photooxidation remains unknown or is speculative (Pandis et al., 1991; Palen et al., 1992).
The few products that have been identified include nopinone, pinanediol, pinonic acid and
5-(l-hydroxy-l-methylethyl)-2-methyl-2-cyclohexen-l-one. Several investigators have studied
the SO A formation from selected anthropogenic hydrocarbons.  The literature data up to
1976 have been reviewed by Grosjean (1977). Other studies focused on toluene and other
aromatic hydrocarbons (Leone et al., 1985; Stern et al., 1987; Gery et al., 1985,  1987;
Izumi and Fukuyama, 1990), styrenes (Izumi and Fukuyama, 1990), cyclic olefins
(Hatakeyama et al., 1985, 1987; Izumi et al.,  1988), cresols and nitrocresols (Grosjean,
1985) and alkenes with more than  six carbon  atoms (Grosjean, 1984b; McMurry  and
Grosjean, 1985; Wang et al., 1992a,b).   Measured and estimated aerosol yields from a
variety of SO A precursors have been tabulated by Grosjean and Seinfeld (1989) and Pandis et
al.  (1992a).
     The calculated contribution of the main individual secondary organic aerosol precursors
to the secondary organic aerosol concentration in Los Angeles on August 28, 1987 is
presented in Table 3-4 (Grosjean and Seinfeld, 1989; Pandis et al., 1992a).   Toluene, the
nonmethane hydrocarbon with the  highest emission rate  in the Los Angeles area (165 t d"1 )
was predicted to contribute 28% of the secondary organic aerosols.  Differences were
attributed to sampling artifacts and calibration uncertainties during the interpretation of the
ambient data.
     Grosjean (1992) calculated the daily production rates of various chemical functionalities
of the secondary organic aerosol formed  in situ during a smog episode in Los Angeles using
the precursor hydrocarbon emission inventory and the results of smog chamber studies.  His
estimates are presented in Table 3-5.  These predictions were compared with the available
measurements of ambient OC functional  group relative abundances (Grosjean,  1992).
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     TABLE 3-4. PREDICTED PERCENT CONTRIBUTION TO SECONDARY
          ORGANIC AEROSOL CONCENTRATIONS AT LOS ANGELES
                                            Contribution
Species
Grosjean and Seinfeld (1989)     Pandis et al. (1992a)
Aromatics
Biogenic Hydrocarbons
Alkanes
Olefins
           58
           10
           21
           11
65
16
15
4
  TABLE 3-5.  AMOUNT OF SECONDARY AEROSOL PRODUCED IN A TYPICAL
    LOS ANGELES SMOG EPISODE ACCORDING TO FUNCTIONAL GROUPS

Precursor
Alkenes
Cyclic olefins
Terpenes
Alkanes
Cycloalkanes
Aromatics
TOTAL

Carbonyls
-
62
295
243
72
-
672
Aerosol produced (kg day"1)
Aliphatic Acids Nitrophenols
608
131
623
-
-
3118
1362 3118

Aliphatic Nitrate
-
9
41
121
72
-
243
Source:  Grosjean (1992).


     Pickle et al. (1990) and Mylonas et al. (1991) argued that the SO A mass size
distribution in urban areas like Los Angeles is typically bimodal with maxima in the 0.1 and
1.0 /^m size ranges.  Our understanding of the mechanisms of creation of these two modes
remains tentative (Pandis et al., 1993).  The effect of relative humidity in the SOA
partitioning between gas and aerosol phases is generally not understood.  Thibodeaux et al.
(1991) developed a theoretical model based on classical adsorption theory and predicted that
as air relative humidity increases (remaining less than 60%) the equilibrium secondary
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organic carbon content on the aerosol particles decreases due to competition for adsorption
sites with water molecules.  This theoretical result seems to be supported by the little
available experimental information, but the  necessary experimental data for the incorporation
of these relative humidity effects on SOA partitioning into an aerosol model do not exist.
Knowledge of the saturation concentrations  of the organic condensable species remains
incomplete.  These concentrations are expected to vary  significantly with temperature.  The
few available relevant measurements include the saturation vapor concentrations of
monocarboxylic and dicarboxylic acids  (Tao and McMurry, 1989) and the /3-pinene aerosol
products  (Pandis et al., 1991).  The saturation vapor concentrations of condensable products
from the oxidation of some aromatic hydrocarbons (toluene, m-xylene, and 1,3,5-
trimethylbenzene) were estimated to lie in the range 3 to 30 ppt (Seinfeld et al., 1987).
McMurry and Grosjean (1985) estimated saturation vapor concentrations for condensable
products  from the oxidation of 1-heptene (0.14 to 1.28 ppb), o-cresol (0.06 to 1.6 ppb) and
nitrocresol (1.7 to 2.2 ppb).

Polycyclic Aromatic Hydrocarbons
     Polycyclic aromatic hydrocarbons  (PAHs) are formed during the incomplete
combustion of organic matter, for example, coal, oil, wood and gasoline fuel (National
Research Council,  1983; Bjorseth and Olufsen, 1983).  Stationary sources (residential
heating, industrial processes, open burning, power generation) are estimated to account for
roughly 80% of the annual total PAH emissions in the U.S.  Mobile sources only account for
20% of the annual total PAH emissions in the U.S., however, they are the major
contributors in urban areas (National Research Council, 1983; Freeman and Cattell, 1990).
More than one hundred PAH compounds have been identified in urban air.  The PAH
observed in the atmosphere range from bicyclic species such as  naphthalene, present mainly
in the gas phase, to PAH containing seven or more fused rings, such as coronene, which are
present exclusively in the aerosol phase (Finlayson-Pitts and Pitts, 1986). Intermediate PAH
such as pyrene and anthracene are distributed in both the gas and aerosol phases (see also
Section 3.3.3.4).
     Measurements of the size distribution  of PAH indicate that while they are found
exclusively in the 0.01 to 0.5 ^m diameter mode of fresh combustion emissions
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(Venkataraman et al., 1994) they exhibit a bi-modal distribution in ambient urban aerosol,
with an additional mode in the 0.5 to 1.0 /xm diameter range (Venkataraman and Friedlander,
1994).  The growth of nuclei-mode particles by condensation of secondary aerosol species
like nitrate,  sulfate and secondary organic aerosol has been proposed as an explanation of this
distribution.
     Polycyclic aromatic hydrocarbons adsorbed on the surfaces of combustion generated
particles are released into an atmosphere containing gaseous co-pollutants including O3, NO2,
SO2, HNO3, PAN, radicals and are exposed to sunlight.  Under these conditions PAH
undergo chemical transformations that might lead to significant degradation and formation of
products more polar than the parent PAH (National Research Council, 1983).  Several
studies have focused on the reaction rates and products of reactions of PAH adsorbed on
specific substrates and exposed in the dark or in  the light to other  pollutants.  However, the
extrapolation of these laboratory results  to real atmospheric conditions remains difficult.
     Benzo(a)pyrene (BaP) and other PAH on a  variety of aerosol substrates react with
gaseous NO2 and HNO3 to form mono-  and dinitro-PAH  (Finlayson-Pitts and Pitts, 1986).
The presence of HNO3 along with  NO2  is necessary for PAH nitrification. The reaction rate
depends strongly on the nature of the aerosol  substrate (Ramdahl et al., 1982, 1984), but the
qualitative composition of the products does not.   The aerosol water is also a favorable
medium for heterogeneous PAH nitration reactions (Nielsen et al., 1983).  Nielsen (1984)
proposed a reactivity classification of PAH  based on chemical and spectroscopic parameters
(Table 3-6). The PAH nitration rate under typical urban  conditions remains poorly
understood.  Bjorseth et al. (1979) observed a lack of significant PAH reactions during their
transport from central to  northern Europe and suggested that these reactions are slow in most
environments.   However,  this may not be the case in heavily polluted areas with high NO2
and HNO3 concentrations and acidic particles (Finlayson-Pitts and Pitts,  1986).  Reactions of
fluoranthene and pyrene with NO2  in the gas phase and condensation the 2-nitro-PAH
derivatives on  the aerosol surface have been proposed as  an alternative reaction pathway for
the production of the observed aerosol nitro-PAH (Pitts et al., 1985a).
     Nitrogen oxide (N2O5) has been proposed as an additional nitrating agent for certain
PAH during nighttime (Kamens et  al., 1990). Pitts et al. (1985b)  exposed six PAH to N2O5
                                         3-81

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 TABLE 3-6.  REACTIVITY SCALE FOR THE ELECTROPHILIC REACTIONS OF
                  POLYCYCLIC AROMATIC HYDROCARBONS
	(REACTIVITY DECREASES IN THE ORDER I TO VI)	
 I    Benzo(a)tetracene, dibenzo(a,h)pyrene, pentacene, tetracene
 II    Anthanthrene, anthracene, benzo(a)pyrene, cyclopenta(cd)pyrene, dibenzo(a,l)pyrene,
      dibenzo(a,i)pyrene, dibenzo(a,c)tetracene, perylene
 III   Benz(a)anthracene, benzo(g)chrysene, benzo(ghi)perylene, dibenzo(a,e)pyrene,
      picene, pyrene
 IV   Benzo(c)chrysene, benzo(c)phenanthrene, benzo(e)pyrene, chrysene, coronene,
      dibenzanthracene, dibenzo(e, l)pyrene
 V    Acenaphthylene, benzofluoranthenes, fluranthene, indeno(l,2,3-cd)fluoranthene,
      indeno(l,2,3-cd)pyrene, naphtalene, phenanthrene, triphenylene
 VI   Biphenyl
Source:  Finlayson-Pitts and Pitts (1986).
and proposed the following reactivity order: pyrene > fluoranthene >  BaP >
benz(a)anthracene >  perylene > chrysene.  Nitro-PAH photodecompose into quinones and
possibly phenolic derivatives.  For example 6-NO2-BaP on silica gel photolyses to the
1,6-, 3,6-, and 6,12-  isomers of BaP quinones and a host of other oxy-PAH (Finlayson-Pitts
and Pitts, 1986). These reactions are expected to depend strongly on the chemical
composition and structure of the aerosol substrate and are not well understood for ambient
particles.
     Aerosol PAH react with O3 to produce oxidized PAH. Pyrene, BaP and anthracenes
react rapidly and the benzofluoranthenes slowly (Finlayson-Pitts and Pitts, 1986;
Alebic-Juretic et al., 1990).  Reaction rates of 15 to 30% hr"1 were observed for  the most
reactive PAH adsorbed on filters during exposure to 200 ppb of O3 (Pitts et al., 1986).
However, other researchers  (Atkinson and Aschmann,  1987; Coutant et al., 1988; De Raat et
al., 1990) have  suggested that the PAH-O3 reaction is of negligible importance for typical
atmospheric conditions.  Relatively little is known about the full ranges of products and the
mechanisms of their formation.  Polycyclic aromatic hydrocarbons exposed to sunlight have
been found to photodegrade in a series of laboratory studies (Valerio et al.,  1984; Behymer
and Hites, 1988).  The photodegradation rates depend strongly on the chemical composition
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and the pH of the aerosol substrate (Dlugi and Glisten, 1983; Valerio et al., 1984; Behymer
and Kites, 1988).  Poly cyclic aromatic hydrocarbons appear to be more stable when adsorbed
on ambient aerosol  than when present in pure form or in solution or on artificial  surfaces
(Back et al.,  1991).  The occurrence of PAH-SOX reactions remains uncertain (Back et al.,
1991).

3.3.3.3   Semi-Volatile Organic Compounds
General
      Species that may exist in the atmosphere either in the gas phase or the condensed
phase, and that may change back and forth between phases as a function of temperature,
concentration, or other atmospheric variables, are known as  semi-volatile substances.
They present special sampling and measurement problems, discussed in Chapter 4,
Sections 4.2.10.2 and 4.3.4.3.
      Semi-volatile organic compounds  (SOCs) may be defined as organic compounds whose
saturation vapor pressures (p£) are in the range of 10~2 to 10~9 torr,  intermediate between
solids and gases. Understanding the factors controlling the relative amounts of SOCs and
semi-volatile inorganic materials in the gaseous (G) and aerosol particulate (P) phases is
important for sampling and health reasons.
      Several processes may lead to partitioning of atmospheric species  between the gasphase
and the condensed phase  (Saxena and Hildemann, 1996).  These include normal equilibrium
vapor pressure, adsorption, absorption, and chemical reaction.

      Equilibrium Vapor Pressure
      A specific organic compound may be characterized by a temperature-dependent
saturation vapor concentration that represents, under equilibrium conditions, the maximum
capacity of the air for this species.  If the gas-phase concentration of the compound exceeds
this saturation concentration, the species can homogeneously nucleate or condense on
available aerosol surfaces such that  at equilibrium its gas-phase concentration equals the
saturation concentration.  If the gas-phase concentration of the species is less than this
saturation concentration, it will not  condense into the liquid phase of the pure compound.
If the concentration of a species  in the gas phase is reduced to less than the saturation
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concentration as a result of dilution, deposition, or chemical reaction, the condensed-phase
component will evaporate to maintain thermodynamic equilibrium.

     Adsorption (Condensation on Solid Surfaces)
     A gas-phase species  can be adsorbed on available aerosol particles even if its
concentration is less than its saturation concentration (Pankow, 1987).  The phase distribution
is estimated by a temperature-dependent equilibrium constant and the relationship is called an
adsorption isotherm (Adamson, 1976; Hanel, 1976).   Several investigators have applied
adsorption theory to study the partitioning of relatively nonpolar compounds such as PAHs
and pesticides to atmospheric aerosol and fog systems (e.g., Jung, 1977; Yamasaki et al.,
1982; Pankow, 1987; Storey  and Pankow, 1992; Valsaraj et al., 1993). Such an adsorption
process has  been found to be significant for polycyclic aromatic hydrocarbons (Ligocki and
Pankow, 1989) but the extent of this process for other secondary  organic aerosol species is
uncertain  (Pankow, 1994).

     Absorption (Condensation on Existing Droplets)
     If aqueous  aerosol particles, cloud or fog droplets are already present (e.g., sea-salt
particles in marine environments; inorganic particles containing sulfate and nitrate in
continental air masses), then  a water-soluble organic compound would distribute between the
vapor and liquid phases according  to its air-water equilibrium constant and the relative
volumes of the two phases.  No threshold gas-phase concentration is needed: absorption,
i.e., condensation onto, or solution into, existing droplets, would take place at all partial
pressures.
     Similar considerations would hold for absorption on or into liquid organic particles (or
components of particles).  Some information is available on the partitioning  of organic and
inorganic  gases with respect to  water (Henry's Law, Table 3-2 ).  However, the properties
for other specific adsorbate and adsorbent pairs are not widely known and the' process of
absorption into complex mixtures is not well understood.  In comparison to  absorption,
adsorption remains poorly understood.  Absorptive phase partitioning of primary organic
emissions (Turpin et al., 1991) and secondary organic species formed by reactions in the
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atmosphere (Pandis et al., 1992a) has been studied.  Organic gases may also dissolve into
aerosol particles containing plant wax (Pankow, 1994a,b).

     Chemical Reaction
     If a gas-phase species reacts with another gas-phase species to form a compound with a
lower saturation vapor pressure, condensed-phase material may form by nucleation or
condensation.  A gas may also react with an existing condensed-phase particle to add to it or
to replace another species.  Such processes are known for inorganic species,
e.g., NH3(g) -I-  HNO3(g)^NH4NO3(s).  However,  similar reactions are possible with
organic species.

Theory
     A useful parameterization of G/P partitioning is (Yamasaki et al., 1982; Pankow, 1991)
                                             Fl TSP
                                    KP   =    —3-                              (3-49)

where: Kp (m3 pig"1)  = partitioning constant;  TSP (/xg m"3) = concentration of total
suspended particulate matter; and F (ng m"3) and A (ng m"3) = the P-associated and G
concentrations of the compound of interest, respectively.  The symbols F and A originate in
the common usage of a filter followed by an adsorbent to collect the P and G portions,
respectively.  With urban particulate matter, a given SOC at a given temperature T tends to
exhibit similar Kp values from sampling event to sampling event.  The fraction  of the total
compound that is on/in the P phase  is given by

                                                                                 (3-50)
                                   _
                                   A + F     Kp TSP  + 1
Though not yet used in practice, it may also prove useful to define K 10  = (F10 / PM10 ) / A
where PM10 (fj,g m"3)  = concentration of particles with aerodynamic diameters smaller than
10 /mi,  and F10 (ng m"3) = PM./0-associated concentration of the compound of interest.
     Theory  (Pankow, 1994a) predicts that the values of K  for a given compound class will
be given by a relation of the form Kp = [C}  + C2] //?£ , where C;/p£ and C2/p£ represent
the adsorptive and absorptive contributions to K , respectively.  Log K values measured
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under given conditions (e.g., T) for a compound class such as the polycyclic aromatic
hydrocarbons (PAHs) will thus tend to be linearly correlated with the corresponding log /?£
values according to log Kp = mr log /?£ + br.  For PAHs sorbing to urban particulate matter
in Osaka, Japan, mr  « -1.028 and br ~ -8.11  (Pankow and Bidleman, 1992).  (Table 3-7
gives PL values for several PAHs at 20 °C.) This correlation allows Kp to be predicted for a
compound that is within the compound class of interest, but was not examined in a given
study.  Kp for a given compound depends on T (Kelvin) according to log Kp =  mpIT + b
where mp depends  on the enthalpy of desorption;  values of the intercept b will be similar
within a given compound class (Table 3-8). Increasing the relative humidity from 40 to 90%
appears to cause K values to decrease by a factor of about two for PAHs sorbing to urban
paniculate matter (Pankow et al., 1993).
     For constant Kp, then <£ will increase as TSP increases.   For constant TSP and T,  as
volatility increases  (i.e., asp£ increases), then Kp and  will decrease.   When 0 =  0, one
can sample just the G phase when determining the atmospheric concentration  of an SOC;
when 0 =  1, one can sample just the P phase; when  is between 0 and 1, one must sample
both phases.

Sampling Methods and Associated Sampling Artifacts
     Atmospheric  SOCs have been determined using a filter followed by an adsorbent.
These collect the P and G portions, respectively.   Filter types include glass fiber filters
(GFFs), quartz fiber filters (QFFs), and teflon membrane filters (TMFs).  Adsorbent types
includes polyurethane foam (PUF), Tenax, and XAD resins.  Safe sampling volumes for
G-phase SOCs on Tenax and PUF can be predicted based on studies of retention volumes  on
these adsorbents (Pankow, 1988 and 1989). Volatilization losses from particles
(i.e., "blow-off") can occur from a filter/adsorbent when T increases during sampling, when
the general level of air contamination decreases during sampling, and/or when a large
pressure drop develops across the filter (Zhang and McMurry, 1991). In the  first case, Kp
for a given compound and the already-filtered particles will decrease, leading to desorption
from the sampled P-phase.  In the second case, even with T constant, if A in  the air being
sampled decreases, then desorption losses from the collected particles can occur.
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	TABLE 3-7. VALUES OF LOG P£ FOR VARIOUS PAHS AT 20 °C
 Compound                                                        log p^ (torr)
   Fluorene                                                           -2.72
   Phenanthrene                                                       -3.50
   Anthracene                                                         -3.53
   Fluoranthene                                                       -4.54
   Pyrene                                                            -4.73
   Benzo[a]fluorene                                                    -5.24
   Benzo[b]fluorene                                                    -5.22
   Benz[a] anthracene                                                   -6.02
   Chrysene                                                           -6.06
   Triphenylene                                                       -6.06
   Benzo [b] fluoranthene                                                -7.12
   Benzo[k]fluoranthene                                                -7.13
   Benzo[a]pyrene                                                     -7.33
   Benzo[e]pyrene                                                     -7.37
Source:  Pankow (1994a).

   TABLE 3-8. mp VALUES FOR PAHS SORBING TO UPM IN OSAKA, JAPAN.
	(Obtained by Fitting to a Common F-intercept BP of -18.48)	
 Compound	mp
  Phenanthrene and Anthracene                                         4,124
  Methylphenanthrene and  Methylanthracene                              4,240
  Fluoranthene                                                        4,412
  Pyrene                                                             4,451
  Benzo[a]fluorene and Benzo[b]fluorene                                 4,549
  Benz[a]anthracene,  Chrysene, and Triphenylene                          4,836
  Benzo [b] fluoranthene and Benzo [k] fluoranthene                           5,180
  Benzo[a]pyrene and Benzo[e]pyrene                                     5,301
Source:  Pankow (1991).

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Volatilization is of particular concern with long sampling times since large overnight T cycles
and/or large changes in the level of contamination are then more likely.  Material volatilized
from the filter will be collected on the adsorbent following the filter. Adsorption gains to
particles from the gas phase due  to decreases in T and/or increases in A during sampling is a
second possible artifact type with filter/adsorbent  samplers. Adsorption to the filter from the
G phase is a third artifact type. In this last case,  a portion of the value of A for an SOC of
interest sorbs directly to the filter and so incorrectly contributes to the measured value of
F for the compound.  It is difficult to generalize regarding the magnitudes of the first two
artifact types.  One can attempt to correct for the third artifact type  through the use of a
backup-filter (Hart and Pankow,  1994).  For sampling of urban particulate matter in
Portland, Oregon, Hart and Pankow (1994) estimated that failure to correct for gas
adsorption to the filter caused F values  for PAHs to be overestimated by a factor of —1.4.
Correction of the G-adsorption effect through the use of a backup filter is subject to two
possible confounding effects:   1) the atmospheres to which the front and back filters are
exposed may differ, making for different G-adsorption to the two filters; 2) organic
compounds sorbed to a backup filter could have in part volatilized from the front filter.
Table 3-9 summarizes how the three artifact types act to cause measured values of F, A, and
 to deviate from the true,  volume-averaged  values.
             TABLE 3-9.  EFFECTS OF THREE TYPES OF ARTIFACTS
          ON VOLUME-AVERAGED VALUES OF  MEASURED USING A
                          FILTER/ADSORBENT SAMPLER

Artifact
Volatilization from collected particles
Adsorption to collected particles
Gas adsorption to filter itself
Artifact Effect
On A On F and 0
Too large Too small
Too small Too large
Too small Too large
      A sampler employing a diffusion denuder may avoid some of the artifact problems of
 filter/adsorbent samplers.  Air drawn into a diffusion denuder can be stripped of G-phase

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SOCs by a sorbent that coats the walls of the denuder:  G-phase SOCs diffuse from the core
of the air flow toward the walls.  Sorbent coatings that have been used include silicones,  gas
chromatographic stationary phases (Krieger and Kites, 1992 and 1994), finely divided XAD
resin (Gundel et al.,  1995; Kamens et al.,  1995), and carbon impregnated filter paper
(Eatough et al., 1995).  The majority of the P-phase SOCs do not deposit on the walls of
the denuder because aerosol  particles have much smaller diffusion coefficients than do
gaseous molecules.  The particles exiting the denuder are collected on a filter. Because the
air stream flowing onto the filter has been largely stripped of G-phase SOCs, some
desorption of the filtered P-phase SOCs can occur, and so an adsorbent is often placed after
the filter to collect any such  desorbed SOCs.  F for a given compound is taken as the sum of
the amounts on the filter and the subsequent adsorbent.  Analysis of the denuder sorbent
provides A.  When the denuder sorbent cannot be analyzed (as with silicone rubber), A can
be determined by difference  using a second, total (A + F) determination for SOCs (Lane
et al., 1988; Coutant et al.,  1988 and 1992; and Eatough et al.,  1989 and 1993).  Although
sampling artifacts are not often discussed for denuder-based samplers, artifacts cannot be
assumed to be absent. In  the denuder section,  less than 100% efficiency for G-phase
collection will tend to make  measured A values too small and F and   values too large;
greater than 0% efficiency for P-phase collection will tend to make measured A values too
large and F and  values too small.   Turpin et al. (1993) have presented a new denuder
design which does not use a  sorbent-coated wall.  Rather, a laminar flow separator is used to
separate a portion of the G phase from a mixed G+P flow;  collection of the G-phase
compounds on a sorbent like PUF allows determination of the G-phase concentrations.
P-phase concentrations are determined by difference.  Other sampling and analysis issues and
a more detailed discussion of the diffusion denuder technique are presented in Chapter  4 of
this document.

3.3.4   Metals and Other Trace Elements
     The major components  of fine  particles are sulfate, nitrate, organic  and elemental
carbon,  ammonium ions and a variety of trace  elements (Godish, 1985; Finlayson-Pitts and
Pitts, 1986).  Trace elements that are found predominantly in the fine particle size  range are
Pb, Zn, Cd, As, Sb,  Ag, In, La, Mo, I, and Sm.  Elements which are found in both fine and
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coarse modes are Na, K, Fe, V, Cr, Co, Ni, Mn, Cu,  Se, Ba, Cl, Ga, Cs, Eu, W, and Au.
Elements found primarily within large particle size range are Ca, Al, Ti,  Mg, Sc, La, Lu,
Hf, and Th (Klee, 1984; Bernstein and Rahn, 1979). The concentrations and the relative
proportions of these species in the various particle size  ranges depend on a number of factors
such as the nature of the emissions, the photochemical activity and the meteorology
(Finlayson-Pitts and Pitts,  1986).  The  concentration ranges of various elements associated
with paniculate matter in the atmosphere are shown in Table 3-10.  For most elements the
range in concentrations is greater than  three orders of magnitude.   This reflects the different
sources and the different pollution control strategies that exist in each area. This information
was compiled by Schroeder et al. (1987), and includes  a large number of studies from the
United States,  and abroad,  which indicates the need to complete site specific evaluations for
high end concentrations (references  can be found in the original paper by Schroeder et al.,
1987).
     In general, remote areas recorded measurable concentrations of some elements
associated with crustal origin, as well as  some elements indicative of anthropogenic sources.
This supports hypotheses which suggest that long range transport occurs in these remote
areas (Schroeder et al., 1987).  The urban data (Table 3-10) reflect elemental concentrations
in different parts of the world.  Elements such as lead,  iron, and copper are measured in
greatest abundance in particulate matter from all locations, while elements such as cobalt,
mercury and antimony are  found in the smallest quantities (Schroeder et al., 1987).
     Potential sources of trace metals found in fine airborne particles are primarily
anthropogenic and include  combustion  of coal and oil, wood burning, waste incineration, and
metal smelting operations.  Biomass burning which  includes residential wood  combustion and
forest fires, is another source for the release of trace elements in the atmosphere.  In a
profile of biomass burning, zinc was the  characteristic trace element present in the fine
particles in concentration (0.0866 ± 0.0355%) of primary mass emitted.  Other trace
elements present were Cl (1.9083 + 0.6396%), K (3.9926 ±  1.2397%) and S (0.5211 ±
0.1761%) (Chow et al., 1992).
     The chemical composition of particulate matter analyzed in New Jersey as part of the
Airborne Toxic  Element and Organic Substances project (ATEOS), identified the trace
elements Pb, Fe, Zn, V and As (Daisey, 1987; Morandi et al., 1991). The main source for
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       TABLE 3-10.  CONCENTRATION RANGES OF VARIOUS ELEMENTS
  ASSOCIATED WITH PARTICIPATE MATTER IN THE ATMOSPHERE (ng/m3)
Elements
As
Cd
Ni
Pb
V
Zn
Co
Cr
Cu
Fe
Hg
Mn
Se
Sb
Remote
0.007
0.003
0.01 -
0.007
0.001
- 1.9
- 1.1
60.0
-64
- 14
0.03 - 460
0.001
0.005
0.029
0.62-
0.005
0.01-
0.0056
0.0008
-0.9
- 11.2
- 12
4,160
- 1.3
16.7
-0.19
- 1.19
Rural
1.0-28
0.4- 1,000
0.6 - 78
2- 1,700
2.7 - 97
11 -403
0.08- 10.1
1.1 -44
3 -280
55 - 14,530
0.05 - 160
3.7 - 99
0.01 - 3.0
0.6 - 7.0
Urban (USA)
2 - 2,320
0.2 - 7,000
1 -328
30 - 96,270
0.4- 1,460
15 - 8,328
0.2 - 83
2.2 - 124
3 - 5,140
130 - 13,800
0.58 -458
4-488
0.2 - 30
0.5 - 171
Source:  Schroeder et al. (1987).
atmospheric lead concentration is the combustion of leaded gasoline in motor vehicles.
However with increased use of unleaded gasoline, levels of atmospheric lead have been
reduced, and other sources of lead tend now to be more significant components of the
residual lead.  Morandi (1985) has reported evidence of contributions to airborne lead from
resuspended soil, oil burning and small scale smelting, which taken together accounted for
more than half of the airborne lead at a New Jersey site.  Vanadium levels were derived
from oil burning for space heating and power production, while Zn is attributed to a zinc
smelter in the area (Daisey,  1987).
     Road dust  aerosols are analyzed for trace elements in a variety of studies (Barnard
et al., 1987,  1988; Warren  and Birch,  1987). Recent source apportionment studies, in
California's South Coast Air Basin, provide additional information on trace element
                                        3-91

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concentrations in roadside dusts as well as in motor vehicle exhaust for particle sizes
< 2.5 Aim (Watson et al., 1994b). In addition to elemental carbon, Al, Si, K, Ca, Ti and
Fe were present in paved road dust in abundances which exceeded 1 %. Elevated
concentrations of Pb and Br were detected, which illustrated the deposition from the tailpipe
exhaust from vehicles that burned leaded fuels (Watson et al., 1994b; Chow et al.,  1992,
1993b). Significant amounts of SO4, Br",  Cl", and Pb were detected in the motor vehicle
exhaust profile, though Pb levels were much lower than those reported in earlier tests
(Watson et al., 1994d; Pierson and Brachaczek,  1983).
     Ambient measurements of the mass and chemical composition of PM10 and PM2 5, and
associated source profiles have been taken through the years.  Data base summaries identify
locations,  sampling times and chemical species of data available since 1988, complementing
previous existing databases (Watson and Chow,  1992; Lioy et al., 1980).  Size specific
measurements show that over 90% of the mass from geological material is in the coarse
particle size fraction, while the combustion related source categories contained —90% of
their mass concentrations in the PM25 size fraction (Chow et al., 1992, 1993b).  In a
municipal incinerator profile,  elements in the fine particle fraction include Cu, Zn, As, Cd,
Sb, Pb and Ba, while trace elements in the coarse particle fraction include Ca, Cr, Mn,  and
Ni (Olmez et al.,  1988).  In an oil-fired power plant, trace elements such as V, Ni, Co, Ba
and Cu are present in both fine and coarse particles (Olmez et al., 1988).
     Although a knowledge of the elemental and ionic composition of ambient particles  is
necessary  in order to understand their sources and chemistry, the chemical forms in which
important species  exist are not known.  For example, sulfate, nitrate and ammonium ions,
which are the main constituents of fine particles, may exist in forms other than simple
ammonium salts (Finlayson-Pitts and Pitts, 1986).  Table 3-11 lists some compounds
identified in aerosols by a roadway at Argonne National Laboratory, and Table 3-12 lists
compounds observed in aerosols in a forested area, at State College, Pennsylvania  (Tani et
al., 1983). However, there are uncertainties associated with the compounds shown in
Tables 3-11  and 3-12.  Tani et al. pointed out that both physical and chemical changes may
occur during or following impaction of aerosol particles on a collector, which would lead  to
the formation of compounds not initially present in the ambient aerosols (Tani et al., 1983).
                                         3-92

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   TABLE 3-11.  COMPOUNDS OBSERVED IN AEROSOLS BY A ROADWAY AT
	ARGONNE NATIONAL LABORATORY	
 SiO2                                      K2Sn(SO4)2
 CaC03                                    (NH4)2Co(S04)2 .  6H2O
 CaMg(CO3)2                               (NH4)3H(SO4)2 (letovicite)
 CaSO4.2H20                               3(NH4N03).(NH4)2SO4
 (NH4)2Pb(S04)2                            2(NH4N03). (NH4)2SO4
 (NH4)2Ca(SO4)2.H2O                       NH4MgCl3.6H2O
 (NH4)HSO4                                NaCl
 (NH4)2S04	(NH4)2Ni(S04)2 .  6H2O	

Source: Tani et al. (1983).
             TABLE 3-12. COMPOUNDS OBSERVED IN AEROSOLS
	IN A FORESTED AREA, STATE COLLEGE, PA
                                   (NH4)2S04
                             (NH4)3H(SO4)2 (letovicite)
                                   NH4HS04
                              2(NH4NO3).(NH4)2S04
                                 (NH4)2Pb(S04)2

Source: Tani et al. (1983).
      Metals such as Al, Ca, Fe, Mg and Pb known to be present in atmospheric aerosols,
also exist in uncertain chemical forms (Finlayson-Pitts and Pitts, 1986).  This is partially due
to the use of analytical techniques that normally provide information on total metal content
(Schroeder et al.,  1987). It is generally assumed that many of the elements, especially from
combustion sources, are present in the form of oxides (Olmez et al., 1988), while trace
elements in incinerator emissions may be  in the form of chlorides (Schroeder et al., 1987).
Data from Los Angeles indicate that arsenic may be present in two chemical forms in
atmospheric aerosols, as arsenite and arsenate.  Both forms were identified in both the fine
and coarse particle fractions (Rabano et al.,  1989).  Fe2O3, Fe3O4, A12O3, and A1PO4 have
been identified in  roadside paniculate matter (Biggins and Harrison,  1980).  Ca and Mg may
exist in the form of oxides (i.e., CaO, MgO), although in the presence of water, Stelson and
Seinfeld (1981) suggest that, on equilibrium considerations, CaO and MgO should react to
                                      3-93

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form their hydroxides, Ca(OH)2 and Mg(OH)2, respectively.  Similarly the oxides Na2O and
K2O should form NaOH and KOH when water is present. Lead has been observed in
roadside paniculate matter in a wide variety of forms, such as PbSO4, Pb3O4,
PbSO4.(NH4)2SO4, PbO.PbS04, 2PbCO3.Pb(OH)2, 2PbBrCl.NH4Cl, PbBrCl, (PbO^PbBrCl,
3Pb3(PO4)2.PbBrCl, and elemental lead (Biggins and Harrison, 1980; Post and Buseck,
1985). Cr is present in the atmosphere in both the hexavalent and the trivalent forms.
However, in the atmosphere the hexavalent form tends to be reduced to the less toxic
trivalent form (Seigneur and Constantinou, 1995).  Information is also available on the
atmospheric compounds of Ni (Schmidt and Andren, 1980) and Se (Ross, 1984).
      Heterogeneous oxidation of sulphur dioxide in air can be catalyzed by species such as
iron, manganese (Barrie and Georgii, 1976) and cadmium, while vanadium is suspected to
catalyze the formation of  sulfuric acid during oil combustion.  Oxides of iron, manganese
and lead are reported to absorb SO2 (Schroeder et al., 1987).
      It has been suggested that the elements arsenic, cadmium, manganese, nickel, lead,
antimony, selenium, vanadium and zinc volatilize at high temperatures during fossil fuel
combustion and condense uniformly on surfaces of entrained fly ash particles as the
temperature falls beyond the combustion zone (Linton et al., 1976).   Accumulation of trace
metals in the fine fraction of airborne dust sampled in iron foundries showed Pb and Zn
localized on the surface of the fine particles (Michaud et al.,  1993).  From the viewpoint of
toxicity, such emissions are more  important than natural sources where trace elements are
usually bound within the matrix of natural aerosols and thus less mobile and bioavailable
(Schroeder et al., 1987).
      Trace metal compounds found in road dust can accumulate from anthropogenic or
natural sources.  Subsequently these can become re-entrained in the  atmosphere. In such
samples lead and zinc were found to be strongly associated with carbonate and iron-
manganese oxide phases,  with small amounts being associated with an organic phase.  Half
of cadmium was associated with carbonate and iron-manganese oxide phases,  while copper
was mainly associated with the organic phase.  These associations influence the relative
mobility and  bioavailability of trace metals in the environment (Harrison et al.,  1981).
      Resuspension of particles from contaminated surfaces may also contribute to an
increase in the toxic trace elements in airborne particles (Kitsa et al.,  1992; Kitsa and Lioy,
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1992; Pastuszka and Kwapulinski,  1988; Falerios et al., 1992).  Kitsa et al. (1992) measured
elemental concentrations in particles resuspended from a waste site in New Jersey.  Close to
the resuspension source, coarse particles were dominant, but farther downwind from the site,
fine particles were prevailing.  The fine particles were enriched in chromium and lead,
indicating the potential for elevated human exposure through inhalation.  Chromium may
exist in different valence states, but the most stable and abundant are the trivalent and
hexavalent  states.  Hexavalent chromium is classified as a known respiratory carcinogen in
humans.
      Oxidation of the species present in aerosols results from interaction with various
atmospheric oxidants, such as molecular oxygen, ozone or hydrogen peroxide.  The presence
of oxides of As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sb, Se, V and Zn has been measured in
emissions of  cement plants, blast furnace and sintering operations, secondary iron foundries,
non-ferrous smelting of arsenic-bearing ores, zinc and lead smelters and many other sources
(Schroederetal., 1987).
      Sulphation, and possibly nitration, of metallic oxides can be surmised to be an
important transformation as particles age.  A statistical  assessment of multielemental
measurements in a study in the rural and urban atmospheres of Arizona showed strong
correlations of lead, copper, cadmium and zinc with sulfates in the rural atmosphere and
moderate correlation of lead and copper with sulfates and nitrates  in urban atmosphere
(Moyers et al., 1977). Nickel has also great affinity for sulfur which may lead to the
emission of nickel sulfate containing particles from combustion sources.  In the absence of
sulfur, nickel oxides or complex metal oxides containing nickel may form (U.S.
Environmental Protection Agency, 1986a).
      Lead was formerly emitted in the air from automobiles as lead halides and as double
salts with ammonium halides (e.g.  PbBrC1.2NH4Cl).  From mines and smelters, the
dominant species are PbSO4, PbO.PbSO4,  and  PbS. In the atmosphere lead is present as
sulfate with minor amounts of halides.  Lead sulfide is the main constituent of samples
associated with ore  handling and fugitive dust from open mounds of ore concentrate.  The
major constituents from sintering and blast furnace operations appeared to be PbSO4 and
PbO.PbSO4 respectively (U.S. Environmental Protection Agency,  1986b).
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3.4   FIELD STUDIES OF TRANSPORT AND TRANSFORMATIONS
      Appropriate and reliable field measurements play a central role in shaping our under-
standing of atmospheric processes, in providing key model inputs, and in the evaluation of
models.  Real-world observations are all the more important in the case of atmospheric
aerosols, which, on the one hand, are the end product of many complex processes and, on
the other hand,  are key precursors of important microphysical cloud processes. Field studies
include  short-term, three dimensional, high-resolution intensive research campaigns, as well
as longer-term surface and upper-air monitoring programs (in routine mode, or in more
comprehensive and higher-resolution research mode). Research studies are generally
mechanistic (targeted at understanding of process rates and mechanisms), and/or diagnostic
(aimed at development and testing of individual process modules or subgrid-scale
parameterizations for use in complex models).  Routine monitoring studies are aimed more at
operational evaluation of overall  model  performance, or at generation of model input data
including those  (e.g., meteorological) which, through dynamic assimilation into the
computations, can improve the realism of the simulations.  Since atmospheric fine  particles
are substantially of secondary origin, measurements of their gaseous precursors and other
reactants are also important. In North America, most of the anthropogenic emissions of fine
particles and their precursors are from large point sources (power plants and smelters) and
from urban-industrial complexes  including vehicle emissions.  Consequently, special attention
is given in this section to measurements in the plumes of such emissions.
      In the 1970s, many field studies were plume studies or urban-scale  studies, and most
models  were Lagrangian and limited to  linearized treatment of chemistry and other non-linear
processes.  Some of these field studies,  along with regional visibility information and back-
trajectories from local pollution episodes, pointed to the existence of long  range transport and
to the regional nature of air pollution and haze (Hall et al.,  1973; Gillani and Husar, 1976;
Wolff et al., 1977). In response, some of the major field studies in the 1980s had a regional
scope with focus on acidic depositions,  oxidants, or aerosols and visibility. That decade  also
saw major strides in measurement technology and in the development of increasingly
sophisticated Eulerian air quality models with explicit treatment of non-linear processes.
In these models, however, the treatment of plumes, particularly point-source plumes, was
grossly  distorted by varying degrees depending on the spatial resolution of the grid.  New
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interest also began to emerge in global climate change, global data, and global modeling.
In the decade of the 1990s, the principal interests in modeling and measurements appear to
be in two areas:  global-scale issues, with particular focus on clouds and aerosols; and,
regional and sub-regional issues,  with special interests in comprehensive linked study of
oxidants, aerosols and acidic depositions, and in multi-scale interactions (e.g., nested
gridding and the treatment of subgrid-scale processes related to plumes, clouds, and air-
surface interactions).
      Topics related to field measurements are also covered in other parts of this document:
methodologies for sampling and analysis of PM and acidic deposition in Chapter 4; ambient
air measurements of PM concentrations and properties in Chapter 6; and field studies of
visibility and PM in Chapter 8.  The focus in this section is on North American field studies
of the past 15 years or so, particularly as they relate to the following objectives:  better
understanding of atmospheric transport and transformation processes which modify the
concentration, size and composition of PM; evaluation of source- or receptor- oriented
models of PM air quality; and generation of model inputs.

3.4.1    Field Studies of Transport Processes
      Except for the gravitational settling of coarse particles (included  in dry deposition),  the
transport of PM is similar to that of gases.  Following their emissions,  gases and fine
aerosols rise due to buoyancy effects, are advected  downwind by the prevailing mean flow
field, and are dispersed horizontally and vertically by ambient turbulence, wind-shear effects,
and cloud processes.  These dispersive mechanisms result from the interaction of large air
masses, or from the disturbance of the larger-scale  flow in a given air mass by insolation-
driven surface fluxes of heat and moisture, and by surface drag effects.  The influence of
these surface effects is largely confined to the atmospheric boundary layer (ABL), the height
of which varies diurnally and seasonally, peaking typically at between 1 and  3 km on
summer afternoons over  the continental U.S.A.  Pollutant emissions may be within the ABL
or above it (depending on emission height,  momentum, and buoyancy),  and their dispersion
is markedly different in the two cases,  being much more rapid and vigorous in the daytime
convective boundary layer (CBL) than in the stable layers aloft or in the stable nocturnal
boundary layer.  Quantitative study of these transport and dispersion processes  requires,
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ideally, simultaneous measurements of a large number of variables related to insolation and
clouds, surface characteristics and surface fluxes of heat and moisture, and dynamic 3-D
fields of flow, temperature, humidity and concentrations of trace pollutants in the ambient
atmosphere.  Transport and dispersion processes also  have a critical influence on plume
chemistry and dry deposition, which are often diffusion-limited.  Meteorological
measurements must therefore be an integral part of any plume study, even when the focus is
on chemistry or deposition.  The shift to Eulerian grid modeling in the 1980s did not, in
general, include adequate measures, particularly at the regional scale, to preserve the essence
of the  sub-grid-scale features of plumes, which were instantaneously dispersed over the entire
emission grid cell (a volume  of  «1012 m3 in RADM  with 80 km horizontal resolution),
thereby also grossly distorting plume chemistry, aerosol formation, and pollutant budgets.
There  is growing awareness now of the need for more realistic treatment of plumes in grid
models.  Two other sub-grid-scale issues which are receiving increasing attention pertain to
pollutant redistribution by clouds (e.g., Hong and Carmichael, 1986b) and surface fluxes of
heat and momentum related to inhomogeneous land use within a grid cell (e.g., Avissar and
Pielke, 1989).
       A large body of literature exists on studies (including field studies) of ABL structure
and dynamics, and on  the characteristics of the wind, temperature and moisture fields in the
ABL and, to a lesser extent,  in the free troposphere aloft. Those studies are outside the
present scope.  Some of  the recent major advances in the knowledge about the ABL are
reviewed by  Briggs and Binkowski (1985).  This discussion is limited to field studies of the
transport and dispersion of PM and their precursors (e.g., SOX and NOX).  Prior to 1975,
most such field studies were  limited to the behavior of point-source plumes in the
-y-mesoscale  range (120  km), i.e., on plume rise and short-range dispersion. Such behavior
is well understood qualitatively; quantitatively, it is well enough represented in models at the
time scales characteristic of most commonly-used plume dispersion models (~ 1 h), but not
at the  much shorter time scales of relevance to plume chemistry  and plume visibility.  In this
near-source range, instantaneous plume behavior is very different from the larger scale
average behavior. In an intercomparison of four plume visibility models, it was concluded
that much of the  variation in visibility observed in the Navajo power plant plume in northern
Arizona was probably  due to fluctuations in source emissions and plume dispersion at scales
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below those resolvable by the models (White et al., 1985).  Since the atmospheric residence
of fine PM in the lower troposphere is on the order of days, our interest here is more on the
transport and dispersion of plumes over the (3- and a- mesoscale ranges (« 20 to 200 and
200 to 2,000 km).  Quantitative determination of transport over the mesoscale requires
special field studies with controlled tracer releases.  Such studies are relatively recent and
very few, and they  represent only a few isolated meteorological  scenarios.

3.4.1.1   Field Measurements Related to Transport Modeling
       Routine meteorological field measurements include surface weather observations of a
broad variety of meteorological variables made every three hours at several thousand sites
across the country by the National Weather Service,  as well as upper-air soundings
(radiosondes) of wind, temperature and relative humidity twice a day (noon and  midnight) at
a much more limited number of sites which, on average, are about 400 km apart.  These
data constitute the principal raw meteorological information  used in regional transport
models, which are either Lagrangian trajectory models or dynamic three  dimensional (3D)
Eulerian grid models.  Most trajectory models are two-dimensional, with atmospheric flow
patterns being analyzed on isobaric or terrain-following surfaces, or in bulk transport layers
confined to the mixed boundary  layer.  These simplifying assumptions  concerning vertical
motions lead to large transport errors on the regional scale (Kuo et al., 1985).  The vertical
velocity can be calculated at grid points in a regional model  domain from the continuity
equation, but the temporal and spatial resolutions of the radiosonde data are so coarse in
most areas that the result would be a gross approximation only.  3D flows may be best
simulated by moist adiabatic trajectories, but  since analysis methods cannot always resolve
the stratified nature of the required moisture fields, the most reasonable practical simulations
of 3D transport are  probably dry adiabatic  (isentropic) trajectories.  Danielsen (1961)
presented a case study showing a separation of  =1,300 km after only 12 h of transport  as
simulated by isobaric and isentropic trajectories.   It was probably an extreme case.  The
gridded wind field in regional Eulerian air quality models is  typically generated by the
application of dynamic 3D mesoscale meteorological  models such as PSU/NCAR-MM5
(Grell et al.,  1994) and CSU-RAMS (Pielke et al., 1992), which incorporate the routine
NWS  observations through a dynamic Four Dimensional  Data  Assimilation (FDDA)
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technique.  The NWS surface weather database also includes a measure of prevailing
visibility as determined by human observers.  A number of field studies have established the
reliability of such subjective visibility observations (e.g., Horvath and Noll, 1969; Hoffmann
and Kuehnemann,  1979).  They have proved to be a very useful indicator of regional haze
and its long-range  transport (Gillani and Husar,  1976), and have been used to study the long-
term trends of the  spatial-temporal variability of regional haze and air quality in the eastern
U.S. over many decades (Husar et al., 1981; Sloane, 1982).
      Special field studies of transport and dispersion are based on observations of the
transport of pressurized (constant density) balloons  (called tetroons if their shape is
tetrahedral), and of the evolution of plumes resulting  from pollutant emissions or controlled
releases of artificial tracers.  Balloons have been used in mesoscale studies in three ways:
as isolated Lagrangian markers of pollutant emissions (e.g., Clarke et al.,  1983); in
sequential releases to provide one-particle diffusion estimates (e.g., Thomas and Vogt, 1990);
and in cluster releases  to study relative diffusion (e.g., Er-El and Peskin, 1981).  Tetroons
generally carry a transponder which permits continuous tracking with a radar, thus providing
the complete detailed 3D trajectory. The range  of the tetroon experiment is normally limited
by the tracking range of the radar (< 100 km).  This range can be extended to the full range
of tetroon transport by including a tag which the finder can return with information, at least,
about the terminal location.   In some studies (e.g.,  Clarke et al., 1983),  tetroons have been
tracked continuously over much longer ranges by sequential tracking from the network of
FAA radars used in support of aviation.  Studies based on tracers and air pollutants also
provide information about plume dispersion.  Most early tracer studies were limited to a
range of about  100 km due to the nature of the tracers then available and limitations of
technology.  Development of new tracers (e.g.,  the PFTs or perfluorocarbon tracers) and
new sampling and analysis techniques have not only extended the range in more recent
experiments by more than an order of magnitude, but the new data are also more reliable.
      Pack et al. (1978) presented  a detailed review  of many early studies in which
observations  of the transport of pollutant plumes, tracers, or balloons were compared with
results of diagnostic trajectory calculations. The models commonly used then were based on
the kinematic approach (using objectively-analyzed wind fields based on measured winds) and
a single transport layer.  The observed winds were used as input in different ways:  for
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example, surface winds or adjusted surface winds representing average winds in the whole
transport layer; or, upper air winds averaged over the transport layer.  The adjustment of
surface winds included enhancement of the speed by as much as a factor of two, and a veer
of the wind direction by as much as 40°, to account for the real-world wind speed shear and
directional veer with height. The advantage of using surface winds was due to their much
higher spatial and temporal resolution, compared to the much coarser resolution of the upper-
air radiosonde winds.  The early results of comparisons of calculated and observed
trajectories evidenced a broad range of discrepancy  (10 to 54% of the trajectory length after
only 100 km, and 55 to 60% after 650 km), and also the presence of large systematic errors,
not always in the same direction, depending on the presence of complex flows due to  fronts,
complex terrain, etc.  The best simulations were often obtained by the use of adjusted surface
winds, and such adjustments varied between studies. The errors were found to be lowest for
transport in the daytime CBL,  and substantially larger for transport in stably-stratified layers.
      Moran (1992) has tabulated (his Table 2-4) basic information about a number of
formal (3- and a- mesoscale tracer experiments since 1973, in which  the release was at
surface level  and the measured transport range was  at least 25 km (and up to 3,000 km).
Table 3-13 summarizes, in chronological order, some of the major field studies of the past
20 years with measurements and modeling of transport extending into the a-mesoscale.
It includes the major tracer studies as well as air quality and tetroon  studies.  The transport
models in these studies were driven either by routine meteorological  observations  or by
additional measurements made as part of the field studies. The following important
observations  are based on the studies listed in Table 3-13:
    •   The  routine data of the radiosonde network (with resolution  of «400 km, 12 h) are
        too coarse both spatially (Kahl  and Samson, 1986, 1988) and temporally  (Rolph and
        Draxler, 1990; Kuo et al., 1985) for accurate simulation of long range transport.
    •   The  error in calculated trajectories is greatest under conditions of high speeds which
        generally accompany complex mesoscale systems (Rolph and Draxler, 1990).
    •   Initial errors in trajectory simulations (both in direction and  vertical spread) play a
        critical role in overall model uncertainty (Draxler et al., 1991).
    •   Single-layer Lagrangian trajectory models do not spread the "plume" adequately,
        while Eulerian models spread it too much.  Multi-layer Lagrangian models perform
        the best in terms of dispersion of point-source emissions (Clark and Conn,  1990).
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        TABLE 3-13. RECENT FIELD STUDIES OF a-MESOSCALE TRANSPORT AND TRAJECTORY MODEL
OJ
t—i

s
Study
INEL Study Idaho Nat'I
Eng. Lab



MISTT
Midwest Interstate
Sulfur Transport and
Transformation Study
VISTTA
Visibility Impairment
due to Sulfur Transport
and Transformation
in the Atmosphere
TPS
Tennessee Plume
Study



NEROS
Northeast Regional
Oxidant Study
Mt. Isa
Smelters Plume Study

Great Plains
Mesoscale Tracer
Expt.

CAPTEX
Cross-Appalachian
Tracer Expt.

ANATEX
Across North America
Tracer Expt.
MISERS GOLD


Period
Tracer(s)
Feb-May 74 Kr-85




Summer 75
Summer 76


Jun, Jul,
Dec-79



Aug-78



15-Aug-78

Summer 79
Summer 80

Jul-79


Jul-80



Sep/Oct 83



Jan-Mar 87


l-Jun-89






Plume sulfur



Anthropogenic
aerosol, ozone



Tetroons (1 cu. m)
with transponder


"

Tetroons (6 cu. m)
Tetroons (1 and 6 cu.
m)
Excess plume S
and Aitken Nuclei
Count (ANC)
Two PFTs (PMCH
and PDCH) and two
heavy methanes
(ME-20, ME-21)
PFT (PMCH)



3 PFTs (PMCP,
PMCH, PDCH)

Indium oxide
(vapor deposits on
particles)
Release Sites(s)
INEL (Idaho) fuel
reprocessing plant
(76 m stack)


Labadie Power
Plant near
St. Louis, MO

Los Angeles Basin




TVA
Cumberland
Steam Plant, TN

"

MD, OH, PA, TN
Columbus, OH

Mt. Isa, Australia
(Sulphide smelters,
-0.6 km apart)
Norman, OK
(1 m AGL)


Dayton, OH
Sudbury, ONT


Glasgow, MT
St. Cloud, MN

White Sands
Missile Range,
NM
Tracking/Sampling
Samplers at
1 1 Midwestern NWS
Maximum Range
(Airshed)
-1,500 km

sites; 10-h day and night
samples.

In-situ aircraft
measurements.


Detailed air quality and
aerosol measurements at
a Grand Canyon site.


Radar to ~ 75 km;
terminal point based on
return tag.

"

Continued FAA radars
Radar and return tag.

Aircraft measts. of Total
S, ANC, COSPEC-SO2.

Surface samplers: 17
on arc at 100 km 38 on
arc at 600 km and
aircraft sampling.
Surface array of
> 80 samplers at arcs
from 300-1, 100 km and
aircraft sampling.
Surface network (77);
Towers (5); and aircraft
sampling.
In-situ aircraft: filter
samples analyzed for
tracer and particles.


-300km



-750 km
Model Comparison(s)
NOAA-ARL trajectory
model with 300 m
vertical resolution


Simple particle
trajectory model


CAPITA Monte Carlo
Ref(s)
Draxler (1982)




Gillani et al. (1978)
Gillani (1986)


Maciaset al. (1981)
particle transport model



-1,000 km
(KY,IN,OH,ONT)


~300km(KY)

-500 km to NE
~ 1,500 km to NE

-1,000 km
(Semi-arid region
in N. Australia)
600 km to N NE



-1,100km
(NE U.S.)


-3,000 km
(Eastern U.S.)

-1,400 km
NM to MO




• NOAA-ATAD
• NCAR isentropic
• CAPITA Monte
Carlo model

3D reg'l dyn. model
NOAA, NCAR,
CAPITA, as above

Simple layered wind
trajectory model;

Different 3D regional
models


Different 3D regional
models; also
MESOPUFF II

3 single-layer LAGR,
6 multi-layer LAGR,
2 multi-layer Eulerian
Gifford's random-force
diffusion theory




Clarke et al. (1983)



Warner (1981)

Clarke et al. (1983)


Carras and
Williams (1981)

Ferberetal. (1981)
Moran (1992)


Ferber et al. (1986)
Moran (1992)
Godowitch (1989)

Draxler et al. (1991)
Comments
Small signal above
b/g; 300 m layered
approach to permit
spread by wind dir'l
shear necessary.
Quasi-Lagrangian
pibal measurements of
winds along plume
transport.
Evidence also of
long-range impact of
Copper smelter
plumes.

Part of a large plume
transport/ chemistry
study, including
aircraft measts.


Part of a large urban
and reg'l oxidant
study.
Exceptionally clean
plume b/g.

Important role of
wind shear effects of
nocturnal jet.

Terrain-effects found
important. Enhanced
upper air met measts.

Enhanced upper air
Rolph and Draxler (1990)met measts.
Clark and Cohn (1990)
Kahl et al. (1991)
Mason and
Gifford (1992)

Dust plume from a
military test
explosion.

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    •   Vertical information about tracer trajectories, based on continuously-tracked tetroons
        and aircraft measurements, contains much useful information not captured by surface
        sampling alone (Clarke et al., 1983).  There is, for example, evidence of cloud
        venting of ABL pollutants into the free troposphere, where their residence time is
        longer and the flow field may be quite different.
    •   Terrain-induced effects played an important role in CAPTEX, and effects related to
        the nocturnal jet were important in the Great Plains Experiment (Moran, 1992).
        Nocturnal wind directional shear plays a major role in effectively  dispersing plumes
        which have been dispersed vertically during the preceding daytime CBL.
    •   Directional wind shear plays an  important role in plume dispersion even in the CBL
        during j3-mesoscale transport (Gillani, 1986).
     The issue of substantial overdispersion by Eulerian models is important because the
state-of-the-art as well as the future direction in mesoscale modeling (meteorological/air
quality/aerosol) appear to favor the Eulerian approach.  A significant source of the problem
must be related to the gross initial overdispersion of plumes  in regional Eulerian models,
particularly of elevated point-source plumes (carriers of most of the U.S. anthropogenic
emissions of sulfur).  The instantaneous false dilution of fresh emissions of NOX into the
NOx-limited surrounding environment (e.g.,  in the  eastern U.S.) greatly  distorts plume
chemistry and aerosol formation.  Proper sub-grid-scale treatment of plumes remains an
important outstanding issue in regional modeling.  Other sub-grid-scale effects in need of
more attention pertain to complex mesoscale flows  (e.g., storms, fronts,  cloud venting,
complex terrain effects, etc.).  They too are  an important source of model  errors.  A few
special field studies have been carried out to investigate such flows: for example, VENTEX
(Ching and Alkezweeny, 1986)  and PRESTORM (Dickerson et  al.,  1987) for cloud venting,
and ASCOT (Allwine, 1993) and the NGS Visibility Study (Richards et al., 1991) for flows
over complex  terrain. Thermal effects and drainage flows also evidently play an important
role in influencing paniculate air quality,  as in the  occurrence of the Denver "brown cloud"
phenomenon (Sloane and Groblicki, 1981).
     There is  considerable  field evidence also for synoptic scale transport (<2,000 km) of
airborne particles (see, for example,  Gordon, 1991). The impact of such transport is
important on the global scale.  That subject is beyond the present scope.
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3.4.1.2  Field Measurements Related to Dispersion Modeling
     Gaussian semi-empirical models have been the basis of most applied diffusion modeling
since their development around 1960. These models were based on Taylor's diffusion theory
of stationary homogeneous turbulence (Taylor, 1922), and were built on a few field
experiments that were quite limited in scope and technology.  The results have been
extrapolated far beyond the intended  range of downwind distance and ambient conditions.
Some of the extrapolations were guided  by statistical theory, but most were freehand
extrapolations (Briggs and Binkowski, 1985). Many research-grade field studies of
atmospheric dispersion have since been performed, but most have been limited to the
y-mesoscale range.  These have been reviewed by Draxler (1984), Irwin (1983), Briggs and
Binkowski (1985) and others.  /3- and a-mesoscale studies, based on observations of the
dispersion of pollutant and tracer plumes have been reviewed by Moran (1992).
     Pollutant plumes remain vertically  narrow in stable flows (e.g., elevated power plant
plumes released at night), but rapidly fill up the CBL after fumigation in the daytime (see,
for example, Gillani et al., 1984).  Information about spreads of plumes in the elevated
stable layers is particularly limited. The most common basis for estimation of such spreads
(expressed as ay and az, the RMS variances  of lateral and vertical plume spreads) over
distances under 100 km or so is the well-known Pasquill-Gifford (P-G) curves for different
stability classes (Gifford, 1961), which make use  of the routine meteorological measurements
to determine applicable  stability class.  The P-G curves were developed mostly from data
collected within the mixing layer.  Another set of parameterizations of elevated plume
spreads was developed by TVA (Carpenter et al., 1971) based on twenty years of experience
in plume observations and aerial monitoring. These require the temperature profile to
establish atmospheric stability.  More recently, Smith (1981) analyzed aircraft measurements
in elevated power plant plumes in different parts of the U.S., mostly in stable layers with
small directional wind shear effects, and determined that the P-G curves overestimated plume
spread in stable layers quite substantially both vertically and horizontally.  Bergstrom et al.
(1981) analyzed a smaller set of data in stable layers in which there was significant
directional shearing of the plume, and found the P-G curves to underestimate horizontal
plume spread. The TVA approach tended to underestimate the  horizontal spread, but
possibly overestimate the vertical spread.  Evidently, there continues to be uncertainty about
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plume spreads even at distances under 100 km.  Of particular interest is horizontal plume
dispersion, both because it is generally far greater over the mesoscale, and because it is
highly variable.  Close to the source,  plume spread is largely by progressively larger
turbulent eddies, but after the plume dimension substantially exceeds the scale of these eddies
(typically less than 1 km), dispersion  is increasingly by directional wind shear with height
(Carras and Williams, 1981; Pasquill  and Smith,  1983; McNider et al., 1988), and possibly
also by other mechanisms involving the diurnal cycle of PEL stability changes and inertial
oscillations (Pasquill, 1974; McNider et al., 1988).  Directional wind shear is relatively
small for the vertically thin nocturnal plume, moderate for the plume  in the CBL, but
maximum for the daytime plume which,  after maximum vertical spread in the CBL,  enters
the nocturnal regime which is often characterized by strong directional shear effects (Gillani
et al., 1984).  Such a nocturnally sheared and stratified plume subsequently becomes
vertically well-mixed following fumigation into the next day's mixing layer.   The average
crosswind spread rates of plumes from a large tall stack power plant emitted within the CBL
on summer days in the Midwest were observed to be in the range 0.25 to  1.0 km per km of
downwind transport until the plume attained a width of about 30 km (Gillani and Pleim,
1995).  Direct observations of the three-dimensional nocturnal shearing of well-mixed
daytime plumes are extremely sparse.
      A common approach  in Lagrangian studies of dispersion over long distances has been
to use semi-empirical "mesoscale" dispersion coefficients by analogy with parameterizations
of microscale turbulent spread.  An important consequence of Taylor's statistical theory was
that, in stationary homogeneous turbulence,  oy grew linearly with time at first for t  «  TL
(the Lagrangian time scale, »1 to 2 min in the CBL), and then asymptotically as t1/2 within
a few kilometers.  Observations of a few a-mesoscale field studies have been interpreted to
suggest that the regime of linear time dependence may apply also at long  distances (see, for
example, Pack et al., 1978), with the characteristic  time scale  (TL) here being related to the
diurnal and/or inertial scale ( = 24 h).  Others have proposed parameterizations of mesoscale
ay which use powers of t ranging from 0.85 to 1.5 (see, for example, Carras  and Williams,
1988).  Thus, there is no consensus about simplistic modeling of mesoscale diffusion over
scales exceeding 24 h. Given the wide range of conditions that plumes can experience
during long range transport in different air masses, over a variety of terrain types, and over
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multiple diurnal cycles during different seasons, such a controversy is not surprising. For
transport in the first 24 h, the time and height of emission are critical influencing variables.
Thus, for example, crosswind spreads  after 24  h of transport of two plumes released from
the same tall-stack power plant at 0800 and 2000 on a given day are likely to be very
different.  During the next diurnal cycle, however, these spreads, as a fraction of downwind
distance travelled, are likely to converge.  Alternate approaches of representing mesoscale
plume dispersion include simulation of relative  dispersion of hypothetical co-emitted
conservative particles (McNider et al., 1988; Uliasz,  1993).  In conjunction with
instantaneous wind  data (e.g., pibal soundings), such models have proved to be satisfactory
over /3-mesoscale distances (e.g., Gillani,  1986), but more work is needed to establish their
application over long distances based on hourly-average gridded wind data such as are
produced by the meteorological preprocessors of regional Eulerian models.  Overall, based
on field evidence, particulate air quality is significantly influenced by regional transport and
dispersion, but quantitative simulation  of these  processes is still  subject to considerable error.

3.4.2   Field  Studies of Transformations
     This section has three subsections.  The first two subsections are focussed on the two
most important transformation processes related to PM, viz., gas-to-particle conversion
(chemical transformation) and the growth  of hygroscopic aerosols by condensation of water
on them (physical transformation).  The latter process is important in clouds, fogs and other
humid environments, and has important implications for atmospheric radiation, chemistry and
pollutant scavenging.  The third subsection is devoted entirely to what was possibly  the most
comprehensive field study of the past decade related to PM, the Southern California Air
Quality Study (SCAQS).

3.4.2.1   Gas-to-Particle Conversion
     A number of field studies of gas-to-particle conversion have been conducted  in the
plumes of large point-sources of SOX and  NOX  (e.g., coal- and oil-fired power plants and
metal smelters).  Fewer studies have focused on urban-industrial plumes.  A number of
studies pertain to the regional background. These studies have focused principally on
quantifying the rates of aerosol formation and,  to a lesser extent, on investigating the
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mechanisms.  Mechanistic studies are more difficult, particularly when multiple mechanisms
are co-active, as is commonly the case.

Point-Source Plume Studies
     In the NAPAP emissions inventory for base year 1985 (Placet et al., 1991), about 70%
of the U.S. anthropogenic emissions of SO2, and about 25% of the corresponding emissions
of NOX, were attributed to large point-sources with stack heights exceeding 120 m (probably
less than 150 individual sources).  The contribution of such sources is even higher in the
eastern U.S., particularly in the Ohio and Tennessee River Valleys.  Clearly, these large
emissions are very important in the context of regional aerosols.  Fortunately, many of these
sources are located in rural areas, and their plume chemistry can be studied in isolation from
the complications of interactions with other plumes.  Much of the remaining anthropogenic
emissions of SOX and  NOX are contributed by urban-industrial area sources.
     Point-source and urban plume studies of SO2-to-sulfate transformations published before
1980 have been reviewed by Newman (1981) and in the earlier 1982 PM/SOX Air Criteria
Document (U.S. Environmental Protection Agency, 1982). Only a brief overview of those
studies is provided here; the main focus here is on plume studies published after 1980.  Since
the plume mass is airborne, the most meaningful plume studies are based on measurements
made from instrumented aircraft.  Early studies (pre-1975) often reported S02 oxidation rates
as high as 50% h"1. They are now generally considered  to be flawed due to limitations in the
measurement technology then available.  This technology has made major strides  since.  For
example, the development of the filter pack (Forrest and Newman, 1973) has proved to be a
useful method of simultaneous collection of high-volume samples of SO^ and paniculate
sulfur.  Such samples, however,  only provide average concentrations over entire plume
cross-sections or, at best, over long crosswind plume traverses.  The development of
continuous monitors for both SO2 and paniculate sulfur (Huntzicker et al.,  1978;  Cobourn
et al., 1978) made it possible to study sulfate formation with crosswind plume detail.  Such
detail during  a single  plume traverse contains a nearly instantaneous snapshot of the full
spectrum of chemistry between the high-NOx regime in plume core to the low-NOx regime at
plume edge (Gillani and Wilson, 1980).  With cross-sectionally averaged measurements, such
a spectrum can only be discerned in measurements ranging from near-source to far
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downwind.  The technology of continuous measurements of nitrogen species with high
sensitivity has also evolved greatly since 1980.
     The period between 1974 and 1981 was very active in terms of plume studies focused
particularly on estimating the  rate of oxidation of SO2.  Studies by Brookhaven National
Laboratory (Newman et al., 1975a,b; Forrest and Newman, 1977a,b) and TVA (Meagher
et al., 1978) in coal- and oil-fired power plant plumes as well as a nickel smelter plume
generally yielded low oxidation of SO2 (seldom exceeding 5 % over 50 km and several hours
of plume transport, with an uncertainty of about a factor of two).  These investigators found
the  oxidation rate to be highest close to the source, where it appeared to be correlated with
plume paniculate loading, and interpreted the oxidation to be due to a heterogeneous second-
order mechanism which became quenched as the plume diluted (Schwartz and Newman,
1978).  These results were in sharp contrast to those of Husar et al. (1976) for a coal-fired
power plant plume, also over about 50 km of plume transport, which showed the oxidation
rate to be slow during an early induction period, increasing thereafter to as much as 5% h"1.
No  mechanistic interpretation was proposed by these authors.
     This controversy was resolved by the subsequent findings of Gillani et al. (1978)
resulting from two case studies which were remarkable for their coverage of downwind range
exceeding 300 km and  10 to 12 h of transport of a coal-fired power plant plume during
daylight as well as dark.  The authors  found the oxidation rate of SO2 to be strongly
correlated with sunlight, and also with the extent of plume dilution, and background ozone
concentration (considered to be a surrogate for background reactivity).  Maximum measured
particulate sulfur as a fraction of total plume sulfur ranged as high as 18%.  The daytime
conversion rate in the plume was slow at first, but  increased  as the plume diluted, reaching
maximum values on the two days of 1.8 and 3.0%  h"1 in the  afternoon.  Such rates are
consistent with theoretical rates based on the SO2-OH reaction (Calvert et al.,  1978; Hov and
Isaksen, 1981).  The entire plume transport on both occasions was in fairly dry environment
(relative humidity  < 70%). Presumably, the mixing of plume NOX and background VOC
led to photochemistry which generated the necessary  oxidants for gas-phase oxidation of SO2.
The measurements of VOC in the background were both sparse and of limited reliability.
The study also found the formation of substantial excess of ozone in aged plumes.  The
interpretation based on plume-background interaction satisfactorily explained the results of
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the BNL and TVA studies in which the measurements of low oxidation of SO2 were all in
coherent stable elevated plumes during early morning and evening hours (low sunlight and
little plume dilution), as well as of Husar et al. (1976) whose measurements were in the
more polluted and convective summer daytime CBL.
     As of the end of the  1970s, a number of factors had been implicated as being relevant
to plume sulfur chemistry.  Gillani and  Wilson (1980) conducted a systematic investigation of
the dependence of ozone and aerosol  formation in power plant plumes on a variety of
possible influencing factors, based on the plume data of five case studies.  They found that
temperature variations in the range 28 to 33 °C, and R.H. variations in the range 50 to 80%
did not have an appreciable influence; the importance of sunlight, plume dilution and
background composition was reconfirmed. Eatough et al. (1981, 1982) have observed a
positive temperature dependence of a linear SO2 oxidation rate in power plant and smelter
plumes in western U.S. in the temperature range 0 to 30 °C.
     Gillani and Wilson (1980) also presented direct evidence and interpretation of the role
of plume-background interactions in plume photochemistry within the context of a common
pattern of diffusion-limited plume chemical evolution through three stages  in a moderately
polluted environment.   In the "early"  stage, the plume is narrow and dominated by a high-
NOX regime in which ozone and other oxidants are  sharply depleted by reaction with plume
NO and SO2; the VOC-NOX chemistry, SO2 oxidation, and aerosol formation are inhibited in
the plume in this stage.  As the plume spreads and dilutes with a background characterized
by relatively high VOC/NOX ratio, the VOC/NOX ratio increases also in plume edges.  This
"intermediate" stage of plume chemistry is characterized by rapid formation of ozone and
aerosols in plume edges, leading to an observed  excess there of ozone over the background
(ozone "wings") while the plume core still has an ozone deficit.  Sharp "wings" of Aitken
nuclei  concentration have also been observed in plume edges at times, indicating directly the
nucleation of new aerosol (Wilson, 1978; Gillani et al.,  1981).  With continuing dilution, the
plume  ultimately develops a condition of low-NOx,  high VOC/NOX ratio and, in the summer,
an ozone "bulge" throughout. In this "mature" stage, the rate of oxidation of SO2 to sulfates
(and presumably also of NOX to secondary products) reaches its peak.
     Gillani et al. (1981) provided a  quantitative interpretation of the above observations by
developing an empirical parameterization of the gas-phase conversion rate  of SO2 to sulfate
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in terms of measured variables representing sunlight, mixing and background reactivity.  The
parameterization was verified based on the "dry" data of three different power plant plumes
over ten days of measurements in two different summer periods. Crosswind-resolved
reactive plume models capable of facilitating plume-background interactions and including
detailed simulation of chemical kinetics have been developed and applied by Hov and Isaksen
(1981), Stewart and Liu (1981), Seigneur (1982), Gillani (1986) and Hudischewskyj and
Seigneur (1989).  The reactive plume models of Seigneur and collaborators also include
simulation of aerosol dynamics.  These models can depict the observed behavior of ozone in
the three plume stages.  Their applications have shown that the evolution of OH in the plume
(a measure of oxidation potential) mimics the above description of ozone evolution (Hov  and
Isaksen, 1981), and that plume oxidant and aerosol formation are very sensitive to
background VOC and their ingestion into the plume (Gillani,  1986).  However, these models
continue to remain unevaluated adequately owing to a continuing lack of data characterizing
the composition of plume background (especially VOC) and the crosswind detail of important
intermediate and secondary species (e.g., OH, HO2, HNO3, etc.).  Reactive plume models
that describe the formation of oxidants and secondary  particulate matter, as well as the
evolution of the aerosol size  distribution, have been developed and  evaluated with available
data  (Eltgroth and Hobbs, 1979;  Seigneur, 1982; Hudischewskyj and Seigneur, 1989). The
most comprehensive model performance  and evaluation available to date is that conducted by
Hudischewskyj and Seigneur (1989).  For example, they conclusively demonstrated that SO2
oxidation occurs at a faster rate in smelter plumes than in power plant plumes,  because in
power plant plumes NO2 competes effectively with SO2 for OH radicals.
     A number of plume studies have verified the sunlight  dependence of the SO2 oxidation
process, observing higher seasonal conversion rates during  summer, and higher diurnal rates
during midday (Husar et al., 1978; Lusis et al., 1978; Roberts and Williams, 1979; Meagher
et al., 1981; Hegg and Hobbs, 1980;  Gillani et al.,  1981; Forrest et al., 1981;  Williams
et al., 1981; Wilson, 1981; Wilson and McMurry, 1981; Liebsch and de Pena, 1982).
In these studies, the peak daytime conversion rate was typically between 1 and 5% h'1 in the
summer (higher under humid conditions), and much lower in  winter.  Wilson (1981)
reviewed the data of twelve power plant  and smelter plumes in the  U.S., Canada and
Australia, covering measurements during day and night, and summer and winter.  The main
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conclusion was that diurnally, midday conversion rates were relatively high and quite
variable (1 to  10% h'1), while the nighttime conversion rates were generally low (under
0.5% h"1). Also, the rates were found to be lower in winter than in summer.
Geographically, the measured plume conversion rates in the arid and relatively clean
southwestern U.S. environment were found to be particularly low (0.5% h"1) at all times,
including  summer midday. Williams et al. (1981) also found the rates to be low in a smelter
plume in the arid, clean environment of north central Australia («0.15% h"1 averaged over
24 h of transport).
     Gillani et al. (1981) were able to formulate the parameterization of the gas-phase
conversion rate by isolating case studies performed entirely in dry  conditions when liquid-
phase contributions were negligible.  They also observed that for all cases when the plume
had any history of wet exposure (clouds, fogs or high humidity), the oxidation of SO2
invariably proceeded at a rate faster  than that predicted by the gas-phase parameterization.
Whereas the typical range of the peak summer daytime conversion rate was 1 to 5 % h"1 in
Project MISTT (Missouri, Illinois), it was  closer to 1 to  10%  h"1 in the more humid
conditions of the  Tennessee Plume Study (Tennessee, Kentucky).  In the wetter daytime
situations, evidently, liquid-phase chemistry was superposed over the underlying gas-phase
chemistry. Gillani and Wilson (1983) focused their study on the plume data of such "wet"
situations. They  attributed to liquid-phase  chemistry the  part of the total measured
conversion rate which was in excess of the rate estimated by  the gas-phase parameterization.
The liquid phase  was found to be due to clouds, fogs and light rain, or due to wetted
aerosols under conditions of high ambient humidity (relative humidity  > 75%). The liquid-
phase contribution to the conversion rate was found to be in excess of 40% of the total in
two-thirds of the  cases analyzed, being as high as 8% h"1 averaged over the whole plume
over 6 h of transport in the most extreme case (clouds and light rain).   Similar increases in
conversion rates in power plant plumes interacting with high  humidity have also been
observed  by others  (e.g., Dittenhoefer and de Pena,  1978; Eatough et  al.,  1984; Richards
etal., 1985).
     Determination of the liquid-phase conversion rate involves quantification not only of the
kinetics, but also of the discrete and variable extent of plume-cloud interaction. Gillani et al.
(1983) formulated a parameterization of the conversion rate for plume-cloud interaction in
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which the physical extent of such interaction was represented probabilistically, and the higher
liquid-phase conversion rate was applied only for the in-cloud portion of the plume.  The
application of the parameterization to a case study corresponding to summer daytime plume
transport within the CBL,  in patchy contact with fair-weather cumulus above, permitted
estimation of the average in-cloud conversion rate averaged over 7 h (1000 to  1700) to be
12% h"1.  Considering that the corresponding average liquid water content in the clouds was
certainly less than 1 g m"3 (1 ppm), much higher actual oxidation rates within individual
droplets are indicated.  Gas-phase photochemistry at a much slower rate was concurrently
quite active in the more extensive drier parts of the plume below, producing ozone and other
oxidants which contributed to gas-phase as well as liquid-phase sulfur chemistry.  It was not
possible to relate  the in-cloud kinetic rate to the critical variables controlling it, such as cloud
liquid water content, H2O2 concentration,  or droplet pH, because  such measurements were
not made.  The role of concurrent gas-phase photochemistry is indeed essential to provide the
oxidizing agents of liquid-phase chemistry. Clark et al. (1984) found the contribution of
liquid-phase chemistry in a power plant plume to be negligible during long-range transport
over water in a shallow stratocumulus-filled boundary layer, with limited plume dilution, low
insolation,  and  little photochemistry.
     A quite different approach based on  aerosol growth laws  applied to aerosol size
distribution data was taken by McMurry et al. (1981) and McMurry and Wilson (1982) to
study relative contributions of the principal mechanisms of gas-to-particle conversion.
Theory predicts different growth  laws for  different chemical mechanisms of aerosol
formation.   The authors examined the functional dependence of calculated particle diameter
growth rate on particle diameter.  By matching field data with theoretical growth laws, it was
possible to differentiate between mechanisms.  Application of this approach indicated gas-
phase chemistry and condensation of the product to be the predominant mechanism of aerosol
formation in several power plant  plumes in eastern and western U.S., with increasing
contribution of heterogeneous mechanisms with increasing humidity (McMurry et al., 1981);
in a case study of the urban plume of St. Louis, 75% and 25% of the aerosol formation were
attributed to homogeneous and heterogeneous mechanisms, respectively, while most of the
aerosol formation in the ambient  air in the Great Smoky Mountains where relative humidities
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were high (up to 95%) was attributed to the droplet-phase mechanism (McMurry and Wilson,
1982).
     In an overview of empirical parameterizations of sulfur transformations in power plant
plumes, Gillani (1985) estimated that on a 24-h average  basis, sulfate formation rates in a
large power plant plume in the U.S. Midwest in July  1976 were likely to be 0.8  ± 0.3% h"1
by gas-phase reactions (midday peak ~ 2.6% h"1) and at least half as much by liquid-phase
reactions.  Winter rates were estimated to be an order of magnitude lower than the summer
rates for the gas-phase mechanism, but comparable for the liquid-phase mechanism.  Since
1981, no new field studies of chemistry in large point-source plumes have been conducted in
the eastern U.S.  A comprehensive plume study with  state-of-the-art aircraft measurements of
primary and secondary sulfur and nitrogen species, as well as VOC and ozone, is planned to
occur in the summer of 1995 as part of the Southern Oxidant Study (SOS) Nashville Field
Measurement Program.
     Smelter plume chemistry is different from that of power plant plumes in some
significant ways.  Based on aircraft measurements made in 1981 in two Arizona smelter
plumes, Richards et al. (1982a,b,c) reported markedly higher SO2 oxidation rates in these
plumes compared to those observed in power plant plumes in similar arid and relatively clean
environments.  The authors also demonstrated that the oxidation mechanism was
predominantly gas-phase, in spite of the relatively high primary aerosol (including iron and
manganese)  and water loading of those smelter  plumes.  They attributed the higher SO2
oxidation rates in smelter plumes to the fact that these plumes contain little or no NOX
emissions, in sharp contrast to the high NOX emissions in fresh  power plant plumes.  As a
result of the absence of NOX, there is no initial depletion of OH in the plume (and the
associated inhibition of SO2 oxidation), nor is there any  competition to SO2 oxidation by OH
from NO2.  It is useful to note also that a major downward change has occurred since 1981
in the contribution of smelters nationally to atmospheric  PM.  The  number of operational
smelters has dropped from 18 to 7;  in those still operational, SO2  emissions have been
reduced by more than an order of magnitude as a result  of improvements in control
technology;  finally,  the primary emissions of aerosols and water have also been sharply
reduced.  Unfortunately, no new detailed field studies of smelter plumes have been conducted
since those reported in 1982.
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     Information about field measurements of nitrate formation in point-source combustion
plumes is much more meager.  Summertime plume measurements suggest that nitrate
formation is principally in the form of nitric acid vapor (Hegg and Hobbs, 1979; Richards
et al., 1981), and that oxidation of NOX to HNO3 may proceed about three times faster than
the rate of oxidation of SO2 (Richards et al.,  1981; Forrest et al., 1979, 1981).  Richards
et al. (1981) observed that along the transport of the Navajo Generating Station in Arizona,
there was adequate ammonia to neutralize the sulfate formed in the plume, but not enough to
form ammonium nitrate.  Forrest et al. (1981) found NH^/SOJ to increase with downwind
distance and was mostly less than  2 (not enough to fully neutralize the sulfate), but
sometimes more than 2, indicating a possibility of the formation of some ammonium nitrate.
Eatough et al. (1981) observed that in the western desert region, the neutralization of sulfuric
acid in plumes was due not only to ammonia, but also to other basic material (e.g., metal
oxides and CaCO3).

Urban Plume Studies
     Field information about secondary formations in urban plumes is scantier than for
power plant plumes for sulfur compounds, but possibly slightly more for nitrogen
compounds.  White et al. (1976, 1983) reported slow formation of ozone and aerosols at first
in the St. Louis urban plume, but  faster rates farther downwind. Average sulfate formation
rates between successive downwind measurement locations  on summer days were estimated
at 2 to 4% h"1. Isaksen et al. (1978) applied a reactive plume model to a subset of the
St. Louis data, and estimated peak rates for the formation of sulfuric  and nitric acid of 5 and
20% h"1, respectively.  Based on the  same data set, Whitby (1980) estimated that about
1,000 tons of secondary fine aerosol may be  produced in the plume in one summer
irradiation day.  Alkezweeny and  Powell (1977) estimated peak sulfate  formation rates in the
St. Louis plume at 10 to 14% h"1.  Miller and Alkezweeny (1980) reported sulfate formation
rates in the Milwaukee urban plume on two summer days in very different air masses to
range from 1% h"1 (clean background) to 11% h"1 (polluted background).  The most
extensive studies of NOX chemistry in urban plumes have been reported by Spicer and
co-workers.  They have reported results for the Los Angeles, Phoenix, Boston and
Philadelphia urban plumes.   In the Los Angeles studies, the transformation rate of NO2-to-
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products was estimated at 5 to 15% h"1 (Spicer, 1977a,b) and 5 to 10% h"1 (Spicer et al.,
1979).  The sum of transformation plus removal rates was estimated for the Phoenix and
Boston plumes at <5% h"1 and 14 to 24% h"1, respectively.  The low rate for Phoenix was
attributed partly to thermal decomposition of PAN after its formation in the plume.  In a
study of the Detroit plume, Kelly (1987) estimated the NOX transformation rate at 10% h"1,
with 67 to 84%  of the products being in the form of HNO3.  Measured concentrations of
nitric acid, however, were much lower because of its higher removal rate.  All of the above
urban plume studies, and most of the power plant plume studies, have been daytime studies.
Field measurements of nighttime chemistry of nitrogen oxides in plumes are almost
non-existent.

Background Field Studies
     Attention is now focused on studies of aerosol formation in background air.  The plume
studies have shown that the rates of oxidation of SO2 and NOX in the background represent
approximately the upper limit of the conversion rates in the plume.  In non-humid,
moderately polluted conditions, the rates typically range between 1 and 5% h"1 for midday
SO2 oxidation in summer in the eastern U.S. (depending on the composition of volatile
organic compounds (VOC) and the variability of VOC/NOX, and up to 1%  h"1  in the cleaner
parts of the Western U.S. Winter rates are about an order of magnitude lower.  By contrast,
observed NOX to nitrate conversion rates are  about three times faster in summer than in
winter (Parrish et al.,  1986).  Aerosol nitrate formation depends strongly on availability  of
NH3 and on temperature.  Background aerosol is generally more aged and its acidity more
neutralized than  plume aerosol.
     The situation is more complex in humid conditions. Field measurements  of the
compositions of  cloudwater, rainwater and the precursor clear-air aerosol have shown that
strong acidity is  substantially greater in cloud and rain water than in the clear-air aerosol
(Daum et al., 1984b; Lazrus et al., 1983; Weathers et al., 1988).  This is indicative of the
contribution of aqueous-phase chemistry to cloudwater acidity in excess of that due to
scavenged aerosol. Based on climatological data of clouds and SO2 distribution, and
assuming aqueous-phase oxidation of SO2 by ozone, Hegg (1985) estimated contribution of
the aqueous mechanism to global tropospheric sulfate production to be at least  10 to  15  times
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greater than that due to the gas-phase mechanisms.  Applications of more comprehensive
global models have given estimated aqueous-phase contributions of 40 to 95 % of the total
sulfate production (Langner and Rodhe, 1991 and references therein). Regional models for
North America suggest 50 to 80% of the sulfate deposited in precipitation to be formed in
clouds (Fung et al., 1991; McHenry and Dennis, 1991).
     A number of ambient studies have attempted to study aqueous chemistry based on
in situ measurements in clouds.  Determination of the rates and mechanisms of aqueous-
phase chemistry is particularly ambiguous for several reasons.  First, it is difficult to
distinguish between the contributions of in situ chemistry and aerosol scavenging to the
observed concentration of the solute in the droplet phase.  Also, aqueous chemistry rate
depends not only on the change in concentration, but also on the change in time.  It is
difficult enough to determine the difference in concentration of even one reactant or product
species, but determining  the corresponding time  difference is even more difficult (Schwartz,
1987; Gervat et al., 1988; Kelly et al., 1989).  In stratiform clouds, in particular, it is  not
always possible to determine what constitutes pre-cloud air corresponding to specific  cloud
water samples (Gillani et al., 1995). Finally, it is difficult, based on field data, to attribute
the inferred chemistry  to specific mechanisms (oxidation by H2O2 or O3, etc.).  The
conclusions regarding rates and mechanisms of aqueous chemistry based on measurements in
clouds are therefore quite uncertain, and have been a source of considerable controversy
(e.g., Hegg and Hobbs,  1982, 1983a,b versus  Schwartz and Newman, 1983).  One important
finding in support of in-cloud oxidation of SO2 by H2O2  , however, is the almost universal
mutual exclusion of these two species in  non-precipitating stratiform clouds (Daum et al.,
1984a; Daum,  1988).  In such clouds, there is generally enough time available for the
species to react fully until the one with the lower concentration in the precursor air is
depleted.  The implication is that the aqueous-phase oxidation of SO2 by H2O2 takes
precedence over other  competing reactions.
      Most field studies have been limited to estimating the amount or fraction of sulfate
formed by the aqueous pathway, rather than the rate of formation.  Liu  et al. (1993) have
summarized the results of a number of cloud studies between 1979 and 1991. In these
studies, a number of different approaches have been used to resolve the  contributions of
aerosol scavenging and in situ chemistry to the observed  cloudwater sulfate.  The study of
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Liu et al.  (1993), which was part of the first intensive (summer 1988) of the Eulerian Model
Evaluation Field Study (EMEFS), used three different approaches for estimating the
scavenged fraction of observed sulfate, and attributed 27 to 55% of cloudwater sulfate to
in situ production.  The inferred results for the aqueous-phase production of sulfate in the
collective  studies  vary widely. In winter studies, such production is low (e.g., Strapp et al.,
1988), while in summer studies,  it is generally higher (e.g., Mohnen and Kadlecek, 1989).
Many studies implicate H2O2 as the principal oxidant (e.g., Van Valin et al., 1990), while
others implicate ozone  (e.g., Hegg and Hobbs, 1986).
     There is a variety of evidence for and against the formation of HNO3 in  the cloud
environment (e.g., Lazrus et al., 1983; Daum et al., 1984b; Hegg and Hobbs,  1986; Leaitch
et al., 1986a). The heterogeneous mechanism involving N2O5 has received attention mostly
as the "nighttime" mechanism (Lazrus et al., 1983; Richards,  1983)  owing to the short life of
the N03 radical (precursor of N2O5) in sunlight.  To account for the comparable measured
amounts of sulfate and  nitrate deposited in winter storms in Ontario, Barrie (1985) suggested
the possibility of the N2O5 mechanism for  wintertime formation of nitrate in clouds.  Leaitch
et al.  (1988) found substantial enhancement of NO3 in and near clouds on 8 of 12 days of
winter measurements in central Ontario under freezing conditions and low insolation.
On these occasions, variations in NOj/SO^ were associated with H+/SO^ in the cloud
water, implicating HNO3. Also, the observed levels of NOJ could not be simulated in a
model without invoking the N2O5 mechanism.  Based on a detailed examination of the
nighttime behavior of the NO3 radical, Noxon (1983) concluded that there was a significant
loss of NO3 compared to N2O5 by  an unknown scavenger (wet particles?).  In measurements
at a rural site in central Ontario in August  1988 as part of EMEFS, Li et al. (1993) observed
a gradual increase in the concentration of aerosol nitrate (NO3) from 1800 to midnight, and
then a gradual decrease.  In a diagnostic model study, they concluded that the  observations
could be explained by heterogeneous reactions of NO3 and N2O5 on  wet particles.  They
attributed more than 80% of the NO3 formation to NO3 and about 10% to N2O5,  and  less
than 5% to HNO3.
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3.4.2.2   Field Studies of Water Uptake by Atmospheric Aerosols
     Water is an important ingredient of atmospheric aerosols.  The water content of
atmospheric aerosols and the behavior of atmospheric aerosols with respect to changes in
ambient humidity are of great  importance in the global water cycle, the global energy budget,
and also  in atmospheric chemistry and optics.  Understanding the relationship between
atmospheric aerosols and water has proven to be a difficult problem.  Most of the water
associated with atmospheric aerosol is "unbound" (Pilinis et al.,  1989) i.e., it can increase or
decrease  with ambient humidity in a non-linear manner. This non-linear relationship depends
on particle size and composition, indeed on size-dependent composition.  More recent studies
have included monitoring of particle size distributions (either directly, or indirectly through
light scattering and use of Mie theory) and size-dependent chemical composition under
controlled relative humidity (e.g., Covert and Heintzenberg,  1984; Rood et al., 1985).  Such
studies have presented increasing evidence in favor of external mixtures in particles.  Covert
and Heintzenberg (1984) found that size spectra of sulfur-bearing species were sensitive to
relative humidity while those of EC were not, and concluded that sulfur and EC are, to some
extent, externally mixed.  Harrison (1985) segregated the particles into CCN (cloud
condensation nuclei) and non-CCN fractions and measured their chemical compositions.
Both fractions contained sulfate, nitrate and soot,  but sulfate was  15%  of the CCN mass and
only 5.8% of the non-CCN mass.  Again, this was taken as evidence  of external mixture to
some extent.  The differential  mobility analyzer has been a useful tool permitting study of
particle properties for monodispersed size classes. Using  this instrument, Covert et al.
(1990) and Hering and McMurry (1991)  showed that monodispersed particles scatter varying
amounts  of light in a single particle optical counter,  indicating different refractive indices,
and hence, different chemical  composition.  Using a tandem differential mobility analyzer,
McMurry and Stolzenberg (1989) showed that hygroscopic and hydrophobic particles of the
same size co-exist frequently in Los Angeles, again an indication of external mixing.
     In visibility studies, the water content of aerosols is of crucial importance. The
estimation of visibility impairment involves use of models in  conjunction with  ambient data
of both aerosols and relative humidity.  Frequently,  both sets of data are not available
concurrently for all stations in a monitoring network such as IMPROVE  (Interagency
Monitoring of PROtected Visual Environments).  In such  cases,  gaps  in information must be
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 filled by the use of empirical relationships between average visibility impairment caused by
 soluble aerosols and average relative humidity derived from the available concurrent data.
 Such an application based on data at the 36 national IMPROVE sites is described by Sisler
 and Malm (1994).
      Another important area which critically involves water uptake by soluble aerosols
 relates to aerosol-cloud interactions.  Such interactions are a critical link in cloud formation
 and the global water cycle,  in cloud optics and the global energy budget, in pollutant
 redistribution  by clouds, in  pollutant wet removal from the atmosphere, and in atmospheric
 chemistry.  Of particular importance is the process of aerosol incorporation in clouds.
 Interstitial aerosols in clouds may become incorporated into cloud droplets by "activation"
 (droplet nucleation), Brownian diffusion, inertial impaction, coalescence,  and  phoretic
 effects.  Of these microphysical cloud processes, aerosol activation is by far the most
 important.  A soluble particle (the CCN) is activated when water vapor supersaturation
 around  it (S) exceeds a critical value (Sc) which depends principally on particle dry size (D0)
 and composition (commonly expressed in terms  of the water-soluble solute fraction, e).  The
 works of Kohler (1936), Junge and McLaren (1971) and Hanel (1976) provide the underlying
 theory for condensation of water on aerosols based on assumptions of internally mixed
 aerosols.  Based on properties of representative  continental and marine CCN,  Junge and
 McLaren predicted that Sc would be sensitive to CCN size,  but to CCN composition only for
 lO.l. Fitzgerald (1973) confirmed the insensitivity to e in the range 0.15 to  0.35 based on
 simultaneous measurements  of CCN size, e and  CCN activation spectra (functional
 dependence of activated fraction of aerosol on S) for S between 0.35 and 0.75%.
     More recently, based on extensive year-long measurements of CCN spectra for
 continental aerosols (representative of eastern U.S. background), separated into narrow size
 bands within the accumulation mode. Alofs et al. (1989) derived a simple semi-empirical
 expression relating Sc to D0 and £ applicable down to S = 0.014%.  They also showed,
based on their own data and a literature review,  that for continental aerosols in industrialized
regions, e  « 0.5 is a reasonable approximation, indicating that the activation  of such
aerosols is unlikely to be sensitive to particle composition. Based on their expression for Sc
and using e = 0.5, a supersaturation of about 0.1%  (characteristic for stratiform clouds)
would be adequate to activate most of the accumulation mode particles exposed to a cloud.
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Cumuliform clouds with higher S would activate many Aitken mode particles also.
In cumulus clouds, peak super saturation is typically attained near cloud base, which is where
maximum activation is likely to occur.  The cloud module of the Regional Acid Deposition
Model (RADM) is based primarily on a cumulus parameterization, and makes the assumption
of 100% cloud scavenging efficiency  for sulfates formed from the oxidation of SO2 (Chang
etal.,  1991).
     The principal interest in quantitative field studies of aerosol-cloud interactions is the
scavenging of acidic aerosol mass by  clouds.  The focus of measurements in these studies
(from aircraft or at fixed mountain sites) was on gross spatial averages (over 10s of km) of
species mass concentrations (mostly of sulfate and nitrate) based on batch samples collected
in cloud  water, and in cloud and clear air (Scott and Laulainen,  1979; Sievering et  al.,  1984;
Daum  et al., 1984b, 1987; Hegg et al., 1984; Hegg and Hobbs, 1986; Leaitch et al., 1986b;
Pueschel et al., 1986).  In some studies, continuous measurements of aerosol size spectra
were used to derive spatially-averaged aerosol volume concentrations (Leaitch et al., 1983;
Hegg et al., 1984; Heintzenberg et al.,  1989) based on which, aerosol volume  scavenging
efficiency was inferred.  In one study, continuous  measurements of light scattering
coefficient were used as a surrogate for aerosol mass  concentration (ten Brink et al., 1987).
In these studies, inferences of the efficiency of aerosol scavenging were generally based on
comparisons of species mass or volume concentrations (or their surrogates) in cloud water
and/or cloud interstitial air with those in putative pre-cloud air.  Such inferences can be
confounded by incorrect identification of pre-cloud air, non-Lagrangian sampling, extended
sampling periods and resultant averaging of spatial inhomogeneities (including clear air
pockets within clouds), and inadequately resolved contributions of aqueous-phase chemistry.
Not surprisingly, the results of the above  studies varied quite widely.  Most commonly,
however, mass scavenging efficiency  was found to be high (>0.8).
     The above studies based on spatially-averaged particle mass concentrations could not
address the issue of main concern with  respect to radiative transfer,  namely, the partitioning
of cloud particles between droplets and  interstitial  aerosol in terms of their local number
concentrations.  Field  studies focused on aerosol scavenging based on particle number
concentrations are relatively scarce.  In the study of Leaitch et al. (1986b) for  stratiform and
cumuliform clouds, the authors took special care to ensure Lagrangian adiabatic
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interpretation by comparing the instantaneous cloud droplet number concentration at a single
location within the adiabatic updraft core near cloud base with the below-cloud aerosol
number concentration. They found that activation efficiencies so defined were generally high
when pre-cloud AMP concentrations were less than about 750 cm"3, but dropped off
non-linearly at higher particle loading.  Raga and Jonas (1993) made a similar observation
when comparing droplet concentrations near cloud top with the sub-cloud aerosol
concentrations on the assumption that the latter represented the pre-cloud condition.
     Gillani et al. (1995) demonstrated that such an assumption  was not generally valid in
stratiform clouds which are layered and may  include sharp inversions decoupling the layers
from each other and from the sub-cloud layer.  For such clouds, the adiabatic assumption
made in 1-D cloud models is not generally valid. To circumvent this difficulty with respect
to identification of pre-cloud air, Gillani et al. defined fractional activation (F) in terms of
local variables only, as the ratio of cloud droplet concentration (activated particles) to total
particle concentration (droplet concentration 4- concentration of  unactivated accumulation-
mode particles, 0.17 to 2.07 pun diameter).  In their study (aircraft measurements in and near
stratiform clouds near Syracuse, NY in the Fall of 1984), continuous in situ measurements
were available for particle number concentrations in 15 size  classes each for the droplets and
for dried (by heating  the probe inlet air) interstitial aerosols.  Thus, they were able to
determine F at a high spatial resolution throughout the clouds  studied (continental stratiform).
It was  determined that accumulation-mode particles larger than 0.37 /^m were efficiently
activated in the cloud under all measurement  conditions, but that particles in the range
0.17 to 0.37 jum were often activated only partially.  Partial activation generally correlated
with high local total particle concentration (> 600 cm"3) and with low temperature lapse rate
(surrogate for cooling rate with ascent, dT/dt =  w. dT/dz, where w=  the mean long-wave
updraft speed), the two conditions most responsible for limiting  supersaturation.  It is
important to note that w is a most difficult quantity to measure,  and is not generally available
in field measurements.  Under the most polluted conditions in a  stable  stratus, fractional
activation of the accumulation-mode particles was as low as  0.1  in the  core of the cloud.
Statistically, based on ten days of measurements  in the Syracuse  study, it exceeded  0.9 in
36% of the data in cloud interior, but was below 0.6 in 28% of  such data.  It was generally
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quite low in cloud edges.  Evidently, the assumption made in RADM of total activation is
questionable for stratiform clouds.
     Simple parameterizations of fractional activation in clouds have been developed based
on 1-D adiabatic Lagrangian models (e.g., Twomey, 1959; Ghan et al., 1994), and generally
highlight the significance of particle loading and updraft speed (model calculated).  The 1-D
adiabatic approach is useful near cloud base and in updraft cores, but it breaks down near
cloud edges and in the upper portions of clouds where entrainment and mixing effects are
substantial.  It is also questionable in the presence of additional complexities such as cloud
layering (Gillani et al., 1995) and lifting and sinking motions (Baker and Latham, 1979;
Pruppacher and  Klett, 1980).  These complex effects result in three-dimensional spatial
inhomogeneities and multi-modal droplet size spectra which are uncharacteristic of the simple
adiabatic model.
     Noone et al.  (1992b) studied activation in ground fogs. They were able to infer size-
segregated volume and number scavenging efficiencies of aerosols (using a counterflow
virtual impactor) in the fog under conditions  of very high particle loading and extremely low
supersaturations. For such highly-polluted fog conditions, they found high activation
efficiencies (>0.8) only for particles larger than 0.8 pun.
     In most cloud and fog studies which include considerations of particle composition, use
is made of the concept of water-soluble mass fraction (e).  This implicitly assumes internally
mixed particles.  As was shown by Zhang et al. (1993), there may really be two  e's, one
(em) for the "more" hygroscopic particles,  and one (s\) for the "less" hygroscopic aerosols.
In the diagnostic modeling study of Pitchford and McMurry (1994), the two-£ concept was
implemented.  For clouds and fogs, this implies that Sc may be different for different
particles in the same size range.
     The interaction between aerosols and clouds modifies not only the clouds, but also the
aerosols.  The condensation-evaporation cycling of aerosols through non-precipitating clouds
generally  results in growth of the nuclei due  to microphysical and chemical processes during
their in-cloud residence (Hoppel, 1988; Hoppel et al.,  1990).
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3.4.2.3  Pertinent Results of the Southern California Air Quality Study
     A separate section is devoted here to the Southern California Air Quality Study
(SCAQS) because it was perhaps the most comprehensive and sophisticated field study
related to PM conducted in  the past decade.  It was a major measurement and modeling
program conducted during 1985 to  1990 under the sponsorship of a number of
local/state/federal government agencies and industrial organizations to study the air quality,
including PM10, of the Southern California Air Basin (SoCAB).  It was a remarkably
comprehensive study in terms of participating  scientists and organizations, pollutants studied,
and measurements made.  Evaluation of measurement methods was one of its stated major
objectives.  Accordingly,  the main field studies of summer and fall 1987 were preceded in
the summers  of 1985 and 1986, respectively, by  the Nitrogen Species Methods Comparison
Study (overview and results published in a number of papers in Atmos. Environ. 22: 1517- ,
1988) and the Carbonaceous Species Methods  Comparison Study  (overview and results
published in a number of papers in the special issue of Aerosol Sci. Technol. 12(1), 1990).
An overview of the  study is contained in Lawson (1990), which also includes a summary of
preliminary results of the 1987 field study presented at the 82nd Annual Meeting of the Air
& Waste Management Association. A SCAQS data analysis meeting was held in Los
Angeles in July 1992, the proceedings of which are available from AWMA as well as the
California Air Resources Board (CARB).  CARB has also compiled a listing of the principal
publications resulting from SCAQS, and has produced a brief unpublished document entitled
"SCAQS Summary of Goals and Conclusions".  What follows is  a brief overview of some of
the principal findings of the SCAQS particulate and related measurements and analyses as
they  pertain to the subject of transformations.  It is based on the CARB document
summarizing  goals and conclusions.  No attempt is made here to  identify the specific
research studies which have generated these conclusions.
     The SCAQS 1987 intensive field measurements were made during summer (11 days)
and fall (6 days),  when a  wide range of air quality measurements were made at up to
36 surface sites. These were augmented by measurements from up to three instrumented
aircraft, surface and upper air meteorological measurements at a number of sites, and other
special measurements including photography.   Some of the principal findings were as
follows:
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Source Characterization
     Primary geological material was the major contributor to PM10 during summer at the
eastern sites in SoCAB.  Its contribution was generally lower in fall.  There was a positive
gradient from the coast inland, where it constituted about 30% of PM10. Primary motor
vehicle exhaust was generally the second largest contributor during the summer, with the
largest fractional contribution (24% of PM10) in downtown Los Angeles. Industrial facilities,
vegetative burning and biogenic emissions were not major contributors.

Concentration, Size, and Composition
     Concentrations of PM10 (24-h average) were highest in fall (>  200 /*g/m3);  highest
concentrations in summer were around 120 /^g/m3. The most abundant PM10 species at all
sites were nitrate, sulfate, ammonium, OC, EC, calcium, sodium, chloride and iron.  PM2 5
constituted 1/2 to 2/3 of PM10 at all sites,  being a higher fraction in  fall than in summer.
Average mass fractions of PM25  were 15-30% OC, 4-9% EC (peaking during the morning
traffic period), 12-36% nitrates (large site-to-site variation and midday peak preceding the
ozone peak by about two hours),  and 3-30% sulfate (large seasonal variation). About 20%
of the total PM2 5 were estimated to be due to non-fossil fuel combustion (modern C).
Aerosols occurred in the local atmosphere in three size modes with relative maxima around
0.2, 0.7 and  ~5 pm diameter. The predominant modes for sulfate and ammonium were
around 0.7 jura, and for nitrate around 0.7 and 4-5
Ammonium Nitrate and Ammonium Sulfate
     Ammonium nitrate concentrations were lowest at Hawthorne (1% of PM10 ) closer to
the coast, and highest at Riverside (24% of PM10) downwind of a large source of ammonia
near Rubidoux.  For ammonium sulfate, the reverse was true, with the highest concentration
at Hawthorne (31%) and the lowest at Riverside (8%).  Together, the two species constituted
about 1/2 to 2/3 of PM2 5-  In summer, ammonium nitrate was 5-10 times larger at Riverside
than at other sites,  its formation apparently being ammonia-limited at the other sites.  In fall,
it was the second highest contributor to PM10 at all  sites, and it could not be determined if its
formation was NH3-limited or HNO3-limited.  Ammonium sulfate was rather uniformly
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distributed over all sites during both seasons, with concentrations in fall being about half of

those in summer.


Secondary Organic Carbon
     Secondary OC was contributed significantly to peak 2-h PM10 during several episodes,

being as high as 70%  of total OC and 14 jugC/m3, and its diurnal peak lagged the ozone peak

by up to 2 h.  Interestingly, its highest concentrations occurred on Saturdays.


Hygroscopic Nature of the Aerosol
     As has been pointed out before, based on TDM A and MOUDI measurements, there

were indications that the particles were to some extent externally mixed, with "more"

hygroscopic and "less" hygroscopic components in monodisperse size classes, with a pattern

of relationship to sulfate-to-carbon ratio.

     A number of other findings with implications for aerosol formation and growth also

resulted from SCAQS.  Some of the principal ones are highlighted below:


     •  Measured concentration ratios VOC/NOX in the  morning traffic period were found
        to be 2 to 2.5 times higher than the corresponding values based on emission
        inventories. SCAQS tunnel studies indicated that this discrepancy may be due to an
        underestimation in the emission inventories of VOC and CO for motor vehicles by a
        factor of about 2. This finding had major potential implications not only for Los
        Angeles and California, but for the whole nation, because similar mobile-source
        emission models are used throughout the nation.  This uncertainty is relevant to
        ozone formation as well as aerosol formation. Urban airshed model simulations
        were found to be in better agreement with ozone measurements when the VOC
        emission estimates were doubled.

     •  Nitrous acid,  directly emitted as well as presumably formed by nighttime reactions
        involving NOX, water and aerosols, may be the single largest source of OH radicals
        in the morning.

     •  Biogenic VOC were found to be relatively negligible in the SoCAB.

     •  Urban airshed model applications to SCAQS episodes were found to underpredict
        NOX oxidation products.  Also, the models did not satisfactorily simulate observed
        layers of ozone and other secondary pollutants near the top of the daytime mixed
        layer.  The sources of these errors  may be related to model formulation
        (terrain-following coordinate system), meteorological inputs, and transport
        simulation.
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3.5   DRY DEPOSITION

3.5.1    Theoretical Aspects of Dry Deposition

     Dry deposition is commonly parameterized by the deposition velocity, Vd (m s"1) which
is defined as the coefficient relating the pollutant deposition flux F (g m'V1) and the
pollutant concentration c (g/m3) at a certain reference height above the surface, i.e.,


                                        F = Vdc                                 (3-51)



The deposition velocity can be expressed as the inverse of a sum of "resistances" in three
layers adjacent to the surface (Sehmel, 1980; Hicks, 1984):
     1. The aerodynamic layer (i.e., the layer in which atmospheric turbulent fluxes are
        constant [typically extending to about 20 m above the ground]).  In this layer,
        pollutant transfer,  whether gas or particle, is controlled by atmospheric turbulence.

     2. The surface (or quasi-laminar) layer, a thin layer (~ 1 mm) just above the surface in
        which transport occurs by molecular diffusion.  In this layer, gases transfer to the
        surface by molecular diffusion and particles undergo Brownian diffusion and inertial
        impaction.

     3. The earth/canopy/vegetation surface, at which the pollutant gas molecule or particle
        is removed from the air by attachment to the surface.

     For gases, the deposition velocity is a function of these three types of resistance  as
follows:
                                 Vd = (r. + rs + r,)-1                           (3-52)
where ra is the atmospheric resistance through the aerodynamic layer,  rs is the surface layer
resistance,  and rc is the canopy/vegetation resistance.  All resistances are in units of s m
     The aerodynamic resistance ra  can be expressed (Wesely and Hicks, 1977) by:
-i
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                                                                                   (3"53)
where zs is the reference height (m) (~ 10 m), z0 is the roughness length (m), k is the von
Karman constant (0.4), «* is the friction velocity (m s"1), and 0h is the stability correction
factor.  Roughness lengths vary from about 10"5 m for very smooth surfaces (ice, mud flats)
to 0.1 m for fully grown root crops, to 1 m for a forested area, to 5-10 m for an urban core
(Seinfeld, 1986).
      The surface layer resistance can be parameterized as a function of the Schmidt number
Sc  = v/D, where v is the kinematic viscosity of air (m2/s) and D is the molecular diffusivity
(m2/s) of the species, as
                                       rs  = d,-                                     (3-54)
                                        s     'ku*
where dlt d2 art empirical parameters (d1 =  1.6 - 16.7, and d2 =0.4-0.8, with a
suggested choice of dl = 5, d2 = 0.66).
      The canopy resistance is  the resistance for gases in the vegetation layer.  There are
three main pathways for uptake/reaction of the pollutant within the vegetation or surface:
(1) transfer through the stomatal pore and dissolution or reaction in the mesophyll cell;
(2) reaction with or transfer through the leaf cuticle; (3) transfer into the ground/water
surface.  In the resistance model, these pathways are treated as three resistances  in parallel.
The canopy resistance rc for a gaseous  species may be parameterized (Yamartino et al.,
1992) as:

                            rc  =  [LAI/rf + LAI/rcut  + l/r^1                      (3-55)


where LAI is the leaf area index (i.e., the ratio of leaf surface area divided by ground surface
area), /y is the internal foliage resistance, rcut is the cuticle resistance, and rg is the ground
or water surface resistance.   Values for //are discussed by O'Dell et al. (1977).  The
resistance rcut is parameterized by Pleim et al. (1984).

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     For gaseous pollutants, solubility and reactivity are the major factors affecting surface
resistance and net deposition velocity.  For particles, the factor most strongly influencing the
deposition velocity is the particle mass or, assuming similar densities, the particle size.
Particles are transported toward the surface by turbulent diffusion, which for larger particles
is enhanced by gravitational settling.  Across the quasi-laminar surface layer very small
particles (< 0.05 pm diameter) are transported primarily by Brownian diffusion, analogous
to the molecular diffusion of gases.  The larger particles possess inertia, which may enhance
the  flux through the quasi-laminar sublayer.
     The downward pollutant  flux is the sum of the turbulent diffusive flux and a flux due to
gravitational settling, i.e.,

                                 F(z) = Fd  + VgC  = VdC                          (3-56)


where Vg is the gravitational settling velocity of the particle.  Whereas in the formulation of
the  algorithm for gases the analogy with electrical resistance is straightforward,  it is less so
for  particles.  This is because  at any height within the aerodynamic layer and surface layer
the  flux of trace gases is diffusive only and hence a function of the concentration gradient.
Consequently, when equating the fluxes through each layer under the steady-state
assumption, the deposition velocity may be cast in a form proportional to the inverse of a
sum of resistances.  Nevertheless, the electrical resistance analogy can still be employed for
particles. The gravitational settling velocity is merely represented by the reciprocal of an
additional resistance acting in parallel with the diffusive resistance.
     As noted earlier, for particles, the resistance in the vegetation layer (rc) is usually
assumed to  be zero, since particles that penetrate the surface layer are assumed  to stick to the
surface.  The expression for deposition velocity in terms of the resistances, modified to
include gravitational settling, is

                              V.  = (r  + r   + r r V r1  + V                       (3-57)
                                d    "as    la s  e'        e
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     Therefore, the deposition velocity of particles may be viewed in terms of electrical
resistance as the reciprocal of three resistances in series  (ra, rs, and rarsVg) and one in
parallel (1/Vp.  The third resistance in series is denoted here as a virtual resistance in view
of the  fact that it is a mathematical artifact of the equation manipulation and not a physical
resistance.  Equation 3-57 is usually implemented with ra (particles) equal to ra (gases), in
which  ra is computed by Equation 3-53,  and the surface layer resistance is
                             rs = (Sc-2/3 +  10-3/V (u*)-1                      (3-58)
where Sc is the Schmidt number based on D, the Brownian diffusivity of the particle in air,
and St is the Stokes number, St=V (u*)2/gv,  where u* is the friction velocity, g is the
                                  o
gravitational constant  and v is the air kinematic viscosity.   The surface layer resistance
incorporates the effects  of both Brownian diffusion, through the Schmidt number, and
inertial impaction effects, through the  Stokes number.
      The gravitational settling velocity V  is a function of the particle size, shape and
                                        o
density.  For spherical particles  (Seinfeld,  1986),
                                    g
                                                 - pa)C                            (3.59)
where d  is the particle diameter (m), p  is the particle density (g /m3), pa is density of the
                                        '1
air (g /m), /i is the viscosity of air (g m'), and C  is the slip correction  factor
                      C  = 1 + (2X/dp)[1.257  + 0.4exp(-0.55dp/X)]               (3-60)
where  A. is the mean free path of air molecules (A. = 6.53 x 10~6 cm at 298K)
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     Figure  3-12 shows particle deposition velocities based  on wind tunnel measurements.
Deposition velocities are presented as a function of particle  diameter, particle density, and
surface roughness height.   Particle deposition velocities exhibit a characteristic  minimum as
a function of particle size.  For the smallest particles, deposition velocity increases as
particle size  decreases  because diffusion by Brownian motion increases as particles get
smaller.  For the largest particles, gravitational settling  becomes important as particles get
larger  so the deposition velocity increases as particles increase in size.  A characteristic
minimum in deposition velocity results in the range of 0.1 to  1.0 jwm diameter where neither
Brownian diffusion nor gravitational  settling is strong enough to control removal.
                        10
                    >
                    §
                       10
                       10
                                 Stable atmosphere with
                                 roughness height cm
Particle density, a/cm
        1.0
        4.0
       11.5
                               10              1            10
                                       Particle Diameter (|im)
                                                                       10
Figure 3-12.  Extrapolations  from correlations  of wind-tunnel measured  deposition
               velocities for z  = 1 m, densities of 1, 4, and  11.5 g cm"3.  VT represents
               terminal settling velocity.
Source:  Sehmel  (1980) as presented by Nicholson (1988).
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     It is possible to obtain a rapid estimate of the atmospheric lifetime of particles with
respect to removal by dry deposition. If the aerosol can be assumed to have a uniform
concentration between the ground and a height h, then the residence time relative to removal
by dry deposition is h/Vd.  For example, for a 1000 m atmospheric  layer, and a particle
deposition velocity of 0.1 cm/s, the estimated residence time is 11.5 days.

3.5.2   Field Studies of Dry Deposition
     In spite of many field measurements and considerable progress since 1980 in  our
understanding of dry deposition processes and their quantification, uncertainties remain
substantial.  The problem is extremely complex involving a large multiplicity of factors, and
their complex interactions, which influence dry  deposition of atmospheric particles  and their
precursors (see, for example, a tabulation of some of these in Davidson and Wu, 1990).
These factors relate to characteristics of the atmosphere, nature of the deposition surface, and
properties of the depositing species.  It is impossible in field studies to measure all the
pertinent variables over large enough spatial and temporal domains.  In essence, knowledge
of dry deposition is limited by the inability to make the necessary measurements in other than
special circumstances.  This was a key statement of the NAPAP Workshop on Dry
Deposition in Harpers Ferry, West Virginia (Hicks et  al.,  1986). The Workshop report also
noted that there is presently a lack of fundamental knowledge concerning the chemical and
biological processes influencing dry deposition,  and there are serious hazards associated with
scaling  input information down from grid level to local, and scaling  up the results  of local
measurements to broader domains.  Information contained in the Workshop report and in
subsequent research publications on the subject were reviewed  by Davidson and Wu (1990).
That review summarizes the results of a large number of field  studies published since earlier
reviews by McMahon and Denison (1979), Sehmel (1980), Hosker and Lindberg (1982) and
Galloway et al. (1982).  It also includes summaries of dry deposition processes, wind tunnel
studies and empirical models, techniques for measuring deposition in the field, and
comparisons of field data and model results. The summary presented in this  section is based
largely on Davidson and Wu (1990).
     Many techniques have been used to measure dry  deposition.  They are generally
grouped into  two classes:  surface analysis methods, which are based on examination of
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contaminant accumulations on natural or surrogate surfaces, and atmospheric flux methods,
which involve ambient measurements of the species of interest and other related variables.
These methods provide the deposition flux out of which the deposition velocity is inferred.
Surface analysis methods include foliar extraction (by washing individual leaves), throughfall
and stemflow (wet measurements above and within the canopy), watershed mass balance,
tracer techniques, snow sampling, collection on surrogate surfaces, etc. These methods may
provide useful data on the flux of coarse particles, but fail to simulate the physical processes
which control  the deposition of sub-micron particles to natural surfaces, and to give
meaningful data on trace gas deposition.  Deposition on surrogate surfaces may not mimic
that on natural surfaces.  Atmospheric flux methods include micrometeorological methods
(eddy correlation and vertical gradients), aerometric mass balance in a box over the
depositing surface,  tracer techniques, etc.  Micrometeorological methods also include what
has come to be known as the inferential approach in which measured concentrations  are
combined with specified or calculated deposition velocities  based on meteorological data and
surface  information.  This approach  is used in long-term monitoring programs in which only
simple measurements are possible at remote sites  (e.g. weekly average species concentrations
and routine meteorological measurements). For details of the various methods, see Davidson
and Wu (1990) and the  report from NAPAP Workshop on  Dry Deposition (Hicks et al.,
1986).
     Estimates of flux in the surface layer have historically been inferred from measurements
of the vertical gradients of mean quantities such as concentration or horizontal wind  speed
under conditions that the gradient-transport theory was assumed to be valid.   Calculations are
modified by corrections of departures from neutral stability.  However, with fast response
instruments it  is possible to directly measure the correlation of  fluctuations in velocity and
transported quantities of interest such as pollutants, water vapor or heat.  For direct
measurement of flux, the eddy correlation method is widely used.  In this case, pollutant flux
and aerodynamic resistance can be measured with appropriate fast sensors with matched time
response. A discussion of these methods may be found in  Hubbe (1989).
     Several limitation  of the methods presently being implemented should be noted. For
example, the aerometric mass balance technique is essentially inferential, relying heavily on
the accurate measurements of air concentrations and  on the evaluation of accurate deposition
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velocities.  The dominant limitations are probably those associated with the ability to evaluate
appropriate deposition velocities.  The knowledge on which any interpretive scheme for
deposition can be based is quite limited. Most information on gas transfer either deals with
average uptake in laboratory conditions (chambers, wind-tunnel, etc.), or is derived from
short-term micrometerological measurements at selected field sites.   Ongoing research
programs are addressing these problems.  However, a major task, confronting all attempts to
monitor dry deposition, is to assess the magnitude of errors arising from the need to apply
poorly-known relationships.

3.5.3   Measured Deposition Velocities
     Measurements of dry deposition in the field and in chambers have primarily involved
six categories of contaminants:  sulfur species, nitrogen species, chloride species, ozone,
trace elements and atmospheric particles.  The results of many of these studies published
between  1978  and 1987 are reviewed in Davidson and Wu (1990), which includes extensive
tabulations of the studies and their results.  Of the reported studies on sulfur species,
20 pertain to SO2.  They  give deposition velocities ranging from nearly 0 to 3.4 cm/s.  The
variations are due to differences in seasonal and diurnal conditions, aerodynamic transfer,
surface characteristics  (especially stomatal resistance), measurement  methods, etc.  Daytime
values are generally higher, as expected (lower aerodynamic and stomatal resistances).
Micrometeorological methods  were used in 16 studies whose average values of vd gave a
grand average of 0.95 ±  0.62 cm/s.  Four studies provided an average value of
0.13 ± 0.09 cm/s for  deposition velocity on snow.  For paniculate sulfur, 34 studies are
included, with 10 also including particle size measurements.  A  graph also includes results of
earlier studies, and gives values of vd in the range 0.01 to 10 cm/s.  Results for vd in cm/s
based on different methods are as  follows:  0.55  ± 0.65 for micromet methods, 0.26  ± 0.25
for surrogate surface exposures, 0.23 + 0.24 for foliar extraction, and 1.00 ± 0.41 for
throughfall.  Since the micromet method is believed to be more  specific for submicron
particles  while the surrogate surface method is biased in favor of larger particles, the
difference in the results of those methods is opposite to that expected.  The surrogate surface
and foliar extraction results are close, but each has a large variance.  Throughfall values are
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the largest probably partly because they include deposition of SO2. Evidently, measurement
methods themselves are an important variable because they do not measure the same thing.
     Twenty two species are reported for nitrogen species,  including NO2, NOX, HNO3,
NO3'- NH3, and NH4+. The inferred values of vd (cm/s) are: 0.012 to 0.5 for NO2
(2 studies), -2.6 to 0.3 for NOX (4 studies), 0 to 2.9 for HNO3 (4 studies), 0.13 to 1.3 for
NO3 - (7 studies), 1.9 ± 1.55 for NH3 (1 study), and  0.06 to 1.0 for NH4+ (4 studies).  The
zero value for nitric acid was for snow in a chamber study; otherwise, the values for nitric
acid are the highest, indicating low surface resistance.  The values for paniculate nitrate are
somewhat larger than for sulfate; this may reflect larger particle size associated with nitrate.
Davidson and Wu (1990) report four studies for chloride-containing particles, giving values
of 1.0 to 5.1 cm/s; and one  study for HC1 gas giving a value for HC1 of 0.73 cm/s on dew.
The  highest values for chloride were in winter, related to road salt.  Deposition velocities to
dew were measured for a number of species including HNO3, NO2, SO2, and aerosol  SO4=
and NHf  in southwest Pennsylvania (Pierson et al., 1986) and in the Los Angeles basin
(Pierson et al., 1988;  Pierson and Brachaczek, 1990).  Low  values were obtained, consistent
with the high atmospheric stability required for dew formation.  Based on 11 studies using
rnicromet methods, vd of ozone on vegetation ranged between nearly 0 and 1.5 cm/s (average
of 15 values = 0.39 ± 0.21). Nighttime values were lower, but the day-night difference
was  less for ozone than for NO2.
     Results of 19 studies included measurements for 21 trace elements, with particle size
data in 15 studies. For these data, crustal element enrichment factors (EF) were determined.
Values of EF «  1 indicate crustal sources, while EF  » 1 (enriched) indicate non-crustal
sources such as anthropogenic, natural combustion (volcanism, forest fires), biogenic,
sea-spray, etc..   Large enrichment factors were found for Ag, As, Cd,  Cu, In, Pb, Sb, Se
and Zn.  Ni and V were marginally enriched.  Other elements were mainly soil-derived.  vd
for these elements were generally higher (> 1  cm/s), while they were generally less than 1
cm/s for the enriched elements (smaller, submicron particles). A figure including these as
well as data of earlier studies is presented, showing a positive correlation between vd and
MMD  (mass median diameter).  For Pb, the values ranged between 0.1  and  1.0 cm/s.
Friedlander et al. (1986) have used CO as a tracer for automobile emissions to estimate the
deposition velocity for Pb, by comparing the ratio Pb/CO in ambient air to that in a tunnel.
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They found the former to be lower, indicating deposition compared to its value in fresh
emission (tunnel). Based on these data, they estimated vd for Pb to be 0.26 cm/s, which is
consistent with the range given above. Davidson and Wu (1990) also report the results of 5
other field studies with micromet measurements of dry deposition for submicron particles,
and particle size measurements also.  vd was generally less than 1 cm/s, in general agreement
with results for sulfate and the enriched trace elements.
      Davidson and Wu (1990) have also presented results of comparisons between measured
values of vd with predicted values based on six model calculations.  These results are from
published studies with size distribution data for aerosol sulfate and trace elements. The
measured values of vd are for the full size range; the model value is the concentration-
weighted average of the calculated values for all size classes.  For sulfate, the predicted
values were generally smaller than the measured values.  Good agreement was, however,  not
expected because of differences in ambient conditions and surface conditions between values
used in the model compared to the corresponding measured values.   Similar comparisons for
24 trace elements were also tenuous:  out of 11 of the 24 elements for which more than one
or two data points only were available, the measured values were in the predicted range; for
Al, Ca and Fe, the predictions were low, while for Zn, the predictions were too high. For
the other 13 elements with sparse measured data, the agreement was generally much poorer.
3.6   WET DEPOSITION
3.6.1   Introduction
     Although detailed physico-chemical models are needed to describe the details of
in-cloud and below-cloud scavenging of particles, there has been a benefit in using
comparatively simple formulations of precipitation scavenging that provide a convenient
picture of the process as a whole.  These simple methods are not designed to explain detailed
variations in wet deposition with time or space, but they are useful in describing average
deposition rates over large areas.  Two alternative techniques have become popular.  The
first relates concentrations of material in precipitation to the quantity available in the air, thus
describing the overall efficiency of precipitation as a removal path.  By relating
concentrations in precipitation to those in the air, dimensionless scavenging ratios can be

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determined.  The second common method is based on the first-order removal of airborne
gases or particles as rain falls through the atmosphere.  Concentrations in the air will
decrease exponentially and a scavenging rate can then be determined.
     Below-cloud scavenging rates for particles of about 3xlO~5 s'1 appear to be typical;
in-cloud scavenging leads to rates typically ten times larger (Hicks and Meyers, 1989).
Hygroscopic particles are scavenged more readily than hydrophobic ones.
     Based on the wet flux W, the wet deposition velocity may be defined as
                                  Vw  =     W      fa                            (3-61)
                                         c(x,y,0,t)
where "A is the vertically averaged scavenging rate. The last equality assumes that the
pollutant is uniformly distributed between z = 0 and z = h.  The wet deposition velocity Vw
can be computed by

                                       Vw  = wrPo                                 (3-62)
where wr is the washout ratio (i.e., the dimensionless ratio of the concentration of material in
surface-level precipitation to the concentration of the material in surface-level air) and p0 is
the precipitation intensity (mm hr"1).  For example, if wr = 106 and p0 =  1 mm h"1, then Vw
= 28 cm s'1, which gives, for h  = 1,000 m, A = 2.8 x 10'4 s'1.  Seinfeld (1986) provides
a detailed discussion of precipitation scavenging  of particles, including the calculation of
collision efficiencies and scavenging rates.
     Scavenging ratios relate concentrations in precipitation to those in air.  Although such
ratios depend on many factors, they provide a simple way to include wet deposition processes
in air quality models.  The washout (or "scavenging") ratio  is
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                                      wr =  [c]rai"Pa                               (3-63)
                                            Maerosol
with [c]rain in mg  g"1, [c]aerosoi in mg m"3, and pa ( = 1,200 g m"3), the density of air. The
definition of this ratio presumes that the aerosol measured at ground level is vertically
uniform and that there are no factors limiting the collection of aerosol by the droplets, such
as solubility.  Scavenging ratios of about 400 appear to be appropriate in the case of particles
well mixed  in the  lower atmosphere but originating near the surface,  while  values of about
800 appear  characteristic of material derived from the free troposphere (Hicks  and Meyers,
1989).

3.6.2   Field Studies of Wet  Deposition
     Removal of accumulation mode aerosol particles from the atmosphere occurs largely by
the precipitation process (e.g., Slinn, 1983).   These particles are the dominant particles on
which cloud droplets form (cloud condensation nuclei, CCN).  Once a cloud droplet (of
diameter of a few  up to about 20 micrometers) is formed, it is much more susceptible to
scavenging and removal in precipitation than is the original submicrometer particle. The
fraction of aerosol particles incorporated in cloud droplets on cloud formation is the subject
of active current research,  which has been reviewed in Section 3.4.2.2.
     The dominance of precipitation removal processes for accumulation mode particles
results in high variability in temporal patterns of aerosol loadings, that may be attributed to
the episodicity of precipitation events and synoptic  scale meteorology that delivers air of
differing origins to a given location (e.g., Waldman et al., 1990). This variability leads to
difficulties in attempts to estimate mean residence times based on budget considerations
(Junge,  1963; Schwartz, 1979).  A unique approach to estimation of the mean  residence time
of accumulation mode aerosol particles was presented by measurement of the decay of
atmospheric concentrations of 137Ce at several mid-latitude surface stations in Europe and
Asia in the weeks following the Chernobyl accident (Cambray et al.,  1987); the 137Ce was
present largely in this size range.  This study led to an estimate for the mean residence time
of 7 days, consistent with other estimates.  It may be noted, however, that this residence
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time is applicable to particles in the free troposphere, where the 137Ce was mainly present
during the several week period.  The mean residence time of accumulation mode particles in
the boundary layer is expected to be somewhat less (Benkovitz et al., 1994).
     Wet deposition measurements are made principally to meet three objectives:  (1) to
determine the regional  spatial-temporal distribution and chemistry of wet deposition; (2) to
study pathways and mechanisms  of pollutant wet removal from the  atmosphere;  and, (3) to
generate data for diagnostic evaluations of precipitation scavenging  modules.  The first of
these objectives is best studied based on data of routine monitoring programs. These were
reviewed in detail by NAPAP (Sisterson et al.,  1991) and are not covered here.  The focus
here is on recent research field studies aimed at objectives (2) and (3) above.
      A significant effort in NAPAP in the  1980s was devoted to development of wet removal
characterizations that directly reflected the cloud physics, attachment, reaction, and
precipitation processes  (Hales, 1991). The PLUVIUS II models, prepared under the auspices
of NAPAP, was a reactive storm model based on multi-phase material balance,  and served as
the basis for the development of the one-dimensional RADM Scavenging  Module, RSM.
A parallel activity in NAPAP was DOE's PRECP (Processing of Emissions by Clouds and
Precipitation) field measurements program which comprised a series of six individual
intensive field studies with the objective of systematically measuring scavenging
characteristics for different classes of storm systems important to regional acid deposition.
In these, studies, the emphasis was  on in situ aircraft measurements.  What follows is a brief
review of such research field studies.  It is based substantially on Hales (1991).  In the
context of precipitation scavenging studies, it is useful to bear in mind that pollutant
particles, on average, undergo a number of repeated cycles in and out of non-precipitating
clouds before finally being removed by precipitation.
      In situ aircraft measurements in clouds and precipitation are of crucial importance in
mechanistic/diagnostic  studies.  Current technology permits continuous aircraft measurements
of NO, NO2, NOy, HNO3, PAN, SO2, O3, H2O2, liquid water content (LWC), and size-
segregated aerosol and cloud/rain droplet concentrations with quite high sensitivity and
precision.  In addition, filter samples and cloudwater samples can provide mass
concentrations of the major ions in  aerosols and droplets at a temporal resolution of a few
minutes. Ground monitoring of precipitation in recent studies has included use  of the
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NAPAP-developed Computer-Controlled Automated Rain Sampler (CCARS) which is a
combination rain gauge and sequential precipitation chemistry sampler, controlled and
monitored by a programmable microprocessor.  Such samplers permit capture  of statistically
valid footprints (multiple sequential event samples) of deposition during the course of a
storm. Upper-air meteorological measurements with fine vertical resolution of wind
components, temperature and moisture are also important. These can be made using radar
profilers and doppler radars.
     Field studies have been conducted in and below point-source plumes (meso-y scale) and
urban plumes (meso-(3 scale). In the former, precipitation scavenging of S and N compounds
was found to be minimal (Granat and Soderland, 1975; Dana et al.,  1976; Drewes and
Hales, 1982), indicating low precipitation scavenging efficiency for SO2 and NOX from fresh
plumes. Hales and Dana (1979) found appreciable removal of S and N compounds from the
urban plume of St. Louis by summer convective  storms.  Patrinos and Brown  (1984),
Patrinos (1985) and Patrinos et al. (1989) found efficient scavenging of these compounds
from the urban plumes of Philadelphia and Washington, DC  by frontal storms. H2O2 data in
rain showed considerable spatial variability in the plumes.
     The major regional-scale field studies  include OSCAR (Oxidation and Scavenging by
April  Rains, April 1981), PRECP (mid-1980s), and the DOE-FBS (Frontal Boundary  Study).
OSCAR (Chapman et al., 1987) included a nested array of ground level sampling (an
extended regional precipitation chemistry network in northeastern United States, with an
embedded high-density network in northeast Indiana) as well as three research aircraft.  The
focus  was  on scavenging by extratropical cyclonic storms. The aircraft made clear air
measurements before and after frontal passage, as well as measurements within the storm, in
the vicinity of the high-density network.  Measurements were made during four storms.
OSCAR data have been used for regional model development and evaluation.
     The six PRECP studies, conducted between 1984 and 1988, were targeted at scavenging
measurements in different types of storm systems.  Three studies were focussed on
convective storms (II, V, and VI)  in summer, and the other three on extratropical cyclonic
and frontal storms during other seasons; five were conducted east of the Mississippi River,
and one in the  Oklahoma-Kansas-Colorado area.  All of them included two or  more research
aircraft, and all also included at least limited area precipitation chemistry networks (PRECP
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IV had three multiscale networks ranging from a coastal "rain-band" network to a truly
regional scale network). The network in PRECP VI was a highly-density network within an
80-km RADM grid cell, designated to provide information about regional sub-grid scale
variability. Two of the studies were conducted jointly with other meteorology-focussed
measurement programs; PRECPII with the NSF PRESTORM study, and PRECP IV with the
NSF-NASA-NOAA Genesis  of Atlantic Lows Experiment (GALE).  Such synergism resulted
in particularly strong meteorological data in these two studies.  PRECP I was intended to be
an exploratory study, but generated a database of which at least one storm has been
extensively studied (Saylor, 1989). PRECP VI, on the other hand was designed as the grand
finale aimed at generating a definitive database for evaluation of the RADM Scavenging
Module, but failed to meet its main objective owing to the extreme drought of the summer of
1988.
     Overall, the studies have developed a substantial database of mechanistic-diagnostic
information suitable for diagnostic model studies.  PRECP II definitively demonstrated the
cloud venting phenomenon transporting boundary layer pollutants to considerable heights in
the free troposphere (Dickerson et al., 1987).  PRECP III provided a significant new
mechanistic insight regarding scavenging in orographically enhanced storms,  e.g., the
observation of an unexpected entrainment mechanism that occurs as orographic lifting occurs,
and which enhances chemical wet removal  appreciably (Hales, 1991).  PRECP V, focussed
on studying vertical profiles  of chemical species  in and around convective storms, resulted  in
one study  (Daum et al., 1990) which showed that while  SO2 was more concentrated in the
lower parts of the ABL, H2O2 was concentrated  near the top, underscoring the importance  of
mixing in  facilitating aqueous-phase of SO2 by H2O2. The same study also found that in the
low-NOx background, H2O2  was correlated with humidity.
     The  Frontal Boundary Study (DOE) was conducted in fall 1989 as part of a global
study of the fate of energy-related pollutants.  The focus was on pollutant redistribution and
removal by stable frontal storms occurring subsequent to pollution episodes associated with
high-pressure stagnation.  Aircraft soundings ahead of, within, and following the passage of
the front showed considerable spatial variability in precipitation amount and composition
(Hales,  1991).
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     The data of the above studies constitute a substantial mechanistic-diagnostic database for
model evaluation.  In addition to these research studies, a number of research-grade
precipitation chemistry networks were also operated in the 1980s. They include the
Canadian CAPMON, and the U.S. MAP3S and UAPSP,  as well as the shorter-term EPRI-
OEN and the EPA-ME35.  Applications of the research network measurements for source-
receptor pathway studies are discussed by Hales et al. (1987).
     Jaffrezo and Colin (1988) studied the wet removal of trace elements in a year-long
study in Paris.  They reported their results in terms of the scavenging ratio,  along with
corresponding information from a number of earlier studies (Table 3-14).  The scavenging
coefficient and the scavenging ratio, in common use in the Lagrangian models of the 1970s,
represent highly lumped representations of the complex of processes involved in wet
removal. They are empirical entities which, by themselves,  contain little mechanistic
information.  While reporting their measurements of scavenging ratio  during a year-long
study in Paris, Jaffrezo and Colin (1988) included a table (Table 3-14) which summarized not
only their own data but also those of other earlier studies. The various results are  not
directly comparable owing, at least partly, to  differences  in measurement methods.
Of particular interest in their study is  the interpretation of elemental composition data.  They
were able to separate the measured elements into three groups which differed in terms of
their solubility and  also, by the mechanisms of their scavenging.  The measured
concentrations in precipitation and in air  were nearly proportional for the insoluble species
Al, Si, and Fe; this was interpreted to imply that their scavenging was mostly a local
mechanism (below-cloud impaction).  At the other extreme,  the local concentrations of the
very soluble species Na and Cl in the two phases were least correlated, indicating a more
complex and progressive process of enrichment of one medium relative to the other (in-cloud
processes).  The remaining soluble species (SOJ, K, Ca, Zn, and Mg) showed an
intermediate behavior. Earlier data at the same site of the relationship between scavenging
ratio and particle mass median diameter (MMD), which showed a minimum in the
scavenging ratio for MMD = 1 to 2 /xm  (reported as  Figure 6-1), were judged to be
supportive of the above interpretation.
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                                        TABLE 3-14.  SCAVENGING RATIOS
                                                   (Dimensionless)a

Reference n G.M.
Cl 78 2,941
S 82 743
Na 81 444
K 82 951
Mg 81 596
Ca 82 1,048
Zn 69 767
Al 82 291
Si 82 373
Fe 82 184
Ti 9 305
Mn 7 146
1 . Jaffrezo and Colin (1988).
2. Harrison and Pio (1983).
3. Arimoto et al. (1985).
4. Buat-Menard and Duce (1986).
5. Lindberg (1982).
6. Gatz (1977).
7. Chan et al. (1986).
8. Peirsonetal. (1973).
9. Cawse (1981).
10. Savoie et al. (1987).
Med.
(1)
2,917
753
530
970
682
1,097
707
283
405
194













S.D.
4.73
1.98
3.17
2.30
2.39
2.49
2.65
2.72
2.35
2.51
1.30
1.36











A.M.
7,710
940
744
1,325
816
1,579
1,226
459
533
267
378
171










A.M. G.M. G.M. A.M. A.M.
(2) (3) (4) (5) (6)
600 350 1,400
700 1,000
560 360 2,100
620 300 2,000 548
850 400 457
1,890 320 1,100 352
790 820 179
580 1,300

390 600 253

250 2,100 3,600 370










G.M. A.M. A.M. A.M.
(7) (8) (9) (10)
2,300 4,100
370b
2,900 5,500 490


2,100
612 1,050 1,030
756 620 430

468 890 270 2

756 760










aG.M. =Geometric mean.
 A.M. = Arithmetic mean.
 Med.=Median.
 S.D.= Geometric standard deviation.
bNon-sea sulfate.

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3.6.3    Overview of Sulfur Dioxide and Nitrogen Oxide Wet Scavenging
     Hales (1991) has presented a useful overview of our understanding of SO2 and NOX wet
scavenging based on field measurements which is very pertinent here, and is recapped below.
SOX:  SO2 is emitted principally from point sources.  It is moderately soluble in water, and
its solubility decreases with increasing acidity of the  solution.  It is not efficiently scavenged
from concentrated fresh plumes, but this efficiency improves as the plumes dilute.  It is
essentially insoluble in ice and cold snows, but tends to be more efficiently scavenged by wet
slushy snow and snows composed of graupel formed by rimming of supercooled cloud water.
Only a small fraction of the SO2 emission is removed as unreacted S(IV) which constitutes
about 20%  of S in precipitation in the eastern U.S. in sold seasons (significantly in the form
of hydroxymethane sulfonate ions), and virtually none in  summer (high acidity  of droplets).
Sulfate removal is also small from fresh plumes (not much there), but increases substantially
with plume dilution as more is formed in the plume.  It is scavenged efficiently by  clouds
and rain. Roughly 1/3  of the S emitted annually in North America is believed  to be removed
by precipitation.
     NOX:  Point sources are a relatively smaller contributor of NOX, but still quite
substantial.  Both NO and NO2 have low solubility in water.  Virtually no NOX is removed
from fresh plumes.  HNO3 formed by gas-phase oxidation of NO2 is very soluble in water
and is the principal source of NO3 in precipitation. NO3, N2O5, and HO2NO2 are also
believed to be significant  intermediates. Since all of the intermediates are secondary
products, NOX scavenging increases  with plume dilution and oxidation.  Mesoscale  studies
show much variation in the efficiency of wet scavenging of SOX and NOX, depending on
storm type and history of plume chemistry.  About 1/3 of the anthropogenic  NOX emissions
in the U.S. are estimated  to be removed by wet deposition.   The distinct seasonal character
of SOX wet deposition is absent in the case of NOX wet deposition. Some likely reasons  are
as follows:  HNO3 has a strong affinity for ice as well as liquid water; its formation has  no
direct dependence on H2O2 which peaks in summer;  and, there are mechanisms for the
formation of HNO3 in low winter sunlight.
                                        3-143

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3.7   PHYSICAL AND CHEMICAL CONSIDERATIONS IN SELECTING
      A SIZE CUT-POINT FOR SEPARATING FINE AND COARSE
      PARTICIPATE MATTER
     Paniculate matter is not a single pollutant but a mixture of many classes of pollutants
that differ in sources; formation mechanisms; composition; size; and chemical, physical and
biological properties.  One of the most fundamental divisions is the natural separation into a
fine particle mode and a coarse particle mode as shown in Figure 3-6.  (The term "fine" and
"coarse" are used in this section to refer to particles in the fine or coarse particle distribution
or modes.  It is understood that the two distribution overlap between 1  and 3 /^m
aerodynamic diameter, Dae.  Fine is also used to refer to particles with a upper cut point of
3.5, 2.5, 2.1, or 1.0 ^m Dae. Coarse is also used to refer to particles between 2.5 and
10 £im Dae or particles collected by the high volume samples as well as the entire coarse
mode.)  Some of the many differences between fine and coarse particles are summarized in
Table 3-15. Because of these many differences it may be advantageous to treat fine and
coarse PM as separate pollutants.
     As will be discussed in Chapter 5, fine and coarse particles have different sources.
Therefore,  in order to devise a cost effective control program it is necessary to know, as a
minimum, the relative amount of fine and  coarse particles in order to know what types of
sources to target for control. Source apportionment analysis, i.e., studies of particle
composition and other properties to determine the contributions of specific types  of sources,
is most effective if fine and coarse particles are collected and analyzed  separately.
     Fine and coarse particles may be anticipated to have different biological properties as
well as different physical and chemical properties.  As discussed later in Chapters 10 through
13, many of the  current hypotheses for health effects at PM concentration levels  near or
below the current standard are increasingly focusing attention on components of fine
particles.  Most of the particle strong  acidity, sulfate, transition metals, toxic elements, and
all of the ultrafine particles are found in the fine particle mode or distribution. On the other
hand, much of the airborne biological material, such as pollen, mold spores and  insect parts,
are found in the  coarse particle mode  or distribution.  Because of the potential for different
types  of biological effects from fine and coarse particles, it may be useful to separate out
relative contributions of each to  observed or projected health risks and  to balance controls for
one or both sizes/types of particles accordingly.
                                        3-144

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                 TABLE 3-15.  COMPARISON OF AMBIENT FINE
                         AND COARSE MODE PARTICLES
                                     Fine
                                          Coarse
 Formed from:

 Formed by:
 Composed of:
 Solubility:


 Sources:
 Atmospheric half-life:

 Travel distance:
Gases

Chemical reaction
Nucleation
Condensation
Coagulation
Evaporation of fog and cloud
droplets in which gases have
dissolved and reacted

Sulfate, SO4=
Nitrate, NO^
Ammonium, NH^
Hydrogen ion, H+
Elemental carbon,
Organic compounds
(e.g.,PAHs, PNAs)
Metals, (e.g., Pb, Cd, V,
  Ni, Cu, Zn, Mn,  Fe)
Particle-bound water

Largely soluble, hygroscopic
and deliquescent

Combustion of coal, oil,
gasoline, diesel fuel, wood
Atmospheric transformation
products of NOX, SO2, and
organic compounds including
biogenic organic species,
e.g., terpenes
High temperature processes,
smelters, steel mills, etc.


Days to weeks
100s to 1000s of km
Large solids/droplets

Mechanical disruption
(crushing, grinding, abrasion
of surfaces, etc.)
Evaporation of sprays
Suspension of dusts
Resuspended dust
(Soil dust, street dust)
Coal and oil fly ash
Oxides of crustal elements,
  (Si, Al, Ti, Fe)
CaCO3, NaCl, sea salt
Pollen, mold, fungal spores
Plant/animal fragments
Tire wear debris


Largely insoluble and
non-hygroscopic

Resuspension of industrial
dust and soil tracked onto
roads and streets
Suspension from disturbed
soil, e.g., farming, mining,
unpaved roads
Biological sources
Construction and demolition,
coal and oil combustion,
ocean spray

Minutes to  hours

< 1 to  10s  of km
Source:  Adapted from Wilson and Suh (1996).
                                        3-145

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     Considerations of the relationships between the concentrations measured at a control
site and the personal exposure of individuals, discussed in detail in Chapter 7, suggest that a
central site monitor may serve as a useful indicator of community exposure to fine particles.
A central monitor is a poor indicator of exposure to coarse particles and other PM
components with localized sources.  Thus for epidemiological or personal  exposure studies it
will be useful to have separate measurements of fine and coarse particles.
     Visibility degradation is due primarily to fine particles since particles with diameters
near the  wavelengths of visible light (0.4 to 0.7 /xm) are much more effective in scattering
light on a unit mass  basis than larger particles (Chapter 8). On the other hand  soiling is
more closely related to coarse particles (Chapter  9).  Again, these differences provide
additional reasons to treat fine and coarse particles separately.

3.7.1    Background
     In  1979 EPA scientists, in a paper entitled  "Size Considerations  for Establishing a
Standard for Inhalable Particles"  recommended that total  suspended particulate matter (TSP),
as defined by the high volume sampler, be replaced by the fraction obtained with a sampler
having a precise upper cut-point (originally 15 /*m Dae, but later changed to 10 ^m Dae); and
that "a second particle size cut-point of < 2.5 pirn Dae be incorporated in  the air sampling
devices" (Miller et al., 1979).  This study found that "the existence of a bimodal distribution
with fine and coarse modes has been clearly demonstrated by.... mass-size distribution
studies and by number distribution studies.  These  size distribution  studies suggest 1 to
3 £im Dae as the most appropriate range for a cut-point for fine and coarse aerosols.
However, practical considerations of reducing plugging of impactor orifices indicate that
2.5 fim Dae is a more appropriate cut-point,  especially for particle size fractionating devices
such as the dichotomous sampler" (Miller et al.,  1979).
     The cut-point of 2.5 /mi Dae, which has been used in many studies since 1979, was
chosen not because it was  ideal but because  it was the smallest cut-point deemed feasible for
a dichotomous sampler at that time.  Current technology has demonstrated the feasibility of
dichotomous samplers with cut-points at 1 pan Dae,  or even lower if desired. Impactor  and
cyclone  technology can also be used for cut-points below 2.5 /mi Dae. Therefore, it is
appropriate at this time to review existing data on size distribution of ambient aerosols so
                                          3-146

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that policy makers may consider whether a change to a smaller cut-point should be
considered. This is especially important in view of the possibility of a major increase in both
research measurements, exposure assessment, and regulatory monitoring of fine particles, as
well as of PM10.

3.7.2   Size Measurements
     Information on the size of fine and coarse particles comes from two basic techniques,
(1) particle-counting techniques that measure the size of individual particles and convert the
particle number distribution to a particle volume distribution and  (2) particle-collecting
techniques that use aerodynamic separation, collection of material in specific size ranges, and
gravimetric or chemical analysis to determine the total mass or the mass of specific
components in the size ranges collected.  Particle counting has the potential advantages of not
causing as much disturbance to the gas/particle equilibrium.  However,  considerable care
must be taken to avoid heating the sample or diluting it with clean or drier air than that
present in the atmosphere. With particle counting techniques it may also be possible to avoid
problems of particle bounce. However, several expensive and complex instruments are
required to cover the desired range of 0.001 to 100 ptm. Because sizes  can be measured very
precisely, the size ranges covered can be very small and an almost continuous function of
number versus size can be obtained.
     Particle collecting techniques have the advantage of obtaining size-differentiated
samples for chemical analysis.  The equipment used  is simpler and less expensive.
However, aerodynamic  separation does  not provide as distinct a classification by size.  Large
particles may bounce from their intended collection surface and be counted in smaller size
ranges. Also, the requirement for long sampling times may result in averages of
distributions that change with time. Particle collection techniques provide a limited number
of size cuts and  yield discontinuous functions of mass versus particle size.
     Both techniques, however,  clearly indicate the natural division of ambient air particles
into fine and coarse modes with a minimum between 1.0 and 3.0 /im diameter.  Size
distributions obtained with particle counting techniques tend to show a lower, broader, and
more distinct minimum than distributions obtained with particle collection techniques such as
impactors.  The  position of the minimum between the accumulation and coarse mode may
                                         3-147

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vary from study to study.  The peak of the fine particle mode tends to increase in size with
increasing concentration and with increasing relative humidity. Several good reviews of
particle size distribution are available:  physical properties of sulfur aerosols (Whitby, 1978),
urban aerosols (Lippmann, 1980), trace elements (Milford and Davidson, 1985), paniculate
sulfate and nitrate in the atmosphere (Milford and Davidson, 1987), and coarse mode aerosol
(Lundgren and Burton, 1995).

3.7.3    Appropriate Display of Size-Distribution Data
     Size-distribution data, if not properly displayed, can give misleading information on the
position and shape of peaks and valleys and can lead to incorrect conclusions, especially in
regard to the position, width, and separation of fine and coarse modes.  For this reason many
workers use a histogram display obtained as follows. The mass, number, surface, or volume
in each size range is divided by the difference of the logarithms of the diameters at the upper
D; and the lower D^ ends of the  size range, and plotted as  rectangles of width log Drlog
Dj.! and height, i.e. mass/ (log Du-log De) on a log diameter scale. This is normally shown
as AC/A log D, dM/d log D, or normalized, for  example, as AM/M*A  log D. Such
histogram plots are especially useful for impactor data,  which normally yield fewer size
intervals than particle-counting techniques.  Examples of such displays are  shown in
Figure 3-13 (Wilson et al., 1977) and Figure 3-14a (John et al., 1990).  Dae is typically used
when the data is presented as aerodynamic diameter and Dp when  the data  is presented as
geometric diameter.
     It is frequently desirable to draw a smooth line through the data in order to identify
modes and the mass  median diameters (MMD) and widths (ag) of  modes.   This can be done
by fitting the data to two or more lognormal distributions, as was  done in Figure 3-13 (also
see Hasan and Dzubay,  1987; and Whitby-DISFIT (TSI, 1993). It is better to use an
inversion process, such as originally developed by  Twomey, to construct a continuous curve
to represent the measurement data as shown in Figure 3-14b (John et al.,  1990; Winklmayr
et al.,  1990).  The continuous curve may then be fit to  one  or more log-normal distributions
as shown in Figure 3-14c.  However, one must be aware that log-normal distributions may
not always provide good fit to actual data (see Figure 3-16).  In this type of presentation the
area in each rectangle or the area under a portion of a curve is proportional to the mass in
                                         3-148

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      8
 «?
  I
  i
 CO
      6
  I
  &  4

  I  3

  I  2
      1

      0
                     Electrical aerosol analyzer
                 i	1—i—i  111MI	1—i  i  111
                            Tl     EAAEZH
                                               TT
                                    220
       0.002     0.01
 0.1              1             10
Geometric Diameter, Dp, urn
100
Figure 3-13.  An example of histogram display and fitting to log-normal functions for
             particle-counting size distribution data.  Instruments used and the range
             covered by each are shown.  Counts are combined into reasonably-sized
             bins and displayed.  Lognormal functions, fitted to the data, are shown
             with geometric mean diameter (DGV, equivalent to volume median
             diameter) of each mode and the width (ag) of each mode. Note the clear
             separation of the nuclei mode (OGV =  0.018 urn), the accumulation mode
             (OGV = 0.21 ion) and coarse mode (OGV = 4.9 /on).  Fine particles, as
             defined by Whitby (1978), include the nuclei and accumulation mode.

Source:  Wilson et al. (1977).
that size range (or the quantity of any other parameter plotted on a linear scale). Plotting
mass per impactor stage versus impactor stage number, or drawing lines connecting the
midpoints of size range at the heights of the mass in each size range, does not provide such
quantitative information.  Once the characteristics of the impactor have been demonstrated,

and once good fits to lognormal distributions have been obtained,  repeated measurements of
the same species may be shown by curves fitted to inversion or lognormal distributions such
as the example in Figure 3-15 (John et al., 1990).
                                       3-149

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   300
£ 250
^ 200
D 150
8" 100
§  50
     0
                              ClaremontNOg 13-Jul-87 0600-0930
                      0.01
   300
<£  250
^ 200

a  15°
| 10°
§  50
     0
                                                               (a)
0.1         1         10
  Aerodynamic Diameter (urn)

    Inverted Size Distribution
                                               100
                                                               (b)
                      0.01       0.1          1         10
                                  Aerodynamic Diameter (urn)
                                               100
                ,-. 30°
                <£ 250
                S 200
                Q  150
                8" 100
                I  50
                     0
                                    Lognormal Fit
                                               (c)
                      0.01       0.1          1         10
                                  Aerodynamic Diameter (jim)
                                               100
Figure 3-14.  An example of an effective display of impactor data:  (a) histograph
              showing mass found on each impactor stage and upper and lower cut
              points of each stage, (b) inverted size distribution, (i.e., a smooth
              distribution that would give the observed distribution considering the
              actual efficiency of each stage;  cut points are  not exact;  each stage allows
              some large particles, which it should collect, to pass through to the next
              stage and collects some small particles which  it should pass on to the next
              stage), (c) the solid line is the distribution obtained by fitting a sum of
              several lognormal functions to the inverted distribution.  The dashed lines
              show the lognormal functions obtained from the fitting process.  In this
              case, the use of log-normal distributions provides a reasonably good fit to
              the data.

Source: John et al. (1990).
                                         3-150

-------
    300

    250

WE  200
I
O  15°
 O)
|  100

     50
             o-.
                                                         0600 - 0930
                                                         1000-1330
                                                         1400-1730
                                                         1800-0100
               0.01
                     0.1           1           10
                      Aerodynamic Diameter
100
Figure 3-15.  Size distributions of sulfate, Long Beach, June, 1987, showing use of fitted
             log-normal distributions to describe diurnal variations in size and
             concentration.
Source:  John et al. (1990).
     In impactor measurements, the maximum size of the upper stage and the minimum size
of the lower stage (or after filter) are not well defined.  Therefore, an arbitrary choice must
be made in order to define the A log Dae.  This choice can have a remarkable influence on
the perceptions of the positions, height, and width of modes.  A particularly  dramatic
example is shown in Figure 3-16, from Sega and Fugas (1984).  The authors chose 0.1 /mi
for the lower limit and 20 fim for the upper limit, suggesting a bimodal distribution with a
fine mode MMAD at about 1.5 /mi, and a coarse mode MMAD at about 10.5 /mi.
However, if 0.4 /mi is chosen for the lower limit and 10 /mi for the upper limit, the display
suggests a fine mode MMAD of about 0.7 /mi and a coarse mode MMAD of about 8 /mi.
                                       3-151

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    a) Yugoslavia, Winter B, Author's Original Endpoints, 0.1 and 20 urn
    400.01
  Q 200.0-
                        Author's
                        originak
                        curve  \
                         Mode MMAD og %Mass

                           1    0.30  3.79  46.5
                           2    6.10  5.95  53.5
        0
  1.0                   10.0
Aerodynamic Diameter, Dae(nm)
100.0
   b) Yugoslavia, Winter B, Replotted with New Endpoints, 0.4 and 11 urn
    400.0

  Q 200.0
                                                              1    0.74  1.53
                                                              2    2.39  1.52
                                                              3   12.00  1.42
                                   Mode %Mass
                                     1     42.3
                                     2     13.6
                                     3    44.1
                               1.0                   10.0
                             Aerodynamic Diameter, Dae(nm)
                                             100.0
Figure 3-16. Effect of changing endpoints. This example of impactor data shows how
            the lack of a well-defined upper and lower size limit can affect the
            perception of the location of fine and coarse particle modes.  A histogram
            with an upper limit of 20 /tin and a lower limit of 0.1 /tin diameter, along
            with the curve drawn by authors of the report, is shown in Figure 3-16a.
            In Figure 3-16b a histogram with a lower limit of 0.4 /tm and an upper
            limit of 10 /tm is shown.  The author's free hand curve suggests a fine
            particle MMAD around 1.5 /tm diameter. A quite different idea of the
            location of the modes is given when different endpoints are chosen. Much
            of the material found between 1.0 and 5.0 /tm is probably smaller particles
            caught on the glass  fiber impactor stages which have very poor separation
            efficiencies. The data has been fitted to a 3-lognormal mode distribution;
            however, log-normal distributions do not provide a good fit to this data.

Source: Sega and FugaS (1984).
                                      3-152

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3.7.4   Comparison of Particle-Counting and Particle-Collection Techniques
      Unfortunately, there have been few efforts to compare results of the two particle-sizing
techniques.  One such effort is shown in Figure 3-17 (Durham et al., 1975). The differences
between the two techniques, as evident in the figure, are qualitatively observed in individual
studies using either of the two techniques.  Particle counting techniques usually give a lower
and wider minimum. Typically particle counting leads to volume distributions plotted versus
geometric size (or more properly, geometric size inferred from mobility or optical size);
whereas impactor separations give mass versus  aerodynamic size.  In Figure 3-17 both
geometric and aerodynamic scales are given.  This figure illustrates the problems involved in
defining particle "size"  and serves as a reminder that each particle sizing technique gives a
different "size".  The upper scale, used for impactor data,  is given in aerodynamic diameter.
The aerodynamic diameter of a particle is the diameter of a particle of density, p = 1.0,
which would behave similarly with respect to impaction as the particle in question. For
spheres in the coarse mode, the aerodynamic diameter, Da, equals Vp Dp, where p is the
density of the particle and Dp is the geometric diameter.  Since coarse particles are expected
to have a greater density than fine particles, converting the volume, geometric-size
distribution to a mass, aerodynamic-size distribution would increase the apparent size of the
volume distribution  above 1 /*m and widen the minimum.  For small particles, below 0.5
/im, or at reduced pressures where the mean free path of the gas molecules is of the same
order, or larger than the particle diameter, the Stokes diameter, which is more closely related
to the diffusion coefficient, is a more useful parameter.  The relationships between Stokes,
aerodynamic and geometric diameter are  discussed in Section  3.1.3.1.
      The particle size distribution shown on the bottom of the graph was derived from a
combination of a mobility counter and several optical counters.  The "mobility size",
obtained from the electrical aerosol analyzer (EAA) in earlier studies and the differential
mobility analyzer (DMA) in more recent  studies is dependent  on the particle shape but not
the density.  For irregularly shaped particles the "mobility" size gives the Stokes diameter,
which is the geometric diameter of a sphere with the same aerodynamic drag.  For a sphere
the Stokes diameter and the geometric diameter  are the same.   By comparing the mobility or
Stokes diameter to the aerodynamic diameter it is possible to measure the density of spherical
particles  (Stein et al., 1994).
                                         3-153

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           0.01
Aerodynamic Particle Diameter, Dae,
       0.1                1.0
                                                             10
     'E
     n° 140
     § 120
     o
        100
o
:§   80
OT
b
o   60

1
|   40
o
O

-------
laser or narrow wavelength optical counters to size changes it is especially difficult to
measure particles in the 0.5 to 1.0 size range (Hering and McMurry, 1991; Kim,  1995).
Since the amount of scattered light depends strongly on the refractive index it would be
useful  to calibrate optical counters with particles of the same refractive index as those in the
atmosphere.  Hering and McMurry (1991) used a differential mobility analyzer to select
particles of a uniform geometric diameter.  The light scattering of these monodispersed
atmospheric particles,  as measured by a Particle Measuring System LAS-X optical counter,
was compared  to that of spheres of polystyrene latex (a substance frequently used to calibrate
optical counters) and oleic acid of the same geometric diameter. The atmospheric aerosols
scattered less light than polystyrene latex sphere (refractive index = 1.9 - O.Oi), but about
the same amount of light as oleic  acid spheres (refractive index =  1.46 - O.Oi) of the same
geometric size.  Relating the variety of sizes measured by particle  counters and impactors,
and displaying  them together on an aerodynamic diameter scale, or other scale, is a major
task which has not yet been adequately addressed.
     The greater width of the coarse modes, as measured by the impactor in Figure 3-17,
may be attributed to the use of glass fiber filter paper for the impactor surface.  It is now
recognized that the use of glass fiber filter material, as contrasted to a flat surface, causes a
severe reduction in the effectiveness of the cut. Large particles bounce off the glass fiber
(Vanderpool  et al., 1987) giving much reduced collection efficiencies; whereas fine particles
penetrate into the fiber and some are captured  in stages that should have near zero collection
efficiencies (Rao and Whitby, 1978).  Many studies that used the Anderson  High Volume
Fractionating Sampler also used glass fiber  filters.  The use of glass fiber filters as impaction
collection surfaces causes any given size range to contain both larger and smaller particles
than predicted and thus tends to spread out the modes and fill  in the minima.  An example of
the smoothing effect of glass fiber collection surfaces, and especially the collection of fine
particles on upper stages, can be seen in Figure 3-16. Nevertheless, the bimodal nature of
the ambient aerosol is  still captured.
                                          3-155

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3.7.5    Review of Size-Distribution Data
3.7.5.1  Early Studies
     In 1978, when EPA scientists debated the best cut-point to separate fine particles from
coarse  particles, there was limited information available.  Particle-counting data from
California studies had been summarized by Whitby and Sverdrup (1980) and are shown in
Figure 3-18.  With the exception of one distribution from Pomona, all distributions showed a
minimum near  1 /*m and indications of significant amounts of coarse particle material
between  1.0 and 2.5  ^im.  (The region between 1 and 2.5 ju,m  will be referred to as the
intermodal region.)  Other studies of size distribution (McMurry et al., 1981) in the
Southeastern United States, provided similar information (Figure 3-19).
     Results from several  impactor studies were also  available, some of which suggested two
modes. However, much of the impactor data were considered unreliable in regard to the
existence and position of modes (Whitby et al., 1974).  However, one of the more extensive
and reliable studies available (Patterson and Wagman,  1977) provided confirmation of the
Whitby bimodal observations.  In this study, mass and composition measurements were made
for four different levels of visibility.  The histograms for mass, sulfate, and iron for two
levels of visibility are shown in Figure 3-20.  It is clear that the major portion of the fine
mass is below 0.6 jum and the  major portion of the coarse mass is greater than 3 /im in
diameter. These  impactor data, coupled with the more extensive number-size distributions
data of Whitby and Sverdrup (1980) led to a preference for a  1 /iin cut-point but an
acceptance of 2.5 /xm on the assumption,  then considered to be the case, that 2.5 /xm
represented the minimum cut-point that was attainable with a dichotomous sampler
(Miller etal., 1979).

3.7.5.2  Recent Work
     In the intervening 15 years, there has been very little additional work in which
particle-counting techniques, covering the entire size range, have been used to measure
ambient aerosols. Most of the particle-counting studies have focused on fine and ultrafine
particles, diameter < 1.0 /mi.  There have however been a number of impactor studies that
provide insight into the size of the fine and coarse modes and into what material is found
between  them.
                                         3-156

-------
   CO
Richmond
San Francisco Airport
Fresno
Hunter Liggett
Harbor Freeway
Pomona
Goldstone
Clean Continental
  Background
                                            1.0    2.5
                          Geometric Diameter, Dp

Figure 3-18. Grand average volume-size distributions from the Aerosol
            Characterization Experiment (ACHEX) in 1972. A size distribution for
            clean continental aerosol is shown for comparison.  Note that with the
            exception of the Pomona size distribution, all distributions show a distinct
            minima near 1.0 /on diameter.  A line has been added at 1.0 /mi, 2.5 jtm,
            and 10 nm diameter to indicate how much of the coarse particle mode is
            observed between 1.0 and 2.5 /on diameter.

Source: Whitby and Sverdrup (1980).
     There are only a few impactor size distribution studies that cover the full size range

from 0.01 to 100 pm (Lundgren and Hausknecht, 1982a,b; Lundgren et al., 1984; Burton

and Lundgren, 1987; Vanderpool et al.,  1987). Lundgren and co-workers used a mobile


                                     3-157

-------
    70

    60

co   50

 f  40
 o.
Q
I  3°

    20

    10
            16 Km Downwind-13:23 15:21
            Average of 18 Distributions
            SD2 " 78 ppb
            23 Km Downwind-16:18 17:07
            Average of 8 Distributions
                " 34 PPb
            Background
       0.01
                              0.1                      1
                            Geometric Diameter, Dp, urn
10
Figure 3-19.  Volume-size distribution taken in the midwestern United States near the
             Cumberland Power Plant in Tennessee. Note that coarse mode decreases
             and fine mode increased as the mobile sampling van moved downwind
             farther from urban influence but allowing more time for reaction as the
             power plant plume mixed with background air and SO2 was converted to
             sulfate and NOX to nitrate.
Source:  McMurry et al. (1981).
unit, the wide range aerosol classifier (WRAC), to measure mass-size distribution in ten size
ranges from <0.4 to >50 /mi.  Two distributions, averages for Philadelphia and Phoenix,
are shown in Figure 3-21.  Both clearly indicate a fine particle mode with an MM AD near
0.5 jum for Philadelphia and below 0.3 /im for Phoenix. Both show a coarse particle mode
with an MMAD near 20 /xm in diameter.  However, there is a significant amount of material
found in the intermodal region, 1 to 2.5 pm. Although the intermodal mass is not a
significant fraction of the total suspended particulate mass or even of TSP, as would be
measured by a high-volume sample (upper cut-point around 25 /mi), it does represent a
                                        3-158

-------
                  Background visibility
                                              Visibility level A
                                              l\4ot-78.5ng/rrP
z.o
2.0
§> 1.5
S 1.0
0.5

	 ' ' ' ' '"" 	
Mass
-
-
i_n_



1 1 1 1 1 1 in * 1 1 it 1 1 ii
-
-
-
1 1 1 1 1 Mil

2.0
1.5
1.0
0.5

1 1 II1III1 1 1 IIIIIII 1 1 1(1111
Mass
-
-
-
i — |


-
-
i — r— |
i i 1 1 1 1 in i i 1 1 1 1 in i iiiiin
         0.1  0.2   0.5   1   2    5  10  20    50100
                Aerodynamic Diameter, D», urn
                                                0.1  0.2  0.5  1   2   5   10  20
                                                       Aerodynamic Diameter, DM, (im
                                                                   50 100

6
CO
<

2.80
Id1
f 2.10

-------
               90.0
                                    Philadelphia-WRAC
                                                  Mode   MMAD    o.  %Mass
                                                   1     0.436   1.5*1    48.2
                                                   2     2.20    1.16     7.4
                                                   3    28.8     2.16    44.3
                                  1.0               10.0
                                 Aerodynamic Diameter,
               90.0
              45.0'
                                       Phoenix-WRAC
               0.0
0.1
                                  1.0               10.0
                                 Aerodynamic Diameter,
                                                   100.0
/^N
Mode
1
2
3
t
/ N
s

MMAD o. %Mass
0.188 1.54 22.4
1.70 1.90 13.8
16.4 2.79 63.9

^
f "• ,<•'

^>^X
^


/
X 	
x**—


X
\
r\
^~
100.0
Figure 3-21. Impactor size distribution measurement generated by Lundgren et al. with
             the Wide Range Aerosol Classifier:  (a) Philadelphia and (b) Phoenix.
             Note the presence of more coarse mode particles in the size range 1 to
             2.5 pm, in the dryer environment of Phoenix.
Source: Adapted from Lundgren and Hausknecht,  1982b.
major portion of the coarse fraction of PM10.  An attempt has been made to fit the
distribution with three, log-normal distributions.  In this case, the fit is poor.  In the Phoenix
case the accumulation mode cannot be defined other than that the MMAD is below 0.2 /xm.
The coarse particle fractions are very wide suggesting the possibility of two or more modes
(Figure 3-24).  The material between 1 and 2.5 pm is not a new mode but an indication of
either an artifact due to particle bounce, or a long-lasting tail of the coarse distribution.
     The existing size-distribution data were recently reviewed by Lundgren and Burton
(1995), with emphasis on the coarse mode.  They concluded that the coarse mode  could be
reasonably well described by a lognormal distribution with a mass median aerodynamic
diameter (MMAD) of 15 to 25 j*m and a mode spread (
-------
mass would be less than 2.5 /mi and only about 0.1% would be less than 1.0 /mi in
diameter.  This conclusion is confirmed by data from Whitby in which a wind change
allowed a measurement of fresh coarse mode aerosol (National Research Council,  1979).  As
can be seen in Figure 3-22, the intermodal mass,  1.0 to 2.5 /mi, was not affected, even
though the mass at 20 /im increased substantially.
                          Hunter-Liggett
                          9-14-72
                            0.1                 1       2.5
                              Geometric Diameter, Dp, (am

Figure 3-22.  Example of aged and fresh coarse mode particle size distributions.
             A sudden wind change brought fresh wind-blown dust to the sampler,
             operated as part of the South Coast Air Quality Study.  Note that there is
             only a very small change in the intermodal mass,  1.0 to 2.5 pm diameter,
             although there is a major increase in the mass between 2.5 and 10 pm in
             diameter.

Source:  National Research Council (1979).
                                       3-161

-------
     Another extensive set of studies covering the full size range, but limited to the Chicago
area, has been reported by Noll and coworkers (Lin et al., 1993, 1994).  They used an
Andersen impactor for smaller particles and a Noll Rotary Impactor for larger particles.
Results of Lin et al. also indicate a bimodal mass distribution.  For the shorter time interval
measurements  (8 or 16 h), the average MMAD for the fine mode was 0.42 /im, with a og
around two. The average MMAD of the coarse mode was 26+8 /^m, with a og varying
from 2.0 to 3.5.  As shown in Figure  3-23, the results of Noll and coworkers (Lin et al.,
1993, 1994) also suggest that in some  instances little coarse mode material is found in the
intermodal  region, 1.0 to 2.5 /mi.  Lin et al. (1993) combined material on the 0.65 to
1.0 pun and the 1.0 to 2.0 /mi stages before weighing.  Therefore, the MMAD of the
accumulation mode is not as well defined as it might be, and could be smaller than that given
by the fitting process.  Therefore, these results cannot be used to show that some fine PM is
found above 1.0 /mi.  When fitted to two log-normal distributions the fit is poor and the
coarse mode is very wide.  The fit with three log-normal distributions is used to show the
possibility of particle bounce or a second mode within the coarse particle  size range
contributing to mass in the intermodal (1-2.5 /mi) region.

3.7.6  Intermodal Region
3.7.6.1 Coarse Mode
     The question then arises, what portion of the coarse mode material found in the
intermodal  region is real  and what portion is artifact?  As discussed in Section 3.3.3.2.4, the
optical size may differ from the geometric or aerodynamic size.  Optical counters  are
normally calibrated with latex particles, or other particles of a  specific refractive index.
Atmospheric particles with different refractive  indices would be incorrectly sized if the
difference in refractive index resulted  in a difference in the amount of light scattered by  the
particles  (Wilson et al., 1988; Liu et al.,  1992; Hering and McMurry, 1991). For particle
counters  using lasers,  particles of different sizes within the 0.5 to 1.0 /mi range may give the
same light scattering  (Hering and McMurry, 1991; Kim 1995).
     In the case of impactors, it is possible that an artifact may arise from particle bounce,
from fragmentation of larger agglomerates, or  from loosening of material from other surfaces
by impacting particles. The problem of particle bounce in impactors has been treated
                                         3-162

-------
     20.0
                                      20.0-
.1         1.0        10.0
    Aerodynamic Diameter,
                                        100.0
0.1
           1.0        10.0
     Aerodynamic Diameter,
                                          100.0
     40.0
                                      30.0-
          1.0        10.0
    Aerodynamic Diameter,
100.0
0.1         1.0        10.0
    Aerodynamic Diameter,
                                                                                  100.0
Figure 3-23.  Size distributions reported by Noll and co-workers from the Chicago area
              using an Anderson impactor for the smaller particles and a Noll Rotary
              Impactor for the larger particles.
Source: Lin et al. (1993).
theoretically and practically in many studies (Wang and John,  1987,  1988).  Most workers
coat the coarse particle stages with a grease or oil to reduce bounce.  However, as the
surface becomes covered with aerosols, a particle may impact another particle instead of the
surface and either bounce to a lower stage or cause deagglomeration and reentrainment of
previously collected particles (John et al., 1991; John and Sethi, 1993).  As impactor
collection plates become loaded or as inlet upper size cut surfaces become dirty, the
magnitude of the  effect increases  (Ranade et al., 1990; John and Wang, 1991). One result is
a lowering of the effective cut point of the inlet and the impactor stages. Thus, it is
uncertain how much of the mass found in the intermodal size range is real and how much is
due to artifacts.
                                         3-163

-------
     There are several reasons to believe, however, that some of the intermodal mass may
be real.  For example, Lundgren and Burton (1995) point out that the lifetime of particles in
the atmosphere is a strong function of their aerodynamic size. Thus, while freshly generated
coarse mode  aerosol may have a MM AD of 20 /im, with only 1 % below 2.5 /mi,  as the
aerosol ages the larger particles will rapidly fall out, leaving a distribution enriched with
particles  in the small-size tail of the distribution.
     A second explanation has to do with the possible multimodal nature of dust generated
by wind  or vehicular traffic.  A study by the U.S. Army (Pinnick et al., 1985) measured the
size distribution of dust generated by heavy vehicles driven on unpaved roadways  in the arid
southwestern United States.  A variety of light-scattering instruments were used and were
recalibrated for the refractive index of the soil particles.  The occurrence of strong surface
winds (gusts of 15 to 20 m s"1) during the study permitted, in addition to the vehicular-
generated dust, some measurements of windblown dust.  There were some differences
between  sandy soil and silty soil, and between dust generated by vehicular traffic and by
wind.  However, all situations produced a bimodal size distributions. The upper mode had
an MMAD that ranged from 35 to 53 /mi, with ae from  1.37 to 1.68.  Of particular interest,
                                               &
however, was a second mode having an MMAD that varied from 6.2 to 9.6 /mi, with a ag
from 1.95 to 2.20.  (One measurement in silty  soil had an MMAD of 19.4 /mi.)  The
MMADs of the smaller coarse particle modes are significantly smaller than those coarse
mode MMADs observed by Lundgren or Noll.  An example of vehicular generated dust is
shown in Figure 3-24. Note that the differential mass is plotted on a logarithmic scale.
These results suggest that in arid areas, significant soil material, generated by traffic or
wind, may be found in the intermodal region.
     A third reason for believing that the intermodal mass is real has to do with the relative
size efficiency of particle removal equipment used on power plants and other large industrial
facilities. Older control  devices, which may  still be used in some applications, allow
significant particle mass  to escape.  Overall mass efficiencies are approximately 80% for
cyclones and 94% for scrubbers. Modern control devices have very high overall
efficiencies, 99.2% for electrostatic precipitators  (ESP) and 99.8% for baghouses. However,
all of these devices have efficiencies for coarse particles  that decrease with decreasing size.
Efficiencies typically reach a minimum between 0.1 and  1 /mi and increase for particles
                                          3-164

-------
           10'
    E
    T3
    £   'o
    II
    Is
    
-------
(B 0.9
.2 0.7
f 0.6
W O-5
^ 0.4
$0.3
^ 0.2
< 0.1
ft A

• No Controls
. 100% Emitted
.
: _H
• -j


P












         1.4

      f,2
      o
      *  1.0
      w
      8 0.8

         "
       | 0.4

      < 0.2

         0.0

       
-------
                         0.05 0.1              1.0     2.5
                                 Stokes diameter, urn
                      400
                      300
                    «200
                    "O
                      100
                           b) After Baghouse
                        0.05  0.1             1.0    2.5
                                 Stokes diameter, urn
10
Figure 3-26. Size distributions from a fluidized-bed, pulverized coal combustor, (a)
             after initial cleanup by a cyclone collector and (b) after final cleanup by a
             baghouse.
Source: Cheng et al. (1985).
     A fourth piece of evidence comes from studies in which measurements are made of the
elemental composition of PM2 5 and PM10 or the coarse fraction of PM10.  Elements
representative of soil type material have been found in the PM2 5 fraction.  In a study in
Philadelphia that used dichotomous samplers, an amount of soil-type material equal to 5% of
the coarse mode fraction of PM10 was found in the PM2 5  fraction (Dzubay et al., 1988).
Since the virtual impactor used in the dichotomous sampler minimizes particle bounce and
reintrainment, this would appear to be the small-size tail of the coarse mode in the 1 to
                                        3-167

-------
2.5 pm size range.  Similar results have been reported from the IMPROVE network
(Eldred et al., 1994).  Elemental analysis suggested that soil-derived material, equal to 20%
of the coarse fraction of the PM10 sample, was found in the PM2 5 sample.
     Thus, one can conclude that coarse mode material is found in the intermodal region.
There are reasons to suspect that a portion of this material is an artifact but that a portion is
real coarse mode material having an aerodynamic diameter between 1.0 and 2.5 pm.
In either  event, this can lead to a misunderstanding of the source of the particles, to
inappropriate control strategies, or to exposure studies that fail to differentiate correctly
between fine and coarse particles.

3.7.6.2  Fine Mode
     This section discusses the source of fine mode material found in the intermodal region.
Early particle-counting data suggested that, with  a few exceptions,  significant mass of fine
mode material would not be found above 1 /mi (see Figures 3-13, 3-18, 3-19, and 3-20).
However, impactor studies, on some occasions, have observed significant mass on stages
with a cut point  of 1 /mi.  In some instances, the MMAD of the fine mode was as large as
1 /mi (John et al.,  1990).  The change in relative humidity produced by a few degrees
change in temperature can significantly modify the MMAD of an ambient aerosol size
distribution.  As the RH approaches 100%, accumulation mode aerosols, with dry sizes
below  1.0 /mi in diameter, may grow larger than 2.5 /mi in diameter,  be rejected by PM2 5
samples,  and be  counted as coarse particles.
     Before examining additional field data demonstrating the effect of relative humidity on
particle size, it is useful to review some basic information on the interaction of water vapor
with the components of fine particles. Sulfuric acid (H2SO4) is a hygroscopic substance.
When exposed to water vapor a H2S04 droplet will absorb water vapor and grow in size
until an equilibrium exists between the liquid water concentration in the H2SO4 solution
droplet and the water vapor concentration in the  air.  The amount of water in the sulfuric
acid droplet will increase and decrease smoothly as the RH increases and decreases.
Ammonium sulfate, (NH4)2SO4, however, is deliquescent.  If initially  a crystal in dry air, it
will remain a crystal until a certain RH is reached; at this point it will absorb water and
become a solution droplet. The RH at which this happens, =80% RH in the case of
                                         3-168

-------
NH4)2S04, is called the deliquescent point.  At RH's above the deliquescent point the
(NH4)2SO4 droplets are hygroscopic, gaining or losing water reversibly as the RH increases
or decreases.  If the RH decreases below the deliquescent point the solution droplet becomes
supersaturated and unstable to crystallization.  However,  sub-micron sized droplets will
remain supersaturated until a significantly lower RH, known as the crystallization or
efflorescent point is reached.  The crystallization point decreases with decreasing droplet size
and decreasing purity (Whitby, 1984).  Thus, for a deliquescent substance,  a plot of droplet
diameter or water content as a function of RH will have two lines, one for  increasing RH
and another for decreasing RH.  An example of this phenomenon, known as hysteresis,  is
shown in Figure 3-27.  Table 3-16 shows the RH at the deliquescent and crystallization
points for some compounds  found in the  atmosphere.
     Much experimental and theoretical effort has gone into understanding  this process.  The
basic theory was elucidated by Hanel (1976).  Much experimental work has been done on
atmospheric species (e.g., Tang and Munkelwitz, 1977, 1993;  Richardson and Spann, 1984).
The  electrodynamic balance, by which single particles can be studied, has advanced the
understanding of particle-water vapor equilibrium, especially for particles in metastable
states, e.g., the supersaturated solution particles which are responsible for the hysteresis loop
shown in Figure 3-27 (Cohen et al., 1987a,b; Chan et al., 1992; Kim et al., 1994).
Ammonium nitrate, NH4NO3, because of its volatility, is difficult to handle but has been
studied successfully by Richardson and Hightower (1987).  The aerosol equilibria models
developed by Seinfeld and co-workers allow calculation of the water content of bulk  solution
as a  function of relative humidity (Kim and Seinfeld). The model SCAPE (Kim  et al.,
1993a,b) has been used to estimate the contribution of water to suspended aerosol mass in the
California South Coast Air Basin  using particle composition data from the 1987 Southern
California Air Quality Study (Meng et al., 1995). From  midnight to early  morning,  when
the temperature is  low and relative humidity is high, water was usually the  predominant
aerosol substance.  Paniculate water in the winter was estimated to be considerably larger
than in the summer at each of the four sites  studied.
     The water content of a sub-micron sized droplet, and therefore its size, depends not
only on the dry size but is a result of a balance between surface tension and solute
concentration (Li et al., 1992). Pure water is in equilibrium with its vapor when the RH
                                         3-169

-------
                                                            8
                                    50        70
                                     RH, %
90
                                                                o
                                                                CL
Figure 3-27. Particle growth curves showing fully reversible hygroscopic growth of
            sulfuric acid (H2SO4) particles, deliquescent growth of ammonium sulfate
            [(NH4)2 SOJ particles at about 80% relative humidity (RH), hygroscopic
            growth of ammonium sulfate solution droplets at RH greater than 80%,
            and hysteresis  (the droplet remains supersaturated as the RH decreases
            below 80%) until the crystallization point is reached.

Source: National Research Council (1993) adapted from Tang (1980).
        TABLE 3-16.  RELATIVE HUMIDITY OF DELIQUESCENCE AND
          CRYSTALLIZATION FOR SEVERAL ATMOSPHERIC SALTS3
Compound
(NH4)2S04
NH4HSO4
NH4NO3
NaCl
Deliquescence
79.9 ± 0.5
39.0 ± 0.5b
61.8
75.3 ±0.1
Crystallization0
37 ± 2


42
aTaken from Tang and Munkelwitz (1993) unless otherwise indicated.
bTang and Munkelwitz (1977).
cShaw and Rood (1990) and references therein.
                                      3-170

-------
equals 100% and is therefore, stable, i.e. the rate of evaporation equals the rate of
condensation. The water in a solution will be in equilibrium with water vapor at a lower
water vapor concentration because the presence of solute molecules or ions lower the rate of
evaporation.  Therefore, a solution will absorb water and become more dilute, increasing the
water vapor concentration needed for equilibrium until the solution water vapor concentration
required for equilibrium matches the ambient water vapor concentration or RH.  As the
droplet size decreases the surface tension increases and the vapor pressure of water required
to maintain equilibrium increases.  Therefore, the smaller the dry size of the particle, the less
the amount of growth as RH increases.
     Theoretical calculations of the growth of various  sizes of ammonium sulfate particles
and an experimental verification of such calculations, using a simulation of the humidification
process in the human lung, are shown in Figure 3-28.  Note the very rapid increase in the
amount of water and in the diameter of the aerosol particle as the relative humidity
approaches 100% RH.  Considering the difficulty of measuring relative humidity accurately
between 99 and  100%, theory and experiment are in reasonable  agreement.  As can be seen
the effect of surface tension is most important for particles with  dry size less than  100 nm
(0.1 /mi).  This  phenomenon may be of importance in  considering the biological effect of
water-soluble pollutants.  Accumulation mode particles will be diluted when exposed to
humidification in the lungs.  Ultrafine  or nuclei mode particles will not be diluted as much.
In the atmospheric aerosol the number distribution will almost always be dominated by
particles below 100 nm (see Section 3.1.2).  However, aerosols  generated in the laboratory
for exposure studies probably lack the  smaller particles found in the atmosphere.  This
provides a hypothesis for the difference in effects observed in epidemiological studies and
laboratory exposure studies.  The importance of this more concentrated, ultrafine droplet
component of the atmospheric aerosol may have been neglected because most of the
experimental studies of hygroscopicity  have used near-micron-sized particles.   However,  in
the modeling of  deposition of hygroscopic particles, workers, such as Martonen (1993), have
corrected the experimental curves of particle size as a function of RH, based on
measurements of near micron-sized particles, to account for the effects of surface tension on
ultrafine particles.
                                         3-171

-------
                                                                        216
                               i              i
                             Theoretical Prediction at22°C
                     ooooo  Experimental Measurements
                              50           100           150
                            NH4 HSO4 Dry Particle Diameter (nm)
200
                               i              i
                              Theoretical Prediction at 22° C
                     ooooo  Experimental Measurements
                                                               RH-98%


                                                               RH-96%
                                                     i   i     i   i   i  i
                    i           i   i  i         i  i   >
                              50           100           150
                            (NH4)2 SO4 Dry Particle Diameter (nm)
200
Figure 3-28.  Theoretical predictions and experimental measurements of growth of
              NH4HSO4 and (NH4)2SO4 particles at relative humidity between 95 and
              100%.
Source: Li et al. (1992).
                                          3-172

-------
     In addition to the laboratory studies discussed above there are some measurements on
the effect of RH changes on atmospheric aerosol. McMurry and co-workers have made use
of a Tandem Differential Mobility Analyzer (TDMA) system (Rader and McMurry, 1986) to
measure  the change in particle size with changes in relative humidity at Claremont, CA, as
part of the  Southern California Air Quality Study (SCAQS) (McMurry and Stolzenberg,
1989) and at the Grand Canyon National Park, AZ,  as part of the Navajo Generating Station
Visibility Study (Zhang et al., 1993; Pitchford and McMurry, 1994). One mobility analyzer
is used to isolate a narrow size distribution.  After humidification the size distribution of this
fraction is measured.  An example is shown in Figure 3-29. Note that Figure 3-29 is a
number size distribution not a mass size distribution. Particle growth with increasing RH is
evident.  However, between 70 and 91 % RH the distribution splits into less-hygroscopic and
more-hygroscopic components.  Pitchford and McMurry (1994) attribute this splitting to
external mixing, i.e.  there are two relatively distinct  classes of particles, both containing
some soluble and some non-soluble material,  with the more hygroscopic component
containing significantly more soluble and hygroscopic material.  A summary of the results  of
these studies is given  in Table 3-17 (Zhang et al., 1993). The difference in growth rates may
be due both to size and to variation in composition as a function of size.  The lower growth
factor for 0.2 /zm particles in Claremont relative to the Grand Canyon may be due to a
higher concentration of non-soluble organic material in Claremont.
     While there is a significant amount of information on the hygroscopic properties of
inorganic compounds, much less is known about the hygroscopic properties of organic
components of the atmospheric aerosol.  Saxena  et al. (1995) have examined the hygroscopic
properties of several  organic species and noted that water soluble organics may be
hygroscopic or deliquescent.  Using concurrent cascade impactor samples, they determined
the composition of the Grand Canyon and Claremont aerosol,  whose size distribution as a
function of relative humidity was discussed above.  They compared the observed water
content at the higher relative humidity with the water content calculated for the inorganic
components.  They concluded "that the aggregate hygroscopic properties of inorganic
particles  are altered when organics are also present.   Furthermore, the alterations can be
positive or negative.   The findings are consistent with the expectation that organics are
                                         3-173

-------
                                                     	1	1	1	
                                                     Initial Relative Humidity 53% RH
                                                     Final Relative Humidity
                                                           •  7% RH
                                                          o 28% RH
                                                           A 49% RH
                                                          ~ 70% RH
                                                             91%RH
 O
 o
  
-------
                     TABLE 3-17.  SUMMARY OF HYGROSCOPIC GROWTH FACTORS3

Dry Size (/xm)
0.05
0.2
0.4
0.5

i Dry Size (j«m)
0.05
0.10
0.20
0.30
0.40
1987 SCAQS, Claremont,
More Hygroscopic Peak
Dp(90 + 3% RH)
rDp(0%RH)
1.14 + 0.05
1.23 + 0.08
1.63 ± 0.11
1.59 ± 0.08
1990 NGS Visibility Study, Grand
More Hygroscopic Peak
D (89 + 4% RH)
rDp(0%RH)
1.36 ± 0.08
1.42 + 0.08
1.49 ± 0.11
1.51 ± 0.09
1.43 + 0.10
CA
Less Hygroscopic Peak
D (87 + 2% RH)
Dp(0% RH)
1.03 ± 0.03
1.02 ± 0.02
1.04 ± 0.05
1.07 ± 0.03
Canyon, AZ
Less Hygroscopic Peak
D (89 + 4% RH)
rDp(0%RH)
1.14 ± 0.10
1.17 ± 0.09
1.17 ± 0.10
1.14 + 0.10
1.07 ± 0.03
aValues are mean ± standard deviations.

-------
     o>
     Q
     o
     JO


     O"
     •o
            •  RH = 99% 8/12/90, 0200 hr

           +  RH < 50%
                              ?+
Sulfate Size Distributions     +   +

                              .••  •  t

          0.01
                 0.1                 1

                     Diameter (urn)
     Q
     o>
     o
              RH = 95% 8/4/90, 0200 hr

              RH < 50%
Sulfate Size Distributions     + •+   *

                             +•   ++.
                 i   i  i
          0.01
                                 Diameter
                                                      10
Figure 3-30. Example of growth in particle size due primarily to increases in relative

           humidity from Uniontown, PA.


Source: Lowenthal et al. (1995).
                                  3-176

-------
     There are also studies of the behavior of ambient aerosols as the relative humidity is
reduced by heating the sampled air.  Shaw and Rood (1990) report a study using a heated
integrating nephelometer in which crystallization RHs of 4 to 67% were observed.  Similar
studies in Washington, D.C. by Fitzgerald et al.  (1982) found no evidence of crystallization
or efflorescence when RH was reduced to 30% RH.
     Further experimental evidence of the effect of decreasing relative humidity on aerosol
size distribution is provided by impactor data reported by Berner (1989) and is shown in
Figure 3-31.  One impactor  sampled aerosol in its humidified  state directly from the
atmosphere.  The inlet of a second impactor was warmed =7 °C above the ambient
temperature of «5 °C in order to  evaporate most of the particle-bound water before
collecting the aerosol.  The water and other volatile material in both the "wet" and the "dry"
samples would evaporate in the laboratory prior to weighing the impactor stages. As can be
seen, in the ambient air most of the non-volatile mass was above 1.0 pm with significant
amounts above 2.5 /xm.  However, after heating the size of the aerosol was reduced  so that
most of the non-volatile mass was below 1.0 /xm.  Berner treated the distributions as
monomodal and derived growth factors of 4.9 for fog and 4.1 for haze. If the observations
are treated as multimodal, good bimodal, or  as shown in Figure 3-31, trimodal fits are
obtained.  This splitting into "more"  and  "less" hygroscopic modes at high relative humidity
has been observed by McMurry and co-workers (McMurry and Stolzenberg, 1989; Zhang et
al., 1993) (Figure 3-29) and Lowenthal et al. (1995) (Figure 3-30).  In some cases,  reported
by Pitchford and McMurry (1994), splitting into three modes of varying hygroscopicity was
observed. However, the separation into two "more" hygroscopic modes may represent, as
suggested by Berner, variations in relative humidity extremes during different parts of the
overnight sampling period.
     In measuring light scattering with the integrating nephelometer,  the aerosol community
has been very concerned about the difference in relative humidity and temperature in the
ambient air and in the volume of air in which particle scattering is actually measured (Covert
et al., 1972; Fitzgerald et al., 1982). Temperature differences  between the measurement
volume and ambient air of 1  or 2 °C can change the relative humidity and change the
observed light scattering. Great efforts have been made to minimize this temperature
                                         3-177

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                  80.0
                                  Bologna Haze, Wet (Bemer, 1989)
                s
                i
o-
Mode MMAD aa %Mass
1 0.204 1.69 9.9
2 1.95 1.97 23.5
3 3.50 2.65 66.5




>

^

/


s~
*\

N

\
                     0.01
0.1             1.0             10.0
   Aerodynamic Diameter, Dn (>tm)
                                                                                   100.0
                 100.0
                8
               0 50.0-
               i
               •a
                                  Bologna Haze, Dry (Berner, 1989)
                       Mode MMAD   a.  %Mass
                         1    0.130  1.42   10.8
                         2    0.589  1.34   57.4
                         3    1.65   1.36   31.8
                             % Dry mass lost
                             upon healing
                             15.8%
                     0.01
0.1             1.0             lO'.O
   Aerodynamic Diameter, DM (urn)
                                                                100.0
                  70.0
                                  Bologna Fog, Wet (Bemer, 1989)
                       Mode  MMAD  ofl   %Mass
                        1    0.310  2.09   30.8
                        2   1.34   1.93   36.4
                        3   5.31   1.91   32.8
                    0.01
                  0.1             1:0
                     Aerodynamic Diameter, DM
                               16.0
                                              100.0
                 200.0
                S
               Q 100.0-
0.0
  0.01
                                  Bologna Fog, Dry (Berner, 1989)
                       Mode MMAD  o.  %Mass
                        1   0.145 1.39  17.8
                        2   0.524 1.36  65.4
                        3   1.56  1.32  13.9
                             % Dry mass lost
                             upon heating
                                    0.1      	i'o      	"16.0
                                       Aerodynamic Diameter, DM(|im)
                                              100.0
Figure 3-31.  Mass size distribution of non-volatile aerosol material.  The aerosol was
               collected at ambient conditions,  "wet", or after evaporation of water,
               "dry".

Source:  Berner (1989).
                                               3-178

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difference.  However, researchers have not been nearly as careful in considering temperature
and relative humidity effects when measuring size distribution, either with impactors or
particle counters, even though effects have been reported in the early literature (Wagman
et al., 1967; Sverdrup and Whitby, 1980).
      A recent paper by Cass and coworkers (Eldering et al., 1994) provides some insight
into how differences in RH resulting from heating can cause differences between
particle-counting distributions and impactor distributions. Particle size distributions were
obtained by counting particles by mobility (electrical aerosol analyzer)  and light scattering
(optical particle  counter).  An example is  shown in Figure 3-32.  Almost no particles were
found between 1.0 and 2.5 /im diameter.  When these particle number data were converted
to total expected light scattering, they agreed with measurements made by a heated, but not
an unheated, integrating nephelometer; and when converted  to expected mass,  agreed with
filter  measurements of dry mass.  Eldering et al. (1994) conclude that even the moderate
heating occurring in mobility and optical counters was enough to change the size of the
particles, especially when the ambient air  was close to 100% RH.  It seems likely that  most
particle counting systems produce some heating of the aerosol, and thus some reduction of
the measured particle size from that existing in the ambient air.  On the other hand, if
particle-size measuring devices were located in air conditioned or heated trailers or
laboratories, the temperature of the sampled air would be changed and the measured particle
size distribution  would be different from that existing in the ambient air (Sverdrup and
Whitby, 1980).
      During the high relative humidities that occur at nighttime, growth of hygroscopic
components can  result in the growth of some fine mode aerosol to diameters greater than
1.0 ^m and perhaps even above 2.5 /xm.  As can be  seen in Figure  3-28, dry ammonium
sulfate particles having a dry diameter of 0.5 ^m will grow to  =2.5 /xm at a relative
humidity between 99 and 100%.  When the relative humidity actually reaches 100%, the
particles will continue to grow to maintain the relative humidity at 100%, and eventually
become fog droplets that are large enough to be collected in the fraction larger than 2.5
Ammonium sulfate particles with dry sizes greater than 0.5 /xm would also grow into the
larger than 2.5 urn size range.
                                         3-179

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CO
  3.
  Q.
 Q
  O)
 _g
 ;o
 •a
    100.00
     80.00"
60.00--
40.00--
     20.00--
       0.(
               August 27, 1987
               Claremont Case B
    )-0900
0900-1300 PST
1300-1700 PST
1700-2400 PST
                    0.01
                         0.1
                  Geometric Diameter, Dp,
                                                        1.0
10
Figure 3-32.  Example of particle-counting volume distribution obtained in Claremont,
             CA.  Compare to Figures 3-14 and 3-31.  Heating of the sampled air by
             the mobility and optical counters are believed to have resulted in a
             distribution representative of a lower than ambient relative humidity.
Source:  Eldering et al. (1994).
     The addition of water to hygroscopic particles, discussed in the previous section, is a
reversible process.  Particles absorb water and grow as RH increases; as RH decreases some
of the particle-bound water evaporates and the particles shrink.  However, the large amount
of liquid water associated with hygroscopic particles at high relative humidity provides a
medium for liquid phase transformation process. A number of atmospheric process, which
convert SO2 to sulfate or NOX to nitrate, can take place in water solutions but not in the gas
phase.  These processes  are not  reversible but lead to an accumulation of sulfate or nitrate
and lead to an increase in the dry size of the particle.  Of course as more sulfate or nitrate  is
added to the particle it will absorb more water so that the wet size will also increase.
     The first observation and clear discussion of these combined effects of relative humidity
on growth and SO2 conversion to sulfate are given by Hering and Friedlander (1982) as
                                        3-180

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shown in Table 3-18.  Using a low pressure impactor, they observed that days with higher
relative humidity had higher sulfate concentration and higher MMAD's compared to days
with lower relative humidity. Hering and Friedlander (1982) named the small mode the
condensation mode and suggested that it was formed by the gas phase conversion of SOj to
sulfate and subsequent nucleation, coagulation, and growth by condensation.  They named
the larger mode the droplet mode and discussed possible formation mechanisms.  This mode
is now believed to result from the reaction of SO2 in fog or cloud droplets (Meng and
Seinfeld, 1994).
           TABLE 3-18. COMPARISON OF SULFATE CONCENTRATION
            AND MASS MEAN DIAMETERS OF AEROSOLS FOR DAYS
               WITH HIGHER AND LOWER RELATIVE HUMIDITY

Minimum RH, %
Maximum RH, %
Sulfate concentration, /ig/m3
Mass median aerodynamic diameter, /xm
Low RH Days
17-35
45-68
3-9
0.20 ± 0.02
High RH Days
26-66
69- 100
3 -52
0.54 ± 0.07
Source: Hering and Friedlander (1982).


     In a series of papers McMurry and co-workers make use of the aerosol growth law,
originally developed by Heisler and Friedlander (1977), to study the mechanism and rates of
sulfate formation in ambient air (McMurry et al., 1981; McMurry and Wilson, 1982, 1983).
They were able to apportion growth to condensation and droplet mechanisms and observed
droplet growth in particles up to 3 /im in diameter.
     A process of aerosol growth due to increasing relative humidity (Figure 3-33) has also
been utilized by Cahill et al. (1990) to explain observations of sulfate size changes during the
1986 Carbonaceous Species Methods Comparison Study in Glendora, CA.  Cahill used a
DRUM sampler to measure sulfate  in nine size ranges. By tracking the mass of sulfate in the
0.56 to 1.15 fj,m size range Cahill et al. could follow the expansion and contraction of
aerosol particles containing sulfate.  Because of the relative high time resolution of the
DRUM sampler (4 h except for an 8-h increment each night from midnight to 8 a.m.),

                                       3-181

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      50
     40

  E
  i
  $
  o  30
     20
      10
       0
             Poor Period, 8/12-8/16
             0000-0800
                                   on
                               a
                     o
                     o
                                                       o Fair Period, 8/17-8/20
                                                       •0000-0800
                      20
40
60
80
100
                                   Relative Humidity (%)
Figure 3-33.  Relative humidity versus sulfur, during the 1986 Carbonaceous Species
             Methods Comparison Study, for particles with Dae > 0.56 /on. The
             approximate trajectories followed during each day by the Dae > 0.56 urn
             sulfur size fraction are shown for period P and period F.  Note that even
             when the  humidities are low, 30 to 50 %, the period P aerosols remain
             coarser by a factor of three than those of period F. The water content
             incorporated in the aerosols during the 0000- to 0800-h time periods is lost
             only slowly, giving a strong hysteresis effect in sulfur size.

Source:  Cahill et al. (1990).
Cahill et al. (1990) could follow this process as the relative humidity increased during the

night and decreased during the day.  These data indicate that during the "Poor Period" (low

visibility) particles grow as relative humidity increases.  However, they did not return to the

smaller size observed during the "Fair Period" (good visibility). This could be due to a

combination of growth due to reaction of SO2 to sulfate within the particles or failure of the

droplet to crystallize thus maintaining particle-bound water in a supersaturated state.
                                        3-182

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     John et al. (1990), in studies in the Los Angeles area,  observed a number of sulfate size
distributions with MMAD near 1.0 /xm.  A histogram of the sulfate MMADs from his study
is shown in Figure 3-34.  John et al. (1990) have provided a qualitative explanation to
account for these large MMADs for fine mode aerosol. In analyzing their data John et al.
plotted sulfate mass as a function of sulfate MMAD and found two distinct regions, as shown
in Figure 3-35.  Distributions with particles near 0.2 /mi diameter are probably still dry; the
particles have not reached their deliquescent point.  As the relative humidity increases they
reach their deliquescent point and grow rapidly into the 0.5  to 0.7 /mi size range.  During
the formation of fog,  the hygroscopic particles act as fog condensation nuclei, and with
relative humidity at 100%, grow into 1 to 10 /mi fog droplets.  Sulfur dioxide dissolves  in
the fog droplets and is rapidly oxidized to sulfate by atmospheric oxidants such as H2O2 or
O3, or by catalysis  by Fe or Mn. These particles lose  some of their water as the relative
humidity decreases below 100% RH, but will have substantially more sulfate than prior to
activation.  Similar processes occur in clouds (Schwartz,  1984a, 1986a).
     This type of process probably accounts for the large size of the fine mode observed in
Vienna (Berner et al., 1979; Berner and Lurzer,  1980).  Winter and summer size
distributions are shown in Figure 3-36. Berner et al. reported that fog occurred during the
night time during the  winter study.  In this European study,  as in American studies, instances
of fine mode size distributions with MMADs near or above  1 /mi seem to occur only when
fog or very  high relative humidity conditions have been present.  Two log-normal
distributions are fit to the accumulation mode to suggest the  separation, at high relative
humidity, into hygroscopic and hydrophobic components.  No  distribution was fit to the
coarse mode because  only a fraction of the coarse size  range was measured.
     Similar results have been observed in sampling with dichotomous samplers.   A large
humidity driven shift  of normally fine mode material into  the coarse mode was observed by
Keeler et al. (1988).  In the extreme case, 60% of the SO4=  and 50% of the PM2 5 mass was
shifted to the coarse fraction.  Such occurrences were not rare, occurring in 12 out of
83 several-hour sampling periods.
     In an analysis of data from the IMPROVE network Cahill and co-workers (Eldred
et al.,  1994) report that 20% of the total sulfate is found in the coarse fraction of PM10.
Studies in Philadelphia using dichotomous samplers have also reported that 20% of the total
                                         3-183

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              I*
              Q>
              CT
              s.
35

30

25

20

15

10

 5

 0
                             Summer All Sites  SOt <
                                                           (a)
   0.1                   1

          Aerodynamic Mode Diameter (urn)
                                                             10
               400  -
            Summer  All Sites  SOt -
(b)
                                         1
                            Aerodynamic Mode Diameter (|im)
                                            10
Figure 3-34.  Data from the South Coast Air Quality Study (John et al., 1990). Plots
             show (a) frequency of sulfate modes of various sizes as a function of mode
             diameter and (b) average sulfate mode concentration as a function of mode
             diameter.  Note that although there are only a few instances when the
             mode diameter is near 1.0 /an, it is these situations that give rise to the
             highest sulfate concentrations.  Modes with diameters above 2.5 /tm may
             be due to collection of fog droplets containing sulfate or reaction of SO2 in
             liquid droplets of NaCl due to  NaCl sea spray droplets in which SO2 has
             dissolved and reacted to form sulfate and release HC1 gas.
                                       3-184

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              1,000^
                    0.1                                     1
                         Aerodynamic Mode Diameter (urn)
Figure 3-35.  Log-log plot of sulfate mode concentration versus aerodynamic mode
             diameter from Claremont, CA, during the summer SCAQS (John et al.,
             1990). The solid lines have slopes corresponding to mode concentration
             increasing with the cube of the mode diameter. A transition between the
             two modes is believed to occur at approximately the sulfate mode
             concentration indicated by the horizontal dashed line.
sulfate is found in the coarse fraction (Dzubay et al., 1988).  Cahill and coworkers suggest
that sulfate particles may grow larger than 2.5 jum in diameter and thus be sampled in the
PM10 fraction but not the PM2 5  fraction.  It is possible for SO2 to react with basic carbonate
coarse particles to form a sulfate coating or to dissolve in wet NaCl particles, from oceans,
lakes, or salt placed on streets to dissolve ice, and be converted to sulfate with the release of
HC1.  However, there also is substantial evidence that some fine sulfate, and therefore
possibly other fine mode material,  may be found in the size range above 1.0 /*m and even
                                        3-185

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                                  Vienna, Summer
                                                                       10
                            Aerodynamic Diameter, Dae,
                                     Vienna. Winter
                0.1                        1.0
                	Aerodynamic Diameter, Dae,
Figure 3-36.  Typical results of size-distribution measurements taken with a Berner
             impactor in a Vienna street with heavy automotive traffic:
             (a) measurements taken during summer at three different elevations, (b)
             measurements taken during winter at three different elevations, fog was
             frequently present during the winter sampling period.

Source:  Berner and Liirzer (1980).
above 2.5 /xm diameter, due to the growth of hygroscopic particles at very high relative

humidity.

     These observations, indicating that, during near 100% relative humidity conditions,

significant amounts of normally fine mode material will be found in the coarse fractions

(>2.5 um diameter), have broader implications than selection of a cut point to separate fine
                                       3-186

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and coarse particles.  Such shifts could cause problems for receptor modeling using chemical
mass balance or factor analysis, for interpretation of exposure data in epidemiological
studies, and in estimated removal of paniculate matter by deposition.

3.7.7   Conclusions
      This review of atmospheric particle-size-distributions was undertaken to provide
information which could be used to determine what cut-point; 1.0 j«m, 2.5 /mi, or something
in between; would give the best separation between the fine and coarse particle modes.  The
data do not provide a clear or obvious answer.  Depending on conditions, a significant
amount of either fine or coarse mode material may be found in the intermodal region
between 1.0 and 3 /im.  However, the analysis does demonstrate the important role of
relative humidity in influencing the size of the fine particle mode and indicates that
significant fine mode material is found above 1.0 /im only during periods of very high
relative humidity.
      Thus, a PM2 5  sample will contain most of the fine mode material,  except during
periods of RH near 100 %.  However, especially in conditions of low RH, it may contain
5 to 20 %  of the coarse mode material below 10 /mi in diameter.  A PMt 0 sample will
prevent misclassification of coarse mode material as  fine but under high RH conditions will
result in some of the fine mode material being misclassified as coarse.
      A reduction in RH, either intentionally or inadvertently, will reduce the size of the fine
mode.  A sufficient reduction in RH will yield a dry fine particle mode with very little
material above 1.0 /mi.  However, reducing  the RH by heating will result in loss of
semi volatile components such as ammonium  nitrate and semi volatile organic compounds.
No information was found on techniques designed to remove particle-bound water without
loss of other semivolatile components.
3.8   SUMMARY
     Atmospheric paniculate matter (PM) refers to solid or liquid particles suspended in air.
The term atmospheric aerosol refers to both the suspended particles and the air (including
gaseous pollutants) in which the particles  are suspended.   However, the term aerosol is
                                         3-187

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frequently used to refer only to the suspended particles.  The terms paniculate matter and
particles will be used most frequently in this document.
     Paniculate matter is  not a single pollutant but rather a mixture of many classes  of
pollutants.  The components of PM differ in sources; formation mechanisms; composition;
size; and chemical,  physical, and biological properties.  Particle diameters span more than
four orders of magnitude,  ranging from a few nanometers (nm) to one hundred micrometers
0*m).  Because of this wide size range, plots of particle-size distribution are almost always
plotted versus the logarithm of the particle diameter. Diameter usually refers to the
aerodynamic diameter,  defined as the diameter of a  spherical particle with an equal settling
velocity but a density of 1 g/cm3.  This normalizes particles of different shapes and densities.
     One of the most fundamental divisions  of atmospheric particles is the naturally
occurring separation into a fine particle mode and a coarse particle mode as shown in Figure
3-3. The terms fine mode particles and coarse mode particles are used to refer to particles  in
the fine or coarse particle  distributions.  The two distributions overlap between 1 and 3 /im
aerodynamic diameter.
     Particles may  also be defined by the size cut of the collection or measuring device.  A
frequently used descriptor  is the 50% cut point.  This is the  aerodynamic diameter  at which
the efficiency of the device for particle collection is 50%.  As particles increase in size above
the 50% cut point, they are collected with decreasing efficiency,  eventually reaching  0%; as
particles decrease in size below the 50%  cut point, they are  collected with increasing
efficiency, eventually reaching 100%.  The indicator for the current particle standard is PM10
(i.e. particles with a 50%  cut point of 10 /urn aerodynamic diameter).  However, PM10
contains some particles larger than 10 fj,m and does  not contain all particles below  10 ^m.
Fine is also  used to refer to particles with an upper  cut point of 3.5, 2.5 (PM2 5), 2.1, or
1.0 nm.  Coarse is  also used to refer to particles between 2.5 and 10 /zm (PM(10.2 5)) or
particles collected by the high  volume sampler as well as the entire coarse mode.
     Size fractions  may also be characterized in terms of their entrance into various
compartments of the body. Thus, inhalable particles enter the respiratory tract, including the
head airways.  Thoracic particles travel past the larynx and reach the lung airways  and the
gas-exchange regions of the lung.  Respirable particles reach the gas-exchange region of the
                                         3-188

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 lung.  PMjQ is an indicator of thoracic particles; PM2 5 is an indicator of fine mode particles;
 and PM(10_2 5) is an indicator of the thoracic component of coarse mode particles.
      The fine and coarse particle distributions are frequently approximated by log-normal
 distributions.  However, finer distinctions can be made.  The fine particles consist of a nuclei
 mode, composed of particles recently formed from gases, and an accumulation mode, into
 which the nuclei grow and accumulate (Figure 3-6).  Ultrafine particles,  defined in this
 document as distributions with mass median diameters below 0.1 jum, are associated with the
 nuclei mode (Figures 3-1,  3-2, and 3-13).  In the presence of fogs or clouds, the
 accumulation mode may split into a smaller, less hygroscopic mode and a larger droplet
 mode.  The latter is formed by gases dissolving  in the fog or cloud droplets,  reacting, and
 forming particles when the water of the droplets evaporates (Figure 3-14).  There may also
 be several modes within the coarse particle distribution or mode but these are usually less
 distinct.
      The terms primary and secondary, anthropogenic and biogenic, outdoor and indoor
 microenvironment have significant applications to particulate matter. Primary fine particles
 are emitted from sources, either directly as particles  or as vapors which rapidly condense to
 form  particles.  Primary coarse particles are usually  formed by mechanical processes.
 Secondary fine particles are formed within the atmosphere as the result of gas-phase  or
 aqueous-phase chemical reactions.  Anthropogenic particles may be formed by primary or
 secondary processes.  Similarly, biogenic particles include primary particles of biological
 origin, including bioallergens, as well as secondary particles formed from biogenic
 precursors such as terpenes emitted into the atmosphere.  The term outdoor refers to
 community atmospheres.  These are the atmospheres which are usually monitored for
 particulate matter. Indoor microenviroments include homes, apartments, schools, office
 buildings and other indoor work places, large enclosed areas such as malls, vehicles used for
 commuting, etc.
      Some general classes of particles, such as organic particles, can occur not only as fine
 or coarse particles, but can be of either anthropogenic and biogenic origin, and can be
produced both in outdoor and indoor microenvironments.  Organic particles also can be
present in air as primary fine particles from combustion processes or as secondary fine
particles formed as a result of atmospheric reactions  involving higher molecular weight
                                         3-189

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volatile anthropogenic alkenes and aromatics or from the atmospheric reactions of volatile
biogenic compounds such as terpenes.  Therefore, there is considerable overlap for chemical
species among the categories listed above.
     A substantial fraction of the fine particle mass, especially during the warmer months of
the year, is secondary PM, formed as a result of atmospheric reactions.   Such reactions
involve the gas phase conversion of SO2 to H2SO4 by OH radicals and aqueous-phase
reactions of SO2 with H2O2, O3, or O2 (catalyzed by Fe and Mn). The NO2 portion of NOX
can be converted to HNO3 by reaction with OH radicals during the day.  During nighttime
NO2 is converted into HNO3 by a  series of reactions involving 03 and the nitrate radical
(NO3). Both H2SO4 and HNO3 react with atmospheric ammonia (NH3).  Gaseous NH3
reacts with gaseous HNO3 to form paniculate NH4NO3.  Gaseous NH3 reacts with H2SO4 to
form acidic HSO4 and neutral (NH4)2SO4.  A number of volatile organic compounds can
react with O3 and/or OH radical to form fine organic particles.  In addition, acid gases such
as SO2 and HNO3 may react with coarse particles such as CaCO3 and NaCl to form coarse
particles of different chemical composition.
     The concentrations of OH radicals, O3, and H2O2, formed by gas phase reactions
involving volatile organic compounds and NOX, depend on the concentrations of the
reactants, and on meteorological conditions including temperature, solar radiation, wind
speed, mixing volume and passage of high pressure systems. Therefore,  formation of a
substantial fraction of fine particles can  depend on the gas phase reactions which also
produce O3 and a variety  of other volatile products.
     The fine particle fraction, in addition to SO4=  and NO§ , contains elemental carbon
(EC), organic carbon (OC), H+ (hydrogen  ions or acidity) and a number of metal
compounds at lower concentrations.  Species such as SO4=  , NO3 and some organic species
are associated with substantial amounts of particle-bound water.  NH4N03 is in equilibrium
with HNO3 and NH3 so it can vaporize  from particles.  Organic particles can also be in
equilibrium with their vapor.  Such species are called semi-volatile.  A number of trace
elements including, but not necessarily limited to, Pb, Zn,  Ni, Cd, Na, Cl, Br, Se and As
have been measured in the PM2 5 fraction of fine particles.  The coarse particles are largely
composed of the crustal elements Si, Ca, Al, and Fe.  However, a considerable number of
elements are found in both the fine and coarse fractions.
                                        3-190

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     Chemical reactions of SO2 and NOX within plumes are an important source of H+,
and NO§.  These conversions can occur by gas-phase and aqueous-phase mechanisms.
     In point-source plumes emitting SO2 and NOX, the gas-phase chemistry depends on
plume dilution,  sunlight and background volatile organic compounds mixed into the diluting
plume.  For the conversion of SO2 to H2SO4, the gas-phase rate in such plumes during
summer midday conditions in the eastern United States typically varies between 1 and 3 % h"1
but in the cleaner western United States rarely exceeds 1 % h"1.  For the conversion of NOX
to HNO3, the gas-phase rates appear to be approximately three times faster than the SC^
conversion rates.  Winter rates for SO2 conversion were approximately  an order of
magnitude lower than the summer rates.
     The contribution of aqueous-phase chemistry to particle formation in point-source
plumes is highly variable, depending on the availability of the aqueous phase (wetted
aerosols, clouds, fog, and light rain) and the photochemically generated gas-phase oxidizing
agents, especially H2O2 for SO2 chemistry. The in-cloud conversion rates of SO2 to SO4=
can be several times larger than the gas-phase rates given above.  Overall, it appears that
SO2 oxidation rates to SO4= by gas-phase and aqueous-phase mechanisms may be
comparable in summer, but aqueous phase chemistry may dominate in winter.
     In the western United States, markedly higher SO2 conversion rates have been reported
in smelter plumes than in power plant plumes.  The conversion is predominantly by a gas-
phase mechanism.  This result is attributed to the lack of NOX in smelter plumes.  In power
plant plumes NO2 depletes OH and competes with SO2 for OH.
     In urban plumes, the upper limit for the gas-phase SO2 conversion rate appears to be
about 5% h"1 under the more polluted conditions. For NO2, the rates appear to be
approximately three times faster than the SO2 conversion rates.  Conversion rates of SO2  and
NOX in background air are comparable to the peak rates in diluted plumes.  Neutralization of
H2SO4 formed by SO2 conversion increases with plume age and background NH3
concentration.  If the NH3 concentrations are more than sufficient to neutralize H2SO4 to
(NH4)2SO4, the HNO3 formed from NOX conversions may be converted to NH4NO3.
     The lifetimes of particles vary with size.  Coarse particles can settle rapidly from the
atmosphere within hours, and normally travel only short distances.  However, when mixed
high into the atmosphere as in dust storms the smaller sized coarse mode particles may have
                                        3-191

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longer lives and travel distances.  Nuclei mode particles rapidly grow into the accumulation
mode. However, the accumulation mode does not grow into the coarse mode.
Accumulation-mode fine particles are kept suspended by normal air motions and have very
low deposition rates to surfaces.  They can be transported thousands of km and remain in the
atmosphere for a number of days.  Both accumulation-mode and nuclei-mode (or ultrafine)
particles have the ability to penetrate deep into the lungs. Dry deposition rates are expressed
in terms of a deposition velocity which varies as the particle size, reaching a minimum
between 0.1 and 1.0 /*m aerodynamic diameter.  Accumulation-mode particles are removed
from the atmosphere primarily by cloud processes. Fine particles, especially particles with a
hygroscopic component, grow as the relative humidity increases, serve as cloud condensation
nuclei, and grow into cloud droplets.  If the cloud droplets grow large enough to form rain,
the particles are removed in the rain.  Falling rain drops  impact coarse particles and remove
them.  Ultrafine or nuclei mode particles are small enough to diffuse to the falling drop and
be removed.  Falling rain drops, however, are not effective in removing accumulation-mode
particles.
     There are many reasons for wanting to collect fine and coarse particles separately.
However, because fine-mode particles and coarse-mode particles overlap in the size range
between 1.0 and 3 //m diameter, it is not clear what 50% cut point will give the best
separation.
     A review  of atmospheric particle-size-distribution data did not provide a clear or
obvious answer. Depending on conditions, a significant amount of either fine or coarse
mode material may be found in the intermodal region between 1.0 and 3 /mi.  However, the
analysis of the existing data did demonstrate the important role of relative humidity in
influencing the  size of the fine particle mode and indicated that significant fine mode material
is found above  1.0 /xm only during periods  of very high relative humidity.
     Thus, a PM2 5 sample will contain most of the fine  mode material, except during
periods of RH near 100 %. However, especially  in conditions of low RH, it may contain
5 to 20 % of the coarse mode material below 10 /xm in diameter. A PMj 0 sample will
prevent misclassification of coarse mode material as fine but under high RH conditions will
result in some of the fine mode material being misclassified as coarse.
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     A reduction in RH, either intentionally or inadvertently, will reduce the size of the fine
mode.   A sufficient reduction in RH will yield a dry fine particle mode with very little
material above 1.0 /mi.  However, techniques to reduce the RH without loss of semivolatile
components  such as ammonium nitrate and semivolatile organic compounds have not yet been
developed.
                                        3-193

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      4.   SAMPLING AND ANALYSIS METHODS  FOR
    PARTICULATE MATTER AND ACID DEPOSITION
4.1   INTRODUCTION
     Assessment of health risks associated with airborne aerosols implies that measurements
be made defining the aerosol characteristics, concentrations and exposures that contribute to,
or simply correlate with, adverse health effects. The proper selection of an aerosol sampling
or analysis methodology to accomplish such measurements requires that rationales be applied
that consider how the resulting data will be used and interpreted,  in addition to the data
quality required. As  an example, treatment of a sample to remove particle-associated liquid
water, either by heating the  sample during the collection process or by equilibrating the
sample at a low relative humidity subsequent to collection, may lead to changes in the
character of the collected particles, relative to the dispersed particles, in addition to the
removal of water (e.g. Meyer et al., 1995).  Similarly, integrated collection of acidic fine
aerosols, without selectively removing the larger, more basic particles, will cause
neutralization (i.e., modification) of the sample on the substrate (Stevens et al.,  1978).  The
same logic applies to  the selective removal of gas phase components during sampling that
might react with the deposited aerosol sample, in a manner inconsistent with naturally
occurring transformation processes.  The assumption that fixed-location measurements are
representative of inhalation exposure implies that the effects of local spatial and temporal
gradients are understood and appropriately applied to the sampler siting criteria  (Spengler
et al., 1994).  Development of relationships between aerosol characteristics and health or
ecological responses requires that the aerosol sampling and analysis processes are truly
representative and adequately defined.
     The application of sampling and analytical systems for aerosols must recognize that
particles exist modally as size distributions generated by distinctively different source
categories and having distinctly different chemistries, as discussed in Chapter 3.  Two
important reasons for making size-specific aerosol measurements are (a) to relate the in  situ
aerosol character to the potential deposition sites,  and thus  toxicity,  of the respiratory system,
and (b) separation of the size distribution modes to identify sources, transformation processes

                                        4-1

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or aerosol chemistry.  The interpretation of particle size must be made based on the diameter
definition inherent in the measurement process.  Since the respiratory system classifies
particles of unknown shapes and densities based on aerodynamic diameter, elucidation of
aerosol relationships with health responses requires that sampling techniques either
incorporate inertial aerodynamic sizers or provide mechanisms to accurately convert the
measured diameters (e.g., optical) to an aerodynamic basis.  All particle diameters described
in this chapter are aerodynamic, unless otherwise specified.
     Friedlander (1977) provided the descriptive matrix shown in Figure 4-1 for placing
measurement techniques that define  aerosol characteristics into perspective, in terms of their
particle sizing capabilities, resolution times and chemical identification attributes.  This
approach defined these characteristics by resolution (single particle or greater), discretizing
ability, and averaging process.  The author notes that the "perfect" aerosol sampler would
characterize particle size with "perfect" resolution,  determine the chemistry of each particle
"perfectly", and operate in real-time with no  "lumping" of classes.  These characteristics
could be amended in "real-world" terms by suggesting that the  "perfect" sampler would  also
have minimal cost and operator intervention.  Also, if the aerosol measurement design goal is
to mimick the respiratory system, physiological averaging characteristics must be considered.
Size-specific,  integrated aerosol measurements have improved significantly and their
capabilities are better characterized since the  1987 PM10 NAAQS, but a "perfect" aerosol
sampling system has not been devised.  As discussed below, the methodologies required to
adequately define the performance specifications of aerosol samplers have yet to be devised.
     Many recent developmental efforts in aerosol measurement technologies have addressed
the need to perfect the chemical characterization of reactive or volatile species collected  on
filtration substrates (e.g., Lamb et al., 1980;  Koutrakis et al., 1988). Some of the most
significant recent advances in aerosol measurement technologies have come in the form of
analysis system "protocols", rather than individual pieces of hardware.  Recognizing that
there is no single "perfect"  sampler, these protocols attempt to merge several  aerosol
sampling and analysis technologies into an adaptable and analytically versatile system.
System attributes typically include one or more size-specific aerosol  inlets, subsequent
fractionators to separate the fine and coarse particle modes, and denuders and/or sequential
filter packs to selectively account for reactive gas phase species.  Examples include EPA's
                                            4-2

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    Instrument
                                    Resolution
Size
Time      Chemical
         Composition
 Quantity
 Measured
(Integrand x N^1)
  Perfect Single
  Particle Counter
    Analyzer
                                               g
 Optical Single
 Particle Counter
  Electrical
    Mobility
      Analyzer
                                         [V2[gdn=dv
 Condensation
    Nuclei
       Counter
                                         Jg dv dnj - 1
  Impactor
                                              dndv
 Impactor
    Chemical
      Analyzer
                                            9 HJ dnjdv
 Whole Sample
 Chemical Analyzer
                                         { {gnj
 Key:
 <0 Resolution of single particle level
       Discretizing process
   /  Averaging process
Figure 4-1. Characteristics of aerosol measurement instruments.
Source: Friedlander (1977).
Versatile Air Pollution Sampler (VAPS) (Conner et al., 1993), the Southern California Air
Quality Study (SCAQS) sampler (Fitz et al., 1989) and the Interagency Monitoring of
PROtected Visual Environments (IMPROVE) sampler (Malm et al., 1994).
     Recognizing that personal exposure concentrations for aerosols may differ from
classical outdoor fixed-location measurements has produced much smaller and less obtrusive
                                      4-3

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samplers using the same sizing techniques for application indoors, or even to be worn on the
body during normal activities.  Miniaturization of aerosol  separators stretches the limits of
current technologies to maintain required sampling precisions and accuracies.  One of the
most significant limitations imposed by the low flowrates inherent in personal exposure
samplers is the extremely small sample size available for chemical analysis.
     This chapter briefly describes the technical capabilities and limitations of aerosol
sampling and analytical procedures in Sections 4.2 and 4.3, respectively, focusing on
(1) those that were used to collect data supporting other sections in this document, (2) those
supporting the existing PM10, TSP1 and Pb regulations, (3) those that were used to support
health  and welfare response studies, (4) those having application in development of a possible
fine particle standard,  and (5) discussing the attributes of several  new technologies.  The
discussion of aerosol separation technologies is divided between (a) devices used to mimic
the larger particle (> 10 /xm) penetration rationales for the upper  airways,  and (b) those
devices generally used to mimic smaller particle penetration (< 10 /xm) to the thoracic
regions.  These device descriptions are followed by sampling considerations for their
applications.  The applications of performance specifications to define these measurement
systems for regulatory purposes are discussed, along with a number of critical observations
suggesting that the current specification process does not always ensure the accuracy or
representativeness necessary in the field.  The EPA program designating PM10 reference and
equivalent sampling systems is then briefly described, along with a current list of designated
devices.  Selected measurement systems used to  provide more detailed characterization of
aerosol properties for  research studies are discussed, with a focus on the determination of
particle size distributions.
     Aerosol sampling systems for specialty applications, including automated samplers,
personal exposure samplers and the sampling systems used in aerosol apportionment studies
are briefly described.  The chapter then presents a short section (4.4) on sampling and
analysis of bioaerosols Nevalainen et al. (1992).  Also, Nevalainen et al.  (1993), and  Qian
et al. (1995) provide excellent summaries of the principles involved in bioaerosol sampling
and the most commonly used  techniques.
'Subsequent identifications in this chapter:  "TSP" for Total Suspended Particulates by high volume sampler,
"PM10" for the fraction less than 10 /nm, "fine" for the fraction less than 2.5 /mi.
                                            4-4

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4.2   SAMPLING FOR PARTICULATE MATTER
4.2.1   Background
      The development of relationships between airborne paniculate matter and human or
ecological effects requires that the aerosol2 measurement process be accurately,  precisely
and representatively defined.  Improvements in sampling methodologies since the 1982 Air
Quality Criteria Document for Paniculate Matter and Sulfur Oxides (U.S. Environmental
Protection Agency,  1982)3 was released, have resulted from improved sensor technologies,
and more importantly, a better understanding of the aerosol character in situ4.  Additionally,
health studies and atmospheric chemistry research in the past decade have focused more
closely on smaller, better-defined aerosol size  fractions  of known integrity, collected
specifically for subsequent chemical characterization.
      The system of aerosols in ambient air is  a continuum of particle sizes in a gas phase
carrier formed as the summation of all size distributions produced by individual sources and
secondary transformations.  Portions of the composite distributions are often found to exist
lognormally (Baron  and Willeke,  1993;  see also  Chapter 3, Section 3.3.3).  Aerosol systems
also exist as a continuum of particle  "ages", resulting from loss and transformation
mechanisms such as agglomeration, settling, volatilization, gas-particle reaction,  and rain-out
affecting freshly  generated particles.  The chemical compositions of the various  portions
(modes) of the aerosol size distribution are more discreet, and sampling strategies must
consider a specific range  of sizes  for a given chemical class.  The constantly changing
character  of the atmosphere (or of indoor air)  places a premium on sampling strategies both
to collect representative aerosol samples from  the air  and to protect their  integrity until
analyzed.
      The 1982 Criteria Document provided basic descriptions of many aerosol measurement
techniques still used today.  These included both older optically-based techniques, such as
"Black Smoke" or "British Smoke" (BS) or "coefficient of haze" (COH) methods and certain
Consistent with recent literature (e.g., see Willeke and Baron, 1993), the term "aerosol" will refer to the continuum
of suspended particles and the carrier gas.
3Referred to in the text subsequently as an entity as the "1982 Criteria Document".
4The in situ characteristics of particles in the ambient air medium can be substantially modified by the sampling
and analysis processes. For example, a particle counter which draws particles through a restrictive or heated inlet
before they reach the sensing volume,  may perceive  the particle properties (e.g. scattering coefficients, size
distributions) differently from those that existed in the ambient.
                                           4-5

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other now lesser used gravimetric methods, that are only briefly mentioned here but not
described in detail. Instead the reader is referred to the earlier Criteria Document (U.S.
Environmental Protection Agency, 1982) for more information on those methods not
extensively covered here. This section mainly highlights the more recent peer-reviewed
research on aerosol measurement technologies since 1982 and notes  salient points that should
be considered in their application. The aerosol sampling section is not intended to be an
exhaustive treatise, but is structured  to highlight important concepts  and technologies relevant
to the development of aerosol measurement/response relationships, or supporting existing and
potential EPA aerosol regulations. Ancillary reference texts, describing basic aerosol
mechanics (e.g., Hinds,  1982; Reist, 1984) and applied aerosol mechanics and measurements
(e.g., Willeke and Baron, 1993; Hering, 1989; Lundgren et al., 1979; Liu, 1976) should be
consulted for more fundamental details.

4.2.2    Large Particle Separators
4.2.2.1   Cutpoint Considerations
     The collection of an aerosol sample is defined by the penetration characteristics of the
inlet, overlaid on the existing in situ size distribution.  Cooper and Guttrich (1981) describe
this process mathematically, and  they estimate the influences of non-ideal penetration
characteristics.  Miller et al. (1979)  described the considerations for the possible selection of
15 ju,m (designated "inhalable") as a  standard for size-selective particle sampling with upper
airway respiratory deposition as the  primary consideration.  The selection of the most
appropriate aerodynamic criteria  for ambient aerosol sampling was only partially resolved by
the 1987 EPA designation (U.S.  Environmental Protection Agency,  1987) of a 10 /xm
(PM 10) cutpoint.  The "ideal" PM10 inlet was referenced to the thoracic penetration model
of Lippmann and Chan (1979).  Ogden (1992) noted that the standardization for aerosol
cutpoint sizes and separation sharpness is still under debate across settings (ambient air,
occupational) and across national and international governmental entities.  As shown in
Figure 4-2 (from Jensen and O'Brien, 1993), the international conventions for cutpoints have
been roughly categorized as Respirable, Thoracic and  Inhalable (previously, Inspirable).
These cutpoints are related to the penetration, respectively, to the gas exchange region of the
lung, the larynx,  and the nasal/oral plane.  The influences of physiological  variables on these
                                           4-6

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e
I
01
1
            100
             80
             60
             40
             20
         0/100
             80
             60
             40
             20
         0/100
             80
             60
             40
             20
                   -Inhalable
• ACGIH(1994)

• Proposed ISO (1992)

-ISO (1983)
 ACGIH (1994)

 Proposed 150(1992)

 180(1983)
                   -Respirable
•ACGIH (1994)

1 Proposed ISO(1992)

•ISO (1983)

' BMRC (1959)
                 0.1
              1
                                                        10
100
                          Aerodynamic Diameter (urn)
Figure 4-2.  American Conference of Governmental Industrial Hygienists (ACGIH),
             British Medical Research Council (BMRC), and International Organization
             for Standardization (ISO) size-selective sampling criteria.

Source: Jensen and O'Brien (1993).
                                             4-7

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cutpoints are described by Soderholm (1989).  The British Standard EN 481 (CEN [European
Committee for Standardization], 1993) describes size fraction definitions for workplace
aerosol sampling, and identifies inhalable "conventions" relative to thoracic, respirable,
extra-thoracic and tracheobronchial penetration (but not necessarily deposition) hi the
respiratory system.  They define a thoracic cumulative lognormal distribution with a median
of 11.64 /mi and  a geometric standard deviation of 1.5, such that 50% of airborne particles
with Da = 10 /*m are in the thoracic region.  The American Conference of Governmental
and Industrial Hygienists (ACGIH, 1994) also adopted these convention definitions.  Owen
et al. (1992) provides an extensive list of the  outdoor and indoor particles by type and source
category that are  found in or overlap these ranges.  Willeke et al. (1992) describe the
sampling efficiencies and test procedures for bioaerosol monitors.
     The concept of using an inlet or separator that has the same sampling  (penetration)
characteristics as  portions of the respiratory system has been discussed by a number of
researchers, including Marple and Rubow (1976), Lippmann and Chan (1979), Vincent and
Mark (1981), Soderholm (1989),  Liden and Kenny (1991) and John and Wall (1983).  They
describe  sampler  design considerations for matching penetration models for respirable,
thoracic and inhalable fractions that have been proposed by a number  of governing bodies.
Since all models proposed for the same fraction do not necessarily coincide, given the
variability and differences in interpretation of respiratory system data, Soderholm (1989)
proposed compromise conventions for each fraction.  Watson et al. (1983),  Wedding and
Carney (1983), and van der Meulen (1986) mathematically evaluated the influences of inlet
design parameters on collection performance relative to proposed sampling criteria.  These
analyses  suggested that factors such as extremes in wind speed and coarse particle
concentration could pose significant problems in meeting performance specifications.
     An analysis of the human head as an aerosol sampler was discussed by Ogden and
Birkett (1977), who noted that breathing is an anisokinetic sampling process.  The concept of
a "total  inhalable" fraction that passes the oral and nasal entry planes was refined by Mark
and Vincent (1986)  with the development of a personal aerosol sampling inlet that mimicked
this penetration as a function of aerodynamic  size. The inlet  was designated the IOM  for the
Institute for Occupational Medicine in Edinburgh, Scotland, where it was developed with the
cutpoint as a function of wind  speed  and aerosol type shown in Figure 4-3.   The total
                                          4-8

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0

	 Inhalable
Convention
Solid Particles

Aloxite

• 1m/s

A 2m/s
• 4m/s
^ 6m/s
V 9m/s


Sodium Fluorescein
O lm/s
A 2m/s
n 4m/s
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V 9m/s
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nFH^
L/cno
O 6m/s
+ 9m/s
      Figure 4-3.   Sampling efficiency of IOM ambient inhalable aerosol sampler for three different types of test aerosol.




      Source:  Mark et al. (1992).

-------
inhalable approach has been adopted by the International Standards Organization (ISO,
1993), European Committee for Standardization (CEN, 1993) and by the American
Conference on Governmental and Industrial Hygienists (ACGIH, 1985; ACGIH, 1994) for
workplace aerosol sampling.  The ACGIH (1985) reference provides a detailed rationale for
the selection of various cut sizes.  The total inhalable fraction using the IOM inlet was
selected for a total human exposure study (Pellizzari et al.,  1995) to provide the total body
burden for metals (lead and arsenic) by the air exposure route.
     Similar thoracic penetration conventions have been adopted by ISO, CEN, ACGIH and
EPA, each with D50 values of 10.0 /xm (ISO, 1993; CEN, 1993; ACGIH,  1994; and
U.S. EPA, 1987).   The EPA definition was based primarily on the data of Chan and
Lippmann (1980).  The exact shapes of each efficiency curve  were mathematically defined by
Soderholm (1989) and are slightly different for each convention.
     The respirable conventions have  had D50 values ranging from 3.5 to 5.0 /*m, but a
compromise convention has been accepted internationally  by several organizations.  It has a
D50 of 4.0 fj.ro. (Soderholm, 1989).  ISO (1993) calls this  the "healthy adult respirable
convention".  Liden and Kenny (1992) discuss the performance of currently available
respirable samplers.  EPA's emphasis  on the 2.5 /im cutpoint was more closely associated
with separating the  fine and coarse atmospheric aerosol modes, rather than mimicking a
respiratory deposition convention.  The exact location  of this minimum in the  atmospheric
size distribution is currently under debate. It is noteworthy that ISO (1993) defines a "high
risk" respirable convention which is claimed to relate to the deposition  of particles in the
lungs of children and adults with certain lung diseases. The respirable  "high risk"
convention has  a D50 of 2.4 /mi, so it could be identified closely with the EPA samplers
having a cutpoint of 2.5 pm.
     The PMjo size fraction has become nearly universal for  ambient air sampling in the
U.S., with the implementation of the 1987 standard (U.S. Environmental Protection Agency,
1987).  The setting of performance specifications, even with their limitations,  has provided a
more consistent PM10 data base, with  better definition of the data quality.  As additional
information becomes available on the sources of biases in aerosol collection methodologies,
further characterizations of older methods may be needed to better  define the quality  of
collected data.  Factors that affect bias, and especially representativeness, should be
                                          4-10

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identified and their influences determined as a function of particle size.  As examples, Appel
et al. (1984) studied gas/particle and particle/substrate interactions for sulfates and nitrates,
volatilization losses of nitrates were reported by Zhang and McMurry (1992), while losses
for organics were reported by Eatough et al. (1993).  Because of the prevalence of these
chemical classes in the fine fraction, the effect of the losses on larger fractions (e.g., PM10,
TSP) would be proportionately smaller and can now be estimated. The losses of larger
particles through aerosol inlet sampling lines (Anand et al., 1992) has a substantial influence
on PM10 coarse fraction samples.  This was demonstrated for the British smoke shade
sampler inlet line by McFarland et al. (1982).  Inlet losses would be expected to play only a
minor role in sampling the fine particle fraction (<2.5 /*m).  Biases in concentration for
samplers with large particle cutpoints are exacerbated by the large amount  of mass present
near the cutpoints and the steep slope of mass versus aerodynamic size.  Thus, small changes
in cutpoint can give significant and hard-to-predict mass biases.

4.2.2.2    Total Suspended Particulates
     The TSP high volume sampler has remained essentially unchanged since the sampler's
identification as a reference ambient sampling device in 1971 (Federal Register, 1971).  The
sampling performance (e.g., wind speed and direction sensitivity) was described in detail in
the 1982 Criteria Document, and the TSP sampler was shown by McFarland and Ortiz
(1979) to collect particles  with aerodynamic diameters exceeding 40 jum. More importantly,
its particle collection characteristics were shown to be significantly sensitive to wind speed
(2 to 24 km/h) and wind direction. Only minor technical updates have been incorporated  in
commercially available units, such as in the types of available  sequence  and elapsed timers
(mechanical, electronic) and in the types of flow controllers (mass flow, volumetric).  Also,
cassettes are now available that protect the fragile glass or quartz fiber filters during handling
and transport.  Size fractionating  inlets for smaller size cutpoints (e.g., 2.5, 6.0 and
10.0 (jLm) and cascade impactors have been developed. Similar to the Pb strategy of using
the TSP high volume  sampler to collect a "total" sample, asbestos sampling utilizes an
aerosol inlet that attempts  to collect a  "total" sample, by using an open-faced filter holder
with a conductive inlet cowling.  Baron (1993) discusses  the potential anisokinetic problems
                                          4-11

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that can occur with such a simple inlet, but notes that the small Stokes number for typical
asbestos fibers provides efficiencies close to 100%.

4.2.2.3    Total Inhalable Particles
     The  toxicity of contaminants such as lead poses health concerns as total body burdens,
suggesting that penetration of all aerosols inhaled into the nose and mouth must be
considered, rather than just thoracic penetration.  The TSP sampler for atmospheric lead is
thought (Federal Register, 1978) to more closely capture this larger size fraction than would
a PM10 counterpart, but was  not specifically designed to mimic inhalability.  The ISO
"inhalable" draft sampling convention (ISO, 1993) is intended to apply to such situations,
defining collection of all particles passing the oral/nasal entry planes.  The total inhalable
cutpoint is currently available only in a personal sampler version.  Mark and Vincent (1986)
described  the development of an inhalable particle inlet (designated as the IOM) meeting the
ISO (1992), CEN (1993) and ACGIH (1994) conventions for inspirable dust.  This inlet was
improved  by Upton et al. (1992) and tested by Mark et al. (1992) and shown to satisfy the
ACGIH criteria for wind speeds of 0.5 and 1.0 m/s.

4.2.2.4    PM10
     The  penetration of ambient aerosols through a size-fractionating inlet to the collection
substrate must be characterized over the ranges of operating conditions (meteorology and
aerosol types) that may be encountered.  The range of conditions currently required by EPA
PM10 performance  specifications was  given in U.S. Environmental Protection Agency
(1987). Ranade et  al. (1990) and John and Wall (1983) described the required testing, which
specifies a controlled flow wind tunnel, monodispersed  fluorescently-tagged wet and dry
aerosols, and an isokinetic nozzle aerosol sampling reference to determine aerodynamic
penetration through candidate PM10 inlets.
     Marple and Rubow (1976) placed inertial impactors on the inlet of an optical particle
counter to provide an aerodynamic calibration of the optical readout for non-ideal particles.
Buettner (1990) noted that an aerodynamically calibrated optical particle counter could in turn
be used to test the sampling performance of other devices only if the particle shape and
refractive  index of the test aerosol were consistent between calibrations.  Maynard (1993)
                                          4-12

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used this approach to determine the penetration of a respirable cyclone to polydisperse glass
micro-spheres, using the TSI, Inc. Aerodynamic Particle Sizer (APS). John and Wall (1983)
noted that inaccurate inlet sizing results may be obtained using poly-disperse AC test dust, as
the result of agglomeration.  Kenny and Lid6n (1991) used the APS to characterize personal
sampler inlets and observed that, on theoretical grounds, calm air sampling  would be
expected to provide  unity aspiration efficiencies for particles below about 8  /mi. Tufto and
Willeke (1982) used an optical particle counter (OPC) to monitor monodisperse aerosols in a
wind tunnel setting to determine the performance of aerosol sampling inlets  relative to an
isokinetic nozzle.  Yamada (1983) proposed using electron microscopy to determine the size
distributions of polydispersed particles using manual counting techniques before and after a
candidate aerosol separator.  Penetration data from this  technique were found to be
significantly less precise and more difficult to interpret compared with data  for the same
separators using fluorometric methods.
     The aerosol cutpoint performance  of two PM10 samplers that have met the EPA
performance specifications is illustrated (see Figure 4-4) by the data for the  Andersen 321A
and Wedding IP10 high volume sampler inlets at 8 km/h from Ranade et al.  (1990).  The data
show that the cutpoint requirements,  defined as a D50 of 10.0 ptm ± 0.5 /wm and mimicking
a modeled  cutpoint sharpness (a ), were met for each of the tested wind  speeds. These
                              &
performance results  were verified by repeating the tests in wind  tunnels located at  two other
research facilities. A diagram (U.S. Environmental Protection Agency, 1992) of the
two-stage Sierra-Andersen PM10 high volume sampler inlet with a design flowrate of
1.13 m3/min is shown in Figure 4-5.  The buffer chamber of this inlet serves to dampen the
particle-laden air stream passing through two sets of acceleration nozzles, which deposit
particles larger than  PM10 on internal collection surfaces.  The PM10 fraction is typically
collected by a glass fiber filter.  An oiled impaction shim was incorporated  into the first
stage fractionator of the 321A to minimize reentrainment of deposited particles during field
sampling.  This  modified version (Sierra-Andersen 32IB)  was designated as an EPA
reference method for PM10 in 1987.  A subsequent single-stage fractionator  (Sierra-Andersen
1200) was developed5 and designated as an EPA reference method, with a D50 of 9.5 /mi
and a hinged design  to facilitate cleaning and oiling of the oiled impaction shim.
5Graseby-Andersen, Inc., Atlanta, GA.

                                          4-13

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                                                               Wedding IR0
                                                               Model 321A
                               4   5  6  7 8 910     15    20
                                    Aerodynamic Diameter (urn)
Figure 4-4. Liquid particle sampling effectiveness curves with solid particle points
            superimposed for the Wedding IP10 (•) and the Andersen Samplers
            Model 321A inlets at 8 km/h.
Source:  U.S. Environmental Protection Agency (1992).
      A diagram of the cyclone-based Wedding6 PM10 high volume sampler inlet (U.S.
Environmental Protection Agency,  1990) with a design flowrate of 1.13 m3/min is shown in
Figure 4-6.  This inlet uses an omni-directional cyclone to accelerate the particle-laden air
stream to deposit particles larger than PM10 on an oiled collection surface.  Two additional
turns are made to alter the flow into a downward trajectory toward the collection filter.
A brush is used to clean the deposited aerosol from the absorber surface through an access
port.  This inlet was designated as  an EPA reference method for PM10 in 1987.
6Wedding and Associates, Fort Collins, CO.
                                         4-14

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H-'
Ui
     Figure 4-5.  Two-stage Sierra Andersen PM10 sampler.


     Source: U.S. Environmental Protection Agency (1992).
      Buffer Chamber


NPL_ Air Flow
      Acceleration Nozzle


      Impaction Chamber

      Acceleration Nozzle

      Impaction Chamber

      Vent Tubes


      Filter Cassette

      Filter
                                                                       Filter Support
                                                                       Screen
                                                                       Motor Inlet

-------
                                  /- Maintenance Access Port
                                 £	
                                                    Vanes

                                                    Vane
                                                    Assembly
                                                    Base
                                                     Insect
                                                    Screen
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     Aerodynamic
     Inlet
     Pathway

Aerodynamic Flow
   Deflector

Outer Tube
Figure 4-6. Sampling characteristics of two-stage size-selective inlet for liquid aerosols.


Source: U.S. Environmental Protection Agency (1992).
                                4-16

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     The aerosol collection performances for 16.67 1pm PM10 inlets for the dichotomous
sampler are described by Wedding et al. (1982) and McFarland and Ortiz (1984) and are
illustrated by the penetration data in Figure 4-7.  The variability of the performance as a
function of wind speed for the Andersen 321A PM10 inlet is shown in Figure 4-8 from data
by McFarland et al. (1984).  This is a dramatic improvement over the variability shown by
the TSP high volume sampler (McFarland and Ortiz,  1979) for the same wind speed range.
An attempt to simplify the complexity and improve the availability of wind tunnels to test
PM10 inlets was addressed by Teague et al. (1992), who describe a compact tunnel 6 m long
by 1.2 m high that is capable of testing inlets against the EPA PM10 specifications.
     Watson and Chow (1993) noted that the EPA PM10 performance specifications allowed
a tolerance range around the D50 that permitted inlets to be undesirably "fine tuned" to
provide a cutpoint on the lower or upper end of the range. Since a significant amount of
mass in the atmospheric aerosol may be associated with particles in the allowable tolerance
range, a  "reduction" in reported  concentrations could be achieved by simply using a lower
(e.g., 9.6 /Ltm) cutpoint inlet that is still within the acceptable D50  range.  The biases
between acceptable samplers have been apparent in the data from field aerosol comparison
studies (e.g., Rodes et  al., 1985; Purdue et al., 1986; Thanukos et al., 1992). Most of the
reported biases between samplers were less than 10%, although some differences greater than
30% were reported.  The data suggested that the collection efficiency of the high volume
sampler PM10 inlets based on cyclonic separation (Wedding, 1985) were consistently lower,
while those based on low velocity impaction (McFarland et al., 1984) were consistently
higher.  Sweitzer (1985) reported results of a field comparison of these two high volume
sampler types at an industrial location and reported average biases of 15%.  It was noted that
this amount of bias was unacceptable for compliance monitoring and more stringent
performance requirements should be used.  Rodes et al. (1985) observed that the PM10
concentration data from the dichotomous sampler (regardless of the inlet design)  gave the
most predictable results.
     Wang and John (1988)  were critical of the EPA PM10 performance specification on
allowable particle bounce (U.S. Environmental Protection Agency, 1987), stating that the
criteria can lead to a 30% overestimation of mass under worst-case conditions. In a related
paper, John et al. (1991)  reported that although reentrainment by air flow alone of particles
                                         4-17

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     100
       80
   CO
   V)
   CD
   c
   
-------
     100  -
      80
       60
    c
    o
    0
    c
    0  Ar.
   0_  40
       20
                       i	1—i—i—i—i—r
                  o    2 km/h

                  A    8 km/h

                  n   24 km/h
                        4       6      8  10           20

                    Aerodynamic Particle Diameter (|im)
40
Figure 4-8. Collection performance variability illustrating the influence of wind speed

           for the Andersen 321A PM10 inlet.


Source: McFarland et al. (1984).
(2.2%/gram deposited) and strongly suggested that periodic cleaning and re-oiling should be


required for PM10 inlets. Ozkaynak et al. (1993) observed that immediately after inlets of
                                     4-19

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the Wedding (1985) design were cleaned, an underestimation (compared to the dichotomous
sampler) occurred of 14%. This bias was followed by a steady "recovery" period of 2 days,
until the expected performance returned.  They also observed a strong influence of diurnal
temperature change on the ratios of concentrations between the Wedding (1985) design
samplers and other PM10 samplers.  This influence could not be attributed to a physical
phenomenon.
     The EPA PM10 performance specification program should be considered successful
(John and Wall,  1983) in providing consistent aerosol collection results during field
sampling.  As noted by Thanukos et al. (1992), the cases of greatest concern were those
where the measured concentrations were near an exceedance level. Wiener et al. (1994)
noted that EPA was scrutinizing the current performance  of designated reference and
equivalent  sampling methods for PM10 in light of reassessment of the existing standard.
A review of the  current PM10 performance requirements and possible amendments of the
existing specifications may be appropriate, given the information base now available.
     Laboratory and field testing reported in the literature since 1987 suggest that the EPA
PM10 Federal Reference Method (FRM) specifications and test requirements have not
adequately controlled  the differences observed in collocated ambient PM10 sampling.  The
most significant  performance flaws have combined to produce excessive (up to 60%) mass
concentration biases.  These biases apparently resulted from the combined factors of
(1) allowing a cutpoint tolerance (10 ± 0.5 ^im), (2) an inadequate restriction placed on
internal particle  bounce, and (3) a degradation of particle separation performance as certain
technology PM10 inlets became soiled.  Particle bounce or soiling problems have not been
reported for the  PM10 inlets for the dichotomous sampler.
     A cutpoint tolerance of ±0.5 /xm was required to account for expected differences
between different wind tunnel laboratories testing the same hardware.  The between-sampler
bias from this tolerance limit alone is predictable and should provide PM10 concentration
differences significantly less than ±10%  in most cases.  Particle bounce allowances are not
as predictable, but design practices (primarily surface coatings with viscous oil, as suggested
by John et al. [1991]) to minimize the penetration caused by bounce and resuspension have
been shown to be very effective when properly  serviced.  The influences  of internal surface
soiling on  PM10 inlet performance were not  recognized when the  FRM was established in
                                          4-20

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 1987, but were found to have severe consequences for some separation technologies.  The
 magnitude of biases from soiling is also not readily predicted,  but can be ameliorated by not
 allowing the inlet to become excessively dirty during operation by routine cleaning prior to
 sampling.
     Although the EPA test procedures have not been formally amended since 1987, the
 manufacturers of the designated PM10 reference methods (see section 4.2.6) have voluntarily
 modified their hardware designs and instruction procedures to accommodate particle bounce
 and soiling concerns. The SA-321b and SA-321c PM10 inlets were voluntarily withdrawn
 from the market by the vendor because of excessive biases attributed to particle bounce.  The
 manufacturer now sells the SA1200 inlet which provides oiled surfaces to eliminate particle
 bounce and access screws to facilitate cleaning.  The manufacturer also amended the
 instruction manuals to require a routine cleaning schedule.  Similarly, the manufacturer for
 the Wedding PM10 inlet now provides an access port in the inlet and a cleaning procedure
 that can be applied prior to  the collection of each sample. Based on our  current
 understanding of the PM10 sampling process, it could be expected that sampling systems can
 be designed and concentration measurements made that are within 10% of the true
 concentrations.

 4.2.3    Fine Particle Separators
 4.2.3.1    Cutpoint Considerations
     Although a particle separation at 2.5 pun has been utilized by the dichotomous sampler
 for a number of years, the 1987 standard reassessment (U.S. Environmental Protection
 Agency, 1987) did not specifically require routine monitoring for fine particles.  It has
 become apparent (see Chapters 8 and 12) that certain health and  ecological responses are
 most strongly correlated with fine particles, significantly smaller than 10  jum, and their
 related chemistry.  Since the mass of a particle is proportional to the cube of its diameter,
 larger particles (especially above 10 /im) can totally dominate the mass of PM,0 and TSP
 samples.  The 2.5 /im cutpoint generally occurs near a minimum in the mass distribution,
minimizing mass concentration differences between samplers with cutpoint biases.  The
development of control strategies based on mass concentrations from a smaller cutpoint
                                         4-21

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standard must be carefully constructed, especially if large particle interference problems
(e.g., particle bounce) cannot be appropriately minimized.
     Practical considerations would be the time and expense required to develop separators
with 1.0 fjtm cutpoints that meet required specifications, conduct validation testing, and
retrofit existing samplers. A virtual impaction "trichotomous" sample was described by
Marple  and Olson (1995) that uses  a PM10 inlet and separators for both 2.5 and 1.0 /mi
cutpoints.   They also noted that technology was not a limiting factor  in providing a fine
particle  separator. Given the body of data available at 2.5 /mi, a focused effort may prove
practical that defines the characteristics of the particle mass and chemistry between 1.0 and
2.5 /mi.  This would add to the technical knowledge base, allow interpretive corrections
between cutpoints to be made,  and  permit continued sampling at 2.5  /mi with a minimum of
additional resources.

4.2.3.2    Virtual Impactors
     The  dichotomous sampler utilizes virtual impaction to separate the fine (<2.5 /mi)  and
coarse (2.5 to 10 /mi) fractions into two separate flowstreams (see, for example, Novick and
Alvarez, 1987) for collection on filters. The  calibration of a nominal 2.5 /mi impactor,
including  wall loss data, is shown in Figure 4-9 (from Loo and Cork, 1988).  The current
separator  design was shown to  provide a relatively  sharp  cutpoint with  minimal internal
losses.  A virtual impactor has been designed with  a 1.0 /mi cutpoint (Marple et al., 1989),
and for cutpoints as  small as 0.12 /mi (Sioutas et al., 1994).  After a cross-channel
correction factor for the coarse mode is applied, the mass concentrations of each fraction and
the total mass (using a PM10 inlet)  can be determined gravimetrically.  An inherent
consideration with virtual separation is contamination of the coarse fraction by a portion  of
the fine fraction, equivalent to  the ratio of the coarse channel flow to the total flow (typically
10%).  Although a straightforward mathematical correction can account for the particle mass
between channels, this can influence subsequent chemical and physical  characterizations,  if
significant differences exist between the chemistry of each fraction (e.g.,  acidic fine fraction
and basic  coarse fraction). Stevens et al.  (1993) utilized  this limited addition of fine particles
to the coarse fraction to advantage  in the SEM analysis of samples collected on Nuclepore
                                          4-22

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   100  -
                                                                        20
Figure 4-9. Aerosol separation and internal losses for a 2.5-/im dichotomous sampler
            virtual impactor.
Source: Loo and Cork (1988).
filters. Keeler et al. (1988) showed that the growth of fine aerosols at elevated relative
humidities can significantly alter the ratio of fine to coarse collection for the dichotomous
sampler. During early morning periods when the humidity approached 100%, an apparent
loss of up to 60% of the fine mass (to the coarse channel) was observed.  Keeler et al.
(1988) concluded that analyzing only the fine fraction of the measured aerosol may not be
appropriate, especially for short integration intervals.
     A high volume (1.13 m3/min) virtual impactor assembly was developed by Marple,
et al.  (1990) that can be placed on an existing high volume sampler to permit larger total
collections than the  dichotomous sampler for chemical speciation by size fraction.
                                          4-23

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By placing a number of virtual impactors in parallel, a separation can be achieved at higher
flows, while reducing the total pressure drop.  Marple et al. (1993) provide a list of
commercially available virtual impactors by flowrate and available cutpoints.  They also note
that virtual separators inherently concentrate the particles in the coarse fraction (typically by
a factor of 10), making them useful as pre-concentrators for sensors with marginal
sensitivities.  John et al. (1983) found that an oiled Nuclepore filter with a nominal 8 fj,m
porosity could provide a D50 cutpoint of 2.5 pm, similar to that of a virtual impactor, if
operated at the appropriate face velocity and for a sampling period short enough to minimize
overloading.

4.2.3.3    Cyclones
      Cyclones have been used as aerosol separators in personal exposure sampling in
occupational settings for many years. Lippmann and Chan (1979) summarized the  cyclones
for sampling aerosol sizes below  10  /Am and noted that the aerosol penetration through a
cyclone can be designed to closely mimic respiratory  deposition.  An intercomparison of
three cyclone-based personal exposure samplers under occupational conditions (concentrations
typically > 1  mg/m3) was described by Groves et al. (1994).  They reported that even
though the cyclones were reportedly designed to mimic similar respirable conventions, biases
as large as a factor of two were noted,  possibly attributable to  overloading problems.
Marple et al. (1993) provided a list of commercially available air sampling cyclones, by
sampling flowrate and D50 range.  Cyclones can be used individually or in a cascade
arrangement to provide a size distribution.  Bartley and Breuer (1982) describe methods to
reduce biases when using a 10 mm (diameter) personal air sampling cyclone, especially as
related to cutpoint shifts caused by flowrate changes.  Saltzman (1984) provided a similar
analysis for atmospheric sampling cyclones.  Sass-Kortsak et al. (1993) observed that
substantial uniformity-of-deposition problems can occur on the  filters downstream of personal
sampling cyclones. Wedding and Weigand (1983) used a cyclone within a high volume
aerosol inlet to provide a PM6 0 cutpoint for ambient sampling  that did not allow penetration
of particles greater than 10.0 pm.
      The simplicity of cyclones has prompted their use as inlets and subsequent separators in
samplers designed to  fractionate the aerosol sample for chemical analysis.  The "Enhanced
                                          4-24

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Method" employed by EPA for sampling acidic aerosols uses a glass cyclone with a 2.5
cutpoint as the sampler inlet (U.S. EPA, 1992).  The percent collection as a function of
aerodynamic diameter is  shown in Figure 4-10 (Winberry et al., 1993).  The modest cutpoint
sharpness exhibited by some cyclones should be considered when attempting to separate
particle size fractions that may interact chemically. Hering et al. (1990) describe several
validated aerosol systems for sampling carbonaceous particles that utilize cyclones with
2.5 /im cutpoints to sample the fine fraction on either Teflon or quartz substrates.  Spagnolo
and Paoletti (1994) describe a dual cyclone ambient aerosol sampler with a 15 pun inlet
(described by Liu and Piu, 1981). This sampler  was designed to collect a 20 to  15 /xm
fraction, a 20 to 4.0 /xm  fraction, and a 0 to 2.5  /xm fraction.  Malm et al. (1994) describe a
sampling system with a PM10 inlet and three parallel channels following a 2.5 /zm cutpoint
cyclone that was used for the 40 site IMPROVE  network.  Over 120,000 fine particle filter
substrates of Teflon®,  nylon and quartz were collected for chemical analysis over a 6 year
period.

4.2.3.4   Impactors
     Impactors have been  developed for a wide range of cutpoints and flowrates. In cascade
arrangements (see Section  4.2.7.1.1) with a characterized inlet,  impactors provide particle
distribution information over a range of aerodynamic sizes. Impactors used as components
of inlets or as in-line fractionators stop and retain the aerosol on a surface (e.g., oil-soaked,
sintered metal or glass) that provides consistent performance  (primarily minimal  bounce) over
the entire sampling interval.  Recovery and analysis of the deposited particles in these
situations are usually not considerations. Koutrakis et al. (1990) described the design of
2.1 /xm cutpoint impactor for a single stage annular denuder system that exhibited internal
losses  of less than 3%.  Marple (1978) described the use of multiple nozzle impactors in a
single  stage to emulate selected respiratory penetration curves.
     Marple et al. (1993)  noted that the three primary limitations of impactors are particle
bounce, overloading of collection stages and  interstage losses.  Particles can bounce from  a
stage after impaction  if the surface forces are not adequate for their retention.  Wang and
John (1988) described the effects of surface loading and relative humidity on particle bounce
and growth, and they noted that if less than 6% of the impact area was covered by deposited
                                          4-25

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   100
    80
    60
 o>
1  40
O
    20
     0
2      2.5               4              6

    Aerodynamic Diameter (urn)
                                                                                     8
10
 Figure 4-10. Percent collection as a function of aerodynamic diameter for the U.S. Environmental Protection Agency
            enhanced method glass cyclone.
 Source: Winberry et al. (1993).

-------
particles, particle-to-particle collisions (and bounce) could be neglected.  They also showed
that ammonium sulfate aerosol growth with increasing humidity resulted in a 25% shift in
cutpoint as the relative humidity increased to 64%. Biswas et al. (1987) showed that,
especially in low pressure zones, the relative humidity and temperature can change rapidly
within a cascade impactor, potentially altering cutpoints and losses.  Wang and John (1988)
in subsequent  work did not observe these shifts, noting that the transit time in a jet is only on
the order of 10 /zs. Turner and Hering (1987) noted  that the stage substrate materials
(Mylar*, stainless  steel and glass) with the same grease (Vaseline*) could produce
substantially different particle adhesion characteristics. Vanderpool et al. (1987) showed that
using glass fiber filters as impactor surfaces can produce drastically reduced performance as
compared to a greased substrate (see Figure 4-11). Markowski (1987) suggested that adding
a duplicate (same cutpoint) serial impactor stage can permit reasonable bounce and
re-entrainment corrections to be made.

4.2.4   Sampling Considerations
4.2.4.1    Siting Criteria
      Selection of aerosol sampling locations  is partially guided by siting criteria under the
1987 PM10  regulation (U.S. Environmental Protection Agency,  1987), which provided
limited guidance for Pb and PM10 samplers.  The details behind  these guidelines for PM10
are provided in a guidance document (U.S. Environmental Protection Agency,  1987), which
relates physical and chemical characteristics of aerosols to  the spatial scales (regional, urban,
neighborhood, middle and micro) required to define the influences of sources on various
populations. Guidance was also provided on the influences of nearby  point, line and area
sources on sampling location as a general function of particle size. Only limited information
was  noted to be available on specific influences of local obstructions and topography
(e.g., trees, buildings) on measured aerosol concentrations. The primary focus  was
establishment  of the degree that a sampling location was representative of a specific scale.
      The high purchase cost,  and occasionally physical size, of aerosol samplers have
restricted  the number of sampling sites used in air monitoring studies.  This may pose
problems  if the selected  sites are not truly representative of the exposures for the populations
at risk. To address the biases resulting from too few aerosol samplers in a field study, a
                                          4-27

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  100

            4th Impactor
    80
          O Greased Substrate
          D Glass-Fiber Filter
 |  60'
'o
1
 o
••5  40
^
 o
O
    20
     0
                          5             10            20             40
                            Aerodynamic Particle Diameter (iim)
Figure 4-11.  Performance of glass fiber filters compared to greased substrate.
Source:  Vanderpool et al. (1987).


"saturation" sampler approach has been used, utilizing an inexpensive, miniature and
battery-powered PM10 sampler that can be deployed at a large number of sites.  Phillips
et al. (1994) reported application of this approach,  using 15 PM10 saturation samplers in
conjunction with one dichotomous sampler to study the contribution of diesel emissions to
total PM levels in Philadelphia.  Although the mean for PM10 concentrations of the saturation
samplers was essentially identical to that of the dichotomous sampler, the saturation data
showed site-to-site mean differences of up to 30 /ig/m3.

4.2.4.2   Averaging Time/Sampling  Frequency
     The collection frequency for samples to support the EPA PM10 NAAQS has typically
been on an every-6th-day schedule.  Shaw et al. (1982) raised a statistically-based concern
                                         4-28

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that infrequent collection increases the coefficient of variation about the overall mean
concentration value; that is, the variability of computed fine mass concentration means
increased as the square root of the number of intervals between individual measurements.
Symanski and Rappaport (1994), using time series analyses, described the influences of
autocorrelation and non-stationary behavior in occupational settings on concentration
distributions constructed from infrequent sampling.  They recommended a random sampling
design where a sufficient number of locations are sampled repeatedly over an adequate period
of time to account for the full range of exposure possibilities.  Hornung and Reed (1990)
described a method of estimating non-detectable  (or missing) values to lessen variance about
the estimate of the geometric mean,  by assuming that  the concentration distribution is
log-normal.
     Insufficient sample collections  can be remedied by more frequent operation of manual
samplers.  The recent PM10 equivalency designations  (see section 4.2.5) of two beta gauge
samplers and the TEOM sampler can provide the necessary  information, with hourly rather
than daily resolution.  The initial cost of an automated sampler is typically 2-3  times that of a
manual, single channel PM10 sampler, but can be offset by savings in operator  labor costs.
If inherent biases described in section 4.2.3.4 for the beta and TEOM samplers can be
overcome (and they are field reliable), these approaches should prove very useful in routine
regulatory and research monitoring studies.  Potential also exists for the integrating
nephelometer  to be an acceptable exceedance monitor7, using site specific calibrations
relating the measured scattering coefficient, bsp,  to fine aerosol mass concentrations (e.g.,
Larson etal., 1992).
     Another consideration for defining  sampling intervals is the setting of start and stop
clock times.  Daily 24-h sampling is most often done  from midnight-to-midnight, but
occasionally from noon-to-noon to either reduce  the number of samplers required or to
reduce operator burden. Sampling locations with highly variable diurnal aerosol
concentration  patterns (e.g., from night time wood smoke influence or day time traffic dust),
or marked differences between week days and weekend days may require special
consideration.  These influences can be especially significant for <24-h sampling periods.
7 A Pollutant Standard Index (PSI) monitor used to estimate when a pre-determined exceedance level has been
reached or exceeded, to potentially trigger the operation of an equivalent PM10 gravimetrically-based sampler.
                                           4-29

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4.2.4.3  Collection Substrates
     The selection of a filtration substrate for integrated collection of particles must be made
with some knowledge of the expected particle characteristics and a pre-determined analytical
protocol.  The expected sampled size distribution places a requirement on the porosity of the
filter media to effectively trap a reasonably high percentage of the particles with a minimum
of pressure drop.  The most common filter types used in air sampling are fiber and
membrane.  Fiber filters tend to be less expensive than membrane filters, have low pressure
drops, and have high efficiencies for all particle sizes.  They are most commonly available in
glass fiber, Teflon coated glass fiber and  quartz materials.  Membrane filters retain the
particles on the surface for non-depth analyses (e.g., X-Ray Fluorescence), can have specific
porosity's, and are available in a wide variety of materials.  Teflon is a popular membrane
material because of its inertness, but is 2  to 4 times as expensive as more common materials.
Liu et al. (1978) summarize the effective penetration characteristics as a function of particle
size  and pressure drops for a wide variety of fiber and membrane filters.  The selection of
filter diameter for a given flowrate influences the face velocity and the loading capacity
before the pressure drop becomes unacceptable.  A 47mm filter provides a surface area that
is 60% larger than that of a 37mm filter.   Polycarbonate filters with well defined porosities
(e.g., Nuclepore®) have been used in "stacked" arrangements as  fine particle separators.
John et al. (1983) describe using an 8 pim porosity filter in series with a back-up  filter to
effectively provide a 3.5 /nm separation of fine and coarse particles in a small, inexpensive
package.  Samplers based on this principle were widely  used in the early 1980's (Cahill
et al.,  1990) and their performance under field conditions was shown  to be equivalent to later
cyclone based PM2 5 samplers in the IMPROVE network.
     The reactivities of filter substrates with the aerosol have been reported extensively.
A common problem with glass fiber filters used on high volume samplers is the basic pH of
the glass material  and  its effective conversion of SO2 to paniculate sulfates (e.g., Pierson
et al.,  1976). Appel et al. (1984) also reported similar conversions of nitrogen oxides to
particulate nitrates on glass fiber filters.  Witz et al. (1990) reported losses of particulate
nitrates, chlorides and ammonium (19, 51 and 65%, respectively) from quartz fiber filters
during storage.  No significant losses of sulfates were reported from quartz filters.
Similarly, Zhang and McMurry (1992) reported the anomalous loss of fine particle nitrates
                                           4-30

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from Teflon filters and noted that predictive loss theories were insufficiently accurate to
permit corrections.  Lipfert (1994) also observed that nitrate artifacts on glass fiber filters
were difficult to quantify on a routine basis. Measurements of paniculate nitrate using nylon
filters by the IMPROVE protocols show, however, that such effects are minor except in
California (Malm et al., 1994).  Eatough et al. (1993) found significant losses of paniculate
organic compounds on quartz filters due to volatilization, such that ambient concentrations of
paniculate carbon may be underestimated substantially.  Lipfert (1994) investigated filter
artifacts in a field study in New York and concluded that positive sulfate  artifacts  inflated
PM10 values from glass fiber filters by 6 /*g/m3.  It was noted that the combination of sulfate
and nitrate artifacts on glass fiber filters may inflate TSP measurements by as much as  10 to
20 /ig/m3.

4.2.4.4    Chemical Speciation Sampling
     The collection of aerosol samples for chemical speciation analysis adds another
dimension to the complexity of the sampling protocol (also  see Section 4.3). The simplest
approach utilizes a characterized inlet or separator to define a size fraction, provides an
aerosol  collection substrate compatible with the analytical technique, and  collects an adequate
quantity of sample for analysis.  This approach is applicable for relatively nonreactive and
stable components such as heavy metals.  An important consideration is the potential
reactivity of the sampling substrate with either the collected aerosols or the gas phase.  Appel
et al. (1984) predicted effects of filter alkalinity on conversion of acid gases  to sulfates and
nitrates  and provided an upper limit estimate for artifact sulfate formation (added mass) for
TSP high volume sampling of 8-15 pig/m3 for a 24-h sample.
     Analyses for semi-volatile  organics found in both the particle and vapor phases must be
collected by adding a vapor trap (e.g., polyurethane foam plug) downstream  of the sampling
filter. Arey et al. (1987) noted  that this arrangement of sequential sampling  reservoirs  may
account for the total mass of organics, but not accurately describe their phase distribution in
situ, due to  "blow-off" from the filter during sampling.  Van Vaeck et al. (1984) measured
the volatilization "blow-off" losses of organic species from cascade impactor sampling to be
up to 30%, while the loss of total mass was only 10%.  McDow and Huntzicker (1990)
characterized the face velocity dependence for organic carbon sampling and provided
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correction models, based on adsorption losses to a backup filter.  Turpin et al.  (1994)
examined organic aerosol sampling artifacts and highlighted the distinction between "organic
carbon" and individual organic species. They observed that organic carbon sampled from the
atmosphere is unlikely to attain equilibrium between that in the gas phase and that adsorbed
on a quartz fiber back-up filter.  They also noted that under typical sampling conditions,
adsorption is the dominant artifact in the sampling of paniculate organic carbon, and  longer
sampling periods reduce the percentage of collected material that is adsorbed vapor.  It was
recommended that collection of aerosols for carbon analyses be made on a pre-fired quartz
filter, with estimates of the adsorption artifact made  from a quartz filter placed behind a
Teflon filter in a parallel sampler.
     For more highly reactive and unstable species,  the recognition of the in situ character
of the aerosol in the air must be identified and preserved during all facets of the sampling
process to provide a representative and accurate sample.  Durham et al. (1978) described a
denuder to remove sulfur dioxide while sampling for sub-micron aerosols. Spicer and
Schumacher (1979) observed that many artifact reactions may occur if stripping of nitric
acid, sulfuric  acid and ammonia is not performed during speciated aerosol sampling.
Appel et al. (1988a) described the various loss mechanisms that apply to the aerosol and
vapor phases while sampling for nitric acid.  They noted that residence time, surface  material
compositions, and conditioning prior to sampling were the predominant variables affecting
transmission efficiency.
     The determination of strong acidity for atmospheric aerosols (U.S. Environmental
Protection Agency, 1992) describes an "enhanced" method that recognizes the inter-
relationships between the vapor and aerosol phases for each constituent and the potential
interferences.   An inlet cyclone or impactor is used to provide a  2.5 pm cutpoint to exclude
the higher pH aerosols found  in the coarse fraction of PM10.  As shown in Figure 4-12,
denuders are used in the flowstream which selectively remove gas phase components  with
minimal, characterized losses  of aerosol.  Ye et al. (1991) determined the aerosol losses
through an 10 1pm annular denuder system as a function of particle size.  They noted that
total particle losses were less  than a few percent whether the denuders were coated or
uncoated.  Also, using parallel annular denuders, Forrest et al. (1982) found aerosol  losses
of only 0.2 to 2.2% for 0.3 to 0.6 jum particles and 4 to 5% for 1  to 2 pm particles.
                                          4-32

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           Filter Pack>
      Coated
      Filters
                .Citric Acid
                Teflon Filter
                    d4
                    d3
                    d2
      Coupler (Typical)—-
                    d1
      Coupler/ ImpactOF-
1

1


T3
0
0
£
NO2
HN03
NH3
f, NH^SOJ, NO;
1
T
NH3
I

co
si
(0
z
T
NO2 , HNO2 , SO2
1

CO
8
CM
CO
HCL, HNO2
HNCJ3, SO2


i
T
HNQj
sa
Figure 4-12. Schematic diagram of an annular denuder system.


Source: U.S. Environmental Protection Agency (1992).
                              4-33

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     Filter packs have been developed, consisting of a sandwich of filters and collection
media of various types in series, to collect aerosols and selectively trap gases and aerosol
volatilization products.  Benner et al. (1991) described an annular denuder sampling system
using Teflon and nylon filter packs  and annular denuders to quantitatively collect the
distributed ammonium nitrate, nitric acid and ammonia in the vapor and aerosol species.
They observed that volatile nitrates  were 71% ± 27% of the total nitrates during the day and
55% ± 30% at night in arid, southwestern U. S. locations. Masia et al. (1994) described
the anomalous uptake of ammonia on the nylon filters, which were expected to collect only
the gas phase nitric acid.  Wang and John (1988) reported  volatilization losses of ammonium
nitrate in the Berner impactor of 7% under hot,  dry (18% Rh) conditions.
     Vossler et  al. (1988) reported the results of improvements in an annular denuder
system, including Teflon coating of the internal glass surfaces.  They found an apparent
particle bounce problem with the cyclone inlets (with or without Teflon coating) and
proposed adding an additional in-line,  greased impactor. John et al. (1988) found that
anodized aluminum surfaces absorb nitric acid efficiently and irreversibly.  Several method
comparison studies have been reported for systems utilizing annular denuder/filter pack
technologies, including Harrison and Kitto (1990), Sickles  et al. (1990), and Benner et al.
(1991).

4.2.4.5  Data Corrections/Analyses
     Aerosol concentration data are reported in units of mass per volume (e.g., jig/m3). The
current EPA regulations for sampling TSP, PM10 and Pb require that sampler flowrates be
controlled and the sampled volumes be standardized to 760 mm Hg and 25 °C.  These
requirements may pose problems in the interpretation of concentrations from aerosol
samplers.  Wedding (1985) notes that the flowrate through inertial impactors should be
maintained at "local" temperatures  and pressures to retain the separator's  aerodynamic
calibration.  Mass flow controllers  may significantly affect the separator flow velocity during
large diurnal temperature changes,  excessively biasing the  resulting cutpoint diameter.
     Subsequent correction of the sampled aerosol volume to "standard" conditions by
mathematically compensating for average meteorological conditions may improperly report
the aerosol concentration measurement.  If the rationale for aerosol sampling was  to mimic
                                          4-34

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respiratory penetration (which occurred at local conditions), a correction after-the-fact may
not be appropriate.  These corrections are typically small (less than a few percent) except in
locations at higher altitudes and those with large diurnal or seasonal temperature changes.
The basis for mandating flowrate controller performance for aerosol samplers is sound, but
the subsequent requirements for concentration corrections for temperature and pressure  are
complex.  Although the issue of sampled volume correction for local  temperature and
pressure is beyond the scope of this document, the scientific bases should be reassessed for
aerosol sampling to determine if this requirement is consistent with EPA goals.
     The  matching of aerosol measurement capabilities with data quality requirements is
discussed  by Baron and Willike (1993). They note that although aerosol sampler precision
can be determined from collocated measurements, field sampling accuracy is more difficult to
define.  Generation of mono- or poly disperse calibration aerosols are  rarely done in field
settings because of the complexity of the calibration process.   Typically, only the aerosol
sampler flowrate accuracy is determined in the field.  Biases between the means from
collocated aerosol samplers using different separation techniques, may result from sampler
operational errors, or from inadequacies in determining the performance specifications during
laboratory testing.

4.2.5  Performance  Specifications
4.2.5.1    Approaches
     A significant step in the standardization process  for aerosol sampling was the EPA
definition (U.S. Environmental Protection Agency, 1987) of the  PM10 size fraction, based on
the aerodynamic diameter of particles capable of penetrating to the thoracic region of the
respiratory system. This definition was followed by implementation of the PM10 provisions
of EPA's Ambient Air Monitoring Reference and Equivalent Methods regulation (U.S.
Environmental  Protection Agency, 1987).  The format of the latter regulation included
adoption of performance specifications for aerosol samplers, based on controlled wind tunnel
testing with mono-dispersed aerosols.   Controlled laboratory testing is followed by limited
field testing,  including tests of candidate equivalent methods to demonstrate comparability to
designated reference methods.   The stringency of the field testing to elucidate potential
                                          4-35

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sampling biases is strongly influenced by the local sampling site environment, including
factors such as wind speed, nearby point sources, and the probability of fugitive dust events.
     This approach was chosen, rather than the design specification approach taken in 1971
(Federal Register, 1971), which identified the high volume  sampler and associated
operational procedures as the reference method for Total Suspended Particulates (TSP). The
1971 regulation had no provisions for the use of alternative or equivalent methods, and
subsequent to this design designation, significant problems of the TSP high volume sampler,
such as wind speed and direction dependency (McFarland et al., 1979) and off-mode
collection (Sides and Saiger,  1976), were reported.  These inherent biases complicated the
interpretation of TSP concentration data (U.S. Environmental Protection Agency, 1982) and
weakened correlations with other measures.  The problems  were estimated to have induced
biases  of less than 10% for most  situations,  but occasionally as high as 30%. The
subsequent development of aerosol testing programs for size selective  aerosol samplers
(e.g., McFarland and Ortiz, 1979; Wedding, 1980; John and Wall, 1983; Ranade et al.,
1990; Hall et al., 1992) more rapidly identified weaknesses in existing technologies and
facilitated the development of better methods.
     No reference standard exists for aerosol concentration measurements in air. The
calibration of aerosol samplers  relies primarily on characterizations under controlled
conditions of the sampler sub-systems, including the size selective inlet, sample conditioning
and transmission system, the flow control system, and, if used, subsequent size separators,
sample collection and storage elements, and sensors and associated electronics.   Although the
precision  of an aerosol sampler is readily obtained by using replicate,  collocated samplers,
the accuracy can only be estimated by comparison with either designated "reference"
samplers or with computations  of expected aerosol mass collections.  Performance
specification limits are used to  control the overall aerosol sampling accuracy. As noted by
John and  Wall (1983) the selection of a comprehensive list of sampling elements requiring
inclusion  and the setting of the performance limits for each element is a difficult task,
especially when the range of "real-world" sampling situations is considered.
     Performance specifications were utilized for the  PM10 standard to allow the broadest
spectrum  of measurement technologies, hopefully encouraging the development of new and
better  methods.  A research program was implemented by EPA in parallel with preparation
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and review of the 1982 Criteria Document to identify the critical specifications and
understand the inter-relationships among the parameters influencing the aerosol sampling
process.  Studies of the influences  of factors such as wind velocity, particle character, flow
rate stability, particle bounce and wall losses on precision and accuracy substantially
advanced the science of large particle sampling.  The performance specification approach was
a significant improvement over the design specification approach used for the TSP high
volume sampler, in that it fostered the development of new information and technologies and
provided for the use  of alternative  methods.  In retrospect, the primary weakness of the
design specification approach for the TSP reference method was not the process per se, but
the technical inadequacy of the development and testing program that produced the high
volume sampler design.
     The utilization of a performance specification approach requires that a minimum level
of knowledge  be available about the measurement process and the associated test procedures.
Some significant drawbacks  subsequently observed in the performance specification approach
for PM10 included the complexity,  expense  and scarcity of aerosol wind tunnel test facilities,
and the difficulty in defining comprehensive specifications that considered all of the nuances
of aerosol sampling.  Wind tunnel  evaluation and limited field tests do not always identify
sampler related problems encountered during extended periods of ambient sampling (e.g.,
John and Wang, 1991). Future performances tests should ideally  include extended field
testing, for example, to evaluate performance in different geographic regions and seasons,  as
well as under  different meteorological conditions.

4.2.5.2   Performance Testing
     Since the 1982  Criteria Document (U.S. Environmental  Protection Agency, 1982a),
aerosol sampling research  studies have  identified numerous factors that influence the
precision and accuracy of  samplers in both wind tunnel and field performance testing. Rodes
et al.  (1985), Purdue et al. (1986), and Cook et al. (1995) showed, in field evaluations under
a variety of sampling situations, that PM10 samplers meeting the EPA performance
specifications provide aerosol concentration measurements with a precision of 10% or less
when samplers of the same model were compared.  However, significant biases were evident
when different types  of samplers were compared.  The Andersen SA-321A PM10 sampler
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was found to collect an average of 58% more mass than a collocated Wedding PM10 sampler
(Perdue et al., 1986). This was partly attributed to the (predicted) bias associated with
cutpoint differences between the inlets.  A more significant bias (not predicted) was
associated with degraded performances in opposite directions (Andersen over-sampling,
Wedding under-sampling) due to soiling of the separators during extended sampling periods.
Rodes et al. (1985) noted that sampler precisions (coefficients of variation) were better than
±10%, with several samplers better than ±5%.  Cook et al. (1995) reported good agreement
(variability less than 15%) among several types of PM2 5 samplers.  Other sampler types
showed significant biases. Under the conditions of the study, high concentrations  of
NH4NO3 and organic carbon (winter in Bakerfield, CA), samplers which heated the collected
particles to 30 °C  or 50 °C during sampling gave lower mass values than filter samples
which were collected at ambient conditions and equilibrated for 24 hours at 23  ±  3 °C and
40 ± 5% relative humidity.   Coefficient of Haze (COH) measurements by an American Iron
and Steel (AISI) tape sampler and light scattering  (bscat) measured by an intergrating
nephelometer heated to 17 °C correlate well  with PM2 5 measurments (COH, r =  0.82 to
0.91;bscat, r = 0.91 to 0.98).
     Mark et al. (1992) reviewed the attributes of wind tunnel testing, and noted that tests
using controlled conditions are a necessity to determine whether an aerosol sampler meets a
basic set of established performance specifications. Hollander (1990) suggested that sampler
performance criteria should be evaluated in controlled outdoor tests, given the inability of
wind tunnels to accurately mimic the influences of outdoor meteorological conditions on
sampling.  The current EPA PM10 performance testing requires field tests to demonstrate
sampler precision and flow rate stability,  and the comparability of equivalent methods to
designated reference methods. The stringency of such tests are highly dependent on the
sampling location chosen, local aerosol sources, the existing meteorology and the  season.
     Kenny and Liden (1991) noted that the EPA PM10 sampler performance specifications
(U.S. Environmental Protection Agency,  1987) provided inadequate consideration for
defining the uncertainty in each parameter, and they suggested that bias mapping approaches
be considered.  Bias mapping relates the allowable precision of a parameter to the critical
values of expected bias that just meet the specifications. A similar but less robust procedure
is used in the EPA performance specifications.  Botham et al.  (1991) recommended that  the
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wind tunnel test system duplicate the expected field sampling scenarios as closely as possible,
including characteristic flow obstructions.  They described the wind tunnel testing of personal
aerosol samplers mounted on an anthropogenically consistent (e.g., breathing, heated)
mannequin.  Hoffman et al. (1988) and John et al. (1991) described the adverse influence of
internal surface soiling on aerosol collection performance during extended field operation,
and noted that the existing EPA PM10 performance specifications only considered clean
samplers.  Mark et al. (1992) noted that even though wind tunnel performance testing  cannot
exactly emulate outdoor turbulence scales,  testing in the controlled  tunnel environment is a
necessity  to adequately characterize particle samplers.
      Significant new innovations in aerosol sensing technologies that  meet the PM10
performance specification and have earned designations as equivalent  methods (see
Section 4.2.6) have occurred since the 1982 Criteria Document. These indirect8 methods
include automated beta attenuation monitors (e.g., Merrifield, 1989; Wedding and Weigand,
1993), and the  automated Tapered Element Oscillating Microbalance  (TEOM®) technology
(Patashnick and Rupprecht,  1991). The TEOM® sampler does not  use gravimetric analysis
on a balance, but computes  mass based on the frequency shift as particles are deposited on an
oscillating element.  These designations added automated sampling  capabilities to the
previously all-manual list of sampling methods.  Recent field tests of  both the beta and
TEOM methodologies suggest that biases compared to gravimetrically-based samplers may
exist that  were  not identified by the EPA performance test requirements.  Arnold et al.
(1992) provide  data suggesting that the mass concentration data from  a Wedding beta gauge
averaged  19% lower than a collocated Wedding PM10 gravimetric sampler.  Several
researchers reported that the TEOM can yield mass concentrations  that are either lower or
higher than those observed in reference method measurements (Hering, et al., 1994; Meyer,
et al., 1992;  Meyer et al.,  1995).  The TEOM operates at an elevated temperature (30 °C or
50 °C) during the collection and measurement process in order to ensure the removal of
liquid water associated with particles.  In the reference method, the particle-associated  water
is removed during an equilibration period in a specified temperature and relative humidity
range.  Both techniques are  subject to loss  of semivolatile materials such as NH4NO3 and
8An alternate technology used instead of direct gravimetric analysis to infer mass concentrations from developed
relationships.
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some organic components.  The TEOM may lose semivolatile material that is volatilized due
to the higher than ambient sampling temperatures. The reference method may lose
semivolatile material during sampling (if concentrations decrease or temperature increases
during the sampling period).  The reference method is  also subject to loss of semivolatile
materials during equilibration and storage prior to weighing.  These processes, in areas or
times during which semivolatile aerosol components are a significant component of the
ambient aerosol mass, can cause either technique to yield a significant under-estimation of
the  mass of particulate matter in the ambient air.  This also applies to some degree to any
integrated sample collected on a  substrate.  Devising comprehensive performance
specifications and test procedures for aerosol samplers, given the complexities of aerosol
chemistry, physics, and mechanics, is a demanding task.
     The size-selective, gravimetrically-based, 24-h manual aerosol concentration
measurement has been the mainstay of compliance sampling for at least two decades.
Although several new sensor technologies have been designated as Equivalent methods for
PM10 by EPA, no superior technology has been developed that is a better reference method
than that based on collection of a discreet aerosol sample followed by gravimetric analysis.
Improvements have been made since 1982 in the accuracy and precision of integrated,
manual aerosol sampling.  Some of the most significant advances have occurred in aerosol
size separation technologies, improved performance characterization test methods, and
speciation sampling techniques.
     As  discussed by Lippmann (1993), there  may be no threshold for health responses
down to  the lowest aerosol concentrations.  This implies that the precision and lower
detection limit requirements will continue to be important for aerosol measurements across
the concentration spectrum.  These factors become even more critical as the size fraction of
interest becomes smaller and fewer total particles are collected.  At low concentrations
(especially with small size fractions),  normally insignificant factors can become important
contributors to biases.  Witz et al.  (1990) reported rapid and substantial  losses of nitrates,
chlorides and ammonium ion (19, 65  and 51%, respectively) from quartz high volume
sampler filters during storage periods of one week prior to analyses.  Transformations can
also occur on  glass fiber substrates during sampling, as reported by Sickles and Hodson
(1989) for the rapid conversion of collected nitrites to nitrates in the presence of ozone.
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Zhang and McMurry (1992) showed that nearly complete evaporative losses of Fine particle
nitrate can occur during sampling on Teflon filters.  Lioy et al. (1988), in a study using
PM10 samplers, reported 25 to 34% lower concentration values resulting from losses of glass
fibers from the filter to the filter holder gasket during sampling.  Feeney et al.  (1984)
reported weight gains in Teflon filters used in contaminated ring cassettes, that posed
significant problems for light aerosol loadings.  Grinshpun et al. (1993) suggest that if
unavoidable changes in the aerosol occur during sampling, development of a model that
permits back-calculation of the in situ characteristics can be considered.

4.2.6  Reference and Equivalent Method Program
     Ambient air PM10 measurements are  used (among other purposes) to determine whether
defined geographical areas are in attainment or non-attainment with the National Ambient Air
Quality Standards (NAAQS) for PM10.  These measurements are  obtained by the States in
their state and local air monitoring station (SLAMS) networks as required under 40 CFR
Part 58.  Further, Appendix C of Part 58 requires that the ambient air monitoring methods
used in these EPA-required SLAMS networks must be methods that have been designated by
the EPA as either reference or equivalent methods.
     Monitoring methods for particulate matter (i.e., PM10) are designated by the EPA as
reference  or equivalent methods under the provisions of 40 CFR Part 53, which was
amended in 1987 to add specific requirements for PM10 methods.  Part 53 sets  forth
functional specifications and other requirements that reference and equivalent methods for
each criteria pollutant must meet,  along with explicit test procedures by which candidate
methods or samplers are to be tested against those specifications.  General requirements and
provisions for reference and equivalent methods are  also given in Part 53, as are the
requirements for submitting an application  to the EPA for a reference or equivalent method
determination.  The distinction between reference and equivalent methods is a technical one.
On one hand,  it provides for detailed, explicit specification of a selected measurement
technology for reference methods.  On the other hand, it allows alternative (including
innovative and potentially improved) methodologies for equivalent methods, based only on
meeting specified requirements for functional performance and for comparability to the
reference method.  For purposes of determining attainment or non-attainment with the
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NAAQS, however, the distinction between reference and equivalent methods is largely,  if not
entirely, immaterial.
     Under the Part 53 requirements, reference methods for PM10 must be shown to use the
measurement principle and meet the other specifications set forth in 40 CFR 50, Appendix J
(Code of Federal Regulations, 1991).  They must also include a PM10 sampler that meets the
requirements  specified in Subpart D of 40 CFR 53.  Appendix J specifies a  measurement
principle based on extracting  an air sample from the atmosphere with a powered sampler that
incorporates inertial separation of the PM10 size range particles followed by collection of the
PM10 particles on a filter over a 24-h period.  The average PM10 concentration for the
sample period is determined by dividing the net weight gain of the filter over the sample
period by the total volume of air sampled.  Other specifications are prescribed in Appendix J
for flow rate  control and measurement, flow rate measurement device calibration, filter
media characteristics and performance, filter conditioning before and  after sampling, filter
weighing, sampler operation,  and correction of sample volume to EPA reference temperature
and pressure.  Also, sampler  performance requirements in Subpart D of Part 53 include wind
tunnel tests for "sampling effectiveness" (the efficacy of the PM10 particle size separation
capability) at each of three wind speeds and "50 percent cutpoint" (the accuracy of the
primary 10-micron particle size separation).  Field tests for sampling  precision and flow  rate
stability are also specified. In spite of the instrumental nature of the  sampler, this method is
basically a manual procedure, and all designated reference methods for PM10 are therefore
defined as manual methods.
     Equivalent methods for  PM10,  alternatively, need not be based on the measurement
principle specified in Appendix J nor meet the other Appendix J requirements.  Instead,
equivalent methods must meet the "sampler" performance specifications set  forth in
Subpart D of Part 53  and demonstrate comparability to a reference method as required by
Subpart C of Part 53.  The provisions of Subpart C specify that a candidate equivalent
method must produce PM10 measurements that agree  with  measurements produced by
collocated reference method samplers at each  of two field test sites.  For this purpose,
agreement means a regression slope of 1 ± 0.1, a regression intercept of 0  ± 5 /xg/m3,  and
a correlation  >0.97.  These  requirements allow virtually  any type of PM10  measurement
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technique, and therefore an equivalent method for PM10 may be either a manual method or a
fully automated instrumental method (i. e., analyzer).
     As of this writing, the EPA has designated seven reference methods and three
equivalent methods for PM10, as listed in Table 4-1.  The reference methods include four
methods featuring high-volume samplers from two manufacturers, with one using a cyclone-
type size separator and the others using an impaction-type separator.  The other reference
methods include a low-volume sampler (from a third manufacturer), a low-volume sampler
featuring a secondary size separation at 2.5 microns (dichotomous sampler), and a medium-
volume, non-commercial sampler.  The three designated equivalent methods are all
automated PM10 analyzers and include two operating on the beta-attenuation principle and
one based on a tapered element oscillating microbalance (TEOM™). It should be noted that
although these latter three automated PM10 analyzers may be capable of providing continuous
or semi-continuous PM10 concentration measurements, only 24-h average PM10
measurements are recognized as approved under their equivalent method designations.

4.2.7    Determination of Size Distribution
     The determination of aerosol size distributions can be a powerful research tool when
studying source contributions and transformation processes.  A number of techniques are
available as described by texts such  as Willeke and Baron (1993) to make near real-time,
single particle aerosol measurement  in addition to cascade impactors.

4.2.7.1   Cascade Impactors
     In cascade applications, the aerosol is impacted and trapped onto a series of removable,
coated  substrates (e.g., greased foils),  including a final total stage collection on a filter for
gravimetric analysis.   Marple et al. (1993) list over 30 single stage and cascade impactors
that are either commercially available or still commonly used. The design and calibration of
a miniature eight-stage cascade impactor for personal air sampling in occupational settings  is
described by Rubow et al. (1987), operating at 2.0 1pm.  Evaluations of the most commonly
used cascade impactor systems have been reported by Vaughan (1989) for the Andersen MK1
and MK2 7-stage  cascade impactors, Marple  et al! (1991)  for the 10-stage Micro-Orifice
Uniform Deposit Impactor (MOUDI), and Wang and John (1988) and Hillamo and
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              TABLE 4-1.  U.S. ENVIRONMENTAL PROTECTION AGENCY-DESIGNATED REFERENCE
                                         AND EQUIVALENT METHODS FOR PM10
     Method No.
          Identification
                 Description
       Type
     Date
RFPS-1087-062
RFPS-1287-063
RFPS-1287-064
RFPS-1287-065
Wedding & Associates PM10
Critical Flow High- Volume
Sampler.

Sierra-Andersen or General Metal
Works Model 1200 PM10 High-
Volume Air Sampler System

Sierra- Andersen or General Metal
Works Model 321-B  PM10 High-
Volume Air Sampler System

Sierra-Andersen or General Metal
Works Model 321-C  PM10 High-
Volume Air Sampler System
High-volume (1.13 m3/min) sampler with cyclone-
type PM10 inlet; 203 x 254 cm (8 x  10 in) filter.
High-volume (1.13 m3/min) sampler with
impaction-type PM10 inlet; 203 x 254 cm (8 x 10
in) filter.

High-volume (1.13 m3/min) sampler with
impaction-type PM10 inlet; 203 x 254 cm (8 x 10
in) filter.  (No longer available.)

High-volume (1.13 m3/min) sampler with
impaction-type PM10 inlet; 203 x 254 cm (8 x 10
in) filter.  (No longer available.)
Manual reference
method
Manual reference
method
Manual reference
method
Manual reference
method
10/06/87
12/01/87
12/01/87
12/01/87
RFPS-0389-071
Oregon DEQ Medium Volume
PM10 Sampler
Non-commercial medium- volume (110 L/min)
sampler with impaction-type inlet and automatic
filter change; two 47-mm diameter filters.
Manual reference
method
3/24/89
RFPS-0789-073
Sierra-Andersen Models SA241 or
SA241M or General Metal Works
Models G241 and G241M PM10
Dichotomous Samplers
Low-volume (16.7 L/min) sampler with impaction-
type PM10 inlet; additional particle size separation
at 2.5 micron, collected on two 37-mm diameter
filters.
Manual reference
method
7/27/89
EQPM-0990-076
Andersen Instruments Model
FH62I-N PM10 Beta Attenuation
Monitor
Low-volume (16.7 L/min) PM10 analyzers using
impaction-type PM10 inlet, 40 mm filter tape, and
beta attenuation analysis.
Automated
equivalent method
9/18/90

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         TABLE 4-1 (cont'd).  U.S. ENVIRONMENTAL PROTECTION AGENCY-DESIGNATED REFERENCE
         	                    AND EQUIVALENT METHODS FOR PM10
     Method No.
         Identification
                Description
       Type
     Date
EQPM-1090-079
EQPM-0391-081
Rupprecht & Patashnick TEOM
Series 1400 and Series 1400a
PM10 Monitors

Wedding & Associates PM10 Beta
Gauge Automated Particle
Sampler
Low-volume (16.7 L/min) PM10 analyzers using
impaction-type PM10 inlet, 12.7 mm diameter
filter, and tapered element oscillating microbalance
analysis.
Low-volume (16.7 L/min) PM10 analyzer using
cyclone-type PM10 inlet, 32 mm filter tape, and
beta attenuation analysis.
Automated
equivalent method
Automated
equivalent method
10/29/90
3/5/91
RFPS-0694-098
Rupprecht & Patashnick Partisol
Model 2000 Air Sampler
Low-volume (16.7 L/min) PM10 samplerwith
impaction-type inlet and 47 mm diameter filter.
Manual reference
method
7/11/94

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Kauppinen (1991) for the 6-stage Berner, low pressure cascade impactor.  The smallest
particle stages of these impactors can have  very small diameter jets and/or very low total
pressures to achieve the sub-micron separations.  The MOUDI impactor has 2000 holes on
the lowest cutpoint  stage. Raabe et al. (1988) describe an 8 stage cascade slit impactor with
slowly rotating impactor drums instead of flat plates.  This arrangement, in combination with
a PIXIE analyzer, permitted aerodynamic sizing of elemental components, with temporal
resolution.   The skill and care required in the operation of cascade impactors suggests that
they are research rather than routine samplers.
     The importance of the aerosol calibration of a cascade impactor is illustrated by
Vaughan (1989) in  Figure 4-13, which compares the experimental data with the
manufacturer's calibrations and indicates biases as large as 1.0 fim.  Marple et al. (1991)
provided a similar type of stage calibration for the MOUDI impactor and included data on
the internal particle losses (see Figure 4-14). These loss data showed that an improperly
designed inlet to the impactor,  combined with the inertial and interception losses  of the larger
particle sizes, can substantially bias the first stage collections.  This was also demonstrated
for the inlet to the Andersen impactor by McFarland et al.  (1977). Cascade impactors that
cover wide particle size ranges inherently require design compromises among competing
factors, including cutpoint sharpness, internal stage losses and the physical size of the device.
     Cascade impactors can be used to construct distributions of mass and speciated
constituents as a function of aerodynamic diameter.  These distributions can be constructed
graphically or using matrix inversion techniques. Marple et al. (1993) notes that impactor
stage calibrations which do not demonstrate sharp cutoffs can cause significant between-stage
sizing  errors if not  accommodated. John et al. (1990) measured distributions over the
0.08 to 16 [j.m range for mass and inorganic ions for several sites in Southern California.
They identified the standard coarse mode and two separate,  previously unreported modes in
the 0.1 to  1.0 /*m range.  This latter  range was referred to by Whitby (1978) as a single
"accumulation" mode.  John et al. (1990) described a "condensation" mode  at 0.2 ±  0.1  /mi
containing gas phase reaction products, and a "droplet" mode at 0.7 ±  0.2 /*m which grows
from the "condensation" mode by the addition of water and sulfates. Fang et al. (1991)
described the effects of flow-inducted relative humidity changes on the  sizing of acid aerosols
in the  MOUDI impactor. They noted that  it may not be possible to measure size
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distributions of small (less than about 0.2 to 0.5 /mi) particles with impactors at relative
humidities exceeding 80%.

4.2.7.2    Single Particle Samplers
     Aerosol size distribution data are  useful for studies of particle transport and
transformation processes, source characterization, and particle sizing and collection device
performance.  In addition to cascade impactors, a number of real time or near real time
sizing instruments are available and described in texts such as Willeke and Baron (1993).
While cascade impactors provide distributions in terms of aerodynamically sized  mass, single
particle sampling devices can produce optically sized distributions as a function of particle
number (count), with surface area and  volume distributions computed during the data
reduction, assuming spherical particles. Particle density and shape information as a function
of size are required to convert from volume distributions to an estimated mass basis.
Individual particle sizing and counting  instruments are generally limited to a particle
detection range of a decade or so, but  several devices can overlap to cover the range of
approximately 0.001 to 10 /mi. The principle of detection of an instrument restricts the
particle sizes which can be detected. For example, instruments using electrical mobility
analysis  are limited to particle sizes less than about 1 /mi.  Optical methods are typically
used to measure particles larger than about 0.1  to 0.3 /mi. Inlet and transport system losses
of coarse particles above about 2 /mi, prior to the sensing volume, must be factored into
reported size distributions.
     The three most commonly used single particle sampler types are  aerodynamic particle
sizers, electrical mobility analyzers and optical  particle counters (OPC's).  Aerodynamic
particle sizers use laser doppler anemometry to measure  the velocity of particles  in a jet.
The acceleration of the particle is related to the aerodynamic particle diameter.  This
technique is typically applied to particles larger than about 0.5 /mi.  In electrical mobility
analysis, aerosol with a known charge  distribution flows through an electric field.  The
particles migrate according to their mobility which can be related to size.  The original TSI
electrical aerosol analyzer (EAA) performed this separation in an integrated manner over the
total size distribution and detected the particles  by unipolar diffusion charging.  A more
versatile approach, the differential mobility analyzer or DMA (Liu et al., 1978), is able to
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examine a narrow slice of the size distribution in an equilibrium charge state, detected by a
condensation nucleus counter (CNC).  Differential mobility analyzers have been employed in
pairs (Tandem Differential Mobility Analyzer, or TDMA) to examine both (a) particle
characteristics such as NH3 and H2SO4 reaction rates (McMurry et al.,  1983) and (b) the
sensitivity of the size distributions of Los Angeles  aerosol to relative humidity (McMurry and
Stolzenburg, 1989).   The latter research used the first DMA to select particles of known
mobility from the input aerosol, a humidification system to condition the selected particles,
and the second DMA to determine mobility changes.  Optical particle counters pass a jet of
aerosol through an optical system.  Light scattered from individual particles is detected and
the signal in processed in a multi-channel analyzer. Discreet signals are counted and sorted
by intensity and by optical size.  One example of a forward-scattering counter with an open
sensing volume  (for use on aircraft) is the Particle Measuring Systems,  Inc., FSSP-300,
which can provide high resolution (31 channel) count distributions over the size range of 0.3
to 20 fim (Rader and O'Hern, 1993).  Gebhart (1993) described currently available OPC's
and their counting efficiencies over a range of diameters.
     Single particle samplers have common considerations, as dicussed below.
     Calibration: They are calibrated with reference aerosol either by the manufacture or by
the user.  If the properties of the aerosol measured are quite different than the calibration,
the indicated size distribution may be quite different than actual distribution.  Brockman
et al. (1988) demonstrated that the APS calibration can vary significantly with the type of test
aerosol and showed substantial response biases between oleic acid and polystyrene  latex
spheres above 10 jan.  Wang and John (1989) described a procedure to correct the APS
response for aerosol  particle density.  Particle shape can also provide serious sizing errors,
and specific calibrations are needed  for particles with shape factors significantly different
from unity (spherical). Yeh (1993)  commented that the calculated geometric standard
deviations (ag) determined by the EAA and DMA  are generally larger than 1.3, even if the
correct value is significantly closer to unity.  Woskie et al. (1993) observed, as did Willeke
and Degarmo (1988), that optical particle counting devices must be appropriately calibrated
using realistic aerosols, especially for low concentration applications.  Harrison and Harrison
(1982) suggested that the ratio of fine particle mass concentration to optical scattering
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extinction will be more variable when a significant contribution is made by irregular (shaped)
particles - an event likely to occur when the mean mass diameter exceeds 1 /mi.
     Particle Concentration Effects:  Gebhart11993) noted that the response of single
particle counters may be influenced by extremely high particle concentrations.  Wake (1989)
and Heitbrink et al. (1991) described the coincidence problems of the APS when sampling
high total particle concentrations, especially for sizes greater than 1 jum.  Baron et al. (1993)
reported that the concentration levels giving 1 %  coincidence in an aerodynamic particle sizer
for 0.8, 3 and 10 pm particles, respectively, are the relatively low values of 558, 387 and
234 particles/cm3.  Optical particle counters can experience coincidence errors (two particles
are detected as  a single particle) and counter saturation at high particle concentrations.  Hinds
and Kraske (1986) described the performance of the PMS, Inc.  LAS-X and noted a sizing
accuracy of ±2 channel widths, with coincidence errors of less than 10% for concentrations
below  10,000 particles/cm3. Clearly,  typical particle concentrations found in the atmosphere
may produce significant errors if sample dilution is not utilized.

4.2.8    Automated Sampling
     Automated methods to provide measures of aerosol concentrations in the air have
existed for decades in an attempt to provide temporal definition of suspended particles and
enhance every-sixth-day sampling schedules with a minimum labor expense.  Arnold  et al.
(1992) collected daily 24-h PM10 samples with an automated monitor and noted that 80% of
the highest 10 daily concentrations between 1989 and 1990 were not encountered by the
every-sixth-day sampling schedule.  Some of the automated samplers (e.g., British Smoke
Shade  and AISI tape samplers) described in the 1982 Criteria Document were indicator
measures of aerosol concentration, using calibrations relating aerosol concentrations to
reflected or absorbed light.  Tape samplers were used in the U. S. primarily as exceedance
(index) monitors.
     The beta attenuation and integrating nephelometer techniques described in the 1982
Criteria Document primarily were  research methods. Since that time, the beta gauge
sampling approach has been refined and a new approach, based on the Tapered Element
Oscillating Microbalance (TEOM)  principle, has been developed.  Samplers based on these
techniques have been designated as equivalent methods  for PM10.
                                         4-51

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     Although one could be readily constructed, there are presently no commercially
available, automated high volume (>  1 m3/min flowrate) aerosol samplers, excluding the
possibility of the timed operation of an array of manual samplers.  The physical size of such
a sampling system using 8 x 10 inch filters is impractical.  The dichotomous  sampler is
currently the only low volume, gravimetrically-based sampler commercially available in an
automated version.

4.2.8.1  Smoke Shade (British Smoke, Black Smoke)
     Historically, the British smoke shade sampler was one of the earliest ambient PM
sampling devices to be developed and to gain widespread use as an automated optical PM
monitoring method.  Key features and limitations of the British or black smoke (BS) method
were discussed in EPA's 1982 Criteria Document.  As indicated in Chapters  3  and 14  of that
Criteria Document, the BS method typically involves use of a sampler that draws ambient air
through an inverted funnel and approximately 3m of plastic tubing to deposit collected
particles on white filter paper. The amount of PM deposited during a given  time period
(e.g., 1-h during severe episodes, or more typically, 24-h) is determined by measuring the
blackness of the stain on the filter paper.  An automated version of the sampler can collect
daily samples sequentially for up to eight days.
     It is important to note, as described in the 1982 Criteria Document, that the BS method
and its variations  (e.g., the OECD version) in routine use typically employ standard
monitoring equipment with a D50 cutpoint =4.5 ^im,  which mainly allows fine-mode particles
and small coarse mode particles  (some ranging up to ~ 8 to  10 /urn) to be collected.  Thus,
regardless of whether larger particles are present in the atmosphere, the BS method collects
predominately small particles. Also, the BS method neither  directly measures mass nor
determines chemical composition of the collected PM.  Rather, it measures light absorption
of particles  as indicated by reflectance from the stain formed by the particles collected on the
filter paper, which depends both on the density of the stain,  or amount of PM collected, and
the optical properties of the collected PM.  Smoke particles composed of elemental carbon,
found in incomplete fossil-fuel combustion products,  typically make the greatest contribution
to the darkness of the stain, especially in urban areas.  Thus, the amount of  elemental
carbon, but not organic carbon,  present in the stain tends to  be most highly correlated with
                                          4-52

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BS reflectance readings.  Other nonblack, noncarbon particles also have optical properties
such that they can affect the reflectance readings, although their contribution to optical
absorption is usually negligible.
     Since the relative proportions of atmospheric carbon and noncarbon PM can vary
greatly from site to site or from one time to another at the same site, the same absolute BS
reflectance reading can be  associated with markedly different amounts (or mass) of collected
particles or, in unusual circumstances, even with markedly different amounts of carbon.
Site-specific calibrations  of reflectance readings against actual mass measurements obtained
by collocated gravimetric monitoring devices are therefore necessary  to obtain estimates of
atmospheric concentrations of paniculate  matter based  on the BS method.  A single
calibration curve relating mass or atmospheric concentration (in /xg/m3) of particulate matter
to BS reflectance  readings  obtained at a given site  may serve as a basis for crude estimates of
the levels of PM (mainly small particles)  at that site over time, so long as the chemical
composition and relative proportions of elemental carbon and noncarbon PM do  not change
substantially.  However,  the actual mass or smoke concentrations present at a particular site
may differ markedly (by factors of two or more) from the values calculated from a  given
reflectance  reading on either of the two most widely used standard curves (the British and
OECD  standard smoke curves)9.  Thus, great care must be taken in interpreting the meaning
of any BS value reported in terms of y«g/m3, especially as employed in the British and other
European epidemiological studies discussed in Chapter 12 of this document.
     There has existed long standing interest with  regard to relationships between ambient
PM  concentrations indexed by BS readings (based  on conversion of reflectance  values to
estimated jwg/m3 concentrations by means of standard calibration curves) and those obtained
by gravimetric methods.  The 1982 Criteria  Document noted that Ball and Hume (1977) and
Waller  (1963) found that such relationships are site, season, and particle-source dependent.
Also, Lee et al. (1972) noted, from collocated TSP hi-vol and smoke shade  sampler
9For this  reason,  smoke data reported  in ^g/m3 based on either the British or OECD  Standard  curve are
appropriately interpreted in terms of "nominal" ftg/m3 smoke units and cannot be accepted as accurate estimates of
airborne PM mass unless corroborated by local site-specific gravimetric calibrations.  In other words, unless based
on local site-specific calibrations, smoke readings in ;*g/m3 cannot yield quantitative estimates of atmospheric PM
concentrations. In the absence of such calibrations, smoke readings only allow for rough qualitative (i.e., <; =;
or >) comparisons of amounts of PM present at a given time versus another time at the same site and do not permit
meaningful comparisons between PM levels at different geographic areas having airborne PM of different chemical
composition (especially in terms of relative proportions of elemental carbon).
                                            4-53

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comparisons made at various sites in England, that the overall correlation coefficients
between these measurements for all sites was 0.618.  However, the individual coefficients
ranged from 0.936 (good correlation) to 0.072 (no correlation).  Bailey and Clayton (1980)
showed that smoke shade measurements correlated more closely with soot (elemental carbon)
content than with gravimetric mass.  Other work by Paschel and Egner (1981) and Clayton
and Wallin (1982) showed consistently higher TSP values than BS readings (converted to
/jg/m3) from collocated samplers in various U.S. and U.K. cities, respectively (as would be
expected  given that the BS measurements of fine and  small coarse mode particles typically
represent only some fraction of the wider range  of particles sampled by TSP measurements).
Clayton and Wallin (1982), not surprisingly, also found widely variable ratios of TSP to BS
readings from different U.K. cities reflecting the varying proportions of small particles
present in the total ambient mix of particles at different sites.  Likewise,  varying (site- and
season-dependent) relationships between BS measurements and ambient PM measurements
made by  various gravimetric methods have been reported  in the Federal Republic of
Germany (Laskus, 1983) and in the semi-arid climate of Baghdad, Iraq (Kanbour et al.,
1990). Lastly, Muir and Laxton (1995) reported that, for Bristol (a moderate size U.K.
city), daily average BS (averaged over six urban background sites) appears to be a reasonable
predictor of daily average PM10 and daily 1-h peak PM10 values;  but different relationships
apply for winter versus summer, indicating that  BS and PM10 measure different components
of airborne PM (i.e., BS may be a better index  of fine-mode particles than PM10, which has
a D50 cutpoint of = 10 /mi).
     Only limited examples exist of derivation of models of interrelationships between BS
readings and gravimetric measurements for particular time periods in a given location.   For
example, see Mage (1995) for discussion of an empirical model relating BS to TSP values
during London winters of the 1950s and 1960s.

4.2.8.2 Coefficient  of Haze (AISI/ASTM Tape Sampler)
     The 1982 Criteria Document also described a second type of automated optical PM
measurement methods.  Developed before 1940, the American Iron and Steel Institute (AISI)
light transmittance method is similar in approach to the BS technique and has been employed
for routine monitoring in some American cities. The instrument collects particles with a D50
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cutpoint of =5.0 /xm aerodynamic diameter and uses an air intake similar to that of the BS
method. Ambient PM is collected on a filter-paper tape that is periodically advanced to
allow accumulation of another stain.  Opacity of the stain is determined by transmittance of
light through the deposited material and the tape.  The results are expressed in terms  of
optical density or coefficient of haze (CoH) units per 1,000 linear feet of air sampled (rather
than in  mass units).  Readings in CoH units are somewhat more responsive to noncarbon
particles than are BS measurements; but, again, the AISI method neither directly measures
mass nor determines chemical composition of the PM collected.  Any attempt to relate CoHs
to j«g/m3 requires site-specific calibration of CoH readings against mass measurements
determined by a collocated gravimetric device, but the accuracy of such mass  estimates are
still subject to question.
     Few attempts have been reported on calibration of COH measurements versus results
from collocated  gravimetric devices.  One notable attempt (Ingram, 1969; Ingram and
Golden, 1973) was reported for New York City, but the results are of very limited
applicability to New York City aerometric data of the 1960's.  Also, Regan et al. (1979)
showed that CoH readings correlate favorably with gravimetric measurements  limited to
smaller particle sizes.  Edwards (1980) and Edwards et al. (1983) have also shown that BS
reflectance measurements can be related to the absorption coefficient of the atmosphere and
that BS measurements can be converted to approximate CoH  measurements made by AISI
tape sampler using the absorption coefficient  relationships.  As several investigators noted,
(e.g., Lodge,  et al.,  1981), if a relationship could be developed between optical and
gravimetric measurements,  it would be site specific, but still  variable because  of seasonal and
long-term differences in the sources of collected particle size fractions and their carbon
content.

4.2.8.3   TEOM® Sampler
     The Tapered Element Oscillating Microbalance (R & P, Inc.) sensor, as  described by
Patashnick and Rupprecht (1991), consists of an oscillating tapered tube with a filter on its
free end (see the diagram in Figure 4-15).  The change in mass of the filter and collected
aerosol produces a shift in the oscillation frequency of the tapered tube that is  directly related
to mass. Rupprecht  et al. (1992) suggested that the filter can be archived after sampling for
                                          4-55

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               Flow
Flow
                                                     Sampling Head
                                              Heated Air Inlet
  Filter Cartridge
  Tapered Element
          Electronic
      Feedback System
              '

        Microprocessor
                                             to Flow Controller
Figure 4-15.  Rupprecht and Patashnick TEOM® sampler.


Source: Patashnick and Rupprecht (1991).
                                    4-56

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subsequent analysis.  The sampler inlet has a PM10 cutpoint and operates at 16.67 1pm.
A flow splitter samples a 3 1pm portion of this flow to be filtered.  Since the fraction of
volatile species (e.g., water, nitrates, organics) in the aerosol is a function of ambient
temperature, the TEOM® sampler heats the inlet air stream to a constant 30 or 50 °C to keep
moisture in the vapor phase.  The mass transducer is also heated to 50 °C to stabilize the
measurement process.   Operation with the flow stream heated to a lower temperature
(e.g., 30 °C) is possible, but care must be taken to avoid moisture condensation that will
confound the measurement.  The transducer is also  heated to 50  °C to stabilize the mass
measurement.  A factory calibration regression is used to electronically correct the computed
mass from the TEOM® sampler to that measured by a reference  PM10 sampler.
     Although several studies (e.g. Patashnick and  Rupprecht, 1991; Kalthoff and Grumpier,
1990) have shown consistent and linear relationships between the TEOM® sampler  and
gravimetric PM10 samplers, a number of studies have shown biases under certain conditions.
Several researchers, including Cahill et al. (1994), Hering (1994) and Meyer et al. (1992)
have reported that the modification of the aerosol by the  elevated operating temperature
appears to have a significant effect (loss) on mass concentration.  Meyer et al.  (1992)
collocated a TEOM® sampler with an PM10 SA1200 gravimetric sampler in Mammoth
Lakes, CA during a winter heating season (heavy wood stove usage).  The regressions
between the TEOM® sampler and PM10 sampler gave strong correlations (r2 >  0.98), with
slopes of 0.55 for operation at 50 °C, and 0.66 for operation at 30 °C.  The negative bias of
the TEOM was attributed primarily to losses of semi-volatile organics from the  filter.
Cahill et al. (1994) reported that the TEOM® sampler showed biases  on the order of 30%
low and poor correlations with PM10 samplers in dry, dusty conditions.  The reasons for this
discrepancy were unknown.  The field comparison data of Patashnick and Rupprecht (1990)
showed near unity (1 ± 0.06) regression slopes for the TEOM with the Wedding IP10 and
Sierra-Andersen dichotomous samplers in El Paso, TX and Birmingham, AL.  Since aerosol
composition is highly dependent on local sources and meteorology, volatilization losses could
be expected to be site- and season-dependent.  This could significantly affect the rigor of
collocated field sampling.  A  WESTAR (1995) council report summarizes the relationships
between TEOM® monitors and other direct gravimetric samplers in at least 10 states in the
western U.S. This report concluded that on average the  TEOM® sampler concentrations
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were 21.8% Lower than other collocated PM10 samplers for concentrations > 50 /ig/m3.
This would significantly affect the TEOM® sampler's ability to be used as a  "trigger"
monitor for control strategy plans.  More data are needed to determine the implications of
these problems on the ability of the TEOM® sampler to be used in a regulatory setting.
Although it is clear that the TEOM® sampler can provide PM10 data comparable  to the
existing reference method samplers, the specific field sampling conditions where  excessive
bias might be expected to occur have not been completely defined. A portion of the bias is
undoubtedly due to concomitant variabilities in the associated gravimetric measurements.

4.2.8.4    Beta Gauge
     The  Andersen FH 62I-N beta attenuation sampler was described by Merrifield (1989)
and uses a 30 mCi Krypton-85 source and detector to determine the attenuation caused by
deposited  aerosols on a filter (see diagram in Figure 4-16).  To improve the  stability over
time, a reference reading is periodically made of a foil with an attenuation similar to that of
the filter and collected aerosol. The Wedding beta attenuation sampler was described  by
Wedding and Weigand (1993) and uses a 100 mCi 14C source.  Both samplers have inlets
with a PM10  cutpoint, with the Andersen sampler operating at 16.67 1pm and the Wedding at
18.9 1pm. The filter material  is contained on a roll and advances automatically on a time
sequence,  or  when a preset aerosol loading is reached.  An automatic beta gauge sampler
was also described by Spagnolo (1989), using a 15 />im inlet and a 14C source. The
calibration of a beta gauge is site specific, and a calibration regression must  be processed
electronically to provide accurate mass readings.  Rupprecht et al.  (1992) suggested that the
closer link between deposited mass and frequency shift for the TEOM principle should
provide less site-specific response, compared to the aerosol compositional sensitivity of the
beta gauge technique.
     Arnold  et al. (1992) provided data over a 2 year period in Denver, CO for the mass
concentration regression data from a Wedding beta gauge, showing a range of correlations
(r2 from 0.72 to 0.86), varying by sampler and season.  The authors  suggested that
installation of a newer technology beta gauge accounted for the higher correlations, but noted
that unexplained outliers resulted in poorer than expected results.  The regression slopes
between the two sampler types showed that the beta gauge averaged 19% lower than a
                                          4-58

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                                          MC
                                          CC
                                          C
Measuring Chamber
Compensation Chamber
Chamber for Dust Precipitation
and Measurement
30 m Ci KR-85 Source
Filter Feed Spool
Filter Takeup
High-Voltage Power Supply
                          mperature / Pressure
Bit
I/O


50-Pin
Connector
              V24/RS232
                          Rotary Vane Pump

Figure 4-16. Andersen beta gauge sampler.

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collocated Wedding PM10 gravimetric sampler. It should be noted that the Wedding PM10
inlet has typically been reported (see Section 4.2.2.4) to be 10 to 15% lower in collocated
field tests  with Sierra-Andersen PM10 inlets.  A WESTAR (1995) council report summarizes
the relationships between beta gauge monitors  and other direct gravimetric samplers in at
least five states in the western U.S.  This report concluded that on average beta gauge
concentrations were 8.6% lower than other collocated PM10  samplers for concentrations
> 20 pig/m3.  Field data from Wedding and Weigand (1993) at two  sites (Fort Collins, CO
and Cleveland, OH) using the same samplers produced regressions exhibiting strong
correlations (r2 = 0.99) with no apparent outliers and a composite slope of 1.00.
Arnold et al. (1992) operated the PM10 high volume samplers on the required every-6th-day
schedule and the beta attenuation monitors continuously, and noted that only 22.5% of the
exceedance days, as measured by the beta monitor, were operational days for the high
volume samplers.

4.2.8.5    Nephelometer
     The  integrating nephelometer is commonly used as a visibility monitor; it measures the
light scattered by aerosols, integrated over as wide a range of angles as possible.
A schematic diagram of the integrating nephelometer is shown in Figure 4-17 (from Hinds,
1982). The measured scattering coefficient of particles, bsp,  can be summed with the
absorption coefficient, bap, and the comparable coefficients for the gas phase to compute the
overall atmospheric extinction coefficient, bext. Methods for estimating absorption and
extinction for atmospheric particles are discussed  in 8.2.2.  The atmospheric extinction has
been related to visibility as visual range. The particle scattering coefficient is dependent
upon particle size, index of refraction and illumination wavelength, as shown by Charlson
et al. (1968) in Figure 4-18, while the absorption coefficient is relatively independent of size.
The field calibration of nephelometers has historically been based on the refractive index of
Freon-12 (and occasionally carbon dioxide), but newer calibration procedures using atomized
sugar aerosols have been proposed (Horvath and Kaller, 1994) as more environmentally
conscious.  Nephelometry over a narrow wavelength band or at a selected wavelength can be
applied to  measure  the laser light scattered from a volume of aerosol containing  a number of
                                          4-60

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o\
       Power
       Supply
                                        Flash Tube
                                       Power Supply
             Clean Air
               Purge
Photomultiplier
    Tube
       Amplifier
                                 Aerosol
                                  Outlet
                        Collimating
                           Disks
                      Recorder
                                        t
                                           €Z9-X)pal Glass
                                        Scattering
                                         Volume
                                                     I A I

                                                   Aerosol
                                                     Inlet
Clean Air
  Purge
    Figure 4-17. Integrating nephelometer.


    Source: Hinds (1982).

-------
                    10
          oo
          CO
                 g
          
-------
particles.  Gebhart (1993) described devices such as the MIE, Inc.10. MINIRAM, often
used in portable applications to estimate real-time aerosol concentrations.  Cantrell et. al.
(1993) showed that MINIRAM calibration was significantly different for diesel and mine
aerosols.  Woskie et al. (1993) described the performance of a MINIRAM (using the
manufacturer's calibration) against gravimetric borate concentrations for particles as large as
30 pim, and found significant biases (a regression slope = 4.48).  This bias was  expected,
since the large mass median particle diameters were substantially outside the respirable
particle range recommended by the manufacturer.
      The relative insensitivity of the nephelometer to particles above ~ 2 /*m results in poor
correlations with PM10 mass. Larson et al. (1992) showed strong correlations (r2 = 0.945)
between bsp and fine fraction mass (see Figure 4-19) for a woodsmoke  impacted
neighborhood near Seattle, WA, with a slope of 4.89 m2/g.  They noted that this slope fell
within the range of values reported by others and was predicted by Mie scattering theory.
The slope of the Larson et al. (1992) data could be compared with other site-specific
calibrations, such as the data of Waggoner and Weiss (1980), which gave a composite slope
of 3.13 m2/g, characterized by the authors as representative of a "wide range" of sites.
Lewis (1981) provided an analysis of the relationships of the features of the ambient size
distribution to bsp.  The inlet air stream to the nephelometers for the latter data was heated
from  5 to 15 °C above background.  Rood et al. (1987) conducted a controlled comparison
of the influence of aerosol properties on bsp in Riverside, CA and reported a regression
slope against fine mass (defined as less than 2.0 /xm) of 2.1 m2/g with an r2 value of 0.92.
In this experiment, the relative humidity for bsp determinations was  controlled  to less than
35% and the gravimetric filter substrate was nylon.  The authors attributed the smaller than
normal slope reading to possible nitrate evaporation from the filtered aerosol and artifact
reactions with the nylon substrate  material. Thomas et al. (1993) demonstrated that the
influence of relative humidity on the relationship between photometer response and collocated
gravimetric particle concentrations can be predicted.
     The data scatter in Figure 4-19 (if assumed to be typical of such comparisons) would
suggest that fine particle mass concentration estimates from bsp values were typically within
5 to 7 fig/m3 of the gravimetrically determined values.  To be useful as a surrogate  measure
10Bedford, MA.
                                          4-63

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2.2
2.0
1.8
1.6
1.4   -
1.2   -
1.0   -
0.8   -
0.6   -
0.4   -
0.2   -
   0
                  Lake Forest Park
                 Weekly Average Values
         January 17,1991 to December 19,1991
                                                      Slope = 4.89m  /g
                                                      R2= 0.945
10
15
20
25
30
35
                                                                       40
                                                                                45
Figure 4-19.  Correlation of bsp and fine fraction mass.
Source:  Larson et al. (1992).
for mass concentration, the site-specific nephelometer calibration should be valid for a wide
range of situations, especially during episodes where the concentration levels approach or
exceed an action limit.  The scattergram of b  versus fine particle mass provided by Rood
et al.  (1987) showed much greater variability, with a given bsp value providing an estimated
20 to  25 jLtg/m3 concentration range.  They noted that metastable H2O contributed 5 to 20%
of the total particle light scattering coefficient, especially during the late afternoon and early
evening. The precisions and biases of the dependent and independent variables between bsp
                                        4-64

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and fine mass concentration are not constants, since at least one factor - moisture content of
the aerosol - affects both measures.  The gravimetric sample filters are typically  equilibrated
to a specific relative humidity range (e.g.,  40 to 60%) to normalize the tare weighings.
     Sloane (1986) and others have noted  that light scattering from particles is not solely a
function of mass but are also very dependent on a summation of the scattering coefficients of
each species.  The scattering cross  section  of a particle is dependent on the water content,
and, hence, the relative humidity in situ.  Pre-heating of the inlet air of the nephelometer
normalizes the response to water content, but  biases the reading relative to the in situ case.
Sloane (1986) also gave the computed and  measured scattering coefficients for ammonium
sulfate and noted that chemical interactions can cause a two-fold variation in scattering
response to a change in the mass of hygroscopic constituents.  It was also observed that the
light scattering efficiency of an aerosol such as ammonium acid sulfate is not a constant, but
varies with the overall aerosol  composition. Eldering et al. (1994) developed and validated a
predictive model for bsp in Southern California.  This model used composite size
distributions constructed  from a TSI,  Inc.11 EAA, a PMS, Inc.12 LAS-X and  a Climet,
Inc.13 multi-channel OPC,  and filter-based  estimates of refractive indices for ammonium
sulfate,  ammonium nitrate, organic carbon, elemental carbon and residual aerosol mass
concentrations as independent variables. The  quality of their comparisons  with nephelometer
data suggested that this approach could be  used to test models that predict visual  range  from
source emissions.  Further research is needed  to determine the effectiveness of the integrating
nephelometer as a predictor of fine particle mass concentrations.

4.2.9    Specialized Sampling
4.2.9.1   Personal Exposure  Sampling
     The application of aerosol measurement technologies to smaller and less obtrusive
samplers have resulted in devices used as fixed-location indoor aerosol samplers  and personal
exposure monitors (PEMs) worn on the body to estimate exposure.  The reduction in
physical  size of personal aerosol sampling  systems to reduce participant burden sometimes
11 Minneapolis, MN.
I2Boulder, CO.
13Redlands, CA.
                                          4-65

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results in poorer aerosol collection performance as compared to the outdoor counterparts.
Wiener and Rodes (1993) noted that personal sampling systems generally have poorer
precisions than outdoor aerosol samplers, due to the smaller sampler collections (from lower
flowrates) and poorer flow controllers.  Ozkaynak et al. (1993) reported that the precisions
of collocated PEMs in the PTEAM study operating at 4.01 pm for a 12-h period were 3 to
4% (RSD).  Wallace et al. (1994) reported biases for the Particle Total Exposure Assessment
Methodology study averaging a factor of two between personal exposure measurements and
fixed location PM10 concentrations.  He was unable  to completely account for the biases, but
attributed portions to proximity to indoor sources, a  difference in inlet cutpoints (11.7 pan
versus 10.0 /mi) and the collection of aerosols  from  the "personal cloud" caused by body
dander. Rodes et al. (1991) showed that the ratio of personal to indoor aerosol
measurements for the EPA PTEAM study appeared  to be log-normally distributed with a
median value of 1.98 and an unexpectedly high value of 3.7 at the 90th ("most exposed")
percentile.  Ingham and Yan (1994)  suggested that the performance of a personal aerosol
sampling inlet in an isolated mode (without mounting on a  representative humanoid bluff
body) can result in substantial under-sampling for larger particles. The relationship between
measured aerosol exposure at some external  location on the body and actual uptake through
oral and nasal entry is very complex.
     Buckley et al. (1991) described the collection efficiency of an MSP, Inc.14 personal
aerosol sampler  at 4.0 1pm as shown in Figure 4-20. They evaluated this sampler in  a field
comparison study with collocated PM10 high volume and dichotomous samplers.  The
precision for the personal sampler was  found to be very good (CV =  +3.2%)  with strong
correlations (r2 = 0.970) with the dichotomous samplers.   Lioy et al. (1988) described a
similar comparison for a 10 1pm Air Diagnostics and Engineering, Inc.15 indoor air
sampler, with a  PM10 inlet characterized by Marple et al.  (1987).  Correlations against the
PM10 dichotomous sampler were also described as very strong (r2 > 0.970), but noted a
substantial  bias caused by the loss of fragments from indoor air sampler's glass fiber  filters.
They recommended that exposure studies using samplers that collect small total volumes
should utilize filters with greater integrity, such as Teflon.  Colome et al. (1992) describe an
14Minneapolis, MN.
15Naples, ME.
                                          4-66

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     100
      80
   I" 60
   o>
   o
   -   40
      20
   o
   O
         1
10
                              Aerodynamic Particle Diameter (urn)
Figure 4-20.  Collection efficiency of the MSP personal aerosol sampler inlet.
Source: Buckley et al. (1991).
indoor/outdoor sampling study using an impactor characterized by Marple et al. (1987) with
a PM10 cutpoint that had duplicate impactors with the same cutpoint in series.  This
sequential arrangement, in combination with a coating of 100 jul of light oil, was used to
minimize particle bounce at 4.0 1pm for 24 h period.
     Personal aerosol sampler systems have typically been characterized as burdensome
(excessive weight, size, noise). The success of passive  detector badges for  gaseous
pollutants has recently prompted research into passive aerosol samplers.  Brown et al. (1994)
described a prototype aerosol sampler utilizing electrostatic charge to move the particles to a
collection substrate.  They noted that preliminary results are  encouraging, but the effective
sampling rate and size-selectivity of the sampler was dependent on the electrical mobility of
the aerosol.   This posed calibration problems for real aerosols with a distribution of electrical
                                          4-67

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mobility's. Hollander (1992) described a passive pulsed-corona sampler that has similar
collection characteristics as a PM10 inlet, with only modest wind speed dependence.
     The performance characterization of PEMs has been considered for occupational
settings by Kenny and Liden (1989), who reviewed the ACGIH, National Institute for
Occupational Safety and Health (NIOSH), and EPA PM10 aerosol sampler performance
programs.  They proposed that an international consensus be reached on the basic principles
underlying the experimental protocols  for testing personal samplers,  as an essential
prerequisite to the setting of standards.  An ISO working group has made progress in
developing such a consensus (Kenny, 1992). As EPA becomes more focused on exposure
assessment and personal exposure sampling, it will become even more important for the
agency to consider establishing performance specifications for personal aerosol samplers.
     Models have become powerful tools in understanding aerosol behavior in the vicinity of
personal exposure samplers.  This is demonstrated by particle trajectory models that can
predict the influences of the geometries and flow field on aerosol capture and losses (e.g.,
Okazaki and Willeke, 1987, Ingham and Yan, 1994, and Tsai and Vincent, 1993).  These
models have not only permitted more rapid  design changes to accommodate new cutpoints
and flowrates, but have added insights as to the influence of air flow obstructions on
sampling efficiencies. Vincent and Mark (1982) suggested that there is a critical particle
trajectory that determines whether a particle is sampled or rejected by an inlet worn on the
body.  An extension of this model applicable to personal exposure sampling by Ingham and
Yan (1994) suggested that testing the performance of a personal aerosol sampling inlet in an
isolated mode (without mounting the inlet on a representative bluff body) can result in under-
sampling for larger  particles by a factor of two. Validation of this model may explain a
portion of the bias reported by Wallace et al. (1994) between personal and indoor sampler
measurements.

4.2.9.2    Receptor Model Sampling
     Receptor modeling has become an  established tool to relate ambient concentrations of
pollutants to major source categories, by apportioning the components in collected ambient
aerosol samples using complimentary source "signatures".  Various approaches developed for
constructing source/receptor relationships were described by Henry et al. (1984),  who also
                                         4-68

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provided a review of modeling fundamentals. They listed the advantages and disadvantages
of multivariate models and discussed multi-collinearity problems associated with the presence
of two or more sources with nearly identical signatures.  Javitz  et al. (1988) described the
basic Chemical Mass Balance (CMB) approach and showed the  influence of the variance in
identifying a component in the source signature sample on the projected apportionment.
Dzubay et al. (1984) described aerosol source and receptor collection schemes that permitted
the separation of ambient samples into fine and coarse fractions for mass, elemental and
volatile carbon, and metals analyses.  Stevens and Pace (1984) suggested the addition of
Scanning Electron Microscopy to permit additional categorization using x-ray diffraction
analysis.  The most widely used aerosol receptor model is the EPA CMB 7.0 model
described by Watson et al. (1990). This paper describes the structure of the model and
computer code and the data requirements to evaluate the validity of the estimates.  Numerous
papers have been published describing the  applications of receptor models to the
apportionment  of the sources of aerosols, with the receptor  modeling conference summary by
Watson et al. (1989b) descriptive of the state-of-the-art.
      Stevens et al. (1993) described (see Figure 4-21) a  modified dichotomous sampler with
a PM10 inlet, two Fine channels operating  at 15 1pm and one coarse channel  operating at
2.0 1pm, designated as the Versatile Air Pollution Sampler (VAPS).  The additional fine
fraction channel permitted sampling on a 47 mm  Teflon filter for elemental analysis and a
47-mm quartz filter for carbon speciation (elemental and volatile). A Nuclepore filter was
used on the Coarse channel  for Scanning Electron Microscopy (SEM) evaluation and energy
dispersive x-ray diffraction analysis for selected particles.

4.2.9.3    Particle Acidity
     An emphasis was placed on sampling sulfuric acidic aerosols in the 1982 Criteria
Document.  This  was followed by a number of research efforts (e.g., Perm,  1986; Koutrakis
et al., 1988;  Pierson et. al., 1989) to identify and study the in situ rate  reactions, develop
sampling strategies to representatively remove the acid particle from the air, identify the
co-existing reactive species (e.g., ammonia, nitric acid, aerosol sulfates and nitrates), and
protect the collected aerosol prior to analysis. A "Standard" and an "Enhanced" method
were subsequently described (U.S. Environmental Protection Agency, 1992) for the
                                          4-69

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         32 l/min
-p.

o
                                    25-urn Cut     Annular Denuder       Teflon Filter
                                   Receiver Jet       Collects           Mass,H+,
                                   (VAPS Body)    SO0, HNCL, HCI  Elemental Composition
                           Accelerator
                               Jet
                     VAPS Impactor
                       10-(imCut
                 Impactor Press
                                                    #47 FP
                                                    Adapter
 PUF Adapter
  with Quick
 Disconnect to
Vacuum Pump
                                                             PUF Trap
                                                          80 mm x 32 mm
      Figure 4-21. Modified dichotomous sampler (YAPS).


      Source: Stevens et al. (1993).

-------
determination of aerosol acidity (titratable H+) using annular denuder technology. The
"Standard" method did not account for potential interferences from nitric acid, ammonium
nitrate aerosol, or other ammonium salts.  The  "Enhanced" method added an additional
denuder prior to filtration,  with nylon and treated glass fiber backup  filters to account for
these species. These sampling technologies utilized either an inlet impactor or a cyclone with
2.5 /tfn cutpoints to sample the fine fraction.  This technology has recently been extended to
other reactive aerosol systems, including semi-volatile organics (e.g., Vossler et al., 1988).
Bennett et al. (1994) describe  a PM2 5 cyclone-based, filter pack sampling system designed
for fine particle network sampling and acidity measurements,  as part of the Acid MODES
program.  The  sampler  operated at 8.8 1pm, and was designed to  selectively remove
ammonia, speciate gas and particle phase sulfur compounds, as well  as collect gas phase
nitric acid.  An intercomparison of 18 nitric acid measurement methods was reported by
Hering et al. (1988), who noted that measurements differed by as much as a factor of four
and biases increased as  nitric acid loadings increased. In general  the filter pack systems
reported the highest acidity measurements,  while the denuder-difference techniques reported
significantly lower measurements.  Benner  et al. (1991)  in a comparison of the SCENES
filter pack sampler with a denuder-based sampler found  excellent  agreement between sampler
types for both nitric acid and total nitrates.  They attributed the close agreement to limited
positive artifact formation,  since the test field site had high nitric  acid gas to particulate
nitrate ratios.  John et al. (1988) noted that internal aluminum sampler surfaces denude nitric
acid, and describe the design of an aluminum denuder for the inlet of a commercially
available dichotomous sampler to quantitatively remove nitric acid for extended periods.
      Brauer et al. (1989) describe the design of a miniature personal sampler to collect  acid
aerosols and  gases. A significant finding was the lower than expected personal acidity
levels, attributed to the  "personal cloud" production of ammonia by the body.  Personal
exposure levels of acid aerosols were reported to be lower than indoor measurements.

4.2.10 Measurement Method Comparisons
4.2.10.1  Nitrate
     Methods for measuring particle nitrate and gaseous nitric acid were  compared in the
field as part of the 1985 Nitrogen Species Methods Comparison Study conducted over an
                                          4-71

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8-day period in the summer of 1985 in Claremont, CA (Bering et al., 1988).  Particle nitrate
methods included sampling with filter packs (teflon and nylon filters operated  in series),
sampling with nylon or impregnated filters operated downstream of a denuder to remove
vapor nitric acid (Possanzini et al., 1983; Shaw et al., 1982; Appel et al., 1981), and
sampling with an impactor (Wall et al., 1988).  Results from that study showed that the
precision for identical samplers  was about 4%  (Anlauf et al., 1988; Solomon et al.,  1988).
Denuded nylon filter methods were used in 6 different samplers operated by 4 different
groups (Appel et al., 1988; John et al., 1988; Pierson et al., 1988; Solomon et al., 1988).
Data from these 6 methods show no systematic bias among samplers.  The average
measurement precision (coefficient of variation) was 11%. Impactor results were also in
agreement with that from the denuded nylon filters (Wall et al., 1988). In contrast, fine
particle nitrate values from teflon filter of the filter packs were 43 to 59% lower than those
measured by denuded nylon filters, with higher discrepancies for longer sampling times
(Soloman et al., 1988).  The lower results on filter pack sampling are due to the
volatilization of nitrate particles from the filter. The vaporized nitrate is measured as nitric
acid on the backup filter (Hering et al., 1988;  Solomon et al., 1988).  To summarize,
sampling with denuded nylon filters or with impactors gave equivalent values  for fine particle
nitrate, whereas teflon filter sampling was biased low  due to the volatilization losses.
     The results of the 1985 Nitrogen Species Methods Comparison Study were confirmed
by data collection as part of the 1987 Southern California Air Quality Study (Chow et al.,
1994). In this study, sampling times were 4 to 7 h.  Samples were retrieved immediately,
within 30 minutes of the end of sampling. Fine particle samples  were collected by teflon
filters, by denuded nylon filters and by impactors.  Results, stratified by time of day and
season, are illustrated in Figures 4-22 and 4-23 for central Los Angeles, CA and Claremont,
CA, respectively.  Losses from the teflon filters are greatest in the summer, especially for
daytime samples (10 a.m. to 2 p.m., and 2 p.m. to 6  p.m.). Over 11 summer sampling days
at 8 basin locations for Claremont, CA, an average of 79% or 9.9/ng/m3 of the fine particle
nitrate was volatilized from the teflon filters for summer daytime sampling. For nighttime
and morning samples, 40% was lost.  The percentage losses are smaller for winter samples,
but the absolute magnitude remains high at 8.9 /xg/m3 for daytime samples. Impactor data
are in much closer agreement with those  from the denuded nylon filter than the teflon filter.
                                          4-72

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         40r
                          Central Los Angeles: Summer

A  30--
    20--
      z
      I
      il
      o
      in
      cvi
                  Summer: Night
                  Summer: Morning
                O Summer: Day
                - 1:1 Line
          10--
          80--

          70--
                     10            20            30
                   PM2.5 Denuded Nylon Filter Nitrate (ng/rn3)

                           Central Los Angeles: Winter
          • Winter: Night
          • Winter: Morning
          O Winter: Day
          - 1:1 Line
                   10      20     30      40     50     60     70
                        PM2.5 Denuded Nylon Filter Nitrate (ng/rn3)
                                                                  80
Figure 4-22. Comparison of PM2.5 nitrate mass measurements from Teflon® filter
            versus denuded nylon filter sample collection for Los Angeles, CA.

Source: Chow, et al. (1994).
                                      4-73

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          40-r
     Filter Comparisons for Claremont: PM2.5 Nitrate
                  Summer: Night
                  Summer: Morning
                O Summer: Day
                - 1:1 Line
          40r
      CO
      I
      ~  30
          20--
      Q.
      o
       E  10--
          10            20            30
        PM2.5 Denuded Nylon Filter Nitrate (iig/nr?)

    Impactor Comparison for Claremont: PM2.5 Nitrate
• Summer: Night
® Summer: Morning
O Summer: Day
- 1:1 Line
                          10            20            30
                        PM2.5 Denuded Nylon Filter Nitrate
                                                    40
Figure 4-23.  Comparison of PM2 5 nitrate mass measurements from Teflon® filter
            versus denuded nylon filter sample collection for Claremont, CA.

Source: Chow, et al. (1994).
                                    4-74

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4.2.10.2  Carbonaceous Particulate Matter
     Methods for measuring carbonaceous aerosol, classified as either "organic" or "black"
carbon, were compared in a similar study conducted in the summer of 1986 in Glendora, CA
(Bering et al., 1990). In that study, analytical methods were compared, as were differences
in simultaneous ambient sampling of PM2 5 aerosol with quartz filters, adsorption-corrected
quartz filters and two types of impactors.  The results showed generally good agreement
among analytical methods  for total carbon, with 5 of the 6 laboratories reporting values
within 9% of each other.  In contrast, ambient sampling results showed variations among
methods.  Quartz filter results, whether or not corrected for carbon vapor adsorption were
within 40%  of each other.  Concentrations from impactors, exclusive of after-filter, were
lower than the mean from the filter samplers by as much as 50%.  Addition of the after-filter
carbon brought impactor values to within 10% of the mean, but the lack of "black" carbon
on these after-filters leads  to the conclusion that vapor adsorption led to a positive bias for
quartz filter sampling on these days.  Similar results were found for the 1987 Southern
California Air Quality Study, for which impactor measurements of carbon were
systematically lower than filter measurements  (Chow, et al., 1994).
4.3   ANALYSIS OF PARTICULATE MATTER
     The interest in the composition of aerosol particles lies in the areas of:  (1) explaining
and inventorying the observed mass, (2) establishing the effect of aerosols on health and
welfare, and (3) attributing ambient aerosols to pollution sources. While any compositional
measurement will address one or more of these goals, certain methods excel for specific
tasks. In general, no single method can measure all chemical species, and comprehensive
aerosol characterization programs use a combination of methods to address complex needs.
This allows each method to be optimized for its objective, rather than be compromised to
achieve goals unsuitable to the technique.  Such programs also greatly aid quality assurance
objectives, since confidence may be placed in the accuracy of a result when it is  obtained by
two or more methods on different substrates and independent samplers.
     In the sections that follow, some of the more commonly used methods that address the
goals stated above are described. The sections are designed to be illustrative  rather than
                                         4-75

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exhaustive, since new methods are constantly appearing as old methods are being improved.
These chemical analysis methods for the following section are divided into four categories:
(1) mass, (2) elements, (3) water-soluble ions, and (4) organics.  Material balance comparing
the sum of the chemical species to the PM mass concentrations show that elements,  water
soluble ions,  and organic and elemental carbon typically explain 65 to 85% of the measured
mass and are adequate to characterized the chemical composition of measured mass  for filter
samples collected in most urban and non-urban areas.  Some of these chemical analysis
methods are non-destructive, and these are preferred because they preserve  the filter for
other uses. Methods which require destruction of the filter are best performed on a section
of the filter to save a portion of the filter of other analyses  or as a quality control check on
the same analysis method.  Table 4-2 identifies the elements and chemical compounds
commonly  found in air using these methods with typical detection limits.
     Less common analytical methods, which are applied to a small number of specially-
taken samples, include isotopic abundances (Jackson, 1981; Currie,  1982; Hirose and
Sugimura,  1984); mineral compounds (Davis,  1978, 1980; Schipper et al., 1993); and
functional groups (Mylonas et al.,  1991; Palen et al., 1992; 1993; Allen et al.,  1994).
Recent advances in infrared optics  and detectors have resulted  in the quantitative
determination of the major functional groups (e.g., sulfate,  nitrate, aliphatic carbons,
carbonyl carbons, organonitrates, and alcohols) in the atmospheric aerosol (Allen et al.,
1994).  The advantages of functional analysis in source apportionment are that the number of
functional groups is much less than the number of organic compounds to be classified.  The
cited references  provide information on sampling and analysis methods for these highly-
specialized methods.
     The following section focuses on:
     •   Physical analysis of elements and single particle size, shape, and composition,
     •   Wet chemical analysis of anions and cations, and
     •   Organic analysis of organic compounds and elemental/organic carbon.
                                          4-76

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TABLE 4-2.  INSTRUMENTAL DETECTION LIMITS FOR
            PARTICLES ON FILTERS
Minimum Detection Limit in ng/m3a

Be
Na
Mg
Al
Si
P
S
Cl
K
Ca
Sc
Ti
V
Cr
Mn
Fe
Co
Ni
Cu
Zn
Ga
As
Se
Br
Rb
Sr
Y
Zr
Mo
Pd
Ag
Cd
In
Sn
ICP/
AESM
0.06
NA
0.02
20
3
50
10
NA
NA
0.04
0.06
0.3
0.7
2
0.1
0.5
1
2
0.3
1
42
50
25
NA
NA
0.03
0.1
0.6
5
42
1
0.4
63
21
AA
FlameM
2d
0.2d
0.3
30
85
100,000
NA
NA
2d
ld
50
95
52
2
1
4
6d
5
4
1
52
100
100
NA
NA
4
300
1000
31
10
4
1
31
31
AA
Furnace13
0.05
< 0.05
0.004
0.01
0.1
40
NA
NA
0.02
0.05
NA
NA
0.2
0.01
0.01
0.02
0.02
0.1
0.02
0.001
NA
0.2
0.5
NA
NA
0.2
NA
NA
0.02
NA
0.005
0.003
NA
0.2
INAAb-f
NAh
2
300
24
NA
NA
6,000
5
24
94
0.001
65
0.6
0.2
0.12
4
0.02
NA
30
3
0.5
0.2
0.06
0.4
6
18
NA
NA
NA
NA
0.12
4
0.006
NA
PIXE«
NA
60
20
12
9
8
8
8
5
4
NA
3
3
2
2
2
NA
1
1
1
1
1
1
1
2
2
NA
3
5
NA
NA
NA
NA
NA
XRFC
NA
NA
NA
5
3
3
2
5
3
2
NA
2
1
1
0.8
0.7
0.4
0.4
0.5
0.5
0.9
0.8
0.6
0.5
0.5
0.5
0.6
0.8
1
5
6
6
6
8
ICb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
ACb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
TORb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
                    4-77

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          TABLE 4-2 (cont'd).  INSTRUMENTAL DETECTION LIMITS FOR
                                PARTICLES ON FILTERS
Minimum Detection Limit in ng/m3a
Species
Sb
I
Cs
Ba
La
Au
Hg
Tl
Pb
Ce
Sm
Eu
Hf
Ta
W
Th
U
Cl-
NO3-
SO^
NH4+
OC
EC
ICP/
AESb-d
31
NA
NA
0.05
10
2.1
26
42
10
52
52
0.08
16
26
31
63
21
NA
NA
NA
NA
NA
NA
AA
FlameM
31
NA
NA
3d
2,000
21
500
21
10
NA
2,000
21
2,000
2,000
1,000
NA
25,000
NA
NA
NA
NA
NA
NA
AA
Furnaceb
0.2
NA
NA
0.04
NA
0.1
21
0.1
0.05
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
INAAb-f
0.06
1
0.03
6
0.05
NA
NA
NA
NA
0.06
0.01
0.006
0.01
0.02
0.2
0.01
NA
NA
NA
NA
NA
NA
NA
PIXEg
NA
NA
NA
NA
NA
NA
NA
NA
3
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
XRFC
9
NA
NA
25
30
2
1
1
1
NA
NA
NA
NA
NA
NA
NA
1
NA
NA
NA
NA
NA
NA
ICb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
50
50
50
NA
NA
NA
ACb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
50
NA
NA
TORb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
100
100
"Minimum detection limit is three times the standard deviation of the blank for a filter of 1 mg/cm2 areal
 density.
 ICP/AES = Inductively Coupled Plasma with Atomic Emission Spectroscopy.
 AA = Atomic Absorption Spectrophotometry.
 PIXE = Proton Induced X-ray Emissions Spectrometry.
 XRF  = Non-Dispersive X-ray Fluorescence Spectrometry.
 INAA = Instrumental Neutron Activation Analysis.
 1C = Ion Chromatography.
 AC = Automated Colorimetry.
 TOR  = Thermal Optical Reflectance.
bConcentration is based on the extraction of 1/2 of a 47 mm quartz-fiber filter in 15 ml of deionized-distilled
 water, with a nominal flow rate of 20 L/min for 24-h samples.
Concentration is based on 13.8 cm2 deposit area for a 47 mm ringed teflon-membrane filter, with a nominal
 flow rate of 20 L/min for 24-h samples with 100 sec radiation time.
dHarman (1989).
eFernandez de la Mora (1989).
fOlmez (1989).
8Eldred et al. (1993).
hNot Available.
                                             4-78

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4.3.1   Mass Measurement Methods
      Paniculate mass concentration is the most commonly made measurement on aerosol
samples.  It is used to determine compliance with PM10 standards and to select certain
samples for more detailed, and more expensive, chemical analyses.  As noted in Section 2,
the beta attenuation and inertial microbalance methods have been incorporated into in situ
measurement systems which acquire real-time mass measurements.  Gravimetric analysis is
used almost exclusively to obtain mass measurements of filters in a laboratory environment.
The U.S. Environmental Protection Agency (1976) has published detailed procedures  for
mass  analyses associated with 20.32 cm X 25.40 cm fiber filters, but the guidance for other
types of filters used for chemical analyses is less well documented.
      Gravimetry measures the net mass on a filter by weighing the filter before and after
sampling with a balance in a  temperature- and relative humidity-controlled environment.
PM10 reference methods require  that filters be equilibrated for 24 h at a constant (within
±5%) relative humidity between 20 and 40% and at a constant (within +3 °C) temperature
between 15 and 30 °C.  These are intended to minimize the liquid water associated with
soluble compounds and to minimize the loss of volatile species.  Nominal values of 30% RH
and 15 to 20 °C best conserve the particle deposits during sample weighing.
     Balances used to weigh  20.32 cm x 25.40 cm filters from high volume PM10 samples
must have  a sensitivity of at least 100 /*g. Balances used  for medium volume PM10 samples
should have a sensitivity of at least 10 /ig, and those used for low-volume PM10 samples
should have a sensitivity of at least 1 /*g. Modifications to the balance chamber are
sometimes needed to accommodate filters of different sizes.  All filters, even those from
high-volume PM10 samplers,  should be handled with gloved hands when subsequent chemical
analyses are a possibility.
     Balance calibrations should be established before and after each weighing session using
Class M and Class S standards, and they should be verified with a standard mass every
10 filters.  Approximately one out of ten filters should be re-weighed by a different person at
a later time.  These re-weights should be used to calculate the precision of the measurement
as outlined by Watson et al. (1989a).
     Feeney et al.  (1984) examined the gravimetric measurement of lightly loaded membrane
filters and obtained excellent precision and accuracy.  The  sensitivity of the electrobalance is
                                         4-79

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about ±0.001 mg, though tolerances on re-weights of Teflon-membrane filters are typically
±0.010 mg.  The main interference in gravimetric analysis of filters results from
electrostatic effects.  Engelbrecht et al. (1980) found that residual charge on a filter could
produce an electrostatic interaction between the filter on the pan and the metal casing of the
electrobalance. This charge can be removed by exposing the filter to a radioactive polonium
source before and during sample weighing.
     Beta attenuation methods have been applied in the laboratory as well as in the field, and
the results are comparable to those of gravimetric measurements. The precision of
beta-gauge measurements has been shown to be  ±5 /zg/m3 or better for counting intervals of
one minute per sample, which translates into ±32 /ig/filter for  37 mm diameter substrates.
This is substantially higher than the ±6 /xg/filter precision determined by gravimetric
analysis using an electrobalance (Feeney et al., 1984).  Jaklevic et al. (1981) found
equivalent accuracy and precision for both techniques as they were used in that study.
Courtney et al. (1982)  found beta attenuation and gravimetric mass measurements to differ
by less than ±5%.  Patashnick and Rupprecht (1991) examine  results from TEOM samplers
operated alongside filter-based PM10 samplers, and Shimp  (1988) reports comparisons with
beta attenuation field monitors; these comparisons all show good agreement for mass
measurements.

4.3.2  Physical Analysis
     The most common interest in elemental composition derives from concerns about health
effects and the utility of these elements to trace the sources of suspended particles.
Instrumental neutron activation analysis (INAA), photon-induced x-ray fluorescence (XRF),
particle-induced x-ray emission (PIXE),  atomic absorption spectrophotometry (AAS),
inductively-coupled plasma with atomic emission spectroscopy  (ICP/AES), and scanning
electron microscopy with x-ray fluorescence (SEM/XRF) have  all been applied to elemental
measurements of aerosol samples.  A AS and ICP/AES  are also appropriate for ion
measurements when the particles are extracted in deionized-distilled water (DDW).   Since air
filters contain very  small particle deposits (20 to 100 pig/cm2),  preference is given to methods
that  can accommodate  small sample sizes. XRF and PIXE leave the sample intact after
analysis so that it can be submitted to additional  examinations by other methods.  Excellent
                                          4-80

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agreement was found for the intercomparison of elements acquired form the XRF and PIXE
analyses (Cahill, 1980).  The analytical measurement specifications of air filter samples for
the different elemental  analysis is shown in Table 4-2.

4.3.2.1   X-Ray Fluorescence of Trace Elements
     In x-ray  fluorescence (XRF) (Dzubay and Stevens, 1975; Hammerle and Pierson,  1975;
Jaklevic et al., 1977; Torok and Van Grieken, 1994), the filter deposit is irradiated by high
energy x-rays  that eject inner shell electrons from the atoms of each element in the sample.
When a higher energy  electron drops into the vacant lower energy orbital, a fluorescent x-ray
photon is released.  The energy of this photon is unique to each element, and the number of
photons is proportional to the concentration of the element. Concentrations are quantified by
comparing photon counts for a sample with those obtained from thin-film standards of known
concentration.
     XRF methods can be broadly divided into two categories:  wavelength dispersive x-ray
fluorescence (WDXRF), which utilizes crystal diffraction for observation of fluorescent
x-rays, and energy dispersive x-ray fluorescence (EDXRF), which uses a silicon
semiconductor detector.  The WDXRF method is characterized by high spectral resolution,
which minimizes peak  overlaps.  It requires high power excitation to overcome low
sensitivity, resulting in excessive sample heating and potential degradation. Conversely,
EDXRF features high sensitivity but less spectral resolution, requiring complex spectral
deconvolution  procedures.
     XRF methods can be further categorized as direct/filtered excitation,  where the  x-ray
beam from the tube is optionally filtered and then focused directly on the sample, or
secondary target excitation, where the beam is focused on a target of material selected to
produce x-rays of the desired energy.  The secondary fluorescent radiation is then used to
excite the samples. The direct/filtered approach has the advantage of delivering higher
incident radiation flux to  the sample for a given x-ray tube power, since about 99% of the
incident energy is lost in  a secondary fluorescence.  However, the secondary fluorescence
approach, produces  a more nearly monochromatic excitation that reduces unwanted scatter
from the filter, thereby yielding better detection limits.
                                         4-81

-------
     XRF is usually performed on Teflon-membrane filters for a variety of trace elements.
A typical XRF system is schematically illustrated in Figure 4-24. The x-ray output stability
should be within ±0.25% for any 8-h period within a 24-h duration.  Typically, analyses are
controlled, spectra are acquired, and elemental concentrations are calculated by software on a
computer that is interfaced to the analyzer.  Separate XRF analyses are conducted on each
sample to optimize detection limits for the specified elements. A comparison of the
minimum detectable limits of Teflon-membrane and quartz-fiber filters is listed in Table 4-3.
Figure 4-25 shows an example of an XRF spectrum.
     Three types of XRF standards are used for calibration, performance testing, and
auditing:  (1) vacuum-deposited thin-film elements and compounds (Micromatter);
(2) polymer films (Dzubay et al., 1981); and (3) National Institute of Science and
Technology (NIST, formerly NBS) thin-glass films.  The thin film standards cover the
largest number of elements and are used to establish calibration curves, while the polymer
film standards are used to verify the  accuracy of the thin film standards.  The  NIST
standards  are used to validate the accuracy of the calibration curves.  NIST produces the
definitive  standard reference materials, but these are only available  for the  species of
aluminum, silicon, calcium, iron, cobalt, copper, manganese, and uranium (SRM 1832), and
silicon, potassium, titanium, iron, zinc, and lead (SRM 1833).  One or more separate
Micromatter thin-film standards are used to  calibrate the system for each element.
     Sensitivity factors (number of x-ray counts per pig/cm2 of the  element) are determined
for each excitation condition.  These factors are then adjusted for absorption of the incident
and emitted radiation in the thin film.  These sensitivity factors are plotted as a function of
atomic number and a smooth curve is fitted  to the experimental values. The calibration
sensitivities are then read from these curves for the atomic  numbers of each element in each
excitation condition. NIST standards are analyzed on a periodic basis to verify the sensitivity
factors. A multi-layer thin film standard prepared by Micromatter  is analyzed  with each set
of samples to check the stability of the instrument response.  When deviations from  specified
values are greater than ±5%, the system should be re-calibrated.
     The sensitivity factors are multiplied by the net peak intensities yielded by ambient
samples to obtain the /ig/cm2 deposit for each element.  The net peak intensity is obtained
                                          4-82

-------
  Sample,
          ^Characteristic
           x-rays
     X-ray excitation
ilicon detector
 FET
 preamp
         " Pulse
          processor
    Secondary
    target
                     Be/
                     window
                                           Analog-to-
                                           digital
                                           converter
                      Electron beam
                      X-ray tube
                                            Multi-
                                            channel
                                           , analyzer
               Data output
                                            Mini-
                                            computer
Video
display
                                                    Signal
                                                    processing
                                             Data
                                             handling
Figure 4-24. Schematic of a typical X-ray fluorescence system.
                               4-83

-------
TABLE 4-3.  MINIMUM DETECTABLE LIMITS3 FOR X-RAY FLUORESCENCE
                   ANALYSIS OF AIR FILTERS
Element
Al
Si
P
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Co
Ni
Cu
Zn
Ga
As
Se
Br
Rb
Sr
Y
Zr
Mo
Pd
Ag
Cd
In
Condition
Numberd
5
5
5
5
4
4
4
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
4
1
1
1
1
Quartz-Fiber
Filterb
Protocol QA-
A ng/cm2 e
NAf
NA
NA
406
30
40
100
50
20
8
7
15
5
4
4
6
8
9
5
5
5
8
8
10
20
20
20
25
30
Teflon Membrane Filter^
Protocol A
ng/cm2 d
10
6.3
5.6
5.0
10
6.1
4.5
2.9
2.5
1.9
1.6
1.5
0.88
0.89
1.1
1.1
1.9
1.6
1.2
1.0
1.0
1.1
1.3
1.7
2.7
11
12
12
13
Protocol B
ng/cm2
7.2
4.4
4.0
3.5
7.4
4.3
3.2
2.1
1.7
1.4
1.1
1.1
0.62
0.63
0.76
0.76
1.4
1.1
0.86
0.72
0.68
0.78
0.92
1.2
1.9
7.6
8.6
8.6
9.5
Protocol C
ng/cm2
3.6
2.2
2.0
1.8
3.7
2.2
1.6
1.0
0.87
0.67
0.56
0.54
0.31
0.31
0.38
0.38
0.68
0.56
0.43
0.36
0.34
0.39
0.46
0.59
0.95
3.8
4.3
4.3
4.8
Protocol D
ng/cm2
2.5
1.4
1.4
1.2
2.6
1.5
1.1
0.73
0.62
0.48
0.40
0.38
0.22
0.22
0.27
0.27
0.48
0.39
0.31
0.25
0.24
0.28
0.33
0.42
0.67
2.7
3.0
3.0
3.4
                            4-84

-------
         TABLE 4-3 (cont'd).  MINIMUM DETECTABLE LIMITS3 FOR X-RAY
        	FLUORESCENCE ANALYSIS OF AIR FILTERS	
                         Quartz-Fiber
                            Filterb                      Teflon Membrane Filter0
             Condition   Protocol QA-      Protocol A   Protocol B  Protocol C Protocol D
  Element   Numberd    A ng/cm2 e          ng/cm2 d      ng/cm2     ng/cm2     ng/cm2
     Sn          1             40               17            12            6~24^4
Sb
Ba
La
Au
Hg
Tl
Pb
U
1
1
1
2
2
2
2
2
50
170
190
NA
20
NA
14
NA
18
52
62
3.1
2.6
2.5
3.0
2.3
13
37
44
2.2
1.8
1.8
2.2
1.7
6.4
18
22
1.1
0.91
0.88
1.1
0.83
4.5
13
16
0.77
0.65
0.62
0.76
0.59
aMDL defined as three times the standard deviation of the blank for a filter of 1 mg/cm2 areal density.
bAnalysis times are 100 sec. for Conditions 1 and 4,  and 400 sec. for Conditions 2 and 3. Actual MDL's for
 quartz filters vary from batch to batch due to elemental contamination variability.
cStandard protocol, developed at the Desert Research Institute, University and Community College System of
 Nevada, Reno, NV, analysis times are 100 sec. for Conditions 1, 4 and 5, and 400 sec. for Conditions 2 and 3
 for Protocol A; 200 sec. for Conditions 1, 4 and 5 and 800 sec. for Conditions 2 and 3 for Protocol B; 800
 sec. for Conditions 1,4 and 5 and 3,200 sec. for Conditions 2 and 3 for Protocol C; and 1600 sec. for
 Conditions 1, 4 and 5 and 6400 sec. for Conditions 2 and 3 for Protocol D.
dCondition 1 is direct mode excitation with a primary excitation filter of 0.15 mm thick Mo.  Tube voltage is
 50 KV and tube current is 0.6 mA.  Condition 2 is direct mode excitation with a primary excitation filter of
 0.13 mm thick Rh.  Tube voltage is 35 KV  and tube voltage is 2.0 mA. Condition 3 uses Ge secondary target
 excitation with the secondary excitation filtered by a Whatman 41 filter. Tube voltage is 30 KV and tube
 current is 3.3 mA.  Condition 4 uses Ti secondary target excitation with the  secondary excitation filtered by
 3.8 /xm thick mylar film.   Tube voltage is 30 KV and tube current is 3.3 mA.  Condition 5 uses direct mode
 excitation with a primary excitation filter consisting of 3 layers of Whatman 41 filters.  Tube voltage is 8 KV
 and tube current os 0.6 mA. Multi-channel analyzer energy range is 0 to 40 KeV for condition 1, 0 -  20 KeV
 for condition 2, and 0 to 10 KeV for conditions 3,4, and 5.
Typical exposed area is 406 cm2 for standard high-volume filters; 6.4 cm2 for 37 mm ringed Teflon-membrane
 filters; and 13.8 cm2 for 47 mm ringed Teflon-membrane filters.
Information not available.
gFor condition 4.
                                              4-85

-------
     26-Oct-1992 18:09:56
     SJTT046
     Vert"    2000 counts  Disp=  1
              Preset-
Comp=  2    Elapsed=
100 sees
400 sees
     San Jose, 1/21/92,  PM 10
     18:01  -  06:00
     Excitation Condition 3
            0.320   Range-    10.230 keV
                                                  Integral 0
                         10.230  >•
                            243425
           1111(111-     1       I
                                      5                                 10
Figure 4-25.  Example of an X-ray fluorescence spectrum.
Source:  Chow and Watson (1994).


by: (1) subtracting background radiation; (2) subtracting spectral interferences; and
(3) adjusting for x-ray absorption.
     XRF analysis of air paniculate samples has had widest application to samples collected
on membrane-type filters such as Teflon- or polycarbonate-membrane filter substrates.  These
membrane filters collect the deposit on then* surfaces,  which eliminates biases due to
absorption of x-rays by  the filter material.  These filters also have a low areal density which
minimizes the scatter of incident x-rays,  and their inherent trace element content is very low.
     Quartz-fiber filters used for high-volume aerosol sampling do not exhibit these features.
As noted earlier, blank  elemental concentrations in quartz-fiber filters that have not
undergone acceptance testing can be several orders of magnitude higher than the
                                        4-86

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concentrations in the particulate deposits.  The concentrations vary substantially among the
different types of quartz-fiber filters and even within the same filter type and manufacturing
lot.  Blank impurity concentrations and their variabilities decrease the precision of
background subtraction from the XRF spectral data, resulting in higher detection limits.
Impurities observed in various types of glass- and quartz-fiber filters include aluminum,
silicon, sulfur, chlorine, potassium, calcium,  iron, nickel, copper, zinc, rubidium, strontium,
molybdenum, barium, and lead.  Concentrations for aluminum, silicon, phosphorus, sulfur,
and chlorine cannot be determined for quartz-fiber filters because of the large silicon content
of the filters.
      Quartz-fiber filters also trap particles within the  filter matrix, rather than on the
surface.  This causes absorption of X rays within the  filter fibers yielding lower
concentrations than would otherwise be measured. The magnitude of this absorption
increases exponentially as the atomic number of the measured element decreases and varies
from sample to sample.  Absorption factors generally  are  "1.2" or less for iron and  heavier
elements, but can be from "2" to  "5" for sulfur.
      Quartz-fiber  filters are much thicker than membrane filters resulting in an increased
scattering of x-rays and a consequent increase in background and degradation of detection
limits.  The increased x-ray scatter also  overloads the x-ray detector which requires  samples
to be analyzed at a lowered x-ray intensity.  These effects alone can result in degradation of
detection limits by  up to a factor of 10 with respect to Teflon-membrane substrates.
      Larger  particles collected during aerosol sampling  have sufficient size to cause
absorption of x-rays within the particles.  Attenuation factors for fine particles  (PM2 5,
particles with aerodynamic diameters equal to or less than 2.5 ^m) are generally negligible
(Criss, 1976), even for the lightest elements, but these attenuations can be significant for
coarse fraction particles (particles with aerodynamic diameters from 2.5 to 10 pirn).
Correction factors for XRF have been derived using the theory of Dzubay and  Nelson (1975)
and should be applied to coarse particle measurements.

4.3.2.2   Particle  Induced X-Ray Emission of Trace Elements
     Particle Induced X-Ray Emission (PIXE) is another form of elemental analysis based on
the characteristics of x-rays and the nature of x-ray detection (Cahill et al., 1987; 1989).
                                          4-87

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PIXE uses beams of energetic ions, consisting of protons at an energy level of 2 to 5 MeV,
to create inner electron shell vacancies.  As inner electron shell atomic vacancies are filled
by outer electrons, the emitted characteristics of x-rays can be detected by wavelength
dispersion (which is  scattering from a crystal) or by energy dispersion (which involves direct
conversion of x-rays).  The development of focusing energetic proton beams (proton
microprobes) has expanded the application of PIXE from environmental and biological
sciences to geology and material sciences.  Figure  4-26 illustrates a typical PIXE setup in a
thin target mode (Cahill, et al., 1989). PIXE analysis  is often used for impactor samples or
small filter substrates, since proton beams can be focused to a small area with no loss of
sensitivity (Cahill and Wakabayashi,  1993).
     Very thick filters or thick particle deposits on filter substrates scatter the excitation
protons and  lower  the signal-to-noise ratio for PIXE.  X-ray analysis methods, such as PIXE
and XRF, require particle size diameter corrections (for low atomic number targets)
associated with a spherical particle of a given diameter (typically particles with aerodynamic
diameters >2.5 /mi) and compositions typical in ambient aerosol  studies.  These analyses
also require correction for sample loadings that reflect  the passage of x-rays through a
uniform deposit layer.  Procedures for instrument calibration, spectrum process, and quality
assurance are similar to those documented in Section 4.3.1.2 for XRF.
     PIXE analysis can provide  information on one of the widest range of elements in a
single analysis, since x-ray results  require two or three separate anodes.  However, attempts
to improve sensitivity of PIXE analysis may result in damage to Teflon-membrane filters.
Recent developments (Malm et al., 1994) using PIXE analysis at moderate sensitivity plus
single anode XRF  analysis at high sensitivity for transition/heavy  metals have achieved the
minimum detectable  limits of less than 0.01 ng/m3. With the addition of hydrogen analysis
(a surrogate for organic matter), almost all gravimetric mass concentrations can be explained
(Cahill, etal.,  1987).
     XRF and  PIXE are the most  commonly used elemental analysis methods owing to their
nondestructive multi-element capabilities, relatively low cost, high detection limits, and
preservation of the filter for additional analyses. XRF sometimes needs to be supplemented
with INAA  when extremely low detection limits are needed, but the high cost of INAA
precludes this method from being applied to large  numbers of samples.  A AS is a good

-------
00
                                                                                       Proton

                                                                              Deposit" Beam
                                                                                       Beam
                                                                                        lollimator
            ,   Faraday Cup   j
PIXE-2
(Fe-Mo)

PIXE-1
(Na-Mn)
     Figure 4-26. Schematic of a PIXE/PESA analysis system.

-------
alternative for water-soluble species, especially for low atomic number.  ICP/AES analysis
is a viable alternative, but it is less desirable because of the sample extraction elements such
as sodium and magnesium, but it requires large dilution factors to measure many different
elements expense and the destruction of the filter.

4.3.2.3   Instrumental Neutron Activation Analysis of Trace Elements
     Instrumental neutron activation analysis (INAA) (Dams et al., 1970; Zoller and
Gordon, 1970; Olmez, 1989; Ondov and Divita,  1993) basically involves irradiation of a
thin membrane filter sample in the core of a nuclear reactor for periods ranging from a few
minutes to several hours.  Bombardment of the sample with neutrons induces a nuclear
reaction of the stable isotopes in the sample.  The energies of the gamma rays emitted by the
decay of this induced radioactivity are used to identify them, and therefore, their parents.
With the use of prepared elemental standards, the amount of parent element in the sample
can be determined since the intensity of these gamma rays are proportional to their number.
     The gamma-ray spectra of radioactive species are usually collected with a high
resolution germanium detector utilizing commercially available amplifiers and multi-channel
analyzers.  Typical  detector efficiencies range from 10 to 40% relative to a 3  x  3 in. sodium
iodide detector.  Detector system resolution, measured as the full-width at half-maximum for
Table 4-4, the 1,332 KeV gamma-ray  peak of 60Co, should be less than 2.3 KeV in order to
provide adequate resolution between isotopes of neighboring energies.
     In order to obtain a full suite of elemental analysis results (often over 40 elements),
multiple counting periods and irradiations are performed on the same sample (e.g., two
irradiations would produce elements separated into short- and long-lived decay products).
An example of the elements determined from multiple irradiations and counting periods and
the irradiation, cooling, and counting times used for ambient paniculate  samples collected on
Teflon-membrane filter material are summarized in Table 4-4 (Divita, 1993).  These
irradiations were performed at the 20-MW NIST Research Reactor operated at 15-MW
(neutron flux of 7.7 x 1013 and 2.7 X 1013 neutron/cm2 x  s).
     The power of INAA is that it is  not generally subject to interferences like  XRF or
PIXE due to a much better ratio of gamma ray peak widths to total spectral width, by a
factor of about 20.  INAA does not quantify some of the abundant species  in ambient
                                          4-90

-------
  TABLE 4-4.  INSTRUMENTAL NEUTRON ACTIVATION ANALYSIS COUNTING
                      SCHEME AND ELEMENTS MEASURED
Counting
Period
Irradiation
Time
Cooling
Time
Counting
Time
Elements Measured
 Short-Lived 1   10 min
 Short-Lived 2
 Long-Lived 1    4-6 h
 Long-Lived 2
                5 min
                20 min
                30 days
             5 min
             20 min
                3-4 days      6-8 h
             12-24 h
              Mg, Al, S, Ca, Ti, V, Cu
              Na, Mg, Cl, K, Ca, Mn,
              Zn, Ga, Br, Sr, In, I, Ba
              Na, K,  Ga, As, Br, Mo,
              Cd, Sb, La, Nd, Sn, Yb,
              Lu, W, Au, U
              Sc, Cr, Fe, Co, Zn, Se,
              Sr, Ag, Sb, Cs, Ba, Ce,
              Nd, Eu, Gd, Tb, Lu, Hf,
              Ta, Th
paniculate matter such as silicon, nickel, tin, cadmin, mercury, and lead. While IN A A is
technically nondestructive, sample preparation involves folding the samples tightly and
sealing it in plastic, and the  irradiation process makes the filter membrane brittle and
radioactive.  These factors limit the use of the sample for subsequent analyses by other
methods. The technique also suffers from the fact that a nuclear reactor is usually used as a
source of neutrons.  However, since the advent of high-resolution gamma-ray detectors,
individual samples can be analyzed for numerous elements simultaneously, most at
remarkably trace levels without the need for chemical separation.  This greatly diminishes the
danger of contamination due to excessive sample handling and introduction of chemical
reagents used for separation procedures.

4.3.2.4   Microscopy Analysis of Particle Size, Shape,  and Composition
     Morphological and chemical features of particles can be used to identify the sources and
transport mechanism of airborne particles.  The chemical analysis of individual particles
allows the attribution of specific pollution sources more straightforward while the abundance
of a specific group is a representative of the source strength.  Both light (optical) and
scanning electron microscopy have been applied in environmental studies to examine the
                                        4-91

-------
single particles (e.g., Casuccio et al., 1983; Bruynseels et al., 1988; Van Borm and Adams,
1988; Van Borm et al., 1989; Cornille et al., 1990; Hopke and Casuccio,  1991; Turpin
etal., 1993a).
     Light microscopy has been used for providing particle size information regarding the
morphology of microscopic features (Crutcher,  1982).  The practical resolution of optical
microscopes is limited by the wavelengths associated with light of the visible  spectrum.
When features of interest occur in micron and submicron size ranges, detailed resolution
cannot be obtained. The practical resolution of light microscopy is  typically 1 to 2 pm
(Meyer-Arendt, 1972).
     The use  of accelerated electrons in electron microscopy (a) allows for the formation of
magnified images and an increased depth of field and (b) provides the resolution of a few
angstroms (10"4 /«n).  Electron microscopy has now evolved to include:  (1) the transmission
electron microscope (TEM); (2) the scanning electron microscope (SEM), and; (3) the
scanning transmission electron microscope (STEM) (Hearle et al. 1972; Lee et al., 1979; Lee
and Fisher,  1980; Lee  and Kelly,  1980; Lee et al., 1981; Johnson et al.,  1981; Mclntyre and
Johnson, 1982; Casuccio et al.,  1983; Wernisch, 1985, 1986; Kim et al.,  1987; Kim and
Hopke, 1988; Dzubay and Mamane, 1989; Schamber, 1993).
     The SEM and STEM use accelerated electrons to strike the sample.  As the electron
beam strikes the samples, various signals (e.g., secondary, backscattered,  and Anger
electrons, characteristic x-rays, photons, and cathodoluminescence)  are generated. These
signals can be collected to  provide highly detailed  information on a  point-by-point basis.  The
secondary electron signal yields  a sample image with three-dimensional prospective, high
depth of field, and illuminated appearance.  Back scattered electron images are used to
separate  phases containing elements of different atomic number.
     The information obtained from light and scanning microscopy  analyses are usually
considered to  be qualitative, due to the limited number of particles counted.  To achieve a
quantitative analysis, a sufficient number of particles must  be properly sized and identified by
morphology and/or chemistry to represent the entire sample.  The selection of filter media,
optimal particle loadings, and sample handling methods are also of  importance.  In this
manner,  the microscopic characteristics can be  directly and reliably related to the bulk or
macroscopic properties of the sample (Casuccio et al., 1983).
                                          4-92

-------
     Microscopic analysis requires a high degree of skill and extensive quality assurance to
provide quantitative information.  The techniques is complex and expensive when quantitative
analysis is required.  The evolution of computer technology has allowed for quantitative
analysis of particle samples of an entire population of features.  With advanced pattern
recognition methods, data from individual particle features  can be sorted and summarized by
size and composition, permitting improved quantitative source apportionment (Bruynseels
et al.,  1988; Hopke and Casuccio, 1991).  Casuccio et al. (1983) summarized the pros and
cons of automatic scanning electron microscopy.
     Recent development of the SEM/XRF allows analysis of elemental compositions and
morphological information on small quantities of material (Bruynseels et al., 1988).  Coupled
with statistical data analysis, computer controlled scanning  electron microscopy shows great
promise for identifying and quantifying complex pollution sources in the field of receptor
modeling source apportionment (e.g., Griffin and Goldberg, 1979; Janocko et al.,  1982;
Johnson et al., 1982; Massart and Kaufman, 1983; Hopke, 1985; Derde et al., 1987, Saucy
et al.,  1987; Mamane, 1988; Dzubay and Mamane, 1989).

4.3.3   Wet Chemical  Analysis
     Aerosol ions refer to chemical compounds that are soluble in water.  The water-soluble
portion of suspended particles associates itself with liquid water in the atmosphere when
relative humidity increases, thereby changing the light scattering properties of these particles.
Different emissions sources may also be distinguished by their soluble and non-soluble
fractions.  Gaseous precursors can also be converted to their ionic counterparts when they
interact with chemicals impregnated on the filter material.
     Several simple ions, such as soluble sodium, magnesium, potassium, and calcium are
best quantified by atomic absorption spectrometry (AAS) as described above.  In practice,
AAS has been very useful for measuring water-soluble potassium and sodium, which are
important in apportioning sources of vegetative burning and sea salt, respectively.
Polyatomic ions  such as sulfate, nitrate, ammonium, and phosphate must be quantified by
other methods  such as ion chromatography (1C) and automated colorimetry (AC).  Simple
ions, such as chloride, chromium III, and chromium IV,  may also be measured by these
methods along with the polyatomic ions.
                                         4-93

-------
     All ion analysis methods require filters to be extracted in DDW and then filtered to
remove the insoluble residue.  The extraction volume needs to be as small as possible, lest
the solution become too dilute to detect the desired constituents.  Each square centimeter of
filter should be extracted in no more than 2 ml of solvent for typical sampler flow rates of
20 to 30 L/min and sample durations of 24 h.  This often results in no more than 20 ml of
extract that can be submitted to the different analytical methods, thereby giving preference to
those methods which require only a small sample volume.  Sufficient sample  deposit must be
acquired to account for the dilution volume required  by each method.
     When other analyses are to be performed on the same filter, the filter must first be
sectioned using a precision positioning jig attached to a paper cutter. For rectangular filters
(typically 20.32 cm by 25.40 cm), a 2.0 cm by 20.32 cm wide strip is cut from the center
two-thirds of the  filter.  Circular filters of 25-, 37-, and 47-mm diameters are usually cut in
half for these analyses, so the  results need to be multiplied by two to obtain the deposit  on
the entire filter.   Filter materials that can be easily sectioned without damage  to the filter or
the deposit must be chosen for these analyses.

4.3.3.1   Ion Chromatographic Analysis for Chloride, Nitrate,  and Sulfate
     Ion chromatography (1C) can be used for both anions (fluoride [F~], chloride [Cl~],
nitrite [NO£], bromide [Br~], nitrate [NO§], phosphate [PO^3], sulfate [SO4=])  and cations
(soluble potassium [K+], ammonium [NH4+], soluble sodium [Na+]) with separate columns.
Applied to aerosol samples, the anions are most commonly analyzed by 1C with the cations
being analyzed by a combination of atomic absorption spectrophotometry (AAS) and
automated colorimetry (AC) (U.S. EPA,  1994). In 1C (Small et al., 1975; Mulik et al.,
1976; Butler et al., 1978) the sample extract passes through an ion-exchange  column that
separates the ions in time for individual quantification, usually by a electroconductivity
detector.  Figure 4-27 shows a schematic representation of the 1C system. Prior to detection,
the column effluent enters a suppressor column where the chemical composition of the eluent
is altered, resulting in a lower background conductivity.  The ions are  identified by their
elution/retention times and are quantified by the conductivity peak area or peak height.  1C is
especially desirable for particle samples because it provides results for several ions with a
single analysis and it uses a small portion of the filter extract with low detection limits.
                                          4-94

-------
           Delivery Module—
   Chromatography Module —
           Detector Module —
                                                   Eluent
                                                 Reservoir
                                                   Pump
                                                  Sample
                                                  Injector
                                                   Guard
                                                  Column
                                                 Separator
                                                  Column
                                                Suppressor
                                                   Device
                                                Conductivity
                                                    Cell
Figure 4-27.  Schematic representation of an ion chromatography system.
                              4-95

-------
Water-soluble chloride (Cl"), nitrate (NOj), and sulfate (SO4~) are the most commonly
measured anions in aerosol samples.  Figure 4-28 shows an example of an 1C anion
chromatogram.  1C analyses can be automated by interfacing to an automatic sampler that
can conduct unattended analysis of as many as 400 samples (Tejada et al., 1978).
18,000-
15,500-
13,000-
10,500-
8,000-
5,500-
3,000-
500-
-2,000-
0.


Fluoride
I


^








I Chloride


Nitrite

Nitrate
\ l\ Phosphate Sffe
\ 1 \ rx / \

30






5.00 10.00

Minutes
Figure 4-28. Example of an ion chromatogram showing the separation of fluoride,
             chloride, nitrite, nitrate, phosphate, and sulfate ions.
     Several independent quality assurance (QA) standards should be used to check the
calibration curve. The standards that are traceable to NIST simulated rainwater standards
are:  Environmental Resource Associates (ERA, Arvada, CA) custom standards containing
the anions measured at a concentration of 100 ug/ml, ERA Waste Water Nutrient Standard,
ERA Waste Water Mineral Standard, and Alltech individual standards at 200 ug/ml. The
QA standards are diluted in DDW to concentrations that are within the range of the
calibration curve.
     Calibration curves are performed weekly.  Chemical compounds are identified by
matching the retention time of each peak in the unknown sample with the retention times of
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peaks in the chromatograms of the standards. The QA standards are analyzed at the
beginning of each sample run to check calibrations.  A DDW blank is analyzed after every
20 samples and a calibrations standard is analyzed after every 10 samples.  These quality
control (QC) checks verify the baseline and calibration respectively.

4.3.3.2    Automated Colorimetric Analysis for Ammonium, Nitrate, and Sulfate
     Automated Colorimetry (AC) applies different colorimetric analyses to small sample
volumes with automatic sample throughput.  The most common ions measured are
ammonium, chloride,  nitrate, and sulfate (Butler et al., 1978; Fung et al., 1979). Since
1C provides multi-species analysis for the anions, ammonium is most commonly measured by
AC.
     The  AC system is illustrated schematically in Figure 4-29.  The heart of the automated
colorimetric system is a peristaltic pump, which introduces air bubbles into the  sample
stream at  known intervals.  These bubbles separate samples in the continuous stream. Each
sample is  mixed with reagents and subjected to appropriate reaction periods before
submission to a colorimeter.  The ion being measured usually reacts to form a colored  liquid.
The liquid absorbance is related to the amount of the ion in the sample by Beer's Law.  This
absorbance is measured by a photomultiplier tube through an interference filter specific to the
species being measured.
     The  standard AC technique can analyze =60 samples per hour per channel, with
minimal operator attention and relatively low maintenance and material costs.  Several
channels can be set up to simultaneously analyze several ions.  The methylthymol-blue
(MTB) method is applied to analyze sulfate.  The reaction of sulfate with MTB-barium
complex results in free ligand, which is measured  colorimetrically at 460 nm.  Nitrate is
reduced to nitrite that reacts with sulfanilamide to  form a diazo compound. This compound
is then reacted to an azo dye for colorimetric determination at 520 nm. Ammonium is
measured  with the indophenol method. The  sample is mixed sequentially with potassium
sodium tartrate, sodium phenolate,  sodium hypochlorite, sodium hydroxide, and sodium
nitroprusside.  The reaction results in  a blue-colored solution with an absorbance measured at
630 nm.  The system determines carry-over by analysis of a low concentration standard
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                                                                       Sampler
                Heater
               (optional)
      Mixing
       Cell
                                   Reagent Line #1
                                   Reagent Line #2
                                    Sample Line
                                   Reagent Line #3
                                   Reagent Line #4
                                                          Reagent Line #5
                                                          Reagent Line #6
             Flow
             Cell
                Optical
                 Filter
Photomultiplier
   Detector
Peristatic
 Pump
Figure 4-29. Schematic of a typical automated colorimetric system.


following a high concentration. The percent carry-over is then automatically calculated and
can be applied to the samples analyzed during the run.
     Intercomparison studies between AC and 1C have been conducted by Butler et al.
(1978) and Fung et al. (1979).  Butler et al. (1978) found excellent agreement between
sulfate and nitrate measurements by AC and 1C. The accuracy of both methods is within the
experimental errors, with higher blank values observed for AC techniques.  Comparable
results were also obtained between the two methods by Fung et al. (1979).  The choice
between the two methods for sample analysis is dictated by sensitivity, scheduling, and cost
constraints.
     Two milliliters of extract in sample vials are  placed in an autosampler that is controlled
by a computer. Five standard concentrations (e.g., (NH4)2SO4, Na2SO4, NaNO3)  are
prepared from American Chemical Society reagent-grade chemicals following the  same
procedure as that for 1C standards.  Each set of samples consists of two DDW blanks to
establish a baseline, five calibration standards and  a blank, then sets of ten samples followed
by analysis of one of the standards and a replicate from a previous batch.  The computer
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control allows additional analysis of any filter extract to be repeated without the necessity of
loading the extract into more than one vial.

4.3.3.3   Atomic Absorption Spectrophotometric (AAS) and Inductive Coupled Plasma
          Atomic Emission Spectro (ICP/AES)  Photometry Analyses for Trace
          Elements
     In atomic absorption Spectrophotometric (AAS) analysis  (Fernandez de la Mora, 1989),
the sample is first extracted in a strong solvent to  dissolve the solid material; the filter or a
portion thereof is also dissolved during this process. A few milliliters of this extract are
introduced into a flame where the elements are vaporized. Most elements absorb light at
certain wavelengths in the visible spectrum, and a light beam with wavelengths specific to the
elements being measured is directed through the flame to  be detected by a monochrometer.
The light absorbed by the flame containing the extract is compared with the absorption from
known standards to quantify the elemental concentrations.  AAS requires an individual
analysis for each element, and a large filter or several  filters are needed to obtain
concentrations for a large number of the elements specified in Table 4-3.  AAS is a useful
complement to other methods, such as XRF and PIXE, for species such as beryllium,
sodium, and magnesium that are not well-quantified by XRF and PIXE. Airborne particles
are chemically complex and do not dissolve easily into complete solution, regardless of the
strength of the solvent.  There is always a possibility that insoluble residues are left behind
and soluble species may co-precipitate on them or on container walls.
     In inductive coupled plasma atomic emission Spectrophotometric (ICP/AES), (Lynch
et al.,  1980; Harman, 1989),  the dissolved sample is introduced into an atmosphere of argon
gas seeded with free electrons induced by high voltage from a surrounding Tesla coil.  The
high temperatures in the induced plasma raise valence electrons above their normally stable
states.  When these electrons  return to their stable states,  a photon of light  is emitted which
is unique to the element which was excited.  This  light is  detected at specified wavelengths to
identify the elements in the sample.  ICP/AES acquires a  large number  of elemental
concentrations using small sample volumes with acceptable detection limits for  atmospheric
samples.  As with AAS, this method requires complete extraction and destruction of the
sample.
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4.3.4   Organic Analysis
4.3.4.1   Analysis of Organic Compounds
     Organic compounds comprise a major portion of airborne particles in the atmosphere,
thus contributing to visibility degradation, and affecting the properties of clouds into which
these particles are  scavenged.  Specific groups of organic compounds (e.g., polycyclic
aromatic hydrocarbons,  PAHs) have also been implicated in human health effects.  However,
due to the very complex composition of the organic fraction of atmospheric aerosols, the
detailed composition and atmospheric distributions of organic  aerosol constituents are still not
well understood.
     Sampling techniques for atmospheric particulate matter have been extensively
investigated, resulting in the development of collection methods suspended in a wide range of
sizes.  Particles are most frequently collected on glass or quartz-fiber filters that  have been
specially treated to achieve low "carbon blanks".  Ambient organic particulate matter has
also been collected on a variety of particle sizing devices,  such as low pressure impactors
and Micro Oriface Uniforms Deposit Impactors("MOUDI").  Very recently, diffusion
denuder based samplers have been used as well (Tang et al., 1994). However,  the task of
sampling organic compounds in airborne particles is complicated by the fact that many  of
these  compounds have equilibrium vapor pressures (gaseous concentrations) that  are
considerably larger than their normal ambient concentrations.  This implies a temperature-
and concentration-dependent distribution of such organics between particulate and vapor
phases. It also suggests that artifacts may occur due to volatilization during the sampling
process (Coutant et al.,  1988).  Such volatilization would cause the under-estimation of the
particle-phase concentrations of organics.  Conversely, the adsorption of gaseous substances
on deposited particles or on the filter material itself, a process driven by the lowered vapor
pressure over  the sorbed material, would lead to over-estimation of the particle-phase fraction
(Bidleman et al., 1986;  Ligocki and Pankow, 1989; McDow and Huntzicker, 1990).  In
addition, several studies have suggested that chemical  degradation of some organics may
occur during the sampling procedure (Lindskog et al., 1985; Arey et al., 1988; Parmar and
Grosjean, 1990).
     The partitioning of semi-volatile organic compounds (SOC) between  vapor  and particle
phases has received much attention (Cautreels and Cauwenberghe, 1978; Broddin et al.,
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1980; Hampton et al., 1983; Ligocki and Pankow, 1989; Gotham and Bidleman, 1992; Lane
et al.,  1992; Kaupp and Umlauf, 1992; Pankow, 1992; Turpin et al., 1993b, 1996).  Most
estimates of partition have relied on high-volume (hi-vol) sampling, using a filter to collect
particles followed by a solid adsorbent trap to collect the gaseous portion of SOC (e.g.,
Kaupp and Umlauf,  1992, Foreman and Bidleman, 1990).  Kaupp and Umlauf (1992)
recently reported that this approach, although not absolutely free from sorption and
desorption artifacts, produces reliable results.  The maximum differences observed between
hi-vol filter-solid adsorbent  sampling and impactor sampling (the latter believed to be less
susceptible to these sampling artifacts) did not exceed a factor of two.
     There is good theoretical and experimental evidence that use of a diffusion denuder
technique significantly improves measurements of vapor-particle phase partitioning (Coutant
et al.,  1988, 1989, 1992;  Lane et al., 1988).  However, at the present state of their
technological development, the reliability of denuders for investigation of atmospheric
partitioning of non-polar SOC needs to be improved, as suggested by contradictions in
published field data (e.g., Kaupp and Umlauf, 1992). Gundel et al.  (1992) used a
proprietary XAD-4-coated tube for  vapor collection,  followed  by filter collection of organic
aerosol particles and a sorbent bed to quantitatively retain desorbed (volatilized)  organic
vapors. Denuders that remove ozone from the air before it reaches the filter reduce the
potential for artifact  formation on the captured paniculate material during sampling (Williams
and Grosjean, 1990).
     Since the organic fraction of airborne paniculate matter is typically a complex mixture
of hundreds to  thousands of compounds distributed over many organic functional groups, its
chemical analysis is an extremely difficult task (Appel et al., 1977; Simoneit,  1984; Flessel
et al.,  1991; Hildemann et al.,  1991; Li and Kamens, 1993; Rogge et al., 1993a, 1993b,
1993c). Analyses of organics generally begin with solvent extraction of the paniculate
sample. A variety of solvents and extraction techniques have been used in the past.  One
common method is sequential extraction with increasingly polar solvents, which typically
separates the organic material into nonpolar, moderately polar, and polar fractions (Daisey
et al.,  1982).  This step is usually followed by further fractionation using open-column liquid
chromatography and/or high-performance liquid chromatography (HPLC) in order to obtain
several less complicated fractions (e.g., Schuetzle and Lewtas, 1986; Atkinson et al., 1988).
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These fractions can then be analyzed further with high resolution capillary-column gas
chromatography (GC), combined with mass spectrometry (GC/MS), Fourier transform
infrared (GC/FTIR/MS) or other selective detectors.
     Much of the recent work on the identification of nonpolar and semi-polar organics in
airborne samples has used bioassay-directed chemical analysis (Scheutzle and Lewtas, 1986),
and has focused on identification of fractions and compounds that are most likely to be of
significance to human health.  In particular, PAHs and their nitro-derivatives (nitroarenes)
attracted considerable attention due to their mutagenic and, in some cases, carcinogenic
properties.  More than 100 PAHs have been identified in the PM2 5 fraction of ambient
paniculate matter (Lee et al., 1981). While most of the nitroarenes found in ambient
particles are also present in primary combustion-generated emissions, some are formed from
their parent PAH in the atmospheric nitration reactions (e.g., Arey et al., 1986; Zielinska
et al., 1989, Ramdahl et al.,  1986).
     Little work has been done to date to chemically characterize the polar fraction in  detail,
even though polar material accounts for up to half the mass and mutagenicity of soluble
ambient particulate  organic matter (Atherholt et al., 1985; Gundel et al., 1994).  Until
recently the polar fraction has remained analytically intractable, since very polar and labile
species interact with conventional fractionation column packing materials and cannot be
recovered quantitatively. Recently, very polar particulate organic matter has been
successfully fractionated using cyanopropyl-bonded silica (Gundel et al.,  1994), with good
recovery of mass and mutagenicity (Kado et al., 1989).  However, ambient particulate  polar
organic material cannot be analyzed with conventional GC/MS because of GC column  losses
resulting from adsorption, thermal decomposition, and chemical interactions. New analytical
techniques,  such as HPLC/MS and MS/MS, need to be applied if the chemical constituents
of polar particulate organic matter are to be identified and quantified.
     Most of the recent work on the identification of particulate organic matter has focused
on mutagenic and carcinogenic compounds that are of significance to human health.
Relatively little work has been done to characterize individual compounds or classes of
compounds  that are specific to certain sources of organic aerosol.  In urban  and rural
atmospheres, as well as in the remote troposphere, organic composition corresponding  to
chemical source profiles for of plant waxes,  resin residues, and long-chain hydrocarbons
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from petroleum residues have been found (e.g., Gagosian et al., 1981; Simoneit,  1984;
Mazurek et al., 1987, 1989, 1991; Simoneit et al., 1991).  However, a variety of smaller,
multi-functional compounds characteristic of gas-to-particle conversion have also been
observed (e.g., Finlayson-Pitts and Pitts,  1986).  These compounds tend to be present in the
polar fraction of ambient organic aerosol  particles, having been formed from atmospheric
chemical reactions of less polar precursors.  Little is currently  known about the chemical
composition of this polar fraction due to the serious analytical difficulties mentioned above.

4.3.4.2    Analysis of Organic and  Elemental Carbon
     Three classes of carbon are commonly measured in aerosol samples collected on
quartz-fiber filters: (1) organic, volatile,  or non-light absorbing carbon; (2) elemental or
light-absorbing carbon; and 3) carbonate carbon.  Carbonate carbon (i.e., K2CO3, Na2CO3,
MgCO3, CaCO3) can be determined on a separate filter section by measurement of the
carbon dioxide (CO2) evolved upon acidification (Chow et al.,  1993b; Johnson et al.,  1981).
Though progress has been made in the quantification of specific organic chemical compounds
in suspended particles (e.g., Rogge et al., 1993a,b,c), sampling and analysis methods have
not yet evolved for use in practical monitoring situations.
     Many methods have been applied to the separation of organic and elemental carbon in
ambient and source paniculate samples (Mueller et al., 1971; Lin et al., 1973;
Gordon,  1974; Grosjean, 1975; Smith et al., 1975; Appel et al., 1976,  1979; Kukreja and
Bove, 1976; Dod et al., 1979; Johnson and Huntzicker, 1979; Macias et al., 1979; Malissa,
1979; Weiss et al., 1979; Cadle et al., 1980a; Johnson et  al., 1981b; Daisey et al.,  1981;
Novakov, 1982;  Cadle and Groblicki, 1982; Gerber, 1982; Huntzicker et al.,  1982; Stevens
et al., 1982; Wolff et al., 1982; Japar et al., 1984; Chow et al., 1993b). Comparisons
among the results of the majority of these methods show that they yield comparable
quantities of total carbon in aerosol samples, but the distinctions between organic  and
elemental carbon are quite different (Countess, 1990; Hering et al., 1990).
     The definitions  of organic and elemental carbon are operational and reflect the method
and purpose of measurement.  Elemental  carbon is sometimes termed "soot", "graphitic
carbon,"  or "black carbon." For studying visibility reduction,  light-absorbing carbon  is a
more useful concept than elemental carbon.  For source apportionment by receptor models,
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several consistent but distinct fractions of carbon in both source and receptor samples are
desired, regardless of their light-absorbing or chemical properties.  Differences in ratios of
the carbon concentrations in these fractions form part of the source profile that distinguishes
the contribution of one source from the contributions of other sources.
     Light-absorbing carbon is not entirely constituted by graphitic carbon, since there are
many organic materials that absorb light (e.g., tar, motor oil, asphalt, coffee).  Even the
"graphitic" black carbon  in the atmosphere has only a poorly developed graphitic structure
with abundant surface chemical groups.  "Elemental carbon" is a poor but common
description of what is measured.  For example, a substance of three-bond carbon molecules
(e.g., pencil lead) is black and completely absorbs light, but four-bond carbon in a diamond
is completely transparent and absorbs very little  light.  Both are pure, elemental carbon.
     Chow et al. (1993b) document  several variations of the thermal (T), thermal/optical
reflectance (TOR), thermal/optical transmission (TOT), and thermal manganese oxidation
(TMO) methods for organic and elemental carbon.  The TOR and  TMO methods have been
most commonly applied in aerosol studies in the United States.
     The TOR method of carbon analysis developed by Huntzicker et al. (1982) has been
adapted by several laboratories for the quantification of organic and elemental carbon on
quartz-fiber filter deposits. While the principle used by these laboratories is identical to that
of Huntzicker et al. (1982), the details differ with respect to calibration standards, analysis
time, temperature ramping, and volatilization/combustion temperatures.  In the TOR method
(Chow et al., 1993b), a filter is submitted to volatilization at temperatures ranging from
ambient to 550°C in a pure helium atmosphere,  then to combustion at temperatures  between
550 to 800°C in a 2% oxygen and 98% helium atmosphere with several temperature ramping
steps.  The carbon that evolves at each temperature is converted to methane and quantified
with a flame  ionization detector.  The reflectance from the deposit side of the filter  punch is
monitored throughout the analysis.  This  reflectance usually decreases during volatilization in
the helium atmosphere owing to the pyrolysis of organic material.  When oxygen is added,
the reflectance  increases  as the light-absorbing carbon is combusted and removed.  Organic
carbon is defined as that  which evolves prior to  re-attainment of the original reflectance, and
elemental carbon is defined as that which evolves after the original reflectance has been
re-attained.  By this definition, "organic carbon" is actually organic carbon that does not
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absorb light at the wavelength (632.8 nm) used, and "elemental carbon" is light-absorbing
carbon (Chow et al., 1993b).  The TOT method applies the same thermal/optical carbon
analysis method except that transmission through instead of reflectance off of the filter punch
is measured. Thermal methods apply no optical correction and define elemental carbon as
that which evolves after the oxidizing atmosphere is introduced.
     The TMO  method (Fung, 1990) uses manganese dioxide (MnO2), present and in contact
with the  sample throughout the analysis, as the oxidizing agent, and temperature is relied
upon to distinguish between organic and elemental carbon. Carbon evolving at 525 °C is
classified as organic carbon, and carbon evolving at 850°C is classified as elemental carbon.
     Carbon analysis methods require a uniform filter deposit because only a small portion
of each filter is submitted to chemical analysis.  The blank filter should be white for light
reflection methods, and at least partially transparent for light transmission methods.  The
filter must also withstand very high temperatures without melting during combustion.
     Since all organic matter contains hydrogen as  the most common elemental species,
analysis of hydrogen by proton elastic scattering analysis (PESA) has been developed by
Cahill et al. (1989).  A correction must be made for hydrogen in sulfates  and nitrates, but
since the analysis is done in a vacuum,  water is largely  absent. PESA has excellent
sensitivity which is approximately 20 times better than combustion techniques.  This method
requires knowledge of the chemical state of sulfates, nevertheless, reasonable agreement was
found as compared to the combustion techniques.

4.3.4.3    Organic Aerosol Sampling Artifacts
     Sampling artifacts contribute to inaccuracies in mass measurements of particulate
organic matter collected by filtration. They can generally be classified into two types:
(1) adsorption on filters or collected particulate matter of organic gases normally in the vapor
phase causes particulate organic mass to be overestimated, and (2) volatilization of collected
organic material during sampling  leads to  an underestimate of particulate organic mass.
These artifacts can cause significant errors in particle mass measurements  in areas where a
large fraction of the particulate mass is  organic.
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Vaporization Artifact
     Significant loss of organic mass from filter samples occurs when clean air or nitrogen is
pumped through them after collection (Commins, 1962; Rondia, 1965; Van Vaeck et al.,
1984). This has frequently been referred to as "blow-off"  or "volatilization artifact"
(Broddin et al., 1980; Konig et al., 1980; Van Vaeck et al., 1984).  Van Vaeck et al. (1984)
found up to 70%  of some n-alkanes volatilized from the filter on exposure to a clean air
stream. Coutant et al. (1988) reported that the amount of fluoranthene and pyrene lost
through the volatilization artifact for a set of ambient samples ranged from 7 to 62%  and
16 to 83%, respectively.  Eatough et al.  (1989) concluded  that 40 to 80% of the organic
material was lost after collection from samples at Hopi Point in the Southwestern United
States. It has been proposed that an upper limit for the volatilization artifact is  reached if the
concentration of the volatilizing species reaches its  equilibrium vapor concentration in the air
exiting the filter,  but that  actual loss from the filter can be substantially lower because of
slow volatilization kinetics or strong adsorption on  particulate matter (Pupp et al., 1974).
The volatilization artifact has been offered as a possible explanation for frequently observed
variations in concentrations of particulate organic matter with flow rate,  face velocity and
sampling period duration (Delia Fiorentina et al., 1975; Appel  et al., 1979; Schwartz et al.,
1981). An increase in pressure drop across the filter during sampling can also promote
volatilization artifact if enough particulate matter is collected (Van Vaeck et al., 1984).
However, pressure drop does not appear to explain artifact behavior under typical sampling
conditions if the pressure  drop across the filter does not change during sampling (McDow
and Huntzicker, 1990; Turpin et al.,  1994).

Adsorption Artifact
      Other workers have  been more concerned with adsorption of the gas-phase organics.
Cadle et al. (1983) reported that adsorbed  vapor accounted for an average of 15% of the
organic carbon collected on quartz fiber  filters. In the recent Carbonaceous Species  Methods
Intercomparison Study it was estimated that organic vapor  adsorption on filters caused
organic aerosol concentrations to be overestimated  by 14 to 53% (Hering et al., 1990).
Significant adsorption of organic  vapors  has  also been observed on backup filters from a
variety of different primary aerosol sources (Hildemann et al.,  1991). The adsorption
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artifact appears to be inversely related to participate organic matter concentration, so that
artifact correction becomes more important at lower concentrations of particulate organic
matter as shown in Figure 4-30 (McDow and Huntzicker, 1990).  Adsorption artifact also
varies with face velocity (McDow and Huntzicker, 1990; Turpin et al., 1993b) and sampling
duration (McDow and Huntzicker, 1993), and significant amounts of adsorbed vapor
volatilizes when clean air flows across the filter (McDow and Huntzicker, 1993).  Because of
this, it is not possible to distinguish between adsorption and volatilization artifacts either by
blowing clean air across a filter or by a  simple comparison of variations of collected organic
mass with face velocity or sampling duration.  Adsorption occurs to a greater extent on
filters which have already collected organics on the filter surface during sampling than on
clean filters not previously used for sampling, suggesting that the filter becomes an
increasingly better adsorbent as adsorbed vapors build up on the  filter (Gotham and
Bidleman, 1992).
     The following compounds have been observed  to be adsorbed on quartz or glass fiber
filters: n-alkanes (Eichmann et al., 1979; Hart and Pankow, 1990), PAH (Ligocki and
Pankow, 1989), and formaldehyde (Klippel and Warneck, 1980).  Appel et al. (1989)
analyzed backup filters for carbonate and ruled out carbon dioxide as a major contributor to
adsorption artifact in Los Angeles on the basis of these analyses.

Artifact Correction
     Appel et al. (1989) advocated a simple backup filter correction procedure described by
Equation 4-1:

                                   Cp  = QQ1- QQ2                               (4-1)

where Cp is artifact corrected particulate concentration, QQ1 represents the mass collected
on filter QQ1 and QQ2 represents the mass collected on downstream backup  filter QQ2
(Figure 4-31).  In some cases a modified backup filter correction procedure described by
Equation 4-2 appears to be more accurate (McDow and Huntzicker,  1990):

                                  Cp =  QQ1 - TQ2                               (4-2)
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       50
       40
  I   30
   o
  S   20
       10
         0
                            4       6        8       10      12
                               Uncorrected OC (ngC/m3)
14
16
Figure 4-30.  Percent correction for vapor adsorption on quartz fiber filters for
             submicrometer particle sampling at a face velocity of 40 cm s-1 for
             13 samples in Portland, OR.
Source: McDow and Huntzicker (1990).
where Cp is artifact corrected particulate concentration, QQ1 represents the mass collected
on filter QQ1 in Figure 4-31, and TQ2 represents the mass collected from filter TQ2, the
backup filter behind a Teflon filter in a parallel sampling port.
     Several approaches have been used to attempt to determine the relative importance of
the adsorption and volatilization artifacts.  Using quartz fiber denuders to remove vapors
upstream of filter samples, Appel et al., (1989) found 59% and Fitz (1990) found 80% on
average of the organic mass adsorbed on the backup filter could be removed by the denuder,
indicating that the 41% or 20% of the organic mass adsorbed on the backup filter was
volatilized from the collected particulate matter.
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         Inlet
Inlet
                   ]  QQ1
             TQ1
                   ]  QQ2
          ]  TQ2
Figure 4-31.  Two types of filter series used for adsorption artifact corrections.  QQ1 is
            a quartz fiber filter, and QQ2 is a quartz fiber backup filter to a quartz
            filter.  TQ1 is a Teflon membrane filter, and TQ2 is a quartz fiber backup
            filter to a Teflon filter.
Source: McDow and Huntzicker (1990).
    McDow and Huntzicker (1990) used Equation 4-3 to correct for adsorption artifacts in
samples simultaneously collected at three different face velocities. They found that in four
experiments more than 80% of the observed difference in organic carbon mass was
eliminated by this correction procedure.  In contrast, if the organic carbon mass on the
backup filter was added to that of the front filter the difference between samples collected at
different face velocities was significantly greater.  This suggests that adsorption artifact is
more likely to account for observed face velocity differences than volatilization artifact.
    Eatough et al. (1989, 1993) felt that both the adsorption and the volatilization artifacts
were important. Eatough concluded that the backup filter, either QQ2 or TQ2 in
Figure 4-32, would adsorb both organic material from the gas phase and organic vapors
volatilized from the collected paniculate matter.  In order to obtain a correct measure of the
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 Sampler 1
DENUDER-
  FILTER
                   Sampler 2
                    FILTER-
                   DENUDER
                                      Legend

                                         Diffusion
                                         Denuder
                                         Quartz
                                         Filter

                                         Sorbent
                                         Filter
Figure 4-32. Schematic of the BYU Organic Sampling System. Sampler 1
         (denuder/filter) and sampler 2 (filter/denuder).


Source: Eatough (1995).
                          4-110

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organic paniculate matter, present in the ambient air in paniculate form, it would be
necessary to eliminate the adsorption artifact and add back the volatilization artifact.
Accordingly, Eatough collected paniculate matter using two parallel sampling trains
described in Figure 4-32 (Eatough et al.,  1989,  1993; Eatough,  1995) and proposed as an
artifact correction equation:

                       Cp = Ql,l  + Ql,2  + CIF1J/E - CIF2J/E                  (4-3)

where:  Cp is artifact corrected particulate concentration; Ql,l and Ql,2 are the  organic
carbon masses collected on the first  and second filters following the denuder in sampler 1 of
Figure 4-32, respectively; CIF1,1 and CIF2,1 are the carbon masses collected on the  sorbent
samplers, carbon impregnated filters (GIF) in samplers  1 and 2 in Figure 4-32, respectively;
and E is the vapor collection efficiency of the denuder.  Eatough (1995) demonstrated that
the denuder, made from carbon impregnated  filter paper (GIF), removed all of the gas phase
organic that could be adsorbed on the quartz  fiber filter material. Thus, the organic material
on Ql,2 would be due to the volatilization artifact only  and Q2,2 -  Ql,2 would give an
indication of the adsorption artifact (assuming independent adsorption of both artifacts).  Any
organic material volatilized from  the organic  particles collected on Q 1,1 and not adsorbed on
Ql.l  or Ql,2 would be adsorbed on CIF1.1. While the denuder is 100% efficient in
removing organic material that would adsorb on quartz fiber filters, it is not  100% efficient
for adsorbing the organic material that would be adsorbed by the carbon impregnated  filters.
Therefore, assuming that all of the organic material vaporized from particles collected on
Q2,l  would be adsorbed on Q2,l, Q2,2 or the denuder  in Sampler  2, CIF2,1 may be used to
correct CIF2,2 for any organic material which passed through the denuder on sampler 1 and
was adsorbed on CIF1,2.  Since the  carbon impregnated filters in the denuders are not 100%
efficient they are each corrected for their efficiency (measured separately by comparing the
organic mass on several carbon impregnated filters in series).
     Several types of samplers have  also been designed to reduce sampling artifacts.  Van
Vaeck et al. (1984) designed a sampler which automatically replaced filters after short time
intervals.  This prevented  large increases in pressure drop across the filter observed during
the relatively long sampling periods they typically used.  Several denuder systems have also
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been designed to reduced sampling artifacts (Appel, et al., 1989; Coutant et al., 1988;
Eatough et al., 1989, 1993; Fitz, 1990).  Turpin et al. (1993b) developed a laminar flow
separator, which also utilizes differences in diffusion rates between vapors  and particles to
reduce sampling artifacts.
     Little is known concerning the chemical species responsible for sampling artifacts, with
the exception of the few species reported here.  Volatile organic compounds (VOCs) such as
formaldehyde make a contribution to the adsorption artifact.  Semi-volatile organic
compounds (SVOCs), those compounds such as n-alkanes and poly cyclic aromatic
hydrocarbons, which are generally  distributed between the vapor phase and paniculate matter
in the atmosphere, play a role in both types of artifacts.
     Equilibrium partitioning of SVOCs between condensed phase, vapor phase and adsorbed
phase depends on their temperature- dependent vapor pressure, the surface area of the
collection material, and their concentration. (Section 3.3.3; Junge, 1977; Yamasaki et al.,
1982; Pankow, 1987).  Some examples of possible causes of SVOC phase  equilibrium shifts
leading to sampling artifacts are (1) changes in temperature, either if the air temperature
changes during sampling, if the sampler is cooled or heated, or if samples  are allowed to
stand in room air with a different temperature than during sampling, (2) changes in surface
area, either in ambient aerosol surface area, or the increase in available surface  area for
adsorption experienced when an SVOC encounters additional filter surface  area, (3) changes
in SVOC concentration,  which can also occur during sampling or  after sample collection if
samples are exposed to room air.  Thus  SVOCs can vaporize  during the  temperature and
relative humidity conditioning prescribed by the Federal Reference Method for measuring
paniculate mass.

Conclusions
     The following conclusions can be drawn from this literature review.  (1) There is
general agreement that sampling artifacts can cause significant errors in the measurement of
paniculate organic mass. (2) Disagreement exists about whether adsorption artifact or
volatilization artifact are the most important sampling artifact  to consider.  It is not clear to
what extent disagreements between studies are caused by differences in the aerosol sampled,
sampling procedures used, or interpretation of sampling results. (3) Little  is known about
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the causes of sampling artifacts or the individual species involved.  (4) Sampling artifacts
may be strongly influenced by changes in temperature or organic vapor concentration during
sampling and storage. Procedures which do not take these factors into consideration are
likely to contribute to sampling artifact errors.  (5) Organic aerosol sampling artifacts can
cause significant errors in particle mass measurements in areas where a large fraction of the
paniculate mass is organic.

4.3.5  Methods Validation
     The use of multiple methods and parallel samplers achieves both optimum performance
and quality assurance. While this has been a part of major research studies since the 1970s,
its extension to long-term monitoring of aerosols was most extensively introduces in the
SCENES and IMPROVE visibility programs (Eldred and Cahill,  1984).  The concept was
labeled, "Integral Redundancy," and was recently adopted by the United Nation's Global
Atmospheric Watch Program.
     The internal consistency checks applied to the IMPROVE network are listed as follows:
     (1) Mass (gravimetric) is compared to the sum of all elements on the Teflon-membrane
         filter of Channel A (PIXE, PESA, XRF analysis; Internally XRF and PIXE are
         compared  for elements around iron).  This was pioneered in the SCENES program
         and is now the standard practice for many aerosol studies.
     (2) Sulfate, by ion chromatography  on Channel B's nylon filter, after an acidic vapor
         denuder, is compared to sulfur (X3) from Channel  A's  Teflon-membrane filter by
         PIXE.  Agreement is excellent, except for summer.
     (3) Organic matter, by combustion on Channel C's quartz-fiber filter stack, is
         compared  to organic matter via PESA analysis of hydrogen on Channel A's Teflon-
         membrane filter. This is an exceptionally sever test due to the nature of organics.
         These comparisons are made for every IMPROVE  analysis, yielding about 25,000
         comparisons to date (Malm et al., 1994).
     These types of data validation checks should be carried out  in every PM measurement
program to ensure the accuracy, precision, and validity of the chemical analysis data.
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4.4   BIOAEROSOLS SAMPLING AND ANALYSIS

4.4.1   Analytical Methods

     Because of the complexity of the particles contained in the term "bioaerosols" no single
analytical method is available that will allow assessment of all of the potential biologically-
derived particle in an aerosol.  Table 4-5 is an overview of the available analytical methods,
examples of the kinds of agents detected, and some sampling considerations.
             TABLE  4-5.  OVERVIEW OF ANALYTICAL METHODS

Culture
Microscopy
Immunoassay


Bioassay
Kinds of Agents
culturable
organisms
recognizable
particles
agents that
stimulate
antibodies

agents exerting
observable effects
Examples
fungal spores, yeasts,
bacteria, viruses (rarely
used)
pollen, fungal spores,
bacteria
allergens, aflatoxin, glucan


endotoxin, cytotoxins
Sampling
Considerations
viability must be
protected
good optical
quality is
required
agents must be
elutable from
sampling
medium.
Activity must be
preserved
same as
immunoassay
 Chemical assays
 Molecular
 techniques
in a biological
system
chemicals with
recognized
characteristics
DNA or RNA-
containing particles
trichothecene toxins
specific organisms
same as
immunoassay
     A good principle to guide the kind of analysis for use in detecting a particular

bioaerosol is to use the approach that best characterizes the agent of disease rather than the
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agent-bearing particle.  Thus, while culture is appropriate where infectious disease is of
concern, or where you know that allergens are only released as a spore germinates, it is
likely to be only a poor indicator for mycotoxin exposure.  Culture always underestimates
actual levels of any viable aerosol because no culture conditions are appropriate for all cells.
The extent of the underestimate can be very large if an aerosol  is damaged or consists
primarily of non-living cells.  The reason culture is not the best approach  for evaluating
mycotoxins is because it is  unlikely that viability is a necessary requirement for mycotoxin
release from spores (although this has not been studied).
     Microscopy allows direct counts of identifiable particles.   Light microscopy will reveal
particles as small as 1.5 /mi reliably.  Identification of the type of particle requires either
some morphological characteristic unique to the particle, or some factor that can be labelled
with a visible dye.  Most pollens and many fungal spores can be placed  in relevant groupings
by microscopy alone.  Bacteria, on the other hand, can only be counted.  Specific techniques
to enhance visibility based on specific immune responses or DNA polymerization techniques
have yet to be developed.
     Immunoassays detect the actual agent of hypersensitivity disease.  Two types are
commonly used: one based on a mixture of polyclonal antibodies that detects a relatively
wide range of allergens, and the other based on monoclonal antibodies that detects only the
single allergen to which the antibody is detected.  Endotoxin is  measured using a bioassay
that involves dose-dependant clotting of lysate from the amoebocytes of horseshoe crabs.
This is not only an agent-specific assay, but actually measures biological activity of the
endotoxin rather than simply the number of molecules.

4.4.2    Sample Collection  Methods
     Bioaerosol particles follow the principles of physics like any other particle type, and are
collected from aerosols by equipment that use these common physical principles.  Bioaerosol
sampling devices were recently  reviewed in depth by Macher et al., 1995.  The most
commonly used bioaerosol samplers are suction sieve impactors that collect particles directly
on culture media.  The second most commonly used types are slit impactors that collect
particles either on rotating plates of agar,  or on grease-coated surfaces.  Rotating arm
impactors are often used for the collection of pollen in clinical allergy practices across  the
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country (American Academy of Allergy and Immunology, 1994).  In addition to the
impactors, bioaerosols are also collected using filtration, either with filters mounted in
cassettes or on large sheets of filter material mounted in high-volume suction samplers.
Liquid impingers are also used under research conditions.
     Analysis of culture plate samples is more or less restricted to static culture,  although
one group has developed a procedure for suspending the catch in a liquid, and using dilution
culture to increase the upper level of sensitivity.  For static culture, the maximum number of
fungal colonies on a 100 mm petri plate that does not result in inhibition between colonies is
about 30. The number of bacteria is much higher (~ 100).  Sieve plate impactors have a
limited number of sites available for deposition,  so that above some given number than
depends on the number of holes in the sieve plate, multiple impactions occur. For biological
aerosols, this means that only one colony of one organism is likely to appear at each site
although several different kinds of organisms might have been collected. Rotating slit culture
samplers do not present this constraint, although the upper limit to prevent competition losses
remains in effect.
     Analysis of samples collected on greased surfaces is generally limited to microscopy,
although some attempts have been to transfer allergens to nitrocellulose membranes and
analyze by immunoassay (immunoblotting).  Filtration samples can be analyzed by culture,
microscopy, and by elution followed by immuno- or bioassay.  Obviously these are the most
versatile devices. However, cultural counts made from filter collections may severely
underestimate actual levels because of desiccation on the filter.  Microscopic analysis
requires  large numbers of particles on the filter, so that, unless long sampling times are used,
the sensitivity can be poor.  Filter collections have been the choice for samples to be
analyzed by immunoassay (e.g., cat  allergens) and bioassay  (e.g., Endotoxin).
4.5  SUMMARY
      Though much of the discussion in the preceding sub-sections has been specific to
different sampling and analysis methods, several generalizations can be drawn.
      First, it is found that samples taken to determine compliance with air quality standards
are often used for other purposes, such as  source apportionment, personal exposure, and
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chemical characterization.  Different sampling systems that are more closely coupled to the
intended analysis methods may be needed to attain additional monitoring objectives.
     Second, though off-site gravimetric analysis of filter samples is straightforward and
relatively inexpensive in terms of equipment, more costly in-situ monitors have the potential
to provide higher time resolution, more frequent sampling intervals, and possibly reduced
manpower requirements. The mass concentrations obtained may not always be comparable
between the manual and automated methods, owing to differences in particle volatilization
and liquid water content of off-site and in situ measurements.
     Third, technology is now proven and available to measure the  major chemical
components of suspended particles, e.g. many separate elements, organic carbon, elemental
carbon, sulfate, nitrate, ammonium, and H+  ions.  With reasonable assumptions regarding
oxide and hydrocarbon forms, most of the measured mass at many locations can be
accounted for by these chemical  measurements.  This  technology could be applied more
routinely than it has in the past to better characterize particles to determine  compliance with
future air quality standards.
     Fourth, since ambient particle size distributions contain fine and coarse particle modes,
with a minimum between them in the 1  to 3 /urn size range, shifts in inlet cut-points near the
2.5 urn size range are not expected to have a large effect on the mass collected owing to the
low proportion of particles with  sizes near this cut-point.  This contrasts to  the sensitivity of
PM10 mass concentrations to small shifts in the cutpoints of PM10 inlets, where the maximum
of the coarse mode occurs between 6 and 25 /im (Lundgren and Burton, 1995).
     Fifth, concentrations of volatile chemicals (such  as ammonium nitrate  or certain organic
compounds) and liquid water may change during sampling, during sample transport and
storage, and during sample analysis.  Liquid water may be removed by lowering the relative
humidity surrounding the sample by heating the sampled air stream, or  by selectively
denuding the airstream of water  vapor.  Several sampling systems involving diffusion
denuders and absorbing substrates operating in series and in parallel have been demonstrated
to quantify volatilized particles, but these are not practical for sustained, long-term
monitoring on limited budgets.
     Finally, collocated studies show substantial differences between mass concentration
measurements acquired by different sampling systems.  They also show differences for
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similar sampling systems for which procedures are somewhat different. Inlet maintenance,
filter handling and storage, laboratory analyses, and quality control procedures are just as
important variables as sampler design in explaining these differences.  Inlet characteristics
and particle volatilization properties are the most important variables that cause mass
concentrations to differ.  The lack of common calibration standards is  one of the major
reasons for differences between certain chemical analysis results.
     This chapter  also briefly describes the technical capabilities and limitations of specific
aerosol  sampling procedures, focusing on those that (1) were used to collect data supporting
other sections in this document, (2) supported the existing PM10, TSP  and Pb regulations,
and (3)  have application in development of a possible fine particle standard.  The discussion
of aerosol separation technologies is divided between devices used to mimic the larger
particle penetration rationales for the upper airways, and those used to mimic smaller particle
penetration to the sub-thoracic regions.  The applications of performance specifications to
define these measurement systems for regulatory purposes  are discussed with observations
suggesting that the current specification process has not always assured the necessary
sampling accuracy. Particle sampling systems  for specialty applications, including automated
samplers and personal exposure monitors, are briefly described.

4.5.1   PM10 Sampling
     Laboratory and field testing reported in the  literature since 1987  suggest that the EPA
specifications and test requirements for PM10 samplers have not adequately  controlled the
differences observed in collocated ambient sampling.  The most significant performance
flaws have combined to produce mass concentration biases as  large as  60%. These biases
appear to have resulted from the  combined factors of (1) allowing a cutpoint tolerance of
10 ± 0.5 f*,m,  (2)  placing an inadequate restriction on internal particle bounce, and
(3) allowing a degradation of particle separation performance as certain technology PM10
inlets became soiled.  The between-sampler bias from a ±0.5 /m tolerance limit is
predictable and should provide PM10 concentration differences significantly less than ±10%
in almost all cases. Design practices (primarily surface coatings with  viscous oil) to
minimize the penetration caused by bounce and resuspension have been shown to be very
effective. The magnitude of biases from soiling events can be accommodated by not
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allowing the inlet to become excessively dirty during operation through routine cleaning prior
to sampling.  Particle bounce or soiling problems have not been reported for the PM10 inlets
for the dichotomous sampler.
     Based on the current understanding of the PM10 sampling process, it could be expected
that sampling systems can now be designed and concentration measurements made that are
within 10% of the true concentration.  This range poses the greatest concern where the
measured concentrations are near a standard exceedance level.  A review by  EPA of the
current PM10 performance requirements and possible amendments of the existing
specifications may be appropriate, given the information base now available.

4.5.2 Fine Particle Sampling
     The technology is available to provide an accurate Fine particle cutpoint (e.g. 1.0 or
2.5 /im) for routine sampling.  Virtual  impactors and cyclones have been shown to be the
most trouble-free and versatile methodologies.  The exclusion of larger particles using a
scalping inlet eliminates many of the transport and  loss problems encountered during PM10
sampling.  The absence of the Coarse particle fraction, however, exaggerates the problems
inherent with Fine particle chemistry, such as particle-substrate interactions and sublimation
losses. Although it could be expected that Fine particle mass concentration measurements
can be made within 10% of the true concentration, accurate chemical speciation may require
more  comprehensive sampling system components, including gas stream denuders and
sequential filter packs.

4.5.3   Concentration Corrections to Standard Conditions
     The appropriateness of the correction of particulate concentrations to a reference
temperature and pressure is currently under review at EPA.  Aerodynamic sampling requires
incorporation of local conditions to provide the correct velocities for accurate particle size
separation. Correcting the collection volume to standard conditions may improperly
influence interpretations of the developed relationships between particle concentration and
adverse health responses.  It appears  to be  more appropriate to compute particle
concentrations at site conditions and provide temperature and barometric pressure data
subsequently, as needed for data interpretation.
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4.5.4   Performance Versus Design Specifications for Sampling Systems
     The current EPA PM10 Reference and Equivalent Method program established in 1987
is based on providing the necessary data quality by using sampling performance
specifications.  Several research studies have recently reported that key elements of the
sampling process were inadequately considered when the original performance specifications
were developed.  The observations from these controlled studies have been bolstered by
reviews of field data from collocated PM10 samplers that showed substantial biases under
certain conditions.  The particle sampling process is complex. Obtaining an acceptable bias
level using performance standards is difficult, but not impossible, if the appropriate
developmental research is identified and implemented. The alternative approach of defining
sampling systems by design specifications seems attractive, but may ultimately pose more
problems than are solved without producing better quality data.  Additionally, specification
of a sampling system by design would have the undesirable attribute of virtually eliminating
further new  technology research.  The approach for specifying particle sampling systems is
currently under review at EPA.

4.5.5   Automated Sampling
      The performances of two sampling methods that are currently designated as Equivalent
PM10 methods  by EPA - beta attenuation and the TEOM sampler - have been evaluated
extensively in field  settings. Although acceptable comparisons with EPA Reference sampling
methods are reported in collocated field studies, attention must be paid to situations where
significant biases existed.  These biases have been attributed to a number of factors,  but
focused on the treatment of the particle sample  during and after collection.  The presence of
highly reactive or unstable particles at sampling locations in the western U.S. appears to
cause the greatest concern, because of a higher proportion of these species. These bias
issues are significant because they complicate the use of automated samplers as "triggers" for
control strategy actions, and they question the adequacy of the existing performance
specifications for equivalent PM10 sampling systems.
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4.5.6   PM Samplers for Special Applications
     Reviews of typical U.S. personal activity patterns suggest that personal exposure
sampling for particulates should also be considered in developing population risk
assessments.  Relatively unobtrusive personal sampling systems have been designed for a
number of particle size cutpoints, and recent studies suggest that acceptable  accuracies and
precisions are possible.  The collection of particle size distribution data can  assist in
identifying paniculate sources and subsequent  studies of particle transport and fate.  Well
characterized cascade impactors are available that cover the aerodynamic size  range from  at
least 0.1 to 10 /mi.  More automated optical systems are also available, providing either
optical or aerodynamic diameter ranges from about 0.5 to 10 /*m.  Source apportionment
sampling systems are available to assist in relating the  chemical attributes of ambient
paniculate matter to the chemical "signatures" from various source categories.  This is
accomplished by using sampling system components and  collection substrates designed to
collect specific chemical classes  (e.g., a suite of individual metals, speciated carbon) in
defined particle size categories.
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                                                   4-148

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                5.   SOURCES AND EMISSIONS OF
                    ATMOSPHERIC PARTICLES
5.1   INTRODUCTION
           Unlike gaseous criteria pollutants (SO2, NO2, CO, O3), which are well defined
chemical entities, atmospheric particles comprise a complex mixture of chemical constituents.
Because of this fact, sources of each constituent of the atmospheric aerosol must be
considered in turn. Since paniculate matter (PM) is composed of both primary and
secondary constituents, emissions of both the primary components and the gaseous precursors
must be considered.  The chemical composition of ambient aerosols was treated in general
terms in Chapter 3.  Information on ambient concentrations of particles of various sizes
(PM10, PM2 5) and their chemical composition, based on specific field studies, is presented in
Chapter 6.
     Tables  5-1A and 5-IB summarize anthropogenic and natural sources for the major
primary and  secondary aerosol constituents of fine and coarse particles.  Anthropogenic
sources can be further divided into stationary and mobile sources. Stationary sources include
fuel combustion for electrical utilities and  industrial processes; construction and demolition;
metals, minerals, petrochemicals and wood products processing; mills and elevators used in
agriculture; erosion from tilled lands; waste disposal and recycling; and fugitive dust from
paved and unpaved roads. Mobile, or transportation related, sources include direct emissions
of primary PM and secondary PM precursors from highway and  off-highway vehicles and
nonroad sources.  Also shown are sources for precursor gases whose oxidation forms
secondary paniculate matter. In general, the nature of sources of paniculate matter shown in
Table 5-1A is very different from that for  paniculate matter shown in Table 5-1B. A large
fraction of the mass in the fine size fraction is derived from material that has been volatilized
in combustion chambers and then recondensed to form primary fine PM, or has been formed
in the atmosphere from precursor gases as secondary PM. Since precursor gases and fine
paniculate matter are capable of travelling great distances, it is difficult to identify individual
sources of constituents shown in Table 5-1A.  The PM constituents shown in Table 5-1B
                                        5-1

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 TABLE 5-1A.  CONSTITUENTS OF ATMOSPHERIC FINE PARTICLES (<2.5 fim)
	AND THEIR MAJOR SOURCES	

                                            Sources
                    Primary
                                                        Secondary
Aerosol
species
S04=
Natural Anthropogenic
Sea spray Fossil fuel
combustion
Natural
Oxidation of reduced
sulfur gases emitted
by the oceans and
wetlands; and SO2
and H2S emitted by
volcanism and forest
fires
Anthropogenic
Oxidation of SO2
emitted from fossil
fuel combustion
 NO,
 Minerals
Erosion,
re-entrainment
 Organic
 carbon
 (OC)
Wild fires
 Elemental    Wild fires
 carbon
 Metals
Volcanic
activity
 Bioaerosols   Viruses,
             bacteria
               Motor vehicle
               exhaust
Fugitive dust; paved,
unpaved roads;
agriculture and
forestry

Motor vehicle
exhaust
Open burning, wood
burning, cooking,
motor vehicle
exhaust, tire wear


Motor vehicle
exhaust, wood
burning, cooking

Fossil fuel
combustion,
smelting, brake wear
                         Oxidation of NOX
                         produced by soils,
                         forest fires, and
                         lighting
                                                      Emissions of NH3
                                                      from wild animals,
                                                      undisturbed soil
Oxidation of
hydrocarbons emitted
by vegetation,
(terpenes, waxes);
wild fires
                     Oxidation of NOX
                     emitted from fossil
                     fuel combustion; and
                     in motor vehicle
                     exhaust
Emissions of NH3
from animal
husbandry, sewage,
fertilized land

Oxidation of
hydrocarbons emitted
by motor vehicles,
open burning, wood
burning
                                             5-2

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    TABLE 5-1B.  CONSTITUENTS OF ATMOSPHERIC COARSE PARTICLES
                     (>2.5 jim) AND THEIR MAJOR SOURCES
Sources

Aerosol species
Minerals
Metals


Miscellaneous
ions
Organic carbon
Organic debris
Bioaerosols
Primary
Natural
Erosion,
re-entrainment
Erosion,
re-entrainment,
organic debris
Sea spray

—
Plant, insect
fragments
Pollen, fungal
spores, bacterial
agglomerates

Anthropogenic
Fugitive dust; paved,
unpaved road dust,
agriculture and forestry
—


Road salting

Tire and asphalt wear
—

Secondary
Natural Anthropogenic
	
_ _


— —

— —
— —

have shorter lifetimes in the atmosphere, so their impacts tend to be more localized.  Only
major sources for each constituent are listed in Tables 5-1A and 5-1B.
      Natural sources of primary PM include windblown dust from undisturbed land, sea
spray, and plant and insect debris.  The oxidation of a fraction of terpenes emitted by
vegetation and reduced sulfur species from anaerobic environments leads to secondary PM
formation.  Ammonium (NH4+) ions which are crucial for regulating the pH of particles are
derived from emissions of ammonia (NH3) gas. Source categories for NH3 have been
divided into emissions from undisturbed soils  (natural) and emissions which are related to
human activities (e.g., fertilized lands, domestic and farm animal waste).  It is difficult to
describe emissions from biomass burning as either natural or anthropogenic.  Clearly, fuel
wood burning is an anthropogenic source of PM, whereas wildfires would be a natural
source.  Forest fires have been included as a natural source, because of the lack of
information on the amount of prescribed burning or accidental fires caused by humans.
                                        5-3

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Similar considerations apply to the biogenic emissions of trace metals which may be
remobilized from anthropogenic inputs.
     Although a large number of potential source contributions have been listed for
paniculate matter and gaseous precursors in Tables 5-1A and 5-1B, it should be noted that
emissions inventories have been compiled for only a limited number of entries for either
aerosol constituents or source categories.  The remainder of the chapter includes discussion
of the processes responsible for the most important sources of primary and secondary PM in
Sections 5.2 and 5.3, respectively, followed by discussion of emissions estimates for the
United States in Section 5.4. Applications of emissions inventories and other techniques, such
as receptor modeling for inferring sources of ambient particulate matter, are then discussed
in Section 5.5.
5.2    SOURCES OF PRIMARY PARTICULATE MATTER
     This section discusses processes responsible for the emissions of primary particulate
matter. The order of sources roughly follows their estimated relative source strengths for the
United States to be presented in Section 5.4. Emissions  of mineral particles produced as the
result of natural wind erosion and human activities are discussed in 5.2.1.  Sources of
primary particulate matter produced by fossil fuel  combustion and other stationary
anthropogenic sources are discussed in 5.2.2, while  sources of secondary particulate matter
are discussed in section 5.3.  Motor vehicle emissions are discussed in 5.2.3.  Vegetation
burning in woodstoves and forest fires is discussed as a source of particulate matter in 5.2.4.
Sea salt aerosol production, the suspension of organic debris, and the production of trace
metals by natural processes are discussed in 5.2.5.  Data for the chemical composition and
particle size distribution for each of these sources  of particulate matter are included where
available along with information about techniques  for measuring source compositions and
emissions rates.

5.2.1  Wind Erosion and Fugitive Dust
     Windblown dust constitutes a major component of the atmospheric aerosol, especially  in
arid and semi-arid areas of the world. Windblown dust represents the largest single category
                                          5-4

-------
in global emissions inventories, constituting about 50% of the total global source of primary
and secondary paniculate matter (IPCC, 1995).  Since the next major category is sea-salt
aerosol production, which is estimated to constitute about 40% of total emissions, it can be
seen that about 70% of non-sea-salt aerosol emitted is in the form of mineral dust.  If one-
half of the dust is assumed to be emitted in the PM10 size  range, then it can be seen that
54% of non-sea-salt PM10 emitted globally is dust, less than about 10% of which originates
in the  United States.
     Many areas of the western United States are classified as arid or semi-arid, potentially
leading to a larger contribution of dust to the mass of the ambient aerosol there compared to
the eastern United States.  Large-scale dust events  are generally associated with  semi-arid
regions where marginal lands are used for agriculture and  herding.  During times of drought,
the denuded and broken soil surface is easily carried away, periodically forming  "dust bowl"
conditions as  in the midwestern U.S. (Prospero,  1995).
     Emission rates of mineral aerosols  are found to be strongly dependent on
meteorological parameters such as wind  velocity  and  precipitation.  Wind tunnel experiments
(Bagnold, 1941; Chepil,  1945) have shown that the motion of loose particles on  the surface
is initiated when the surface wind stress  (The wind stress acting on the surface is supplied by
the downward transport of momentum from the mean winds.  In micrometeorological
applications, u*, or the square  root of the ratio of the wind stress to the air density is  used.)
acting  on erodible particles exceeds the downward force of gravity and the interparticle
cohesion forces acting on the particles.  Particle motion occurs when u* exceeds  the threshold
friction velocity, u*t, which is dependent on particle properties. Values of u*t are strongly
size dependent, with a minimum for particles having diameters of about 60 /im (Bagnold,
1941).  Individual smaller particles are held by cohesive forces and larger particles are
constrained by the force of gravity.  Measurements of u*t are available for a number of
different soil types (e.g., Gillette et al., 1980).
     Three types of particle motion were characterized in the early wind tunnel experiments:
suspension, saltation, and creeping.  Suspension refers to the upward transport of dust
(d< 60 f*m) by turbulent eddies; saltation to the  horizontal motion of particles (60 <  d <
2000 jLtm) which can reach heights of up to a meter above the surface before they fall  back;
                                           5-5

-------
creeping to particles too massive (d > 2000 /xm) to be lifted from the surface so they roll
along.
     Because of strong cohesive forces in soil crusts and rock surfaces,  particles are not
suspended directly by the transfer of momentum from the wind but by sandblasting and
abrasion by saltating particles.  The impact  of saltating grains then results in the emission of
smaller particles (Shao et al., 1993). The flux of saltating particles increases rapidly with
wind speed, and varies as (u*)2(u*-u*t). The size distribution of the suspended aerosol  is then
controlled by the aerosol microphysical processes of coagulation and sedimentation.
     Non-erodible elements on natural surfaces cut down on the surface area available for
erosion, and they take up wind momentum which would otherwise be available for erosion.
Soil moisture, salts, and organic  matter mainly affect soil cohesion (e.g., Gillette et al.,
1982) and thus the size distribution of soil particle aggregates.   Chepil (1956), Belly (1964),
Bisal and Hsieh (1966), and Svasek and Terwindt (1974) show that substantially greater wind
                                            €>
forces are needed when soil surface moisture  is increased by less than 1% from its dry state.
The moisture content of soils will vary throughout the year depending on the  frequency and
intensity of precipitation events, irrigation, and the relative humidity and temperature of the
surrounding air.  Large amounts of rain falling during 1 mo of a year will not be as effective
in stabilizing dust as the same amount of  rain interspersed at intervals throughout the year.
     An operational difficulty arises because u* is derived from anemometers placed at a
height of 5 or 10 m above the surface and requires assumptions about the wind profile down
to the surface.  The challenge is to derive values for wind stress acting on erodible elements
(Alfaro and Gomes,  1995) which are valid for large areas.  Alfaro and Gomes  (1995) have
derived relations between wind velocity measurements made at conventional heights and
surface wind stresses  using radar imagery to characterize surface roughness.  Surface
roughness is determined by the presence of vegetation, structures, rocks and boulders,
topographic irregularities and surface obstructions.   Marticorena and Bergametti (1995) have
developed parameterizations including these physical considerations suitable for use in large
scale models.
     Apart from the large-scale, mean flow small-scale atmospheric vortices are also capable
of suspending dust.  Dust devils, so-called because of the dust they entrain, may be found  in
arid areas along roads or where the surface has  been disturbed by human activity (Hall,
                                           5-6

-------
 1981; Snow and McClelland,  1990).  Hall (1981) proposed that dust devils could constitute
 the major source of suspended dust on hot summer days with light winds and convectively
 unstable conditions, as an example in Pima Co., AZ demonstrates.  Hall (1981) estimated
 that large scale winds could raise  171 kg km"2 day"1 and motor vehicles could raise
 48 kg km"2 day"1 on an annually averaged basis, while dust devils could raise up to
 250 kg  km"2 day"1 of dust (in  all size ranges) on hot summer days.  Atmospheric vortices are
 not a source component currently  treated in emissions inventories.
     Apart from sources within the continental United States, an additional source of
 windblown dust involves the long-range transport of dust from the Sahara desert westward
 across the  Atlantic  Ocean.  Individual dust storms have been tracked across the Atlantic,
 after emerging from the northwest coast of Africa, to the east coast of the United States (Ott
 et al., 1991).  Saharan dust is carried into the Miami area, capable of producing dense hazes
 during the  summer (Prospero et al.,  1987).  While summertime monthly mean dust
 concentrations are about 10 jwg/m3 (Prospero et al., 1993), dust events are highly sporadic
 and  of short duration.  In a one-year study of Saharan dust deposition in Miami, Prospero
 et al. (1987) found that 22% of the annual deposition occurred in one day and 68% in rain
 events that occurred during two dust episodes spread over a total of four days.  Gatz (1995)
 has found evidence suggesting that Saharan dust has reached as far as central Illinois in  at
 least one episode which occurred during the summer of 1979.  Up to 20 /*g/m3 of  the
 ambient aerosol may have originated in the Sahara desert and the Sahel during this episode.
 These dust events are highly sporadic and more work needs to be done to  characterize the
 frequency,  magnitude,  and  variability of these events.   Similar dust transport may  also occur
 from the deserts of Asia across the Pacific Ocean (Prospero, 1995), but it is not clear to
 what extent any of this dust reaches the United States (See Chapter 6 for more information
 on long distance transport of dust particles  into the United States from  Africa or Asia.)
     The compositions of soils and average crustal material are shown in Table 5-2 (adapted
 from Warneck, 1988).  Two entries are shown as representations of average crustal material.
 Differences from the mean  soil composition shown can result from local geology and climate
conditions.  Major elements in both soil and crustal profiles are Si, Al, and Fe which are
found in the form of various minerals. In addition, organic matter constitutes a few percent,
                                          5-7

-------
                TABLE 5-2. AVERAGE ABUNDANCES OF MAJOR
                    ELEMENTS IN SOIL AND CRUSTAL ROCK
Elemental Abundances (ppmw)
Element
Si
Al
Fe
Ca
Mg
Na
K
Ti
Mn
Cr
V
Co
Soil
(a)
330,000
71,300
38,000
13,700
6,300
6,300
13,600
4,600
850
200
100
8
Crustal
(b)
277,200
81,300
50,000
36,300
20,900
28,300
25,900
4,400
950
100
135
25
Rock
(c)
311,000
77,400
34,300
25,700
33,000
31,900
29,500
4,400
670
48
98
12
Source:  (a) Vinogradov (1959); (b) Mason (1966); (c) Turekian (1971), Model A; as quoted in Warneck
(1988).
on average, of soils.  In general, the soil profile is similar to the crustal profiles, except for
the depletion of soluble elements such as Ca, Mg, and Na.
     Because of the enormous difficulties encountered in developing theoretical estimates of
windblown dust emissions, most current estimates rely on the results of empirical studies.
These studies typically involve the placement of wind tunnels over natural surfaces and then
measuring emission rates and size distributions for different wind conditions.  The emissions
of fugitive dust raised as the result of human activities are also extremely difficult to
quantify. Fugitive dust emissions arise  from paved and unpaved roads, building construction
and demolition, storage piles, and agricultural tilling in addition to wind erosion.
     Figure 5-1  shows examples of size distributions in dust from paved and unpaved roads,
agricultural soil,  sand and gravel, and alkaline lake bed sediments which were measured in a
                                          5-8

-------
    100
      80
      60
      40
      20
  Paved
Road Dust
   Unpaved
   Road Dust
:1.0|im  [^<
Agricultural
   Soil
Soil/Gravel
                                                         ITSP
                                                                     Alkaline
                                                                     Lake Bed
Figure 5-1.   Size distribution of particles generated in a laboratory resuspension
             chamber.
Source: Chow et al. (1994).
laboratory resuspension chamber as part of a study in California (Chow et al., 1994).  This
figure shows substantial variation in particle size among some of these fugitive dust sources.
The PMt 0 abundance (6.9%) in the alkaline lake bed dust is twice its abundance in paved
and unpaved road dust.  Approximately 10% of TSP is in the PM2 5 fraction and
approximately 50% of TSP is in the PM10 fraction.  The sand/gravel dust sample shows that
65% of the mass is hi particles larger than the PM10 fraction. The PM2.5 fraction of TSP is
approximately 30% to 40% higher in alkaline lake beds and sand/gravel than hi the other soil
types.  The tests were performed after seiving and with a short (< 1 min) waiting period
prior to sampling.  It is expected that the fraction of PMt 0 and PM2 5 would increase with
distance from a fugitive dust emitter as the larger particles deposit to the surface at a larger
velocity than the smaller particles.  Additional data shown in Figure 5-2 (Houck et al., 1989,
1990) were obtained hi a study characterizing particle sources hi California.
                                          5-9

-------
     100
      80
      60

      40
      20
                 52.3%
                 10.7%
                 (<2.5n)

                 4.5%
                             92.8%
                             82.7%
                             81.6%
                                           95.8%
  93.1%
  (<2.5n!

  92.4%
96.2%
(<10ji)
92.3%
(<2.5|*)
91.8%
99.2%


97.4%
(<2.5n)



87.4%
                                                                               34.9%
          Road and   Agricultural   Residential     Diesel
          Soil Dust     Burning      Wood       Truck
                                  Combustion    Exhaust
                   Crude Oil  Construction
                  Combustion     Dust
            Code:    f""~l >10(i
2.5ji -
       - 2.5\i
Figure 5-2.  Size distribution of California source emissions, 1986.


Source: Houck et al. (1989, 1990).
                                            5-10

-------
      Unpaved roads and other unpaved areas with vehicular activity are essentially unlimited
 reservoirs of dust loading when vehicles are moving. These surfaces are always being
 disturbed, and wind erosion seldom has an opportunity to increase their surface roughness
 sufficiently to inhibit particle suspension.  The U.S.  EPA AP-42 emission factor (U.S.
 Environmental Protection Agency,  1995a) for unpaved road dust emissions contains variables
 which account for silt loading, mean vehicle speed, mean vehicle weight, mean number of
 wheels, and number of days with detectable precipitation, to determine annual PM10 dust
 emissions for each vehicle-kilometer traveled.  Dust  loadings on a paved road surface build
 up by being tracked out from unpaved areas such as  construction sites, unpaved roads,
 parking lots, and shoulders; by spills from trucks carrying dirt and other particulate
 materials; by transport of dirt collected on vehicle undercarriages; by wear of vehicle
 components such as tires, brakes, clutches, and exhaust system components; by wear of the
 pavement surface; by deposition of suspended particles from many emissions sources; and by
 water and wind erosion from adjacent areas.  Moisture causes dust to adhere to vehicle
 surfaces so that it can be carried out of unpaved roads, parking lots, and staging areas.
 Carry out also occurs when trucks exit heavily watered construction sites.  This dust is
 deposited on paved roadway surfaces as it dries, where it is available for suspension far from
 its point of origin.  Fugitive dust emissions from paved roads are often higher after
 rainstorms in areas where unpaved  accesses are abundant, even though the rain may have
 flushed existing dust from many of the paved streets.
      The size distribution of samples of paved road dust obtained from a source
 characterization study in California is shown in Figure 5-2.  As might be expected, most of
 the emissions are in the coarse  size mode.  The chemical composition of paved road dust
 obtained  in Denver, CO, during the winter of 1987-1988 is shown in Figure 5-3.   The
 chemical composition of paved road dust is much like an ambient PM10 sample, which
 consists of a complex mixture of particulate matter from a wide variety of sources.  Hopke
 et al. (1980) found that  the inorganic composition of urban roadway dust in samples from
 Urbana, IL could be described in terms of contributions from natural soil,  automobile
exhaust, rust, tire wear, and salt. Automobile contributions  arose from exhaust emissions
enriched in Pb; from rust as Fe; tire wear particles enriched in Zn; brake linings enriched in
Cr, Ba, and Mn; and cement particles derived from roadways by abrasion.  The complexity
                                         5-11

-------
       &e&&f&&&&/P^#      ^    ^GP" ^c!^
            A> y-O .<*w ^- oo      ^       "A  Cj* *$r               ~     ^"
                               Chemical Compound
*>*
     ^
Figure 5-3.  Chemical abundances for PM2.s emissions from paved road dust in Denver,
           CO.  Solid bars represent fractional abundances, and the error bars
           represent variability in species abundances. Error bars represent detection
           limits when there are no solid bars.
Source: Watson and Chow (1994).
of paved road dust is also evident in the comparison of a paved road dust profile reported by
Chow et al. (1991) for Phoenix, AZ, with profiles from other geological sources in the area.
Chow et al. (1991) noted that the abundance of organic carbon in the profile was 11 ±9%,
larger and more variable than its abundance in profiles from agricultural land, construction
sites, and vacant lots.  The presence of particles produced by automotive emissions, tire
wear, organic detritus, and engine oils may account for this enrichment for organic carbon.
    The abundances of Pb and Br in Phoenix paved road dust were more than double the
concentrations in the other geological profiles, indicating the presence of tailpipe exhaust
                                     5-12

-------
 from vehicles burning leaded fuels.  The contribution of tire wear could have been from
 4 to 45 % of that of motor vehicle exhaust, based on the results of Pierson and Brachaczek
 (1974).  Enrichments in species from clutch and brake  wear were not detectable in the
 Phoenix paved road dust profiles.  These are often composed of asbestos and/or semi-metal
 carbon composites.  Cooper et al. (1987) examined the elemental composition of semi-metal
 brake shoes and found abundances of -45% Fe, -2% Cu, -0.5% Sn, -3% Ba, and
 — 0.5%  Mo.  None of these species were found in the  Phoenix paved road dust profiles at
 levels significantly in excess of their abundances in other geological sub-types.
      Many fugitive dust sources are episodic rather than  continuous emitters.  Though
 windblown dust emissions are low on an annual average,  they can be quite large during those
 few episodes when wind speeds are high.  In Coachella Valley, CA, the South Coast Air
 Quality Management District (1994) calculated 24-h emissions based on a worst windy day.
 On a day when wind gust speeds  exceeded 96 km/h, fugitive dust emissions could account
 for 20% of the entire annual emissions. Since the rate  of dust suspension varies as the cube
 of the wind speed for large wind  speeds, estimates of windblown dust emissions use highest
 wind speeds quoted in National Weather Service Local  Climatological Summaries.
 Construction activities are also episodic in nature.  Reeser et al. (1992) reported that fugitive
 dust emissions during wintertime  in Denver, CO, were  44% higher than those found in the
 annual inventory using standard emissions inventory methods.
      Finally,  the spatial disaggregation for fugitive dust emissions is poorer than that for all
 other source categories.  Whereas most mobile sources  are confined to established roadways
 and most area sources  are located in populated regions, suspendable dust sources are
 everywhere.  Most fugitive dust emissions are compiled on a county-wide basis and are not
 allocated to specific fields, streets, unpaved roads, and construction sites possibly
 contributing to high airborne PM  concentrations.  Several of these limitations may be
 impossible to overcome, but many result from old methods being applied to the problem.
      The inherent variability of fugitive dust emissions  may preclude absolute emissions
 estimates.  Nevertheless, this examination of physical processes  shows that better knowledge
 of the locations of these emissions, the joint frequencies of activities and different
meteorological conditions, and more site specific measurements of key parameters  could
provide much better absolute emissions rates than are now available.
                                         5-13

-------
5.2.2   Stationary Sources
     The combustion of fossil fuels, such as coal and oil, leads to the formation of both
primary and secondary paniculate matter. Fossil fuels are mainly composed of a mixture of
the remnants of plant matter and surrounding soils which have been processed at elevated
temperatures and pressures over periods of up to three hundred million years.  The process
of coal formation results in a matrix of high molecular weight, highly cross-linked
polyaromatic carbocyclic and heterocyclic ring compounds containing C, H, O, N, P, and S,
and crustal materials. The hydrogen, nitrogen and phosphorus contents of coal are lower
than the original biomass, reflecting losses by microbial utilization and thermal processing.
Petroleum consists of long chain straight and branched alkanes with high carbon numbers
(i.e., C25-C50), alkenes  and aromatic hydrocarbons.  The trace element content of these fuels
reflects the trace element content of the initial organic matter and soil,  subsequent
hydrothermal alteration  during diagenesis and  industrial processing.  Because of the inherent
variability in each of these factors,  the trace element content of fossil fuels is highly variable.
     Coal combustion in the  high temperature combustion zones of power plants  results in
the melting and volatilization of refractory crustal components, such as aluminosilicate
minerals which condense to form spherical fly ash particles.  Fly ash is enriched with metals
compared to ordinary crustal  material by the condensation of metal vapors.  The sulfur
content of fossil fuels ranges  from fractions of a percent to about 4%.  The sulfur in the fuel
is released primarily as  SO2 along with smaller amounts of sulfate. Ratios of sulfate S to
total S range from about 1 %  for modern coal  fired power plants to several percent in
residential, commercial  and industrial boilers (Goklany et al., 1984).
     The elemental composition of primary particulate matter emitted in the fine  fraction
from a variety of power plants and industries in the Philadelphia area is shown in Table 5-3
as a representative example of emissions from stationary fossil combustion sources (Olmez
et al., 1988).  Entries for the coal fired power plant show that Si and Al followed by  sulfate
are the major primary constituents produced by coal combustion, while  fractional abundances
of elemental carbon  were  much lower and organic carbon species were  not detected.  Sulfate
is the major particulate  constituent released by the oil fired power plants examined in this
study; and,  again, elemental and organic carbon are not among the major species emitted.
Olmez et al. (1988)  also compared their results to a number of similar studies and concluded
                                          5-14

-------
TABLE 5-3. COMPOSITION OF FINE PARTICLES RELEASED BY
VARIOUS STATIONARY SOURCES IN THE PHILADELPHIA AREA
Species
(Units)
C-v (%)
C-e (%)
NH4 (%)
Na(%)
Al (%)
Si(%)
P(%)
S(%)
S04 (%)
Cl (%)
K(%)
Ca(%)
Sc (ppm)
Ti(%)
V (ppm)
Cr (ppm)
Mn (ppm)
Fe (%)
Co (ppm)
Ni (ppm)
Eddystone Coal-
Fired Power
Plant
ND
0.89 + 0.12
1.89 + 0.19
0.31 + 0.03
14 + 2
21.8 ± 1.6
0.62 + 0.10
3.4 + 0.6
11.9 + 1.2
0.022 ±0.11
1.20 ± 0.09
1.4 ± 0.5
42 + 2
1.1 ± 0.2
550 + 170
390 ± 120
290 ± 15
7.6 ± 0.4
93 ± 10
380 ± 50
Oil-Fired Power Plants
N

3
3
3
3
9
9
9
3
3
9
3
3
3
3
3
3
3
3
9
Eddystone
2.7 ± 1.2
7.7 + 1.5
3.5 ± 1.6
3.0 + 0.8
0.45 + 0.09
1.9 + 0.6
1.5 ± 0.4
11 ± 2
40 ±4
0.019 ± 0.009
0.16 ±0.05
3.6 ± 1.0
0.17 ±0.02
0.040 + 0.044
11500 ± 3000
235 ± 10
380 ± 40
1.6 ± 0.2
790 ± 150
15000 + 5000
N
3
3
3
3
3
9
9
9
3
2
9
3
3
9
3
3
3
3
3
9
Schuylkill
0.75 ± 0.63
0.22 ± 0.17
3.7 ± 1.7
3.3 ± 0.8
0.94 ± 0.08
2.6 ± 0.4
1.0 ± 0.2
13 ± 1
45 ± 7
ND
0.21 ± 0.03
2.3 ± 1.0
0.47 ± 0.02
0.12 ± 0.02
20000 ± 3000
230 ± 70
210 ± 50
1.7 ± 0.4
1100 + 200
19000 ± 2000
N
4
4
4
3
3
11
11
11
4

11
3
3
11
3
3
3
3
3
11
— Secondary
Al Plant
1.6 ± 1.5
0.18 ± 0.10
2.2 ± 0.9
16.3 ± 0.8
1.74 ± 0.09
3.1 ± 2.2
0.45 ± 0.27
3 ±4
5.9 ± 2
21 ± 4
10.9 ± 1.5
0.12 ± 0.09
0.092 ± 0.039
0.024 ± 0.003
36 ± 7
410 ± 20
120 ± 15
0.31 ± 0.02
13 ± 2
300 ± 100
N
2
2
2
1
1
2
2
2
2
1
2
2
1
2
1
1
1
1
1
2
Fluid Cat.
Cracker
ND
0.16 ± 0.05
0.43 ± 0.22
0.38 ± 0.05
6.8 ± 1.2
9.8 ± 20.0
ND
4.2 ± 12.6
38 ± 4
ND
0.031 ± 0.005
0.030 ± 0.004
2.7 ± 0.4
0.38 ± 0.1
250 ± 70
59 ± 8
14 + 3
0.20 ± 0.03
15 ± 2
220 ± 30
N

3
3
3
3
9

9
3

9
9
3
3
3
3
3
9
3
9
Municipal
Incinerator
0.57 + 0.26
3.5 ± 0.2
0.36 ± 0.07
6.6 ± 3.5
0.25 ± 0.10
1.7 ± 0.3
0.63 ± 0.12
2.9 + 0.8
6.8 ± 2.3
29 ± 5
7.6 ± 2.3
0.23 ± 0.10
0.11 ± 0.02
0.030 ± 0.015
8.6 ± 5.3
99 ± 31
165 ± 40
0.22 ± 0.05
3.7 + 0.8
290 ± 40
N
4
4
4
3
3
10
10
10
4
3
10
10
1
10
2
3
3
3
3
10

-------
                    TABLE 5-3 (cont'd). COMPOSITION OF FINE PARTICLES RELEASED
                    BY VARIOUS STATIONARY SOURCES IN THE PHILADELPHIA AREA
ON
Species
(Units)
Cu (ppm)
Zn(%)
As (ppm)
Se (ppm)
Br (ppm)
Rb (ppm)
Sr (ppm)
Zr (ppm)
Mo (ppm)
Ag (ppm)
Cd (ppm)
In (ppm)
Sn (ppm)
Sb (ppm)
Cs (ppm)
Ba (ppm)
La (ppm)
Ce (ppm)
Nd (ppm)
Sm (ppm)
Eddystone
Coal-Fired
Power Plant
290 + 20
0.041 + 0.005
640 ± 80
250 + 20
35 + 8
190 ± 80
1290 + 60
490 ± 190
170 + 60
ND
ND
0.71 + 0.04
ND
(a)
9.2 ± 0.9
ND
120 + 10
180 ± 10
80 ± 26
23 ± 2
Oil-Fired Power Plants
N
9
3
3
3
3
1
9
9
2


2


2

3
2
3
3
Eddystone
980 + 320
1.3 ± 0.3
33 ± 6
26 + 9
90 ± 60
ND
160 ± 50
140 ± 180
930 ± 210
ND
ND
ND
320 + 230
370 + 410
ND
1960 + 100
130 ± 30
89 + 23
28 + 5
3.7 ± 0.7
N
9
3
1
3
9

9
9
3



9
3

3
3
3
2
3
Schuylkill
1100 + 500
0.78 ± 0.30
50 + 16
23 + 7
45 ± 17
ND
280 + 70
100 ± 120
1500 ± 300
ND
ND
ND
200 ± 80
1020 ± 90
ND
2000 + 500
450 + 30
360 ± 20
230 + 20
20.5 + 1.5
N
11
3
3
3
11

11
11
3



11
3

3
3
3
3
3
Secondary
Al Plant
450 ± 200
0.079 ± 0.006
15 + 6
66 + 3
630 ± 70
97 ± 38
ND
ND
ND
ND
ND
ND
550 ± 540
6100 + 300
ND
ND
19 + 2
ND
ND
ND
Fluid Cat.
N Cracker
2 14 ± 8
1 0.0026 + 0.0007
1 ND
1 15 + 1
2 5.6 + 1.8
1 ND
36 ± 6
130 + 50
ND
ND
ND
ND
2 ND
1 7.7 ± 1.5
ND
290 + 90
1 3300 + 500
2700 + 400
1800 + 250
170 ± 20
N
9
3

3
9

9
2





3

2
3
3
3
3
Municipal
Incinerator N
1300 ± 500 3
10.4 + 0.5 3
64 + 34 3
42 ± 16 3
2300 + 800 10
230 + 50 2
87 ± 14 10
ND
240 ± 130 10
71 ± 15 3
1200 ± 700 3
4.9 + 1.4 3
6700 ± 1900 10
1300 + 1000 3
5.9 + 3.0 3
ND
1.1 + 0.5 1
ND
ND
ND

-------
                     TABLE 5-3 (cont'd).  COMPOSITION OF FINE PARTICLES RELEASED
                     BY VARIOUS STATIONARY SOURCES IN THE PHILADELPHIA AREA
Species
(Units)
Eu (ppm)
Gd (ppm)
Tb (ppm)
Yb (ppm)
Lu (ppm)
Hf (ppm)
Ta (ppm)
W (ppm)
Au (ppm)
y. Pb (%)
^^
•~j
Th (ppm)
% mass
Eddystone
Coal-Fired
Power Plant
5.1 + 0.5
ND
3.3 + 0.3
10.3 + 0.5
ND
5.8 + 0.8
ND
20 + 8
ND
0.041 + 0.004

24 + 2
24 + 2
Oil-Fired Power Plants
N
3

3
1

3

1

9

3
6
Eddystone
ND
ND
ND
ND
ND
0.39 ± 0.07
ND
60 ± 5
0.054 + 0.017
1.8 + 0.6

1.9 + 0.5
93.5 + 2.5
N





1

2
2
9

2
6
Schuylkill N
0.65 + 0.23 3
ND
0.90 ± 0.29 3
ND
ND
ND
ND
ND
ND
1.0 + 0.2 11

ND
96 + 2 6
Secondary
Al Plant
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.081 + 0.014

ND
81 + 10
Fluid Cat.
N Cracker
4.9 ± 0.7
71 + 10
8.9 + 1.3
3.7 + 0.4
0.59 ± 0.17
0.99 + 0.08
0.56 ± 0.10
ND
ND
2 0.0091 ± 0.0021

6.2 + 0.7
2 97 + 2
N
3
3
3
3
3
3
3


9

3
7
Municipal
Incinerator
ND
ND
ND
ND
ND
ND
ND
ND
0.56 ± 0.27
5.8 + 1.2

ND
89 ± 2
N








3
10


7
N = Number of samples.
ND = Not detected.
The "% mass" entries give the average percentage of the total emitted mass found in the fine fraction.
(a) Omitted because of sample contamination.

Source: Adapted from Olmez et al. (1988).

-------
that their data could have much wider applicability to receptor model studies in other areas
with some of the same source types.  The high temperature of combustion in power plants
results in the almost complete oxidation of the carbon in the fuel to CO^ and very small
amounts of CO. A number of trace elements are greatly enriched over crustal abundances
(in different fuels), such as Se in coal and V and Ni in oil.  In fact, the higher V content of
the fuel oil than in coal could help account for the  higher sulfate seen in the profiles from the
oil-fired power plant compared to the coal-fired power plant since V is known to catalyze the
oxidation of reduced  sulfur species. Although Table 5-3 only gives values of the fine particle
composition, measurements of coarse particle composition were also reported by Olmez et al.
(1988) which were qualitatively similar.
     The composition of the organic carbon produced by stationary sources has not been
well characterized. Information is available  for the composition of polycyclic aromatic
hydrocarbons, or PAH's (Daisey et al., 1986), while data for the composition of other
classes of organic compounds are sparse.  In addition, the phase distribution of a number of
PAH's and other organic compounds will depend strongly on ambient atmospheric
conditions.   It may be expected that the composition of emissions in systems operating at low
temperatures (e.g., residential coal combustion) will reflect  that of the unburaed fuel.
     Emissions from  stationary sources are determined mainly by stack sampling with a
variety of techniques.  All these techniques rely on measurements of stack flow rates and
concentrations of pollutants to determine emissions. Method 5 (Federal Register,  1977)
consists of  a sampling train which is commonly used to measure emissions of various trace
metals.  The method is cumbersome and is limited in the number of species that can be
sampled. Based on the realization that direct sampling of hot undiluted stack gases may  not
yield an accurate representation of the chemical composition and size distribution of particles
leaving the stack, dilution sampling has also  been used  (e.g., Olmez et al., 1988).
Condensation, coagulation, and chemical  reactions  occur as stack gases are cooled and
diluted. In dilution sampling, stack gases are diluted with filtered ambient air in an attempt
to partially simulate processes occurring in upper portions of the stack and in the plume
leaving the stack.  Another advantage in the  use of dilution systems is that the same sampling
substrates and analytical techniques used in ambient sampling can be used.  As a result, a
wider variety of constituents can be sampled than in conventional direct sampling techniques
                                          5-18

-------
and biases resulting from the use of separate sampling systems in source apportionment
studies are eliminated.  Remote monitoring methods (e.g., differential optical absorption
spectroscopy) have also been used to determine emissions of species such as Hg.  The size
distribution of particles emitted by burning crude oil is shown in Figure 5-2.  As can be
seen, almost all of the mass is  in the fine fraction.
      Apart from emissions in the combustion of fossil fuels, trace elements are emitted as
the result of various industrial processes such as steel and iron manufacturing and
non-ferrous metal production (e.g., for Pb, Cu, Ni, Zn, and Cd) as may be expected,
emissions factors for various trace elements are highly source-specific (Nriagu and Pacyna,
1988).  Inspection of Table 5-3 reveals that the emissions from the catalytic cracker and the
oil-fired power plant are greatly enriched in rare-earth elements such as La compared to
other sources.
      Emissions from municipal waste incinerators are dominated by Cl arising mainly from
the combustion of plastics and metals that form volatile chlorides.  The metals can originate
from cans or other metallic objects and some metals such as Zn and Cd are also additives in
plastics or rubber. Many elements such as S, Cl, Zn, Br, Ag, Cd, Sn, In,  and Sb are
enormously enriched compared to their crustal abundances.  A comparison  of the trace
elemental composition  of incinerator emissions in Philadelphia, PA (shown  in Table 5-3)  with
the composition of incinerator emissions in Washington D.C., and Chicago, IL, (Olmez et
al., 1988) shows agreement for most constituents to better than a factor of two.  High levels
of Hg associated with emissions from medical waste incinerators from discarded
thermometers, mercurials, mercury batteries, etc., have been declining because of reductions
in the use of Hg for medical purposes (Walker and Cooper,  1992). A  sizable fraction of the
Hg may  be particulate  Hg(II) as opposed to  gas phase Hg°.

5.2.3   Mobile Sources
      Particulate matter from motor vehicles originates from  tailpipe exhaust and from
friction acting on individual components such as tires and brakes.   Both diesel  and gasoline
fueled vehicles are sources of primary and secondary particulate matter.  The rates of
emission and the composition of particles emitted by motor vehicles have been measured
using dynamometers with samples collected directly in the exhaust of individual vehicles
                                          5-19

-------
(e.g., Lang et al., 1982) or at the vents of inspection facilities (e.g., Watson et al.,  1994a);
or in tunnels and along open roadways (e.g., Pierson and Brachaczek, 1983; Szkarlat and
Japar, 1983). Each approach has its merits and limitations and each approach is best used to
augment the other.  The principal components emitted by diesel and gasoline fueled vehicles
are organic carbon (OC) and elemental carbon (EC) as shown in Table 5-4.  As can be seen,
the variability among entries for an individual fuel type is large and overlaps that found
between different fuel types.  On average, the abundance of elemental carbon is larger than
that of organic carbon in the exhaust of diesel vehicles, while organic carbon is the  dominant
species  in the exhaust of gasoline fueled vehicles.  There appears to be a tendency for
emissions  of elemental carbon to increase relative to emissions  of organic carbon for gasoline
fueled vehicles as simulated driving  conditions are changed from a steady 55 km /hr to those
in the Federal Test Procedures (FTP's).  Also shown are the results of sampling from mixed
vehicle  types along roadsides and in tunnels.
     The results shown in Table 5-4 were obtained during the late 1980's, and, so,  the
results may not be entirely representative of current vehicles.  Examples of data for the trace
element composition of motor vehicle emissions obtained in Phoenix, AZ are shown in
Table 5-5.  SO2 emissions are also shown in relation to the mass of fine particles emitted.
As can be seen, small quantities of soluble ions  such as SO4= and NH4+ are emitted.  The
ammonium may be emitted as the result of an improperly functioning catalytic converter, or
may simply be the result of contamination during sample handling and analysis.  Four
fractions are given for the organic carbon fraction and  three for elemental carbon.  These
refer to  abundances measured at different temperatures in a thermographic analysis.
Temperatures for OC1, OC2, OC3,  and OC4 are 120 °C, 250 °C,  450 °C, and 550 °C,
respectively; and, for  EC1, EC2, EC3, they are 550 °C, 700 °C, and 800  °C, respectively,
in He/2%  O2. The  abundances of trace  elements are all quite low,  with most being less than
1 %. It  is not clear what the source  of the small amount of Pb  seen in the auto exhaust
profile is.  It is extremely difficult to find suitable tracers for automotive exhaust since Pb
has been removed from gasoline.  However, it should also be remembered that restrictions in
the use  of leaded gasoline have  resulted  in a dramatic lowering of ambient Pb levels. Huang
et al. (1994) attempted to identify marker elements in motor vehicle emissions, based on
sampling the exhaust of 49 automobiles.  They proposed that the combination of Zn, Br, and
                                         5-20

-------
        TABLE 5-4.  FRACTIONAL ORGANIC AND ELEMENTAL CARBON
                 ABUNDANCES IN MOTOR VEHICLE EMISSIONS
Fuel Type
Diesel
Denver, COa
Los Angeles, CAa
Bakersfield, CAb
Phoenix, AZb
Unleaded gasoline
Denver, COa
Los Angeles, CAC
Los Angeles, CAa
Phoenix, AZb
Leaded gasoline
Denver, COa
Los Angeles, CAC
Los Angeles, CAa
Mixed (tunnel and roadside)
Denver, CO
Los Angeles, CAd
Phoenix, AZ
Organic

23
36
49
40

76
93
49
30

67
52
31

50
38
39

±
±
±
±

±
±
±
±

±
±
±

±
±
±
Carbon

8%
3%
13%
7%

29%
52%
10%
12%

23%
4%
20%

24%
6%
19%
Elemental Carbon

74
52
43
33

18
5

±
±
±
+

±
±

21%
5%
8%
8%

11%
7%
39 ± %
14

16
13
15

28
38
36
±

±
±
±

±
±
±
8%

7%
1%
2%

19%
5%
11%
Ne

3
2
3
8

8
11
11
9

3
3
3


3

Sources

1,2
3, 4, 5,
7
8

1,2
3, 4, 5,
3, 4, 5,
8

1,2
3, 4, 5,
3, 4, 5,

1,2
3
8


6




6
6



6
6




Sources:  (1) Watson et al. (1990a), (2) Watson et al. (1990b), (3) Cooper et al. (1987), (4) NBA (1990a),
        (5) NEA (1990b), (6) NBA (1990c), and (7) Houck et al. (1989), cited in (8) Watson et al. (1994a).

Notes: (a) Modified Federal Test Procedures followed in dynamometer tests; (b) Roof monitoring at
      inspection station; (c) 55 km/hr steady speed in dynamometer tests; (d) Rt. 1 tunnel at LA airport,
      (e) N = Number of samples.
Sb could be used for this purpose.  However, the relative abundances of these species in

automobile exhaust were shown to be highly variable, implying that other  sources of these
elements may limit their usefulness as automotive tracers in some locations.  To minimize
                                         5-21

-------
TABLE 5-5. PHOENIX PM2 5 MOTOR VEHICLE EMISSIONS PROFILES (% MASS)
Chemical Species
NO3-
SO42'
NH4+
OC
OC1
OC2
OC3
OC4
EC
EC1
EC2
EC3
Al
Si
P
S
Cl
K
Ca
Ti
Cr
Mn
Fe
Cu
Zn
Sb
Ba
La
Pb
S02a
Auto
3.9 ± 2.9
2.3 ± 1.3
1.7 ± 1.0
30.1 ± 12.3
11.3 ± 3.5
9.2 ± 6.8
4.6 ± 2.2
3.5 ± 1.5
13.5 ± 8.0
11.7 ± 7.2
3.1 ± 1.6
0.15 ± 0.30
0.41 ± 0.20
1.64 ± 0.88
0.11 ± 0.07
1.01 ± 0.48
0.34 ± 0.32
0.25 ± 0.14
0.71 ± 0.41
0.07 ± 0.13
0.02 ± 0.01
0.10 ± 0.04
0.68 ± 0.42
0.07 ± 0.06
0.27 ± 0.22
0.02 ± 0.13
0.06 ± 0.40
0.15 ± 0.51
0.16 ± 0.07
32.8 ± 13.9
Diesel
0.31 ± 0.40
2.4 ± 1.0
0.87 ±0.13
40.1 ± 6.6
21.0 ± 6.3
9.1 ± 1.9
5.9 ± 1.3
4.0 ± 1.5
32.9 ± 8.0
4.4 ± 1.3
27.9 ± 5.6
0.69 ± 0.82
0.17 ±0.12
0.46 ± 0.18
0.06 ± 0.06
1.24 ±0.28
0.03 ± 0.06
0.04 ± 0.03
0.16 ± 0.06
0.00 ±0.15
0.00 ± 0.01
0.01 ± 0.01
0.16 ± 0.07
0.01 ± 0.01
0.07 ± 0.02
0.01 ± 0.14
0.14 ± 0.47
0.18 ± 0.59
0.01 ± 0.03
66.9 ± 24.0
Source:  Watson et al. (1994a).

Note: Elemental abundances <0.01% (V, Co, Ni, Ga, As, Se, Br, Rb, Sr. Y, Zr, Mo, Pd, Ag, Cd, In, Sn,
     Au, Hg, Tl, U) in XRF analyses excluded; OC = organic carbon; EC = elemental carbon.
"Relative to total PM2 5.
                                        5-22

-------
errors arising from the loss of Br from filters, samples should be analyzed as soon as
possible after collection (O'Connor et al., 1977).
      The chemical mechanisms responsible for the formation of carbonaceous particles in
diesel engines are  not well established but are thought to involve the intermediate formation
of poly cyclic aromatic hydrocarbons, or PAH's (U.S. Environmental Protection Agency,
1993).  Elemental  carbon particles may be formed by the polymerization of gaseous
intermediates adsorbed on a core of refractory material.  The particles are in the form of
chain or cluster agglomerates at temperatures above 500  °C.  At temperatures below 500 °C,
high molecular weight organic compounds condense on the carbon chain agglomerates.
Roughly 10-40% of paniculate emissions from diesels are extractable into organic solvents
(National  Research Council, 1982). In a typical profile,  50% of the extract is composed  of
aliphatic hydrocarbons with  14-35  C atoms and alkyl substituted benzenes; 4% are PAH's
and heterocycles; and about  6% are PAH oxidation products including a small fraction of
nitro-PAH's.  The highly polar fraction of the organic emissions has not been as well
characterized (Johnson,  1988).  Inorganic compounds such as sulfuric acid are also produced
in diesel engines (Truex et al., 1980).
      Particulate matter is also formed in internal combustion engines as the result of the
incomplete combustion of gasoline with contributions from engine oil.  The particles consist
essentially of a solid carbon core with a coating of organic compounds, sulfate,  and trace
elements.  The composition of PAH's, oxy-PAH's and their alkyl homologues in tailpipe
emissions from gasoline fueled vehicles is similar  to that produced  in diesel engines
(Behymer and Hites, 1984).  Particles produced by gasoline fueled vehicles range from
0.01 to 0.1 pirn in  diameter with a  peak at around 0.02 /xm, while the majority of particles in
diesel exhaust range from 0.1 to 1.0 /xm with a peak at around 0.15 pim (U.S. Environmental
Protection Agency, 1993).
      The concentrations of particulate matter and total hydrocarbons in the exhaust of
gasoline fueled vehicles  were found to be roughly correlated with each other by Hammerle
et al.  (1992).  Emission factors for particulate matter in the exhaust of gasoline fueled
vehicles range from 0.011 g/km for light duty vehicles to 0.12 g/km for heavy duty gasoline
vehicles, and from 0.23  g/km in the exhaust of diesel passenger vehicles to  1.20 g/km for
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heavy duty diesel vehicles (Radwan,  1995). These values are based on characteristics of the
motor vehicle fleet in 1990.
     As mentioned before, the composition of automotive emissions is sampled using
individual vehicles on chassis dynamometers or by collecting aerosol samples along roadsides
or in tunnels.  The control over operating characteristics by using dynamometers allows the
development of models which can predict emissions on the basis of variables such as vehicle
make and age and driving cycle.  The representativeness of dynamometer test data can be
questioned if volunteered vehicles, as opposed to randomly selected  vehicles, are sampled.
In addition, measuring emissions from individual vehicles  is  also costly and the sample
numbers are usually small, as reflected in the small  number  of samples shown in Table 5-4.
Moreover, a number of driving practices are not reflected in the Federal Test Procedures
leading  to significant underestimates  of emissions of CO and hydrocarbons (Calvert et al.,
1993).   It is still not clear what effects superemitters and off-cycle driving practices have on
particle  emissions rates.  If the relation between paniculate matter and hydrocarbon
emissions discussed above is  representative of the vehicle fleet,  the effects could be
substantial.  Hansen and Rosen (1990) measured the ratio  of light-absorbing carbon to CO2
in the exhausts of 60 gasoline fueled vehicles.  They found a factor  of 250 difference
between the highest and lowest ratios measured. Larger scale studies designed to assess the
variability of paniculate emissions from motor vehicles are lacking.
     Roadside and tunnel measurements  sample large numbers of vehicles of different types
and have demonstrated their potential for validating  the predictions of emissions models.
However, the extent to which traffic conditions in the tunnel reflect those in the situation
under study must be defined for the results to be considered  representative.  The same
considerations can be extended to dynamometer studies and to open-road studies along road
segments.  Results from some tunnel studies are of  limited usefulness because  they have been
obtained under highway driving conditions which may not be representative of the conditions
found in most urban and suburban areas. Additional uncertainties result from  resuspended
dust in using tunnel and roadside  studies to characterize motor vehicle emissions.  However,
methods are available for estimating  contributions from tire wear (Pierson and Brachaczek,
1974, 1976). Remote measurements of elemental carbon in  the exhaust plumes of individual
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vehicles (Hansen and Rosen, 1990) can overcome many of these difficulties, but the method
cannot yet be applied to aerosol constituents other than elemental carbon.

5.2.4   Biomass Burning
      In addition to fossil fuels, biomass in the form of wood may be burned in forest fires or
as fuel for heating or cooking.  At first glance these two broad categories might seem to
serve to distinguish between natural and anthropogenic sources.  However, many forest fires
result from human intervention, either deliberately through prescribed burning in forest
management or accidentally through the improper disposal of flammable material or fugitive
sparks (e.g., Andreae, 1991).  On the other hand, human intervention also suppresses
lightning triggered fires.  Not enough data is available to assess  the effects of humans on
forest fires, except for land clearing for agriculture.  In contrast to the mobile and stationary
sources discussed earlier, emissions from biomass burning in woodstoves and forest fires are
strongly seasonal and can be highly episodic within their peak emissions seasons.  Burning
fuelwood is confined mainly to  the winter months and  is acknowledged  to be a major source
of ambient air particulate matter in the  northwestern United States during the heating season.
Forest fires mainly occur during the driest seasons of the year in different areas of the
country and are especially prevalent during prolonged droughts.
      An example of the composition of fine particles (PM2 5) produced by woodstoves is
shown in Figure 5-4.  These data  were obtained in Denver during the winter of 1987-1988
(Watson and Chow, 1994).  As was the case for motor vehicle emissions, organic and
elemental carbon are the major  components of particulate emissions from wood burning.
It should be remembered that the relative amounts shown for organic carbon and elemental
carbon will vary with the type of stove, the stage of combustion and the type and condition
of the fuelwood.  Potassium (K) is by far the major trace element found in woodstove
emissions (Watson and Chow, 1994), making it suitable for use as a tracer for vegetation
burning (Lewis et al., 1988).  Fine particles are dominant in studies  of wood burning
emissions.  For instance, the mass median diameter of wood-smoke particles was  found to be
about 0.17  nm in a study of the emissions from burning hardwood, softwood and synthetic
logs (Dasch,  1982).
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Figure 5-4.   Chemical abundances for PM2>5 emissions from wood burning in Denver,
             CO.  Solid bars represent fractional abundances, and the error bars
             represent variability in species abundances.  Error bars represent detection
             limits when there are no solid bars.
Source:  Watson and Chow (1994).
     Measurements of aerosol composition, size distributions, and aerosol emissions factors
have been made in biomass burning plumes either on towers (Susott et al.,  1991) or aloft on
fixed wing aircraft (e.g., Radke et al., 1991) or on helicopters (e.g., Cofer et al., 1988).
As was found for woodstove emissions, the composition of biomass burning emissions is
strongly dependent on the stage of combustion (i.e., flaming, smoldering, or mixed), and the
type of vegetation (e.g., forest, grassland, scrub).  Over 90% of the dry mass in paniculate
biomass burning  emissions is composed of organic carbon  (Mazurek et al.,  1991).  Ratios of
organic carbon to elemental carbon are highly variable ranging  from 10:1 to 95:1, with the
highest ratio found for smoldering conditions and the lowest for flaming conditions.
Ambient particle concentrations were  about two mg/m3 during the measurement period.
Available measurements suggest that K is by far the most abundant trace element in biomass
burning plumes.  Although there is considerable inter-sample variation,  results from tower
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samples also suggest that S, Cl, and K are highest during flaming stages, while Al, Si, Ca,
and Fe tend to increase during the smoldering phase (Susott et al., 1991).  Emissions factors
for total paniculate emissions increase by factors of two to four in going from flaming to
smoldering stages in the individual fires studied by Susott et al. (1991).  These measurements
were made when ambient particle concentrations ranged from 15 to 40 mg/m3.
     Particles in biomass burning plumes from a number of different fires were found to
have three distinguishable size modes, namely a nucleation mode, an accumulation mode,
and a coarse mode (Radke et  al., 1991).   Based on-an average of 81 samples, approximately
70% of the mass was found in particles < 3.5 pm in aerodynamic diameter.  The fine
particle composition was  found to be dominated by tarlike,  condensed hydrocarbons and  the
particles were usually spherical in shape.  Additional information for the size  distribution of
particles produced  by vegetation burning was shown in Figure 5-2.

5.2.5  Sea-Salt Production and Other Natural Sources of Aerosol
     Although sea-salt aerosol production is confined to salt water bodies, it is included  here
because many marine aerosols can exert a strong influence  on the composition of the ambient
aerosol in coastal areas.   In some respects, the production of sea-salt aerosols is like that of
windblown dust  in that both are produced by wind agitation of the surface.   The difference
between the two categories arises because sea-salt particles  are produced from the bursting  of
air bubbles rising to the sea surface.  Air bubbles are formed by the entrainment of air into
the water by breaking waves.   The surface energy  of a collapsing bubble is converted to
kinetic energy in the form of  a jet of water which can eject drops above  the sea surface.  The
mean diameter of the jet drops is about 15% of the bubble diameter (Wu, 1979).  Bubbles in
breaking waves range in size  from a few /xm to several mm in diameter.  Field
measurements by Johnson and Cooke (1979) of bubble  size spectra show maxima in
diameters  at around 100 ^m,  with the bubble size distribution varying as (d/dg)"5 with
do =  100  /xm.
     Since the water jet receives its  water from the surface layer, which is enriched in
organic compounds, the aerosol drops are composed  of this organic material in addition to
sea salt (about 3.5% by weight in sea water).  Na+ (30.7%),C1" (55.0%), SO4= (7.7%),
Mg2+ (3.6%), Ca2+  (1.2%),  K+ (1.1%), HC(V (0.4%), and Br (0.2%) are  the major ionic
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species by mass in sea water (Wilson, 1975).  The composition of the marine aerosol also
reflects the occurrence of displacement reactions which enrich sea-salt particles in SO4" and
NO3" while depleting them of Cl" and Br".  As the drops travel upward above the water
surface, they encounter lower relative humidities and lose water until they come into
equilibrium with their environment.  The resulting marine aerosol size distribution reflects
the processes of coagulation, coalescence, and sedimentation.
      Seasalt is concentrated in the coarse size mode with a mass  median diameter of about
7 jwm for  samples  collected in Florida, the Canary Islands and Barbados (Savoie and
Prospero, 1982).  The size distribution of sulfate is distinctly bimodal.  Sulfate in the coarse
mode is derived from sea water but sulfate in the submicron aerosol arises from the oxidation
of dimethyl sulfide (CH3SCH3) or DMS.  DMS is produced during the decomposition of
marine micro-organisms.  DMS is oxidized to MSA (methane sulfonic acid) a large fraction
of which  is oxidized to sulfate (e.g., Hertel et al., 1994).
      Apart from sea spray,  other natural sources of particles include the suspension of
organic debris and volcanism.  Particles are released from plants  in the  form of seeds,
pollen, spores, leaf waxes and resins, ranging in size from 1 to 250 /*m (Warneck, 1988).
Fungal spores and animal debris such as insect fragments are also to be found in ambient
aerosol samples in this size range.  While material from all the foregoing categories may
exist as individual particles, bacteria are usually found attached to other dust particles
.(Warneck, 1988).  Smaller bioaerosol particles include viruses, individual bacteria, protozoa,
and algae (Matthias-Maser and Jaenicke, 1994). In  addition to natural sources,  other sources
of bioaerosol include industry (e.g., textile mills), agriculture, and municipal waste disposal
(Spendlove, 1974).
      Trace metals are emitted to the atmosphere from a variety of sources such as sea spray,
wind blown dust,  volcanoes, wild fires and biotic  sources (Nriagu,  1989).  Biologically
mediated  volatilization processes (e.g., biomethylation) are estimated to account for 30-50%
of the worldwide total Hg, As, and Se emitted annually, whereas other metals are derived
principally from pollens,  spores, waxes, plant fragments, fungi, and algae.  It is not clear,
however, how much of the biomethylated species are remobilized from anthropogenic inputs.
Median ratios of the natural contribution to globally averaged total sources for trace metals
are estimated to be 0.39 (As), 0.15 (Cd), 0.59 (Cr), 0.44 (Cu), 0.41 (Hg), 0.35 (Ni),
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0.04 (Pb), 0.41 (Sb), 0.58 (Se), 0.25 (V), and 0.34 (Zn), suggesting a not insignificant
natural source for many trace elements.  It should be noted though that these estimates are
based on emissions estimates which have uncertainty ranges of an order of magnitude.
5.3   SOURCES OF SECONDARY PARTICIPATE MATTER (SULFUR
      DIOXIDE, NITROGEN OXIDES, AND ORGANIC CARBON)
     Secondary paniculate matter is an important contributor to suspended particle mass.
Sulfate is formed by the oxidation of SO2, nitrate by the oxidation of NO2, and aerosol
organic carbon species by the oxidation of a large number of precursors.  Thus, the
formation rate of a substantial fraction of aerosol mass is given by a complex function of
both emission rates of precursor gases and the rates of photochemical processes in the
atmosphere.  In order to use precursor emissions estimates effectively, however, it is
necessary to understand the nature of the processes that cause them to convert to paniculate
matter.  Mechanisms for the oxidation of SO2 to SO4=, and NO2 to NO3", have been
discussed in Chapter 3.  Both species are oxidized during daytime in the gas phase by
hydroxyl (OH) radicals. At night, NOX is also oxidized to nitric acid by a sequence of
reactions initiated by  O3, that include nitrate radicals (NO3) and dinitrogenpentoxide (N2O5).
SO2  is also oxidized by heterogeneous reactions occurring in films of atmospheric particles
and in cloud and fog droplets.  Data for primary and secondary  components of aerosol mass
at a number of locations across  the United States can be found in Chapter 6.
     While the mechanisms and pathways for forming inorganic secondary paniculate matter
are fairly well known, those for organic secondary aerosol are not well understood.
Numerous precursors participate in these conversions, and the rates at which these convert
from gas to particles are highly dependent on the concentrations of other pollutants and
meteorological conditions.  Pandis et al. (1992) identified three mechanisms for secondary
organic PM formation:  (1) condensation of oxidized end-products of photochemical reactions
(e.g., ketones, aldehydes, organic acids, and hydroperoxides); (2) adsorption of organic
gases onto existing  solid particles (e.g., polycyclic aromatic hydrocarbons); and
(3) dissolution of soluble gases which can undergo reactions in particles (e.g.,  aldehydes).
The first and third mechanisms  are expected to be of major importance during the
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summertime when photochemistry is at its peak.  The second pathway can be driven by
diurnal and seasonal temperature and humidity variations at any time of the  year.  Turpin and
Huntzicker (1991) and Turpin et al.  (1991) provided strong evidence that secondary PM
formation occurs during periods of photochemical ozone formation in Los Angeles.
     Haagen-Smit (1952) first demonstrated that hydrocarbons irradiated  in  the presence of
NOX produce light scattering aerosols.  Results of later studies summarized by Altshuller and
Bufalini (1965) indicated that aerosols  are produced by the irradiation of mixtures of NOX
and numerous six-carbon and higher molecular weight acyclic and cyclic olefins and aromatic
hydrocarbons.  Cyclic olefins were shown to be more effective in aerosol formation than
acyclic olefins of similar molecular weight by Stevenson et al.  (1965). The possibility that
aerosols might be formed from biogenic hydrocarbon emissions was investigated by Went
(1960) and Rasmussen and Went (1965).  Analyses of the aerosol produced  from the
photooxidation of a-pinene and NOX mixtures indicated the presence of pinonic acid and
norpinonic acid (Wilson et al.,  1972).
     Numerous smog chamber studies of the formation of secondary organic aerosol from
the photooxidation of terpene precursors have been performed since these earlier studies.
A study of the reaction of a-pinene and /3-pinene  with O3 by Hatakeyama et al.  (1989)
obtained aerosol carbon yields (mass of aerosol carbon produced per mass of C  reacted), or
ACY's, of 18% and  14%, respectively, for HC levels ranging from 10-120  ppb C.  In this
study, pinonaldehyde, pinenic acid, nor-pinonaldehyde, and nor-pinonic acid accounted for
less than 10% of the aerosol yield from the reaction  of a-pinene.  Hatakeyama et al. (1991)
subsequently obtained ACY's of 56  ± 4% and 79 ±  8% following the reaction of a-pinene
and /3-pinene, respectively, for initial HC levels of 820-3170 ppb C and NOX levels of
210-2550 ppb.  Pandis et al. (1991)  obtained ACY's ranging from 0.1 to 8% for the
oxidation of /3-pinene for HC levels  ranging from 20-250 ppb C and NOX levels ranging from
39 to about 700 ppb. Zhang et al. (1992) obtained ACY's ranging from 0 to 5.3% for
HC levels ranging from 37-582 ppb  C and NOX levels ranging from 31-380 ppb for the
oxidation of a-pinene.  Results from the above studies showed that aerosol yields  strongly
depend on the initial concentration of terpenes and the ratio of hydrocarbons (HC) to NOX in
the reaction chamber. However, Hooker et al. (1985) did not  find a significant dependence
of aerosol yield on initial HC abundance for HC  levels ranging from 3.1-50 ppb C.  Their
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approach differed from that used in all of the above studies because they used 14C-a-pinene.
Of the 14C-a-pinene which reacted, 38-68% was found in aerosol products, 6-20% was found
in gas phase products, and 11-29% was lost to the walls of their reaction chamber.
     After reaction of the a-pinene with OH radicals or O3, the radical product will add
O2 to form a peroxy radical.  Zhang et al. (1992) proposed that the peroxy radical may react
with NO initiating a series of reactions forming pinonaldehyde, which may condense
depending on its concentration, or the peroxy radical may react with HO2 or other free
radicals to form aerosol products.  The inhibition of the  second pathway by the addition of
NO was proposed by Zhang et al. (1992) to explain the decrease of aerosol yield with added
NO.  They also suggested that the dependence  of aerosol yield on initial HC concentration
arises because the concentration of pinonaldehyde can more easily exceed its saturation value
and the rate of formation of aerosol products in the other pathway will also increase.
     Pandis et  al. (1991) found no aerosol products formed in the photooxidation of
isoprene, although they and Zhang et al. (1992) found that the addition of isoprene to
reaction mixtures increased the reactivity of the systems  studied. Based on their
experimental results and the high ratio of terpene to NOX concentration ratios found in rural
and remote areas, Zhang et al. (1992) suggested that the upper limits for aerosol yields they
obtained should be used in estimating the aerosol yields from the oxidation of biogenic
hydrocarbons.
     The aerosol forming potentials of a wide variety of individual anthropogenic and
biogenic hydrocarbons were compiled by Pandis et  al. (1992) based mainly on estimates
made by Grosjean and Seinfeld (1989) and data from Pandis et al. (1991) for j8-pinene and
Izumi and Fukuyama (1990) for aromatic HC's.  The estimates given by Pandis et al. (1992)
were converted to aerosol carbon yields below.  Examples of compounds with zero ACY's
are all CrC7 alkanes, all C2-C6 acyclic alkenes, benzene, and aldehydes; examples of
compounds with lowest ACY's (< 2.0%) are C8-C10 alkanes, C6-C8 cycloalkanes, C7-C9
acyclic alkenes, C5 cyclic alkenes and p-xylene; examples of compounds with intermediate
values (2.0%-4.0%) are Cn-C14 alkanes,  C9-C10 cycloalkanes, alkyl benzenes other than
p-xylene, C10-C13 alkenes and C6+ cycloalkenes; and examples of compounds with high
values (>4.0%) are C15+ alkanes, Cn+ cycloalkanes, C14+ cyclic alkenes and
monoterpenes.
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     Studies of the production of secondary OC in ambient air have focussed on the Los
Angeles Basin.  Based on aerosol yields shown above, Pandis et al. (1991) suggested that
about 1-4 tons day"1 of secondary OC in the Los Angeles basin is formed from the oxidation
of monoterpenes which are emitted at the rate of 10-40 tons day"1.  This estimate may be
compared to the secondary OC production rate of 7.5 tons day"1 estimated to result from the
oxidation of anthropogenic hydrocarbons which are emitted at the rate of 1200 tons day"1
(Grosjean and Seinfeld, 1989). The overall yield of secondary OC from anthropogenic
sources in this example is about 0.6%.  Pandis et al.  (1991) also proposed that most of the
secondary  OC in highly vegetated urban areas such as Atlanta is produced by the oxidation
of monoterpenes.
     As part of the Southern California Air Quality Study (SCAQS), Turpin and Huntzicker
(1991) measured elemental and organic carbon at Claremont, CA in the summer of 1987
with an in situ carbon analyzer with 2 hour time resolution.  During an air pollution episode
centered on August 28, 1987, airmass trajectories arriving at Claremont were directed
eastward (i.e., inland from the coast), allowing the entrainment of substantial hydrocarbon
precursors during transit.  Peak OC concentrations (23 jig/m3) and highest OC to EC ratios
(4.6 ± 0.4) occurred together at Claremont from about 1500 to 1700 PDT.  In addition,
correlations between EC and OC were low throughout the day (R2 =0.38).  Turpin and
Huntzicker (1991) also measured OC and EC concentrations at Long Beach in November of
1987 with the same instrumentation. On the basis of these data, they suggested that OC to
EC ratios of 2.2 + 0.7 are characteristic of primary OC in the Los Angeles area.
     Pandis et al. (1992) constructed a Lagrangian trajectory model to simulate the chemical
formation, transport and deposition of secondary OC  during the August episode.  They used
estimates of aerosol yields from HC oxidation compiled by Grosjean and Seinfeld (1989),
updated as necessary  (e.g., Pandis et al., 1991) along with estimates of daily emissions, to
predict that 28% of the peak secondary OC on Aug. 28 at Claremont resulted from the
oxidation of toluene,  38% from other aromatic HC's, 9% from biogenic HCs, 21% from
alkanes and cycloalkanes, and the remaining 4% from other species.  Values were somewhat
different on a daily average basis (19% from toluene, 46% from other aromatic HC's, 16%
from biogenic HC's,  15% from alkanes, and 4% from alkenes).  There was reasonable
agreement with the data of Turpin and Huntzicker (1991) throughout most of the day, but
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calculated peak secondary OC levels (  ~- 5 jig/m3) were about half those inferred by Turpin
and Huntzicker (1991).  A combination of factors could have contributed to this
underprediction including errors in emissions, deposition rates, chemical reaction rate data
and aerosol yields.  In general, the calculated secondary OC represented 15-27% of the daily
average total OC at inland locations (Burbank, Claremont, Azusa, and Rubidoux) on August
28, 1987 and 5-19% of the average total OC at coastal sites.
     Attempts were made during SCAQS to determine the composition of the organic carbon
fraction of the ambient aerosol.  Organic nitrates were measured on size segregated samples
collected on zinc selenide disks which were later analyzed by transmission FTIR by Mylonas
et al. (1991).  Concentrations of organic nitrates in the particle phase ranged from 0.8 to
4.0 /ig/m3, with maximum mass loadings in the 0.05 to 0.075 pirn and 0.12 to 0.26 pirn size
ranges. Concurrently, Pickle et al.  (1990) used infrared spectroscopy to measure the total
abundance  of compounds containing carbonyl groups and aliphatic compounds.  Maximum
absorption  at wavelengths characteristic of carbonyl groups was observed for particles in the
0.12 to 0.26 jum and 0.5 to 1.0 pirn size ranges.  These results suggest that carbonyl
compounds are largely of secondary origin and that IR absorption by aliphatic compounds in
particles smaller than 0.12 /xm was correlated directly with automotive emissions.
     Kao and Friedlander (1995) examined the statistical properties of a number of PM
components in the South Coast Air Basin.  They found that the concentrations of non-
reactive, primary components of PM10 have approximately log normal frequency
distributions and constant values of geometric standard deviations (GSDs) regardless of
source type and location within their study area.  However, aerosol constituents of secondary
origin  (e.g., SO4=, NH4+, and NO3") were found to have much higher GSD's.  Surprisingly,
the GSD's  of organic (1.87) and elemental (1.74) carbon were both found  to be within la
(0.14)  of the mean GSD (1.85) for non-reactive primary species, compared to GSD's of 2.1
for sulfate, 3.5 for nitrate,  and 2.6 for ammonium.  These results suggest that most of the
OC seen in ambient samples is of primary origin. Pinto et al.  (1995) found similar results
for data obtained  during the summer of 1994. Further studies are needed to determine if
these relations are valid at other locations and to determine to what extent the results might
be influenced by the evaporation of volatile constituents after sampling.
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     It must be emphasized that the inferences drawn from field studies in the Los Angeles
Basin are unique to that area and cannot be extrapolated to other areas of the country.
In addition, there is a high degree of uncertainty associated with all aspects of the calculation
of secondary OC concentrations which is compounded by the volatilization of OC during and
after sampling.  Grosjean and Seinfeld (1989) derived a factor of five range in estimates of
production rates of secondary OC in the Los Angeles area by comparison of results obtained
from four different methods. Aerosol yields from the oxidation of individual hydrocarbons
reported by different investigators vary by  an order of magnitude (Grosjean and Seinfeld,
1989). Significant uncertainties always arise in the interpretation of smog chamber data
because of wall reactions.  Limitations also exist in extrapolating the results of smog
chamber studies to ambient conditions found in urban airsheds and forest  canopies.
Concentrations of tetpenes and  NOX are  much lower in forest canopies (Altshuller, 1983)
than are commonly used in smog chamber  studies.  The identification of aerosol products of
terpene oxidation has not been a specific aim of field studies, making it difficult to judge the
results of model calculations of secondary  OC formation.  Uncertainties may also arise
because of the methods used to measure biogenic hydrocarbon emissions.  Khalil and
Rasmussen (1992) found much  lower ratios of terpenes to other hydrocarbons (e.g., isoprene)
in forest air than were expected, based on  their relative emissions strengths and rate
coefficients for reaction with OH radicals and O3.  They offered two explanations, either the
terpenes were being rapidly removed by some heterogeneous process or emissions were
artificially enhanced by feedbacks caused by the bag enclosures they used. If the former
consideration is correct,  then the production of aerosol carbon from terpene emissions could
be substantial; if the latter is correct, then  terpene  emissions could have been overestimated
by the techniques used.
5.4   EMISSIONS ESTIMATES FOR PRIMARY PARTICIPATE
      MATTER AND SO2, NOX, AND VOCs IN THE UNITED STATES
     The emissions of a pollutant can be expressed by the following equation:

                             E = £  Aj-Fj-a-Cj)                               (5-1)
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where E is the total emissions rate from all sources;  Aj is the activity rate for source i; Fj is
the emissions factor for the production of the pollutant by source i; and Ceff j is the fractional
efficiency of control devices used by source i.  Activity rates  relevant to the entries shown
in Tables 5-6 to 5-10 might refer to the electricity generated by power plants, the amount of
coke produced by a coke oven, the distance travelled by motor vehicles, the amount of
biomass consumed by forest fires per year, etc.  The mass of pollutant emitted per unit
activity of a source is then expressed in terms of an emissions factor (e.g., amount of NOX
emitted per kw-hour of energy generated or per vehicle  mile travelled). Emissions factors
are given in compilations (e.g., AP-42 [U.S.  Environmental Protection Agency,  1995a]) or
are calculated  by emissions models, which include a  number of variables which can affect
emissions.  Examples include the U.S. Environmental Protection Agency's PARTS model for
estimating particulate motor vehicle emissions, and BEIS which is used to calculate emissions
of hydrocarbons from vegetation (Geron et al., 1994).  The product of Aj x Fj yields an
estimate of the uncontrolled emissions from a particular source i. These are then multiplied
by a factor incorporating the effects of any control devices that might be used.  It is
acknowledged that control equipment breaks down, and its efficiency might not be
maintained over its lifetime of operation.  Therefore, the optimum efficiencies  of control
devices are multiplied by a rule effectiveness  factor.  The default value for the rule
effectiveness factor is taken to be 0.8 in the inventory calculations, unless a better factor can
be justified (U.S. Environmental Protection Agency,  1989).  Equation 5-1 was used in the
preparation of the emissions inventories shown in Tables 5-6 through 5-10. Further details
about collection and reporting methods may be found in the National Emissions Inventory
Trends data base (U.S. Environmental Protection Agency, 1994).
     Table 5-6 shows the primary PM10 emissions estimated for the period of  1985 through
1993 using the National Emissions Inventory Trends  data base (U.S.  Environmental
Protection Agency,  1994).  Emissions are shown in the original units used in their
calculation.  A short ton is equal to 2,000 pounds or  9.08 x 105 gm.  Between 1985 and
1993, PM10 emissions from stationary and mobile sources decreased almost 10 percent.
During this period, contributions from highway vehicles decreased by 27  percent, reflecting
emissions controls on diesel vehicles.  Contributions  from industrial fuel production
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o\
                        TABLE 5-6.  NATIONWIDE PRIMARY PM10 EMISSION ESTIMATES FROM
                                  MOBILE AND STATIONARY SOURCES, 1985 TO 1993
(Thousands short tons/year)
Source Category
Fuel Combustion - Electric Utilities
Fuel Combustion - Industrial
Fuel Combustion - Other
Chemical and Allied Product Manufacturing
Metals Processing
Petroleum and Related Industries
Other Industrial Processes
Solvent Utilization
Storage and Transport
Waste Disposal and Recycling
Highway Vehicles
Off-Highway
Total
1985
284
234
896
67
147
32
317
2
57
279
271
368
2,953
1986
289
231
902
68
137
31
321
2
56
275
265
372
2,949
1987
282
226
910
68
131
30
314
2
54
265
261
350
2,893
1988
278
230
918
73
141
29
314
2
54
259
256
387
2,942
1989
278
229
922
74
142
28
308
2
54
251
253
372
2,909
1990
291
228
930
74
140
28
306
2
54
242
239
372
2,907
1991
253
229
942
72
136
28
300
2
53
245
223
367
2,849
1992
255
223
819
75
137
27
303
2
53
246
210
379
2,729
1993
270
219
723
75
141
26
311
2
55
248
197
395
2,661
    Note: The sums of sub-categories may not equal total due to rounding (1 short ton = 9.08 x 105 gins).

    Source: U.S. Environmental Protection Agency (1994).

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       TABLE 5-7.  MISCELLANEOUS AND NATURAL SOURCE PRIMARY PM10 EMISSION ESTIMATES,
                                                 1985 TO 1993
(Thousands short
Source Category
Fugitive Dust
Unpaved roads
Paved roads
Construction/mining and quarrying
Agriculture and Forestry
Agricultural crops
Agricultural livestock
Other Combustion
!*» Wildfires
Managed burning
Other
Natural Sources wind erosion
Total
1985

14,719
6,299
13,009

6,833
275

142
523
59
3,565
45,424
1986

14,672
6,555
12,139

6,899
285

142
530
59
9,390
50,671
1987

13,960
6,877
12,499

7,008
330

142
536
59
1,457
42,868
1988

15,626
7,365
12,008

6,090
376

142
555
59
17,509
60,730
1989

15,346
7,155
11,662

6,937
397

142
549
59
11,862
54,073
tons/year)
1990

15,661
7,299
10,396

6,999
381

717
546
59
4,192
46,250

1991

14,267
7,437
10,042

6,965
363

457
537
59
10,054
50,181

1992

14,540
7,621
10,899

6,852
386

341
547
59
4,655
45,900

1993

14,404
8,164
11,368

6,842
394

418
549
59
628
42,826
Note: The sums of sub-categories may not equal total due to rounding (1 short ton = 9.08 x  105 gms).

Source:  U.S. Environmental Protection Agency (1994).

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                    TABLE 5-8. NATIONWIDE SULFUR OXIDES EMISSION ESTIMATES, 1984 TO 1993
00
Source Category
Fuel Combustion - Electric Utilities
Fuel Combustion - Industrial
Fuel Combustion - Other
Chemical and Allied Product
Manufacturing
Metals Processing
Petroleum and Related Industries
Other Industrial Processes
Solvent Utilization
Storage and Transport
Waste Disposal and Recycling
Highway Vehicles
Off-Highway
Miscellaneous
Total

1984
16,023
2,723
728
229
1,387
707
923
0
0
25
445
198
9
23,396

1985
16,273
3,169
578
456
1,042
505
425
1
4
34
446
208
7
23,148

1986
15,701
3,116
611
432
888
469
427
1
4
35
449
221
7
22,361

1987
15,715
3,068
663
425
616
445
418
1
4
35
457
233
7
22,085
(Thousands
1988
15,990
3,111
660
449
702
443
411
1
5
36
468
253
7
22,535
short tons/year)
1989
16,218
3,086
623
440
657
429
405
1
5
36
480
267
7
22,653
1990
15,898
3,106
597
440
578
440
401
1
5
36
480
265
14
22,261
1991
15,784 15
3,139 2
608
442
544
444
391
1
5
36
478
266
11
22,149 21
1992
,417
,947
600
447
557
417
401
1
5
37
483
273
10
,592
1993
15,836
2,830
600
460
580
409
413
1
5
37
438
278
11
21,888
    Note: The sums of sub-categories may not equal the totals, due to rounding (1 short ton = 9.08 X 105 gms).




    Source:  U.S. Environmental Protection Agency (1994).

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                            TABLE 5-9. NATIONWIDE NO/ EMISSION ESTIMATES,  1984 TO 1993
u»
(Thousands short tons/year)
Source Category
Fuel Combustion - Electric Utilities
Fuel Combustion - Industrial
Fuel Combustion - Other
Chemical and Allied Product Manufacturing
Metals Processing
Petroleum and Related Industries
Other Industrial Processes
Solvent Utilization
Storage and Transport
Waste Disposal and Recycling
Highway Vehicles
Off-Highway
Miscellaneous
Total
1984
7,268
3,415
670
161
54
70
203
0
0
90
8,387
2,644
210
23,172
1985
6,916
3,209
701
374
87
124
327
2
2
87
8,089
2,734
201
22,853
1986
9,909
3,065
694
381
80
109
328
3
2
87
7,773
2,777
202
22,409
1987
7,128
3,063
710
371
76
101
320
3
2
85
7,662
2,664
203
22,386
1988
7,530
3,187
737
398
82
100
315
3
2
85
7,661
2,914
206
23,221
1989
7,607
3,209
730
395
83
97
311
3
2
84
7,662
2,844
205
23,250
1990
7,516
3,256
732
399
81
100
306
2
2
82
7,488
2,843
384
23,192
1991
7,482
3,309
745
401
79
103
298
2
2
81
7,373
2,796
305
22,977
1992
7,473
3,206
735
411
80
96
305
3
3
83
7,440
2,885
272
22,991
1993
7,782
3,176
732
414
82
95
314
3
3
84
7,437
2,966
296
23,402
     "Emissions calculated as NO2.




     Note: The sums of sub-categories may not equal total due to rounding (1 short ton = 9.08 x 105 gms).




     Source:  U.S. Environmental Protection Agency (1994).

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      TABLE 5-10.  NATIONWIDE VOLATILE ORGANIC COMPOUND EMISSION ESTIMATES, 1984 TO 1993
(Thousands short tons/year)
Source Category
Fuel Combustion - Electric Utilities
Fuel Combustion - Industrial
Fuel Combustion - Other
Chemical and Allied Product Manufacturing
Metals Processing
Petroleum and Related Industries
Other Industrial Processes
Solvent Utilization
Storage and Transport
Waste Disposal and Recycling
Highway Vehicles
Off-Highway
Miscellaneous
Total
1984
45
156
917
1,620
182
1,253
227
6,309
1,810
687
9,441
1,973
951
25,572
1985
32
248
508
1,579
76
797
439
5,779
1,836
2,310
9,376
2,008
428
25,417
1986
34
254
499
1,640
73
764
445
5,710
1,767
2,293
8,874
2,039
435
24,826
1987
34
249
482
1,633
70
752
460
5,828
1,893
2,256
8,201
2,038
440
24,338
1988
37
271
470
1,752
74
733
479
6,034
1,948
2,310
8,290
2,106
458
24,961
1989
37
266
452
1,748
74
731
476
6,053
1,856
2,290
7,192
2,103
453
23,731
1990
36
266
437
1,771
72
737
478
6,063
1,861
2,262
6,854
2,120
1,320
24,276
1991
36
270
426
1,778
69
745
475
6,064
1,868
2,217
6,499
2,123
937
23,508
1992
35
271
385
1,799
72
729
482
6,121
1,848
2,266
6,072
2,160
780
23,020
1993
36
271
341
1,811
74
720
486
6,249
1,861
2,271
6,094
2,207
893
23,312
Note: The sums of sub-categories may not equal total due to rounding (1 short ton = 9.08 x 105 gms).




Source:  U.S. Environmental Protection Agency (1994).

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decreased by 6 percent, leading to an overall decrease of about 10% in emissions from all of
these categories from 1985 to 1993.
      Table 5-7 shows PM10 emissions from natural and miscellaneous sources for 1985 to
1994. Fugitive dust is the largest source in the miscellaneous category.  No clear trend is
evident in overall fugitive dust emissions, because increases in paved road dust are offset by
decreases in the mining and quarrying and construction categories.  The large year-to-year
variability in wind erosion reflects changes in precipitation and regional soil conditions.  For
instance, the values for 1993 reflect the flooding and  extremely wet conditions that occurred
in the midwestern United States.
      Tables 5-8 through 5-10 show nationwide emissions for sulfur dioxide, oxides of
nitrogen, and VOC's, which are all precursors for secondary aerosol formation, for the
period from 1984 through 1993.  Electric utilities account for the largest fraction of sulfur
dioxide, nearly 70% of total emissions in 1993 (Table 5-8).  Estimates of sulfur dioxide
emissions from industrial fuel combustion increased by approximately 16%  from 1984 to
1985, and decreased by 11% between  1985 and 1993. Sulfur dioxide emissions from
chemical manufacturing doubled between 1984 and 1985, with emissions leveling off between
0.42 and 0.46 million short tons/year after 1985.  Sulfur dioxide emissions from highway
vehicles were estimated to have increased by 8% from 1984 to 1989, then levelling off and
then decreasing by about 10% from 1992 to 1993, reflecting the introduction of regulations
for the desulfurization of diesel fuel.  Off-highway vehicle  emissions increased from 0.20
million short tons per year in  1984 to 0.28 million short  tons per year in 1993.  Major sulfur
dioxide emissions reductions were observed for petroleum processing and other industrial
processes, with decreases of 40% to 50% over the ten-year period.  In total, however, sulfur
dioxide emissions estimates in 1993 decreased by 6%  from those given for 1984.
      Table 5-9 shows no significant variations in total nitrogen oxides emissions over the
10-year period.  Electric utility and motor vehicle emissions each account for about one-third
of total emissions.  Emissions from (a) industrial and  other fuel combustion and (b) from
off-highway vehicles each account for about one-sixth of total emissions.  There is little
change in total emissions from 1984 to 1993.  Moderate  increases are seen in the electric
utility, industrial and other fuel combustion, and off-highway vehicles categories with much
                                          5-41

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larger relative increases for chemical manufacturing and metals processing. These increases
are offset by decreases in fuel combustion by industry and on-highway vehicles.
     Volatile organic compound (VOC) emissions in Table 5-10 are dominated by highway
vehicles and solvent use.  These two sources together account for 50 to 60% of total
emissions.  Off-highway vehicles, petroleum-related industries,  chemical manufacturing, and
petroleum storage and transport account for most of the remaining amounts.  VOC emissions
from highway vehicles were reduced between  1984 and 1993 by 35%, in spite of increased
vehicle mileage. Most of this decrease is due to the presumed effectiveness of emissions
controls on newer vehicles.  VOC emissions from petroleum industries also were reduced by
43% between 1984 and 1993.  Total VOC emissions decreased by 9% between 1984 and
1993. It should be noted that emissions from natural sources are not reflected in the above
discussion.
     Although total emissions of gaseous  precursors (SO2, NOX, and VOC's) are  shown in
Tables 5-8, 5-9, and 5-10, it should be remembered that these values cannot be directly
translated into production rates of paniculate matter.  Dry deposition and precipitation
scavenging of some of these gases can occur before they are oxidized to aerosols in the
atmosphere. In addition, some fraction of these gases are transported outside of the domain
of the continental United States before being oxidized. Likewise, emissions of these gases
from areas outside the United States can result in the transport of their oxidation products
into the United States.  While the chemical oxidation of SO2 will lead quantitatively to the
formation of SO4=,  the formation of aerosol from the oxidation of VOC's will be much less
because only a small fraction of VOC's react to form particles, and those that do have
efficiencies less than 10%  (c.f. Section 5.3). The oxidation of NO2 will yield HNO3, some
of which may  dry deposit or be scavenged by  precipitation,  and the remainder will form
paniculate nitrate.
     Projections of future emissions of primary PM10, SO2, and NOX are shown in
Table 5-11.  Controls mandated by the Clean Air Act Amendments of 1990 are expected to
reduce PM10 emissions in nonattainment areas. However, because emissions in
nonattamment areas constitute a small  subset of total emissions, overall emissions  are
projected as still likely to increase.  Fugitive dust sources contribute the major share of the
increase.  Changes in emissions after 1996 solely reflect activity level changes with the
                                         5-42

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         TABLE 5-11. PROJECTED TRENDS IN PARTICIPATE MATTER (PM10), SULFUR DIOXIDE (SOj),
                           AND OXIDES OF NITROGEN (NOJ EMISSIONS (1Q6 short tons yi"1)
PM10 Source Categories
Fuel Combustion3

1990
1993
1996
1999
2000
2002
2005
2008
2010
Natural3
4.36
1.98
4.36
4.36
4.36
4.36
4.36
4.36
4.36
Misc.a-b
36.3
37.9
43.6
48.5
49.8
51.8
54.9
57.4
59.0
Electric
Utilities
0.28
0.26
0.31
0.33
0.34
0.35
0.37
0.40
0.42
Industrial
0.24
0.23
0.21
0.20
0.20
0.19
0.19
0.18
0.18
Other
0.55
0.54
0.66
0.59
0.66
0.59
0.64
0.69
0.73
OSC
0.90
0.91
0.89
0.93
0.94
0.97
1.01
1.04
1.06
Mobile3
On-Road
0.36
0.32
0.15
0.13
0.12
0.13
0.13
0.13
0.12

Nonroad
0.37
0.40
0.44
0.47
0.48
0.50
0.53
0.55
0.56
Total3
43.3
42.5
50.6
55.9
56.9
59.0
62.2
64.7
66.4
S02d
22.4
21.5
18.1
17.6
17.4
17.1
16.7
16.1
15.7
N0xd
23.0
23.3
21.9
21.8
20.5
20.5
20.8
21.3
21.6
aSame categories as used in Tables 5-6 and 5-7.
bThe miscellaneous category includes fugitive dust from unpaved and paved roads, and other sources; wildfires and managed burning; and agricultural and
 forestry related emissions.
COS refers to other stationary sources such as chemical manufacturing, metal processing, petroleum refining, other industrial processes, solvent utilization,
 storage and transport, waste disposal and recycling.
dOnly total emissions are shown.

Source: U.S. Environmental Protection Agency (1995b).

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exception of on-road vehicles.  Emission factors for on-road vehicles are expected to
decrease mainly because of stringent standards for diesel emissions. Diesel vehicle emissions
are expected to decrease nationwide by about 70% from 1990 to 2010 (U.S. Environmental
Protection Agency, 1993).  This decrease results mainly from a roughly 90% decrease in
emissions factors which are partially offset by an increase in total diesel vehicle miles
travelled. As can be seen from Table 5-11,  emissions from non-road sources (e.g., marine
vessels, railroads, aircraft, vehicles used in construction, industry, agriculture, airport
services, and landscaping) are projected to exceed those from on-road vehicles from 1990
to 2010.
     Emissions of SO2 from  fossil fuel combustion by electric utilities show an expected
continued decline through 2010. Emissions  from all other categories in Table 5-7 show a
slight increase from 1993 to 2002 and then level off to the year 2010.  Total NOX emissions
show a decrease of over 10% from 1993 to  2002, then increase by about 5%  by the year
2010.  This pattern reflects projected emissions for the major categories of fuel combustion
by electric utilities and on-road vehicles.
     Emissions of ammonia and ammonium are not included in the U.S. Environmental
Protection Agency inventories for criteria pollutants. Dentener and Crutzen (1994) have
constructed a global inventory of NH3 emissions. Anthropogenic sources (animals kept  for
human use, fertilizer applications, and biomass  burning) and natural sources (wild animals,
vegetation, and the oceans) were included.   Emissions from sewage were not included,
though.
     Vegetation was found to be either a source or a sink for NH3 depending on ambient
concentrations and vegetation type.   Animals kept for human use represent the largest single
source category.  Highest emission rates in North America  were found in the central United
States.  Matthews (1994) found that  about 75% of U.S. NH3 emissions from the application
of nitrogenous fertilizers  occur in the central United States, with the remainder about evenly
divided between the eastern and western United States.  Emissions of approximately 0.51 Tg
NH3-N yr"1 were calculated for the United States. The Dentener  and Crutzen (1994)
estimate of NH3 emissions for North America of 5.2 Tg N  yr"1 may be compared to a wet
deposition rate  of NH4+ in the United States of 3 -4.5 Tg N yr"1, and three separate emission
inventories yielding values of 1.2,  8.8, and 2.8 Tg N yr"1 for the U.S. (Placet et al., 1991).
                                          5-44

-------
     While emissions of organic carbon (OC) and elemental carbon (EC) are included
implicitly in the emissions inventories for PM10, it is still useful to consider independent
estimates.  Zhang et al. (1992) estimated the total production of secondary organic aerosol to
be about 1.2 Tg yr"1 in the United States. Liousse et al. (1996) have constructed OC and EC
emissions inventories for use in a global scale chemical tracer model. They estimate OC
emissions of 0.80 Tg OC yr"1 from live biomass combustion, 1.4 Tg OC yr"1 from fossil fuel
combustion, and 0.59 Tg OC yr"1 from the oxidation of naturally emitted terpenes assuming
a fractional aerosol yield of 5 %. Carbon values for OC sources have been multiplied by a
factor of 1.2 to account for  the presence of oxidized species.  EC emissions from the
combustion of live biomass  and fossil fuels are estimated to be 0.11 Tg EC yr"1 and
0.30 Tg C yr"1, respectively.  These estimates are roughly 8% of total particulate emissions
shown in Tables 5-6 and 5-7. Comparisons of model results  with observations from the
IMPROVE/NESCAUM network by Liousse et al. (1996) suggest that both the OC and
EC emissions derived for their model may be systematically underestimated by at least a
factor of two.
     The  regional nature of total primary particulate matter emissions is illustrated in
Figure 5-5. At least 80% of the emissions in any single region arises from fugitive dust
sources and wind erosion.  SO2 regional emissions are  shown in Figure 5-6 as a reminder
that they are highest in the eastern United States and that the  oxidation of SO2 to SO4= can
constitute  a substantial fraction of the aerosol mass in the eastern United States.  It can also
be seen that the ratio of SO2 to primary PM10 emissions tends to be much higher in the
eastern  than in the western United States.
     Annual averages do not reflect the seasonality of certain emissions.  Residential wood
burning in fireplaces and stoves, for example,  is a seasonal practice which reaches its peak
during cold weather.  Cold weather also affects motor vehicle exhaust particulate emissions,
both in  terms of chemical composition and emission rates (e.g., Watson et al., 1990b; Huang
et al., 1994). Planting, fertilizing, and harvesting are also seasonal activities.  Forest fires
occur mainly during the local dry season and during periods of drought.
     Several of the sources  in Tables 5-6  through 5-10 are  episodic rather than continuous in
nature.  This is especially true of prescribed and structural fires and fugitive dust emissions.
Although windblown dust emissions are low on an annual average, they are likely to be  quite
                                          5-45

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Figure 5-5.  Estimates of primary PM10 emissions by U.S. Environmental Protection
             Agency region for 1992.

Units = 106 short tons/yr (1 short ton = 9.08 x  105 gins).

Source: U.S. Environmental Protection Agency (1993).
Figure 5-6.  Estimates of sulfur dioxide emissions by U.S. Environmental Protection
             Agency region for 1992.

Units = 106 short tons/yr (1 short ton = 9.08 x 10s gms).

Source: U.S. Environmental Protection Agency (1993).

                                           5-46

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 large during those few episodes when wind speeds are high.  The transport of Saharan dust
 to the continental United States is also highly episodic.
 5.5   APPLICATIONS AND LIMITATIONS OF EMISSIONS
       INVENTORIES AND RECEPTOR MODELS
      This section examines requirements for the design and construction of emissions
 inventories and potential areas of uncertainty and limitations in their use. Receptor modeling
 methods to apportion sources to mass components in ambient aerosol measurements, and
 results for a number of aerosol monitoring studies, will then be presented.  Some general
 considerations of the relative strengths and weaknesses of using emissions inventories and
 receptor models to assign sources to particulate matter components in ambient samples will
 then be discussed.  Finally, results from specific receptor modeling studies in the eastern and
 western United States will be discussed.

 5.5.1   Uncertainties in Emissions Estimates
      It is difficult to assign uncertainties quantitatively to entries in emissions inventories.
 Methods that can be used to verify or place constraints on emissions inventories are sparse.
 In general, the overall uncertainty in the emissions of a given pollutant includes contributions
 from  all  of the terms on the right hand side of Eq. 5-1 (activity rates, emissions factors, and
 control device efficiencies).  Additional uncertainties can arise during the compilation of an
 emissions inventory because of missing sources and arithmetical errors.  The variability of
 emissions can cause errors when annual average emissions are applied to applications
 involving shorter time scales.
     Activity rates for well-defined point sources (e.g., power plants) should have the
 smallest uncertainty associated with their use,  since accurate production records need to be
kept.  On the other hand, activity rates for a number of areally dispersed fugitive sources are
extremely difficult to quantify. Emissions factors for  easily measured fuel components which
are quantitatively released during combustion (e.g., CO2 and SO2) should be the most
reliable.  Emissions of components formed during combustion  are more difficult to
characterize as the emissions rates are dependent on factors specific to individual combustion
                                         5-47

-------
units and on combustion stage (i.e., smoldering or active).  Although the AP-42 emissions
factors (U.S. Environmental Protection Agency, 1995a) contain extensive information for a
large number of source types, these data are very limited in the number of sources sampled.
The efficiency of control devices is determined by their age, their maintenance history, and
operating conditions.  It is virtually impossible to assign uncertainties in control device
performance due to these factors.  It should be noted that the largest uncertainties occur for
those devices which have the highest efficiencies (>90%).  This occurs because the
efficiencies  are subtracted from  one and small errors in assigning efficiencies can  lead to
large errors in emissions.
     Ideally an emissions inventory should include all major sources of a given pollutant.
This may be an easy task for major point sources, but becomes problematic for poorly
characterized area sources.  As  an example,  it was recently realized that meat cooking could
be a significant source of organic carbon (Hildemann et al., 1991).  Further  research is
needed to better characterize the sources of pollutants in order to reduce this source of
uncertainty. Errors can arise from the misreporting of data, and arithmetic errors can occur
in the course of compiling entries from thousands of individual sources.  A quality assurance
program is required to check for outliers and arithmetic errors.
     Because of the variability in emissions rates, there can be errors  in the application of
inventories developed on an annually averaged basis (as are the inventories shown in
Tables 5-6 to 5-10) to episodes  occurring on much shorter time scales.  As an example, most
modeling studies of air pollution episodes are carried out for periods of a few days.
     Uncertainties in annual emissions were estimated to range from 4 to 9% for SO2 and
from 6 to 11% for NOX in the 1985 NAPAP inventories for the United States (Placet et al.,
1991).  Uncertainties in these estimates increase as the emissions are disaggregated both
spatially and temporally.  The uncertainties quoted above are conservative estimates and refer
only to random variability about the mean, assuming that the variability in emissions factors
was adequately characterized and that  extrapolation of emissions factors to sources other than
those for which they were measured is valid. The  estimates do not consider the effects  of
weather or variations in operating and maintenance procedures.  Fugitive dust sources, as
mentioned above,  are extremely difficult to quantify, and stated emission rates may only
represent order-of-magnitude estimates.  As rough estimates, uncertainties in emissions
                                          5-48

-------
estimates could be as low as  10% for the best characterized source categories, while
emissions figures for windblown dust should be regarded as order-of-magnitude estimates.
Given (a) uncertainties in the deposition of SO2 and its oxidation rate, (b) the variability seen
in OC and EC emissions from motor vehicles  along with the findings from past verification
studies for NMHC and CO to NOX ratios, (c)  ranges of values found among independent
estimates for emissions of individual species (NH3, OC), and (d) the predominance of
fugitive emissions, PM emissions rates should be regarded as order-of-magnitude estimates.
     There have been few field studies designed to test emissions inventories observationally.
The most direct approach would be to obtain cross-sections of pollutants upwind and
downwind of major urban areas from aircraft.  The computed mass flux through a cross
section of the urban plume can then be equated to emissions from the city chosen.  This
approach has been attempted  on a few occasions. Results have been ambiguous because of
contributions from fugitive sources, non-steady wind flows, and general logistic difficulties.
     Greater success, albeit on a smaller scale, has been achieved in studies that tested
predictions of the State of California EMFAC  emissions model.  An ambient-air study in the
Los Angeles basin (Fujita et al., 1992) showed that motor vehicle emissions  of CO and
nonmethane hydrocarbons (NMHC) were being systematically underpredicted in the
emissions model by a factor of about 2.5,  assuming that NOX emissions were much better
known; i.e., the CO to NOX and NMHC to NOX ratios were underpredicted  by the model.  A
study performed in a tunnel in the Los Angeles basin (Ingalls, 1989; Pierson et  al., 1990)
showed that motor vehicle NOX emission rates (g/mi) were predicted approximately correctly
but that the CO and NMHC emission rates were systematically underpredicted in the
emissions model by factors of two to three.  Similar  tests need to be performed for
particulate matter emissions from motor vehicles.
     A completely different approach to obtaining area-wide emissions  of pollutants relies on
the construction of inversion algorithms applied in the context of atmospheric transport
models (Brown, 1993).  Emissions of a pollutant that are required to produce a  specified
distribution of surface concentrations are solved for by using model-derived transport and
chemical loss terms.  Uncertainties in the emissions fields are then generated in terms of
specified uncertainties in  the observed data and in the model transport and chemistry fields.
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     A number of factors limit the ability of an emissions inventory driven, chemical tracer
model  to determine the effects of various sources on particle samples obtained at a particular
location apart from uncertainties in the inventories given above.  Air pollution model
predictions represent averages over the area of a grid cell, which in the case of the Urban
Airshed Model typically has been 25 km2 (5 km X 5 km).  The contributions of sources to
pollutant concentrations at a monitoring site are strongly controlled by local conditions which
cannot be resolved by an Eulerian grid-cell model.   Examples would be the downward
mixing of tall stack emissions and deviations from the mean flow caused by buildings.  The
impact of local sources may not be accurately predicted, because their emissions  would be
smeared over the area of a grid cell or if the local wind flow were in the wrong direction
during sampling.
     For these reasons, receptor models have been used to  determine source contributions  to
paniculate matter at individual monitoring sites.  Receptor models are strictly diagnostic in
their application and do not have the prognostic, or predictive, capability of chemical
transport models.  In addition, receptor models have been developed for apportioning sources
of primary particulate matter and are not formulated to include the processes of secondary
paniculate matter formation which are explicitly included in the chemical transport models.

5.5.2    Receptor Modeling Methods
     Receptor models relate source contributions to ambient concentrations based on
composition analysis of ambient particulate samples.  They  depend on the assumption of
mass conservation and the use of a mass balance.  As an example, assume that the total
concentration of particulate lead measured at a site can be considered to  be the sum of
contributions from a number of independent sources,

                    Pbtotal = Pbmotor vehicle + Pbsoil  + PbSmeUer +   '  '   '              (5'2>
Since most sources emit particles that contain a number of chemical elements or  compounds,
the atmospheric concentration of an element can be considered to be the product of the
abundance of the element of interest (ng/mg) in the  effluent and the mass concentration of
particles  from that source in the atmosphere (mg/m3).  For lead from motor vehicles, for
example,
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                                   motor vehicles     Pb, mv J mv
where apb mv is the abundance of lead in motor vehicle emissions, and fmv is the mass
concentration of motor vehicle emitted particles in the atmosphere.  Extending  this idea to
m chemical elements, n samples, and p independent sources,

                                               •ft 4                                (5"4)

where Xy is the ith elemental concentration measured  in the jth sample (ng m"3), aik is the
gravimetric abundance of the 1th element in material from the k1*1 source (ng mg"1), and
fkj is the airborne mass concentration  of material from the kth source contributing to the
jl  sample (mg m" ).  The fk: are the quantities to be determined from Equation 5-4. To
distinguish the contributions of one source type  from another using  receptor models, the
chemical and physical characteristics must be such that (1) they are present in different
proportions in different source emissions, (2) these proportions remain relatively constant
for each source type, and (3)  changes in these proportions between  source and receptor are
negligible or can be empirically  represented.
      A number of specialty conference  proceedings, review articles, and books have been
published to provide  greater detail about source  apportionment receptor models (Cooper and
Watson, 1980; Watson et al.,  1981; Macias and  Hopke, 1981; Dattner and Hopke, 1982;
Pace, 1986; Watson et al., 1989; Gordon, 1980,  1988; Stevens and Pace, 1984; Hopke,
1985, 1991; Javitz et al., 1988).  Watson et al. (1994b) present data analysis plans which
include receptor models as an integral part of visibility and PM10  source  apportionment and
control  strategy development.
      The first  step in attempting to relate ambient particulate matter measured at  a
particular location to  source contributions is typically data evaluation. The objectives for
data evaluation are:  (1) to summarize the accuracy and precision  of measurements; (2) to
identify and investigate extreme  and inconsistent values;  (3) to perform data comparisons
and investigate discrepancies;  and (4) to  estimate the  equivalence  of measurements of the
same variable by different methods.
      Even with the most stringent quality assurance, it is prudent  to perform several
straightforward analyses to identify the presence of any discrepancies in atmospheric
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particulate data and to correct, flag, or eliminate them.  Investigating the equivalence  of
different measurement methods for the same variable  is especially important for particulate
chemical measurements, which may  show substantial  differences in concentration depending
upon how they were made.  Data evaluation activities include: (1) plotting and  examining
pollutant time series data to identify  spikes  and outliers for investigation;  (2) comparing the
sum of chemical  species with PM,0 mass measurements; and (3) comparing measurements
of the  same variables at the same or nearby sites using different measurement devices and
procedures.
     After data evaluation the next step in an analysis of particulate air quality  in a region
is a process that  can be termed a descriptive air quality analysis.  The objectives of a
descriptive air  quality analysis are:  (1) to identify similarities and differences in air quality
at different sampling sites; and (2) to depict temporal  and spatial variations in particulate
and gaseous precursor measurements.  Descriptive  air quality analysis includes:
(1)  statistical summaries of median and extreme values of air quality variables for different
sites, episodes, and times of day; (2) time series plots of PM10 and selected chemical
components;  (3)  spatial pie plots of particulate chemical composition;  and (4) spatial  and
temporal correlations between PM10  and chemical  composition measurements.   The product
of this analysis is a quantitative  overview of particulate concentrations during the period of
interest.
     Performed at the same time as  a descriptive  air quality  analysis,  a descriptive
meteorological analysis is carried out to:  (1) describe the spatial structure, temporal
variability, and statistical distribution of meteorological  conditions;  (2) describe the
transport and mixing patterns in the  study domain; and (3) identify relationships between
meteorology  and atmospheric particulate concentrations.  Data normally available would
include wind speed, wind direction, temperature, relative humidity, and solar radiation at
ground level and aloft (if available).
     Descriptive meteorological analysis activities include: (1) statistical summaries of
meteorological variables; (2) time series and spatial plots of meteorological variables,
including wind vectors, with examination  for phenomena such as inter-basin transport,
stagnation, slope flows, convergence zones, and recirculation; (3) identification  of layers
and orographic phenomena that  change with elevation; (4) tabulations of fog occurrences,
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frequencies, locations, and intensities; and (5) meteorological descriptions and comparisons
with meteorology during high PM]0 episodes from prior years.
     The product of these analyses is a conceptual understanding of how meteorological
phenomena influence atmospheric particulate concentrations in a particular region.
     The next step in receptor modeling for particulate  matter is a source profile
compilation.  The objectives of source profile compilation analysis are: (1) to combine
profiles from  individual samples into composite profiles; and (2) to assign source profiles  to
source categories based on their degree of similarity  or  difference.  Data needed for this
study are the  chemical measurements  on samples  from representative  source  types that are
expected to contribute to airborne particulate matter during study periods.  Major source
types include, for example:  (1)  suspended geological material from roads  and from
agricultural and unpaved areas;  (2) primary particle exhaust from gasoline- and
diesel-powered vehicles; (3) industrial sources; (4) residual oil combustion; and
(5) secondary  ammonium sulfate and ammonium  nitrate originating from  gaseous
precursors.  Source profile compilations include:  (1) tables  and plots of individual  profiles
and their uncertainties; (2) calculation of averages and standard deviations for category
profiles;  and (3) calculation of weighted composite profiles  for source categories which are
found for the  source apportionment modeling described  below.  It is important to emphasize
that source and ambient samples must be analyzed using the same protocols  and methods
(U.S. Environmental Protection  Agency, 1994).
     The chemical mass balance (CMB) receptor  model is the model most commonly used
for particulate matter source apportionment.   The  CMB  model uses  the chemical and
physical  characteristics of gases and particles measured  at source and receptor to both
identify the presence of, and quantify  source  contributions to, the receptor (Friedlander,
1973).
     The CMB consists of an effective variance least-squares solution to the set of linear
equations (5-4) that expresses each concentration  of a chemical species at a receptor site as
a linear sum of products of source profile species  and source contributions.   The source
profile species, i.e., the fractional amount of the species in the emissions from each source
type, and the receptor concentrations, with appropriate uncertainty estimates,  serve as input
data to the CMB model.  The output  consists of: (1)  the source contribution  estimates of
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each source type; (2) the standard errors of these source contribution estimates; and (3) the
amount contributed by each source type to each chemical species.  The model calculates
values for the contributions from each source type and the uncertainties  associated with
those values.  Input data uncertainties are used both to weight the importance of input data
values in the solution and to calculate the uncertainties  of the source contributions.  The
CMB model assumptions are: (1) compositions of source emissions are constant over the
period of ambient and source sampling;  (2) chemical species do not react with each other,
i.e., they add linearly; (3) all sources with a potential for significantly contributing to the
receptor have been identified and their emissions have been characterized; (4) the source
compositions are linearly independent of each other; (5) the number of sources  or source
categories is less than or equal to the number of chemical species;  and (6) measurement
uncertainties are random, uncorrelated, and normally distributed.  Assumptions  1 through
6 for the CMB model are fairly restrictive  and will never be completely satisfied in actual
practice. Fortunately, the CMB model can tolerate reasonable deviations from these
assumptions, although these deviations increase  the stated uncertainties of the source
contribution estimates.
     The CMB modeling procedure requires: (1) identification of the contributing source
types;  (2) selection of chemical species  to be included;  (3) estimation of the fraction of each
of the chemical species  which  is contained in each source type (i.e., the source
compositions); (4) estimation of the uncertainty  in both ambient concentrations  (including
artifacts during sampling and storage such  as gas absorption  or volatilization in filter
samples) and source compositions; (5) estimation of differential losses during transport
from source to receptor; (6) solution of the chemical mass balance equations; and
(7) validation and reconciliation.  Each  of these steps requires different types of data.
Uncertainties in the modeling results can be noticeably  reduced by obtaining source profile
measurements which correspond to the period of ambient measurements (Glover et al.,
1991; Dzubay et al.,  1988; and Olmez et al., 1988). Stratifying data according  to wind
direction can also increase the number of source types that can be  resolved as shown in  the
above studies.
     Emissions inventories are examined to determine the types of sources that are most
likely to influence a receptor.  These emissions  inventories for particulate  matter are
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frequently  far from complete, however, and other measures are needed to infer the influence
of uninventoried  sources.  The Principal Components Analysis and Empirical Orthogonal
Function models  described below can aid in this identification.  Once these sources have
been identified, profiles  acquired from similar sources can be examined to select the
chemical species  to be measured.  The more species measured,  the better the precision of
the CMB apportionment.
     The Principal Components Analysis (PCA) receptor model classifies variables into
groups identifiable as causes of particulate matter levels measured at receptors.  Typical
causes are emissions sources, chemical interactions, or meteorological phenomena.  The
PCA model uses  ambient concentrations of chemical species and  meteorological data as
inputs.  PCA does not use source emissions measurements, as does the CMB model,  but it
may require 50 or more  measurements of many species from different time periods at a
single receptor site.
     The PCA procedure is as follows:  (1) select the chemical species and measurement
cases to be included; (2) calculate the correlation  coefficients between the species;
(3) calculate the eigenvectors and eigenvalues  of the correlation matrix; (4) select
eigenvectors to be retained; (5) rotate these eigenvectors into a more physically  meaningful
space; and (6)  interpret the rotated vectors as air pollution sources based on the  chemical
species with which they  are highly correlated.  Freeman et al. (1989)  describe the computer
software and methods required to use the PCA model for PM10 source assessment.  See
also Henry (1991).
     The PCA model assumptions are: (1) compositions of source emissions are constant
over the  period of ambient and source sampling; (2) chemical species concentrations  add
linearly;  (3) measurement errors are random and uncorrelated; (4) the case-to-case
variability  of actual source contributions is much larger than the variability due to other
causes, such as measurement uncertainty or changes in source profiles due to process and
fuel changes; (5)  causes  of variability that affect all sources  equally (such as atmospheric
dispersion) have much smaller effects than causes of variability for individual source  types
(such as wind direction or emission  rate changes); (6) the number of cases exceeds the
number of variables in the PCA model to an extent that statistical stability is achieved; and
(7)  eigenvector rotations are physically meaningful.
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     There are a number of examples of the application of PCA models.  Photochemical
factors were found to influence particulate matter measurements  from Los Angeles, CA,
New York, NY (Henry and Hidy, 1979), St. Louis, MO (Henry and Hidy, 1982),
Lewisburg, WV (Lioy et al.,  1982), and Detroit, MI (Wolff et al., 1985a). These
photochemical factors were consistently associated with daily average and maximum ozone
(O3), maximum temperatures, and absolute humidity.  The photochemical factors found for
Los Angeles data (Henry and Hidy, 1979) were highly correlated with daily  maximum and
minimum relative humidity measurements.  Local source factors were found for Salt Lake
City (Henry and Hidy, 1982) and Los Angeles (Henry and Hidy,  1979) and were highly
correlated with sulfur dioxide (SO2) and the wind direction  frequency distributions.
Dispersion/stagnation  factors  were found for St. Louis, Salt Lake City, and Lewisburg.  The
variables correlated with the dispersion/stagnation factor were nitric oxide (NO), nitrogen
dioxide (NO2), wind speed at midnight and noon, average wind speed, morning mixing
height, maximum hourly precipitation,  and average  precipitation.  PCA has also been used
to identify sources which may not be inventoried  (Wolff and  Korsog,  1985; Cheng et al.,
1988; Henry and Kim, 1989;  Koutrakis and Spengler, 1987; Zeng and Hopke, 1989).
     The PCA procedure as outlined above provides only a qualitative assessment  of air
pollution sources.  In some circumstances, however, the procedure can be extended to
produce quantitative estimates of the source impacts.  For example, a chemical  species
strongly associated  with a single PCA  group may be suitable  as a source tracer for use in a
subsequent multiple linear regression receptor model (Kleinman et al., 1980)
     The Empirical Orthogonal Function (EOF) receptor model is applied to  a  spatially
dense network of measurements  to identify the locations of emissions sources and  to
estimate the net fluxes (emissions minus  deposition) of those  pollutants.   The EOFs
manifest themselves as isopleth maps of flux density.  When  a major  point source  is the
emitter, such as a coal-fired power plant, the EOFs have been shown  (Gebhardt et al.,
1990) to surround that source. EOFs have been applied to air pollution  measurements by
Peterson (1970), Ashbaugh et al. (1984),  Wolff et al. (1985b), and Henry et al.  (1990).
Henry  et al. (1990) were the  first researchers to place this method 6n a firm theoretical
foundation and to demonstrate that EOFs reproduce the  net fluxes used as input to a
dispersion model.
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     In prior studies, the EOF model was applied to single chemical constituents, such as
sulfur dioxide,  sulfate, and total particulate mass concentrations.  In a recent study (Watson
et al., 1991), the EOF model was applied to the source contribution estimates calculated for
each sample from the CMB modeling described above.  In this way, source-type specific
patterns of net  flux were determined.  For example, the geological source contributions may
be represented  as a linear sum of EOFs which correspond to a  dirt road, a construction site,
and an area of intense agricultural activity. The motor vehicle  exhaust source contributions
may be represented by a linear sum of EOFs  which correspond to a major highway, a large
truck stop, or an urban core area. The EOF model may also be applied to specific chemical
species which are identified as potential markers for uninventoried sources.
     The EOF procedure is similar to the PC A procedure: (1) select the source  contribution
estimates  and measurement  cases to be included; (2) calculate the covariance coefficients
between the species  measured at the same time at several sites; (3) calculate  the
eigenvectors and eigenvalues of the covariance matrix; (4) select  eigenvectors to be
retained; (5) rotate these eigenvectors into a more physically meaningful space;  and
(6) interpolate between the values of these eigenvectors to supply the net flux patterns.  The
main difference between PCA and EOF is that PCA operates on many samples from a
single site taken over an extended time period, while EOF operates on many samples from
many sites taken over a  single time period.
     The formulation of Henry et al. (1990),  termed Source Identification Through
Empirical Orthogonal  Functions (SITEOF), uses wind velocities as input in addition to the
spatially  distributed source contribution estimates.  The SITEOF assumptions are: (1) net
fluxes of  spatially-distributed pollutants add linearly;  (2) pollutants are homogeneously
distributed vertically  in the mixed layer; (3) measurement errors are random and
uncorrelated; (4) the number of sampling  sites exceeds the number of source locations to an
extent that statistical stability is achieved; and (5) measurement locations are located in
positions to maximize spatial gradients from major source emissions.  The major unknown
concerning the  SITEOF  model is the extent to which assumptions 4 and  5 can be met in
actual practice.   Motor vehicle exhaust is  confined to specific areas (e.g., roads and parking
lots), and  it is a straightforward task to locate monitors close to and far from these known
locations.  Fugitive dust, on the other hand, can be emitted  from many locations.
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     The SITEOF model is one of a class of procedures referred to as "hybrid receptor
models".  Such models  make use of not only the ambient species concentration
measurements that form the input data for a pure receptor model, but in addition source
emission rates or atmospheric dispersion or transformation information characteristic of
dispersion models.  By  exploiting simultaneously the strengths of the two complementary
approaches their individual weaknesses  should  be minimized.  A survey of hybrid  receptor
models  is available (Lewis and Stevens, 1987).
     Ashbaugh et al., (1985) developed the concept of the potential source contribution
function (PSCF) for performing the apportionment  of secondary  species,  for combining air
parcel back trajectories  from a receptor site with chemical  data at the site to infer possible
source locations.  The PSCF is an estimate of the conditional probability that a trajectory
which passed through a given cell in the emissions grid (g-) contributed a concentration
greater than some threshold value to ambient concentrations  at the  receptor site. Gao et al.
(1993) extended the PSCF analysis to provide an apportionment  of secondary  species.  By
multiplying the PSCF by the emissions  rate in  g^, an estimate of the maximum contribution
of sources in gy to the  concentrations measured at the  receptor site is obtained. Further
research is needed to quantify the uncertainties  associated with this method. These
uncertainties  are related to unidentified  sources, background  sources, emissions estimates at
the time of calculation,  the differential  loss of species (e.g., by deposition), and mixing of
air parcels from different cells during transit from source to receptor.  Gao et al. (1993)
have applied PSCF's along with emissions estimates  to the apportionment of SO2 and  SO4=
at sites  in the South Coast Air Basin, and  Cheng et al.  (1996) have also applied this
technique to the apportionment of NOX and NH3 in this area.
     The use of 14C isotopic analysis has been  used to distinguish  between fossil fuel  and
biomass sources of carbon in aerosol samples.   An example  would be to determine the
fraction of ambient aerosol mass concentration in wintertime samples originating from
woodburning. This method has been particularly useful in validating  less expensive
receptor methods of achieving the same goal (Wolff et al., 1981; Lewis et al.,  1988).
     The preceding  sections  have dealt with receptor models that rely on chemical
information  obtained from bulk samples.  It is worth  noting that in addition there are
powerful receptor modeling methods  which also use the morphology and composition  of
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individual particles to provide clues to their source origin (Dzubay and Mamane, 1989).
Scanning electron microscopy (SEM) along with energy dispersive X-ray analysis (EDX)
has allowed the size distribution of particles to be characterized according to shape and
elemental  composition.  This technique has proven to be extremely useful for distinguishing
between fly ash and soil derived particles;  both types of particles have  similar  composition,
but fly ash particles are spherical while soil particles are irregularly shaped.
     Manually performing SEM/EDX analyses of the large number individual  particles
necessary  to characterize a size distribution is extremely time consuming.  Automated
methods have been developed for the rapid characterization of the shapes of hundreds of
particles in aerosol samples (Xie et al., 1994a,  1994b).  The morphology  data can then be
used along with EDX data to assign particles to clusters related to specific source types
(Van Espen,  1984).

5.5.3   Source  Contributions  to Ambient Particles Derived  by Receptor
        Models
     Receptor modeling has been used for obtaining information about the nature of
sources of ambient aerosol samples.  The results of several studies will be discussed to
provide an indication of different sources of particulate  matter across the  United  States.
First, results obtained by using the  CMB approach  for estimating contributions to PM2 5 and
PM(10_2 5)  from different source categories  at monitoring sites in the United States east  of
the Mississippi River will be discussed.  Estimated contributions from a number  of source
categories to PM10 in ambient samples, obtained mainly at sites west of the Mississippi
River,  will then be discussed.
     Dzubay et al. (1988) estimated source category contributions to 24-hour PM25 and
PM/jQ.25)  samples obtained  by a dichotomous sampler at three widely separated sites in the
Philadelphia, PA area (NE airport in Philadelphia, PA; Camden, NJ; and  a site about 30 km
to the SW of Camden, NJ) during the summer  of 1982.  They used  a composite  of CMB,
multiple linear regression  (MLR), and wind trajectory receptor models.  Source
compositions shown in Table 5-3 were  obtained partly to provide input to this  study (Olmez
et al., 1988).  Ambient concentrations of individual species used by Dzubay et al. (1988)
are shown in Table 6A-2a (Chapter 6, Appendix A).  Sulfate, associated NH4+ and water
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constituted about 70% of PM2 5.  Since the mean fractional abundances of PM2 5 to PM10
was 0.75, it can be seen that sulfate components contributed approximately 53% of PM10.  Coal-
 and oil-fired power plants located east of the Mississippi  River were found to contribute
50 ± 6% and 11 ± 4% of PM2 5, by using Se as a tracer for coal combustion and V and Ni
as tracers for oil combustion, based on an MLR analysis.
     The study was performed during a period (summer of 1982) when the  Pb content of
gasoline  was declining markedly, and so  Pb could not be used as a unique tracer of motor
vehicle emissions. CMB was used to determine nonvehicular Pb, which was subtracted
from the measured Pb concentration to yield a tracer for vehicle exhaust.  Motor vehicle
exhaust was then found to contribute about 8%, on  average, to  PM2 5.  Local sources  of
sulfate (determined from the MLR intercept) were found to contribute  13%, on average,
with smaller contributions from local industrial sources, incinerators, and crustal matter to
PM2.5.
     Crustal matter constituted about 76%, on average, of PM^10.2 5).  Sulfate and
associated NH4+ and water constituted only  about 7% of PM/10_2 5y  Other contributions to
PM,10_2 5) were found to  arise from sea-salt and incinerators.  In a study of the Philadelphia
aerosol in the summer of 1994, Pinto et al. (1995) found close  agreement with Dzubay et
al. (1988) both in measured concentrations  and in the magnitude of the inferred fractional
contribution of regional sulfate sources.
     Glover et al. (1991) estimated the contributions of different source categories to
24-hour PM2 5 and PM(10_2 5-> samples obtained with a dichotomous  sampler  at a site in
Granite City, IL. Again, sulfate was the  major constituent of PM25, constituting from
59% of PM2 5 with SSW winds to 86.6% of PM2 5  with NNW  winds.  Inferred
contributions from specific source types were also shown  to be strongly dependent on wind
direction. Inferred contributions from iron works ranged from  3.4% with NNW winds to
16.4% with SSE winds.  Inferred contributions from a Pb smelter ranged from 2.8% with
WNW winds to 11.6% with SSW winds.  Inferred contributions from other  sources (e.g.,
motor vehicles, incinerators, other smelters,  and soil) were all typically a few per cent.
     Sulfate was a relatively minor constituent (< 10%) of PM(10_25) samples.  Major
inferred contributions were from iron works, ranging from 5.7% with WNW winds to
53.8% with ENE winds;  soil, ranging from 4.2% with WSW winds to 35.8% with ESE
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winds; street dust, ranging from 1.4% SSE winds to 45.6% with WNW winds; with
generally smaller contributions  from the other sources listed for PM25.
     These results  demonstrate the different nature of PM2 5 and PM,10_2 5) sources
(i.e., PM25 was derived from regional sources, while PM^]0.25^ was derived from local
industries); the utility of wind sectoring to help  locate sources; and the need to obtain
site-specific source composition profiles.  The use of site-specific source profiles instead of
profiles culled from the literature resulted in decreases in predicted error values, especially
for fugitive dust.
     Results obtained at a number of monitoring sites in the central and western United
States obtained by using the CMB model are shown in Table 5-12 for PM10.  The sampling
sites represent a variety of different source characteristics within different regions of
Arizona, California, Colorado, Idaho, Illinois,  Nevada and Ohio.  Several  of these  are
background sites, specifically Estrella Park, Gunnery  Range, Pinnacle Peak, and Corona de
Tucson,  AZ, and San Nicolas Island, CA.  Definitions of source categories also vary from
study to study.  In  spite of these differences, several features can be observed  from the
values in this table.
     Fugitive dust  (geological material) from roads, agriculture and erosion appears as a
highly variable contributor to PM10 at nearly all sampling  sites shown in Table 5-12,
contributing about 40% of the average PM10 mass concentration.  The average fugitive dust
source contribution is highly variable among sampling sites within the same urban areas, as
seen by differences between the Central Phoenix (33  /ig/m3) and Scottsdale (25 /^g/m3) sites
in Arizona, and it is also seasonally variable, as evidenced by the summer and fall
contributions at Rubidoux, CA.  These studies found  that the source profiles for fugitive
dust were chemically  similar, even though the dust came from different emitters, so that
further apportionment into sub-categories  was  not possible by the CMB model alone.  Road
sand often contains salts that allow it to be distinguished from other fugitive dust sources.
It is usually the only exposed fugitive dust source  when  other sources are covered  by
snowpack.   Dust from some construction activities and cement plants  can also be separated
from other sources  due to enrichments in  calcium content of these  emissions, as seen in
studies at Rubidoux, CA, Rillito, AZ (near cement plants), and Pocatello,  ID (near chemical
and fertilizer production plants).
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TABLE 5-12. RECEPTOR MODEL SOURCE CONTRIBUTIONS TO PM
                                                    10
ftg/m3
Sampling Site
Central Phoenix, AZ (Chow et al., 1991)
Craycroft, AZ (Chow et al., 1992a)
Hayden 1, AZ (Garfield) (Ryan et al., 1988)
Hayden2, AZ (Jail) (Ryan et al., 1988)
Phoenix, AZ (Estrella Park) (Chow et al., 1991)
Phoenix, AZ (Gunnery Rg-) (Chow et al., 1991)
Phoenix, AX (Pinnacle Pk.) (Chow et al., 1991)
Rillito, AZ (Thanukoset al., 1992)
Scottsdale, AZ (Chow et al., 1991)
West Phoenix, AZ (Chow et al., 1991)
Bakersfield, CA (Magliano, 1988)
Bakerfield, CA (Chow et al., 1992b)
Oi
OS Crows Landing, CA (Chow et al., 1992b)
10 Fellows, CA (Chow et al., 1992b)
Fresno, CA (Magliano, 1988)
Fresno, CA (Chow et al., 1992b)
Indio, CA (Kim et al., 1992)
Kern Wildlife Refuge, CA (Chow et al., 1992b)
Long Beach, CA (Gray et al., 1988)
Long Beach, CA (Summer) (Watson et al., 1994b)
Long Beach, CA (Fall) (Watson et al., 1994b)
Riverside, CA (Chow et al., 1992c)
Rubidoux, CA (Gray et al., 1988)
Rubidoux, CA (Summer) (Watson et al., 1994b)
Rubidoux, CA (Fall) (Watson et al., 1994b)
Rubidoux, CA (Chow et al., 1992c)
San Nicolas Island, CA (Summer) (Watson et al.,
1994b)
Primary
Time Period Geological
Winter 1989-1990
Winter 1989-1990
1986
1986
Winter 1989-1990
Winter 1989-1990
Winter 1989-1990
1988
Winter 1989-1990
Winter 1989-1990
1986
1988-1989

1988-1989
1988-1989
1986
1988-1989

1988-1989
1986
Summer 1987
Fall 1987
1988
1986
Summer 1987
Fall 1987
1988
Summer 1987

33.0
13.0
5.0
21.0
37.0
20.0
7.0
42.7
25.0
30.0
27.4
42.9

32.2
29.0
17.1
31.8
33.0
15.1
20.7
11.1
11.3
32.6
43.1
34.9
19.2
48.0
1.6

Primary
Motor
Primary Vehicle
Construction Exhaust
0.0
0.0
2.0b
4.0"
0.0
0.0
0.0
13.8b
0.0
0.0
3.0
1.6

0.0
1.4
0.7
0.0
3.0
2.0
0.0
0.0
0.0
0.0
4.0J
4.5
16.1
0.0
0.0

25.0
8.3
0.0
0.0
10.0
5.5
2.9
1.2f
19.0
25.0
5.5
7.7

2.2
2.1
4,0
6.8
4.4
2.2
5.11
6.3
42.8
7.0
5.61
17.3
30.3
10.2
0.9

Primary
Vegetative
Burning
2.3
0.0
0.0
0.0
0.9
0.0
1.0
0.0
7.4
10.0
9.61
6.5

3.4
3.4
9.2'
5.1
7.1
4.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

Secondary
Ammonium
Sulfate
0.2
0.7
4.0
4.0
1.6
1.0
0.9
0.0
0.6
0.4
5.6
5.5

2.8
5.1
1.8
3.6
3.6
3.3
8.0
10.9
3.8
4.8
6.4
9.5
2.1
5.3
3.7

Secondary Misc.
Ammonium Source
Nitrate 1
2.8
0.6
0.0
0.0
0.0
0.0
0.0
0.0
3.6
3.1
0.0
12.7

6.5
7.5
0.0
10.4
4.1
1.5
9.2
0.8
23.2
21.4
21.3
27.4
31.6
21.7
0.5

0.0
1.2'
74.0C
28.0C
0.0
0.0
0.0
11.6s
0.0
0.0
0.5J
1.0m

0.5m
7.0m
0.1'
0.3m
0.2J
0.5m
0.1J
0.1J
0.0)
0.3J
0.3J
0.0>
0.0*
0.4>
0.0)

Misc.
Source
2
0.0
0.0
5.0"
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.5"

1.5"
1.4"
0.0
1.0"
1.0"
1.5"
2.0h
2.2h
2.7h
1.3"
1.0h
5.1h
1.1"
1.5"
4.3h

Misc. Misc.
Source Source
3 4
0.0
0.0
1.0"
1.0"
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.6k

1.2"
1.4"
0.0
O.lk
0.0
0.7k
6.4"
0.0
0.0
1.1°
5.9k
0.0
0.0
5.7°
0.0

0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

Measured
PM10
Concentration
64.0
23.4
105.0
59.0
55.0
27.0
12.0
79.5
55.0
69.0
67.6
79.6

52.5
54.6
48.1
71.5
58.0
47.8
51.9
46.1
96.1
64.0
87.4
114.8
112.0
87.0
17.4


-------
                         TABLE  5-12 (cont'd).  RECEPTOR MODEL SOURCE CONTRIBUTIONS TO PM10
/ig/m3



Sampling Site
Stockton, CA (Chow et al., 1992b)
Pocatello, ID (Houcketal., 1992)
S. Chicago, IL (Hopke et al., 1988)
S.E. Chicago, IL (Vermette et al., 1992)
Reno, NV (Chow et al., 1988)
Sparks, NV (Chow et al., 1988)
Follansbee, WV (Skidmore et al., 1992)
Mingo, OH (Skidmore et al., 1992)
Steubenville, OH (Skidmore et al., 1992)



Time Period
1989
1990
1986
1988
1986-1987
1986-1987
1991
1991
1991


Primary
Geological
34.4
8.3
27.2
14.7V
14.9
15.1
10.0
12.0
8.3


Primary
Construction
0.5
7.51
2.4
0.0
0.0
0.0
0.0
0.0
0.0
Primary
Motor
Vehicle
Exhaust
5.2
0.1
2.8
0.9f
10.0
11.6
35.0
14.0
14.0

Primary
Vegetative
Burning
4.8
0.0
0.0
0.0
1.9
13.4
0.0
4.1
0.8

Secondary
Ammonium
Sulfate
3.1
0.0
15.4s
7.7
1.3
2.7
16.0
15.0
14.0

Secondary
Ammonium
Nitrate
7.0
0.0
__
-
0.6
0.9
—
-
-

Misc.
Source
1
0.7m
0.0
15.1'
0.8'
0.0
0.0
9.3'
3.4'
3.8'

Misc.
Source
2
1.8"
0.0
2.2"
0.3h
0.0
0.0
0.0
11.0"
5.0*

Misc.
Source
3
0.0k
84. lr
0.0
1.1"
0.0
0.2k
0.0
0.0
0.0

Misc.
Source
4
0.0
0.0
0.0
7.78
0.0
0.0
0.0
0.0
0.0

Measured
PM10
Concentration
62.4
100.0
80.1
41.0
30.0
41.0
66.0
60.0
46.0
"Smelter background aerosol.
bCement plant sources, including kiln stacks, gypsum pile, and kiln area.
cCopper ore.
dCopper tailings.
'Copper smelter building.
fHeavy-duty diesel exhaust emission.
8Background aerosol.
hMarine aerosol, road salt, and sea salt plus sodium nitrate.
'Motor vehicle exhaust from diesel and leaded gasoline.
JResidual oil combustion.
Secondary organic carbon.
'Biomass burning.
""Primary crude oil.
"NaCl + NaN03.
"Lime.
PRoad sanding material.
qAsphalt industry.
'Phosphorus/phosphate industry.
'Regional sulfate.
'Steel mills.
"Refuse incinerator.
"Local road dust, coal yard road dust, steel haul road dust.
"Incineration.
Unexplained mass.

-------
     Dust sources constitute 88% of the annual average PM10 National Emissions Inventory
(U.S. Environmental Protection Agency, 1994), but they average more than 50% of the
contribution to average PM]0 concentrations  in only about 40% of the entries shown in
Table 5-12.  The reasons for this apparent discrepancy  are not clear.  In addition to errors
in inventories  or source apportionments, weather-related factors (wind speed and ground
wetness) and the dominance of local sources on spatial scales too small to be captured in
inventories may be involved.  It should be remembered that dust emissions are widely
dispersed and  highly sporadic.  Dust particles also have short atmospheric residence times
and as a result their dominance in emissions inventories may  not be reflected in samples
collected near specific sources.  The contributions from primary motor vehicle exhaust,
residential wood combustion, and industrial sources would be underestimated if values from
the National Emissions Inventory Trends data base (U.S. Environmental  Protection Agency,
1994) were used.  Some  of these deficiencies,  such as fugitive dust emissions, are probably
intractable, and the best that can  be done is to estimate the uncertainties in these emissions
and to value the data  accordingly when decisions are made.
     In Table 5-12, primary motor vehicle exhaust contributions account for up to 40% of
average PM10 at many of the sampling sites.  Vehicle exhaust contributions are also
variable at different sites within the same study area.  The mean value and the variability of
motor vehicle  exhaust contributions reflects the proximity of sampling sites to roadways
and traffic conditions  during the time of sampling.  Vegetation burning,  which includes
agricultural fires, wildfires, prescribed  burning, and residential wood combustion, was found
to be significant at residential sampling sites such as:  Craycroft, Scottsdale, and West
Phoenix, AZ;  Fresno, Bakersfield, and Stockton,  CA; Sparks,  NV; and Mingo, OH. The
predominance  of these contributions  during winter months and the local rather than regional
coverage indicates that residential wood combustion was the major sub-category, even
though chemical profiles are too  similar to separate residential combustion from other
vegetative burning  sources.  For example, Chow  et al. (1988) show substantial differences
between the residential Sparks, NV and urban-commercial  Reno, NV burning contributions
even though these sites are separated by less than 10 km.  Sites near documented  industrial
activity show  evidence of that activity, but not necessarily involving primary particles
emitted by point sources.  Hayden, AZ, for example,  contains a large smelter, but the major
                                          5-64

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smelter contributions  appear to arise from fugitive emissions of copper tailings rather than
stack emissions.  Secondary  sulfate contributions at Hayden were low, even though SO2
emissions from the stack were substantial during the time of the study.  Fellows, CA is in
the midst of oilfield facilities that burn crude oil for tertiary oil extraction.  These have
been converted to natural gas since the 1988 to 1989 study period.  The Follansbee,  WV,
Mingo, OH, and  Steubenville, OH sites are all close to each other in the Ohio River Valley
and show evidence of the widespread  steel mill emissions in that area.
     Marine aerosol is found, as expected, at coastal sites such as Long Beach (average
3.8% of total mass), and San Nicolas Island  (25%). These contributions are relatively
variable and are larger at the more remote sites.  Individual values reflect proximity  to local
sources.  Of great importance are the contributions from secondary ammonium sulfate and
ammonium nitrate in the western United States.  These are especially  noticeable at sites in
California's San Joaquin Valley (Bakersfield,  Crows Landing, Fellows, Fresno, Kern
Wildlife, and Stockton) and  in the Los Angeles area.
     In addition  to these commonly measured components, it is  possible that isotopic ratios
in source  emissions may vary in an informative way with the nature of the combustion
process and with the  geologic age and character of the source input material.  Carbon-14,
for example, has been used to separate contemporary carbon due to vegetative burning from
carbon emitted by fossil fuel combustion  (Currie et al., 1984). Organic compounds (Rogge
et al.,  1991, 1993a, 1993b,  1993c, 1993d,  1993e; Lowenthal et al., 1994; Hildemann et al.,
1991, 1993) show great promise  for further differentiation among sources, but measurement
methods need to  be standardized and made more cost-effective to take advantage of
extended chemical source profiles.
     Several aspects  of the data in Table 5-12 limit the generalizations that can be drawn
from it:
     •  The source contribution estimates for the receptor sites shown are for  different years
        and different times of year.  The results, therefore, do not show the temporal
        variability which may exist in relative  source contributions and should not be used
        to infer differences between  sites.
     •  Samples selected for chemical analysis are often biased toward the highest PM10
        mass concentrations  in these studies, so average source contribution  estimates are
        probably  not representative of annual averages.
                                          5-65

-------
       Many studies were conducted during the late  1980s, when a portion of the vehicle
       fleet still used leaded gasoline.  While the lead and bromine in motor vehicle
       emissions facilitated the distinction of motor vehicle contributions from other
       sources, it was also associated with higher emission rates than vehicles using
       unleaded fuels.  Lead has been virtually eliminated from vehicle fuels.

       Uncertainties of source contribution  estimates are not usually reported with the
       average values summarized in Table  5-12.  Estimates of standard errors are
       calculated in source apportionment studies,  and typically range from 15 to 30% of
       the source contribution estimate.  They are  much higher when the chemical source
       profiles for  different sources are highly uncertain or too similar to distinguish  one
       source from another.

       Different measurement  sites within the same airshed show different  proportions of
       contributions from the same sources.  Most often, the sites in close proximity  to an
       emitter show a much larger contribution from that emitter than sites that are distant
       from that emitter, even by distances as short as  10 km  (e.g., Chow et al.,  1988;
       1992c).

       Given the mass, trace element, ion, and carbon components measured in source and
       receptor samples in most of the studies from Table 5-12, greater differentiation
       among sources (e.g., diesel and gasoline vehicle  exhaust, meat cooking and other
       organic carbon sources, different sources of fugitive dust, and secondary  aerosol
       precursors)  is not possible for the studies shown  in Table 5-12.
5.6   SUMMARY AND CONCLUSIONS

     Ambient particulate matter contains both primary and secondary components.  Due to
the complexity of the composition of ambient PM10, sources are best discussed  in terms of

individual constituents of both primary and secondary PM10.  Each of these constituents can
have anthropogenic and natural sources, as shown in Tables 5-1A and 5-IB.  The

distinction between natural and anthropogenic sources is not always obvious.  While

windblown dust might seem to be the result of natural processes, highest emission rates are

associated with agricultural activities in areas that are susceptible to periodic drought.

Examples include the dust bowl region of the midwestern United States and the Sahel of

Africa.  Most forest fires in the United States may ultimately  be of human origin, either

through prescribed burning or accident.

     Windblown dust from whatever  source  represents the largest single source of PM in

U.S. and global emissions inventories. Although dust emissions (88% of total U.S.  PM10)

                                         5-66

-------
are far in excess of any other source of primary or secondary PM10 in any region of the
country, measurements of soil constituents  in ambient samples suggest that the overall
contribution from this source could be much lower.  The reasons for this apparent
discrepancy are not clear.  In addition to errors in inventories or source apportionments,
weather-related  factors (wind speed and ground wetness) and the dominance of local
sources on spatial scales too small to be captured in inventories may be involved.  It should
be remembered  that dust emissions are widely dispersed and highly sporadic. Dust particles
also have short  atmospheric  residence times and,  as a result, their  dominance in emissions
inventories may not be reflected in samples collected near specific  sources.
     There is a great deal of spatial and temporal variability which is still not reflected in
emissions inventories.  Apart from seasonal variability,  many of the sources discussed  in
this chapter are  highly  episodic even within their peak emissions seasons.  Examples
include the long-range  transport of Saharan dust to the United States, regional dust storms,
volcanism, and  forest fires.  Their spatial variability  is also  evident.  Annual estimates  for
an area can easily be exceeded  in a few days by unusual events involving these sources.
Less dramatic examples of strong seasonal  variability, such  as wood burned for home
heating in the northwestern United States, may be the major source  of winter PM there.
     It might be thought that enough data are  available to adequately characterize mobile
and stationary source emissions.  However,  data characterizing the  variability of PM
emissions from  mobile sources  are quite sparse.  Available data suggest that elemental
carbon followed by organic carbon species  are the major components of diesel particulate
emissions,  while organic carbon emissions  are larger than elemental carbon emissions in the
case of gasoline fueled vehicles.
     Emissions  from biomass burning are also composed mainly of organic  carbon  species
and elemental carbon, although the ratio of organic carbon to elemental carbon is  much
higher than in motor vehicle emissions.  Power plant emissions are not significant sources
of aerosol carbon.  The fractional yield of secondary organic carbon from the oxidation of
natural and anthropogenic  hydrocarbons is highly uncertain.   Yields from the oxidation of
anthropogenic hydrocarbons  are probably less than a few percent, and larger yields are
found in the oxidation of terpenes emitted by vegetation.
                                          5-67

-------
     As seen in Table 5-IB, emissions  of surface dust, organic debris, and sea spray are
concentrated mainly in the coarse fraction of PM10 ( > 2.5 urn aero. diam.). A small
fraction of this material is in the PM25  size range ( <  2.5 urn aero, diam., c.f. Figure 5-1).
Nevertheless, concentrations of crustal material can be appreciable especially during dust
events.  It should also be remembered that all of the Saharan dust reaching the United
States is in the PM2 5 size range.  Emissions from combustion sources (mobile and
stationary sources, biomass burning) are predominantly in the PM2 5 size  range.
     As shown in Table 5-6, estimated  primary PM10  emissions decreased by about 10%
from 1985  through 1993. A high degree of variability is evident for emissions from
miscellaneous  (fugitive dust, biomass burning, and agriculture) and natural (wind erosion of
natural surfaces) categories shown in Table 5-7.  Estimated SO2 emissions decreased by
several per cent from 1984 through 1993 as shown in  Table 5-8.  Estimated emissions of
NOX show  little variation over the same time period as shown in Table 5-9.  Emissions of
primary PM10 are  projected to increase  to the year 2010 mainly because of increases in
fugitive dust emissions, while emissions of SO2 and NOX are expected to decrease  over the
same time period.
     Uncertainties in emissions inventories  are difficult to quantify.  They may be as low
as 10% for well-defined  sources (e.g., for SO2) and may range up to a factor of 10 or so
for windblown dust.  As a rule, total  PM  emissions rates should be regarded as
order-of-magnitude estimates. Because of the large uncertainty associated with emissions
of suspended dust, trends of total PM10 emissions should be viewed with caution and
emissions from specific source categories are best discussed on an individual basis.
     Emissions  inventories  are generally not the most appropriate way to apportion material
in ambient samples.  Receptor modeling has proven to be an especially valuable tool in this
regard.  Compositional profiles developed for receptor modeling applications are perhaps
the most accessible and reliable means  to characterize  the  composition of emissions.
Quoted uncertainties  in source apportionments of constituents in ambient aerosol samples
typically range from  15 to 30%.  Receptor modeling studies in the western United States
have found that motor vehicles and fugitive dust are major sources of PM10.  Likewise,  a
limited number of studies in the eastern United  States  have found that fossil fuel
combustion and fugitive dust are major sources  of PM10.  Techniques are currently being
                                          5-68

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developed to use receptor modeling techniques along with ambient data to refine emissions
inventory estimates.  Because of the site-specific nature of receptor modeling results, more
rigorous methods for determining site locations and methods for applying receptor model
results to larger spatial  scales are needed for this purpose.  Again, it should be emphasized
that, because of limitations in receptor modeling methods in treating secondary components,
these efforts are more likely  to be successful for primary components,  although it should be
mentioned that methods are being developed to apportion secondary constituents.
                                          5-69

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         6.  ENVIRONMENTAL CONCENTRATIONS
6.1   BACKGROUND, PURPOSE, AND SCOPE
     This chapter summarizes the concentrations of particulate matter over the United States,
including the spatial, temporal, size and chemical aspects.  The information needs for
assessing the major aerosol effects of concern are summarized in Table 6-1. The general
approach followed in preparing this chapter was to organize, evaluate, and summarize the
existing large scale aerosol data sets over the United States. Emphasis was placed on complete
national coverage as well as the fusion and reconciliation of multiple data sets.
     Space is the main organizing dimension used to  structure this chapter.  Aerosol
concentration data are presented on global, continental, national, regional, and
sub-regional/urban scales.  Within each spatial domain, the spatial-temporal structure, size,
and chemical composition are presented.  An overview of the pattern analysis methods is given
in the remainder of Section 6.1.  The presentation of aerosol patterns begins from the global
and continental perspective (Section 6.2).  Next, nationwide aerosol patterns (Section 6.3)
derived  from nonurban and urban PM10 and PM2 5 monitoring networks are examined.
Section 6.3 also includes a discussion of various measures of background PM2 5 and PM10.
In Section 6.4 the aerosol characteristics over seven subregions of the conterminous United
States are examined in more detail. The 10-year trends, seasonal patterns, relationships
between PM2 5 and PM10, and fine particle chemical composition are examined for each
region.  Section 6.5 focuses further on the subregional and urban-scale aerosol pattern over
representative areas of the United States.  Section 6.6 presents more detailed information on
the chemical composition of the aerosol from a number of intensive field studies.  Section 6.7
deals with measurements of fine particle acidity.  Section 6.8 focuses on the concentration  of
ultrafine particles and Section 6.9 on the chemical composition of ultrafine particles.  Section
6.10 examines trends and relationships for PM2 5,  PM(10.2.5), and PM10 in data bases having
long term data on both components.
     Aerosol concentration data for the United States have been reported by many aerosol
researchers over the past decade. This chapter draws heavily on the contribution, of research
                                        6-1

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groups that have produced data, reports, and analyses of nonurban data. However, their
maps, charts, and computations have been re-done for consistency with urban data reports.

6.1.1    Dimensionality and Structuring of the Aerosol Data Space
     Aerosol concentration patterns contain endless detail and complexity  in space, time, size,
and chemical composition.  Aerosol samples from the conterminous United States reveal the
coexistence of sulfates, hydrogen ions, ammonium, organic carbon (OC), nitrates, elemental
carbon (EC [soot]), soil dust, sea salt, and trace metals. This chemically rich aerosol mixture
arises from the multiplicity of contributing aerosol sources, each having a unique chemical
mixture for the primary aerosol at the time of emission. The primary aerosol chemistry is
further enriched  by the addition of species during atmospheric chemical processes. Finally,  the
immensely effective mixing ability of the lower troposphere stirs these primary and secondary
particles into a mixed batch with varying degrees of homogeneity, depending on location and
time.
     A major consideration in structuring the aerosol pattern analysis is that it has to be
consistent with the physical and chemical processes that determine the concentrations of the
aerosol.  The concentration of paniculate matter (C),  at any given location and tune is
determined by the combined interaction of emissions (E), dilution (D), and chemical
transformation and removal processes (T),expressed as:

                                      C=f(D,T,E)

     Dilution, transformation/removal, and emissions are generic operators and can, in
principle, be determined from suitable measurements  and models.  However, for consideration
of aerosol pattern analysis it is sufficient to recognize and separate these three major causal
factors influencing the aerosol concentration pattern.
     It is convenient to categorize the highly variable aerosol signal along the following major
dimensions: space, time, size and  chemical composition. The  dependence of concentration on
space and time is common to all pollutants.  However, both the distribution with respect to
particle size as well as the chemical distribution within a given size range constitute unique
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dimensions of paniculate matter that are not present for other pollutants.  The concentrations
of single-compound gaseous pollutants can be fully characterized by their spatial and temporal
pattern. This classification by dimensions is consistent with the size-chemical composition
distribution function introduced by Friedlander (1977). It could be said that particulate matter
is a composite of hundreds of different substances exhibiting a high degree of spatial and
temporal variability.

6.1.2   Spatial Pattern and Scales
     The spatial dimension covers the geographic scale and pattern of aerosols. Based on
consideration of emissions,  meteorology, and political boundaries, the spatial dimension can
be broken into global, national, regional-synoptic,  meso,  urban, and local scales. Some of the
characteristics of these spatial scales are illustrated in Table 6-1.
                   TABLE 6-1. SPATIAL REGIONS AND SCALES
Global
Continent
10,000-
50,000 km
National
Country
5,000 -
10,000km
Regional
Multi-state
1,000-
5,000 km
Meso
State
100-
1,000km
Urban
County
10 - 100 km
Local
City center
1-10 km
6.1.3   Temporal Pattern and Scales
     The time dimension of aerosols extends over at least six different scales (Figure 6-1).
A significant, unique feature of the temporal domain is the existence of periodicities.  The
secular time scale extends over several decades or centuries. Given climatic and chemical
stability of the atmosphere the main causes of secular concentration trends are changes in
anthropogenic emissions. Emissions, atmospheric dilution, as well as chemical/removal
processes, can be influenced by the seasonal cycle.  The synoptic scale covers the duration of
                                          6-3

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                                      Dilution |X I Chemistry/Removal |sa| Concentration
          Secular
           Yearly
          Weekly
         Synoptic
             Daily
       Microscale
                       Minutes
Figure 6-1.  Time scales for particle emissions.


synoptic meteorological events (3-5 days).  Its role is primarily reflected in dilution and
chemical/removal processes.  The daily cycle strongly influences the emissions, dilution, and
chemical/removal processes.  Microscale defines variation of the order of an hour caused by
short-term atmospheric phenomena.  In the analysis that follows we will emphasize secular
trends and yearly cycles,  with some consideration of daily aerosol pattern.  The microscale
patterns will be largely ignored.

6.1.4    Space-Time Relationships
     The spatial and time scales of aerosol pattern are linked by the atmospheric residence
time of particles.  Short residence times restrict the aerosol to a short transport distance from a
source, causing strong spatial and temporal gradients. Longer residence times yield more
                                          6-4

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            10'
            10'
e
"5
E
20 urn)
           10
                      10'
10'
10'
10'
10'
                                Residence Time, seconds
10
Figure 6-2.  Relationship of spatial and temporal scales for coarse and fine particles.
uniform regional patterns caused by long range transport.  The relationship between spatial
and temporal scales for coarse and fine particles is illustrated in Figure 6-2.
     The aerosol residence time itself is determined by the competing rates of chemical
transformations and removal. Secondary aerosol formation tends to be associated with multi-
day long range transport because of the time delay necessary for the formation. For sulfates,
for example, the residence time is 3-5 days. For fine particles, 0.1 //m to several /urn, the
main removal mechanism involves cloud processing, while coarse particles above 10 Atm are
deposited by sedimentation.  Ultrafine particles, below 0.1 fj.m, also rapidly coagulate to form
particles in the 0.1 to 1.0 /urn size range.  Another factor which must be considered is local
turbulence.  As a consequence of low removal rates, aerosols in the 0.1-1.0 /um size range
reside in the atmosphere for longer periods than either smaller or larger particles (Figure 6-3).
                                          6-5

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                     10'
                        10~~  10'°   10'"   10"'  10"   10'   10"   10
                                   Radius, pen
Figure 6-3.  Residence time in the lower troposphere for atmospheric particles from 0.1 to
             1.0 jim.  ( — Background aerosol, 300 particles cm*3; — continental aerosol,
             15,000 particles cm'3.)
Source: Jaenicke (1980).
 If aerosols are lifted into the mid- or upper-troposphere their residence time will increase to
 several weeks. Large scale aerosol injections into the stratosphere through volcanoes or deep
 convection result in atmospheric residences of a month or two months for ash and ^ 2 years
 for sulfates formed from SO2 oxidation.
       In the context of the specific analysis that follows, the space-time-concentration
 relationship in urban and mountainous areas is of particular importance (Figure 6-4). Urban
                                ^'4,
 areas have strong spatial emission gradients and also may have corresponding concentration
 gradients for directly emitted species, particularly in the winter under poor horizontal and
 vertical transport conditions.
       In mountainous regions, the strong concentration gradients are caused by both
 topography that limits transport as well as the prevalence of emissions in valley floors.  Strong
                                             6-6

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 Summer
 rural
 Winter
 rural
urban
 -•;.-:V.r-:----^
urban
rural
   ^•{•^^•'
rural
                               Summer
mountain
valley
                                                Winter
                                                  mountain
                                                 valley
Figure 6-4.  Space-time relationship in urban and mountainous areas.
mountain
                                           mountain
wintertime inversions tend to amplify the valley-mountain top concentration difference. Fog
formation also accelerates the formation of aerosols in valleys

6.1.5   Particle Size Distribution
     The aerosol size distribution is of importance in quantifying both the formation
(generation) as well as the effects of aerosols.  Condensation of gaseous substances during
combustion in the atmosphere generally produces fine particles below 1 jum in diameter.
Resuspension of soil dust and dispersion of sea spray produces coarse particles above 1 /*m.
     The size distribution of particles also influences both the atmospheric behavior and the
effects of aerosols.  Atmospheric coagulation, cloud scavenging,  and removal by impaction
and settling are strongly size dependent (Figure 6-3). The effects on human health depend on
size-dependent lung penetration. The effects of light scattering on visibility and climate are
also strongly dependent on particle size.
     Measurements over the past decades (Whitby et al., 1972; Whitby, 1978) show that
atmospheric aerosols may be classified as fine mode particles or coarse mode particles. The
size distribution of atmospheric particles is discussed in Section 3.7. The sources, formation
mechanisms, and chemical compositions of these two aerosol modes are different. In general,
                                           6-7

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the two aerosol size modes have independent spatial and temporal patterns as described
throughout this chapter. Coarse dust particles tend to be more variable in space and time and
can be suspended through natural or human activities.  Fine particles during the wanner
months of the year are largely of secondary origin and their spatial-temporal pattern is more
regional.  Notable exceptions are urban-industrial hotspots and mountain valleys where
primary submicron size smoke particles can prevail.

6.1.6   Aerosol Chemical Composition
     The chemical composition of atmospheric aerosol is believed to influence the effect on
human health.  While the causal mechanisms are not fully understood, the acidity,
carcinogenicity, and other forms of toxicity are chemical properties considered relevant to
human health.
     The aerosol chemical composition has also become an important property for identifying
source types based on chemical "fingerprints" in the ambient aerosol. Since aerosols reside in
the atmosphere for days and weeks,  there is a substantial amount of mixing that takes place
among the contributions of many sources. At any given "receptor" location and time, the
aerosol is a mixture of many source  contributions each having a chemical signature for
possible source type identification.
     Fine particles  are generally composed of sulfates, hydrogen ions, ammonium, organics,
nitrates, elemental carbon (soot), as  well as a portion of the trace metals (Section  6.6). Each
major chemical form has sub-species such as acidic and neutral sulfates, light and heavy
organics, ammonium and sodium nitrates, etc.
     The chemical composition of coarse particles is dominated by the elements of the earth's
crust, Si, Al, Fe, and other elements commonly found in soil. Near industrial sources, coarse
particles may be contaminated by lead and other trace metals.  At ocean shores, coarse
particles may consist of sea salt arising from breaking of waves.   Both resuspended dust and
sea salt are primary particles, carrying the chemical signatures of their sources.
                                          6-8

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6.2   GLOBAL AND CONTINENTAL SCALE AEROSOL PATTERN
     There are two data sets which can be used to provide information on fine particle
concentration patterns on continental and global scales. Routine visibility distance
observations, recorded hourly at many U.S.  airports by the U.S. Weather Service, provide an
indication of fine particle pollution over the United States. The visibility distance data have
been converted to aerosol extinction coefficients and used to access patterns and trends of
aerosol pollution over the United States (Husar et al.,  1994; Husar and Wilson, 1993).
Routine satellite monitoring of backscattered solar radiation over the oceans by  the Advanced
Very High Resolution Radiometer sensors on polar orbiting meteorological satellites provides
a data set which can be used to give an indication of aerosol over the world's oceans.  These
two data sets have been merged to provide a global and continental perspective.  The data
analyses presented here were performed for this Criteria Document and have  not yet been
published elsewhere.
     Aerosol detection over the oceans is facilitated by the fact that the ocean reflectance at
0.6 /mi  is only 0.02.  Hence, even small backscattering from aerosols produces a measurable
aerosol  signal. The backscattering is converted to a vertically integrated equivalent aerosol
optical thickness assuming a shape for the aerosol size distribution or phase function.  Clouds
are eliminated by a cloud mask, so the data are biased toward clear-sky conditions.  The
oceanic  aerosol maps represent a two-year average (July 1989-June 1991) prior to the eruption
of Mt. Pinatubo, when the stratosphere was unusually clear of aerosol.  Consequently, the
images reflect mainly the spatial pattern of tropospheric aerosol.
     A  continental-scale perspective for North America is shown  in Figure 6-5. Seasonal
depictions of the oceanic aerosol  for the entire globe are shown in Figure 6-6.  The average
aerosol  map of Eastern North America for June, July and August (Figure 6-5) shows areas of
high optical depth over the Mid-Atlantic States and over the Atlantic Ocean.  The aerosol
concentration over the oceans is highest near the coast and declines with distance from the
coast. This indicates that the aerosol is of continental origin and represents a plume
originating in eastern North America, heading north-east across the Atlantic ocean.  This
plume can also be seen in the spring and summer season oceanic aerosol patterns shown in
Figure 6-6.
                                          6-9

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                           March,  April,
                 ontinental - Visibility
Oceanic - AVHRR Satellite:
                         July,  August,   Sept
                         - Visibility
                            	
                                            Oceanic  - AVHRR Satellite
Figure 6-5.  Continental scale pattern of aerosols derived from visibility observations over

            land and satellite monitoring over the oceans:  North America.
                                       6-10

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           9
Figure 6-6.   Global pattern of oceanic aerosols derived from satellite observations.

-------
     The continental aerosol extinction coefficient data for the southwest coast of North
America indicate elevated aerosol extinction over southern California. The area includes the
hazy South Coast and the San Joaquin Valley air basins. It is interesting to note that somewhat
elevated aerosol optical thickness is also recorded  over the Pacific near Southern California.
However, the low aerosol signal and the semi-quantitative satellite data preclude a clear cause
and effect association.
     The seasonal aerosol pattern over the oceans reveals that the highest aerosol signal is
found  near the tropics, where wind-blown dust and biomass burning in Africa and southern
Asia produce 5,000 km long aerosol plumes (Figure 6-6). Further aerosol belts which may be
of marine origin are observed just north of the Equator and at 30 to 60° latitudes in both
hemispheres. The backscattering in the summer hemispheres exceeds the winter values by a
factor of 5 to 10. There is a pronounced seasonality in each aerosol region (Figure 6-7); the
higher aerosol levels appear in the summer hemisphere although many continental and marine
regions show a spring maximum. Thus, the global tropospheric aerosol is a dynamic
collection of independent aerosol regions, each having unique sources and temporal patterns.
     The seasonal oceanic aerosol maps show  two distinctly different spatial patterns:  aerosol
plumes originating from continents, and oceanic aerosol patches that are detached from the
continents. The continental aerosol plumes are characterized by high values near the coastal
areas and a decline with distance from the coast. The  most prominent aerosol plume is seen
over the equatorial Atlantic, originating from West Africa and crossing the tropical Atlantic.
It is the well known Sahara dust plume.  Additional continental plumes emanate form
Southwest Africa, Indonesia, China-Japan, Central America and  eastern North America.
Aerosols which may be of marine origin dominate large zonal belts (30 to 60° N and S) in the
summer hemispheres as well as near the Equator.  In summary, the global tropospheric aerosol
is a collection of largely independent aerosol regions, each having a bio-geochemically active
source and unique spatial temporal pattern.
     Based on  the above global and continental-scale observations, it can be concluded that the
continental plume from eastern North America is not as intense as those from  other industrial
and non-industrial regions of the world.  However, quantitative aerosol comparisons of global
regions are not available.
                                          6-12

-------
    0
     H DO cr tr >
     < LJJ < Q. <
     ^ "- S < 2
        -i o Q-  !TT > o
        33E8i£
                                      160
                                      140
                                      120
                                      100
                                       80
                                       60
                                       40
                                       20
                                        0
                                                       NW Pacific.                b

                                                                          N Atlantic
                EC Pacific
                                                         New Zealand
       CD
       LU
CC>Z=iOQ-l->O
0-<^2^lijOoiiJ
<5-5^<(/>OzQ
                                                                           SE Pacific
                  N 30-60
              S 30-60
             Shifted 6 months
                                                             N Hemisphere
                                                                    f
Figure 6-7.   Seasonal pattern of oceanic aerosols derived from satellite observations.
                                        6-13

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6.3   U.S. NATIONAL AEROSOL PATTERN AND TRENDS
     Our current understanding of the U.S. national aerosol pattern arises from nonurban,
regional background monitoring networks, the Interagency Monitoring of Protected Visual
Environments (IMPROVE) (Sisler et al., 1993) and the Northeast States for Coordinated Air
Use Management (NESCAUM) (Poirot et al., 1990, 1991), and from a mainly urban network,
the Aerometric Information Retrieval System (AIRS) (AIRS,  1995). The nonurban and urban
networks yield markedly different national patterns, particularly over the western United
States.  For this reason the results from the two sets of observations are presented separately
and the differences between two networks are evaluated. The data analyses presented here
were performed for this Criteria Document and have not yet been published elsewhere.

6.3.1   Nonurban National  Aerosol Pattern
     Nonurban aerosol concentrations are measured at remote sites, away from
urban-industrial activities.  Size-segregated aerosol mass and chemical composition data are
available for 50 sites,  through the IMPROVE (Joseph  et al., 1987; Eldred et al., 1987, 1988,
1990; Eldred and Cahill, 1994) and NESCAUM (Poirot et al.,  1990, 1991;  Flocchini et al.,
1990) networks.  These are located mostly in national  parks and wilderness  areas.  The PM10
and PM2 5 mass concentrations are sampled and analyzed on separate filters. The sampling
frequency is generally twice a week (Wednesdays and Saturdays) for 24 hours. The PM2 5
samples are analyzed for chemical composition which  makes  the data sets suitable for chemical
mass balance computations (e.g.,  Sisler et al., 1993; Malm et al., 1994b). The
IMPROVE/NESCAUM aerosol data are available from 1988 through  1993.
     Measurements of PM are available from the IMPROVE/NESCAUM network at a
smaller number of sites compared to the number of sites for which measurements are available
from the AIRS network. The nonurban sites also have very different geographical
distributions from those sites in the urban network.  Therefore, the ability to compare PM10
concentrations from the nonurban and urban networks is severely limited by these factors.
     The monthly distributions of chemical species, the chemical mass balances, obtained
from the measurements at nonurban sites are incomplete. Only sulfate, organics, soil, and
soot (elemental carbon) are considered. The contributions of hydrogen ion, water, trace

                                        6-14

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metals and sea salt are not listed.  The contribution of nitrate is included on a national basis,
but not in the subsequent discussion for regions.
     The results of the national spatial and temporal pattern analysis are presented in quarterly
contour maps and monthly seasonal time charts. The contours drawn for the eastern United
States are derived from only 15 to 20 stations.  As a consequence, these contour lines are to be
taken as guides to the eye and not as actual patterns.  The quarters of the year are calendrical.

6.3.1.1   Nonurban PM2 5 Mass Concentrations
     Maps of seasonal average nonurban PM2 5 concentrations are shown in Figure 6-8.  The
maps divide the country roughly into eastern and western halves. The eastern United States is
covered by large, contiguous PM2 5 concentrations that range from 10 /ug/m3 in Quarter 1, and
17 //g/m3 in Quarter 3.  During the transition seasons (Quarters 2 and 4) the eastern U.S.
nonurban PM2 5 concentrations are at about 12 /ug/m3.  Within the eastern U.S., there are
subregions such as New England that have lower concentrations ranging between 8 and
12 Aig/m3. During the third quarter, there is a wider range of geographic distribution of PM2 5
concentrations in the eastern United States than in other quarters of the year.
     The lowest nonurban PM2 5 concentrations are measured over the central mountainous
western states.  The low winter concentrations are at about 3 /wg/m3, while the summer values
are around 6 Atg/m3.  Somewhat elevated PM2 5 concentrations are observed over the
southwestern border adjacent to Mexico as well as in California and the Pacific Northwest.
The nonurban fine particle mass clearly shows multiple aerosol regions over the conterminous
U.S., each exhibiting unique spatial and seasonal characteristics.

6.3.1.2   Nonurban  Particulate Matter Coarse Mass Concentrations
     In classifying size fractions of PM, PM10 refers to PM collected in a sampler with a
50% cutpoint of 10 /*m aerodynamic diameter and PM2 5 to PM collected in a sampler with a
cutpoint of 2.5 /mi aerodynamic diameter.  PMCoarse or coarse will be used to refer to the
PM between the cutpoints of 2.5 and 10 /un, whether determined by  subtracting a PM2 5
sample mass from a PM10 sample mass or determined directly from the coarse particle channel
of a dichotomous sampler with a PM10 (or PM15) /mi diameter upper cutpoint.  Fine will also
                                         6-15

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                                                  Quarter 1
Quarter 2
o\
                            Fine Mass
                                                  Quarter 3
                                                                                       Fine Mass
                            Fine Mass        '                                            Fine Mass




     Figure 6-8. Fine mass concentration derived from nonurban IMPROVE/NESCAUM networks.
Quarter 4

-------
be used to refer to PM2 5. PM2 5 is an indicator of the fine mode particle mass but it is not an
exact indicator, since PM2 5 may contain some coarse mode PM. Likewise, PMCoarse or
coarse refers to the inhalable fraction of the coarse mode, not the entire coarse mode.  Under
high relative humidity conditions PMCoarse may contain some fine mode PM.
     The nonurban coarse aerosol mass concentration in the size range 2.5 to 10 pm is given
in the seasonal maps in Figure 6-9. It is plotted on the same concentration scale as the
nonurban PM2 5 and PMCoarse maps to show that the nonurban coarse mass concentration is
less than the fine mass concentration over most of the country. The lowest nonurban coarse
particle concentration is recorded during the first, second, and fourth calendar quarters when
virtually the entire conterminous United States showed values <  10 //g/m3.  The industrialized
Midwest, adjacent to the Ohio River, shows low PMCoarse concentration (< 10 Mg/m3)
comparable to the relatively clean Rocky Mountains states. The  highest nonurban coarse mass
concentrations appear during quarters 2  and 3. In quarter 2, the  southwestern United States
adjacent to the Mexican border shows the highest nonurban coarse mass concentrations. In
quarter 3,  the monitoring sites hi Florida and Southern California exhibit high concentrations
6.3.1.3   Nonurban PM10 Mass Concentrations
     Maps of seasonal average nonurban PM10 concentrations are shown hi Figure 6-10.
PM10 is the sum of the PM2 5 and PMCoarse. The spatial pattern from east to west,  including
the delineation of aerosol regions, is generally similar to the PM2 5. However, the PM10
concentrations exceed the PM2 5 by up to a factor of two depending on region and season.
The sparseness of nonurban sites over large areas of the central United States limits the
reliability of profiles in these areas.
     In the eastern U.S., PM10 concentrations range between 12 //g/m3 in Quarter 1  and
25 jug/m3 in Quarter 3.  During the transition seasons (Quarters 2 and 4) the eastern  U.S.
non-urban PM10 concentrations are about 15 ^g/m3, except hi New England. The lowest
PM10 concentrations are  measured over the central mountainous states, 5 A*g/m3 hi Quarter 1,
10 /ig/m3 in Quarter 3, and 7 yug/m3 during the transition seasons.  Higher PM10
concentrations, between  10 and 20 //g/m3, were measured over the southwestern United States
                                         6-17

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                                                    Quarter 1
Quarter 2
                             Coarse Mass
                                                                                        Coarse Mass
o\
H-*
00
                                                    Quarter 3
Quarter 4
                             Coarse Mass
                                                                                        Coarse Mass
     Figure 6-9.  Coarse mass concentration derived from nonurban IMPROVE/NESCAUM networks.

-------
                                        Quarter 1
Quarter 2
                   PM10 Mass
                                                                             PMIOMass
                                        Quarter 3
Quarter 4
                   PM10 Mass
                                                                             PM10 Mass
Figure 6-10. PM10 mass concentration derived from nonurban EVfPROVE/NESCAUM networks.

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as well as over the Pacific states from California to the Northwest than over the central
mountainous states.

6.3.1.4     PM2 5/PM10 Ratio at Nonurban Sites
     The PM10 aerosol mass is composed of fine mass (PM2 5) and coarse mass,  below
(Figure 6-10). Both the sources and the effects of fine particles differ markedly from those of
coarse particles.  For this reason it is beneficial to examine the relative contribution of PM2 5
and PM10 concentrations.  Figure 6-11 shows the seasonal fine mass as a fraction of PM10.
     Nationally, the fine fraction at nonurban sites ranges between 0.4 and 0.8.  The highest
fine fraction is recorded east of the Mississippi River, where 60 to 70% of the PM10 mass is in
particles  <2.5 jum in size.  This is also the region that shows the highest PM10 concentrations;
thus, fine particles dominate the nonurban aerosol concentrations east of the Mississippi River.
The fine  fraction exceeds the coarse fraction at the nonurban northwestern sites.  The fine
fraction is the lowest in the southwestern United States (<  50%), particularly in  the spring
season (Quarter 2).
     Spatial and seasonal variation of the fine fraction is a further indication for the existence
of different aerosol regions over the conterminous U.S. This is further illuminated in Section
6.4 where the aerosol characteristics over different regions of the United States are discussed.

6.3.1.5    Nonurban Fine Particle Chemistry
     The elemental composition of nonurban fine particles over the conterminous United
States is now reasonably well understood.  The IMPROVE/NESCAUM network  provides over
five years of aerosol mass and  chemical composition data.  The data from these networks
allows the chemical apportionment of the fine particle mass into aerosol types such as sulfates,
organic carbon, elemental carbon, and fine soil (Schichtel and Husar,  1991; Sisler et al., 1993,
Sisler and Malm,  1994).  The quantification of these aerosol types is relevant to both the
determination of aerosol effects and source apportionment of particle mass. It should be
emphasized that urban areas, mountain valleys, and remote monitoring sites are likely to have
different  relative concentrations of the  aerosol types.  Also, the quantification of  semivolatile
organic compounds, nitrates, and other unstable species is subject to major uncertainties.
                                         6-20

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                                                                                                                    Quarter 2
                        Fine Fraction of PM10 Mass
                                                                                       Fine Fraction of PM10 Mass
to
                                                     Quarter 3
                        Fine Fraction of PM10 Mass   2                                        Fine Fraction of PM10 Mass
     Figure 6-11. Fine fraction of PM10 derived from nonurban EMPROVE/NESCAUM networks.
Quarter 4

-------
Much work remains in order to define the chemical, as opposed to the elemental, composition,
especially for organic compounds.
     At nonurban eastern U.S. sites, a large fraction of the fine aerosols are composed of
sulfate and related species (ammonium ions, hydrogen ions, and associated water) and organic
compounds. In the northeastern and southeastern U.S., organic carbon appear to equal sulfate
in the fourth quarter of the year.  In the southwestern U.S., wind blown dust is a major
component of fine mass while sulfate is less important (Schichtel and Husar, 1991).
     Annually averaged fine particle sulfate, as ammonium sulfate; organic carbon; elemental
carbon; and nitrate, as ammonium nitrate, concentrations from the IMPROVE network across
the U.S.  are shown in Figures  6-12 and 6-13 (Sisler et al., 1993; Malm et al., 1994b).  The
station density is limited, especially in the eastern U.S.  The contour lines in the annual
average maps are to be used as guides to the eye, rather than precise values. Concentrations of
sulfate in the eastern U.S. (Figure 6-12a) exceeds those over the mountainous western states by
factor of five or more. Elevated sulfate in excess of 1 Atg/m3 is also reported over the Pacific
coast states. Sulfates typically contribute over 50% of the fine particulate mass in the eastern
U.S., while sulfates contribute 30% or below in the West.
     Fine particle nitrates (Figure 6-12b) are highest in California, exceeding 4 ^g/m3  at most
sites. Their share of the fine mass at several California sites exceeds 20%.  Organic carbon
concentrations (Figure 6-13a) are high over California and northwestern sites,  as well as at the
eastern U.S. sites. Organic carbon contributes over 50%  of the fine particle mass in the
Northwest, and about 30% throughout the eastern U.S.  There is a high degree of uncertainty
associated with the measurement of particulate nitrate and organic carbon because of artifacts
arising from the adsorption of vapors or the loss of semivolatile materials. The elemental
carbon concentrations (Figure 6-13b) are significant over the Northwest and southern
California, as well as at the Washington, DC, site.  Over most of the country elemental carbon
is 5% or less of the fine particle mass.
     The chemical composition of PM10 and PM2 5 aerosols in the IMPROVE network
(Eldred et al.,  1994b) revealed that the average coarse mass does  not differ significantly
between  the East and West; however, the fine mass is higher in the East.  Also about 80% of
                                          6-22

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to
U)
                                                                                                             % NO
    Figure 6-12. Yearly average absolute and relative concentrations for sulfate and nitrate.



    Source:  Sisler et al. (1993) and Malm et al. (1994b).

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ON
                                               Organic
                                               Carbon, pg/m3
Elemental
Carbon, ug/m3
                                                                                                 % Elemental
                                                                                                   Carbon
    Figure 6-13. Yearly average absolute and relative concentrations for organic carbon and elemental carbon.

    Source: Sisler et al. (1993) and Malm et al. (1994b).

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soil elements and 20% of sulfur were found in the coarse fraction.  Most trace elements were
found hi the fine fraction, both hi the East and in the West. The spatial and seasonal patterns
hi particle concentrations and then- relationships to optical extinction in the United States from
the IMPROVE network were summarized by Malm et al. (1994b).
      In studying the regional patterns of nonurban trace metals in the IMPROVE network,
Eldred et al. (1994a) found a good correlation between selenium and sulfur at all sites hi the
East. The correlation in the West is lower.  Comparison of the S/Se ratios for summer and
whiter shows that there is approximately twice the sulfur relative to selenium in summer
compared to whiter.  Se is a tracer for S emitted from coal-fired fossil fuel power plants; this
shift hi S/Se from summer to winter is consistent  with a substantial secondary photochemical
contribution to SO^" during the summer. Zinc is highest at the sites in the central East.
It does not correlate well with sulfur. Lead and bromine are relatively uniform,  with slightly
higher mean concentrations hi the East. There is  poor correlation between lead and bromine.
Copper and arsenic are highest hi the Arizona copper smelter region.  Copper is also higher hi
the central East.
      Trends (1982 to 1992) of nonurban fine particle sulfur, zinc, lead, and soil elements
were reported by Eldred et al. (1994a) using the IMPROVE network data.  They observe that
hi the southwest, sulfur trends in spring, summer, and fall decreased, while most of the whiter
trends increased.  The trends hi the Northwest increase slightly.   The two eastern sites
(Shenandoah and Great Smoky Mountains) have increased almost 4% per year in summer,
increased 1 to 3% hi spring and fall, and decreased 2% hi whiter. The annual increase was
between 2 and 3%.  Generally, there were no significant trends in zinc and the soil elements.
Lead at all sites decreased sharply through 1986, corresponding to the shift to unleaded
gasoline. The ten year trends reported by Eldred et al. (1994b) have not been compared and
reconciled with other compatible  data.

6.3.1.6  Seasonally of the Nonurban Chemistry
     This section discusses the seasonality of size segregated chemical composition at
non-urban monitoring sites (IMPROVE/NESCAUM) over the entire U.S. (Figure 6-14).
                                         6-25

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       PM 2.5 Concentration - U.S.
          IMPROVE/NESCAUM Data
    PM10, PM2.5 and PMC-U.S.
       IMPROVE/NESCAUM Data
                                                40,000
                                                35,000
                                                30,000
                                             E
                                             01  25,000

                                             o
                                             O
                                                20,000
                                                15,000
                                                 10,000
                                                 5,000
                                                                                 (b)
                                                  1989  Mar  May   Jul   Sep   Nov

                                                   -S-PM10  -^~ PM2.5  -A- PM Coarse
    Chemical Fine Mass Balance - U.S.
          IMPROVE/NESCAUM Data
 IB
 I
                                   (C)
0.0
1989  Mar   May

   -fr- Sulfate
                                                4,000
                                                3,500
                                                3,000
                                             E
                                             01  2,500
      Chemical Tracers - U.S.
       IMPROVE/NESCAUM Data
                                             o
                                             I
                                             o
                                             O
                                                2,000
                                                 1,500
                                                1,000
                                                 500
                               (d)
                    Jul   Sep   Nov

                   ^OC        -HSoil

                   -o- Sulfate + OC + Soil + EC
1989   Mar   May   Jul

-&- Sulfur -Max = 4000

~i~Vanadium - Max = 10
  Sep   Nov

-B-Selenium - Max = 4

-e- S/Se - Max = 4000
Figure 6-14. Seasonal pattern of nonurban aerosol concentrations for the entire
             United States:  (a) monitoring locations; (b) PM10, PM2 5, and PMCoarse
             (PMC); (c) sulfate, soil, organic carbon (OC), and elemental carbon (EC)
             fractions; and (d) tracers.
                                         6-26

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     The nationally aggregated average PM10, PM2 5 and PMCoarse is shown in
Figure 6-14b (See Section 6.3.1.2 for a definition of PMCoarse.). The nonurban PM10
concentration ranges from 8 |Ug/rn3 in the winter, December through February, to about
15 /ug/m3 in June to August. On the national scale the PM10 seasonality is clearly sinusoidal
with a summer peak.  Fine particles over the nonurban conterminous United States account for
about 50 to 60% of the PM10 mass concentration throughout the year.  The coarse mass
accounts for 40 to 50% throughout the year.  Hence, the fine-coarse aerosol ratio does not
change dramatically for the average nonurban aerosol.
     The relative chemical composition of the aggregated nonurban aerosol pattern is shown
in Figure 6-14c, including sulfates, organic carbon, soil, and elemental carbon as a fraction of
the fine particle mass concentration. The Figure also shows the sum of these four aerosol
species to indicate the fraction of the fine aerosol mass that is not accounted for. Most notable
among the missing species is the contribution of nitrates, ammonium ion, and hydrogen ion.
     There is mild seasonality  in the nationally aggregated sulfate and organic carbon
fractions. Throughout the year, sulfate aerosol, including the ammonium cation, accounts for
30 to 40% of the fine mass.  Organic carbon also contribute 30 to 40% of the nationally
averaged fine particle mass.  Thus, sulfates and organic  carbon are the two dominant species,
contributing about 70% of the fine aerosol mass.
     The contribution of soil dust to the fine mass ranges between 4% in the winter months to
12% during April through July. Elemental carbon is about 2% during the summer and 5%
during the winter.
     The sum of the four measured fine mass components,  sulfates, soil, organic carbon, and
elemental carbon add up to about 80%  of the measured fine mass throughout the year.  The
remaining, unaccounted fine mass may be contributed by nitrates,  trace metals (e.g.,  Pb, Br,
sea salt [NaCl], etc.).
     The seasonal pattern of concentration of primary emission tracers, selenium, Se and
vanadium, V is shown Figure 6-14d. Se is a known tracer for coal combustion, while V is a
trace constituent of fuel oil (Altshuller, 1980; Kleinman  et al., 1980; Cass and McRae, 1983;
Tuncel et al.  1985).  The Figure also shows the monthly average concentration of fine particle
sulfur as well as  the S/Se ratio.
                                         6-27

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     The national average Se concentration is rather uniform over the seasons, ranging
between 400 to 600 pg/m3.  Since Se is a primary pollutant, the seasonal invariance means that
the combined effect of emissions and dilution is seasonally invariant over the year.
     The concentration of V is between 500 to 700 pg/m3, with the higher concentrations
occurring in the winter season. Evidently, the contributions from V-bearing fuel oil are more
pronounced during the cold season. The monthly average sulfur in aerosols exhibits  the
highest concentrations 1.5 ^g/m3, during June, July, and August, and the lowest values
0.9 Aig/m3, during November, December, and January.
     The S/Se mass ratio is about 700 during November to January and climbs to about
1,500 during April through September. The higher S/Se ratio during the warm season is an
indication of secondary sulfate production from SO2 in the plumes of coal fired power plants
(Chapter 3).

Eastern United States
     The seasonal pattern of the eastern U.S. aerosol chemistry is shown in Figure 6-15.  The
concentration of PM10, PM2 5, PMCoarse (Figure 6-15b) indicates a similar seasonality,
highest concentrations in the summer, and lowest in the winter. The PM10 levels range
between 12 to 24 /ug/m3, the PM2 5 ranges between 8 to 12 Mg/ni3, while PMCoarse ranges
between 4 to 7 Mg/m3 over the year. The size segregated aerosol data for the nonurban East
show that the fine mass concentration (8 to 12 //g/m3) is higher than the national average (4 to
8 //g/m3), while the coarse mass concentration is comparable to the national average. Eastern
U.S. nonurban fine particles contribute 60 to 70% of the fine mass throughout the year.
     The apportionment of the fine particle mass into its chemical components (Figure 6-15c)
favors sulfates which amount to 40 to 50% of the fine mass throughout the year,  compared to
about 30% of organic carbon. The contribution of soil dust is about 5% throughout the year,
while soot is more important in the  winter (6%) than in the summer (3%). The above three
aerosol chemical components account for 85 to 90% of the measured fine particle mass,
leaving only relatively small contribution to nitrates, hydrogen ions, trace metals, and sea salt.
     The coal tracer  selenium (Figure 6-15d) exhibits a modest winter peaked seasonality
between 600 to 800 ng/m3.  The fuel oil tracer vanadium on the other hand, is factor of two
                                         6-28

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     PM2.5 Concentration - Eastern U.S.
           IMPROVE/NESCAUM Data
                                                 PM10, PM2.5 and PMC - Eastern U.S.
                                                        IMPROVE/NESCAUM Data
                                                 40,0001	'	.	.	.	.	.	.	.	.	.	•-
                                              o
                                              o
                                                 35,000
                                                 30,000
                                                 25,000
                                                 20,000
                                                 15,000
                                                 10,000
                                                  5,000
                                                                                 (b)
                                                   1989 Mar  May   Jul   Sep   Nov
                                                       -B-RM10  H-PM2.5  -A-PM Coarse
Chemical Fine Mass Balance - Eastern U.S.
          IMPROVE/NESCAUM Data
                                                    Chemical Tracers - Eastern U.S.
                                                         IMPROVE/NESCAUM Data
    o.e
    0.8
  
-------
higher in the winter (1,500 ng/m3) compared to the summer (750 ng/m3). Evidently, the
primary contribution from fuel oil is winter peaked. The S/Se ratio is about 1,000 in the
winter, and it is over 2,000 in the summer months.  This suggests the seasonality of secondary
sulfate formation during the summer months.

Western United States
     The aggregated western U.S. aerosol seasonality is presented in Figure 6-16.  The
non-urban aerosol concentrations for PM10, PM2 5, and PMCoarse are well below the
concentrations over the eastern United States (Figure 6-16b).  The  western United
States differs from the eastern United States,  having lower fine mass concentrations, which
range between 3 to 5 ,ag/m3. The coarse mass concentration (4 to 8 ^g/m3) and seasonality is
similar over the East and the West. It is worth emphasizing, however, that these measurements
are at remote national parks and wilderness areas in both East and West. The examination of
monitoring data in urban areas and confined airsheds (Sections 6.4 and 6.5) reveals a highly
textured pattern in space and time.
     The fine particle chemical mass balance (Figure 6-16c) for the aggregated western United
States shows the substantial contribution of organic carbon, which account for 30 to 45 % of
the fine mass.  The higher organic carbon fraction occurs in the November through January
season.  Sulfates range between 20 to 25 % throughout the year. Soil dust plays a prominent
role in the western fine mass balance, contributing 20%  in April through May, but declining to
5% by January.  Elemental carbon ranges between 5% in the winter and 2 % during the
summer. About 25% of the fine  mass over the western United States is not accounted for by
sulfates, soil, organic carbon, and elemental carbon.  It is known that nitrates are major
contributors to the fine particle mass in the South Coast Basin, as well as other western
regions (White and Macias, 1987a; Chow et al., 1992a,  1993a, 1995a).
     The concentration of the trace substances (Figure 6-16d) selenium and vanadium shows
both low concentrations and  weak seasonality.  The sulfur concentrations are also less than
half of the eastern U.S. values.  The S/Se ratio is about 500 in the winter months and 1,000
during the summer. The lower S/Se ratios compared to  those in the eastern U.S. are the result
                                         6-30

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     PM2.5 Concentration -Western U.S.
            IMPROVE/NESCAUM Data
                                              1
                                              o
                                              o
Chemical Fine Mass Balance -Western U.S.
           IMPROVE/NESCAUM Data
    0.6
    O.B
    0.7
  
-------
of selenium emitting coal-fired power plants not being the only sources of western U.S. sulfur.
Smelters make a contribution to S but not Se in the atmosphere.
     The above general discussion of national pattern of chemical and size dependence do not
provide the more detailed spatial and temporal texture of the U.S. aerosol pattern discussed in
the following sections.  However, it provides the national scale gross features and serves as a
broader context for the more detailed examinations.

6.3.1.7  Background Concentrations of Particle Mass and Chemical Composition
     The concentration and chemical composition of background paniculate matter can very
with geographic location, from monitoring site to monitoring site; with season of the year; and
with meteorological conditions which affect  the emissions and secondary production of
biogenic or geogenic species to the background.
     A number of types of background can be considered.  These backgrounds include the
following:  (1) a "natural" background excludes all anthropogenic contributions.  This
background includes any natural sources contributing to the background for chemical  species
in North America or globally; (2) a background which excludes all anthropogenic sources
within North America, but not from anthropogenic sources contributing to background from
outside of North America; (3) a background which excludes the anthropogenic sources inside
the United States, but not from elsewhere in North America; (4) a background which  excludes
anthropogenic sources from other regions into a specified region hi the United States; (5) a
background which would exclude all sources of paniculate matter except those associated with
a particular urban area.  The two backgrounds directly relevant to the Criteria Document are
backgrounds  (1) and (2).  The problems and limitations  in obtaining reasonably accurate
annual average and  seasonal values for these backgrounds are discussed below.  Backgrounds
(4) and (5) can be more readily be obtained  by measurements.  These backgrounds are relevant
to subsequent stages in the implementation process. The averaging period over which
background levels are defined should also be stated.  Annual and seasonal averages may be
more appropriate for risk assessments but daily peak values may be more relevant for control
strategy implementation.
                                          6-32

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     More specifically, the term non-manmade is meant to encompass sources such as
geogenic dust plumes and sea salt as well as biogenic sources.  Biogenic sources include
(a) combustion products of biomass burning caused by lightning; (b) emissions of volatile
sulfur compounds from marshes, swamps or oceans; (c) organic paniculate matter formed by
the atmospheric reactions of biogenic volatile organic compounds such as terpenes; and
(d) particulate nitrates formed by the atmospheric reactions of NOX emitted from soils. There
is an intermediate class of sources associated with agricultural activities. These include
biomass burning caused by human intervention and the addition of fertilizers to soils resulting
in emissions of NH3 and NOX (Section 5.2, 5.3).
     Anthropogenic sources include vehicular and stationary sources which emit particles
directly or precursors such as sulfur dioxide, nitrogen oxides, or those volatile organic
compounds capable of reacting in the atmosphere to form organic particles. Stationary sources
of primary particulate matter as well as sulfur oxides and nitrogen oxide precursors include
fossil fuel power plants, while smelters are sources of primary particles and sulfur oxides.
Vehicles emit primary particulate matter as well as nitrogen oxides and volatile organic
compounds.  Solvent usage,  agricultural coatings, and many other industrial operations also
may emit precursors or particulate matter. Wood burning for heating of homes is a  source of
organic carbon and elemental carbon (Section 5.2, 5.3).
     The formation of sulfates from sulfur dioxide emitted by power plant plumes can occur
over distances exceeding 300 km and 12 h of transport (Section 3.4.2.1). Nitric acid also can
be formed in these plumes and it can be converted to ammonium nitrate, if sufficient ammonia
is available to first neutralize the sulfate in plumes. Similar transport can occur in urban
plumes. The transport distances in plumes depend on both formation rates of particles and
their removal by deposition processes. However, the residence times of fine particles  can be
long. For example, if the dominant removal process is dry deposition, fine particles
transported through a 1000 m deep mixed layer near the surface with deposition velocities of 1
to 0.1 cm/s have atmospheric residence times ranging from 1  to 11 days (Section 3.5.1,
3.5.3).  When particles are trapped in a layer well aloft they may survive even longer periods.
Therefore, transport distances of several hundred to several thousand kilometers are  possible.
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     Direct evidence of such transport aloft is available from satellite monitoring of back
scattered solar radiation. The most prominent plume is that of Sahara dust from West Africa
(Section 6.2). This plume has been observed to extend during the spring and summer months
to the east coast of the United States, especially over Florida (Figure 6-6).  Ground level
measurements in Bermuda indicate that southeasterly winds bring high concentrations of soil-
and crustal-related aerosols which appear to be from the Sahara desert (Wolff et al., 1986).
Other large plumes exist, such as the Asian plume.  However, the satellite observations do not
indicate that it reaches the west coast of the United States (Figure 6-6).
     Field measurements and modeling studies can be  used as aids in the derivation of
background  values for aerosol constituents.  Either approach is subject to considerable
uncertainty and each has its own advantages and limitations. Field data would be the  most
logical choice if it could be shown to be completely free of anthropogenic influences
originating within North America, i.e., background (2), (following the guidelines set  out
above for defining background levels unaffected by pollution sources within North America).
A number of difficulties arise in interpreting field data for this purpose, namely:  (a) there are
very few tracers (e.g., 14C) which can be used to distinguish between anthropogenic and
biogenic source categories of aerosol constituents; (b) multilayer trajectories should be used to
identify source regions since layer-average trajectories may underestimate the geographic area
contributing pollutants to the air mass sampled; (c) sampling must also be carried out  for long
enough periods to obtain statistically representative values over seasonal tune scales.
Determining the history of air parcels is difficult in locations subject to small scale circulations
such as cumulus convection and land-sea or mountain-valley breezes.  In addition, all small
localized anthropogenic sources of particulate matter must be identified during sampling.
Ideally, measurements should be carried out long enough for the measurements to be  shown to
be generally representative of the time period of interest e.g., seasonal average, annual
average.
     Alternatively, models which include only natural sources and anthropogenic sources
located outside North America could be used.  Their utility is limited by inadequacies in model
formulation,  such as grid spacing and knowledge of the strengths, locations, and variability of
various sources.  Since a large fraction of particulate matter is secondary, uncertainties in the
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chemistry of precursor gases will play a large role in determining the uncertainty of the final
results. These uncertainties are especially large for the yield of aerosol produced by the
oxidation of biogenic hydrocarbons as pointed out in Chapters 3 and 5.  Uncertainties in the
chemistry of NOX and SO2 are also important hi that they affect estimates of the yield of
aerosol products versus the deposition of intermediate species.
     Trijonis  (1982,  1991) has attempted to estimate PM2 5 and PM10 concentrations
corresponding to background (1), the "natural" background. His approach was to obtain
concentration  values only from those biogenic and geogenic sources which are at or below
those possibly associated with preindustrial conditions over North America.  Annual average
concentrations of the  chemical species in particulate matter were estimated for the eastern
United States  and for mountain/desert regions of the western United States.  Seasonal
"natural" background concentration values were not estimated.  The annual average
concentrations of fine particles were estimated separately for sulfates; as NH4HSO4, nitrates;
as NH4NO3; organic  carbon; elemental carbon; soil dust and water (Trijonis, 1982, 1991).  In
the later work, coarse particle concentration values were also estimated (Trijonis, 1991).  In
addition, in the later work, it was emphasized that the concentration values proposed can have
error factors ranging  from 1.5 to 3 for individual  chemical species in particulate matter.
     In the earlier work (Trijonis, 1982), a fine particle "natural" background for the eastern
United States  is estimated at 5.5 ± 2.5 //g/m3.  Excluding water, the background value would
be 4 ±  2 ,ug/m3 with the largest contribution, 2 //g/m3, from organic carbon.  In the later
estimates (Trijonis, 1991), a fine particle "natural" background for the eastern United States of
3.3 //g/m3  is estimated.  Excluding water, this background would be 2.3 Atg/m3 with
1.5 //g/m3  associated  with organic carbon.  A separate estimate is given for the fine particle
"natural" background over the mountain/desert regions of the western United States of
1.2 /ug/m3.  Excluding water, this background would be 1 /^g/m3 with 0.5 ^g/m3 associated
with organic carbon.  The coarse particle "natural" background for both the eastern and
western United States is estimated at 3 /ug/m3.
     Fernam et al. (1981) also estimated "natural" background concentrations for PM2 5
constituents in the eastern United States during summer.  They estimated natural  contributions
                                          6-35

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to sulfate of 0.5-1.9 yug/m3, to organic carbon of 3.7 /ug/m3, and to crustal material of
     To obtain these "natural" background estimates, a wide range of approaches are used
varying from natural SO2 and NOX emissions inventories to SO 4, NO 3 and elemental carbon
concentration measurements in remote locations in the northern and southern hemispheres.
Carbon isotope ratios and organic composition measurements for organic components are used
from several sites in the southwestern United States.
     Results of three-dimensional  models that could be used to estimate each of the five
background levels for all the major categories of aerosol composition listed above are not
available.  Liousse et al. (1996) have performed three-dimensional chemical tracer model
simulations of the global distribution of elemental and organic carbon.  Background values
assuming only natural sources (background 1) were also calculated.  Average organic carbon
concentrations calculated for the month of July were all less than 1 jwg/m3 in the United States.
These calculations were made assuming a 5% yield of secondary organic carbon from the
oxidation of terpenes (cf.  Section 5-3).
     Another approach is to use results from rural/remote sites in national parks,  wilderness
areas and national monuments from the IMPROVE monitoring measurements.  Results for the
period between March 1988 and February 1991 have been published (Malm et al., 1994).  The
tabulations of results are given on  an annual average basis for individual IMPROVE sites and
on a seasonal basis by IMPROVE  subregion for fine mass; sulfate, as (NH4)2SO4; nitrate, as
NH4NO3;  organic and elemental carbon;  fine soil and coarse mass.  These measurements do
not differentiate between anthropogenic and non-anthropogenic contributions and do not
stratify measurements values by wind direction or by use  of trajectories representing various
air masses (Malm et al., 1994).  However,  a large set of measurements, including seasonal
measurements, are provided at a substantial number of rural/remote sites, especially in the
western United States.
     In stratifying the IMPROVE  results a problem arises because the Colorado plateau
"subregion" with seven sites straddles the boundary between the southwest and northwest used
subsequently (Figure 6-28).  Four  of the sites are north of the boundary in Utah and Colorado
and three of the sites are south of the boundary in Arizona and New Mexico. The authors
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place the Colorado plateau in the southwest for purposes of a fine mass composition budget
(Malm et al., 1994). Since they assign only one other subregion, Sonora desert, with two sites
to the southwest, the method of assigning sites can significantly affect the resulting estimates
of regional fine mass concentrations. This problem can be avoided for  the annual average
values which are shown by  individual sites, but not for the seasonal values which are lumped
by subregion. This lumping also requires deciding whether a subregion with five  sites, central
Rocky Mountains, should be given the same or five times  the weight of the other subregions in
the northwest with only one or two sites  each.  For the annual average values given in
Table 6-2 the sites are assigned consistent with the division between the northwestern and
southwestern regions shown in Figure 6-28, excluding three  sites in the northern California
coastal mountains considered separately.  A transitional region between the western mountains
and deserts and the eastern United States has been considered consisting of five sites in three
subregions  from West Texas (2), to South Dakota (1) up to the Boundary Waters  subregion (2)
near the Canadian border.  In addition, the result for paniculate matter from the Appalachian
subregion (2) are given. Previous measurements of particulate matter at sites in the eastern
mountains are available (Stevens et al., 1980); Pierson et al., 1980b;  Wolff et al.,  1983). The
measurements listed in Table 6-2 include PM2 5 sulfate, as (NH4)2SO4,  organic carbon, and
PM(10-2.5)-
      The annual average PM2 5 increases substantially from  west to east in Table 6-2 from a
value of 3.55 /ug/m3 in the northwestern  United States to 10.91 /ug/m3 in the Appalachian
mountains.   The annual average (NH4)2SO4 concentration  increases even more substantially
from west to east from a value of 0.88 Mg/m3 in the northwestern United States to 6.33 /ug/m3
in the Appalachian Mountains.  The lowest annual average organic carbon concentration of
1.38 £tg/m3 occurs in the southwestern United States and increases to 2.97 jUg/m3 in the
Appalachian Mountains. A smaller range of concentrations occurs for organic carbon from
west to east than for PM2 5 and (NH4)2SO4. The (NH4)2SO4, as  a percentage of PM2 5,
increases into the transitional region and  the Appalachian Mountains from as low as 25 % of
the PM2 5 at sites in the northwestern United States up to 58% at sites in the Appalachian
Mountains.  Conversely, organic carbon decrease as a percentage of PM2 5 from 46% at sites
in northwestern United States down to 27% in the Appalachian Mountains.  Within the
                                         6-37

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            TABLE 6-2.  ANNUAL AVERAGE CONCENTRATIONS AND
        CHEMICAL COMPOSITION FROM IMPROVE MONITORING SITES

Northwest3
Southwest15
California Coastal Mountains0
Transitional Regiond
Appalachian Mountains0
No. of
15
5
3
5
2
Annual

3.55
3.91
4.99
5.15
10.91
Average Concentrations, /ug/m3 and Composition
(NH4)2S04/
0.88/25
1.28/33
1.41/28
1.97/38
6.33/58
Organics/
1.63/46
1.38/35
1.95/39
2.01/39
2.97/27
PM
4.46
5.62
8.85
6.54
6.24

8.0
9.5
13.8
11.7
17.2
aCascades (1), central Rocky Mt. (5), Great Basin (1), N. Rocky Mt. (1), Sierra Nevada (1), Sierra Humboldt
(2), and Colorado Plateau (4)
"Colorado Plateau (3), Sonora Desert (2)
cSame as subregion
dWestern Texas (2), northern Great Plains (1), Boundary Waters (2).
 western United States there are somewhat higher percentages of (NH4)2SO4 and lower
percentages of organic particles in the southwestern United States than in the northwestern
United States.  (NH4)2S04 plus organic carbon account for from 67% to 85% of PM2 5, with
the higher percentages at IMPROVE sites east of the Rocky Mountains (Table 6-2).
     Compared to the estimates discussed by Trijonis (1982, 1991) for "natural" background,
PM2 5 values in the western United States of 1 Aig/m3, the average measured contractions of
PM2 5 in the northwestern and southwestern United States of 3.55 /ug/m3 and 3.91 /ag/m3
suggest anthropogenic contributions. The IMPROVE measurements are likely to include
anthropogenic contributions from sources within North America (background 3). Even the
lowest annual average PM2 5 value in the contiguous United States of 2.5 Mg/m3 at Bridger
Wilderness Area, WY, is over twice the  "natural" background.  The Denali NP in Alaska has
an average annual PM2 5 of 2 /ug/m3 (Malm et al., 1994).  The organic carbon concentrations
measured there are somewhat closer to the estimated "natural" background in the western
mountains/desert of 0.5  /ug/m3 (Trijonis, 1991).  However, average annual concentrations in
the northwestern and southwestern United States are higher with values of 1.63 /ug/m3 and
1.38 //g/m3.  The annual average values at several IMPROVE monitoring sites in the Rocky
Mountains are near 1  /ug/m3, while the Denali NP in Alaska has an average annual organic
                                        6-38

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carbon concentration of 0.85 /ug/m3.  These latter organic carbon concentration values ares at
the two fold upper limit of uncertainly in the estimate of "natural" background.  On the other
hand, the (NH4)2SO4 concentrations measured in the west are far above the "natural"
background for (NH4)2SO4 of 0.1 /ug/m3 (Trijonis, 1991).  The lowest measured annual
average (NH4)2SO4 at several sites are near 0.5 /ug/m3. For PM(10_2 5), the annual average
concentrations in the northwestern and southwestern United States of 4.46 /ug/m3 and
5.62 /ug/m3 are within the two fold upper limit of uncertainty in the estimate of "natural"
background.  At a number of individual sites, annual average PM(10_2 5) concentrations are
3 /ug/m3 to 3.5 /ug/m3, close to the estimated "natural" background. Therefore, the largest
deviations from the "natural" background estimates for a major component occur for
(NH4)2S04.
     Comparisons of the measured concentration values in the "transitional" area of the
eastern United States, using sites from west Texas to the Boundary Waters, find that the
average annual concentrations for PM2 5 of 5.15 /ug/m3; (NH4)2SO4 of 1.97 /ug/m3; organic
carbon of 2.01 /ug/m3 and PM(10_2 5) of 6.54 /ug/m3 (Table 6-2) usually are well above the
estimates of "natural" background in the eastern United States (Trijonis, 1991) for PM2 5 of
2.3 /ug/m3; (NH4)2SO4 of 0.2 /ug/m3;  organics of 1.5 /ug/m3;  and PM(10.2 5) of 3 /ug/m3. As in
the western United States, the measured (NH4)2SO4  concentration values are far above the
"natural" background value, while the measured concentrations of organics are well within the
two fold uncertainty in the "natural" background value.
     Another source of lower PM10 concentrations are rural/remote AIRS monitoring sites.
Based on 1993 measurements, the lowest values of PM10 are as follows: Rosebud Co., MT
(maximum  of 10 /ug/m3, annual mean of 4.5 /ug/m3); Campbell Co., WY (maximum of
15 /ug/m3, annual mean of 7.0 /ug/m3); and Washington Co., ME (maximum of 23 /ug/m3,
annual mean of 8.8 /ug/m3).  These PM10 values agree  within a factor of two with the
estimated "natural" background PM10 in the western United States of 4 /ug/m3, and in the
eastern United States of 5.3 /ug/m3 (Trijonis, 1991).
     Seasonal variations in particulate matter are also important and have been considered.
The source  used for these seasonal values in particulate matter is the IMPROVE monitoring
network (Malm et al., 1994).  Because the seasonal values are reported only by IMPROVE
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subregions, there is no good approach to averaging values from differing numbers of sites
within the varying geographical extent of IMPROVE subregions.  Therefore, the values of
annual average, summer and winter values for PM2 5, (NH4)2SO4, organic carbon, and
PM(10-2.5) are listed for a number of IMPROVE subregions (Table 6-3).
    TABLE 6-3. ANNUAL SUMMER AND WINTER CONCENTRATIONS FROM
                       IMPROVE MONITORING SITES"
Subregion
Central Rockies


Colorado Plateau


Coastal Mountains


Sonora Desert


West Texas


Northern Great
Plains

Boundary Waters

Appalachian
Mountains

No of Seasons of
Region of U.S. Sites the Year
NW 5 annual
summer
winter
NW-SW 7 annual
summer
winter
NW 3 annual
summer
winter
SW 2 annual
summer
winter
Transitional to 2 annual
east summer
winter
Transitional to 1 annual
east summer
winter
Transitional to 2 annual
east summer
winter
Eastern U.S. 2 annual
summer
winter
PM
3.3
4.8
2.0
3.4
4.1
2.9
5.0
4.5
5.6
4.4
5.6
3.2
5.4
6.6
3.6
4.5
5.6
3.4
5.3
6.2
5.2
10.9
16.6
6.5
(NH4)2S04
0.8
1.0
0.5
1.1
1.3
0.9
1.4
1.9
0.9
1.5
2.1
1.2
2.1
2.5
1.5
1.5
1.8
1.2
2.0
2.2
2.0
6.3
10.5
3.0
Organics
1.5
2.4
0.9
1.2
1.6
1.1
1.9
1.4
2.3
1.5
1.8
1.1
1.5
1.7
1.1
1.5
2.2
1.1
2.1
3.1
1.4
3.0
4.4
2.0
PM
Coarse
4.8
7.5
3.0
4.7
6.4
3.2
8.9
10.7
7.7
6.0
7.6
3.3
7.5
7.4
5.1
6.3
9.7
3.9
5.7
8.2
3.2
6.2
11.2
3.1
a From Malm et al., 1994.
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     Annual average concentration almost always are intermediate between the summer and
winter concentration of particulate matter listed in Table 6-3.  With a few exceptions, the
summer concentrations are higher than winter concentrations.  The exceptions are the higher
winter concentrations for PM2 5 and organics in the coastal mountains.  Ratios of summer to
winter concentrations can equal or exceed two for all listed particulate components in both the
central rockies subregion and the Appalachian Mountains.  The summer to winter
concentration ratios for PM2-5 are within the 1.5 to 2.5 range except for the coastal mountains
and Boundary Waters subregion. The summer to winter concentration ratios for  PM coarse
equal or exceeds two except for the coastal mountains.  Therefore, in most rural  remote sites
hi IMPROVE subregions summer concentrations of particulate matter substantially exceed
winter concentrations.  However, it must be emphasized that it is not appropriate to
extrapolate these results obtained at IMPROVE sites in 1988 to 1991 to other sites or even to
other years of monitoring at IMPROVE sites.
     Within the continental United States, there are measurements of particulate  mass and
chemical composition under conditions identified as "clean" background conditions
(Wolff et al., 1983). These are based on 7 days of measurements during the summer of 1978
at a site 40 km northwest of Pierre, South Dakota and 18 days during the summer of 1979 at a
site 15 km north of the Gulf Coast, near Abbeville, LA. At the South Dakota site the small
variations in anthropogenic pollutants observed was attributed  to a lack of any major pollution
sources along the trajectories. In contrast, at the Louisiana site the days were stratified into
"clean" days when the air had passed over the Gulf of Mexico for several days and much more
polluted episode days when the maritime air was modified by air which had undergone
transport from the midwestern and northeastern United States.
     Fine particle mass on "clean" days averaged 11  to 13 /ig/m3 and coarse mass between 9
and 19 /tg/m3 at the two sites.  The total mass averaged between 21 and 32 /ig/rn3.  Organic
carbon at both sites was the most important fine particle species averaging 4 to 8  /ig/m3
(organic mass multiplied by 1.2 to include H and O),  while sulfate averaged 3 /Ag/m3.
     At the closest IMPROVE  site, the Badlands National Monument, SD in the  northern
great plains subregion (Table 6-3), for the summers of 1988 and 1989 (Malm et al., 1994) the
concentrations were PM2.5, 5.6 jig/m3; (NH4)2SO4, 1.8 ,ug/m3; organic carbon, 2.2 yUg/m3 and
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PM(10.2 5)» 9.7 //g/m3.  These concentration values are substantially lower than those obtained
at the site 40 km northwest of Pierre, SD in the summer of 1978 as follows:  PM2 5,
13 //g/m3, (NH4)2SO4, 3.2 /ug/m3; organic carbon 3.8 Aig/m3 and PM(10.2 5), 19 /ug/m3.
     There are several reasons for the differences between the "clean" values and the
IMPROVE values,  (a) The measured background varies from year to year and site to site.
(b) Precipitation periods were excluded by  Kelly et al. (1982) and Wolff et al. (1983), but the
IMPROVE monitoring network measurements include all weather conditions. All other
conditions being the same, the inclusion of precipitation events in the IMPROVE
measurements probably biases the results low because of rain-out of particulate matter,  (c) On
the other hand,  the presence of material from anthropogenic sources probably biases the results
high. Wolff et  al. (1983) used trajectory analyses to exclude periods with intrusions of
polluted air from their analysis. This was not done with the IMPROVE results. However, the
layer-averaged trajectories used by Wolff et al.  (1983) may have underestimated the mixing of
air parcels from surrounding geographical areas leading to an underestimate of the potential
for anthropogenic contributions.  The exact causes for the differences between these two types
of "background" estimates cannot be quantitated from available data.
     For sulfate, it is possible to make a limited comparison with measurements at rural sites
outside of St. Louis with air flow from the northwest during the third quarters of 1975 and
1976 (Altshuller, 1987), background 5.  The average third quarter  sulfate concentrations at
these sites for these two years was 7 yug/m3, a substantially higher sulfate concentration than  in
South Dakota (Wolff et al., 1983), but lower than measured in other wind directions. These
measurements outside of St. Louis also indicate substantially lower sulfate concentrations
during the first  and fourth quarters of 1975, 1976, and 1977 averaging 3.4 /^g/m3, comparable
to the third quarter sulfate concentrations in South Dakota.
     It is important to emphasize that the "background" for particulate matter moving toward
cities along the  east coast over the Great Smoky Mountains (Stevens et al., 1980); the
Allegheny Mountains (Pierson et al.,  1980b) and  the Blue Ridge Mountains (Wolff et al.,
1983), background 4, are much higher than for the "clean" air days in South Dakota and
Louisiana.  For example, the fine particle matter  at the Blue Ridge Mountain site in July and
August 1980 with trajectories from the midwest source areas and the Tennessee Valley source
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area averaged 27 and 24 //g/m3, approximately twice the values under "clean" air conditions in
South Dakota and Louisiana (Wolff et al, 1983).  The sulfate concentrations for these two
trajectory directions averaged 14 and 9 //g/m3, with sulfate substantially exceeding organic
carbon.  This result is a reversal in the chemical composition under the "clean" air conditions
in South Dakota and Louisiana, but more consistent with the chemical composition under
"episodic" conditions in Louisiana when the sulfate concentration averaged 20 //g/m3 and the
organic carbon averaged 15 /ug/m3.
      Because of the repeated occurrence of (NH4)2SO4 concentrations far above "natural"
background even at rural/remote sites, this  aspect justifies  additional consideration.
      A low contribution of natural sources  of gaseous sulfur (both terrestrial and marine)
occurs in the eastern United States (Trijonis,  1991). However, a more detailed consideration
of the contribution of natural sources of gaseous  sulfur indicates wide variations over the
United States (Placet, 1991).  The following estimates for the ratios of total natural gaseous
sulfur to total anthropogenic gaseous sulfur by region (Placet, 1991) are as follows: northeast,
0.01; southeast, 0.03; west gulf, 0.03; southwest, 0.12;  northwest, 0.19. The corresponding
ratios for coastal areas are higher with an estimate of 0.52  for the California coastal areas.  If
these ratios are converted to ratios of total natural gaseous  sulfur to total gaseous sulfur, the
ratios would be 0.11 in the southwest and 0.16 in the northwest.  If the following assumptions
are made (a) both natural and anthropogenic sulfur are converted to (NH4)2SO4 to about the
same extent; (b) the concentrations of natural (NH4)2SO4 can be obtained by multiplying the
above ratios by the measured (NH4)2SO4 concentrations, the natural sulfur concentrations in
the southeast would range from 0.1 to 0.15  /ug/m3 and in the northwest from 0.08 Atg/m3 to
0.2 Mg/m3.
     A more detailed consideration of the contribution of natural gaseous sulfur at  sites near
the Pacific coast is available (Kreidenweis,  1993).  In particular, comparisons with  measured
(NH4)2SO4 concentrations were made at the Crater Lake National Park in southwestern
Oregon with estimates of natural (NH4)2S04 concentrations.  The measured annual  average
concentration at this site of (NH4)2SO4 was  0.5 Atg/m3 and  an average  "low" concentration was
approximately 0.13  fj.g/m3 (Kreidenweis, 1993).  This latter value can be compared with
several estimates of natural (NH4)2SO4 concentration based on the following approaches (a) a
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natural source column burden between 35 to 50° north of 0.05 to 0.15 /ug/m3; (b) a Pacific
natural source column estimate between 35 to 50° N of 0.18 /ug/m3 and (c) a 3 D model value
of 0.14 to 0.28 Atg/m3. Other approaches gave higher possible values for natural (NH4)2SO4
(a) "clean" rainfall sulfate concentrations of 0.1 to 0.5 ^g/m3 and (b) another 3-D model value
of 0.6 Aig/m3. These comparisons results in a wide range of annual average values of
(NH4)2SO4 from less than 0.1 Mg/m3 to less than 0.5 /ag/m3 (Kreidenweis, 1993).
     Even an upper limit value for natural (NH4)4SO4 of 0.5 Mg/m3 would be a third to a half
of the measured (NH4)2SO4 at IMPROVE sites near the Pacific Coast (Malm et al., 1994).
Further inland, at interior western sites, the marine sources of natural sulfur should make an
even smaller contribution to the measured concentrations of (NH4)2SO4. Comparison of these
(NH4)2SO4 with the estimates based on regional sulfur inventories (Placet, 1991) of 0.08 to
0.2 Atg/m3 would indicate a significant anthropogenic contribution even at relatively remote
western IMPROVE sites. This result suggests that background 3 may have a substantial
contribution from anthropogenic sulfur sources in North America.
     As a summary to the discussion in Section 6.3.1.7, the estimated lower limit and upper
limit background concentrations for PM10 and PM2 5 are given on an annual average basis and
for winter and summer for the western and eastern United States in Table 6-4.
  TABLE 6-4.  SUMMARY OF ANNUAL AND SEASONAL AVERAGE RANGES OF
         BACKGROUND CONCENTRATION LEVELS OF PM10 AND PM2 5
PM
PM10
PM2.5
PM10
PM2.5
PM10
PM2.5
Annual or Seasonal
Annual average
Annual average
Winter
Winter
Summer
Summer
Concentrations, yug/m3
Western United States Eastern United States
4-
1-
4-
1-
4-
1-
8
4
6
3
12
5
5-
2-
5-
2-
5-
2-
11
5
8
4
14
6
                                       6-44

-------
     The lower limit concentrations are based on the "natural" background midrange
concentrations discussed (Trijonis 1991).  There are error factors associated with the chemical
species used to obtain these concentrations range from 1.5 to 3.
     The upper limit concentrations are based on measured concentrations from IMPROVE
monitoring sites (Malm et al., 1994). The PM2 5 concentrations are the sum of concentrations
measured for individual chemical species. As noted earlier  in Section 6.3.1.7, these measured
concentrations can include some anthropogenic source contributions within North America.
Therefore,  the upper limit concentrations represent conservative values for the upper end of
the background concentration type.
     To obtain the upper limit concentrations, the averages were obtained from the
concentrations for nine subregions in the western United States giving each region equal
weight and also weighing the contribution of each subregion by the number of sites in the
subregion.  The median values were also obtained. For the eastern United States, the  averages
were obtained from the concentrations for three subregions in the "transitional" region.  For
the annual average from 23 individual sites in the western United States and  5 sites in  the
"transitional" region (Table 6-3).  The resulting values for upper limit concentrations were
closely clustered usually with a 1 Atg/m3 range. Within these values, the lower whole  value
concentration was listed in Table 6-4.
     As a supplement to the data collected in the IMPROVE/NESCAUM networks, seasonal
and annual average PM10 concentrations were also taken from AIRS (1990 - 1995).  Four
inhabited areas with the lowest annual average PM10 concentrations were chosen in areas
without nearby IMPROVE/NESCAUM sites.  Annual, summer, and winter averages for
Penobscot Co., ME (11.1, 13.8, and 10.0 /ig/m3); Marquette, MI (11.2, 15.5, and
7.0 /ig/m3); Mercer Co., ND (11.7, 12.9, and 10.6 /ig/m3); and Lakeport, CA (11.6,  14.3,
and 10.0 /ig/m3) all fall within the upper  limits set for PM10 shown in Table 6-4. All  areas
exhibit summertime maxima and wintertime minima.  The similarity of these results to the
upper limits shown in  Table 6-4 suggests an anthropogenic component to those upper limits,
since the AIRS values  were obtained in inhabited areas.
     Again, it should  be mentioned that seasonal or annual average "background" values
presented above will likely underpredict 24-hour maximum  "background" values. Ambient
                                         6-45

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data could be used to estimate 24-hour maximum values, but their use is subject to
considerable uncertainty because of possible anthropogenic inputs.

6.3.2 Urban National Aerosol Pattern—Aerometric Information Retrieval
       System
     The urban monitoring network is operated by state and local agencies as mandated by the
Clean Air Act.  The data from this network are used to determine exceedences above the
paniculate matter standards. Federal regulations also require that these monitoring data be
submitted to the EPA Aerometric Information and Retrieval System (AIRS).  In what follows,
AIRS PM10 refers to the PM10 mass concentration extracted from the AIRS database. The
AIRS database is a useful resource for analyzing trends and concentration patterns, and
relationships between the fine, coarse, and PM|0 components of the atmospheric aerosol
(Husar and Frank, 1991; Husar  and Poirot, 1992).
     The  national average AIRS concentrations were calculated utilizing all of the available
data since the beginning of 1985, when less than 100 monitoring stations were operational
(Figure 6-17).  Since that time, the  number of monitoring stations has risen to more than
1,300 (Figure 6-17). The  implications of the changing stations density to the above described
national PM10 trend is not well studied. The emergence of new stations appeared to be in
rough proportion to the final station density shown in Figure 6-17. In other words, in 1985,
the national coverage had a pattern  similar to 1994, except less dense. Changes in sampling
equipment and monitoring protocols are also possible causes of systematic errors in the
reported spatial pattern and trends.
     The  AIRS PM10 database reports the concentrations every sixth day for a  24-h sampling
period, synchronously over the entire country. The sample duration is one day which, over
the long run, provides the  concentration distribution function of daily samples.  For
determination of the effects (human health, visibility, acid deposition) the concentration has to
be known at the specific location where the sensitive receptors reside. Also the concentrations
have to be known at a short (e.g., daily) time scale, as well as over the long term.
     In order to characterize the one day-scale temporal variation over a given region, the
entire available data aggregated  over the entire region for each monitoring day are plotted as
                                         6-46

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                           Valid  F»IS/I1O  Stations
                                    LJS, XX.II  Stations
                     1 986
                                   1 988
                                                  1 99O
                                                                 1 992
Figure 6-17. Trend of valid PM10 monitoring stations in the AIRS database.
 time series.  It is recognized that during the other five non-monitored days, the concentrations
 may be different from the reported value.  The six day sample increment ensures that both
 weekday and weekend data are properly taken into account.
      The AIRS PM10 stations are mostly in urban areas but some suburban and nonurban sites
 are also reported. The analysis presented in this section is based on PM10 and PM2 5 data
 retrieved from AIRS in October 1994.
      The results of AIRS PM10 aerosol pattern analysis are presented in quarterly contour
 maps, as well as seasonal time charts.  For valid monthly and quarterly aggregation, it was
 required to have at least two samples a month, and six samples per quarter.  For the seasonal
 maps all the  available data between 1985 to 1994 were used.
      The seasonal contour maps also  show the location of the PM10 monitoring sites. The
 size of the rectangle at each site is proportional to the quarterly average PM10 concentration
                                          6-47

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using all available data between 1985 to 1994. Hence, sampling biases due to station density
that changed over time can not be excluded.
       The quarterly concentration pattern of PM10 is shown in Figure 6-18.  The high
sampler density allows the resolution of spatial texture on the scale of 100 km, particularly
over major metropolitan areas.  However, remote regions in the central and western states
have poor spatial density. In the absence of rural monitoring data computerized contour
plotting of PM10 is biased toward extrapolating (spreading) high concentrations over large
areas.  This bias is particularly evident in the maps for Quarters 1 and 4 in the western states,
where the area of high concentration hot spots is exaggerated.
       The AIRS PM10 concentrations over the eastern United States are lowest during
Quarter 1, ranging between 20 to 30 /ug/m3.  The higher concentrations exceeding 30 Mg/m3
are confined to metropolitan areas.

6.3.2.1  National Pattern and Trend of Aerometric Information Retrieval System PM10
       Two trend analysis approaches were used to obtain the 1988 to 1993 trends in PM10
shown in Figure 19b are subsequent figures providing AIRS concentration patterns.  One of
these approaches uses all of the available stations operational each year between 1988 and
1994.  The second approach uses only those stations operational from 1988 to 1994, the long
term coverage, trend, stations.
       During the  1988 to 1994 period there were decreases in the annual average PM10 for
the continental U.S. from 33 //g/m3 to 25 /ug/m3, for all sites and from 35 Aig/m3 to 28 /ug/m3
for trend sites resulting in 24% or 20%  reductions in PM10.
       The Figure 6-19b also shows the standard deviation among the yearly average PM10
concentrations for each year. On the national scale the standard deviation of yearly average
concentrations is about 40%  of the mean.
       The concentrations of PM2 5 and PM10 are compared in the scatter chart in
Figure 6-19c.  Each point represents a pair of PM2 5-PM10 monthly average concentrations.
The diagonal line is the 1:1 line and shows the fine particle concentration ranges between
20 and 85% of PMi0. The heavy solid  line is derived from a linear best fit regression.  The
detailed correlation statistics is reproduced in the upper-left corner of the scatter charts. The
                                          6-48

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                                                 Quarter 1
                        Quarter 2
                          PMIOMass
                                                          ng/m3
                                                            50
                                                           140
                                                            30
                                                            20
                                                           [10
                                                             0
PM10 Mass
                                                 Quarter 3
                        Quarter 4
                          PM10 Mass
                                                          u.g/m3

                                                           150
                                                           140
                                                           130
                                                           120
                                                           [10
                                                             0
PM10 Mass
Figure 6-18. AIRS PM10 quarterly concentration maps using all available data.

-------
       PM10 Average - Continental US
                                        (a)
                                             PM10 Cone. Trend - Continental U.S.
                                                       EPA AIRS database
   150

   140

   130

   120

   110

   100
 m
 ^  90
  o>
 ^
 uT 80
 e\i
 I  70

    60

    50

    40

    30

    20

    10
    PM2.5 vs. PM10 - Conterminous U.S.
          EPA AIRS - Monthly Averages
CORRELATION STATS:
Avg X :     33.67
AvgY:     19.23
Avg Y/Avg X :  0.57
CorrCoeff:   0.82
Slope :     0.56
Y offset •    0.24
Data Pointi • 2269
(c)
         20
              40   60
                       80
                  PM10(pg/rri )
                            100  120  140
                           3v
                                            1988   1969   1990   1991   1992   1993   1994
                                              -A- Avg for all sites    -B- Avg for trend sites
                                              -+- Avg + Std. Dev.    -9- Avg - Std. Dev.

                                            Seasonal PM Pattern - Continental U.S.
                                                       EPA AIRS Database
                  50
                                          5
                                          a. 25
                                            20
(d)
                                             1986  Mar   May   Jul    Sep   Nov
                                                -A-PM10  -B-PM2.5 -t-PM Coarse
Figure 6-19.  AIRS PM10 and PM2.5 concentration patterns for the conterminous
              United States.
                                            6-50

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ratio of overall average PM2 5 and overall average PM10 is also indicated.  For the data when
both PM2.5 and PM10 data were available, nationally aggregated PM2 5 particles accounted for
57% of the PM10 mass.
      The seasonal pattern of the national PM10 concentration is also depicted in
Figure 6-19d, utilizing all available data in AIRS.  The national  average PM10 seasonally
ranges between 27 /ug/m3 in March and April, and 33 /ug/m3 in July and August, yielding a
modest 16% seasonal modulation. There is also evidence of slight bimodality with the
December through January peak.
      The seasonal chart also shows the annual variation of PM2 5, and PM10-PM2 5 (i.e.,
coarse particles). The national fine particle concentration shows clear evidence of bimodality
with peaks in July  and December. It is shown below that the fine particle winter peak arises
from western sites, while the summer peak is due  to eastern U.S. contributions.  The national
average coarse particle concentration has a 50 % yearly modulation with a single peak in July.
      Stratifying the national PM10 concentrations one can obtain results showing that the
country has several major aerosol regions, as discussed in more  detail below.  Each region has
a discernible geographic extent as well as seasonal pattern. Over the plains of the  eastern
United States the spatial texture of PM10 is driven by the pattern of the emission fields, while
the seasonality of concentrations is likely to be determined by the chemical transformation and
removal processes, as well as by the regional dilution. In the mountainous western and Pacific
states, pockets of wintertime PM10 concentrations  exist that well exceed the eastern U.S.
values.  It is believed that haze and smoke in confined mountain valleys and air basins are
strongly influenced by topography which in turn influences the emission pattern, dilution, as
well as the chemical transformation  and removal rate processes.
      Given the regionality of the aerosol concentration pattern much of the discussion that
follows will be focused on the characteristics of these aerosol regions.  The Rocky Mountains
produce a natural division between the eastern and western aerosol regimes which will be
discussed next.
                                          6-51

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6.3.2.2  Eastern U.S. PM10 Pattern and Trend
      During the 1988 to 1994 period there were decreases in the annual average PM10 for
the eastern U.S. from 31 Mg/rn3 to 26 //g/m3 for all sites and from 34 /ug/m3 to 28 fj.g/m3 for
trend sites resulting in 16% or 18% reductions in PM10 (Figure 6-20b). The decline is rather
steady  over time.
      The highest eastern U.S. AIRS PM10 concentrations are recorded in Quarter 3
(Figure 6-20d). The  peak concentrations are over the Ohio River Valley stretching from
Pittsburgh to West Virginia, southern Indiana and St. Louis.  In this region, the PM10
concentration over the industrialized Midwest during the summer can exceed 40 /ug/m3.
Additional hot-spots with > 40 //g/m3 are recorded in Birmingham, AL, Atlanta, GA,
Nashville, TN, Philadelphia, PA and Chicago. IL. The summertime PM10 concentrations in
New England and upstate Michigan are < 20 //g/m3.
      The transition  seasons Quarters 2 and 4 (Figure 6-20d) show PM10 concentrations
ranging from 25 Atg/m3 to about 30 /ug/m3 over much of the eastern U.S., with concentration
hot-spots over the industrial Midwest as well as in the Southeast, Atlanta, GA and
Birmingham, AL.  The PM10 concentrations in urban-industrial  "hot-spots" exceed their rural
surrounding by less than a factor of two.
      The spatial variability of PM10 occurring over the eastern United States is driven
primarily by the varying primary aerosol emission density.  This can be deduced from the
coincidence  of higher concentrations within urban industrial areas.  The atmospheric dilution
(i.e., horizontal and vertical dispersion) in these areas is not likely to be spatially variable.
Also, the chemical aerosol formation and removal processes are likely to have weak spatial
gradients when averaged over a calendrical quarter. Hence, the main factor that is believed to
be responsible for the spatial variability is the emission field of primary PM10 particles and the
SO2, NOX, and VOC precursors of secondary aerosols.
      PM10 concentration in excess  of 30 //g/m3 is recorded over the agricultural states of
Iowa, Kansas, Nebraska, and South Dakota. The elevated PM10 concentrations over this
region tend to persist over all four seasons.    The eastern PM10 seasonality (Figure 6-20d) is
rather pronounced, with winter concentrations (December through March) of 24 /ug/m3, and
                                         6-52

-------
        PM10 Average - Eastern US
                                               PM10 Cone. Trend - Eastern U.S.
                                                       EPA AIRS database
       PM2.5 vs. PM10 - Eastern U.S.
          EPA AIRS - Monthly Averages
   140

   130

   120

   110

   100
 co  90
CORRELATION STATS:

AvgX-     31.4
AvgY:     18 86
Avg Y/Avo X .  06
CorrCoeff.   0.63
Slope      0.58
Y off»et:     0.35
Data Polntt • 1651
                                 (c)
                                           198B   1989   1990   1991   1992   1993   1994
                                             •A- Avg for all sites    -H-Avg for trend sites
                                             -l-Avg + Std. Dev.    -e-Avg - Std. Dev.

                                             Seasonal PM Pattern - Eastern U.S.
                                                       EPA AIRS Database
55
50
45
                                                  40
                                                0)
                                                  25
                                                  15
          20   40   60   80  100  120
                  PM10(Mg/m3)
                                    140
                               (d)
                                            1986  Mar   May   Jul   Sep   Nov
                                               -A-PM10  -B-PM2.5 -I-PM Coarse
Figure 6-20.  AIRS concentration data for east of the Rockies:  (a) monitoring locations;
              (b) PM10 concentration trends; (c) PM10 and PM2.5 relationship; and
              (d) PM10, PM2 5, and PMCoarse seasonal pattern.
                                          6-53

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July through August peak of 35 ^g/rn3.  The amplitude of the PM10 seasonal concentrations is
about 30%.
     The scatter chart of the eastern AIRS PM2 5-PM10 relationship shows a significant
amount of scatter, with a slope of 0.58 (Figure 6-20b). The ratio of the overall average PM2 5
and PM10 concentration is 0.6 such that 60% of PM10 in the sub 2.5 pan size range. The
seasonally of the fine AIRS particle concentration over the East is bimodal with a major peak
in July and a  smaller winter peak in January (Figure 6-20d).  As shown in Figure 6-15b, the
nonurban IMPROVE/NESCAUM network results for the eastern U.S. for PM2 5 show a peak
in summer but does not show a winter peak. The coarse particle concentration shows a single
broad peak over the warm season, April through October (Figure 6-20d), but with a somwhat
different pattern than shown in Figure 6-15b for nonurban cities in the eastern U.S.  It is
therefore evident that fine and coarse particles (from urban and nonurban measurements) have
different seasonal dynamics in the East.

6.3.2.3  Western U.S. PM10 Pattern and Trend
     The mountainous  states, west  of the Rockies (Figure 6-21) have higher PM10
concentrations in Quarters 1 and 4 than in Quarters 2 and 4 and shown ever higher PM10
concentrations (>50 /-fg/m3) at localized hot-spots. These higher concentrations occur over
both metropolitan areas such as Salt Lake  City, as well as in smaller towns in mountain valleys
of states west of the Rockies.
     The main geographic feature regions considered in California are the Los Angeles basin
and the San Joaquin Valley.  Both basins show monthly PM10 concentrations sometimes in
excess of 50 /ug/m3.  These basins are also confined by surrounding mountains that  limit the
dilution, facilitate cloud formation,  and have emissions that are confined to the basin floor.
Accordingly, they represent airsheds with characteristic spatial and temporal pattern.  It is
likely that the actual  local effects on the PM10 concentration field in the mountainous western
states are greater than depicted in Figure 6-21a.
     It appears that the spatial pattern of these high concentration hot  spots is driven by
emissions as  well as by the restricted wintertime ventilation due to mountainous  terrain. Over
the mountainous western states the  atmospheric dilution by horizontal and vertical dispersion is
                                          6-54

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         PM10 Average -Western US
                                                  PM10 Cone. Trend - Western U.S.
                                                           EPA AIRS database
  150

  140

  13D

  120

  110

  100

  80
dp

| B0

in 70
CM

Q. 60

  50

  40

  30

  20

  10
        PM2.5 vs. PM10 - Western U.S.
           EPA AIRS - Monthly Averages
        CORRELATION STATS:

        AvgX:
        AvjY:
        Avg Y/Avg X
        Corr Coftff:
        Slope :
        Y offwt:
        Data Points : 618
          20   40   60   80  100  120  140
                 PM10 (pg/m3)
                                                 40
                                               I351
                                                                                 (b)  .
                                                 1988   1989   1990   1991   1992   1993  1994
                                                  -A- Avg for all sites  -B- Avg for trend sites
                                                  -I- Avg + Std. Dev.  -©- Avg - Std. Dev.

                                                 Seasonal PM Pattern - Western U.S.
                                                           EPA AIRS Database
                                                 55
                                               5
                                               a.
                                                                                 (d)
                                                 1986   Mar   May   Jul   Sep   Nov
                                                    -A- PM10 -B- PM2.5 -+- PM Coarse
Figure 6-21.  AIRS concentration data for west of the Rockies: (a) monitoring trends;
              (b) PM10 concentration trends; PM10 and PM2t5 relationship; and (d) PM10,
              PM2 5, and PMCoarse seasonal pattern.
                                         6-55

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severely restricted by mountain barriers and atmospheric stratification due to strong and
shallow inversions.  Radiative cooling also causes fog formation which enhances the
production rate of hydroscopic aerosols in the valleys.  As a consequence, mountain tops are
generally protruding out of haze layers. Emissions arising from industrial, residential,
agricultural, unpaved roadways and other sources are generally confined to mountain valleys.
In the wintertime the mountain valleys are frequently filled with fog.  All three major factors
that determine the ambient concentrations (i.e., emissions, dilution, and chemical rate
processes) are strongly influenced by the topography. For this reason, many of the maps
depicting the regional pattern use shaded topography as a backdrop.
     In the western half of the U.S., west of and including the Rockies,  there was a decrease
in the PM10  concentration of 1988 to 1994 from 36 /ug/m3 to 25 jUg/m3 for all sites and from
39 (Ug/m3 to 28 ,ug/m3 for trend sites (Figure 6-2 Ib).  The reductions were 31% for all sites
and 28% for trend sites.  Standard deviation among the western stations of yearly average
PM10 concentrations is about 40%.
     The western AIRS PM2 5-PM10 relationship (Figure 6-21c) shows that on the average
about 50% of the PM10 is contributed by fine particles.  The scatter of data points
(Figure 6-21c) also shows that during high concentration PM10 episodes the fine fraction
dominates.
     The western PM10 seasonality (Figure 6-2Id) is also rather pronounced, having about
30% amplitude.  However, the lowest concentrations (26 Atg/m3) are reported in the late spring
(April through June), while the highest values occur in late fall (October through January).
     The seasonality of PM2 5 west of the Rockies (Figure 6-21d) is strongly peaked in
November through January.  In fact,  the PM2 5 is several times higher than the summertime
values.  On the other hand, the coarse fraction shows a broad peak during late summer, July
through October. It is  to be noted that in Figures 6-20 and 6-21, the fine and coarse particle
concentrations do not add up to PM  10, because size resolved samples were only available for
tens of sites, while the  PM10 concentrations were obtained from hundreds of monitoring
stations.
     In summary, there is a 20 to 24% reduction of PM10 concentrations for the continental
U.S. between 1988 and 1993.  On the national average the PM10 seasonality is weak.
                                         6-56

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Desegregation of the national averages into east and west of the Rockies, shows that the
downward trend west of the Rockies is more pronounced than over the eastern half of the U.S.
The east-west desegregation also shows that the lack of national PM10 seasonally arises from
two strong seasonal signals that are phase shifted, the eastern United States has a summer
peak, the West a fall and winter peak, and the sum of two signals is a weakly modulated
seasonal pattern.  Nationally, PM2 5 mass accounts for about 57%  of PM10 mass.  The East
and West show comparable average fine particle fractions (60% in the East and 50% in the
West).  Fine particles tend to dominate during the fall and winter season in the western U.S.,
except in the  southwest.
     It is evident that further examination discussed in the next sections will show that the
East-West division itself is rather crude and that dividing the conterminous United States into
additional subregions is beneficial in explaining the PM10 concentration pattern and trends.

6.3.2.4  Short-Term  Variability of PM10 Concentrations
     The previous aerosol concentration patterns were expressed as quarterly averages.
However, for health and other effects, the variance of the concentration, in particular the
occurrence of extreme high concentrations is of importance. The PM10 concentrations exhibit
marked differences in the shape of their distribution functions  around the mean values. For
example in Figure 6-22, the day to day variations of PM10 concentrations in Knoxville, TN are
about 40% of the mean value of 35 /ug/m3. On the other hand, the concentration time series
for Missoula, MT shows  a coefficient of variation of 60% over the mean of 34 ^g/m3. During
the winter season the coefficient of variation is even higher. It is therefore evident, that for
comparable mean concentrations the Missoula, MT site exhibits significantly higher short-term
variations.  Also note the large variations from a high concentration day to the lower
concentrations on the day before and/or the day after (Figure 6-22).
     The variability of concentration is examined spatially and seasonally by computing
logarithmic standard deviation (ratio of 84/50 concentration percentiles) for each monitoring
site.  These deviations were then contoured for each season. The results are depicted in the
seasonal maps of the logarithmic standard deviation (Figure 6-23).  The highest logarithmic
standard deviation is recorded over the northern and northwestern states during the cold
                                          6-57

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                       '239
                                                  Mean
                                                  CoVa
                                                  Min
                                                  Max
                                                  Points
        1988
80


60
  1989
                         1990
1991
1992
1993
       40
       20
           Mean
           CoVa
           Min
           Max
           Points
35
39.92
9
73
258
        1988
  1989
                         1990
1991
1992
1993
 Figure 6-22.  Short-term PM10 concentration time series for Missoula, MT, and
              Knoxville, TN.
season, Quarters 1 and 4.  Regionally, the logarithmic standard deviation in the north-

northwest is about 2.0 with pockets of high winter variability such as Salt Lake City, UT, and

Missoula, MT.  The lowest variability prevails over the warm season, Quarters 2 and 3,

covering the southeastern and southwestern states. Over multistate regions in the southern

states the summertime logarithmic standard deviation is  below 1.5. This means that these

areas are covered more or less uniformly by summertime PM10, while the northern states are

more episodic.
                                          6-58

-------
                                                 Quarter 1
                                                                                                            Quarter 2
o\
                                                 Quarter 3
                                                                                                            Quarter 4
                               'Q                                                          "9
    Figure 6-23. Geographic variation of the standard deviation, og, of the lognormal distribution of PM10 concentrations from
                 the AIRS.

-------
6.3.2.5   Aerometric Information Retrieval System PM2 5 Concentrations
     The mass concentration of fine particles in urban areas is not well known. Sampling and
analysis of PM25 is limited by small number of stations (<50), sampling period restricted to
few years, and different, non-standard sampling equipment was utilized for PM2 5
     The yearly average AIRS PM2 5 concentrations are shown in Figure 6-24. Figure 6-24
also shows the location and magnitude of PM2 5 concentrations from measurements of
IMPROVE/NESCAUM monitoring networks.  The fine particle data from the
IMPROVE/NESCAUM show a pattern of high concentrations (> 15 /-ig/m3) occurring over
the eastern United States.  This pattern of nonurban fine particle concentrations was discussed
in Section 6.3.1.

6.3.2.6  Other National Surveys
     A summary of urban PM10, PM2 5, PMCoarse at eight urban areas, Birmingham, AL,
Buffalo, NY, Houston, TX, Philadelphia, PA, Phoenix, AZ, Pittsburgh, PA, Rubidoux, CA,
and Steubenville, OH was reported by Rodes and Evans (1985).  The overall ratio of the PM10
to Total Suspended Paniculate (TSP) was 0.486. The relationships between PM10 and the
15 fj.m fraction (IP) are linear for all sites. With exception of Phoenix, AZ, and Houston, TX,
PM2 5 exceeded the PMCoarse mass concentration in all six urban areas.
     Spengler and Thurston (1983) reported PM concentrations in six  U.S. cities: Portage,
WI, Topeka, KS, Kingston, TN, Watertown, MA, St. Louis, MO, and Steubenville,  OH,
using dichotomous virtual impactors in the two size ranges, PM2 5 ,  having dp<2.5 //m, and
coarse particle mass with 2.5
-------
                                      AIRS PM2.5 - IMPROVE PM2.5 Comparison
Os
                                    AIRS PM2.5
IMPROVE/NESCAUM PM2.5
      Figure 6-24. Annual PM2-5 concentration pattern obtained from IMPROVE/NESCAUM and AIRS networks.

-------
      30
      20
      10
                       Portage, Wl
  • IP mass
  * Fine mass
_  • Course mass
  • Total sulfate mass
        JFMAMJJASONDJFMAMJJASONDJFMAMJJASOND

             1979         1980         1981


                      Harriman, TN
OU
50
40
CO
E
^>30

20
10
: / V*.
•. \
/ *
> \ *
* • / \-«
/ *
•^'s^v.V
*• •• «> *
•<
•IP mass
* Fine mass .
•Course mass
* Total sulfate mass .
» ,^B
V
•
•
k •*
.•••*
f"
        JFMAMJJASONDJFMAMJJASONDJFMAMJJASOND

             1979         1980         1981
                                                 90

                                                 80

                                                 70

                                                 60

                                                 50

                                                 40

                                                 30

                                                 20

                                                 10
                                                                      Topeka, KS
                                                                                • IP mass
                                                                                A Fine mass
                                                                                • Course mass
                                                                                •Total sulfate mass
                                                                   \    /
                                                     60
                                                     50
                                                   JFMAMJJASONDJFMAMJJASONDJFMAMJJASOND

                                                       1979         1980         1981


                                                                 Watertown, MA
                                                     40
                                                  m
                                                   E
                                                     20


                                                     10
                                                     • IP mass
                                                     • Fine mass
                                                    . • Course mass
                                                     • Total sulfate mass
                                                   JFMAMJJASONDJFMAMJJASONDJFMAMJJASOND

                                                       1979         1980         1981
  70


  60


  50

CO
E 40
o>




  20


  10
                      St. Louis, MO
  • IP mass
  •Fine mass
  'Course mass
  'Total sulfate mass
        JFMAMJJASONDJFMAMJJASONDJFMAMJJASOND

             1979         1980         1981
  90

  80

  70

  60
m
E50

=L40

  30


  20

  10
                                                                    Steubenville, OH
                                                                                •IP mass
                                                                                •Fine mass
                                                                                •Course mass
                                                                                •Total sulfate mass
     1        *


A  A'  ^    /'  r.
    li.  .  i  •  .   \    i \ i
                                                   JFMAMJJASONDJFMAMJJASONDJFMAMJJASOND

                                                       1979         1980         1981
Figure 6-25. Monthly mean concentrations in micrograms per cubic meter of PM15 (IP,
              inhalable mass), PM2 5 (fine mass), coarse mass (PM15-PM2 5), and total
              sulfate as (NH^SC^ in Portage, WI; Topeka, KS; Harriman, TN;
              Watertown, MA; St. Louis, MO; and Steubenville, OH.
                                              6-62

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     The quartz content and elemental composition of aerosols, collected in dichotomous
samplers in selected sites in the EPA Inhalable Participate network, were reported by Davis
et al. (1984).  For all network sites, an average of only 4.9 weight per cent of the coarse
particle mass and 0.4 weight per cent of the fine mass consisted of quartz. Continental interior
sites show the highest average quartz content as well as the greatest variability. The coastal
regions and eastern interior sites reveal the lowest quartz concentrations. The complete X-ray
spectra from some samples in Portland, OR, show that Si comes primarily from minerals such
as feldspars, where the Si in the Buffalo, NY aerosols comes from quartz.

6.3.3   Comparison of Urban  and Nonurban Concentrations
     Seasonal maps of the AIRS PM10-IMPROVE/NESCAUM PM10 spatial concentrations
are given in Figure 6-26.  In evaluating the  subsequent comparisons of the differences between
AIRS and IMPROVE/NESCAUM spatial concentrations possible sampling biases and
differences in sampling equipment and monitoring protocols may be significant.  In addition,
the differences in geographical location between the stations for the two networks also can
influence the reliability of these comparisons.  The AIRS PM2 5 concentrations everywhere
exceed their adjacent IMPROVE/ NESCAUM concentrations.  The highest AIRS PM2 5 are
reported over the eastern urban industrial centers, such as Philadelphia and Pittsburgh, where
the concentrations of 20 to 30 /ug/m3 exceed the nonurban PM2 5 by a factor of 2 to 3.
However,  the excess urban PM2 5 concentrations are evidently confined to the immediate
vicinity of urban centers.  This indicates that over the eastern United States a regionally
homogeneous background of PM2 5 concentration exists that has smooth spatial gradients.
Superimposed on the smooth regional pattern are local hot-spots with excess concentrations of
factor of 2 to 3 that are confined within a few miles of urban industrial centers.  The regional
homogeneity is an indication that the eastern U.S. PM2 5 is  composed of a secondary aerosol
that is produced several days after the emission of its gaseous precursors.  Similar results have
been discussed for SO42" since the 1970's (Altshuller, 1980). The excess PM2 5 concentration
in urban centers suggests that primary emissions such as automobile exhaust and heating
furnaces are responsible for much the urban PM2 5 hot-spots.
                                         6-63

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                                             Quarter 1
                                                                                                        Quarter 2
                   PM10 AIRS - PM10 IMPROVE
                                                                             PM10 AIRS - PM10 IMPROVE
                                             Quarters
Quarter 4
                   PM10 AIRS - PM10 IMPROVE
                                                                             PM10 AIRS - PM10 IMPROVE
Figure 6-26.  Spatial maps of PM10 concentration difference between AIRS and IMPROVE/NESCAUM networks.

-------
     The reported AIRS PM2 5 concentrations over the Pacific states are generally higher and
average at 20 to 50 /ug/m3.  This is 5 to 10 times higher than their companion IMPROVE
PM2 5 concentrations. The dramatic difference is attributable to the pronounced concentration
differences between urban-industrial-agricultural centers that exist in mountainous air basins
and the concentrations monitored at remote national parks and wilderness areas that are
generally at higher elevations.  However, it is fair to presume that the AIRS and IMPROVE
PM2 5 data sets represent the extreme of aerosol concentration ranges that exist over the
western U.S. The challenging task of filling in the details (i.e., spatially and temporally
extrapolating the aerosol concentrations over the rugged western United States) is discussed in
further detail in later regionally and locally focused sections below.
     Comparisons have been made of the seasonality of the urban (AIRS) concentrations
relative to the nonurban (IMPROVE/NESCAUM) data. In Figure 6-27 the difference in
PM10, PM2.5, and PMCoarse between AIRS and IMPROVE/NESCAUM sites,  using all
available data, is used to indicate the urban excess particle concentration compared to the rural
concentration.  No attempt has been made to evaluate the possible uncertainties in these
difference values.
     Nationally, the urban excess fine particle concentration ranges between 18 //g/m3 in
December through February  and  10 /^g/m3  in April through June (Figure 6-21 a).  The urban
excess coarse mass concentration ranges between 10 to 7 ,ug/m3.  The sum of the fine and
coarse national urban excess  mass concentration is about 25 ,ug/m3 in the winter season, and
18 /ug/m3 during the spring season. Hence, the nationally aggregated urban and nonurban data
confirm that urban areas may have excess concentrations on the order of 20 yug/m3, and well
over half is due to fine particles, particularly in the winter season.
     The urban excess (AIRS-IMPROVE/NESCAUM difference) over the eastern United
States (Figure 6-27b) shows fine particles excess of 8 to 12 /-tg/m3, with higher  value
occurring during both winter and summer.  The urban excess coarse mass in the eastern
United States is only 5 to 8 //g/m3,  peaking during spring and summer.  The sum of fine and
coarse urban excess is 15 to 18 ,ug/ni3 throughout the year.
                                         6-65

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                                       Urban Excess
  60
      I—I—I—I—I—I—I—I—I—I—I
   Jan  Mar  May  Jul  Sep  Nov  Jan  Jan  Mar  May  Jul  Sap  Nov  Jan  Jan  Mar  May  Jul  Sep  Nov  Jan
                         Fine + Coarse Mass
Fine
— c— Coarse
Figure 6-27. Urban excess concentrations (AIRS minus IMPROVE) for (a) the United
            States, (b) the eastern United States, and (c) the western United States.
     The excess urban (AIRS-IMPROVE/NESCAUM) aggregated over the western United
States is much more pronounced in magnitude and seasonality.  The urban excess fine mass is
about 30 A^g/m3 in November through January and drops to 8 to 10 Mg/m3 in April through
August. The urban excess coarse mass is less in magnitude and seasonality  15 to 18 y.g/m3 in
July through December, and 10 to 12 yug/m3 in March through May. The sum of the urban
excess fine and coarse mass is 40 to 50 //g/m3 in November through January and about
20 Mg/m3 in the spring March through June. The urban AIRS and nonurban IMPROVE)
networks in the western United States monitor aerosols differently because of different goals
and mandates. The urban nonurban difference is such that the western nonurban
concentrations contribute little to the much higher urban values, particularly in the winter
season.  On the other hand, the eastern urban sites are greatly influenced by the  nonurban,
regionally representative concentrations, particularly in the summer season.
                                         6-66

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6.4   REGIONAL PATTERNS AND TRENDS
     This section describes the spatial, temporal, size, and chemical characteristics of seven
aerosol regions of the conterminous U.S.  The sizes and locations of these regions were
chosen mainly on the basis of the characteristics of their aerosol pattern.  The main criteria for
delineating a region were (1) the region had to possess some uniqueness in aerosol trends,
seasonality, size distribution, or chemical composition; (2) each territory of the conterminous
United States had to belong to one of the regions; and (3) for reasons of computational
convenience the shape of the regions were selected to be rectangular on unprojected latitude
longitude maps. The resulting criteria yielded seven rectangular aerosol regions as shown in
Figure 6-28. It is recognized that this selection is arbitrary and for future analysis additional
regional definition criteria would be desirable.  The limitations in the data bases of the two
different networks discussed previously also apply to the  subsequent discussion.
     For sake of consistency and intercomparisons each region is described using maps
delineating the spatial pattern and the sampling locations in the subsequent figures (Section a).
For the figures showing AIRS monitoring results, Section b shows trends  in average PM10
concentrations and ± a. As discussed in Section 6.3.2.1 included in (b) are the results of two
trend analyses.  One of these uses the annual concentrations from  all available stations in
operation any time in the 1985 to 1994 period.  The second approach uses the annual
concentrations from only those stations operated continuously from 1985 to 1994, the long
term coverage or trend stations. Section c show plots and correlations relating PM10 and
PM2.s. Monthly AIRS concentrations (Section d) for a given region were computed by
averaging all the available data for the specific month.  In case of nonurban aerosol chemistry
some regions only had two to four monitoring stations. The monthly nonurban PM2 5,
PMCoarse and PM10 shown in the subsequent figures (Section b) over regions illustrate the
relative seasonality of each aerosol type. The nonurban regional average chemical
composition is presented as seasonal charts of chemical aerosol components as a fraction of the
fine mass concentration (Section c). The role of some primary sources, such as coal and fuel
oil combustion is indicated through seasonal charts of selenium (coal) and vanadium (fuel oil)
trace metals (Section d). In addition,  for each region figures will be provided showing shorter
term variability of PM10 concentrations and PM10 urban excess concentrations.
                                         6-67

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                    Northwest
                    PM10 = 28
                    PM2.5= 16
                    PM2.5/10 = 0.59
                     Upper
                     Midwest
                     PM10 = 31
                     PM2.5 = 12
                     PM2.5/10 = 0.38
Industrial
Midwest
PM10 = 29
PM2.5=17
PM2.5/10 = 0.59
Northeast
PM10 = 34
PM2.5 = 21
PM2.5/10 = 0.62
   S.California
   PM10=53
   PM2.5=26
   PM2.5/10=0.49
                                                                           Southeast
                                                                           PM 10=29
                                                                           PM2.5=17
                                                                           PM2.5/10=0.58
Southwest
PM10=34
PM2.5=12
PM25/10=037
Figure 6-28.  Aerosol regions of the conterminous United States.
 6.4.1   Regional Aerosol Pattern in Eastern New York, New Jersey, and the
         Northeast

      The Northeast aerosol region covers the New England states, including eastern

 Pennsylvania and eastern Virginia to the south (Figure 6-29a). In the Northeast, terrain

 features that significantly influence regional ventilation occur over the mountainous upstate

 New York, Vermont and New Hampshire.  Throughout the year,  the Northeast is influenced

 by Canadian as well as Gulf air masses.  The region includes the Boston-New York
 megalopolis, as well as other urban-industrial centers.  It is known that the Northeast is

 influenced by both local sources, as well as long range transport of fine particles and

 transformations of precursors to particles from other regions, as well as transport

                                          6-68

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     PM2.5 Concentration - Northeast
         IMPROVE/NESCAUM Data
 Chemical Fine Mass Balance - Northeast
          IMPROVE/NESCAUM Data
    0.7
    0.5
 o
 c
 o
 73  0.4
    0.0
    1989  Mar
May  Jul    Sep   Nov

 -B-oc        -(-Soil

 -©-Sulfate + OC + Soil + EC
                                 PM10, PM2.5 and PMC - Northeast
                                       IMPROVE/NESCAUM Data
                                               40,000
                                                1989  Mar   May  Jul    Sep   Nov

                                                              -+- PM2.5  -A- PM Coarse
                                    Chemical Tracers - Northeast
                                       IMPROVE/NESCAUM Data
                                               4,000
                                               3.500
                                               3,000
                                               2.500
                                            a
                                            1  2.000
                             c
                             o
                             O
                                               1,500
                                               1,000
                                                500
                                                                              (d)
1989  Mar   May  Jul

-A- Sulfur - Max = 4000

-+- Vanadium - Max =10
  Sep  Nov

-B- Selenium - Max = 4

-e- S/Se - Max = 4000
Figure 6-29. IMPROVE/NESCAUM concentration data for the Northeast:  (a) monitoring
            locations; (b) PM10, PM2 5, and PMCoarse (PMC); (c) sulfate, soil, organic
            carbon (OC), and elemental carbon (EC) fractions; and (d) tracers.
                                        6-69

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and transformation of precursors in single and multiple urban plumes within the region
(Chapter 3).

6.4.1.1   Nonurban Size and Chemical Composition in the Northeast
     The summary of the nonurban aerosol chemical composition in the Northeast is presented
in Figure 6-29c.  The region has 14 monitoring sites, 8 of which are part of NESCAUM in
upper New England.  The geographic locations with respect to nearby urban areas vary from
those sites within the northeast corridor to rural sites near the Canadian border.
     The PM10 concentration exhibits a factor of two seasonal amplitude between 12 /ug/m3 in
the winter, and 25 //g/m3 in June and July (Figure 6-29b). About 60% of PM10 is contributed
by fine particles throughout the year.  The PM2 5 also contributes to the summer-peaked
seasonality.
     Data from a two year fine particle network in the Northeast (Bennett et al., 1994) yielded
a geometric mean concentration of PM2 5 of 12.9 ,ug/m3 and particulate sulfur (1.4 Mg/m3,
equivalent to 4.2 A*g/m3 of sulfate), which is somewhat lower than other comparable rural
data.
     Sulfates are the most important contributors of the fine particle mass in the Northeast,
particularly in the summer season when they account for half of the fine mass (Figure 6-29c).
The regionality of sulfate in the northeastern U.S. has been dicussed for many years
(Altshuller, 1980).  The organic carbon ranges from 30 to 40%, with the higher percentages
occurring in the fall and winter,  September through January.  In fact, during the late fall the
contributions of sulfate and organic carbon are comparable at 40%.  Fine particle soil is
unimportant throughout the year (<5%).  Elemental carbon, on the other hand, is somewhat
more significant, particularly during the fall when it contributes about 10%  of the fine mass.
The sum of the above four nonurban fine particle aerosol  components,  account for over 90%
of the measured fine particle mass throughout the year.  These results would appear to indicate
ammonium ion, hydrogen ion, nitrates, trace metals and sea salt are of minor importance in
the northeastern U.S. fine particle chemical mass balance.
                                         6-70

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     The seasonality of both selenium and vanadium indicates a winter peak (Figure 6-29d).
In particular, the vanadium concentration increases by a factor of two for December and
January compared to the summer values.  Also, the V concentration is higher than over any
other region indicating the strongest contribution of fuel oil emissions. The S/Se ratio is
strongly seasonal  with a winter value of 1,000 and a summer peak of 2,000 to 2,500 consistent
with a substantial secondary photochemical contribution of SO42" during the summer.

6.4.1.2   Urban Aerosols in the Northeast
     In the northeastern U.S. there was a decrease in the annual average PM10 concentration
between 1988 and 1994 from 28 /ug/m3 to 23 //g/m3 for all sites and  from 31 /ug/m3 to
25 ,ug/m3 for trend sites (Figure 6-30b). The reductions were  18% for all sites and  19% for
trend sites. The standard deviation among the monitoring stations for any given year is about
30%. The map of the Northeast shows the magnitude of PM10 concentrations in proportion of
circle radius. The highest AIRS PM10 concentrations tend to occur in larger urban centers
(Figure  6-30a).
     The seasonality of the urban Northeast PM10 concentration (Figure 6-30d) is a modest
20%, ranging from 25 to 31 Mg/m3, smaller than the seasonality of the nonurban northwest
PM10 (Figure 6-29b). There is a summer peak in July, and a rather uniform concentration
between September and May showing only a slight winter peak.  The PM2 5-PM10 relationship
(Figure  6-30c) shows that on the average 62% of PMj0 is  contributed by fine particles.
     In general, the regional scale emissions are not expected to vary significantly from one
day to another. However, both meteorological transport (i.e.,  dilution), as well as aerosol
formation and removal processes,  are important modulators of daily aerosol concentration.
The daily concentration of particulate matter exhibits strong fluctuation from one day to
another, mainly due to the role of the meteorological transport variability.
     The regionally averaged daily concentration is associated with the regional scale
meteorological ventilation.  High regionally averaged concentrations  indicate poor ventilation
(i.e., a combination of low wind speeds and low mixing heights and the absence of fast aerosol
removal rates, such as cloud scavenging and precipitation). Low regional concentrations, on
the other hand, represent strong horizontal transport, deep mixing heights, or high regional
                                         6-71

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            PM10 Average - Northeast
                                        PM10 Cone. Trend - Northeastern U.S.
                                                  EPA AIRS database
       140

       no

       120

       110

       100
     5
     a.
            PM2.5 vs. PM10 - Northeast
             EPA AIRS - Monthly Averages
                                        ?988  1989   1990   1991   1992   1993  1994
                                          -A- Avg for all sites -B- Avg for trend sites
                                          -f-Avg + Std. Dev. -©-Avg - Std. Dev.

                                           Seasonal PM Pattern - Northeast
                                                  EPA AIRS Database
                                   (c)
CORRELATION STATS
Avg X :     34 28
AvgY:
Avg Y/Avg X
Corr Coeff:
Slop* :
YoffMt:
D»ta Points •
80

55

so

45
                                                i35
                                                                       (d)
             20  40   60   80  100  120  140
                    PM10(pg/m3)
                                         1986  Mar  May   Jul
                                           -A-PM10  -B-PM2.5
                       Sep   Nov
                     • PM Coarse
Figure 6-30  AIRS concentration data for the Northeast:  (a) monitoring locations;
             (b) regional PM10 concentration trends; (c) PM10 and PM2>5 relationship;
             and (d) PM10, PM2.s> and PMCoarse seasonal pattern.
                                             6-72

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 removal rates.  Advection of high aerosol content air masses from neighboring regions may
 also be a cause of elevated concentration in a given region.
      The daily variation of the regional averaged urban PM10 concentration for the Northeast
 is shown in Figure 6-31. The single day concentration data for every sixth day are connected
 by a line between the data points, although five in-between days are not monitored. The
 lowest regionally averaged daily urban PM10 is about 10 /wg/m3, while the highest is about
 55 //g/m3, with a regional average in the early 1990s of 25 /ag/m3.  The highest concentrations
 (> 40 /ug/m3) occur primarily in the summer season.  The time series also indicate that the
 high concentration episodes do not persist over consecutive six day periods.  This is consistent
 with the notion that the regional ventilation that is caused by synoptic scale air mass changes,
 which typically occur every four to seven days over eastern U.S. The daily time series also
 convey the fact that day to day variation in PM10 is higher than the seasonal amplitude. In
 fact, in Figure 6-31 the concentration seasonality is barely discernible. It can be stated,
 therefore, that the PM10 concentration in the Northeast is highly episodic (i.e., the temporal
 concentration variation is both substantial and irregular). The excess urban PM10 (AIRS-
 IMPROVE) is shown in Figure 6-32.  The excess urban PM10 concentration in the Northeast
 is a relatively small part of the total urban PM10 concentration between May and October.
 The reliability of such estimates of excess regional urban PM10 concentrations discussed
 earlier should be considered (Section 6.3.3).

 6.4.2   Regional Aerosol Pattern in the Southeast
     The Southeast rectangle stretches from  North Carolina to eastern Texas (Figure 6-33).
 From the point of view of regional ventilation the Southeast terrain is flat,  with the exception
 of the mildly rolling southern Appalachian Mountains.  The region is known for increasing
 population over the past decades, high summertime  humidity, and poor regional ventilation
 due to stagnating high pressure systems.

 6.4.2.1    Nonurban Size and Chemical Composition in the Southeast
     Only six nonurban stations were available in the Southeast with the absence of
monitoring over the southern (Gulf Coast) part of the region, except for Florida.  The
                                         6-73

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                          Northeast Every Sixth Day
     CO
     5
     a.
                       1991
1992
1993
 Figure 6-31. Short-term variation of PM10 average for the Northeast. Data are reported
           every sixth day.
   40
                     Northeast urban excess
   35  --

   30  --

   25
 O)
   20  +
   10  --
    0
      Jan      Mar       May      Jul      Sep      Nov      Jai
Figure 6-32. Urban excess concentration (AIRS minus IMPROVE) for the Northeast.
                                  6-74

-------
    PM2.5 Concentration - Southeast
          IMPROVE/NESCAUM Data
 Chemical Fine Mass Balance - Southeast
         IMPROVE/NESCAUM Data
    0.9
 «  °7
 «
 m
 S
 a  0.8
    0.3
                                  (c)
0.0
1989  Mar
-^Sulfate
-e-EC
              May   Jul   Sep  Nov
                -B-QC        n-Soll
                -e>-Sulfate + OC + Soil + EC
   PM10, PM2.5 and PMC - Southeast
         IMPROVE/NESCAUM Data
  40,000
                                             O

                                             1
                                               35,000
                                               30,000
                                               25,000
                                               20,000
                                             O
                                             O
                                               15,000
                                               10,000
                                               5,000
                                                                              (b)
                                                1989  Mar   May  Jul    Sep   Nov
                                                               -+- PM2.5 -A- PM Coarse
      Chemical Tracers - Southeast
         IMPROVE/NESCAUM Data
                                               4,000
                                               3,500
                                               3.000
01 2.500
                                            I
                                            ~ 2,000
                                            8
                                             o
                                             o
                                               1.500
                                               1,000
                                                500
                                  (d)
   1989  Mar   May   Jul
    -A- Sulfur - Max = 4000
    -+- Vanadium - Max =10
 Sep   Nov
-B- Selenium - Max = i
-e- s/Se - Max = 4000
Figure 6-33. IMPROVE/NESCAUM concentration data for the Southeast:  (a) monitoring
            locations; (b) PM10, PM2 5, and PMCoarse (PMC); (c) sulfate, soil, organic
            carbon (OC), and elemental carbon (EC) fractions; and (d) tracers.
                                        6-75

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nonurban PM10 concentration in the Southeast (Figure 6-33b) is roughly comparable to the
Northeast, exhibiting about factor of two seasonal concentration amplitude between 12 ,ug/m3
in the winter, and 25 /ug/m3 in the summer.  An anomalous high PM10 concentration is shown
in July which appears to be contributed by an excess coarse particle concentration of about
10 /ug/rn3.  With exception of July, the fine particle mass accounts for about 70% of the
nonurban PM10, leaving the coarse mass of 30% or less throughout the year (Figure 6-33b).
     The most prominent aerosol species in the Southeast are sulfates contributing 40 to 50%
of the fine mass (Figures 6-33c).  The anomalously low sulfate fraction (35%) during July
coincides with the high (20%) soil contribution during July. For the other months, soil
contribution is < 5% of the fine mass.  The relative role of the organic carbon in the nonurban
Southeast is most pronounced during the winter (40%), but declines to 25% during the
summer months. The contribution of elemental carbon varies between 2% in the summer to
6% in the winter months.
     The trace element concentrations of selenium and vanadium (Figure 6-33d) are constant
throughout the year, implying that the combined role of emissions and dilution is seasonally
invariant.  The concentration of sulfur, on other hand shows a definite summer peak, that is
two to three times higher than the winter concentrations. Consequently, the S/Se ratio is
strongly seasonal.  In fact, the warm season S/Se ratio of 2,500 is higher than over any other
region of the country. If Se-bearing coal combustion is the dominant source of sulfur in the
Southeast, then the high S/Se ratio implies that the secondary photochemical sulfate production
in the summer is several times that in the winter.

6.4.2.2  Urban Aerosols in the Southeast
     In the southeastern U.S. there was a decrease in the annual average PM10 concentrations
between 1988 and 1994 from 33 /ug/m3 to 27 //g/m3 for all sites and from 35 /zg/m3 to
29 //g/m3 for trend sites (Figure 6-34b).  The reductions were 18% for all sites and 17% for
trend sites. The Southeast PM10 concentration trends and the PM10 seasonality resemble  the
industrial Midwest described below.  A unique feature of the Southeast is the uniformity of the
aerosol concentration among the monitoring stations. In fact the 17% station to station
                                         6-76

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            PM10 Average - Southeast
  150

  140

  130

  120

  110

  100

«*"• eo

3 80
U)
« 70
O.
   60

   50

   40

   30

   20

   10

   0
PM10 Cone. Trend - Southeastern U.S.
           EPA AIRS database
80
           PM2.5 vs. PM10 - Southeast
             EPA AIRS - Monthly Averages
      1989   1990   1991   1992   1993   1994
  -A- Avg for all sites  -B-Avg for trend sites
  -t-Avg + Std. Dev.  -*-Avg - Std. Dev.

   Seasonal PM  Pattern - Southeast
           EPA AIRS Database
          CORRELATION STATS:
          AvgX:    29.19
          AvgY-    18.32
          Avg Y/Avg X :  0.55
          CorrCoeff:   0.63
          Slopa :     0.43
          Y offtet:     3.61
          D»ti Polnte : 352
 50
 45

                                                                                    (d)
             20   40  60   80  100  120  140
                    PM10(jjg/m3)
  1986  Mar   May  Jul    Sep   Nov
    -A-PM10  -B-RM2.5  -t-PM Coarse
Figure 6-34. AIRS concentration data for the Southeast: (a) monitoring locations;
             (b) regional PM10 concentration trends; (c) PM10 and PM2>5 relationship;
             and (d) PM10, PM2.s» antl PMCoarse seasonal pattern.
                                          6-11

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standard deviation is by far the lowest among the aerosol regions (Figure 6-34b). This result
would appear to be associated with regional meteorological patterns.
     The Southeast is also characterized by high seasonal amplitude of 37%, ranging between
22 //g/m3 in December through February and 35 //g/m3 in July through August
(Figure 6-34d). There is no evidence of a winter peak for the southeastern U.S.
     The scattergram of PM2 5-PM10 for the Southeast (Figure 6-34c) shows an average of
58% fine particle  contribution, with considerable scatter. It should be noted, however, that
size segregated samples were available only briefly and these only for two monitoring sites
which may not be representative for the large southeastern region.
     The regionally averaged daily PM10 concentrations over the Southeast (Figure 6-35)
shows a clearly discernible seasonally.  The concentrations during the winter months are
about factor of two lower than during the summer.  Overall, the lowest concentrations are
about 12 //g/m3, and the highest about 50 //g/m3, which is about factor of four.  However,
seasonality of the  temporal signal accounts for about half of the variation. Hence, within a
given season the sixth day to sixth day variation is only about 50%.  It can be concluded that
the PM10 concentration over the southeastern United States region is quite uniform during
shorter tune intervals, although it exhibits a substantial seasonality.  The southeastern United
States also exhibits the highest spatial homogeneity (i.e., the smallest average deviations of
average concentrations between the stations). The PM10 urban excess (AIRS-IMPROVE) for
the southeast region is given in Figure 6-36.  The range of monthly urban excess
concentrations in the Southeast is within approximately the same range, 5 Mg/m3 to 10 //g/m3,
as for the Northeast.  The one distinct feature is the sharp decrease in the urban excess in July
which corresponds to  the sharp peak attributed to the nonurban coarse soil contribution in July
for the Southeast (Figure 6-33).

6.4.3      Regional Aerosol Pattern in the Industrial Midwest
        This aerosol  region stretches between Illinois and western Pennsylvania, including
Kentucky on the south (Figure 6-37a). The industrial Midwest is covered by flat terrain west
of the Appalachian Mountains. In the winter the region is under the influence of cold
Canadian air masses, while during the summer moist air masses transported from the Gulf
                                          6-78

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                          Southeast Every Sixth Day
eo

 O)

 O~
                      1991
1992
1993
Figure 6-35.  Short-term variation of PM10 average for the Southeast. Data are reported
           every sixth day.
                        Southeast urban excess
      4O
        Jan       Mar       May        Jul        Sep       Nov

 Figure 6-36. Urban excess concentration (AIRS minus IMPROVE) for the Southeast.
                                   6-79

-------
      PM2.5 Concentration - Industrial Midwest
              IMPROVE/NESCAUM Data
                             PM10, PM2.5 and PMC - Industrial Midwest
                                      IMPROVE/NESCAUM Data
                                                 40,000
                                                 35,000
                                                 30,000
                                              £  20,000
                                               O
                                               O
                                                 15,000
                                                 10,000
                                                  5,000
                                                                               (b)
                                                   1989  Mar   May
                                                        -B-PM10  -
                                               Jul    Sep   Nov
                                               PM2.5 -A- PM Coarse
  Chemical Fine Mass Balance - Industrial Midwest
              IMPROVE/NESCAUM Data
      i
      I
        0.4
        0.1
                                     (C)
        0.0
        1989  Mar
        -A-Sulfate
        -9-EC
                                Chemical Tracers - Industrial Midwest
                                      IMPROVE/NESCAUM Data
                               4,0001	'	1	.	1	1	1	1	.	i-
                                                 3,500
                               3,000
                            «">
                            i
                            »  2,500


                            I
                                               o
                                               O
                                                 2,000
                                                 1,500
                                                 1,000
                                                             (d)
May  Jul   Sep  Nov
  -B-oc        -HSoil
  -e-Sulfate + OC + Soil + EC
1989  Mar  May  Jul
-A- Sulfur - Max = 4000
-+- Vanadium - Max =10
 Sep  Nov
-B- Selenium - Max = 4
-e- S/Se - Max = 4000
Figure 6-37.  IMPROVE/NESCAUM concentration data for the industrial Midwest:
              (a) monitoring locations; (b) PM10, PM2 5, and PMCoarse (PMC);
              (c) sulfate, soil, organic carbon (OC), and elemental carbon (EC) fractions;
              and (d) tracers.
                                           6-80

-------
Coast prevail. However, the northern most portion of this region in Michigan and Wisconsin is
cooler and may be influenced by Canadian air flow at times during the summer. This region
includes the Ohio and Mississippi River Valleys that are known for high sulfur emission
densities.  The region also includes major metropolitan areas.

6.4.3.1   Nonurban Size and Chemical Composition in the Industrial Midwest
     The seasonal pattern of the nonurban aerosol in the Industrial  Midwest is shown in
Figure 6-37b.  Only five nonurban monitoring sites  are available widely separately
geographically between those at the northern most sites and those in the southern portion of
the region with no sites over most of the region. Their representativeness is questionable.
The PM10 concentrations range between 10 and 22 /ug/m3, comparable to the nonurban levels
in other eastern U.S. regions. From 70 to 80% of PM10 is contributed by fine particles
throughout the year.  The coarse particle concentrations are 4 to 5 //g/m3, which is lower than
over any other region of the U.S.  Hence, the contribution of wind  blown dust, fly ash, or
other man-induced dust entrainment is not a significant factor in the nonurban areas of the
Industrial Midwest.
     The chemical mass balance (Figure 6-37c) shows that sulfates are 45 to 55% of the fine
mass which is higher than the sulfate fractions in other regions. The concentration of
vanadium, which is a tracer for oil combustion, is low throughout the year.  The concentration
of fine particle sulfur Organics exhibit a variable  contribution that  is high (40%) during the
cold season (October through February) and quite low (20%) in July and August.  The strong
winter peak for the organic fraction differs markedly from the Northeast where the organics
are seasonal.  Another unusual feature of the chemical mass balance is that the sum of sulfate,
organic carbon, soil, and elemental carbon is about 75% during the summer and 95%  in the
winter.  It is not known  what is the composition of the missing 25% during the summer tune,
but the missing fraction could be associated with nitrates, ammonium ion, hydrogen ion, and
water.
     Chemical tracer data are shown in Figure 6-37d.  The chemical tracer for coal
combustion, selenium ranges between 1,000 and 1,500 pg/m3, which is higher than in any
other region.  There is a sizeable month to month variation in Se concentration (partly due to a
                                         6-81

-------
small number of data points) and the seasonality is not appreciable. This means that the
combined effects of coal combustion source strength and meteorological dilution are seasonally
invariant over the industrial Midwest,  exhibits random monthly variation but indicates a
summer peak.  The S/Se ratio is a rather smooth seasonal curve ranging between 1,000 in the
winter and 2,000 during the summer months. Hence, the sulfate yield is about twice as high
during the summer as during winter months.  For comparison both the Northeast and
Southeast exhibit somewhat higher seasonality (factor of 2.5) in S/Se ratio. A possible
explanation for this change in S/Se ratio is that over the industrial Midwest the average age of
the SO2 emissions traveling downwind may be less than over the Northeast or Southeast.

6.4.3.2  Urban Aerosols in the Industrial Midwest
     In the industrial midwester U.S. there was a decrease in the annual average PM10
concentrations between 1988 and 1994 from 33 fj.g/m3 to 29 ^g/m3 for all sites and from
37 ,ug/m3 to 30 ^g/m3 for trend sites (Figure 6-38b). The reductions were 12% for all sites
and 19% for trend sites.  There is also a 28% deviation among the stations within the region.
As in the Northeast, the higher concentrations occur within the larger urban-industrial areas
(Figure 6-38a).  The PM10 seasonality (Figure 6-38d) is virtually identical (37% amplitude) to
the seasonality of the Southeast: the lowest concentrations (25 /^g/m3) occur between
November and February, while the highest values are recorded in June through August
(40 /ug/m3).  The  trends and the seasonality of the midwestern PM10 aerosols are comparable
to those of the Southeast.
     Fine particles contribute 60% of the PM10 concentration on the average (Figure 6-38c),
and high PM10 can occur when either fine or coarse particles dominate.
     Daily concentration over the industrial Midwest (Figure 6-39) varies between 14 and
75 //g/m3. The lowest regional concentrations occur during the winter months, while the
highest values (in excess of 40 /ug/m3) occur during the summer.  It is evident that seasonality
is an important component of the time series, accounting for about half of the variance.  The
elevated concentrations occur only one sixth day observation at a time, consistent with the low
frequency of prolonged episodes.  The industrial Midwest also shows substantial spatial
variability.  The urban excess PM10 (AIRS-IMPROVE) for the industrial midwest is given in
                                         6-82

-------
         PM10 Average - Industrial Midwest
                                         PM10 Cone. Trend - Industrial Midwest
                                                   EPA AIRS database
         PM2.5 vs. PM10 - Industrial Midwest
              EPA AIRS - Monthly Averages
                                               1989   1990   1991   1992   1993   1994
                                           -A- Avg for all sites  -B- Avg for trend sites
                                           -l-Avg + Std. Dev.  -»-Avg - Std. Dev.

                                        Seasonal PM Pattern - Industrial Midwest
                                                   EPA AIRS Database
        130

        120

        110

        100
     CO
      E
      «. 80
      CM
      5
      0. 70
 CORRELATION STATS:

. Avg X     29.02
 AvgY-    17.62
 Avg Y/Avg X :  0.6
 Corr Coeff    0.66
• Slope •     0.53
 Y offtet.     2 08
 Dita Points : 465
                                    (c)
                                                    55
45
                                       0.
                                                                        (d)
          0    20   40  60   BO   100  120 140
                     PM10
                                          1986  Mar   May   Jul
                                             -A-PM10  -B-PM2.5  '
                       Sep   Nov
                     -PM Coarse
Figure 6-38. AIRS concentration data for the industrial Midwest: (a) monitoring
             locations; (b) regional PM10 concentration trends; (c) PM10 and PM2.5
             relationship; and (d) PM10, PM2.5, and PMCoarse seasonal pattern.
                                              6-83

-------
                              Industrial Midwest Every Sixth Day
 o
 i
 Q_
                         1991
1992
1993
Figure 6-39.  Short-term variation of PM10 average for the industrial Midwest.  Data are
             reported every sixth day.
  Figure 6-40.  The pattern for the urban excess PM10 differs seasonally from that in the
  northwest (3-32) or southeast (6-34).

  6.4.4     Regional Aerosol Pattern in the Upper Midwest
       The upper Midwest covers  the agricultural heartland of the country (Figure 6-41). The
  region is void of any terrain features that would influence the regional ventilation.  Industrial
  emissions and the  population density are comparatively low. However, the relatively high
  PM10 concentrations in this region warrant a more detailed examination. In the winter, the
  region is covered by cold Canadian air masses, while in the summer moist Gulf air alternates
  and drier Pacific air masses occur.
  6.4.4.1  Nonurban Size and Chemical Composition in the Upper Midwest
       There is a lack of nonurban monitoring sites in the upper midwest (Figure 6-4la).
  Compared to the urban sites (Figure 6-42a), these nonurban sites are poorly representative of
                                          6-84

-------
                     Industrial  Midwest urban excess
       o
                                                                         Nov
        Jan         Mar        May         Jul        Sep
Figure 6-40.  Urban excess concentration (AIRS minus IMPROVE) for the industrial
             Midwest.
the region.  Based on these few sites in the upper Midwest, the PM10 concentration is about
8 /ug/m3 during the November through April winter season, and increases to 15 Mg/rn3 during
the summer. Fine and coarse particles have a comparable contribution to the PM10 mass
(Figure 6-4 Ib).
     The chemical mass balance (Figure 6-41c) indicates that during the March through May
spring season sulfates dominate, but during July through October season organics prevail.
This is a rather unusual pattern not observed over any other region.  The contribution of fine
particle soil exceeds 10% in the spring as well as in the fall season.
     Chemical tracers are shown in Figure 6-41d. Selenium concentration is low throughout
the year (400 to 600 pg/m3), with the highest concentrations observed during the summer.
This suggests that either the Se sources from coal-fired power plants or the Se transport into
the Upper Midwest from other regions is stronger in the summer.  The concentration of the
fine particle sulfur is < 500 ng/m3 throughout the year, but somewhat higher during March
and April. The spring peak of fine particle sulfur has not been observed in any other region.
It is also worth noting that S/Se ratio is the highest during the spring and lowest in July
                                        6-85

-------
      PM2.5 Concentration - Upper Midwest
             IMPROVE/NESCAUM Data
                                        PM10, PM2.5 and PMC - Upper Midwest
                                                IMPROVE/NESCAUM Data
                                                40.000
                                                35,000
                                                30,000
                                                25,000
                                              5
                                              o
                                              O
                                                20,000
                                                15,000
                                                10,000
                                                6,000
                                                                               (b)
                                                 1989  Mar   May   Jul   Sep  Nov

                                                       -0-PM10  -t-PM2.5  -&-PM Coarse
  Chemical Fine Mass Balance - Upper Midwest
             IMPROVE/NESCAUM Data
    |
    £  0.4
                                    (C)
1989  Mar

 -A-Sulfate
                                           Chemical Tracers - Upper Midwest
                                                IMPROVE/NESCAUM Data
                                         4,000
                                                3,500
                                                3.000
                                              •& 2.500


                                              i
                                              S 2,000
                                       o
                                       O
                                                1.500
                                                1,000
                                                                        (d)
                 May  Jul    Sep   Nov

                    -B-OC        H-Soil

                    -e-Sulfate + OC + Soil + EC
1989  Mar  May   Jul

 -A- Sulfur- Max = 4000

 -I- Vanadium - Max =10
Sep   Nov

-B- Selenium - Max = 4

-e- S/Se - Max = 4000
Figure 6-41. IMPROVE/NESCAUM concentration data for the upper Midwest:
            (a) monitoring locations; (b) PM10, PM2 5, and PMCoarse (PMC);
            (c) sulfate, soil, organic carbon (OC), and elemental carbon (EC) fractions;
            and (d) tracers.
                                          6-86

-------
          PM10 Average - Upper Midwest
                                             PM10 Cone. Trend - Upper Midwest
                                                      EPA AIRS database
                                                         1989   1990  1991   1992  1993   1994
                                                     -A-Avg for all sites  -B-Avg for trend sites
                                                     -l-Avg + Std. Dev.  -©-Avg - Std. Dev.
          PM2.5 vs. PM10 - Upper Midwest
              EPA AIRS - Monthly Averages
     "
150

140

130

120

110

100

 90
     — 60
     in
        40

        30

        20

        10
                                   (C)
           CORRELATION STATS
           AvgX:   31.41
           AvflY.   12.16
           Avg Y/Avg X : 0.38
           CorrCoeff:  0.54
           Slope:    0.18
           Y oH»t .    6.46
           O>t« PoinU : 34
             20   40   60   80  100  120  140
                     PM10 (\iglm3)
                                            Seasonal PM Pattern - Upper Midwest
                                                      EPA AIRS Database
         (d)
                                            1986  Mar   May  Jul
                                               -A-PM10  -B-PM2.5
Sep   Nov
PM Coarse
Figure 6-42.  Aerometric Information Retrieval System (AIRS) concentration data for the
              upper Midwest:  monitoring locations; regional PM10 monitoring trends;
              PM10 and PM2>5 relationship; and PM10, PM2-5, and PMCoarse seasonal
              trends.
                                            6-87

-------
through September. It needs to be pointed out again that the above chemical patterns are based
on only two monitoring stations.

6.4.4.2 Urban Aerosols in the Upper Midwest
     The agricultural upper Midwest (Figure 6-42b) shows the smallest decline in PM10
concentrations among the regions. In the upper midwestern U.S. there was a decrease hi the
annual average PM10 concentration between 1988 and 1994 from 30 ptg/m3 to 25 jtg/m3 for all
sites and from 32 //g/m3 to 26 /ug/m3 for trend sites (Figure 6-42b). The reductions were 17%
for all sites and 19% for trend sites.  As over the eastern U.S., the highest concentrations
occur in the vicinity of urban areas.  Some of the station-to-station concentration spread arises
from low concentrations over western North Dakota.  On the average, the deviation among the
stations over the region is a moderate 30% (Figure 6-39). The upper Midwest is also unique
in that it shows the regionally lowest seasonal amplitude of 19%, with the slightly lower
concentrations occurring in December and January. The sparse size segregated data indicate
that only 38% of PM10 is contributed by fine particles.  This is an indication that coarse wind
blown dust from natural or man-induced sources prevails. In this sense, the region is similar
to the Southwest (see below).
     The daily regionally averaged PMi0 concentrations in the upper Midwest  (Figure 6-43)
range between 14 and 45 /^g/m3.  The highest values (>40 /ug/m3) generally occur in the
summer season, while the low regional concentrations occur mainly in the cold season, but
low values also occur in the summer. It is interesting that the lowest PM10 concentrations
over the upper Midwest (15 /ug/m3) are comparable to the Southeast and the industrial
Midwest, but differ from these regions by the absence of immediately subsequent high
concentration events or episodes.  In fact, the PM10 "episodes" over the upper  Midwest are  all
in the 40 to 45 /zg/m3 concentration range, compared to 50 to 75 //g/m3 in the Midwest.  The
seasonality is barely discernible from the tune series confirming that the day to day variation
exceeds the seasonal modulation.  The urban excess PM10 (AIRS-IMPROVE) for the upper
midwest is given in Figure 6-44, but its reliability may be in question because of the very
small number of nonurban sites.

-------
                         Upper Midwest Every Sixth Day
 CO
 5
 CL
                     1991          1992           1993
Figure 6-43.  Short-term variation of PM10 average for the Upper Midwest. Data are
           reported every sixth day.
       Jan
                    Upper Midwest urban excess
Mar
Nov
                             May       Jul        Sep
Figure 6-44.  Urban excess concentration (AIRS minus IMPROVE) for the Upper
           Midwest.
                                 6-89

-------
6.4.5   Regional Aerosol Pattern in the Southwest
     The Southwest covers the arid states from western Texas to Arizona (Figure 6-45a). The
Southwest is characterized by mountainous terrain features between the southern Rockies and the
Colorado Plateau.  The industrial activity and agriculture is minor compared to other regions.
Major population centers include El Paso, Phoenix, and Tucson. The meteorology of the region
is characterized by low annual precipitation, except during the periods when moist air penetrates
from the Gulf of Mexico toward these states, bringing moisture and precipitation.

6.4.5.1   Nonurban Size and Chemical Composition in the Southwest
     The PM10 concentrations at nonurban southwestern sites show a double peak, one during
the late spring  (April through July), and another in October. This bimodal seasonality is
imposed by the coarse particle mode.  The PM2 5 mass concentration is unimodal with a
summer maximum.  Overall, the nonurban PM10 concentrations are comparatively low (8 to
15 /ug/m3) and over 60% contributed by coarse particles (Figure 6-45b).
     The chemical mass balance (Figure 6-45c) shows sulfates to be the larger contributor
during the winter (December through March) as well as in late summer (July through
October).   However, sulfate and organic carbon contributions are comparable during March
through June as well as during November through December.  Fine particle soil plays a
prominent role in the spring fine particle chemical mass balance reaching 25 %, but the
contribution of soil decreases during the summer, and during December through February
dwindles to below 10%.
     The selenium and vanadium concentrations  (Figure 6-45d) are very low and rather
invariant throughout the year.  The fine particle sulfur concentration is low and exhibiting a
weak maximum during August. The S/Se ratio is comparatively low and bimodal, with peaks
in April through May as well as August through October.

6.4.5.2   Urban Aerosols in the Southwest
     In the southwestern U.S. there was a decrease in PM10 concentrations between 1988 and
1994 from 38 ^g/m3 to 24 Aig/m3 for all sites and from 43 ^g/m3 to 29 /ug/m3 for trend sites
(Figure 6-46b). The reductions were 37% for all sites and 33% for trend sites. The
                                         6-90

-------
      PM2.5 Concentration - Southwest
           IMPROVE/NESCAUM Data
                                               PM10, PM2.5 and PMC - Southwest
                                                     IMPROVE/NESCAUM Data
                                             40.0001	1	,	1	,	1	.	,	1	,	,	r-
                                                35,000
                                                30,000
                                                25,000
                                                20,000
                                            5   15.000
                                                10,000
                                                5,000
                                                                              (b)
                                               Jan   Mar   May   Jut

                                                -EHPM10  -HPM2.5
                                                                      Sep  Nov

                                                                        Coarse
  Chemical Fine Mass Balance - Southwest
            IMPROVE/NESCAUM Data
  0.7




H 0.8


B
c
~ 0.6
  •5

  I
                                  (C)
    0.0
    Jan   Mar

     -fi-Sulfate

     -e-soot
             May  Jul   Sep   Nov

               -B-Organics    -t-Soil

                     Org + Soil + Soot
                                               4,000
                                               3,600
                                               3,000
                                               2,500
                                                 Chemical Tracers - Southwest
                                                     IMPROVE/NESCAUM Data
                                             B 2.000
                                             O
                                             O
                                               1,000
                                                                              (d)
Jan   Mar   May   Jul   Sep   Nov

 -A-Sulfur-Max = 4000   -B-Selenium - Max = 4

 -+- Vanadium -Max = 10 -©- S/Se - Max = 4000
Figure 6-45.  IMPROVE/NESCAUM concentration data for the Southwest:
             (a) monitoring locations; (b) PM10, PM2.5, and PMCoarse (PMC);
             (c) sulfate, soil, organic carbon (OC), and elemental carbon (EC) fractions;
             and (d) tracers.
                                        6-91

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            PM10 Average - Southwest
                                                   PM10 Cone. Trend - Southwest
                                                          EPA AIRS database
  150

  140

  130

  120

  110

^ 100

I  >°

10  BO

2
Q.  70

   60

   50

   40

   30

   20

   10
           PM2.5 vs. PM10 - Southwest
             EPA AIRS - Monthly Averages
                                                     1989   1990   1991   1992  1993   1994
                                                    -A-Avg for all sites  -B-Avg for trend sites
                                                    -l-Avg + Std. Dev.  -e-Avg - Std. Dev

                                                  Seasonal PM Pattern - Southwest
                                                          EPA AIRS Database
CORRELATION STATS .
AvgX.    3758
AvgY:    1328
AV9 Y/Avg X : 0.35
Corr Coeff:
Slope :
Y offtet :
Data Polntt. 107
                                                   45
                                                "E  3S
                                                 - 30
                                                s
                                                a.
                                                   is

                                                   20

                                                   15

                                                   10
                                                                                  (d)
            20  40   60   80  100  120  140
                    PM10(M9/m3)
                                                 1986  Mar   May  Jul
                                                   -A-PM10  -B-PM2.5  -
                                                                Sep   Nov
                                                               • PM Coarse
Figure 6-46.  AIRS concentration data for the Southwest: (a) monitoring locations;
              (b) regional PM10 monitoring trends; (c) PM 10 and PM2.5 relationship; and
              (d) PM10, PM2.5, and PMCoarse seasonal trends.
                                           6-92

-------
 downward trends in PM10 concentrations were not monotonic. In the Southwest is the large
 concentration spread of 45% among the monitoring sites (Figure 6-46b). Sites with low
 concentrations (<20 Atg/m3) occur adjacent to high concentration sites (>50 ^tg/m3).
      Seasonally, the Southwest PM10 concentration shows two peaks, one in late spring April
 through June, and another during the fall October through November.  The concentration dip
 in August and September has not been observed for any other region. The late summer
 concentration drop coincides with the occurrence of the moist air flows from the Gulf of Mexico.
      The size segregated aerosol samples from the Southwest clearly show that coarse
 particles make the major contribution to the PM10 concentration, the fine particles contributing
 only 37% (Figure 6-46a).  The scatter in Figure 6-46c indicates that high PM10 concentration
 months can occur with low concentrations of fine particles. In the Southwest natural and man-
 induced coarse particle dust is a major contributor  to PM10 aerosols (Figure 6-45c).
      The short term PM10 concentration over the Southwest (Figure 6-47) exhibits a highly
 irregular pattern, that ranges between 11 to 52 f^g/w? regional average. Both the lowest (10 to
 15 /ug/m3) as well as the highest values are dispersed throughout the year.
   o
   s
   Q_
                             Southwest Every Sixth Day
                           1991
1992
1993
Figure 6-47.  Short-term variation of PM10 average for the Southwest. Data are reported
             every sixth day.
                                         6-93

-------
     The urban excess PM10 (AIRS-IMPROVE) for the Southwest is given in Figure 6-48,
and the urban excess is substantially larger than in the regions discussed previously.
                             Southwest urban excess
         Jan         Mar         May         Jul        Sep        Nov
  Figure 6-48.  Urban excess concentration (AIRS minus IMPROVE) for the Southwest.
6.4.6   Regional Aerosol Pattern in the Northwest
     The Northwest is defined to cover the bulk of the western United States north of the
Arizona border (Figure 6-49a). It is covered by mountainous terrain of the Rockies, as well as
the Sierra-Cascade mountain ranges. The Northwest is actually a collection of many aerosol
subregions. The meteorology is highly variable between the Pacific Northwest and the Rocky
Mountains with prevailing winds generally from the west. The main feature of the Northwest
is pronounced elevation ranges between mountain tops and valleys, and the resulting
consequences on emission pattern (confined to the valleys) and limited ventilation.  The
Northwest has also industrial population centers, such  as Seattle, Portland, Salt Lake City and
Denver.
                                       6-94

-------
    PM2.5 Concentration - Northwest
         IMPROVE/NESCAUM Data
Chemical Fine Mass Balance - Northwest
         IMPROVE/NESCAUM Data
  0.8
  0.7
 
-------
6.4.6.1   Nonurban Size and Chemical Composition in the Northwest
     The nonurban PM10 concentrations show low values ranging between 7 to 14 //g/m3 in
the northwestern U.S. The seasonality shows a peak in the summer which is contributed by
both fine and coarse particles. Coarse particles account for more than half of the PM10,
particularly during March through June spring season (Figure 6-49b).
     The chemical mass balance (Figure 6-49c) shows  roughly comparable contributions from
sulfates and organics, but their seasonality is phase shifted.  Sulfates prevail during the spring
season while organics dominate  during late fall (October through January).  Fine particle soil
dust contributes 20% during April and May, but decline well below 10% during the winter
months (November through February).  Overall, about 80% of the fine mass is accounted for
by the sulfates, organic carbon,  soil, and elemental carbon.
     Examining the carbonaceous particles and regional haze in the western and northwestern
U.S., White and Macias (1989)  concluded that in the rural areas the concentrations of
particulate carbon are comparable to those of sulfate. Examining paniculate nitrate, White and
Macias  (1987) showed that the particulate nitrate concentration in the northern states (MT,
ID, WY) were substantially higher than sulfate concentrations. Aerosol particulate nitrates
over rural mountainous West were also episodic (i.e.,  few  samples contributed a large
fraction of the fine particle integrated dosage).
     Both selenium and vanadium concentrations (Figure 6-49d) are low in the Northwest, but
there is an indication of a summer peak of Se. The S/Se ratio is between 500 to  1,000, which
is the lowest among the regions.  This ratio  has both spring peak as well as fall peak, similar
to the pattern observed for the southwestern United States.

6.4.6.2  Urban Aerosols in the Northwest
     In the northwestern U.S. there was a decrease in the annual average PM10 concentration
between 1988 and  1994 from  33 ^g/m3 to 24 yug/m3 for all  sites and from 35 Aig/m3 to
27 /^g/m3 for trend sites (Figure 6-50b).  The reductions were 27% for all sites and 23% for
trend sites. However, the 1985  to 1994 reductions may be  overestimates because of the low
station density in the early years.  Once again, the average  1993 concentration is 25 //g/m3
which is comparable to the 1993 concentrations of the eastern U.S. regions.  The spread of
                                         6-96

-------
             PM10 Average - Northwest
    PM10 Cone. Trend - Northwest
           EPA AIRS database
  140

  130

  120

  110

  100

— 90
M

ra ao

S ^o

* 60

   50

   40

   30

   20

   10
            PM2.5 vs. PM10 - Northwest
              EPA AIRS - Monthly Averages
      1989   1990  1991   1992   1993   1994
  !r- Avg for all sites   -B- Avg for trend sites
  4- Avg + Std. Dev.   -G- Avg - Std. Dev.

   Seasonal PM Pattern - Northwest
           EPA AIRS Database
           CORRELATION STATS
           Ava X :     29.85
           AvgY:     17.29
           Avg Y/Avg X 0.57
           CorrCoaff-  0.9
           Slop* .     0.72
           YoH»t.    -4.42
           Data Polnta • 347
                                    (c)
60

55

SO

45
                                                  1
                                                  s
                                                  Q_
                                                                                 (d)
             20  40   60  80   100  120  140
                     PM10
 1986  Mar  May   Jul    Sep   Nov
    •A- PM10  -B- PM2.5 -I- PM Coarse
Figure 6-50.  AIRS concentration data for the Northwest:  (a) monitoring locations;
              (b) regional PM10 monitoring; (c) PM10 and PM2 5 relationship; and
              (d) PM10, PM2>5, and PMCoarse seasonal trend.
                                             6-97

-------
concentration among the Northwest stations is large, with standard deviation of
45% (Figure 6-50b). This spread in the  concentration values is also evident from the various
circle sizes of the Northwest map (Figure 6-50a). The highest PMjQ concentrations in the
Northwest occur in more remote mountainous valleys, rather than in the center of
urban-industrial areas.
     The seasonally of the northwestern United States has an amplitude of 36% which is
comparable to the strong seasonality of the eastern U.S.  The peak PM10 concentrations occur
in the winter. The lowest PM10 concentration occurs during March through May and
gradually increases to a peak in December through January, falling sharply between January
and March.
     The limited PM2 5-PM10 data for the Northwest indicate that on the average 57% of
PM10 particles are PM2 5.  Figure 6-50c also indicates that the extreme PM10 concentrations
are contributed mainly by fine particles.  Furthermore, the extreme PM10 concentrations also
occur in the winter season.
     The daily concentration when averaged over the large and heterogeneous northwestern
region exhibits a remarkably small sixth day to sixth day variation (Figure 6-51).
Furthermore, there is clear seasonality with a strong whiter peak. Within a given season, the
regionally averaged  concentrations only vary by 20 to 40% from one sixth day to another.
Examination of the logarithmic standard deviation (Figure 6-50b) shows that the Northwest is
spatially the most heterogeneous and has the highest logarithmic standard deviation among all
regions. Evidently, in the Northwest high concentration PM10 pockets in topographically
confined airsheds result in strong spatial and temporal variations. However, large scale
elevated PM10 concentrations that cover the entire Northwestern region do not exist because
high concentrations are not "synchronized" among the different airsheds.  In this sense, the
Northwest differs markedly from the eastern U.S., where large regional scale air masses with
elevated PM10 determine the regionally averaged values.  The urban excess PM10 (AIRS-
IMPROVE) for the Northwest is given in Figure 6-52. The winter urban excesses are almost
as large as in the Southwest (Figure 6-48).  However, if the region is a collection of aerosol
subregions, the small number of nonurban sites may not be representative of this collection of
subregions.
                                         6-98

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                        Northwest Every Sixth Day
^>
                     1991            1992            1993
Figure 6-51.  Short-term variation of PM10 average for the Northwest. Data are reported
           every sixth day.
                        Northwest urban excess
        Jan        Mar        May       Jul       Sep       Nov
 Figure 6-52.  Urban excess concentration (AIRS minus IMPROVE) for the Northwest.
                                   6-99

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6.4.7   Regional Aerosol Pattern in Southern California
     The region covers California south of San Francisco Bay (Figure 6-53a). It was
considered as a separate region primarily because of the known high aerosol concentrations in
the Los Angeles and San Joaquin basins.  Meteorologically the region is exposed to the air
flows from the Pacific that provide the main regional ventilation toward the south and
southeast.  The precipitation in the region occurs in the winter season, with the summer being
hot and dry. The regional ventilation of the San Joaquin Valley is severely restricted by the
Sierra Nevada Mountain range.  Also, the San Gabriel Mountains constitute an air  flow
barrier east of the Los Angeles basin. Both basins  have high population, as  well as industrial
and agricultural activities.  Hence, human activities are believed to be the main aerosol sources
of the region.

6.4.7.1  Nonurban Size and Chemical Composition in Southern California
     The PM10 concentration at the few nonurban sites over southern California ranges
between 10 ,ug/m3 during December through February, and 20 to 25 /ig/m3  in April through
October.  Coarse particles contribute more than 50% of the PM10 during the warm season
May through October. Both the fine and coarse aerosol fractions are lowest during the winter
months (December through March).  The summer peak fine particle seasonally at nonurban
southern California sites is in marked contrast to the strongly fall peaked urban fine particle
concentrations (Figures 6-53b, 6-54d).
     The chemical mass balance (Figure 6-53c) of nonurban southern California aerosol has  a
substantial contribution by organics of 30 to 40% throughout the year.  Sulfates account for
only 10 to 15% of the fine mass in the winter, and  about 20% in the summer months. The
sulfate fraction of the nonurban southern California fine mass is the lowest among the regions.
Fine particle soil dust is about 10% between April through November and drops to 5% during
the winter months. A notable feature of the southern California chemical mass balance is that
45% of the winter, and 35% of the summer fine mass concentration is not accounted by
sulfates, soils, organic carbon,and elemental carbon.  Nitrates are a major contributor to the
southern California aerosols (Solomon et al., 1989).
                                         6-100

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         PM2.5 Concentration - S. California
               IMPROVE/NESCAUM Data
    PM10, PM2.5 and PMC - S. California
             IMPROVE/NESCAUM Data
        Chemical Fine Mass Balance - S. California
                 IMPROVE/NESCAUM Data
                                        (C)
      1989 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

        -A-Eulfate        -Q-Organic«       -4— Soil
        -©-Soot
                       -e-Sulf + Org + Soil* Soot
                                                     35,000
                                                     30,000
                                                     25,000
                                                     20,000
                                                     15,000
                                                     10,000
                                                      5,000
                                                                                           (b)
                                                        1989 Feb Mar Apr May Jun Jul  Aug Sep Oct Nov Dec

                                                             -B-PM10    -I-PM2 5    -A-PM Coaraa
       Chemical Tracers - S. California
            IMPROVE/NESCAUM Data
                                                      4,000
                                                      3,500
                                                      3,000
                                                      2,500
                                                      2,000
                                                      1,500
                                                      1,000
                                   (d)
1989 Feb Mar Apr May Jun Jul  Aug Sep Oct Nov Dec
     -A-Sulfur- Max- 4000     -B-Selenium - Max - 4

     -(-Vanadium - Max- 10    -©-S/Se - Max - 4000
Figure 6-53.  IMPROVE/NESCAUM concentration for Southern California:
               (a) monitoring locations; (b) PM10, PM2 5, and PMCoarse (PMC); (c) sulfate,
               soil, organic carbon (OC), and elemental carbon (EC) fractions; and
               (d) tracers.
                                               6-101

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     Both selenium and vanadium (Figure 6-53d) show low values throughout the year
without significant seasonally. On the other hand the fine particle sulfur concentration  shows
a definite summer peak at 500 ng/m3, compared to 200 ng/m3 during the winter.
Consequently, the S/Se ratio increase from 500 in the winter 1,000 to 1,500 in the summer.

6.4.7.2 Urban Aerosols in Southern California
     In the southern California region there was a decrease in the annual average PM10
concentration between 1988 and 1994 from 41 /ug/m3 to 30 Atg/m3 for all sites and from
42 //g/m3 to 32 ,ug/m3 for trend sites (Figure 6-54b). The reductions were 27%  for all sites
and 241 % for trend sites. There is a sizable concentration spread among the stations (40%
standard deviation). Inspection of the circle sizes in the map points (Figure 6-54a) to
uniformly high concentrations in the San Joaquin Valley as well as in the Los Angeles basin.
The low concentration sites are located either on the Pacific coast outside of the Los Angeles
basin or in the Sierra Nevada Mountains.  Thus there are clear patterns of basin-wide elevated
PM10 concentrations with lower values in more remote areas (Figure 6-54a).
     The seasonality of the PM10 pattern in southern California is significant at 27%.
Furthermore, the seasonal pattern is unique that the highest concentrations occur in November
and the lowest in March. However, it is a see saw rather than a sinusoidal pattern.
     On the average, about half of southern California PM10 is  contributed by fine particles as
shown in the PM2 5-PM10 scattergram. Most of the high PM10 concentration months
dominated by fine particles tend to be in the fall.
     The sixth day average time series for the southern California region (Figure 6-55) shows
remarkably high sixth daily variance, between 10 and 75 /ug/m3. The lowest values tend to
occur between January and April, while the highest concentrations (>50 /ug/m3) tend to occur
during October through  December. Concentration excursions of a factor of two are common
between two consecutive six day time periods.  However, visual inspection of the sixth daily
signal also reveals  a substantial seasonality highest in the fall (September through December)
and lowest in the spring.
     The urban excess PM10 (AIRS-IMPROVE) for Southern California is given in
Figure 6-56.  The urban excesses are larger especially in winter, as are the urban excesses
                                         6-102

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      PM10 Average - Southern California
                                             PM10 Cone. Trend - S. California
                                                     EPA AIRS database
     150

     140

     130

     120

     110

     100
   c*^
   £  90
   en
   •2  BO
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   5  70
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      60

      SO

      40

      30

      20

      10
         PM2.5 vs. PM10 - S. California
            EPA AIRS - Monthly Averages
                                                          1989   1990  1991   1992   1993   1994
                                                      -A-Avg for all sites   -B-Avg for trend sites
                                                      -l-Avg + Std. Dev.   -e-Avg - Std. Dev.
                                            Seasonal PM Pattern - S. California
                                                     EPA AIRS Database
CORRELATION STATS
AvgX     54.1
Avg Y :    26 76
Avg Y/Avg X :  0 49
CorrCoiff:   0.87
Slope :     0 66
Y ofl.et:    -9
Data Pointa : 209
                          (c)
so
                                           15
                                 (d)
            20
                40   60   80
                    PM10
                              100  120  140
                                            ^986  Mar   May   Jul
                                               -A-PM10  -B-PM2.5
                        Sep   Nov
                       • PM Coarse
Figure 6-54.  AIRS concentrations for Southern California: (a) monitoring locations;
              (b) regional PM10 monitoring trends; (c) PM10 and PM2.S relationship; and
              (d) PM10, PM2.5, and PMCoarse seasonal trend.
                                           6-103

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   i
   0-
                      Southern California Every Sixth Day
                        1991
                                1992
1993
 Figure
6-55.  Short-term variation of PM 10 average for Southern California. Data are

      reported every sixth day.
                 Southern California urban excess
       Jan        Mar        May        Jul        Sep        Nov

Figure 6-56. Urban excess concentration (AIRS minus IMPROVE) for Southern

           California.
                                   6-104

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in the Northwest.  Again, these results depend on measurements from a small number of
nonurban sites.
6.5 SUBREGIONAL AEROSOL PATTERNS AND TRENDS
     The health and other effects of aerosols are imposed on individuals, and the density of
population varies greatly in space. Consequently, the evaluation of effects requires the
knowledge of aerosol concentrations over specific locations where sensitive receptors reside.
The purpose of this section is to characterize the aerosol pattern at specific sites, small airsheds
or subregions. The discussions is organized by region and then by monitoring site within a
region. Most urban aerosol sampling is confined to PM10 or in some instances to PM2 5 and
PMCoarse . However, detailed chemical composition data are reviewed for several urban areas.

6.5.1  Subregional Aerosol Pattern in the Northeast
     In the northeastern region, the Shenandoah National Park and Washington, DC constitute
a useful urban-nonurban set of size and chemically resolved aerosol data.  New York City and
Philadelphia are also major metropolitan areas with substantial aerosol data bases.  Whiteface
Mountain site distinguishes itself from its background by high elevation.

6.5.1.1 Shenandoah National Park
     The PM10 concentration at the Shenandoah National Park IMPROVE site (Figure 6-57'a)
exhibits a pronounced summer peak (27 /^g/rn3), which is a factor of three higher than the
winter  value of 9 /ug/m3.   The strong  seasonality is driven by the seasonal modulation of the
fine mass which accounts for 70 to 80% of the PM10 mass (Figure 6-57a).  The coarse particle
concentration ranges between 3 and 6 Mg/m3, which is small compared to the fine particle
mass, particularly in the summer season, when it accounts for < 25% of the PM10. It is clear
that at this nonurban site, in the vicinity of industrial source regions, fine particles determine
the magnitude of PM10.
     The chemical mass balance  for the Shenandoah IMPROVE monitoring site
(Figure 6-57b) clearly documents the dominance of sulfate aerosols, which contribute about

                                        6-105

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60% of the fine mass during April through September and about 50% during the winter months.
Organic carbon, on the other hand, range from 20% in summer to 30% in the winter months.
The contribution of fine particle soil and elemental carbon is well below 5%. Throughout the
year about 90% of the fine mass is accounted for by these measured substances. At the
Shenandoah site, the sulfate aerosols constitute a higher percentage of the chemical mass
balance, and lower percentages of organic carbon and elemental carbon than for the averaged
nonurban Northeastern sites (Figure 6-29).
     Chemical tracer data are shown in Figure 6-57c. The concentration of coal-tracer selenium
shows two maximum, one during December through March, and another in June through
September. Vanadium is relatively constant throughout the year. The fine particle sulfur
concentration is almost a factor of five higher in August (3,300 ng/m3) than in December
(700 ng/m3).  This extreme sulfur seasonally is stronger at the Shenandoah site relative to the
averages for sulfur seasonality at all nonurban Northeastern sites (Figure 6-29). The S/Se ratio
has a remarkably smooth but highly seasonal variation that varies by about factor of four
between the winter (700) and summer (2,600) values. If Se-bearing coal combustion is the
exclusive source of sulfur at the Shenandoah National Park, then the sulfate production from the
SO2 associated with coal-fired sources is 3 to 4 times higher in the summer than in the winter.
     An examination of the nature and sources of haze in the Shenandoah Valley/Blue Ridge
Mountains area (Ferman et al., 1981) showed that sulfate aerosols were the most important
visibility reducing species. Averaging 55% of the fine particle mass, sulfates (and associated
water) accounted for 78% of the total light extinction. The second most abundant fine particles,
accounting for 29% of the fine mass, was organic carbon. The remaining particle mass and
extinction were due to crustal materials.
     Using an in-situ rapid response measurement of H2SO4/(NH4)2SO4 aerosol in Shenandoah
National Park, VA, Weiss et al. (1982) found that the summer sulfate and ammonium ions
average 58% of particle mass smaller than  1 mm.  The particle composition in terms of
NH4+/SO42" molar ratio ranged from 0.5 to 2.0 with strong diurnal variation. The particles were
most acidic at 1500 EDT and least acidic in the period 0600 to 0900 EOT. The water contained
in ambient aerosol particles was more strongly associated with sulfate and ammonium ions than
with the remainder of the fine particle mass.
                                         6-107

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6.5.1.2  Washington, District of Columbia
     The PM10 concentration at Washington DC (at the top of the National Park Service
Headquarters building) is virtually constant over the seasons at 25 to 30 /ug/ni3.  Fine particles
contribute over 70% of PM10 throughout the year (Figure 6-5 8a). The weak seasonally in the
fine particle mass is in sharp contrast to the factor of three seasonal fine mass modulation at the
Shenandoah National Park. The coarse particle concentration in Washington, DC is 8 to
10 Mg/m3 throughout the year, exhibiting virtually no seasonality.
     PM2 5 at the urban Washington, DC site (figure 6-5 8b) is dominated by sulfates during the
summer months (over 50%), but declines to 30% in January. Organic carbon, on the other hand,
is 40% during October through January but only 30% during May through August.  Thus, the
relative roles of organics and sulfates at the Washington, DC urban site is fully phase shifted by
half a year. Elemental carbon is a substantial contributes  9 to 12% during October through
December. Fine particle soil contributes a  low 2 to  5% to PM2 5 at this urban site.
     The chemical tracer species are shown in Figure 6-5 8c. The concentration of the coal tracer
selenium ranges between 1.5 to 2.0 pg/m3 without appreciable seasonality.  The urban  Se in
Washington, DC, is much higher than the Se at the northeastern nonurban sites.  Vanadium, the
tracer for fuel oil, varies by factor of two between the high winter values (>8 pg/m3) and low
summer values (3 pg/m3). The pronounced V concentration seasonality is a clear indication of
that the emissions from fuel oil  and other vanadium sources are seasonal. The fine particle sulfur
concentration varies by about factor of two between 1,400 ng/m3 winter concentration, and about
3,000 ng/m3 summer peak. The seasonal modulation of sulfur in Washington, DC is only factor
of two  compared to the factor of four fine sulfur modulation at Shenandoah National Park. The
difference is primarily due to the elevated winter sulfur in Washington, DC. The S/Se  ratio is
about 600  in the winter and about 1500 in the summer.  It differs from Shenandoah by  the lower
summer S/Se ratios. This result may be associated with differences in the air parcels involved in
long-range transport and transformation of SO2 to sulfate at Shenandoah compared to
Washington, DC.
                                         6-108

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6.5.1.3   Comparison of Nonurban (Shenandoah) to Urban (Washington, District of
         Columbia) Aerosols
     The Washington, DC, urban site and the companion nonurban Shenandoah monitoring site
constitute a rare data pair that allows the quantification of urban-rural differences in fine and
coarse particle concentration, and chemical composition. Within Washington, DC, industrial
emissions are moderate compared to the industrial midwestern cities. However, both
automobile emission density and emissions from winter time heating are expected to be high. In
this section the excess aerosol concentrations in Washington, DC, over the Shenandoah site are
examined to elucidate the urban influence.
     The Washington, DC, excess PM10 concentration (Figure 6-59a) ranges between
15-20 Atg/m3 in the winter, and <3 //g/m3 in the summer. Hence, there is almost an order of
magnitude higher urban excess during the winter, compared to the summer. The seasonally of
the excess PM10 is driven by the winter peak excess fine particle concentration of 10-12 ,ag/m3.
The modest excess coarse particles is in the 3 to 6 £tg/m3 range throughout the year. Thus, the
urban Washington, DC  concentration exceeds its nonurban regional aerosol values during the
winter season, and the excess winter time urban aerosol is largely contributed by fine particle
mass. This indicates the smaller role of coarse particle fly ash, road dust resuspended by
automobiles or construction, road salt and all other sources of urban coarse particles in
Washington, DC, in winter.
     The chemical composition of the excess fine particle concentration over the Shenandoah
nonurban background is also shown in Figure 6-59b. Fine organic carbon dominates the urban
excess ranging between 1 /ug/m3 during the summer, and 5.5 Atg/m3 during the winter.  The
seasonally of excess organic carbon also drives the seasonality of excess fine mass. There is an
excess sulfate concentration of 1 to 2 //g/m3 in Washington, DC, except during July, August, and
September.  In fact, in August in Washington, DC, sulfate concentration is about 0.3 //g/m3
below the  Shenandoah values. The urban excess elemental carbon concentration is 1  to 2 //g/m3
throughout the year. The  soil contribution to the fine particle mass is identical to the  values of
the Shenandoah National Park, yielding virtually no excess fine soil contribution in the urban
area.
                                         6-110

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                      PM10, PM2.5, and PMC Monthly Average
                    Washington DC - Shenandoah NP Difference
                    Chemical Fine Mass Balance
               Washington DC - Shenandoah NP Difference
                    1989  Mir  May  Jul
                    -H-RM10    -+PM2.5
Sep  Nov
 •*" PH Coarse
1989  Mar  May Jul   Sep  Nov
  -fr Sulfate  -B- QC   -+- Soil  -«- EC
  -*• Sulfate + OC + Soil + EC
  Figure 6-59.  Excess aerosol concentration at Washington, DC, compared to Shenandoah
                National Park: (a) PM10, PM2 5, and PMCoarse (PMC); (b) concentration of
                sulfate, soil, organic carbon (OC), and elemental carbon (EC).
      The short-term fine mass concentration at Washington, DC and Shenandoah National Park
for the year 1992 is shown in Figure 6-60a. Although the sampling is conducted Wednesdays
and Saturdays for 24 h, the data points have been connected. The figure also compares the daily
(Wednesdays and Saturdays) fine particle sulfur concentrations at the two monitoring sites. The
fine mass concentration time series for Washington, DC, show elevated concentrations
(>30 Atg/m3) that can occur throughout the year. On the other hand, high fine mass levels at
Shenandoah are recorded only during the summer season. Particulate sulfur concentrations at the
urban and nonurban site are often comparable during the summer (Figure 6-60b).  This indicates
that particulate sulfur often is a large part of the regional air mass that at any given day
influences Washington, DC, and the Shenandoah National Park. Fine particle mass, on the other
hand, shows an excess concentration at Washington, DC, particularly during the winter months.
The fine mass daily time series clearly indicates that the concentration change from one daily
sample to another can be an order of magnitude different. Consequently, most of the
concentration variance is due to random synoptic air mass changes, and to a lesser degree due to
periodic seasonal variations.
                                          6-111

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 O)
 c
 in
 in
 n
 S
         1992  Mar   May   Jul
          •A" Washington D.C.
          -a-Shenandoah National Park
Nov
                  1992   Mar   May   Jul   Sep   Nov
                    -*• Washington D.C.
                    -EJ- Shenandoah National Park
 Figure 6-60.  Daily concentration of (a) fine mass and (b) fine sulfur at Washington, DC,
               and Shenandoah National Park.
6.5.1.4    New York City, New York
     The New York City metropolitan area is characterized by high population density,
moderate industrial activity, and relatively flat terrain. The PM10 concentration over the
metropolitan area is shown in Figure 6-6 la. The circles in the map show the locations of the
monitoring sites and the magnitude of each circle is proportional to the average PM10
concentration at that site using all available data. The observed average concentrations change by
about of factor of two to three from one location to another. Higher average concentrations tend
to occur near the center of the metropolitan area.
     In the New York City metropolitan area there was a decrease in the annual PM10
concentration between 1988 and 1994 from 35 ^g/m3 to 27 /ug/m3 for all sites and from
41 /^g/m3 to 34 Aig/m3 for trend  sites (Figure 6-61b).  The reductions were 23% for all sites and
17% for trend  sites.  There was unusually large difference between the two trends. The average
                                          6-112

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        PM10 Cone. Trend - New York City
                  EPA AIRS database
                                    Seasonal PM Pattern - New York City
                                              EPA AIRS Database
   i
   0.
                                                60
                                                55
                                                45
                                                40
                                                35
                                   30
                                                25
                                                20
                                                15 -
                                                10 -,
                                       PM10 Station Months : 1676
                                       PM2.5 Station Months : 258
                                       PMC Station Months : 258
                                                                                 (C)
1989  1990   1991   1992   1993   1994   "1986  Mar   May  Ju,    Sep
    for all sites   ^Avg for trend sites         ^pM1Q   ^PM25
Avg + Std. Dev.   -®-Avg - Std. Dev.
                                                                       PM Coarse
Figure 6-61. New York City region:  (a) aerosol concentration map, (b) trend, and
             (c) seasonal pattern.
                                        6-113

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seasonal pattern over the subregion (Figure 6-6 Ic) is 25 to 30 //g/m3 throughout the year, but
rises to about 40 /ug/m3 in July.
     The seasonal pattern at three different individual monitoring sites in the New York City
metropolitan area is shown in Figure 6-62a.  The three sites all show similar seasonally with a
summer peak, but with elevated concentrations closer to the city center.
     Size segregated aerosol samples in New York City (Figure 6-62b,c) show that at both sites,
PM10 concentrations are contributed primarily by fine particles.  Based on the discussion of the
more extensive Washington, DC (Section 6.5.1.2) measurements, it can be inferred that the
summer peak in the fine mass is mainly due to the regional formation of the fine aerosols, while
the winter peak is contributed by the local sources, confined to the inner metropolitan area.
     As part of the New York Summer Aerosol study (Leaderer et al., 1978) continuous size
monitoring confirmed the expected bimodal volume distribution with modes between 0.1 to
1.0 fj.m and >3.0 fj.m. A number of interesting patterns were observed when the size distribution
was averaged by hour of day. The diurnal average total number concentration showed a pattern
which corresponded closely with the normalized diurnal traffic pattern. Particles <0.1 //m
showed the most marked diurnal variation, following the total number curve. Moreover,
particles in size ranges >0.1 //m showed little variation in the diurnal pattern. Analysis of
samples processed by the diffusion battery indicated that approximately 54%±18% of the sulfate
measured was in the suboptical range (approximately 0.04 um to 0.3 jam) with the remainder
above 0.3 /um. Little sulfate mass was found in particles in the nuclei range (<0.04 fj.m).
Analysis of impactor samples for sulfates consistently showed that more than 85% of all water
soluble sulfates were <2.0 /j,m in size.  Virtually no nitrate was present in the nuclei size range
while the suboptical size range accounted for approximately 30% of the total nitrate. 70% of the
total nitrate was found in the size range >0.3 //m.  Analysis of large stages of Anderson impactor
showed that approximately 50% of particulate nitrate was greater than 5.5 //m in size.
     Urban and rural particulate sulfur monitoring near New York City in the summer (Leaderer
et al., 1982) indicated that sulfate concentration distributions were regionally homogeneous and
increased with increasing ozone levels and covariant with several other pollutant and
meteorological parameters.  Sulfate concentrations correlated strongly with ammonium and
strong acid at all sites. Strong acid concentrations were highest at the rural and semi-rural sites,
                                          6-114

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                              70
                             .50
                              40
                              30
                              20
                                                                (a)
                               1985   Mar   May   Jul    Sop   Nov
                                    -A-- PM10AVG NEW YORK CITY
                                    -B- = PM2.5 AVG NEW YORK CITY
                                    -I- = PMC AVG NEW YORK CITY
       90
                                         (b)
                                                  100
                                                  90
                                                  80
                                                  70
                                                  50
                                                  40
                                  (c)
       1985  Mar    May  Jul    Sep   Nov
            -A- = PM10 AVG NEW YORK CITY
            -B- = PM2.5 AVG NEW YORK CITY
            -+- = PMC AVG NEW YORK CITY
1985  Mar   May   Jul    Sep    Nov
     -&-= PM10AVG NEW YORK CITY
     -B-= PM2.5 AVG NEW YORK CITY
     -H= PMC AVG NEW YORK CITY
Figure 6-62a,b,c.  Fine, coarse, and PM10 particle concentrations at three New York City
                   sites.
                                          6-115

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lowest at the urban sites, increased with increasing ozone levels and exhibited diurnal patterns
which matched the ozone diurnal patterns.
     Size dependent, mass and composition of New York aerosol for low, medium, and high
visibility levels was reported by Patterson and Wagman (1977).  At all levels of visibility,
bimodal or multimodal particle size distribution were observed for total mass and for individual
components.  Decreased visibility corresponded to increased particle mass concentrations
especially in the fine particle fraction. Increases in the proportion of particulate sulfate and to a
lesser extent of nitrate, chloride, ammonium, and carbon were also associated with decreased
visibility.
     Aerosol pattern analysis of a major wintertime (1983) pollution episode near New York
City in northern New Jersey (Lioy et al., 1985) revealed that the  intensity of the episode was the
greatest in the area of the highest commercial, residential and industrial activities, and that the
atmospheric stagnation conditions resulted in the significant accumulation of aerosol mass. The
aerosol mass was primarily fine aerosols, and the extractable organic matter comprising about
50% of the particle mass.

6.5.1.5   Philadelphia, Pennsylvania
     The metropolitan area of Philadelphia includes urban-industrial emissions over flat terrain.
Relatively uniform PM10 concentrations throughout the metropolitan area, with the exception of
a single site (AIRS #421010149) in the middle of the urban area (Figures 6-63 and 6-64).
     The downward trends in PM10 concentrations between 1988 and 1994 were largely or
completely negated by the upward trends in  1993 and 1994 (Figure 6-63b). The decrease in
annual PM10 concentrations at trend sites between 1988 and 1994 for all sites was from 39 /ug/m3
to 32 //g/m3, a decrease of 18%.  The seasonal concentration of PM|0 (Figure 6-63c) is about
30 to 35 /-tg/m3 throughout the year, except during the summer months when it rises above
40 Aig/m3.
     The seasonal average PMi0 concentrations for four sites near the center of Philadelphia is
shown in Figure 6-64. The high concentration site noted on the metropolitan map in
Figure 6-63a and two nearby sites in the industrial area long the riverfront are shown in
Figure 6-64a. The average PM10 concentration at that site ranges between 100 to 150
                                         6-116

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        PM10 Cone. Trend - Philadelphia
                EPA AIRS database
      198B   1989   1990   1991   1992   1993   1994
               for all sites fl-Avg for trend sites
               + Std. Dev. -e-Avg - Std. Dev.
 Seasonal PM Pattern - Philadephia
          EPA AIRS Database
                                                  60
                                                  55
                                                  SO
                                                  45
                                                  40
                                                  35
                                                  30
                                                  25
                                                  20
                                                  15
                                                  10
   PM10 Station Months : 1263
   PM2.5 Station Months : 59
   PMC Station Months : 59
                                                                                   (C)
1986   Mar   May   Jul    Sep   Nov
           ^PM2.5  -HPM Coarse
Figure 6-63.  Philadelphia region: (a) aerosol concentration map, (b) trend, and
              (c) seasonal pattern.
                                          6-117

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  E
  OJ
       1985   Mar   May   Jul     Sep    Nov
             PM10, Philadelphia. AIRS #42-101-xxxx
           Sites,  -A-=0149, -B-= 0449, -+-=0049
                                                 100
                                                  90
                                                  80
                                                  70
                                                  60
                                                  50
                                                  40
                                                  30
                                                  20
                                                  10
                                                                                      (b)
°1985  Mar    May   Jul    Sep   Nov
         Philadelphia, AIRS #42-101-0004
    -A-= PM 10, -B-= PM 2.5, H-= PM Coarse
figure 6-64a,b.   Seasonal particle concentrations at four Philadelphia sites. (Note scale
                  for (a) is 150 ug/m3.)
which is a factor of 2 to 3 higher than the concentration at the neighboring sites. The daily
concentrations at these source monitoring sites correlate poorly with a nearby site (Figure 6-64b)
that is only 4 km away but outside the industrial area. This is an indication that the
concentrations at the industrial sites are under the influence of a strong local source of PMi0. In
contrast, community sites in downtown and suburban Philadelphia that are as much as 30 km
apart show a strong correlation of daily measurements, indicating that a spatially uniform
regional aerosol influences the daily values in Philadelphia.
      Size segregated aerosol samples (Figure 6-64b) show that fine particles contribute more
than coarse particles to the PM10 at this site.  It is possible, however, that at other sampling sites,
e.g., the industrial sites (Figure 6-64a), coarse particles may prevail.
      Outdoor summertime sulfate (SOJ) concentrations were found to be uniform within
metropolitan Philadelphia (Suh et al., 1995). However, aerosol strong acidity (H+)
                                           6-118

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concentrations were found to vary spatially. Also, the wintertime sulfate pattern was likely to be
more heterogeneous in space and time. This variation generally was independent of wind
direction, but was related to local factors, such as the NH3 concentration, population density, and
distance from the center of the city.

6.5.1.6   Whiteface Mountain, New York
              The AIRS sampling location at the Whiteface Mountain in Upstate New York is a
high mountain top site elevated from the surrounding  terrain.  The monitoring site offers the
possibility of comparing mountain top concentrations to the surrounding lower elevation sites.
The seasonal pattern of PM10 concentration for Whiteface Mountain and the surrounding low
elevation sites, Saranac Lake and Saratoga Springs, is shown in Figure 6-65.  The concentration
at the three sites is virtually identical during June through September. However, during the
winter the mountain top site at Whiteface has a PM10  concentration which is only one third of the
low elevation sites. This indicates that during the winter, the Whiteface mountain top is above
the surface-based aerosol layer, while during the summer the height of the well mixed aerosol
layer rises above the mountain top producing a reasonably uniform concentration at all sites.

6.5.2    Subregional Aerosol Pattern in the  Southeast
6.5.2.1  Atlantic Coast States
     The average yearly concentration in the southeast Atlantic coast states for all sites and trend
sites has decreased from 32 to 24 ug/m3 and 25 ug/m3 (Figure 6-66a,b). The reductions were
25% and 22%. Seasonal concentrations show a summer peak largely due to PM2 5
(Figure 6-66c). Comparison of three AIRS PM10 monitoring sites in North Carolina's
Piedmont, Winston-Salem, Greensboro, and Raleigh-Durham (Figure 6-66d) shows virtually
identical concentrations (within 10%), both in absolute magnitudes and in the seasonally with
summer peaks in PM10.  This is an indication that these sites in this subregion are exposed to
essentially the same air masses throughout the year.  It also suggests that the excess PM10
concentrations due to local urban sources probably are not signficant.
                                         6-119

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         i
                                - - RM10 AVO
                                " — PM1O A.VO »Atf*ANAtG I_
                                - - F»M10 A\/O SARATOGA
Figure 6-65.  PMlo concentration seasonally at Whiteface Mountain and neighboring
             low-elevation sites.
      Size segregated monitoring data for Winston-Salem (Figure 6-66f) show that fine particles
 contribute 70 to 80% of the PMlo mass of 25 to 35 A*g/m3. Coarse particles are seasonally
 invariant at about 10 //g/m3 which is typical for eastern U.S.
      The PMjQ concentration at monitoring sites in Florida (Orlando, Miami, Tampa) show
 virtually identical concentrations ranging between 25 to 30 yug/m3 throughout the year, without
 appreciable seasonality (Figure 6-66e).
 6.5.2.2   Texas and Gulf States
      The average yearly concentration between 1 988 and 1 994 in the Texas-Gulf states has
 decreased for all sites and tend sites from 28 to 25 ^ig/m3 (Figure 6-67b), a reduction of 1 1%.
 Seasonal concentrations show a summer peak largely due to PM2.5 (Figure 6-68c). The seasonal
 PMlo concentration at sites in Odessa, Amarillo, and Lubbock, TX, and in New Orleans, LA,
 Mobile and Birmingham, AL show uniformity (20 to 40 /ig/m3) with modest seasonality
                                          6-120

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                                                                   Southeast Atlantic Coast States
                                                                            PM10 Cone. Trend
                                                                                                                     SMMMlPMPMkm
                                                                                                                                            (C)
fo
                                                                                                                                             0)
                                Miy    Jul    8«p
                               •A- • ran /wa WMSTOH-SALEM
                               -&-PM10AVO GREENSBORO
                               + -PM10AVO RALEIGH
toy     Jul    Sw
 * -raiOAVG ORLANDO
 &-PM10AVQ TAMPA
 + >rail>AVGMAIII
 Mty    Jul    S^>
-ft- • PMU AVG WMTON-SALEII
•e-• PMU AVG WMSTON-SA1EH
-I— PMC AV6 WMSTON-MUM
            Figure 6-66.  Aerosol concentration patterns for the Southeast Atlantic Coast states and sites in  North Carolina and Florida:
                           (a) monitoring sites, (b) trends, (c) seasonal pattern, (d) North Carolina sites, (e) Florida sites, and (f) seasonal
                           pattern for Winston-Salem.

-------
                                                            PM10 Cone. Trend - S. Texas/Alabama
                                                                 EPA AIRS database
            Seasonal PM Pattern - Texas/Alabama
                  EPA AIRS Database
                                                     1988   1989   1990   1991    1992   1993
                                                          -A-Avg for all sites  -B-Avg for trend sites
                                                          H-Avg + Std. Dev.  -e-Avg - Std. Dev.
                                                                                  1994
      60

      55

      50

      45

      40

      35

      30

      25

      20

      15

      10
PM10 Station Months : 6774
PM2.5 Station Months: 1B5
PMC Station Months : 1B5
(C)
                                                                      Sep    Nov
                                                                     ODESSA
                                                        -B-= PM10 AVG AMARILLO
                                                        -H= PM10 AVG LUBBOCK
                                            50
                                            40
                                        «
                                         E  30
                                         75>
                                         =•20
                                         5
                                         °-  10
       1986  Mar    May   Jul    Sep    Nov
          -A-PM10   -B-PM2.5  H-PM Coarse
                                             1985   Mar   May   Jul     Sep   Nov
                                                   -&-= PM10 AVG NEW ORLEANS
                                                   -H-= PM10AVG MOBIL
                                                   += PM10 AVG BIRMINGHAM
Figure 6-67a,b,c,d,e,f,g,h,i.  Aerosol concentration patterns in Texas and Gulf states.
                                                6-122

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      100
       90
       80
       70
    £
    CD
    a.
   a.
       60
50
       40
       30
       20
       10
                                          (f)
       1985   Mar    May   Jul    Sep   Nov
                    -&-= PM10 AVG HOUSTON
                    -Q-= PM10 AVQ AUSTIN
                    H-- PM10 AVG SAN ANTONIO
      100,
      90
      80
      70
      60
      50
      40
      30
      20
       10
                                          (h)
       1985   Mar   May   Jul     Sep    Nov
              -A-=PM10AVG FORTWORTH
              -3- = PM2.5 AVG FORTWORTH
              -H= PMC AVG FORTWORTH
100

 90

 80

 70

 60

 50

 40

 30

 20

 10
                                                                                         (9)
                                               1985   Mar   May   Jul     Sep    Nov
                                                     -6-= PM10 AVG CORPUS CHRISTI
                                                     -B-= PM2.5 AVG CORPUS CHRISTI
                                                     -+- = PMC AVG CORPUS CHRISTI
                                                    100
                                                     90
                                                     80
                                                     70
                                                     60
                                                     50
                                                     40
                                                     30
                                                     20
                                                     10
                                                                                  0)
                                               1*985   Mar   May   Jul    Sep    Nov
                                                      -A-= PM10AVG NEW ORLEANS
                                                      -B- = PM2.5 AVG NEW ORLEANS
                                                      -+- = PMC AVG NEW ORLEANS
Figure 6-67 (cont'd).  Aerosol concentration patterns in Texas and Gulf states.
                                                 6-123

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(Figure 6-67d,e). The sites in Houston, Austin, and San Antonio, TX show a wider range of
PM10 values with summer peaks (Figure 6-67f).
     The size segregated aerosol samples collected in the cities of the Gulf states, Corpus
Christi, Forth Worth and New Orleans, LA (Figure 6-67g,h,i) all show fine particle
concentrations that are relatively low (10 to 20 //g/m3) compared to large eastern cities. Coarse
particle concentrations, on the other hand, can account for more than half of the PM10 mass. The
coarse particle contribution is most pronounced during the summer season.
     In Houston, TX, Dzubay et al. (1982) found that in summertime fine particle mass
contained 58% sulfate and 18% of carbonaceous material.  They also found that the coarse
fraction (2.5 to 15 fj.ro) consisted of 69% crustal matter,  12% carbon, and 7% nitrate species.

6.5.2.3     Atlanta
     Characterization of the Atlanta area aerosol (Marshall et al., 1986) show that elemental
carbon and particulate sulfur represent, respectively 3.1 to 9.9% and 1.9 to 9.4% of the total
suspended particulate mass. The concentrations of elemental carbon, sulfur, and TSP exhibit
strong seasonal variations, with elemental carbon decreasing from winter to summer, and sulfur
and TSP increasing. Elemental carbon appears to be statistically separate from sulfur, indicating
that the sources for elemental carbon and particulate sulfur are distinct.

6.5.2.4  Great Smoky Mountains
     Size segregated fine and coarse aerosol concentrations were measured at the Great Smoky
Mountains National Park in September of 1980 (Stevens et al., 1980). Sulfate and its associated
ions contributed to 61% of the fine particle mass, followed by organics (10%) and elemental
carbon (5%).

6.5.3   Subregional Aerosol Pattern in the  Industrial Midwest
     Since the turn of the century, the major cities in the industrial midwestern states had air
pollution problems due to smoke and dust. Pittsburgh, St. Louis, Chicago, and Detroit were
among the formerly notorious air pollution hot spots.  The recently acquired PM10 database now
                                         6-124

-------
 allows the re-examination of these metropolitan areas in the industrial Midwest for their
 concentration pattern in the 1990s.

 6.5.3.1   Pittsburgh, Pennsylvania
      The average PM10 concentrations for sites in the extended metropolitan area is shown in
 Figure 6-68. The Pittsburg, PA subregion includes the industrial cities, Steubenville, OH, and
 Weirton, OH, located on the Ohio River. The average PM10 concentration at the 80 sites shown
 on the map varies only by about 20% from site to site. Outstanding high concentration hot spots
 are now absent.  It is thus evident that during the 1985 to 1993 period, the average PM10
 concentrations in the Pittsburgh subregion were spatially rather uniform.
      In the Pittsburgh, PA metropolitan area there was a decrease in the annual average PM10
 concentrations between 1988 and 1994 from 37 /ug/m3 to 32 //g/m3 for all sites and from
 41 /ug/m3 to 33 /^g/m3 for trend sites (Figure 6-68b).  The reductions were 14% for all sites and
 19% for trend sites.   Figure 6-68b also marks the concentration standard deviation among the
 monitoring sites for each year, which is about 15 to 20% and shrinking over time.
      The seasonality of the PM10 pattern (Figure 6-68c) is dominated by a summer peak
 (45 //g/m3),  which is about 50% higher than the winter concentrations (30 //g/m3). The sites in
 Pittsburgh, PA, Weirton, OH, and Steubenville, OH (Figure 6-69) show comparable seasonality
 and values that are slightly above the subregional average.  Hence, the particles at these formerly
 highly polluted locations are now virtually identical to their subregional background.
      Size segregated aerosol samples in Pittsburgh, PA and Steubenville,  OH (Figure 6-69)
 show that fine particles contribute 70 to 80% of the PM10 mass, and also dictate the summer-
peak seasonality of the PM10 concentrations.  As in other urban monitoring sites in the eastern
U.S.,  the coarse particle concentration in Pittsburgh is about 10 /ug/m3 and seasonally invariant.
The size segregated  seasonal data for Steubenville, OH, exhibit more random fluctuations as well
as a discrepancy between the sum of fine and coarse on one hand, and PM10 on the other. The
discrepancy  is attributed to the small number of size segregated aerosol samples.
      Sulfate acidity measurements (Waldman et al., 1991) at Chestnut Ridge, PA (east of
Pittsburgh), suggest higher acidity occurred in the overnight period (0000-0800) in the late fall,
while sulfate had its highest levels in the morning to afternoon period.
                                         6-125

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          PM10 Cone. Trend - Pittsburgh
                 EPA AIRS database
E
O)
3.

5
O.
            1969   1990    1991    1992   1993   1994

         -A- Avg for all sites   -B- Avg for trend sites

         -+- Avg + Std. Dev.   -e- Avg - Std. Dev.
                                                    60


                                                    55


                                                    50



                                                    45


                                                    40


                                                    35


                                                    30


                                                    25


                                                    20


                                                    15


                                                    10


                                                     5
                                                     Seasonal PM Pattern - Pittsburgh
                                                              EPA AIRS Database
                                                      PM 10 Station Months : 2937

                                                      PM2.5 Station Months : 159

                                                      PMC Station Months : 162
                                (C)
1986   Mar
                                                               May   Jul

                                                               -B-PM2.5
Sep   Nov

PM Coarse
Figure 6-68.  Pittsburgh subregion:  (a) aerosol concentration map, (b) trends, and
              (c) seasonal pattern.
                                           6-126

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       100
       90
       80
       70
    E
    o>
    3.
    s
    Q.
       80
SO
       40
       10
                                               (a)
        1985   Mar   May    Jul    Sep    Nov
                       A . PM10 AVQ PITTSBURGH
                      -B-. PM10 AVO WEIRTON
                      -+-• PM10 AVQ STEUBENVILLE
   100

    90

    80

    70

m   60
 E
 ^  so
 2
 Q.
    40
       20
       10
                                               (c)
        1°985    Mar   May    Jul     Sep    Nov
                     -A-" PM10 AVG PITTSBURGH
                     -B-. PM2.5 AVO PITTSBURGH
                     ~t-- PMC AVQ PITTSBURGH
                                                        100
                                                         90
                                                         80
                                                         60
                                                         40
                                                         20 -
                                                                                          (b)
                                                  1985   Mar    May    Jul     Sep    Nov
                                                              -&-- PM10 AVQ STEUBENVILLE
                                                              -B-- PM2.5 AVO STEUBENVILLE
                                                              -H- PMC AVQ STEUBENVILLE
                                                       100
                                                        90
                                                        70
                                                        60
                                                        40
                                                        20
                                                        10
                                                                                          (d)
                                                  1985   Mar    May    Jul    Sep   Nov
                                                                -&-- PM10 AVQ PITTSBURGH
                                                                -B-» PM2.5 AVQ PITTSBURGH
                                                                H-- PMC AVG PITTSBURGH
Figure 6-69a,b,c,d.   Fine, coarse, and PM10 concentration at sites in or near Pittsburgh.
                                                6-127

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     Pierson et al. (1980b, 1989) found no appreciable night/day difference in aerosol H+
(or NH4+ or SO42") - and almost no diurnal variation in O3 - at two elevated sites (Allegheny
Mountain and Laurel Hill, elevations 838 and 850 m) in southwest Pennsylvania. The contrast
with behavior at lower sites and particularly with the concurrent measurements at Deep Creek
Lake (Vossler et al., 1989) is attributable to isolation from ground-based processes at the
elevated sites at night.
     The remarkable uniformity of fine particle mass and elemental composition from site to site
in the Ohio River Valley was also shown by Shaw and Paur (1983). Sulfur was the predominant
element in fine particles. Factor analysis of element concentrations indicated three clusters
throughout the year (1) coarse particle crustal elements (2) fine particle sulfur and selenium
(3) fine particle manganese, iron and zinc.
     The chemical mass balance of Weirton-Steubenville aerosol was examined by Skidmore
et al. (1992). Primary aerosols from motor vehicles and secondary ammonium sulfate were
dominant contributors to the PM2 5 aerosol. Steel emissions were also significant contributors to
PM2 5. Wood burning and oil combustion were occasionally detected.  Geological material was
the major contributor to the coarse aerosol fraction.  Primary geological material, primary motor
vehicle exhaust, and secondary sulfate were the major contributors to PM10 at all five monitoring
sites.
     The composition of size-fractionated summer aerosol in nearby Charleston, West Virginia
was reported by Lewis and Macias (1980). Ammonium sulfate was the largest single chemical
component (41%) of the fine aerosol mass. Carbon was also a large component of both fine and
coarse particle mass constituting 16% and 12% respectively. Factor analysis indicated that four
factors were sufficient to satisfactorily represent the variance of 26 measured parameters.  The
factors were characteristic of crustal material, ammonium sulfate, automotive emissions, and
unidentified anthropogenic sources.

6.5.3.2   St. Louis, Missouri
     Historically, the St. Louis metropolitan area has been known for high particulate
concentrations. The map of the metropolitan area (Figure 6-70a) shows about factor of 2 to
3 concentration differences among the PM10 monitoring stations.
                                         6-128

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                PM10 Cone. Trend - St. Louis
                      EPA AIRS databate
        Seasonal PM Pattern - Pittsburgh
                EPA AIRS Databat •
             1989   1990   1991   1992    1993   1994
        -&- Avg for all sites     -B- Avg for trend sites
        -+- Avg + Std. Dev.     -©- Avg - Std. Dev.
                                                           PM10 Station Months : 2937
                                                           PM2.5 Station Months : 159
                                                           PMC Station Months : 162
1986  Mar   May   Jul    Sep   Nov
            -B-PM2.5  H-PM Coarse
Figure 6-70.  St. Louis subregion: (a) aerosol concentration map, (b) trends, and
               (c) seasonal pattern.
                                             6-129

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     In the St. Louis metropolitan area there was a decrease in the annual average PM10
concentration between 1988 and 1994 from 37 //g/m3 to 30 [J,g/m3 for all sites and from
40 /ug/m3 to 31 yug/m3 for trend sites (Figure 6-70b). The reductions were 23% for all sites and
22% for trend sites.  This decline is comparable to the average reductions over the industrial
midwestern region.  The seasonality of the sub-regionally averaged concentrations (Figure 6-70c)
shows the summer peak with 40 to 50 /ug/m3 which is about 50% higher than the winter
averages.
     Seasonal comparison of the individual monitoring sites in the area shows that Granite City,
IL and East St. Louis, IL have higher PM10 concentrations throughout the year compared to
western St. Louis, MO sites.
     Size segregated aerosol samples at three sites west of the Mississippi River (Ferguson, MO,
Affton, MO, and Clayton, MO) show that fine particles are mostly responsible for PM10,
including the seasonality (Figure 6-71).  Coarse particles contribute 10 /ug/m3 or less throughout
the year, although corresponding size segregated aerosol data for the more polluted east side of
the Mississippi River are not available.
     Monitoring the diurnal and seasonal patterns of particulate sulfur and sulfuric acid in
St. Louis,  Cobourn and Husar (1982) noted an afternoon increase in particulate sulfur
concentration of about 20%.  For the summertime, particulate sulfur concentration was  higher
than the annual mean by 40%.
     Measurements were made using dichotomous samplers of PM10 (expressed as PM20),
PM2 5 and S at urban, suburban, semi-rural, and rural sites in and around St. Louis, MO, during
1975 to 1976 as part of the Regional Air Pollution Study (RAPS) (Altshuller, 1982). Unlike the
nonurban sites compared from the IMPROVE/NESCAUM network with urban sites from AIRS,
these rural sites were within 45 km of the center of St. Louis. The comparisons are between
three urban sites (103,  105,106) and three rural sites (118,112,124).
     The PM2 5 constituted 45 to 60% of the PM10 with the percentages at rural sites ranging
from 0 to 10% higher than at urban sites. The ratios of the concentrations of PM2 5 at urban sites
to PM2 5 at rural sites ranged from  1.4 to 1.5 for the six quarters between the third quarter of
1975 to the fourth quarter of 1976. The ratios of the concentrations of PMCoarse at urban sites
to PMCoarse at rural sites ranged from 1.5 to 1.8 for the same six quarters. For fine S,  the ratios
                                         6-130

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          80
          70
       O9
       =•  50
       S
       o.
          40
          30
          10
                                              (a)
          1965   Mar    May   Jul    Sep   Nov
                    -A- ,
                       • PM10AVOST LOUIS
                       • PM10 AVO GRANITE CITY
                       • PM10 AVO EAST ST LOUIS
         80
      «  60
       E
       o>
         30
                                              (c)
1985   Mar   May   Jul    Sep    Nov
          ~^~ - PM10 AVG CLAYTON
          ~B' - PM2.5 AVG CLAYTON
          ~~l"~ - PMC AVG CLAYTON
                                                         100
                                                         80
                                                         80
SO
                                                         40
                                                         10
                                     (b)
1985    Mar    May   Jul    Sep   Nov
            A 'PM10 AVG FERGUSON
           ~B~ * PM2.5 AVO FERGUSON
           ~+~ * PMC AVG FERGUSON
                                                        •0
                                                         70
                                                         30
                                                        20
                                                         10
                                     (d)
                                                          1985   Mar   May   Jul     Sep   Nov
                                                                     -A- ,
                                                                     -B-
                PM10AVG AFFTON
                PM2.S AVG AFFTON
                PMC AVG AFFTON
Figure 6-71a,b,c,d.   Fine, coarse, and PM10 seasonal concentration patterns in or near
                        St. Louis.
                                                 6-131

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between urban and rural sites ranged from 1.1 to 1.2, while for coarse S, the ratios between urban
and rural sites ranged from 1.7 to 2.6 for the same six quarters.
     These results indicate a very strong regional influence on fine S with a lesser regional
influence on PM2 5.  The ratios of PMCoarse and coarse S indicate stronger local influences on
their concentrations than on fine S and PM2 5. The percentage of fine S expressed as (NH4)2 SO4
to the PM2 5 was consistently higher at rural sites than at urban sites in and around St. Louis
(Altshuller, 1982). In the third quarters of 1975 and 1976, these percentages averaged 70% at
rural sites and 55% at urban sites, while in the fourth quarters of 1975 and 1976, these
percentages averaged 45% at rural sites and 35%  at urban sites.
     As observed near New York City (Leaderer et al., 1982), the fine S in the St. Louis area
was regionally homogenous and, during episodic periods, the fine S concentrations followed the
variations in O3 concentrations reasonably closely (Altshuller, 1985). A linear relationship was
obtained for fine S and O3 flows into St. Louis. The fine S with increasing fine S concentration
constituted an increasingly large percentage of the PM2 5 at an urban site (Altshuller, 1985).

6.5.3.3   Chicago, Illinois
     Historically, Chicago has been known for industrial dust, smoke, and haze, as in adjacent
East Chicago and Gary, IN.  The average PM]0 concentrations over the Chicago subregion
(Figure 6-72a) vary by a factor of two or less throughout the subregion. In the Chicago
subregion, there was a decrease in the annual average PM10 concentrations between 1988 and
1994 from 32 /ug/m3 to 29 /ug/m3 for all sites and from 39 //g/m3 to 31 /ug/m3 for trend sites
(Figure 72b). The reductions were 9% for all sites and 20% for trend sites.  The seasonality of
PM10 is also typical  with the summer peak of 40 /ug/m3 and winter values of 20 to 30 /ug/m3.
     Superposition of seasonal PM10 data at Chicago,  IL, East Chicago, IL, and Gary, IN,
demonstrates significant spatial uniformity, as well as indicating in more recent years
comparatively low PM10 concentrations in this area that has historically been a smoky and dusty
industrial subregion.
     In the Chicago subregion there was a decrease in the annual average PM10 concentration
between 1985 and 1994 from 40 /ug/m3 to 29 /ug/m3 for all sites and from 40 /ug/m3 to 31 /ug/m3
for trend sites (Figure 6-72b). The reductions were 28% for all sites and 23% for trend sites.
                                         6-132

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                                                        PM10 Cone. Trend - Chicago
                                                               EPA AIRS database
           Seasonal PM Pattern - Chicago
                   EPA AIRS Database
                                                         1989   1990   1991   1992   1993   1994
                                                      -&- Avg for all sites    -B- Avg for trend sites
                                                      -i- Avg + Std. Dev.    -e- Avg - Std. Dev.
        15
            PM10 Station Months : 3245
            PM2.S Station Months : 0
            PMC Station Months : 0
          (c)
   150

   140

   130

   120

   110

   100

   90
rt
 E »"
 CP)
 =• 70
 S
 Q. 60

   50

   40

   30

   20 -

   10
(d)
              Mar   May  Jul
           -A- PM10  -B- PM2.5
Sep   Nov
PM Coarse
   1°985  Mar   May   Jul    Sep
          -A-= PM10AVG CHICAGO
                                                 Nov
                                                          -B-= PM10 AVG EAST CHICAGO
                                                          -H- PM10AVGGARY
Figure 6-72.  Chicago subregion:  (a) aerosol concentration map, (b) trends, (c) and
               (d) seasonal patterns.
                                            6-133

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     Chemical composition measurements in Chicago (Lee et al., 1993) showed that mean
concentrations for SO42- (5.55 //g/m3), NH4+ (2.74 Aig/m3), NH3 (1.63 Atg/m3), HNO3
(0.81 ^g/m3), HNO2 (0.99 /ug/m3), for SO2 (21.2 /ug/m3), NO3' (4.21 //g/m3), and H+
(7.7 nmol/m3). The highest values occurred in the summer, except for HNO2 and NO3" which
had the highest values in the winter.
     Comparison of atmospheric coarse particles at an urban and nonurban site near Chicago,
IL, show that the concentration were 50% higher during mid-day than at night. Dry ground
samples were 30 % higher than wet ground and 90% higher than frozen ground samples. (Noll
etal., 1985).
     The analysis of coarse particles in Chicago, IL (Noll et al., 1990) show that the coarse
particle mass could be divided into two categories: material that was primarily of crustal origin
(Al, Ca, Fe, and Si) and material that was primarily of anthropogenic origin (Cd, Cu, Mn, Ni, Pb,
and Zn). The mass of crustal material varied between 14 and 24% of the total coarse mass. The
mass of Cd, Cu, Mn, Ni, Pb, and Zn totaled less than 1%.
     The composition of atmospheric coarse particles at urban (Chicago, IL) and nonurban
(Argonne, IL) were reported by Noll et al. (1987). Limestone and silicates were the main source
of material at the non urban site.  Anthropogenic sources, represented by flyash and coal, were
present in the industrial  sector sample and rubber tire was present in the commercial sector
sample.

6.5.3.4  Detroit, Michigan
     In Detroit, in July, 1981 (Wolff and Korsog, 1985) the average fine mass was found to
be 42.4 A*g/m3.  The chemical composition of the fine particles (Wolff et al.,  1982) was 52%
sulfates, 27% organic carbon, 4% elemental carbon, 8% soil dust.  Nitrate was found to be
absent from fine mass.  Fine particles themselves contributed about 64%  of the  aerosol mass.
The sulfate associated with coal combustion contributed to 50% of the fine particles.  The
coarse fraction, which averaged as 25.8 /^g/m3, was dominated by crustal material which
accounted for about two-thirds of the coarse material.  Significant contributions were also
identified from motor vehicles (mostly due to re-entrained road dust) and iron and steel industry
emissions.
                                        6-134

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     The seasonal variations in nitric acid, nitrate, strong aerosol acidity, and ammonia in
Warren, MI, was examined by Cadle (1985).  The greatest variations was for ammonia, which
was 8.5 times higher in summer than winter.  The least variation was for particulate nitrate which
had a summer maximum only 1.8 times higher than in spring minimum. It was noted that
ammonium nitrate volatilization from filters  and impactors can cause large errors in
summertime measurements, but the errors are not significant during the winter.
     The influence of local and regional sources on the concentration of particulate matter in
urban and rural sites near Detroit, MI was investigated by Wolff et al. (1985). Analysis of spatial
variations of the various particulate components revealed: (1) at all four sites the PM2 5 was
dominated by regional influences rather than local sources. The site in industrial sector had the
largest impact of local sources, but even at his site the local influences appears to be smaller than
the regional ones. (2) The regional influences were most pronounced on the sulfate levels which
accounted for 40 to 50% of the PM2 5.  (3) Organic carbon compounds were the second most
abundant PM2 5 species accounting for 20 to 40% of the mass. Organic carbon seems to be
controlled by both local and regional organic  carbon influences.  Vehicular emissions and
possibly secondary reactions appear to affect the organic carbon concentrations.  (4) Elemental
carbon appears to be dominated by local emission.  (5) PMCoarse was dominated by local
sources, but at the industrial site unknown non-crustal elements were significant components
of coarse mass.

6.5.5    Subregional Aerosol Pattern in the Southwest
     The arid southwestern U.S. includes metropolitan areas (El Paso, TX, Phoenix-Tucson,
AZ) with modest industry and national parks (Grand Canyon)  where the prevention of
visibility degradation has been stated as a national goal.  The southwest is a dusty region and
much of the discussion below pertains to coarse particles and soil dust.

6.5.5.1  El Paso, Texas
     The PM10 concentration in the El Paso, TX, subregion shows that the high and low
concentration sites occur near each other (Figure 6-73a). This is an indication that local
sources of PM10 with limited range of impact are important. In the El Paso, TX, subregion
                                        6-135

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         PM10 Cone. Trend - El Paso
               EPA AIRS database
          1989   1990   1991   1992   1993   1994
       -A- Avg for all sites    -Q- Avg for trend sites
       H-Avg + Std. Dev.    -6-Avg - Std. Dev.
                                                 60
   Seasonal PM Pattern - El Paso
          EPA AIRS Database
                                                 55
                                                 50 -
                                                 45
                                                 40
                                                 35
                                                 25
                                                 20
                                                 15
                                                 10
   PM10 Station Months : 1108
   PM2.5 Station Months : 32
   PMC Station Months : 32
                                                                                  (C)
1986   Mar  May   Jul    Sep   Nov
   -&- PM10 -a- PM2.5 -+- PM Coarse
Figure 6-73.  £1 Paso subregion:  (a) aerosol concentration map, (b) trends, and
              (c) seasonal pattern.
                                          6-136

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 there is a decrease in the annual average PM10 concentration between 1988 and 1994 from
 46 Mg/m3 to 25 //g/m3 for all sites and from 57 //g/m3 to 34 /ug/m3 for trend sites
 (Figure 6-73b).  The reductions were 46% for all sites and 40% for trend sites.  This
 substantial reduction exceeds the PM10 decline over the entire southwestern region
 (Figure 6-46b).
      The seasonality of PM10 over the El Paso, TX subregion (Figure 6-73c) is bimodal with
 peaks in the spring time, March through July, as well as another stronger peak, October
 through November.  This double peak seasonality at El Paso, TX, also parallels the
 seasonality of the entire region. The concentration reduction in August which coincides with
 the arrival of moist flow from the Gulf of Mexico into states in the southwest (Figure 6-46d).
      Size segregated aerosol samples for El Paso, TX (AIRS #481410037) show that coarse
 particles dominate the PM10 concentrations, accounting for about 70% of the PM10 mass
 (Figure 6-74a).  This is consistent with the important role of coarse particles over the arid
 Southwest. In comparison, size segregated data for San Antonio, TX (Figure 6-74b) located
 closer to the Gulf Coast in Texas, show that fine and coarse mass have comparable contributions,
 similar to Houston, TX.

 6.5.5.2    Phoenix and Tucson, Arizona
      The Phoenix-Tucson subregion (Figure 6-75a) shows a substantial PM10 concentration
 range. Samplers within the Phoenix or Tucson area indicate 2 to 3 times higher concentrations
 than the more remote sites, particularly the ones in the mountains. For the Phoenix-Tucson
 subregion there was a decrease in the annual average PM10 concentration between 1988 and 1993
 from 39 A*g/m3 to 28 A*g/m3 for all sites and from 49 Atg/m3 to 32 fj.g/m3 for trend sites
 (Figure 6-75b).  The reductions were 28% for all sites and 35% for trend sites.  The decrease in
 PM concentration were not monotonic. The average PM10 seasonality of the Phoenix-Tucson
 subregion (Figure 6-75c) shows the bimodal spring and fall peak pattern which is characteristic
 for the entire Southwest region.
     During the Phoenix Urban Haze Pilot Study during the winter 1988 to 1989 (Frazier, 1989)
a definite diurnal cycle in PM2 5 concentrations was observed.  The maximum, generally but not
                                        6-137

-------
  100
   90 -
   BO
   70 -
   60
I
 3. 50
5
Q.
   40
   30
   20
   10
                                       (a)
                                                100
                                                 90
                                                 80
                                                 70
                                                 SO
                                                 50
                                                 40
                                                 30
                                                 20
                                                 10
                                  (b)
     1985   Mar    May   Ju\     Sep    Nov
              -A- = PM10AVGELPASO
              -B- = PM2.5AVGELPASO
              -+- = PMC AVG EL PASO
19B5   Mar    May   Jul     Sep    Nov
          -&- = PM10AVG SAN ANTONIO
          -H- = PM2.5 AVG SAN ANTONIO
          -+- = PMC AVG SAN ANTONIO
Figure 6-74a,b. Fine, coarse, and PM10 concentration patterns in El Paso and San
                Antonio.
 always, occurred at night, which is consistent with the meteorological observations of poor
 dispersion and dilution.
      The wintertime aerosol chemical pattern in Phoenix was reported by Chow et al. (1990)
 and Solomon and Moyers (1986).  These investigators found fine particle crustal species,
 sulfates, nitrates, and organic and elemental carbon to be at least five times higher in
 concentration when comparing samples during a period of limited visibility to samples taken
 during good visibility.
      A chemical characterization  of wintertime fine particles in Phoenix, AZ (Solomon and
 Moyers, 1986) showed a dominance of organic carbon and nitrate aerosols.  The composition in
 Phoenix is most like that of Denver, CO, a city which also experiences wintertime inversions
                                           6-138

-------
PM10 Cone. Trend - Phoenix/Tucson
          EPA AIRS database
Seasonal PM Pattern - Phoenix/Tucson
           EPA AIRS Database
                                           60
                                           55
                                           50
                                          45
                                          40
                                          35
                                          30
                                          25
                                          20
                                          15
                                          10
    PM10 Station Months: 1630
    PM2.5 Station Months : 0
    PMC Station Months : 0
                                                                           (C)
                                            1986  Mar   May  Jul
                                              -&- PM10  -B- PM2.5
                        Sep   Nov
                       PM Coarse
        198B   19B9   1990  1991   1992   1993   1994
         -A- Avg for all sites   -Q- Avg for trend sites
         ~+- Avg + Std. Dev.   "^ Avg - Std. Dev.
Figure 6-75.  Phoenix-Tucson subregion:  (a) aerosol concentration map, (b) trends, and
              (c) seasonal pattern.
                                6-139

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(Pierson and Russell, 1979; Countess et al., 1980; Groblicki et al., 1981). In both cities, the
average measured NO3" concentrations were about 1 to 2 times that of the average SOJ
concentration.  In addition, the average SOJ concentration measured in Phoenix was much lower
than those observed at other locations throughout the U.S., but similar to the regional values
observed in the Southwest (Moyers, 1982).
     Wintertime PM10 and PM2 5 chemical compositions  and source contributions in
Tucson, AZ (Chow et al., 1992a) show that the major contributors to the highest PM10
concentrations were geological material (>50%) and primary motor vehicle exhaust (> 30%) at
three urban sampling sites. Secondary ammonium sulfate, secondary ammonium nitrate, and
copper smelter aerosols were found to contribute less than 5% to elevated PM10 concentrations.
     The OC/EC ratio was one to one at Phoenix sites.  The average arsenic concentrations in
Phoenix was four times higher than observed in other cities, which indicates the potential
influence of Arizona smelters located within 100 miles of Phoenix.  Average sulfate levels in
Phoenix were higher than they were in Denver, which has less local emissions of SO2.

6.5.5.3   Grand Canyon National Park
     McMurry and Zhang (1989) reported the size distribution of ambient organic and
elemental carbon near the Grand Canyon and in the Los Angeles basin. Virtually all of the
carbon was found in the submicron range, some below 0.1 ^m.  However, positive sampling
artifacts for sub 0. Iptm organics were considered significant.
     At the Grand Canyon National Park, Zhang et al. (1994) showed that sulfates and
carbonaceous particles were the major contributor to PM2 5 particle scattering during the three
winter months and that their contributions were comparable. Scattering by nitrates and soil
dust was typically a factor of five to ten smaller. The low pressure impactor measurements
also showed that sulfur size distributions vary considerably (0.07 to 0.66 //m).

6.5.6   Subregional Aerosol Pattern in the Northwest
     The mountainous northwestern United States has many aerosol regions with different
characteristics. The discussion below will examine South Lake Tahoe, as a case study for
                                        6-140

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mountain-valley difference, Salt Lake City, UT, Denver, CO, Idaho-Montana sites, and
several Washington-Oregon sites.

6.5.6.1  South Lake Tahoe
     South Lake Tahoe IMPROVE monitoring site is located in a in a populated area on the
south shore of Lake Tahoe. The Bliss State Park IMPROVE monitoring site is to the
northwest, elevated (700ft) and removed from the populated areas. The pair of sites illustrates
the populated area-remote difference hi aerosol pattern. The aerosol and visibility at the two
lake Tahoe sites were also examined (Molenar et al., 1994).
     The concentration of all aerosol components is substantially higher on the south lake
shore compared to the more remote site.  The seasonally and chemical composition is also
substantially different.  The excess PM10 concentration at the S. Lake Tahoe site compared to
Bliss State Park (Figure 6-76) is about 5 /ug/m3 during the warm season, May through
September, and it climbs  to 28 /^g/m3 excess hi January.  The factor of five seasonal
modulation for valley excess PM10 is likely contributed by winter  tune emission sources, poor
dispersion compared to the summer, as well as fog, all of which tend to enhance the aerosol
formation.  Fine and coarse particles contribute roughly equally to excess  PM10 mass
concentrations. However, fine particles contribute about 60% during the  fall season and
coarse particles prevail (> 60%) during the spring.  Both fine and coarse particles show a
winter peak concentration.
     The chemical composition of the valley excess fine particle mass  concentration also
shows a strong seasonality for organic carbon and elemental carbon. In fact, the excess
organic carbon concentration hi the whiter (13 //g/m3) is almost an order of magnitude higher
than the summer values.  The seasonal concentration of excess elemental  carbon is similar to
that of the organic carbon. However, the relative magnitude of organic carbon compared to
elemental carbon is higher in the winter (factor of five) than in the summer (factor of two).
The concentration of fine particle sulfate is virtually identical for South Lake Tahoe and Bliss
State Park.  This implies that the South Lake Tahoe aerosol sources  do not contain sulfur.  It is
also worth noting that the  excess fine particle soil at South Lake Tahoe is below 1 /^g/m3, which
is a small fraction  of the coarse mass. Thus, the crustal component of the South Lake
                                         6-141

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^
ff
w
*
o
85
n

3
09
i
a
o
B
=
O
5
•a
o
09
                                      Mass
o
B
                                 Concentration pg/m3
§
«•+•
tf
r

B*
BT
O
A
O
O

5
I

8-
a
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                                                      -k   ro   to   to

-------
Tahoe aerosol contributes to the coarse mass but not appreciably to the fine mass
concentration.
         In summary, there is a significant excess PM10 aerosol concentration at S.  Lake
Tahoe compared to the adjacent Bliss State Park remote site, particularly during the winter
season (28 ,ug/m3). The excess mass is about equally distributed between fine and coarse
particles.  The fine mass is largely composed of organics.

6.5.6.2  Salt Lake City, Utah, Subregion
     Salt Lake City, Ogden, and Provo, UT, are part of an airshed that is confined by tall
mountains to the East, limiting the dispersion by westerly winds.
     The seasonal average PM10 concentration at three AIRS sites in Salt Lake City, Ogden, and
Provo, UT, is shown in Figure 6-77b.  All three sites show virtually identical seasonality, having
peak concentrations during December through January.  This confirms that the three sites belong
to the same airshed with similar source pattern, meteorological dispersion and chemical
transformation and removal processes.
     During the  1988 to 1994 period there were overall decreases in the annual average PM10
for the Salt Lake City, UT subregion from 49 /ug/m3 to 29 fj-g/m3 for all sites and from 54 //g/m3
to 30 /^g/m3 for trend sites (Figure 6-77b). The reductions were 41% for all sites and 48%  for
trend sites.  The trends were not monotonic, but showed substantial shifts upwards and
downwards during the 1988 to 1994 period.
     The size segregated fine and coarse concentration data  exhibit a dynamic seasonal pattern.
Fine particles clearly dominate the high winter concentrations reaching 40 to 50 /ug/m3,
compared to summer concentrations of 10 ^ug/m3.  This magnitude of fine mass concentration is
among the highest recorded in the AIRS data system.  Coarse particles are  less seasonal and they
are more important during the dry summer season. The formation of sulfate and nitrate during
winter inversion fogs near Salt Lake City, UT were studied by Mangelson et al. (1994).
                                         6-143

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                                                                       PM10 Cone. Trend - Salt Lake City
                                                                            EPA AIRS d
                                                Seasonal PM Pattern - Salt Lake City
                                                      EPA AIRS Dltabut
                                               (d)
                     * • PM1D AVG SALT LAKE CITY
                     -B-- PMtO AVG 06DEN
                     + • PMtO AVG PROVO
                                                                                                                                                      (c)
                                                                                                                             Miy     Jill
                                                                                                                           PMtO   -B- PM2.5
                                                                 S>p
                                                               - PM CMIM
  • PMtO AVG NOTINA CITY
O » PM2 5 AVG NOTINA CITY
-I- » PMC AVG NOTINA CITY
 My     M      Sip
• PM10 AVO SALT LAKE CITY
* PM2 5 AVG SALT LAKE CITY
- PMC AVG SALT LAKE CITY
Figure 6-77.   Salt Lake City region:  (a) aerosol concentration map, (b) trends, (c) seasonal pattern, and (d,e,f) seasonal patterns
                 at sites in or near Salt Lake City.

-------
 6.5.6.3     Denver, Colorado
      The Denver brown cloud is a manifestation of high wintertime concentration of particles
 and gases. Several recent studies have focused on the characterization of the Denver brown
 cloud aerosols.
      Size distribution measurements of winter Denver aerosol (Countess et al., 1981) show that
 on high pollution days that the mass median aerodynamic diameter of the accumulation mode
 aerosol was about 0.31 //m with Og±2.0. Wolff et al. (1981) found that on the average motor
 vehicles were responsible for 27% of the elemental carbon while wood burning was responsible
 for 39% of the elemental carbon.
      The chemical composition of wintertime Denver fine aerosol mass (16.4 //g/m3) (Sloane
 et al., 1991) shows the dominance of total carbon consisting of organic carbon (8.1 //g/m3)
 and elemental carbon (2.6 //g/m3) over sulfate (1.2 //g/m3) and nitrate  (3.4 //g/m3).  The fine
 particle size distribution of sulfate and nitrates were bimodal.

 6.5.6.4  Northern Idaho-Western Montana Subregion
      The mountainous northern Idaho and western Montana subregion is characterized by
 deep valleys and the absence of major industrial sources or large urban-metropolitan areas.
 Nevertheless, PM10 monitoring sites in northern Idaho and western Montana report
 concentrations that are among the highest in the nation, as illustrated hi Figure 6-78a, while
 neaby sites are among the lowest. The large spatial concentration variability is evidently
 related to the rugged terrain.  Most of the monitoring sites are located in the flat valleys.
     In the northern Idaho-western Montana subregion there was a decrease in the annual
 average PM10 concentrations between 1988 and 1993 from 41 //g/m3 to 30 //g/m3 for all sites
 and from 40 //g/m3 to 31 //g/m3 for trend sites.  The reductions were 27% for all sites and
 23% for trend sites (Figure 6-78b). The average seasonality of the subregion is strongly
 whiter peaked (Figure 6-78c)  with a factor of two modulation between 25 and 45 //g/m3.
     The high spatial variability is illustrated hi an example from northern Idaho
 (Figure 6-79a).  Three sites in Missoula, MT, show winter monthly averaged peak
concentrations from less than  40 to more than 100 //g/m3.   This is higher than the monthly
average PM10 concentration anywhere in the  eastern U.S.  The site closest to the city center
                                         6-145

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    PM10 Cone. Trend - N. Idaho/NW Montana  Seasonal PM Pattern - Idaho/Montana
                 EPA AIRS database                          EPA AIRS Database
                                                 60
                                                 55
                                                 50
                                                 45
                                                 40
                                                 30
                                                 25
                                                 20
                                                 15
                                                 10
PM10 Station Months : 1985
PM2.5 Station Months : 0
PMC Station Months : 0
                                                                                 (C)
      1988   1989   1990   1991   1992   1993   1994     19B6  Mar   May  Jul    Sep   Nov
         -A- Avg for all sites   -B-Avg for trend sites           ^ PM10  -a- PM2.5 -t- PM Coarse
               + Std. Dev.   ^Avg - Std. Dev.
Figure 6-78.  Northern Idaho-Northwestern Montana subregion:  (a) aerosol concentration
             map, (b) trends, and (c) seasonal pattern.
                                        6-146

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     100
                            10 -
                 •P985
                                   Mar   May  Jul    Sep   Nov
                                     •*"= PM10AVG MISSOULA
                                     -&-= PM10AVG MISSOULA
                                     -*-= PM10AVG MISSOULA
                                               100
                                               90
                                               70
                                               SO
                                               40
                                               30
                                               20
                                                10
                                                                               (C)
Mar
                 May  Jul    Sep   Nov
               = PM10 AVG BOISE CITY
               sPM 10 AVG SALMON
               = PM10AVG IDAHO FALLS
1985   Mar   May   Jul   Sep   Nov
« PM10 AVG ANACONDA- DEER LODGE COUNTY
= PM10 AVG ANACONDA DEER LODGE COUNTY
= PM10 AVG ANACONDA- DEER LODGE COUNTY
Figure 6-79a,b,c.  PM10 concentration patterns at sites in the Northern Idaho-
                  Northwestern Montana subregion.
                                      6-147

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shows the highest winter peak (> 100 Mg/m3), but has summer values that are comparable to
the other two sites.  It is evident that in Missoula, MT, high concentration gradients exist
between the populated areas and remote sites.  Boise and Salmon, ID (Figure 6-79b) also show
elevated PM10 concentrations during the cold season.  Idaho Falls, ID, on the other hand, is
seasonally uniform at about 30 //g/m3, which is comparable to the lowest Missoula, MT, site.
     Unusually low PM10 concentrations of 10 /ug/m3 are reported at three PM10 monitoring
sites near Anaconda-Deer, ID (Figure 6-79c).  This result is unexpected because the sites are
in a valley. The characteristic winter peak is completely absent. This suggests that pristine,
low, PMjQ sites can exist in the northwestern valleys, and hence the region is not uniformly
covered by wintertime haze or smoke.

6.5.6.5   Washington-Oregon Subregion
     The Pacific Northwest is also a mountainous subregion that exhibits unique aerosol
characteristics. During 1988 to 1994, there were decreases in the annual average PM10
concentrations for the Washington-Oregon subregion from 36 /jg/m3 to 26 /xg/m3 for all sites
and from 39 /*g/m3 to 28 /xg/m3 for trend sites. The reductions were 28% for both all sites
and trend sites.  The subregion shows a strong seasonally with a winter peak due to PM2 5
(Figure 6-80b).  PM10 monitoring sites in Seattle, Bellevue, and Tacoma, WA (Figure 6-80d),
show relatively low concentrations and a lower seasonality although higher values occur in the
winter.  A much more pronounced seasonality of PM10 concentrations is recorded in southern
Oregon.  Medford, Grants Pass, and Klamath Falls, OR (Figure 6-80e) evidently belong to an
airshed hi which emissions, dispersion, and aerosol formation mechanisms are conducive to
the formation of whiter tune aerosol (60 to 80 /ug/m3).
     Fine and coarse particle data collected over a limited period in 1987 show that the winter
peak of PM10 is entirely due to the strong winter peak of fine particle mass (50 to 100 /ug/m3).
Coarse mass, on the other hand, is seasonally  invariant at about 10 to 20 /ug/m3.  Fine
particles  clearly are responsible for the whiter peak.  This is somewhat different from the
observations at South Lake Tahoe, where the winter peak was attributed to both fine and
coarse particles.
                                         6-148

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                                                               PM10 Cone. Trend - Washington/Oregon
                                                                            EPA AIRS databau
          Seasonal PM Pattern -Washington/Oregon
                       EPA AIRS Database
    n   35
     E
       25
            PM 10 Station Months 5142
            PM2.5 Station Montht 68
            PMC Station Month! : 97
                                              (C)
        1986    Mar    May     Jul
           -6-PM10    -B-PM25
  8ep     Nov
~PM Coarte
                                                            f9B8    1989    1990    1991    1992    1993    1994
                                                                      for all cite*   "O"Avo for trend cites
                                                                   -Avg * Std. Dev.   -S-Avg - Std. D«v.
                                                             40
                                                             30
                                                                   (d)
1985   Mar    May    Jul     Sep
          -A- » PM10 AVQ SEATTLE
                                                                                                Nov
                                                                        -B-
                                                                            • PM10 AVG BELLEVUE
                                                                            = PM10AVQ TACOMA
Figure 6-80a,b,c,d,e,f,g,h.   Aerosol concentration patterns in Washington State and
                                 Oregon.
                                                     6-149

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   1985   Mar    May    Jul    Sep    Nov
              A = PM10AVG MEDFORD
             ~B~ » PM10 AVQ GRANTS PASS
             ~+~ = PM10 AVQ KLAMATH FALLS
 100
  80
  70
  BO
  40
  30
  10
                                         (g)
  1985   Mar    May    Jul     Sep   Nov
                 -&~ -PM 10 AVQ BEND
                 -B- -PM2.SAVO BEND
                 -*-" = PMC AVQ BEND
                                                    100
                                                     BO
                                                     BO
                                                     70
                                                     60
                                                     SO
                                                     30
                                                     20
                                                     10
                                                                                            (f)
1985   Mar   May     Jul    Sep    Nov
             -&- = PM10AVQ MEDFORD
             -B- - PM2.5 AVO MEDFORD
             ~+~ = PMC AVQ MEDFORD
                                                   100
                                                    90
                                                    80
                                                    70
                                                    BO
                                                    40
                                                    30
                                                    20
                                                    10
                                       (h)
1985   Mar    May   Jul     Sep    Nov
            ~&~ « PM10 AVQ CENTRAL POINT
            -B- » PM2.S AVQ CENTRAL POINT
            ~+~ = PMC AVQ CENTRAL POINT
Figure 6-80 (cont'd). Aerosol concentration patterns in Washington State and Oregon.
                                             6-150

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      The size segregated aerosol data for Bend and Central Point, OR (Figure 6-80g,h), show
 diminishing concentrations compared to Medford (Figure 6-80f), where the reduction of PM10
 is mainly due to the decrease of the fine particle mass during the winter season.
      In Portland, OR, carbonaceous aerosol was found to account for about 50% of fine
 aerosol mass (Shah et al.,  1984).

 6.5.6.6   Other Northwestern Locations
      Dresser (1988) investigated the winter PM10 concentrations in a small ski resort town,
 Telluride, CO, and found that the street dirt and sand are major contributors, particularly
 during the dry post snow period.  Wintertime source apportionment attributed to 45% of the
 PM10 mass to residential wood combustion in San Jose, CA (Chow et al., 1995a).

 6.5.7   Subregional Aerosol Pattern in Southern California
      The southern California region has two subregions, the San Joaquin Valley and the
 Los Angeles-South Coast Air Basin, discussed separately in sections below.

 6.5.7.1  San Joaquin Basin
      The wide air basin between the coastal mountain ranges of California to the west and the
 Sierra Nevada Mountains to the east shows reasonably uniform PM10 concentrations as
 indicated on the map (Figure 6-8la).  There is evidence of PM10 concentration reduction but
 the trend is not conclusive  (Figure 6-8 Ib).  The seasonal modulation amplitude over the San
 Joaquin Valley  (Figure 6-81c) is about factor of 2.5 between the low spring concentration
 30 to 35 Mg/m3, and high fall concentration (60 to 70 //g/m3). The unique  feature of this
 seasonality is the fall peak  which differs from the summer peak in the eastern United States
 and winter peak over the mountainous northwestern states.
     The AIRS database contains valuable size segregated fine and coarse particle concentration
 data within the San Joaquin Valley, as shown in Figure 6-82 for Fresno,  Madera, Visalia, and
 Bakersfield, CA. These monitoring sites show virtually identical concentration patterns for fine
 and coarse mass. Both coarse and fine particles are important contributors to the San Joaquin
Valley PM10 aerosol.  However, their respective prevalence is phase shifted.  Fine particles are
                                         6-151

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    100
     80
     80
     70
     40
     20
     10
          PM10 Cone. Trend - San Joaquin Valley
            	EPA AIRS ditibMQ	
                                           (b)
   Seasonal PM Pattern - San Joaquin Valley
              EPA AIRB DotablM
                                                        100
                                                         to
                                                         BO
  PM10 Station Month! : 1335
 . PM2.5 Station Month* : 123
  PMC Station Months : 123
                                                         10
      ?8BB    19(t    1«<0    1»1   1992    1883    1894
           •A- AVB for ill >tt*>  •& Avg tor Irond (Ho*
           -H Avg + std. Dov.  -9- Avg - Std. Dov.
1886 Fob Mar Apr May Jun Jul Aug Sop Oet  Nov Doe
  •A-PM10   -B-PM2.5   -HPMCoorao
Figure 6-81.  San Joaquin Valley: aerosol concentration map, trends, and seasonal
                pattern.
                                                6-152

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    100
     ao
     70
     •o
 S
 a.
     so
     40
     30
     20
     10
                                          (a)
     1985   Mar   May    Jul     Sep   Nov
                -*-. PM10 AVG FRESNO
                -B-. PM2.5 AVG FRESNO
                -•--PMC AVG FRESNO
    100
     to
     so
     70
 5
 Q.
     SO
     40
     30
     20
     10
                                          (C)
     1985   Mar    May   Jul     Sep   Nov
                  -*- • PM10 AVG VISALIA
                  -B-« PM2.5 AVG VISALIA
                  ~*~ • PMC AVG VISALIA
                                                     100
                                                      90
                                                      SO
                                                      70
                                                      eo
                                                      so
                                                      40
                                                      30
                                                      20
                                                      10
                                    (b)
1985   Mar   May    Jul    Sep    Nov
           A « PM10 AVQ MADERA
           -B-- PM2.S AVQ MADERA
           -+-- PMC AVG MADERA
                                                     100
                                                      •0
                                                      BO
                                                      70
                                                      eo
                                                      so
                                                      40
                                                      30
                                                      20
                                                      10
1985   Mar   May    Jul    Sep    Nov
            -&- - PM10 AVQ BAKERSFIELD
            -B- - PM2.S AVQ BAKERSFIELD
            H-« PMC AVG BAKERSFIELD
Figure 6-82.  Fine, coarse, and PM10 seasonal patterns in the San Joaquin Valley.
                                             6-153

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most important during the November through February winter season, while coarse particles
prevail during April through September.  In November, both coarse and fine particles contribute
to the seasonal peak of PM10. During March through May, neither fine or coarse particles are
abundant and the PM10 concentration is lowest during the spring season.
     The temporal dynamics of the emissions, ventilation and aerosol formation in the San
Joaquin Valley has been the subject of detailed aerosol monitoring, and source apportionment
studies.
     The aerosol composition at nonurban sites (Chow et al., 1995b) provides further
characteristics of the central California aerosol pattern (Figure 6-82). A PM10 aerosol study was
 carried out at six sites in California's San Joaquin Valley from  14 June 1988 to 9 June 1989, as
part of the 1988  to 1989 Valley Air Quality Study (VAQS). Concentrations of PM10 and
PM2 5 mass, organic and elemental carbon, nitrate, sulfate, ammonium, and elements were
determined in 24-h aerosol samples collected at three urban (Stockton, Fresno, Bakersfield)
and three non-urban (Crows Landing, Fellows,  Kern Wildlife  Refuge) locations (Chow et al.,
1993a).  The VAQS data indicate the federal 24-h PM10 standard of 150 ^g/m3 was exceeded
at four out of the six sites and for reasons which differ by season and by spatial region of
influence. The annual average source contributions to PM10 at Bakersfield, the site with the
highest annual average, were 54% from primary geological material,  15% from secondary
ammonium nitrate, 10 % from primary motor vehicle exhaust, 8% from primary
construction, the remaining 4%  is unexplained. The results of the source apportionment at all
sites show that geological contributions dominate in summer and fall months, while secondary
ammonium nitrate contributions derived from direct emissions of ammonia and oxides of
nitrogen from agricultural  activities and engine exhaust are largest during winter months.
(Chowetal., 1992b).

6.5.7.2   Los Angeles-South Coast Air Basin-Southeastern Desert Air Basin
      The Los Angeles basin is confined by the San Gabriel Mountains which limit the
ventilation during  westerly winds. Intensive emissions from automotive and industrial  sources
produce  the Los Angeles smog with numerous secondary photochemical reaction products
from primary emissions. The map of the Los Angeles subregion shows (Figure 6-83a) the
                                         6-154

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                                                  1988   1989   1990   1991    1992   1993
                                                  1985  Mar  May   Jul   Sep   Nov
Figure 6-83.  Los Angeles: (a) aerosol concentration map, (b) trends, and (c) seasonal
             pattern.
magnitude of PM10 concentrations for individual monitoring stations.  Isopleths of PM10
concentration for 1992 are consistent with these results showing the highest PM10
concentrations are measured in the center of the LA basin with the lower concentration of
PM10 near the ocean and out in the desert and the mountains (Hoggan et al., 1993).
     There has been a substantial reduction of subregion average PM10 concentration from
1988 to 1993 from 54 /zg/m3 down to 38 ^g/m3 (Figure 6-83b), a reduction of 30%.  The
seasonality of the basin averaged PM10 concentration shows a 50% amplitude, with the peak
concentration (60 //g/m3) during October and the lowest values (40 //g/m3) during January
through March (Figure 6-83c). Hence, this fall peaked seasonality is similar to the fall peak over
the San Joaquin Valley.
                                         6-155

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     The PM10 air quality in the California South Coast Air Basin (CSCAB) and to a limited
extent in the Southeastern Desert Air Basin have been analyzed for the 1985 to 1992 period
(Hoggan et al.,  1993).  Although a larger number of monitoring stations exist in more recent
years, the analysis involved only the monitoring stations with complete data in Long Beach,
Burbank, El Toro, Ontario, Rubidoux, Banning, and Indio. Measurements in downtown Los
Angeles also are used in parts of the analysis (Hoggan et al., 1993).  The annual average PM10
trend line for 1985 to 1992 showed a statistical  significant trend downwards with the decrease
averaging 3% per year.  The sulfate and nitrate also were measured and they accounted for
about one-third of the decrease in PM10.  The decreases between 1989 and  1993 for this set of
stations were smaller than for the larger group of stations (Figure 6-83b).  There was a
statistically significant decrease (0.05 level of significance) at Burbank, Long Beach,
Rubidoux, and Banning.  Use of both a decision tree analysis and a multiple linear regression
analysis showed that the temperature at 850 mb, a measure of mass stability, was an important
variable associated with PM10 in the CSCAB.  Use of this variable suggests that the observed
decreases in annual average PM10 concentrations between 1987 and 1992 are not an artifact of
meteorology. A more detailed discussion of these analyses as related to various aspects of
meteorology is given (Hoggan et al., 1993).
     The diurnal patterns of PM10 also are discussed (Hoggan et al.,  1993). The Rubidoux
monitoring station showed peaks in PM10 at about the time of peak commuter traffic.  The Los
Angeles monitoring station showed higher PM10 concentrations in the morning and evening
than at midday.  Azusa and Long Beach monitoring stations showed broad daytime peaks.
The Indio monitoring station showed an evening peak.
     The weekday to weekend mean PM10 concentrations at all monitoring stations showed
significantly lower concentrations on weekends (Hoggan et al., 1993). At the two SEDAB
stations, Indio and Banning, Saturday PM10 concentrations were slightly lower than weekdays,
but Sunday PM10 concentrations fell within the range of weekday means.
     Some seasonal characteristics of the Los Angeles basin are depicted in Figure 6-84.  The
monitoring sites at different parts of the basin have markedly different seasonal concentration
patterns. Hawthorne and Long Beach  near the  Pacific Coast and Burbank in an inland valley
have the higher PM10 concentration in late fall and early winter (Figure 6-84b,c).  On the
                                         6-156

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     150

     140

     130
     120
     110

     100

     90
 CO

  I"
  5  70
  Q.
     60
     50
     40

     30

     20

     10
(a)
      1985   Mar   May    Jul     Sep   Nov
                ~*~ - PM10 AVG HAWTHORNE
                ~&~ - PM10 AVQ RUBIDOUX
                -+- - PM10 AVQ BURBANK
    100
     90
     SO
     70
  co  60
   e
   o»
   - 50
  Q.
     40
     30
     20
     10
                                            (c)
1985   Mar   May   Jul     Sep
            "*" • PM10 AVG AZUSA
            ~B~ - PM2.5 AVG AZUSA
            -+- • PMC AVG AZUSA
                                       Nov
                                                      100
           90
           70
           80
           50
           40
           30
           20
           10
                                     (b)
           1985   Mar    May    Jul    Sep    Nov
                     ~&~ - PM10 AVG LONG BEACH
                     -B- - PM2.5 AVG LONG BEACH
                     ~+- - PMC AVG LONG BEACH
                                                      100
                                                       80
                                                       80
                                                       70
                                                       60
           50
                                                       40
                                                       30
                                                       20
                                                       10
1985   Mar
                         May
                                                                           Jul    Sep
                                                                              RUBIDOUX
                                                                      PM2.5 AVG RUBIDOUX
                                                                      PMC AVG RUBIDOUX
                                 Nov
Figure 6-84a,b,c,d.   Fine, coarse, and PM10 seasonal patterns near Los Angeles. (Note
                       scale for (a) is 150 ug/m3.)
                                              6-157

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other hand, Azuza and Rubidoux in the eastern part of the basin exhibits the higher PM10
concentration during the May to October 'smog season' (Hoggan et al., 1993)
(Figure 6-84b,d,e).  The main causes of different seasonalities are likely to be associated with
seasonally varying meteorological, transport, and chemical transformation patterns.  The role
of coarse and fine particles in the Los Angeles basin is also illustrated in Figure 6-84.
At Long Beach, near the coast (adjacent to Hawthorne), the fine particles dominate the PM10
during the November through February winter season (40 to 50 //g/m3). Coarse particles at
Long Beach are constant throughout the year at about (20 yug/m3). At Azuza and Rubidoux
fine and coarse particles contribute roughly equally to the high PM10 concentrations. Thus,
the PM10 aerosols over the smoggiest parts of the Los Angeles basin are not dominated by fine
secondary aerosols but contributed by both fine and coarse particles.
     The Rubidoux site in 1985 to 1988 showed violations of the 24-h PM10 standard
approximately 12%  of the tune with a large contribution from ammonium nitrate (Chow et al.,
1992c). A large group of dairies and animal husbandry operations hi the Chino area
approximately 13 km west of the Rubidoux site were identified as major ammonia emitters
(Russell and Cass, 1986). To better evaluate the immediate area, measurements were made at
the Rubidoux, Riverside-Magnolia, and Riverside sites.  The results indicated that the
Rubidoux site did represent urban-scale contributions of primary motor vehicle exhaust,
secondary sulfate, and secondary nitrate.  However, there also were significant
neighborhood-scale and urban-scale contributions of primary geological sources and
lime/gypsum sources contributing to the PMj0 concentration (Chow et al.,  1992c).
     The Los Angeles smog has been the subject of extensive spatial, temporal, size and
chemical composition studies since the 1960s (Appel et al., 1976, 1978, 1979; Hidy et al.,
1980). A number of individual  studies are discussed below.
     The chemical characteristics of the PM10 aerosols were measured throughout 1986
(Solomon et al., 1989).  Five major aerosol components (carbonaceous material, elemental
carbon and organic carbon [measured value multiplied by 1.4 to account for O and H associated
with C], nitrate, sulfate, ammonium, and soil-related materials, as measured) accounted for over
80% of the 1986 annual average  PM10 mass. In all, measured chemical components were
included from 80 to 94% of the PM10 mass was chemically identified. The nitrate and
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ammonium concentrations were substantially higher at the Rubidoux and Upland sites than at
other sites.  Measurements made off the coast at San Nicolas Island suggest that from 28 to 44%
of the annual average concentration at individual on-land sites can be associated with a regional
background (Solomon et al., 1989).
     More recently the LA aerosol characteristics during  11 summer days and 6 fall days in
1987 have been further elucidated by  Southern California Air Quality Study (SCAQS) (Lawson,
1990). Several of the SCAQS studies reported are discussed below. The SCAQS study is also
discussed in Chapter 3, Section 3.4.2.3.
     Nitrate, sulfate, ammonium, and organic and elemental carbon were the most abundant
species in the PM2 5 fraction during SCAQS (Chow et al., 1994a).  The coarse particle fraction
was composed largely of soil-related elements (e.g., aluminum, silicon, calcium, iron) at the
inland sites and with marine-related elements (e.g., sodium, chloride) at the coastal sites.
Average concentrations for most chemical compounds were higher during the fall than during the
summer, except for sulfate which was more abundant in summer.  The PM2 5 constituted one-half
to two-thirds of PM10 at all sampling  sites. PM2 5 nitrate and ammonium concentrations were
negatively  biased for daytime samples compared to nighttime samples, consistent with diurnal
changes in temperature and the effect of these changes on the equilibrium between particulate
ammonium nitrate and gaseous ammonia and nitric acid. (Chow et al., 1994a; Watson et al.,
1994a).
     Wolff et al. (1991) measured the smog aerosol pattern during SCAQS at Claremont, CA,
and Long Beach, CA, in the eastern  and western Los Angles basin, respectively. Claremont's
air quality during the summer was characterized by high concentrations of photochemically
produced pollutants including ozone, nitric acid, particulate nitrate, and particulate organic
carbon (OC). The highest concentrations of these species were experienced during the
daytime sampling period (0600 to 1800) and were associated with transport from the western
part  of the basin.  Long Beach's air quality during the fall was characterized by frequent
periods of air stagnation that resulted in high concentrations of primary pollutants including
PM10, OC and elemental carbon (EC) as well as particulate nitrate. Night -tune levels of most
constituents exceeded daytime levels due to poorer night-time dispersion conditions. At
Claremont, OC and nitrate compounds accounted for 52% of PM10 , while at Long Beach they
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accounted for 67% of PM10. On the average, there appears to be sufficient paniculate
ammonium to completely neutralize the nitrate and acidic sulfates.
     In situ, time resolved analysis for aerosol organic and elemental carbon in Glendora, CA
(Turpin et al.,  1990), showed strong diurnal variations with peaks occurring in the daylight
hours.  Comparison of the diurnal profile of organic carbon with those of elemental carbon
provided evidence for the secondary formation of organic aerosol in the atmosphere. Turpin
et al. (1991) observed that secondary organic aerosol appears to have contributed roughly half
of the organic aerosol hi Pasadena during midday summer conditions.
     Turpin and Huntzicker (1991) also found that the organic and elemental carbon
concentrations exhibit strong diurnal variations.  Peak concentrations  occur during the daylight
hours in the summer and at  night in the fall. The maximum concentrations observed in the fall
(maximum total carbon, 88  Mg/m3) were two to three times higher than the summer maxima
(maximum total carbon, 36  /ug/m3).  Measurements of elemental and organic carbon have been
carried out by Gray et al. (1986). Extensive efforts have been made by Cass and coworkers
(e.g. Rogget et al., 1993; Hildemann et al., 1991) to identify the molecular composition of the
organic component. While  some tracers have been identified, only a fraction of the organic
PM has been characterized in terms of its molecular composition.
     Gaseous nitric acid and fine particulate nitrate at Claremont, CA (Pierson and
Brachaczek, 1988) both showed pronounced (~ 10-fold) diurnal variations; however, coarse
particles showed little diurnal variation. The average concentrations over the September 11 to
19 study period were for HNO3, 7.1 Mg/rn3; fine NO^, 7.29 /ug/m3; and coarse NO^,
7.1 /^g/m3. Fine NO§ may  have been underestimated due to volatilization during or after
sampling. This problem is discussed in Chapter 4, Section 4.2.10.1.
     Careful size distribution measurements in the Los Angeles basin (John et al., 1990) shed
light on the size spectrum dynamics for ammonium, sulfate and nitrate. Three modes, two
submicron and one coarse, were sufficient to fit all of the size distributions. The smallest
mode, at 0.2±0.1 /um aerodynamic diameter, is probably a condensation mode containing gas
phase reaction products. A larger mode at 0.7±0.2 ^m is defined as a droplet mode.  Most of the
inorganic particle mass was found in the droplet mode. The observed condensation and droplet
modes characterize the overall size distribution in the 0.1 to 1.0 yum range, previously described
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by Whitby and coworkers as a single accumulation mode (Whitby et al., 1972; Whitby, 1978).
Wall et al. (1988) also found that in September 1985 at Claremont, CA fine particle nitrate was
associated with ammonium, while coarse mode nitrate was associated with both ammonium and
sodium. Sulfate was primarily in two submicrometer modes.
     A clear demonstration of the effect of relative humidity and aerosol loading on atmospheric
sulfate size distributions is given by Hering and Friedlander (1982).  Days of high relative
humidity and aerosol loading correspond to high mass median diameters (0.54±0.07 yum) for the
sulfate while low relative humidity and low aerosol loadings correspond to small mass median
diameters (0.2±0.02 /um).  According to their interpretation, the larger (0.54 /nm) sulfate particles
resulted from aqueous phase reactions of SO2.  The finer (0.2 /mi) sulfate resulted from
homogeneous gas phase reactions leading to the nucleation of sulfuric acid particles.
     McMurry and Stolzenburg (1989) provide evidence that Los Angeles smog aerosols are
externally mixed. Monodisperse ambient aerosols were often found to split into  nonhygroscopic
(no water uptake) and hygroscopic portions when humidified. An average of 30% of the
particles in the 0.2 to 0.5 /mi range were nonhygroscopic. However, the proportion of particles
that were nonhygroscopic varied considerably from day to day and on occasion was 70 to  80%
of the particles. The data show that for the hydrophilic aerosol, the larger particles (0.4 to
0.5 /mi) grew more when humidified than did smaller particles (0.05 to 0.2 /mi).
     Size distributions of aerosol phase aliphatic and carbonyl groups at Claremont, CA (Pickle
et al., 1990) showed maxima in the 0.12 to 0.26 /mi and the 0.5 to 1.0 /mi size functions. From
the aliphatic carbon absorbency, the ambient samples generally showed maxima in the 0.076 to
0.12 fj,m size fraction. The authors attribute the carbonyl absorbance almost entirely attributed to
products of atmospheric reactions and the aliphatic absorbencies in particles smaller than 0.12
yum to automotive emissions.
     Cahill et al. (1990) found that the sulfate aerosol size at Glendora, CA, is smaller, 0.33 /mi
(MMD) during clear days compared to 0.5 /mi on smoggy days.
     The size distributions of organic nitrate groups in ambient Los Angeles aerosol were
typically bimodal (Mylonas et al., 1991). During periods of high photochemical activity, the
maxima in the mass loadings were in the 0.05 to 0.075 /mi and the 0.12 to 0.26 /mi size
fractions.  During periods of low-moderate ozone concentrations, the distributions were shifted to
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slightly larger sizes, with maxima appearing in the 0.075 to 012 fj.m and the 0.5 to 1.0 //m size
fractions. A principal component analysis of the organonitrate loadings revealed strong
correlations with ozone concentrations and with aerosol phase carbonyl loadings.
     The analysis of coarse particles in Claremont, CA (Noll et al., 1990) show that the coarse
particle mass could be divided into two categories: material that was primarily of crustal origin
(Al, Ca, Fe, and Si) and material that was primarily of anthropogenic origin (Cd, Cu, Mn, Ni, Pb,
and Zn). The mass of crustal material varied between 33 and 49% of the total coarse mass, while
the mass of anthropogenic elements listed above were <1%.
     The daily frequency distribution of the chemical components of the Los Angeles aerosol
measured over a 1-year period were approximately lognormal (Kao and Friedlander, 1994).  For
nonreactive aerosol components, the geometric standard deviation (GSD) is nearly constant at
1.85±0.14 even  for components from different source types.  An apparent bimodal frequency
distribution for sulfates probably corresponds to the two differing reaction pathways by which
gas-to-particle conversion occurs. However, the bimodal sulfate distribution function was not
found at other Los Angeles sites (Kao and Friedlander, 1995).  The authors suspect a relationship
between GSD and the level of complexity of the stochastic physical and chemical processes
affecting the distributions of the individual species. They also point out that the chemical
concentration of the Los Angeles aerosol that corresponded to the peak in the (nearly) lognormal
frequency distribution of the total mass is lower than he simple average chemical concentration.
      A long term data base for organic and elemental carbon has been constructed (Cass et al.,
1984; Gray et al., 1984).  The average elemental carbon concentrations at seven monitoring sites
in the Los Angeles area, for the 24-year period (1958 to 1982), were estimated to range from
6.4 Atg/m3 at downtown Los Angeles to 4.5 Atg/m3 at West Los Angeles.  At most monitoring
sites studied, elemental carbon concentration were lower in recent years than during the late
1950s and early 1960s.
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6.6   CHEMICAL COMPOSITION OF PARTICULATE MATTER
       AEROSOLS AT URBAN AND NONURBAN SITES
     This section summarizes selected data from a number of studies for the composition of
atmospheric particles in suburban, urban, and a few rural areas for comparison purposes.
Emphasis has been placed on the Harvard six-city study and the inhalable particulate network
(1980-1981).  Data for fine particle mass and elemental composition were available from these
studies. Data for sulfate, nitrate, and elemental and organic carbon content are included from
other studies to provide an overview of the chemical composition of the atmospheric aerosol in
the United States. Tables presented in Appendix 6A provide relatively detailed representations
of the properties of atmospheric particles to which U.S. populations are exposed.  Unfortunately,
data this complete are generally collected only during intensive studies. The tables are meant to
provide examples of the types of information that could be collected as part of future monitoring
efforts in support of human exposure investigations.
     A summary of all the aerosol sampling studies included in this compilation is given in
Tables 6A-la, 6A-lb, and 6A-lc. Sampling studies have been grouped by geographical region
roughly corresponding to the eastern, central, and western United States. Data are tabulated for
the PM2.5 (d < 2.5 urn), the coarse fraction of PM10 (2.5 um< d < 10 urn) and PM-10 (d < 10
/^m) size  fractions of the ambient aerosol in Tables 6A-2a, 6A-2b, and 6A-2c. Compositional
data for all size fractions were broken down into the following major components: sulfate, as
SOJ; carbon, as organic carbon (OC), which as been multiplied by a factor of 1.4 to account
for the presence of oxidized species, and elemental carbon (EC); nitrate as NO3"; and remaining
trace elements. The NH4+, that would be required to neutralize all acidic species in the  samples,
is shown  as (NH4+)*. Representing sulfate as ammonium sulfate and using a factor of 1.4 to
account for the mass of organic carbon present in oxidized forms allows a firm lower limit to be
placed on the fractional mass that is not chemically identified in filter samples. Acidity is given
in units of nmoles/M3 in Tables 6A-2a and 6A-2c. The masses of the trace elements from
sodium through lead have been calculated by assuming they are in their most stable  forms for
conditions at the earth's surface. Reconstructed masses calculated in this way are shown by the
entry, Sum, along with measured masses, and the ratio of the two are shown at the bottom of the
individual summaries for each size fraction. Not all compositional categories were measured in
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the studies for inclusion in the tables.  For instance, data for characterizing the carbon or nitrate
content of the ambient aerosol are not available for many of the studies listed. Average data are
shown in graphical form in Figures 6-85a, 6-85b, and 6-85c for studies in the eastern, central,
and western United States.
     As can be seen from inspection of Figure 6-85a, sulfate is the major identified component
of mass for fine particles (34.1%), followed by elemental and organic carbon (24.8%), minerals
(4.3%), and nitrate (1.1%) for studies in the eastern United States. However, this last inference is
based on only a few studies in which nitrate was measured. Pierson et al. (1980a,b, 1989)
measured nitrate as constituting only 0.8% to 1.4% of aerosol mass at Allegheny Mountain and
Laurel Hill in southwest Pennsylvania in the summers of 1977 and 1983. Presumably, the low
nitrate in these and other studies in the eastern United States is related to aerosol acidity. Coarse
particles are seen to consist mainly of mineral forming elements (51.8%) and sulfate (4.9%). Not
enough data were available to determine abundances of carbon species  and nitrate in the coarse
fraction.  A sizable fraction of both the fine (22.8%) and coarse (41.5%) particle mass is shown
as unknown. This unknown mass is assumed to be mainly water, either bound as water of
hydration or associated with hygroscopic particles. A small fraction of the mass, especially in
the coarse fraction, may be present as  carbonates.  Carbonates are difficult to quantify, in part
because of artifact forming reactions with atmospheric CO2 and acids on filters. Stable
carbonates could be identified by SEM in regions where they are known to represent a
substantial fraction of soil composition.
     Fine particles sampled in the studies shown in Table 6A-1 in the central United States
(Figure 6-85b) are seen to consist mainly of sulfate (22.3%), minerals (7.6%), and elemental and
organic carbon (53.6%). The reconstructed mass percentages sum to 124.8%. This could be due
to an overestimation of the carbon content which was estimated from only a few samples
collected during winter in woodsmoke impacted areas.  Coarse particles were found to consist
mainly of minerals (62.8%), sulfate (3.1%) and an unknown fraction (33.0%). No nitrate or
carbon data were available for the coarse fraction from the studies in the central United States.
     While gross fine particle composition appears to be broadly similar between the eastern and
central United States on the basis of the studies shown in Tables 6 A-la, 6A-lb,  and 6A-lc, the
fine particle composition is seen to be distinctly different in the western United  States
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                              PM2.5 Mass Apportionment

                                              •Minerals 4.3%
                   Unknown 22.8%
                     EC 3.9%
                                                        SOj 34.1%
                   OCx 1.4 20.9%
                            NO;  1.1%  —     "     
-------
                             PM2.5 Mass Apportionment

                            EC 9.0%  	v     /	 Minerals 7.6%
                 OCx1.4 44.6%
                                                        so; 22.3%
                                                      (NHJ)* 10.2%
                                                   NOj 8.1%
                               Reconstructed sum = 124.8%
                             Coarse Mass Apportionment
                Unknown 33.0%
                   (NHJ)* 1.1%

                    SO° 3.1%
                                                        Minerals 62.8%
                        Insufficient Nitrate, OC, and EC data available
                              PM10 Mass Apportionment
                      EC 29.6%
                                                        Minerals 35.8%
                  OCx 1.4 5.0%      	
                                                     S04  3.3%

                           NO,  23.7% 	'             (NHJ)*  6.5%

                   Nitrate based on 2 studies; OC and EC based on 4 studies

                               Reconstructed sum = 103.9%
Figure 6-85b.  Major constituents of particles measured at sites in the central United States,
              as  shown  in  Tables 6A-2a, 6A-2b, and 6A-2c.  (NH4+)* represents the
              concentration of NH4+ that would be required if all SO 4= were present as
              (NH4)2SO4 and all NO3' as NH4NO3. Therefore, (NH4+)* represents an upper
              limit to the true concentration of NH4+.
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                              PM2.5 Mass Apportionment
                         EC 14.7%
                                                          j 10.8%
                   OCX 1.4 38.9%
                                                        (NHJ )* 7.5%
                                                         j 15.7%
                               Reconstructed sum = 102.2%

                             Coarse Mass Apportionment
                  Unknown 27.0%
                     )* 0.8%
                   SO" 3.1%
                                                       Minerals 69.9%
                         Insufficient Nitrate, OC, and EC data available
                              PM10 Mass Apportionment

                              EC 5.1%
                 OCX 1.4 30.0%
                                                         Minerals 36.3%
                          NO-3  24.0%  	'             (N   )* 6.7%

                                Reconstructed sum = 111.4%
Figure 6-85c.  Major constituents of particles measured at sites in the western United States,
              as  shown  in Tables 6A-2a, 6A-2b, and 6A-2c.  (NH4+)* represents the
              concentration of NH4+ that would be required if all SO4 = were present as
              (NH4)2SO4 and all NO3' as NH4NO3. Therefore, (NH4+)* represents an upper
              limit to the true concentration of NH4+.
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(Figure 6-85c).  Elemental plus  organic carbon species (53.6%) are the major identified
component of mass, instead of sulfate (10.8%), and minerals and nitrate account for a larger
fraction of total mass. While minerals are seen to account for most of the coarse particle mass
(69.9%), available data were insufficient to estimate the contributions of elemental and organic
carbon species to the coarse mass.  Table 6A-3  shows a comparison of selected ratios of mass
components for studies conducted in each of the three broad regions of the United States.
     Many of the studies listed in Table 6A-3 involved data collected at more than one site
within an airshed. Information about the variability of particle mass within an airshed can yield
information about the nature of sources of the particles. The variability of mean concentrations
measured at multiple sites within a study area is used as a measure of the intersite variability in
fine particle composition and is shown in Tables 6A-4a and 6A-4b.
6.7   ACID AEROSOLS
6.7.1    Introduction
     Acid aerosols are secondary pollutants formed primarily through oxidation of sulfur
dioxide (SO2), a gas emitted by the combustion of fossil fuels.  Oxidation of SO2 forms sulfate
(SO4=), the major component of acid aerosols. Sulfate is formed to a lesser extent through the
oxidation of sulfur species (H2S and CH3SCH3) from natural sources. The oxidation of SO2
occurs through a series of heterogeneous (gas-particle) or homogeneous (gas or aqueous) phase
oxidation reactions that convert SO2 to sulfuric acid (H2SO4) particles.  The sulfate species are
typically expressed in terms of total SOJ, with the acidic fraction expressed in terms of titratable
H+ ([H+] + [HSO^]) and referred to as aerosol strong acidity. The chemical aspects of oxidation
of S02 and formation of aerosol strong acidity are discussed in Chapter 3, Section 3.3.1. H+ is
usually found in the fine particle size fraction (aerodynamic diameter (Dp) < 1.0 um) (Koutrakis
and Kelly, 1993; Pierson et al., 1980a, 1989). However, acidity may be found in larger particles
during periods of fog or very high relative humidity. Keeler et al. (1988) and Pierson et al.
(1989) report finding acidity in the > 2.5 /um size range when the relative humidity was close to
100%. Although recent research has shown a high correlation between SOJ and acidity, data
                                         6-168

-------
from summertime sampling have shown that SO4 is not always a reliable predictor of H+ for
individual events at a given site (Lipfert and Wyzga, 1993).
     A major determinant of the lifetime of H+ in the atmosphere is the rate of neutralization by
ammonia (NH3). Ammonia reacts with H2SO4 to form ammonium sulfate [(NH4)2SO4] and
ammonium bisulfate (NH4HSO4). The major sources of ammonia in the environment are
animals and humans (Fekete and Gyenes, 1993). The then current state-of-knowledge regarding
acid aerosols was reviewed by EPA in 1989 (U.S. Environmental Protection Agency, 1989) and
by Spengler et al, 1990. A more recent summary is given by Waldman et al. (1995).

6.7.2  Geographical Distribution
      In North America, ambient concentrations of H+ tend to be regional in nature with the
highest concentrations found in the northeastern United States and southwestern Canada.
Spengler et al. (1990) have collected information on maximum values of SO4 and H+ found
across the U.S. and southern Canada. This information is shown in Table 6-5.

6.7.3  Spatial Variation (Regional-Scale)
     Recent evidence has shown that meteorology and regional transport are extremely
important to acid sulfate concentrations. Elevated levels of ambient H+ were measured
simultaneously during a regional episode at multiple sites located from Tennessee to Connecticut
(Keeler et al., 1991).  Lamborg et al. (1992) measured H+ concentrations to investigate the
behavior of regional and urban  plumes advecting across Lake Michigan. Results suggested that
aerosol acidity is maintained over long distances (up to 100 km or more) in air masses moving
over large bodies of water. Lee et al. (1993) reported that H+ and SOJ concentrations measured
in Chicago over a year were similar to levels measured in St. Louis.  In an analysis of acid sulfate
concentrations measured at Pittsburgh, State College, and Uniontown, PA, Liu et al. (1996)
reported high correlations for H+ between all three locations.  The three locations are separated
by large distances (approximately 60 to 240 km) and have vastly different population densities.
It is commonly believed that the source region for most of the H+ precursors (primary inorganic
pollutant gases —SO2 and NOX) is the Ohio River Valley (Lioy et al., 1980). The conversion of
the primary gases to secondary pollutants takes place as the prevailing winds carry the precursors

                                        6-169

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            TABLE 6-5. MAXIMUM 804 AND H+ CONCENTRATIONS
                  MEASURED AT NORTH AMERICAN SITES
           (H+ concentrations expressed as sulfuric acid (H2SO4)equivalents;
                  "SC" indicates semi-continuous measurements.)
Location
Lennox, CA
Smoky Mountains
High Point, NJ
Brookhaven, NY
Tuxedo, NY
St. Louis, MO
St. Louis, MO
Los Angeles, CA
Harriman, TN
Watertown, MA
Fairview Lake, NJ
Warren, MI
Whiteface Mt, NY
Toronto, ON, Canada
Allegheny Mt., PA
Laurel Hill, PA
Harriman, TN
St. Louis, MO
Topeka, KS
Watertown, MA
Steubenville, OH
Portage, WI
Kanawha Valley, WV
Dunville, ON, Canada
Hendersonville, TN
Livermore, CA
Morehead, KY
Monroeville, PA
Pembroke, ON, Canada
Springdale, AR
Newtown, CT
Allegheny Mt., PA
Uniontown, PA
State College, PA
Philadelphia, PA
Pittsburgh, PA
Sample Duration (h)
2-8
12
6
3
1-12
SC
SC
12
SC
SC
SC,4
24
24
8,16
7,10
7,10
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
12
12,24
12
24
6,24
Maximum
SO^g-m'3)
18
17
37
24
41
25
43
10
47
31
27
37
59
75
45
56
28
40
14
23
56
33
46
31
23
9
23
42
29
11
26
33
52
47
39
27
Concentration
H2S04(Mg-m-3)
0.1
10
18
10
9
7
34
3
18
14
12
9
14
19
31
42
14
6
3
9
18
4
22
15
11
2
14
18
14
2
8
20
39
25
9
15
Source: Spengler et al. (1990).
                                   6-170

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from the source region, northeastward to the northeastern United States and southwestern
Canada. This type of northeasterly wind flow occurs on the backside (western side) of
mid-latitude anti-cyclones (high pressure systems).
     Pierson et al. (1980a,b, 1989) conducted studies of atmospheric acidity on Allegheny
Mountain and Laurel Hill in southwest Pennsylvania, 80 and 100 km southeast of Pittsburgh, in
the summers of 1977 and 1983. The aerosol H+ appeared to represent the net after H2SO4
reaction with NH3(g).  The resulting H+/SO4 ratio depended on SO4 concentration, approaching
that of H2SO4*at the highest SO4 concentrations. The atmospheric was acidic; the average
concentrations of HNO3 (78 nmole/m3) and aerosol H+ (205 nmole/m3), NH4+ (172 nmole/m3),
and SO4 (201 nmole/m3), and the dearth of NH3 (<15 nmole/m3), show that the proton acidity of
the air exceeded the acid-neutralizing capacity of air by a factor of >2, with one 10-hour period
averaging 263 nmole/m3 for HNO3 and 844 nmole/m3 for H+. SO2 added another 900 nmole/m3
(average) of potential H+ acidity. HNO3 and aerosol H+ episodes were concurrent, on 7-8 day
cycles, unrelated to SO2 which existed more in short-lived bursts of apparently more local origin.
NOX was sporadic like SO2. Laurel and Allegheny, separated by 35.5 km, were essentially
identical in aerosol SO4, and in aerosol H+, less so in HNO3; apparently, chemistry involving
HNO3 and aerosol H+ or SO4 was slow compared to inter-site transport times (1-2 hours).  From
growth of bscat and decline of SO2, daytime rate coefficients for SO2 oxidation and SO2 dry
deposition were inferred to have been, respectively, -0.05 and ^0.1 hr"1-
     HNO3 declined at night.  Aerosol H+ and SO4 showed no significant diurnal variation, and
03 showed very little; these observations, together with high PAN/NOX ratios, indicate that
regional transport rather than local chemistry is governing. The O3 concentration (average
56 ppb or 2178 nmole/m3) connotes an oxidizing atmosphere conducive to acid formation.
     Highest atmospheric acidity was associated with (1) slow westerly winds traversing
westward SO2 source areas, (2) local stagnation, or (3) regional transport around to the back side
of a high pressure system. Low acidity was associated with fast-moving air masses and with
winds from the northerly directions; upwind precipitation also played a moderating role in air
parcel acidity.  Much of the SO2 and NOX, and ultimately of the HNO3 and aerosol H+, appeared
to originate from coal-fired power plants.  An automotive contribution to the NOX and HNO3
could not be discerned.
                                         6-171

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     Size distributions of aerosol H+ and SO4 were alike, with MMED -0.7 //m, in the optimum
range for efficient light scattering and inefficient wet/dry removal. Thus, light scattering and
visual range degradation were attributable to the acidic SO^ aerosol.  With inefficient removal of
aerosol H+, and inefficient nighttime removal of HNO3, strong acids may be capable of long-
distance transport in the lower troposphere.  Water associated with the acidic aerosol was shown
to account for much of the light scattering.

6.7.4    Spatial Variation (City-Scale)
     A study of acid aerosols and ammonia (Suh et al., 1992) found no significant spatial
variation of IT1" at Uniontown, Pennsylvania, a suburb of Pittsburgh.  Measurements at the central
monitoring site accounted for 92% of the variability in outdoor concentrations measured at
various homes throughout the town. There was no statistical difference (p > 0.01) between
concentrations of outdoor H+ among five sites (a central site and four satellite sites) in Newtown,
Connecticut (Thompson et al., 1991). However, there were differences in peak values which
were probably related to the proximity of the sampling sites to ammonia sources. These studies
suggest that long-term averages should not substantially differ across a suburban community,
although peak values may differ significantly.
     In small suburban communities outdoor concentrations of H+ are fairly uniform, suggesting
that minor differences in population density do not significantly affect outdoor H+ or NH3
concentrations (Suh et al., 1992). In urban areas, however both H+ and NH3 exhibit significant
spatial variation. Waldman et al. (1990) measured ambient concentrations of H+, NH3, and SOJ
at three locations in metropolitan Toronto. The sites, located up to 33 km apart, had significant
differences in outdoor concentrations of H+. Waldman and co-workers reported that the sites
with high NH3 measured low H+ concentrations.  However, the  limited number of sampling sites
did not allow for a conclusive determination of the relationship between population density,
ammonia concentrations, and concentrations of acid aerosols.
     An intensive monitoring study has been conducted during the summers of 1992 and 1993
in Philadelphia (Suh et al., 1995).  Twenty-four hour measurements of aerosol acidity (H+)
sulfate and NH3 were collected simultaneously at 7 sites in metropolitan Philadelphia and at
Valley Forge, 30 km northeast of the city center.  The researchers reported that SOJ was evenly
                                         6-172

-------
 distributed throughout the measurement area but H+ concentrations varied spatially within
 metropolitan Philadelphia. This variation was related to local NH3 concentrations and the local
 population density (Figure 6-86). The amount of NH3 available to neutralize H+ increased with
 population density, resulting in lower H+ concentrations in more densely populated areas. The
 extent of the spatial variation in H+ concentrations did not appear to depend on the overall H+
 concentration. It did, however, show a strong inverse association with local NH3 concentrations.
              o
              i
                 0.5
                 0.4
                 0.3
                 0.2
                 0.1
                 0.0
                       in-** 3
                                 120
                                 90
!  60
                               O
                               
-------
  September (Figure 6-87).  Little or no seasonal variation was observed at sites with low acidity.
  These findings were supported by those of Thurston et al. (1992) in which H+ concentrations
  measured at Buffalo, Albany, and White Plains, NY, were found to be highest during the
  summertime.  Thurston and co-workers also reported that moderate concentrations of H+ could
  occur during non-summer months

n
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1 90-
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c
&
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0
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n-
0 Hendersonville, TN • Morehead. KY

SDunnville, Ontario, Canada

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^Livermore, CA





n
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DSpringdale, AR




i

,
1





1
i
1 UH ^1 Ufil
                  JAN  FEB  MAR APR MAY JUN  JUL AUG  SEP OCT NOV DEC
                                             Month
Figure 6-87.  Average monthly aerosol strong acidity for Year 1 sites of the Harvard 24-City
             Study.

Source:  Thompson et al. (1991).
  6.7.6   Diurnal Variation
       Evidence exists of a distinct diurnal pattern in outdoor H+ concentrations.  Wilson et al.
  (1991) examined concentration data for H+, NH3, and 804 from the Harvard 24-City Study for
  evidence of diurnal variability (Figure 6-88).  This investigation found a distinct diurnal pattern
  for H+ concentrations and the H+/SC>4 ratio, with daytime concentrations being substantially
                                           6-174

-------
         •o
         c
         (D
         at
         3
         O
         CO
         0)
         o
         E
         c
                                    9  '   '?'   '  ?
                    a Sulfate

                    A Hydrogen Ion
                       20    40    60    80    100   120   140   160   180   200

                                            Hours
        to
        TJ
        c
        CO
        CO
        E
        "to
        q>
        o
        E
        c
3.2

3.0

2.8

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0
-D-Sulfate

~*— Hydrogen Ion
                   0    2   4    6    8   10   12   14   16   18   20  22   24


                                            Hour

Figure 6-88.  Diurnal pattern of sulfate and hydrogen ion at Harriman, TN, weekly pattern

              and daily average.


Source: Wilson etal. (1991).
                                         6-175

-------
higher than nighttime levels.  Both H+ and SO4 concentrations peaked between noon and
6:00 pm. No such diurnal variation was found for NH3. Wilson and co-workers concluded that
the diurnal variation in H+ and SOJ was probably due to atmospheric mixing. Air containing
high concentrations of H+ and SOJ mixes downward during daylight hours when the atmosphere
is unstable and well-mixed.  During the night, ammonia emitted from ground-based sources
neutralizes the acid in nocturnal boundary layer, the very stable lower part of the atmosphere, but
a nocturnal inversion prevents the ammonia from reacting with the acid aerosols aloft. Then in
the morning as the nocturnal inversion dissipates, the acid aerosols mix downward again as the
process begins anew. Spengler et al. (1986a) also noted diurnal variations in sulfate and sulfuric
acid concentrations and suggested atmospheric dynamics as the cause. The diurnal variation in
SOJ has been observed by other workers and discussed in terms of atmospheric dynamics by
Wolff et al. (1979) and Wilson and Stockberger  (1990).
     This diurnal variation in mixing heights and concentrations does not seem to hold at
elevated sites. For example, Pierson et al. (1980a,b,  1989) found no appreciable night/day
difference in aerosol H+ (or NH4+ or SOJ), and  almost no diurnal variation in O3, at two
elevated sites (Allegheny Mountain and Laurel Hill, elevations 838 and 850 m) in southwest
Pennsylvania. They contrasted this behavior with that at lower sites, and particularly with the
concurrent measurements at Deep Creek Lake (Vossler et al., 1989). The differences were
attributed to isolation from ground-based processes at the elevated sites at night.

6.7.7   Indoor and Personal Concentrations
     Several studies have examined indoor concentrations of acid aerosols and personal
monitoring. Brauer et al. (1989) monitored personal exposures to particles (including acidic
 sulfates) and gases in metropolitan Boston in the summer of 1988, and compared these to
measurements collected at a centrally located ambient monitor.  They found that personal
concentrations of acidic aerosols and gases differed significantly from those measured at the
centrally located site. Summer and winter concentrations of acid aerosols and gaseous pollutants
also collected in Boston (Brauer et al., 1991) showed indoor/outdoor ratios of H+ to be 40-50%
of the indoor/outdoor SO4= ratio indicating neutralization of the acid by the higher indoor NH3
levels, which were reported.
                                         6-176

-------
     Indoor, outdoor, and personal acid aerosol monitoring was performed for children living in
Uniontown, Pennsylvania, during the summer of 1990 (Suh et al., 1992).  The indoor, outdoor,
and personal measurements were compared to outdoor measurements collected from a centrally
located ambient monitor. Personal concentrations were lower than corresponding outdoor levels
but higher than indoor levels. Air  conditioning was found to be an important predictor of indoor
H+, while NH3 was found to influence indoor and personal H+ concentrations. Similar results
were obtained in a study of the relationships between indoor/outdoor concentrations of H+ and
NH3 conducted in State College, PA, in 1991 (Suh et al., 1994).
     In a study characterizing H+  concentrations at child and elderly care facilities, Liang and
Waldman (1992) measured indoor and outdoor acid aerosol concentrations. Results from this
study showed that indoor/outdoor H+ and SOJ ratios were comparable to those measured inside
residential buildings. Air conditioner use and indoor NH3 concentrations were again identified
as important determinants of indoor H+ concentrations.
6.8   NUMBER CONCENTRATION OF ULTRAFINE PARTICLES
6.8.1    Introduction
     Recent work has suggested that ultrafine particles may be responsible for some of the
health effects associated with exposure to particulate matter (Chapter 11, Section 11.4). The
hypothesis for explaining a biological effect of ultrafine particles is based on the number,
composition and size of particles rather than their mass (Seaton et al., 1995). This has led to an
interest in the number concentration of ambient particles. This section examines data on particle
number concentration and the relationship between particle number and particle mass or volume.

6.8.2    Ultrafine Particle Number-Size Distribution
     In the context of ambient particles, the term ultrafine particles refers to those particles with
diameters below 0.1 //m.  Ultrafine aerosol size distributions from an urban site at Long Beach,
California (Karch et al., 1987), and from a background site in the Rocky Mountains, Colorado
(Kreidenwies and Brechtel, 1995) are shown in Figures 6-89 and 6-90. Both of these sets of data
were obtained by electrical mobility measurements. For the urban aerosols of Long Beach, the
                                        6-177

-------
    120,000 T
    100,000"
  "  80,000"
   o
  ^*
  Q 60,000"
   o>
   o
  Z 40,000"
  T>
     20,000"
         0.00
            (a)
                                  Long Beach, CA
 •1200-2400
 •1200-1300
 •1400-1500
 •2100-2200
                     0.01
            Particle Diameter (^m)
0.10
            (b)
                                   Long Beach, CA
CO
E
o
*r s-
E
a.
Q. R.
Q 6
_o
T3



-•- 1200-2400
-A- 1200-1 300
-O- 1400-1 500
-0-2100-2200



          2"
          o-
          0.00
-o—o—a—a—a—a—a i o  o«-"
                     0.01
            Particle Diameter (pm)
0.10
Figure 6-89. Aerosol number (a) and volume (b) size distributions from an urban site at
            Long Beach, CA.
                                     6-178

-------
     1.200T
                                 Rocky Mountains, CO
                                                                 11/23/941304
                                                                 11/23/941804
                                                                 11/24/941205
                                           0.1
                                 Particle Diameter (urn)
       0.6T
                                 Rocky Mountains, CO
dV/dlogDp (|jm3/cm3)
0.5'
0.4'
0.3'
0.2'
•' ' -0-11/23/941304
-•-11/23/941804
-0-11/24/941205

                                 Particle Diameter, Dp (urn)
Figure 6-90.  Aerosol number (a) and volume (b) size distributions from a background site in
             the Rocky Mountains, CO.
                                      6-179

-------
number geometric mean diameter can vary from 0.012 ^m to 0.043 //m.  Some of the ultrafme
distributions, such as that shown for the 1,200 to  1,300 PST time period, are bimodal. The
number concentrations were higher in the early afternoon, 1400-1500 PST, as shown in
Figure 6-91. For the background aerosols from Rocky Mountains the number geometric mean
diameter of the ultrafine aerosols was somewhat larger than for Long Beach, with geometric
mean diameters ranging from 0.047 to 0.075 fj.m  for periods without urban influence. A bimodal
character for the ultrafine distribution was also observed for some measurements, as seen in
Figure 6-90.
            90,000-
            80,000"
         E  70,000-
         a.
         °  60,000-
         v  50,000-
         CO
         I  40,000"
         n  30,000 ••
         a.
         o>  20,000-
         n
         1  10,000"
         z
                ol
                 12
14
16
20
22
24
                                            18
                                         Time of Day
Figure 6-91.  Number concentrations as a function of time of day at Long Beach, CA.
     The contrast between urban and background ultrafine aerosol size distribtution is
demonstrated in Figure 6-92, where a change in the wind direction brought transport from an
urban area to the background site at Rocky Mountains. Within a 2-h period, the number
                                        6-180

-------
      60,000 T
                                    Rocky Mountains, CO
   TJ
   z 20,000
   "D
                                                                   12/25/94 1524
                                                                   12/25/941550
                                                                   12/25/94 1648
                                             0.1
                                    Particle Diameter, Dp (Mm)
           3T
                                    Rocky Mountains, CO
              (b)
dV/dlogDp (jjm3/cma
2.5'
2"
1.5-
r
-0- 12/25/94 1453
-*- 12/25/94 1546
-0- 12/25/94 1653

         0.5"
                                             0.1                                1
                                   Particle Diameter, Dp (urn)
Figure 6-92.  Number (a) and volume (b)  size distributions  at the Rocky Mountain  site
             showing an intrusion of urban air.
                                      6-181

-------
concentration increased from 850 cm"3 to 19,000 cm'3, an increase of more than a factor of 20.
In contrast, the volume distribution increased by less than a factor of 5.  The number geometric
mean diameter decreased from 0.052 /urn for the background aerosol to 0.024 //m for the urban
influenced aerosol. For the urban influenced size distributions, over 96% of the particle number
was measured in particles below 0.1 fj,m, while 80% of the particle volume was associated with
particles above that size.

6.8.3   Relation of Particle Number to Particle Mass
     In general, the majority of airborne particle volume and mass is associated with particles
above 0.1 //m, while the highest number concentration of particles is found in particles below
0.1 /^m. This was shown for volume in Figures 6-89 to 6-92 and can be seen for mass in the
recent data collected in the Los Angeles, CA shown in Figure 6-93.  As with the data of Whitby
and Sverdrup (1980), the size distributions of Figure 6-93 show data collected by several
instruments. Physical size distributions were measured with an electrical aerosol analyzer for
particles between 0.01 and 0.4 /zm, and with a laser optical particle counter for particles between
0.14 and 3 /^n.  Additionally, Berner (John et al., 1989,1990) and MOUDI (Marple et al., 1991)
impactors were used to measure the mass size distribution of inorganic ion species and
carbonacous species. These data have been combined (Hering et al., 1996) to give a total mass
distribution from which the number distribution has been calculated  assuming an effective
aerosol density of 1.6 g/cm3 and assuming that the water associated with the aerosol is 15% of
the measured dry particle mass (see McMurry and Stolzenburg, 1989). The optical particle
counter was calibrated with ambient particles, size classified by a differential mobility analyzer.
The ambient aerosol has a lower effective refractive index than the polystyrene latex usually used
for calibration (Hering and McMurry, 1991). No fitting was applied to match the different size
distributions in the region of overlap.
     Figure 6-93 shows the average of distributions collected over six different days in the fall
of 1987 in downtown Los Angeles, as part of the Southern California Air Quality Study. Particle
number distributions emphasize the ultrafine particles, or "nuclei" mode. Volume distributions
place importance on 0.1 to 1 /urn particles which are associated with  the "accumulation" mode.
For this average distribution 88% of the particle number is associated with particles below 0.1
                                         6-182

-------
     125,000

  .•^
 ^  100,000
  o^

  Q"  75,000
  o
  ^
  z   50,000
  "O
      25,000
           0.01
 0.1                  1
Particle Diameter, Dp(um)
                             Impactor  -O— OPC
                         EAA
                     Los Angeles Particle Volume and Mass Distribution
                                0.1                  1                  10
                               Particle Diameter, Dn(um)
Figure 6-93.  Number (a), and volume and mass (b) size distributions from Los Angeles, CA,
            showing comparison of three measurement techniques.
                                    6-183

-------
fj.m, but 99% of the particle volume is from particles above that size. Both the impactor and
optical counter data indicate a weakly bimodal character for the accumulation mode aerosol.
     For unimodal, log normal size distributions, the particle volume Vis simply related to the
particle number N by the relation:
                               V = - D  3exp  -In20 \N
                                   6   *"      2    '}
where D  is the number geometric mean diameter, and ogis the geometric standard deviation.
However, because of the multimodal character of ambient aerosol size distributions, one does not
expect this simple relationship to hold in the atmosphere.  The relationship between particle
number and particle volume was examined for data from the Southern California Air Quality
Study collected at Riverside, CA over 11 days in the summer of 1987, and at downtown Los
Angeles in the fall of 1987 using the methods described above. As shown in Figure 6-94,
particle number concentrations are correlated with the volume associated with particles below
0.1 fj,m, but are not correlated with the total fine particle volume.  Similar results are found for
the data reported from Rocky Mountains, CO and for the data reported by Whitby and Sverdrup
(1980).

6.8.4   Conclusion
     The size distribution measurements of aerosols in urban and continental background
regions indicate number geometric mean diameters which vary from 0.01  to 0.08, with the larger
values found in background regions.  Particle number concentrations may vary from less than
1,000/cm3 at clean, background sites to over 100,000/cm3 in polluted urban areas.  Particle
number concentrations are dominated by the ultrafine or nuclei mode aerosols. In contrast, the
volume (or mass) of fine particles is associated with particles above 0.1 jun, which are associated
with the accumulation mode identified by Whitby and coworkers (Willeke and Whitby, 1975;
Whitby and Sverdrup, 1980). Particle number concentrations are correlated with the volume of
particles below 0.1 //m. The number concentration of ultrafine particles results from a balance
between formation and removal. The rate of removal by coagulation with accumulation mode
                                         6-184

-------
760,000-
740,000-
•T 720,000-
I
f 100,000-
o>
•Q
| 80,000"
| 60,000-
'•5
0. 40,000
20,000
0
0.(
- , \ m
O
'..-
•**
' &B$±*
A
* \ \ \ \
30 2.00 4.00 6.00 8.00
Volume < 0.1 urn (um3/cm3)
160,000-
140,000-
^120,000-
i: 7 00, 000-
| 80,000-
•§ 60,000-
'•5
a 40,ooo-
20,000'
(
\(t» '
0
•
• •
'.
• •
• • •
J • • ••
1 o tf •
• •
1 '° "o« o D
i sffr * i
? 50 100 150 200



• Los Angeles
O Riverside
A Whitby Background
0 Whitby Urban
A Rocky Mountains







• Los Angeles
D Riverside
A Whitby Background
0 Whitby Urban
A Rocky Mountains

                         Volume < 2.5um  (um3/cm3)

Figure 6-94.  Relationship between particle number and particle volume ([a] volume <0.1 and
            [b]<2.5,
                                      6-185

-------
particles will increase as the number (and mass and volume) of accumulation mode particles
increases.  Therefore, a correlation between number and accumulation mode volume or mass on
a short term (e.g., hourly basis), would not be anticipated. However, as suggested by the
differences in particle number concentrations from 850 cm'3 at a remote site in the Rocky
Mountains, to 19,000 cm"3 in air transported from an urban area, to in excess of 105 cm"3 in
polluted urban areas, a correlation, between the total number concentration and the total fine
article mass or volume, might be expected if comparisons were made over longer periods, e.g.
days.  However, no such studies have been  done.
6.9  AMBIENT CONCENTRATIONS OF ULTRAFINE METALS
6.9.1   Introduction
     Nucleation theory (Seinfeld, 1986) indicates that ultrafine particles will consist of materials
that have very low vapor pressure but which will, at some time, exist in significant vapor
concentrations.  This could be the result of rapid formation of a condensible vapor from chemical
conversion of a gas or the formation of a vapor at relatively high concentrations during
combustion. Very small particles, because of their high curvature, have a higher vapor pressure
than larger particles. This is known as the Kelvin effect and becomes increasingly important as
the particle size decreases below 0.1 /^m in diameter. The critical size, at which a particle will
grow instead of evaporating, depends on the saturation ratio, the ratio of the vapor pressure of the
particle, pA, to the vapor pressure over a flat surface, p^) (S = PA\PA)» me surface tension; and
the molar volume of the condensed phase. Thus, materials such a elemental carbon, formed in
flames, or metal (or metal compound) vapor, formed during  combustion, are likely candidates
for ultrafine particles. Sulfuric acid can also form ultrafine particles (Weber et al., 1995) but
whether it nucleates into ultrafine particles or condenses on existing particles depends on the
balance between the formation rate of sulfuric acid and the surface area of preexisting particles
(Seinfeld,  1986).
     Thus, ultrafine aerosols may be primary, formed from vapor generated during combustion,
or secondary, formed from vapor generated by chemical reactions in the atmosphere.  Because of
their small size, ultrafine particles diffuse rapidly and are lost by deposition to surfaces or by

                                         6-186

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growth into larger particles by coagulation. Ultrafine particles also serve as nuclei for
condensation of vapors. Thus, ultrafine particles grow rapidly by coagulation and
condensation, into the accumulation mode.  For these reasons, the mass of ultrafine particles
in the ambient atmosphere is generally much smaller than that of the accumulation mode,
where removal rates of particles reach a minimum in non-cloud conditions.  The result is that
in ambient conditions, the ultrafine mode is generally indistinct or absent from mass or volume
profiles of aerosol particles versus size.  However, a distinct ultrafine mode below 0.1 //m
diameter has been observed in quasi-ambient samples taken close to combustion sources. In
these cases, the distinct ultrafine particle mode is referred to  as the nuclei mode (Whitby,
1978).
     While there is consensus that ultrafine metal particles are produced and emitted into the
atmosphere, there is little information on ambient concentrations  of ultrafine metals. The few
direct measurements available can be extended with some confidence using indirect methods;
i.e., from particle counting techniques that have size information but no chemical information,
or from filter collection methods that have limited size information but detailed compositional
information. Nevertheless, it is clear that more data on ultrafine  metals are urgently needed to
gain confidence in the spatial and  temporal concentration profiles of this key atmospheric
component.

6.9.2   Formation  of Ultrafine Particles
     Nucleation theory establishes that high temperature processes are generally required to
form ultrafine metallic  aerosols.  Such processes are usually anthropogenic, although natural
fires, volcanic eruptions, and other such events can contribute to  ultrafine transition and heavy
metals in some circumstances. Table 6-6, taken from Seeker (1990), gives the vaporization
temperature of EPA-regulated metals (Federal Register, 1986) as  a function of temperature,
with and without chlorine available in the combustion process.
     Note the dramatic shift in temperature for several elements, including lead, for the
chlorine-rich combustion scenario. A similar process has been used to prevent lead from
coating surfaces in internal combustion engines using leaded gasoline.  The process used
                                         6-187

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   TABLE 6-6. REGULATED METALS AND THE VOLATILITY TEMPERATURE
With No Chlorine
Metal
Chromium
Nickel
Beryllium
Silver
Barium
Thallium
Antimony
Lead
Selenium
Cadmium
Osmium
Arsenic
Mercury
Volatility
Temp. (°F)
2935
2210
1930
1660
1560
1330
1220
1160
605
417
105
90
57
Principal
Species
CrO2/CrO3
Ni(OH)2
Be(OH)2
Ag
Ba(OH)2
T1203
Sb2O3
Pb
SeO2
Cd
OsO4
As2O3
Hg
With 10% Chlorine in Waste
Volatility
Temp. (°F)
2930
1280
1930
1160
1660
280
1220
5
605
417
105
90
57
Principal
Species
CrO2/CrO3
NiCl2
Be(OH)2
AgCl
BaCl2
T1OH
Sb203
PbCl4
SeO2
Cd
OsO4
As2O3
Hg
Source:  Seeker (1990).


chlorine and bromine-containing additives to form compounds such as PbBrCl which are
gaseous at combustion temperatures but form ultrafine particles after leaving the vehicle.
     Numerous theoretical and laboratory studies have shown that the typical size of metals
derived from combustion is ultrafine (Friedlander, 1977; Senior and Flagan, 1982; Seeker,
1990).  Analysis of particles from coal combustion by Natusch and Wallace, 1974 and Natusch
et al., 1974 showed an additional aspect.  There is a tendency for the condensing metal vapors
to form relatively uniform thickness surface coatings on more refractory particles present in
the combustion effluent stream.  If the particles upon which the metals coat themselves are
crustal, as in coal fly ash, this results hi a final particle whose enrichment factor compared to
crustal averages depends upon the initial size of the refractory particle—minor for large
particles, extreme for ultrafine particles (Davison et al., 1974).  This result also places the
(potentially) toxic metals on the biologically-accessible surface.
                                        6-188

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       Thus, the presence of metals in a combustion process such as incineration of biological
  and chemical wastes or treatment of contaminated soils poses a problem. Raising the
  temperature of combustion high enough to completely (> 99.99%) destroy the biological and
  chemical species will also enhance the volatilization of metallic components hi the feed stock,
  requiring more efficient removal methods for ultrafine and accumulation mode metals.
  Figure 6-95 shows the enhanced volatilization of metals as the combustion temperature is
  raised from 1000 °F (540 °C) to 1800 °F (980  °C) (Seeker, 1990).
                                              As
        40
        30
    E
    .c
    o
    ,5   20
    0)
        10
                     1000F
                                                 Cd
Pb
       1800F
   Sb
                                                          Cu
                                                             Zn
                                                               Cr
Figure 6-95.  Impact of treatment temperature on the enrichment of metals in the fly ash
             after the thermal treatment of soils from a Superfund site.
Source: Seeker (1990).
                                         6-189

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     The combustion effluent can be partitioned into three components (Seeker, 1990;
Barton et al., 1990); emitted (as fly ash), captured (assuming there is an attempt to capture
fine particles), and collected in the bottom ash.  Assuming no particle removal equipment is in
place on the combustion process, emitted particles will include both the "emitted"  component
and most of the "captured" component.  In an uncontrolled incineration facility, 96% of
mercury, 88% of cadmium, 58% of lead, and 11% of copper might by emitted into the
atmosphere. If control is attempted, the capture efficiency is only 25% for mercury, but is
better for most other metals, ranging from 86% for cadmium to 91 % for copper (Barton
et al., 1990). In addition, the chemical state of the metals in the ultrafine mode can vary from
the more toxic phases (for example, arsenite versus arsenate) as a function of combustion
conditions (Chesworth et al.  1994). Thus, we must expect that ultrafine metallic components
will be emitted from high temperature processes in both toxic and less toxic forms.

6.9.3   Techniques for Collecting and Analyzing Ultrafine Metals
     Relatively little information exists on concentrations of ultrafine metal particles in
ambient air samples away from combustion sources. There are many reasons.  The ultrafine
mode falls off rapidly away from the combustion source, due to the rapid migration of some
types of ultrafine particles into the accumulation mode, and increased dispersion as one moves
away from the source.  Many sources of ultrafine metals use tall exhaust stacks, which
enhances dispersion.  The largest of the ultrafine particles can overlap the smallest particles of
the much more abundant accumulation mode, roughly 0.2 to 0.7 //m aerodynamic diameter.
Particles must be size-separated using a device with a sharp cut point, ususally a multistage
physical impactor, that entails problems  in particle collection and analysis.  Since ultrafine
particles may be hard and dry, adhesive  coatings are essential in order to avoid particle bounce
in the impactors. Particle bounce typically translates coarser particles onto finer stages,
contaminating the ultrafine particles with the enormously more abundant coarser particles.
Finally, one can collect only a few monolayers of particles (at most) on the adhesive stages
before particle bounce becomes important, assuming the particles themselves are not "sticky".
A few monolayers of particles of 0.1 //m diameter amounts to only about 50 /ug/cm2 of total
deposit. If one then desires to perform minor or trace elemental analysis of the deposit, one is
                                        6-190

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then faced with analytical requirements that reach picogram (10'12 gm) sensitivities.  This
clearly limits analytical options.
     For these reasons, much of the data available on ultrafme particles does not depend on
compositional analysis. Most information on the presence of ultrafme particles is derived
from particle counting techniques such as the Electrical Mobility Analyzer (EMA), in
situations in which the source is well known (source-enriched).  This was the method
pioneered in the 1972 ACHEX studies of Los Angeles (Whitby, 1978). Particle counting
devices do not normally result in collection of ultrafme particles in a manner suitable for
compositional analysis, although some of the devices ("particle classifiers") could be modified
to provide samples for subsequent compositional analysis,  if desired. The same can be argued
for devices such as diffusion batteries, but to date little has been done along this line in
ambient conditions.
     Integrated samples of fine particles can be collected on substrates suitable for analysis.
While some optical information is available as one approaches the ultrafine size, most optical
techniques do not work in the ultrafine size range, which is well below the wavelength of
light. A Scanning Electron Microscope (SEM) beam can still resolve ultrafine particles
although some details are lost.  The ultrafine particle distribution can then be derived by
particle counting techniques, either manual or automated, and metal composition can be found
by X-ray analysis of the single particles.  The enormous gain in signal to noise ratio by
selecting individual particles offsets the loss of X-ray sensitivity (typically parts per thousand)
caused by use of the  electron beams to induce the X rays.  SEM and electron microprobe
analyses rarely achieve any better than one part per thousand sensitivity.  However, for single
particles, this  is often enough to classify them by source. Proton microprobes are, at present,
not quite able  to operate in the 0.1 //m diameter region, but can perform Proton Induced X-ray
Emission (PIXE) analysis to one part per million by mass on single particles as small as
0.3 /mi (Cahill, 1980).
     Impactors are designed to separate particles by aerodynamic size in such a way as to
allow compositional analysis.  Yet here, too, ultrafine particles pose problems.  First, most
impactors can not operate effectively below 0.1 ^m.  The Stokes number for separation of a
0.1 //m diameter particle from an air stream requires either extremely high jet velocities,
                                         6-191

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extremely low pressures in the gas stream, or both.  While such performance can be achieved
in a physical impactor, most impactors used for ambient particle collection in the 1970's and
early 1980's did not possess this capability.  For example, the very popular cyclones and
virtual impactors are ineffective below about 0.5 //m diameter. The Lundgren-type impactors
widely used in California studies (Lundgren,1967; Flocchini et al., 1976; Barone et al. 1978)
used 0.5 fj.m as the lowest cut point. Everything smaller was collected on a filter.  The
Battelle-type samplers (Mercer, 1964) favored by other groups (Van Grieken et al., 1975) used
a lowest cut point of 0.25 //m diameter. Thus, while both these units generated copious
information on aerosol composition, they could not separate ultrafine aerosols from
accumulation mode aerosols.
     In the mid-1980's four new impactors were developed capable of providing information
on the composition of particles near 0.1 fj.m diameter: the Low Pressure Impactor, (LPI)
(Hering et al., 1978), the Berner Low Pressure Impactor (BLPI) (Berner and Ltirzer, 1980;
Wang and John, 1988), the  Davis Rotating-drum Unit for Monitoring impactor,  (DRUM)
(Cahill et al., 1985; Raabe et al., 1988), and the Multiple Orifice Uniform Deposit Impactor
(MOUDI) (Marple et al., 1986; Marple et al., 1991).  Battelle-type impactors were also
modified to add two size cuts below 0.25 /urn diameter.  However, unlike the other four units,
no certification of performance has been published to date on its performance in the ultrafine
region.  The development of reliable, clean adhesive coatings such as Apiezon™-L grease was
also a major advance in the  field (Wesolowski et al., 1977; Cahill, 1979), allowing separation
of abundant soils from ultrafine size ranges even in dry, dusty conditions. For nominally
PM-10 soils,  for example, a ratio of coarse to ultrafine soils was measured at 6,600:1  at a
temperatures  above 30 °C and relative humidity below 20% (Cahill et al., 1985).
Performances and specifications of all these units is  included in a recent review paper  (Cahill
and Wakabayashi, 1993)
     It is important to mention, however, that the motivation for development of this ultrafine
capability was not for extensive studies of ultrafine metals, but rather to get a more complete
picture of the accumulation mode behavior of sulfates, nitrates, organics, and other major
components of the fine aerosol mix. Thus, compositional analysis was often limited to these
species even when suitable samples had been collected.  For example, many LPI samples were
                                         6-192

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collected on stainless steel substrates, ideal for combustion analysis of sulfur, but unsuitable
for analysis of transition metals by X-ray techniques.

6.9.4   Observations of Very Fine Metals
     Few techniques exist for collecting particles below 0.1 yum diameter for chemical
analysis. No compositional data was found for particles below 0.1 /urn diameter.  However,
since ultrafine particles rapidly grow into the accumulation mode, it may be assumed that
measurements of the small-size tail of the accumulation mode provide some insight into the
composition of the ultrafine particles.  Thus, the concentration of metals in the smallest
available size-cut will be examined. In order to avoid problems with definitions, particles in
the smallest size-cut, which may extend to diameters above 0.1 ^m, will be called "very fine"
and ultrafine will be reserved for particle distributions with a mass mean diameter below
0.1 /um.

6.9.4.1   Stack and Source-Enriched Aerosols
     Observation of very fine metals in source or source-enriched situations lessens problems
with dilution of the sample and identification of the source.  This eases both particle collection
and analysis.  Figure 6-96 shows the results of such a study on a coal fired power plant
(Maenhaut et al., 1993) using the Berner Low Pressure  Impactor (BLPI).  The extreme
volatilization of selenium is clearly seen, which is also confirmed in aircraft sampling of
power plant stacks. Note, however, that the enrichment factor, as a function of particle size,
for both sulfur and its chemical  analog selenium.  More refractory elements, on the other
hand, are strongly enhanced in the very fine particles as compared to coarser  modes.
     The BLPI cuts are as  follows:  Stage number 1-0.011 ^m diameter, 2-0.021, 3-0.032,
4-0.07, 5-0.17, 6-0.30, 7-0.64,  8-1.4, 9-2.6, 10-5.5,  11-10.7 ^m. All are for particle density
2.45 g/cm3 and a temperature 120 °C, the conditions of stack sampling in the coal fired power
plant. Both these figures were normalized to Earth crustal averages.  Thus, even a two order
of magnitude rise in the normalized concentration may not result in a visible "combustion
mode" since the mass  of soil falls very rapidly as one moves towards very fine particles.  This
is exactly what is predicted by the results of Natusch et al. (1974). Thus, source testing
                                         6-193

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      1,000
      8 10
      •o
      O
      Z
       0.1
                                   •e-
                                   0.1 xSe
                                   -»-
                                   S
                                   -e-
                                   Ca
                                   -*-
                                   At
                                   •+•
                                   Si
                                   -*-
                                   K
1,000 a
           123456789  10
                 Stage number
       23456789 10
            Stage number
 Figure 6-96. Average normalized concentrations as a function of stage number, for
              selenium (Se), sulfur (S), calcium (Ca), aluminum (AI), silicon (Si),
              potassium (K), molybdenum (Mo), tungsten (W), nickel (Ni), and chromium
              (Cr) for five BLPI samples from a coal fired power plant.  The smallest size
              mode is to the left, Stage number 1, 0.011 to Stage number 11, 10.7 //m
              diameter.  Normalization is to average crustal composition.

 Source: Maenhaut et al. (1993).
confirms nucleation theory and the laboratory studies and predicts emissions of metals in the
very fine particle size range from many types of high temperature combustion sources.


6.9.4.2  Ambient Aerosols

Direct Observations
     Because of the difficulties in sampling and analysis, there is relatively little information
on the concentrations of very fine metal particles in ambient air. Some quantitative
determinations of ambient concentrations have become available in the past 15 years, however,
                                        6-194

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generally as a result of a number of short but intensive aerosol studies. Examples include the
extensive studies near the Grand Canyon National Park (NP) in 1979 (Macias et al., 1981) to
the Mohave Studies near the Grand Canyon NP in 1993 and the Southern is California Air
Quality Study (SCAQS) in 1985-1987 (Hering et al., 1990; Cahill et al., 1990; Cahill et al.,
1992a);  studies at Shenandoah NP in 1991 (Cahill and Wakabayashi,  1993) and Mt. Rainier
NP in 1992 (Malm et al., 1994a; Cahill and Wakabayashi, 1993), and others. While almost
all of these studies used several different types of impactors with ultrafine capabilities,
relatively few were analyzed for trace metal content.
     An example of very fine particles persisting in ambient air is shown in Figure 6-97 using
data collected at Grand Canyon NP 1984 (Cahill et al., 1987).  The  very fine particles behave
independently from the accumulation mode, in fact often showing a  net anti-correlation in
concentrations of sulfur as well as dramatic differences in metals (Table 6-7). The very fine
particles in Table 6-8 can be attributed to non-ferrous metal smelting activities in the region
(Eldred et al., 1983;  Small et al., 1981), which puts the nearest important sources a hundred
miles away from the  sampling site. The completely different behaviors of the accumulation
and very fine particles in this arid site also show that mis-sizing by particle bounce is not
significant.
     Table 6-8 presents a summary of more recent data for major EPA-regulated metals (lead,
nickel) and other metals, at Long Beach, CA, December in 1987 (Cahill et al., 1992a) and at
Shenandoah NP in 1991 (Cahill and Wakabayashi, 1993).  The elements span the range from
refractory metals like nickel and vanadium to metals with low melting temperatures such as
zinc and lead. These data were all taken with the same unit, the Davis Rotating-drum Unit for
Monitoring (DRUM) using greased stages and a single orifice impactor (Cahill et al., 1985).
The last two stages were modified form the Gand Canyon configuration as a result of
theoretical and laboratory studies (Raabe et al., 1988), yielding 0.069 to 0.24 //m for Stage 8,
and 0.24 to 0.34 fj.m diameter for Stage 7.
     The DRUM data were used for  several reasons: the DRUM'S slowly rotating greased
stages have a documented ability to handle  large amounts of coarse,  dry soils without
contaminating the very fine stages (Cahill et al., 1985; Cahill and Wakabayashi, 1993), the
elemental data are of unprecedented sensitivity for ambient very fine trace metals (PIXE and
                                         6-195

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   400
   200


fc  400
15
E  200
o
        400
     
-------
 TABLE 6-7.  COMPOSITION OF THE AEROSOLS PRESENT AT GRAND CANYON
 NATIONAL PARK IN THE SUMMER OF 1984 FOR THE SULFATE EPISODES OF
       AUGUST 15 (ACCUMULATION MODE, STAGE 6) AND AUGUST 16
                      (VERY FINE PARTICLES, STAGE 8)
Elements
Sodium
Silicon and Aluminum
Sulfur
Chlorine
Potassium
Calcium
Titanium
Vanadium
Iron and Nickel
Copper
Zinc
Arsenic
Bromine
Lead
Stage 8,
0.088-0. 15 Aim
(ng/m3)
420
8
204
208
59
150
2
2
2
100
931
13
2
63
Stage 6,
0.24-0.34 /zm
(ng/m3)
10
6
392
5
3
5
4
3
2
1
2
2
2
4
Source: Cahill et al. (1987).


source. In order to obtain sulfate, multiply sulfur by 3.0. These average values, however,
obscure a great deal of structure as a function of time.
     The variability as a function of size and tune is shown in Figure 6-98 for nickel,
selenium, and lead in Long Beach, CA as part of the SCAQS studies of 1987. By 1987, much
of the lead was no longer automotive, and there are significant changes hi the very fine
fraction over periods of four to twelve hours. Note the behavior of very fine metals; almost
total absence for selenium, partial absence for nickel, and constant presence for lead.  Almost
all elements at almost every site show similarly complex behavior.  Thus, the summary of
Table 6-8 can include only the most basic types of information on fine and very fine metals in
the atmosphere.
                                     6-197

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        TABLE 6-8. MEASUREMENTS OF FINE AND VERY FINE METALS
Site Name
Duration
Frequency
Dates
Particle
Aerodynamic
Diameters
Very Fine
Particles




Accumulation Mode



Long Beach, CA
6 days
6 samples/day
(11, 12/87)
Mean detectable
limit - 0.3 ng/m3


Shenandoah NP
21 days
6 samples/day
(9/91)
Mean detectable
limit - 0. 15 ng/m3



(Dae, Mm)

Element

Vanadium
Nickel
Zinc
Selenium
Lead
Sulfur8

Vanadium
Nickel
Zinc
Selenium
Lead
Sulfur3
From
To
0.069
0.24
Maximum
Values
(ng/m3)

6.6
3.4
51
MDL
199


1.2
1.2
3.8
2.7
50

From
To
0.069
0.24



2.5
1.3
17.6
MDL
71.4
200

0.24
0.58
1.42
0.14
5.38
334
From
To
0.24
0.34

Mean

6.1
4.4
46.3
0.32
47.6
250

0.67
0.48
2.16
0.11
5.49
929
From
To
0.34
0.56

Values (ng/m

10.5
7.7
From
To
0.56
1.15

3)

12.2
4.5
140.4 189.4
3.0 1.4
59.9
350

0.52
0.13
2.60
0.52
3.01
1235
69.9
500

0.30
0.03
1.92
0.35
10.87
1727
From
To
1.15
2.5



8.6
0.5
39
0.65
25.4
250

0.80
0.01
1.66
0.14
16.06
101
"Estimated from graphs.
Source:  Cahill et al. (1992a, 1996a).


   In addition to the limited US data, comparison data have also become available from
foreign sources such as from the Kuwaiti oil fires (Reid et al., 1994) and a study in Santiago,
Chile (Cahill et al., 1996). While the former is a unique situation, the Santiago data are
                                       6-198

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              'o.o-
                Dec. 10
                                    Long Beach, CA Nickel
                                 Dec. 11
                                                   Dec. 12
                                                                    Dec. 13
                                       Selenium
                Dec. 10
                                 Dec. 11
                                                   Dec. 12
                                                                    Dec. 13
                                         Lead
               0.0
                Dec. 10
Dec. 11
                                                   Dec. 12
                                   Dec. 13
Figure 6-98. Concentration, in micrograms per cubic meter, of fine and very fine metals
             (nickel, selenium, and lead) in Long Beach, CA, December 10 through 13,
             1987, in 4-h increments. Stage 8 is very fine, 0.069-0.24 //m; then 0.34,
             0.56, 1.15, 2.5 /urn aerodynamic diameter for the upper size-cut.

Source: Cahill et al. (1992a).
                                        6-199

-------
especially useful since leaded gasoline is still routinely used in Chile and other countries,
generating data impossible to obtain hi the United States.  Table 6-9 summarizes some of these
data for a refractory element, nickel, and a volatile metal, lead. However, the full data set
includes 450 samples of four to six hours duration, each analyzed in five fine size fractions,
generally with about 20 elements found in each fraction, or approximately 40,000 individual
elemental values.
              Some general observations can be made from the data; first, there is an
enormous variation in the concentration of fine and very fine metals, sometimes spanning 4 or
5 orders of magnitude in a few days. Such behavior can be modeled by plumes of particles
that sweep over the site episodically, as opposed to area or regional sources. Second, one
often finds a mixture of very fine particle or nuclei mode behavior as well as accumulation
mode behavior. However, these modes may be physically separated in time.
              Lead in the United States follows a variety of very  different patterns. In the
rural samples, lead tends to be bimodal, with a coarse component above 1.0 fj,m diameter and
a very fine component below 0.34 //m diameter.  This can be modeled by a very fresh fine
particle mode and a coarser mode associated with resuspended soil. Urban sites, however,
both in the U.S. and in Santiago, show lead in very fine particles  as well as in the
accumulation mode.  Lead in resuspended soil is  found in the coarse particle mode.
              Other metals at Long Beach, however, lack a distinct concentration of very fine
particles all the time (selenium) or part of the time (nickel), merely possessing an accumulation
mode that closely mimics sulfates and other secondary species (Cahill et al.,  1990). It is well
known that nickel and vanadium were derived from high temperature combustion sources, and
since each is highly refractory, they will occur primarily as very fine particles near the source.
Thus, the similarity between the distributions of these elements and less refractory elements
such as zinc can be understood through a rapid condensation and coagulation of the abundant
secondary species around these metals, leading to an accumulation mode distribution as the
secondary acidic species hydrate.  Clearly, such processes are weaker at dry sites such as the
arid west in summer (Table 6-8).  On the other hand, Shenandoah NP has a mixture of urban
and rural behavior, with occasional sharp peaks of very fine metals (nickel) superimposed on
an accumulation mode behavior (sulfur, selenium) with some  coarse contribution (lead,
                                         6-200

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        TABLE 6-9. MEASUREMENTS OF FINE AND VERY FINE METALS
                              (LEAD AND NICKEL)
Site
Duration
Frequency
Dates Element





Long Beach Lead
6 days
4 samples/
day (11/87) Nickei

Shenandoah NP Lead
21 days
6 samples/
day (9/91) Nickei

Mt. Rainier NP Lead
28 days
6 samples/
day (7, 8/92) Nickei

Santiago, Chile Lead
14 days
6 samples/
day (9/93)
Kuwait Lead
14 days
4 samples/
day(6/91) Nickel

Particle
Aerodynamic
Diameters



(Dae, //m)

Mean
Maximum
Mean
Maximum
Mean
Maximum
Mean
Maximum
Mean
Maximum
Mean
Maximum
Mean
Maximum

Mean
Maximum
Mean
Maximum
Very
Fine
Particles
From
To
0.069
0.24
ng/m3
71.4
199
1.3
3.4
5.4
50
0.58
1.2
2.3
6
Always less
MDL
101
920

429.9
2580
1.5
5
Accumulation
From
To
0.24
0.34
ng/m3
47.6
95
4.4
11.4
5.5
20
0.48
1.6
6.5
15
From
To
0.34
0.56
ng/m3
59.9
129
7.7
15.0
3.0
16
0.13
0.8
2.0
21
From
To
0.56
1.15
ng/m3
69.9
164
4.5
13.4
10.9
70
0.03
1.0
3.4
14
Mode
From
To
1.15
2.5
ng/m3
25.4
58
0.5
3.7
16.1
130
0.01
0.14
6.7
29
than MDL
0.4
53
340

154.2
580
2.5
18
0.8
38
320

84.7
128
4.3
11
0.4
108
640

44.7
86
3.7
8
0.7
41
270

38.1
70
6.0
9





MDLa
0.45

0.22

0.2

0.09

0.5

0.07

8


0.35

0.22

aMDL = minimum detectable limit at 95% confidence level, in nanograms per cubic meter

Source: Cahill et al. (1992a,b, 1996a), Malm et al. (1994a), Reid et al. (1994), Cahill and Wakabayashi (1993).

                                       6-201

-------
vanadium).  Only through a detailed study of meteorology and knowledge of emission sources
can such ambient behavior be understood.

Indirect Methods
     Lacking a large body of direct data on very fine metallic aerosols, there are indirect ways
to increase our knowledge of such aerosols;
     1.  Combustion studies have established the formation mechanism of very fine metallic
        aerosols, and,
     2.  Considerable ambient data exist that, when combined with known combustion
        processes, yield estimates for the concentration of very fine metallic aerosols by time
        and locations.
     3.  In conditions of low ambient concentrations of particles and low humidity, very fine
        particles have been shown to persist for many hours. (Cahiil et al., 1985).
     Thus, the numerous observations of fine (Dp  < 2.5 //m) metallic aerosols in low
humidity conditions can yield estimates of the presence of such metals in the very fine particles
and set upper limits on their concentrations. The relatively  small number of actual
measurements can then serve as tests or as confirmation  of our level of understanding of these
biologically  important aerosols.  As an example, Figure  6-99 shows concentration profiles of
sulfur, selenium, zinc, and arsenic, all of which can occur as very fine particles in the western
United States. Arsenic and zinc are annual averages, March, 1993 to February, 1994, while
the sulfur  (for sulfate, times 3.0) and selenium are for summer,  1993.  This was done to
exhibit the correlation of these elements, which are chemically akin, during  the eastern U.S.
sulfate maximum each summer.  The regional nature of the  elements is  very evident, as are
certain strong sub-regional sources such as the copper smelter region of Arizona and New
Mexico (arsenic).
     The  non-urban values shown in Figure 6-99,  which are derived from the cleanest areas of
the United States, are surprisingly relevant to urban areas in the same region for some of the
species. Table 6-10 compares major and minor fine elements at Shenandoah NP, where there
are detailed measurements of particle size, and Washington, DC, where such size information
is lacking. Summer 1993 is the comparison period. Finally, two western sites are compared,
both downwind of Los Angeles; San Gorgonio Wilderness,  and  Grand Canyon NP.
                                         6-202

-------
ON

fc
O
UJ
                     Arsenic
                   Oonafl Notional Park  OJO7
                   Hatoakala Not. Pan  QJ37

                             '    ai*
                                                                                               Selenium
    Hadaral Park 0.03

limaftliillll Not. Pw« Q.07

Vlrqirt »IOIMtt M>   0.21
      Figure 6-99.  Patterns of zinc, arsenic, sulfur, and selenium in the United States.

-------
     TABLE 6-10.  COMPARISON OF SELECTED SPECIES AT SHENANDOAH
          NATIONAL PARK; WASHINGTON, DISTRICT OF COLUMBIA;
               SAN GORGONIO WILDERNESS, CALIFORNIA; AND
           GRAND CANYON NATIONAL PARK DURING SUMMER 1993
                          Shenandoah     Washington,      San Gorgonio      Grand Canyon
Concentration (Mg/m3)	National Park	DC	Wilderness	National Park
Mass-PM10                  31.00         34.90             21.70           9.37
Mass-PM25                  22.50         26.50             10.30           4.50

Composition - PM2 5
Ammonium sulfate              11.80         14.60              2.55           1.09
Ammonium nitrate               0.40           1.47              4.44           0.25
Organic matter                  2.84           5.42              3.88           1.22
Soil                          1.41           1.55              0.86           0.63
Trace compositon (ng/m3)
Nickel                        0.24           0.97              0.18           0.09
Copper                       1.06           3.37              0.76           0.30
Zinc                         7.93         13.90              3.72           0.63
Arsenic                       0.22           0.56              0.16           0.18
Selenium                      1.58           2.48              0.44           0.18
Bromine                      2.14           4.18              3.67           2.11
Lead                         2.17           4.48              1.36           0.51
Bio-smoke tracer                8.33        <  2.00             10.00           32.30
(non-soil fine potassium)

Optical Absortion               19.60         41.90             13.90           5.40
   (b(abs), lO^m'1)

Source:  Malm et al. (1994b).
Inhalation of Very Fine Metals
     An extensive literature exists on the deposition of fine metals in the human lung, much of
which was derived from laboratory studies, some using radioactive tracer isotopes. But an
example of one of the few direct measurements of lung capture of ambient very fine metals is
found in Desaedeleer et al. (1977) and shown in Figure 6-100. The  lower cut point is only
0.25 /urn, but even so, the increased capture efficiency of the lung for very fine and very fine
particles is clearly shown.
                                       6-204

-------
       *-*
        C
        g>
       !5

        E
       ^

       •o
       j>
        n
       c.
        x
o

01
            0.2
       •2   0.1
        (0
0>
o
c
o
O
           0.05
           0.02
                    A A  Pb

                    • D  Br

                    •    Cl
                     6
                       543

                          Particle Size Class
                                                                0

                                                                20


                                                                40



                                                                60
                                                                80
90

92


94



96
                                                                98
                                                                     c
                                                                     o

                                                                     'w
                                                                     o
                                                                     Q.
                                                                             0)
                                                                             O

                                                                             Q>
Figure: 6-100.  Apparent deposition of automotive lead aerosol in the respiratory tract of

               one of the authors as determined by cascade impactor and Proton Induced

               X-ray Emissions (PIXE), as a function of aerodynamic diameter for >4,4 to

               2,2 to 1,1 to 0.5,0.5 to 0.25, and < 0.25 //m particles of size classes 1

               through 6, respectively. Extension of the curve to particles of diameter

               >2 fj.m (classes 2 and 1) is supported by separateexperiments using chalk

               dust aerosol.


 Source: Desaedeleer et al. (1977).
 6.9.5      Conclusions


      There are few data on ambient concentrations of ultrafine metals.  The few direct


 measurements can be extended with some confidence using indirect methods; i.e., particle


 counting techniques that have size information but no chemical information, or filter collection


 methods that have limited size information but detailed compositional information.
                                         6-205

-------
Nevertheless, it is clear that more information is needed on the size and concentration and the
spatial and temporal concentration profiles of ultrafine metal particles.
     Ultrafine metals are produced by a wide variety of anthropogenic activities and emitted
into the ambient air. Ambient concentrations of such metals have been seen not only in urban
settings but also at the cleanest sites in the United States. Concentrations are highly variable
as a function of site and time.  While ultrafine metals have been seen to persist for many
hours, or more, in the clean, dry environment of the arid west, they appear to be rapidly
transformed into the accumulation mode in polluted urban or humid rural sites.
6.10 FINE AND COARSE PARTICULATE MATTER TRENDS
      AND PATTERNS
     Data for characterizing PM10 are available from a number of AIRS sites across the country.
However, data for characterizing PM2 5 and PM(10_2 5) as well as PM10 are not readily available.
As discussed in 6.3.1.7, data for PM2 5 and PM(10_2 5) have been obtained at sites in the
IMPROVE/NESCAUM networks. However, these sites are located in uninhibited areas.
Measurements suitable for determining trends and patterns of PM2 5 and PM(10_2 5) in populated
areas are available from only a few sites.
     Most such data have been obtained with dichotomous samplers which measure PM2 5 (an
indicator of fine mode particles) and PM(10.2.5) (an indicator of the coarse fraction of PM10).
These two fractions may be added together to give PM10. PM2 5 is sometimes referred to as
fine and PM(10_2 5) as coarse although it is understood that PM2 5 will contain that fraction of
the coarse mode PM below 2.5 //m diameter and neither PM10 nor PM(10_2 5) will contain that
portion of the coarse mode above 10/^m diameter.  Sources of PM2 5 (fine) and PM(10.2 5)
(coarse) data include EPA's Aerometric Information Retrieval System (AIRS) (AIRS, 1995),
IMPROVE (Eldred and Cahill, 1994; Cahill, 1996), The California Air Resources Board
(CARB) (CARB,  1995), the Harvard Six-Cities Data Base (Spengler et al.,  1986b; Neas,
1996), and the Harvard Philadelphia Data Base (Koutrakis, 1995).  The Inhalable Paniculate
Network (IPN) (IPN, 1985; Rodes and  Evans, 1982) provides TSP, PM15 and PM2 5 data with
only a small amount of PM10 data.
                                       6-206

-------
     Data suitable for characterizing the daily variability in PM2 5 and PM10 are available
from only one site in southwestern Philadelphia.  The National Weather Service provides daily
observations of visual range, which when suitably treated, can provide an indication of fine
mode particle concentration. The Harvard Six Cities study obtained data for PM2 5 and PM15
every other day for several years. The California Air Resources Board operates about twenty
sites that collect PM2 5 and PM(10_2 5) data with a sampling frequency of every sixth day.
Every sixth day data for a few sites may be found in AIRS.  Because of the small number of
data sets for PM2 5 and either PM(10.2 5) or PM10 levels detailed intercomparisons of the
behavior of these aerosol size fractions in different regions of the United States cannot yet be
made.  Data for characterizing the daily and seasonal variability of PM2 5, PM(10_2 5), and
PM10 will be discussed in 6.10.1, the longer term variability (i.e.,  trends) of PM2 5, PM10_2 5,
and PM10 will be discussed in 6.10.2,  and the interrelations and correlations among the
various PM components and parameters will be discussed in 6.10.3.
     The results presented in this section were derived from data bases available to the public.
Except for the visibility and National Park trend data, the results presented in this section were
prepared for this Criteria Document and have not yet been published elsewhere.

6.10.1  Daily and Seasonal Variability in PM2 5 and PM10
     In addition to considering patterns of seasonal variations over broad geographical areas,
a great deal of information, useful for  relating ambient concentrations to health effects, can be
obtained by analyzing long time series of concentration data at a single site.  Collocated
24-hour PM2 5 and PM10 filter samples were collected at a site in southwestern Philadelphia
from May 1992 through April 1995  (Koutrakis,  1995).  This unique data set was collected on
a nearly daily basis, thereby allowing an assessment of day-to-day variability in aerosol
properties.
     The data are presented as box plots showing the lowest, lowest tenth percentile, lowest
quartile,  median,  highest quartile, highest tenth percentile, and highest PM2 5 values in
Figure 6-101.  The four three-month averaging periods shown (March-May, June-August,
September-November, December-February) correspond to the so-called climatological or
meteorological seasons.  Highest median (20.8 jig/m3)  and extreme (72.6 /ig/m3)
                                         6-207

-------
ou -

70 -

60 -
m
o> 50 -
c
o 40 -
2
"c
m _ _
o 30 -
c.
o
O
20 -
10 -
n -













•







•







n


- A
4
I
? T
Philadelphia - PBY site
PM2.5

(n = 1024)

••
_










m






A A
LI LI
	 1 	 1 	 = 	
               Mar-May
Jun-Aug
Sep-Nov
Dec-Feb
Figure 6-101.  Concentrations of PM2 5 measured at the PBY site in southwestern
              Philadelphia. The data show the lowest, lowest tenth percentile, lowest
              quartile, median (black circles), highest quartile, highest tenth percentile,
              and highest PM2 5 values.
 PM2 5 concentrations were found during summer, with a difference of 50 ^ig/rn3 between
 them.  Median PM2 5 concentrations are 14.6, 14.2, and 13.4 ^ig/m3 for the three quarterly
 periods from September through May, while maximum concentrations ranged from 41 to
 55 /ig/m3.  Corresponding PM10 data are shown in Figure 6-102. PM10 concentrations exhibit
 strong maxima during both the summer (82.4 /ig/m3) and winter (77.5 pg/m3).  Maximum
 PM10 concentrations during spring and fall are 54.7 and 58.5 /xg/m3. The difference between
 median and maximum values was 54.4 ^ig/m3 during summer and 58.3 pig/m3 during winter.
 The median PM10 concentration was 28.0 /jg/m3 in summer, and ranged between 19.2 and
 20.9 /ig/m3 during the other seasons.
      PM2 5 and PM10 concentrations were highly correlated (r=0.92). PM10 and PM(10.2 5)
 concentrations were less highly correlated (r=0.63) and PM2 5 and PM(10_2 5) concentrations
 were even less well correlated (r=0.30). The day-to-day difference in PM2 5 concentrations
 was 6.8 ± 6.5 /ig/m3 and the maximum difference was 54.7 /xg/m3, while the day-to-day
                                        6-208

-------
      E
      o>
      o>
      o
      c
      o
      o
9O --

8O --

7O -•

60 --

5O -•

40 --

30 --

2O --

1O --
                                               Philadelphia - PBY site
                                                      PM1O
                                                    (n = 1O24)
   n
   •
   i
                 Mar-May
Jun-Aug
                                          Sep-Nov
Dec-Feb
Figure 6-102. Concentrations of PM10 measured at the PBY site in southwestern
             Philadelphia. The data show the lowest, lowest tenth percentile, lowest
             quartile, median (black circles), highest quartile, highest tenth percentile,
             and highest PM2 5 values.
difference in PM10 concentrations was 8.6 ± 7.5 /*g/m3 with a maximum difference of
50.4 fig/m3. The day-to-day difference in PM(10.2>5) concentrations was 3.7 ± 3.5 jig/m3 with
a maximum difference of 35.1 /*g/m3. The ratio of PM2 5 to PM10 throughput the
measurement period was 0.71 ±  0.13.  The high correlation coefficient between PM2 5 and
PM10 along with the high ratio of PM2.5 to PM10 suggests that variability in PM2 5 was driving
the variability in PM10 levels.
     Frequency distributions for the entire three-year PM2 5, PM(10.2 5), and PM10 data sets
are shown in Figures 6-103, 6-104, and 6-105, respectively.  Concentrations predicted from
the log-normal distribution, using mean values and geometric standard deviation derived from
the data, are also shown. The small number of apparently negative PM(10_2 5)  values reflects
measurement error at low concentration levels.  Frequency distributions of aerosol
concentrations at several sites in the South Coast Air Basin (Kao and Friedlander, 1995) have
also been shown to be reasonably approximated by log-normal distributions.
                                        6-209

-------
     350
                                                       PM
                                                           2.5
                                    geometric  mean = 15.2 pg/m
                                                      og= 1 .69
1O     2O     3O     4O
           Concentration
                                                 5O     6O
                                                       3)
  7O
                                                   8O
Figure 6-103.  Frequency distribution of PM25 concentrations measured at the PBY site in
             southwestern Philadelphia. Log-normal distribution fit to the data shown as
             solid line.
     450
     4OO  -•

     35O  --

 £   300  --
 o_
 £   25O  --
 CO
 CO
"o   2OO  --
 o
z   1 5O  --

     1OO  --

      5O  -•
            n.
         .Fl.
1O
6O
                                                      TO
                               2O     3O     4O     5O
                             Concentration  (|jg/m 3)
Figure 6-104.  Frequency distribution of coarse mode mass derived by difference between
             PM10 and PM2.5. Log-normal distribution not shown because of derivative
             nature of entries.
                                     6-210

-------
      25O
                                                                  PM10
                                               geometric mean = 21.4  pg/m3
                                                                og= 1.66
                    10     2O      30     4O      5O      60
                                   Concentration  (Mg/m3)
7O
8O
Figure 6-105. Frequency distribution of PM10 concentrations measured at the PBY site in
              southwestern Philadelphia. Log-normal distribution fit to the data shown as
              solid line.
     In general, the highest PM2 5 values are observed when winds are from the southwest
during sunny but hazy high presure conditions.  In contrast, the lowest values are found after
significant rainstorms during all seasons of the year.  The highest PM2 5 values were observed
during episodes driven by high sulfate abundances and are due, at least partly, to higher
sulfate concentrations.  Correlation coefficients between SO4=  and PM2 5 were 0.97 during the
summer of 1993. Similar correlations between SO4=  and PM2 5 were found at a site in
northeastern Philadelphia (24 km distant from the site under discussion) during the summer of
1993.  In addition, PM2 5 was found to be stongly correlated (r > 0.9) between seven urban
sites and one background site (Valley Forge, PA) during the summer of 1993 (Suh et al.,
1995). The same relations were also found during the summer of 1994 at four monitoring
sites as part of a separate study (Pinto et al., 1995).  The results from these studies strongly
suggest that PM2 5 and SO4=  concentrations are spatially uniform throughout the Philadelphia
area, and that variablility in PM10 levels is caused largely by variability in PM2 5 (Wilson and
                                        6-211

-------
 Suh, 1996). However, not enough data are available from regional sites to define the total
 areal extent of the spatial homogeneity observed in the urban concentrations.
          Different conclusions could be drawn about data collected elsewhere in the United
 States. PM2 5 and PM(10_2 5) data were obtained at a number of sites in California on a
 sampling schedule of every six days with dichotomous samplers (California Air Resources
 Board, 1995). As an example, frequency distributions of PM2 5, PM(10.2.5), and PM10
 concentrations (calculated as the sum of PM2 5 and PM(10_2 5) obtained at Riverside-Rubidoux
 from 1989 to 1994 are shown in Figures 6-106, 6-107, and 6-108, respectively.  It can be seen
 that the data cannot be satisfactorily fit by a single function, mainly as the result of the
 complexity of the concentration distribution of the coarse size mode shown in Figure 6-107.
       so
       70-
                   20
                          4O    SO     8O    1OO   12O   14O   1 SO
                                Concentration (pg/m3)
18O
Figure 6-106. Frequency distribution of PM2-5 concentrations measured at the Riverside-
              Rubidoux site.
         The data are also presented as box plots showing the lowest, lowest tenth percentile,
 lowest quartile, median, highest quartile, highest tenth percentile, and highest PM2 5 values in
 Figure 6-109 for four three-month averaging periods (January-March, April-June,
                                         6-212

-------
      a>
     «*_
      o

      d
so-




7O-




6O-




5O-




4O-




3O-




20-




10-
               0     20    40    60     8O   100   120   14O   160    18O

                                Concentration (pg/m3)



  Figure 6-107. Frequency distribution of PM(10_2.5) concentrations measured at the

                Riverside-Rubidoux site.
            50-
            40-
         S.  30-
         a.


         re
         in
         o 20-
            10-
                       —
                      20    40    60    80    100  120   140   160   180

                                 Concentration (pg/m3)
Figure 108.  Frequency distribution of PM10 concentrations calculated as the sum of

            PM2 5 and PM(10_2-5) masses measured at the Riverside-Rubidoux site.
                                        6-213

-------
                                                           Riverside-Rubidoux
        f
        o
        o


140-
120-
100-
80-
60-

40-
20-
Fine
(n = 382)














r

•


i &
I II F
J-U |




>



i
•
y v Y
Jan - Mar Apr-Jun Jul -
1 st Qtr 2nd Qtr 3rd










Y
Sept Oct - Dec
Qtr 4th Qtr
Figure 6-109.  Concentrations of PM2 5 measured at the Riverside-Rubidoux site. The
              data show the lowest, lowest tenth percentile, lowest quartile, median
              (black squares), highest quartile, highest tenth percentile, and highest
              PM2>5 values.
 July-September, October-December). Data for PM(10_2 5) and reconstructed PM10 are
 similarly plotted in Figures 6-110 and 6-111.  As can seen from these figures, variability in
 concentrations within an averaging period is high. Differences  between median and maximum
 PM2 5 levels range from 40 /ig/m3 during the  spring to 123 /ig/m3 during the winter, while
 differences between median and maximum PM^o^.s) levels range from 23  /ig/m3 during
 winter to 83 /ig/m3 during summer.  Variations in both size fractions combine to yield
 differences between median and maximum PM10 levels ranging between 83 /ig/m3 and
 136 /ig/m3.  Median PM2 5 levels do not show a clear seasonal cycle.  However, PM(10_2 5)
 concentrations show a maximum during the summer which causes a weak maximum in PM10
 levels.  In fact, median PM2 5 (30 /ig/m3) and PM(10_2 5) (34 /ig/m3) levels  are identical during
 the spring and fall quarters. The ratio of PM2 5 to PM10 mass throughout the measurement
 period was 0.48 ± 0.13 and PM2 5 and  PM10 levels were moderately correlated (r = 0.47).
      An examination of the data from Philadelphia, PA and Riverside, CA indicates that
 substantial differences exist in aerosol properties between widely separated geographic
                                         6-214

-------
                                                           Riverside-Rubidoux


120-
_ 100-
€0
f
3: 80-
c
o
1
1 6°-
I
° 40-
20-
o-'
Coarse
(n = 382)



i









•




A
n
•
g V
V i
T
Jan - Mar Apr













T
.



LJ
V


L y
1 1
- Jun Jul - Sept Oct - Dec
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
Figure 6-110. Concentrations of PM(10.2-5) measured at the Riverside-Rubidoux site.  The
              data show the lowest, lowest tenth percentile, lowest quartile, median
              (black squares), highest quartile, highest tenth percentile, and highest

              PMcoarse values-
             200
                                                           Riverside-Rubidoux
Concentration
             150-
-L
o
o
i
en
o
i
                  PM10
                  (n = 382)
                            n.
                                                              a
                                                   tr
                         Jan - Mar
                          1st Qtr
Apr -Jun
 2nd Qtr
Jul - Sept
 3rd Qtr
Oct - Dec
 4th Qtr
Figure 6-111.  Concentrations of PM10 measured at the Riverside-Rubidoux site.  The
              data show the lowest, lowest tenth percentile, lowest quartile, median
              (black squares), highest quartile, highest tenth percentile, and highest
              PM10 values.
                                        6-215

-------
regions.  Fine mode particles make up most of the PM10 mass observed in Philadelphia and
appear to drive the daily and seasonal variability in PM10 concentrations there.  Coarse mode
particles  are a larger fraction of PM10 mass in Riverside and drive the seasonal variability in
PM10 seen there.  The range in the seasonal variation of the ratio of PM2 5 to PM10 mass is
much smaller in Philadelphia (0.70 to 0.75) than in Riverside (0.41 to 0.57) for the four
averaging periods used. Differences between median and maximum concentrations in any size
fraction are much larger at the Riverside site than at the Philadelphia site. Many of these
differences could reflect the more sporadic nature of dust suspension at Riverside.  These
considerations demonstrate the hazards in extrapolating conclusions about the nature of
variability in aerosol characteristics inferred at one location to another.

6.10.2  Fine  and Coarse Participate Matter Trends and Relationships
6.10.2.1  Visual Range/Haziness
     Observations of visual range, obtained by the National Weather Service and available
through the National Climatic Data Center of the National Oceanic and Atmospheric
Administration, provide one of the few truly long-term, daily records of any parameter related
to air pollution. After some manipulation, the  visual range data may be used as an indicator of
fine mode particle pollution.   The data reduction process and analyses of resulting trends have
been reported by Husar et al. (1994), Husar and Wilson (1993), and Husar et al. (1981).
     Visual range i.e., the maximum distance at which an observer can discern the outline of
an object, is an understandable and for  many purposes an apporpriate measure of the optical
environment. It has the disadvantage, however, of being inversely related to aerosol
concentration.  It is usual, therefore, to convert visual range to a direct indicator of fine mode.
particle concentration.  The quantitative measure of haziness is the extinction coefficient, Bext,
defined as Bext=K/visual range, where K is the Koschmieder constant.  The value of K is
determined both by the threshold sensitivity of the human eye and the initial contrast of the
visible object against the horizon sky.  Husar et at. (1994) use K= 1.9 in accordance with the
data by Griffmg (1980).  The extinction coefficient is in units of km"1 and is proportional to
the concentration of light scattering and absorbing aerosols and gases.  The radiative transfer
                                         6-216

-------
characteristics which determine the visual range depend on time of day.  Only local noon
observations are used.

Haze Trend Summary
     The U.S. haze patterns and trends since 1960 are presented in 16 haze maps that
represent four tune periods and four seasons (Figure 6-112). The selected time periods are
5 year averages centered at 1960, 1970, 1980, and 1990. The quarters are calendrical,
i.e., winter is January, February, and March.  View horizonally for secular trends by quarter.
View vertically for seasonal variation by decade.
     The overall national view shows two large contiguous haze regions, one over the eastern
U.S. and another over the western Pacific states.  The two haze regions are divided by a
low-haze territory between the Rocky Mountains and the Sierra-Cascade mountain ranges.
This general pattern is preserved over the past 30-year period.  However, notable trends have
occurred over both the western and eastern haze regions.
     The haziness in the western Pacific states covers all of the coastal states, with California
having the highest values. In the 1960s a large fraction of western California was very hazy,
particularly during Quarters 1 and 4.  By the 1990s the magnitude of the Pacific Coast
haziness has declined markedly for all seasons.
     The eastern haze region extends from the East Coast to the Rocky Mountains.  The
western boundary of the eastern haze region has been markedly constant over both the seasons
and the years.  In fact, haze in the  mid-section of the U.S., extending from the Rocky
Mountains to the Mississippi River, has changed little over the 30-year history.
     The most dynamic pattern can be observed over the eastern U.S., extending from the
Mississippi River to the East Coast.  The eastern U.S. shows a significant seasonal variation.
There is  also a significant trend over the past 30 years. Furthermore, these seasonal and
secular (long-term) trends are different for sub-regions within the eastern U.S., such as the
Northeast, Mid-Atlantic and Gulf States regions.
     In the 1960s, the highest extinction values were recorded for the cold season (Ql, Q4),
with significantly lower values for the warm quarters (Q2, Q3). The remarkable reduction in
haziness  during the cold season and the strong increase during  the warm season has shifted the
                                         6-217

-------
o\
tl>
i—»
00
      Figure 6-112. United States trend maps for the 75th percentile extinction coefficient, Bcxt for winter (Ql), spring (Q2),
                   summer (Q3), and fall (Q4).  Bext [km'1] is derived from visual range, VR, data by Bext=1.9/VR.  Data
                   obtained during natural obstructions to vision (i.e., rain, snow, fog) were eliminated.

-------
haze peak from winter to summer.  This seasonal change has been accompanied by a regional
shift in highest haze pattern. In the 1960s, the worst haziness occurred around Lake Erie and
the New York-Washington megalopolis, during the cold season.  By the 1990s the area with
the worst haze had shifted southward toward Tennessee and Carolinas and occurred in the
summer season.
     The decade of the 1980s shows less change than the earlier decades. However, there has
been a continued haze reduction in the Northeast, north of the Ohio and east of the Mississippi
Rivers.  The southeastern U.S. as well as the Pacific states remained virtually unchanged in
the 1980s.

Regional Pattern
     Trends for specific regions in the eastern U.S., and the number and location of visual
range reporting stations for each region, are shown in Figure 6-113.  The trend graphs
represent the 75th percentile of Bext for the stations located within the designated  region. The
trends are presented for Quarters 1  (winter) and 3 (summer) separately.  The northwestern
U.S. exhibits an increase of Quarter 3 haze between 1960 and 1970, and a steady decline
between 1973 (0.22) and 1992 (0.12).  In the winter quarter the haziness has steadily declined
from 0.15 to 0.10 in the 30-year period. The Mid-Atlantic region that includes the Virginias
and Carolinas shows a strong summer increase between 1960 and 1973, followed by a decline.
The winter haze was virtually unchanged over the 30-year period.  The haziness over the Gulf
states increased between 1960 and 1970, and remained virtually unchanged since then. The
central Midwest including Missouri and Arkansas exhibit virtually no change during the winter
season and a slight increased in the summer (1960-1970). The upper Midwest (Figure 14) shows
an opposing trend for summer and winter. While summer haze has increased, mostly 1960-1973,
the winter haze has declined.

6.10.2.2  IMPROVE
     The National Park Service-EPA monitoring network for Class I areas is  designed to
monitor visibility in national parks and other designated areas. Most of these  are remote.
However, data from two southeastern sites, Shenandoah National Park and the Great Smoky
                                         6-219

-------
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Mountains National Park, provide useful information on the regional background of sulfate
(Eldred and Cahill, 1994; Cahill et al., 1996b). As shown in Figure 6-114, there is a distinct
increase in sulfate. This increase can be correlated with increases in SO2 emissions in the
summer from power plants in the Tennessee Valley (Cahill et al., 1996b). The increased
emissions may be related to an increase in demand for power for air conditioning. The increase
in regional background will impact urban centers along the eastern U.S. Visibility
measurements over the northeastern U.S. show an increase in haze from 1960 to 1970 in both
winter and summer. Between 1970 and 1983, there was a decrease in haze in the winter but little
change during the summer (Husar and Wilson, 1993; Husar et al., 1994).  Concern has been
expressed that the indicated trends may have been impacted, or even produced, by changes in
monitoring protocols (White, 1996a,b). However, these issues have been addressed by Cahill
etal. (1996b).

6.10.2.3   Philadelphia
       Philadelphia is of special interest because of the extensive monitoring conducted there
and the use of Philadelphia data in epidemiological studies.  Extensive measurements of TSP
have been conducted in Philadelphia. Several data sets have been combined to give an indication
of long-term trends in Philadelphia (Figure 6-115). The TSP data set was construed from the
AIRS  data base  (Wyzga and Lipfert, 1996; Li and Roth, 1995). There was a steady decrease in
TSP from 1973 to 1983 with variable but slightly increasing TSP levels between 1983 and 1990.
       Fine PM was estimated from the Inhalable Particle Network (Rodes and Evans, 1985)
from 1980 to 1983, from AIRS (AIRS, 1995), from 1987 to 1990, and from the Harvard Data
Base (Koutrakis, 1996) for 1993 and 1994. During the period 3/79 to 12/83, the Inhalable
Particulate Network conducted measurements in Philadelphia with dichotomous samplers. These
used 15 (j,m upper cut points except for a period at the end of the study (3/82 to 12/83) when two
co-located PM10 samplers were run at one site. The IPN data set allows construction of four
annual averages for 1980 through 1983 by averaging PM2 5 data from PMl5fPM2 5 dichotomous
samplers from the several IPN sites across Philadelphia. These are shown in Figure 6-115, along
with the one year of PM2 5 data from PM10/PM2 5 dichotomous samplers at the South Broad St.
site.
                                        6-221

-------
                           Sulfate Concentration Trends
            1982
1984
1986
1988
1990
1994
                 1983      1985      1987      1989      1991
                       a Shenandoah   + Smoky Mountains
                                              1993
 Figure 6-114.  Eastern U. S. regional background trend of sulfate indicated by seasonal
              trend data from Shenadoah and Great Smoky Mountains National Parks.
      A PM10/PM2 5 dichotomous sampler, run in the Philadelphia area from 1987 through
1990 allows annual averages of PM2 5 for those years to be added to Figure 6-115.  Harvard
University measured PM10 and PM2 5 at the Presbyterian Home site from 5/92 to 5/92 allowing
annual averages for 93 and 94 to be added to the graph. Since PM2 5 is expected to be relatively
uniform across Philadelphia (Wilson and Suh, 1996), this data can be used to estimate a PM2 5
trend from 1979 to 1994. A downward trend is indicated.
      The samplers were not at the same sites during the different time periods. Since
PM(10_2 5) does not seem to be uniform across Philadelphia (Wilson and Suh, 1996), no PM10 or
PM,10_2 5) trend could be constructed. Comparisons of PM10 and PM(|0_2 5) and PM2 5/PMi0
(Figure 6-116) for 1983  and 1993 are shown. Differences in PM(10_2 5) and the ratio of
                                      6-222

-------
       95
       90
       85
    «  80
     E
    O.
    CO
       75
       70
       65
       60
       55
                              TSP and PM25Trends
                        IPN, AIRS, and Harvard Databases
                                                  A
30

25

20 "
   "o>
15  *
    >O
    CN
10 Q.
            1973  1975  19771 1979  1981 1983  19851 19871 1989 1991 I  1993
               1974 1976  1978 1980  1982  1984  1986  1988 1990  1992 1994
                                       Year
   D TSP    + PM2s.IPNAvg     o PM25. IPN, SBROAD    A PM2J..AIRS    x PMJ5,PBY
Figure 6-115.  TSP and PM2-5 trend data for the city of Philadlphia from AIRS, IPN, and
              Harvard database.
PM2 s/PM10 may represent geographical differences in the coarse fraction of PM10 as well as
relative changes in PM2 5 and PM(10_2 5).

6.10.2.4  Harvard Six-Cities Study
       During 1979 to 1986, the Harvard School of Public Health measured paniculate matter in
6 cities in eastern and central United States (Spengler et al., 1986b; Neas, 1996). Means and 90th
percentiles for fine, coarse, PM15, and TSP are shown in Figures 6-117 to 6-119. (Measurements
were made with dichotomous samplers with a 15 yum diameter cut point from 1979 to 1984 and
with a 10/^m diameter cut point from 1984 to 1986. The coarse fractions of PM10 and PM15
were not significantly different during the overlapping year.) In the dirtier cities, Steubenville,
St. Louis, and Harrison, there were decreases in all PM indicators, especially in the earlier years.
                                        6-223

-------
o\

§
    30
                            S. Broad, 1983
                            PM,., and PM,10.2J>
                                                  PHILADELPHIA
                                                                                                               PBY, 1983
                                                                                                            PM23 and PM,,.
                24-Jan-83  2S-Mi
                    23-Fab-83
     1


   0.9


   0.8


   0.7


   0.6


   0.5


   0.4


   0.3


   0.2
                    r-83 I 24-May-B3 I 23-Jul-83 { 21-Sap-83  ZO-Nov-83 I
                    24-Apr-83  23-Jun-B3  22-Aug-»3  21-Oct-83  20-Dac-83
                               Oat*
                               as a Fraction of PM ,„
                                                (c)
       24-Jan-83 I  25-Mar-83 I 24-May-B3 I  23-Jul-83 I 21-Sep-83
           23-F»b-83  -' -  —  —  •  —  — •   ~ -- -
                                                   2O-NOV-83
                    24-Apr-S3  23-Jun-83  22-AuS-83 21-Oct-83  20-DOC-83
                                Data
                                                                O1JAN93  I OSMAR93 I O6MAY93 I  O8JUL93 I  O8SEP93 I  OTNOV93
                                                                     01FEB93  OSAPR93  O8JUN93  O8AUO93  07OCT93  08DEC93
                                                                                         Data
                                                                             n PM2
                                                                                               2.
                                                                                                 PM25 as a Fraction of PM ,
                                                                O1JAN93 I  05MAR93 >  06MAY93 I  06JUL93 I  D8SEP93 \ 07NOV93 I
                                                                    01FEB93  OSAPR93  08JUNB3   08AUO93  07OCT93   08OEC93
Figure 6-116
                   Data                                                                    Data

>. Comparison of fine and coarse particle parameters in Philadelphia in 1983 and 1993: (a) PM2 5 and PM(10.2 5j at
  South Broad St. site, 1983; (b) PM2 5/PM10 at South Broad St. site, 1983; (c) PM2 5 and PM(10_2 5) at
  Presbyterian Home site, 1993; (d) PM2 5/PM10 at Presbyterian Home Site, 1993.

-------
            80

            70

            BO

            50

            4O

            30

            20

            1O

            O
                                  Stubenville
S.
            150
            140
            13O
            120
            11°
            100
            90
            "°
            70
            60
            50
            «
            30
            20
            10
             0
                                               Harvard Six Cities Data
                                                               80
                                                                                              St. Louis
                      1980
                                             1984
                  1981   1982   1983
                            Year
            O PM2.5   • PM-C   • PM15   • TSP

                        Stubenville
150
140
130
120
11°
100
 90
 80
 70
 60
 50
 40
 30
 20
 10
                                                                                   980    1981    1982   1983   1984
                                                                                                  Year
                                                                                   I PM2.5  • PM-C   • PM15   • TSP

                                                                                               St. Louis
                                                                                                              (d)
       1979   1980   1981   1982   1983   1984   1985   1986
                            Year
             O PM2.5   • PM-C   • PM15  • TSP
                                                                                   I PM2.5
                                                                                          81    1982   1983
                                                                                                  Year
                                                                                          • PM-C   • PM15
                                                                                                                 1985
                                                                                                                       1986
                                                                                                          • TSP
Figure 6-117. Trend data from the Harvard Six-Cities Study:  (a) Steubenville, fine, coarse, PM15, and TSP means;
               (b) Steubenville, fine, coarse, PM15, and TSP 90th percentiles;  (c) St.  Louis, fine, coarse, PM15, and TSP
               means; (d) St. Louis, fine, coarse, PM10, and TSP 90th percentiles.

-------
a\
                                                             Harvard Six Cities Data
                         19BO
                               1981
               150
               14O
               130
               120
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             •S 110
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             ID 90
             £ 80
             i «
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             ^ 50
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               30
               20
               10
                0
                                     1982   1983
                                     Year
                         OPM2.5   » PM-C   *PM15

                                    Harriman
 1984

• TSP
             (b)
                   1979
                         1980
                               1981
                                     1982   1983
                                     Year
                         OPM2.5   • PM-C   «PM15
 1984

• TSP
                                                            1986
  150
  140
  130
  120
£ 110
| 100
£ 90
9
a- BO
S 7°
a. 80
«•>
£ 50
» 40
   30
   20
   10
    0
             1980   1981    1982   1983
                           Year
             OPM2.5   • PM-C   *PM15
                                                                                              Watertown
 1984

• TSP
             (d)
                                                                                    1980
                                                                                          1981
                        1982   1983
                           Year
            OPM2.S   • PM-C   »PM15
 1984

• TSP
                                                                                                                 1985
                                                                                                                       1986
     Figure 6-118.  Trend data from the Harvard Six-Cities Study:  (a) Harriman, fine, coarse, PM15, and TSP means;
                     (b) Harriman, fine, coarse, PM15, and TSP 90th percentiles; (c) Watertown,  fine, coarse, PM15, and
                     TSP means; (d) Watertown, fine, coarse, PM15, and TSP 90th percentiles.

-------
                  "a 30
                     20

                     10

                     0
                                          Portage
                                           Harvard Six Cities Data
                                                          80
                                                                  (a)
                              1980
                                    1981
                                                      1984
                                          1982   1983
                                            Year
                              OPM2.5   • PM-C   »PM15   • TSP
                                                          70

                                                          60

                                                          50

                                                          40

                                                          30

                                                          20

                                                          10

                                                           0
Topeka
                                                                                                        (c)
                                                                                        1980
                                                                                              1981
                                                                                                                1984
                                                                                1982   1983
                                                                                 Year
                                                                    OPM2.S   • PM-C   *PM15   »TSP
ON
150
140
130
120
110
100
 90
 80
 70
 60
 50
 40
 30
 20
 10
                                          Portage
                                                                  (b)
                         1979
                               1980
                                     1981
                              O PM2.5
                                          1982   1983
                                            Year
                                      • PM-C   • PM15
                                                            1985
                                                                  1986
  150
  140
  13O
  120
•1 110
« 100
a  90

I  8°
O  70
                                                                               40
                                                                               30
                                                                               20
                                                                               10
                                                                                Topeka
                         (d)
                                                     • TSP
                                                                                        1980
                                                                                        O PM2.5
                                                                            1   1982 '  1983
                                                                                  Year
                                                                            • PM-C    • PM1S
                                                                                                                      1985
                                                                                                                            1986
                                                                                                               • TSP
        Figure 6-119. Trend data from the Harvard Six-City Study: (a) Portage, fine, coarse, PM15, and TSP means; (b) Portage, fine,
                      coarse, PM15, and total TSP 90th percentiles; (c) Topeka, fine, coarse, PM15, and TSP means; (d) Topeka, fine,
                      coarse, PM15, and TSP 90th percentiles.

-------
There was also an apparent decrease in Topeka, one of the cleaner cities. No trend can be
discerned in Watertown or Portage.  It is difficult to determine whether there was a greater trend
in fine or coarse particles.

6.10.2.5  AIRS
     The AIRS data base was searched for sites with 4 or more years of fine and coarse data
(AIRS, 1995). Five such sites were found. Values for the mean and the 90th percentile are
shown in Figures 6-120 to 6-123. No significant trends are evident in PM2 5 or PM(i0_2 5) either
in the means or the 90th percentile values.  PM10 and PM(i0_2 5) at the dirtier site  in New York
City do appear to have decreased from 1988 to 1992 but to have increased between 1992 and
1994.

6.10.2.6  California Sites
     The California Air Resources Board conducted dichotomous sample measurements, every
sixth day, beginning in 1989 at a number of California sites (CARB, 1995). Some results from 8
sites are shown in Figures 6-124 to 6-130.  The means (Panel a) and 90th percentile values
(Panel b) are given for PM2 5, PM(10_2 5), and PM10.  Most of the sites show slight downward
trends for PM10 and both PM2 5 and PM(10_2 5).
     The California sites are of special interest because of the substantial seasonal and daily
variability. The individual every-sixth-day values are plotted for 1991 (plus 1 day in the
preceeding and following years)(Panel c). Strong seasonal and daily variation are evident.
Based on the every-sixth-day measurements, it would appear that the day-to-day variability at
the California sites  is higher than in Philadelphia. Also shown is the PM2 5 fraction of PM10
(Panel d). These ratios are also show a strong seasonal variation.

6.10.3     Interrelations and Correlations
     The availability of data on four PM size fractions at several sites for a number of years
makes it possible to examine relationships and correlations among PM2 5, PM(10_2.5), PM10, and
TSP. It is also possible to examine the distribution of values in the upper range and the
relationship of the fine fraction to other PM parameters.  Sufficient data for these purposes are
                                         6-228

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                                                        New York, NY
0\
to
to
                                  Site 69
                         Annual Arithmetic Mean (ug/m3 )
         Site 71
Annual Arithmetic Mean (ug/m3 )
70
60
50
40
30
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                             90th Percentile (ug/m3)
    90th Percentile (ug/m3)
100
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       Figure 6-120.  Trend data from AIRS:  (a) New York City, Site 69, fine, coarse, and PM10 means; (b) New York City,
                     Site 69, fine, coarse, and PM10 90th percentiles; (c) New York City, Site 71, fine, coarse, and PM10 means;
                     (d) New York City, Site 71, fine, coarse, and PM10 90th percentiles.

-------
                           Detroit, Ml
                 Annual Arithmetic Mean (ug/m3 )
        St. Louis,MO-IL
Annual Arithmetic Mean (ug/m3 )
70
60
50
40
30
20
10
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                            Detroit, Ml
                      90th Percentile (\iglm3)
       St.Louis,
    90th Percentile (ug/m3)
100
80
60
40

20
«





6 E





7 8
O R4 mo c
Figure 6-121.  Trend data from AIRS: (a) Detroit, fine, coarse, and PM10 means; (b) Detroit, fine, coarse, and PM10 90th
              percentiles; (c) St. Louis, fine, coarse, and PM10 means; (d) St. Louis, fine, coarse, and PM10 90th percentiles.

-------
                                 Philadelphia, PA - NJ
                              Annual Arithmetic Mean  (pg/m3 )
ro
6O
so
4O
3O
20
10
'






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6 8


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4
         1OO

          8O

          6O

          40

          2O
              86
                                    9Oth Percentile
                                                                         (b)
                 87      88
                   PM 1 O
 89      9O     91
	Coarse
92      93      94
	  PM2.5
       Figure 6-122. Trend data from AIRS: (a) Philadelphia, fine, coarse, and PM10 means;
                    (b) Philadelphia, fine, coarse, and PM10 90th percentiles.
1
2
3
4
5
6
7
available from several sites in California (CARB, 1995) and from Philadelphia (IPN, 1985;
AIRS, 1995; Harvard 1995).  However, only the Philadelphia data allows examination of the
relationship of PM2 5 and PM10 with TSP.

6.10.3.1  Upper Range of Concentration for Various PM Size Fractions
     Some information on the upper range of concentrations and relationships among the four
PM size fractions are shown in Tables 6-11 and 6-12. The  maximum value; the 2nd, 3rd, 4th,
                                             6-231

-------
                                        Annual Arithmetic Mean (|Jg/m )
to
u>
to
                         100






                          80






                          60






                          40






                          20
                                        (a)
	;	I	J	! NAAQS
                                            90th Percentile (pg/m  )
San Jose, CA




             100




             90




             80




             70




             BO




          ~5> so
           3.


             40




             30




             20




             10
                                                                                                          Every Sixth Day. 1991
                                                       1




                                                      0.9




                                                      0.8




                                                      0.7




                                                   2°  0.6

                                                   a.


                                                   I  0.5




                                                   8  0.4




                                                      O.3




                                                      0.2




                                                      0.1




                                                       0
(c)
                                                                                          01/06/91 I 03AM/91  I  OS/D6/91 I  07/05/91 I  09/0*3/91  I 11/0*2/91  112/20/91

                                                                                              02/05/91   04/06/91   06/05/91   08 AM/91    10/03/91   12/02/91

                                                                                                                Date

                                                                                                          O Coarse   *  Fine




                                                                                         	PMa.5 as a Fraction of PMio	
                                                                                         01/17/90 I  01/06/91 I 01/01/92 I  01/01/93 I  01/02/94 I 01/04/95 109/30/95

                                                                                             07/04/90   07/05/91   O7/05/92   07/06/93   07/O1/94  07/02/95


                                                                                                                 Date
           Figure 6-123.  Trend data from San Jose from CARB:  (a) Fine, coarse and total means;  (b) Fine, coarse and total

                             90th percentiles; (c) Every sixth day fine and coarse mass for 1991; (d) Fine and coarse mass as a

                             fraction of PM10.

-------

-------

Is)

-------
                                                               Bakersfield.CA
                           Annual Arithmetic Mean (ug/m3)
             100
                         90      91      92      93      94      95
 110

 100

  go

  80

  70

:  80

L SO

  40

  30

  20

  10
                                                                                               Every Sixth Day. 1991
                                                                              01/08/91 I 03/07/91   I  05/1*2/91 I  07/0*5/91  I 09/04/91  I 11/0*2/91  I 12/28/91
                                                                                 02/08/91   04/08/91   06/05/91   08/04/91    10/03/91   12/02/91
                                                                                                      Date
                                                                                               D Coarse    + Fine
                                                                              1

                                                                            0.9

                                                                            0.8

                                                                            0.7

                                                                            0.8

                                                                            0.5

                                                                            0.4

                                                                            0.3

                                                                            0.2

                                                                            0.1

                                                                              0
                                                                                             PM2 5  as a Fraction of PM 10
                                                                               01/04/88  I 01/0*5/90 I  01/06/911  01/01/921  01/01/93 Id 1/08/94*
                                                                                    07/03/89   07/04/90    07/05/91   07/07/92    07/06/93 04/08/94
Figure 6-126.  Trend data from Bakersfield from CARB:  (a) Fine, coarse and total means; (b) Fine, coarse and total
                 90th percentiles; (c) Every sixth day fine and coarse mass for 1991; (d) Fine and coarse mass as a
                 fraction of PM10.

-------
U)
              180
              160 J

              140
              120^

              100

               8 CM

               60

               40

               20

                0
                            Annual Arithmetic Mean (ug/m3)
                                                                  Azusa, CA
100
80
60
40
20
0
8


^^
' — "•••?•.


^
«.
•- »-t*"


\
•>
""* *"*'•*.










I'X* .— ..
(a)

NAAQS

9 90 91 92 93 94 9
                                90th Percentile (ug/m^
                                      (b)
                  89
  90
• Total
91
92      93
Coarse
 94      95
	Fine
                                                                            t
                                                      110

                                                      1OO

                                                       90

                                                       80

                                                       70

                                                       60

                                                       SO

                                                       40

                                                       30

                                                       20

                                                       10

                                                        0
                                              1

                                            0.9

                                            0.8

                                            0.7

                                            0.6

                                            0.5

                                            0.4

                                            0.3

                                            0.2

                                            0.1
                                                                                                  Every Sixth Day, 1991
                                                                                   01/12/91 03/13/91   05/08/91  07/05/91
                                                                                     02/05/91  04/D6/81
                                                                                                   08/05/91   08/04/91
                                                                                                         Date
                                                                                                   Coarse   + Fine
                                                                                               PM2 5 as a Fraction of PM10
                                                                                                          (d)
                                                                               01/04/8903/38/89 07/04/90 107/05/91 IO7/05/92 I 07/06/93 lo7/01/94 dr/02/9sl
                                                                                  05/28/89 01/05/90 01/12/91 01/01/92  01/01/93  01/08/94  01/03/95 09/30/95
                                                                                                         Date
      Figure 6-127.  Trend data from Azusa from CARB:  (a) Fine, coarse and total means; (b) Fine, coarse and total
                      90th percentiles; (c) Every sixth day fine and coarse mass for 1991; (d) Fine and coarse mass as a
                      fraction of PM10.

-------
ON
                          Annual Arithmetic Mean (\ig/m  )
             100

              80

              60

              40

              20

               0
       : I-.
        •I-
    Riverside-Rubidoux, CA
    (a)
89      90      91      92      93
 	Total      	Coarse
              90th Percentile (wg/m3)
                                                                 95
             180
             160
             140
             120
             100
              80
              60
              40
              20
               0
                                                            Fine
                                            (b)
                 89
                         90
                       • Total
                        92      93
                     —  Coarse
94      95
	Fine
                                                             0.8
                                                             0.7
                                                          I o.e
                                                          •s
                                                           | 0.5
                                                          I 0.4
                                                             0.3
                                                             0.2
                                                             0.1
                                                                                  Every 6th Day, 1991
                          01/08/91  I 03/01/91   105/12/91 I  07/D5/9ll 09/D3/91I  11/0*2/91 lo 1/01/92
                               02/05/91    04/08/91 06/O5/91  08/10/91    10/03/91   12/02/91
                                              Date
                                         D Fine    + Coarse
                                       PM2 5  as a Fraction of PM 10
                                                                                                                 (d)
01/22/89 I 02/17/90 I 03/13/91 I 04/30/92 I 05/25/93 I 06/01/94 I 05/27/95
    08/02/89  08/21/90  10/15/61  11/26/92  11/27/93  11/28/94
                     Date
      Figure 6-128.  Trend data from Riverside-Rubidoux from CARB:  (a) Fine, coarse and total means; (b) Fine, coarse and
                       total  90th percentiles; (c) Every sixth day fine and coarse mass for  1991; (d) Fine and coarse mass as a
                       fraction of PM10.

-------
U)
oo
                            Annual Arithmetic Mean (pg/m3)
100
80
60
40
20
0
e




^—-^





- —





•















(a)

NAAQS


I9 90 91 92 93 94 9
Lone Pine, CA


              4O|—
                               90th Percentile (kig/m 3)
180
160
140
120
100
80
60
40
ZO
0 '













































(b)








89 90 91 92 93 94 9

                                                                               25



                                                                               20



                                                                               1S



                                                                               10



                                                                                5
Every Sixth Day, 1991
                                                                                                                                     (C)
                                                                                   01/06/91  I 03/01/91   I 05/06/91  b7/03/9ll 09/03/81  I  11/02/91  112/14/91
                                                                                        02/05/91   04/06/91  06/29/11 08/04/91   10/03/91    12/02/91

                                                                                                         Date

                                                                                                   a Coarse   + Fine
                                                                               0.9


                                                                               0.8


                                                                               0.7


                                                                             s o.e


                                                                            i  0.5


                                                                            I  0.4


                                                                            *  0.3


                                                                               0.2


                                                                               0.1
                                                                                                PM2 5  as a Fraction of PM10
                                                                                  01/16/80 I  01/05/80 I 01/08/81 I 01/31/82 I 01/01/83  b 1/02/94 101/08/85
                                                                                     07/03/89    07/04/90  07/05/91  07/05/92   07/08/93 07/01/94

                                                                                                         Date
      Figure 6-129. Trend data from Lone Pine from CARB:  (a) Fine, coarse and total means; (b) Fine, coarse and total
                       90th percentiles; (c) Every sixth day fine and coarse mass for 1991; (d) Fine and coarse mass as a
                       fraction of PM10.

-------
                     Annual Arithmetic Mean (|jg/m3)
                                                             El Centre, CA
            89      90      91      92      93      94      95
            	 Total     	Coarse    	 Fine

                          90th Percentile (M9/m3)
                    90      91      92      93      94      95
                   Total     	Coarse    	 Fine
                                                                         120

                                                                         110

                                                                         100

                                                                          90

                                                                          80

                                                                          70
                                                                       «
                                                                       E  60
                                                                       Ol
                                                                       *  50

                                                                          40

                                                                          30

                                                                          20
                                                                         0.9

                                                                         0.8

                                                                         0.7

                                                                         0.6

                                                                         0.5


                                                                         "
                                                                         0.3

                                                                         0.2

                                                                         0.1
               Every Sixth Day, 1991
01/06/91 I 03/01/91  I  05/08/91 I   07/11/91 I 09/06/91 I 11/02/91  M2/14/91
    02/05/91    04/08/91  06/05/91   06/O4/91    1O/O3/91    12/O2/B1
                       Date
                D Coarse    + Fine
                                                                                          PM2 s as a Fraction of PM10
                                                                                                                               (d)
01/04/69|o9/19/6B|oS/2l3/9oloi/3'iO/9l|lO/03/91l06/23/92|o3fO2/93|lO/26/93l07/Oi'7/94|o3/22/95
   05/22/89 01/17/90 09/14/90 06/05/91 02/18/92 11/02/92 06/30/93 03/03/94 11/16/94

                       Date
Figure 6-130. Trend data from El Centro from CARB:  (a) Fine, coarse and total means; (b) Fine, coarse and total
                90th percentiles; (c) Every sixth day fine and coarse mass for 1991; (d) Fine and coarse mass as a
                 fraction of PM10.

-------
 TABLE 6-11. MAXIMUM VALUE; 2ND, 3RD, 4TH, AND 5TH HIGHEST VALUES;
98TH AND 95TH PERCENTILE VALUES; 50TH PERCENTILE VALUE (MEDIAN); A,
 THE DIFFERENCE BETWEEN THE MEDIAN AND THE MAXIMUM VALUES AND
 #, THE NUMBER OF MEASUREMENTS AVAILABLE FROM EIGHT CALIFORNIA
                  AIR RESOURCES BOARD SITES:
                  (a) PMj* (b) PMno.2.Si,and(c)PM10
PM25
SITE
Riverside
Azusa
Bakersfield
Visalia
Stockton
San Jose
El Centre
Lone Pine
Max
142
98
447
140
94
105
73
29
2nd
130
95
147
121
92
88
62
23
3rd
129
88
119
105
91
86
52
22
4th
122
88
100
91
75
69
49
19
5th
121
87
98
91
75
66
47
18
98%
114
84
93
82
70
59
39
17
95%
77
60
77
69
55
44
26
13
50%
29
23
16
15
11
9
11
6
A
113
75
431
125
83
96
62
23
#
368
371
296
389
381
341
392
322
PM(10-2.5)
Riverside
Azusa
Bakersfield
Visalia
Stockton
San Jose
El Centre
Lone Pine
123
108
320
86
66
55
324
107
114
98
104
75
57
45
176
105
87
71
99
74
57
41
160
84
86
62
98
73
56
39
150
71
86
61
90
70
56
32
132
67
76
57
76
64
54
64
108
42
68
50
61
51
41
51
63
26
34
24
27
21
16
11
27
10
89
84
293
65
50
44
297
97
368
371
296
389
381
341
392
322
PM10
Riverside
Azusa
Bakersfield
Visalia
Stockton
San Jose
El Centre
Lone Pine
194
203
766
187
126
151
347
122
189
152
218
164
119
109
228
120
189
139
183
138
112
102
222
101
182
139
163
137
110
87
167
93
182
135
144
130
102
85
158
76
178
127
135
109
98
76
130
54
130
99
120
98
82
61
90
36
68
50
48
43
30
22
39
16
126
153
718
144
96
129
308
106
368
371
296
389
381
341
392
322
                             6-240

-------
 TABLE 6-12. MAXIMUM VALUE; 2ND, 3RD, 4TH, AND 5TH HIGHEST
 VALUES; 98TH AND 95TH PERCENTILE VALUES; 50TH PERCENTILE
VALUE (MEDIAN); A, THE DIFFERENCE BETWEEN THE MEDIAN AND
 THE MAXIMUM VALUES AND #, THE NUMBER OF MEASUREMENTS
   AVAILABLE FOR STIES IN PHILADELPHIA FROM 1979 TO 1995:
         (a) PM2 s  (b) PM,1ft.^, and (c) PM1fl,AND (d) TSP
Philadelphia
Site
IPN
Average
IPN
S. Broad
AIRS

Harvard
PBY
Dates
3/79
12/83
3/82
12/83
1/87
12/90
5/92
5/59
Max
98

54

55

73

PM25
2nd
94

54

55

72

3rd
74

52

47

56

4th
65

50

46

53

5th
65

50

45

53

98%
61

53

46

43

95%
50

50

43

36

50%
21

22

18

15

A
74

32

37

58

#
366

91

219

1014

PMnn-9 «
IPN
Average
IPN
S. Broad
AIRS

Harvard
PBY
3/79
12/83
3/82
12/83
1/87
12/90
5/92
5/59
NA

28

39

40

NA

25

39

28

NA

20

38

27

NA

19

37

25

NA

17

30

24

NA

25

37

18

NA

18

25

15

NA

9

12

6

NA

19

27

34

0

91

219

970

PM,n
IPN
Average
IPN
S. Broad
AIRS

Harvard
PBY
3/79
12/83
3/82
12/83
1/87
12/90
5/92
5/59
NA

71

86

82

NA

66

83

78

NA

66

82

72

NA

65

79

64

NA

64

73

64

NA

67

79

54

NA

64

60

48

NA

30

31

22

NA

41

55

60

0

91

219

1025

TSP
IPN
Average
IPN
S. Broad
AIRS

Harvard
PBY
3/79
12/83
3/82
12/83
1/87
12/90
5/92
5/59
196

116

131

NA

150

107

124

NA

148

105

116

NA

140

101

116

NA

138

99

112

NA

129

109

116

NA

114

100

104

NA

64

61

56

NA

132

55

75

NA

366

91

219

0

                         6-241

-------
and 5th highest values; the 98th and 95th percentile values; the 50th percentile (median value)
and the difference between the median and the maximum value are given for the measurement
period available at each site.  The maximum PM2 5, PM(10-2.5)» and PM10 levels were
substantially higher at all the California sites, including the site at Lone Pine (estimated
1980 population, 1800),  than at the Philadelphia sites. Differences between maximum and
median levels are also larger at the California sites.  The causes for the extremely high values
observed at the Bakersfield site are not known.  Data on the upper ranges of TSP are shown
for Philadelphia sites as available.

6.10.3.2  Relationships Between PM2 5; PM(10_2 5), PM10, and TSP in Philadelphia
     Epidemiologists have made extensive use of a long-term TSP data set from Philadelphia
(Chapter 12; Wyzga and Lipfert, 1996; Li and Roth, 1995) to investigate the statistical
relationships between TSP and mortality. It is possible, however, that PM2 5 or PM10, instead
of TSP, may be the causal agent and that TSP may serve as an indicator for PM2 5 or PM10.
PM indicators for Philadelphia, other than TSP, have not been available until recently.
Therefore, an examination of relationships between TSP, PM2 5, and PM10 in the  Philadelphia
area may provide data that will be useful in interpreting the epidemiological results obtained in
Philadelphia with TSP.  Such relationships are displayed in a series of Figures (6-131 to
6-135) that show: (Panel a) TSP plotted versus PMX (where PMX is either PM2 5 or PM10)
(Panel b) the distribution of values of PMX/TSP, (Panel c) PMX/TSP plotted versus PMX, and
(Panel d) PM/TSP plotted versus TSP.
     It would appear from Figures 6-131 to 6-135 that there is some relationship between PMX
and TSP and that the relationship improves at higher values of TSP.  The PMX/TSP  ratio does not
appear to vary significantly with PMX. However, the ratio does appear to increase with TSP
until a certain level of TSP is reached and then levels off.  These visual observations are
quantified by comparison of the PMX/TSP ratios at various levels and statistical regressions of
PMX with various TSP fractions shown in Table 6-13.
                                         6-242

-------
                                                                                   Frequency Distribution of PM^./TSP
to
                                                     PHILADELPHIA, IPN, 3/79 to 12/83
                           Comparison of TSP and PM2i
40   60    80   100  120  140   160  180  200

            TSP. (jg/m*
                                                                                0.15  0.25  I 0.35 I  0.45  I 0.55   0.65  0.75 I
                                                                             0.1    0.2   0.3    0.4   0.5   0.6    0.7   0.8
                                                                                           PM2S/TSP
00
.o
0.7


0.6

0.0.5
CO
t
S0.4

o.
0.3
0.2

0.1
Q
uunipciii»uii ui riYi25 MMU ri«25M or n „ comparison OT i or ana KM25/I SK
n
-
D
n a D
D D a
D D D a Da a
on Da EJ o o D
^ ODD%^|^*DD^DD "

B 2qfi'r»!£B*_n™n Q
~
-
&5ag> a a
a
a
i i i i i i i i i i
u.o
0.7


0.6

a. °'5
CO
S 0-4

a.
0.3
0.2

0.1
n
n
-
D
n n
a o D
D DDnaDna
no DD aa D o D
- 0° DD^^D^^ ° "S» 1 D

'"b a^asiuSr ^SL j=pS raP D DD
DaI"^n^^^&^nDQn °
°^||ppvD *
iPftna^D a a
D
n
I I I I I I I I I I I 1 1 1 1 1 1 1 :
0 20 40 60 80 100 " 20 40 60 80 100 120 140 160 180 200
PM , (jg/m TSP, |jg/m!
      Figure 6-131. PM2 5 and TSP Relationships in Philadelphia, IPN Average, 3/79 to 12/83: (a) comparison of PM2 5 with
                    TSP,  (b) frequency distribution of PM2 5/TSP, (c) comparison of PM2 5 /TSP with PM2 5 , (d) comparison
                    ofPM25/TSPwithTSP.

-------
                                            PHILADELPHIA, IPN, S. BROAD, 3/82-12/83; PM2
       60
       50
   -   40
    E
 S  30
s
o.

   20



   10
              0°
        20
      0.7
      0.6
      0.5
    a.
    to

    3 0.4
      0.3
      0.2
      0.1
                  Comparison of PM2,5 and Average TSP
                               D a
                               DD
                         D O
 a
aa
 o
                        ffl  D
                 40
                                                100
                           60        80

                           TSP, pg/m3

                  Comparison of PM25 /Avg TSP and PM 2,
                                                         120
                            a  a
  g   a o
                 D  Dg,    DD a n


                n a  a   D
             a n a          a
                                a „  an
                                      D
                     15
                              25       35

                             PM,., |jg/m3
                                                45
                                                         55
                                                                     0.7
                                                                     0.6
                                                                 0.5

                                                               a.
                                                               CO
                                                               t
                                                               2 0.4
                                                                     0.3
                                                                     0.2
                                                                     0.1
                                                                                  Distribution of PM ;5/Average TSP
                                                                              0.15 0.2 0.25 0.3  0.35 0.4  0.45  0.5  0.55  0.8 0.65 0.7

                                                                                           PM2S/AveraoeTSP

                                                                                 Comparison of PM2>/TSP and Average TSP	
    a  a
a   a
        mi
                                                D
                                                    a D
                                            a   o   a°*

                                            D   a   ™ a   a    D
                                               a  a   a
                                                                          20
                                                                                   40
                                                                                        60

                                                                                       TSP, (jg/m3
                                                                                                     80
                                                                                                              100
                                                                                                                       120
Figure 6-132. PM2 5 and TSP Relationships in Philadelphia, IPN, South Broad Site, 3/82 to 12/83: (a) comparison of PM2 5
              with TSP, (b) frequency distribution of PM2 5/TSP, (c) comparison of PM25/TSP with PM25,
              (d)  comparison of PM2 5/TSP with TSP.

-------
Os
            S
            0.
               60
               50
               40
               30
               20
                10
                             Comparison of TSP and PM;
                                                        PHILADELPHIA, AIRS, 1987-1990; PM2.5
                                  D


                                 G
                      a
                      D
                     a
               DO I

              -,0 D.
                        ID  aafaa n  n °

                            oa    a
                                                 D

                                               D   D

                                                 D  D
                             D

                             D
               1.1

                1


               0.9

               0.8


               0.7


               0.6

               0.5


               0.4

               0.3


               0.2


               0.1


                0
20     40      60      80     100

                  TSP, ug/m3

         Comparison of TSP and PM
                                                         120
                                                  i?_
               3D
             DDD
           n    °
          ,    D a  a
°BDD  OUB£  D°o   D  D

                >s    D
                                 20
                                                 40
                                      PM
                                                                140
                                                                 60
                                                                     Distribution of PM25/Average TSP
                                                         I 0.15  0.25  0.35  0.45 I 0.55 I 0.65 I 0.75 I 0.85
                                                        0.1   0.2   0.3   0.4   0.5   0.6   0.7   0.8   0.9

                                                                       PMj.5/TSP

                                                            Comparison of TSP and PM2.5






E
GK
0."
03
t
s
a.





i.i

1
0.9

0.8
0.7

0.6

0.5
0.4
0.3
0.2

0.1
n

n


D
n
n
n n
d3 D n
Q j-i Q
D n
% eg D o a
o^ D«Dana D Qa a D
- *DP|O 0DDOrfSP^ J^n*8^ DDD °n
" QDo ^fl'J'^D D^ff0*1 a^ B* * D' °
D Da#*aig]?i Dcptn'°D DQ Dan °
" a go a
D D
i i i i i i i i i i i i i
                                                                                                    TSP, ug/m3
       Figure 6-133.  PM25 and TSP Relationships in Philadelphia, AIRS, 1987 to 1990:  (a) comparison of PM2 5 with TSP,
                     (b) frequency distribution of PM2 5/TSP, (c) comparison of PM2-5 /TSP with PM2.5, (d) comparison of
                     PM25//TSP with TSP.

-------
                                                     PHILADELPHIA, IPN, S. BROAD, 3/62-12/83; PM,0
ON

tb
4*.
ON






E
"3»
a.

S
0.








80
70


60

SO



40

30



20

10
n
Comparison of PM10 and Average TSP

__ D
D Dn
n
~~ D
D
D ° 0 D "
D DQ °
n a
0 a
a aa D GD
n ana D
a n o a a
Q SU on B D
a D Q, n a n n
BaH3 a DO a
TID OD
a am rP a Q n
cP D a ^ H D
n ^b n D

i i i i i i i i i i i
            a.
            eo
0.9



0.8



0.7 -



0.6 -



0.5 -



0.4



0.3 -



0.2



0.1



 0
                        TSP, (jg/m*


               Comparison of PM

 0.4



 0.3



 0.2



 0.1



  0
                                                                                           Distribution of PM10 /Average TSP
                                                                                       0.3  0.35 0.4 0.45 0.5 0.55 0.8  0.85  0.7  0.75  0.8 0.85


                                                                                                   PM10 /Average TSP


                                                                                          Comparison of PM10 /Average TSP and PM,0
a

a

cP
         D a a1
             I


          D urn



          a a
                                                                                                   a


                                                                                                   D
                                                                                  10
                                                                                               30
                                                                                                            50
                                                                                                                         70
                                                                                                   PM10 , pg/m'
        Figure 6-134.  PM10 and TSP Relationships in Philadelphia, IPN, South Broad Site, 3/82 to 12/83: (a) comparison of PM10

                       with TSP, (b) frequency distribution of PM10/TSP, (c) comparison of PM10/TSP with PM10, (d) comparison

                       ofPM10/TSPwithTSP.

-------
ON
80



70



60



50



40



30



20



10



 0
                            Comparison of TSP and PM1
                                                       PHILADELPHIA, AIRS, 1987-1990;
                                cna
                   20
                          40
                                 60      60

                                    TSP,
                                               100
                                                       120
                                                              140







E
Q>
a.
o."
ia
t
0
s
Q.







1.6
1.5
1.4

1.3

1.2
1.1
1
0.9
0.8
0.7

0.6
0.5

0.4
0.3
n ?
Comparison of PM,n /Avg TSP and Avg TSP

n
_
D
-
D
D
n
c^
- n D
D Dn n DD o D cP D
DD rJ?n ^ ° @a
-------
oo
           TABLE 6-13. RELATIONSHIPS BETWEEN PMX (PM2 5 OR PM10) AND TSP AS A FUNCTION OF TSP
           CONCENTRATION LEVELS FOR SEVERAL SITES IN PHILADELPHIA: (a) RATIO OF PMX TO TSP,
                               (b) COEFFICIENT OF DETERMINATION (R2)
(a) Ratio of PMX/TSP

Philadelphia
Site
IPN
Average
IPN
S. Board
AIRS


Philadelphia
Site
IPN
Average
IPN
S. Board
AIRS


Dates
3/79
12/83
3/82
12/83
1/87
12/90



Dates
3/79
12/83
3/82
12/83
1/87
12/90

TSP
All
0.335 ±0.108
0.371 ± 0.105
0.345 ±0.137


TSP
All
0.64
0.57
0.45
PM25/TSP
TSP
<80
0.325 + 0.107
0.361 ± 0.106
0.350 ± 0.114

PM2 5 with
TSP
<80
0.36
0.38
0.29

TSP
>80
0.363 ±0.107
0.416 ± 0.090
0.317 ± 0.083
(b) Coefficients

TSP
>80
0.50
0.48
0.34

TSP
All
NA
0.525 ±0.105
0.573 ±0.187
of Determination, R2

TSP
All
NA
0.78
0.55
PM10/TSP
TSP
<80
NA
0.516 ±0.107
0.581 ±0.194

PM10 with
TSP
<80
NA
0.57
0.42

TSP
>80
NA
0.573 ± 0.079
0.528 ±0.131


TSP
>80
NA
0.61
0.24

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6.10.3.3 Correlations Between PM2>5, PM(10.2.s)> and PM10
     The analysis of epidemiological results suggest that the smaller size fraction of
particulate matter may have a stronger association with health outcomes than fractions that
contain larger size particles (Chapter 12).  It is of interest, therefore, to examine the
correlations between PM2 5, PM(10_2 5), and PM10.  The means of these fractions and the
coefficient of determinination, R2, for their relationships are shown in Table 6-14 for eight
sites in California (CARB, 1995) and in Table 6-15 for several sites and times for Philadelphia
(IPN, 1985; AIRS, 1995; Harvard, 1995).
     If correlation between PM2 5 and PM10 is high but the correlation of PM(10_2 5) with both
PM2 5 and PM10 is low, it is possible that PM10 is serving as an indicator of PM2 5 and that
any health effects of PM(10_2 5> would be masked by the larger PM2 5 (Wilson and Suh, 1996).
This may be the case in Philadelphia since PM2 5 to PM10.  In general, PM(10_2 5) is a larger
fraction of PM10 at the California sites than at the Philadelphia site. However, there is still
substantial variability (-40% from minimum to maximum) in this ratio in the data sets from
California.  Correlations between PM2 5 and PM(10_2 5) are highly variable at the sites in
California and encompass the  Philadelphia value.  The large correlations seen between PM2 5
and PM(10_2 5) at several California sites suggest a significant contribution from crustal
material to PM2 5. In contrast, at the Philadelphia site, only PM2 5 and not PM(10_2 5) was
highly correlated with PM10.  These data support the  desirability of having independent data
on fine mode particles  and coarse mode particles for epidemiological investigations.

6.10.3.4 Fine Fraction
     The fine fractions of PM10 (PM2 5/PM10) were shown for Philadelphia in Figure 6-116
(Panels c and d) and for California sites in Figures  6-123 to 6-130.  A strong seasonal
variation is evident at the California sites but not in Philadelphia. Numerical values of the
PM2 5 fractional contribution to PM10 are given for Philadelphia and for several California
sites in Table 6-16.  These variations in PM2.5/PM10 demonstrate the difficulty of inferring
PM2 5 from PM10 measurements unless some information is available on PM2 5/PM10 on a
seasonal and geographic basis.
                                        6-249

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 TABLE 6-14. MEANS AND STANDARD DEVIATIONS FOR PM2 5, PM(10_2 ^
AND PM10 AND COEFFICIENTS OF DETERMINATION (R2) BETWEEN PAIRS
   FOR EIGHT CALIFORNIA AIR RESOURCES BOARD SITES DURING
                   THE PERIOD 1989 TO 1990
Mean ± Standard Deviation
Site
Riverside
Azusa
Bakersfield
Visalia
Stockton
San Jose
El Centra
Lone Pine
PM2.5
34.1 ±24.3
25.9 ± 17.2
24.2 ± 24.2
23.0 ± 20.5
17.4 ± 16.7
13.9 ± 14.1
12.3 ± 8.2
6.5 ± 3.7
PM(10-2.5)
34.5 ± 19.5
25.5 ± 14.5
33.7 ± 33.6
23.3 ± 15.9
17.8 ± 10.8
11.9 ±6.7
31.5 ± 25.4
12.1 ± 11.7
PM10
68.6 ± 37.6
51.3 ± 27.7
57.0 ± 27.7
46.3 ± 26.7
35.6 ± 21.8
25.8 ± 17.9
43.8 ± 30.5
18.6 ± 13.8
Coefficient of Determination, R2
Site
Riverside
Azusa
Bakersfield
Visalia
Stockton
San Jose
El Centra
Lone Pine
PM2 5 to PM(10.2.5)
0.21
0.27
0.36
0.36
0.05
0.16
0.27
0.19
PM2 5 to PM10
0.79
0.79
0.86
0.66
0.77
0.88
0.50
0.42
PM(10.,5)toPMlo
0.67
0.71
0.74
0.41
0.44
0.48
0.94
0.94
                           6-250

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 TABLE 6-15. MEANS AND STANDARD DEVIATIONS FOR PM2 5, PM(10_2>5), PM10,
   and TSP AND COEFFICIENTS OF DETERMINATION (R2) BETWEEN PAIRS
             FOR SEVERAL SITES IN PHILADELPHIA DURING
                       PERIODS FROM 1979 TO 1995
Philadephia
Site
IPN Average
IPN S. Board

AIRS

Harvard PBY


Site
IPN Average
IPN S. Board

AIRS

Harvard PBY

Mean ± Standard Deviation
Dates
3/79
12/83
3/82
12/83
1/87
12/90
5/92
5/95

Dates
3/79
12/83
3/82
12/83
1/87
12/90
5/92
5/95
PM2.5 PM(10.2.5) PMjo TSP
23.3 ± 13.3 NA
22.6 ±11.0 9.7 ±4.

19.9 ± 10.0 13.1 ± 6

17.4 ± 9.4 7.0 ± 4.

Coefficient of
PM2 5 with PM2 5
PM(io-2.3) with PM
NA NA
0.14 0.90

0.32 0.86

0.11 0.88

NA 68.2 ± 24.7
7 32.1 ± 13.5 61.1 ±20.5

.7 33.0 ± 14.9 58.4 ± 21.9

3 24.3 ± 11.5 NA

Determination, R2
PM(iO-2.5) PM2.5
10 with PM10 with TSP
NA 0.64
0.42 0.57

0.69 0.45

0.41 NA

6.11 SUMMARY AND CONCLUSIONS
    This chapter presents ambient concentration measurements of paniculate mass, PM10,
PM2 5, and PM(10_2 5), and of the chemical composition of particulate matter.  For PM10
measurements the number of urban monitoring stations in the AIRS network increased rapidly
hi the years immediately after 1985, but the increase slowed substantially hi the early 1990s.
The measurements of PM10 at most of these stations were made every 6th day. Measurements
                                  6-251

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     TABLE 6-16. PM2 S/PM10 (FRACTION OF PM10 CONTRIBUTED BY PM2 s)
Mean Standard Deviation
Philadelphia
Mar-May
Jun-Aug
Sept-Nov
Dec-Feb
Azusa
Visalia
San Jose
Riverside
Stockton
Bakersfield
Lone Pine
El Centre
Riverside
Winter
Spring
Summer
Fall
0.71
0.73
0.73
0.72
0.75
0.50
0.49
0.49
0.49
0.46
0.44
0.38
0.29

0.57
0.48
0.41
0.48
0.13
0.14
0.16
0.17
0.15
0.13
0.22
0.15
0.14
0.18
0.19
0.14
0.10

0.14
0.13
0.09
0.15
Coefficient of Variation
(%) Range
18
19
22
24
20
26
45
31
29
39
43
37
34

25
27
22
15

0.09-1.09
0.30-1.56
0.17-1.81
0.03-1.55









0.22-0.99
0.22-0.76
0.23-0.69
0.16-0.74
of chemical species in urban areas usually are obtained in special studies of limited duration.
Data for chemical species in urban areas are discussed as appropriate in the text.
     The mass concentration measurements in urban areas have been used to obtain (a) annual
trends in PM10, (b) ratios and correlations of PM2 5 to PM(10_2 5) and PM10 and (c) seasonal
variations in PM10, PM2 5, and PM(10.2.5)-
     The measurements at non-urban sites were collected at a much smaller number of
locations relative to the number of urban stations by region.  The geographical location of the
sites in the IMPROVE/NESCAUM networks were not selected to optimize their locations
relative to AIRS stations in the same region. As a result, not only are there small numbers of
                                       6-252

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non-urban sites by region, but most of these sites are geographically well displaced from urban
areas.
     The non-urban concentration measurements include both mass and chemical composition
so they were used to obtain (a) the variations in PM10, PM2 5, and PM(10_2 5) with month of the
year, (b) the chemical balances for sulfates, organic carbon, elemental carbon, and soil with
month of the year and (c) the variations in the concentrations of S, Se, and V and the S to Se
ratio with month of the year.
     From the urban and non-urban PM10 concentration measurements, an "urban excess" was
obtained from the monthly differences in AIRS and IMPROVE/NESCAUM PM10 values.
Because of the limitations mentioned above and the lack of tests of statistical significance,
these "urban excess" values should be viewed as preliminary and used very cautiously with
respect to quantitative results.
     Additional sections of Chapter 6 include the following discussions:  (1) the mass
apportionment of chemical species obtained from a group of selected research studies of the
chemical composition at locations in the eastern, central and western U.S.; (2) acid sulfate
study results by (a) their geographical distribution in the U.S. and  southern Canada, (b) spatial
variations on a city and urban scale, (c) seasonal variations, (d) diurnal variations, and
(e) indoor and personal monitoring relative to outdoor hydrogen ion concentration
measurements; (3) particle number concentrations with emphasis on ultrafine particles;
(4) some information on metals potentially present in ultrafine particles; and (5) information
on fine and coarse PM trends and patterns for sites where both fine and coarse PM
measurements were available.
     Based on these various concentration measurements a considerable number of
conclusions may be obtained. Many of these conclusions are limited by (a) the number of
monitoring sites available, (b) their geographical location, (c) the frequency of measurement
and (d) differences in methodology used between networks or stations as well as between
individual studies of chemical composition.
     Trends in PM10 mass concentration, averaged over regions or by city, usually indicate a
substantial decrease in PM10 concentrations by year from 1988 to 1994.  There are exceptions
to this  significant  downward trend in Philadelphia and at some locations within the Southern
                                         6-253

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California Basin.  The trend plots shown in Chapter 6 have not been tested for statistical
significance.  The trend plots can also be influenced by the approach taken in the selection of
stations.  Since the number of stations increased rapidly between 1985 and 1990, the trends
that might be obtained using early data could be biased by the added stations being influenced
by location towards higher or lower PM10 concentration measurements. For this document,
the set of stations in operation from 1988 to 1994 was used to obtain PM10 concentration
trends during this period.  It should also be noted that meteorological influences which are
known to be important for deducing trends of O3 concentrations also may  affect PM10
concentrations on a year-to-year basis.
     Keeping the limitations mentioned above in mind, urban trend analyses for PM10 are
presented using all stations in operation in a given year and the smaller set of trend stations in
operation over the entire 1988 to 1994 time period.  The range for the averaged decrease in
PM10 between 1988 and 1994 at urban stations was:  for the contiguous U.S., all sites, 24%,
trend sites, 20%; for the eastern U.S., all sites, 16%, trend sites, 18%; and for the western
U.S., all sites, 31%, trend sites 28%.  There were appreciable differences between regions in
the range of averaged decreases in PM10 between 1988 and 1994 with the decrease for urban
stations in the northeast ranging from 18% (all) to 19% (trend) while in the industrial midwest
the decreases ranged from 12% (all) to 19% (trend). The ranges of averaged decreases for the
three western regions were from 27% to 37%  (all) and 23% to 33% (trend).  These decreases
in PM10 concentrations resulted in 1994 annual average regional AIRS concentrations in the
range of 25 Mg/m3 to 32 /ug/m3.
     For individual cities, both between and within cities, the decreases in PM10 for individual
stations could show substantial variability.  In the Los Angeles Basin, 3 of 6 stations showed
statistically significant downward trends in PM10 while other stations showed no significant
trends.  In the western U.S. several large cities showed larger downward  trends in PM10 than
the regional averages. PM2.5  and PM(10-2.5) or PMIO data, suitable for determining trends
of both fine and coarse components of PMIO,  are available from only a few sites in the eastern
United States and a few sites in California.  While a general decrease is evident in both fine
and coarse components of PMIO at most sites  where data is available, it is not possible to
ascertain differential trends in the two components.
                                         6-254

-------
     A few attempts to infer various types of background levels of PM2 5 and PM10 have been
made.  The backgrounds most relevant to the Criteria Document include a "natural"
background which excludes all anthropogenic sources anywhere in the world, and a
background which excludes anthropogenic sources in North America, but not elsewhere.
Annual average natural background levels of PM10 have been estimated to range from 4 to
8 fig/m3 in the western United States and 5 to 11 /ig/m3 in the eastern United States.
Corresponding PM2 5 levels have been estimated to range from 1 to 4 /-ig/m3 in the western
United States and 2 to 5 /Ltg/m3 in the eastern United States.  Twenty-four hour average
concentrations may be substantially higher than the  annual or seasonal average background
concentrations presented in Chapter 6.  The 24-hour averages are usually  considered for
control strategies while the annual and seasonal averages are suitable for risk analyses.
     Based either on the correlation of individual values or on the average PM2 5 to PM10
values, the annual ratios of PM2 5 to PM10 from urban stations fell within a  relatively narrow
range of 0.55 to 0.6, for both the entire eastern and western U.S.  However, for two regions,
the upper midwest and southwest, the correlations yielded ratios of less than 0.2 while the
average PM2 5 to PM10 values yielded ratios between 0.3 and 0.4.
     Ratios of PM2 5 to PM(10_2 5) from urban  stations can vary with season as well as
between regions. In the northeast, southeast, and industrial midwest regions, there is
appreciable uniformity with PM2 5 exceeding PM(10_2 5) during all seasons of the year.
In contrast, in the southwest, the PM2 5 is less than the PM coarse during all seasons of the
year.  In the northwest and in southern California, PM2 5 exceeds PM10 in the fall and winter
with the reverse occurring  in the spring and summer.
     Measurements of the  day to day variability in PM2 5 and PM10 are available from only
one site located in Philadelphia,  PA. The data show day to day variations of 8.6±7.5 /ig/m3
for PM10,  6.8±6.5 pcg/m3 for PM2 5, and 3.1 ±3 A /*g/m3 for PM10_2 5 from May 1992 to
April  1995. Maximum day to day differences were 50 /ig/m3 for PM10, 55 /-ig/m3 for PM2 5,
and 35 /*g/m3 for PM(10.2 5). The ratio of PM2 5 to  PM10 was 0.72±0.16 over the
measurement period and the correlation between PM2 5 and PM10 was 0.86  (R2) suggesting
that variability in PM2 5 was forcing the variability in PM10.  Data collected by dichotomous
samplers at several  sites in California showed that PM(10_2 5) accounted for roughly half of
                                        6-255

-------
PM10 and that both PM2 5 and PM(10_2 5) were highly correlated with PM10. Differences
among the Philadelphia data set and the California data sets illustrate the dangers in
extrapolating relations among different size fractions from one region of the country to other
regions.
     Comparisons of seasonal profiles of PM10 show summer peaks for both urban and
nonurban sites in the northeast, southeast, and industrial midwest.  These summer peaks
usually, but not exclusively, are associated with the summer peaks in PM2 5. The PM2 5
concentrations at non-urban sites in the northeast, southeast, and industrial midwest exceed the
PM(10_2 5) concentrations in all seasons of the year, as is the case for urban stations.  The
northwest urban PM10 and PM2 5 concentrations show a spring and early summer minimum
with the highest values in fall and winter, while the non-urban PM10 and PM2 5 concentrations
show a summer peak similar to the seasonal profiles in the eastern U.S.  In southern
California,  the urban PM10 and PM2 5 seasonal profiles show fall peaks, while the non-urban
seasonal profiles have a relatively flat maximum from spring into early fall. Again it must be
emphasized that with so few nonurban sites in most regions any conclusions drawn from the
comparisons above are very tentative for most regions of the U.S.
     The every-sixth-day urban PM10 averaged concentrations for most regions of the
United States ranged during 1990 to 1994 from 10 to 15 //g/m3 up to 40 to 60 Mg/m3. The
southern California region had PM10 values  averaging up to 70 to 75 //g/m3. Day-to-day
variations in PM10 concentrations in Knoxville, TN, ranged from 10 to 20 Mg/m3, while hi
Missoula, MT, PM10 concentrations ranged  from < 10 to 120 to 140 Aig/m3 with one value
over 200 Mg/m3.
     A quantity termed an urban excess has been discussed extensively in the text of
Chapter 6.  In view of the distinctions discussed above between the number and geographical
distribution of urban and non-urban sites, the quantitative results probably should be
interpreted  with considerable caution.  While it is reasonable that additional sources within
cities should increase PM10 concentrations significantly above those at non-urban sites, the
quantitative differences can be sensitive to the location of the non-urban sites with respect to
individual cities.  The most striking feature of the urban excess is its large increase hi the fall
and whiter  in the western United States compared to the eastern United States.
                                         6-256

-------
     The chemical compositions at the nonurban IMPROVE/NESCAUM sites are discussed
within the earlier sections of Chapter 6. Later in Chapter 6 an independent evaluation of
chemical composition is given based on a mixture of intensive studies at both urban and
nonurban sites.  The results from both approaches appear reasonably consistent in showing
geographical variations in chemical composition.
     Both approaches indicate that sulfate, presumably present either as (NH4)HSO4 or as
(NH4)2SO4, is the largest contributor to the chemical species measured in the eastern
United States. Other results indicate that a large regional background of sulfate is
superimposed on a smaller urban contribution. Results also indicate that sulfate is relatively
uniform in concentration throughout much of the eastern United States.  These results are less
pronounced in the late fall and winter months. The contribution of sulfate to PM10 is
somewhat smaller than sulfate is to PM2 5.  Comparisons of the eastern  United States with the
central United States and western United States show a decreasing contribution of sulfate to the
chemical composition.  Conversely, the soil and/or mineral concentrations become an
increasingly important contributor to PM10 and PM2 5  going from the eastern to the western
United States. The nitrates, as NH4NO3, also appear to be a much more important contributor
to the composition in areas of the western United States than in the eastern United States.
Organic compounds also appear to increase in importance relative to sulfate going from the
eastern to the western United States.  For PM(10_2-5), sulfates are relatively unimportant. Soil
or mineral components dominate the PM(10_2 5), but there is a substantial unknown fraction of
PM(10-2.5)'
     Particle strong acidity, defined as H2SO4 plus HSO4, is a regional pollutant fairly evenly
distributed  across large areas of the central portion of the eastern United States.  It is relatively
evenly distributed across small cities, but in the one large urban area from which results have
been reported, the higher concentrations of ammonia in the central city apparently neutralize a
significant portion of the acidity. Thus, higher concentrations of acidity are found in rural
areas, small towns,  and suburban areas than in the centers  of larger urban areas.  The
concentration of acidity is higher in the summer and peaks during the early afternoon in urban
areas. Indoor, outdoor, and personal monitoring indicates that indoor and personal
concentrations of acidity  are lower than outdoor concentrations, presumably due to
                                         6-257

-------
neutralization by indoor ammonia.  Particle strong acidity is normally found exclusively in the
fine particle mode.  Coarse particles tend to be basic.  Exceptions may occur during periods of
fog or very high relative humidity.
     The number concentration of particles is generally dominated by particles below 0.1 /urn
or 100 nm in diameter, termed ultrafine particles.  When a distinct mode is present, it is called
the nuclei mode.  Number geometric mean diameter ranged from 12 to 43 nm in Long Beach,
CA and 47 to 75 nm in clean air in the Rocky Mountains. Particle number concentrations
varied from less than 1,000/cm3 at clean background sites to over 100,000/cm3 in polluted
urban areas and were correlated with the volume of particles below 0.1 /urn.  Particle number
concentrations were not found to be correlated with accumulation mode volume on  an hourly
basis. Correlations of particle number and accumulation mode volume might be expected if
compared over longer time intervals (e.g., days), but such studies have not yet been done.
     An examination of the size distribution of metals suggests that metals that may be
volatilized during combustion may appear as ultrafine particles. Such metals include copper,
zinc, and lead and possibly nickel and vanadium, as well as nonmetals selenium and sulfur.
Ultrafine particles appear to exist longer under conditions of low concentrations and high
relative humidity.
                                         6-258

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        G. M.; Mueller,  P. K.; Grosjean, D.; Appel, B. R.; Wesolowski, J. J., eds. The character and origins
        of smog aerosols: a digest of results from the California Aerosol Characterization Experiment (ACHEX).
        New York, NY: John Wiley & Sons, Inc.; pp. 477-517. (Advances hi environmental science and
        technology: v. 9).

Whitby, K. T.; Husar, R. B.; Liu, B. Y. H. (1972) The aerosol size distribution of Los Angeles smog.
        J. Colloid Interface Sci. 39: 177-204.

White, W. H.  (1996a) Alternative perspectives on a trend hi eastern fine-particle sulfur concentrations. Environ.
        Manager: hi press.

White, W. H.  (1996b) Deteriorating air or improving measurements?—on interpreting concatenate time series.
        J. Geophys. Res. [Atmos.]: submitted.
                                                6-274

-------
White, W. H.; Marias, E. S. (1987) Particulate nitrate measurements in rural areas of the western United States.
        Atmos. Environ. 21: 2563-2571.

White, W. H.; Macias, E. S. (1989) Carbonaceous particles and regional haze in the western United States.
        Aerosol Sci. Technol.  10: 111-117.

Willeke, K.; Whitby, K. T. (1975) Atmosperic aerosols: size distribution interpretation. J. Air Pollut. Control
        Assoc. 25: 529-534.

Wilson, W. E.; Stockburger, L. (1990) Diurnal variations in aerosol composition and concentration.
        In: Masuda, S.; Takahashi,  K., eds. Aerosols: science, industry, health and environment: proceedings of
        the third international aerosol conference, v. 2; September; Kyoto, Japan. Oxford, United Kingdom:
        Pergamon Press pic; pp. 962-965.

Wilson, W. E.; Suh, H. H. (1996) Fine and coarse particles: concentration relationships relevant to
        epidemiological studies. J. Air Waste Manage. Assoc.: accepted.

Wilson, W. E.; Koutrakis, P.; Spengler, J. D. (1991) Diurnal variations of aerosol acidity, sulfate, and ammonia
        in the atmosphere. Presented at: 84th annual meeting and exhibition of the Air & Waste Management
        Association; June; Vancouver, BC, Canada. Pittsburgh, PA: Air & Waste Management Association;
        paper no. 91-89.9.

Wolff, G. T.; Korsog, P. E. (1985) Estimates of the contributions of sources to inhalable paniculate
        concentrations hi Detroit. Atmos. Environ. 19: 1399-1409.

Wolff, G. T.; Monson, P. R.; Ferman, M. A. (1979) On the nature of the diurnal variation of sulfates at rural
        sites hi the Eastern United States. Environ. Sci. Technol. 13: 1271-1276.

Wolff, G. T.; Countess, R. J.; Groblicki, P. J.; Ferman, M. A.; Cadle, S. H.; Muhlbaier, J. L. (1981)
        Visibility-reducing species in the Denver "brown cloud"~II. sources and temporal patterns. Atmos.
        Environ. 15: 2485-2502.

Wolff, G. T.; Ferman, M.  A.; Kelly, N. A.; Stroup, D. P.; Ruthkosky, M. S. (1982) The relationships between
        the chemical composition of fine particles and visibility hi the Detroit metropolitan area. J. Air Pollut.
        Control Assoc. 32: 1216-1220.

Wolff, G. T.; Kelly, N. A.; Ferman, M. A.; Morrissey, M. L. (1983) Rural measurements of the chemical
        composition of airborne particles in the eastern United States. J. Geophys. Res. C: Oceans Atmos.
        88: 10,769-10,775.

Wolff, G. T.; Korsog, P. E.; Stroup, D. P.; Ruthkosky, M. S.; Morrissey, M. L. (1985) The influence of local
        and regional sources on the concentration of inhalable paniculate matter hi southeastern Michigan.
        Atmos. Environ.  19: 305-313.

Wolff, G. T.; Ruthkosky,  M. S.; Stroup, D. P.; Korsog, P. E.; Ferman, M.  A.; Wendel, G. J.; Stedman, D. H.
        (1986) Measurements of SOX, NOX and aerosol species on Bermuda. Atmos. Environ. 20:  1229-1239.

Wolff, G. T.; Ruthkosky,  M. S.; Stroup, D. P.; Korsog, P. E. (1991) A characterization of the principal PM-10
        species hi Claremont (summer) and Long Beach (fall) during SCAQS. Atmos. Environ. Part A
        25: 2173-2186.

Wyzga, R. E.; Lipfert, F. W. (1996) [Philadelphia TSP data.] Palo Alto, CA: Electric Power Research Institute.
                                                 6-275

-------
Zhang, X.; Turpin, B. J.; McMurry, P. J.; Herring, S. V.; Stolzenburg, M. R. (1994) Mie theory evaluation of
        species contributions to the 1990 wintertime visibility reduction in the Grand Canyon. J. Air Waste
        Manage. Assoc. 44: 153-162.
                                                6-276

-------
           APPENDIX 6A:
TABLES OF CHEMICAL COMPOSITION OF
       PARTICIPATE MATTER
               6A-1

-------
TABLE 6A-la.  SUMMARY OF PM2 s STUDIES
EAST
Smoky Mtn.
Shenandoah
Camden
Philadelphia
Deep Creek
Roanoke
Raleigh
Watertown
Hartford
Boston
Res.Tr. Pk.
Charlotte
Allegheny Mtn.
Allegheny Mtn.
Laurel Hill














REF
1
1
2
3
4
5
5
6,7
8
8
8
20
44
45-50
45-50














NOTE WEST
Boise
Tarrant CA
b Five Points CA
Riverside CA
c San Jose
d Honolulu
d Winnemucca NV
Portland
a Seattle
a Southern California
a San Joaquin Valley
e Phoenix
Nevada
















REF
5
8
8
8
8
8
8
8
8
9,31
10
11
12
















NOTE CENTRAL
d Albuquerque
a St. Louis
a Steubenville
a Harriman
a Portage
a Topeka
a Inglenook AL
a Braidwood IL
a Kansas City KS
g,h Minneapolis
i St. Louis
j Kansas City MO
f Akron
Cincinnati
Buffalo
Dallas
El Paso
Denver
Urban Denver
Non-urban
Denver
Chicago
Houston
St.Louis
Harriman
St. Louis
Steubenville
Brownsville
Ontario
REF
5
6,7
6,7
6,7
6,7
6,7
8
8
8
8
8
8
8
8
8
8
8
13
14
14

15
16
17
17
18
21
24
37
NOTE
d





a
a
a
a
a
a
a
a
a
a
a

m
aa





k

n
1
                6A-2

-------
TABLE 6A-lb. SUMARY OF COARSE FRACTION STUDIES
EAST
Smoky Mtn.
Shenandoah
Camden
Philadelphia
Watertown
Hartford
Boston
Res.Tr. Pk.
Allegheny Mtn.
Allegheny Mtn.
Laurel Hill










REF NOTE
1 0
1 0
2 b
3 ab
6,7 o,p
8 a,o
8 a,o
8 a,o
44
45-50
45-50










WEST
Tarrant CA
Five Points CA
Riverside CA
San Jose
Honolulu
Winnemucca NV
Portland
Seattle
Southern California
San Joaquin Valley
Phoenix










REF
8
8
8
8
8
8
8
8
9,31
10
11










NOTE
a,o
a,o
a,o
a,o
a,o
a,o
a,o
a,o
g
i
j










CENTRAL
St. Louis
Steubenville
Harriman
Portage
Topeka
Inglenook AL
Braidwood IL
Kansas City KS
Akron
Cincinnati
Buffalo
Dallas
El Paso
Denver
Chicago
Houston
St. Louis
Harriman
St. Louis
Brownsville
Ontario
REF
6,7
6,7
6,7
6,7
6,7
8
8
8
8
8
8
8
8
13
15
16
17
17
18
24
37
NOTE
o.p
o,p
o,p
o,p
o.p
a,o
a,o
a,o
a,o
a,o
a,o
a,o
a,o
0
s
o


k,r
n
1
                    6A-3

-------
TABLE 6A-lc. SUMMARY OF PM10 STUDIES
EAST
Smoky Mtn.
Shenandoah
Camden
Philadelphia
Kingston
Watertown
Hartford
Boston
Res.Tr. Pk.
Allegheny Mtn.
Allegheny Mtn.
Laurel Hill















REF NOTE
1 o,q
1 o,q
2 b
3 ab
6,7 p,q
6,7 p,q
8 a,q
8 a,q
8 a,q
44
45-50
45-50















WEST
Tarrant CA
Five Points CA
Riverside CA
San Jose CA
Honolulu HI
Winnemucca NV
Portland OR
Seattle
Southern California
San Joaquin Valley
Phoenix
San Fran. Bay
San Jose
Palm Springs
Pocatello, ID
Tuscon
Rillito, AZ










REF
8
8
8
8
8
8
8
8
9,31
10
11
29
29
38
39
40
42










NOTE
a,q
a,q
a,q
a,q
a,q
a,q
a,q
a,q
g,h
i
j
V
w
t

u











CENTRAL
St. Louis
Harriman
Steubenville
Portage
Topeka
Inglenook AL
Braidwood IL
Kansas City KS
Minneapolis
St. Louis
Kansas City MO
Akron
Cincinnati
Buffalo
Dallas
El Paso
Denver
Chicago
Houston
St. Louis
Harriman
St. Louis
Brownsville
Utah Valley
Ontario
SE Chicago, IL
Ohio
REF
6,7
6,7
6,7
6,7
6,7
8
8
8
8
8
8
8
8
8
8
8
13
15
16
17
17
18
24
26
37
41
43
NOTE
p,q
p,q
p,q
p,q
p,q
a,q
a,q
a,q
a,q
a,q
a,q
a,q
a,q
a,q
a,q
a,q
q
s
q


X


1

y
                6A-4

-------
                  FOOTNOTES FOR TABLES 6A-la THROUGH 6A-2c
a.    Inhalable Particle Network (IPN) Data. Only represents days of elevated concentrations—dichot filter
      loadings >50 /tg/cm2.
b.    Data from Site 28 only.
c.    Average of all 6-h samples.
d.    Avg over all day/nite samples.
e.    Average of all 12-h samples at 2 incin. sites and 2 background sites.  Only XRF values which
      exceeded associated uncertainties more than half the time at all four sites were included.
f.    Average from Sparks site and Reno site.
g.    Sampling only during intensive episodes.
h.    Averages based on 12-h day/nite samples. There were 59 sampling days at Claremont and 23 sampling
      days at Long Beach.
i.    Avg over all sites: Stockton, Crow's Landing, Fresno, Kem, Fellows, and Bakersfield.
j.    Average of Central Phoenix, West Phoenix, and Scottsdale sites.
k.    Avg of RAPS site 106.
1.    Average from Walpole, Windsor 1, and Windsor 2 sites.
m.    Avg of 3 urban sites: Auraria, Federal, and Welby.
n.    Median VAPS values from Central site.
o.    2.5-15 iaa.
p.    Coarse concentrations may be 30% or more underestimated due to losses from handling filters.
q.    PM15.
r.    2.4-20 ftm.
s.    No upper size cutoff on VAPS inlet.
t.    Average of Palm Springs and Indio, CA.
u.    Avg. of Downtown Tuscon, Orange Grove, Craycroft, and Corona de Tuscon sites.
v.    Mean of annual avgs (1988-1992) from ~9 sites in Alameda, San Francisco, and Santa Clara counties.
w.    24-h average  of day/nite concentrations at two sites in San Jose.
x.    PM20.  Average  from RAPS site 106.
y.    Avg. of Follansbee, Mingo, Sewage Plant, Steubenville, and WTOV Tower sites in Ohio.
z.    Average of urban sites: Fresno, Bakersfield and Stockton.
aa.    Average of nonurban sites: Brighton and Tower.
ab.    Castor Avenue site only.
                                              6A-5

-------
                      APPENDIX FOR TABLES 6A-la THROUGH 6A-2c.  BIBLIOGRAPHY FOR PM STUDIES
      Ref No.
Sites
Dates
Types of Samples
Data
Comments
>
                1) Smoky Mtn.
                2) Shenandoah Valley
                3) Abastumani Mtn.

                Philadelphia - 3 sites
                Philadelphia
                Deep Creek Lake
                1) Albuquerque
                2) Raleigh
                3) Boise
                4) Roanoke
                Portage,
                Topeka,Harriman,
                Kingston, St. Louis,
                Steubenville, Watertown
               1) Sept 1978
               2) Jul-Aug 1980
               3) Mar 24-Jul 1979

               Jul 14-Aug 13 1982
               Jul 25-Aug 14 1994
               August, 1983
               1) Dec 1984-Mar 1985
               2) Jan 1985-Mar 1985
               3) Dec 1986-Mar 1987
               4) Oct 1988-Feb 1989
               1979-1981
               Multi-season
               F+C(2.5-15), EC, OC,
               SO^, Nitrate. 12-h
               samples.

               F+C(2.5-10), EC, OC,
               S04=, NOj.  12-h (0600-
               1800) and (1800-0600).
               Fine mass, elements, OC,
               EC, SD, uncert., from 4
               sites

               Day/nite sampling (1000-
               2200, 2200-1000).
               Dichots. FM, CM, OC, EC,
               Gases, FP nitrate

               F & C (2.5-10) + Carbon,
               EOM, VOCs. 12-h samples,
               Day/night:
               0700-1900,1900-0700.
               FP & IP(2.5-15).  24-h
               (midnite-midnite), every
               other day.  No Carbon data.
                      1) Comparison of avg F&C
                      composition for 3 sites.


                      1) F+C composition at site 28.
                      2) 9-source CMB source app. for
                      site 28.
                      3) Mass Balance for 3 sites.

                      1) Measured PM2 5 mass, OC, EC,
                      elements, SD, unc. at each site.


                      1) Mean FP mass, OC, EC,  nitrate,
                      elements stratified by day/nite/all.
                      1) Mean comp. of F mass, EC,
                      OC EOM, at 4 sites.  2) daytime/
                      nightime/24-havgs for key species
                      at 4 sites.
                      1) Mean-t-SE by city for F+C
                      mass, metals.
                      2) Box-line plots by city showing
                      means and percentiles for F+C
                      mass, sulfate, Cl.
                      3) Time-series plots of F+C mass
                      & tot Sulfate.
                      4) Data summaries  only-no raw
                      data.
                    No CP data
                    presented;
                    Sampling only in
                    winter; focus on
                    woodstove
                    impact

                    Source of info
                    on geographical
                    and temporal PM
                    composition
                    variability.

-------
           APPENDIX FOR TABLES 6A-la THROUGH 6A-2c (cont'd).  BIBLIOGRAPHY FOR PM STUDIES
Ref No.
Sites
Dates
Types of Samples
Data
Comments
          Harvard 6-cities
             1) 1977-1985 TSP
             2) 1979-1985 PM10
             &PM25
             3) 1979-1984
             Sulfate
          IPN study -25 sites.   Throughout 1980.
          Los Angeles
          (SCAQS)
          40 locations

          Aerosol composition
             Summer (11
             episode days) and
             fall (7 episode
             days) 1987
             F+C(2.5-15), 24-h sample
             every 6th day. Only moderately
             or highly-loaded samples were
             included. No Carbon.

             Sequential 4-, 5-, and 7-h
             PM2 5 and PM10 on summer
             episode days, and 4- and 6-h
             samples in fall.

             Mass, elements, ions, sulfate,
             nitrate, Carbon, ammonium.
                       1) Table of Mean Air pollution
                       values for 6 cities: TSP,
                       Inhalable, Fine, Sulfate. No
                       comp.


                       1) F+C mass for -25 sites.
                       2) F+C mass, composition for
                       22 sites (No carbon)


                       1) Avg & Max PM10 and PM2 5
                       mass, ions, comp, Cv, Ce
                       stratified into summer and fall.
                       2) Plots of temp and spatial
                       variations of PM2 5 and PM10,
                       PM2 5 nitrate.
                       3) Ctot/EC for some sites
                  Temp and spatial
                  variations of PM2 5
                  and PM10
10        San Joaquin Valley
          6 sites

          Aerosol Composition
             Jun 1988-Jun 1989    24-h PM10 & PM2 5 every
                                6 days.

                                Mass, elements, ions (K+,
                                SO^, NH4+,Na+), EC, OC
                                         1) Summary of annual geometric
                                         avg, arith. avg, max 24-h PM10
                                         and PM2 5 mass by site.

                                         2) Ann. Avg Mass and comp. for
                                         PM10 and PM2 5 by site.
                                                     PM10 highest in winter
                                                     and dominated by F
                                                     mass; C >50% of
                                                     PM10 in summer and
                                                     fall. Data show
                                                     spatial and temporal
                                                     variations of PM10 and

-------
                APPENDIX FOR TABLES 6A-la THROUGH 6A-2c (cont'd).  BIBLIOGRAPHY FOR PM STUDIES
Ref No.
11

12







13


Sites
Phoenix PM Study

Phoenix
4 sites
Also comparison
aerosol data from
Denver, Reno, and
Sparks


Denver


Dates
Oct. 1989 - Jan.
1990
Sept. 1989 - Jan.
1990






Jan. 11-30, 1982


Types of Samples
F&C mass, elements, uncertainties
from 6 sites
6-h samples, 2x/day, (0600-1200,
1300-1900)
PM10 & PM2 5: mass, elements,
HNO3, SO2, NH3, FP NO3and SO^,
ionic species, OC, EC.



Dichotomous sampler, OC, EC,
nitrate, sulfate

Data


1) temporal variation of PM2 5
mass at 4 sites.
2) Mean, SD, & Max: PM2 5, EC,
OC, NOj, SO|% NH^ and
elements for 3 Phoenix sites
3) Same for Denver (11/87-1/88)
4) Same for Reno (11/86-1/87)
5) Same for Sparks (11/86-1-87)
1) Measured PM2 5 and Coarse,
elements, OC, EC, nitrate,
day/night samples; light extinction.
Comments


Moudi size-
resolved (0-
5.6 pm in 9 bins)
mass, NOj SO^,
OC, EC.



Source
apportionment for
F&C particles and
     14
oo
     15

     16


     17
Denver (SCENIC)
Nov. 1987-Jan.
1988
2x daily (0900-1600, 1600-0900).
PM2 5 mass, comp, sulfate, nitrate,
OC, EC, ionic species, gases
Chicago

Houston


St. Louis & Harriman
July, 1994

Sept. 10-19, 1980
Sept. 1985 - Aug.
1986
VAPS & Dichot. FM, CM, OC, EC,
elements, S02> HONO, HNO3.
Dichotomous sampler: 0.1-2.5, 2.5-
15. 4 sites. Consecutive 12 h samples.

Daily F&C (2.5-10^m). Also SO2,
NO2, and 03.
1) Avg, SD, Mm, Max PM2 5
mass for 6 sites.
2) Avg, SD, Min, Max, for PM2 5
mass, ionic species, EC, OC,
elements for 3 sites.
3) Source profiles
4) SCE for 4 sites by day and
night
1) Avg VAPS mass, SD, uncert.
for F&C, OC, EC.
1) Average F&C mass, elements,
Carbon, NH^~, NOj, Sulfate

1) Mean, SD, range for PM10,
PM25, S04=, H+, S02, N02, 03
for both sites.
extinction.
Source
Apportionment
study
Source
apportionment.

-------
            APPENDIX FOR TABLES 6A-la THROUGH 6A-2c (cont'd).  BIBLIOGRAPHY FOR PM STUDIES
Ref No.
        Sites
      Dates
        Types of Samples
                 Data
   Comments
18
19
20
21
22
23
24
St. Louis
1) Albuquerque
2) Denver
Charlotte (2 incin
sites and 2 control
sites).
Steubenville
Review of PM
studies
Phoenix
                      10
Jul 1976-Aug 1976
(St. Louis)

RAPS data for St.
Louis exist for May
1975-Mar 1977 but
were not in this
article
F(<2.4) & C (2.4-20) 6-12 hr.
No Carbon.
1) Jan 3-4, 1983
2) Jan 19-20, 1982
F & C (2.5-10) + Carbon, Nitrate
& Sulfate (1C) 12-h samples,
Day/Night:
0700-1900,1900-0700.
Apr 30-Jun 4, 1992 VAPS F&C + Acid gases.
& Sept 21-28,      no carbon. 12-h samples
1992.
Jan-Dec 1984      24-h, F+C. No Carbon
1984-1990
Jan 5-27, 1983
PM
Brownsville —        Spring+Summer
residential and central  1993
sites.
                                                  10
F(<2.8)+C(>2.8).  1800-0800
12 h samples.

1)FP MES indoor/outdoor
2) VAPS central site
3) Dichot central site
1) 2-mo avg of F+C mass, metals, sulfate,
for one site.
2) F+C composition of selected samples
(different sites) during events.
3) CMB apportionment of F+C fractions to
6 components (crustal shale, crustal
limestone, ammonium sulfate, motor
vehicles, steel, paint).
4) Plots ofintercity variations in source
component concentrations
1) Mean daytime and nightime comp. of
F&C, EC, OC, nitrate, sulfate, for each site.
2) Source app. of Denver winter FP
composition.

1) Mean ambient FP cone.  + XRF unc. at 4
sites
2) CMB results for FP.
1) avg F mass + comp.
2) avg source contributions by SRFA
3) SRFA-derived source profiles
1) SCE's for PM10 mass for -15 studies
l)avg F+C nightime comp, mass, Cv.Ce,
1) Crude CMB
source
apportionment of
FP with 6
sources.
More complete
source app
results in Lewis
& Enfield paper.
ambient PM10
data sources are
cited but no data
is presented
                                                  2) CMB of FP
                                                  1) min, med, max for fine MES comp+mass  No avg values,
                                                  2) min, med, max F+C comp, mass for      only median .
                                                  VAPS and dichot at central site

-------
            APPENDIX FOR TABLES 6A-la THROUGH 6A-2c (cont'd).  BIBLIOGRAPHY FOR PM STUDIES
Ref No.
        Sites
      Dates
                                                              Types of Samples
              Data
    Comments
25
26
27
28
>
h—'
o
29
30
Sparks, Reno, Verdi,
NV (SNAPS)
Utah Valley (Linden
site)

Santa Clara County
San Joaquin Valley
6 sites

Source apportionment

SF Bay Area
2 sites
Los Angeles
(SCAQS)
40 locations

CMB Source Apport.
Apr 1986-Mar
1987
Apr 1985-Dec 1989
1980-1986: only
Nov, Dec, Jan data
used.
Jun 1988-Jun 1989
Dec 16, 1991-Feb
24, 1992
Summer (11
episode days) and
fall (7 episode
days) 1987
1) PM2 5 & PMj0 every 6th day.
24-h samples.  Also diurnal
sampling.

1) PM10 for 1736 days.  Also, SO2,
NO2, O3,  acidity data.

"COH" -coefficient of haze.
[COH/PM10=1.87or 1.64 (1985
and 1986)].
24-h PM10 & PM2 5 every 6 days.

Mass, elements, ionic species,
Carbon,

12-h daily day & nite (0600-1800,
1800-0600) PMi0 samples.
Mass, elements, ions (K+,C1,
SOJ , NH^ , Na+) Carbon,
ammonium.
                                                      Sequential 4-, 5-, and 7-h PM2 5
                                                      and PM10 on summer episode days,
                                                      and 4- and 6-h samples in fall.

                                                      Mass, elements, ions, sulfate,
                                                      nitrate, Carbon, ammonium.
                                                                                      1) Seasonal avg SCE for PM10 at
                                                                                      3 sites,  (geological, motor veh,
                                                                                      construction, vegetative, sulfate,
                                                                                      nitrate, OC, EC)
                                                                                      l)avg PM10 = 47 jig/m3.
                                                                                      sd=38, (min.max)=(1,365 ftg/m3).
                                                                                      2) freq distribution of PM10 mass.
                                                                                      1) Plots of COH vs daily mortality
                                                                                      for 2-yr periods.

                                                                                      1) Table of arm.  avg. SCE to PM10
                                                                                      and PM2.5 for data above, by site
l)Table of arm. avg. PM10 mass,
sulfate, nitrate statistics at 3 sites for
1988-1992
2) Avg. & Max day & nite PM10
mass, ions, comp, EC, OC, for both
sites
3) Source profiles
4) SCE pie charts for each site.
1) Source profiles
2) PMjQ SCE for summer and fall.
3) Diurnal SCE to PM10 at each site.
                                 No raw data
no comp. data.
Highest pmlO
during winter.
Examines relation
between mortality
and COH
For PM10 Mass,
Sulfate, and
Nitrate data, see
ref 27.

1. Highest PM10
mass during Nov,
Dec, Jan.
2. Wood combust.
 contributes
-45% of PM10.
Data show diurnal
changes in SCE
for PM10 mass.

-------
            APPENDIX FOR TABLES 6A-la THROUGH 6A-2c (cont'd).  BIBLIOGRAPHY FOR PM STUDIES
Ref No.
         Sites
     Dates
                        Types of Samples
Data
                                      Comments
31
32
33
34
35
1) Claremont (SCAQS)    1) Summer 1987
                       (59d)
2) Long Beach (SCAQS)  2) Fall 1987
                       (23d)
CADMP - 8 sites:
Gasquet, Fremont,
Bakersfield, Yosemite,
Sequoia, Long Beach,
Los Angeles, Azusa
Central California -53
sites in SF Bay area,
Sacramento Valley, San
Joaquin Valley, North
and South Central Coast,
Mountain Counties

Birmingham
Philadelphia
                Continuous 12-h
                PM10 and PM25.

                Mass, elements, ionic species,
                EC, OC
Summer 1988
1) 1989
                                2) July & August,
                                1988
1986-1989
                2 samples every 6th day.
                0600-01800, 1800-0600.
                PM2 5, PM10. Mass, ionic
                species,
                PM10 every 6th day. Sulfate and
                nitrate measured on a subset of
                these samples.
1973-1980
                Daily 24-h PM]0 mass. Also
                Ozone data.

                No composition data.

                24-h (midnite-midnite) TSP.

                No composition data.
1) Mean, SD, & Max: PM10, FPM,
CPM, EC, OC, N03 , SOJ ,
NH4+ .
2) Mean values of above species
during intensive and non-intensive
periods.
3) Day/nite values of above
4) PM10 and PM2 5 mass balances
5) Summary of EC, OC data.
1) Graph of avg PM10 & PM2 5
mass and ratio at 8 sites
2) Graphs of PM10 & PM2 5 ionic
concentrations.

1) 1989 Max and Avg PM10 mass,
Sulfate, and Nitrate for ~53 sites.
2) Summertime 1988 Avg, SD, and
Max PM10 and  PM2 5 Mass, comp,
OC.EC, Ionic species, for 3
SJVAQS sites.  [Annual data
summary is in ref 20].
1) Table of percentile points of the
distribution of PM10, O3, T,
DewPoint, Pneumonia, Chronic
obstructive pulmonary disease.
2) Avg PM10 and O3 by season
1) Table of percentile points of the
distribution of TSP, SO2, T,
DewPoint, Mortality.
                   Ask Chow/Watson for
                   raw data.
                   Aside: Indoor/Outdoor
                   ratios of 0.63 for PM10
                   were reported in
                   Tuscon.
36
State College, PA
summer 1990
                Indoor, outdoor, personal
                H+,andNH3
                   Validation of personal
                   exposure models

-------
 44
 45-50
             APPENDIX FOR TABLES 6A-la THROUGH 6A-2c (cont'd).  BIBLIOGRAPHY FOR PM STUDIES
Ref No.
37
38-43
Sites
Southern Ontario
3 sites
Miscellaneous sites
14 sites
Dates
Jan.-Nov., 1991
1984-1990
Types of Samples
24-h, midnite-midnite, every 6th
day. PMjQ dichot sampler.
PMj0 concentrations.
Data
l)Avg mass, elements, for F&C
fractions, for 3 sites. No OC, EC.
1) Measured PM10 mass and avg
source contributions (up to 10 source
Comments
Primary reference
is Ref 10.
Allegheny Mtn. SW PA July 24-Aug. 10
elev. 838 m            1977
Allegheny Mtn. and
Laurel Hill, SW PA
separation 35.5 km
Aug. 5-Aug. 28,
1983
Filters, impactors, gas samplers,
day/night

Filters, dichotomous samplers,
impactors, denuders, gas analyzers,
day/night
                                                   categories).
                                                   Aerosol mass, elements, H+,
                                                   SO 4  , NO3 , total C, size distributions,
                                                                                    scat'
Fine, coarse, and PM10 mass,
elements, EC, H+, NH| , SO^ ,
NC>3 , size distributions, CN counts,
bscat, babs, Lv, HNO3 and other gases,
rain, dew, 2-site correlation
Strong aerosol H+
found, associated
with SO^
Coordinated with
Deep Creek Lake
experiment, Ref.
4, =60 km to
SSW
References:
1.  Stevens et al. (1984)
2.  Dzubay et al. (1988)
3.  Pinto et al. (1995)
4.  Vossler et al. (1989)
5.  Stevens et al. (1993)
6.  Spengler and Thurston (1983)
7.  Dockery et al. (1993)
8.  Davis et al. (1984)
9.  Chow et al. (1994a)
10. Chow et al.  (1993a)
11. Desert Research Institute (1995)
12. Chow et al (1990)
13. Lewis et al.  (1986);
   Lewis and Dzubay (1986)
                          14. Watson et al. (1988)
                          15. Stevens, R. K (1995) [Unpublished
                              data].
                          16. Johnson et al. (1984)
                          17. Dockery et al. (1992)
                          18. Dzubay (1980)
                          19. Stevens (1985)
                          20. Mukerjee et al. (1993)
                          21. Koutrakis  and Spengler (1987)
                          22. Chow et al.  (1993b)
                          23. Solomon and Moyers (1986)
                          24. Ellenson et al. (1994)
                          25. Chow et al.  (1988)
                          26. Pope et al. (1992)
                                              27. Fairley (1990)
                                              28. Chow et al. (1992b)
                                              29. Chow et al. (1995a)
                                              30. Watson et al. (1994a)
                                              31. Wolff etal. (1991)
                                              32. Ashbaugh et al. (1989)
                                              33. Chow et al. (1994b);
                                                 Watson et al. (1994b)
                                              34. Schwartz (1994)
                                              35. Schwartz and Dockery
                                                 (1992)
                                              36. Suh et al. (1993)
                                              37. Conner et al.  (1993)
                                              38. Kim et al. (1992)
                                              39. Houck et al. (1992)
                                                             40. Chow et al. (1992a)
                                                             41. Vermette et al. (1992)
                                                             42. Thanukos et al. (1992)
                                                             43. Skidmore et al. (1992)
                                                             44. Pierson et al. (1980b)
                                                             45. Pierson et al. (1986)
                                                             46. Japar et al. (1986)
                                                             47. Pierson et al. (1987)
                                                             48. Keeler et al. (1988)
                                                             49. Pierson et al. (1989)
                                                             50. Keeler et al. (1990)

-------
TABLE 6A-2a. PM2 5 COMPOSITION FOR THE EASTERN UNITED STATES (/ig/m3)
Ref
Site
Dates
Time
Duration
(h)
Number
Mass
OC
EC
Nitrate
Sulfate
Acidity$
Al
As
Ba
Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
1
Smoky Mm.
9/20-26/78
0-12-24
12
12
24.00
2.22
1.10
0.30
12.00

<0.054
<0.003

0.018
0.016

< 0.010

0.003
0.028
0.040





1
Shenandoah
7/23-5/08/80
0-12-24
12
28
27.00
0.44
1.12

13.60

< 0.105
< 0.003

0.008
0.035

0.010

0.005
0.054
0.061





2(b)
Camden
7/14-8/13, 1982
6-18-6
12
50
28.70
2.05
1.87
<0.48
11.20

0.053
0.001

0.029
0.040
0.002
0.003
0.002

0.091
0.101

0.006
0.001
0.146
0.011
3
Philadelphia
7/25-8/14/94
9-9
24
21
32.18
4.51
0.76



0.114


0.009
0.058

0.026

0.007
0.127
0.060
0.023
0.003

0.070
0.007
4(c)
Deep Creek
8/83
4x daily
6
98
40.00
1.45
0.18
0.57



0.001

0.005
0.048




0.058
0.044

0.003

0.034

46, 49, 50
Allegheny Mm.
8/5-28/83
day /night
-10
44
49
2
1.2
0.5
17
9
0.058
0.0005
0.0048
0.004
0.027
0.0004
0.061
0.0016
0.0012
0.046
0.041
0.011
0.0032
0.0037
0.036
0.0009
46, 49, 50
Laurel Hill
8/6-27/83
day /night
-10
39
46
2
1.4
0.6
18
10
0.048
0.0006
0.0033
0.004
0.023
0.0004
0.038
0.0011
0.0020
0.062
0.040
0.009
0.0038
0.0031
0.034
0.0011
5(d)
Raleigh
1/85-3/85
7-19-7
12
NR
30.30
10.00
0.50



0.009
0.001

0.028
0.018

0.007

0.020
0.044
0.159

0.003
0.001


5(d)
Roanoke
10/88-2/89
7-19-7
12
NR
19.90
7.30
1.50



0.176
0.002

0.005
0.047

0.053
0.001
0.007
0.114
0.177

0.012
0.001


6,7
Watertown
5/79-6/81
00-24
24
354
14.90



5.85
20.300



0.088
0.041

0.084


0.074


0.004


0.009
8(a)
Hartford
1980
NR
24
2
26.75





0.035


0.036
0.070


0.003
0.043
0.125
0.171

0.007


0.010
8(a)
Boston
1980
NR
24
1
34.80






0.002

0.020
0.070


0.004
0.035
0.121
0.096

0.001


0.012
8(a)
Res.Tr.Pk
1980
NR
24
3
28.77





0.073
0.002

0.007
0.035



0.016
0.120
0.148

0.003


0.001

-------
              TABLE 6A-2a (cont'd).  PM2 5 COMPOSITION FOR THE EASTERN UNITED STATES 0*g/m3)
Ref
Site
Dates
Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
Si
Sn
> Sr
i
£ Ti
V
Zn
1
Smoky Mm.
9/20-26/78
00-12-24
12
12

0.097

3.744

0.001
0.038



< 0.006
<0.004
0.009
1
Shenandoah
7/23-5/08/80
00-12-24
12
28

0.052

4.539

0.001
0.116



< 0.010
<0.010
0.011
2(b)
Camden
7/14-8/13 '82
6-18-6
12
50

0.249

4.200
0.079
0.002
0.103
<0.012
< 0.002

< 0.027
0.013
0.082
3
Philadelphia
7/25-8/14/94
9-9
24
21
0.015
0.019

3.251

< 0.002
0.165



< 0.042
<0.013
0.041
4(c)
Deep Creek
8/83
4x daily
6
98

0.048

6.700
0.001
0.003
0.150




0.001
0.013
44, 45-50
Allegheny Mm.
8/5-28/83
day/night
-10
44
0.013
0.035
0.0005
5.9
0.0006
0.0018
0.23

0.0026

0.0041
0.0019
0.010
45-50
Laurel Hill
8/6-27/83
day /night
-10
39
0.019
0.039
0.0002
5.5
0.0006
0.0020
0.21

0.0027

0.0047
0.0017
0.012
5(d)
Raleigh
1/85-3/85
7-19-7
12
NR

0.096

1.729

0.002
0.076




0.003
0.015
5(d)
Roanoke
10/88-2/89
7-19-7
12
NR

0.027

1.177

0.002
0.077




0.004
0.083
6,7
Watertown
5/79-6/81
00-24
24
354

0.329

1.800

0.001
0.100




0.022

8(a)
Hartford
1980
NR
24
2

0.510

2.219

0.001
0.177



0.002
0.017
0.079
8(a)
Boston
1980
NR
24
1
0.009
0.285

3.869

0.001
0.144




0.020
0.046
8(a)
Res.Tr.Pk
1980
NR
24
3
0.042
0.106

2.835

0.002
0.350





0.018
References are listed in Table 1 Appendix. Associated notes are explained in Table 1.
"Values for this size fraction are calculated from the average measured values reported for the other two size fractions.
'Units for acidity are nmoles/m3.
NR = not reported.

-------
             TABLE 6A-2a (cont'd). PM2 5 COMPOSITION FOR THE WESTERN UNITED STATES (/ig/m3)
ON
Ref
Site
Dates
Time
Duration (h)
Number
Mass
OC
EC
Nitrate
Sulfate
Acidity*
Al
As
Ba
Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
9(g)
Los Angeles
Summer 1987
NR
4,5 and 7
1 1 days
41.10
8.27
2.37
4.34
9.41

0.035
0.022
0.015
0.013
0.022

0.093
0.022
0.063
0.099
0.041
0.024
0.016

0.202
0.005
9(g)
Los Angeles
Fall 1987
NR
4 and 6
6 days
90.20
18.46
7.28
22.64
4.38

0.250
0.015
0.043
0.065
0.335

0.453
0.025
0.273
0.557
0.217
0.075
0.043

0.466
0.007
100)
San Joaquin
Valley
6/88-6/89
NR
24
-35
29.89
4.87
3.24
8.17
3.00

0.152

0.012
0.010
0.0%
< 0.007
0.094
0.003
0.0%
0.180
0.188

0.006


0.016
H(j)
Phoenix
10/13/89-
1/17/90
NR
6 h, 2x/day
- 100 days
29.37
10.10
7.47
3.60
1.33

0.130
<0.020
<0.106
0.011
0.170
<0.018
0.365
0.003
0.015
0.216
0.207

0.023
<0.006

0.003
5(d)
Boise
12/86-3/87
7-19-7
12
NR
35.70
12.70
1.70



0.102
0.002

0.014
0.026

0.122
0.001
0.011
0.022
0.145

0.002
0.002


12(1)
Nevada
11/86-1/87
00-24
24
24
56.92
19.97
15.17
2.43
1.67

0.275
0.001
0.013
0.033
0.215

0.145
0.002
0.010
0.310
0.280

0.015


0.006
8(a)
Tarrant CA
1980
NR
24
6
57.05





0.177


0.102
0.455


0.002
0.047
0.316
0.186

0.032


0.003
8(a)
Five Points
CA
1980
NR
24
3
31.80





0.239


0.015
0.150

0.004
0.001
0.024
0.216
0.244

0.005


0.025
8(a)
Riverside
CA
1980
NR
24
4
35.18





0.036


0.037
0.301

0.009

0.040
0.127
0.120

0.007


0.007
8(a)
San Jose
CA
1980
NR
24
6
36.28





0.123
0.001

0.188
0.089

0.050
0.003
0.043
0.148
0.248

0.006


0.006
8(a)
Honolulu
1980
NR
24
1
21.10





1.127


0.017
1.024

0.518
0.004
0.018
0.726
0.371

0.020


0.002
8(a)
Winnemucca
1980
NR
24
5
9.68





0.361


0.006
0.243



0.026
0.231
0.149

0.003


0.001
8(a) 8(a)
Portland Seattle
1980 1980
NR NR
24 24
4 1
37.18 10.70





0.581 0.002
0.012 0.006

0.093 0.019
0.154 0.037

0.021
0.009 0.002
0.072 0.024
0.270 0.098
0.218 0.080

0.052 0.004


0.027 0.006

-------
              TABLE 6A-2a (cont'd). PM2 5 COMPOSITION FOR THE WESTERN UNITED STATES Gig/m3)
Ref
9(g)
9(g)
10(i)
H(j)
5(d)
12(f)
8(a)
8(a)
8(a)
Five Points Riverside
Site

Dates

Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
ON
> Si
i
ON Sn
Sr
Ti
V
Zn
Los Angeles

Summer 1987

NR
4, 5 and 7
1 1 days
0.060
0.038

2.832

0.013

0.052


0.019
0.005
0.006
0.090
Los Angeles

Fall 1987

NR
4 and 6
6 days
0.046
0.185

1.998

0.011

0.520


0.028
0.060
0.007
0.298
San Joaquin
Valley
06/88-06/89

NR
24
-35
0.007
0.029
0.001
1.242
< 0.002
0.001

0.460

<0.015
0.002
0.017
0.015
0.078
Phoenix

10/13/89-
1/17/90
NR
6 h, 2x/day
- 100 days
< 0.051
0.039
< 0.0025
0.437
< 0.033
< 0.002

0.430

<0.028

< 0.030
< 0.016
0.056
Boise

12/86-3/87

7-19-7
12
NR

0.045

0.603

0.001

0.069




0.001
0.019
Nevada

1 1/86-1/87

00-24
24
24
0.041
0.115
0.001
0.765



0.860


0.004
0.043
0.009
0.033
Tarrant CA

1980

NR
24
6

0.619

2.578



0.583



0.010

0.095
CA

1980

NR
24
3
0.007
0.087

1.129

0.001

0.656



0.005
0.006
0.016
CA

1980

NR
24
4

0.376

1.653

0.001

0.234




0.003
0.029
8(a)
San Jose
CA

1980

NR
24
6
0.013
0.891

0.852



0.292




0.002
0.061
8(a)

Honolulu

1980

NR
24
1
0.002
0.071

0.313



2.363



0.063
0.001
0.011
8(a)

8(a)

Winnemucca Portland

1980

NR
24
5

0.042

0.358



0.914



0.009

0.011

1980

NR
24
4
0.017
0.422

1.944

0.001

0.377



0.005
0.014
0.081
8(a)

Seattle

1980

NR
24
1
0.006
0.215

0.831

0.001

0.092





0.059
References are listed in Table 1 Appendix. Associated notes are explained in Table 1.
"Values for this size fraction are calculated from the average measured values reported for the other two size fractions.
$Units for acidity are nmoles/m3.
NR = not reported.

-------
TABLE 6A-2a (cont'd).  PM2 5 COMPOSITION FOR THE CENTRAL UNITED STATES
Ref
Site
Dates
Time
Duration (h)
Number
Mass
OC
EC
Nitrate
Sulfate
Acidity*
Al
As
Ba
Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
5(d)
Albuquerque
12/84-3/85
7-19-7
12
NR
20.60
13.20
2.10



0.077


0.085
0.059

0.036


0.045
0.074





13
Denver
1/11-30/82
6-18-6
12
-26
20.73
7.11
2.15
2.22
2.06

0.394
< 0.002
0.031
0.103
0.047
0.006
0.052
< 0.009
0.010
0.079
0.079

0.011


0.003
14(m) 14(aa)
Urban Denver Non-urban Denver
11/87-1/88 11/87-1/88
9-16-9 9-16-9
7&17 7&17
-136 -150
19.67 10.35
7.25
4.41
3.96
1.55

0.037


0.018
0.058
0.005
0.141
0.003
0.017
0.111
0.077

0.012


0.002
15
Chicago
7/94
8-8
24
16
13.57
5.39
1.31



0.046
< 0.003
<0.091
0.004
0.045
< 0.029
0.011
< 0.005
0.011
0.089
0.061
0.012
0.005
< 0.002
0.022
<0.001
16
Houston
9/10-19/80
NR
12
20
38.60
5.68
1.42
0.59
14.61

0.123
< 0.005
0.048
0.055
0.155
< 0.003
0.032
< 0.005
0.028
0.162
0.119

0.014

<0.38
0.004
6,7
Harriman
5/80-5/81
00-24
24
256
20.80



8.10
36.1



0.038
0.150

0.021


0.120


0.017


BQL
17 6,7
Harriman Kingston
9/85-8/86 5/80-6/81
NR 00-24
24 24
330 169
21.00 24.60



8.70
36.1



0.044
0.120

BQL


0.097


0.010


BQL
6,7
Portage
3/79-5/81
00-24
24
271
11.00



4.95
10.5



0.011
0.045

0.027


0.049


0.003


BQL
6,7
Topeka
8/79-5/81
00-24
24
286
12.50



4.40
11.6



0.045
0.250

0.031


0.090


0.004


BQL
8(a)
El Paso
1980
NR
24
10
27.16





0.155
0.025

0.070
0.332


0.001
0.036
0.134
0.127

0.004


0.001
8(a)
Inglenook
1980
NR
24
8
32.03





0.082
0.001

0.040
0.326

0.003
0.002
0.032
0.281
0.408

0.037


0.001

-------
             TABLE 6A-2a (cont'd). PM2 s COMPOSITION FOR THE CENTRAL UNITED STATES Otg/m3)
ON
00
Ref
Site
Dates
Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
Si
Sn
Sr
Ti
V
Zn
5(d)
Albuquerque
12/84-3/85
7-19-7
12
NR

0.237

0.507


0.076




0.007
13
Denver
1/11-30/82
6-18-6
12
-26
0.043
0.326
< 0.003
0.709


0.277

< 0.003
< 0.027

0.046
14(m)
Urban Denver
11/87-1/88
9-16-9
7&17
-136

0.075

0.642
0.004
0.001
0.272
0.006
0.001
0.009

0.031
14(aa) 15
Non-urban Denver Chicago
11/87-1/88 7/94
9-16-9 8-8
7&17 24
-150 16
0.008
0.027

1.321
< 0.042
<0.001
0.074
< 0.049

< 0.029
<0.009
0.052
16
Houston
9/10-19/80
NR
12
20
0.028
0.465
< 0.002
4.834
0.006
< 0.002
0.210
< 0.005
< 0.002
<0.014
<0.008
0.084
6,7
Harriman
5/80-5/81
00-24
24
256

0.180

2.500

0.002
0.120



BQL

17 6,7
Harriman Kingston
9/85-8/86 5/80-6/81
NR 00-24
24 24
330 169

0.194

2.400

0.002
0.200



BQL

6,7
Portage
3/79-5/81
00-24
24
271

0.061

1.400

0.001
0.075



BQL

6,7
Topeka
8/79-5/81
00-24
24
286

0.163

1.100


0.190



BQL

8(a)
El Paso
1980
NR
24
10

0.481

0.823

0.002
0.436


0.003

0.055
8(a)
Inglenook
1980
NR
24
8
0.008
0.309

2.655

0.001
0.685




0.133

-------
TABLE 6A-2a (cont'd). PM2 5 COMPOSITION FOR THE CENTRAL UNITED STATES Gig/m3)
Ref
Site
Dates
Time
Duration (h)
Number
Mass
OC
EC
Nitrate
Sulfate
Acidity*
Al
As
Ba
Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
8(a)
Braidwood
1980
NR
24
1
28.20





0.089


0.003
0.084



0.024
0.071
0.052

0.001


0.001
8(a)
Kansas City KS
1980
NR
24
8
25.66





0.091
0.003

0.027
0.519


0.004
0.032
0.189
0.311

0.006


0.002
8(a) 8(a)
Minneapolis Kansas City MO
1980 1980
NR NR
24 24
6 3
15.50 16.77






0.004 0.007

0.047 0.064
0.103 0.213


0.001 0.002
0.035 0.021
0.087 0.140
0.092 0.142

0.005 0.006


0.001 0.001
8(a)
Akron
1980
NR
24
7
36.09





0.046
0.012

0.039
0.110


0.010
0.037
0.609
0.268

0.085


0.006
8(a)
Cincinnati
1980
NR
24
2
29.80





0.062
0.013

0.024
0.062


0.003
0.024
0.174
0.136

0.011


0.004
8(a)
Buffalo
1980
NR
24
14
38.75





0.192
0.009

0.003
0.218


0.002
0.026
0.671
0.310

0.033


0.008
8(a)
Dallas
1980
NR
24
4
28.93





0.111
0.033

0.223
0.691


0.005
0.043
0.248
0.125

0.015


0.002
8(a)
St. Louis
1980
NR
24
5
23.06





0.119
0.003

0.025
0.090



0.018
0.076
0.126

0.002


0.002
18(k)
St. Louis
8-9/76
NR
6-12
NR
34.00





0.203
0.002
0.020
0.132
0.132
0.004
0.087
0.006
0.029
0.275
0.261

0.036


0.004
6,7
St. Louis
9/79-6/81
00-24
24
306
19.00



7.40
10.3



0.078
0.101

0.052


0.190


0.021


0.003
17 6,7
St. Louis Steubenville
9/85-8/86 4/79-4/81
NR 00-24
24 24
311 499
17.70 29.60



8.00 10.94
9.7 25.2



0.042
0.097

0.092


0.590


0.029


0.005

-------
              TABLE 6A-2a (cont'd).  PM2 5 COMPOSITION FOR THE CENTRAL UNITED STATES (jig/m3)
Ref
Site
Dates
Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
Si
Sn
|> Sr
Ni Ti
O
V
Zn
8(a)
Braidwood
1980
NR
24
1

0.041

2.060

0.001
0.220





0.011
8(a)
Kansas City KS
1980
NR
24
8
0.013
0.180

1.816

0.001
0.434


0.004


0.034
8(a)
Minneapolis
1980
NR
24
6

0.308

0.907

0.001
0.169





0.045
8(a)
Kansas City MO
1980
NR
24
3

0.369

0.763


0.177





0.046
8(a)
Akron
1980
NR
24
7
0.059
0.412

3.419

0.008
0.522


0.009


0.150
8(a)
Cincinnati
1980
NR
24
2
0.043
0.343

2.876

0.005
0.328


0.003


0.053
8(a)
Buffalo
1980
NR
24
14
0.060
0.359

3.706

0.005
0.241




0.001
0.078
8(a)
Dallas
1980
NR
24
4
0.018
1.066

1.514


0.442


0.007

0.002
0.054
8(a)
St. Louis
1980
NR
24
5
0.020
0.277

2.333

0.002
0.170





0.023
1800
St. Louis
8-9/76
NR
6-12
NR
0.001
0.688

4.655
0.006
0.004
0.458
0.009
0.002
0.112

0.002
0.101
6,7
St. Louis
9/79-6/81
00-24
24
306

0.327

2.100

0.002
0.160




BQL

17 6,7
St. Louis Steubenville
9/85-8/86 4/79-4/81
NR 00-24
24 24
311 499

0.216

4.700

0.005
0.290




0.011

References are listed in Table 1 Appendix. Associated notes are explained in Table 1.
"Values for this size fraction are calculated from the average measured values reported for die omer two size fractions.
JUnits for acidity are nmoles/m3.
NR = not reported.

-------
          TABLE 6A-2b. COARSE PARTICLE COMPOSITION FOR THE EASTERN UNITED STATES
to
Ref
Site
Dates
Time
Duration (h)
Number
Mass
OC
EC
Nitrate
Sulfate
Acidity*
Al
As
Ba
Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
Ko)
Smoky Mm.
9/20-26/78
NR
12
12
5.60





< 0.300
<0.001

0.005
0.322

< 0.012

< 0.005
0.118
0.108




< 0.002
Ko)
Shenandoah
7/23-5/08/80
NR
12
28
7.40



0.78

0.311
< 0.002

0.003
0.304

0.179

0.006
0.158
0.129

< 0.006


< 0.003
2(b)
Camden
7/14-8/13 '82
6-18-6
12
50
11.40
<3.00
0.42
0.57
<0.90

0.550


0.015
0.360
< 0.006
0.069
< 0.009

0.490
0.151

0.011


0.004
3(ab)
Philadelphia
7/25-8/14/94
NR
24
21
8.42





0.325


0.003
0.421

0.047

0.014
0.352
0.100
0.104
0.006

0.136
0.002
4(c) 46,49,50
Deep Creek Allegheny Mm.
8/83 8/5-28/83
4x daily day /night
6 -10
98 44
15





0.39
0.0002
0.007
0.0011
0.27
0.0004
0.044
0.0014
0.0016
0.24
0.11
0.060
0.0063
0.0026
0.054
0.0008
46,49,50 5(d)
Laurel Hill Raleigh
8/6-27/83 1/85-3/85
day/night 7-19-7
-10 12
39 NR
13





0.39
0.0002
0.006
0.0011
0.28
0.0003
0.039
0.0015
0.0025
0.24
0.10
0.061
0.0068
0.0021
0.044
0.0009
5(d) 6,7(o,p)*
Roanoke Watertown
10/88-2/89 5/79-6/81
7-19-7 00-24
12 24
NR 354
9.30



0.65




0.022
0.209

0.305


0.276


0.006



8(a,o)
Hartford
1980
NR
24
2
27.85





1.875


0.046
0.864

0.302
0.008
0.026
1.070
0.310

0.021


0.005
8(a,o)
Boston
1980
NR
24
1
105.60





3.458
0.001

0.025
1.069

0.301
0.004
0.023
1.612
0.533

0.029


0.022
8(a,o)
Res.Tr.Pk
1980
NR
24
3
8.17





0.606


0.003
0.086


0.002
0.010
0.182
0.068

0.003




-------
          TABLE 6A-2b (cont'd).  COARSE PARTICLE COMPOSITION FOR THE EASTERN UNITED STATES Gtg/m3)
NJ
Ref
Site
Dates
Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
Si
Sn
Sr
Ti
V
Zn
1(0)
Smoky Mm.
9/20-26/78
NR
12
12

0.014

< 0.560

< 0.0006
0.580


0.018

< 0.004
1(0)
Shenandoah
7/23-5/08/80
NR
12
28

0.009

<0.711

<0.001
0.813


0.017

0.006
2(b)
Camden
7/14-8/13 '82
6-18-6
12
50

0.054

0.230
0.181
<0.0015
1.610
<0.009
0.002
0.065
0.007
0.030
3(ab)
Philadelphia
7/25-8/14/94
NR
24
21
0.027
0.013

BQL

BQL
0.933


0.030
BQL
0.052
4(c) 46,49,50
Deep Creek Allegheny Mtn.
8/83 8/5-28/83
4x daily day /night
6 -10
98 44
0.006
0.007
0.0004
0.59
0.0002
0.0003
1.48

0.0029
0.029
0.0011
0.010
46,49,50 5(d)
Laurel Hill Raleigh
8/6-27/83 1/85-3/85
day /night 7-19-7
-10 12
39 NR
0.007
0.007
0.0005
0.56
0.0002
0.0003
1.41

0.0025
0.027
0.0010
0.011
5(d) 6,7(o,p)*
Roanoke Watertown
10/88-2/89 5/79-6/81
7-19-7 00-24
12 24
NR 354

0.076

0.200


1.000





8(a,o)
Hartford
1980
NR
24
2
0.033
0.171

0.428


4.517


0.094
0.008
0.054
8(a,o)
Boston
1980
NR
24
1
0.016
0.177

0.502


6.760


0.154
0.008
0.054
8(a,o)
Res.Tr.Pk
1980
NR
24
3

0.013

0.223


1.387


0.021

0.007
    References are listed in Table 1 Appendix. Associated notes ate explained in Table 1.
    "Values for this size fraction are calculated from the average measured values reported for the other two size fractions.
    $Units for acidity are nmoles/mj.
    NR = not reported.

-------
       TABLE 6A-2b (cont'd). COARSE PARTICLE COMPOSITION FOR THE WESTERN UNITED STATES Oig/m3)

-------
          TABLE 6A-2b (cont'd).  COARSE PARTICLE COMPOSITION FOR THE WESTERN UNITED STATES Otg/m3)
ON
N>
Ref
Site
Dates
Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
Si
Sn
Sr
Ti
V
Zn
9(g)*
Los Angeles
Summer
1987
NR
4,5 and 7
1 1 days
0.127
0.046

0.520

BQL
1.988

BQL
0.072
BQL
0.024
9(g)*
Los Angeles
Fall 1987
NR
4 and 6
6 days
0.053
0.066

0.264

BQL
1.642

BQL
0.106
0.003
BQL
10(i)*
San loaquin
Valley
6/88-6/89
NR
24
-35
0.052
0.032

0.222


7.577

0.012
0.130
BQL
0.016
110)
Phoenix
10/13/89-
1/17/90
NR
6 h, 2x/day
- 100 days
0.038
0.022
0.003
0.178
< 0.030
< 0.002
7.013
< 0.026
0.014
0.121
<0.014
0.034
5(d) 12(f) 8(a,o)
Tarrant
Boise Nevada CA
12/86-3/87 11/86-1/87 1980
7-19-7 00-24 NR
12 24 24
NR 24 6
0.002
0.167

0.310


5.208


0.083

0.052
8(a,o)
Five Points
CA
1980
NR
24
3
0.148
0.018

0.293


16.001


0.272
0.007
0.016
8(a,o)
Riverside
CA
1980
NR
24
4
0.144
0.113

0.720


7.544


0.182

0.030
8(a,o)
San Jose
CA
1980
NR
24
6
0.032
0.228

0.257


5.214


0.086

0.044
8(a,o)
Honolulu
1980
NR
24
1

0.022

0.258


3.766


0.067

0.008
8(a,o)
Winnemucca
1980
NR
24
5

0.021

0.215


11.903


0.164

0.015
8(a,o)
Portland
1980
NR
24
4
0.011
0.115

0.427


12.128


0.186
0.004
0.038
8(a,o)
Seattle
1980
NR
24
1

0.077

0.121


4.332


0.091

0.034
     References are listed in Table 1 Appendix. Associated notes are explained in Table 1.
     Values for this size fraction are calculated from the average measured values reported for the other two size fractions.
     JUnits for acidity are nmoles/m3.
     NR = not reported.

-------
TABLE 6A-2b (cont'd). COARSE PARTICLE COMPOSITION FOR THE CENTRAL UNITED STATES (jig/m3)
Ref
Site
Dates
Time
Duration (h)
Number
Mass
OC
EC
Nitrate
Sulfate
Acidity*
Al
As
Ba
Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
5(d) 13(o)
Albuquerque Denver
12/84-3/85 1/11-30/82
7-19-7 6-18-6
12 12
NR -26
35.73



0.39

2.900

0.058
0.024
0.658
0.012
1.235
< 0.009
0.008
0.954
0.648

0.021


0.005
14(m) 14(ab) 15(s)
Urban Denver Non-urban Denver Chicago
11/87-1/88 11/87-1/88 7/94
9-16-9 9-16-9 8-8
7 & 17 7 & 17 24
-136 -150 16
14.97





0.223
<0.0013
<0.038
0.007
0.716
<0.012
0.036
< 0.0024
0.006
0.344
0.101
0.106
0.008
< 0.0017
<0.017
< 0.0007
16(o)
Houston
9/10-19/80
NR
12
20
24.80
3.10

1.63
0.91

1.093
<0.006
0.091
0.036
2.780
< 0.006
0.366
0.007
0.018
0.604
0.170

0.021

<0.74
0.004
6,7(o,p)* 17 6,7(o,p)*
Harriman Harriman Kingston
5/80-5/81 9/85-8/86 5/80-6/81
00-24 NR 00-24
24 24 24
256 330 169
11.70 9.00 10.80








0.014 0.012
1.650 0.840

0.029 0.018


0.570 0.263


0.021 0.018


0.001 BQL
6,7(o,p)*
Portage
3/79-5/81
00-24
24
271
7.20



0.35




0.003
0.335

0.056


0.181


0.006


0.001
6,7(o,p)*
Topeka
8/79-5/81
00-24
24
286
13.90



0.40




0.010
2.150




0.490


0.016


0.001
8(a,o)
El Paso
1980
NR
24
10
49.05





2.748
0.012

0.033
3.632

0.043
0.003
0.047
0.812
0.496

0.023


0.001
8(a,o)
Inglenook
1980
NR
24
8
40.43





2.426


0.021
2.598


0.004
0.027
1.193
0.309

0.041


0.002

-------
TABLE 6A-2b (cont'd). COARSE PARTICLE COMPOSITION FOR THE CENTRAL UNITED STATES Otg/m3)
Ref
Site
Dates
Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
Si
Sn
Sr
Ti
V
Zn
5(d) 13(o)
Albuquerque Denver
12/84-3/85 1/11-30/82
7-19-7 6-18-6
12 12
NR -26
0.113
0.099
0.005
<0.48


7.460

0.009
0.090

0.039
14(m) 14(ab) 15(s)
Urban Denver Non-urban Denver Chicago
11/87-1/88 11/87-1/88 7/94
9-16-9 9-16-9 8-8
7 & 17 7 & 17 24
-136 -150 16
0.027
0.005

0.043
<0.017
<0.0006
0.739
<0.021

0.019
< 0.004
0.038
16(o)
Houston
9/10-19/80
NR
12
20
<0.1
0.124
<0.003
<1.29
< 0.009

2.990
<0.009
< 0.008
0.036
<0.03
0.058
6,7(o,p)* 17
Harriman Harriman
5/80-5/81 9/85-8/86
00-24 NR
24 24
256 330

0.057

SQL


1.880





6,7(o,p)*
Kingston
5/80-6/81
00-24
24
169

0.040

SQL


1.700





6,7(o,p)*
Portage
3/79-5/81
00-24
24
271

0.013

SQL


0.905





6.7(o,p)*
Topeka
8/79-5/81
00-24
24
286

0.040

BQL


2.310





8(a,o)
El Paso
1980
NR
24
10

0.191

0.249

0.001
5.377


0.077

0.057
8(a,o)
Inglenook
1980
NR
24
8
0.022
0.079

0.314


6.312


0.116

0.055

-------
TABLE 6A-2b (cont'd). COARSE PARTICLE COMPOSITION FOR THE CENTRAL UNITED STATES Qtg/m3)
Ref
Site
Dates
Time
Duration (h)
Number
Mass
OC
EC
Nitrate
Sulfate
Acidity*
Al
As
Ba
Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
8(a,o)
Braidwood
1980
NR
24
1
28.70





1.931
0.002

0.003
1.406


0.002
0.020
0.656
0.303

0.017


0.001
8(a,o)
Kansas City KS
1980
NR
24
8
41.67





2.284
0.003

0.029
3.754

0.530
0.004
0.015
0.979
0.361

0.025


0.002
8(a,o)
Minneapolis
1980
NR
24
6
30.85





2.191
0.001

0.022
1.571

0.293
0.002
0.022
0.744
0.310

0.026


0.001
8(a,o)
Kansas City MO
1980
NR
24
3
41.67





2.284
0.003

0.029
3.754

0.530
0.004
0.015
0.979
0.361

0.025


0.002
8(a,o)
Akron
1980
NR
24
7
34.81





2.509
0.003

0.025
1.431

0.572
0.014
0.018
1.640
0.324

0.044


0.005
8(a,o)
Cincinnati
1980
NR
24
2
33.15





2.910


0.017
1.312

0.103
0.002
0.014
0.883
0.363

0.021


0.003
8(a,o)
Buffalo
1980
NR
24
14
44.57





2.808


0.012
2.550

0.728
0.015
0.022
2.040
0.206

0.078


0.009
8(a,o)
Dallas
1980
NR
24
4
32.63





1.294
0.006

0.051
3.436

0.029
0.005
0.023
0.720
0.210

0.020


0.002
8(a,o)
St. Louis
1980
NR
24
5
33.76





3.837
0.001

0.021
1.784

0.053
0.001
0.014
0.587
0.291

0.017


0.002
18(k,r)
St. Louis
8-9/76
NR
6-12
28.00





1.209
0.001
0.034
0.047
2.817
0.001
0.257
0.009
0.014
1.218
0.392

0.035


0.005
6,7(o,p)*
St. Louis
9/79-6/81
00-24
24
306
12.40



0.70




0.021
1.499

0.093


0.580


0.019


0.002
17 6,7(o,p)*
St. Louis Steubenville
9/85-8/86 4/79-4/81
NR 00-24
24 24
311 499
9.90 16.90



1.86




0.010
1.023

0.211


1.610


0.039


0.004

-------
     TABLE 6A-2b (cont'd).  COARSE PARTICLE COMPOSITION FOR THE CENTRAL UNITED STATES 0*g/m3)











I
oo

Ref
Site
Dates
Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
Si
Sn
Sr
Ti
V
Zn
8(a,o)
Braidwood
1980
NR
24
1
0.014
0.013

0.572

0.001
5.767

0.083
0.012
8(a.o)
Kansas City KS
1980
NR
24
8

0.109

0.280


4.809

0.074
0.040
8(a,o)
Minneapolis
1980
NR
24
6

0.098

0.224


4.679

0.062
0.027
8(a,o)
Kansas City MO
1980
NR
24
3

0.109

0.280


4.809

0.074
0.040
8(a,o)
Akron
1980
NR
24
7

0.097

0.451


5.009

0.107
0.069
8(a,o)
Cincinnati
1980
NR
24
2
0.037
0.099

0.389


6.633

0.096
0.148
8(a,o)
Buffalo
1980
NR
24
14

0.108

0.765


2.675

0.051
0.043
8(a,o)
Dallas
1980
NR
24
4

0.252

0.240


3.210

0.051
0.030
8(a,o)
St. Louis
1980
NR
24
5

0.095

0.279


4.468

0.058
0.021
18(k,r)
St. Louis
8-9/76
NR
6-12

0.098
0.189
0.002
0.533
0.001
0.001
4.470
0.001
0.007
0.475
0.004
0.074
6,7(o,p)*
St. Louis
9/79-6/81
00-24
24
306

0.088

0.200


1.940

BQL

17 6,7(o,p)*
St. Louis Steubenville
9/85-8/86 4/79-4/81
NR 00-24
24 24
311 499

0.043

0.800


2.010

0.002

References are listed in Table 1 Appendix. Associated notes are explained in Table 1.
"Values for this size fraction are calculated from the average measured values reported for the other two size fractions.
$Units for acidity are nmoles/m3.
NR = not reported.

-------
TABLE 6A-2c.  PM10 COMPOSITION FOR THE EASTERN UNITED STATES Otg/m3)
Ref
Site
Dates
Time
Duration (h)
Number
Mass
OC
EC
Nitrate
Sulfate
Acidity*
Al
As
Ba
Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
Ko.q)*
Smoky Mm.
9/20-26/78
NR
12
12
29.60
2.22
1.10
0.30
12.00

BQL
BQL

0.023
0.338

BQL

0.003
0.146
0.148




BQL
l(o,q)*
Shenandoah
7/23-5/08/80
NR
12
28
34.40
0.44
1.12

14.38

0.311


0.011
0.339

0.189

0.011
0.212
0.190

BQL


BQL
2(b)*
Camden
7/14-8/13 '82
6-18-6
12
50
40.10
2.05
2.29
0.57
11.20

0.603
0.001

0.044
0.400
0.002
0.072
0.002

0.581
0.252

0.017
0.001
0.146
0.015
3(ab)* 4(c)
Philadelphia Deep Creek
7/25-8/14/94 8/83
NR 4x daily
24 6
21 98
40.60
4.51
0.76



0.439


0.012
0.479

0.073

0.021
0.479
0.160
0.126
0.010

0.206
0.009
5(d) 5(d) 6,7(p,q)
Raleigh Roanoke Watertown
1/85-3/85 10/88-2/89 5/79-6/81
7-19-7 7-19-7 00-24
12 12 24
NR NR 354
24.20



6.50




0.110
0.250

0.389


0.350


0.009


0.011
8(a,q)*
Hartford
1980
NR
24
2
54.60





1.910


0.082
0.934

0.302
0.011
0.069
1.195
0.481

0.028


0.015
8(a,q)*
Boston
1980
NR
24
1
140.40





3.458
0.003

0.045
1.139

0.301
0.008
0.058
1.733
0.629

0.030


0.034
8(a,q)*
Res.Tr.Pk
1980
NR
24
3
36.93





0.679
0.002

0.010
0.121


0.002
0.026
0.302
0.216

0.006


0.001

-------
               TABLE 6A-2c (cont'd).  PM10 COMPOSITION FOR THE EASTERN UNITED STATES 0*g/m3)
Ref
Site
Dates
Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
Si
Sn
> Sr
g Ti
V
Zn
Ko,q)*
Smoky Mm.
9/20-26/78
NR
12
12

0.111

3.744

0.001
0.618

0.018
BQL
0.009
Ko.q)*
Shenandoah
7/23-5/08/80
NR
12
28

0.061

4.539

0.001
0.929

0.017
BQL
0.017
20))'
Camden
7/14-8/13/82
6-18-6
12
50

0.303

4.430
0.260
0.002
1.713
BQL
0.002
0.065
0.020
0.112
3(ab)* 4(c)
Philadelphia Deep Creek
7/25-8/14/94 8/83
NR 4x daily
24 6
21 98
0.042
0.032

3.251


1.098

0.030
0.092
5(d) 5(d) 6,7(p,q)
Raleigh Roanoke Watertown
1/85-3/85 10/88-2/89 5/79-6/81
7-19-7 7-19-7 00-24
12 12 24
NR NR 354

0.405

2.000

0.001
1.100

0.022

8(a,q)*
Hartford
1980
NR
24
2
0.033
0.681

2.647

0.001
4.694

0.096
0.025
0.133
(a,q)*
Boston
1980
NR
24
1
0.025
0.462

4.371

0.001
6.904

0.154
0.028
0.100
8(a,q)*
Res.Tr.Pk
1980
NR
24
3
0.042
0.119

3.058

0.002
1.737

0.021
0.025
References are listed in Table 1 Appendix. Associated notes are explained in Table 1.
"Values for this size fraction are calculated from the average measured values reported for the other two size fractions.
$Units for acidity are nmoles/m3.
NR = not reported.

-------
TABLE 6A-2c (cont'd). PM10 COMPOSITION FOR THE WESTERN UNITED STATES (/tg/m3)
Ref
Site
Dates
Time
Duration (h)
Number
Mass
OC
EC
Nitrate
Sulfate
Acidity5
Al
§> As
W Ba
^ Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
9(g)
Los Angeles
Summer 1987
NR
4,5 and 7
11 days
67.40
11.61
3.19
9.47
11.28

0.758
0.007
0.070
0.016
0.585

1.119
0.023
0.022
0.836
0.237
0.335
0.033

1.632
0.005
9(g)
Los Angeles
Fall 1987
NR
4 and 6
6 days
98.70
23.35
8.49
27.50
5.39

0.847
0.019
0.127
0.072
1.190

0.880
0.042
0.178
2.192
0.460
0.287
0.063

0.518
0.005
10(i)
San Joaquin Valley
Jun. 1998-Jun. 1989
NR
24
-35
74.05
10.59
5.62
10.55
3.62

3.570

0.051
0.015
1.057

0.487
0.010
0.087
1.633
0.820

0.037


0.010
ll(j) 5(d)*
Phoenix Boise
10/13/89-1/17/90 12/86-3/87
NR 7-19-7
6 h, 2x/day 12
- 100 days NR
62.45
14.56
8.30
4.46
2.34

2.669
BQL
0.013
0.014
2.099
BQL
0.559
0.011
0.036
1.475
0.876
BQL
0.054
BQL
BQL
0.006
12(f) 8(a,q)*
Tarrant
Nevada CA
11/86-1/87 1980
00-24 NR
24 24
24 6
100.90





2.407


0.149
4.543


0.007
0.077
1.257
0.441

0.067


0.006
8(a,q)*
8(a,q>*
Five Points Riverside
CA CA
1980
NR
24
3
124.37





7.317


0.019
1.786

0.026
0.007
0.037
3.275
1.437

0.055


0.037
1980
NR
24
4
8(a,q)*
San Jose
CA
1980
NR
24
6
8(a,q)*
Honolulu
1980
NR
24
1
106.20 66.68 46.90





3.549


0.065
5.082

0.173
0.005
0.061
2.015
1.081

0.049


0.013





2.053
0.001

0.250
0.771

0.480
0.009
0.071
1.214
0.508

0.027


0.014





2.992


0.023
1.981

1.456
0.009
0.025
1.384
0.665

0.034


0.005
8(a,q)*
Winnemucca
1980
NR
24
5
8(a,q)*
Portland
1980
NR
24
4
65.42 117.55





6.925


0.010
2.177

0.176
0.006
0.043
1.995
1.200

0.044


0.003





6.932
0.014

0.121
1.459

0.197
0.019
0.109
2.059
0.805

0.108


0.036
8(a,q)*
Seattle
1980
NR
24
1
36.00





2.296
0.008

0.033
0.585

0.228
0.005
0.041
1.001
0.231

0.022


0.007

-------
                    TABLE 6A-2c (cont'd).  PM10 COMPOSITION FOR THE WESTERN UNITED STATES Gig/m3)
N)
Ref
Site
Dates
Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
Si
Sn
Sr
Ti
V
Zn
9(g)
Los Angeles
Summer 1987
NR
4,5 and 7
11 days
0.187
0.084

3.353

0.008
2.040

0.018
0.077
0.005
0.114
9(g)
Los Angeles
Fall 1987
NR
4 and 6
6 days
0.099
0.251

2.262

0.010
2.162

0.024
0.165
0.009
0.293
10(0
San Joaquin Valley
Jun. 1988 -Jun. 1989
NR
24
-35
0.059
0.061
0.004
1.463

0.001
8.037

0.014
0.147
0.014
0.094
ll(j) 5(d)* 12(f)
Phoenix Boise Nevada
10/13/89-1/17/90 12/86-3/87 11/86-1/87
NR 7-19-7 00-24
6 h, 2x7day 12 24
- 100 days NR 24
0.054
0.062
BQL
0.615
BQL
BQL
7.443
BQL
0.014
0.136
BQL
0.090
8(a.q)*
Tarrant
CA
1980
NR
24
6
0.002
0.786

2.888


5.791


0.093

0.147
8(a,q)*
Five Points
CA
1980
NR
24
3
0.155
0.105

1.422

0.001
16.657


0.277
0.013
0.032
8(a,q)*
Riverside
CA
1980
NR
24
4
0.144
0.489

2.373

0.001
7.778


0.182
0.003
0.059
8(a,q)*
San Jose
CA
1980
NR
24
6
0.045
1.119

1.109


5.506


0.086
0.002
0.105
8(a,q)*
Honolulu
1980
NR
24
1
0.002
0.093

0.571


6.129


0.130
0.001
0.019
8(a,q)*
Winnemucca
1980
NR
24
5

0.063

0.573


12.817


0.173

0.026
8(a,q)"
Portland
1980
NR
24
4
0.028
0.537

2.371

0.001
12.505


0.191
0.018
0.119
8(a,q)*
Seattle
1980
NR
24
1
0.006
0.292

0.952

0.001
4.424


0.091

0.093
     References are listed in Table 1 Appendix. Associated notes are explained in Table 1.
     "Values for this size fraction are calculated from the average measured values reported for the other two size fractions.
     $Units for acidity are nmoles/m3.
     NR = not reported.

-------
TABLE 6A-2c (cont'd). PM10 COMPOSITION FOR THE CENTRAL UNITED STATES Oig/m3)
Ref 8(a,q)*
Site Albuquerque
Dates 12/84-3/85
Time 7-19-7
Duration (h) 12
Number NR
Mass
OC
EC
Nitrate
Sulfate
Acidity*
Al
As
Ba
Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
P
Pb
13(q)*
Denver
1/11-30/82
6-18-6
12
-26
56.46
7.11
2.15
2.22
2.45

3.294
< 0.004
0.089
0.127
0.705
0.018
1.287
<0.018
0.018
1.033
0.727

0.031


0.008
0.155
0.424
14(m) 14(aa) 15(s)*
Urban Denver Non-urban Denver Chicago
11/87-1/88 11/87-1/88 7/94
9-16-9 9-16-9 8-8
7 & 17 7 & 17 24
-136 -150 16
28.54
5.39
1.31

5.46

0.269
< 0.0043
< 0.130
0.011
0.761
<0.041
0.047
< 0.0073
0.017
0.432
0.161
0.118
0.013
< 0.0041
0.022
<0.0018
0.035
0.032
16(q)*
Houston
9/10-19/80
NR
12
20
63.40
8.78
1.42
2.22
15.52

1.216
<0.015
0.139
0.091
2.935
<0.012
0.398
0.007
0.046
0.766
0.289

0.035

<1.49
0.008
0.128
0.589
6,7(p,q)
Harriman
5/80-5/81
00-24
24
256
32.50



8.10




0.052
1.800

0.050


0.690


0.038


0.001

0.237
17* 6,7(p,q)
Harriman Kingston
9/85-8/86 5/80-6/81
NR 00-24
24 24
330 169
30.00 35.40



8.70
36.1



0.056
0.960

0.018


0.360


0.027


ND

0.234
6,7(p,q)
Portage
3/79-5/81
00-24
24
271
18.20



5.30




0.014
0.380

0.083


0.230


0.009


0.001

0.074
6,7(p,q)
Topeka
8/79-5/81
00-24
24
286
26.40



4.80




0.055
2.400

0.031


0.580


0.020


0.001

0.203
8(a,q)*
El Paso
1980
NR
24
10
76.21





2.903
0.037

0.103
3.964

0.043
0.004
0.083
0.946
0.623

0.027


0.002

0.672
8(a,q)*
Inglenook
1980
NR
24
8
72.45





2.508
0.001

0.061
2.924

0.003
0.006
0.059
1.474
0.717

0.078


0.003
0.030
0.388

-------
TABLE 6A-2c (cont'd). PM10 COMPOSITION FOR THE CENTRAL UNITED STATES (jig/m3)
Ref 8(a,q)*
Site Albuquerque
Dates 12/84-3/85
Time 7-19-7
Duration (h) 12
Number NR
Rb
S
Sb
Se
Si
Sn
Sr
Ti
V
Zn
13(q)*
Denver
1/11-30/82
6-18-6
12
-26
0.005
0.709
<0.004
< 0.004
7.737
<0.004
0.009
0.09
<0.004
0.085
14(m) 14(aa) 15(s)*
Urban Denver Non-urban Denver Chicago
11/87-1/88 11/87-1/88 7/94
9-16-9 9-16-9 8-8
7 & 17 7 & 17 24
-136 -150 16

1.363
< 0.059
< 0.0017
0.813
< 0.070

0.019
<0.013
0.090
16(q>*
Houston
9/10-19/80
NR
12
20
< 0.006
4.83
0.006
< 0.003
3.200


0.036
<0.045
0.142
6,7(p,q)
Harriman
5/80-5/81
00-24
24
256

2.500

0.002
2.000



ND

17* 6,7(p,q)
Harriman Kingston
9/85-8/86 5/80-6/81
NR 00-24
24 24
330 169

2.400

0.002
1.900



ND ERR

6,7(p,q)
Portage
3/79-5/81
00-24
24
271

1.500

0.001
0.980



ND

6,7(p,q)
Topeka
8/79-5/81
00-24
24
286

1.200


2.500



ND

8(a,q)*
El Paso
1980
NR
24
10

1.072

0.003
5.813


0.080

0.112
8(a,q)*
Inglenook
1980
NR
24
8

2.969

0.001
6.997


0.116

0.188

-------
TABLE 6A-2c (cont'd). PM10 COMPOSITION FOR THE CENTRAL UNITED STATES Qtg/m3)
Ref
Site
Dates
Time
Duration (h)
Number
Mass
OC
EC
Nitrate
Sulfate
Acidity5
Al
As
ON
> Ba
i
$ Br
Ca
Cd
Cl
Cr
Cu
Fe
K
Mg
Mn
Mo
Na
Ni
8(a,q)*
Braidwood
1980
NR
24
1
56.90





2.020
0.002


0.006
1.490


0.002
0.044
0.727
0.355

0.018


0.002
8(a,q)*
Kansas City KS
1980
NR
24
8
70.33





2.144
0.003


0.036
4.371


0.010
0.048
0.989
0.660

0.026


0.005
8(a,q)*
Minneapolis
1980
NR
24
6
46.35





2.191
0.005


0.069
1.674

0.293
0.003
0.057
0.831
0.402

0.031


0.002
8(a,q)*
Kansas City MO
1980
NR
24
3
58.43





2.284
0.010


0.093
3.967

0.530
0.006
0.036
1.119
0.503

0.031


0.003
8(a,q)*
Akron
1980
NR
24
7
70.90





2.555
0.015


0.064
1.541

0.572
0.024
0.055
2.249
0.592

0.129


0.011
8(a,q)*
Cincinnati
1980
NR
24
2
62.95





2.972
0.013


0.041
1.374

0.103
0.005
0.038
1.057
0.499

0.032


0.007
8(a,q)*
Buffalo
1980
NR
24
14
83.32





3.000
0.009


0.015
2.768

0.728
0.017
0.048
2.711
0.516

0.111


0.017
8(a,q>*
Dallas
1980
NR
24
4
61.55





1.405
0.039


0.274
4.127

0.029
0.010
0.066
0.968
0.335

0.035


0.004
8(a,q)*
St. Louis
1980
NR
24
5
56.82





3.956
0.004


0.046
1.874

0.053
0.001
0.032
0.663
0.417

0.019


0.004
18(x)*
St. Louis
8-9/76
NR
6-12
62.00





1.412
0.003
0.054

0.179
2.949
0.005
0.344
0.015
0.043
1.493
0.653

0.071


0.009
6,7(p,q)
St. Louis
9/79-6/81
00-24
24
306
31.40



8.10





0.099
1.600

0.145


0.770


0.040


0.005
17* 6,7(p,q)
St. Louis Steubenville
9/85-8/86 4/79^/81
NR 00-24
24 24
311 499
27.60 46.50



8.00 12.80
9.7




0.052
1.120

0.303


2.200


0.068


0.008

-------
               TABLE 6A-2c (cont'd).  PM10 COMPOSITION FOR THE CENTRAL UNITED STATES (jig/m3)
Ref
Site
Dates
Time
Duration (h)
Number
P
Pb
Rb
S
Sb
Se
Si
Sn
>. Sr
U> Ti
V
Zn
8(a,q)*
Braidwood
1980
NR
24
1
0.014
0.054

2.632

0.002
5.987


0.083

0.023
8(a,q)*
Kansas City KS
1980
NR
24
8
0.013
0.237

2.031

0.001
4.976


0.076

0.060
8(a,q)*
Minneapolis
1980
NR
24
6

0.406

1.131

0.001
4.848


0.062

0.072
8(a,q)*
Kansas City MO
1980
NR
24
3

0.478

1.043


4.986


0.074

0.086
8(a,q)*
Akron
1980
NR
24
7
0.059
0.509

3.870

0.008
5.531


0.116

0.219
8(a,q)*
Cincinnati
1980
NR
24
2
0.080
0.442

3.265

0.005
6.961


0.099

0.201
8(a,q)*
Buffalo
1980
NR
24
14
0.060
0.467

4.471

0.005
2.916


0.051
0.001
0.121
8(a,q)"
Dallas
1980
NR
24
4
0.018
1.318

1.754


3.652


0.058
0.002
0.084
8(a,q)*
St. Louis
1980
NR
24
5
0.020
0.372

2.612

0.002
4.638


0.058

0.044
18(x)*
St. Louis
8-9/76
NR
6-12

0.099
0.877
0.002
5.188
0.007
0.005
4.928
0.010
0.009
0.587
0.006
0.175
6,7(p,q)
St. Louis
9/79-6/81
00-24
24
306

0.415

2.300

0.002
2.100



ND

17* 6,7(p,q)
St. Louis Steubenville
9/85-8/86 4/79-4/81
NR 00-24
24 24
311 499

0.259

5.500

0.005
2.300



0.013

References are listed in Table 1 Appendix. Associated notes are explained in Table 1.
"Values for this size fraction are calculated from the average measured values reported for the other two size fractions.
$Units for acidity are nmoles/m3.
NR = not reported.

-------
          TABLE 6A-3. SELECTED RATIOS OF PM COMPOSITION BY
                            GEOGRAPHIC REGION
EAST

FM/CM
FM/PM10
Tot Carbon/FM
SO4= /FM
Mean
2.59
0.65
0.25
0.34
N
8
8
7
12
WEST
Mean
0.89
0.41
0.54
0.11
N
11
11
5
13
CENTRAL
Mean
1.06
0.51
0.64
0.28
N
25
25
5
28
N = number of studies contributing to the calculated ratios.
FM, CM, PM10 = Mass concentrations of PM2 5, Coarse fraction, and PMi0 respectively.
Total Carbon = (OC x 1.4 + EC).
                                    6A-37

-------
u>
00

Study Area
No. of Sites
Study Dates
Reference

Fine Mass
OC
EC
Nitrate
Sulfate
Al
Br
Ca
Cl
Cr
Cu
Fe
K
Mn
Ni
Pb
S
Si
Ti
V
Zn

TABLE 6A-4a.
Denver Metropolitan
3,a
11/2/87- 1/31/88
14
Mean
19.672
7.245
4.409
3.956
1.547
0.037
0.018
0.058
0.141
0.003
0.017
0.111
0.077
0.012
0.002
0.075
0.642
0.272
0.009

0.031
Spread
2.889
0.789
0.780
0.931
0.162
0.006
0.006
0.001
0.013
0.002
0.008
0.023
0.009
0.003
0.002
0.017
0.077
0.009
0.001

0.008
SITE-TO-SITE VARIABILITY OF PM2 5 CONCENTRATIONS
Phoenix
3,b
10/13/89 - 1/17/90
11
Mean
29.379
10.089
7.490
3.597
1.329
0.131
0.011
0.167
0.366
0.003
0.015
0.216
0.209
0.023
0.003
0.039
0.436
0.430


0.056
Spread
3.493
2.690
1.710
0.370
0.240
0.015
0.003
0.034
0.356
0.001
0.003
0.035
0.020
0.010
0.001
0.009
0.038
0.066


0.030
Philadelphia
4,c
7/25/94 - 8/14/94
3
Mean
32.183
4.164
0.685

13.426
0.114
0.009
0.058
0.026

0.007
0.127
0.060
0.003
0.007
0.019
3.251
0.165

0.019
0.041
Spread
2.172
0.935
0.215

0.333
0.009
0.005
0.014
0.007

0.001
0.037
0.008
0.000
0.002
0.010
0.081
0.022

0.003
0.018
San Joaquin Valley
6,d
6/14/88 - 6/9/89
10
Mean
29.888
4.873
3.242
8.165
3.003
0.152
0.010
0.096
0.094
0.003
0.096
0.180
0.188
0.006
0.016
0.029
1.242
0.460
0.017
0.015
0.078
Spread
10.020
2.695
2.580
2.270
1.325
0.055
0.006
0.050
0.070
0.002
0.036
0.060
0.080
0.003
0.030
0.021
0.565
0.245
0.004
0.028
0.027
     Mean  = Mean over all sites of the average concentrations determined at each site for the sampling period.
     Spread = ABS ({Highest Mean Cone. - Lowest Mean Conc.}/2) for all the sites.
     a.  Federal, Auraria, and Welby sites in urban Denver.
     b.  Central Phoenix, Scottsdale, and Western Phoenix sites.
     c.  Broad Street, Castor Avenue, Roxboro, and Northeast Airport sites.
     d.  Stockton, Crow's Landing, Fresno, Kern, Fellows, and Bakersfield sites.

-------
     TABLE 6A-4b.  SITE-TO-SITE VARIABILITY OF PM10 CONCENTRATIONS
Study Area
No. of Sites
Study Dates
Reference

Fine Mass
OC
EC
Nitrate
Sulfate
Al
Br
Ca
Cl
Cr
Cu
Fe
K
Mn
Ni
Pb
S
Si
Ti
V
Zn
San Jose
2,a
12/16/91 -
29
Mean
64.950
19.390
9.015
10.900
2.240
0.845
0.012
0.670
0.728
0.003
0.029
0.834
0.823
0.014
0.003
0.035
1.147
2.905
0.088
0.007
0.065
2/24/92
Spread
1.650
0.150
0.415
0.600
0.090
0.035
0.001
0.049
0.032
0.001
0.002
0.027
0.021
0.001
0.000
0.004
0.091
0.045
0.024
0.003
0.005
Phoenix
3,b
10/13/89 -
11
Mean
62.465
14.549
8.327
4.459
1.704
2.670
0.014
2.096
0.559
0.011
0.036
1.475
0.878
0.054
0.006
0.062
0.615
7.442
0.121

0.090
1/17/90
Spread
7.064
3.481
1.777
0.452
0.287
0.273
0.003
0.317
0.349
0.002
0.009
0.170
0.083
0.014
0.002
0.013
0.041
0.862
0.024

0.034
San Joaquin Valley
6,c
6/14/88 -
10
Mean
62.920
7.870
3.505
9.437
3.565
2.993
0.012
0.950
0.388
0.009
0.084
1.413
0.720
0.030
0.019
0.039
1.472
7.517
0.128
0.022
0.085
6/9/89
Spread
17.280
4.150
2.760
3.015
1.460
1.570
0.005
0.390
0.225
0.003
0.046
0.445
0.220
0.011
0.032
0.027
0.605
1.765
0.033
0.031
0.029
Mean  = Mean over all sites of the average concentrations determined at each site for the sampling period.
Spread = ABS ({Highest Mean Cone. - Lowest Mean Conc.}/2) for all the sites.
a.  San Carlos St. and Fourth St. sites.
b.  Central Phoenix, Scottsdale, and Western Phoenix Sites.
c.  Stockton, Crow's Landing, Fresno, Kern, Fellows, and Bakersfield sites.
                                          6A-39

-------
 7.  HUMAN EXPOSURE TO PARTICULATE MATTER:
          RELATIONS TO AMBIENT AND INDOOR
                         CONCENTRATIONS
7.1   INTRODUCTION
     The 1982 Air Quality Criteria Document for Paniculate Matter and Sulfur Oxides
(U.S. Environmental Protection Agency, 1982) thoroughly reviewed the PM exposure
literature through 1981. The later "Second Addendum to Air Quality Criteria for Particulate
Matter and Sulfur Oxides (1982)" (U.S. Environmental Protection Agency, 1986a) added
coverage of newly available health effects information up to  1986.  This chapter first
summarizes key points from the 1982 Criteria Document, and then thoroughly reviews the
PM exposure literature from 1982 through 1995 and includes some  literature published and
in press  through February,  1996.
     The U.S. Environmental Protection Agency (U.S. EPA) regulatory authority for PM
only extends to the ambient air, defined in 40 CFR 50.1(e) as that portion of the atmosphere,
external  to buildings, to which the general public has access  (Code of Federal Regulations,
1994).  By the operative definition of ambient air, polluted air inside a building, or on
private property owned or controlled by the source of pollution, is not regulated by the
National Ambient Air Quality Standards (Costle, 1980; Bennett, 1983).  However, it is
necessary to consider total personal exposure to PM, both from the  regulated ambient air and
non-regulated indoor air. This is because ambient (outdoor)  particles penetrate into non-
ambient  environments (indoors) where people spend approximately 85% of their time (U.S.
Environmental Protection Agency, 1989). Therefore, when people are indoors, they are
exposed  to a mixture of ambient PM and particles generated  indoors from non-regulated
sources,  such as PM from cigarette  smoke and personal activities.
     Personal exposure  to total PM is important in itself, because the body may react
differently to ambient and non-ambient particles of identical size but different chemical
composition. Comparison of personal exposures to indoor and outdoor concentrations may
provide clues as to whether or not these two types of PM have similar toxicity on a unit size
and mass basis. Personal exposure may also act as a confounder in epidemiological studies

                                      7-1

-------
which use an inferred community exposure to ambient PM as a parameter to correlate with
community health parameters, and an individual's personal exposure to total PM is a critical
parameter for analysis if that person is a member of a cohort whose health outcomes are
being tracked individually.  Therefore, this chapter examines not only indoor air quality in
regard  to PM, but also community and individual exposures to PM, which include that
portion of ambient PM which penetrates into indoor microenvironments (^Es).  This is to aid
in interpretation of acute and chronic epidemiology studies assessed in Chapter 12, in which
ambient PM concentrations are assumed to be an indicator or a surrogate for mean
community exposure to ambient PM or an individual exposure to ambient PM.  Thus, this
chapter has three objectives:  (a) to provide a review of pertinent studies of indoor and
personal exposures to PM; (b) to evaluate linkages between monitored personal exposures
and exposures estimated from a fixed-site monitor located at some  central monitoring site;
and (c) to quantify the contribution of ambient air to personal PM  exposure.
     In this chapter, Sections 7.1.1-7.1.3 discuss the concept of  ambient PM as a surrogate
for a personal exposure  and the relationship of a measured personal PM exposure to the
ambient and nonambient concentrations of PM that may influence it.
     Section 7.2 next reviews PM concentrations found indoors where people spend about
85% of their time (U.S. Environmental Protection Agency, 1989).  This subject is discussed
in detail because of the importance of indoor conditions for understanding total exposure to
PM. Indoor air particles from indoor sources may be an important factor in the analysis and
interpretation of epidemiology studies, because they may influence both the personal PM
exposure and personal health of the exposed people.
     Section 7.2.5 reviews the literature covering biological aerosols, which may produce
direct health effects or act as a source of antigens capable of sensitizing people to the effects
of other PM exposures.
     Section 7.3 reviews the fundamental principles of personal PM monitoring  and factors
that influence the personal PM measurement.
     Section 7.4 covers the literature on direct measurements of personal exposures to PM
and PM constituents such as sulfates.
                                          7-2

-------
     Section 7.5 reviews the literature on indirect exposure estimation procedures that
predict exposures from time-weighted averages of concentrations measured indoors and
outdoors.
     Section 7.6 discusses the relationship of individual PM exposures to ambient PM
concentrations and establishes a linkage between average personal PM exposures in a
community to the ambient PM concentrations.
     Section 7.7 discusses implications of PM exposure relationships for mortality and
morbidity analyses.
     Section 7.8 provides a Summary of Conclusions for Chapter 7.

7.1.1    Ambient Particulate Matter Concentration as a Surrogate for
         Particulate Matter Dosage
     The health effects of PM experienced  by an individual depend upon the mass, size and
composition of those particles deposited within various  regions of the respiratory tract during
the time interval of interest. The amount of this potential dose will depend on the
concentration inhaled (e.g., the instantaneous personal exposure); the ventilation rate
(a function of physical activity and basal  metabolism); and the fractional deposition, which is
a function of ventilation rate, mode of breathing (e.g.,  oral or nasal), and any alterations due
to lung dysfunction.  If all people had identical ventilation rates and deposition patterns, then
the potential-dosage distribution could be linearly scaled to the personal exposure distribution
which would serve as a suitable primary  surrogate.  The usage of ambient PM concentration
in health studies as a surrogate for personal PM exposure, and thereby a secondary surrogate
for the PM dosage, would be suitable if ambient concentration was also linearly related to the
personal  exposure (Mage, 1983).
     Adult ventilation rates are lowest (mean « 6 L/min) during the night while asleep, at a
maximum (mean  « 12 L/min; peak = 60 L/min) during the  day while awake
(Adams,  1993), and in phase with PM exposure, which is also lower at night than during the
day (Clayton et al., 1993).   Consequently, the product  of the  24-h average PM exposure, the
24-h average ventilation rate, and the average deposition parameter for the average
ventilation would seriously under-predict the amount of PM deposited in the respiratory tract
(Mage, 1980).
                                          7-3

-------
     In practice, when relating human health to PM pollution variables (as in Chapter 12)

one is forced to use time-weighted-average (TWA) ambient PM concentration as a surrogate
for PM exposure and PM dosage because only fragmentary data are typically available on
personal exposures to PM in populations.  Data are also limited on ventilation rates as a
function of basal metabolism and physical activities (Adams,  1993), as are data on pulmonary
deposition rates of particles people are inhaling, since the size distribution is unknown and
deposition is affected by unmeasured individual physiological parameters. According to
Hodges and Moore (1977), "even when an explanatory  variable (ambient PM concentration)
can be measured with negligible error it may often be standing as a proxy for some other

variable (dosage) which cannot be measured directly, and so  it (dosage) is subject to

measurement error". Pickles (1982) shows "that (such) uncertainties in air pollution levels
lead to two kinds of error in the air pollution/mortality  regression coefficient - a systematic
underestimate and a random scatter".  In addition,  measurement error can also bias a
threshold in the dose-response function towards zero (Yoshimura, 1990).
     In the  sections  that follow, the relationships between ambient  PM concentration, indoor
PM concentrations and personal exposures to PM are discussed hi detail.  The following five

caveats should be kept in mind while reading this chapter:

     1. Ambient PM concentrations are often measured as a 24-h  time-weighted-average
        (TWA) expressed as /ig/m3.  This quantity, by necessity, is assumed to be a
        surrogate for the mass of ambient PM deposited hi people's respiratory tracts per
        unit body weight, expressed as jig/kg-day.

     2. This daily quantity of ambient PM deposited per unit body weight  is in turn a
        surrogate for the amount of the true (but unknown)  species and/or  size fraction of
        the total PM that is the specific etiologic toxic agent(s) that act by  a presently
        unknown mechanism.  This latter quantity should be the independent variable for
        delineating  underlying relationships between ambient PM TWA concentrations to
        the health indices used as the dependent variables.

     3. Virtually  all analyses and discussions  of exposure presented here are based on
        personal exposure to PM of non-smokers.  Only Dockery and Spengler (1981b)
        included 6 smokers out of 37 subjects.  Smokers are often excluded from these
        studies because a personal exposure monitor (PEM) on a smoker will not capture
        the main-stream tobacco smoke that is directly inhaled.  In Section 7.2 on indoor air
        pollution, it is shown that side-stream environmental tobacco smoke (ETS) is the
        largest identifiable indoor source of PM where smoking occurs. For the average
        smoker, the amount of direct inhalation (several milligrams of PM per cigarette) can
        be two-to-three orders of magnitude greater than the microgram amounts of ETS

                                          7-4

-------
         which the PEM captures (Federal Trade Commission, 1994).  The relationships
         presented below,  of ambient PM concentration to individual total PM exposure,
         therefore only apply to non-smokers.

     4.  A total TWA personal exposure to PM (ambient PM plus indoor PM) will be a poor
         surrogate for the  personal exposure to PM of ambient origin for those people whose
         personal exposures are dominated by indoor (residential and occupational) sources,
         such as ETS.

     5.  All studies of indoor concentrations and personal exposures described below
         evaluated subjects recruited either in a nonrandom manner or in a scientific
         probability sampling scheme.  In the former case, the results cannot be extrapolated
         with confidence beyond the subjects themselves.  In the latter case,  the results can
         be extrapolated with a known confidence to the target population from which the
         sample was drawn. However, in both cases, there is a cohort of people who are
         nonresponders. If the reason for their refusal to participate in the survey is directly
         or indirectly related to their PM exposure, then the study results represent a sample
         with a bias of unknown sign and magnitude.
7.1.2 General Concepts for Understanding Particulate Matter Exposure
       and Microenvironments

     Particulate matter represents a generic class of pollutants which requires a different
interpretation of exposure in contrast to that for the other specific criteria gaseous pollutants,
such as CO (Mage, 1985).  Whereas a molecule of CO emitted from a motor vehicle is
indistinguishable from a molecule of CO emitted from  a fireplace, a l-/xm aerodynamic
diameter (AD) particle emitted from a motor vehicle and a 1-^im particle emitted from a
fireplace can have a different shape, mass, chemical composition, and/or toxicity.  Thus,
a "particle" can be a single entity, or an agglomeration of smaller particles, such as a small
Pb particle bound to a larger crustal particle.  Furthermore, indoor sources of particles
produce a wide variety of particles of varying size and composition that people are exposed
to, as shown in Figure 7-1 (Owen et al., 1992). Given that the health effects of inhalation of
any particle can depend upon its mass and chemical composition, it would be of use to
measure PM exposure in terms of mass and chemical composition as a function of size
distribution (Mage,  1985).
     The total PM exposure of an individual during a period of time is composed of
exposure to many different particles  from various sources in different microenvironments
(/xE). A pE was defined by Duan (1982) as  "a chunk of air space with homogeneous
                                          7-5

-------
                                                                Particle Diameter (urn)
0
Plant
Animal
Mineral
Combustion
Home/
Personal
Care
Radioactive
.01 0.1 1 10 100 1.000

- Coffee Roast Soot "4 	 Jjlold 	 ^. Coffee ^.
^ Starches ^
- nnmxtnmh ». ^ Milled Flow. <3rourjo^Corn_ »- "" Grain Dusts
.MusWlg. ^Ginger g Tea Dust
-_ Carbon Black -* ^ *"
~~ ^ Channel Black ^

^ Pudding Mix ^Com Cob Chaff » ^
^ Snuff •» - ^ Cayenne Pepper »
Bacteriophage -« Droplet Nuclei 1 ^ Bacteria
^ * ^ ^ Gelatin ^ ,,
> 	 V1""""5 	 »» Spider Web ""^ Hair " ^

•^ 	 ** Spray Dried Milk • ni.«» UM«»
•* 	 »• Disintegrated Feces •* 	 i=i^ 	 ^ •< Bu*1 Nll'es »
" — r-eces Bono Dust ^
_ Asbestos _



-< Clay * »- ^
^ ^Caldum. Zinc • Lead Dust ^ "

MMMF' ~ Special Use insulation ^ Fiberglass C3ia»» wool -^
MetaJuraical Dusts and Fumes Textiles ^ 	 ^ »* 	 FettlllZBT, Ground Limnstona 	 ^
- NH^CI Fume fc Cement Dust ^
^ SeaSalt »»
_, Tobacco Smoke ^
Burnlna Wood
^ Rosin Smoke "* 	 *" Smouldering or Flaming Cooking Oil
"* Coal Flue Qas - °" Sm°ke Fly Ash



^— - = Spray Paint Spray pa]nt Dus,
^ Air Freshener ^ *
" Anti-Stick Sorav L.
- Humidifier £ 	 < Fabric Protector
. . _. -" ^ ^ Nebulizer Droos to
— Paint Pigments ^ ^ ^
;, Alkali Fume « 	 Insecticide Du^ts > 	 ^-^ Emolllems
Face Powder: > 	 ^^ 	 »• Clumps ^ ^ MoCO, ^
CnniarTonm- Pioment fc „ Binder
Copier Toner. ,- •• - ^ 	 ».Artlflclal Textile Fibers
— Radon Progeny ^

                  1 Liquid droplets containing bacteria etc.. sneezed. Me
                  2 Man-made mineral fibers
Figure 7-1.  Sizes of various types of indoor particles.

Source:  Owen et al. (1992).

-------
pollutant concentration"; it has also been defined (Mage, 1985) as a volume in space, during
a specific time interval, during which the variance of concentration within the volume is
significantly less than the variance between that fiE and its surrounding jiEs.  For example,  a
kitchen with a wood stove can constitute a single pE for total PM when the stove is off, and
all people in the kitchen would have similar PM exposures. When the stove  is in operation,
the kitchen could have a significant vertical PM concentration gradient and a  child on the
floor in a far corner and an adult standing at the stove  could be exposed to significantly
different PM concentrations.
      In a given pE, such as one in the kitchen example, the particles may come from a wide
variety of sources.  PM may be generated from within (e.g. the stove, deep frying, burning
toast), from without (ambient PM entering through an open window), from another indoor
/iE (cigarette smoke from the living room), or from a personal activity that generates a
heterogeneous mix of PM (sweeping the kitchen floor and resuspending a mixture of PM
from indoor and outdoor sources that had settled out).
      In general, as people move through space and time, they pass through a series of /iEs
and their average total exposure (X /xg/m3) to PM for the  day can be expressed  by the
following equation,
                                  X  =  EXjtj/Etj                             (7-1)
where X4 is the total exposure to PM in the Ith /wE, visited in sequence by the person for a
time interval tt (Mage, 1985).
      With appropriate averaging over sets of 4 classes  of jtEs (e.g., indoors,
ambient-outdoors, occupational, and in-traffic) Equation 7-1 can be simplified as follows
(Mage,  1985):
                 **• = (Xjn tin +  Xout tout + Xocc  tocc  + Xtra ttjj) / T            (7-2)
where each value of X is the mean value of total PM concentration in the j*E class while the
subject is in it, tune (t) is the total time  the subject is in that /iE during the day,  and T is
equal to the sum of all times (usually one day).  Similar equations may be written for
personal exposures to particles from specific sources (e.g.,  diesel  soot), for specific
chemicals (e.g., Pb), or for  specific size intervals (PM < 2.5 /mi AD).
                                          7-7

-------
     Many excellent studies have reported data on air quality concentrations in pE settings
that do not meet a rigorous definition of an exposure, which requires actual occupancy by a
person (Ott, 1982).  Section 7.2, on Indoor Concentrations and Sources of PM, cites
Thatcher and Layton (1995) who report that "merely walking into a room increased the
particle concentration by 100%".  Consequently, an integrated measurement of air quality in
an enclosed space that includes time when it is unoccupied may not be a valid measure that
can be used to estimate an exposure while occupied.   If this measure includes periods of time
when the space is unoccupied, it will tend to be biased low as a measure of the exposure
within it during periods of occupancy. For example,  it is incorrect to associate an average
PM exposure to a person while  cooking  at a stove hi a kitchen with a kitchen concentration
measurement that is influenced by periods when the stove was off (Smith et al., 1994).
     The literature on 24-h average PM concentrations in indoor /tEs,  such as residential
settings, is treated separately in Section 7.2, as is done for 24-h average ambient PM
concentrations in Chapter 6.  In the exposure portion of this chapter, specific reference is
made to some studies where simultaneous personal PM exposures and indoor PM
measurements have been made,  so that the relationship between indoor concentration and
personal exposure can be examined.
     In practice, a cascade sampler can  collect ambient PM samples by size fractionation for
separate chemical analyses, but  such a complete definition of personal exposure to PM by
chemistry and size is difficult to obtain.  Although some personal monitors can be equipped
with a cyclone or impactor separator and several filters to capture several PM sizes (e.g.,
<2.0 /am, 2.0 to 10 pirn, and > 10 /xm;  Tamura et al.,  1996), most published studies of PM
exposure used a PEM with a single  integrated measurement of particle mass collected (e.g.,
<2.5 pm or  < 10 /mi).  Consequently, health studies on individuals are usually only able to
develop associations between their observed health effects and their observed exposure
expressed as an integral mass of PM collected and its average chemical composition.
     Health studies  on populations can make multiple measurements of ambient and indoor
PM concentrations simultaneously (e.g., PM2 5, PM10, TSP) along with components of PM,
such as polycyclic aromatic hydrocarbons (PAHs), to help understand the size distribution
and chemistry of the particles in the ambient and indoor atmospheres.
                                          7-8

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7.1.3   Summary of State-of-Knowledge in the 1982 Criteria Document
     In 1982 it was known, from personal monitoring and indoor monitoring, that SC^ is
almost always lower indoors than outdoors because of the virtual  absence of indoor sources
and the presence of sinks for SO2 in indoor settings (exceptions can occur if high sulfur coal
or kerosene are used as fuel in a poorly vented stove  or space heater). However, this
relationship does not hold for PM, as the indoor and personal monitoring data show both
higher- and lower-than ambient PM concentrations  in indoor settings as a function of particle
size and human activity patterns.
     The largest coarse mode particles (> 10 pirn), which are generally of nonanthropogenic
origin (wind blown dust, etc.), require turbulence to provide vertical velocity components
greater than their settling velocity to allow them to remain suspended in the air (Figure 7-1).
Outdoor particles enter into an indoor setting either by bulk flow, as through an open
window, in which all particles can enter at the inlet condition, or by pressure driven drafts
and diffusional flows through cracks and fissures in the barriers of the building envelope
when all windows are closed.  In the latter mode of entry,  velocities are relatively lower,
thereby settling out the largest coarse particles (> 25 /xm AD) in  the passage through the
barriers (Thatcher and Layton, 1995).
     Indoor settings are usually quiescent (Matthews et al., 1989), and ambient particles that
enter  indoors quickly settle out by gravity or electrostatic forces,  leading to familiar dust
layers on horizontal surfaces and vertical TV screens  that require constant cleaning
(Raunemaa et al.,  1989). However, human activity in indoor settings, such as smoking,
dusting, vacuuming and cooking, does generate fine particles (<2.5 /im) and coarser
particles (>2.5 /im) and resuspends coarse particles (> 10 p.m) that previously had settled
out (Thatcher and Layton, 1995; Litzistorf et al., 1985).
     Only three studies of personal PM exposures, compared to ambient PM concentrations,
were referenced in the  1982 Criteria Document (U.S. Environmental Protection Agency,
1982). Binder et al. (1976) reported that "outdoor air measurements do not accurately reflect
the air pollution load experienced by  individuals who  live in the area of sampling", in a study
in Ansonia, CT, where personal exposures to PM5  were double the outdoor PM
concentrations measured as TSP (115 versus 58 /ig/m3).  Spengler et al. (1980) was cited as
reporting that "there was no correlation [R2 = 0.04] between the  outdoor level [of respirable
                                          7-9

-------
particles] and the personal exposure of individuals" in a study in Topeka, KS.  Figure 7-2,
from Repace et al. (1980), was cited as an example of the variability of PM exposures which
show very little influence of ambient concentration.  Thus, at the time of the 1982 Criteria
Document, two major factors were known to influence ambient PM relationships to indoor
PM air quality: (1) the variability of indoor levels of PM compared to outdoor
concentrations as a function of particle size (e.g., fine indoor  > fine outdoor, and coarse
indoor <  coarse  outdoor); and (2) the variation of exposures of individuals related to
different activities involved in local generation of particles in their immediate surroundings
(smoking, traffic, dusting  and vacuuming at home, etc.).  This understanding was
summarized on pg. 5-136  of the 1982 Criteria Document, as follows:
     •  long term personal exposures to fine fraction PM (<2.5 jtrn) of outdoor origin, may
        be estimated by ambient measurements of the <2.5 /zm PM fraction.
        Personal activities and indoor concentrations cause personal exposures to PM to vary
        substantially. Ambient measurements appear to be a poor predictor of personal
        exposure to PM.
        Tobacco smoke is an important contributor to indoor concentrations and personal
        exposures where smoking takes place (U.S. Environmental Protection Agency,
        1982).
7.2   INDOOR CONCENTRATIONS AND SOURCES OF PARTICIPATE
      MATTER
7.2.1   Introduction
     Although EPA regulates particles in ambient air, which excludes the air internal to
buildings, it is still important to consider indoor air.  Most people spend most of their time
indoors.  A U.S. Environmental Protection Agency (1989) report indicates that U.S.
residents spend 85.2% of their time indoors, 7.4% in or near a vehicle, and only 7.4%
outdoors.  Also, it is important to understand how outdoor particles are affected as they cross
building envelopes.  For a home with no indoor sources,  how much protection is offered
against particles of various size ranges?  How do parameters such as volume of the house,
air exchange rate, cleaning frequency and methods, and materials in the home affect
                                        7-10

-------
            280
            260
            240
            220
        CO
         E  200
         ?180
160
140
120
100
 80
 60
 40
 20
   0
         I
         o
        O
                    I   I   I   I    I   I   I   I   I   I   I   I   I   I   I    I   I    I   I   I   I   I   T
    • Indoors
    • In Transit
    O Outdoors
                                   Cafeteria, Smoking Section

                               Behind Smoky Diesel Truck
                                               Office
                                  Commuting •  ^  ^
                                  . Bedroom .Ji
                                               Well-Ventilated Kitchen
                                     Outside Cigar
                                     Smoker's Office
                                         Street Suburbs, Outdoor
                                         Suburbs
                                           Vehicle
                                                In City
                    I   I   I   I    I   I    I
                                          Library. Unoccupied Cafeteria
                                                                  Cafeteria,
                                                                  Nonsmoking
                                                                  Section
                                                                          Sidewalk
                                                                                         edroom
                                                                                            Livin_ _
                                                                                  Commuting  Room —
                                                                                       Suburbs    _
                                                                                       ogging
                                                                                 Living. Room
                                                                      Dining ~i
                                                                       ;oom
                                            City, Outdoor
                                             I   I   I   I

               12  1
            Midnight
234
                     567
                       A.M.
                                            8  9  10  11 12  1
                                                        Noon
                                                    Time of Day
Figure 7-2.  An example of personal exposure to respirable particles.
234
567
  P.M.
8  9 10 11 12
Source: Repace et al. (1980).

-------
concentrations of particles of outdoor origin?  This section has several parts that address
these questions.
     The first part (7.2.2; 7.2.3; and 7.2.4) deals with field studies of particles indoors and
outdoors, focussing mainly on large-scale surveys of many homes and buildings.  Besides
presenting observed indoor and outdoor particle concentrations, information on important
parameters such as air exchange rates, source emission rates, and deposition rates is also
reported.  This section also discusses a few studies dealing with inorganic and organic
constituents of particles, as well as other considerations such as the role of house dust in
exposure to metals.  Section 7.2.3 provides a brief introduction to indoor air quality models.
Finally, Section 7.2.4 summarizes the main findings.
     The second part (7.2.5) is a discussion of bioaerosols from  plants, molds, insects, etc.
Although these sources of PM are uncontrolled by EPA, they affect measured PM indoors
and can potentiate the effects of PM from other sources through  allergenic properties.
     In keeping with EPA's regulatory responsibilities, the many studies in industrial
workplaces and the "dusty trades" are omitted, as are  studies of lead  (Pb) in indoor locations,
since lead is a separate criteria pollutant and such studies are reviewed in a separate lead
criteria document (U.S. Environmental Protection Agency, 1986b).

7.2.2    Concentrations of Particles in Homes and Buildings
     At  least seven major reviews of field studies of indoor particles have been published
since 1980 (Sterling et al., 1982; National Research Council, 1986; Repace, 1987; Guerin
et al.,  1992; U.S. Environmental Protection Agency, 1992; Holcomb, 1993; Wallace, 1996).
The last  of these reviews reports on several recently completed important studies, including
EPA's major probability-based PTEAM Study.  Since the two microenvironments where
people spend the most time are (a) home and (b) work or school, studies of these
environments are summarized in turn, with emphasis on the former.

7.2.2.1   Particle Concentrations in Homes: Large-Scale Studies  in the United States
     There have been three large-scale studies (greater than 150 homes) of airborne particles
inside  U.S. homes.  In chronological order, these are:
                                          7-12

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     1.   The Harvard Six-City study, carried out by the Harvard School of Public Health
         from  1979 through 1988, with measurements taken in 1,273 homes;
     2.   The New York State ERDA study, carried out by Research Triangle Institute (RTI)
         in 433 homes in two New York State counties during 1986;
     3.   The EPA Particle TEAM (PTEAM) study, carried out by RTI and Harvard School
         of Public Health in 178 homes in Riverside, CA in 1990.
The findings of each are discussed in detail, since these studies present the most complete
investigations to date of indoor and outdoor concentrations of particles.
7.2.2.1.1   The Harvard Six-City Study
     The Harvard Six-City Study is a prospective epidemiological study of health effects of
particles and sulfur oxides.  Focused mainly on children, it has included pulmonary function
measurements  on  more than 20,000 persons in the six cities, chosen to represent low
(Portage, WI and  Topeka, KS), medium (Watertown, MA and Kingston-Harriman, TN), and
high (St. Louis, MO and Steubenville, OH) outdoor particle and sulfate concentrations.
     The study took place in two measurement phases.  The first involved monitoring of
about 10 homes in each city for respirable particles (PM3 5), with measurements made every
sixth day (24-h samples) for one to two years.  In the second phase, a larger sample of 200
to 300 homes was selected from each city,  with week-long PM2 5 samples collected both
indoors and outdoors during two weeks of sampling in summer and winter.  Ultimately,
more than 1,200 homes were monitored in  this way.
     Spengler et al. (1981) described the first five years of the study.  During the Phase I
period, pulmonary function measurements were made for 9,000 adults, and 11,000 children
in grades 1 through  6.  In each home, a 24-h sample (beginning at midnight) was collected
every sixth day, using a cyclone sampler with a cut point of »3.5 /im at a flow rate of
1.7 Lpm.  About  10 sites in each city were kept hi operation for two years.   The annual
mean indoor and outdoor PM3 5 concentrations are shown in Figure 7-3. The indoor
concentrations  exceeded the outdoor levels in all cities except Steubenville, OH, where the
outdoor levels  of about 46 jug/m3 slightly exceeded the indoor mean of about 43 ^g/m3.   The
authors noted that the major source of indoor particles was cigarette smoke, and categorized
their data by number of smokers in the home (Table 7-1).
                                         7-13

-------
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                     P  T  K W SL  S
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Figure 7-3.  The annual mean concentration of respirable particles (PM3 5) for the
           highest and lowest site from the network of indoor and outdoor monitors in
           each city (P-Portage, T-Topeka, K-Kingston/Harriman, W-Watertown,
           SL-St. Louis, S-Steubenville) in the Harvard Six-City study.  Overall
           composite mean and the number of samples are also shown.

Source: Spengler et al. (1981).
     TABLE 7-1.  CONCENTRATIONS OF PARTICLES (PM3 5) IN HOMES OF
       CHILDREN PARTICIPATING IN THE HARVARD SIX-CITY STUDY
 Location
No. of Homes
No. of Samples    Mean (SD) G*g/m3)
 Indoors
 No smokers
 One smoker
 Two or more smokers
 Outdoors
      35
      15
       5
      55
      1,186
       494
       153
      1,676
24.4(11.6)
36.5 (14.5)
70.4 (42.9)
21.1 (11.9)
Source: Spengler et al. (1981).
                                    7-14

-------
     Dockery and Spengler (1981a) provided additional data analyses drawn from the same
study but including data from 68 homes compared to the 55 reported on in Spengler et al.
(1981).  Annual (every sixth day) mean indoor PM3 5 concentrations (in /ig/m3) were 20 and
23 in the two "clean" locations (Portage and Topeka); 31 and 36 in the two "medium"
locations (Watertown and Kingston-Harriman); and 39 and  47 in the two "dirty"  locations
(Steubenville and St. Louis). Outdoor PM2 5 concentrations measured by dichotomous
samplers every other day ranged from 13 /ng/m3 in Portage and Topeka to 20 /xg/m3 in St.
Louis, 24 jig/m3 in Kingston-Harriman, and 36 /xg/m3 in Steubenville (Spengler and
Thurston, 1983). A mass balance model allowed estimation of the impact of cigarette
smoking on indoor particles. Long-term mean infiltration of outdoor PM3 5 was estimated to
be 70%  for homes without ah* conditioners, but only 30% for homes with air conditioners.
A contribution of 0.88 /ig/m3 per cigarette (24-h average) was estimated for homes without
air conditioning; for homes with air conditioning, it increased to 1.23 pig/m3 per cigarette.
A residual amount of 15 ^ig/rn3 not explained by the model was attributed to indoor sources
such as  cooking, vacuuming and dusting.
     From the one to two years of indoor-outdoor data on  57 homes in the  six cities, Letz
et al. (1984) developed an equation relating indoor  to outdoor particle concentrations:
                      Cm = 0.385 Cout + 29.4 (Smoking) + 13.8.

Thus, homes with smokers had a PM3 5 ETS component of 29.4 /ig/m3. The residual of
13.8 /^g/m3 was assumed to be due to other household activities.
     Neas et al. (1994) presented summary results  for the entire Phase 2 of the Six-City
Study (1983 to 1988).  In Phase 2, for 1,237 homes containing white, never-smoking
children, 7 to 11 years old at enrollment, three questionnaires were completed and two weeks
of summer and whiter monitoring indoors and outdoors for PM2 5 was done, using the
Harvard PM2 5 impactor.  At the start of the indoor monitoring study,  55%  of the children
were exposed to ETS in the home, and 32% were exposed  to two or more smokers.
Household smoking status changed for 173 children, (13%  of smoking households ceased to
smoke, and 15% of the nonsmoking households became smoking ones).  The annual (winter
and summer) household PM2 5 mean concentration for the 580 children living in consistently
smoking households was 48.5  ± 1.4 (SE) /ig/m3 compared to 17.3  ± 0.5 /ig/m3  for the
                                        7-15

-------
470 children in consistently nonsmoking households.  Among the 614 exposed children for
whom complete information on smoking consumption was available,  36% were exposed to
< 1/2 pack daily, 40% to 1/2 to 1 pack daily, and 25% to > 1 pack daily. The distribution
of household concentrations for children in these smoking categories  is shown in Figure 7-4.
     Spengler et al. (1985) reported on the Kingston-Harriman,  TN data from the Six-City
Study.  Of 101 participants, 28 had cigarette smoke exposure at  home, and each had an
indoor and personal monitor (cutpoints of 3.5 ^tm). Each town had a centrally located
outdoor dichotomous sampler providing two size fractions (2.5 pm and 15 /mi).  Both towns
had similar outdoor PM2.5 concentrations of 18 /xg/m3,  so the values  were pooled for
subsequent analyses. Indoor concentrations averaged 42 + 2.6 (SE)  /ig/m3.  Indoor values
in homes with smoking averaged 74 ± 6.6 /jg/m3, compared to  28 + 1.1  /*g/m3 in homes
without smoking (p < 0.0001).  No significant correlations between  indoor and outdoor
concentrations were observed.
     Lebret et al. (1987) reported on  the Watertown, MA portion of the Six-City Study
where 265 homes were monitored for two one-week periods. Homes with smoking averaged
54 /ig/m3  (N  = 147 and 152 during weeks 1 and 2),  while homes without smoking averaged
21.6 /ig/m3 (N =  70 and 74).  The effect of smoking one cigarette/day was  estimated at
0.8 /xg/rn3 of PM25.
     Spengler et al. (1987) reported on a new round  of measurements in three Six-City
Study communities:  Watertown, MA; St. Louis, MO;  and Kingston-Harriman, TN.  In each
community, about 300 children were selected to take part in a year-long diary and indoor air
quality study.  PM2 5 measurements were taken indoors at home for two consecutive weeks
in winter and in summer, using the automated Harvard  sampler  which collected an integrated
sample for the week except for 8 a.m. to 4 p.m. weekday periods when the child was at
school.  During this 40-h period, samples  were taken in one classroom in each of the
elementary schools involved.  Results were presented for smoking and non-smoking homes  in
each city by season (Figure 7-5); the authors noted that mean concentrations in homes with
smokers were about 30 /xg/m3 greater than homes without smokers, the difference being
greater in winter than in summer for all cities.
     Santanam et al. (1990) reported  on a more recent  and larger-scale monitoring effort in
Steubenville and Portage  as part of the Six-City Study;  140 homes in each city, equally
                                         7-16

-------
     120-1
 00
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      20-
90th %tile
75th %tile
50th %tile
                              1 0th %tile
    l           I
 Never   Changed
  and      Status
Former
                                         i           i           i
                                         Consistently Smoking
                                      Pack
                   1/2-1
                    Pack
Packs
Figure 7-4. Distribution percentiles for annual average concentrations of indoor
          respirable particulate matter (PM2>5) by household smoking status and
          estimated number of cigarette packs smoked in the home during Phase 2
          Harvard Six-City study.

Source: Neas et al. (1994).
                                  7-17

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140-
130-
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Watertown St. Louis Kingston
Figure 7-5.  PM2>5 (/ig/m3) in smoking (S) and nonsmoking (N) homes in three of the
            Harvard Six-City Study sites.
Source:  Spengler et al. (1987).
distributed among households with and without smoking were monitored for one week in
summer and in winter.  The Harvard impactor sampler was used to collect PM2 5 samples
between 4 p.m. and 8 a.m. on weekdays and all day on weekends, corresponding to likely
times of occupancy for  school-age children.  Outdoor samples were collected from one site in
each city.  Target elements were determined by XRF.   A source apportionment using
principal components analysis (PCA) and linear regressions on the elemental data were
carried out (Table 7-2a,b). Cigarette smoking was the  single largest source in smokers'
homes, accounting for 20 to 27 jug/m3 indoor PM2-5 in Steubenville (Table 7-2a) and 10 to
25 /ig/m3 hi Portage (Table 7-2b).  Wood smoke was estimated to account for about 4 /ig/m3
indoors and outdoors in Steubenville in winter, but only for about  1 /ig/m3 indoors and
outdoors in Portage.  Sulfur-related sources accounted for 8 to 9 /xg/m3 indoors and
16 ptg/m3 outdoors in Steubenville in the summer, but were apparently not important in
winter.  Auto-related sources accounted for 2 to 5 /ig/m3  in the two cities. Soil sources

                                         7-18

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          TABLE 7-2a. RECONSTRUCTED SOURCE CONTRIBUTIONS
              TO INDOOR PM2 5 MASS FOR STEUBENVILLE, OH1
Source
Soil
Wood smoke
O.C.-I
Tobacco Smoke
Sulfur-related

Auto-related
O.C.-II

Indoor dust
Unexplained

Total

Smokers'
Homes
7.9 (3.45)
9.5 (4.15)
10.3 (4.47)
45.6 (19.9)
NS

NS
NS

NS
26.7(11.6)

100 (43.57)
WINTER
Non-Smokers'
Homes
17.6 (3.45)
21.2(4.15)
22.9 (4.47)
NA
NS

NS
NS

NS
38.3 (7.47)

100 (19.54)

Outdoor
Site
9.6(1.79)
23.0 (4.31)
24.8 (4.65)
NA
NS

NS
NS

NA
42.6 (7.95)

100 (18.7)

Smokers'
Homes
NS
NS
NS
53.7 (26.8)
17.8 (8.90)

7.3 (3.65)
8.8 (4.40)

7.4 (3.70)
5.0 (2.4)

100 (49.85)
SUMMER
Non-Smokers'
Homes
NS
NS
NS
NA
33.3 (8.23)

14.8 (3.65)
16.5 (4.07)

15.0 (3.70)
20.4 (5.05)

100 (24.7)

Outdoor
Site
NS
NS
NS
NA
52.5
(15.5)
5.3(1.55)
26.0
(7.67)
NA
16.2
(4-78)
100 (29.5)
!A11 entries in %
NS = not significant.
NA = not applicable.
O.C.-I: Iron and steel, and auto-related sources.
O.C.-II:  Iron and steel, and soil sources.

Source: Santanam et al. (1990).
          TABLE 7-2b. RECONSTRUCTED SOURCE CONTRIBUTIONS
                 TO INDOOR PM2 5 MASS FOR PORTAGE, WI1
Source
Sulfur-related
Auto-related
Soil
Tobacco Smoke
Wood smoke
Unexplained
Total

Smokers'
Homes
13.2 (4.56)
5.1 (1.78)
3.8(1.31)
71.0(24.6)
2.7 (0.94)
4.2(1.38)
100 (34.6)
WINTER
Non-Smokers'
Homes
30.7 (4.56)
12.0(1.78)
8.8(1.31)
NA
6.3 (0.94)
42.2 (6.23)
100 (14.8)

Outdoor
Site
39.2 (4.04)
17.3 (1.78)
13.4 (1.38)
NA
13.0(1.34)
17.1 (1.80)
100 (10.3)

Smokers'
Homes
23.3 (5.80)
18.1 (4.50)
7.5(1.86)
40.1 (9.99)
NA
11.0(2.75)
100 (24.9)
SUMMER
Non-Smokers'
Homes
38.1 (5.30)
29.6(4.12)
13.4(1.86)
NA
NA
18.9 (2.62)
100 (13.9)

Outdoor
Site
45.8 (6.23)
35.6 (4.84)
16.5 (2.25)
NA
NA
2.10(0.28)
100 (13.6)
   entries in % 0*g/m3)
NA = not applicable.

Source: Santanam et al. (1990).
                                    7-19

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accounted for only about 1 to 3 /xg/m3 of indoor and outdoor PM2 5 concentrations.
Nonsmoking homes in both cities had indoor mean PM2 5 concentrations very close to the
outdoor mean concentrations.  Quite large percentages of particle concentrations were due to
unexplained sources.

7.2.2.1.2 The New York State ERDA Study
     Sheldon et al.  (1989) studied PM2 5 and other pollutants in 433 homes in two New
York State counties. One goal of the study was to determine the effect of kerosene heaters,
gas stoves, wood stoves or fireplaces, and cigarette smoking on indoor concentrations of
combustion products.  A stratified design included all 16 combinations of the four
combustion sources and required about 22,000 telephone calls to fill all cells.  The sampler
was a portable dual-nozzle impactor developed at Harvard University.  Two oiled impactor
plates in series were used to reduce the probability that some particles larger than 2.5 /-im
would reach the filter.  Samples were collected in the main  living area and in one other room
(containing a combustion source if possible) using a solenoid switch to collect alternate
15-min samples over a 7-day period.  Outdoor samples were collected at a subset of 57
homes.  All samples were collected during the winter (January to April) of 1986.
     PM2 5 mean concentrations indoors for all homes, with and without any combustion
sources, were approximately double those outdoors in both counties (Table 7-3).  However,
in homes without combustion sources, PM2 5 concentrations were approximately equal
(Leaderer et al., 1990).  Of the four combustion sources, only  smoking created significantly
higher indoor PM2 5 concentrations in both counties (Table 7-4).  Use of kerosene heaters
was associated with significantly higher  concentrations in Suffolk (N = 22) but not in
Onondaga (N =  13).  Use of wood stoves/fireplaces and gas stoves did not significantly
elevate indoor concentrations in either county.
     Leaderer et al. (1990) extended the analysis of these data by collapsing the  gas stove
category, reducing the number of categories from 16 to 8 (Table 7-5).   By inspection of
Table 7-5, it is clear that smoking was the single strongest source of indoor fine particles,
with geometric means of indoor PM ranging from 28.5 to 61.4 /^g/m3, whereas the four
nonsmoking categories ranged from 14.1 to 22.0 jug/m3.
                                          7-20

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TABLE 7-3. WEIGHTED SUMMARY STATISTICS BY NEW YORK COUNTY FOR
  RESPIRABLE SUSPENDED PARTICIPATE (PM2 5) CONCENTRATIONS Qtg/m3)
Main Living Area

Percent Detected
Sample Size
Population Estimate
Arithmetic Mean (/*g/m3)
Arithmetic Standard Error
Geometric Mean (pig/m3)
Geometric Standard Error
Minimum (/*g/m3)
Maximum (/ig/m3)
Percentiles
10th
16th
25th
50th (median)
75th
84th
90th
95th
99th
Onondaga
98.9
224
94,654
36.7a
2.14
25.7a
1.07
0.72
172

9.93
11.2
13.5
23.9
48.4
68.0
85.2
112
136
Suffolk
99.6
209
286,580
46.4
2.77
35.9
1.06
2.18
284

13.8
16.8
18.9
33.6
62.8
76.6
89.4
112
155
Outdoors
Onondaga
100
37

16.8
1.00
15.8
1.06
6.32
28.4



12.8
15.1
20.5




Suffolk
100
20

21.8
4.54
18.6
1.11
12.0
106



13.6
16.7
22.3




a Significantly different between counties at 0.05 level.
Source: Sheldon et al. (1989).


     Leaderer and Hammond (1991) continued analysis of the New York State data by
selecting a subset of 96 homes for which both nicotine and PM2 5 data were obtained. In the
47 homes where nicotine was detected (detection limit  = 0.1 /xg/m3), the mean concentration
of RSP was 44.1 (± 25.9 SD) jig/m3 compared to 15.2 (± 7.4) ng/m3 in the 49 homes
without detected nicotine.  Thus, homes with smoking had an increased weekly mean PM2 5
concentration of about 29 jig/m3.  Imperfect agreement with reported smoking was observed,
with nicotine being measured in 13% of the residences that reported no smoking, while
nicotine was not detected in 28% of the residences that reported smoking.  A regression on
                                      7-21

-------
     TABLE 7-4. WEIGHTED ANALYSIS OF VARIANCE OF RESPIRABLE
 SUSPENDED PARTICULATE (PM2 5) CONCENTRATIONS (/tg/m3) IN THE MAIN
       LIVING AREA OF HOMES VERSUS SOURCE CLASSIFICATION

Onondaga (R2 = 0.17)
Model
Independent variables:
Intercept
Gas stove
Kerosene heater
Tobacco smoking
Wood stove/fireplace
Suffolk (R2 = 0.21)
Model
Independent variables:
Intercept
Gas stove
Kerosene heater
Tobacco smoking
Wood stove/fireplace
F Value

20.5
1.87
1.06
81.6
2.42

36.9
0.13
12.0
114
0.71
Probability

0.00
0.17
0.30
0.00
0.12

0.00
0.72
0.00
0.00
0.40
Coefficient


20.3
5.25
5.05
45.1
7.81


26.1
-1.52
30.1
46.8
9.88
Source: Sheldon et al. (1989).
        TABLE 7-5. RESPIRABLE SUSPENDED PARTICULATE (PM2 5)
        CONCENTRATION Qtg/m3) IN HOMES BY SOURCE CATEGORY
                            Suffolk                   Onondaga
Source
None
W
K
S
KW
SW
SK
SKW
Outdoor
N
30
15
7
61

29
23
6
19
Mean
17.3
18.1
22.0
49.3

38.0
61.4
30.3
16.9
Standard
1.7
1.6
1.6
1.8

1.8
2.0
1.4
1.3
N
45
16
4
80
4
31
4
4
36
Mean
14.1
19.1
21.2
36.5
19.7
33.9
35.3
28.5
15.8
Standard
1.7
1.7
1.0
2.4
1.5
2.2
1.5
1.6
1.5
Abbreviations: W = woodstove; K = kerosene heater; S = tobacco smoking.

Source:  Leaderer et al. (1990).
                                7-22

-------
all (smoking and nonsmoking) homes of PM2.5 on total number of cigarettes smoked during
the week (T) gave the result:

                     PM2.5  = 17.7 + 0.322T (N = 96; R2 = 0.55).

For the subset of 47 homes with measured nicotine, the regression gave the result:

                     PM2 5  = 24.8 + 0.272T (N = 47; R2 = 0.40).

Thus each cigarette produces  about a 0.3 (±0.03) /tg/m3 increase hi the weekly mean PM2 5
concentration,  equivalent to a 2.1  (±0.2) jiig/m3 increase in the daily concentration.
     Koutrakis et al. (1992) also analyzed  the New York State data, using a mass-balance
model  to estimate PM2 5 and elemental source strengths for cigarettes, wood burning stoves,
and kerosene heaters.  Homes with cigar or pipe smoking and fireplace use were eliminated,
resulting in 178 indoor air samples.  PM2 5 source strength for smoking was estimated at
12.7 ± 0.8 (SE) mg/cigarette; but PM2 5 source strengths could not be estimated for wood
burning or kerosene heater usage (only seven homes in each category were available for
analysis). For a residual category of all other indoor sources, a source strength of 1.16 mg/h
was calculated.  For nonsource homes (N  =  49), the authors estimated that 60% (9 jig/m3)
of the total PM2 5 mass was from  outdoor  sources and 40% (6 pig/m3) from unidentified
indoor sources.  However, indoor concentrations were not significantly correlated with
outdoor levels. For smoking  homes, they  estimated that 54% (26 /xg/m3) of the PM2 5 mass
was from smoking, 30%  (15 jig/m3) from  outdoor sources, and 16% (8 /ig/m3) from
unidentified sources.   The elemental emissions profile for cigarettes included potassium
(160 /ig/cig), chlorine (69 /xg/cig), and sulfur (65 /ig/cig), as  well as smaller amounts  of
bromine, cadmium, vanadium, and zinc. The woodburning profile  included three elements:
potassium (92  /xg/h), silicon (44 /*g/h) and calcium (38 /xg/h).  The  kerosene heater profile
included a major contribution from sulfur (1500 /tg/h) and fairly large inputs of silicon
(195 /ig/h) and potassium (164 /xg/h).  A drawback of the mass-balance model was an
inability to separately  estimate the value of the penetration coefficient P and the decay rate
                                         7-23

-------
k for particles and elements; Koutrakis et al. (1992) assumed a constant rate of 0.36 h"1 for
fc, and then solved for P.

7.2.2.1.3   The U.S. Environmental Protection Agency Particle Total Exposure Assessment
           Methodology Study
     EPA designed a study of exposure to particles and associated elements in the late
1980s. Personal exposure and indoor and outdoor PM2 5 and PM10 concentrations were
measured.  The personal exposure portion of the study is discussed in 7.4.1.1.1.  The study
was carried out under the Total Exposure Assessment Methodology (TEAM) program, and is
known as the Particle TEAM, or PTEAM Study.
     A pilot study  was undertaken in nine homes in Azusa, CA in March of 1989 to test the
sampling equipment.  The first five households were monitored concurrently for seven days
(March 6-13, 1989; Wiener,  1988, 1989; Wiener et al., 1990;  Spengler et al., 1989); the last
four households were then monitored for four consecutive days (March 16-20, 1989). Indoor
and outdoor particle concentrations were monitored using impactors with a 10  Lpm pump
(Marple et al.,  1987).  Indoor monitors, capable of sampling both fine and inhalable  particles
simultaneously, were placed in different rooms in each house to determine the magnitude of
room-to-room variation.
     Room-to-room variation of  12-h integrated  particle levels was generally less than 10%.
Therefore the several indoor values in a particular house were averaged to provide a  single
mean indoor value  to compare to the corresponding outdoor value.  The mean (SE) 24-h
indoor PM10 concentration was 58.7 (3.4) pig/m3 compared to the outdoor mean of
62.6 (3.5) /ig/m3.  Corresponding PM2 5 concentrations were 36.3 (2.6) /zg/m3 indoors and
42.6 (3.0) /ig/m3 outdoors.
     Regressions of indoor on outdoor concentrations (N = 26 for each size fraction and
time period) resulted in the following equations for PM10:
     Cfc(day)   = 36 (11) + 0.44(0.14) Cout   (R2 = 0.17)
     Cin (night) = 44 (11) + 0.14 (0.19) Cout   (R2 = 0.01)
and for PM2 5:
     qn(day)   = 18 (5) + 0.47(0. 10) COM,    (R2 = 0.30)
                                         7-24

-------
     Cin (night) = 24 (6) + 0.23 (0.15) Cout    (R2 = 0.05)
where the values in parentheses are the standard errors of the parameter estimates.  (In most
epidemiology studies, PM exposures are  related to PM concentrations at a community
ambient monitoring station, rather than to these PM concentrations measured outside
indivdual homes).
     The R2 values unproved considerably when the regressions for individual homes were
calculated (Wallace, 1996; see also Table 7-6).  For the five homes with seven days of
monitoring (14 12-h periods) all slopes were significant, and R2 values ranged from 0.34 to
0.79 for PM10 and from 0.49 to 0.85 for PM2 5.  For the four homes having only four days
of monitoring, only home 8 had significant slopes and R2 values above 0.5.
          TABLE 7-6. REGRESSIONS OF INDOOR ON OUTDOOR PM10
     AND PM2 5 CONCENTRATIONS (/tg/m3):  PARTICLE TOTAL EXPOSURE
               ASSESSMENT METHODOLOGY PREPILOT STUDY
PM10 G*g/m3)
House
1
2
3
4
5
6
7
8
9
N
13
13
14
13
14
8
8
8
7
Intercept
23
-25
13
16
14
175
30
-2.7
48
SE
9
17
7
9
13
38
34
23
42
P
0.026
NS
NS
NS
NS
0.004
NS
NS
NS
Slope
0.27
1.14
0.64
0.52
0.67
-1.52
0.34
1.38
0.94
SE
0.12
0.23
0.1
0.14
0.16
0.78
0.62
0.5
0.87
P
0.038
0.0003
0.00002
0.004
0.001
NS
NS
0.03
NS
R2
0.34
0.7
0.79
0.54
0.59
0.39
0.05
0.56
0.19
PM2 5 (/xg/m3)
House
1
2
3
4
5
6
7
8
9
N
14
14
14
13
14
8
8
8
8
Intercept
14
-12
7.3
6
11
65
10
-0.34
37
SE
3.4
9
4.5
5
6
26
8
13
47
P
0.001
NS
NS
NS
NS
0.046
NS
NS
NS
Slope
0.19
0.96
0.72
0.52
0.58
-0.32
0.35
0.99
0.78
SE
0.06
0.16
0.09
0.13
0.1
1.01
0.22
0.39
1.3
P
0.005
0.00007
0.00001
0.002
0.0001
NS
NS
0.045
NS
Rz
0.49
0.74
0.85
0.6
0.72
0.02
0.3
0.51
0.05
Source:  Data from PTEAM Prepilot Study upon which R2 values were generated as reported by
       Wallace (1996).
                                       7-25

-------
     After the pilot study in Azusa, CA, the EPA sponsored a study of personal, indoor, and
outdoor concentrations of PM10, and indoor and outdoor concentrations of PM2 5 in
Riverside, CA (Pellizzari et al., 1992, 1993; Perritt et al., 1991; Sheldon et al., 1992;
Clayton et al., 1993; Thomas et al., 1993; Ozkaynak et al., 1993a,b, 1996).  Personal
exposure results of this study are discussed hi Section 7.4.1.1.2.  The main goal was to
estimate the  frequency distribution of exposures to PM10 for all nonsmoking Riverside
residents aged 10  and above; and 178 households were selected, using probability sampling
to represent  about 61,000 households throughout most of the city of Riverside.  Homes were
sampled between  September 22 and November 9, 1990, and each home had two 12-h
samples for both size fractions. A  central site operated throughout the 48 days of the study,
producing 96 12-h samples collected by side-by-side reference samplers (dichotomous
samplers and modified hi-volume samplers) along with the low-flow (4 Lpm) impactors with
nominal cutpoints at 2.5 and 10 /nm designed for this study.  (Laboratory tests  [Thomas
et al., 1993] revealed that the actual cutpoints were 2.5 /un and 11.0 /mi, but this section
shall refer to PM10 in keeping with the investigators [Clayton et al., 1993] who reported their
data as PM10).  A subset of the homes was monitored for PAHs (Sheldon et al., 1992);
125 were monitored indoors  and 65 of those were monitored outdoors for two  consecutive
12-h periods.
     The precision of the three types of particle samplers at the central site was excellent,
with median RSDs of about 4 to 5% (Wallace, et al., 1991a).  The low-flow sampler
produced estimates about 12% greater than the dichotomous sampler, which was about 7%
greater than the modified hi-vol sampler (Wallace, et al., 1991b). Part of the difference may
be due to the different cutpoints (estimated to be 11 pun for the new sampler, 9.5 for the
dichot, and 9.0 for the modified hi-vol), and part due to particle bounce (large particles
bouncing off the impactor and being re-entrained in the flow to the filter), such that the
PM2 5 and PM10 fractions in the low-flow sampler may be contaminated with a small number
of larger-size particles.   However,  particle bounce was found in laboratory tests to account
for less than 7% of the total  mass.
     The population-weighted distributions of personal (PEM),  indoor (SIM), and outdoor
(SAM) particle concentrations are provided in Table 7-7. PM10 mean concentrations
(150 /ig/m3) were more  than 50% higher  than either indoor or outdoor levels (95
                                         7-26

-------
                      TABLE 7-7.  WEIGHTED DISTRIBUTIONS OF PERSONAL, INDOOR, AND
                                 OUTDOOR8 PARTICLE CONCENTRATIONS



DAYTIME
PM2.5

Sample size
Minimum
Maximum
Mean
(Std. error)
Geometric Mean
(Std. error)
Std. deviation
Geometric std. deviation1*
Percentiles
10th
25th
50th (median)
75th
90th
Std. errors of percentiles
10th
25th
50th
75th
90th
SAM
167
7.4
187.8
48.9
(3.5)
37.7
(2.5)
37.6
2.07

14.9
23.4
35.5
60.1
102.2

1.6
2.1
4.0
3.9
4.6
SIM
173
2.8
238.3
48.2
(4.1)
35.0
(3.3)
41.2
2.25

11.5
19.3
33.5
61.5
101.0

3.4
1.4
4.5
3.3
6.7
SAM
165
16.2
506.6
94.9
(5.5)
82.7
(4.1)
57.2
1.68

42.8
56.9
84.1
110.8
157.2

2.3
4.5
4.7
4.0
7.2
NIGHTTIME
PM10
SIM
169
16.6
512.8
94.7
(5.7)
78.2
(5.0)
61.4
1.88

30.9
49.5
81.7
127.2
180.7

3.4
4.3
8.3
9.4
11.0
PM2.5
PEM
171
35.1
454.8
149.8
(9.2)
128.7
(8-5)
84.3
1.75

59.9
86.1
129.7
189.1
263.1

4.0
9.4
7.5
10.8
12.0
SAM
161
3.4
164.2
50.5
(3.7)
37.2
(3.1)
40.3
2.23

14.5
23.0
35.0
64.9
120.7

2.1
2.7
2.4
4.6
5.8
SIM
166
2.9
133.3
36.2
(2.2)
26.7
(1.9)
29.5
2.21

10.0
14.8
25.9
48.9
82.7

0.9
1.3
2.4
5.3
5.8
SAM
162
13.6
222.9
86.3
(4.4)
74.5
(4.0)
47.7
1.74

39.3
53.6
74.1
103.7
167.8

7.4
3.4
4.8
5.1
4.3
PM10
SIM
163
14.1
180.3
62.7
(3.2)
53.1
(3.1)
37.4
1.78

25.2
33.5
51.6
84.8
116.9

1.5
2.4
3.5
4.7
5.3

PEM
168
19.1
278.3
76.8
(3.5)
67.9
(3.1)
39.7
1.64

36.6
48.1
66.2
98.8
135.0

1.5
3.1
4.3
8.2
10.1
"Statistics other than the sample size, minimum, and maximum are calculated using weighted data; they provide estimates for the target population of person-days
(PEM) or of household-days (SIM, SAM).
bln contrast to the other statistics, the gsd is a unitless quantity.

Source: Pellizzari et al. (1992).

-------
Overnight mean personal PM10 concentrations (77 pig/m3) were similar to the indoor
(63 /ig/m3) and outdoor (86 fig/m3) levels.  The reason for the higher daytime personal
exposures (PEM) than daytime SIM or SAM  is not completely understood:  it may be due to
persons often being close to sources of particles (e.g., cooking, dusting,  or vacuuming) or to
re-entrainment of household dust (Thatcher and Layton, 1995).  It appears not to be due to
skin flakes or clothing fibers; many skin flakes were found on filters but their mass does not
account for more than 10% of the excess personal exposure (Mamane, 1992).
     Mean PM2 5 daytime concentrations were similar indoors (48 /xg/m3) and outdoors
(49 /ig/m3), but indoor concentrations fell off during the sleeping  period (36 /xg/m3)
compared to  50 /ig/m3 outdoors.  Thus the  fine particle contribution to PM10 concentrations
averaged  about 51% during the day and 58%  at night, both indoors and outdoors.  The
distributions  of these ratios are provided in Table 7-8.
                  TABLE 7-8.  WEIGHTED DISTRIBUTIONS8 OF
                      PM2 5/PM10 CONCENTRATION RATIO
Daytime

Sample Size
Mean
(Std. error)
Geometric Mean
(Std. error)
Percentiles
10th
25th
50th (median)
75th
90th
Std. errors of percentiles
10th
25th
50th
75th
90th
Statistics other than sample
Outdoor
160
0.470
(0.016)
0.444
(0.017)

0.274
0.371
0.469
0.571
0.671

0.018
0.018
0.015
0.019
0.012
size are calculated usini
Indoor
167
0.492
(0.021)
0.455
(0.022)

0.250
0.347
0.498
0.607
0.735

0.030
0.046
0.020
0.024
0.028
z weighted data: they PI
Nighttime
Outdoor
154
0.522
(0.017)
0.497
(0.019)

0.308
0.406
0.515
0.646
0.731

0.023
0.028
0.022
0.027
0.016
rovide estimates for the
Indoor
160
0.550
(0.014)
0.517
(0.016)

0.301
0.440
0.556
0.694
0.771

0.023
0.017
0.015
0.023
0.012
target
 population of household-days.
Source:  Pellizzari et al. (1992).
                                        7-28

-------
      Unweighted distributions are displayed in Figures 7-6 and 7-7 for 24-h average PM10
 and PM2 5 personal, indoor, and outdoor concentrations.  For 24-h data, the indoor PM is
 less than the outdoor PM at all percentiles.  Most of the distributions were not significantly
 different from log-normal distributions, as determined by a chi-square test.   About 25% of
 the nonsmoking population of Riverside was estimated to have 24-h personal PM10 exposures
 exceeding the 150 jtg/m3 24-h NAAQS for ambient air.  Since participants were monitored
 for only one day, the percentage of persons with exposures exceeding the outdoor 24-h
 standard more than once per year would be greater than 25 %.
                      a.
                      o
 300
 270
 240
 210
 180
 150
,120

  90
                        60
                        30
    -  o"'
                                                    Personal
                                                 -A- Indoor
                                                    Outdoor
300
270
240
210
180
150
120

90


60
                             25      50     75    90  95   96 99
                                   Cumulative Frequency (%)
                                       30
Figure 7-6. Cumulative frequency distribution of 24-h personal, indoor, and outdoor
            PM10 concentrations in Riverside, CA.
Source: Adapted from PTEAM study data (Pellizzari et al., 1992).
     The 48-day sequence of outdoor PM10 and PM2-5 concentrations is shown in Figure 7-8
(Wallace et al., 1991a). At least two extended episodes of high fine-particle concentrations
occurred, and four days of high Santa Ana winds, with correspondingly high coarse-particle
concentrations from desert sand, were observed.
                                         7-29

-------
                                   50    75    90  95  98 99
                                   Cumulative Frequency (%)
                                                            20
Figure 7-7.  Cumulative frequency distribution of 24-h indoor and outdoor PM2>5
            concentrations in Riverside, CA.

Source: Adapted from PTEAM study data (Pellizzari et al., 1992).
           200
                    Dichot coarse   —Dichot-10
                          20           40          60          80           100
                             12-Hour Periods Beginning Sept. 22,1990
Figure 7-8. Forty-eight day sequence of PM10 and coarse PM (PM10 - PM2-5) in
            Riverside, CA, PTEAM study. Santa Ana wind conditions are noted by
            an asterisk.

Source: Wallace et al. (1991a).
                                         7-30

-------
     Central-site PM2 5 and PM10 concentrations agreed well with back yard concentrations.
Pearson correlations of the log-transformed data were 0.96 and 0.92 for overnight and
daytime PM2 5 and 0.93 for overnight PM10 values (Ozkaynak et al., 1993a), but dropped to
0.64 for daytime PM10 values.  However, two homes in one Riverside area showed very
high outdoor concentrations of 380 and 500 /Lig/m3 on one day, while two homes in another
Riverside area and the central-site monitor showed more typical concentrations.  A local
event likely produced the higher concentrations at the former two homes.  If they are
removed from the data set, the correlation improves from 0.64 to 0.90, suggesting that a
single central-site monitor can represent well PM2 5 and PM10 concentrations throughout a
wider area such as a town or small city (at least in the Riverside area) except for unusual
local conditions.
     Daytime indoor PM10 and PM2 5 concentrations showed low-to-moderate Pearson
correlations of 0.46 and 0.55, respectively, with outdoor concentrations (N =  158 to 173).
At night, the correlations improved somewhat to 0.65 and 0.61, respectively
(N = 50 to 168). Outdoor PM10 concentrations explained about 27% of the variance of
indoor levels (Figure 7-9) with the two outliers included.
     Simple regressions of indoor on outdoor PM10 and PM2 5 resulted in the  following
equations (standard errors in parentheses):

     Indoor PM10 = 48 (9) + 0.51 (0.08) x Outdoor PM10 (day)    N=159   R2  = 0.22
     Indoor PM10 = 20 (5) + 0.52 (0.05) X Outdoor PM10 (night)  N = 151   R2  = 0.42
     Indoor PM2 5 = 14 (4) + 0.70 (0.07) X Outdoor PM2 5 (day)   N=162   R2  = 0.42
     Indoor PM25 = 9 (3) + 0.56 (0.04) X Outdoor PM25 (night)  N = 153   R2  = 0.54

     Simple regressions of personal PM10 on outdoor and indoor PMj0 resulted hi the
following equations:
     Personal PM10 =  71 (9) -I- 0.78 (0.08) x Indoor PM10 (day)   N = 163   R2  = 0.40
     Personal PM10 =  21 (4) + 0.90 (0.05) X Indoor PM10 (night)  N=158   R2  = 0.65
     Personal PM10 = 100 (12) + 0.48 (0.10) X Outdoor PM10 (day) N=158   R2  = 0.12
     Personal PM10  =  31 (6) + 0.53 (0.06) x Outdoor PM10 (night) N=155   R2  = 0.38
                                        7-31

-------
-600

~ 500
o
   400
                                          Indoor - 0.54*Outdoor + 32
                                          R2=27% (n = 309)
                    100        200         300         400         500         600
                         Average 12-h outdoor concentration (ng/m3)
Figure 7-9. Average indoor and outdoor 12-h concentrations of PM10 during the
            PTEAM study in Riverside, CA.
Source: Ozkaynak et al. (1993b).
     Correlation analyses and regressions relating personal to indoor, indoor to outdoor, and
personal to outdoor concentrations of the 14 prevalent elements were carried out for the
appropriate size fractions and both 12-h monitoring periods.  For most of the elements, as
with particle mass, moderate correlations were noted for personal-indoor and indoor-outdoor
concentrations but low correlations for personal-outdoor concentrations.  One element was a
strong exception to this rule:  sulfur.  Unlike any of the other elements, sulfur  was not
elevated in the PEM relative to the SIM, and, thus,  personal concentrations were much more
closely related to indoor concentrations (rs = 0.91 during the day and 0.95 at night).
Moreover,  because few sources of sulfur are found indoors, the indoor-outdoor correlations
were high (rs varied between 0.90 and 0.95 for both size fractions), and even the personal-
outdoor correlations showed little degradation (the Spearman correlation rs = 0.85 during the
day and 0.92 at night).
     Regressions of outdoor sulfur on indoor levels gave the following results for PM10
sulfur (/ig/m3):
                                         7-32

-------
     Sta (day) = 0.26 (0.06 SE) + 0.80 (0.02) 5out            N =  164   R2 = 0.88
     5in (night) = 0.20 (0.06) + 0.71 (0.03) 5out              N =  155   R2 = 0.84
and for fine (PM2 5) sulfur:

     S.m (day) = 0.046 (0.04 SE) + 0.85 (0.02) 5out           N =  164   R2 = 0.92
     S-m (night) = 0.061 (0.04) + 0.80 (0.02) 5out             N =  154   R2 = 0.89

     Stepwise regressions resulted in smoking, cooking, and either air exchange rates or
house volumes being added to outdoor concentrations as significant variables (Table 7-9).
Homes with smoking added about 27 to 32 jig/m3 to the total PM2 5 concentrations and about
29 to 37 jwg/m3 to the PM10 values.  Cooking added 12 to 26 ^g/m3 to the daytime PM10
concentration and about 13 /ig/m3 to the daytime PM2 5 concentration, but was not significant
during the overnight period.
     A model developed by Koutrakis et al.  (1992) was solved using nonlinear least squares
to estimate penetration factors, decay rates, and source strengths for particles and elements
from both size fractions in the PTEAM study.  In this model, which assumes perfect
instantaneous mixing and steady-state conditions throughout each 12-h monitoring period, the
indoor concentration of particles or elements is given by
                                       PaCout + Qis'V                             (7.3)
                                       -     -
where
     Cin   = indoor concentration (ng/m3 for elements, /ig/m3 for particles)
     P     = penetration coefficient
     a     = air exchange rate (h"1)
      out
     Qh   = mass flux generated by indoor sources (ng/h or /ig/h)
     V     = volume of room or house (m3)
     k     = decay rate due to diffusion or sedimentation (h"1)
     Cout   = outdoor concentration (ng/m3 or /tg/m3)
     From initial multivariate analyses, the most important indoor sources appeared to be
smoking and cooking.  Therefore the indoor source term QK was replaced by the following
expression:

                                          7-33

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        TABLE 7-9.  STEPWISE REGRESSION RESULTS FOR INDOOR AIR
                 CONCENTRATIONS OF PM10 AND PM2 5 0*g/m3)
              COEFFICIENTS (STANDARD ERRORS OF ESTIMATES)
                                   PM10
Variable
N
R2
Intercept

Outdoor air

Smoking3

No. cigarettes1"

Cooking0

Air exchange

House volumed

All
310
41%


0.52
(0.05)
37
(6)
3.2
(0.7)
20
(5)
5.2
(2.0)
-0.08
(0.02)
Day
158
39%
57
(21)
0.66
(0.09)
29
(8)
3.0
(1.0)
26
(9)


-2.7
(1)
Night
147
58%


0.45
(0.05)
38
(11)
3.9
(0.9)
12
(5)
12
(5)


All
324
55%


0.64
(0.04)
28
(3.5)
2.5
(0.4)
9.4
(2.9)




Day
156
53%
21
(7.8)
0.71
(0.07)
27
(7)
2.4
(0.6)
13
(5)


-2.0
(0.6)
Night
149
71%


0.53
(0.04)
32
(10)
4.0
(0.6)


4.5
(2)


All listed coefficients significantly different from zero at p < 0.05.
aBinary variable: 1 = at least one cigarette smoked in home during monitoring period.
bThis variable was interchanged with the smoking variable in alternate regressions to avoid colinearity problems.
cBinary variable: 1 = cooking reported for at least one min in home during monitoring period.
dVolume in thousands of cubic feet.

Source: Ozkaynak et al. (1996).
                         Qis  = ("dgScig + TcookScook)/T + Qother                    (7-4)

where

     T     =   duration of the monitoring period (h)
     Wcig   =   number of cigarettes smoked during monitoring period
     5"cig   =   mass of elements or particles generated per cigarette smoked (ng/cig or
     rcook =  time spent cooking (min) during monitoring period
     •^cook =  mass °f elements or particles generated per min of cooking (ng/min or
               ptg/min)
     Cother =  mass flux °f elements or particles from all other indoor sources (ng/h or
                                         7-34

-------
     With these changes, the equation for the indoor concentration due to these indoor
sources becomes
                                    ^cig^cig + ^cook^cook     Qother                 (7.5)
                                        (a + k)V T       (a + k)V

     The indoor and outdoor concentrations, number of cigarettes smoked, monitoring
duration, time spent cooking, house volumes, and air exchange rates were all measured or
recorded. The penetration factor, decay rates, and source strengths for smoking, cooking,
and all other indoor sources  (Cother) were estimated using a nonlinear model (NLIN in SAS
software).  The Gauss-Newton approximation technique was used to regress the residuals
onto the partial derivatives of the model with respect to the unknown parameters until the
estimates converge. On the  first run, the penetration coefficients were allowed to "float"
(no requirement was made that they be < 1).  Since nearly all coefficients came out close to
1, a second run was made bounding them from above by 1. The NLIN program provides
statistical uncertainties (upper and lower 95 % confidence intervals) for all parameter
estimates. However, it should be noted that these uncertainties assume perfect measurements
and are therefore underestimates of the true uncertainties.
     Results are presented in Table 7-10 for the combined  day and night samples.  The
penetration factors were very close to unity  for nearly all particles and elements. The
calculated average  decay rate (lower and upper 95% confidence levels) for PM2 5 was
0.39 (0.22; 0.55) h'1, and for PM10 was 0.65 (0.36; 0.93)  h'1.  Since PM10 contains the
PM2.s fraction, a separate calculation was made for the coarse particles (PM10 — PM2 5)
with a resulting decay rate of 1.01 (0.6; 1.4) h"1. Each cigarette emitted 22 (14; 30) mg of
PM10 on average, about two-thirds of which 14 (10; 17) mg is in the fine fraction.  Cooking
emitted 4.1 (2.6; 5.7) mg/min of inhalable particles, of which about 40%  or 1.7 (1.0; 2.3)
mg/min, was in the fine fraction.  All  target elements emitted by cooking  were limited
almost completely to the coarse fraction.  Sources other than cooking and  smoking emitted
about 5.6 (2.6; 8.7) mg/h of PM10, of which only about 1.1 mg/h (0.0; 2.1) (20%) was in
the fine fraction.
     Decay  rates for elements associated  with the fine  fraction were generally lower than for
elements associated with the  coarse fraction, as would be expected.  For example, sulfur,
                                         7-35

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 TABLE 7-10.  PENETRATION FACTORS, DECAY RATES, AND SOURCE STRENGTHS: NONLINEAR ESTIMATES
Penetration
VAR
PM2.S>
Al
Mn
Br
Pb
Ti
Cu
Sr
Si
Ca
Fe
K
S
Zn
Cl
PM10»
Al
Mn
Br
Pb
Ti
Cu
Sr
Si
Ca
Fe
K
S
Zn
Cl
Mean
1.00
1.00
0.87
0.90
195 u95
0.89 1.11
0.95 1.05
0.78 0.95
0.81 0.99
Decay Rate (1/h)
Mean
0.39
0.03
0.23
0.28
195b
0.22
-0.03
0.07
0.15
u95
0.55
0.09
0.38
0.41
S cook (pg/min)
Mean
1.7
0.9
0.1
0.1
195b
1.0
-1.4
-0.1
0.0
u95
2.3
3.1
0.2
0.2
Mean
13.8
9.0
0.2
1.9
S smoke (/tg/cig)
195"
10.2
-2.5
-0.4
1.3
u95
17.3
20.5
0.8
2.5
Other Sources (figfti)
Mean
1.1
3.0
0.5
0.6
195b
0.0
-3.7
0.2
0.3
u95
2.1
9.8
0.9
0.9
Fail to converge
Fail to converge
1.00
0.97
0.98
1.00
1.00
1.00
1.00
0.71
0.50
1.00
1.00
1.00
1.00
1.00
1.00
0.83
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.94
0.56 M
0.93 .01
0.75 .20
0.65 .35
0.76 .24
0.81 .19
0.97 .03
0.57 0.86
0.28 0.72
0.85 .15
0.80 .20
0.80 .20
0.90 .10
0.89 .11
0.80 .20
0.62 .05
0.83 .16
0.81 .19
0.68 .32
0.80 .20
0.83 .17
0.96 .04
0.81 .19
0.44 .43
1.63
0.07
0.54
0.61
0.70
0.16
0.16
0.78
0.64
0.65
0.80
0.69
0.21
0.14
0.60
0.77
0.62
0.62
0.63
0.66
0.46
0.21
0.37
2.36
0.38
0.01
0.04
-0.02
0.11
-0.04
0.12
0.31
0.05
0.36
0.38
0.30
0.11
0.01
0.22
0.18
0.28
0.26
0.06
0.26
0.17
0.17
0.10
0.48
2.88
0.12
1.05
1.25
1.29
0.37
0.19
1.25
1.24
0.93
1.21
1.07
0.32
0.26
0.98
1.36
0.97
0.97
1.20
1.06
0.75
0.26
0.64
4.24
0.6
0.0
6.1
11.9
4.5
0.0
1.0
0.4
5.9
4.1
69.5
0.9
0.1
0.0
4.0
0.5
0.3
149.3
118.7
46.7
17.6
6.8
1.2
45.7
0.0
0.0
-8.6
-0.6
-3.3
-4.4
-3.9
-0.5
0.1
2.6
16.6
0.1
0.0
-0.3
0.3
0.0
0.0
26.9
37.3
8.5
0.1
-0.7
-0.2
17.6
1.2
0.0
20.9
24.4
12.3
4.4
5.9
1.2
11.6
5.7
122.4
1.7
0.3
0.3
7.8
1.1
0.5
271.8
200.1
84.8
35.2
14.3
2.5
73.9
3.7
0.1
14.4
165.6
23.8
121.3
27.1
2.9
102.6
21.9
97.6
1.1
1.8
2.1
10.0
3.5
2.6
296.4
800.0
73.0
215.7
68.0
4.0
320.2
0.2
-0.1
-58.3
72.0
-16.3
85.7
2.4
-1.5
54.0
13.6
-159.0
-2.7
1.2
0.4
-8.4
0.4
1.2
-293.9
329.0
-109.8
116.9
29.3
-3.0
107.0
7.2
0.2
87.2
259.1
63.9
156.9
51.7
7.4
151.2
30.2
354.2
4.9
2.5
3.9
28.4
6.5
3.9
886.6
1271.0
255.9
314.5
106.7
11.0
533.4
3.8
0.1
57.3
34.1
23.8
8.9
4.0
7.5
20.6
5.6
154.5
1.2
0.4
0.0
10.3
3.2
0.9
237.8
107.6
51.5
43.6
22.7
7.4
148.4
1.4
0.0
12.5
3.4
1.8
-0.5
-3.7
4.2
7.2
2.6
52.0
-0.2
0.1
-0.6
2.6
1.3
0.3
16.1
-27.0
-15.5
8.6
10.4
3.4
49.4
6.3
0.2
102.0
64.8
45.7
18.3
11.7
10.9
34.0
8.7
257.0
2.6
0.6
0.6
18.1
5.1
1.5
459.6
242.3
118.5
78.5
34.9
11.4
247.4
"Mass units in mg for PM2 5 and PM10 only.
bA negative lower confidence interval implies a nonzero mean is not statistically significant.

Source: Ozkaynak et al. (1993a).

-------
which has the lowest mass median diameter of all the elements, had calculated decay rates of
0.16 (0.12; 0.19) h'1 and 0.21 (0.17; 0.26) h'1 for PM25 and PM10 fractions, respectively.
The crustal elements (Ca, Al, Mn, Fe) had decay rates ranging from 0.6 to 0.8 h"1.
     Based on the mass-balance model, outdoor air was  the major source of indoor particles,
providing about 3/4 of fine particles and 2/3 of thoracic particles in the average home. It was
also the major source for most of the target elements, providing 70 to 100% of the observed
indoor concentrations for 12 of the 15 elements.  It should be noted that these conclusions
are applicable only to Riverside, CA.  In five of the six cities studied by Harvard and in both
New York counties, outdoor air could not have provided as much as half of the indoor air
particle mass for the average home,  because  the observed indoor-outdoor ratios of the mean
concentrations were > 2.  However, for homes without  smoking or combustion sources
(Santanam et al.,  1990; Leaderer et  al., 1990; Table 7-5), indoor-outdoor ratios were «  1.
In general, homes in areas with colder winters (such as New York) would be expected to
have tighter construction than homes in warmer areas (such as Riverside) and, therefore,
more  protection against outdoor air particles.
     Unidentified indoor sources accounted for most of the remaining particle and elemental
mass collected on the indoor monitors. The  nature of these sources is not yet completely
understood (Thatcher and Layton, 1995).  They apparently do not include smoking, other
combustion sources, cooking, dusting, vacuuming, spraying, or cleaning, since all these
sources together account for less than  the unidentified sources.  For example, the unidentified
sources accounted for 26% of the average indoor PM10 particles, whereas smoking accounted
for 4% and cooking for 5% (Figure 7-10).
     Of the identified indoor sources,  the two most important were smoking and cooking
(Figures 7-11 and 7-12).  Smoking was estimated to increase 12-h average indoor
concentrations of  PM10 and PM2 5 by  3.2 and 2.5 /ig/m3 per cigarette, respectively.  Homes
with smokers averaged about 30 /xg/m3 higher levels of PM10 than homes without smokers,
most of this increase being in the fine  fraction. Cooking increased  indoor concentrations of
PM10 by about 0.6 /-ig/m3 per minute of cooking, most of the increase being in coarse
particles.
     Emission profiles for target elements were obtained for smoking and for cooking.
Major elements emitted by cigarettes were K, Cl, and Ca; those from cooking included Al,
                                         7-37

-------
                   Outdoor
                    76%
                                      Cooking
                                        4%
                                              Other Indoor
                                                  14%
                                                   Smoking
                                                     5%
            N = 352 Samples from 178 homes
                                       Cooking
                                         5%
                Outdoor
                 66%
                                                  Other Indoor
                                                     26%
Smoking
  4%
            N - 350 Samples from 178 homes

Figure 7-10. Sources of fine particles (PM2 5) (top) and thoracic particles (PM10)
           (bottom) in all homes (Riverside, CA).

Source: Ozkaynak et al. (1993a).
                                   7-38

-------
                                  Coftin9  Other Indoor
                                    3/0        7%
              Outdoor
                60%
                                                    Smoking
                                                      30%
            N - 61 Samples from 31 homes
                                  Cooking
              Outdoor
               56%
                                              Other Indoor
                                                  16%
                                                   Smoking
                                                     24%
            N - 61 Samples from 31 homes

Figure 7-11. Sources of fine particles (PM2.5) (top) and thoracic particles (PM10)
           (bottom) in homes with smokers (Riverside, CA).

Source: Ozkaynak et al. (1993a).
                                   7-39

-------
                                               Cooking
                                                 25%
              Outdoor
                62%
                                                 Smoking
           N - 62 Samples from 33 homes
                                                    Other Indoor
                                                        8%
                                               Cooking
                                                 25%
             Outdoor
               56%
                      \              V       /
                                                  Other Indoor
                                                      16%

                                           Smoking
                                              4%
           N - 62 Samples from 33 homes

Figure 7-12. Sources of fine particles (PM2.5) and thoracic particles (PM10), top and
           bottom panels, respectively, for homes with cooking during data collection
           (Riverside, CA).

Source: Ozykaynak et al. (1993a).

                                  7-40

-------
Fe, Ca, and Cl.  Other household activities such as vacuuming and dusting appeared to make
smaller contributions to indoor particle levels. Commuting and working outside the home
resulted in lower particle exposures than for persons staying at home. As with the particle
mass, daytime personal exposures to 14 of 15 elements were consistently higher than either
indoor or outdoor concentrations.  At night, levels of the elements were similar in all three
types of samples.

7.2,2.1.4  Comparison of the Three Large-Scale Studies
     The three studies had somewhat different aims and therefore different study designs.
The Harvard Six-City study selected homes based on various criteria, especially a
requirement that a school-age child be in the  home, but did not employ a probability-based
sample.  Therefore the results strictly apply only to the homes in the sample and not to a
wider population; however,  the very large number of homes suggests that the results should
be broadly applicable to homes with school-age children in the six cities.   The New York
State study used a probability-based sample, but stratified on the basis of combustion
sources.  Hence,  there are likely  to be a higher fraction of homes with kerosene heaters,
wood stoves, and fireplaces  in the sample than in the general population.   The PTEAM study
used a fully probability-based procedure, and its results are likely the most broadly applicable
to the entire population of Riverside households.  However,  the participants were limited to
nonsmokers, so homes with only  smokers were excluded; as a consequence, maximum
indoor concentrations were likely underestimated.  Also,  the three  studies used different
monitors, with different cutpoints precluding  exact comparisons.  However, large differences
between the PM3 5 and PM2 5 cutpoints  and the PMn and PM10 cutpoints are not likely
(Willeke and Baron, 1993); thus,  these results can be more readily compared.  In what
follows, the term "fine particles"  refers  to the PM3 5 and PM2.5 size  fractions collected in the
three studies.
     Indoor-Outdoor Relationships.  Outdoor concentrations of fine particles in five of the
Harvard six cities and the two New York counties were relatively low, typically in the range
of 10 to 20 ptg/m3 (Table 7-11).   Only Steubenville, with an annual mean of 45 jtg/m3 (but a
range among the outdoor sites of 20 to 60 pcg/m3) approached the mean outdoor level of
50 Mg/m3 observed in Riverside.  It is interesting to note  that average indoor concentrations
                                          7-41

-------
      TABLE 7-11.  INDOOR-OUTDOOR MEAN CONCENTRATIONS (/tg/m3)
             OF FINE PARTICLES IN THREE LARGE-SCALE STUDIES
Study Name
Harvard Six-City Study
Portage, WI
Topeka, KS
Kingston-Harriman, TN
Watertown, MA
St. Louis, MO
Steubenville, OH
New York State ERDA Study
Onondaga County
Suffolk County
EPA Particle TEAM Study
Riverside, CA
Homes

11
10
8
8
10
8

224
209

178
Out

10
10
18
15
18
45

17
22

50
In

20
22
44
29
42
42

37
46

43
In/Out

2.0
2.2
2.4
1.9
2.3
0.9

2.2
2.1

0.9
Harvard: PM3 5 measured using cyclone sampler.  Samples collected every sixth day for one year (May 1986
        to April 1987).
NYS:    PM2 5 measured using impactor developed at Harvard. Samples collected for one week at each
        household between January and April 1986.
PTEAM: PM2 5 measured using Marple-Harvard-EPA sampler. Samples collected for two 12-h periods at each
        home between September and November 1990.
Source:   Harvard data—Spengler et al. (1981); NYS data—Sheldon et al. (1989); PTEAM data—Pellizzari
        et al. (1992).
exceeded outdoor concentrations in the seven sites with low outdoor levels, (indoor/outdoor
ratios were contained in a small range between 1.9 and 2.4), but were slightly less than
outdoor concentrations  in the two  sites with high outdoor levels (ratios of 0.9).
     Effect of Smoking.  All three studies found cigarette smoking to be a major source of
indoor fine particles. Multivariate calculations in all three studies result in rather similar
estimates of the effect of smoking on fine particle concentrations.  Spengler et al. (1981)
estimated an increase of about 20  /ig/m3 per smoker based on 55 homes from all six cities.
Since the 20 homes with at least one smoker averaged at least 1.25 smokers per home, this
corresponds to about 25 //g/m3 per smoking home.  Spengler et al.  (1985) found a smoking
effect of about 32 /zg/m3 for smoking  homes in multivariate models based on the Kingston-
Harriman data. Santanam et al. (1990) found a smoking-related increase of 20-27 /ig/m3 in
Steubenville and Portage (winter only) but only  10 jug/m3 in Portage in summer.  Sheldon
et al. (1989) found an increase of 45 (Onondaga) and 47 (Suffolk) ftg/m3 per smoking home
in a multivariate model of the New York State data.  Ozkaynak et al. (1993b) found an
                                         7-42

-------
increase of about 27 to 32 jwg/m3 in homes with smokers in a multivariate regression model
of the PTEAM PM2 5 data.  Thus,  the effect of a home with smokers on indoor fine particle
concentrations was estimated to be  about 20 to 30 /xg/m3 in the Six-City and PTEAM
studies, but about 45 jug/m3  in the New York State study.
     Dockery and Spengler (1981a) found an effect of 0.88 jug/m3 per cigarette for homes
without air conditioning, and 1.23 /ig/m3 per cigarette for homes with air conditioning, based
on 68 homes from all six cities.  Lebret et al. (1987) found an effect of 0.8 ^ig/m3 per
cigarette for homes in the Watertown,  MA,  area.  Leaderer and Hammond (1991) found an
effect ranging between 1.9 and 2.3 /xg/m3 per cigarette contribution to a 24-h average. In a
series of stepwise regressions on  the PTEAM data, Ozkaynak et al. (1993b) found an effect
ranging between 1.2 and 2.4 /ig/m3 per cigarette  smoked during a 24-h period.  Taking the
midpoint of these ranges leads to estimates for the Harvard Six-City, New York State and
PTEAM studies of about  1.1, 2.1,  and 1.8 /-ig/m3 increases in fine particle concentrations per
cigarette smoked in the home over  a 24-h period.
     Both the New York State study and the PTEAM study were able to estimate source
strengths for different variables using a mass-balance model.  The estimates for PM2 5
emissions from cigarettes were very comparable,  with Koutrakis et al. (1992) estimating
12.7 mg/cig compared to the PTEAM  estimate of 13.8 mg/cig (Ozkaynak et al.,  1993a).
Both studies also found similar elemental profiles  for smoking, with potassium and chlorine
being emitted in substantial amounts.
     Effect of Other Variables.  In the PTEAM  Study, the second most powerful indoor
source of PM10,  and possibly PM2 5 particles, was cooking.  Quite large emission strengths
of several mg/minute of cooking  were determined from the mass-balance model,  while
multiple regressions indicated that cooking could contribute between 10  and 20 /tg/m3 PM10,
and somewhat smaller amounts of PM2 5,  to the 12-h concentration.
     Both the New York State and PTEAM studies  also measured air exchange in every
home, and both studies found that air exchange significantly affected indoor particle
concentrations. In the PTEAM study, increased air exchange led to  increased indoor air
concentrations for both PM2 5 and PM10 at night only, perhaps because  outdoor
concentrations were larger than indoor levels at night.  In the New York State study,
increased air exchange led to decreased RSP concentrations in Onondaga (p < 0.02) but no
                                         7-43

-------
effect was noted in Suffolk (p < 0.90). In both of these counties, indoor levels generally
exceeded outdoor levels, so increased air exchange would generally reduce indoor
concentrations.

7.2.2.2 Other Studies of PM Indoors
     Several other large-scale studies of indoor PM in homes have taken place in other
countries, and a number of smaller U.S. studies have been conducted.  These are discussed
below hi order of the number of homes included in the study.
     Lebret et al. (1990) carried out week-long RSP measurements (cutpoint not described)
in 260 homes in Ede and Rotterdam, The Netherlands, during the winters of 1981 to 1982
and 1982 to 1983, respectively; 60% of the Ede homes and 66% of the Rotterdam homes
included smokers.  Diary information collected during the measurement period indicated that,
on average,  one to two cigarettes were smoked during the week, presumably by guests, even
in the nonsmoking homes. Homes with one smoker averaged seven cigarettes smoked per
day at home in Ede (N = 53) and  11 per day in Rotterdam (N = 35). Homes  with two
smokers averaged 21 cigarettes per day in Ede (N  =  23) and 25 per day in Rotterdam
(N = 15).
     Geometric means for the combined smoking and nonsmoking homes were  similar in the
two cities (61 and 56 jig/m3, respectively), with maxima of 560 and 362  /*g/m3. Outdoor
concentrations averaged about 45 jig/m3 (N not given).  Indoor concentrations in the homes
with smokers averaged about 70 |Kg/m3 (calculated from  data in the paper), compared to
levels in the nonsmoking homes of about 30 /-ig/m3. Multiple regression analysis indicated
that the number of smoking occupants explained about 40% of the variation in the log-
transformed RSP concentrations—family size, frequency  of vacuuming, volume of the living
room,  type of space heating, and city (Ede versus Rotterdam) had no significant effect on
RSP concentrations. In a second regression, the number of smoking occupants  was replaced
by the number of cigarettes and cigars smoked during the week.  The regression equation
was
               log(RSP) = 1.4 + 0.37 log(# cigarettes) + 0.53 log(# cigars)
                                + 0.03 log(family size)
                     R2 = 0.49; d.f. =250  F = 83.7  p  < 0.0001
                                         7-44

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From this equation, the authors estimated that one cigarette smoked per day would increase
weekly average indoor RSP concentrations by 2 to 5 /ig/m3, whereas one cigar smoked per
day would increase indoor levels by 10 /ig/m3.  Instantaneous RSP concentrations were
measured using a TSI Piezobalance on the day the technicians were setting up the equipment.
Table 7-12 shows the influence of smoking on these measurements.
         TABLE 7-12.  INFLUENCE OF RECENT CIGARETTE SMOKING
         ON INDOOR CONCENTRATIONS OF PARTICIPATE MATTER1
Time Since Smoking
No smoking
More than 1 h ago
Between 1/2 and 1 h ago
Less than 1/2 an hour ago
During the measurements
N
98
18
7
27
54
RSP (geom. mean) (/*g/m3)
41
52
76
141
191
'Size cuts for measured particles not specified.
Source:  Lebret et al. (1990).


     Heavner et al. (1995) studied PM3 5 at home and at work for 104 New Jersey and
Pennsylvania females.  The personal sampler used consisted of a cyclone sampling head
attached to a 37-mm Fluoropore filter, connected by Tygon tubing to a 1.7 Lpm pump.  The
sampling head was worn on a lapel, collar, or pocket  in the breathing zone of the participant
until she went to bed, when the sampler was placed on the bedside table.  The "home"
pumps were turned on at 6 p.m. and sampled until about 8 a.m. the next morning
(an average of 14 h); the "work" pumps were turned on at work and sampled for an average
of 7 h. Participants were selected to include those with exposure to smoking at home or at
work or both or neither. The 14-h evening and overnight concentrations hi the homes
averaged 86.7 ±  145.4 (SD) /*g/m3 for 30 homes with smokers and 27.6 ±  19.9 jig/m3 for
58 homes without smokers. Corresponding values for workplaces were 67.0 ± 44.3 /ig/m3
for those 28 allowing smoking and 30.3 ± 17.6 ng/m? for the 52 without smoking, the
differences being  significant at p  <  0.0001 (Wilcoxon rank sum) for both comparisons.

-------
     Diemel et al. (1981) measured particles in 101 residences in an epidemiological study
related to a lead smelter in Arnhem, the Netherlands.  The indoor sampler collected samples
at a flowrate of 1 to 1.5 Lpm.  The authors stated that particles < 3 to 4 j«n diameter
should have been sampled efficiently, but presented no data on measured cutpoint size.  The
outdoor samplers (number not given) were high-volume samplers.  The 28-day average levels
indoors ranged from 20 to 570 jig/m3, with an arithmetic mean of 140 pig/m3 (SD not
presented) and a geometric mean of 120 jig/m3; corresponding outdoor concentrations (2-mo
averages of 24-h daily samples) ranged from 53.7 to 73.3 /*g/m3 (N not given), with nearly
identical arithmetic and geometric means of 64 /ig/m3.
     Kulmala et al. (1987) measured indoor and outdoor air in approximately 100 dwellings
(including some office buildings) in Helsinki, Finland between 1983 and 1986.  Samples
were collected on Nuclepore filters using a stacked foil technique.  The geometric mean for
the combined fine particle (< 1 /mi) samples indoors was 16 /zg/m3, with a 95% range of
4 to 67 /xg/rn3.  The corresponding value for the indoor coarser particles (> 1 /mi) was
13 /ig/m3 with a range of 3 to 63 /xg/m3.  Outdoors, the fine particles had a geometric mean
of 20 /ig/m3 with a 95% range of 5 to 82 jug/m3, and the coarser particles had a geometric
mean of 16 /Ag/m3 with a range of 3 to 91 /ig/m3.
     Quackenboss et al. (1989) reported PM10 and PM2 5 results from 98 homes in the
Tucson, AZ area selected as part of a nested design for an epidemiological study. The
Harvard-designed dual-nozzle  indoor air sampler (Marple et al., 1987) was used for indoor
air measurements.  Outdoor air was measured within each geographic cluster by the same
instrument; supplementary  data were obtained from the Pima County Air Quality Control
District, but these data did not include PM2 5 measurements and some data were apparently
PM15. Homes were classified by (a) tobacco smoking and (b) use of evaporative ("swamp")
coolers, which apparently act as a removal mechanism for particles  (Table 7-13). Homes
without smoking averaged about 15 /-ig/m3 PM2 5, compared to 27 /ig/m3 for homes reporting
one or less pack a day, and 61 /ig/m3 for homes reporting more than one pack a day. PM2 5
particles accounted for about half of the PM10 fraction in nonsmoking homes, increasing with
the amount of smoking to about 80% in those homes with heavy smoking.  Outdoor PM10
particles were not strongly correlated with indoor levels (R2 = 0.18; N =  90).
                                         7-46

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            TABLE 7-13.  INDOOR AVERAGE PM2 5 AND PM10 Gig/m3)
         BY REPORTED SMOKING IN THE HOME AND EVAPORATIVE
       COOLER USE DURING SAMPLING WEEK FOR TUCSON, AZ STUDY
Smoking
Cigarettes/Day
None


1-20


>20


Evaporative
Cooler
Yes
No
Total
Yes
No
Total
Yes
No
Total

Mean
8.8
20.3
15.2
19.3
32.3
27.3
36.2
82.7
60.8
PM25
S.D.
5.0
19.0
15.5
8.8
28.5
23.6
32.9
55.4
50.8

Homes
(20)
(25)
(45)
(10)
(16)
(26)
(8)
(9)
(17)

Mean
21.0
38.4
30.3
33.9
53.4
46.2
47.4
102.5
75.0
PM10
S.D.
9.7
22.9
19.9
12.0
33.9
29.1
39.6
60.6
57.2

Homes
(20)
(23)
(43)
(10)
(17)
(27)
(9)
(9)
(18)
PM2 5: Significant (p < 0.01) main effects for smoking and evaporative cooler use; two-way interaction nearly
      significant (p = 0.06).
PMi0: Significant (p < 0.01) main effects for evaporative cooler and smoking.
Source:  Quackenboss et al. (1989).

     Quackenboss et al. (1991) extended the analysis of the Tucson homes over three
seasons.  Median indoor PM2 5 levels in homes with smokers were about 20 /xg/m3 in the
summer and spring/fall seasons compared to about 10 /ig/m3 in homes without smokers in
those seasons.  In winter,  however, the difference was considerably increased, with the
median level in 24 homes  with smokers at about 36 jwg/m3 compared to 13 /ig/m3 in
26 homes without smokers.
     Sexton et al. (1984) reported on a study in  Waterbury, VT.  This study included
24 homes, 19 with wood-burning appliances, and none with smokers.  24-h samples were
collected in each home every other day for two weeks, providing  163 valid indoor samples.
Indoor RSP levels ranged  from 6 to 69 /ig/m3 with a mean value of 25 /xg/m3.  Outdoor
levels ranged from 6 to 30 jtg/m3 with a mean value of 19 /ug/m3.  Indoor concentrations
were not significantly correlated with outdoor concentrations (r = 0.11, p > 0.16.)
     Kim and Stock (1986) reported results for 11 homes in the Houston, TX area.
(Year and the season not reported in the paper.)  For most homes, two 12-h PM2 5 samples
(day and night) were collected for approximately one week.  Sampling methods were not
fully discussed, but apparently they involved samples collected using a mobile van  near each
home.  The mean weekly concentrations in the five smoking homes  averaged
33.0 ± 4.7 (SD) /ig/m3, versus mean outdoor concentrations averaging 24.7  ± 7.4

                                        7-47

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(calculated from data presented in paper).  Indoor concentrations in the six nonsmoking
homes averaged 10.8 ± 4.9 /xg/m3 compared to outdoor levels of 12.0 ± 5.9 /tg/m3.
     Morandi et al. (1986) reported on 13 Houston, TX, homes monitored during 1981 as
part of a larger personal monitoring study of 30 nonsmoking participants. The TSI
Piezobalance (cutpoint at about PM3 5) was  employed for personal monitoring, with
technicians  "shadowing" the participants and taking consecutive 5-min readings.  At the
homes, dichotomous samplers (cutpoints at PM2 5 and PM10) were used for two 12-h daytime
samples (7 a.m. to 7 p.m.) both inside and  outside the homes for seven consecutive days.
Little difference was  noted in the indoor concentrations at homes (25 ± 30 (SD) jig/m3) and
at work or school  (29 ± 25 ^g/m3).  The highest overall respirable suspended particle (RSP)
concentrations occurred in the presence of active smoking (89  jig/m3), significantly different
from mean RSP values measured in the absence of smokers (19 /ig/m3; p <  0.0001).
Among homes with smokers, those homes with central air conditioning were significantly
(p< 0.0001) higher (114 versus 52 /ig/m3) than those with no air conditioning.  Cooking was
associated with significantly higher RSP concentrations (27 /xg/m3 compared to 20 pig/m3,
p <  0.01). The single highest RSP concentration (202 jug/m3) was found in a home with no
smokers and no air conditioning but with active cooking.  The authors concluded that
cooking was a more important source of indoor RSP than smoking, at least in the few homes
they  studied.
     Coultas et al. (1990) measured PM2 5  in 10 homes containing at least one smoker, using
the Harvard aerosol impactor.  Samples were collected for 24  h every other day for  10 days
and then for 24 h  every other week for 10 weeks, resulting in 10 samples per household.
The mean concentrations of PM2.5 ranged from 32.4 + 13.1 (SD) to 76.9 ± 32.9 /*g/m3.
Outdoor particle concentrations were not reported; thus it is difficult to calculate the portion
of the observed PM2 5 that might be due to  ETS.
     Kamens et al. (1991) measured indoor particles in three homes without smokers in
North Carolina in November and December 1987 (no measurements of outdoor particles
were taken).  Two dichotomous samplers (PM2 5 and PM10), several prototype personal
samplers (also PM2 5 and  PM10), three particle sizing instruments including a TSI electrical
aerosol mobility analyzer  (EAA) with 10 size intervals between 0.01 and 1.0 ptm, and two
optical scattering devices  covering the range of 0.09 to 3.0 and 2.6 to 19.4 pm were
                                         7-48

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employed.  Air exchange measurements were made using SF6 decay over the course of the
seven 8-h (daytime) sampling periods. There were also three 13-h (evening and overnight)
sampling periods.  For the entire study, 37% of the estimated total mass collected was hi the
fine fraction, and another 37% was  > 10 /mi.  The remainder (26%) was in the inhalable
coarse (PM10 — PM2 5) fraction.  However, considerable variation was noted in these size
distributions. For example, on one day with extensive vacuuming, cooking,  and vigorous
exercising of household pets,  52% of the total mass appeared hi the fraction  > 10 /mi, but
only 18% hi the fine fraction.  The peak in particle mass on that day coincided with
vacuuming  and sweeping of carpets and floors.  On another day, cooking of stir-fried
vegetables and rice produced a large number of small particles, with those <0.1 /mi
accounting  for 30% of the total EAA particle volume, much more than the normal amount.
The cooking contribution of that one meal to total 8-h daytime particle volume exposure was
calculated to be in the range of 5 to 18%.  The authors concluded that the most significant
indoor source of small particles (<2.5 /mi) in all three of these nonsmoking homes was
cooking, while the most significant source of large particles (> 10 /mi) was vacuum
sweeping.  Inhalable coarse particles (PM10 —  PM2.5) appeared to be  of largely biological
(human dander and insect parts) and mineral (clay, salt, chalk, etc.) origin.
     In a test of a new sampling device (a portable nephelometer), Anuszewski et al. (1992)
reported results from indoor and outdoor sampling at nine Seattle, WA, homes sampled for
an average  of 18 days each during the whiter of 1991 to 1992.  The nephelometer is a light-
scattering device with rapid (1-min) response to various household activities such as
sweeping, cigarette smoking, frying, barbecuing, and operating a fireplace. Homes with
fewer activities showed high correlations of indoor and outdoor light-scattering coefficients,
both between hourly averages and 12-h averages.  However, homes with electrostatic
precipitators, with weather-stripped windows or doors, and with gas cooking or heating
devices showed weak 12-h indoor-outdoor correlations.
     Chan  et al. (1995) studied particles and nicotine hi seven homes  with one smoker each
in Taiwan.   Sampling was carried out in summer and winter of 1991.  Each home had one
indoor PM5 sampler in the living room and another in the yard.  In the winter study, two
homes had  PM10 samplers added  inside and outside and at two central sites.  Indoor mean
                                         7-49

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PM5 concentrations averaged 44 ± 32 (SD) /ig/m3 in summer compared to outdoor levels of
27 ± 15 /ig/m3.  Corresponding winter values were  107 ± 44 /ig/m3 and 92 ± 40 /tg/m3.
     Daisey et al. (1987) measured RSP, PAH, and  extractable organic matter (EOM) in
seven Wisconsin homes with wood stoves; one 48h (1,000 m3) sample was collected during
woodburning and a second sample was collected when no woodburning occurred.  Five of
seven homes had somewhat higher RSP levels during woodburning, but the mean difference
was not significant.
     Highsmith et al. (1991) reported on two 20-home studies in Boise, ID and Roanoke,
VA.  The Boise study assessed the effects of wood burning on ambient and indoor
concentrations in the area.  Ten homes with wood burning stoves were matched with
10 homes without such stoves.  One matched pair of homes was monitored from Saturday
through Tuesday for eight consecutive 12-h periods.  Ambient PM2 5 levels increased by
about 50% at night, suggesting an influence of woodburning.  Indoor PM2 5 concentrations
also were increased (by about 45%) in the homes with the wood burning stoves compared to
those without (26.3 versus  18.2 /ig/m3),  although coarse particles showed no increase
(10.2 versus 9.7 /ig/m3).  The Roanoke study, designed to assess the effects of residential oil
heating, showed no effects on indoor levels of fine or coarse particles.
     Lofroth et al.  (1991) measured particle emissions from cigarettes, incense sticks,
"mosquito coils," and frying  of various foods.  Emissions were  27 and 37 mg/g for two
brands of Swedish cigarettes, 51 and 52  mg/g for incense sticks and cones, and 61 mg/g  for
the mosquito coil.  Emissions from frying pork, hamburgers,  herring,  pudding,  and Swedish
pancakes ranged from 0.07 to 3.5 mg/g.
     Mumford et al. (1991) measured PM10, PAH, and mutagenicity in eight mobile homes
with kerosene heaters. Each home was monitored for 2.6 to 9.5 h/day (mean of 6.5 h) for
three days a week for two weeks  with the kerosene heaters off and for two weeks with them
on (average on-time of 4.5 h).  Mean PM10 levels were not significantly increased when the
heaters were on (73.7 ± 7.3 (SE) /tg/m3 versus 56.1 ± 5.7 /ig/m3), but in two homes levels
increased to 112 and 113 /ig/m3 when the heaters were on. Outdoor concentrations averaged
18.0 ± 2.1 /ig/m3.
     Colome et al. (1990) measured particles using PM10 and PM5 (cyclone) samplers inside
and outside homes of 10 nonsmokers, including eight asthmatics, living in Orange County,
                                        7-50

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CA. Indoor PM10 samples were well below outdoor levels for all homes (mean of
42.5 ± 3.7 (SE) /ig/m3 indoors versus 60.8 ± 4.7 /ig/m3 outdoors).  No pets, wood stoves,
fireplaces, or kerosene heaters were present in any of these homes.
     Lioy et al. (1990) measured PM10 at eight homes  (no smokers) for 14 days in the
winter of 1988 in Phillipsburg, NJ, which has a major point source consisting of a grey-iron
pipe manufacturing company.  The Harvard impactor was used indoors to collect 14 24-h
samples beginning at 4:30 p.m. each day; Wedding hi-vol PM10 samples were deployed at
three outdoor sites. A fourth outdoor site was located on a porch of one of the homes,
directly across the street from  the pipe manufacturer. The first three sites showed little
difference from one another, whereas on day 4 and day 6 of the study, the outdoor sampler
on the porch had readings that were considerably (about 40 /xg/m3) higher than the other
outdoor samplers,  suggesting an influence of the nearby point source.  The geometric mean
outdoor PM10 concentration was 48 /ig/m3 (GSD not provided) compared to 42 /ig/m3
indoors.  A simple regression equation for all homes (N = 101 samples) explained 45% of
the cross-sectional variance in  indoor PM10:

               Indoor PM10 = 0.496 Outdoor PM10 + 21.5     (R2 =  0.45)

However, individual regressions by home showed much better R2 values in most cases,
ranging from 0.36 to 0.96 (Table 7-14).  All slopes were significant.
     Thatcher and Layton (1995) measured optical particle size distributions inside and
outside a residence in the summer.  Measured deposition velocities for particles between
1 and 5 /im closely matched the calculated gravitational settling velocities; however, for
particles >5 /mi, the  deposition velocity was less than  the calculated settling velocity,
perhaps due to the non-spherical nature of these particles.  The deposition velocities
determined by the authors corresponded to a particle deposition rate k of 0.46 h"1 for
particles of size range  1 to 5 /ma and 1.36 h"1 for particles of  size range 5 to 10 /im. These
values are very comparable with the  values of 0.39 h"1  for particles less than 2.5 /tm and
1.01 h"1 for particles between 2.5 and 10 /mi found by  the PTEAM Study.  The authors
measured the penetration factor P by the following method: They first carried out vigorous
house cleaning activities to raise the level of resuspended dust well above outdoor levels.
                                          7-51

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           TABLE 7-14.  REGRESSION OF INDOOR ON OUTDOOR PM10
        CONCENTRATIONS:  THEES STUDY, PHILLIPSBURG, NJ Otg/m3)
House
1
2
3
4
5
6
7
8
N
14
14
9
14
13
13
12
14
Intercept
19
16
9
20
6
-1
24
27
SE
9
14
5
21
10
18
25
8
P
NS
NS
NS
NS
NS
NS
NS
S
Slope
0.44
0.40
0.55
0.73
0.43
0.89
0.70
0.54
SE
0.06
0.08
0.04
0.15
0.07
0.13
0.29
0.05
P
S
S
S
S
S
S
S
S
R2
0.79
0.68
0.96
0.66
0.75
0.81
0.36
0.91
S = Significant
NS = Non-significant
Source:  Data from THEES Study (Lioy et al., 1990).
They then left the house, while automated instruments measured the deposition rate k for the
different particle sizes and the ah- exchange rate a for SF6 tracer gas.  With these values of
a and k in hand, they solved the equation for P, using the steady-state values for Cin and Cout
observed long after the dust had settled:

                                    P -  ^*>                                (7-6)
                                          c«,"

For all size ranges tested, including the largest (10 to 25  /on), the experimentally determined
value for P was not significantly different from 1  (Figure 7-13).  This result is hi agreement
with the PTEAM conclusion that P is 1 for both fine and coarse particles, although the latter
conclusion was  derived from a nonlinear (statistical) approach whereas the present result was
experimentally obtained.
     The resuspension results of Thatcher and Layton (1995) (Figure 7-14)  show the effect
of a vigorous housecleaning activity.  The authors concluded "Although particles larger than
5 /*m show significant resuspension hi these experiments, particles smaller than 5 fan are not
readily resuspended, and particles less than 1 pun show almost no resuspension even with
vigorous activity." Figure 7-15 shows that just one person walking hi and out of a carpeted
                                         7-52

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               1 to 3 urn
               i        i
3 to 6 urn
1 to 5 urn
       I
       §1
       s.
             1234

                                   Experiment Number

Figure 7-13. Results of six penetration experiments in a test home.


Source: Thatcher and Lsyton (1995).
                                                                  Particle Diameter
                                                                    •	0.3 to 1 urn


                                                                    B	1 to 5 urn


                                                                    *	5 to 10 urn


                                                                    e	10 to 25 urn


                                                                    ^	> 25 urn
                 10
                     50
               60
                          20      30      40
                             Time (minutes)

Figure 7-14.  The change in suspended particle mass concentration versus tune, as

              measured by optical particle counter, assuming spherical particles of unit
              density.  All resuspension activities are stopped at t = 0.


Source: Thatcher and Layton (1995).
                                          7-53

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                Cleaning
 2Min
Walk/Sit
4 People
5 minutes
 4 People
30 minutes
Walk In
               0.5 to 1 urn
          m          5 to 10
        Particle Diameter (\im)
                          10 to 25 jim
Figure 7-15. The ratio of the suspended particle concentration after a resuspension
             activity to the indoor concentration before that activity, by particle size.
             The activities tested are (1) vigorous vacuuming and housecleaning,
             (2) 2 min of continuous walking and sitting in the living area by one
             person, (3) 5 nun of normal activity by four people, (4) 30 min of normal
             activity and (5) one person walking into and out of the living area.

Source: Thatcher  and Layton (1995).
living area can increase indoor particle concentrations in the ranges 5 to 10 /tin by 100% and
10 to 25 /im by 200%.  The absolute increase in indoor concentrations by this activity is a
function of the surface dust loading in those size ranges.  Surface dust loadings (/ig/m2)

increase with the time since last cleaning (Raunemaa et al., 1989; Wilmoth et al., 1991).

     Because  fluffy house dust can be resuspended, it will contribute to total airborne

exposure to particles and constituents such as metals and pesticides.  Roberts et al. (1990)

studied 42 homes in Washington State.  Geometric mean lead concentration hi 6 homes

where  shoes were removed on entry was 240 /ig/m2 on carpets, compared to 2,900 /ig/m2 on
carpets in 36 homes where shoes were kept on.  In Japan, where shoes are removed on  entry

and straw mats (tatami) are usually used instead of carpets, Tamura et al.  (1996) found
                                          7-54

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evidence of negligible PM10 resuspension.  These findings suggest that most of the carpet
dust in a home enters via track-in on shoes rather than by infiltration of ambient air.

7.2.2.3 Personal Exposures to Environmental Tobacco Smoke.
     Jenkins et al. (1995a) reported on the first 12 cities of a 16-city sampling survey
comparing ETS exposures at home and at work.  About 100 nonsmoking persons in each city
were recruited to wear a personal monitor at work and  another personal monitor away from
work.  The monitors collected PM3 5 particles, which were then analyzed for tobacco smoke
markers (UVPM,  FPM and solanesol).  Nicotine and other gas-phase markers were also
collected.  Subjects provided saliva samples, which were used to screen out smokers
reporting themselves as nonsmokers.  (Using different cutoff points of  10, 30, or 100 jug/L,
between 1.82 and 5.2% of the 1073 subjects would have been misclassified as nonsmokers).
Four cells were defined: persons exposed to smoke at home and at work (N = 119); persons
exposed at home but not at work (110); persons exposed at work but not at home (163); and
persons exposed neither at home nor at work (504).  All four particle markers agreed in
ranking the four cells for total ETS exposure in the order listed—that is,  nonwork (including
home) ETS exposures were greater than work exposures as shown in Table 7-15. The
authors identified several problems  with the selection of the sample. First, the sample was
68% female.  Secondly, the socioeconomic level was biased  high, with about twice  as many
persons having  some college or being college graduates as the population as a whole. It is
well known that smoking rates decrease as education and income rise,  and  this study
confirmed that observation-when broken out by  income, ETS markers decreased by factors
of 2 to 5 as annual income rose from $10,000 to $100,000.  The authors compared ETS
levels in offices with no smoking (N = 629), restricted smoking (N = 297) and unrestricted
smoking (N  =  113). Median (mean) levels of RSP increased from 13 (18) to 16 (28) to
33 (58) /ig/m3 in the three categories,  with corresponding nicotine medians (means) of
0.025 (0.11), 0.09 (0.87), and 0.44 (2.7) /xg/m3.
     Jenkins et al. (1995b) updated the results to the full 16 cities.  The final number of
participants in the four cells were 157, 234, 281, and 808, respectively.  The median RSP
(PM3 5) values changed only slightly, increasing  to 33.6 from 32 /xg/m3 in  Cell 1 and
decreasing to 23.3 /xg/m3 in Cell 2, with no changes in the remaining two  cells.
                                         7-55

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                   TABLE 7-15.  MEDIAN VALUES (jig/m3) FOR
                ENVIRONMENTAL TOBACCO SMOKE MARKERS
Cell (N)
1 (119)
2 (110)
3 (163)
4 (504)
Nonwork
S
S
NS
NS
Work
S
NS
S
NS
RSP
32
24
20
15
UVPM
12
7.6
2.3
1.1
FPM
7.7
5.9
1.2
0.6
Solanesol
0.113
0.058
0.003
ND
Nicotine
1.46
0.56
0.11
0.02
S = smoker; NS = nonsmoker; ND = not detectable.
Source:  Jenkins et al. (1995a).
     ETS Exposures in Restaurants and Buildings.  Oldaker et al. (1993) reported results
of analyzing ETS markers in four office buildings.  Median RSP levels were 30 and
34 jug/m3 in two buildings allowing smoking, compared to 5 and 7 jug/m3 in two buildings
without smoking.  Grouse et al. (1989) reported on measurements of RSP (PM3 5) in
42 North Carolina restaurants.  Geometric mean (arithmetic mean) values were 5.3 (8.6),
26.1 (34.1) and 62.0 (80.8) jig/m3, respectively.  Oldaker et al. (1990) measured PM3 5 in
33 restaurants in the Winston-Salem, NC,  area during the summer of 1986 and the winter of
1988 to 1989; in the winter, the cutpoint was changed to PM2 5.  A wide range of particle
concentrations was noted, from 18 to 1,374 jug/m3 in the summer,  and <25 to 281 jig/m3 in
winter.

7.2.2.4 The Fraction of Outdoor Air Particles Penetrating Indoors
     Having reviewed the literature on particles in homes, it is useful to return to one of the
questions we asked at the outset:  For a home with no indoor sources or resuspension of
settled dust of ambient origin, how much protection is offered against outdoor particles of
various size ranges?
     The governing equation in this case is
                                      Cin     Pa
                                                                                (7-6)
                                        7-56

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Thus, there are three parameters affecting the fraction of outdoor air particles to be found
indoors:  the penetration factor P, the air exchange rate a, and the particle deposition rate k.

     Penetration Factor P.  The penetration factor P is a measure of the ability of a gas or
particle to penetrate the building envelope; 0 < P <  1. For a nonreactive gas, such as CO,
the factor is expected to be 1.  For large particles, the factor would be expected to go to zero
with increasing particle size and decreasing air exchange rate.  The question is at what
combinations of size range and air exchange rate does the factor P begin to decrease
significantly from unity for PM?
     Two recent studies  have attempted to determine the value of P for different particle size
ranges.  The PTEAM study (Ozkaynak et al., 1996) found a value of P «  1 for both PM2 5
and PM10 particles.  The value was determined statistically by a nonlinear solution of
Equation 7-5 (including all indoor sources) for 178 homes.  Thatcher and Layton (1995) also
found a value of P «  1 for all size ranges tested, including the ranges 1 to 3 jum, 3 to 6 /xm,
1 to 5 pm, 5 to  10 pun, and 10 to 25 jrni.  The authors determined their values
experimentally by direct measurement on one  instrumented house.  The results for the first
two size ranges were obtained in five replicate experiments; for the last three size ranges, in
only one experiment (Figure 7-13).  Thus the  two studies used different methods but arrived
at the same conclusion: particles  less than 10 \im in aerodynamic diameter penetrate
building envelopes with an efficiency approaching that of nonreactive gases.  Clearly, more
work needs to be done to test this finding at lower air exchange rates.

     Air Exchange Rate a.  Air exchange rates in residences depend on three major factors:
building construction, ambient conditions, and resident activities.
     The building construction determines the lower bound of the air exchange rate. That
is, rates cannot be reduced below  the rate allowed by diffusion through the building cracks,
holes, and other uncontrolled means of particle ingress in the absence of wind  and buoyancy
differences. Tests by building pressurization (e.g., using "blower doors") are able to
determine a parameter ("crack length") that quantifies this lower bound. Buildings that are
extremely tightly constructed for energy efficiency are able to reduce the lower bound of the
air exchange rate to the order of 0.1 air change per hour (ach, or h"1).
                                          7-57

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     Ambient conditions, particularly temperature and wind velocity, can also drive air
exchange rates.  Strictly speaking, it is the difference between indoor and outdoor
temperatures that creates either a pressure difference (closed windows) or a convective
behavior (open windows) leading  to higher air exchange rates as the temperature difference
increases. As wind velocity rises, pressure differences also increase and therefore the air
exchange rate  rises.  Besides these immediate ambient conditions we also have climatic
conditions.  A region that can expect a daily sea breeze is more likely to use open windows
than air conditioning for ventilation.  Northern areas are more likely to have tightly
constructed buildings than southern areas in the USA.
     In most cases, by far the most important factor affecting air exchange rates is the
behavior of the resident(s).  This  includes such considerations as the number of residents, the
number and age of children, the number of pets that spend time outdoors,  whether or not air
conditioning is used, and how much time doors and windows  are open.  Since residents are
more active  during the day,  and doors are opened and closed more often, air exchange rates
during the day typically exceed those at night, both in winter and in summer.  In the
PTEAM Study, the median daytime air exchange rate was 1.02 h"1 compared to an overnight
median of 0.80 h'1 (Wallace et al., 1993).  In the Parkville community of Baltimore, MD, in
the spring, the daytime median was 0.40 h"1 and the overnight median was 0.28 h"1.  In Los
Angeles coastal communities in the summer, the daytime median was 2.2 h"1 and the
overnight median was 1.2 h"1.  (All values derived from U.S. Environmental Protection
Agency, 1995)
     Fortunately,  a large number of surveys have been carried out in which air exchange
rates of homes have been measured.  These include the three major particle studies already
mentioned, and some studies of other pollutants. A paper collecting results from many
surveys found a geometric mean for 2844 U.S. residences of 0.53 h"1 with a geometric
standard deviation of 2.3 (Murray and Burmaster,  1995).  The mean value for all
2844 homes was 0.76 h"1, which  corresponded to the 70th percentile.  However, the
geometric means varied by season (a low of 0.31 h"1 hi fall and a high of 1.00 h"1  in
summer) and by region (a low of 0.31 h"1 in the North and a high of 0.69 h"1 in the
South—mainly southern California).  The geometric standard deviations for individual
seasons and regions  were generally very close to 2, ranging from 1.9 to 2.5.  (It should be
                                          7-58

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noted here that the homes were not selected to represent the nation, and that there are very
great disparities in the number of homes sampled in any one region.)
     A second paper (Koontz and Rector,  1995) used a nearly identical data set, but
weighted the 2889 measured homes by the state populations to estimate more closely the
national distribution.  Their estimates are similar to those of Murray and Burmaster (1995)
with an arithmetic mean of 0.63 h"1, a geometric mean of 0.46 h"1  and a GSD  of 2.25.
     However, certain smaller areas with pronounced climatic conditions could have very
much higher air exchange rates.  In a region such as the South Bay of Los Angeles, Wallace
et al. (1991c) found that 49 of 50 homes had no air conditioning and depended on the daily
land-sea breeze for ventilation. In this area, winter air exchange rates had a geometric mean
of 0.75 h"1 and summer air exchange rates were much higher, with a geometric mean of
2.16 h"1.  Both these ranges are much higher than the typical values reported above.  Thus, it
is important to consider the individual geographic region of study and its local  climatic
characteristics before selecting a range of air exchange rates to characterize the region.
     With that caveat, the empirical distribution for a large number of U.S. homes across all
seasons, but with disparate representation among the various regions of the country, appears
to have a median value of about 0.5 h"1, with a one geometric standard deviation (+ a) range
of 0.2 to 1.1 h'1, and a ±2o range of 0.1 to 2.2 h'1  (Murray and Burmaster, 1995; Koontz
and Rector, 1995).

     Deposition Rate k.  In a residence, the deposition rate k depends on many factors, such
as scale of turbulence, and the size, shape, electrostatic charge, and density of the particle.
For larger  particles, the deposition rate  is determined largely by gravitational settling; for
smaller particles, deposition on vertical surfaces by diffusion may also be important
(Nazaroff et al., 1993).  Unfortunately, fine particle deposition rates are not well
characterized.  Typically, one must measure over very long periods of time (weeks to
months) to collect enough particles for analysis by sophisticated techniques.  A series of
studies in nearly unoccupied buildings containing telephone-switching electrical equipment
resulted in average values for the deposition velocity of sulfate particles  ranging from
0.003 to 0.005 cm/s (Sinclair et al.,  1988,  1990, 1992;  Weschler et al.,  1989); these values
correspond to values of k (using a surface to volume ratio of 3 m"1) of 0.3 to 0.5 h"1.
                                          7-59

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However, another series of studies in museums resulted in values an order of magnitude
smaller (Ligocki et al., 1990; Nazaroff et al., 1990a,b).  Results for the sulfur (PM2 5)
deposition rate in the PTEAM studies were 0.16 h"1, lying between the values found by these
two groups.  Nazaroff et al.  (1993) concluded that deposition rates could vary as a result of
different  surfaces or near-surface air flows, amount of thermal isolation of the surfaces from
building walls, turbulence, and many other factors. Thus it is not likely that theoretical
calculations of deposition rates will provide trustworthy estimates.  Nor is it likely that
chamber  studies, with their limited ability to reproduce the variety of floor coverings and air
flows found in residences, can provide much information relevant to real-world residences.
     In the absence of precise theory or widely applicable chamber study estimates, the
largest study of residences including a calculation of empirical deposition rates is the
PTEAM  study.  The estimate for PM25 was 0.39 h'1,  for PM10 it was 0.65  h'1,  while for the
coarse fraction (the difference between PM10 and PM2 5) it was 1.01 h"1.

What Is the Fraction of Outdoor Air Particles Found Indoors at Equilibrium?
     Based on the values of P, a, and k discussed above, an answer can be provided to this
question.  Figure 7-16 shows  the fraction of outdoor fine and coarse particles  found in homes
under equilibrium conditions for a range of air exchange rates.  This fraction is calculated
using the value of P  = 1 determined in the PTEAM and the Thatcher and Layton (1995)
studies, and the values of k for fine and coarse particles calculated in the PTEAM study.
The fractions are displayed over the 95% range of observed air exchange rates (0.1 to
2.2 h"1) in  studies reported  on by Murray and Burmaster (1995).  It can be seen that at the
mean air exchange rate of 0.76 h"1 reported in Murray and Burmaster (1995),  the fractions of
outdoor fine (<2.5 jirn) and coarse particles (>2.5 and < 10 /im) that will be found indoors
under equilibrium conditions are 66% and 43%, respectively. The fraction of PM10 found
indoors will lie between these two curves, with the exact placement dependent on the relative
proportions of fine and coarse particles constituting the PM10.
     The actual  distribution of values of a/(a+k) observed in the PTEAM Study is provided
in Table 7-16 for PM10 and for its fine and coarse fractions. As can be seen,  the average
values across day and night were about 67% for fine particles and 47% for coarse particles,
with PM10  exactly between the two size fractions at 57%.
                                         7-60

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          u.
                                                                   2.5
                         0.5         1         1.5         2
                               Air exchange rate (air changes per hour)
            Deposition rate - 0.39/h for fine particles, 1.01/h for coarse
Figure 7-16.  Fraction of indoor participate matter (PM) from outdoor airborne PM,
              under equilibrium conditions, as a function of air-exchange rate, for two
              different size fractions.
Source: Calculated from PTEAM database (Ozkaynak et al., 1993a; Wallace, 1996).
     These results suggest that if persons at risk of health effects from outdoor particle
pollution are  able to significantly decrease the air exchange rates in their homes
(by weatherization, installation of air conditioning to reduce use of windows, etc.) they could
decrease the fraction of outdoor air particle concentration in their homes.  A decrease in the
air exchange  rate from the mean level of 0.76 h'1 reported above to an achievable (16th
percentile) value of 0.25 h"1 would decrease the indoor air level of outdoor-generated fine
PM2 5 particles from 66% to 39% of the outdoor level,  and of PM10 from 54% to 28%.

7.2.2.5   Studies of PM in Buildings
     The single largest study of particles in buildings was carried out by the Lawrence
Berkeley Laboratory (LBL) for the Bonneville Power Administration (BPA) (Turk et al.,
1987, 1989).  Thirty-eight buildings were chosen from two climatic regions in the Pacific
Northwest: Portland-Salem, OR (representing mild coastal conditions), and Spokane-Cheney,
                                          7-61

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               TABLE 7-16. FRACTION OF CONCENTRATION OF
        OUTDOOR PARTICLES ESTIMATED TO BE FOUND INDOORS AT
   EQUILIBRIUM: RESULTS FROM THE PARTICIPATE TOTAL EXPOSURE
                     ASSESSMENT METHODOLOGY STUDY
Daytime (N =
Statistic
Mean
Standard deviation
Standard error
Geometric mean
Minimum
25th percentile
Median
75th percentile
Maximum
Fine
0.68
0.17
0.013
0.66
0.28
0.55
0.70
0.83
0.95
PM10
0.58
0.19
0.015
0.55
0.19
0.42
0.58
0.75
0.93
174)
Coarse
0.49
0.20
0.015
0.45
0.13
0.32
0.47
0.65
0.89
Overnight (N = 175)
Fine
0.66
0.15
0.012
0.64
0.28
0.55
0.66
0.79
0.94
PM10
0.55
0.17
0.013
0.53
0.19
0.43
0.54
0.69
0.90
Coarse
0.46
0.17
0.013
0.42
0.13
0.32
0.43
0.59
0.85
Fractions calculated from the formula Pa/(a+k), where
/>= 1;
k = 0.39 h'1 for fine particles, PM2 5;
k = 0.65 h4 for PMi0; and
k = 1.01 h"1 for coarse particles 2.5 ^m < AD < 10 ftm.
Values for a measured in 175 homes during the PTEAM Study.
Source of data: Values calculated from PTEAM database (Wallace, 1996).
WA (representing extreme inland conditions). The buildings were studied for a variety of
pollutants to determine how ventilation rates  affect indoor air quality.  Buildings were
measured in winter (21 buildings in both regions), spring (10 buildings in both regions) and
summer (nine buildings in the inland region only). All but four buildings were government
or public properties, and therefore the 38 buildings cannot be considered to  represent the full
mix of building types.
     Each building was monitored for  10 working days over a two-week period.  From four
to eight particle sampling sites were chosen in each building according to size. The sampler
was an LBL-developed flow controlled device with a 3 /xm cutpoint. The pumps sampled
only during hours the building was  occupied. If filters had to be changed due to excessive
loading, the combined weight of all filters from one site was determined—thus all values are
                                       7-62

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approximately 10 working-day (80-h) averages.  Buildings had varied types of smoking
policies, from relatively unrestricted to very tightly controlled, as in one elementary school.
In most buildings, an attempt was made to site at least one monitor in an area where smoking
was allowed.  Data were obtained from smoking areas in about 30 of the 38 buildings.
     Results comparing smoking and non-smoking areas are provided in Table 7-17 and
Figure 7-17. Mean RSP concentrations in the smoking areas were more than three times
higher than  in the non-smoking areas (70 versus 19 /xg/m3).  Since these arithmetic means
showed evidence of being driven by one or two high values, the geometric mean (averaged
across all sites in a building) may be a better comparison.  Here the ratio is very close to
3 to 1  (44 versus  15  jiig/m3).  Outdoor results at 30 sites had the identical arithmetic mean as
the indoor non-smoking sites: 18.9 /^g/m3.
     Repace and Lowrey (1980) sampled 19 establishments allowing smoking (seven
restaurants,  three  bars, church bingo games, etc.) and 14 where no smoking occurred
(including five residences and four restaurants) between March and early May of 1978.
Sampling occurred for short periods of time (2 to 50 min) using a TSI Piezobalance to
measure PM3 5.  Indoor concentrations ranged from 24 to  55 ptg/m3 in the areas without
smoking, and from 86 to 697 pig/m3 in places with active smoking.
     Miesner et al.  (1989) sampled particles and nicotine in 57 locations within 21 indoor
sites in Metropolitan Boston, MA,  between July 1987 and February 1988. PM2 5 was
sampled using Harvard aerosol impactors.  Sampling times ranged from about 3 h in a bus
station to 16 h in  a library,  depending partly on how "clean" the environment was perceived
to be.  PM2 5 concentrations ranged from 6 /xg/m3 (in the library) to 521 /ig/m3 in a smoking
room in  an office building.  For 42 measurements in  non-smoking areas, the mean PM2 5
concentration was 25 ± 30  (SD) /^g/m3.  Six of these measurements included a classroom
with visible  levels of chalk dust on the impactor, four measurements in subways, and the bus
station.  The remaining 36 nonsmoking areas had a mean PM2 5 concentration of
15  ± 7 /ig/m3. The 15 smoking areas ranged from 20 to 520 /ig/m3 with a mean of
110 ± 120/ig/m3.
     Sheldon et al. (1988a,b) reported on the EPA  10-building study of hospitals, homes for
the elderly,  schools, and office buildings.   Particle measurements were taken in six buildings
using a National Bureau of Standards portable particle sampler (McKenzie et al., 1982) to
                                         7-63

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                TABLE 7-17.  SMOKING, NONSMOKING, AND
              OUTDOOR RSP CONCENTRATIONS AND RATIOS


Building No.
1
2
3
4
5
6
7
8
9
10
11"
12
13
14
15
16
17b
18
19
20
21
22
23
24
25
26
27
28
29
30b
31
32
33
34
35
36*
37
38
39
40
AM
ASD
GM
GSD



fcg/m3)
ND
ND
ND
8
BD
35
35
8
8
9
8
ND
10
6
BD
10
7
7
7
18
17
20
11
11
68
32
52
65
29
33
13
ND
ND
16
18
20
19
14
11
11
19
16
14
2.2

InHnnr

0»g/m3) Arithmetic Mean (Range)

Nonsmoking
25(19-36)
19(18-21)
ND
7(6-8)
13(13)
12(11-13)
38(32-44)
7(7-8)
11(11)
65(53-74)
23(9-49)
10(10)
5(5-6)
ND
11(7-14)
9(8-11)
11(10-13)
ND
ND
11(10-11)
11(9-12)
18(18)
9(BD-20)
44(10-77)
35(32-38)
45(20-70)
36(33-38)
36(29-43)
10(8-12)
24(20-30)
12(8-18)
13(10-17)
ND
13(10-16)
20(6-35)
14(9-18)
21(12-32)
7(BD-9)
8(8-9)
10(8-12)
19
14
15
1.9

Smoking0
ND
ND
20(16-25)
ND
14(14)
35(23-59)
39(39)
ND
16(13-20)
95(67-127)
209(209)
63(63)
ND
30(26-34)
12(12)
73(73)
105(105)
19(19)
20(11-29)
ND
ND
57(22-165)
ND
24(24)
109(109)
82(55-123)
61(33-89)
BD
144(144)
113(113
268(268)
36(21-52)
29(12-74)
54(13-117)
50(50)
72(17-127)
27(11-62)
308(308)
13(11-14)
26(11-40)
70
73
44
2.7
"Repeated test of building #11.
'"Repeated test of building #17.
cSmoking within
10 m radius of site



Meand
25(19-36)
19(18-21)
20(16-25)
7(6-8)
13(13-14)
28(11-59)
38(32-44)
7(7-8)
15(11-20)
86(53-127)
63(9-209)
36(10-63)
5(5-6)
30(26-34)
11(7-14)
31(8-73)
40(10-105)
19(19)
20(11-29)
11(10-11)
11(9-12)
50(18-165)
9(BD-20)
37(10-77)
60(32-109)
67(20-123)
48(33-89)
24(BD-43)
32(8-144)
37(20-113)
64(8-268)
21(10-52)
29(12-74)
28(10-117)
23(6-50)
28(9-127)
25(11-62)
46(BD-308)
11(8-14)
15(8-40)
30
19
24
2.0
NA
ND
BD

Ratios
Indoor Indoor
Nonsmoking •*• Smoking -s-
Outdoor
NA
NA
NA
0.9
NA
0.3
1.1
0.9
1.3
7.0
2.9
NA
0.5
NA
NA
0.9
1.6
NA
NA
0.6
0.7
0.9
0.8
4.0
0.5
1.4
0.7
0.6
0.3
0.7
0.9
NA
NA
0.8
1.1
0.7
1.1
0.5
0.7
0.9
1.2
1.3
0.9
2.0
= Not applicable.
= No data collected.
= Below detection limit.
Outdoor
NA
NA
NA
NA
NA
1.0
1.1
NA
2.0
11.0
26.1
NA
NA
5.0
NA
7.3
15.0
2.7
2.9
NA
NA
2.9
NA
2.2
1.6
2.6
1.2
NA
5.0
3.4
20.6
NA
NA
3.4
2.8
3.6
1.4
22.0
1.3
2.4
6.0
7.2
3.6
2.6




Indoor
Mean -s-
Outdoor
NA
NA
NA
0.9
NA
0.8
1.1
0.9
1.9
9.6
7.9
NA
0.5
5.0
NA
3.1
6.1
2.7
2.9
0.6
0.7
2.5
0.8
3.4
0.9
2.1
0.9
0.4
1.1
1.1
4.9
NA
NA
1.8
1.3
1.4
1.3
3.3
1.0
1.4
2.3
2.2
1.7
2.3



dArithmetic average of all sites in building.

Source: Turk et al. (1987).
                                  7-64

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     80
     70
  ^ 60
 CO
  I so
  3 40
  I  30
     20
     10
      0
                    Mean Concentrations
         Smoking areas  Nonsmoking areas   Outdoors
 CO
50

40

30
                          Geometric Means
  a>  20
  DC
     10
         Smoking areas  Nonsmoking areas   Outdoors

Figure 7-17. Comparison of respirable particles in smoking and nonsmoking areas of
          38 buildings in the Pacific Northwest.

Source: Turk et al. (1987).
                              7-65

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collect two size fractions: PM3 and a coarse fraction between PM3 and PM15.  The sampler
employed two filters in series:  an 8.0 fan Nuclepore filter for PM15 and a 3 jum Ghia Zefluor
Teflon filter for fine particles.  The flow rate was 6 Lpm for a 24-h sample.  Three
consecutive 24-h samples were collected at each building.  Additional particle monitoring
was provided at certain locations (e.g.,  smoking lounge, cafeteria) using a Piezobalance
(PM3 5) and a dichotomous sampler (PM2 5 and PM10).
     In areas without smoking, indoor concentrations  of both size fractions were generally
lower than outdoor levels; for example, the coarse fraction ranged from 0.2 to  0.66 of the
outdoor level (13 to 17 ^g/m3) in the three buildings with no smoking.  The fine fraction  was
present at higher indoor-outdoor ratios,  ranging from 0.56 to 0.99 in the same  three
buildings (outdoor fine fraction ranged from 16 to 33  ptg/m3).  The fine  fraction was elevated
in the regions of smoking (range of 14 to 56 jug/m3).  Piezobalance results for  several
buildings showed uniformly low (7 to 29 /ig/m3) for 800 min of monitoring in  nonsmoking
areas.
     Concentrations in the areas allowing smoking were more often in the 40 to 60 ^g/m3
range,  with short-term peaks as high as 345 jug/m3. It was possible to use the  observed
declines in PM3 5 following cessation of smoking to calculate an effective air exchange rate
and thus a source strength for PM3 5 emissions from cigarettes.  Four  estimates gave an
average value of about 6 mg/cigarette, somewhat below the chamber study estimates of 10 to
15 mg/cig.  An estimate due to Repace  and Lowrey (1980) of concentrations of respirable
particulates due to smoking was also tested, with good agreement.  The  Repace and Lowrey
equation is

                                 C fig/m3 = 27.6 Pala                            (7-7)

where  Pa is smoking occupancy in persons per 100 square meters and a is the  air exchange
rate h"1.  Equation 7-7 was developed assuming one of every three occupants are smokers
who smoke two cigarettes per hour.  Assuming a background concentration of  15 /zg/m3,  the
measured values for the smoking lounge for zero, three, and nine smokers were 10, 78, and
284 /ig/m3, respectively. Equation 7-7  predicts 0, 99, and 296 /ig/m3, respectively. In two
of the  homes for the elderly, apartments with smokers and nonsmokers were measured for
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three consecutive days using the NBS samplers.  In one building, the smoker's apartment had
a 2-day PM3 average of 39  ptg/m3, compared to 9.4 pig/m3 in the nonsmoker's apartment; in
the other home for the elderly, where two smokers shared one apartment, the average 2-day
PM3 concentration was 88 /-ig/m3 compared to 8.6 jwg/m3 in the nonsmoking apartment.  The
simultaneous ambient values were not measured at Home 1.  At Home 2, the ambient value
was 11 /ig/m3.
     Owen et al. (1990) studied particle size distributions in an office under varying
conditions of ventilation and occupancy.  The unoccupied office using minimum outdoor air
had concentrations  at least as low as the occupied office using maximum outdoor air.  PM3  5
concentrations  (measured using the TSI Piezobalance) were about twice as high (75  versus
39 /ig/m3) in the occupied office  when the dampers were closed as when they were  open.
The main source of particle generation appeared to be the hallway, suggesting that
resuspension of tracked-in dust was an important indoor source of particles as reported by
Roberts et al. (1990) for residences.

7.2.3    Indoor Air Quality  Models and Supporting Experiments
     Indoor concentrations  of particles are a  function of penetration  of outdoor particles and
generation of particles indoors.  The concentrations are modified by  air exchange  rates and
deposition rates of the particles onto indoor surfaces.

7.2.3.1  Mass Balance Models
     Mass balance models have been used for more than a century in various branches of
science.  All such models depend on the law  of the conservation of mass. They simply state
that the change in mass of a substance  in a given volume is equal to  the amount of mass
entering that volume minus  the amount leaving the  volume.   Usually they are written in the
form of first-order  linear differential equations. That is, consider a volume V filled with a
gas of mass m.  The change in mass Am over a small time Ar will simply be the difference
between the mass entering the volume (/nin) and the mass leaving the volume (mout):
                                         7-67

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                                 Am  _ \mm   mout)                              (7_g)
                                 "AT         At
Taking the limit as At approaches zero, we have the differential equation for the rate of
change of the mass:
                                — =  — (m   - m  )                            (7-9)
                                 dt    dt   m     out

     If we require that the mass be uniformly distributed throughout the volume at all times,
we have a condition that the physical chemists call  "well-mixed". We assume that any mass
gained or lost in the volume V is instantaneously distributed evenly throughout the volume.
We may then replace the mass term (m) by the concentration C = m/V, so that
dm/d?=  VdC/dt.
     The above equations are the basis for all such mass-balance models.  Equation 7-9 takes
on many  forms depending on the type of processes involved in transporting mass into or out
of the volume being considered. A large class of models assume that the volume  V is a
single perfectly mixed compartment. More complex models assume multiple compartments
to allow for incomplete mixing in the total volume  V (Mage and Ott, 1996). A detailed
mass-balance model that includes changes  in particle size, chemical composition, and
turbulence is described in Nazaroff and Cass (1989).

7.2.4   Summary of Indoor Particulate Matter Studies
     At low outdoor levels of fine (PM3 5 or PM2 5) particles (as in most of the cities in the
Harvard Six-City and New York State studies),  mean indoor concentrations have been found
to be twice as high as outdoor levels. However, for homes without smokers or combustion
sources, indoor levels are often roughly equal to outdoor levels (Santanam et al.,  1990;
Leaderer et al., 1994; Neas et al., 1994).  At high outdoor levels, mean indoor
concentrations have been about 10% lower than the mean outdoor concentrations in the two
areas studied (Steubenville, OH, and Riverside, CA).  Indoor concentrations are considerably
higher during the day, when people are active, than at night.  Based  on a mass-balance
model, outdoor air was the major source of indoor particles in the PTEAM study, providing
                                         7-68

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about 3/4 of fine particles (PM2 5) and 2/3 of inhalable particles (PM10) in the average home.
However, outdoor air contributed less than half of the indoor particle concentrations at seven
out of eight other sites with extensive indoor-outdoor measurements.  Indoor concentrations
are much higher during the day, when people are active, than at night.
     In the PTEAM study (with very high outdoor particle concentrations), indoor levels
were significantly influenced by outdoor levels, but with relatively low R2 values ranging
between 0.22 and 0.54.  In the other two major studies, no significant indoor-outdoor
relation was observed.  Regressions  of indoor on outdoor particles seldom explained more
than half the variance of any study (R2  < 50%). However, in those studies with repeated
measures  on the same house, (e.g., the PTEAM prepilot [Table 7-6], the Phillipsburg, NJ,
study [Table 7-15] and Tamura et al. [1996]  in Section 7.4.2.1), longitudinal regressions of
indoor on outdoor particles often had much higher R2 values of 0.6 to 0.9 for each individual
house.  Since the epidemiological studies of health effects of particles have been studies of
variation over time, the longitudinal  regressions by individual home are expected to be more
relevant to the epidemiology  studies  than cross-sectional regressions across all homes in the
study.  The better relationship showed by these regressions suggests  that whatever structural
or behavioral characteristics affect indoor particle concentrations in the home tend to persist
or be repeated over tune. This gives better support to the epidemiological findings than
would  be  inferred from the typically low R2 values reported for the cross-sectional
regressions performed in most studies.
     Deposition rates k ranged from 0.16 h"1 for sulfur to 0.4 h'1 for fine (PM2 5) particles
to 1  h"1 for coarse particles (PM10 - PM2 5), with an intermediate estimate  of 0.65 h"1 for
PM10.  The penetration factor P for  both fine and coarse fractions was estimated to be unity.
For a home with no indoor sources whatever and a typical air exchange rate of about
0.75 h"1, these values for k and P would imply  that sulfur indoors would be about
0.75/(0.16 + 0.75) =  82% of the outdoor value at equilibrium, fine particles indoors would
be about 0.75/(0.4+0.75) =  65% of the outdoor value at equilibrium, indoor PM10 would
be about 54% of outdoor levels, and indoor coarse particles would be about 43% of outdoor
levels.  Since very few homes were observed to have concentrations  this low, it can be
inferred that very few homes are free of important indoor sources of particles.
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     A crucial question is the impact of outdoor particles on indoor particle concentrations.
It was  found that the governing equation is a function of only two parameters: air exchange
rate a and particle deposition rate k: a/(a+k).  Air exchange rates measured in the United
States appear to follow a roughly log-normal distribution with a geometric mean of 0.5 and a
geometric standard deviation about 2.  With the values for the deposition rates provided
above, one can calculate the impact of outdoor particles on indoor concentrations for any
given value of the air exchange rate.  At a low air exchange  rate of, say, 0.4 h"1, sulfates
indoors will be 71% of their outdoor values, fine particles indoors will be 50% of their
outdoor values, while coarse particles will be 0.4/1.4 or 28% of their outdoor values.  At a
higher air exchange rate  of 1 h"1, sulfates  will be 86% of their outdoor concentration, fine
particles will be 1/1.4 or 71% of their outdoor concentration, whereas coarse particles  will
be 50% the outdoor concentration.  The difference in both cases between the two size
fractions is about 0.2; that is, for the entire range  of realistic air exchange rates (from 0.2 h"1
to 2 h"1), if the fraction of outdoor coarse (PM10  —  PM25) particles found indoors is/, then
the fraction of fine particles found indoors will be approximately/ + 0.2. It can be seen
that a reduction in air exchange rate would reduce the impact of outdoor air on indoor  air
particle concentrations.

7.2.5   Bioaerosols
     Biologically-derived particles  are frequently ignored components  of both ambient  and
indoor aerosols.  This lack of attention is, in part, due to the fact that  the bioaerosols are
considered "natural"  and not amenable to control.  Methods  for their analysis are, in many
cases,  highly variable, and very little exposure or  exposure/response information is available.
Measurement  methods for bioaerosols are discussed in Chapter 4 (Section 4.4).   Various
health effects  associated  with bioaerosols  are discussed in Chapter 11.  A few reference
works that focus on bioaerosols  include Gregory (1973), Edmonds (1979), Cox (1987),
Lighthart and Mohr (1994), and Cox and  Wathes (1995).
     For bioaerosols, there is considerable confusion among the terms reservoir, source,
particle, and agent.  For the purposes  of this chapter, the following  definitions apply:
     •   Reservoir: the environmental niche in which source organisms are living
     •   Source: the organism that produced the particle
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     •   Particle: the particle shed from the organism
     •   Agent: the part(s) of the particle that actually mediate the disease process.
Examples of bioaerosol sources, particles and agents are presented in Table 7-18.
           TABLE 7-18.  AN OVERVIEW OF ORGANISMS, AEROSOLS,
                               AND DISEASE AGENTS
 Sources
              Aerosol Particles
    Disease Agents
 Plants

 Animals
 Fungi


 Bacteria

 Viruses
Pollen and pollen fragments, fragments of
other plant parts, spores (ferns, mosses), algal
cells
Skin scales,  secretions (saliva, skin secretions),
excreta, body parts (arthropods)
Spores, hyphae, yeast cells, metabolites
(toxins, digested substrate material)
Cells, fragments, metabolites (toxins, digested
substrate material)
Viral particles
Glycoprotein allergens
Glycoprotein allergens
Glycoprotein
allergens, infectious
units, glucans,
mycotoxins
Infectious units,
allergens, endotoxin,
exotoxins
Infectious units
7.2.5.1  Plant Aerosols
Pollen
     Pollen is produced by vascular flowering plants: trees (pines, cedars, birch, elm,
maple, oak, hickory, walnut, etc.),  grasses, and weeds (ragweed, sage, Russian thistle,
lambs quarters, etc.). Within these large groupings, specific types are regionally common.
For example, ragweed is most common in the eastern United States.  Birch pollen dominants
the spring pollen season in New England, while mountain cedar pollen is abundant early in
the year hi the  southwest (Lewis et  al.,  1983).
     Pollen levels outdoors are controlled by the number of plants available for pollen
release, the amount of pollen produced by each plant, factors that control pollen release and
dispersion from the plant, and factors that directly affect the aerosols (Edmonds, 1979). The
number of plants available depends  on the many environmental factors that control plant
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prevalence, some of which are human factors.  As an example, the abundance of the ragweed
plant in a particular year depends on the number of plants that produced seed in the previous
year, disturbed ground available for seed germination and growth, and meteorological factors
during the growing season.  Once a crop of ragweed has been produced, pollen production
depends on temperature, rainfall, and day length.
     Pollen grains are relatively large complex particles that consist of cellular material
surrounded by a cell membrane and a complex wall.  Pollen grain structure has been well
studied.  Pollen shed is controlled by temperature, humidity, wind, and rain.  Pollen levels in
air depend on all of these factors as well as wind and rain conditions after release, and on
surfaces available for impaction.  Figure 7-18 represents day to day ragweed pollen
prevalence in Kalamazoo, MI, for 1994.
                                  1994 Kalamazoo, Michigan
  1,000
£
3
   100
I   10
                                                    nut nit  i   i
                                          NlWIN   I   II
     Jan-1   Jan-31   Mar-2    Apr-1    May-1   May-31   Jun-30   Jul-30   Aug-29   Sep-28   Oct-28  Nov-27   Dec-27
                                    24-hour Total Pollen Counts

Figure 7-18.  Chart of ragwood pollen prevalence.  Sampling was not conducted before
              April and during the first few days of October.

Source:
     Pollen allergens are (apparently) water-soluble glycoproteins that rapidly diffuse from
the grain when it contacts a wet surface.  The glycoproteins are (generally) specific to the
type of pollen, although large groups may be represented by a single allergen. For example,
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many different kinds of grasses carry similar allergens in their pollen grains.  A number of
pollen allergens have been characterized:  Amb a I (ragweed), Bet p I (birch), Par j I
(parietaria), etc.

Other Natural Plant Aerosols
     Other plant-derived particles that are a natural part of outdoor air include algal cells;
spores of mosses, liverworts, club mosses, and ferns; and fragments of all kinds of plants.
Very little  has been reported about the prevalence or human impact of any of these aerosol
particles, although they are presumed to carry allergens.

Man-Made Plant Aerosols (Soy,  Latex, Occupational)
     Man-made accumulations of plant material that are subsequently handled inevitably
produce bioaerosols. The most common practices that involve such accumulations are
storage, handling, and  transport of farm products (hay, straw, grain), composting, and
manufacturing processes that involve the use of plant material.  In addition, the use of some
plant products can result in disease-causing aerosols (Alberts  and Brooks, 1992).  The
aerosols produced from most of these processes are complex, and few have been accurately
characterized.
     Grain Dust.  It is well-recognized that grain dusts include respirable-size particles
(< 10 nm) although the exact nature of the particles and the  agents of disease remain
speculative.  Soybean dust aerosols released from freighters unloading the beans in port have
been blamed  for epidemics of asthma.
     Wood Dust.  Wood trimmer's disease (from particles released from wood during high-
speed cutting). Sewage composting involves the use of wood chips that can release
allergenic aerosols.
     iMtex.  Latex-containing powder aerosols are produced  when surgical gloves are used.
Latex particles also may be released  from automobile tires.
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7.2.5.2   Animal Aerosols
Mammalian Aerosols
     All mammals produce aerosols, from humans to the smallest mouse.  Human aerosols
(skin scales, respiratory secretions) do not cause disease except, of course, for agents of
infection (see below).  Other mammals release aerosols that cause hypersensitivity diseases.
The most common of these are cats, dogs, farm animals, laboratory animals, and house
mice, although all animals release aerosols that could be sensitizing under appropriate
conditions (Burge, 1995).  Mammals only cause human disease when appropriate exposure
conditions occur.  For cats, simply having a cat in a house will create such conditions, as
will handling any animal regardless of the environment. Cat allergens apparently become
aerosolized on  very small particles (< 1 /xm) shed from skin and saliva.  There is some
indication that dog, mouse, and other rodent allergens are borne on dried urine particles, and
particle sizes are similar to those of cat allergen.  Little is known about other mammalian
aerosols.  Cat and dog allergens have been characterized (Pel d I, Can f I) and other
mammalian allergens are under active study.

Avian Aerosols
     Wild and domesticated birds associated with disease-causing aerosols include for
example:  starlings (histoplasmosis); pigeons (histoplasmosis, pigeon-breeders disease);
parrots  (psittacosis); poultry (poultry-handlers disease); etc.  Of these diseases, only the
hypersensitivity diseases (pigeon breeders and poultry handlers disease) are caused by "bird"
aerosols.  The  others are infections caused by agents inhabiting the birds (see below).  The
birds that release antigens  that have  caused human disease are those that are confined or
congregate close to people. The avian aerosol-hypersensitivity diseases are almost
exclusively confined to sites where birds are bred and handled extensively, especially in
indoor environments.  Relatively little is known about avian aerosols.  Probably skin scales,
feather  particles, and fecal material are all released as antigen-containing aerosols.  The
antigens (allergens) responsible for avian-related diseases have not been characterized.
                                           7-74

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Insect Aerosols
     Dust Mites.  Dust mites are arthropods belonging to the family Pyrogliphidae.  There
are two common species in temperate climates: Dermatophagoides farinae (which
proliferates under relatively dry conditions) and D. pteronyssinus which dominates
populations in more humid environments (Arlian, 1989).  Dust mites thrive in environments
where relative humidity consistently exceeds 60 % and where skin scales and fungal spores
are available as a food source.  Primary reservoirs for exposure are bedding and carpet dust.
The mite itself is about 100 /-im long, but it excretes 20 /mi membrane-bound fecal particles
that contain the allergens. Exposure to dust mite allergens apparently occurs only when
reservoirs are disturbed. Dust mites produce allergens that are a major cause of sensitization
hi children.   The allergens are digestive enzymes that gradually diffuse from fecal particles
after deposition on mucous membranes.   Several dust mite allergens have been characterized
and monoclonal antibodies against each have been raised and cloned.  These include Der f I
and II, and Der p I and II (Platts-Mills and Chapman, 1987).

     Cockroaches. Cockroaches are insects belonging to the Orthoptera (Mathews, 1989).
The most common cockroach infesting temperate climate  buildings is Blatella germanica, the
German cockroach. Cockroaches are nocturnal, and inhabit dark environments where food
and water are available.  Common food sources include stored animal or human food, and
discarded food (garbage). Cockroaches  are extremely prolific, given appropriate
environmental conditions. Population pressure will eventually drive the roaches into the
daylight in search of food. Cockroaches shed body parts, egg cases, and fecal particles, all
of which probably carry allergens.  Little is known about the  particles that actually carry the
allergens. Two German cockroach allergens have been characterized:  Bla g I,  and Bla g II.
The function within the cockroach of these allergens is unknown.  Cockroach allergens are
probably a major cause of asthma for some populations of children.

     Other Insects.  Fragments of gypsy moths and other insects  that undergo massive
migrations can become abundant in ambient air.  Sizes, nature, and  allergen content of such
particles have not been studied.  Cases of occupational asthma from exposure to insects (e.g.,
sewer flies) have been reported.
                                          7-75

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Other Animal Allergens
     It is likely that proteinaceous particles shed from any animal could cause sensitization if
exposure conditions are appropriate.  For example, exposure to proteins aerosolized during
seafood processing have caused epidemics of asthma.

7.2.5.3  Fungal Aerosols
     Fungi are primarily filamentous microorganisms that reproduce and colonize new
environments by means of airborne spores.  Most use complex non-living organic material
for food, require oxygen, and have temperature optima within the human comfort range.
The major structural component of the cell wall is acetyl-glucosamine polymers (chitin).
Cell walls also may contain B-glucans, waxes, mucopolysaccharides, and a wide variety of
other substances.  In the process of degrading organic material, the fungi produce CO2,
ethanol, many other volatile organic compounds, water, organic acids, ergosterol, and a
broad spectrum of secondary metabolites including many antibiotics and mycotoxins.
     The fungi colonize dead organic materials in both outdoor and indoor environments.
Some fungi are able to invade living plant tissue and cause many important plant diseases.
A few fungi will invade living animal hosts, including people. Fungi are also universally
present in indoor environments unless specific efforts are made for their exclusion (i.e., as in
clean rooms).  The kinds of fungi that are able to colonize indoor materials are generally
those with broad nutritional requirements (e.g., Cladosporium sphaerospermum), those that
are able to colonize very dry environments (e.g., members of the Aspergillus glaucus group),
or organisms that readily degrade the  cellulose and lignin present  in many  indoor materials
(e.g., Chaetomium globosum, Stachybotrys atra, Merulius lacrymans).  Yeasts (which are
unicellular fungi) and other hydrophilic taxa (e.g., Fusarium, Phialophora) are able to
colonize air/water interfaces.  Water,  in fact, is the most important factor controlling indoor
fungal growth, since food sources are ubiquitous (Kendrick,  1992).
     Particles that become airborne from fungal growth include spores (the unit of most
fungal exposure), fragments of the filamentous body of the fungus, and fragments of
decomposed substrate material.  Fungal  spores range from about 1.5 /icm to > 100 /mi in size
and come in many different shapes.  The simplest are smooth spheres; the most complex are
large multicellular branching structures.   Most fungal spores are near unit density or less.
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Some include large air-filled vacuoles. Fungal spores form the largest and most consistently
present component of the outdoor bioaerosols.  Levels vary seasonally, with lowest levels
occurring during periods of snow.  While rain may initially wash large dry spores from the
air, these are immediately replaced by wet (hydrophilic) spores that are released in response
to the rain.
     Some kinds of spores are cosmopolitan hi outdoor air (e.g., Cladosporium herbarum,
Alternaria tenuissima).  Others produced by fungi with more fastidious nutritional
requirements are only locally abundant.  A typical indoor fungal aerosol is composed of
particles penetrating from outdoors, particles released from active growth  on indoor
substrates, and reaerosolized particles that have settled into dust reservoirs.  Indoor fungal
aerosols  are produced by active forcible discharge of spores,  by mechanisms intrinsic to the
fungus that "shake" spores from the growth surface, and (most commonly) by mechanical
disturbance (e.g., air movement, vibration).
     Allergic rhinitis and asthma are the only commonly reported diseases resulting  from
fungal exposures outdoors, and which also commonly occur indoors.  The allergens  of fungi
are probably digestive enzymes that are released as the spore germinates.  Other spore
components (of unknown function) may also be allergenic.  Only very few fungal allergens
(out of possibly hundreds of thousands) have been characterized:  (e.g., Alt a I, Cla h I, and
Aspfl).
     Allergic fungal sinusitis and allergic bronchopulmonary mycoses occur when fungi
colonize thick mucous hi the sinuses or lungs of allergic people.  The patterns of incidence of
allergic fungal  sinusitis may be explained hi part by geographic variability in ambient fungal
exposures. Figure 7-19 shows total  fungal spore counts in Kalamazoo, MI, for 1994.  This
disease is most commonly caused by Bispora, Curvularia, and other dark-spored fungi.
Exposure patterns required for allergic bronchopuhnonary mycoses  are unknown.  This
disease is usually caused by Aspergillus fumigatus.  Histoplasmosis  and Coccidioidomycoses
are fungal infectious diseases that result from outdoor exposures to  Histoplasma capsulatum
(a fungus that contaminates damp soil enriched with bird droppings) and Coccidioides inmitis
(a fungus that growth in desert soils.  Indoor aerosol-acquired fungal  infections are rare, and
restricted to immunocompromised people (Rippon,  1988).
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                                   1994 Kalamazoo, Michigan
  10,000
  1.000
     Jan-1    Jan-31   Mar-2   Apr-1    May-1   May-31   Jun-30   Jul-30   Aug-29   Sep-28   Oct-28   Nov-27   Dec-27
                                     24-hour Total Spore Counts
Figure 7-19. Chart of fungal spore prevalence in Kalamazoo, MI, for 1994.
Source:


     Toxic agents produced by fungi include antibiotics, mycotoxins, and some cell-wall
components that have toxic or irritant properties.   The antibiotics and mycotoxins are
secondary metabolites that are produced during fungal digestion of substrate materials, and
their presence depends, in part, on the  nature of the substrate.  The locations of the toxins in
spores or other mycelial fragments are  unknown, as are the dynamics of release in the
respiratory tract.  Aerosol exposure to  fungal antibiotics in levels sufficient to cause disease
is unlikely.  Mycotoxicoses have been reported as case studies from exposure to spores of
Stachybotrys atra (Croft et al., 1986), and epidemiologically for Aspergillus flavus (Baxter
etal.,  1981).
7.2.5.4   Bacterial Aerosols
     Bacteria,  in contrast to plants, animals and fungi, contain neither nuclei or
mitochondria.  Most are unicellular, although some form "pseudo" filaments when cells
remain attached following cell division. The actinomycetes are bacteria that do form
filaments and (in some cases) dry spores designed for aerosol dispersal. The bacteria can be
broadly categorized into two groups based on a response to the Gram stain procedure.  The
cell walls of Gram positive bacteria are able to absorb a purple stain; the walls of Gram
                                           7-78

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negative bacteria resist staining.  The Gram negative cell wall contains endotoxin (see
above).
     Most infectious agents are maintained in diseased hosts. A few, including Legionella
pneumophila, reside in water-filled environmental reservoirs such as water delivery systems,
cooling towers,  air conditioners, and (outdoors) oceans, lakes, streams, etc.
     Infectious  agents are often released from hosts in droplets released from the respiratory
tract.  Each droplet contains one or more of the infectious agent, probably one or more other
organisms, and  respiratory secretions.  Most droplets are very large and fall quickly.
Smaller droplets dry quick to droplet nuclei, which range in size from the size of the
individual organism (< 1  /zm for the smallest bacteria) to clumps of larger organisms
(> 10  /xm for larger bacteria).  Environmental-source aerosols are produced by mechanical
disturbances that include wind, rain splash, wave action, and by mechanical disturbance such
as occurs in recirculation and sprays of washes and  coolants, and in humidifiers.   Particle
sizes from all of these activity cover a wide range from well below 1  /xm to >50 /an.  The
thermophilic actinomycetes produce dry aerial spores that require only slight air  movements
to stimulate release.  Each spore is about 1 ^m in diameter.
     Whole living bacteria are agents of infectious disease  (e.g., Tuberculosis, Legionnaires'
disease).  For tuberculosis, a single virulent bacterial cell deposited in the appropriate part of
the lung is likely to cause disease in a host without specific immunity. For Legionnaires'
disease,  the number of organisms required to make  disease development likely depends on
how well the host's general protective immune system is operating. Some bacteria release
antigens that cause hypersensitivity pneumonitis. The antigens may be enzymes  (e.g.,
Bacillus subtilis enzymes used in the detergent industry) or may be cell wall components as
in the  thermophilic actinomycetes. Bacteria also produce toxins of which endotoxin is the
most important  from an aerosol exposure point of view.

7.2.5.5   Viral Aerosols
     The viruses are units of either RNA or DNA surrounded by a protein coat.  They have
no intrinsic mechanism for reproduction, and require living cells whose enzyme systems they
utilize to make  new particles. They can be crystallized and remain able to reproduce,  and
are often considered intermediates between non-life  and life.  Because viruses require living
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cells to reproduce, reservoirs for them are almost exclusively living organisms.  Rarely,
viruses survive (but do not reproduce)  in environmental reservoirs from which they are
re-aerosolized to cause disease.  The Hanta virus that causes severe respiratory disease in
people exposed to intense aerosols of infected mouse urine is an example of this
phenomenon.  Viral aerosols are produced when the infected organism coughs, sneezes, or
otherwise forces respiratory or other secretions into the air.  The  viral particles are coated
with secretions from the host, and, as for the bacteria, there may  be one to many in a single
droplet.  The size of a single viral particle is very small (a small  fraction of a ^m).
However, infectious droplets are probably within a much larger size range (1 to  10 pm).
Each kind of virus produces a specific disease,  although some of  the diseases present with
similar symptoms. Thus, the measles virus produces measles, the chicken pox viruses
produces chicken pox and shingles.  Influenza and common colds are produced by a range of
viruses all of which produce symptoms that are similar (but not necessarily identical).

7.2.5.6   Ambient and Indoor Air  Concentrations of Bioaerosols
     A general rough estimate of the contribution of bioaerosols to collected PM mass can
be made as follows:  for an "average" 3  /jm spherical  spore of 0.9 density, each spore would
weigh «13 x 10"6 jug; for a clean indoor environment with  «103 spores/m3 the mass would
be on the order of 0.01 /*g/m3; for a typical outdoor condition, with « 50 x 103 spores/m3,
the contribution would be on the order of 0.5 pig/m3.  In contaminated indoor environments,
where spore levels above 106 spores/m3 are possible, the spore weight could be  on the order
of 10 pig/m3 or more.
     In summary, the minor mass concentrations of bioaerosols in ambient and  indoor air
are independent of the concentrations of  the non-bioaerosol constituents in ambient and
indoor air.  However, the deposition of bioaerosols at the same respiratory tract loci as the
other PM can cause irritation and  infection foci that may make the affected host more
susceptible to the effects of other deposited PM.
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7.3   DIRECT METHODS OF MEASUREMENT OF HUMAN PM
      EXPOSURE BY PERSONAL MONITORING
7.3.1  Personal Monitoring Artifacts
     Human exposure to air pollution can be measured by placing a personal exposure
monitor (PEM) close to the breathing zone of an individual. However, the very act of
studying the subjects can alter their behavior,  which influences the measured values of their
exposures and  creates an erroneous reading.  This influence, known as the "Hawthorne
Effect" (Mayo, 1960; Last, 1988), arises because the subjects are aware of the study
objectives, and the presence of the PEM on their body is a constant reminder.
     The physical location of the monitor inlet, as worn by the subject, can also influence
the subject's PM exposure and the recorded PM (Cohen et al., 1982, 1984).  The movements
of the subject's body and the PEM sampling flow rate can alter the air currents in the
subject's breathing zone.  "The presence of the body and its movement affect what a
personal sampler collects" (Ogden et al., 1993).  When in close proximity to a source
actively emitting PM (within a meter) a small change in PEM position (e.g. from left side to
right side) can vary  the PM measurement.  The vertical position of the personal monitor
sampling inlet  (e.g., at the waist or at the lapel near the breathing zone), can influence the
captured amount of PM that is generated from the floor and stuffed furniture (Aso et al.,
1993).
     In performance of a personal monitoring study, people often refuse to participate.  The
refusal rate increases with the burden on the respondents due to the time required to
complete questionnaires, diaries and the need  to carry the personal  monitor with them
throughout the study.  If the cohort of people  who refuse to participate have significantly
different personal PM exposures than the participants, then the study will produce a biased
estimate of the exposures of the total population.
     Two other important errors that influence the personal exposure measurements are:
(1) "the monitor  effect", by which the monitor reduces PM concentration in the breathing
zone by "self dilution" (Cohen et al., 1984), the alteration of stream lines in the area of the
nose and mouth,  or by electrostatic charge on a plastic cassette filter holder collecting
charged particles (Cohen et al., 1982); and (2) "the subject effect", by which the subject
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contaminates the data set by a purposeful action, such as blowing smoke into the inlet, or
forgetting to wear the monitor and not admitting that error in the log of daily activity.
     These unquantifiable  "errors" in a PM PEM measurement study may be greater than
the filter weighing errors and flow rate measurement errors that can be quality controlled
through calibration procedures.  This may be important for interpretation of published
PM PEM data because these errors likely inflate the variance of the measurements.

7.3.2    Characterization of Particulate Matter Collected by Personal
         Monitors
     The amount of PM collected by different types of personal monitors with the identical
nominal cut-point can be variable. The difference between two PM measurements, made by
two nominally identical monitors of different design,  can be a function of the wind speed and
the size distribution of the  PM in the air mass being sampled.  A recent field comparison by
Groves et al.  (1994) of different types of respirable dust samplers used in occupational
settings where coarse mode PM predominates shows that there is considerable difference
between the mass collected by sets of paired cyclones and paired impactors sampling in a
concentration range of 500 to 6600 A*g/m3.  The cyclones collected from 53  to 165% of the
mass collected by the impactors. This type of comparison study has not been done for
personal monitors used in nonoccupational  studies at ambient and indoor respirable PM
concentrations on the order of 10 to  100 /ig/m3, where the fine mode can be more important.

7.3.3    Microscale Variation and the Personal Cloud Effect
     The study of Thatcher and Layton (1995) described in Section 7.2.2.2 reports the
increase of indoor PM of various size  ranges from household activities, such as walking into
and out of a room.  The tendency for such human activity in the home or at work to generate
a "personal activity cloud" of particles from clothing and other items (stuffed furniture,
carpet, etc.),  that will be intense in the breathing zone and diluted near an area monitor
located several meters away, has also been cited as a contributing factor to the discrepancy
between personal measures of exposure and time-weighted-average (TWA) exposures using
microenvironmental measurements (Martinelli et al.,  1983;  Cohen et al., 1984; Rodes et al.,
1991).  Fletcher and Johnson (1988) also measured metal concentrations (measurement
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method and size unspecified) in an occupational exposure situation (metal spraying of
spindles on a lathe) and found 50% higher concentrations measured from the left lapel
compared to the right lapel, which reflected the orientation of the operator to the lathe.
7.4  NEW LITERATURE ON PARTICLE EXPOSURES SINCE 1981
     The following sections review studies that measured PEM PM in the general non-
smoking population.  In these studies, the subjects spent tune at home and in other indoor
environments that include time at work.   In the USA, recent data indicate that on a daily
basis, an average US resident spends approximately 21 h indoors (85.6%), 100 minutes in
(or near) a vehicle (7.2%), and 100 minutes outdoors (7.2%) (U.S. Environmental Protection
Agency, 1989).
     Almost all the studies of PM exposure in the general public have been conducted on
urban and suburban residents. These  subjects are often working in occupations that do not
require PM monitoring to assure that occupational standards are being met (e.g. in an
office).  However, PM monitoring in  an industrial workplace by a subject - independently of
an official corporate industrial hygiene program - can have legal or security implications for
an employer. A further complication  arises from the fact that industrial exposures tend to be
dominated by a specific type of particle.  Coal miners are exposed to coal dust, textile
workers are  exposed to cotton dust, etc.

7.4.1   Personal Exposures in U.S. Studies
     Dockery and Spengler (1981b) compared personal PM3 5 exposures and ambient PM3 5
concentrations in Watertown, MA, and in Steubenville,  OH.  In Watertown, 24-h personal
samples were collected on a 1-in 6-day schedule,  and in Steubenville, 12-h personal samples
(8 a.m.  to 8  p.m.) were collected on a Monday-Wednesday-Friday schedule.  A correlation
coefficient of 0.692 between the mean personal and the mean ambient concentration for
37 subjects,  18 in Watertown and 19 in Steubenville, was reported for the pooled data.
However, this appears to be an artifact of two separate clusters formed by these data, each
with considerably lower correlation. When these data are analyzed separately, the regression
                                        7-83

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coefficient between personal and ambient for Watertown is R2 = 0.00 and for Steubenville it
isR2 = 0.18.
     Sexton et al. (1984) studied personal exposures to respirable particles (PM3 5) for
48 nonsmokers during a winter period in Waterbury, VT,  where firewood was either the
primary or secondary heating source for the subject. Their results showed that personal
exposures were 45 % higher than indoor averages (36 /*g/m3 versus 25 /*g/m3) and indoor
averages were 45% higher than outdoor averages (25 /xg/m3 versus 17 /*g/m3).  Ambient air
pollution, measured by an identical  stationary ambient monitor (SAM) outside each residence
(a pump contained in a heated box was connected to an external cyclone and filter), had no
correlation with the residents'  personal exposures (R2 = 0.00) and 95%  of the subjects had
personal exposures greater than the  median outdoor concentration.
     Spengler et al. (1985) reported a study of PM3 5 exposures in the non-industrial cities of
Kingston and Harriman, TN, during the winter months  of February through March,  1981.
In this study, two Harvard/EPRI PM3 5 monitors were used for each person.  One stationary
indoor monitor (SIM)  remained indoors in the home, and the second monitor (PEM) was
carried for 24-h to obtain the personal exposure.  In each community, identical
Harvard/EPRI samplers (SAM) were placed  at a central site to represent ambient PM3 5
concentrations.  The results of the study are  shown in Table 7-19. In both communities,
95% of the subjects had personal exposures to PM3 5 greater than the average ambient
concentrations.  The mean personal  exposure and indoor concentrations (44 + 3 /ug/m3 and
42 + 3 /ig/m3) were more than 100% greater than the mean ambient average of
18 + 2 fjigfm3 sampled on the same days.
     For the complete cohort, the correlation between PM PEM and PM SAM  was r =  0.07
(p = 0.30),  and between PM PEM  and PM  SIM was r = 0.70 (p = 0.0001).  The
correlation between simultaneous PM PEM and PM SAM was r = 0.15 for 162 nonsmoke
exposed individual observations (p =  0.06).  For 63 observations on smoke exposed
individuals,  the correlation r = 0.16 was not significant (p = 0.16) between PM PEM and
PM SAM.  An important finding was that in nonsmoking households, the PM PEM is always
higher than SIM and SAM. "This implies that individuals encounter elevated concentrations
away from home and/or that home concentrations are elevated while they are at home and
reduced while they are away". This observation is supported by the  findings of Thatcher and
                                         7-84

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        TABLE 7-19.  QUANTILE DESCRIPTION OF PERSONAL, INDOOR,
                AND OUTDOOR PM3S CONCENTRATIONS Gig/m3),
               BY LOCATION IN TWO TENNESSEE COMMUNITIES
City
Kingston


Harriman


Total3


Group
Personal
Indoor
Outdoor
Personal
Indoor
Outdoor
Personal
Indoor
Outdoor
N
133
138
40
93
106
21
249
266
71
"Includes samples from 13 subjects living
personnel residing in these communities.
N = number of samples.
S.E. = Standard error.
95%
99
110
28
122
129
34
113
119
33
outside
75%
47
47
22
54
45
23
48
46
23
Kingston and
50%
34
31
16
35
27
15
34
29
17
Harriman
25%
26
20
12
24
18
13
26
20
13
town limits
5%
19
10
6
15
10
9
17
10
7
and from
Mean
42
42
17
47
42
18
44
42
18
S.
2.
3.
2.
4.
4.
4.
2.
2.
2.
E.
5
5
7
8
1
0
8
6
1
four field
Source:  Spengler et al. (1985).
Layton (1995), reported in Section 7.2.2.2:  merely walking into a room can raise the
concentrations of PM by 100%.  This study is relevant to the analyses by Dockery et al.
(1992) of PM mortality in St. Louis, MO, and in Eastern Tennessee counties surrounding
Kingston and Harriman as discussed in Chapter 12.  Although the Spengler et al. (1985) and
Dockery et al. (1992) studies are not directly comparable, because different years of data
were used (1981 versus 1985/1986), the authors' assumption in Dockery et al. (1992) that
the Harriman, TN, data represent exposures to PM in all of eastern Tennessee is called into
question.
     Morandi et al. (1988) investigated the relationship between personal exposures to PM
and indoor and outdoor PM concentrations, using a TSI Model 3500 piezobalance  that
measures respirable particles in the range <3.5 /mi.  For the group of 30 asthmatics in
Houston, TX, that were studied, outdoor concentrations averaged 22 /xg/m3,  indoor
concentrations averaged 22% higher than outdoor (27 /ig/m3)  and, in motor vehicles, the
average concentration of particles was 60% higher than the  average outdoors (35/xg/m3).
                                       7-85

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Personal 12-h (7 a.m. to 7 p.m.) daytime exposures to PM were not predicted as well by
fixed site dichotomous sampler ambient monitors (R2 = 0.34) as by the indoor exposures
(R2 = 0.57). However, for 1-h exposures, they found no correlation (R2 = 0.00) between
the personal exposures to PM5 and the indoor exposures measured with a TSI model 5000
stationary continuous piezobalance located in the "den" area of the home. The  authors noted
that use of home air conditioning and recirculation tended to increase the PM exposures.
     Lioy et al.  (1990) reported a study done during the winter (January 1988) in the
industrial community of Phillipsburg, NJ, where personal PM10 was monitored along with
indoor and outdoor PM10.  They collected PM10 (fine plus coarse particles on a single filter).
In this study of eight residences of 14 nonsmoking individuals not smoke exposed at home,
geometric mean 24-h concentrations were 68, 48 and 42 /ig/m3 for  personal, outdoor and
indoor sites, respectively.  The arithmetic mean personal PM exposure of 86 /xg/m3 was 45%
higher than the mean ambient concentration of 60 /*g/m3.  The higher ambient than indoor
concentrations in this study, a reversal of the relationships found in the  Sexton et al. (1984),
Spengler et al. (1985) and Morandi et al. (1988) studies, may be caused by the local
industrial source of coarse particles in that community and the absence of cigarette smokers
in the residences sampled.  This difference also may be partially explained by the 10 /xm
particle  sizes sampled in the NJ study and the 3.5 /mi particle sizes in the other studies. The
regression coefficient between personal  and ambient PM10 for all 14 people on  the 14 days of
the study (n =  191 valid personal values) was 0.19 (R2 = 0.037, p = 0.008).   With three
personal exposure  extreme values removed (n = 188 personal values) and without correction
for missing data, the coefficient was 0.50 (R2 = 0.25, p =  0.007).
     Lioy et al.  (1990) report individual regression equations of PEM and SAM for the six
of 14 subjects with significant relationships (p  < 0.01).  These data are shown in
Table 7-20.  For individuals with constant daily activities in the same microenvironments, the
increment of PM exposure due to nonambient sources is repeatable with lower variability
than that of the ambient PM. Therefore their variation of personal exposure from day-to-day
is highly driven by the variation of the ambient PM. For subjects with intermittent
exposures to  nonambient PM, through non-repetitive activity patterns or intermittent source
operation, the regression of PEM on SAM can become non-significant.  This improvement in
                                         7-86

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        TABLE 7-20.  REGRESSION EQUATION OF THOSE INDIVIDUALS
          HAVING STATISTICALLY SIGNIFICANT RELATIONSHIPS OF
       EXPOSURE (PEM) WITH OUTDOOR AIR CONCENTRATIONS (SAM)
Participant
01
31
52
62
81
91
y
y
y
y
y
y
= 0.62
= 0.55
= 0.63
= 1.29
= 1.07
= 0.59
Equation
(0.12)
(0.07)
(0.11)
(0.27)
(0.24)
(0.12)
X +
X +
X +
X +
X +
X +
26.5 (17.
7.3 (9.9)
15.3 (14.
33.0 (37.
39.0 (32.
42.0 (19.
3)

7)
1)
6)
9)
R2
0
0
0
0
0
0
.66
.83
.74
.67
.63
.63
N
14
14
14
13
14
13
P
< 0
< 0
< 0
< 0
< 0
< 0

.01
.01
.01
.01
.01
.01
y = Personal air PM-10.
X = Outdoor air PM-10.
() = Confidence interval.
Source: Lioy et al. (1990).
correlation was also shown for their indoor versus outdoor relationships, between cross-
sectional and individual comparisons, as described in Section 7.4.2.3.
     In all these studies, the personal PM was measured to be higher than either the indoor
or the outdoor PM measurements.  This relationship of PEM >  SIM and PEM  > SAM has
also been found in the PTEAM study (Clayton et al., 1993) described in detail in
Section 7.2.2.1.3 and later in Section 7.4.1.1.  For the PTEAM study during the day
(7 a.m. to 7 p.m.) average personal PM10 exposure data (150 Mg/m3) were 57% higher than
the average indoor and outdoor concentrations, which were virtually  equal (95 /xg/m3).
Consequently, a tune-weighted-average (TWA) of the daytime indoor and outdoor PM
concentrations appears to always underestimate the personal exposures to PM because the
daytime PEM data are higher than either the SIM or SAM data.  At night (7 p.m. to 7  a.m.)
average PM10 personal exposures (77 jig/m3) were higher than the average indoor
concentrations (63 /-ig/m3) but lower than the average outdoor concentration (86 /*g/m3).
     It has been proposed (World Health Organization, 1982a; Spengler et al.,  1985; Mage,
1985) that such a discrepancy between the TWA and the personal monitoring measurements
may be caused by two factors described as follows: (1) human exposure to PM at work and
in traffic are only partially accounted for in a TWA of indoor and outdoor ambient PM
                                       7-87

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values; and (2) indoor and outdoor averages reflect periods of low concentration during
which the subject is not present.  The PM pollution generating activities in a home usually
occur only when a person is at home, as discussed in Section 7.1.2 concerning Equation 7-2.
Therefore, the PM in a home will be higher when a person is present than when the home is
unoccupied.  A 24-h average of the indoor concentration thereby underestimates the average
exposure of a person while  in that home.
     Ambient PM  is also higher during the day (when industry and traffic are active, and
wind speeds are high)  than at night when PM generating activities are at a minimum and  the
air is still (Miller and Thompson, 1970).  Consequently, a 24-h  average ambient PM value
generally underpredicts the concentrations during the daylight hours and the exposures of
people going outdoors during that period.

7.4.1.1    The Particle Total Exposure Assessment Methodology Study
     In 1986, the U. S. Congress mandated that EPA's Office of Research and Development
"carry  out a TEAM Study of human exposure to particles." The main goal of the study was
to estimate the frequency distribution of exposures to particles for nonsmoking Riverside,
CA, residents. Another goal was to determine particle concentrations in the participants'
homes and immediately outside the homes. The detailed analyses of the indoor PM and
outdoor PM data were described  in Section 7.2.2.1.3.

7.4.1.1.1  Pilot Study
     Study Design
     A prepilot study, described  in Section 7.2.2.1.3, was undertaken in nine homes in
Azusa, CA in March of 1989 to test the sampling equipment (Ozkaynak et al., 1990).
Newly-designed personal exposure monitors (PEMs) were equipped with thoracic (PM10) and
fine (PM2 5) particle inlets.  The  PEMs were impactors with 4-Lpm Casella pumps (Wiener,
1988). Two persons in each household wore the PEMs for two consecutive 12-h periods
(night and day).  Each day they alternated inlet nozzles.  A central site with a PEM, a
microenvironmental monitor (MEM), and two EPA reference  methods (dichotomous and
high-volume samplers) with a 10 /xm size-selective inlet was also operated throughout the
11 days (22  12-h periods) of the study.
                                         7-i

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Results
     The personal exposure levels were about twice as great as the indoor or outdoor
concentrations for both PM10 (Table 7-21a) and PM2 5 (Table 7-21b).  Considerable effort
was expended to demonstrate that this was not a sampling artifact, due for example to the
constant motion of the sampler; however, no evidence could be found for an artifactual
effect. Nonetheless, to reduce chances for an artifactual finding in the main study, it was
decided to use identical PEMs for both the personal and fixed (indoor and outdoor) samples
in the main study.  Cross-sectional personal  exposures were essentially uncorrelated (slightly
negatively) with outdoor concentrations (R2  = 0 to 2%) (Ozkaynak et al., 1993a).  However,
a serial correlation analysis of these  pilot PTEAM data were performed for the six or eight
12-h averages that comprised the three or four 24-h averages reported for the residents of the
first five homes in Table 7-21a,b. The residents of four homes only carried the PEM for
two days,  so  the four 12-h individual measurements were too few for development of a
meaningful serial relationship.  The  results for the ten people in homes  1  to 5 are  shown in
Table 7-22.  The medians of R2 equal 0.12 for PEM PM2 5 vs SAM PM2 5 and 0.07 for
PEM  PM10 vs SAM PM10, neither of which is significant.  More importantly,  the serial
slopes were positive for 15 of the 20 cases which is the expected behavior, as opposed to the
counter-intuitive negative correlation found for the pooled PEM vs SAM data for all
residents of the nine homes.
     In Azusa, the excess  PM2 5 and PM10 generated by personal activities increased the
personal exposures by approximately 100%  above the average  of the  indoor and outdoor
values. These results are in marked contrast to the data of Tamura and Ando (1994) and
Tamura et al.  (1996) in which  seven Japanese elderly housewives and male retirees had PM10
PEM  exposures less than the time weighted  average of SIM and SAM PM10 concentrations.

7.4.1.1.2   Main Study
     Study Design
     Ultimately 178 residents of Riverside,  CA took part in the study in the fall of 1990.
Respondents represented 139,000 ± 16,000 (S.E.) nonsmoking Riverside residents aged
10 and above. Their homes represented  about 60,000 Riverside homes.  Each participant
wore  the PEM for two consecutive  12-h  periods.  Concurrent PM10 and PM2 5 samples  were
                                         7-89

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 TABLE 7-21a. PARTICLE TOTAL EXPOSURE ASSESSMENT METHODOLOGY
        PREPILOT STUDY: 24-HOUR PM10 CONCENTRATIONS (/tg/m3)
House
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
5
5
5
6
6
7
7
8
8
9
9
Mean
SD
SE
Day
1
3
5
7
1
3
5
7
1
3
5
7
2
4
6
2
4
6
8
10
9
11
9
11
8
10



Person 1 Person 2
102
142
158
92
109
99
131
62
98
100
143
76
109
90
99
80
70
80
130
150
209
80
135
97
136
273
117.2
44.9
8.8
86
125
150
127
158
140
87
56
107
141
132
103
92
77
122
104
77
78
152
102
126
71
178
151
102
91
112.9
30.8
6.0
Indoors
54
38
49
34
122
37
41
32
86
39
71
36
77
34
36
76
62
54
114
106
46
29
73
38
63
121
60.3
28.5
5.6
Outdoors
132
49
70
49
112
48
70
46
115
45
79
44
102
47
37
99
65
50
39
51
72
39
59
28
43
48
63.0
27.1
5.3
Source: Data from PTEAM Prepilot Study used to calculate R2 values as shown in Table 7-22 and published by
      Wallace (1996).
                                   7-90

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 TABLE 7-21b. PARTICLE TOTAL EXPOSURE ASSESSMENT METHODOLOGY
          PREPILOT STUDY:  24-H PM2 5 CONCENTRATIONS (jtg/m3)
House
1
1
1
2
2
2
3
3
3
4
4
4
4
5
5
5
5
6
6
7
7
8
8
9
9
Mean
SD
SE
Day
2
4
6
2
4
6
2
4
6
1
3
5
7
1
3
5
7
9
11
8
10
8
10
9
11



Person 1
44
55
55
58
46
51
53
62
109
75
46
118
40
65
59
40
34
71
77
64
111
53
110
178
105
71.2
32.7
6.5
Person 2
96
88
382
53
100
50
66
94
88
61
43
94
40
69
70
56
53
81
75
135
67
100
1453*
48
58
140.8*
275.5
55.1
Indoors
22
25
21
31
27
28
48
30
39
33
19
31
17
62
35
42
25
56
53
17
32
27
35
70
42
34.7
13.7
2.7
Outdoors
67
39
33
52
43
40
58
35
39
71
29
46
26
96
38
55
28
33
18
27
35
27
35
40
28
41.6
16.8
3.4
* Horseback riding at an indoor ring. If this point is deleted, mean = 86.1.

Source: Data from PTEAM Prepilot Study used to calculate R2 values as shown in Table 7-22 and published by
      Wallace (1996).
                                     7-91

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   TABLE 7-22. REGRESSIONS OF PERSONAL EXPOSURE ON INDOOR AND
     OUTDOOR PM10 AND PM2 5 CONCENTRATIONS: PARTICULE TOTAL
         EXPOSURE ASSESSMENT METHODOLOGY PREPILOT STUDY
House
PM10:
1

2

3

4

5

PM2.5:
1

2

3

4

5

Person
Personal vs.
1
2
1
2
1
2
1
2
1
2
Personal vs.
1
2
1
2
1
2
1
2
1
2
N
Intercept
SE
P
Slope
SE
P
R2
Outdoor
8
8
8
8
8
8
6
6
6
6
124
134
47
26
83
116
87
106
47
22
42
60
44
52
47
54
20
28
31
26
0.03
NS
NS
NS
NS
NS
0.01
0.02
NS
NS
-0.0004
-0.16
0.77
1.22
0.3
0.07
0.2
-0.15
0.42
0.9
0.51
0.73
0.58
0.68
0.61
0.7
0.29
0.4
0.41
0.35
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
0
0.01
0.23
0.35
0.04
0.002
0.1
0.03
0.2
0.63
Outdoor
6
6
6
6
6
6
8
8
8
8
41
274
8.8
47
87
40
40
45
27
46
20
266
20
34
58
54
24
22
15
16
NS
NS
NS
NS
NS
NS
NS
NS
NS
0.03
0.22
-1.8
0.96
0.47
-0.29
0.97
0.7
0.34
0.42
0.3
0.4
5.3
0.41
0.7
1.25
1.2
0.48
0.45
0.24
0.27
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
0.07
0.03
0.58
0.1
0.01
0.15
0.26
0.09
0.34
0.17
NS = not significant (p > 0.05).
N = Number of 12-h observations.
Source: Wallace (1996).
collected by the stationary indoor monitor (SIM) and stationary ambient monitor (SAM) at
each home. A total of ten particle samples were collected for each household (day and night
samples from the PEM10, SIM10, SIM2 5, SAM10, and SAM2 5).  Air exchange rates were
also determined for each 12-h period.  Participants were asked to note activities that might
involve exposures to increased particle levels.  Following each of the two 12-h monitoring
                                    7-92

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periods, they answered an interviewer-administered questionnaire concerning their activities
and locations during that time. A central outdoor site was maintained over the entire period
(September 22, 1990 through November 9, 1990).  The site had two high-volume samplers
(Wedding & Assoc.) with 10-jum inlets (actual cutpoint about 9.0 jum), two dichotomous
PM10 and PM2 5 samplers (Sierra-Andersen) (actual cutpoint about 9.5 pm), one PEM, one
PM10 SAM, and one PM2 5 SAM.

Results
     Of 632 permanent residences contacted, 443 (70%) completed the screening interview.
Of these, 257 were asked to participate and 178 (69%) agreed.

Quality of the Data
     More than 2,750 particle samples were collected, about 96% of those attempted.  All
filters were analyzed by X-ray fluorescence (XRF) for a suite of 40 metals.  More than
1,000 12-h average air  exchange rate measurements were made.  A complete discussion of
the quality of the data is found in Pellizzari et al. (1993) and in Thomas et al. (1993).

Concentrations
     Concentrations of particles and target elements have been reported (Clayton et al.,
1993; Ozkaynak et al.,  1993a; Pellizzari et al., 1993;  Wallace et al.,  1993).  Population-
weighted daytime personal PM10 concentrations averaged about 150 ^g/m3, compared to
concurrent indoor and outdoor mean concentrations of about 95 /ig/m3 (Table 7-23). The
overnight personal  PM10 mean was much lower (77 /xg/m3) and more similar to the indoor
(63 /itg/m3) and outdoor (86 jug/m3) means. About 25% of the population was estimated to
have exceeded the 24-h National Ambient Air Quality Standard for PM10 of 150 /xg/m3.
Over 90% of the population exceeded the 24-h California Ambient Air Quality  Standard of
50 /ig/m3.

Correlations
     The central site appeared to be a moderately good estimator of outdoor particle
concentrations throughout the city.  Spearman correlations of the central-site concentrations
                                         7-93

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       TABLE 7-23.  POPULATION-WEIGHTED8 CONCENTRATIONS AND
                   STANDARD ERRORS (/ig/m3) PTEAM STUDY
Sample type
Daytime PM10
Personal
Indoor
Outdoor
Overnight PM10
Personal
Indoor
Outdoor
Daytime PM2 5
Indoor
Outdoor
Overnight PM2 5
Indoor
Outdoor
N

171
169
165

168
163
162

173
167

166
161
Geom.
Mean

129
78
83

68
53
74

35
38

27
37
GSD

1.75
1.88
1.68

1.64
1.78
1.74

2.25
2.07

2.21
2.23
Arith.
Mean ± SE

150 ± 9
95 ± 6
94 + 6

77 ± 4
63 ± 3
87 ± 4

48 ± 4
49 ± 3

36 ± 2
51 ±4
Percentile
90% ± SE

260 ± 12
180 ± 11
160 ± 7

140 ± 10
120 ± 5
170 ± 5

100 ± 7
100 ± 6

83 ± 6
120 ± 5
98%

380
240
240

190
160
210

170
170

120
160
aPersonal samples weighted to represent nonsmoking population of 139,000 Riverside residents aged 10 or
 above.  Indoor-outdoor samples weighted to represent 61,500 homes with at least one nonsmoker aged 10 or
 above.
Source:  Pellizzari et al. (1993).
measured by all three methods (PEM-SAM, dichot, Wedding) with outdoor near-home
concentrations as measured by the SAMs ranged from 0.8 to 0.85 (p< 0.00001). Linear
regressions indicated that the central-site 12-h readings could explain 57% of the variance
observed in the near-home  12-h outdoor concentrations (Figure 7-20).
     Outdoor 12-h concentrations of PM10 could explain about 25 to 30% of the variance
observed in indoor concentrations of PM10, but only about 16% of the variance in 12-h
personal exposures to PM10 (Figure 7-21).  This is understandable in view of the importance
of indoor activities such as smoking, cooking, dusting, and vacuuming on exposures to
                                        7-94

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         600
         500
      o  400
      o
      E  300
      CO

     "t  20°

     <  100

           0
                                           Backyard = 1.03*Central + 17.6
                                           R2 - 0.57  N - 323
                        50          100          150         200
                        Central site reference monitor mean (ug/m3)
                                                    250
Figure 7-20.  Residential outdoor monitors versus central-site mean of two dichotomous
             samplers in Riverside, CA.  R2 = 57%.

Source of Data:  Pellizzari et al. (1993).
         500
         400
      w
      8>  300
      V)
      §.
      CO
      o
         200
         100
           0
                                          Pers = 0.54*Out + 62
                                          R2- 16%  N - 312
                              u°
100       200       300       400
       Backyard concentrations
                                                               500
600
Figure 7-21.  Personal exposures versus residential (back yard) outdoor PM10
             concentrations in Riverside, CA.  R2 = 16%.

Source of Data:  Pellizzari et al. (1993).
                                       7-95

-------
particles.  The higher daytime exposures were even less well represented by the outdoor
concentrations.
     Indoor concentrations accounted for about half of the variance in personal exposures.
However, neither the indoor concentrations alone, nor the outdoor concentrations alone, nor
time-weighted averages of indoor and outdoor concentrations could do more than explain
about two-thirds of the observed variance in personal exposures.  The remaining portion of
personal exposure  is assumed to arise from personal activities or unmeasured
microenvironments that are not well represented by fixed indoor or outdoor monitors.

Discussion
     The more than 50% increase in daytime personal  exposures compared to concurrent
indoor or outdoor  concentrations suggested that personal activities were  important
determinants of exposure.  However, the nature of this "personal cloud" of particles has not
yet been determined.  An approach to the composition  of the personal cloud is elemental
analysis, using X-ray fluorescence.  Analysis of all  personal and indoor  filters showed that
14 of 15 elements  were elevated by values of 50 to 100% in the personal filters compared to
the indoor filters (Figure 7-22).  This observation suggests that a component of the personal
cloud is an aerosol of the same general composition as  the indoor aerosol.  This could be
particles created by activities (e.g., cooking) or re-entrained household dust from motion
(walking across carpets or sitting on upholstered furniture; Thatcher and Layton, 1995).
House dust is a mixture of airborne outdoor PM (primarily coarse mode), tracked-in soil and
road dust, and PM produced by indoor sources. As such, it should contain crustal elements
from soil, lead and bromine from automobiles, and other elements from combustion sources.
This would be consistent with the observation that nearly all elements were elevated in
personal samples.  The lack of elevated values for sulfur may be due to  the fact that
submicron particles are not resuspended by human activity (Thatcher and Layton, 1995).
The personal overnight samples that showed smaller mass increases than the personal
daytime samples are also consistent with the fact that the participants were sleeping for much
of the  12-h overnight monitoring period and were thus  not engaging in these particle-
generating or reentraining activities.
                                          7-96

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                                40       60       80       100
                            Percent increase in personal cloud
120
Figure 7-22.  Increased concentrations of elements in the personal versus the indoor
             samples.
Source:  Ozkaynak et al. (1996).
     A source apportionment of the personal PM10 mass during the daytime period is shown
on Figure 7-23 (Ozkaynak et al., 1996). This chart is derived by subtracting the average
SIM and SAM (95 /xg/m3) from the mean PEM (150 /*g/m3) given on Table 7-23.  The
55 jttg/m3 difference is shown as the 37% fraction of the total of 150 /tg/m3 labelled Personal
37%. The source of this  "personal cloud" is indeterminable from the SIM, SAM and PEM
data.  As discussed previously,  it is likely to consist primarily of resuspended dust that would
have a composition of a mixture of all the other sources.  The 15% other-indoor PM
represents the indoor mass that  could not be assigned to ETS, cooking or ambient PM.  It is
likely that the 52% of other-indoor plus personal-cloud categories contains an appreciable
amount of ambient PM that came indoors over a long period of time and is resuspended by
activity.  If so, then the PEM would be about 50% of ambient origin.

7.4.2   Personal  Exposures in International Studies
     As part of World Health Organization/United Nations Environment Programme
(WHO/UNEP) Global Environment Monitoring System (GEMS) activities, four pilot studies
                                        7-97

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                     Outdoor
                       42%
                                                           Personal
                                                             37%
                              Smoking
                                3%
                     N- 166 Samples
         Cooking
           3%
Other Indoor
   15%
Figure 7-23.  Source apportionment of PTEAM PM10 Personal Monitoring (PEM) Data.
             "Other Indoor" represents PM found by the indoor monitor (SIM), for
             which the source is unknown.  "Personal" represents the excess PM
             captured by the PEM that cannot be attributed to either indoor (SIM) or
             outdoor (SAM).

Source:  Clayton et al. (1993).
of personal exposure to PM were conducted in: Zagreb (World Health Organization, 1982a);

Toronto (World Health Organization, 1982b); Bombay (World Health Organization,  1984);

and Beijing (World Health Organization,  1985).  In these studies, people who worked in the

participating scientific  institutes were recruited to carry a PM sampler, and their exposures

were matched to the ambient concentrations measured outside their home or at a central

station in their communities. The results of these studies, expressed as mean personal

exposure (PEM) and mean ambient (SAM) concentration, and the cross-sectional regression

R2 between them are presented in Table 7-24.

     The net result of these four international studies is that they appear to confirm the lack

of a consistent cross-sectional relationship between individual personal PM exposures and

ambient concentrations as found in the U.S. studies described in Section 7.4.1.
                                        7-98

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        TABLE 7-24.  SUMMARY OF WHO/UNEP GLOBAL ENVIRONMENT
     MONITORING SYSTEM/PERSONAL EXPOSURE PILOT STUDY RESULTS
Location
Season
Toronto
Winter
Summer
Zagreb
Summer
Winter
Bombay
Winter
Summer
monsoon
Beijing
Winter
Summer
PM Size
Cut (pan) N m
25* 13
72
78
5 12
12
12
3.5 15
105
102
101
3.5 20
71
40
PEM
Time Mean ± SE
8-h
1-wk
24-h
24-h
1-wk
122 ±9
124 ±4
114±?
187 ±?
127 ±6
67±3
58±3
177±?
66±?
SAM
Mean ± SE
68 ±9
78 ±4
55±?
193 ±?
117±5
65 ±3
51±2
421 ±?
192±?
R2PEM
vs. SAM
0.15
0.10
0.00
0.50
0.26
0.20
0.02
0.07
0.03
P
NS
NS
NS
NR
NR
NR
NS
NS
NS
N = number of subjects carrying personal exposure monitor (PEM).
m = total number of observations.
PEM = mean ± SD of PM concentrations (in /ig/m3) from personal exposure monitors.
SAM = mean ± SD of PM concentrations (in ng/m3) from stationary ambient monitors.
NR  = Not Reported, but listed as significant.
NS  =  Not significantly different from 0.
? = Not Reported.
*25  jj.m AD computed from flow rate and open filter design.
Source: World Health Organization (1982a,b, 1984, 1985).
7.4.2.1   Personal Exposures in Tokyo  (Itabashi Ward), Japan
     Tamura and Ando (1994), National Institute for Environmental Studies (1994) and
Tamura et al. (1996) report results of a PM personal monitoring study conducted during
1992 in Tokyo.  Seven elderly non-smoke exposed individuals who lived in traditional
Japanese homes with "tatami" reed mat or carpeting on tatami or wooden flooring, and
cooked with city gas, carried a PEM cascade impactor with cut-points of 2 /mi and 10 /xm
(Sibata Science Technology, Ltd.). The seven individuals lived near the Itabashi monitoring
station close to a main road.  Indoor PM (SIM) and outdoor PM (SAM) were measured
simultaneously for 11 48-h periods distributed in all four seasons  of the year.  The dataset
was screened to remove observations that included indoor combustion source exposures, such
as ETS from visitors, and burning of incense or mosquito coils. The reported findings were
as follows:
                                         7-99

-------
     1.  The cross sectional correlation coefficient of SIM vs SAM was "relatively high"
        (r2 = 0.72), but the individual coefficients for each house were higher as shown in
        Figure 7-24.

     2.  The cross sectional correlation coefficient of PEM vs SAM (measured under the
        eaves of the subject's house) was "relatively high" (r2 = 0.70), but the individual
        coefficients for most of the subjects were higher as shown in Table 7-25.

     3.  The cross sectional correlation coefficient of PEM vs PM measured at the Itabashi
        monitoring station was slightly lower than that for the outside air (r2  = 0.68), as
        shown in Figure 7-25, and the individual coefficients for most of the  subjects were
        higher as shown in Table 7-25.

     4.  The individual SAM values were all linearly related with the central monitor at the
        Itabashi station with the coefficient of regression (R2) in the range between 0.70 and
        0.94.

     5.  The individual PEM values varied from 30% to 50% of the SAM values. These
        {PEM <  SAM} data are quite different  from the US data sets, such  as PTEAM,
        where {PEM > SAM}, because they were designed to  measure the influence of the
        outdoors on personal exposures.  The difference may be due to the exclusion of
        ETS exposure and incense/mosquito coil burning and the Japanese  customs of using
        reed mat (tatami) flooring and taking shoes off when entering a home.  These
        factors would all tend to reduce the generation and resuspension of PM in the home.
        Tamura and Ando (1994) and Tamura et al. (1996) confirm the findings of Thatcher
        and Layton (1995) that PM < 5 jrni AD has negligible  resuspension  in homes.
        Their SIM PM2 and SIM (PM10 - PM2) were highly correlated with the SAM of
        identical size (r  = 0.879 and 0.839 respectively) but there was a negative
        correlation between the SIM and SAM (TSP - PM10) fraction (r  =  - 0.036).

     The importance of this study is that it demonstrates that there are very strong

correlations between PEM and SAM (0.747 <  r  < 0.964) when the masking influences of
indoor combustion sources are removed and resuspension of PM  is minimized. This

provides strong support to the use of an ambient monitoring station to represent the exposure

of people in the community to PM of ambient origin.
7.4.2.2   Personal Exposures in the Netherlands

     Janssen et al. (1995) preliminarily reported in an abstract results of personal PM

monitoring conducted during 1994 in Amsterdam and Wageningen, NL as part of a doctoral

study.  Participants were  13 non-smoking adults (age 50 to 70) in Amsterdam (urban) with
                                        7-100

-------
12U-
110-
100-
— 90-
™E 80-
|70-
§50-
140-
30-
20-
10-
Q

E

a •
a a
o c%
BB° "
Ift'tf1 B r- 0.922
™B Winter r- 0.920
Summer r - 0.961
\iiitiiii
             40
                     80
                            120
                                    160
             40
                     80
                            120
                                    160
             40
                     80
                            120
                                    160
                    Outdoor (iig/m3)
                                           200
izu —
110-
100-
"i?
•5™
J 50-
1 40-
30-
20-
10-

F

a a
QB
B
mm •*
g g
0 0™ ™
JlB1 r - 0.897
tf Winter r - 0.702
Summer r - 0.970
\i\\\\\\\
                                           200
1
-------
   TABLE 7-25. SUMMARY OF CORRELATIONS BETWEEN PM10 PERSONAL
 EXPOSURES OF 7 TOKYO RESIDENTS AND THE PM10 MEASURED OUTDOORS
   UNDER THE EAVES OF THEIR HOMES, AND THE PM MEASURED AT THE
                    ITABASHI MONITORING STATION
Subject ID
A
B
C
D
E
F
G
A-G
Number of Samples
48-h PM10
9
9
11
9
10
7
9
64
Correlation between
Personal and Outdoor at
home (r)
0.958
0.874
0.846
0.922
0.960
0.776
0.961
0.834
Correlation between
Personal and Itabashi
Station (r)
0.876
0.747
0.848
0.964
0.925
0.801
0.952
0.830
Source: Tamura et al. (1996).
   140-
   130-
   120-
 «•£ 110-
 5100-
 ¥ 90-
 o 80^
 I
    60-

  o
 2  40-
 O  30-
    20-
    10- ""
     0^
       0
                                         n
                                         B
y - 1.07 x - 0.4 (R = 0.901)

                            n
                                y = 0.46 x
 I     f    I    T    \    1    \    I     \    Y
     20       40        60       80      100
                                                          0.825)
120
                         Itabashi Monitoring Station (ng/m3 )
140
Figure 7-25. Correlations between PM10 at the Itabashi monitoring station and PM10 in
           outdoor and personal exposure (D= outdoor; + = personal).

Source: Tamura and Ando (1994); Tamura et al. (1996).
                                  7-102

-------
no occupational exposure to PM, and 15 children (age 10 to 12) in Wageningen (rural) who
are presumably non-smokers.  Four to eight measurements were obtained for each subject
which allowed for correlating PEM and SAM within individuals (longitudinally). Only the
median individual regressions were reported, as follows:  adults, PEM = 26 +  0.70 SAM,
R = 0.57, R2 =  0.32; and children, PEM  = 78 +  0.43 SAM, R = 0.67, R2 = 0.44.  For
the children,  parental smoking explained 35% of the variance between PEM and SAM.  For
the adults, "living near a busy road", time spent in traffic, and exposure to ETS explained
75% of the variance between PEM and SAM.  The  authors interpreted their preliminary
results to  "suggest a reasonably high correlation between personal  and ambient PMIQ within
individuals".  Janssen et al. (1995) also note that the low correlations observed  in most of the
other studies  reported in the literature were cross-sectional (calculated on a group level), and
were therefore mostly determined by the variation between subjects (e.g., ETS exposed and
non-ETS exposed subjects combined in the  same regression).

7.4.2.3  Reanalysis  of Phillipsburg, NJ Data
     With insight from the Jansen work, Wallace (1996) reanalyzed the complete Lioy et al.
(1990) data from  Phillipsburg, NJ, as shown partially in Table 7-20 (see also Table 7-37).
Wallace (1996) compared the cross-sectional regressions of PEM on SAM for all the
14 subjects on each of the 14 days sampled, to the longitudinal regressions of each of the
14 subjects on all 14 days sampled. He found that the median R2  (range) of the
14 individual (longitudinal) regressions was 0.46 (0.02 to 0.82); and that for the 14 daily
(cross-sectional) regressions was 0.06 (0.00 to 0.39). The difference appears to indicate
that, although one household may have a smoker and another not,  the relationship of the
indoor air in  each home to the outdoor air may be the same from day to day
(i.e., consistently  higher than ambient in the first case, but may be consistently  similar in the
second).  Because it provides a linkage between PEM and SAM, it bears reiteration to make
certain that it is clearly understood. This PEM vs SAM relationship can be visually
demonstrated with the following hypothetical example as shown on Figure 7-26a,b.
     • Let two people live next door to each other  at a location where the ambient
       PM for 5 consecutive days has a sequence {1, 2, 3, 4, 5}.
     • Let person A live without ETS exposure and have a corresponding PEM
       series {1, 2, 3, 4, 5}, (R2  = 1).
                                         7-103

-------
                           5 -
                                          SAM
Figure 7-26. Example of difference between serial correlation (a) and cross-sectional
             correlation (b) of PEM and SAM, showing how pooling of individuals
             together can mask an underlying relationship of PEM and SAM.
     •  Let neighbor B live with ETS exposure and have a corresponding PEM
        series {11, 12, 13, 14, 15}, (R2 = 1).

     •  When their PEM values are pooled so that they are analyzed together
        (cross-sectionally) {(1,11), (2,12), (3,13), (4,14), (5,15)} vs the SAM set
        {1, 2, 3, 4, 5},  thenR2 = 0.074.

     •  However, had the two PEM series been averaged each day, the sequence of
        averages {6, 7,  8, 9,  10}  would have a correlation of R2 = 1 with the same
        SAM sequence.  This averaging process is described  later in more detail in
        Section 7.6.2.
The explanation by Janssen et al. (1995) for the low cross-sectional correlations of PM PEM

with PM SAM found in the literature and the new analyses reported by Tamura et al. (1996),
Jansen et al. (1995), and Wallace  (1996) represent a major advance in our understanding of

contributions of ambient PM to personal exposures.

                                        7-104

-------
7.4.2.4  Overview of Comparison of Personal Exposure to Ambient PM Concentrations
     The PTEAM Study and the other key PEM studies discussed in this chapter so far are
summarized in Table 7-26.  This table shows that many of the early studies reported no
statistically significant correlation between PEM and SAM.  However, these early studies
were all characterized by a  non-probability sample and a relatively small sample size.  The
PTEAM study in Riverside  which was a probability sample (Clayton et al., 1993) and the
Lioy et al.  (1990) study in Phillipsburg,  which was not a probability sample, have large
sample sizes and achieved significance.  The other studies, such as World Health
Organization (1982a,b) or Morandi et al. (1988) are equivocal.  In the following sections,
PEM/SAM comparisons for some PM constituents and two means of visualizing the complex
relationships of PM measured by a SAM and a PEM are developed.

7.4.3    Personal Exposures to Constituents of Particulate Matter
     Suh et al.  (1993) measured personal exposures to sulfate (SO4=) and acidity (H+), and
ambient and indoor concentrations in State College, PA, summer 1991.  The correlations
between personal and  ambient values of  sulfate and acidity were R2  = 0.92 and
0.38 respectively, which is  in marked contrast to the R2 « 0 between earlier reported
ambient PM and personal PM studies (Table 7-26). This relationship is supported by
Figure 7-22, indicating that personal activities in the PTEAM study  do not generate or
resuspend sulfates less than 10 /mi.
     Figure 7-27 shows the consistent relation between ambient and personal sulfate
measurements (slope = 0.78 ± 0.02), and Figure  7-28 shows the improvement in prediction
by using the TWA with a correction factor (estimated personal sulfate = 0.885TWA,
R2 = 0.95  with slope = 0.96 ± 0.02).  Personal acidity was also computed by the same
equation with a correction for personal ammonia (NH3) exposure that gave an R2 =  0.63.
As opposed to PM which has both indoor and outdoor sources, the sulfate and acidity are
virtually all of outdoor origin.  Consequently, only the characteristics of the indoor
environment, such as air conditioning and ammonia sources, modify the personal exposures
indoors.
                                        7-105

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o
ON
                TABLE 7-26. COMPARISON OF PERSONAL EXPOSURE MONITOR (PEM) EXPOSURE OF
                   INDIVIDUALS TO THE SIMULTANEOUS AMBIENT PARTICULATE MATTER (SAM)
                           CONCENTRATION IN SEVERAL U.S. AND FOREIGN CITIES (jig/m3)
Reference
Binder et al.
Dockery and Spengler
Dockery and Spengler
Spengler et al.
World Health
Organization



Spengler et al.

World Health
Organization

Sexton et al.
World Health
Organization


World Health
Organization

Morandi et al.
Lioy et al.

Perritt et al.

Clayton et al.
Tamura et al.
Year2
1976
1981b
1981b
1980
1982a
Winter
Summer
Winter
Summer
1985

1982b
Summer
Winter
1984
1984
Winter
Summer
Monsoon
1985
Winter
Summer
1988
1990

1991

1993
1996
Location
Ansonia
Watertown
Steubenville
Topeka
Toronto
Non-asthmatic
Non-asthmatic
Asthmatic
Asthmatic
Kingston/
Harriman
Zagreb


Waterbury
Bombay



Beijing


Houston
Phillipsburg

Azusa

Riverside
Tokyo
PM/um
5
3.5
3.5
3.5
25




3.5

5


3.5
3.5



3.5


3.5
10

2.5
10
10
10
N
20
18
19
46

13
13
13
13
97

12


48
15



20


30
14"
14C
9
9
141
7
Time
24-h
24-h
12-h
12-h

8-h
8-h
8-h
8-h
24-h

1-wk


24-h
24-h




24-h
1-wk
12-h
24-h
24-h
24-h
24-h
24-h
48-h
Mean PEM
115
35
57
30

122
124
91
124
44


114
187
36

127
67
58

177
66
27
86
76
79
115
113
37
Mean SAM
59
17
64
13

68
78
54
80
18


55
193
17

117
65
51

421
192
16
60
60
43
62
84
56
R2 PEM vs SAM
NS
0.00
0.19
0.04

0.15
0.10
0.00
0.07
0.00


0.00
0.50
0.00

0.26
0.20
0.02

0.07
0.03
0.34
0.04
0.25
0.01
0.01
0.23
0.68
P
NS
NS
NR
NS

NS
NS
NS
NS
NS


NS
NR
NS

NR
NR
NS

0.09
NS
<0.05
0.008
0.001
NS
NS
NR
0.000
    N = Number of individuals carrying personal monitors.
    NS = Not statistically significant from 0.
    NR = p Value not reported, but mentioned as significant.
    a = Year of publication.
     = 14 Subjects carried PEMS for 14 days for 191 valid measurements.
    c = Three outliers are removed and regression is for 188 measurements.

-------
                      600
                      500
                      400
                      300
                      200
                      100
                               100
200
300
400
500
600
                                    Outdoor Sulfate (nmoles/m J)
Figure 7-27.  Personal versus outdoor SO4  .  Open circles represent children living in
              air conditioned homes; the solid line is the 1:1 line.

Source: Suh et al. (1993).
                                                                 500
                                   Measured Sulfate {nmoles/m3)
Figure 7-28. Estimated ("best fit" model) versus measured personal SO4=.  Model
             includes indoor and outdoor concentration and activity data.  Open circles
             are air conditioned homes; the solid line is the 1:1 line.

Source: Suh et al. (1993).
                                         7-107

-------
     Similar high correlations for total sulfur were found by Ozkaynak et al. (1996) in the
PTEAM study.  Regressions of personal exposures in the PM10 fraction on outdoor sulfur
gave the following results (^g/m3):
     5pers (day) = 0.62 (0.07 SE) + 0.69 (0.03) 5out           N = 168 R2 = 0.78
     5pers (night)  = 0.27 (0.06) +  0.68 (0.03) 5out             N = 162 R2 = 0.81
     Another important consideration in evaluating personal exposures, from the indoor and
outdoor environmental measurements, is that the chemical composition of the excess in
personal exposure compared to the TWA exposure calculation may be significantly different
than that predicted from the indoor  and ambient data alone.
     In addition to the  two factors cited just above, a microscale "personal cloud" can be
generated  by the person's activities  which complicates the exposure measurement process.
This effect is most important in occupational settings where personal exposures are not
readily comparable to weighted area sampling  measurements.  For example, Lehmann et al.
(1990) measured workers exposure  to diesel engine exhaust by personal monitoring of PM10
with a range of 0.13 to 1.2 mg/m3, compared  to an area estimate range of 0.02 to
0.80 mg/m3.  The U.S. Centers for Disease Control  (1988) reports the exposures of nurses
and respiratory therapists  to the aerosols of ribavirin during treatment of patients by  ribavirin
aerosols administered inside an oxygen tent. Bedside area monitors averaged 317 /ixg/m3
while personal exposures  ranged from 69  to 316 /xg/m3 with an average of 161 /*g/m3.
     Environmental Tobacco Smoke (ETS) is  a category of PM found in many  indoor
settings where smoking is taking place or  recently occurred. As  stated in Section 7.2, ETS
is the major indoor source of PM where smoking occurs.  Because of the depth of discussion
of ETS in Section 7.2.2.2, no  further discussion is made here other than to note that ETS
adds on the order of 25 to 30 /ig/m3 to 24-h average personal exposures and residential
indoor environments where smoking takes place (Holcomb, 1993; Spengler et al., 1985).
     The  random ETS  increment will tend to  reduce the correlation between PEM and SAM.
If one were able to subtract out the ETS from the PEM PM data, the  correlation of  SAM
with the non-ETS PEM PM might be improved (Dockery and Spengler, 1981b). As stated
as a caveat in the introductory section 7.1, the inhalation of main-stream tobacco smoke will
be a major additive exposure to PM for the smokers, which dwarfs the nonsmoker's
PEM PM. Therefore the results presented so  far apply only to nonsmokers, and a major
                                         7-108

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proportion of the US population (e.g., smokers) has a total exposure to PM that is at least
one order of magnitude greater than that of the nonsmokers.
7.5   INDIRECT MEASURES OF EXPOSURE
7.5.1  Time-Weighted Averages of Exposure
     The early air pollution literature related health to ambient paniculate matter (TSP)
concentrations as a surrogate for personal exposures to PM. Although this relationship has
been shown to be highly questionable for specific individuals, it still is used in studies such
as Pengelly et al.  (1987) who estimated TSP exposures of school children in Hamilton,
Ontario, by interpolation of ambient TSP concentrations to  the school  locations.
     The first usage of a time-weighted-average (TWA) of environmental exposures to
estimate total human personal exposure to an air pollutant (Pb) was by Fugas et al. (1973).
In theory, a human exposure to PM could be estimated by use of Equation 7-2 and
knowledge of the  average PM concentration while in each microenvironment (/^E) that a
person experiences and the duration of the exposure in each such /*E (Duan, 1982; Mage,
1985).  For a room with no source in operation, the whole  room could be treated  as a
single fjE.  However, when a PM source is in operation and gradients exist, that very same
room may need to be described by multiple jiEs.  These fiEs could have dimensions of an
order of a few centimeters close to the source and of several meters farther from the source.
     Ogden et al. (1993) compared exposures from personal sampling and static area
sampling data for cotton dust exposures.  The British cotton dust standard specifies static
sampling,  because the 1960 dose-response study used to set the standard  used static sampling
data to compute worker exposure and dosage.  Ogden et al. (1993) found median personal
exposures of 2.2 mg/m3 corresponding to a mean static background concentration of
0.5 mg/m3.  They concluded that "The presence of the body and its movement affect what a
personal sampler collects, so static comparisons cannot be used to infer anything about the
relationship of the (static) method with personal sampling."  Ingham and Van (1994)
confirmed this finding by modelling the human body as a cylinder and showing that unless
the personal monitor length/diameter ratio was  greater than four, the aspiration efficiency
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(the fraction of particles sampled that would be sampled in the absence of the body) could be
greatly affected.
     Rodes et al. (1991) compared the literature relationships of personal exposure
monitoring (PEM) to ptE area monitoring (MEM) for PM, as shown in Figure 7-29, to which
Ogden et al. (1993) is added as a single point.  The authors  found that PEM/MEM ratios
ranged from 3 to 10 in occupational settings, and from 1.2 to 3.3 in residential settings.
These combined data show that approximately 50% of all measured PEM PM values are
more than 100% greater than the estimated simultaneous MEM values using the TWA
approach.  Their explanation points to this excess PM as due to the spatial gradient about
indoor sources of PM which are usually well away from area monitors which thus fail to
capture the high exposures individuals may get when in close proximity to a source.  They
suggest that clothing lint and skin dander could only add, at  most,  a few percent to the total
PM mass collected by a personal exposure monitor.
     The Tokyo PM10 data of Tamura et al. (1996), added on Figure 7-29, show that for
their cohort of five elderly housewives and two male retirees that there is no evidence of a
large personal cloud effect as seen in the other studies listed. Japanese people customarily
take shoes off before entering a home and do not use wall-to-wall carpets,  which would
reduce track-in of soil and eliminate a major reservoir for resuspension of dust.  However,
this same cohort does display a "personal cloud" effect for the PM  greater than PM10, with a
maximum PEM/MEM value of 3.3 for PEM = 55 ^g/m3 vs MEM  17 /*g/m3.  This is
consistent with the findings of Thatcher and Layton (1995) showing,  on Figure 7-15, an
indoor increase due to human activity, primarily for the PM greater than 10 pm in size, and
Sheldon et al.  (1988a,b) showing  two U.S. homes for the  elderly with less than 10 jug/m3
PM3 over a 72-h period in a nonsmoker's room.

7.5.2 Personal Exposure Models Using Time-Weighted Averages of Indoor
       and Outdoor Concentrations of Particulate Matter
     Several studies have used the relationship of Equation 7-2 to compute the time-
weighted-average (TWA)  PM exposure of subjects.  The procedure calls for a time-activity
diary  to be kept so that the time at-home, outdoors, at-work, in-traffic, etc., can be defined.
By use of fiE monitoring data from the study itself (or literature  values of PM concentrations
                                        7-110

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            100
                                                              95   98
                  ° Stevens (1969)
                   Fletcher and Johnson
                  O Parker etal. (1990)
                  oLioyetal. (1990)
                   EPA PTEAM data
                  ©Ogdenetal. (1993)
                  ^Tamuraetal. (1996)
                                               Data  median
                                   30     50    70
                                Cumulative % less than

Figure 7-29. Personal activity cloud (PEM) and time-weighted average exposure
            (MEM).

Source:  Rodes et al. (1991), Ogden et al. (1993), Tamura et al. (1996).
in similar /xEs) and concurrent ambient monitoring, one can predict the concentration that
would be measured if the subject had carried a PEM.
     Because people in the United States spend, on average, 21 h indoors each day (U.S.
Environmental Protection Agency, 1989), the concentration hi indoor /*Es is a most
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important quantity for usage within a TWA PM model.  The important articles on indoor air
quality for PM have been reviewed extensively by Wallace (1996) and are covered in Section
7.2.  The articles that are discussed here predict PM exposures of non-smokers that include
ETS, and most provide PEM data for comparison.  As opposed to the gaseous pollutants for
which continuous hour-to-hour time series of SAM data are available, PM SAM monitoring
data have been often only available as a time series of 24-h SAM measurements.
Consequently, in much of the early PM TWA literature, the modelers assumed, by necessity,
the same ambient PM in the morning and evening, which might not be accurate (Dockery
and Spengler, 1981b).
     Spengler et al. (1980) in a study of PEM, SAM and SIM in Topeka, Kansas, found the
averages of PEM = 30 /xg/m3, SIM  = 24 /Ag/m3 and SAM = 13 /^g/m3.  They note
"It suggests that somewhere in an individual's daily activities,  they are being exposed to PM
at concentrations higher than what is  measured either indoors or outdoors". This relationship
has been found  in almost all other studies, such as PTEAM (Clayton et al., 1993) where
daytime PEM averaged 150 pig/m3 and SIM and SAM averaged just under 100 ^g/m3.
Spengler et al. (1985) measured 24-h PEM, SIM and SAM. The resulting relationship based
on Equation 7-1 was:  PEM = 17.7 /ig/m3 + 0.9 TWA.  The authors noted, in addition to
the previous suggestion, that the excess of PEM over TWA may be due to an incorrect
assumption that the indoor and outdoor are constant during the 24-h sampling period.
     Koutrakis  et al. (1992), in a study discussed in Section 7.2 on Indoor Air, report that
their source-apportionment  mass-balance model predicts penetration from outdoors to
indoors on the order of 85-90% for Pb and sulfur compounds.  The authors claim that:
     "We can satisfactorily predict indoor fine aerosol mass and elemental concentrations
     using the respective outdoor concentrations, source type and usage, house volume and
     air exchange rate."
The authors further note that this may be a cost-effective approach to estimating peoples'
exposure while indoors, since the necessary ambient data may be available and the housing
profile may be collected with a simple interview.
     Colome et al.  (1992) measured indoor and outdoor PM-10 at homes of asthmatics in
California. Their personal  monitoring data, limited to three individuals, confirmed the
relation in Figure 7-16 that "some protection from higher outdoor concentration is afforded
by shelter if smokers and other particulate sources are not present".  This observation may
                                         7-112

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be important for estimating the exposure of elderly and infirm people who are assumed to be
the susceptible cohort (Sheldon et al., 1988a,b).
     Klepeis et al. (1994) present an up-to-date TWA PM Model that uses, as an input, real-
time hourly PM SAM data and a mass balance equation to predict exposures of nonsmokers
in various indoor settings based on ambient PM data, presence of PM sources such as
smokers, and other variables relating to air exchange rates. The inclusion of the additive
terms that allow for sources, such as cooking and presence of smokers adds to the TWA of
Equation 7-2, which in effect is a correction for the underprediction of the fjE concentration.
     In summary, as described by several authors, the PM PEM exposure of individuals who
are not smoke exposed has been shown to be higher than their corresponding TWA of SIM
and SAM in U.S. studies.  The exact reason for this excess in PM, sometimes called a
"personal cloud", is not known (Rodes et al., 1991). It has been thought to reflect the fact
that the person's presence itself can stir up loosely settled-dust by induced air motion and
vibration (Ogden et al., 1993;  Aso et al., 1993).  Thatcher and Layton (1995) gave an
example where merely walking into and out of a room raised the total suspended dust (PM10)
by 100%.  A study by Litzistorf et al. (1985) of asbestos type fibers in a classroom showed
how fibers (f) were stirred up  when it was occupied.  The levels rose from below the
detectable level of 10000 f/m3 to 80000 f/m3 when occupied,  and they returned to below
detectable levels within 1 h after the end of the class.  Millette and Hays (1994) present a
detailed discussion of the general topic of resuspended dust in their text  on settled asbestos
dust.
     It may not be a proper procedure to use a 24-h average  concentration in a physical
setting, such as a kitchen, to estimate a person's exposure while in the kitchen.  As described
previously in the discussion of the definition of a  microenvironment in Section 7.1.2, the
same kitchen can constitute one or more /*Es depending on the source operation pattern.
In many studies, such as Spengler et al.  (1985), the SIM  sampled the indoor residential
setting for 24-h in phase with the  PEM.   The resulting average SIM will often underestimate
the person's exposure while they are at home and may contribute to the  difference between a
TWA exposure and the PEM.
     In a similar manner, a person's workplace exposure may be more or less than that in
their home. In the PTEAM study (Clayton et al., 1993), there was a general decrease in
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exposure for those employed outside their home. However, employment in a "dusty trade",
such as welding, may increase their PM PEM.  Lioy et al. (1990) give an example of a
subject with a hobby involving welding having a 24-h PEM reading of 971 /ig/m3.
     Indirect estimation of a person's time-weighted-average (TWA) PM exposure may be a
cost-effective alternative to direct PEM PM measurement.  Mage (1991) compared the
advantages and disadvantages of the TWA indirect method compared to the direct PEM
method.  The primary advantages of the indirect method are the lower cost and lower burden
on the subject,  because it uses only a time-activity diary and no PM PEM is required; the
disadvantage is the lower accuracy.  The primary advantage of the PEM PM method is that it
is a higher accuracy direct measurement; the main disadvantages are the higher cost and
higher burden on the subject (see Section 7.3.1). Mage (1991) proposed a combined  study
design in which direct measurements on a subset of subjects can be used to calibrate the
TWA estimates of other subjects.  Duan and Mage (1996) present an expression for the
optimum fraction of subjects to carry the PEM as a function of the relative cost of the PM
PEM to the TWA PM estimate and the correlation coefficient between the PM PEM data and
the PM TWA estimates.
7.6   DISCUSSION
7.6.1   Relation of Individual Exposures to Ambient Concentration
     The previous sections discussed the individual PM PEM vs PM SAM relationships of
the studies listed in Table 7-26.  In many of the cross-sectional PM studies, no statistically
significant linear relationship was found between PEM and SAM, but in some other studies
the relationship is positive and statistically significant. However, as shown by Lioy et al.
(1990), Janssen et al. (1995), and Tamura et al. (1996), the serial correlations between PEM
and SAM within an individual's tune series are often highly positive and significant.  This
section discusses these data in terms of understanding the complex relationship between the
SAM concentrations and the  individual PEM exposures.  In the following  section, the
relationship of the SAM to the mean PEM in the community surrounding the  SAM will be
presented.
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     The principle of superposition is offered as a basis for visualization of the process
involved in creating a total exposure.  A linear system will exist for respirable-PM PEM
exposures  if the expected PEM response to a source emitting 2 mg/min of PM is exactly
twice the PEM response to that identical source emitting 1 mg/min of identical PM.
If superposition applies, then we can construct the total exposure by adding  all the
increments of exposures from the various source classes and activities that a subject performs
on a given day.
     Let the SAM be representative of the macroscale ambient PM concentration in the
community as shown on Figure 7-30a. This is the exposure that would be measured for a
person if they spent 24-h per day outdoors near the SAM site.  Neglecting local microscale
variation (e.g. backyard barbecue or  leaf burning), while people are outdoors they are
exposed to 100%  of the SAM value (Figure 7-30b).  Assume that this exposure is also the
baseline PM for a location in traffic which occurs outdoors.  The increment produced by the
local traffic is considered later.
     While people are indoors, they  are exposed to a variable fraction of tune-lagged
SAM PM.  This constitutes an amount of (1) the fresh PM which depends on recent SAM
and the air exchange rate between indoors and outdoors, and the PM deposition sinks
(filtration of recirculated air, surfaces, etc.), and (2) PM from outdoor sources that had been
deposited in the past but is resuspended due to human activity and air currents.   PTEAM
(Ozkaynak et al., 1996),  as cited in Section 7.2, found  that outdoor air  was  the major source
of indoor particles,  accounting for 75% of the fine fraction (<2.5 /xm AD)  and 67%  of the
thoracic fraction (< 10 fjan AD) in indoor air.  It is noted that these average fractions will
be lower in communities with lower average SAM values.   Lewis (1991) reported an
apportionment of indoor air PM  in 10 homes within a wood burning community in
Boise,  ID.  The results showed that 50% of the fine PM was of outdoor origin (SAM), and
in 9 of 10 homes, 90% of the  sulfur  was from outdoors (one home had an anomalous sulfate
injection from a humidifier using tap water).  This is consistent with indoor  sources varying
independently of the SAM in a stationary manner (constant mean and variance),  so that the
relative contribution of indoor  sources to indoor exposures decreases as SAM increases.
Figure 7-30c represents the increment to PEM from outdoor sources of SAM while the
                                         7-115

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"E
 0>
           6      12      18      24
            Time - Hours
           6      12      18
            Time - Hours
             6       12      18
               Time - Hours
                                 24
             SAM
                  SIM
                 Indoors
                non-ETS
                non-SAM
                                                         SAM
                                                         Traffic
                                                       Increment
                                                        to SAM
                                                     6       12     18      24
                                                       Time - Hours
                                                                 Occupational
                                                                   Exposure
                                                                   Increment
                                                                     to SIM
                                                                      SAM
                                             0       6      12     18      24
                                                       Time - Hours
                                                                       ETS
                                                                   Exposure
6       12     18      24
  Time - Hours
     0       6       12     18     24
               Time - Hours
Figure 7-30. Components of personal exposure.
"E
E
•*•
"g
20 -*
Cigarettes
Smoked
SAM
^> .



--



s>*



'*















*-,



-..










^

                                                  6      12      18      24
                                                    Time - Hours
                                   7-116

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subjects are indoors at home and at work. The SAM value is shown as the dotted line for
reference in this and all the following Figures 7-30c to 7-30h.
     While people are indoors, at home, and at work, they also are exposed to-PM emitted
by indoor sources - other than ETS from passive smoking and specific occupational sources.
These  sources, such as cooking, lint from clothing and furnishings, mold, insects, etc.,
create PM  that agglomerates and deposits as visible dust that can be continuously
resuspended, which constitutes an additional PEM increment.  Figure 7-30d shows the
additive effect of this source. In traffic, or near vehicles in a parking garage or parking lot,
people are  exposed to an increment of PM over and above the SAM value for that location.
Figure 7-30e shows the additive PM for this setting that would be added to Figure 7-30b for
the local vehicular emissions.
     At work in a "dusty trade" (e.g., welder, mechanic, or miner) there is an increment of
exposure associated with these occupational activities that generate PM.  Figure 7-30f
represents the additive PM for these activities which are assumed to take place "indoors".
     In an indoor setting, in the presence of a  smoker or the wake of a smoker, a PEM will
record an increment of ETS associated with the act of smoking. Figure 7-30g shows the
added PM  increment for this source.
     Last,  but not least, is the physical act of smoking itself.  As described previously,  the
main stream smoke from a cigarette,  cigar,  or pipe is inhaled directly without being sampled
by a PEM.  The mass of PM directly inhaled from smoking one-pack-per-day of cigarettes
rated as delivering "1 mg 'tar' per cigarette by FTC method" is 20 mg per day (Federal
Trade Commission, 1994).  If this were  distributed into a nominal 20 m3 of air inhaled per
day, it would be an additive increment on the order of 1 mg/m3 to a 24-h PEM reading.  Tar
emissions as rated by the Federal Trade  Commission (1994) range from <0.5 mg/cigarette
to 27 mg/cigarette.  Therefore one-pack-per-day smokers can have a PM exposure standard
deviation that is much larger than the mean exposure to PM of non-smokers,  simply from
choice of brand. Figure 7-30h represents the impact of the act of smoking as  creating
exposures  represented by the vertical spikes with an integral area > 1 mg-day/m3 per day.
     For all subjects, by the principle of superposition, the sum of the areas shown in
Figures 7-30b and 7-30c represents the exposure of an individual to the PM constituents that
are characterized by a SAM PM concentration.  The additional exposure categories that are
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independent of the SAM concentration (Figures 7-30d through 7-30g) and are appropriate for
that subject would represent the portion of 24-h PEM PM that is not associated with SAM.
Variance of SAM should explain much of the variance in the SAM related PEM fraction as
defined by Figures 7-30b and 7-30c.  The summation over a full day for all categories 7-30b
to 7-30g would be the PEM for any subject,  such as is shown in Figure 7-2 (Repace and
Lowery,  1980).
     Although there are no data for PEM PM exposures of individuals living in homes
without any  indoor sources of PM, there are  data for PEM sulfate as discussed previously in
Section 7.4.3.  Given that  there are negligible sources of sulfur (S) that originate in the home
(matches, low-grade kerosene, humidifiers using tap water),  the high correlation of
PEM sulfate and SAM sulfate (R2 =  0.92) of Figure 7-27 reported by Suh et al. (1993),
where no appreciable sources of S were present, is an indication that the same relationship
should hold  for all SAM PM  of that size range.  The data of Anuszewski et al. (1992) show
that light scattering particles measured by nephelometry had  a very high correlation between
indoor and outdoor concentrations (R2 >  0.9) for one home, but were  lower for others.
Lewis (1991) and Cupitt et al. (1994) report  that PM10 appears to penetrate with an average
factor of 0.5 in Boise homes without woodburning.  The factor goes up to 0.7 with
woodburning, and the authors assume that the factor would go up to 0.9 in the summer when
homes are less tightly sealed.   However, the  authors did not consider the deposition rate k.
This is in contrast to the data of Thatcher and Lay ton (1995), who measured k and found
penetration factors of 1.0 for  all PM  sizes < 10 /im.
     If the variance of the PEM PM portion which is uncorrelated to SAM (Figure 7-30d to
7-30g) is very large,  the percentage of the variance of the PEM PM that can be explained by
the variance of SAM PM will be very small.  It may be possible that the different
populations sampled, cited in the studies of Table 7-26, have widely different home
characteristics,  occupations, mode of commuting, and smoking exposures that contribute to
the different PEM vs SAM relationships.  In some of the cleaner communities (such as
Watertown,  MA; Topeka,  KS; Waterbury, VT; and Kingston and Harriman, TN) SAM
averaged less than 20 /*g/m3.   The non-SAM increments to PEM exposure in these locales
were greater than the SAM and may have been so variable between people (eg. ETS and
non-ETS exposures pooled together) that the PEM PM became insignificantly correlated with
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the SAM PM data. The exception is Houston, TX, with a SAM = 16 ^g/m and a
significant R2= 0.34 (0.005 < p < 0.05). However, Morandi et al. (1988) note that
deletion of two outlier observations would reduce R2 and make it nonsignificantly different
from 0 (p > 0.2).  This is in contrast to the three studies in communities with high SAM
levels (Tamura et al., 1996; Clayton et al., 1993; Lioy et al., 1990), where the relations
between PEM  and SAM were  significant.
     All discussions above relate to nonsmokers.  As for the smoker, the exposure from
Figure 7-30h would outweigh the sum of all the other exposures, 7-30b through 7-30g. This
smoking  increment may have an important implication for interpretation of epidemiology
studies that relate ambient PM, as a surrogate of exposure, to mortality or morbidity.
     Because the daily amount of individual smoking and other exposures from indoor
sources (cooking, ETS, resuspension of settled dust by walking into carpeted rooms, hobbies)
is independent of the daily  SAM value, the variance of the PM SAM value is a surrogate for
the variance component of  total personal exposures to PM associated with PM SAM.  For
nonsmokers ambient PM reflects about 50 to 70% of their PM10 exposure that by definition
does not  contain directly inhaled smoke exposure (Tamura et al., 1996; Ozkaynak et al.,
1996). This relationship would also hold for the total PM exposure of smokers minus the
effective  increment they receive from their direct smoking which is independent of PM
SAM.  Therefore, a relationship between ambient PM (SAM) and human exposure to PM
(PEM) that makes sense, is that the SAM value is a surrogate for personal exposure to PM
(PEM) from PM originating in the ambient air.  This relationship would apply to everyone,
smokers and nonsmokers alike.  However, treating SAM as a surrogate for total personal
exposure to PM from all sources, including those major sources of PM that vary
independently of SAM (active  smoking and occupational exposures), would be wrong.

7.6.2    Relation of Community Participate Matter Exposure to Ambient
         Particulate Matter Concentration
     For the morbidity/mortality studies described in  Chapter 12 that use SAM as the
independent variable,  that SAM can be interpreted to  stand as a surrogate for the average
community exposure to PM from sources that influence the SAM data.  These sources of
ambient PM do not include indoor sources such as the "personal cloud" of skin flakes and
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lint, ETS, cooking fumes, and resuspended PM from walking on a dirty carpet. Thus, if we
could subtract off from each PEM measurement the contribution to the total exposure from
the indoor sources, such as smoking, cooking, carpets,  and personal clouds, the residual PM
from ambient sources would probably improve the correlation with SAM, as described by the
data of Tamura et al. (1996) for nonsmoking-noncarpeted homes  occupied by elderly people.
Mage and Buckley (1995) tested the relationship of the  mean PEM to SAM as a means to
minimize the affect of variations of these indoor sources of PM on the relation of PEM to
SAM, and their results, with modifications, are presented in the following section.
     There are several different models for these  analyses and although most describe the
same linear relationship, the models differ greatly in their assumptions about the error terms.
The discussion of the various models is followed by U.S. EPA reanalyses of five different
PEM-SAM data sets described previously in Section 7.4.

7.6.2.1   Methodology
Methods for Missing Data
     One common difficulty in the use of aerometric data is the presence of missing data
elements.  For example, consider the following PEM data from the study of Tamura et al.
(1996).  The authors measured the 48-h personal exposure to PM10 for seven individuals
living near a main road for 11 periods  in four seasons distributed over a complete year.  This
example has a great  deal of missing data, and for purposes of computation, the data were
split into a group living close to the road (persons A, B, C, and D), and a group living
farther from the road (persons E,  F, and G).  Their indoor and outdoor data were shown
previously on Figure 7-24.  The PEM data for the first group are shown in Table  7-27.
     Unless pairwise correlations are computed, the standard solution to the problem is  to
delete all observations for which any of the variables are missing. This approach, known as
a complete-case analysis, is standard in the majority of the statistical packages.  For this
example, we would be left with only 5 of the original 11 periods of observation.  This
section will describe a model which will allow for the inclusion of all available data.
     The reason for the missingness of the data is extremely important because it determines
our ability to obtain  maximum likelihood estimates (MLE). The following definitions are
paraphrased from Little and Rubin (1987):  If the probability of being missing is independent
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         TABLE 7-27.  48-HOUR PERSONAL EXPOSURE TO PM10 (jtg/m3)
              (Data Taken by Subjects Living Along a Main Road in Tokyo)
Period Person A
1 43.7
2 27.4
3 30.2
4 22.4
5 57.4
6 M
7 M
8 24.6
9 31.0
10 22.9
11 68.7
Person B
40.4
31.5
39.2
29.2
43.2
26.1
37.9
M
34.5
M
51.8
Person C
37.5
29.8
32.7
25.9
43.3
27.9
35.8
41.4
36.0
24.3
52.6
Person D
52.3
26.0
M
38.2
M
39.9
34.6
39.8
45.6
30.6
68.1
M = Missing observation.
Source:  Tamura et al. (1996).


of both the variables missing and the variables present, then the data are said to be missing
completely at random (MCAR).  If the probability of being missing depends on the variables
present, but not on the  variables missing, then the data are said to be missing at random
(MAR).  If neither situation holds, then there are no general solutions to the problem.  This
would  happen if the value of the missing variable (which is not known to us) is directly
related to its probability of being missing.  Laird (1988) discusses models used for maximum
likelihood estimation with missing data, as well as a detailed discussion of the non-response
mechanism.
     One solution is to assume that  the measurements are distributed as a multivariate
normal distribution (or  to assume that some transformation of the data give a multivariate
normal distribution). The estimation of the parameters of a multivariate normal model with
missing data is  a problem which has been discussed for many years (see Afifi and Elashoff,
1966).  The first general solution to the problem of estimating a mean vector and covariance
matrix from a multivariate normal distribution with data missing at random was given by
Woodbury and  Hasselblad (1970).  The solution,  referred to as the "Missing Information

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Principle", was generalized to other missing data problems by Orchard and Woodbury
(1972).  Proof that the method always improved the likelihood was given by Dempster et al.
(1977), and the generalized solution method was named the E-M algorithm.
     To describe the problem, the following notation will be used. Let  x = x1,x2,...,xk be a
k-dimensional random vector from a multivariate normal distribution
                                            1S |
(7-10)
where E is a symmetric positive definite matrix and A* is a vector.  The mean of the vector x
is n and its covariance is £.  Assume that we have n observations  from this distribution,
X1,X2,...,Xn.
     The E-M algorithm can be used to estimate the parameters of a multivariate normal
distribution.  The method starts with any reasonable first estimate  of the parameters.
Assume that we have initial estimates of the parameters jt and E, which can be obtained by
filling in the missing data with the column means and then estimating the parameters in the
usual manner.  The E step consists of estimating the sufficient statistics. For this model, the
sufficient statistics are the sums and sums of squares of cross products.
     Assume that at one particular point,  Xj, some of the observations are missing and some
of the observations are present.  Without loss of generality, we will  drop the subscript, i, and
rearrange the subscripts so that the vector X is [Xj, X2] where all of the observations, Xj,
are missing and all the observations X2 are present.  Partition the  mean vector n and the
covariance matrix E in a similar fashion
                 and
                                  12
(7-11)
Compute the regression of the missing observations on the observations present
                -i
      P =  S12S22-
(7-12)
Estimate the missing values, Xlf by their expected values
                                          7-122

-------
                         -14)-                                                  (7'13)

Compute the correction to the expected sums of squares
                        _-i _                                                    r?-14^

Now add the vector X to the sums and XX' to the sums of squares and cross products using
their expected values for the missing values; remember to add EU i 2 to the cross products
corresponding to Xj.
     The M step consists of recomputing the estimates of /* and £ from the completed sums
and sums of squares and cross  products.  This procedure will converge,  typically taking five
to 20 iterations for a moderately sized problem.  Using the methods just described, the
estimates of both the missing values and the parameters for the data of Tamura et al. (1996),
based on U.S. EPA reanalyses, are shown in Table 7-28.
     This method was also used to fill in the missing values for persons E, F, and G (shown
in Table 7-29).  Once the missing data were estimated, the average across all seven persons
was computed and compared with the  ambient measurement monitor as shown in Table 7-30.
These data will be used as examples for the next section.

Linear Regression Models
     The various linear regression models are illustrated next using the average personal
exposure values from the Tamura et al. (1996) data set which were described in the previous
section. For these examples, the average personal exposure will be considered the
dependent variable and the ambient concentration at the Itabashi site will be the independent
variable.
     The first model is often referred  to as the fixed independent variable model (see Dunn
and Clark,  1974, p. 225).  The model assumes that the dependent variable is a linear
function of the independent variable with random error which is normally distributed (this is
a bad assumption but this is the most commonly used model).  This can be written as
                                         7-123

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     TABLE 7-28.  PARAMETER ESTIMATES FOR 48-HOUR PM10 PERSONAL
          EXPOSURE MONITOR DATA TAKEN BY SUBJECTS LIVING
                     NEAR A MAIN ROAD IN TOKYO Otg/m3)
                   (Estimated Missing Values Shown in Parentheses)
Day
1
2
3
4
5
6
7
8
9
10
11
Means
Person A
43.7
27.4
30.2
22.4
57.4
(29.3)
(28.9)
24.6
31.0
22.9
68.7
35.1
Person B
40.4
31.5
39.2
29.2
43.2
26.1
37.9
(43.3)
34.5
(26.7)
51.8
36.7
Covariance/Correlation Matrix
Person A
Person B
Person C
Person D
215.8
0.745
0.819
0.888
83.9
58.9
0.949
0.731
Person C
37.5
29.8
32.7
25.9
43.3
27.9
35.8
41.4
36.0
24.3
52.6
35.2
(Correlation below
96.4
58.4
64.3
0.816
Person D
52.3
26.0
(37.4)
38.2
(58.4)
39.9
34.6
39.8
45.6
30.6
68.1
42.8
diagonal)
157.4
67.6
79.0
145.6
Source:  Parameter estimates, including the calculation of estimated missing values, and covariance/correlation
       matrix results from reanalyses by U.S. EPA of data from Tamura et al. (1996).
     Yj = P0 +  pjXj + e,, where                                               (7-15)

i = l,2,...,n, n is the number of observations, and et is normal with mean 0 and variance o2.
No assumption is made about the distribution of the independent variable since it is
considered to be fixed.
     Using the previous example, the estimated coefficients are given in Table 7-31, and the
results are shown graphically in Figure 7-31.
     The second model is often referred to as the bivariate normal model (see Dunn and
Clark, 1974, p. 239). This model assumes that the dependent variable and the independent
variable are both normally distributed.  Actually, the assumption is stronger—it assumes that
                                       7-124

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 TABLE 7-29.  PARAMETER ESTIMATES FOR 48-H PM10 PERSONAL EXPOSURE
  MONITOR DATA TAKEN BY SUBJECTS LIVING FARTHER FROM THE SAME
           TOKYO MAIN ROAD DESCRIBED IN TABLE 7-28 (in /tg/m3)
                   (Estimated Missing Values Shown in Parentheses)
Period
1
2
3
4
5
6
7
8
9
10
11
Person E
57.1
(30.9)
26.8
32.9
68.6
31.2
26.5
35.8
40.7
29.8
62.5
Person F
62.2
26.5
23.1
(30.6)
(69.2)
26.6
24.0
(28.7)
(36.9)
27.5
51.2
Person G
(37.1)
(29.0)
25.3
27.2
48.0
24.4
29.7
37.7
35.4
22.4
61.0
Source:  Parameter estimates, including the calculation of estimated missing values, based on reanalyses by
       U.S. EPA of data from Tamura et al. (1996).
the joint distribution of the two variables is bivariate normal.  The bivariate normal
distribution is a special case of the multivariate normal distribution described earlier.  The
intercept, (3Q, and regression coefficient, j8ls are estimated by the same formulas as were used
in the first model even though the assumption is not the same.  The R-squared term is also
the same, but the ANOVA Table no longer makes any sense.
     The third linear model is the same as the first except that a lognormal error term is
used.  This kind of model requires the use of a general linear model fitting routine.  The
model gives less weight to large deviations  about the predicted line where the predicted
values are already large.  The model still assumes that the independent variable is fixed  and
measured without error.  The  fit to the previous example is shown in Table 7-32.  There is
no measure comparable to R2, but the log-likelihoods can be compared directly.  Note that
                                       7-125

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   TABLE 7-30. AVERAGE PERSONAL EXPOSURE DATA COMPARED WITH
                   ITABASHI SITE MONITOR (PM10; jig/m3)
Period
1
2
3
4
5
6
7
8
9
10
11
Itabashi Site
66.5
30.1
37.9
50.3
90.5
40.7
40.5
55.1
70.6
31.9
99.5
Average Personal
47.2
28.7
30.7
29.5
55.4
29.3
31.1
35.9
37.2
26.3
59.4
Source: Data from Tamura et al. (1996).
   TABLE 7-31.  RESULTS OF LINEAR REGRESSION ANALYSIS, ASSUMING A
	NORMAL ERROR USING THE EXPOSURE DATA FROM JAPAN	
Linear regression
Y = intercept + slope X
Variable                      Beta                          Std. Err. Beta
Intercept                       11.32                            3.025
Slope	0.466	          0.050
                                  ANOVA Table
Source                   Sum of Squares       Mean Square Error       D.F.      F-value
Regression                    1194.3               597.2          2          42.9
Error                         125.3                13.9          9
TOTAL                      1319.6               120.0         11
R-squared = 0.905
Log-likelihood = -28.99

Source: U.S. EPA reanalyses of data from Tamura et al. (1996).
                                    7-126

-------
       80
       60
    (8
    §  40
    J2
    S.
    
-------
the linear model with a lognormal error fits slightly better than the normal error model,
although the difference of 0.49 in the log-likelihood is not statistically significant.

Orthogonal Regression Models
     Orthogonal regression is also known as principle components regression.  There is no
real assumption about  the model.  The purpose of the analysis is to pass a line through the
data such that as much of the variation is explained as possible.  Variation is measured as the
squared distance from  the points to the fitted line.  Because no distributional assumptions are
made, no confidence limits can be placed on the estimated line.  The measure of the total
variation is
     Total variation  = ouo22 -  o12.                                                (7-16)
The fraction of the variation explained is derived from the eigenvalues of the covariance
matrix, and the regression line corresponds to the first eigenvector.  That is, the eigenvalues
are the solution of
                      / -,    /%  \
                                   = 0.                                             (7-17)
°11

a!2
°U

°22,

- A

1

0
0 \

1 J
The values of X which satisfy equation (7-17) are
      .  _  gll + °22 ±    qil- q22   +  °2                                         (7-18)
      A —  	 .
The slope of the line corresponding to the largest eigenvalue, Xl5 is
                                                                                    (7-19)
The intercept, 00, is easily calculated because the line must pass through the mean of the
data.
                                          7-128

-------
     The measure, percent of variation explained, is a generalization of the multiple R2
measure from a single dependent variable, but its behavior is somewhat different.  For a two
variable problem it can be calculated as X^CXj + X2).  In general, for correlations near 1, it
will be  about twice as good (.975 to  .98 instead of .95), but for correlations near 0, the
behavior is not as simple.  As a result, it can only be used to compare one orthogonal
regression with another.  Because the standard correlation coefficient is a non-parametric
measure of association, it can be used for orthogonal regression as well.  The results of
fitting by U.S. EPA of an orthogonal regression model to the previous example are in
Table 7-33.  The slope and intercept  are almost identical to the normal error model values
shown in Table 7-31.
           TABLE 7-33.  RESULTS OF AN ORTHOGONAL REGRESSION
	ANALYSIS OF THE EXPOSURE DATA FROM JAPAN
 Y = intercept + slope X
 Variable                                                         Beta
 Intercept                                                         10.83
 Slope                                                            0.475
 Total variation                                                5686.9
 Percent explained                                                98.5
Source of data: U.S. EPA reanalyses of data from Tamura et al. (1996).
Measurement Error Models
     In general, most linear regression analyses assume the independent variable has no
measurement error. When this error exists and no correction is made for it, the estimated
regression coefficients tend to be biased towards zero.  Because we often have multiple
monitors we can often attempt to estimate these components of variation, and therefore
correct our estimated regression coefficients. The solution usually requires some additional
assumptions—in particular the assumption of multivariate normality is necessary for most of
the solutions. Additionally, some information must be available about the error variance.
Either the error variance of the independent variable or the dependent variable, or the ratio
                                        7-129

-------
of the error variance to the variance of the dependent variable must be known exactly.
In some cases, these values are known with sufficient accuracy from other experiments so
that the values can be treated as known.
     Much of the material on measurement error in continuous variables comes from the
work of Kendall and Stuart (1961) and Fuller (1987). Both authors make the same
distinction that was made in the earlier section regarding the fixed or random nature of the
independent variable.  We will consider the more interesting case of measurement error in an
independent random variable.
     This subsection assumes a model with a continuous dependent variable and a continuous
independent variable whose values are considered to be  random and measured with error.
For example, Hasabelnaby et al. (1989) described an analysis of pulmonary function data
using measurements of NO2 exposure as a covariate.  The true NO2 exposure was assumed
to be a random variable which was estimated by sampling NO2 levels in the home for two
weeks out of the year.  The other terms in the model were height and gender of the
individual, and these were measured with little or no error.
     The single random independent variable model assumes a single independent variable
whose values, xi5 are random values. The model is
     y, = Po +
and we wish to estimate /30 and j3l.  Assume that the expected value of x is f^, the expected
value of y is /iy, and that the variance of x is axx. We do not observe yt and Xj, but rather Y;
and Xj, where

      Yf = y{  + Yj  and                                                           (7-21)
      Xt - xt  + 6,,                                                               (7-22)

and where % is normal with mean 0 and variance 
-------
assumption implies that the vector (Y,X) is distributed as a bivariate normal vector with
mean
                        = P
                                                    (7-23)
and covariance
      °rr  °XY
            XX
 r l~xx
a~ + O
                                     yy.
                                                                                   (7-24)
Let $! be the standard regression estimate based on the observed data,
                        VI n
            E (*<  -
The expected value of ^ is
                                                    (7-25)
                                                                                   (7-26)
Thus, for the bivariate normal model, the least squares regression coefficient is biased
towards zero.  The ratio, axx l°xx *s known by several names including the attenuation, the
reliability ratio,  and in genetics as the heritability (Fuller, 1987).
     Maximum  likelihood equations can be set up for the bivariate normal model with
measurement error.  The first and second moments,  which are sufficient to determine the
distribution, will give five equations in the six unknown parameters, JLIX,  axx, 
-------
maximum likelihood estimate of aYy, and SXY be the maximum likelihood estimate of aXY.
The maximum likelihood estimate of #1 becomes


     P,  = V/(*a-<0-                                                      (7-27)
Note that this estimator reduces to equation (7-25) when the measurement error in x, axx,
isO.
     If the measurement error in Y, ayy, is known, then there is a comparable solution.  Let
Sxx, SYY, and SXY be defined as before.  The maximum likelihood estimate of /3j becomes

     P, =  (*„ - ow)/Sw.                                                      (7-28)
     All of this was based on the assumption that there was a true relationship between x and
y that had no error. If, in fact, there was some error so that
     yt = Po +  M,  +  «,.                                                       <

where  ej is normal with mean 0 and variance 
-------
Components of Variance Models
     If we have measurements from several individuals over time or several ambient
monitors over time, then these measurements can be used in an analysis of variance
(ANOVA) model.  The purpose of the model is to estimate the variation between individuals
and/or the variation between monitors. This information can then be used to adjust our slope
estimates as described earlier, as well as letting us estimate the correlation between ambient
and personal monitors assuming we had an infinite sample of both.
     The logical analysis for this kind of data is a repeated measures design (see Winer,
1962, pp.  105-124).  For most examples, the necessary components can be obtained from the
results of a standard two-way  ANOVA table.  For example, consider the data of Tamura et
al. (1996) after the missing values have been estimated (Tables 7-28, 7-29). There are
7 individuals measured over 11 48-h periods, resulting in the following ANOVA Table 7-34.
  TABLE 7-34. RESULTS OF AN ANOVA ANALYSIS OF THE EXPOSURE DATA
                                   FROM JAPAN
Source of Variation
date
person
date x person
Total
D.F.
10
6
60
76
S.S.
9235.41
634.53
2248.66
12118.60
M.S.
923.54
105.76
37.48

Source of data:  U.S. EPA reanalyses of data from Tamura et al. (1996).
     These results indicate that the mean square error for person is 105.76.  This represents
an estimate of 7 ayy + aee (mean squared error).  The value, 37.48, represents an estimate
of aee, so that ayy can be estimated by (105.76 - 37.48) / 7 = 9.75. Because we will
actually use the mean of 7 persons to estimate the average, the variance component we need
for equation (7-28) is estimated by 9.75/7 = 1.39.
     For example, consider the data of Tamura et al. (1996).  From the above analysis, we
have an estimate  of the person variation, 
-------
7.6.3   U.S. EPA Analysis of Data Sets
7.6.3.1   Tokyo, Japan Data Set
     The data set of Tamura and Ando (1994) and Tamura et al. (1996) presents an
interesting problem.  Shown in Table 7-35 is the correlation matrix for average personal
exposure with the two nearby ambient sites as well as their average.  The Yamato site is
located near a highway intersection 0.7 km from the central Itabashi site.
   TABLE 7-35.  COVARIANCE AND CORRELATION MATRIX FOR AVERAGE
       PERSONAL EXPOSURE AND AMBIENT EXPOSURES FROM JAPAN
Covariance/Correlation Matrix (Correlation below diagonal)

Average person
Itabashi site
Yamato site
Average site
Average Personal
119.97
(0.951)
(0.736)
(0.840)
Itabashi Site
232.83
499.30
(0.874)
(0.949)
Yamato Site
308.81
748.50
1467.62
(0.983)
Average Site
270.82
623.90
1108.06
865.98
Source of data: U.S. EPA reanalyses of data from Tamura et al. (1996).
     Note that the correlation of the average personal exposure is much higher with the
Itabashi site than with the Yamato Site or the Average of the two sites. The estimated
components of variance can give strange results when there are only two sites and one is
much more highly correlated.  For this reason, only the Itabashi site is used in the following
analyses.  If there  had been additional sites it would have been possible to make all of the
analyses in Table 7-36, but only those single site analyses are included at this time.

7.6.3.2   Phillipsburg, New Jersey Data  Set
     The personal exposure data (Lioy et al., 1990) contained some missing values and three
outlier values, and they all were estimated as described earlier. The results of U.S. EPA
reanalyses are shown in Table 7-37.  In order to estimate the error variances, these data were
used in an analysis of variance as described earlier.  The results are shown in Table 7-38.
                                       7-134

-------
                  TABLE 7-36.  SUMMARY OF RESULTS OF THE
               ANALYSIS OF THE EXPOSURE DATA FROM JAPAN
Regression Model
Linear, normal error
Linear, lognormal error
Orthogonal
Linear adjusted for person error
Linear adjusted for ambient error
Measures of Association
Correlation of personal averages with Itabashi site
Correlation adjusted for measurement error
Average correlation of ambient with mean person
Average correlation of person with mean ambient
Fraction of variation explained by orthogonal regression
01 00
0.466 11.3
0.431 13.1
0.475 10.8
0.509 8.9
(Not available)
Value
0.951
(Not available)
(Not available)
0.872
0.985
Source:  U.S. EPA reanalyses of data from Tamura et al. (1996).


     The site monitoring data contained some missing values, and they were estimated by
U.S. EPA as described in Section 7.6.2.1.  The means, covariances and correlations were
also estimated.  The results are in Table 7-39. In order to estimate the error variances, the
same data were used in an analysis of variance as described earlier.  The results of the EPA
analyses are shown in Table 7-40. The individual exposure values were averaged as well as
the site exposure values. These means are  shown in Table 7-41.
     The same regression analyses described earlier were performed by U.S. EPA.  A plot
of the linear regression is shown in Figure 7-32.  The orthogonal regression gives virtually
an identical plot and is not shown.  The results of the analyses are in Table 7-42.
     Note that all estimated regression equations are quite similar.  The interesting value is
the correlation adjusted for measurement error.  This represents an estimate  of the
correlation between the mean of an infinite number of personal samplers and the mean of an
infinite number of fixed site samplers.  This value is relatively close to one,  but we do not
have good estimates of its variance to tell if the value is really different from one.
                                        7-135

-------
o\
              TABLE 7-37. PERSONAL EXPOSURE SUSPENDED PARTICULATE MATTER DATA FROM
       PHILLIPSBURG, NEW JERSEY. MISSING VALUES ESTIMATED (); OUTLIER VALUES RECOMPUTED [ ].
Person Identifier (/xg/m3)
Day
1
2
3
4
5
6
7
8
9
10
11
12
13
14
01
59
52
74
115
65
45
75
104
84
55
10
39
26
45
02
85
58
69
88
37
16
77
81
29
29
60
59
44
44
11
54
85
94
136
139
56
65
79
48
70
65
80
65
89
31
39
17
56
104
38
22
35
67
56
35
25
23
35
17
41
(53.2)
(76.7)
86
65
77
34
36
83
85
59
36
127
31
105
42
36
45
77
116
64
27
80
32
122
81
[48.1
57
47
117
51
41
50
90
112
56
28
27
69
30
25
49.4]
32
114
(24.8)
52
28
53
93
120
52
21
34
61
36
39
43
35
67
24
61
123
104
200
125
184
60
92
112
57
199
93
121
47
117
62
67
56
134
272
190
58
(110.2)
91
96
77
84
95
95
63
81
96
50
166
193
79
57
124
144
156
63
99
31
71
44
82
79
49
81
98
49
12
77
69
123
41
32
45
18
14
91
50
66
77
164
(95.7)
54
107
96
91
66
78
63
31
57
92
32
63
187
172
89
99
184
198
[100.6]
135
122
72
109
108
    Source: Data from Lioy et al. (1990).  Missing values estimates and recomputed outlier values calculated by U.S. EPA.

-------
     TABLE 7-38. RESULTS OF AN ANOVA ANALYSIS OF THE PERSONAL
            EXPOSURE DATA OF PHILLIPSBURG, NEW JERSEY
Source of Variation
Date
Person
Date x Person
Total
d.f.
13
13
169
195
s.s.
119,600
103,300
149,900
372,800
m.s.
9202
7942
887

Source:  U.S. EPA reanalyses of data from Lioy et al. (1990).
       TABLE 7-39. SAM SITE CONCENTRATIONS, PM10 DATA (/tg/m3)
                   FROM PHILLIPSBURG, NEW JERSEY
                       [Missing Values Estimated ()].
Day Site 101 Site 102
01
02
03
04
05
06
07
08
09
10
11
12
13
14
Means
Covariance/Correlation
Site 101
Site 102
Site 103
Site 020
26
51
94
148
76
15
44
101
59
46
37
28
27
21
55
41
(55.6)
(101.8)
155
81
17
47
105
67
52
36
33
27
23
.2 60.1
Matrix (Correlation below
1313
0
0
0
.9 1346.5
.995 1393.8
.996 0.994
.943 0.935
Site 103
28
55
112
165
76
13
49
119
68
50
35
28
27
19
60.3
diagonal)
1538.9
1581.4
1816.2
0.929
Site 020
24
46
98
209
85
50
51
99
66
57
34
28
25
38
65.0

1596.6
1630.9
1850.1
2183.4
Source:  U.S. EPA reanalyses of data from Lioy et al. (1990).
                                 7-137

-------
  TABLE 7-40. RESULTS OF AN ANOVA ANALYSIS OF THE SITE EXPOSURE
                   DATA OF PHILLIPSBURG, NEW JERSEY
Source of Variation
Site
Day
Site x Day
Total
d.f.
3
13
39
55
s.s.
671
90286
3615
94572
m.s.
223.6
6945.1
92.7

Source: U.S. EPA reanalyses of data from Lioy et al. (1990).
TABLE 7-41. AVERAGE PERSONAL PM10 EXPOSURE DATA COMPARED WITH
        THE SITE EXPOSURE DATA FOR PHILLIPSBURG, NEW JERSEY
Day
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Ambient Average (/jg/m3)
29.75
51.55
101.45
169.25
79.5
23.75
47.75
106
65
51.25
35.5
29.25
26.5
25.25
Average Personal (/ig/m3)
60.15
58.91
106
134.29
86.76
42.07
80.23
91.86
79.19
69.57
60.74
62.79
57.14
62.04
Source: U.S. EPA reanalyses of data from Lioy et al. (1990).


7.6.3.3   Beijing, China Data Set
     The Beijing, China data set reported by the World Health Organization (1985) is listed
in Table 7-43.  From these data, daily mean values of the ambient and personal exposure
values were computed.  An U.S. EPA reanalysis of these data is shown in Table 7-44 and in
Figure 7-33. The results of the analysis indicate that there is not a significant linear
relationship between the personal and ambient monitoring data.  For this reason, it does not
                                    7-138

-------
            200
             150
         Q.
             100
         <   50
                             50            100           150
                                   Average Site PMi0 ng/rrr3
200
Figure 7-32. Plot of relationship between average personal PM10 exposure versus
             ambient PM10 monitoring data from Phillipsburg, NJ and regression line
             calculated by U.S. EPA.
Source: Lioy et al. (1990).
make any sense to adjust the coefficient for measurement error. The subjects all worked at
the same institute so their daytime personal exposures may not have been independent of
each other.

7.6.3.4    Riverside, California Data Set
     Both the personal exposure and the monitoring data used in analyses by Clayton et al.
(1993) contained some missing values, and they were estimated by U.S.  EPA as described
earlier.  The estimated correlation/covariance matrix results of U.S. EPA reanalyses of these
data are shown in Table 7-45.
     Because the individual monitors were placed on different individuals each period, we
can't really estimate the variation between individuals.  Based on previous analyses, we know
that most  of the residual is  variation between individuals, and so we will use this as a
                                         7-139

-------
               TABLE 7-42.  RESULTS OF THE ANALYSIS OF THE
             EXPOSURE DATA FROM PHILLIPSBURG, NEW JERSEY
Regression Model
Linear, normal error
Linear, lognormal error
Orthogonal
Linear adjusted for person error
Linear adjusted for ambient error
Measures of Association
Correlation of averages
Correlation adjusted for measurement error
Average correlation of ambient with mean person
Average correlation of person with mean site
Fraction of variation explained by orthogonal regr.
01 ft)
0.546 42.3
0.560 41.4
0.556 41.9
0.556 41.9
0.587 40.1
Value
0.955
0.974
0.944
0.633
0.984
Source:  U.S. EPA reanalyses of data from Lioy et al. (1990).


surrogate. On average there were 3.5 persons per period and this number of individuals was
used in the analysis of variance shown in Table 7-46.  The dichot monitoring data contained
little missing data,  and  so it was analyzed against the personal monitoring data for those days
with data. The results  of the linear regression are in Table 7-47 and are shown graphically
in Figure 7-34. The individual exposure values were averaged so that they could be
compared with the  site  exposure values.  These means are shown in Table 7-48. Note that
the orthogonal regression slope is larger than either of the linear regression slopes.  Note
also that the linear  regression slope adjusted for measurement error is larger than any of the
other slopes.

7.6.3.5 Azusa, CA Data Set
     The Azusa, CA data set for PM10 reported on by Wiener et al. (1990) was described
earlier in Section 7.4.1.1.1  and presented in Table 7-21a. The same regression analyses
described earlier in this section were performed on the 24-h cross-sectional data and the
results are shown in Table 7-49.  A plot of the linear regression analysis, resulting in a
                                        7-140

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              TABLE 7-43.  PERSONAL AND AMBIENT EXPOSURE
                       DATA FOR BEIJING, CHINA (mg/m3)
Day
1
2
2
2
2
2
3
3
3
4
4
4
4
4
4
5
5
5
5
5
5
5
Personal
0.13
0.15
0.10
0.12
0.23
0.14
0.11
0.09
0.09
0.31
0.12
0.13
0.35*
0.12
0.25
0.10
0.22
0.32
0.12
0.08
0.13
0.07
Ambient
0.19
0.25
0.25
0.25
0.25
0.25
0.31
0.31
0.31
0.33
0.33
0.33
0.33
0.33
0.33
0.36
0.36
0.36
0.36
0.36
0.36
0.36
Day
6
6
6
6
6
6
7
7
7
8
9
9
9
9
10
11
11
11
11
11
11
11
Personal
0.15
0.17
0.13
0.16
0.21
0.08
0.35
0.24
0.20
0.15
0.23
0.18
0.10
0.38
0.11
0.23
0.32
0.11
0.21
0.11
0.20
0.29
Ambient
0.42
0.42
0.42
0.42
0.42
0.42
0.44
0.44
0.44
0.53
0.55
0.55
0.55
0.55
0.59
0.69
0.69
0.69
0.69
0.69
0.69
0.69
*The only personal value higher than the ambient value.

Source:  World Health Organization (1985).


          TABLE 7-44. RESULTS OF LINEAR REGRESSION ANALYSIS
	FOR THE BEIJING, CHINA EXPOSURE DATA 	
Linear regression analysis of average personal exposure versus ambient exposure
Y = intercept + slope X
Variable                         Beta            Std. Error Beta
Intercept                       0.116                0.040
Slope	0.142	0.088	
                                     ANOVA Table
Source                     Sum of Squares       Mean Square Error       D.F.      F-Value
Regression                      0.0179              0.00893            2        1.2911
Error                          0.2835              0.00692            41
TOTAL                        0.3014              0.00701            43
R-squared = 0.05925, r = 0.2434
Log-likelihood = -46.95	

Source:  U.S. EPA reanalyses of data from World Health Organization (1985).

                                       7-141

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             400
             300
             100
                          200        400        600
                                Ambient PM10(ng/m3)
800
Figure 7-33.  Plot of means of personal exposures and ambient PM10 from Beijing,
            China and regression line calculated by U.S. EPA.

Source: U.S. EPA reanalyses of data from World Health Organization (1985).
      TABLE 7-45. ESTIMATED MEAN VECTOR, COVARIANCE MATRIX,
           AND CORRELATION MATRIX OF PERSONAL EXPOSURE
           PM10 DATA FROM RIVERSIDE,  CALIFORNIA (24-h,
Monitor

Means
Personal
109.9
Covariance/Correlation Matrix
Personal
Indoor
Backyard
Dichot
Wedding
PEM-SAM
1055.0
(0.849)
(0.725)
(0.707)
(0.721)
(0.736)
Indoor
79.9
(Correlation below
917.4
1107.6
(0.703)
(0.767)
(0.753)
(0.776)
Backyard
91.7
diagonal)
1024.7
1017.9
1893.2
(0.821)
(0.832)
(0.858)
Dichot
71.2

749.0
832.7
1165.6
1063.4
(0.956)
(0.989)
Wedding
68.4

838.9
897.0
1296.9
1116.6
1282.8
(0.976)
PEM-SAM
80.4

913.7
987.4
1427.4
1232.9
1337.1
1462.3
Source: U.S. EPA reanalyses of data reported on by Pellizzari et al. (1992).
                                    7-142

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             TABLE 7-46. RESULTS OF AN ANOVA ANALYSIS OF
       THE PERSONAL EXPOSURE DATA OF RIVERSIDE, CALIFORNIA
Source of Variation
period
residual
Total
D.F.
46
114
160
S.S.
167,400
275,000
442,400
M.S.
3640
2412

Source: U.S. EPA reanalyses of data reported on by Pellizzari et al. (1992).
              TABLE 7-47.  RESULTS OF THE ANALYSIS OF THE
              EXPOSURE DATA FROM RIVERSIDE, CALIFORNIA
Regression Model
Linear, normal error
Linear, lognormal error
Orthogonal
Linear adjusted for person error
Linear adjusted for ambient error
Measures of Association
Correlation of averages
Correlation adjusted for measurement error
Fraction of variation explained by orthogonal regr.
01
0.6174
0.6185
0.8071
0.9675
(Not applicable)




ft)
59.7
57.1
44.2
31.0

Value
0.721
(Not applicable)
0.864
Source:  U.S. EPA reanalyses of data reported on by Pellizzari et al. (1992).
negative slope, is shown in Figure 7-35.  There clearly is no relationship between the pooled
PEM and SAM variables for this data set. The statistical explanation for the negative
correlation and slope (PEM decreases with increasing SAM) is that one of the observations
(PEM = 273 jig/m3, SAM = 48 /-ig/m3, for House 9, Day 10, person 1, as shown in
Table 7-21a) is an outlier (273 /-ig/m3 >  mean + 3*SD). Removal of this single datum
point changes both the correlation and the slope to slightly positive values of similar
magnitude.  Because of the insignificance of the slope and correlation, further adjustments
for measurement error do not make sense.

                                      7-143

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                 200
                  150
              n
              I

              I  100
                  50
                               50         100         150
                                     Ambient SAM ng/m3
200
Figure 7-34.  PTEAM mean 24-h PM10 data compared for personal PEM and SAM.
Source:  U.S. EPA reanalyses of data reported on by Pellizzari et al. (1992).
7.6.4  Discussion of Statistical Analyses:  Mean PEM Versus Mean SAM
     The Beijing study had an insignificant positive slope and the Azusa study gave an
estimated slope less than zero that becomes insignificant positive with the removal of one
outlier.  Possible explanations for the low slope of the Beijing study may be related to the
unusually low ratio of PEM to SAM of order 0.4.  Either the SAM PM3 5 monitor that was
used may have been influenced by a local PM source, and thereby was not representative of
the Beijing locality where the subjects worked and lived, or the air exchange between indoors
and outdoors during the winter period was greatly minimized for personal comfort.
     In the Beijing dataset of 44 pairs of simultaneous SIM and SAM (Table 7-43) only one
PM3 5 PEM value was greater than  SAM, as opposed to Azusa  where in the 50 pairs of
simultaneous SIM and SAM (Table  7-21b) only six PM2 5 PEM values were less  than SAM.
On a day where SAM PM3 5 reached 690 /ig/m3 in Beijing, seven simultaneous PEM values
all ranged between 110 ng/m3 and 320 /ig/m3.  In relation to Figure 7-16, these PEM/SAM
ratios between 0.16 and 0.45 correspond to low air exchange rates of order 0.1 to 0.3 air
changes per hour. In the tightly-sealed poorly-heated building where all the subjects worked
                                        7-144

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     TABLE 7-48. AVERAGE 24-HOUR PM10 PERSONAL EXPOSURE DATA
          COMPARED WITH THE PEM-SAM SITE EXPOSURE DATA
                   FOR RIVERSIDE, CALIFORNIA (/tg/m3)
Period
1
3
5
7
9
11
15
17
19
21
23
25
27
29
31
37
39
41
43
47
49
51
53
57
59
61
63
65
67
69
71
73
75
77
79
81
83
85
87
89
91
93
95
Average Personal
48.3
83.6
108.6
88.3
68.3
121.0
68.2
95.8
102.5
116.8
160.5
97.7
72.2
107.6
103.0
165.3
144.4
135.6
168.2
173.8
144.9
65.0
76.7
110.9
78.4
136.1
103.1
142.4
163.6
153.7
144.2
150.6
125.4
112.1
63.7
67.5
102.2
92.0
100.0
88.9
113.0
82.4
97.3
PEM-SAM Site
35.1
41.7
56.9
64.1
51.7
55.8
56.0
69.1
92.0
108.2
126.4
79.4
60.7
52.9
87.4
66.8
106.2
138.5
107.5
175.9
112.9
77.9
42.8
17.6
46.7
61.1
78.4
77.9
127.6
150.4
147.4
166.4
139.6
59.2
42.7
61.4
75.8
35.7
65.3
75.3
122.7
48.8
57.1
Source:  U.S. EPA-calculated 24-h averages, based on 12-h data reported on by Pellizzari et al. (1992).
                                 7-145

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             TABLE 7-49. RESULTS OF THE LINEAR REGRESSION
       ANALYSIS OF THE EXPOSURE DATA FROM AZUSA, CALIFORNIA
Variable
Intercept
Slope
Covariance Matrix of Parameter Estimates
Intercept
Slope
Log-likelihood = -263.4
Beta
119.1
-0.054
Intercept
' 189.7
-2.543

Std. Error Beta
13.77
0.201
Slope
-2.543
0.040

                                  ANOVA Table
Source
Regression
Error
TOTAL
R-squared = 0.0015
Sum of Squares
111.2
76590
76700

Mean Square Error
55.6
1531.8
1475.1

D.F.
2
50
52

F-Value
0.0363



Source: U.S. EPA reanalyses of data reported on by Wiener et al. (1990).
                300
             LLI
             Q.
              ° 200
             Q.  100
                        •
50         100        150
   PMio Ambient SAM ng/m3
                                                             200
Figure 7-35. Plot of ambient and personal monitoring PM10 data from Azusa, CA and
            calculated (slightly negative slope) regression line, which becomes positive
            if single outlier value (/} is deleted.

Source: U.S. EPA reanalyses of data reported on by Wiener et al. (1990).
                                     7-146

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during the Beijing winter, a small variation in air exchange could result in a relatively large

difference in the indoor PM, which would result in PEM that appears to be uncorrelated with

SAM.  If a contribution of PM generated by personal activity and ETS is subtracted from the

PEM values then the estimated air exchange rates would be even lower.  The remaining

discussion will be based on the other three studies,  realizing that the discussion is not

supported by these two studies.

      The major conclusions which can be reached from the remaining three studies are as

follows.

      (1)  The average of several ambient monitors correlates better with mean personal
          exposure than does an individual site (as would be predicted by the Central Limit
          Theorem).

      (2)  The average of several personal monitors correlates better with mean ambient
          exposure than does the ensemble of individual monitors.

      (3)  There is no evidence of the existence of a maximum (ceiling) correlation between
          personal and ambient measurements.  The only study with fixed multiple (n >  2)
          ambient SAM locations and multiple personal monitors is the Phillipsburg, NJ,
          study.  The estimated correlation adjusted for measurement error was 0.97. The
          true (unknown) correlation between an infinite average of personal monitors with
          an infinite average of fixed site monitors may be different (smaller) in other
          locations, but we do not have the data to evaluate that.

      (4)  The correlation coefficient is probably the best measure of association between
          personal and ambient measurements.  It can be used independent of the regression
          technique or model and does not assume a distributional form.  The "percent of
          variation explained" as derived from orthogonal regression is not comparable to
          any  measure used for other models.

      (5)  The choice of a model (linear, linear with lognormal error, orthogonal) makes less
          difference  than the adjustment for measurement error.

      (6)  Based on the results of the Phillipsburg, NJ,  analysis,  one or more fixed site
          monitors can do an excellent job of predicting the average of all personal exposures
          (if they could be measured) even though the prediction for most individual
          exposures is quite poor.  This is also supported by the Tokyo, Japan,  data set
          (Tamura et al., 1996).  The other data sets did not provide adequate information to
          either confirm or deny this conclusion.

     The value of the improvement of the mean PEM relationship to SAM is that it provides

a better visualization that helps in understanding how mean PEM varies with SAM.   It thus

provides a measure of the validity of the use of a daily PM SAM as a surrogate for the mean

                                         7-147

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PM PEM in the community for nonsmokers.  It is clear that the uncertainty in predicting
mean personal exposure PM is much smaller than the uncertainty in predicting the personal
exposure PM for a nonsmoking individual when we note that the means have a much smaller
variability about the line as shown in Figures  7-31, 7-32, and 7-34.
     There appears to be two distinct categories of cross-sectional exposure studies that were
examined:  In the first type of study, such as  Lioy et al.  (1990), Clayton et al. (1993), and
Tamura et al.  (1996), there is a significant R2 between individual PM PEM and PM SAM.
In this category, there is an appreciable improvement in  correlation between the mean PEM
and SAM.  It has been suggested that these cases with higher correlation of PEM PM with
SAM PM may arise where the fine portion  of the ambient PM  (PM2 5) is highly variable
from day-to-day, and the ambient coarse fraction is relatively constant (Wilson and Suh,
1995).  In an urban area, the fine particle composition and the  fine particle concentration are
often highly correlated  from site-to-site on any given day.  This is due,  in part, to the gas
phase reactions of SOX and NOX, associated with regional sources, to produce sulfates and
nitrates in the submicron range.  Because of the long residence times of these species due to
their negligible deposition velocities, they are well mixed throughout the air mass (Suh et al.,
1995; Burton etal., 1996).
     On the other hand, ambient coarse particles are generated locally,  and they have higher
deposition velocities than the fine particles.  Their impact may  then be limited by fallout to a
locality downwind of their emission point, as  they are not readily transported across an urban
area.  Therefore,  during an air pollution episode, people living in an urban area may be
exposed to fine PM of similar  chemical composition and concentrations, whereas  they will be
exposed to coarse PM of ambient origin with a chemical composition that can depend on the
location of the exposure.  Because ambient  PM penetrates readily into a nonambient setting,
the correlation between the mean PM2 5 PEM and PM2 5 SAM would be high because  all the
people would have similar exposure to the ambient fine PM - plus exposure to indoor
generated PM2 5 which may have  less fluctuation in the absence of smoking.
     In the second type of study,  such  as Sexton et al. (1984), Spengler et al. (1985),  and
Wiener et al. (1990), there is negligible correlation between individual PEM  PM  and SAM
PM, and consequently there will be little correlation between their mean PEM and the  SAM.
In these cases, if the fine fraction is not an  appreciable portion of the ambient PM, or there
                                         7-148

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are significant indoor sources, then the correlations between mean PM PEM and PM SAM
will be lower and possibly not significantly different from zero.
7.7  IMPLICATIONS FOR PARTICULATE MATTER AND
     MORTALITY MODELING
     PM related mortality may be specific to the most highly susceptible portion of the
population. Such a cohort may be the elderly people with the most serious chronic
obstructive pulmonary disease (COPD) and cardiac insufficiency.  Smithard (1954) relates
the findings of Dr. Arthur Davies (Lewisham coroner) who autopsied 44 people who died
suddenly during the 1952 London Fog:
     "The great majority of deaths occurred in people who had pre-existing heart and lung
     trouble,  that is to say they were chronic bronchitic and emphysematous people with
     consequent commencing myocardial damage. The suddenness of the deaths, Dr. Davies
     thought, was due to a combination of anoxia and myocardial degeneration resulting in
     acute right ventricular dilatation."
     Mage and Buckley (1995) hypothesized that these people with compromised cardio-
pulmonary systems may be relatively inactive, while selecting to live in homes or
institutional settings without sources of indoor pollution.  When their tune is spent in clean
settings (e.g. where smoking is prohibited), they would have little exposure to PM other than
from the ambient pollution that intrudes into their living quarters (Sheldon et al., 1988a,b).
The exposure to PM of this cohort,  would be highly correlated with PM SAM, and so would
be their mortality, if this ambient PM was reactive in their pulmonary  tracts as described by
West (1982).  However, there have  been no results reported of an exposure study done on
people with COPD who correspond  to the Lewisham mortality cohort.  The cohort of five
elderly housewives and two male retirees in Tokyo (Tamura et al., 1996) may come close to
this susceptible cohort.  Individual PM PEM of people outside these cohorts, who could be
relatively insensitive to ambient PM, might not be significantly correlated with PM SAM, as
reported in most of the other studies of nonsmokers cited in Table 7-26. This suggests a
model to relate PM and mortality as follows.  Let any person (j) on a given day have a
probability of mortality, p(m) = kj Xj, where kj is the unit probability of mortality per /ig/m3
of PM per day, .Xj j& the daily average exposure to PM, /ig/m3, independent of kj.  Let us
                                        7-149

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assume that each individual (j) has their own personal value of fc that can vary from day-to-
day with changes in their respiratory health, such as a transient pulmonary infection (West,
1982).
     The expectation of total mortality (M) in a community of size N can be shown to be the
summation of k X over all individuals (j = 1  to N) as follows:

                                     M = E kj Xj                                (7-31)

If kj is independent of Xj, then we can define K as (1/N) E kj, and the mean community
exposure X as (1/N)  E Xj, and it follows

                                      M = NKX                                 (7~32)

     This implies that, given a linear relationship of mortality with PM PEM exposure  (X)
as assumed in most studies discussed in Chapter  12, the expected mortality is proportional to
the mean community personal exposure to PM. The individual in the community, on any
given day,  with the highest probability of dying from a PM exposure related condition is that
individual with the highest product kj Xj, not  necessarily the highest exposed individual with
the maximum value of Xj (West, 1982).
     The Phillipsburg, NJ, data  set is a case  in point.  In this study, three subjects had
excessively high PEM PM.  These values  were caused  by a hobby involving welding in a
detached garage  (971 ^g/m3), a home remodeling activity (809 jug/m3) and usage of an
unvented kerosene heater (453 pig/m3).  Excessive PM generating activities are not expected
of elderly people who may have compromised pulmonary systems.  In fact, the elderly  and
infirm husband of the remodeler had a personal exposure of 45 /xg/m3 on the day of the
remodeling activity.  The indoor monitors in  the homes of the welder and remodeler only
recorded 55 ^g/m3 and 19 /wg/m3,  respectively, during  those events, indicating the specificity
of the high exposure to only the individual involved. These three outliers were removed
from the analysis and were replaced by the procedure for missing data of section 7.6.2.1,
which estimates their exposures as if they  had not done those specific activities responsible
for their noncharacteristic exposures (see Table 7-37).  This procedure is reasonable,  since it
                                         7-150

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is unlikely that these activities would be performed by individuals with pulmonary conditions
similar to those of the Lewisham mortality cohort (Smithard, 1954).  As shown on
Table 7-42 and  Figure 7-32, the regression improves markedly to a value of R2 = 0.914.
     It is this relation of the average PM PEM exposure to PM  SAM concentration, as
shown in Figure 7-32 that may be a better representation of the true situation underlying the
PM vs mortality relationships because of the "healthy worker" effect.   Chronically ill people
who are sensitive  to PM might change their behavior to minimize their exposure to irritants.
Consequently, healthy people with high  PEM PM measures in occupations and indoor
settings can cause the regression R2 between PEM and SAM for nonsmokers to be low, but
they may not be the individuals at highest risk of the acute effects of PM exposure.

7.7.1 Relative Toxicity of Ambient Particulate Matter and Indoor
       Particulate Matter
     In the previous sections the SAM PM was evaluated as a predictor of PEM PM of
nonsmokers on the implied basis that the health effects of PM were only mass dependent,
and independent of chemical composition.  It was shown in Table 7-26 that many early PM
studies of PEM had a low correlation between PEM and SAM on a cross-sectional basis that
was  often not significantly different from zero.  But, in the later studies (Tamura et al.,
1996; Lioy et al., 1990), a significant relationship was observed between PEM and SAM on
an individual basis.  Further analysis showed that on a daily basis, SAM would appear to be
a good predictor of mean community exposure to ambient PM10 of nonsmoke exposed people
from the results of the Tokyo,  Japan;  Riverside, CA; and Phillipsburg, NJ; studies.
However, there can be a large  difference in toxicity of PM per unit mass which is related to
the chemical composition, solubility and size  of the particles.  For example, mercury (Hg)
and arsenic (As) have significantly different toxicities in their inorganic and organic forms.
Hexavalent chromium (Cr) is more toxic than trivalent Cr.  Anthropogenic PM,  from
combustion of fossil fuels, is much more toxic than PM of natural origin (Beck and Brain,
1982; Mage et al., 1996).  Fine urban particulate matter generated by coal smoke during the
1952 London Fog at concentrations of order 2,000 /xg/m3 caused thousands to die (Holland et
al., 1979; United Kingdom Ministry of  Health, 1954) but 2,000 /ig/m3 of soil dust from dust
storms (Hansen et al., 1993) would not  have  been as deadly.
                                        7-151

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     Soil constituents that are tracked-in to a home on shoes, and are subsequently
resuspended, contribute to the personal cloud (Roberts et al., 1990; Thatcher and Layton,
1995).  "Even if this crustal PM is relatively inert, its presence in the lung potentiates the
toxicity of the anthropogenic particles because it increases the residence time of the more
toxic PM (WHO, 1995)" (Mage et al., 1996). This increase in soil constituents was also
shown in the PTEAM study (Ozkaynak et al., 1996) on  Figure 7-22 "by observation that
nearly all [soil] elements  were elevated in personal samples" but sulfur, which is in the
ambient fine mode, was not a personal cloud constituent.  This is consistent with the
observations of Wilmoth  et al. (1991) that "extremely small particles  (below two
micrometers) require local airflow (sampling) velocities  near 100 miles per hour [45 m/s] to
overcome surface attraction  forces and dislodge [them] for sampling".
     Figure 7-36 shows an example of resuspension of Pb in a Denver, CO, home
(Moschandreas et al., 1979). During the one-week sample,  a wind shift brought a clean air
mass to below 0.01 /ig/m3.  In this time period,  the average indoor Pb dropped from
0.085 to 0.048 /xg/m3. The residual  0.048 ^g/m3 represents the effect  of resuspension by
human activity.  When the wind shifted again, and ambient Pb rose to 0.360 /xg/m3 the
indoor Pb rose to 0.180 /*g/m3. Note  the peaks in the indoor concentration of Pb up to and
above 0.10 /ig/m3 during the clean air period which are  indicative of variations in
resuspension by human activities.
     There  is also some  indication in laboratory animal  studies, using transpleural
catheterization and intratracheal instillation, that products of fossil fuel  combustion are more
acutely toxic to animals than wood smoke and soil constituents (U.S. Environmental
Protection Agency, 1982, Table 12-6; Beck and Brain, 1982). Although these laboratory
animal studies may have  no direct relation to toxicity in humans, they provide an indication
of their relative toxicity in animals when administered by those two routes.
     In summary, there is evidence that not all PM constituents have the same toxicity per
unit mass.  These differences are due to differences in aerodynamic diameter and chemical
composition.  As shown  on  a Venn diagram (Figure 7-37, Mage [1985]), the focusing of the
description of a PM10 exposure increases the ability to estimate the potential toxicity of the
exposure. In the sequential description given below, the uncertainty  in the toxicity of the
mixture is decreased as more information is provided.
                                         7-152

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   0.90
 I
  o
  To 0.10
  8
  o
 O
   0.01
I     I     I
    Pb Outdoors
                       V
                               0.90
                             c
                             g
                             to 0.10
                             o
                             O
                               0.01
                                                           \     I     l
                                                         Pb Indoors
0.90
0.10
                  40   80   120  160        ""      0    40   80   120  160
                   Time, hours                              Time, hours
Figure 7-36.  Comparison of indoor and outdoor concentrations of lead in a home in
             Denver, October 1976, for 1 week, starting at 1600 h. Mean values are
             given by horizontal bars.

Source:  Moschandreas et al. (1979).
0.01
     1. 2 /ig/m3 of PM10.

     2. 2 /ig/m3 of PM10 in the size interval 2 to 2.5 /mi.

     3. 2 /ig/m3 of PM10 in the size interval 2 to 2.5 /un, 50% of automotive origin and
        50% of indoor source origin.

     4. 2 /ig/m3 of PM10 in the size interval 2 to 2.5 /im, 50% of automotive origin and
        50% of indoor source origin, 0.5 /ig/m3 of Pb, 0.5 /xg/m3 of BaP and 1 /ig/m3 of
        unspecified inorganic material.


     As applied to human exposure to PM, this concept of differential toxicity suggests that
data collections might benefit by providing data that would allow the toxicity of a PM
exposure to be evaluated in terms of chemical information, in addition to the mass collected

per unit volume.
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Figure 7-37. Venn diagram (Mage, 1985) showing focusing of information to more
            completely specify toxicity of a given PM mixture:  (1) universe of all
            possible mixtures of PM with concentration of 2 /ig/m3; (2) subuniverse of
            all combinations of PM with concentration of 2 /*g/m3 in size interval
            2.0 to 2.5 pm; (3) subuniverse of all combinations of PM with
            concentration of 2 jig/m3 in size interval 2.0 to 2.5 pm AD with 50% of
            automotive origin and 50% from indoor sources; and (4) subuniverse of all
            combinations of PM with concentration of 2 /tg/m3 in size interval 2.0 to
            2.5 fim AD with 50% of automotive origin and 50%  from indoor sources;
            25% Pb, 25% BaP and 50% unspecified inorganic materials.
7.7.2   Summary:  Linkage of Ambient Concentrations of Participate
        Matter to Personal Exposures to Particulate Matter

     As described by Wilson and Suh (1995), total exposure to ambient PM (X.,e) of any

given size range is equal to the summation of exposures to ambient PM over both ambient
(Xa) and nonambient (Xna) microenvironmental conditions.  Total exposure to PM is equal to
Xae plus exposure to nonambient PM concentrations generated independently of personal

activities (Xnai) and nonambient PM concentrations generated dependently on personal
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activities (Xnap) which may correspond to smoking and the personal cloud effect.  For a
period (T) of constant ambient PM a subject spends time Ta outdoors and time (T - Ta) in
n different nonambient microenvironments.  The total exposure to ambient PM can be
expressed as:
                              „      [TaXa  + (T-Ta)XJ                        „
     For a nonambient microenvironment, the equilibrium concentration of ambient particles
in it will be equal to
                                            Xa P a
                                     X   = —	
                                      118    (a  + *)
where P = penetration fraction of PM in the ambient air entering the nonambient
microenvironment,
     a = air exchange rate, h"1
     k = deposition rate (a function of AD), h"1.
     As discussed in section 7.2, the penetration factor P is virtually equal to 1 for all
particles less than 10 too. (Thatcher and Layton, 1995) and the fraction of Xj,a/Xa is as shown
on Figure 7-16.  Combining equations 7-33 and 7-6, we obtain
                                    Xa[Ta  * SWfrj + *)]                        (7-34)
                               ae
 where T - Ta  = £ tj, total time spent indoors,
     j = 1  to n, index of indoor microenvironment visited.
     Defining z as the overall ratio of exposure to ambient PM (X^) to the ambient
 concentration  (Xa),  so that Xae = z Xa,  letting v = Ta/T, the fraction of time the subject is
 outdoors, we obtain the average relation,
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                                      d-y)     «    ,                         (7-35)
                                                a + A:
                      where I—-—  is a time weighted average.
                             a + k
     As shown on Figure 7-38, on a daily basis, z can vary by an appreciable amount by
spending a fraction (y) of time outdoors.  For y = 1/3 (8 h), exposures to fine ambient
PM2 5 increase by 100% for people living in homes with an ah" exchange rate a  = 0.1 h"1.
     The total exposure (X) can now be written as,
                          x  = z x  +         j           J                      (7-36)
                                  a
where £ [(Xnai)j  + (Xnap)j] tj / T = |8, the personal exposure hicrement produced by sources
that do not influence the ambient concentration as measured by a stationary ambient monitor
(SAM). Simplifying, we can rewrite  Equation 7-36 as,
                                    X  =  z Xa + p                               (7-37)

which gives a physical significance to the  slope and intercepts of the regressions of PEM (X)
versus SAM (Xa) as discussed hi Section 7.6.
     The values of z, which depend on y, a, k and P can be determined from their
independent measurements described previously.  P = 1 for all PM <  10 pm A.D.
(Thatcher and Layton, 1995) and y = 0.074 [U.S. mean fraction of tune spent outdoors per
day; U.S.  Environmental Protection Agency (1989)].  From PTEAM (Wallace et al., 1993),
a = 0.9 h"1 as a median value for night and day. Ozkaynak et al.  (1993a,b) have determined
values for k as follows:
     For sulfatefc = 0.16 hA
     For PM2 5 k = 0.39 h'1
     ForPM10fc =1.01 h'1
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                            Air exchange rate (air changes per hour)
Figure 7-38. Fraction of ambient PM to which people are exposed (z) as a function of
             fraction of time outdoors (y) and air exchange rate for fine (PM2 5) and
             coarse (PM10 - PM2t5) particles.
From the equation z = y  + (1 -y) P a/(a + k)
     for sulfate, z = 0.074 + 0.926 (0.9)7(0.9 + 0.16) = 0.859
     for PM2 5 it is z  = 0.074 +  0.926 (0.9)/(0.9 + 0.39) = 0.720
     for PM10 it is z = 0.074 + 0.926 (0.9)7(0.9 + 1.01) = 0.512
These predicted values match  closely to the reported values of z cited in this Chapter 7 as
follows:
     Suh et al. (1993) report z =  0.87 ± 0.02 (r2 = 0.92) for SO4=
     Tamura et al. (1996) [Table 7-32] report z = 0.466 (r2  = 0.905) for PM10,
     Lioy et al.(1990) [Table  7-44] report z = 0.546 (r2  = 0.91) for PM10
It is not known what the average values of y and a were for the State College, PA, and
Phillipsburg, NJ, cohorts  of Suh et al. (1995) and Lioy et al. (1990),  or the Tokyo, Japan,
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cohort of Tamura et al. (1996). Therefore these results can only be considered as tentative
at this time.
     The parameter 6 in Equation 7-37 represents the contribution to personal exposures
(PEM) from nonambient sources both independent of and dependent on personal activities.
In general the composition of the PM emitted by  indoor sources (or resuspended by human
activity) that influence 6 will be different from the PM emitted into the ambient atmosphere
from sources  controlled by State Implementation Plans (SIP)s.  The nonambient /*E emissions
are from the activities of the subject (cooking, heating, smoking, resuspension of housedust,
hobbies, etc.) or independent activities of others in the same juE that are independent of the
ambient concentration (Xa).
     For the  situation in Tokyo (Tamura and Ando,  1994;  Tamura et al.,  1996) the
PM10 PEM vs PM10 SAM correlation is good for all subjects individually, as well as their
average PEM, because the data were collected  in a manner to minimize 6.  These data for
the seven nonsmoke exposed elderly subjects were culled to remove observations which were
influenced  by overt particle generating activities such as visitors' smoking, burning of
incense, and burning of antimosquito coils.  The  custom of taking  off shoes on entry into
Japanese residences and use of "tatami" mat flooring minimized resuspension of PM less than
10 ^m AD, although indoor activity did raise dust above 10 /im AD (Tamura et al., 1996).
     For the U.S. cities of Phillipsburg, NJ, and Riverside, CA, with large numbers of
observations, the correlations  of PEM vs SAM for PM10 were significantly positive but less
than for Tokyo, Japan, possibly due to the passive smoking and house dust generation in the
Riverside,  CA, and Phillipsburg, NJ, studies.  Even so, in Riverside,  CA, ambient sources
provided about 67%  of PM10 mass measured indoors (Ozkaynak et al., 1996). Finally, the
results of the studies in Beijing, China, and Azusa, CA, gave positive correlations of PEM
and SAM that were not significantly different from zero (If one outlier is included in the
Azusa analysis, the PEM vs SAM correlation is negative).  These  low correlations may be
due to low air exchange rates in Beijing during the winter as evidenced by the low
PEM/SAM ratios, and the presence of indoor sources in Azusa, as evidenced by the PEM
almost double the SIM or SAM. These latter studies are typical of the results in other U.S.
cities such as Kingston and Harriman, TN (Spengler et al., 1985), where ambient pollution is
relatively low, so that the personal cloud and indoor source effects predominate.
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     In summary, it appears that the first exposure conclusion of the previous PM criteria
document (U.S. Environmental Protection Agency, 1982), quoted in section 7.1.3, has been
generally supported by recent studies. If the relation of equation 7-35 which appears to
predict the observed relations in several studies cited in this document is a reasonable model
of the personal exposure to ambient PM, then that conclusion can be adjusted more
specifically as follows:

     1 . Long-term personal exposures to fine PM sulfates of outdoor origin may be
        estimated by approximately 85% of the sulfate in the fine fraction of ambient PM.
     2. Long-term personal exposures to PM < 2.5 /tm A.D.  of outdoor origin may be
        estimated by approximately 70% of the PM < 2.5 /xm A.D.  in the ambient PM.
     3. Long-term personal exposures to PM < 10 ^m A.D. of outdoor origin may be
        estimated by approximately 50% of the PM < 10 /mi A.D. in the ambient PM.
     These relationships still need to be  validated in populations other than those from which
they were derived. Variations will exist for cohorts with different fractions of time spent
outdoors (y) and air exchange rates (a) than the values chosen for representing the national
averages.
     Ambient concentrations of PM10 measured at properly sited monitoring stations are
highly uniform in urban areas (Burton et al., 1996,  Suh et al., 1995), have no losses in
penetration into /xEs (Thatcher and Lay ton, 1995), and may be highly correlated with
personal exposures to PM10  (Tamura et al., 1996) where indoor  sources of PM10 are
minimal.  Even where indoor sources of PM10 exist, they tend to produce different chemical
species than those found in the PM2 5 fraction, as shown by the sulfates which do not appear
in the personal cloud (Ozkaynak et al., 1996;  Suh et al., 1993).
     It is therefore concluded that the presence of variable indoor sources of PM10 tends to
lower the  observed correlations between  PEM PM10 (derived from both ambient and
nonambient sources) and SAM PM10 (derived only from ambient sources) and even achieve
values nonsignificantly different from zero.  Consequently,  the use of an ambient
concentration of PM2 5 or PM10 in relation to daily  changes of mortality and morbidity may
be a reasonable surrogate for the average personal exposure of people in the community to
the PM2 5 or PM10 generated by ambient sources.  "The consistently higher R2 values
observed in the longitudinal regressions support the epidemiological  findings more strongly
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than the poor correlations noted in the standard cross-sectional regressions" (Wallace, 1996),
as per the U.S. EPA reanalyses shown in Tables 7-36 and 7-42.
7.8  SUMMARY AND CONCLUSIONS
     For PM, the total exposure of an individual consists of the summation of the
individual's exposure to PM in a variety of microenvironments.  This typically includes
exposures while (a) outdoors and (b) indoors (at-home or in microenvironments such as
shops and public buildings; at-work in an office or factory; and in a vehicle).  The principle
of superposition is a useful mechanism to visualize the summation process.  A simplification
of this summation process  for an arbitrary individual, described in detail by Figure 7-30, is
illustrated in Figure 7-39.  In each  sub-figure (a to d) of Figure 7-39, the shaded area
represents PM exposure (in /xg-h/m3) of ambient origin appropriately indexed by a central
(community) monitoring station.  The clear area represents  that PM exposure (in jttg-h/m3)
the individual is exposed to which is not characterized by the PM measured at the central
monitoring station.
     Figure 7-39a shows that while outdoors, the subject can be exposed to (a) widely
dispersed ambient PM that is represented by  the community monitoring station and,
independently, also to (b) proximal PM that does not markedly influence the monitoring
station reading (from tobacco smoking, standing over a grill at a backyard barbecue,
"personal cloud", etc.). For example, in the PTEAM Study, backyard concentrations of
PM2 5  and PM10 had a correlation on the order of 0.9 with a central monitoring station.
Also, in Tokyo (Figure 7-25), outdoor concentrations immediate to the homes of subjects
studied by  Tamura et al. (1996) had a correlation of 0.9  with the local ambient monitoring
station.
     Figure 7-39b shows that, while indoors (not at work), the subject can be exposed  not
only to (a) ambient PM (represented by the monitoring station) that infiltrates indoors but
also to (b)  PM of indoor origin that does not influence the ambient monitoring station reading
(from smoking, cooking, vacuuming, "personal cloud", etc.).  Obviously, the proportion of
exposure to PM of ambient origin versus that of indoor origin can vary widely, depending
on: outdoor concentrations of the ambient PM; the air exchange rate of indoor spaces; the
                                         7-160

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 a.
  x
 UJ
                                                                          24 hours
                       -14 hours
    (a) Outdoors
Figure 7-39.
      (b) Indoors
     At-home, etc.
      (Non-work)
(c) Indoors
 At-work
(d) In-traffic
 (e) Total
Exposure
Conceptual representation of potential contributions of PM of ambient
origin  and PM generated indoors to total human exposure of a
hypothetical individual. The total personal exposure (e) of that
individual will consist of the sum of exposures to widely dispersed PM
of ambient origin (shaded areas) characterized by measurements at a
centrally-located community monitoring site and all other exposures
(non-shaded areas) to proximally generated particles either outdoors
or indoors  in situations designated for (a), (b), (c), and (d). Times of
exposure in the various situations reflect typical time-action patterns
for U.S. adults.  Depicted exposures to PM of non-ambient origin may
vary greatly from those shown there for qualitative impression only,
depending  on various factors described in the text.
presence or absence of indoor PM sources; and the removal efficiency of indoor sinks for

specific constituents of the respective PM of ambient or indoor origin. In the absence of

major indoor PM sources (e.g., smoking), the percentage of total exposure contributed by

PM of ambient origin can be substantial.  For example, as shown in Table 7-2, between 60%

and 80% of indoor air PM was estimated by source apportionment methods to  be of ambient

origin in non-smokers' homes in two U.S. cities (Steubenville, OH; Portage, WI) included in

the Harvard Six-City Study.   Even in smokers' homes, it was estimated that 60% of the non-

smoking related PM was of ambient origin in the same two cities.  The New York State
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ERDA Study (see page 7-23) also showed that, in homes without combustion sources, 60%
of the total indoor PM2 5 was from outdoor sources. For homes with smokers in the same
study, about 66% of the non-tobacco smoke indoor particles were found to be of ambient
origin.  Similarly, based on the Tamura et al. (1996) data shown in Figure 7-24, it can be
estimated that as much as 80% of the measured indoor PM10 in Japanese homes without
combustion sources  was of ambient origin.
     Figure 7-39c shows that while indoors at work the subject can also be exposed to
(a) ambient PM (represented by the community monitoring station) which infiltrates indoors,
and (b) PM of indoor origin that does not influence the monitoring station reading (from
smoking, welding, machining, "personal cloud",  etc.).  It can be expected that, for office-
type work, similar relationships as described above for the other indoor conditions (e.g.,
smokers' or non-smokers'  homes) would apply.  However, for work conditions involving
particle generation (e.g., wood working, welding, mining,  etc.), the personal exposure of
"dusty-trade" workers to indoor-generated particles can be several orders of magnitude
greater than their exposure to indoor particles of ambient origin.
     Figure 7-39d shows that while in traffic, the subject can be exposed to (a) ambient PM
that is represented by  the monitoring station (via  ambient air infiltration into the vehicle), and
(b) PM of on-board or proximal  vehicle origin that does not directly influence the community
monitoring station reading (from smoking, exhaust penetration from nearby vehicles, etc.).
For example, in one study, Morandi et al. (1988) found that the average concentration of
PM3 5 in motor vehicles in traffic (55 /*g/m3) was 60% higher than the average outdoor
PM3 5 level  (35 jig/m3).
     Figure 7-39e is a simple rearrangement of the shaded and non-shaded areas to show
that an individual's  total daily exposure (/ig-h/m3) can be thought of as the sum of two
quantities: (a) exposure to PM characterized by the local community monitoring  station, and
(b) exposure to PM of immediately proximal origin that varies independently of the PM
measured at the monitoring station. Conceptually, everyone  in the community will be
exposed to the mix  of PM represented by the shaded area that is characterized by the local
monitoring station, due to their time  outdoors and the penetration of PM into indoor
microenvironments  and vehicles.  However, not everyone in the community will be exposed
to the identical mix of PM represented by the clear area, because this exposure and its
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chemical composition is idiosyncratically related to their individual habits and practices

(smoking, home cleanliness, hobbies,  "personal cloud", etc.), their occupation (home maker,

student, office worker, welder, miner, etc.) and their mode and usage of transportation (car,

bus, train, etc.).

     Evaluation of information useful in determining relative contributions of ambient

(outdoor) and non-ambient (indoor) particles to total human exposures leads to the following

key conclusions:

     (1)  For PM, the ambient environment can be a major source of indoor pollution due
          to air exchange and infiltration. Whether the ambient is the dominant source of
          indoor PM depends on the  relative magnitude of indoor sources of PM.

     (2)  For PM of size fractions that include coarse particles, some studies have identified
          statistically significant relationships between personal exposures and ambient
          concentrations,  while other studies have not, probably due to overwhelming effects
          of indoor sources,  "personal clouds" and other individual activities.

     (3)  Cross-sectional  regressions  of personal exposure on outdoor PM2 * and PM10
          concentrations generally explain less than 25% of the variance (R^ < 0.25).
          However, longitudinal regressions for each person in the study (in those cases
          where the person was measured repeatedly) often show much better  relationships
          between personal exposure  and outdoor air concentrations.

     (4)  Personal exposures to outdoor-generated PM of any  size fraction <  PM10 can be
          estimated from  the fraction of time spent indoors and an estimate of the air
          exchange rate and deposition  rate associated with that size  fraction.

     (5)  The relationship between ambient concentration and personal exposure is better for
          finer size fractions of ambient PM, than for coarser PM.  Higher correlations
          between ambient concentration and personal  exposures have been found for  fine
          PM constituents (such as sulfates) without  indoor sources.

     (6)  For a study population of nonsmokers in which there is a significant positive
          correlation between personal exposures and ambient concentrations,  the ambient
          concentration can predict the mean personal exposure with much less uncertainty
          than it can predict the personal exposure of any given individual.

     (7)  For Riverside, CA, where 25% of the nonsmoking population was estimated to
          have personal exposures on the day  they were monitored that  exceeded the 24-h
          National Ambient Air Quality Standard for PM10 of 150 /ug/rn3, approximately
          50% of this mass was found to be of ambient origin.

     (8)  The personal exposure to PM of smokers is dominated by the milligram quantities
          of PM inhaled with each cigar, pipe, or cigarette smoked.


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(9)   For the U.S. studies, almost all personal exposures to PM are greater than the
     ambient concentrations.

(10)  The penetration factor from outdoors to indoors for both PM2 5 and PM10 was
     found to be unity in the PTEAM and Thatcher and Layton (1995) studies.

(11)  Deposition rates in indoor microenvironments for PM10 and its fine and coarse
     fractions were determined in the PTEAM Study. Similar deposition rates were
     found by Thatcher and Layton (1995).  Deposition reduces exposure to ambient
     PM; coarse mode PM is removed more rapidly than PM2 5, which is removed
     more rapidly than sulfate.

(12)  Under equilibrium conditions, residential indoor concentrations of outdoor-
     generated PM of any size fraction < PM10 can be estimated for any given air
     exchange rate, by employing the deposition rate associated with that size fraction.

(13)  For PM, studies have detected a "personal cloud" related to the activities of an
     individual who may generate  significant levels of airborne PM in his/her vicinity
     which may not be picked up by an indoor PM monitor at a distance.

(14)  There is some evidence that nonsmoke-exposed elderly people have lower
     residential indoor PM concentrations than the simultaneous ambient PM
     concentrations, as opposed to the general population who have indoor PM
     concentrations comparable to or greater than ambient PM concentrations.

(15)  Measured indoor air concentrations of PM2 5 and PM10 generally exceed outdoor
     air concentrations (often by a factor of two) except in areas where outdoor
     concentrations are high (e.g., Steubenville, OH and Riverside, CA).

(16)  Indoor concentrations are higher during the day than  at night.

(17)  Correlations between indoor and outdoor particle mass concentrations were not
     significant in two of the three major studies reviewed. In the third (PTEAM)
     study, they  ranged between 0.22 and 0.54.

(18)  Regressions of indoor on outdoor PM2 5 and PM10 concentrations generally
     explain less than half of the variance (R2 < 50%) if  the regressions are  carried
     out simultaneously on all homes in the study.  However,  regressions for a single
     home (in those cases where homes were measured repeatedly) often have much
     better indoor-outdoor relationships (R2 up to 90%).  Since most epidemiological
     studies deal with repeated measurements over time, "longitudinal" regressions by
     individual home may be more relevant to these studies than "cross-sectional"
     regressions  across all homes.

(19)  The largest identified indoor source of particles in both homes and buildings is
     cigarette smoking.  Homes with smokers have an ETS-related PM2 5 concentration
     increment ranging between 25 and 45

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(20) The second largest identified indoor source of particles is cooking.  Homes with
     cooking had increased levels of PM10 on the order of 10 to 20 /ig/m3.

(21) Unknown indoor sources accounted for a substantial fraction (25%) of indoor
     concentrations of both PM2 5 and PM10 in the PTEAM Study. These sources
     appear to be due to personal activities, including resuspension of house dust.

(22) Variations in personal exposure due to fluctuations produced by  indoor sources of
     PM are independent of the variations in personal exposure produced by ambient
     sources.
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