EPA-450/2-89-010
                           April 1989
ASSESSING MULTIPLE POLLUTANT

MULTIPLE SOURCE CANCER RISKS

     FROM URBAN AIR TOXICS
     Summary of Approaches and Insights From
           Completed and Ongoing
       Urban Air Toxics Assessment Studies
   U. S. ENVIRONMENTAL PROTECTION AGENCY
           Office Of Air and Radiation
      Office Of Air Quality Planning And Standards
      Research Triangle Park, North Carolina 27711

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This report has been reviewed by the Office of Air Quality Planning
and Standards, U. S. Environmental Protection Agency, and has been
approved for publication.  Mention of trade names or commercial
products does  not constitute endorsement or recommendation for use.
                      EPA 450/2-89-010

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                         TABLE  OF CONTENTS
EXECUTIVE SUMMARY	i
       Background	i
       Purpose of Report	iv
       Report Organization	iv
       Report Emphasis	v
       The Changing Nature of Urban Assessments	vii
       References	viii
Chapter 1. An Introduction to Urban Air Toxics Studies	1
       1.1     Defining the Urban Air Toxics Problem	1
       1.2     Summary of the Completed and Ongoing Urban Air Toxics
              Assessments	2
                 The Six Months Study	3
                 The Integrated Environmental Management Project (IEMP)	..4
                 Clark County Study	5
                 South Coast—MATES	6
                 5 City Controllability Study	....8
                 Motor Vehicle Study	..9
                 Integrated Air Cancer Project (IACP)	11
                 TEAM (Total Exposure Assessment  Methodology) Studies	12
                 Staten Island/New Jersey Study	14
               -  Southeast Chicago Study	14
                 Urban Air"Toxics Monitoring Program....	'.	15 .
                 Bay Area Toxics Monitoring Study (BAAQMD, 1987)	16
       1.3     Other Urban Air Toxics Assessment Activities	17
       References	:	20
Chapter 2. Ambient Air Quality Monitoring	23
       2.1     Use of Ambient Air Monitoring Data in Multi-Pollutant, Multi-
              Source Urban Assessments	23
       2.2.    Decisions Affecting Monitoring  Plan Development	25
                 Studies  with Stated Goals for Air Quality Monitoring  Programs	27
       2.3.    Pollutant Coverage	33
                 Trends Toward Broader Pollutant Coverage	38
       2.4.    Site Selection	39
                 Formal [Quantitative] Approaches to Site Selection	39
                 Informal [Qualitative] Approaches to Site  Selection	45
                 Physical Siting Criteria Common to Ail Studies..	47
       2.5     Sampling Periods, Frequency and Duration	48
                 Sampling Period	49
                 Sampling Frequency	51
                 Sampling Duration	52
       2.6     Sampling and Analytical Techniques	54
                 Volatile Organics	54
                 Semivolatile Organics	58
                 Metals	58
                 Aldehydes/Ketones	,	59
       2.7.    Evolving Monitoring Technologies.	59

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                 TEAM 59
                 The Integrated Air Cancer Project	61
                 Special Monitoring Techniques	62
                    TAGA®	.62
                    ROSE  62
       2.8    Insights into the Use of Monitoring in Air Toxics Programs....	.62
                 Cost-Saving Measures	62
                 General Issues	66
Chapter 3. Emission Inventories	73
       3.1    Use of Emission Inventories in Multi-Pollutant, Multi-Source
             Urban Air Toxics Assessments	73
       3.2    Pollutant Coverage..	74
                 Treatment of Polycyclic Organic Matter (POM)	80
                 Treatment of Chromium	,	81
                 Treatment of Beryllium and Nickel	81
                 Secondary Pollutants	82
       3.3    Source Coverage	,	82
       3.4    Estimating Emissions	83
                 Emissions Estimation from Existing Data Bases	83
                 Source-Specific Survey Data	84
       3.5    Spatial and Temporal Resolution	86
                    Grid Size and Grid Cell Resolution	86
                    Point Source Resolution	87
                    Area Source  Allocation	88
                    Temporal Allocation	*....	89
       3.6    Emission Inventory Quality Assurance	I.........:....	89
                 Emission Inventory Review.	90
                 Data Verification	90
               ,  Monitoring vs. Modeling  Comparisons	91
       3.7    Insights into Compiling Inventories for Urban Air Toxics
             Assessment	91
       References	;	94
Chapter 4. Dispersion Modeling	95
       4.1    Use of Models in Estimating Exposure in Multi-Pollutant, Multi-
             Source Urban Assessments	95
       4.2    Decisions Affecting Modeling Protocols	97
       4.3    Model Selection	103
                 Model Characteristics	103
                 Rationale for Model Selection	106
       4.4    Release Specifications for Emissions	110
       4.5    Selection of Receptor Network Array	Ill
       4.6    Meteorological Data	119
                 Treatment of Meteorological Parameters	120
                 Treatment of Meteorological Variability	122
       4.7    Decay, Deposition, and Transformation	124
                 Decay 124
                 Deposition	124
       4.8    Model Execution	125
       4.9    Model Performance Evaluation	127
                 Evaluating Model Performance	127

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                 Improving Model Performance	129
       4.10   Insights into the Use of Modeling in Air Toxics Evaluations	137
                 Modeling Techniques	137
                 Cost-Saving Measures	142
       References	'.	144
Chapter 5. Exposure and Risk Assessment	146
       5.1    The Use of Exposure and Risk Assessment in Air Toxics Studies	146
       5.2    Methodological  Issues	149
                 Exposure Assessments	149
                 Risk-Assessment	155
       5.3    Comparison of Approaches to Interpreting Exposure and Risk	161
                 Ranking the Relative  Importance  of Pollutants	162
                 Ranking the Relative Importance of Sources or Source
                        Categories	162
                 Evaluating Exposure and Risk Patterns	163
       5.4    Insights into the Use of Exposure and Risk Assessment in Multiple
             Air Toxics Studies	169
       References	171
Chapter 6. Control Strategy Simulation  and Evaluation	173
       6.1    The Use of Control Strategy Simulation and Evaluation in Urban
             Air Toxics Assessments	~..-.	173
       6.2    .Comprehensive vs. Site-specific Strategy Analyses	174
                 Extent of Source Coverag'e.,.1	175
                 Multiple vs. Limited  Control Options	175
               -  Inclusion of Source Growth and Retirement	."	175
       6.3    Control Strategy"Simulation Procedures in the 5 City
             Controllability Study	i...	176
                 RIM Operation	176
       6.4    Control Strategies  Evaluated in  5 City Controllability Study	178
       6.5    Assumptions Inherent in 5 City Controllability Study Control
             Analysis	179
       6.6    Control Strategy Evaluation  in  the lEMPs	180
                 Philadelphia IEMP Control  Analysis	180
                 Baltimore IEMP Control Analysis	184
                 Santa Clara IEMP  Control Analysis	185
                 Analysis of Areawide vs. Most Exposed Individual (MEI) Risk
                        Reduction	186
       6.6.   Insights on Control Strategy Simulation and Evaluation	188
Chapter 7. Computerized Data Handling	191
       7.1    Data Handling Considerations in Urban Air Toxics Studies	191
                 Study Type	192
                 Computer Accessibility	192
       7.2    Data Handling Aspects of Emission Inventory/Dispersion Modeling
             Studies	193
                 PIPQUIC	193
                 Other Data Handling Systems	199
                 Cost Saving Techniques	205
       7.3    Data Handling Aspects of Ambient Air Monitoring Studies	205
       7.4    Insights on Computerized Data  Handling	206

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Chapter 8.  Emerging Methods: Receptor Modeling and Biological Testing.....	210
       8.1    The Use of Receptor Modeling in Urban Assessments	210
                 Pollutants and Source Categories Addressed	....212
                 Source Signature Testing and Tracer Analysis.....	.212
                 Measured Ambient Air Quality Data Sets	213
                 Statistical Techniques Used to Estimate Apportionment	214
                 Spatial  and Temporal Representativeness of Results	215
       8.2    Comments on the Use of Receptor Modeling for Urban Air Toxics
              Studies	216
       8.3 -    The Use of Biological Testing in Urban Air Toxics Studies	217
       8.4    Comments on the Use of Biological Testing in Air Toxics Studies	219
                 Relative Importance of Gas Phase Pollutants	219
                 Indoor/Outdoor Differences	220
                 Use of Biological Testing to Set Priorities	220
       References	221
Appendix A. Glossary of Terms	A-i
Appendix B. Evaluation of Optimum Size for Air Toxics Monitoring Network
       Based on Spatial Correlations of Concentration	B-i
       Introduction	B-i
       B.I    Method	B-i
       B.2    Example Application:  Philadelphia IEMP	B-iii
                 Siting Analysis Based on Modeled Data	B-iii
                 Siting Analysis Based oil Measured  Data	„	B-iv
                 Method Evaluation	B-vii

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                             EXECUTIVE SUMMARY
Background

       Increasing attention is being given to the problem of air toxics exposures in urban
areas.  This attention initially focused on maximum exposures of individuals living near
large  point sources.  Lately, however,  some attention has shifted to the potential for
widespread risks in urban communities resulting from smaller, more dispersed emitters of
air toxics.

       "Recent Environmental Protection  Agency (EPA) studies1  suggest that multiple
pollutant, multiple source exposures to air toxics may be causing  1,000 to 2,000 excess
cancer cases annually  in the United States and average lifetime individual cancer  risks
ranging from 1 in 10,000 to 1 in 1,000 (10~4 to 10'3) in urban areas. These  studies further
suggest that air emissions from small point, area, and highway vehicle  sources may be the
largest contributors to urban air toxics cancer incidence.

        Under EPA's National Air Toxics Strategy (EPA, 1985), a major activity has been
the assessment of high risk urban problems.  Numerous  assessment efforts  have  been
initiated in the past five years or are under way in many U.S. cities to evaluate both the
nature  and magnitude of the air toxics problem  in urban environments.  Some of  these
studies are examining  the potential  for  risk mitigation  through  various, control
alternatives. EPA, in carrying out the National Air Toxics Strategy, has been encouraging
urban areas to engage in these kinds  of assessment activities. Moreover, EPA is promoting
 1  Haemisegger, et al., 1985; Manale, et al., 1987; EPA, 1986; Hinman, et al., 1986; EPA,
    1987a; Shikiya, et al., 1987; Shikiya, et al., 1988; EPA, 1987b; EPA, 1988.

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the coordination of State and local air toxics control programs  with ozone (63) and
particulate matter (PMio) control programs to assure that, whenever possible, future
State Implementation Plans (SIPs)  incorporate measures  that reflect co-control of air
toxics. (EPA, 1987c) .

       Current Assessment Approaches—Most urban air toxics assessments to date have
used either  of two basic approaches:  ambient air monitoring  and emissions modeling.
Many, in fact, have used a combination of both approaches to minimize the weaknesses of
each.

       The ambient air monitoring approach is conceptually simple.  Measured ambient
levels of air toxics in an urban area are multiplied by cancer unit risk factors to calculate.
individual risks.  (Unit risk factors  relate specific probabilities of contracting cancer to
lifetime exposures to 1 ug/m3 of a specific pollutant.)  Individual risks are then multiplied
by population data to calculate aggregate cancer incidence.. Because of data limitations,
such analyses are usually fairly crude.  For example, some studies may assume that one or
several measurements of each, .pollutant represent long-term,  .areawide  population
exposures.   Ideally, ambient assessments of  urban air  toxics risks .would be more
sophisticated, involving many more ambient samples at more representative times and
locations.

       The  second  major approach involves dispersion  modeling of emissions.   In this
approach, an emission inventory is compiled for the air toxics of concern and is modeled
(using various long-term dispersion models) to predict ambient air concentrations over the
urban area.  These modeled ambient air concentrations are then used to estimate individual
risks and aggregate  incidence as if they were measured  data.   The dispersion modeling
approach requires a comprehensive emission inventory of point,  area, and highway vehicle
sources.

       There are advantages and disadvantages to each of these two  assessment
approaches.  The use of reliable and representative ambient air monitoring data avoids the
errors inherent in emission inventories and dispersion models, which can be considerable.
In addition, the ambient air monitoring approach allows one to handle secondarily  formed
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pollutants that are not emitted directly (e.g., photochemically formed formaldehyde) or
pollutants  that exist  in  the atmosphere resulting  from gradual  global buildup of
background levels  (e.g.,  carbon tetrachloride)  or transport  from other urban areas.
(Ambient monitoring data can also be used to help verify modeling results.)

       Important advantages of the dispersion modeling approach are that it allows one
to predict risk reductions as a function of anticipated emission changes  and it allows
ambient air levels to be projected at many more receptors than may be possible in most air
sampling networks.  It also  allows one to handle pollutants (e.g., hexavalent chromium)
for which ambient sampling methods are not yet available or  cost-effective. Dispersion
models also allow one to estimate the impact of specific sources on particular receptors,
which is not possible with ambient monitoring data.

       Future Assessment Approaches—Alternative approaches  are being  developed for
assessing  the..urban air toxics problem,  the most notable being  EPA's Integrated Air
Cancer Project (IACP) and Total Exposure Assessment Monitoring (TEAM) studies.  The
IACP is being conducted by.the  EPA  research labs  in Research. Triangle Park, North
Carolina.   It  is a  long-term  interdisciplinary research  program  aimed at developing
scientific methods and data bases to identify the major sources of carcinogenic chemicals
emitted into  the air  or  arising from  atmospheric  transformations.   This project is
performing extensive  ambient  air  monitoring, receptor modeling, bioassays,  and
atmospheric transformation  studies to identify the principal airborne carcinogens and, to
the extent feasible, quantify the human cancer risk they may pose.  Thus far, the project
has concentrated on the  effects  of emissions from wood burning and mobile  sources.
 Subsequent phases of this project will add other residential combustion sources  (e.g., oil
 combustion) and industrial sources. (Lewtas, 1987)

        The TEAM approach (Ott, 1986) assesses exposures by  measuring pollutant levels
 in an individual's immediate physical environment throughout the day. In contrast to a
 fixed monitoring station  that collects pollutants as they  are carried by the ambient (or
 indoor) air to the location of the devices, TEAM monitoring devices are located on  vests
 worn by individuals during their daily routine.   In addition, TEAM studies measure the
 concentration of chemicals in an individual's  drinking water and exhaled breath to
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determine the actual amount of the pollutant that has been taken up by the individual.
Personal sampling directly  addresses the variability of exposures within a population.
TEAM may best contribute to future  urban assessments by improving the  exposure
assumptions that are typically made in current assessments.

Purpose of Report

       The primary purpose of this report  is to assist State, local, and  other agency
personnel  by describing  methods  that have been used  in  assessing multiple  source,
multiple pollutant risks from air toxics exposures in urban areas.

       This report is a synthesis of assessment methods that have been made to date to
quantify multi-pollutant, multi-source urban  air toxics exposures and risks.  It does not
constitute formal EPA requirements for conducting an urban risk assessment of air toxics,
nor does it recommend a single  approach. Instead, it identifies techniques that others have
elected to employ, and it offers  insights tha't may assist the reader in selecting a particular
set of techniques for use in a given locale.  In some cases, this report discusses problems
encountered in previous studies so that prospective study managers might learn some
lessons. A State or local agency will have to decide from the procedures which (if any) to
adapt in its particular urban  assessment based on program goals, timing, and resources. -

Report Organization

       Chapter 1 of this report presents a brief overview of the major studies completed
in the past several years that have assessed  the multi-pollutant, multi-source urban air
toxics problem.  This overview  is provided because these studies are cited throughout the
body of this report. To a limited extent, the  major findings of each completed study are
summarized to give the reader a sense of the available evidence that suggests the existence
of an urban air toxics cancer problem.

       Chapters 2 through  7 describe specific activities that are common to urban air
assessments.  These major activities are highlighted below:
       •      Ambient air monitoring;
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       •      Emission inventorying;
       •      Dispersion modeling;
       •      Exposure and risk assessment;
       •      Control alternative simulation and evaluation; and
       •      Data processing.

       For each of these activities, a synopsis is given in the respective chapter of the
procedures employed in the  various urban assessments, and insights are offered on the
suitability of these procedures in particular  applications.  It should be noted that not all
of the above activities would  necessarily be involved in a given urban assessment.

       Chapter  8 summarizes various assessment methods that are currently under
development and may be employed in future urban assessments.  These methods, which
include bioassays, receptor modeling, and personal monitoring, are being developed by
EPA research laboratories.  Although not the focus  of this report, it is useful to be aware
of these evolving techniques.

       A glossary of terms relating to urban risk assessment is included in Appendix A
for  those not conversant with the terminology used herein.  Appendix B discusses  a
method  for  representative monitoring site  location relating to  material  discussed  in
Chapter 2.

Report Emphasis

       Cancer Risk—Cancer  has been the major focus of urban air toxics studies to  date.
As  such, this report focuses primarily on techniques employed by various study managers
to assess cancer risks.  These techniques generally involve the evaluation of long-term
(e.g., annual average) exposures to multiple  pollutants. Techniques for evaluating specific
non-cancer risks associated  with short-term (acute and subchronic) exposures are  only
discussed briefly, reflecting the limited work done in this area. Note that this report does
not deal with the development of dose-response relationships and cancer unit risk factors.
It is assumed that potency data for different pollutants are available from other sources.

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       Measures of Cancer Risk—Two common measures of excess cancer are incidence
and individual risk.  Excess cancer incidence is a measure of the number of excess cancer
cases associated with air toxics exposures within an urban area, whereas individual risk is
the probability that an individual will contract cancer from a particular exposure.

       There are many variations of each of these measures. For example, incidence can
be additive, covering multiple pollutants.  Incidence can reflect areawide cancer cases or
can be specific to particular "hotspots,"  such as neighborhoods or industrial parks.
Incidence can reflect the number of cases expected in a single year or within a 70-year
"lifetime."  Incidence is  sometimes population-normalized, for  example, adjusted per
million population.  A commonly used measure in multi-pollutant, multi-source urban air
toxics assessments is annual, areawide excess cancer incidence.

       La the same way, individual risk can refer to the cancer probability associated with
multiple  or single pollutant exposures, the probability  associated with  the average
exposure within a broad area or  with "the exposure  at a particular receptor, or the
probability of contracting cancer in a single year or during a 70-year lifetime.

       Screening  Assessments—Most of the  urban assessments completed to  date are
best described as screening (or scoping) studies, performed to yield an order-of-magnitude
estimate of the relative nature of the urban cancer problem, rather than to provide
absolute predictions of incidence and individual risks.  Screening studies  are often
considered more credible for prioritizing subsequent assessment efforts than for defining
direct  regulatory measures or for  predicting risks  to  specific individuals by specific
sources and pollutants.  If, for example, the screening  analyses described herein would
point to a single point source or a  cluster  of sources as posing particularly high risks,
more detailed assessment techniques might be needed, especially in situations (e.g., permit
review or standard setting) where  specific regulatory actions were contemplated.

       Risk Assessment vs. Risk Management—Quantitative risk assessment is  a process
by which a factual base of information is  used to estimate the probability  of incurring
some risk (e.g., cancer)  due to exposure to a specific chemical or chemical  mixture.  In
contrast, risk management is the decision-making process by which some action is taken or
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some policy is formed concerning a potential risk to the environment and/or to human
health.  Risk management differs from risk assessment in that management of risk usually
considers political, economic, and social issues in the decision-making  process.   This
document mainly deals with the methods to assess urban air toxics risks, including the
projection of risk reductions associated with control alternatives.

The  Changing Nature of Urban Assessments

       There  are  myriad  techniques,  assumptions,  and data involved  in urban
assessments, many of which may  change  in the next few years.  Cancer unit risk factors,
for example,  are subject to  significant, often order-of-magnitude, changes as  new dose-
response data become available. Ambient air monitoring and source sampling  techniques
for air toxics  are  evolving, allowing certain compounds to be measured accurately at
levels  not previously possible.  Techniques  are also  becoming available to evaluate
mutagenicity of specific urban air compounds-as they age and transform photochemically.

       It is important to be aware of the changing nature of urban assessments for two
reasons.  First,  it  means that-no-, two completed assessments have likely used the  same
procedures, have made the same  assumptions, or have used the same data in estimating
urban risk. If one examines the emission factors, unit risk factors, modeling assumptions,
pollutant coverage, etc., in the various studies, significant  divergence is  often apparent.
Hence, care needs to be taken  when "relating results from one  study to another to avoid
"apples and oranges" comparisons.

        Second, the information  presented in the  following chapters will  undoubtedly
 change as the perception of the urban problem matures and as more assessment options
 become available.  Moreover, the underlying emission  inventory and ambient air quality
 data bases upon which such assessments are based, should improve with time.  Thus,
 those responsible for urban risk  assessments  should keep  abreast of these changes and
 reflect them as much as possible.
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                              EXECUTIVE  SUMMARY
References
EPA,  1985.   "A  Strategy to Reduce Risks  to  Public  Health from Air Toxics," U.S.
Environmental Protection Agency, Washington, D.C.

EPA, 1986. Integrated Environmental Management Project: Philadelphia Phase n Report.
Regulatory Integration Division, Washington, D.C.

EPA, 1987a.  Kanawha Valley West Virginia Toxics Screening Study Report.
Environmental Services Division (Region HI) and the Regulatory Integration Division,
Philadelphia, Pennsylvania.

EPA,  1987b.  National Air Toxics Information  Clearinghouse Report;  "Qualitative and
Quantitative  Carcinogenic  Risk  Assessment,"  EPA 450/5-87-003, U.S. Environmental
Protection Agency and STAPPA/ALAPCO, Washington, D.C., pp. 11-1 through 11-6.

EPA,  1987c.  "State Implementation Plans;  Approval of Post-1987 Ozone and Carbon
Monoxide Plan Revisions for Areas Not Attaining the National Air Quality Standards;
Notice,"  Federal  Register,  Vol.  52, No. 22fi, Part II, Tuesday, November 24,  1987.
p. 45072.

EPA,  1988.  Preliminary results from 5-City Controllability Study being conducted  by
E.H. Pechan  and  Associates under  contract  to  U.S. Environmental Protection Agency,
Research Triangle  Park, N.C. (Tom Lahre, EPA project officer).

Haemisegger, E.,  et al.  1985.   "The Air Toxics Problem in the United States: An
Assessment  of Cancer Risks for Selected Pollutants," U.S. Environmental Protection
Agency, Washington, D.C.

Hinman, K., et al.,  19861   "Santa Clara Integrated Environmental Management Project.
Stage Two Report," U.S., Environmental Protection Agency, Washington, D.C.

Lewtas, J., and L.  Cupitt, 1987.   "Overview  of the Integrated Air  Cancer Project,"
Proceedings  of the 1987 EPA/APCA Symposium on Measurement of Toxics and Related
Air Pollutants, Research Triangle Park, N.C., pp.  555-561.

Manale, A, et al.,  1987.  "Baltimore Integrated Environmental Management Project.  Phase
H Report:  Air Toxics," (Draft) U.S. Environmental Protection Agency, Washington, D.C.

Ott, W., et al., 1986. "EPA's Research Program on Total Human Exposure," Environmental
International, Vol. 12, pp. 475-494.

Shikiya, M.,  C. Liu, E. Nelson, and R. Rapoport. 1987.  The Magnitude of Ambient Air
Toxics Impacts from Existing Sources in the South Coast Air Basin: 1987 Air  Quality
Management Plan Revision Working Paper Number 3. South Coast Air Quality
Management District, June 1987, El Monte, California.
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Shikiya, M., W.  Barcikowski, and M. I. Kahn.  1988.  Analysis of Ambient Data from
Potential Toxics "Hotspots" in the South Coast Air Basin.  South Coast Air Quality
Management District,  October 1988.
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                                    CHAPTER i

                 AN INTRODUCTION TO THE URBAN AIR TOXICS
                                     STUDIES
1.1
The following subjects are covered in this chapter:

•      Defining the urban air toxics problem

•      Summary of completed and ongoing urban air toxics assessments

•      Other urban air-toxics assessment activities


Defining the Urban Air Toxics Problem
       The perception has been evolving over the last few years that an air toxics "soup"

exists over cities that is imposing an uncertain, but possibly significant, public health risk

of increased cancer. This concern has been described by the following terms:

       •      Urban hot spot problem

       •      Multiple source, multiple pollutant problem

       •      High-risk urban problem

       •      Urban soup

Whatever term one uses to describe the phenomenon, the concerns are basically the same:

       •      That traditional air toxics programs, which typically emphasize major
              point sources and single pollutant analyses, may not be addressing  the
              most significant cancer risks in urban areas;

       •      That criteria pollutant control programs,  which are known to effect some
              air  toxics reductions through  indirect  means,  may  not be sufficiently
              addressing certain air toxics problems;

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       •      That small point and area sources and road vehicles may be causing more
              of a problem than has been recognized heretofor in air toxics programs;
       •      That simultaneous  exposures to urban air  toxics  mixtures  from many
              sources can lead to significant aggregate risks in urban areas, risks that are
              not being realistically assessed  by current pollutant-by-pollutant risk
              assessments; and
       •      That byproducts of irradiated and aged urban air mixtures may be much
              more potent than "fresh" (i.e., point of release) emissions traditionally
              analyzed in some risk assessments.

       Thus, whether one calls the problem "urban soup" or something else,  the point of
conducting an urban air toxics assessment  is the same—to  shed light  on these concerns
and determine whether additional air toxics  controls are needed.

1.2    Summary of the Completed and Ongoing Urban Air Toxics Assessments

       This report is a compilation of procedures that are being employed for conducting
multi-pollutant, multi-source urban air toxics assessments.  The procedures described in
this report are derived from afhumber of urban studies either" completed in the last several
years or currently under way.

       The purpose of this  chapter is to introduce the  reader to the  studies alluded to
throughout the remainder of this report.  Hence, what follows is a brief discussion of the
purpose and approach of each study, along with an overview of its major findings.  As a
rule, a short descriptive title is assigned to each study (e.g., the "Six Months Study") so
that hereafter, for ease of reference, the full  title of each assessment need not be repeated.

       The reader is cautioned that all of the risk and incidence values  cited herein, while
appearing very precise and certain in some cases, are acknowledged to be very preliminary
and tentative by the respective study authors and by the authors of this report. The many
uncertainties, assumptions, and limitations inherent in the procediires used in  these
studies are documented in the remainder of this report, and should be kept in  mind before
using any of the following data and conclusions.

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       The Six Months Study

       This study is formally entitled The Air Toxics Problem in the United States:  An
Analysis of Cancer Risks for Selected Pollutants. (Haemisegger, 1985)  It represents EPA's
first comprehensive analysis  of the  air toxics problem and provides  a basis for the
Agency's National Air Toxics Strategy. (EPA, 1985)  The Six Months Study is regarded as
a "scoping" study only, useful in a relative sense to yield rough approximations of total
incidence and individual risks.

       Three major analyses were undertaken as part of the Six Months Study to estimate
cancer incidence and individual lifetime risks. An "Ambient Air Quality Study" used
ambient data for 5 metals, 10 volatile organic compounds, and benzo(a)pyrene [B(a)P] to
assess risks. Two other analyses—a "NESHAP Study" and a "35-County Study"—used
emission estimates and exposure models to estimate incidence and maximum individual
risks associated with  the pollutants selected.  In these analyses,  a "B(a)P surrogate"
approach was used to  estimate cancer incidence associated with products of incomplete
combustion (PIC). To  do this", a dose-response coefficient relating lung cancer incidence
and PIC was generated from epidemiological studies, and  cancer incidence associated with
PIC exposure was estimated by applying this dose-response coefficient to B(a)P  levels.
Finally, quantitative risk assessments available from other EPA activities for asbestos,
radionuclides, and gasoline marketing were incorporated into the study.

        The  Six Months Study shows  that both point and area  sources contribute
significantly to the air toxics problem.  Large point sources are  associated  with high
maximum individual lifetime risks  (commonly 10"4 to 10"2), whereas the additive lifetime
individual  risk due to  simultaneous  exposure to  10 to 15  pollutants  range from
10'4 to 10'3.  The latter risks do not appear related to specific point sources, but instead,
are associated with the complex mixtures typical of urban ambient air. For the pollutants
examined, additive cancer incidence averaged about 6 excess cases per million people per
year.

        This study very tentatively suggests that EPA's  criteria pollutant programs have
 significantly reduced air toxics levels. An analysis of 16 pollutants was completed using

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both monitoring and emission data in order to evaluate progress made on air toxics
between 1970 and 1980.  The estimated cancer incidence rate for these air pollutants in
1980 was less than half that for 1970.

       The Integrated Environmental Management Project (TEMPI

       Under the Integrated Environmental Management Project (IEMP), EPA's Office of
Policy, Planning, and Evaluation (OPPE) conducted a number of urban (or "geographic")
studies to evaluate the multi-media aspects of various environmental issues, with general
emphasis  on toxics  exposures from drinking  water, hazardous wastes, and air.  The
geographic areas  covered by the IEMP studies to  date are Philadelphia, Pennsylvania
(EPA,  1986);  Baltimore,  Maryland (Manale,  1987); Santa  Clara  ("Silicon Valley"),
California (Hinman,  1986); and Kanawha Valley, West Virginia (EPA, 1987a).  Another
IEMP is under, way in Denver, Colorado, at this writing.

       The  concept  of integrated environmental management developed out of EPA's
recognition that there are potential drawbacks to the traditional approaches for developing
environmental regulations. Agencies have traditionally focused on individual industries,
pollutants, and media—thereby potentially limiting their ability to determine  where,
among the various media, their resources  could best be  employed to optimize health
protection.  Moreover, the traditional approach may not ensure that pollution controls are
not merely shifting risk from one medium to another. Thus, comparing the different media
risks to help set priorities allows environmental managers to focus limited resources in a
manner that will achieve the greatest public benefit.

       The  cancer  assessment approaches used  in each  of the  four  IEMP studies
completed to date are fundamentally similar. First,  each study modeled emissions data to
project ambient air concentrations.  These projected concentrations were then distributed
over a population grid to estimate exposures, which, in turn, were multiplied by unit risk
factors to estimate individual cancer risks and incidence.  Some ambient air measurements
were made as part of these studies to complement the modeling results and to help check
for errors and omissions in the inventory.

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                                   1
       The results of the lEMPs generally support the conclusions of the Six Months
Study.  Excess.cancer incidence from exposures to 10 to 20 toxic air pollutants ranges
from about 1 to 18 cases per year per million persons.  The relative contributions of point
and area sources vary, with area sources generally predominant (except in Kanawha Valley
with its numerous, large chemical complexes).  A combination of metals (e.g., chromium),
polycyclic organic matter (e.g., B(a)P), and toxic volatiles (e.g., benzene) account for most
of the excess urban cancer incidence. Additive individual lifetime cancer risks to the most
exposed individuals around point sources vary considerably, ranging from 10'4 to as high
as 10"2. Average individual lifetime risks of 10"4 are common in many areas not impacted
by particular point sources.

       Several of the lEMPs  attempted to evaluate noncancer risks.  The only pollutant
present at levels associated with noncancer_health effects was  benzene, both around a
large steel manufacturing complex and near major traffic intersections.  One IEMP study
also suggested that ambient xylene concentrations near large wastewater treatment plants
may have the potential for causing noncancer health effects.

       Clark County Study

       In early  1985, after reviewing EPA's Six Month Study, the  Clark County Health
District attempted to address the magnitude and nature of the air toxics problem in
Las Vegas, Nevada, using a  simple ambient monitoring approach. (EPA, 1987b)  The
District initially estimated  average, annual Valley-wide levels for 11 chemical substances,
based on short-term data, and multiplied these levels by unit risk factors to arrive at an
additive cancer  incidence. The District had some knowledge of the emissions and/or air
quality  levels  of the  11  substances  in the Las  Vegas Valley.   Most air quality
measurements were obtained from an established  station in east  central Las Vegas (which
has  a  history of high CO and TSP levels) and from a station in the southeast Las Vegas
Valley (which has a history of high ozone levels and complaints of chlorine odor and eye
irritation). The toxics data were derived from the District's efforts  to characterize urban
haze and to develop specific hydrocarbon profiles to support ozone modeling exercises.
The urban haze research provided short-term data for various metals and carbonaceous
material.

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       Results  of the initial screening compilation show that products of incomplete
combustion (PIC), asbestos, benzene, perchloroethylene, and chromium account for most
of the cancer incidence from air toxics exposures in the Valley.  Following the initial
screening,  the  District then refined  its estimates of annual averages for particular
substances of interest and  recalculated  at the annual Valley-wide cancer incidence.
Totaling the chemicals, the District calculated that the annual incidence  is 4.3 to 9.4
cancers per million people per year.  PIC is the overwhelming contributor to total
incidence from air toxics generated almost equally by three source categories: (1) cars
using leaded gasoline, (2) diesel trucks and buses, and (3) wood burning fireplaces. Other
pollutants of lesser importance include arsenic, benzene, cadmium, and chromium.

       South Coast—MATES

       The South Coast Air Quality Management District (SCAQMD) has conducted one
of the most comprehensive studies to  date of the urban air toxics problem.  This study,
termed  the Multiple-Air Toxics Exposure  Study  (or "MATES"),  is a multiple,  year
evaluation  of community exposure  to air, toxics  in the Los Angeles metropolitan area.
MATES has been the focal point for urban air toxics study by SCAQMD since 1985;
however, various other independent activities that have  been ongoing have provided data
for MATES.  These other activities include certain monitoring and emission inventory
activities, an "in-vehicle" characterization of exposures to commuters inside their vehicles,
and the development of a dispersion/exposure/risk model named "SCREAM" (South Coast
Risk and Exposure Assessment Model).  SCREAM is an enhanced version of EPA's Human
Exposure Model (HEM).  (Barcikowski, 1988)

       The MATES project is documented in a series of working papers, each reflecting
major components of activity. This series includes, in order:
       •     A quality assurance plan (SCAQMD, 1985)
       •     A monitoring site selection plan  (SCAQMD, 1987a)
       •  .   An emissions data summary (SCAQMD, 1987b) -
       •     A modeling results summary (SCAQMD, 1987c)

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       •      A monitoring results summary (SCAQMD, 1988).

       The MATES project utilized  data from two different ambient sampling efforts.
One set of data came from ongoing  sampling activities at existing SCAQMD sampling
sites.  The second effort was carried out by SCAQMD as  part of MATES and involved
setting up ten new/temporary sampling sites in areas suspected of having  elevated
concentrations, based on dispersion modeling of emissions data and various on-site
criteria.

       Risk estimations at the sampling locations  indicate  that there are areas within the
South  Coast Basin where the combined impact of multiple air toxics are significantly
higher than the Basin average.  The combined individual risks from simultaneous exposure
to the  20 pollutants under  study (14  organic gases and 6 metals) ranged from about 1 in
10,000 to 1 in 1,000, predominantly due to benzene and chromium. Higher concentrations
of most of the pollutants were measured in" the winter than in the summer because of such
meteorological conditions  during the winter months  as low ambient temperatures and
longer hours of surface inversions leading to reduced vertical mixing. (Barcikowski, 1988)

       The modeling effort in MATES used, as input, emission inventories of point, area,
and highway vehicle emissions compiled in 1982 and updated to 1984. The estimation of
population exposures to one  or more air toxics was conducted by first using dispersion
modeling of emissions  data  to  calculate the  long-term concentrations  at centroids of
census areas  and then multiplying the calculated concentrations with the  population that
each centroid represents.  The areawide risks, in terms of incremental cancer cases, were
then calculated by multiplying the population exposure with the chemical-specific unit
risk factors  developed by the  California Department of Health Services.  A linear
relationship was assumed  and the exposure/risks associated with multiple sources and
with species of air toxics  are assumed additive.  SCREAM was used to apportion the
number of excess cancer cases by source category and by pollutant, and to identify high-
risk chemical substance  and source categories. SCREAM  can also be used to identify high-
risk locations and to estimate control measure effectiveness in reducing exposure, cancer
risk, and number of cases.

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       Of the 20 air toxics studied in the emission inventory/dispersion modeling portion
of MATES, benzene and hexavalent chromium have the greatest impact on the Basin's
population.  Almost the entire population is exposed to ambient benzene and hexavalent
chromium concentrations corresponding to an upper-bound risk of 1 in 10,000 or higher.
As an upper-bound estimate, this portion of the study found that about 160 excess cancer
cases would result annually in the Basin  due to the combined exposure to all 20
pollutants, or about 15 excess annual cases per million population.  (Barcikowski, 1988;
Shikiya, 1987c)

       Subsequent activities include the enhancement  of SCREAM and. the development
of various risk mitigation strategies. The SCREAM computer model is being enhanced and
will include options for population mobility, indoor-versus-outdoor exposures,  and
noninhalation routes  of exposure.  SCREAM will also be enhanced to provide  better
estimates of individual risk and community, .cancer burden in regions and subregions of the
Basin.

       The  two major toxic air contaminants  of concern,  benzene  and  hexavalent
chromium, will be reduced through control efforts at the local District level and by
programs  at the  State  level.   SCAQMD  has recently adopted  a measure to control
emissions from chrome platers and is moving  forward  with rulemaking to control
hexavalent chromium emissions from cooling towers.  Options for  reducing  benzene
contents in  motor vehicle fuels are being evaluated.   More stringent requirements on
motor  vehicles that are aimed at reducing hydrocarbon emissions will also reduce
emissions of benzene from motor vehicles.  (Barcikowski, 1988)

       5 Citv Controllability Study

       This study (EPA, 1988a), currently under way, is modeling ambient exposures to
25 air toxic compounds in five major urban areas.  It also estimates how exposure  levels
are likely to change in the future as a result  of alternative control scenarios.  The  urban
areas were chosen to represent a cross-section of potential  problem types and to ensure
geographical diversity. Air toxics exposures  in the five study areas were  estimated via
EPA's  Human  Exposure Model (HEM).   A 1980 baseline  emissions inventory was

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established to quantify emissions data for input into the model.  Emissions  data were
developed by various techniques, building on the source data in EPA's National Emissions
Data System  (NEDS) and special State and local  inventories.  Various 1995  projection
inventories, reflecting expected and alternative control scenarios, will be simulated to
evaluate the  effectiveness  of various mitigation  options.  Risks from secondary (i.e.,
photo chemically  formed) formaldehyde were estimated by superimposing  measured
concentrations over the modeling domain.  Polycyclic Organic Matter  (POM)  risks were
estimated using "comparative potency factors" for individual source categories rather than
the "B(a)P surrogate approach" used in the Six Months Study. Comparative potency
factors are being developed in EPA's Integrated Air Cancer Program (IACP) and relate the
potency of various organic mixtures to known carcinogens by comparing their  respective
mutagenicities. (Lewtas, 1987)

       The primary measure  of risk  in  this study is  aggregate cancer  incidence.
Preliminary results suggest that cancer incidence in the five cities ranges from  about 2 to
10 excess cases annually per- million persons, in general agreement  with other urban
assessments.  Urbanwide individual lifetime risks range from about 1.5 x 10~4 to 7 x 10"4.
The major pollutants of concern are  polycyclic organic  matter  (POM), hexavalent
chromium, 1,3-butadiene, formaldehyde, and benzene.  Small point  and  area sources
contribute most to total incidence, with road vehicles, cooling towers, chromium  platers,
solvent use, and wood combustion being predominant.

       An important finding of this study is that considerable air toxics control  will be
achieved by  1995 from measures reasonably anticipated to be in effect because of State
Implementation Plans (SIPs), New Source Review  (NSR),  the Federal Motor Vehicle
Control Program (FMVCP),  and  National  Emission Standards  for Hazardous  Air
Pollutants  (NESHAPs). As suggested by the Six Months Study, some of this reduction is
indirectly due to criteria pollutant measures rather than to direct controls on air toxics.

       Motor Vehicle Study

       This  study, entitled Air Toxics  Emissions from Motor Vehicles. (Carey, 1987),
focused primarily on cancer risks posed by road vehicle emissions in the United States.  It

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is not an urban assessment per se, but the results are useful in an urban soup context as
most vehicular travel is in urban areas.  The report considered all vehicular air emissions
for which EPA has cancer unit risk estimates. Specific pollutants and pollutant categories
included  are  diesel particulate, formaldehyde, benzene,  gasoline vapors,  gas  phase
organics,  organics associated with non-diesel particulate,  dioxins, asbestos, vehicular
interior emissions, and metals.

       Several unique  aspects to this study are worth highlighting.  First,  an activity
pattern model was used to reflect changing exposures to sets of population groups as they
change location from one microenvironment to another during their day-to-day activities.
These microenvironments included street canyons, parking garages, and indoor  air, as well
as various urban and suburban settings.  This  approach for estimating exposures  differs
markedly from all other urban assessments to date.   Second, this study included an
approach to evaluate risk from polycyclic organic matter (POM) that used a comparative
potency method.  In this method, the potency of organic extracts of diesel particulate and
gasoline particle  organics was- compared with the potencies of extracts from sources for
which epidemiological data were available (Albert, 1983).  This  contrasts with the so-
called "B(a)P  surrogate approach" used in EPA's Six Months Study wherein B(a)P was
used as a surrogate for all POM.

       This study associated 385 to 2,286 annual excess cancer cases nationwide with air
toxics  emissions from road vehicles. From 2 to 11  excess cancer cases are predicted per
year per million  people in urban settings.  This incidence drops  roughly 40  percent by
1995 because  of more stringent diesel particulate standards for both light and heavy duty
vehicles and because of the increasing use of three-way catalyst equipped vehicles coupled
with the phaseout of non-catalyst equipped vehicles. The pollutants  contributing most to
total cancer incidence are, in rough order  of importance, diesel particulate,  1,3-butadiene,
benzene, gasoline particulate-associated organics, formaldehyde, and asbestos.  The bulk
of the formaldehyde  risk  is  due to secondary (i.e.,  photochemically  produced)
formaldehyde.
                                         10

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       Integrated Air Cancer Project flACPl

       The Integrated Air Cancer Project (IACP) is being conducted by the EPA research
labs in Research Triangle Park, North Carolina.  It is an interdisciplinary research program
(Lewtas, 1987) aimed at developing scientific methods and data bases to identify the major
sources of carcinogenic chemicals emitted into the air  or  arising from atmospheric
transformations. This project is performing extensive ambient air monitoring, receptor
modeling, bioassaying, and atmospheric transformation studies. Thus far, the study has
concentrated  on the effects  of emissions  from wood burning and  mobile sources.
Subsequent phases of this project will add other residential combustion sources (e.g., oil
combustion) and industrial sources.

       The major long-term goals of the project are as  follows:
       1.
       2.


       3.
To identify the principal airborne carcinogens.
To  determine which emissions sources are the major contributors of
carcinogens to ambient air. Major carcinogenic emissions sources will be
determined by-field studies with simultaneous emission characterizations
and ambient monitoring, followed by source apportionment calculations.
To  improve estimates of comparative human cancer risk from specific air
pollution emission  sources.   The study  will  develop  a  comparative
methodology to evaluate and apply short-term mutagenesis  and animal
carcinogenesis  data on emission sources.  Improved human exposure
estimates will be developed for complex emission products and individual
carcinogens, including transformation products.
       Field  studies  have  been  conducted to date  in Raleigh,  North  Carolina,
 Albuquerque,  New Mexico, and Boise, Idaho, with plans to add Roanoke, Virginia.  Most
 of the work that has been reported has been in Raleigh and Albuquerque, and has focused
 mainly on methods development.  The Boise results are not available as of this writing.

       An  important  result has been  the observation  of elevated mutagenicities of
 irradiated and aged gas phase mixtures.  The mutagenicities of wood smoke, auto exhaust,
 and several common  industrial organics (toluene, propylene, and  acetaldehyde) all
 increase by an order-of-magnitude or more after the mixtures are irradiated and allowed to
 react for  several hours.  These results  suggest that the transformations  of complex
                                        11

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mixtures may contribute significantly to the total burden of mutagens or carcinogens in
the environment and should be considered in assessing risk. (Shepson, 1987)

       TEAM (Total Exposure Assessment Methodology! Studies

       Current urban assessments traditionally assume that the  individual's entire
exposure to air toxics is a result of continuous exposure to  outdoor air.  The simplistic
assumptions and procedures involved in most current assessments, therefore, do  not
realistically approximate actual personal  risks from  other routes of exposure,  such as
from indoor air, workplace exposures, automobile interiors, etc.

       In contrast, EPA's TEAM studies (Ott, 1986; Ott, 1988) have been designed to  test
statistical  and chemical methodologies  for estimating total  human exposure across
different microenvironments and various  media.  This research program has sought to
establish, for each of 20 or so organic chemicals, the relative importance of certain routes
of exposure (air, -water) and whether predictable correlations exist between exposure  and
  1°.              *        - — — —
body burden (as measured through analysis of breath).

       The TEAM approach assesses exposures by measuring the amounts of pollutants
in an individual's immediate physical environment throughout the day. In contrast to  a
fixed monitoring station that collects pollutants as they are c.arried by the ambient (or
indoor) air to the location of the devices,  TEAM monitoring devices are located on vests
worn by individuals throughout their daily routine.   In addition, TEAM measures the
concentration of chemicals in an individual's drinking water and  exhaled breath in order to
determine the actual amount of the pollutant that has been taken up by the individual.
Personal sampling directly addresses the variability of exposures within a population  and
should help to improve the  exposure assumptions  that are typically made in urban
assessments.

       Since  1979, the TEAM studies have measured  the personal exposures of hundreds
of people across the United States to 20 to 26 volatile organic compounds (VOCs) in air
and drinking water.   The chemicals were selected on the basis of their  toxicity,
carcinogenicity, mutagenicity, production volume, presence in the air or drinking water at
                                         12

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the field sites, existence of National Bureau of Standards (NBS) permeation standards, and
amenability to collection on the sorbent Tenax.  The following cities are among those that
have been or are being evaluated:  Bayonne-Elizabeth,  New Jersey; Greensboro, North
Carolina; Devil's Lake, North Dakota; the California cities of Los Angeles and Antioch;
Pittsburgh, Pennsylvania; and Baltimore, Maryland. The latter application was coupled
directly with the Baltimore TEMP in order to evaluate the appropriateness of the use of
ambient monitoring and modeling data as proxies  for total individual exposures in risk
assessments.  The interpretation of the Baltimore TEAM results is still in progress at this
writing.  Results from the TEAM  studies "indicate  that even  in  major  chemical
manufacturing and petroleum refining areas such as Los Angeles and northern New Jersey,
personal  exposures  to  many  toxic  and  carcinogenic  VOCs  exceeded  outdoor
concentrations by  100 to 400 percent.  Breath measurements of the 550 residents who
took part in the study were significantly correlated with the preceding air exposures.  It
was concluded that personal  activities' such as smoking,  using  room air deodorizers,
wearing dry-cleaned-clothes, and even using hot water were responsible for a major
"portion of most people's exposure to benzene, para-dichlorobenzene, tetrachloroethylene,
and chloroform, respectively."   (Wallace, 1988)  Prior TEAM studies  (Ott,  1985)  have
shown that levels of  11 important organic compounds,  some of which are regarded as
potential carcinogens, are found to be significantly higher indoors than outdoors.  These
chemicals included chloroform, 1,1,1-trichloroethane,  benzene, carbon tetrachloride,
trichloroethylene, perchloroethylene,  styrene,  meta- and  para-dichlorobenzene,
ethylbenzene, and jrylene isomers. The sources appear to be inside the home,  probably in
furniture,  paint,  solvents, drapes, carpets, spray  cans, clothing, and construction
materials.

        In addition to  the above described method, which directly measures daily exposure
profiles for a representative cross-section of population within an area, TEAM  is also
constructing  estimates of personal exposure profiles by combining information on human
activities  with microenvironmental  field data.  In  essence,  concentration is measured in
selected microenvironments in  which a person will be exposed throughout a  day.  Then,
an integrated  exposure is computed as the product of (1) the  concentration of each
pollutant  encountered in each microenvironment and (2) the time the person spends there,
                                        13

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divided by the total time of exposure. For VOCs, typical microenvironments may include
gas stations, dry-cleaning stores, freshly painted rooms, and households where solvents
are stored indoors.  The construction of time-activity  exposure  profiles allows the
extrapolation of the results to larger populations and  other locales, using models that
incorporate human activity patterns.  (Ott, 1985).

       Staten Island/New Tersey Study

       The Staten Island/New Jersey study (EPA, 1986b), begun in 1986, was initiated to
provide data on the scope and magnitude of the urban  air toxics problems in the Staten
Island/New Jersey (Middlesex and Union Counties) areas.  This study will rely primarily
on ambient monitoring to characterize air quality in the study area for selected  toxic
pollutants.  The measured data will be used to estimate exposures and to understand
better the interrelationship between indoor  and ambient  exposures. Plans are for emission
inventory data to be compiled to formulate hypotheses linking major contaminants to
potential sources and "to evaluate, general abatement strategies.

       The Staten Island area was selected for intensive study based on the high density
of industry in the area, a long-standing history of odor complaints leading to a perception
that an air toxics problem exists, and the high quality of  technical expertise available from
State and local organizations to study the  situation.  As of this writing, no results have
yet been reported.
                                                                 ,/
       Southeast Chicago Study

       The Southeast Chicago study (Summerhays, 1987) was initiated by EPA as a result
of citizen concerns regarding the environmental safety of the heavily industrialized area in
the vicinity of Lake Calumet,  located in southeast Chicago, Illinois.  A broad range of
environmental concerns was raised relating to air, water, and land pollution issues.  As a
response,  an emission modeling  study was undertaken by EPA's Region V office to
ascertain whether air toxics exposures may be causing significant cancer risks.. The initial
step was the development of an air toxics emission  inventory over a broad area of
Chicago, encompassing the receptor area of concern. The inventory included 47 suspected
                                         14

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carcinogens—22 nonhalogenated compounds, 17 halogenated compounds, and 8 inorganic
species (mostly metals). As part of the inventory process, questionnaires were sent to 29
of the 88 point sources in the area.

       Dispersion modeling using ISC (for point sources) and CDM (for area sources)  was
performed to estimate ambient concentrations of air toxics in the  receptor grid.   The
resulting concentrations were applied to population data for the same receptor grid  and
multiplied by cancer unit risk factors to estimate individual risk and aggregate incidence.

       No results are  available  from this modeling study as of this writing.

       Urban Air Toxics Monitoring Program

       In 1987,  EPA initiated a screening  program  entitled the  Urban  Air  Toxics
Monitoring Program (EPA, 1987c). The primary purpose of the program is to collect air
quality data to  support State and local agency efforts to assess the nature and magnitude
of the urban air toxics problem.in their respective areas.  In .1987, 19 State and local
agencies  agreed to participate in this program. Depending on future funding allocation and
interest shown by State and local agencies, this program may be continued for  several
years.

       EPA's objectives in promoting and supporting this monitoring program are:
       •      To provide estimates of annual concentrations of selected air toxics;
       •      To provide information for prioritizing  and planning future work  and
              sampling on a more in-depth and pollutant-specific basis in local areas;
       •      To provide a means to identify prevailing pollutants  and possible source
              types that may need further assessment; and
       •      To identify a means to evaluate and prioritize future air toxics mitigation
              programs.

       The  program  calls for  State  and local agency personnel to collect ambient air
samples  for subsequent analysis either by EPA or an EPA contractor for  specific toxic
compounds.  Under EPA direction, a central contractor will bring the necessary sampling
train to each site, assemble the apparatus, instruct the sampling technician  on equipment
                                         15

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operation  and  maintenance,  and  provide  detailed  sample  shipping  information.
Participating personnel will schedule a meeting date and location for the contractor to set
up and demonstrate the sampling trains.

       Three different types of ambient air samples are collected.  The first set of air
toxics samples is collected in stainless steel canisters for 24-hour periods every 12 days
for one year.  After sample collection, the canisters are air shipped to a central laboratory
for analysis.  The samples are analyzed for about 30 volatile organic compounds by gas
chromatography equipped with multi-detector capability.  The second set of samples is
collected in cartridges for determination of formaldehyde and other aldehydes.  The third
set is total suspended particulate (TSP) matter collected from a high-volume air sampler
for determination of about 14 metals and B(a)P.

       No results are available as of this writing.

       Bay..Area Toxics Monitoring ....Study CBAAQMD. 1987)

       The Bay Area Air Quality Management District (BAAQMD), in conjunction with
the California Air Resources Board, conducts ambient sampling for 11 volatile air toxics at
a network of 15 monitoring sites in the Bay Area, comprising one of the largest urban air
toxics networks operating in the United States.  The network is oriented to population
exposure, with a background site north of San Francisco near the ocean. The pollutants
analyzed  are believed by the Bay Area AQMD  to represent a large part of the risk of
volatile toxic substances to the general population.  Twenty-four hour  samples are taken
in Tedlar bags and are analyzed by gas chromatography. Sampling frequency is twice per
month per site.

       Measurements were started in 1986 and are ongoing. Results to date suggest the
benzene levels constitute the majority of risk associated with the  measiired exposures and
are largely derived from vehicular activity.
                                         16

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1.3    Other Urban Air Toxics Assessment Activities

EPA has been encouraging State and local air pollution control agencies to assess their
urban air toxics problems.  Numerous assessment activities have been undertaken in the
past several years in the areas of ambient air monitoring, emissions inventorying, exposure
and risk characterization, and mitigation analysis.  Figure 1-1 shows those U.S. cities
where some type of assessment activities have been initiated involving one or more of
these activities. Not all of these activities will lead to multi-pollutant, multi-source
cancer risk assessments. Table 1-1 lists the specific activities undertaken in 30 urban
areas having populations exceeding one million persons.
                                          17

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                                    Chapter 1
References
Albert, R., et  al., 1983.  "Comparative Potency Method for Cancer Risk Assessment:
Application to Diesel Particulate Emissions," Risk Analysis, Vol. 3, No,, 2, pp.  101-117.

BAAQMD, 1987. Letter and Attachment from D.A. Levaggi to M. Stenburg, Bay Area Air
Quality Management District.  939  Ellis Street,  San  Francisco, California  94109.
September 10, 1987.

Barcikowski, W., 1988.  South  Coast  Air Quality Management District.  Letter with
attachments to Tom Lahre, U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina.  October 24, 1988.

Carey, P., 1987.  Air Toxics Emissions  from Motor Vehicles, EPA-AA-TSS-PA-86-5, U.S.
Environmental Protection Agency, Ann Arbor, Michigan,.

EPA, 1985.   A Strategy  to  Reduce Risks_ to  Public Health from  Air Toxics,  U.S.
Environmental Protection Agency, Washington," D.C.

EPA, 1986, Final  Report  of  the Philadelphia  Integrated Environmental Management
Project.  U.S. Environmental Protection Agency, Washington, D.C.

EPA, 1987a.  Kanawha Valley.  Toxics Screening Study Final Report, U.S. Environmental
Protection Agency, Washington, D.C.

EPA, 1987b.  National Air Toxics Information Clearinghouse report  entitled "Qualitative
and  Quantitative Carcinogenic Risk Assessment," EPA 450/5-87-003, U.S. Environmental
Protection Agency and STAPPA/ALAPCO, pp. 11-1 through 11-6.

EPA, 1987c.    Urban  Air Toxics Monitoring Program, EPA-450/4-87-022,  U.S.
Environmental Protection Agency, Research Triangle Park, North Carolina.

EPA 1988a.  Preliminary results from 5 City Controllability Study  being conducted by
E. H. Pechan  and Assoc.  under contract to U.S. Environmental  Protection Agency,
Research Triangle Park, North Carolina.  (Tom Lahre, EPA project officer).

EPA, 1988b.  Staten Island/New Jersey Urban Air Toxics Assessment Project.  Revised
Status Report. U.S. EPA Region in.

Haemisegger,  E., et  al.,  1985.  The Air Toxics Problem in  the  United States:   An
Assessment of Cancer Risks for Selected Pollutants, U.S.  Environmental  Protection
Agency, Washington, D.C.

Hinman, K.,  et al., 1986.   Santa Clara Integrated Environmental Management Project.
Stage Two Report.  U.S. Environmental Protection Agency, Washington, D.C.
                                        20

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Lamason,  1988.  Personal communication.   U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina. August 1988.

Lewtas, J.,and  L.  Cupitt, 1987.  "Overview of the Integrated  Air Cancer Project,"
Proceedings of the 1987 EPA/APCA Symposium  on Measurement of Toxics and Related
Air Pollutants,  U.S. Environmental Protection Agency, Research Triangle Park, North
Carolina, pp. 555-561.

Machlin, Paula R., 1986.   "Denver Integrated Environmental  Management  Project:
Approach to Ambient Air Toxics Monitoring," EPA Region 8, Denver Colorado, November
1986.

Manale, A., et  al., 1987.  "Baltimore Integrated  Environmental Management  Project.
Phase II Report:  Air Toxics," (Draft), U.S. Environmental Protection Agency, Washington,
D.C.

Ott, W., 1985.  "Total Human Exposure," Environmental Science Technology, Vol.  19,
No. 10, 1985.

Ott,  W.,  et al.,  1986.   "EPA's  Research  Program  on Total  Human Exposure,"
Environmental International, Vol. 12, pp. 475:494.

Ott, W., 1988.  "Human Exposure to Environmental Pollutants," Presented at the 81st
Annual Air Pollution Control Association Meeting, June 20-24, 1988, Dallas, Texas.

SCAQMD (1985). MATES Working Paper #1, Quality Assurance Plan, June 1985, South
Coast Air Quality Management District.

SCAQMD  (1987a).  MATES Working Paper #2, Air Toxics Monitoring Site Selection,
September  1987, South Coast Air Quality Management District.

SCAQMD (1987b).  MATES Working Paper #3, Toxic Emissions Data for the South Coast
Air Basin, April 1987,  South Coast Air Quality Management District.

SCAQMD  (1987c).  MATES Working Paper  #4,  Urban Air Toxics  Exposure Model:
Development and  Application, October 1987,  South  Coast  Air Quality Management
District.

SCAQMD (1988).  MATES Working Paper #5, Analysis of Ambient Data  from Potential
Toxics "Hot Spots" in the South Coast Air Basin, September 1988, South Coast Air
Quality Management District.

Shepson,  P., T. Kleindienst, and E. Edney,  1987.  The Production  of Mutagenic
Compounds as a Result of Urban Photochemistry, EPA 600/3-87-020, U.S.  Environmental
Protection Agency, Research Triangle Park, North Carolina.

Summerhays, 1987.  "Air Toxics Emission Inventory for the Southeast Chicago Area."
U.S. EPA Region V.
                                       21

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Wallace,  L., et al., 1988.   "Preliminary Results from the  Baltimore  TEAM Study."
Presented  at the  81st  Air  Pollution   Control Association  Annual  Meeting,
June 19-24, 1988, Dallas, Texas.
                                        22

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                                     CHAPTER 2
                      AMBIENT AIR QUALITY MONITORING
2.1
The following subjects are covered in this chapter:
•      Use of ambient air monitoring data in urban assessments
•      -Pollutant coverage       .......
•      Site .selection
•      Sampling periods, frequencies, and duration
•      Sampling and analytical techniques
•      Evolving monitoring techniques
•      Insights into the use of monitoring in air toxics programs

Use of Ambient Air Monitoring Data in Multi-Pollutant. Multi-Source Urban
Assessments
       Multi-pollutant, multi-source urban air toxics assessments commonly use ambient
air monitoring data to help characterize exposures and risks related to toxic air pollutants.
Ambient air data are most  often  used in conjunction  with dispersion modeling of
emissions data to minimize the limitations inherent in using either type of data alone.

       Estimating  exposures using data from ambient air monitoring programs  in the
studies under review has been conceptually straightforward.  Ambient levels of air toxics
have been measured over urban areas  to yield estimates of population exposures.  These
                                        23

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exposure levels were then multiplied by the respective potencies (i.e., cancer unit risk
factors) of each  pollutant to estimate individual cancer  risks,  which, in turn, are
superimposed over  population  distributions to  estimate excess cancer  cases.  Such
analyses are often fairly crude because only a comparatively few measurements of each
pollutant are  available  to  represent long-term, area-wide population  exposures.
Fortunately, the increasing emphasis on ambient air toxics monitoring by States and local
agencies is providing more complete data for exposure and risk assessments.

       There are important advantages in using ambient air monitoring data as part of
urban assessments, the principal one  being  the  ability  to  directly  characterize
concentrations at key receptor sites of interest.   In this regard, the  use of reliable and
representative ambient air monitoring data, instead of modeled concentrations based on
emissions data, circumvents the errors inherent in emission inventories and dispersion
models, which can  be considerable.   In .fact, some pollutants that  may be covered by
ambient air quality monitoring programs may not be included in emissions inventories. In
addition, ambient air quality-monitoring allows one  to evaluate pollutants that are not
emitted directly, such as photochemically formed formaldehyde or peroxyacetyl nitrate
 (PAN)  or other  transformation products,  or to measure compounds that exist in the
 atmosphere because of gradual build-up of background levels (e.g., carbon tetrachloride) or
 transport from other urban areas.  Finally, ambient  air measurements provide a direct
 reference from which to assess dispersion model performance and emission inventory
 accuracy.

        Ambient air monitoring data were used in many of these studies for purposes in
 addition to multi-source, multi-pollutant cancer risk assessments.  For example, ambient
 air measurements were used in the Philadelphia and Kanawha Valley studies to help
 identify maximum individual exposures in the vicinity of major point sources. This might
 also be done to help verify that the  conditions  in an operating  permit  are being met.
 Ambient air measurements can also be  used  to assess the adequacy of an emission
 inventory and/or dispersion model are used to predict ambient air concentrations.  Poor
 agreement of measured and predicted ambient concentrations  may reflect  inventory error
                                          24

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(e.g.,  a major overlooked source) and/or poor model performance.  Finally,  ambient
monitoring  data are  now being used for  mutagenicity  bioassays  and  source
apportionments.  Regulatory agencies may want to consider multiple program uses of any
measurements that are taken as part of an urban soup assessment to optimize the use of
its ambient monitoring resources.

       The  major disadvantages of ambient air  monitoring are the high costs and
uncertainties involved with measuring low concentrations of specific air toxics. Obtaining
a sufficient number of samples to show variations in concentration in time and space can
be prohibitively expensive.  Also, high detection  limits, sample contamination (in the
field,  in transit, and in the laboratory), uncertain identification of target compounds, and
other  sources of error have hampered many data collection efforts; these should be
anticipated  and carefully evaluated during Jhe  design stages of monitoring programs.
Finally, ambient sampling  and/or analytical procedures  are  not yet proven for all
pollutants (e.g., hexavalent chromium,  1,3 butadiene, acrylonitrile, and ethylene oxide).
All of. these disadvantages suggest that EPA and State and local  agencies must optimize
the number  of sampling sites and measurements in any assessment, and must coordinate
with  other  applied programs and  the  research community before initiating  a major
sampling effort.

       In the Evolving Monitoring Technologies section of this chapter, several promising
approaches  are described to  reduce the  cost of monitoring  programs  for toxic air
pollutants.   Techniques such as sample compositing, sampling over longer time  periods,
and periodic expansion  to  a larger sampling network may dramatically  increase the
information  provided per monitoring dollar spent.

2.2.   Decisions  Affecting Monitoring Plan Development

       Design  of an  effective monitoring plan  is the most  important  step in the
development of a monitoring program.  The two basic elements of concern are (1) clearly
specified data quality objectives (definition of the type, accuracy,  and coverage of data to
be gathered) and (2) an adequate quality assurance plan against which achievement of data
                                        25

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quality objectives can be reviewed.  Not only are these steps essential in ensuring the

cost-effective deployment of resources, their scope and design should closely support the

goals and objectives of the overall study.  Inadequacies in monitoring program design can

seriously compromise the ability of a study to define the exposures and potential risks it

seeks to investigate.


       As the studies reviewed here illustrate, cost constraints inevitably lead to design

compromises or trade-offs among multiple competing objectives.  Any ambient air quality

monitoring program is inherently expensive, but air toxics studies tend to be relatively

more expensive  than conventional  programs.   Sampling  techniques  are often not

standardized; per-sample laboratory costs are comparatively high,  and the number  of

compounds of potential interest is large.  Furthermore, in the  absence of specific  ambient

air quality standards for toxic air pollutants_ (which would  define specific statistics  of

regulatory concern), study objectives tend  to  include  multiple  aspects of air toxics

exposures: emission rates, fenceline concentrations, source-receptor relationships, average

population exposures, or maximum individual exposures. It is therefore highly important

for designers to reach firm agreement on a monitoring program's objectives, especially in

terms of how these objectives relate to the broad technical questions  of the study as a

whole.


       Examples  of technical  trade-offs that  should  be  addressed in  designing  a

monitoring plan include:

       «       Sample accuracy:  The most accurate sampling and laboratory procedures
               are not necessarily the most desirable for every study.  Achieving highly
               accurate results for individual samples may compromise  the number of
               sites  sampled, the number of compounds evaluated,  the frequency  of
               sampling, or the length of the  sampling period.  Undue emphasis  on
               individual sample accuracy may  actually reduce  rather than enhance the
               effectiveness of the measured data in supporting program goals.  This is
               especially true in cancer assessments because of  the inherent uncertainty
               that exists in cancer unit risk factors.

        •      Site selection!  Many factors influence site selection, including the gradient
               of concentrations expected in each area  (locations  with steep gradients
               require the  most  careful location of sites),  laboratory detection limits
               (location of sites in areas where  concentrations often fall  below detection
                                         26

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              limits may waste resources), or air quality statistics of concern (average
              exposures versus maximum exposures).

       The studies under review here illustrate these points in various ways.  Some of the
reports produced by these studies listed specific, formal monitoring objectives; for others,
we have had to infer monitoring goals from the  stated overall  study objectives and
technical approaches taken.

       The  advantage of presenting detailed, specific objectives prior to initiating a
monitoring program is that it provides a focal point for consensus between program
managers and technical  analysts.  In addition to helping these groups  collaborate  on a
detailed and cost-effective QA/QC plan, setting formal objectives provides a benchmark to
assess how well a program meets its  goals.

       Table 2-1 Summarizes the stated or implied  monitoring goals of the eight studies
with monitoring programs.  Before examining individual technical issues in more detail, it
is  useful to discuss the stated goals of these eight monitoring programs and the extent to
which they were achieved.

       Studies with Stated Goals for  Air Quality Monitoring Programs

       The IEMP programs in Kanawha Valley, Philadelphia, Baltimore, and Denver; the
Clark County Study; and the Urban Air Toxics Monitoring Program and the South Coast
MATES project all documented their  monitoring objectives.

       Kanawha Valley—Designers  of the Kanawha Valley program listed  five  general
monitoring program objectives  that clearly supported overall program  goals for  this
unique industrial area:1
   These are quoted verbatim from the study report.
                                        27-

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              Table 2-1.  Summary of Objectives for Air  Toxics
                Monitoring Programs Reviewed In This Study

Study
South Coast— MATES
Clark County
lEMPs
Philadelphia
Baltimore
Santa Clara
Kanawha
Denver
Urban Air Toxics
Program -
USES
Estimate
Average
Exposures
X
X
X
X
Evaluate
Model
Performance
X

Support
Emissions
Verification
X

X X
X X
-Goals Unspecified 	
X X
~~~

SCALES ADDRESSED
Define Define
Population Maximum
Exposures Concentrations
X X
X
X X
X X
	 Goals Unspecified 	
X
X
x
   1.     To 'qualitatively and quantitatively confirm  the  presence of targeted
          compounds in the  ambient air.   EPA Region Ill's minimum objective was
          to confirm the presence of compounds for which emissions  data were
          available  from the West  Virginia Air Pollution Control Commission's
          inventories.  There was  concern that some emissions data may have been
          outdated  or  otherwise inadequate and  that  modeled predictions  of
          concentrations  of  certain  pollutants could be in error, both because of
          technical difficulties in modeling the valley (see Chapter 4) and because of
          possible chemical reactions during transport.

          This  goal was largely achieved by  the program.2   Most predicted
          compounds were documented, though ambiguities remained with regard to
          ethylene oxide.
Although the overall objectives of this study were met fairly well, Phase I of the two-
phase monitoring plan developed for the study did not meet its study objectives. The
strategy of Phase I was to conduct a limited three- or four-day screening study using
bag sampling with GC  analysis  (and  some GS/MS analysis for confirmation) to rank
potential  sites  and select pollutants  for the  larger-scale  effort  in  Phase  II.
Unfortunately, the GC and GC/MS results  were not in agreement and no clear pattern
emerged to distinguish upwind and downwind sites near major sources.
                                    28

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       2.      To determine whether a more in-depth emissions verification of facilities
              and processes is needed.  To the extent that models could predict ambient
              concentrations, the study sought to determine which compounds in the
              inventories were least well  characterized and required additional follow-
              up.

              This goal was also achieved. The study did find  that certain measured
              concentrations  did appear to  be significantly higher  or lower  than
              predicted.  As  of this writing, however,  the study has not reconciled
              specific differences  by  following up  to determine whether certain
              emissions were overestimated or underestimated.

       3.      To  assist  in model  selection for potential  future  study.  Technical
              difficulties in modeling the Valley (see Chapter 4) are substantial.  To
              fulfill this monitoring goal, the study data were to be interpreted to the
              extent  feasible  to  determine whether model modifications  should be
              attempted to better define transport and dispersion for this area.3

              This goal has not yet been achieved because the necessary analysis has not
              yet been done.

       4.     ..To help determine whether a more detailed monitoring program is needed
              at a later date. If significant most exposed individual MEI  concentrations
              of study pollutants were detected in populated neighborhoods, and if
              monitoring confirmed that modeled results reasonably  predicted the
              presence and locations of these MEI concentrations, additional monitoring
              might be appropriate to confirm model predictions in other locations or for
              additional compounds.

              This goal  was  achieved;  predicted high  concentrations  of certain
              compounds were tentatively confirmed in target neighborhoods, indicating
              the appropriateness of further monitoring.

       5.      To evaluate a newly designed  24-hour  canister sampling system. The
              Kanawha project used a relatively new evacuated canister  system for
              sample collection.  The final goal of the program was to evaluate the
              effectiveness of this method for the compounds of interest.

              The program confirmed the general effectiveness of the canister system.4


       Overall, the Kanawha program clearly stated its monitoring program goals and

documented its progress in meeting these goals in its final report.  It was  the only study.
3  Specific issues included whether increased use of turbulent intensity data and a
   specific treatment for Valley wall reflection would be possible and desirable.
4  Difficulties were encountered, however, with the formaldehyde sampling techniques
   used in the study, invalidating results for this pollutant.
                                        29

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reviewed that tracked achievement of its stated goals. Further discussions in this chapter

and elsewhere will deal with how these program goals were reflected in details of the

monitoring plan itself.


       Denver—The Denver IEMP project also listed general objectives for its monitoring

effort that directly  supported overall study goals.   They were, however,  markedly

different in many respects than the Kanawha effort.

       1.      Collect ambient concentration data on selected toxic air pollutants that are
              likely to he present in  metropolitan  Denver using the best  available
              sampling and analysis  techniques.  Denver's air pollution problem,  is
              considered to be regional: there are few industrial sources, and much of the
              air pollution is attributable  to complex meteorological conditions and
              possibly  transformation of pollutants from automobiles and other
              dispersed sources.  Study designers determined that in  this case it was
              necessary and  appropriate to use the  most current sampling methods'
              available.   The  generalized, nature  of  the pollution  implied  that
              concentrations would not vary extensively from site to site,  permitting use
             "of a small number of stations. The main issue of concern was to quantify
              multiple pollutant species  in depth,  using the  most  current methods
              available from EPA's Office of Research and Development.

              This objective is expected to be achieved. Data analysis is still in progress.

       2.      Collect ambient concentration data during high episodes  for the routinely
              collected pollutants,  and  conduct  broad-scan analyses** for  those not
              routinely measured and which may warrant further study.  This program
              objective called for increased  12-hour coverage of VOCs on days  with peak
              CO and TSP concentrations.

              This objective was not  fully achieved despite the  use of  independently
              analyzed 12-hour samples taken during predicted peak air pollution days.
              The goal of collecting additional samples on peak days was limited to one
              extra day of sampling.

       3.      Produce  ambient measurements that are  spatially and temporally
              representative of annual and acute [short-term6] exposures experienced by
              the general population. This program objective overlaps somewhat  with
              objective 2.  It called for distribution of sampling stations over the general
5   "Broad-scan" refers to a detailed GC/MS library search for each peak to attempt to
    estimate  the concentration of as many pollutants as possible.   More  typically,
    analysis is limited to a preselected number of routinely quantified peaks.
6   Use of the word "acute" implies short-term concentrations, not concentrations high
    enough to produce acute toxic responses in humans.
                                        30

-------
             metropolitan area, sited both to reflect annual average concentrations and
             to capture high short-term concentrations.
             Spatial representativeness was achieved, but, as noted, definition of short-
             term peak concentrations does not appear to be fully achieved.
       4.     Help identify and prioritize future research needs. The program's final goal
             was to set priorities for future studies in terms of compounds of interest
             and spatial and temporal resolution.  It was expected that some
             compounds for which risk  values are not available may be identified at
             relatively high  levels,  and  that  final  evaluations  of risk would be
             postponed until suitable potency estimates are forthcoming.
             It is  not possible  to assess the  attainment  of this goal until  data
             interpretation is complete.

       Philadelphia  and Baltimore—These projects were among the earliest,  if not the
first, projects to attempt  urban scale monitoring for  air toxics.  They had  one  basic
monitoring goal—to use  a source-oriented siting'approach to help evaluate and verify local
emission inventories.  They were both partly successful in reaching this goal, though
perhaps more elaborate and comprehensively developed goal statements  could have
improved the program results.

       Clark County—The  objective of this study was to determine annual average
concentrations of most volatile organic compounds, selected metals,  and asbestos.  These
compounds were expected to represent toxics of concern within the Las Vegas study  area,
which is  heavily  influenced by mobile sources.  The study appears to have largely
achieved its stated monitoring objectives.

       Urban Air Toxics Monitoring Program—This is a continuing, multi-city program in
which each monitoring study reflects the general objectives of the overall program.  These
objectives include  the following:
       1.      To provide estimates of annual concentrations of selected air toxics.
       2.      To  provide information for prioritizing and planning future  work and
              sampling on a more in-depth and pollutant-specific basis in local  areas.
       3.      To  provide a means to identify prevailing pollutants and possible source
              types  that may need further assessment.
                                        31

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       4.      To identify a means to evaluate and rank  future air toxics mitigation
              programs.

       South  Coast—MATES—The  South  Coast Air Quality  Management  District

(SCAQMD) developed a very detailed monitoring protocol to cover all important aspects

of ambient sampling, as itemized below:

       •      Monitoring objectives

       •      Chemical species measured

       «      Sampling methods

       •      Analytical methods

       •      Identification of potential sites

       •      Sampling strategy

       •      "Data quality objectives    	

       •      Quality assurance^ considerations


       Specific monitoring objectives were:

       1.     Measure multiple toxic air contaminant concentrations in areas where the
              probability of the occurrence of elevated risk  is greatest.

       2.     Collect data representative of the potentially toxic chemical species that
              are expected to be significant in the South Coast Air Basin.

       3.     Measure both organic  compounds in the gaseous phase  and metals  on
              suspended particulate matter.

       4.     Collect samples that are representative  of a winter/spring" season and a
              summer/fall season, and from which meaningful estimates of annual
              average concentrations may be determined.

       5.     Collect data that can be  compared with information from other ongoing
              toxics programs in the SCAB.


       Details of the MATES monitoring program are given in (Shikiya, 1988).
                                        32

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 2.3.   Pollutant Coverage

       Table 2-2 presents the pollutants covered in each, monitoring program.  As is clear
 from this table, studies to date have emphasized volatile organics and, to a considerably
 lesser extent, metals.  Coverage of semivolatile organics and aldehydes/ketones has been
 very limited.

       The focus on volatile organics was apparently a result  of emphasis placed on
 volatile organics in  available emission inventories, which often preceded these studies.
 Even where these inventories were developed expressly to evaluate toxic air  pollution (as
 in Philadelphia), they tended not to look comprehensively at all categories of potential
 toxics, but  to  emphasize the high-volume, often carcinogenic,  organic solvents.   The
 implicit assumption was that these high-volume carcinogens were likely to account for the
 major fraction of the risk from toxic air pollutants. This bias toward volatile organics is
 clearly evident in the IEMP studies in Philadelphia, Kanawha Valley, and  Santa Clara,
 whose goals were, explicitly linked to verification  or review of available  emission
 inventories.  These studies" included coverage of pollutants released from industrial
 sources,  as  well as those related to the fueling, combustion, or evaporative emissions
 from mobile sources.   Pollutants in this latter group include benzene, toluene, ethyl
benzene, and the isomers of xylene.

       Once the emphasis  on  volatile organics was established, most studies covered
pollutants that could be measured by readily available methods.  For the majority of
studies  conducted during  the  period of  1980 through  1985,  Tenax®/GC or GC/MS
methods7 generally dictated pollutant coverage, producing a heavy emphasis on common
solvents  such as perchloroethylene  and trichloroethylene (solvents such as methylene
chloride and 1,1,1-trichloroethane tended to pose contamination problems).  In addition,
7  The only other generally available method for organics sampling was to use charcoal
   as the sorbent, but charcoal was generally considered substantially inferior to Tenax.
                                        33

-------
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       Tenax® is  not suitable for  measuring pollutants such as  vinyl  chloride, 1,3-
butadiene, or acrylonitrile, which is why these organics were not included in early studies.

       Trends Toward Broader Pollutant Coverage

       Recent  studies are moving toward more comprehensive pollutant coverage.
Volatile organics are still an important class of pollutants and are given major emphasis,
but  there  is  a  growing  realization  that  metals,  semivolatile   compounds,
aldehydes/ketones, and possibly asbestos may contribute significantly to potential health
risk in certain urban locations.  This, in conjunction with improvements in sampling
methods,8 supports a freer and more inclusive selection from multiple, classes of toxic
pollutants.

       For  example, the South Coast MATES  selected pollutants for inclusion in the
monitoring programs based on four  factors: toxicity, emissions inventory  coverage,
 comparability with" ongoing .toxics programs, and constraints of sampling and analytical
 methods. This systematic approach led to the coverage of 6 metal and 14 volatile organic
 compounds, with a fairly balanced mix between these two major classes.  The Clark
 County  Study also chose  a mix of VOCs  and metals,  and included  some  asbestos
 monitoring as well.

        Similarly,  the more recent Staten Island  and Denver studies were designed for
 relatively  comprehensive  pollutant  coverage,  with volatile  organics,  metals,  and
 aldehydes included.  Denver also  considered  semivolatile organics.9 The more recent
 studies broaden our understanding of the magnitude and variability of the components of
 the urban soup. The data collected by the Staten Island and Denver monitoring programs,
 8   For example, although Tenax is generally not well suited for sampling methylene
     chloride and vinyl chloride, these and other compounds can now be  sampled by
     canister methods, which are replacing Tenax for many applications, especially tor
     fixed-station use.                                      .                   _    0
 9   For reasons discussed under site selection, the Denver project also monitored tor OU
     and fine particulates to help extrapolate data for broader spatial coverage.
                                         38

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 in particular, have greatly expanded the coverage into each of the major classes listed
 above.

 2.4.    Site Selection

        A variety of site selection methods were used in these studies.  Differences in
 approaches generally relate to differences in the objectives of each study, but study
 objectives must inevitably be balanced against practical siting considerations.  Where
 studies have attempted to  support multiple monitoring objectives, and most have, siting
 has involved a complex series of tradeoffs.

        Of the studies reviewed,  four  used monitoring expressly to estimate average
 population exposures.  Some  sited monitoring stations  according to explicit technical
 criteria (examples include the South Coast MATES, Philadelphia, Baltimore, Kanawha, and
 Denver studies.); others approached the  problem using no prescribed protocol (the Staten
 Island and Santa Clara studies).

      .  Formal ["Quantitative!  Approaches to Site Selection

        South Coast MATES—The, objectives of the ambient monitoring portion of this
 study were to quantify  maximum individual  risk,  estimate  average  background
 contaminations, and assess model performance and  its emission inputs.  Eleven existing
 SCAQMD sampling sites were used to estimate average background concentrations and to
 compare with SCREAM model outputs.  Ten new sampling sites,  selected by a method
 outlined below, were meant to be  located in areas of  high concentrations, and thus
 indicate maximum individual risks.

       The study developed a three-step approach to site selection  that could be adapted
to meet needs in similar locations.  Site assessment analysis covered a large area—70 by
60  kilometers—or 4,200  1  km2 grid cells, based on the  1  km grid spacing used in  the
study's dispersion modeling and for site  selection.
                                        39

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       In step one, SCAQMD conducted  dispersion modeling of emissions to estimate
annual average concentrations of each individual target compound within each grid cell.
These concentrations  were then  normalized  as  ratios between each  predicted
concentration and the 40th highest concentration for the pollutant in question. (This ratio
provides an indication of the strength of the concentration gradients  for each pollutant.)
The ratios in each cell were then summed  across pollutants.  According to the study
report, this procedure identified those areas with the greatest exposure potential for site
ranking purposes.

       In step two, the designers selected ten separate  clusters of cells as  candidates for
monitoring sites, based on the following considerations:
               Emphasizing cells with maximum concentration estimates above  or close
               to  detection limits for the test pollutants;
1.

2.
3.
              ...Giving priority to cells with .the highest concentration estimates;
               Providing adequate coverage of mobile and stationary sources; and
        4.      Emphasizing  cells with relatively high overall  potential  health risk,
               considering the unit risk associated with individual  pollutant species.

        The  first two considerations  essentially sorted  out cells with  the highest
 individual concentrations and the highest sums of ratios.  Using the third and  fourth
 considerations, the study selected final monitoring clusters based on representativeness of
 multiple source categories, as well as high potential health risks.10

        In the third step of the site selection process, the study considered practical siting
 factors, such as availability of power, security, access, source proximity,  receptor profile,
 species mix, and micrometeorological factors.

        Details  of SCAQMD's site selection protocol are in (Shikiya, 1988).
 10  The criteria for balancing these factors, whether qualitative or  quantitative, are not
     discussed in the study documentation.
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       Denver—The IEMP Denver study used  a rather  different, but  also  formally
structured, approach to monitoring station siting.  The site selection analysis used is based
on  some  unique factors pertinent to this  metropolitan area, such as mobile  source
dominance, but may have applicability to other sites where characterizing  broad regional
variations in the urban soup is the  goal, and industrial sources do not create substantial
gradients  in concentrations.   This  study's site selection problems were  simpler, since
model performance evaluation and  emissions inventory verification were  not among the
study's objectives.

       The  study objectives called for locating three sites at which detailed,  in-depth,
broad-spectrum sampling and  analysis would be conducted.  Because data on emissions
and ambient concentrations of air  toxics were limited at the outset of this study, the
designers used  criteria  pollutant  data as surrogates  for  selected classes  of  toxic air
pollutants in the site selection process.

       The  site-selection  process  involved  four steps.  In step one, available  criteria
monitoring data from various  monitoring stations were analyzed statistically. The study
computed means and  standard deviations of pollutants at  each monitoring location and
conducted paired t-tests to  compare means among sites.

       In step two, sites  were  grouped into neighborhoods that  showed the  closest
correlations and  summary statistics.  Defining neighborhood groups involved some
judgment, however. For instance, CO data were given more weight than the other criteria
pollutants because  previous  studies had concluded that mobile  sources would be the
most significant toxics contributors.

       Step three selected three  neighborhoods  that  study designers believed best
represented the region as a whole.  Again, although the supporting analysis  was
quantitative, some judgment was  involved.

        In step four, actual sites  were located within the three selected neighborhoods,
based mainly on practical  operational considerations.  At this time, however, one of the
                                         41

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three sites was located nearer to a traffic corridor—a deviation from the general program
objective of establishing average annual concentrations—to better define the high range of
air toxics concentrations from mobile sources.  To compensate for any resulting  bias in
regional exposure estimates, the study- established a fourth monitoring site to collect CO
and fine particulate data as an aid in interpreting the results of the monitoring program.
Although the selection of this  fourth site resulted in a  departure  from formal study
objectives,  the effort to quantify possible biases.is a practical example of compromises
that are often needed to achieve multiple objectives in an air toxics monitoring program.

       Philadelphia and Baltimore—If dispersion modeling is used to estimate risks, the
comparison of measured and modeled data can reveal how well the model is performing
and point to where model performance can be improved.  Criteria for locating sites in
these two studies were similar to those discussed in the South Coast study. Again, the
principal  concern  was  to  ensure  that  the maximum  number  o;F  samples show
concentrations above laboratory detection limits, and that there is good representation of
source categories contributing detectable concentrations at each monitoring site.

       The Philadelphia and Baltimore monitoring programs also emphasized emission
inventory verification.11  The siting strategy in these two projects, therefore, emphasized
siting in industrial neighborhoods  where concentration gradients of targeted VOC
pollutants were  expected to be  steepest.

       The IEMP Philadelphia  and (to a lesser extent) Baltimore  studies used  dispersion
modeling" in selecting neighborhoods  for  coverage in • the  monitoring  program.
Neighborhoods  (e.g., 1 km by  1 km areas) expected to have the sharpest gradients in
ambient concentrations, as well as the highest concentrations, were identified through the
use  of screening-level modeling.12   Network  coverage  was  sought that  provided
11  The South Coast study also was concerned with evaluating model performance, but
    was  equally  concerned with  estimating average annual  exposures—not a formal
    objective in Philadelphia and Baltimore.  This in part explains the somewhat different
    siting strategy used by the South Coast study.
12  This is similar to the analytical approach used in the South Coast study, but the IEMP
    modeling analyses were much less detailed than in the South Coast study.
                                        42

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representative coverage within each selected neighborhood. Actual selection of sites was
limited by the finite number of practical sites within many of the neighborhoods identified
for inclusion in the monitoring program.  The primary problem was identifying sites that
were secure, accessible, and not unduly affected by minor local sources, such as adjacent
roadways, gas stations, and so forth.     .

       The Philadelphia study provides  a special case objective  for testing model
performance near two major sources. In the Philadelphia study, two of the ten sites were
selected to  be  in the  vicinity  of  major  point sources to  help  assess maximum
concentrations in these areas. The remaining sites were selected to be more representative
of typical exposures in residential neighborhoods.  For the two special-case monitoring
sites, the goal was to be as representative as possible of maximum concentrations.

       For the two special sites,  the selection process  was guided by review of the
emissions inventory rather than by dispersion modeling.  One area of concern was near a
major pharmaceutical company,. located downtown.  The actual monitoring site was
selected approximately 500 m from the source, within the same street canyon.   This
distance was chosen because of concerns that vent releases from the large building might
not mix effectively prior to reaching a closer monitoring site.   Unfortunately, based on
records collected from the facility, emissions of chloroform, the  key pollutant of concern
at this source, were highly variable. Significant emissions were measured only during a
few days of the 30-day monitoring program, and at that time there was little wind flow
toward this site.   The objective of evaluating impacts near this  source was therefore not
effectively achieved.

        The second special  monitoring site was placed near the major refineries  in the
southeast section of  Philadelphia. The station was established just downwind of this
major industrial  complex, approximately 500 m from the fenceline.  Because emissions
from this source region were more uniform  and  because the wind  flow toward the
monitoring site was more frequent, this site achieved its objective.  Monitoring data from
this station helped to corroborate emissions data, indicating lower emissions rates than
anticipated at the outset of the study.
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       Kanawha Valley—Site selection in this study area was simplified because the
emissions of toxic air pollutants were heavily clustered into three separate areas located
within the confines of a narrow, 1 to 2 km wide, river valley. Two of the three areas were
selected hased on the magnitude of emissions of toxic pollutants and the amenability of
the canister sampling method to cover the key pollutants emitted  by  the industries  in
these areas, i.e., the Belle Complex (which included  a Diamond Shamrock and DuPont
plant) and the Institute Complex (which included a Union Carbide plant).

       Kanawha Valley is a good example of how the availability of monitoring methods
can heavily influence site selection. The emissions data that were  available  during the
design stage of the monitoring program suggested that the Institute and  South Charleston
complexes were  of greatest priority in terms of risk—a tentative hypothesis subsequently
borne out by more detailed emissions modeling .data. Belle was selected for a monitoring
site in place of South  Charleston because the solvents released in Belle (e.g., carbon
tetrachloride, chloroform, methylene chloride) were more amenable  to canister sampling
than the pollutants of greatest-concern in the South Charleston complex, namely ethylene
oxide and acrylonitrile.

       The narrow valley width facilitated the selection of an upvalley and downvalley
site in each of these two areas, because the major consideration was distance from the
boundaries of these tightly  clustered industrial  areas. With few exceptions, the major
releases of toxic  air pollutants in the Kanawha Valley are from near-grcund-level sources,
which would be expected to produce maximum ambient annual average impacts near or at
the fenceline  of  the plant boundaries.  This factor  together with the restricted flow
produced by the terrain greatly simplified the search for sites by making it essentially a
one-dimensional problem.

       For this study, sites were located within a specific range of  between 0.5 km and
2 km from industrial  fencelines, rather than at  residences directly  adjacent to the
fencelines (i.e., within 100 m  of the property boundaries). This was done because the
emphasis of  the  monitoring study   in  Kanawha  Valley was  on  evaluating  the
reasonableness of local  emissions inventory data.  The 0.5 km to 2.0 km distance range
                                        44

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was selected because, with only 15 days of sampling to be available, the study's designers
were concerned that plumes from industrial complexes might travel around or beside
monitoring sites located closer  than 0.5  km to  the fenceline.  There was  also  the
possibility that plumes might  extend above the monitors  on specific  days.  Both
eventualities would complicate interpretation of such a comparatively short (15-day) data
set. The 2 km maximum distance was selected because the Gaussian models used in the
study would produce more uncertain results for greater distances because of the influence
of the valley walls and complication in flow behavior. The measured and modeled data
were in reasonable agreement, suggesting that the modeled data were at least within the
right order of magnitude and thus were achieving one of the basic objectives of the study.

       Informal [Qualitative! Approaches to Site Selection

       Staten Island—The  Staten  Island  study is an  example in which selection of
monitoring sites does not rely  on analysis of available measured  or emissions data.
Instead, this  study sought to estimate exposures mainly by establishing wide  coverage
throughout the geographic bounds of the study area—a more subjective approach.

       While more  subjective placement of samplers was generally performed in the
Staten Island study than was performed in South Coast, established guidelines were
followed  for site selection, especially at the  microscale.  EPA guidance  for SO2 was
followed in locating  volatile organics sampling sites; EPA's particulate guidance was used
for the  high  volume samplers collecting metals data.  These guidance  procedures are
focused principally on microscale siting factors such as the height for sampling, distance
from nearby sources, and so forth, rather than  on selecting neighborhoods analytically in
relation to sources and predicted concentrations.

       The main limitation  of this more subjective approach to siting monitors involves
the interpretation of the relationship between average concentrations at the monitoring
sites and the average concentrations in neighborhoods elsewhere  throughout the study
area; such relationships  need to  be  established in order to estimate exposure based on
measured data.  While it is possible to complete this step after data collection, the main
                                        45

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risk is that some  of the sites may be found to  be in areas that do not best represent
exposures within the study area.  At that point, the data would need to be extrapolated to
better estimate more general exposures.  On the other hand, if this were found during the
site  selection stage, a different, more representative site could be selected.  This is in
contrast to South  Coast MATES where the representativeness of monitoring sites to the
full  receptor array could be more quantitatively demonstrated based  on the modeling
results prior to the collection of the first  sample.

       Urban Air  Toxics Monitoring Program—In this study, single sampling sites were
informally selected to be broadly representative  of urban exposures and not influenced
strongly by nearby point sources. (Note:  multiple sampling sites were encouraged, but
most participating  agencies had inadequate resources for more than one site.)

       Santa Clara—The Santa Clara study provides another example of qualitative site
selection.  In this  study,  site selection was  limited to existing air quality monitoring sites.
Five existing air  quality monitoring sites, part of the  Bay Area, Air Quality Control
Region's existing criteria pollutant network, were selected.  One of the five sites was in
Gilroy, a town near the southern extreme of the study area, in order to provide background
data13 to compare with the more heavily  developed northern portion of the study  area.
The remaining four sites were selected to obtain coverage in the more urban commercial
and residential areas.   None of the existing  sites was adjacent to a major  industrial facility;
instead, they  were  chosen  to  be  more representative  of selected neighborhoods,
distributed as widely  as possible throughout the study area. In this sense,  site selection
goals were similar to the Staten Island Study.

       The Staten Island  and Santa Clara studies  have  used similar site  selection
procedures, but it appears that the Staten  Island study is more likely to benefit from the
measured data set. The primary reason  is the large spatial and temporal coverage of the
 13  The concept of a background site has benefits in terms of interpreting the measured
    data set, and  potentially for assessing background values for  monitoring analysis.
    Because most  studies can afford only a limited number of sites, background stations
    generally have not been established-—none except this one was noted in any of the
    studies reviewed.
                                         46

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Staten Island  study.  The 13 site network, collecting data over nearly two  years, is

developing a large reservoir of data to partition and interpret as a function of wind flow

and  other  factors.   This richness provides information to help  separate potentially

unrepresentative data from data that better represent typical concentrations.  Furthermore,

sites found to be generally unrepresentative on this basis could be dropped from future

exposure assessments, if necessary. The Santa Clara project, on the other hand,  with

5 sites collecting- data over just 5 days, did not provide  enough data to support  an

exposure  assessment or  model performance  evaluation.   Moreover, the  limited

quantitative understanding of the selected sites further weakened the utility of this data

set.


       Physical Siting Criteria Common to All Studies


       Certain common  practical considerations  affect any  ambient  air monitoring

program.  Studies of toxic air pollution are no exception, and in fact  may often be  more

sensitive to certain localized factors than studies of most criteria pollutants. In selecting

sites for an air toxics ambient air.monitoring network, designers must be sure that each

site meets the following general criteria:

       •      Representativeness—The goal typically should be to collect data that
              would be representative  of a broad area, such as a  neighborhood.  In
              general,  sites  should be selected to  avoid local sources and  flow
              interference  from  nearby  structures  or  vegetation,  including
              micrometeorological influences from nearby hills, bodies of water, valley
              drainage patterns, and so forth. Sampling line intakes should be within a
              typical range of 2 to 10 m in height above the ground.  The probe should
              extend at least 2 m from a supporting structure; if located on a building, it
              must  be  mounted on  the windward side.   The  distance  between any
              obstruction and the sampler should not be closer than two times the height
              of the obstruction.

       •      Physical security—Securing instruments from tampering or theft often has
              led to rooftop sampling or the use of existing air quality monitoring sites
              that are part of local criteria pollutant monitoring networks.  When new
              sites must  be selected, rooftop monitoring is frequently used.  For these
              sites, the main issue was the avoidance of localized emissions from roofing
              material or vent releases.  The use of rooftop sampling for urban soup and
              other studies  of  toxic  air pollutants have  been questioned, however.
              (Wallace, 1987) Documentation was  not available for any of these studies
                                        47

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              to demonstrate that rooftop sampling did not interfere with the collection
              of representative data.
       •      Adequate  power and access—These are operational factors concerning
              issues such as the availability of power for all instruments and support
              equipment, space considerations,  24-hour access, etc., that enter into the
              site selection process.

       The above  factors impose practical constraints  that are common to air toxics or
criteria pollutant monitoring programs and will not be addressed further in this report.  To
the best of the authors'  knowledge, they were followed in all of the studies  reviewed
here.

2.5    Sampling Periods. Frequency and Duration

       The primary averaging period for these studies was annual because annual average
concentrations were  used as the basis for "estimating exposures to  carcinogenic  air
pollutants. Increasing the number of hours" that  monitors operate  (assuming an unbiased
sampling schedule) should improve the accuracy with which measured data can estimate
annual average concentrations.  Three interrelated design factors determine the number of
hours used to estimate seasonal or annual averages at each site in a sampling network:
(1) the sampling period, (2) the sampling duration, and (3) the sampling frequency.

       Sampling  period  refers to the length of time during which the field program is
operational, from the  first sampling day to the last.  The number of hours that compose
each sample is the sampling duration. Sample frequency is the manner in which days for
sampling are separated, e.g., every second day, every third day, every sixth day, and so
forth.

       Figure  2-1 presents the sampling periods, sampling durations, and  sampling
frequencies for each of the studies evaluated. As shown in Figure 2-1, there is a wide
range in the extent of data collection across these studies, ranging from 5 days of coverage
in Santa Clara, to  1.5  years of every 6th day  sampling in the Staten Island project. In all
cases, funding has been the key element affecting temporal coverage. Ideally, all studies
that emphasize cancer risks, as reviewed here, would prefer complete monitoring over at
                                        48

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 least a one-year period.  Since the cost of such monitoring is prohibitively expensive,
 program designers must generally define the minimum data needed to adequately meet
 each project's stated objectives.
                 Figure 2-1.  Summary of Monitoring Periods, Sampling
                         _ Frequencies,  and Sample  Duration
  Urban Air Toxics Monitoring
         Program
                Baltimore
                  Denver
           Kanawha Valley
              Philadelphia
               Santa Clara
      South Coast—MATES
             Staten Island B8flaafiflfiaflaaflaa«flftfi«^^                                  24
      Sampling Period (months)  M Sampling Duration (hours)
Sampling Frequency (nth
day)
         Monitoring schedule was selected based on short-term meteorological forecasting.
               NOTE: the Denver study also conducted limited 12-hour sampling.
       Sampling Period

       The selection of an appropriate sampling period is driven by the end use of the
data set. For studies to date that evaluated cancer risks, there were two major approaches
used: (1) use of measured data to directly estimate annual average concentrations, and
(2) use of measured data  to assess,  and improve if possible, model performance.  The
second approach was then followed by predicting concentrations, and thereby exposure
and risks, through the same basic modeling approach.
                                         49

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       Denver—The IEMP Denver study used monitoring in the winter and  summer
seasons to estimate annual average concentrations  for  all pollutants.   The  study
concluded, based on interpreting criteria pollutant data, that averaging data from the
summer and  winter  periods provided reasonable  estimates of  annual  average
concentrations.  These sampling periods were also selected to track periods with peak
concentrations.  Photochemically formed compounds peak during the summer and have
minimum concentrations during the winter. The less reactive pollutants, such as benzene,
carbon tetrachloride, and  other volatile compounds, were  expected to reach maximum
concentrations during the winter and  minimum values during the summer. Although the
periods with generally high and low  values for these different pollutants  are opposite to
one another in the annual cycle, in either case averaging the results "collected during the
summer and winter seasons was thought to reasonably represent peak and annual average
concentrations.

       To  check this  assertion,  the study  analyzed  criteria  pollutant  data to
quantitatively evaluate how well, the summer and winter seasons, on balance represent
annual average concentrations. CO was used as the primary indicator pollutant for the
dominant  mobile source emissions, and TSP as  an  alternative  indicator pollutant.
Preliminary statistical review of CO and TSP data suggested that the summer and winter
seasons would provide balance in covering a wide range of pollutant classes.  For these
criteria pollutants, estimates of annual  average concentrations based on the summer and
winter periods were within 10 to  15  percent of the annual averages  as calculated using a
full year of data.

        South Coast MATES—At the 11 existing sites, sampling has been ongoing for  an
indefinite  period. At the 10 new sites selected to capture high exposures, two sampling
periods were defined,  each five months long.  One period corresponded  to the
winter/spring seasons and the second, the summer/fall seasons.  Thus, samples were
taken at the new sites during periods  representative of an entire year.

        IEMP  in Philadelphia.  Baltimore-  Kanawha  Valley—The  IEMP studies  in
 Philadelphia, Baltimore, and Kanawha Valley selected a sampling period  of one season to
                                         50

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serve as a checkpoint on model performance.  For this objective, a single season appears to
be a reasonable minimum sampling period so long as there are not likely to be strong,
poorly characterized seasonal variabilities in the emissions of key compounds. For many
of the volatile organics addressed, this is probably a reasonable assumption.  Wood
burning, however, is an  example where distinct  seasonal trends occur.  The IEMP
monitoring programs  were performed during the winter and spring seasons primarily
because of project schedule constraints.

       Santa Clara—For this study, a five-day monitoring  program was undertaken on
consecutive days to obtain data to directly assess the general magnitude of concentrations
at selected locations  within the study  area.   The intent was  to  obtain  some direct
measurements to complement the modeling-based exposure  assessment.  In retrospect, it
appears that the short  duration of this program hampered the validity of comparisons with
modeled data? the  data set was too limited and  short to reasonably assess general
concentrations.

       Sampling Frequency

       Most of the studies used a sampling frequency of every 3rd, 6th, or 12th day.  In
general, the use of  longer sampling frequencies and predetermined sampling intervals
allows for: (1) extending the collection of measured data throughout a season, (2) avoiding
bias introduced by a non-random selection criteria,  and (3) obtaining more  independent
data than would be collected with monitoring during consecutive days.

       The South Coast MATES study chose an alternative approach.  Two  ambient
sampling efforts were conducted: (1) sampling at regular intervals  at a network of existing
sampling sites and (2) sampling at new sites  believed to represent locations of maximum
individual risk.  Sampling at the 11 existing sites was conducted at regular intervals of
about once every two  weeks for organics and about  once every week for metals.  At two
contributing  EPA sites,  sampling was  conducted  once  every 10  to  12  days for
benzo(a)pyrene and metals.  At the 10 new sites, sampling  days  were selected based on
short-term meteorological  forecasting rather  than on a regular sampling frequency.  No
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samples were collected at the new sites on Saturdays or Fridays, since they could not be
processed within 24 hours.  (Barcikowski, 1988)

       The approach used in the South Coast MATES project for the sites selected to
represent locations of maximum individual risk is useful in that sampling days could be
selected that best met project objectives (such as days in which wind flow is consistent or
days  with no precipitation).   It offered  the potential to  fill  a matrix  of pre-selected
conditions, which, if properly weighted, could also be used to represent annual average
conditions. This approach, however, requires that the analyst show that the days selected
for sampling adequately represent annual average conditions.  In contrast, when using
every 3rd, 6th,  or  12th days regular sampling frequencies  the random nature of the
selection process should act to statistically represent the monitoring period without the
need to  demonstrate representativeness.  Even the random selection of sampling days,
however, could require a  demonstration  of representativeness to  annual  average
conditions if only a small number of sampling days are included.

       Sampling Duration

       Concentrations can vary markedly as  a function of time at any given site because
of periodic variations related to diurnal cycles in meteorological conditions or emissions
patterns.  Industrial processes with rather uniform, low-level emissions may, for instance,
show substantially higher  concentrations caused by relatively poor dispersion at night.
Mobile source emissions tend to be greatest during the day, while wood smoke and other
residential-related emissions are generally higher at night.  All of these cases point to the
importance of considering diurnal variations when using  measured or modeled data to
support  an exposure assessment.

       Two basic ways that study managers have selected sample durations  to account
for this  variability are  as follows: (1) collect 24-hour  samples to expand the  number of
hours integrated into each sample (and avoid potential bias by using less than 24-hour
durations) or (2)  collect sets  of  12-hour daytime and nighttime samples  to show the
diurnal differences while retaining the ability to compute daily averages.
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       In the South  Coast  MATES project, all of the samples at the new sites were
collected over a 24-hour period beginning at approximately 0900 Pacific Standard Time.
The choice of starting hour was chosen so that data would be comparable to that from the
"background" ambient toxics program, which was running concurrently.  The rationale for
choosing this particular 24 hour period as opposed to a midnight-to-midnight period was
to collect samples over a contiguous meteorological drainage period  (stable conditions)
rather than splitting the drainage period.  (Shikiya, 1988)

       Of the operational studies  reviewed,  the Denver study was  the  only one that
routinely collected 12-hour samples at  some sites.14  The other studies used 24-hour
duration samples.  The decision to  use 12-hour samples for a subset of the program was
made because of the following factors:
       •      The  diurnal patterns in the Denver metropolitan areas are accentuated,
              especially during the winter season, by  topographic  complexities.
              Collecting 12-hour samples provided the opportunity to  better  interpret the
              measured  data sets to show the relative importance of source categories
              and to better characterize peak concentrations.
 .„-.                        - --  •"
       •      Receptor modeling was a secondary objective; this could be substantially
              enhanced  by  the   collection  of  12-hour   samples  because source
              culpabilities15 could  be more easily distinguished.

       The advantage of collecting  12-hour  diurnal samples is that incremental impacts
from source categories can be better distinguished.  The obvious disadvantage is that two
samples, instead of one, need to be analyzed to represent one day, cutting in half the
number of hours measured per monitoring dollar  spent.   Twelve  hour samples  are
typically taken from 7 a.m. to 7 p.m. and from 7 p.m. to 7 a.m. each day.
14 Refer to Section 2.7 for a description of the TEAM and IACP, where 12-hour sampling
   also was done to help display diurnal differences in emissions, and to account for
   distinct differences in subject activity patterns.
15 The "culpability" of a source refers to that proportion of a measured or modeled
   concentration at a particular geographic location attributable to that source.
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2.6    Sampling and Analytical Techniques

Tables 2-3 and 2-4 present a summary  of techniques  used to sample  and analyze,
respectively,- the classes  of compounds measured in these studies.  The wide range of
methods can be briefly summarized by pollutant class: those pertaining respectively to
volatile organics, semivolatile organics, metals, and aldehydes/ketones.

       Volatile Organics

       This class is historically the most widely covered.  For the studies reviewed, there
were two major methods used to sample VOCs: Tenax® and canisters, with Tenax® being
absorbent material  and the canisters collecting samples of ambient air.  Generally, a
combination of gas chromotography (GC) and gas chromotography/mass spectrometry
(GC/MS) has been used in conjunction with these methods to analyze the samples.

       The trend has been toward greater reliance on canisters because of the documented
unreliability of Tenax®.16   Even though canister sampling generally suffers from higher
detection limits than  Tenax®, canister sampling appears to offer greater day-to-day
consistency in results and is therefore preferred for most applications,17 despite its higher
detection limits.

       Uncertainties in using Tenax® can be  reduced  by using a distributed volume
sampling protocol, in which four sampling tubes collect samples at  different flow rates.
(Walling, 1984) There is no evidence in the documentation of any of the studies reviewed
here,  however, to show that  the distributed volume technique has  been used  when
Tenax® was the sampling medium. Disadvantages of distributed volume sampling include
a near quadrupling  of analytical costs and relatively high rejection rates among samples
tested. Adherence to this method effectively renders Tenax® unacceptable for operational
studies.
16 See Walling 1984.  Problems include (1) the apparent variability in effectiveness as a
   function  of   ambient  temperature,  relative humidity, and specific  mixtures of
   pollutants, (2) vulnerability to breakthrough, and (3) vulnerability to contamination.
17 An exception is for personal monitoring, where  Tenax continues to be used (see
   Section 2.5).
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       gemlvolatile Organics

       The  class  with the most limited coverage among  the  studies  reviewed  is
semivolatile organics.  These samples can be collected on polyurethane foam (PUF) or on
XAD-2, another sorbent compound.  Analysis is by gas chromatograph/mass spectrometer
(GC/MS), or by GC/MS coupled with high performance liquid chromatography (HPLC).
XAD-2 is generally preferred when biological testing is to be performed, as it has been in
the Integrated Air Cancer Project (IACPJ.  The IEMP  Denver study used PUF sampling and
GC/MS analysis to cover this  class of pollutants.  One reason  that the sampling  of
semivolatile organics is so limited is cost: analyses can range  from $1,000 to $2,000 per
sample, as shown in the documentation for the Denver study. (Machlin, 1986) The trend
toward sample compositing for semivolatiles is an attempt to extend the number of hours
integrated into each sample to stretch analytical dollars within  the generally tight budgets
of applied programs.18

       Metals             "                                                '

       Metals have been included in many urban studies.  In the South Coast MATES and
Urban Air Toxics Monitoring Studies, high volume samplers were  used to collect metals
samples, a different procedure from that  used by the Denver studies, which collected fine
particulate  mass using fine particulate samplers.  Atomic  adsorption spectrometry,
neutron activation  analysis, inductively coupled  plasma  spectroscopy,  and X-ray
fluorescence spectroscopy are the methods generally preferred for metals analysis.

       Hexavalent chromium (Cn+f>), an important contribution to urban air toxics risk,
has not been distinguished from total chromium in most ambient measurements.  Cr+6 was
reported in ambient samples collected as part of the SCAQMD MATES project.  (Shikiya,
1988)  Many studies that have analyzed  Cr+6 risk have  either  (1) assumed  a certain
percentage  of  total ambient chromium  is Cr+6  (e.g.,  10 percent or 100  percent) or
(2) modeled Cr+f> and Cr+3 emissions distinctly.
 18 See Section 2.8 for a more detailed discussion of sample compositing.
                                        58

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        Aldehydes/Ketones

        Formaldehyde and the remaining aldehydes/ketones were addressed  by 2,4-
 dinitrophenylhydrazine (DNPH) cartridges and HPLC analysis in Denver, Staten Island,
 and the Kanawha Valley studies and in the Urban Air Toxics Program.

        The results of the Denver and Staten Island studies  were not available at this
 writing. Kanawha Valley, however, showed  serious  contamination problems developed
 under this  method, invalidating the use of its samples. This is not to suggest that there is
 a general flaw with DNPH cartridges or HPLC analysis, but the Kanawha report does not
 discuss the cause of, or possible corrections  for, this problem.  This problem has been
 overcome in the Urban Air Toxics  Monitoring Program by careful preparation of the
 sample matrix before use.

 2.7.    Evolving Monitoring Technologies

       New developments in  technology for analyzing  air toxics offer the promise of
 increased breadth and depth lor "air toxic studies, with greater reliability and  possibly
 lower costs.  Two major EPA research efforts  are providing measured data that can help
 guide present and future applied studies of the urban soup.  These are the Total Exposure
 Assessment Methodology (TEAM) and the Integrated Air  Cancer Project (IACP).  In
 addition, two specialized monitoring techniques that were used in the studies under
 review are briefly discussed later in this section—the TAGA® and Remote Optical Sensor
 (ROSE) systems.
       The TEAM studies have  collected extensive  measured  data  sets of personal
exposure and ambient concentrations of selected toxic air pollutants.   These  studies, of
which the principal  ones were conducted in New Jersey and California,19 measured
exposure of 20 toxic air pollutants to a total of over 600 subjects.   The TEAM data base
19 Additional field studies have been conducted in North Dakota, North Carolina and
   most recently, in Baltimore, Maryland, a study still in progress as of this writing"
   More studies are planned.
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provides measured ambient and personal data to display more fully the range, of personal
exposures than can be revealed by the more traditional  approach of using measured or
modeled ambient concentrations as a surrogate for personal exposure.  Chapter 4 more
fully describes the exposure data collected  in the TEAM studies.   This section briefly
discusses TEAM'S measured data set.

       The  TEAM study personal samples  were collected by human subjects  wearing
vests containing a Tenax® cartridge and a sampling pump; samples are collected over two
12-hour periods for each subject.20  Gas chromatography/mass spectrometry (GC/MS) have
been  used for analysis of 20 preselected compounds.   In parallel with the personal
sampling, each TEAM study took ambient samples using traditional fixed site samplers.
In each case, approximately 20 liters of air were collected during the sampling  period and
the 12-hour integrated averages were compiled.  Tenax® was used for both personal and
ambient samples in the early TEAM studies.  The recent Baltimore TEAM study employed
a combination of Tenax® (for personal sampling) and canisters (for fixed stations).

        During the TEAM studies, several criteria were used to select the target chemicals
 (EPA, 1986). These criteria included:
        1.      Toxicity, carcinogenicity, mutagenicity;
        2.      Production volume;
        3.      Presence in ambient air or drinking water;
        4.      Existence of National Bureau  of Standards permeation standards; and
        5.      Amenability to collection on Tenax®.
 20  Despite the problems with Tenax discussed by Walling (Walling 1984), TEAM studies
     continue to rely on Tenax for at least two  reasons.  First, Tenax  appears  to  be
     acceptable for characterizing distributions of air concentrations, which is an important
     output of the TEAM studies; Tenax problems are most severe when the goal is to
     measure minor variations in concentrations over time, as is typical with fixed-station
     programs.  Second, 'no satisfactory substitute for Tenax appears  to be available  for
     portable use in personal monitoring vests.
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        A  total of 20  compounds  have been  routinely measured  by TEAM, but 11
 compounds were given particular emphasis during interpretation because: (1) this subset
 was identified as being the most amenable to the Tenax®/GC/MS sampling and  analytical
 techniques, and (2) these  pollutants were identified as being of greatest interest from a
 health perspective. The pollutants emphasized were:
        1.      Chloroform
        2.      1,1,1-Trichloroethane
        3.      Benzene
        4.      Carbon tetrachloride
        5.      Trichloroethylene
        6.      Tetrachloroethylene
        7.     "Styrene
        8.      meta or para-Dichlorobenzene
        9.      Ethylbenzene
        10.     o-Xylene.
        11.     meta or para-Xylene.

       The Integrated Air Cancer Project

       Drawing on the combined expertise  of four EPA research laboratories, the IACP
program probably provides the best  opportunity to further develop monitoring methods
for toxic air pollutants. The broad nature of IACP, with the goal of identifying species
most likely to be carcinogenic and  their sources, requires  development  of  monitoring
methods to better identify specific carcinogens in a wide range of classes, including VOCs,
semivolatile/particulate organic compounds, and inorganic pollutants.  In addition to
developing monitoring methods for directly characterizing pollutant species, IACP is also
advocating the state of the art in monitoring methods  for biological sampling and data
collection to support receptor modeling applications.
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      Special Monitoring Techniques

      This section briefly describes the TAGA® and ROSE® systems.
      TAGA®
      The TAGA® system is a mobile mass spectrometry/mass specliometry (MS/MS)
system that has been used to collect near real-time air quality concentrations.  TAGA® is a
mobile system that can be driven to many pre-selected sites,  or used to more randomly
search for "hot spots." Some limited sampling was performed  in Santa Clara (EPA, 1984)
and Denver (Dumdei, 1986) using this system over a one-week period in each study area.

       Problems with detection limits lessened the ability of this system to meet the
objectives of the Denver and Santa  Clara studies—most  compounds at most sites were
present in the  ambient air at levels  well below the  detection limits of the TAGA®,
limiting sampling to major intersections- during rush hour  in Denver and some vent
sampling in Santa .Clara.  Data gathered were not useful to support the broader objectives
of these studies. •                  '                            .
        ROSE
        The Remote Optical Sensing  System (ROSE®) is a portable infrared sensor housed
in a van. It was used to monitor at selected municipal and hazardous waste landfills in
New Jersey as part of the Philadelphia study (EPA, 1986).  As with the TAGA® system,
high detection limits adversely affected meeting the goals  of the study.  The data  were not
usable  for meeting the study's stated objectives.

 2.8     Tnsights into tlis Use of Monitoring in Air Toxics Programs

        Cost-Saving Measures

        Since monitoring programs are often the most expensive component of an urban-
 scale air toxics study, they often offer the  greatest potential for  employing cost-saving
 measures.  Perhaps the key point to consider when designing a cost-effective monitoring
 program is that the goal is to best characterize air quality, often for a metropolitan area.
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Assuming funds are fixed, then tradeoffs exist between the coverage of the sampling
program (i.e., the number of sites and days of sampling per site) and the data quality
objective of each sample.

       If cost-saving measures can be instituted to improve spatial and temporal coverage,
and if the data quality of each sample can be documented to be within limits defined as
acceptable to the project, methods such as those mentioned below may help improve
spatial and temporal coverage.

       Sample Compositing—Sample compositing (combining short-term samples before
analysis to increase the temporal representativeness of each composited sample) can be an
attractive concept, especially if the major concern is with carcinogenic risks, where annual
averages are the most important  statistic of concern.  Although metals  have been
composited effectively for many years and the concept has been used with source testing,
only during the past several years has compositing gained acceptance as a viable potential
option for ambient "air sampling._

       Instead of combining  short-term (e.g., <24 hr) samples after they are collected, a
variation of sample compositing involves the collection of long-term, but intermittent,
samples.  An example of this might be for each sample to be collected over a period of
several  days or a week, but actually "pulling" sample air for only 15 minutes within each
hour  of sample duration.  Thus, the sample compositing is being done, in effect, by the
sampling device rather than by the analyst in the lab.  One concern with this approach is
the potential  for pump failure,  which could result  in  substantial loss  in coverage.
Another concern is  the possibility for sample loss or  degradation over the long collection
period or during subsequent storage.

       To date, sample compositing has only been used in laboratory testing and  was not
used in any of these studies, with the exception of semivolatile organic compounds in the
Denver study. While further documentation appears to be needed to better describe the
tradeoffs in terms of accuracy, especially for VOCs, sample compositing appears to offer
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the potential for making effective air toxics monitoring programs more affordable at the
local level.

       Indicator Pollutants—Another potential cost-saving measure is the  use of indicator
pollutants to priortize and/or limit subsequent sample analysis.  The concept here is to
identify an easily measured substance or property  that reasonably relates to  other
substances or properties that are more expensive to measure. Thus, one can rank the
samples by the value of the indicator pollutant  to isolate a subset of the samples that
warrants further analysis.  In the Denver IEMP study, for example, total organic carbon
will be used to rank semivolatile  samples collected at each site.  The groups of low,
medium, and high concentrations based on  total organic carbon will  be analyzed in
composited batches.  This offers the benefit  of  performing  individual  analysis for the
upper  end of the  distribution,  which may be of greatest interest in terms of short-term
exposures, and also provides  a means of quality assurance of the compositing step.

        Periodic-Network  Enhancement—This technique is based on the concept of a
routinely operated core monitoring network (such as three sites for a metropolitan area),
which can be expanded to a larger network (such as eight to ten  sites for a metropolitan
area) to collect very detailed data for a short time  period every two to three years. Having
a screening-level monitoring  program with  a larger network in place aids the selection of
optimal sites to address local needs. Performing follow-up monitoring every two to three
years for a one-season program could provide documentation concerning changes in the
relationship between the core and supplemental sites. This approach provides the benefit
of an  expanded network to characterize spatial  distributions, without  the continuous
collection of data at the supplemental sites  and attendant costs. None of  the studies have
applied this concept to date.

        Selective Analysis—Another approach  to reduce the costs  of monitoring programs
is the  concept  of selective analysis. If samples can be collected at a fraction of the costs
of analysis (as is the case for many methods), a larger  data set can be collected than
analyzed, i.e., every sample that is collected does not  need to  be analyzed.  By this
approach, samples can be selected to fill out a matrix of conditions of greatest importance
                                         64

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to a  study;  for example, worst-case  dispersion,  flow from  specific  facilities, or
nonprecipitation days. By having broad coverage, the objectives of a monitoring program
may be achieved by selecting those samples that best meet prespecified objectives.  If
estimates of annual averages are to be based on user-selected days, however, the burden is
on the user to demonstrate that the averages are not biased by selecting unrepresentative
days. For example, a weighting scheme may be needed to effectively represent long-term
averaging.  The South Coast study, which is using a form of selective analysis for its
monitoring program, could benefit by describing the representativeness of the measured
data set to estimate annual average conditions.

       The "Supersite"  Concept—As  part of  the Philadelphia study, a quantitative
methodology was  developed to select optimal number and location of monitoring sites
based on dispersion modeling of available emissions data (see Appendix B). The goal was
to maximize the use of available informa.ti:pn to select the core sites that  best met project
goals.  The cost of air toxics monitoring is expensive, especially the cost of maintaining
long-term  sites that  might- be~Tised to help track trends in urban  air toxics levels.
Allocating  a fraction of these resources to help support the selection of sites that provide
the most independent data is one way of obtaining the most information per monitoring
dollar spent.

       This approach can be briefly summarized as follows:
       1.     Model the available emissions data for the key pollutants to be  addressed
              in the monitoring program. Perhaps 5 to 10 pollutants could be used to
              guide the selection of monitoring sites.  Normalized modeling with tight
              resolution would be recommended, such as a 1 km grid spacing.
       2.     Based  on the initial results, select a long list of 25 to  30 potential site
              areas (at the block or neighborhood level).
       3.     Compute  the correlation  of  modeled  hourly  or   daily  ambient
              concentrations among all sites for each of the targeted pollutants based on
              meteorological and emissions data that are  representative  of seasons  in
              which monitoring programs will be done.21
21     Consideration of summary statistics could also be included in consideration of
independence.
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       4.      Select a maximum correlation, such as a correlation coefficient in the range
              of 0.6 to 0.8.   For purposes of illustration, consider 0.7 as the desired
              cutoff.
       5.      For each pollutant, group all sites that are correlated by 0.7 or higher as a
              cluster.
       6.-     Develop  a scoring technique that  identifies sites that provide the most
              independent information.  (Refer to  Appendix A for the  details of the
              proposed scoring technique.)
       7.      Develop an iterative procedure that eliminates sites that provide the least
              amount of information, until the only sites left are  the most independent.

       The Philadelphia Study presented the opportunity to test this approach by using
this methodology for the actual monitoring sites, and then comparing the model-based
results with rankings based on the actual  measured data.  Figure 2-2 shows the optimal
four sites based on modeling and monitoring. _As shown , there was overlap on two of the
four sites.  Appendix B provides a more -detailed description of this potential cost saving
measure.
               Issues
       Accuracy. Precision, and Representativeness — It is a common misconception that
measured air quality data are inherently "better" than modeled concentrations.  As the
experience  of these  studies shows,  measured  data can have as many  limitations as
modeled estimates of ambient air concentrations  and  can be equally misleading — more so,
perhaps, because of their presumptive validity.  Especially in air toxics studies, where
costs tend to be high and funding limited, the number of sites and samples are often very
small.  Data sets developed to characterize urban-scale concentrations may often not be
representative either in space or in time.  In addition, the uncertainties of sampling and
analytical methods, as well as the errors that can be present in identifying and quantifying
pollutants, can often reduce the effectiveness  of monitoring approaches.

       This is not to say that monitoring should not be an important element of air toxics
studies,  but rather to suggest that monitoring  should, in most cases, (1)  be carefully
planned  in relation to its own study objectives and (2) be integrated as much as possible
                                         66

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                                                                                          1
                                 PHILADELPHIA
                                     COUNTY
                                            LEGEND TO IDENTIFY OPTIMAL TOP FOUR
                                            SITES BASED ON CORRELATION ANALYSIS

                                               @ » BASED ON MEASURE DATA

                                               ^ * BASED ON MODELED DATA   .

                                               ^ - BASED ON BOTH MODELED
                                                    AND MEASURED DATA
                          SAMPLING SITE
1. NAVAL HOSPITAL
2. GOODYEAR
3. FD 16
4. FO7
5. ST. JOHN
 6. LARONER'S POINT
 7. FD71
 8. FO36
 9. SAM BAXTER
10. NE AIRPORT
 * ROADWAY METEOROLOGICAL/MONITORING SITE
  FIGURE 2-2  OPTIMAL TOP FOUR MONITORING SITES FOR PHILADELPHIA PROGRAM
                BASED ON MODELED AND MEASURED DATA
                                  67

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into complementary analytic processes—namely, emissions inventory development and
air dispersion modeling.

       Vulnerability  of Programs to Prnjant  Schedules—In a number  of  instances,
monitoring results have been to some degree compromised by overall program  schedules.
Especially for estimating carcinogenic risk, where the key parameter of concern is annual
average concentrations (either for the population as a whole or for the maximum exposed
individual), monitoring programs  of single seasons (or even shorter periods)  must be
carefully reviewed  to  ensure that the risks are not unduly biased by unrepresentative
meteorological  conditions or seasonal variations in emission rates from key  sources or
source categories. Ideally, concentrations should be estimated over a period of more than
one full year, as has been routinely possible with the criteria pollutants.  Unfortunately,
air toxics funding tends to be provided on a~special project basis, and few projects can
afford more than  selective seasonal  sampling programs.  Much  has  been  done to
compensate for this perennial problem through analysis of annual meteorological data,
links to air modeling efforts, and the like, but the issue remains a significant one.

       Lack of Comprehensive Pollutant Coverage—Limitations in  funding,  lack of
suitable ambient sampling and analytic methods for  some pollutants, and  lack of
available potency data mean that all monitoring studies must concentrate on a subset of
potential pollutants of concern. The subset may be limited to carcinogens only, to certain
classes of pollutants (metals, volatiles, etc.), or to  a selected group of substances from  a
variety of  classes;  however, the effect  is the same—a  possible  underestimation of
exposures and risks from certain pollutants.  For example,  even studies such  as IACP or
the Denver study, where the goal is broad pollutant coverage, the limitations of the state
of the art for measuring many compounds in the ambient  air are substantial.  Although
measurement techniques continue to  improve, only those compounds that -have been
validated for the selected methods can be accurately quantified.

        Compromise Among  Program Obiectives—Most, if not all, of the studies
 reviewed were compelled to make compromises  among multiple monitoring objectives,
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with varying  effects  on project  results.   In  many cases, these compromises  did not
seriously hinder the quality or usefulness of study results, but in others they apparently
have.  There  is a tendency to attempt to respond to as many pertinent questions as
possible.  Serious problems can occur when compromises prevent attainment of one or
more primary goals.

       Use  of New  Technologies—Studies  that  have  attempted  to apply evolving
laboratory techniques to operational field studies  have  frequently failed to meet  their
original program objectives.  The recurring theme is that the detection limits for which
these technologies were developed may not be  appropriate for field studies of ambient air.
Although there is every need to evolve new laboratory techniques for operational studies,
projects on  limited budgets should be skeptical of applying innovative  sampling and
analysis techniques  in the absence  of concrete information on their  performance
(especially detection  limits and  interference. and contamination problems), reliability,
suitability to planned  field conditions, and costs.

       Spatial  Coverage—Generally,  measured data are collected to help meet the
objective of  characterizing concentrations within a large area. It is impractical, however,
to equip a large number of sites to meet this goal because of the high cost of monitoring
most noncriteria pollutants.  The  range in the  network size among these studies was 1 to
13 sites in an urban area. For applied studies, therefore, the analyst must extrapolate data
from a limited number of sampling points to represent a much larger area.

       The  extrapolation of measured data to a geographic area broader than  specific
monitoring sites needs to be carefully considered.  For example, the 13-station network
for the Staten Island study has by  far the most extensive  spatial  coverage among the
studies reviewed.  The 13 sites, however, are a very small subset of the points in a study
area that likely has large concentration gradients in some subsections.  The question is
where these  specific sites fall within  the spatial distribution of concentrations.

       Dispersion modeling can help shed light on this problem, which again points to the
need to more fully integrate monitoring  and modeling components of  such studies.
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Dispersion  modeling  provides the  opportunity in some  cases  to  estimate likely
contamination gradients throughout an urban area prior to  establishing a monitoring
network.  Interpreting the modeled results, including evaluating exposure correlation
among candidate monitoring sites, may improve the site selection process.   Refer to
Appendix B for further details.

       Temporal Coverage—Ambient measurements will reflect tremendous variability as
a function of time because of meteorological  and emissions variability.  This is most
significant for major point source releases, as  was shown  in the measured data for
Kanawha Valley where the industrial clusters dominated impacts for selected compounds.
It was also  shown in the Philadelphia IEMP study (for 1, 2 dichloropropane and 1, 2
dichloroethane), where a major wastewater treatment plant was a dominant emitter that
could show two orders of magnitude difference in ambient concentrations as a function of
wind flow  alone.  Area source-dominated, receptors are less  affected,  but seasonal
variability and diurnal patterns in emissions and meteorology will nevertheless produce
substantial variations in concentrations.

       For  carcinogens, the goal  in these studies was to estimate annual average
concentrations, so the question becomes one of  how well the short-term measurements
for each site represent annual average conditions.  The IEMP Philadelphia study made this
comparison during a 30-day winter sampling program.   Concentrations were modeled to
match the days of the monitoring period, and also for a five-year climatological average
data set.  These modeled data were compared with the measured data set and found to be
in reasonably close agreement.

       In the studies reviewed, the sampling program ranged from 5 days per year in Santa
Clara up to 50 days in Staten Island.  Intuitively, it appears that Santzi  Clara data would
be totally inappropriate for use in estimating annual average conditions,.  Staten Island, on
the other hand, should  reasonably represent annual average at these sites.   Modeling
similar to what was done for the Philadelphia study could help confirm this hypothesis.
                                        70

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       Acute noncancer  risks, because of their relation to short-term exposures, pose
substantial  challenges in terms  of temporal coverage,   Here, the problem involves
characterizing peak concentrations, such as over a 1- to 24-hour period. None of studies to
date provides sufficient measured  data to effectively evaluate acute exposures.

       Methods Limitations—It appears that methods for measuring toxic air pollutants
are becoming more standardized over time, but there still are no standard methods such as
those that exist for  criteria  pollutants.   Until standardization is achieved, inherent
variability in the data collected by different studies will persist.  This may inadvertently
lead to invalid comparisons of the resulting risk estimates across different metropolitan
areas.
                                        71

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                                    Chapter 2
References
Barcikowski, W.,  1988.  South Coast Air Quality Management District.  Letter and
attachments to Tom Lahre, U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina.  October 24, 1988.

Dumdei, Bruce, David Mickunas and James Zoldak, 1984, "Characterization of the Urban
Plume in Denver, Colorado; February through March 1986," prepared for EPA Region VIII,
1986.

EPA, 1984, Keith Hinman et al., "Santa Clara Study Report," EPA Integrated
Environmental Management Division, Washington, D.C., 1984.

EPA, 1986, Final Report of the Philadelphia Integrated Environmental Management
Project.  U.S. Environmental Protection Agency, Washington, D.C.

Machlin, Paula R., 1986.  "Denver Integrated Environmental Management Project:
Approach to Ambient Air Toxics Monitoring,""EPA Region Vffl, Denver, Colorado, 1986.

Shikiya, et al., 1988.  Analysis of Ambient Data From Potential Toxics "Hot Spots" in the
South Coast Air Basin- Multiple Air Toxics Exposure Study.  Working Paper No. 5,
September 1988.     ,    	                              '

Wallace, L., USEPA,  Office of Research and Development, personal correspondence,
March 1988.

Walling, Joseph F., 1984.  "Experience from the Use of Tenax in Distributed Ambient Air
Volume Sets,"  presented at APCA/ASQC Specialty Conference on Quality Assurance in
Air Pollution Measurements, Boulder, Colorado, October 1984.
                                       72

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                                     CHAPTERS
                             EMISSION INVENTORIES
       The following topics are discussed in this chapter:
       •      Use  of emission inventories in multi-pollutant, multi-source urban  air
              toxics  assessments
       •      Pollutant coverage
       •      Source coverage- —                                     •      .
       •      Estimating emissions
       •      Spatial and temporal resolution
       •      Quality assurance
       •      Insights into compiling inventories for urban air toxics assessments

3-1    Use of Emission Inventories in Multi-Pollutant. Multi-Source Urban Air Toxics
       Assessments

       Compiling an emission inventory is one of the first steps normally taken in urban
air toxics assessment  programs.  Emission  inventories can be  used in many ways in  air
toxics programs.  They can be used to identify sources and emission strengths, patterns,
and trends. They can be used to store information from related programs, such as permit
or right-to-know data.  They can be used to predict ambient concentrations and to assist in
the development of control strategies and regulations.
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       Most commonly, in urban air toxics assessment studies, emission inventories are
used as input to ambient air dispersion models for estimating concentrations, exposures,
and risks across an urban area.  In the  dispersion modeling approach, all  inventoried
sources within a study .area are modeled, using one or several dispersion models to
estimate pollutant concentrations across a representative receptor network.  The resulting
model-predicted concentrations are applied to a population distribution to yield estimates
of'the  number of persons exposed to each concentration.   The resulting person-
concentration totals are then multiplied by cancer unit risk factors to estimate aggregate
risk, which is a measure of the number of excess cancer cases expected in an area due to
combined exposures  to the pollutants of concern.  Model-predicted concentrations can
also be used to estimate individual risks, such an average areawide risk or risk to the
maximum exposed individual.   Another expression of risk" could simply be the number of
persons exposed to various levels of each pollutant.

       A major advantage of the dispersion modeling approach is that it allows the agency
to project changes in ambient-^ir quality and risk as a function  of projected changes in
emissions.  This allows the agency to test the impact of growth and alternative control
measures on air quality and makes the emission inventory an  important tool in control
strategy development.  A second advantage is that  concentrations and  risk can  be
estimated for many more receptors than  could  reasonably be covered in an ambient air
monitoring network.

3-2     Pollutant Coverage

       Table 3-1 shows the pollutants that were inventoried and modeled in the urban
studies reviewed in this report.  The fact that 70 compounds were covered in one or
another of these studies is indicative of the difficult task confronting  the study manager—
he/she  must choose from  literally thousands of  potential pollutants to  define the
coverage of the emission inventory, after which the inventory must be compiled for each
of these compounds.
                                        74

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TABLE 3-1.  Pollutants Inventoried in Urban Air Toxics Studies
POLLUTANT


ACETONE
ACRYLAMIDE
ACRYLONITRILE
ALLYL CHLORIDE
ARSENIC
ASBESTOS
BENZENE
BENZO(a)PYRENE
BENZYL CHLORIDE
BERYLLIUM
BROMOCHLOROMETHANE
BROMOFORM
BUTADIENE, 1,3-
CADMIUM
CARBON TET
CFC-113
CHLOROFORM
CHROMIUM(TOTAL)
CHROMIUM+6
COKE OVEN EMISSIONS
DICHLOROBENZENE
DICHLOROETHANE, 1,2-
DICHLOROETHYLENE
DICHLOROPROPANE, 1,2-
DIETHANOLAMINE
DIMETHYLNITROSAMINE
DIOCTYL PHTHALATE
DIOXIN
EPICHLOROHYDRIN
ETHYL ACRYLATE
ETHYL BENZENE
ETHYLENE
ETHYLENE DIBROMIDE(EDB)
ETHYLENE DICHLORIDE(EDC)
ETHYLENE OXIDE
FORMALDEHYDE
GASOLINE VAPORS
GLYCOL ETHERS
ISOPROPYL ALCOHOL
ISOPROPYLIDENEDIPHENOL,
SIX MONTHS
STUDY
(35 COUNTY)


X

X

X
X

X


X
X
X

X
X

X

(SEE EDO)









X
X

X
X


4,4-
SIX MONTHS
STUDY
(NESHAPS)

X
X
X
X

X

X
X


X
X
X

X ~
. ...x

X

(SEE EDO)

X
X
X

X
X

X
X
X
X
X



X
KANAWHA
VALLEY
IEMP


X
X
X

X
X

X


X
X
X

X




(SEE EDO)
X









X
X
X
X




SANTA CLARA
IEMP







X
X


X
X


X
X
X



X
(SEEEDC)










X
X

X
X
X

PHILLY IEMP
IEMP







X







X

X




(SEE EDO)
X









X


X



                    75

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POLLUTANT
                       TABLE 3-1. (confd) Pollutants Inventoried in Urban Air Toxics Studies
SIX MONTHS SIX MONTHS KANAWHA   SANTA CLARA   PHILLYIEMP
  STUDY      STUDY    VALLEY        IEMP         IEMP
(35 COUNTY) (NESHAPS)   IEMP
LEAD
MANGANESE
MELAMINE
MERCURY
METHYL BROMIDE
METHYL CHLORIDE
METHYL CHLOROFORM
METHYLENE CHLORIDE
METHYLENE DIANILINE, 4.4-
NICKEL
               X
               X
NICKEL SUBSULFIDE
NITROBENZENE
NITROSOMORPHOLINE
PCBs
PENTACHLOROPHENOL X
PERCHLOROETHYLENE ' X
PHENOL
POM/PIC X
PROPYLENE DICHLORIDE
PROPYLENE OXIDE -
STYRENE X
TEREPHTHAUCACID
TITANIUM DIOXIDE
TOLUENE
TRICHLOROETHANE, 1,1,1- '
TRICHLOROETHYLENE X
VINYL CHLORIDE X
VINYUDENE CHLORIDE
XYLENE ISOMERS
X
X
X
X
X
x - x
X
X X
X
X
X
X X
X X
X X

X X
X
x
X
X
x x
X
x
                                    76

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                           TABLE 3-1. (cont'd) Pollutants Inventoried in Urban Air Toxics Studies
POLLUTANT
                           BALTIMORE    SE CHICAGO     FIVE CITY   SOUTH COAST
                             IEMP                 CONTROLLABILITY   MATES*
MOTOR VEHICLE
ACETONE
ACRYLAMIDE
ACRYLONITRILE
ALLYL CHLORIDE
ARSENIC
ASBESTOS
BENZENE
BENZO(a)PYRENE
BENZYL CHLORIDE
BERYLLIUM
BROMOCHLOROMETHANE
BROMOFORM
BUTADIENE, 1,3-
CADMIUM
CARBON TET
CFC-113
CHLOROFORM
CHROMIUM(TOTAL)
CHROMIUM+6
COKE OVEN EMISSIONS
DICHLOROBENZENE
DICHLOROETHANE, 1,2-
DICHLOROETHYLENE
DICHLOROPROPANE, 1,2-
DIETHANOLAMINE
DIMETHYLNITROSAMINE
DIOCTYLPHTHALATE
DIOXIN
EPICHLOROHYDRIN
ETHYL ACRYLATE
ETHYL BENZENE
ETHYLENE
ETHYLENE DIBROMIDE(EDB)
ETHYLENE DICHLORIDE(EDC)
ETHYLENE OXIDE
FORMALDEHYDE
GASOLINE VAPORS
GLYCOL ETHERS
ISOPROPYL ALCOHOL
ISOPROPYLIDENEDIPHENOL,
X
X
X
X
X
X
X
X
(SEE EDO)

X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
(SEE EDC)
X
X
X
X
X
X
X
X
X
X
X X
X
X X
X
X X
X
X X
X X
X X
X X
X
(SEE EDC) (SEE EDC)

X
X X
X
X
X

X
X
X
X
X

•• (SEE EDC)'

X
X
X
X
                                          77

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TABLE 3-1. (cont'd) Pollutants Inventoried in Urban Air Toxics Studies
POLLUTANT
LEAD
MANGANESE
MELAMINE
MERCURY
METHYL BROMIDE
METHYL CHLORIDE
METHYL CHLOROFORM
METHYLENE CHLORIDE
METHYLENE DIANILINE, 4.4-
NICKEL
NICKEL SUBSULFIDE
NITROBENZENE
NITROSOMORPHOLINE
PCBs
PENTACHLOROPHENOL
PERCHLOROETHYLENE
PHENOL
POM/PIC "
PROPYLENE DICHLORIDE
PROPYLENE OXIDE
STYRENE
TEREPHTHALJCACID
TITANIUM DIOXIDE
TOLUENE
TRICHLOROETHANE, 1,1,1-
TRICHLOROETHYLENE
VINYL CHLORIDE
VINYUDENE CHLORIDE '
XYLENE ISOMERS
BALTIMORE SE CHICAGO FIVE CITY S
IEMP CONTROLLABILITY
X
X
X X
X
X
X X X
X X
X
X
X
xxx
X
X - X X
X
X
X
X X
X X X
X X
X
X X
OUTH COAST MOTOR VEHICL
MATES*
X
*
X
X

X
X
X
X
X
X
X
 * South Coast actually included many compounds in their inventory
 not shown in this table.  Only those pollutants
 included in their MATES are shown here.
                       78

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       Note in Table 3-1 that some compounds were inventoried in more studies than

 others.  This gives some indication of what compounds might logically form a core list for

 consideration by a study manager at the outset of a new assessment.  The following

 compounds were inventoried in five or more of the studies reviewed:
                     Arsenic
                     Benzene
                     B(a)P
                     Beryllium
                     Butadiene 1,3-
                     Cadmium
                     Carbon tetrachloride
                     Chloroform
                     Chromium (total or +6)
                     Ethylene dibromide
Ethylene dichloride
Gasoline vapors
Ethylene oxide
Formaldehyde
Methylene chloride
Perch loroethylene
POM
Trichloroethylene
Vinyl chloride
      • It is important to note that the above compounds account for the vast majority of

aggregate cancer incidence in most of the studies reviewed.  Of these pollutants, POM,

formaldehyde,  1,3  butadiene, chromium, and benzene generally contributed most to

aggregate incidence. .      •-. — —.-                .              -


       In addition, the above list of compounds conforms  well with the recommended

list of compounds  given in Table  9 of earlier EPA guidance on compiling air toxics

emission inventories (EPA, 1986), even though the lists were independently derived.


       Some of the pollutants listed in Table 3-1 were inventoried out of concern for

their  contribution  to maximum  exposed individual (MEI) risks  rather than to their

contribution to areawide cancer incidence.  Some pollutants were included because of

their potential for noncancer health  effects, while others were probably included because

of general public concern.


       The  treatment of several  pollutants  contributing significantly to cancer risk—

polycyclic  organic matter  (POM),  hexavalent  chromium  (chromium  +6),  and

formaldehyde—is important to note in the studies reviewed and discussed below.  The

treatment of beryllium and nickel is also discussed below, since, like chromium, the
                                       79

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chemical  form assumed in one's analysis will have an important effect on cancer

incidence.


       Treatment of Polvcvclic Organic Matter (POM)


       There was considerable divergence among the studies on the treatment of POM,

which is sometimes (as in the Six Months Study) called PIC, or products of incomplete

combustion.  Two distinct approaches have been used in those studies where POM was

addressed:

       1.      BfalP  Surrogate Approach.  The Six Months Study, Southeast  Chicago
              Study, and  the Santa  Clara  IEMP assumed  that  all POM could  be
              represented (with acknowledged uncertainty) by a surrogate  compound,
              benzo(a)pyrene.  The Six Months Study inventoried B(a)P,, specifically, and
              then applied a potency  factor that represented  all PIC.   Somewhat
              differently, the Southeast Chicago Study and Santa Clara IEMP  inventoried
              all POM and then applied the- potency factor for B(a)P.  The Baltimore
              JEMP and Motor Vehicle Study also used this approach as onie of several
              alternatives for assessing risk from POM.                    '

       2.      Comparative Potency Factor Approach.  This approach (described in greater
              detail in Chapter 6) applies a potency score,(or  cancer unit risk factor) to
              the entire mixture of POM  emitted by each source category rather than to a
              particular surrogate  compound.   Comparative  potency  factors  are
              forthcoming from EPA's Integrated Air Cancer Program and have been
              developed for road vehicle exhaust  (diesel and  gasoline fueled vehicles),
              wood smoke, and various other combustion sources.  In the studies using
              comparative potency factors  (Baltimore IEMP, 5 City Controllability, and
              Motor  Vehicle),  total solvent-extractable organic particulate was
              inventoried for  those categories for which comparative potency factors
              existed.  In  these studies, POM was assumed  to be represented by the
              solvent-extractable  fraction  of total particulate.   As an  option, total
              particulate can be inventoried  rather than just the organic fraction if the
              comparative potency factors  are increased accordingly to account for the
              non-solvent extractable particulate fraction that contains POM.  The latter
              option  allows the direct use of existing particulate emissions data.


       The divergence that exists in the treatment of POM reflects the lack of consensus

among study managers on which of these  approaches (if any)  is most suitable for

estimating cancer risk. Hence,  at present, POM incidence estimates must be considered to

be highly uncertain.
                                        80

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       Treatment of Chromium

       Chromium is an important contributor to aggregate risk in the studies reviewed,
but only the Cr+6 valence state  is considered carcinogenic.  Hence, the treatment of
chromium is very important and has been approached in several ways.  One approach is to
treat all chrome emissions the same in the inventory (i.e., inventory total chromium) and
then assume that some fraction of the resulting modeled ambient concentrations is Cr+6.
Early studies, such as the Six Months Study, very conservatively assumed that all chrome
was Cr+6, as did the South Coast MATES project in some of its risk estimates.. The Santa
Clara IEMP assumed 10 percent of total chromium was Cr+6, whereas the Baltimore BEMP
assumed that varying fractions—from 0 to 100  percent—were Cr+6.

       The  5 City Controllability Study distinguished  between Cr+6 and total chromium
directly  in  the  inventory and thus was  able-to model both ambient Cr+6 and total
chromium levels.  One advantage of this approach is  that it allows one to project what
fraction of ambient chromium is  Cr+6.  This  is important since Cr+6 cannot be directly
measured at typical ambient "levels.

       Treatment of Beryllium and Nickel

       As with  chromium, it is important to be aware of the chemical  form of other
metals such as beryllium and nickel. In the 5 City Controllability Study, most beryllium
was assumed to  be in the oxide form rather than to  be present in the more carcinogenic
ore, fluoride, phosphate, or sulfate forms.  Similarly, in this study, no nickel emissions
were assumed to be in the carcinogenic refinery dust or  subsulfide forms; hence, no cancer
incidence was associated with nickel emissions. It appears from the various studies that
conservatively assuming all beryllium  and nickel emissions, to be as carcinogenic as the
most potent compounds containing these elements would greatly overestimate risks from
these two substances.
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       Secondary Pollutants

       None of the studies assessed secondarily formed (e.g., photochemical) pollutants
via dispersion modeling since no validated models yet exist for predicting transformation
products such as  formaldehyde, peroxyacetyl nitrate (PAN), and the like. Hence, none of
the studies  expressly developed emission inventories of precursors of these secondary
products for modeling purposes.

       Generally, secondary pollutant levels have been  approximated by using ambient
air monitoring data.   The 5 City Controllability Study,  for example, used measured
ambient  air levels of formaldehyde in each city to estimate exposures to both primary
formaldehyde emissions  and photochemically formed formaldehyde.   After backing out
that fraction of ambient formaldehyde directly attributable to primary emissions, the
balance  was assumed to be due to secondarily formed formaldehyde.  The 5 City
Controllability'Study then assumed, since formaldehyde  is photochemically formed from
VOC emissions, that each VOC emitter was culpable for that fraction of risk  attributable
to- secondary formaldehyde in "direct proportion to  its level of VOC emissions.           .

3.3    Source Coverage

       Because the emphasis in urban air assessments is  on multi-source, multi-pollutant
exposures and risks, most studies have tried to be comprehensive in their coverage of
point,  area,  and  mobile sources.   So-called "nontraditional" sources have also  been
included in several studies, such as wastewater treatment facilities (including publicly
owned treatment  facilities or POTWs); treatment, storage, and disposal  facilities (TSDFs),
which include  aeration tanks, landfills, and surface impoundments, for waste handling;
waste oil combustion; hazardous waste combustion; and ground-water aeration.   Most
nontraditional sources involve pollutant transfer from another media (e.g., water or  solid
waste) to air and are not yet well characterized.

       By and  large, the sources  covered in most urban air toxics assessments are the
same sources covered in criteria pollutant inventories. However, most of the study
managers were conscious  of the need- to inventory important sources of air toxics that may
                                        82

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not have  been included in their criteria pollutant inventories, because either (1) their
criteria pollutant emissions were below some point source cutoff level or (2) they did not
emit any  criteria pollutants at all.  Important examples of this kind of source include
chrome platers, cooling towers (using chromium corrosion inhibitors), hospital sterilizers,
wood stoves and fireplaces, and small surface coating/degreasing operations that emit
toxic but photochemically nonreactive VOCs (e.g., methylene chloride).

       Some sources received particular or unique emphasis in certain studies.  The Santa
Clara IEMP, for example, included drinking water treatment plants, municipal landfills,
and  semiconductor manufacturing in  the inventory.  The Kanawha Valley  IEMP
emphasized  fugitive  equipment  and  vent  releases from the major organic chemical
manufacturing plants.  The Baltimore IEMP and Southeast Chicago Study both placed
particular emphasis on large iron and steel facilities in each of their respective urban  areas.

3.4    Estimating Emissions

       Two approaches  were used in the  studies reviewed for estimating  air toxics
emissions.   In one  approach,  emissions  were derived  from  existing  data  bases.
Alternatively, emissions  data were  developed from  source-specific surveys.   A
combination of these approaches was used in most studies.

       Emissions Estimation from Existing Data Bases

       To differing degrees in the studies reviewed, existing data bases were used as a
starting point for locating sources  of air toxics and estimating emissions therefrom.
Existing data  bases included criteria pollutant inventories, published emission factors,
species profiles, and information in the general literature. There were two variations on
this approach. In one, an air toxics emission factor was applied to the existing throughput
or activity level to estimate emissions.  This works well for sources where the air toxics
emission factors are expressed in the  same units as the criteria pollutant factors (e.g.,
Ib/ton  of  fuel burned or per vehicle  mile  traveled).   All  studies  used this emission
estimating approach for some sources.
                                        83

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       A second variation is to apply species factors to existing VOC and particulate
matter (PM) emission totals to  estimate emissions for particular toxics.  The Southeast
Chicago Study provides an example of the use of the species factor method to estimate
point source emissions. First, emissions estimates for total VOC and TSP were obtained
from EPA's National Emissions Data System (NEDS) for each process within 88  point
sources.   Species factors (representing the fraction of the total TSP or VOC emitted as
various individual species from each process) were then multiplied by the process-specific
emission  totals  to  yield emission levels of specific toxics.  Information on species
fractions are available from EPA. (EPA, 1988)

       Source-Specific  Survey Data

       Local survey data were  used in a number of the studies, including the 5 City
Controllability. Study,  the  Southeast  Chicago Study, the South Coast Study and the
IBMPs, to estimate the emissions of selected air toxics from point sources.  The lEMPs
illustrate the use "of "local suryey.data; with the  exception of the Denver IEMP study, each
study included the incorporation of  survey-based emissions data info the emissions
inventory used for the exposure  assessment. Each survey was done somewhat differently,
as shown below:

       Philadelphia—The Philadelphia Air Management Services (PAMS)  conducted a
survey of 345 potential point  sources of air  toxics  in the Philadelphia  study  area.
Emissions estimates at the facility level were solicited from industry; these were, in turn,
subjected to engineering review by the PAMS  engineering  staff.  This survey did not,
however, include a large wastewater treatment  plant.  IEMP  estimated releases from this
plant based on influent,  effluent,  and sludge data to  help complement the PAMS
inventory.

       Baltimore—Surveys  were  conducted  by the  Maryland  Air  Management
Administration (AMA) to obtain input data  needed to estimate the emissions  from
approximately 200 point sources.  AMA staff then computed emissions estimates at the
facility level.
                                       84

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       Kanawha  Valley—The  West  Virginia Air  Pollution Control  Commission
(WVAPCC) conducted a survey at the release point level for all major sources addressed
in the study.  WVAPCC staff conducted limited verification of these emissions data.  A
total of 17 facilities", 2,258 release points, and 570 pollutants were covered.

       The WVAPCC provided guidance to the industries on generating emission rates for
three major categories:  process, combustion,  and fugitive releases.   Unlike the other
studies, the Kanawha Valley study had  very detailed release specifications, grouped into
logical modeling units, (e.g., building sources, area sources, and stacks).  The other studies
did not subdivide the data below the facility level, resulting in relatively crude treatment
of release specifications.

       The  vast majority of emissions  within  this study were from vents and fugitive
releases  rather than from  stacks.  Since the Kanawha Valley experiences  such wide
variations in meteorological conditions as a function of daytime or nighttime conditions, it
was important to  account for_the variability in emissions  on.this basis to avoid
introducing model bias for sources that primarily emit during the daytime hours.  Hours of
operation data were thus considered when using the inventory for dispersion modeling
purposes.

       Southeast Chicago—Questionnaires were sent to 29 of the 88 facilities in the
source grid.  These 29 were selected in a two-step process to rank the most important
sources.  First, all chemical manufacturing facilities were automatically listed because of
presumed importance.  Second, the remaining sources were ranked by probable impact of
their VOC/PM emissions on the receptor area  and on the probability of these sources
emitting air toxics.

       A questionnaire was sent to  each of the  29 companies asking them to  make their
own emission estimates of about 50 pollutants and also asking for certain modeling data.
Substantial  follow-up was  often necessary to obtain, clarify, or confirm company
responses. In  many cases, the companies asked EPA for species fractions to estimate their
own emissions.
                                        85

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       South Coast  MATES—The  South  Coast Air  Quality Management District
(SCAQMD) conducted a mail survey of 1,606 companies  in the South Coast Air Basin to
update its toxics emission inventory.  A number of local information systems were used
to compile the emissions inventory.  These information systems included SCAQMD's:
       1. Automated Equipment Information System
       2. Emissions Inventory System
       3. Annual Emission Fee Reports
       4. New Source Review Files.

       Literature searches were conducted, letter mail-outs were sent, and telephone calls
were made in cases where insufficient information was received.
3.5
Spatial and Temporal Resolution
       Spatial resolution is a. measure of how finely emissions data are subdivided (i.e.,
resolved) in space, whereas temporal resolution is a measure of how finely emissions data
are subdivided in time.  The  resolution of any risk  estimates resulting from an urban
assessment cannot exceed the resolution of the emissions (and other) data used as input.
       Grid Size and Grid Cell  Resolution

       Many of the studies superimposed a dispersion modeling grid over the urban area.
Each grid comprised many, usually square, grid cells.  The South Coast and Southeast
Chicago studies did this as did the lEMPs in Baltimore, Kanawha Valley, Santa Clara and
Philadelphia.  The grid at once defines the receptor network for dispersion modeling
(receptors being defined as one or several points within each grid cell) as well as the
necessary resolution of the emission inventory.

       Grid cell sizes varied from 1 km x 1 km in the South Coast MATES project to 5 km
x 5 km in the Philadelphia and Baltimore lEMPs.  (The Baltimore IEMP actually defined a
"refined" grid, comprised of 2.5 km x 2.5 km grid cells over Baltimore City  within the
larger grid.) Southeast Chicago adopted a 2 km x 2 km grid cell size and the Kanawha
                                       86

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 Valley ffiMP used a 2.5 km x 2.5 km grid cell size.   (Note that the IEMP studies also
 included special  discrete  receptors in residential areas near major facilities to support
 modeling of the maximally exposed individuals.)

        Southeast  Chicago actually  defined separate source and receptor  grids.  Their
 source grid was 46 km x 46 km, comprised of .529 2 km x 2 km grid cells.  The emission
 inventory was compiled for this area.  In contrast, their receptor grid was only 13 km x
 13 km and was comprised of 169 1 km x 1 km grid cells. The idea here was to define a
 smaller receptor grid  within a larger source grid to assure coverage of all sources  that
 could reasonably be expected to impact on the receptor grid.  This same concept was  also
 employed in the Baltimore and Philadelphia lEMPs.  In the Southeast Chicago study, the
 receptor grid was located  within the source grid, but skewed slightly off center in the
 direction of the prevailing winds.
        Point Source Resolution          ......

  _._     Typically,  point source -locations are known to the nearest 0.1 km in emission
 inventories, which is adequate resolution for areawide cancer assessments. Several of the
 studies (e.g., the Baltimore IEMP) may have lost a measure of point source resolution
 because the emissions  data were submitted at the facility level and could not be assigned
 to specific stacks, vents, and the like, within the facility.

       In studies  evaluating maximum individual exposures and risks, more attention
 needs to be given to the actual release configuration within large, sprawling facilities  that
 have  different types of release points.   The  Kanawha Valley IEMP, of the studies
 reviewed,  best delineated different kinds  of releases  within  the large chemical
manufacturing plants in the study area.  Instead of assuming complex facilities could be
characterized by one or several release points, up to 20 release  groups were defined.
Stacks were modeled individually.  All vents on each building were modeled as a volume
source based on the dimensions of the applicable building.  Fugitive  releases extending
over an area, such as  tank farms or equipment leaks, were modeled as industrial area
                                        87

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sources.  The Southeast Chicago Study also characterized several large coking operations
at this level of resolution.
       Area Source Allocation

       All of the studies that used grid networks necessarily had to disaggregate area
source emissions to the grid cell level. The 5 City Controllability Study did not define a
modeling grid for each urban area, nor did it allocate emissions to the subcounty level.
This  is because EPA's Human Exposure  Model (HEM)—used therein  for  dispersion
modeling and risk assessment—does not require  subcounty apportioned data. HEM (1)
internally apportions county level area source emissions to the Block Group/Enumeration
District (BG/ED) level based on the population within each BG/ED, (2) runs a simplified
box model to calculate population exposure at each subcounty level, and (3) sums the
resulting BG/ED  risks to produce a measure of aggregate risk in the entire study area. HEM
does not yield'spatially disaggregated exposure and risk results as do the models  used in
the studies where gridding was done.

       The basic allocation approach used in most of the  inventories was to apportion
county level emissions to individual grid cells based on some surrogate indicator(s) such
as population, employment within certain SICs,  or vehicle miles  traveled (VMT).  The
inherent assumption is that area source emissions are distributed according to the known
distribution of the surrogate indicator.

       The simplest approach is to apportion all area source emissions  by population.
This was done in the South Coast MATES study for all area sources but road vehicles and
service stations. Road vehicle emissions of toxics were distributed based on the known
distribution of VOC in the criteria pollutant inventory.  Service station emissions were
clustered at street intersections based on a weighting model developed from a telephone
survey.

       In the Southeast Chicago study and the Baltimore, Santa Clara, and Philadelphia
lEMPs, area source emissions were apportioned by a mix of surrogate parameters.  In
Baltimore and Philadelphia, digitized  United States Geological Service (USGS) land use
                                        88

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 data were used, including residential land  use, commercial/services  land use,  and
 industrial and  transportation  land use.  As an example, dry-cleaning emissions were
 apportioned by  commercial/service land use.  As another example, Southeast Chicago used
 VMT distributions to apportion gasoline marketing emissions and used manufacturing
 employment to allocate degreasing emissions.  In the  Southeast Chicago study, special
 surveys were used to assess the potential locations of hospital sterilizers and chrome
 platers.
        Temporal Allocation

        Most of the studies appear to have collected  annual emissions data with  no
 consideration of diurnal, seasonal, or batch operation variability. This has generally been
 considered  satisfactory for cancer assessments where the  emphasis  is on long-term
 exposures to pollutants.

        Since the dispersion models used in  urban assessments distinguish daytime and
 nighttime dispersion conditionsrsome  error is introduced, if emissions are assumed to  be
 uniformly .emitted both day  and night.   In the  Kanawha Valley,  Baltimore, and
 Philadelphia ffiMPs, engineering estimates were made of diurnal emissions variability for
 certain source categories, and these  estimates were then used  as  input to evaluate
 dispersion model performance  (Sullivan, 1985a).  This was done to reduce some of the
 bias introduced by assuming uniform emission rates throughout the day.  The assumption
 of uniform  emissions  can result  in  a  disproportionate  amount of emissions being
 associated with the poor nighttime dispersion conditions.

 3.6    Emission Inventory Quality Assurance

       Quality assurance checks were not explicitly discussed in most of the studies
reviewed, so  for the most part the following discussion infers measures that were taken to
assure emission inventory quality.  Some of these measures  may not have been  done
intentionally  as quality assurance activities, but nevertheless served in that capacity.
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       Emission Inventory Review

       A number of the studies incorporated reviews at various times to help assure that
the best possible inventory procedures and data were being used.  Ideally, such reviews
would be  done during the planning stages, at the end of the data collection effort, and
when the results were compiled.  As an example of this, the 5 City Controllability Study
regularly reviewed source and emissions data coming out of EPA's regulatory programs
and incorporated changes to reflect these data.  Two particular EPA  programs worth
following are the NESHAPS  program and the motor vehicle  testing program.   The
NESHAPS  program routinely generates source,  emissions and risk data for specific
facilities based on "114 letters," i.e., responses obtained under the authority of  Section
114 of the Clean Air Act.  As a result of following these programs, significant changes
were made during the 5 City Study for several major source categories and pollutants. In
addition, previously uninventoried sources and pollutants were uncovered and added.

       Data Verification

       In a number of the studies,  including the Baltimore, Philadelphia, and Kanawha
Valley lEMPs, State or local agency personnel spot-checked the adequacy of emissions
data submitted by  industry.  In some cases, site inspections were made.  Emissions
estimates were reevaluated and revised or updated as appropriate.

       The Kanawha Valley IEMP  inventory was  resolved at the stack/vent level of
detail for large organic chemical manufacturing facilities.  The State agency spot-checked
release points that emitted large quantities of highly potent pollutants.  For example, for
the most important facility in the study area, the State requested backup calculations for
15 to 20 selected processes to confirm the emissions data.

       In the Southeast Chicago Study, the emissions data for the largest point sources in
the study area—several major coking  operations—were personally reviewed by agency
personnel who had inspected each facility.
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        Monitoring vs. Modeling Comparisons

        The IEMP methodology (Sullivan, 1985b) is based on using measured data as  a
 means  of facilitating the quality control of emissions data.  For many major facilities,
 especially where there are large uncertainties in fugitive  and vent releases, it can be
 prohibitively expensive to perform comprehensive source testing.  However, by measuring
 the composite plume, such as at points 500 to 1,000  m from the facilities, comparisons
 with modeled  data  can be an effective means of checking the reasonableness of the
 emissions data. This approach was used in designing the Kanawha Valley monitoring
 program.

       Comparing model predictions with measured ambient levels was also done in the
 Philadelphia and Baltimore lEMPs to check emissions data. In one case, discrepancies led
 to significant increases in the inventory estimates  for a large garment manufacturer. In
 another case, such discrepancies led to the identification of sewer influent "and effluent
 lines to a major wastewater treatment plant as a missing source of toxic emissions.   The
 lEMPs developed a computer-program called the Monitoring and Data Analysis Module
 (MADAM), which facilitates this  comparison of measured and  model-predicted  data.
 (MADAM is discussed further in Section 4.9.)

       Receptor models represent another  potential technique for quality assuring
emission inventories, but have not been used for this purpose  in any  of the studies
reviewed in this report.

3-7    Insights  into Compiling Inventories for Urban Air Toxics Assessment

       The following insights can be drawn from the studies conducted to date:
             The study area (grid) should be denned to be as large as possible to assure
             that all sources are included in the inventory that may impact on the
             proposed receptor sites.  The Southeast Chicago study and  Baltimore and
             Philadelphia  lEMPs  defined  source grids larger  than their respective
             receptor grids  to account for  all local  sources impacting on the receptor
             sites.   The Southeast Chicago study source grid was about 13 times larger
             m area than the receptor grid  and accounted  for "upwind" sources up to
             20 km away in the direction of the  prevailing wind.  Theoretically, this
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approach should help account for local background levels of pollutants
that are not secondarily formed or due to gradual global buildup.

The chemical  form  of  some  pollutants  is  important in cancer  risk
assessments.  For example, it is important to distinguish between total
chromium and Cr+6; between total nickel and the carbonyl/subsulfide
forms  of nickel; and between the  oxide  and other forms of beryllium
(particularly  beryllium sulfate).  Only some  forms of these metals are
considered carcinogenic.  Limited test data are becoming available to allow
this kind of speciation in emissions inventories.

Consideration should be given  to  how  POM will be handled in the
inventory.  Depending on the risk approach adopted in the urban study,
emissions of either total POM emissions or some surrogate—probably
B(a)P—will be required.

Broad  source coverage is important in urban air toxics assessments, as
many sources contribute  to aggregate risk.   Area and road vehicle sources
must be included for reasonable  completeness.   Chrome platers  and
cooling towers  should be included, whether treated  as area or point
sources.  Wood  smoke  should -be  included.  Formaldehyde emitters—
particularly road vehicles—should be included, even though aggregate risk
from primary  formaldehyde  emissions   is  probably  less  than  from
secondary formaldehyde.

The spatial relationship  of release points  within large, complex  facilities .
"(e.g., iron and steel plants or synthetic organic chemical manufacturing
facilities) may need to be characterized if one's study will assess maximum
individual risks near these facilities (<  1 km).   This requires better
delineation of stacks,  vents, building dimensions,  equipment leaks, and
other  fugitive sources than is  typically  available in  most  inventories
without special  site surveys.  For broad screening  of areawide aggregate
cancer incidence, simpler point source emissions characterizations may be
adequate.

The spatial  resolution of the area  source inventory needs to be
commensurate with the level of spatial resolution desired in the  resulting
risk estimates. To the extent possible, the  study area manager should pick
and choose from several socioeconomic  and/or land  use parameters as
surrogates for allocating area source emissions rather than rely on a single
indicator such as population. Use of the appropriate variables for different
area source categories should improve the accuracy of the study results
since the distribution of emissions within an urban area can have a great
effect on population exposures.

The use of EPA's Human Exposure  Model (HEM) frees the study manager
from having to  allocate emissions  to the  subcounty level because HEM
performs this step internally (down to the BG/ED level) based  on U.S.
Census Bureau data.  A downside is that HEM only allows this subcounty
allocation to be done by population  or else a spatially uniform distribution
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 is assumed, neither alternative being as accurate as the use of direct survey
 techniques and better surrogate apportioning variables.

 Limited data  suggest  that  increasing  the  temporal  resolution  in  the
 emissions data may improve the accuracy of the study results. Typically,
 most studies have just compiled annual average emissions. However, since
 emission rates differ considerably for some sources, both  diurnally and
 seasonally,  and since most models reflect different diurnal and seasonal
 dispersion patterns, it  may behoove the study area manager to consider
 diurnal and/or seasonal patterns when compiling the emission inventory,
 especially if a more detailed assessment is contemplated or if short-term
 concentrations and maximum exposures  are desired as output. No studies
 reviewed have attempted to compile short-term (e.g., hourly) variability in
 emissions data. This level of temporal resolution would probably not be
 warranted in an urban assessment whose  focus was on cancer, but could be
 important for evaluating acute, noncancer effects.

 As with any inventory, data quality  objectives and quality assurance  are
 important.  As a planning step, a formal data quality protocol should  be
 considered that reflects  all  anticipated end uses of the emissions inventory.
 During and after inventory compilation,  as many review steps as possible
.should be planned. These  reviews should involve parties who will use  the
 data, parties who  might be affected by the study results, and parties who
 have particular expertise .concerning certain aspects of the inventory.

 Comparing monitoring data with modeling results can provide insights into
 missing sources, missing pollutants, or erroneous emission estimates in the
 air toxics emission inventory.
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                                   Chapter 3


References

EPA., 1986.  Compiling Air Toxics Emission Inventories, EPA-450/4-86-010, Office of Air
Quality Planning and Standards, Durham, North Carolina, 1986.

EPA., 1988a.  Air Emissions Species Manual, EPA-450/2-88-003a—VOC Species Profiles,
Office of Air Quality Planning and Standards, Durham, North Carolina.

EPA  1988b. Air Emissions Species Manual, EPA-450/2-88-003b—Particulate Matter
Species Profiles, Office of Air  Quality Planning and Standards, Durham, North Carolina.

Sullivan D  A., 1985a.  Evaluation of the Performance of the Dispersion Model SHORTZ
for Predicting Concentrations of "Air Toxics in the U.S. Environmental Protection Agencys
Philadelphia Geographic Study.  U.S. Environmental Protection Agency, Integrated
Environmental Management Divisions, Washington, D.C.

Sullivan, D.A.,  and C. Carter, 1985b. A Screening Methodology for Air Quality Analysis
(Draft).   U.S;- Environmental  Protection. Agency.   Regulatory  Integration Division,
Washington D.C.
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                                     CHAPTER 4

                              DISPERSION MODELING
4.1
The following subjects are covered in this chapter:


•      Use of models for estimating exposure in multi-pollutant, multi-source
       urban  assessments.


•      Decisions affecting modeling protocols


•      Model selection- —                               •             o


•      Release specifications


•      Selection of receptor network


•      Meteorological data


•      Decay, transformation, deposition


•      Model execution


•      Model performance evaluation


•      Insights



Use of Models in Estimating Exposure in Multi-Pollutant. Multi-Source Urban
Assessments
       Most studies of toxic air pollutants in the urban environment rely on dispersion

modeling as their  chief means  of  estimating ambient concentrations of pollutants.

Modeling is used at scales ranging from microscale to urban-wide, sometimes including
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transport from neighboring regions.   As shown  by the studies under review here,
concentration estimates are generally used directly as input to risk assessments, although
they may also be used as a preliminary step in the planning of monitoring programs, as
input to receptor modeling analyses, or, in conjunction with monitoring data,  for the
verification of emission inventories.1

       The Gaussian models in use for toxic pollutant analysis were originally developed
for analyzing ambient air quality for criteria pollutants and represent nearly two decades
of practical development experience.  There has been no need to  develop new classes of
dispersion  models specifically  for  the  noncriteria  pollutants—Gaussian  model
performance has been demonstrated by White (White, 1984) and others to be effective for
the annual or seasonal  averages that are the most common averaging periods for air toxics
studies.  As discussed in this chapter, however, there are a variety of areas (e.g., dense gas
releases) where the application of  existing, models  to air toxics studies  requires careful
review and  analysis to achieve best performance.  How well a model performs for a
specific use depends on the-accuracy of emissions data, including release specifications;
the representativeness  of meteorological data for the area under study; and how well the
application matches the source and terrain types  for which the model was originally
developed.

       In the studies  under review here, emission  inputs were compiled for specific
industrial facilities, area sources (such as mobile sources, commercial development, and
residential heating), and a variety of miscellaneous sources sometim.es referred to as
"nontraditional sources," such as comfort cooling towers, volatilization, of organics from
sewage treatment plants, wood burning  furnaces or  stoves, and other small point  sources
not generally included (at least until recently)  in urban emission inventories.  Some
studies relied predominantly on individually defined emission rates; most used default
estimates for at least some categories of sources (see Chapter 3).
    Potential future applications of dispersion models could include estimating deposition
    rates  (to  analyze indirect pathways of toxic chemical uptake), or analysis of
    hypothetical or actual accident scenarios.
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        Meteorological data are input to dispersion models to characterize the direction
 and speed of transport, estimate horizontal and vertical dispersion rates, and account for
 the vertical extent of turbulent mixing.  For the modeling analyses  of these studies,
 meteorological data sets were chosen that yielded  annual average concentrations because
 this averaging period was needed for exposure assessments of cancer effects.

        The advantages of the use of air dispersion modeling in urban scale assessments
 are straightforward.   Modeling analyses generally require only a small fraction of the
 resources needed to conduct comprehensive  ambient air quality monitoring programs.
 They  can provide extensive spatial and temporal resolution of estimated concentrations
 and can cover pollutants for which suitable ambient air monitoring methods are not
 available. Modeling analyses can isolate the effect of any single source or evaluate the
 impact of any aggregate of sources.  Finally, modeling can analyze hypothetical situations,
 such as the imposition of a range of control scenarios on existing sources, addition of new
 sources, or operational changes in the utilization patterns of facilities.

        An important disadvantage of modeling lies in the limited validation 'of available
 models and the consequent uncertainties this produces.  Available validation data apply
 to only a small subset of the scenarios in which models can be used, and models are often
 applied under conditions quite different from those  for which they were developed.  Also,
 models  cannot  accurately  estimate concentrations unless  sources  are  adequately
 characterized, which is problematic in many cases.

 4-2    Decisions Affecting Modeling Protocols

       Unlike monitoring programs, for which it is  common (and often required) practice
 to prepare quality assurance/quality control (QA/QC)  plans, modeling protocols  have
 typically  not been prepared to guide the design and  execution  of modeling programs.
 Ideally, all studies would benefit from  defining protocols that would: (1) clearly define
 objectives to be achieved in relation to general program goals and (2) present the details of
the approach to meet these objectives.  Detailed elements of the approach would include:
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Level of analysis: In modeling, "screening level" or "scoping" analyses are
often used as the first phase in a multi-stage study, such as to estimate the
scope of potential  problems  or aid in design  of monitoring  programs.
Depending on their  purposes, screening level programs can use less
detailed input data and more generalized modeling techniques.  Where the
purpose of a program is to develop refined modeling, input requirements
are more stringent and modeling protocols  are likely to be more elaborate.

Source categories/emissions data;  Study managers may elect to model only
certain categories of sources (e.g., industrial point source:?) or may attempt
to be comprehensive, including a  wide range of area sources and minor
point sources.   In addition, the  level  of detail  in emissions  data
specifications may vary considerably from study to study.  For instance,
for screening purposes a major industrial source might be modeled as a
single point, but for refined modeling might be treated as a combination of
point sources, volume sources, and area sources.

Pollutants to be modeled:  Both gaseous and particulate pollutants may be
of concern  to air toxics studies.   For most analyses, the design and
execution of the modeling program is seldom  significantly affected  by
pollutant selection.  An exception would be where analysis of atmospheric
reactions or transformations is required, but none of the studies reviewed
here attempted such analysis:

Receptor  networks/Scale  of  analysis; Design  of modeling programs is
significantly affected by whether the scale of interest  is to define average
urban-wide pollutant concentrations or to define concentrations for the
most exposed individual (MEI).   These concerns strongly influence the
design of the grid used to structure model outputs.

Terrain: Terrain is a key factor of concern; modeling complexity increases
significantly if terrain is defined as "complex"—i.e., if some receptor points
in the study area are at higher elevation than  release points, or if the study
has  unusual  geographic  features (proximity to large bodies  of water,
location in valleys or in mountainous areas, etc.).

Meteorological  data:   The two factors  of concern are  the  data's
appropriateness and  detail.   Available recording stations for  the region
must adequately represent conditions  in the study  area,,  In addition, for
uses such  as running models in  complex terrain, evaluating model
performance, or estimating short-term concentrations,  detailed hour-by-
hour data may be required. In some cases, air toxics studies collected and
used their own meteorological data set.

Averaging  period  of interest:   All the  studies  reviewed included
consideration of carcinogenic pollutants,  for which average annual
exposures are the statistic of concern.  Such studies can use  more
simplified data to achieve study goals.  Modeling is also  capable of
estimating  short-term exposures  (e.g., exposures  averaged over  a few
minutes to a few hours). Although few air toxics studies have investigated
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               short-term exposures
               the future.
i, short-term exposures may be of increasing interest in
        All of the modeling studies reviewed in this report dealt with these issues, even if
 they did not develop formal protocols.   Table  4-1  summarizes their general goals, as
 reflected in published reports.  Of the studies reviewed  here, only the Kanawha Valley
 study prepared a formal modeling protocol covering the  elements listed above.  The
 Kanawha study was the exception because of the highly unusual geography of the study
 region; the Kanawha River lies in a deep, winding valley with steep walls, a situation that
 no single  available EPA model is designed to model effectively.  This study therefore
 developed a formal modeling and meteorological data collection protocol for review by
 the EPA Science Advisory Board to clarify the project's goals and document its methods
 (see discussion below).

        A broader discussion of the above decision elements is given below.

        Level of Analysis—Both the NESHAPS study and the 35 County study (within the
 general Six Months Study) can be considered screening level or scoping studies in their
 entirety. Several other studies used screening-level analysis for preliminary evaluations.
 The South Coast study conducted a screening-level evaluation of 4,200 grid cells to select
 sites for the monitoring program (see  Chapter 2 for a  full  discussion).  The Philadelphia
 study used a screening level analysis  to help select study area boundaries.  Major point
 sources across the general region, including sources  in Delaware,  were modeled using
generalized data to see whether the effects of the Delaware sources were significant in the
Philadelphia metropolitan area; their impacts were found to be negligible, so the Delaware
sources were not included in the program. In Baltimore, screening-level modeling of VOC
sources helped in the selection  of monitoring sites.   In the Kanawha study, simplified
screening helped select pollutants and  sources of greatest concern for refined modeling.2
   Another type of screening, not involving air dispersion modeling, was used in the 35-
   Lounty study: counties across the country were evaluated for inclusion in the study
   by ranking  them (1) by total emissions  and (2)  by total emissions multiplied by
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                Table 4-1.  Goals for Dispersion Modeling Analysis In
                  Multi-Pollutant, Multi-Facility Air Quality  Studies
                 (NOTE: See Chapter 1 for a summary of each of these studies.)


Baltimore
Santa Clara
Kanawha Valley
NESHAPSf
35 County Studyf
South Coast
Southeast Chicago
Philadelphia
5-C'rty Controllability
Scales Addressed
Spatial Resolution
(* included, ** emphasized)
Average
Exposures
**
**
*
**
**
**
* *
**
* *
ME!
Exposures
*
*
**
*
*
*
*
*
*
Temporal Resolution
(* included, ** emphasized)
Annual
Average
*
*
*
*
*
*
*
*
*
Short-Term
Evaluations


tt




Model Performance
only
Components of the
Six Months Study
                                                  ft Anticipated'study.
       Source Categories/Emissions Data—All the studies in Table 4-1 attempted to cover
all categories of sources, both point and area. They varied, however, in the level of detail
of their emissions statistics and release terms.  As discussed further below, the Kanawha
Valley study, for  instance, developed highly disaggregated release points within the large
chemical complexes of interest within the study; it also investigated diurnal differences in
release terms, primarily because dispersion conditions within the valley sometimes vary
dramatically from day to night.

       Pollutants to be modeled:  With the exception of the Kanawha study, none of the
studies reviewed here  evaluated  pollutants that posed unusual  technical problems in
modeling.   Where particulate matter was  modeled, the implicit assumption was that
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 particle size was less than or equal to 10 microns,  and that gravitational settling was
 negligible and that all particulates could be modeled the same  as gases.  The Kanawha
 project investigated the possible effects of decay of pollutants within the modeling area:
 separate model runs were done to evaluate allyl chloride.3

        Receptor Networks/Scale  of  Analysis—Mn«t of the studies concentrated  on
 developing estimates of population weighted average annual exposures. From the point of
 view of dispersion modeling, estimation  of these average  exposures poses the fewest
 technical problems and requires the most generalized types  of data (e.g., average annual
 emissions and average annual meteorological data).

        The Kanawha Valley study, and, to a lesser extent, the Philadelphia study,  were
 the only studies that put relatively heavy  emphasis on modeling of MEI (most exposed
 individual) exposures.   The main objective ~in the Kanawha study was to determine
 whether maximum exposures  in the  neighborhoods of the large chemical complexes
 located along the 50 km valley corridor between Nitro and Belle are significant.  It was
 also pertinent.to focus the modeling effort on. relatively near-field  effects because of
 concerns, such as those expressed by the EPA Science Advisory  Board, that modeling of
 broader exposed areas was technically questionable.  The Philadelphia study modeled
 potential MEI exposures primarily because of concern in one heavily industrialized area of
the city that air toxics exposures were high.

       The primary technical difference in conducting  MEI evaluations  in these  two
studies had to do with resolution of the emission inventories.  Because of the large size of
the chemical complexes in the Kanawha Valley, several of which  span many hundreds  of
acres, the study coded hundreds of individual release points (vents, stacks, storage tanks)
within  each complex so  as  to  support  detailed estimates of  MEIs  offsite.4   The
   Although this pollutant has a relatively short atmospheric half-life (about five hours)
   compared with others  in  the  study, the effects of decay were determined to be
   negligible within the short transport distances of interest within the study area.
   Accommodating the  large  size of the source complexes  also led to location of
   monitoring  sites at between 1.5  and 5 km from  the fencelines  so  as  to  ensure
   maximum coverage of multiple emission points (see discussion in Chapter 2).
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Philadelphia emission inventory was not resolved at this level of detail, although some
further detail was added for select facilities to support the goals of the MEI analyses.

       Terrain—EPA modeling guidance stipulates that if  any receptor points within a
modeling area are higher in elevation than physical stack  heights of any sources, then
complex terrain models should be used to  evaluate exposures  for those locations.  None
of the studies, however, used complex terrain models for this purpose, even though the
geography in several of the areas could be formally defined as "complex."5  The reason for
not including complex terrain modeling in the South Coast and Kanawha studies is that
the influence of terrain on predicted concentrations would be minor for the distances  and
stack heights evaluated and, therefore, would not be critical to project objectives. Two
complex terrain models were used, SHORTZ (Philadelphia Study) and LONGZ (Kanawha
Valley Study), but terrain data were not input. - These  models were used because of the
treatment of dispersion.                	                  .

       Meteorological" Data—Most of the studies relied on available meteorological data,
but three developed their own sets.  The Philadelphia and Baltimore studies developed
independent data sets, including sequential data for one-year periods, in order to better
interpret their studies' monitoring data and to support model performance evaluation. The
Kanawha Valley  study developed its  own data set because the closest available
meteorological data station was located at  an airport on a plateau above  the valley, and
was therefore entirely unrepresentative of conditions within the study area.

       Averaging Periods of Interest—As noted, the risk evaluations  conducted by these
studies emphasized chronic  health  effects from  long-term  (i.e.,  annual  or  lifetime)
exposures.  The Philadelphia and Baltimore studies, however, used short-term modeling
to aid  in model performance evaluation..   In addition, future phases of the Kanawha
Valley study are planning the use of short-term  exposure  analysis for evaluation of
possible health  effects (such as respiratory or neurological  impacts) of less-than-lifetime
exposures to toxic air pollutants.
5   As discussed below, however, LONGZ—a complex terrain model—was used in several
    studies for other reasons.
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4.3    Model Selection
       The recommendations in EPA Guideline on Air Quality Models were used as  a
basis to select models for the Baltimore, Kanawha Valley and Southeast Chicago Studies.
A combination of models (ISCLT and CDM) was used because none of the current models
in the Guideline provide adequate treatment of complex industrial sources and urban-scale
area sources.  The NESHAPS  Study, 35-County  Study, South Coast Study, and 5 City
Controllability Study (see Table 4-2), all used exposure models that contain a dispersion
model  that is not listed in the Guideline as  a recommended model for this application.
This does not  imply that the model selection in these studies, or the Philadelphia Study,
is not appropriate.  A major advantage of using a Guideline recommended model, however,.
is that such a model has greater consistency among studies. Table 4-2 lists the dispersion
models used in these .studies.
                 Table  4-2.  Dispersion Models Used in the  Reviewed
                                     Studies
.-"-
ISCLT
CDM
SHORTZ
LONGZ
HEM-SHEAR
SCREAM
GAMS
- • Santa Kanawha 35- South Southeast Phila- 5 City
Baltimore Clara .". Valley NESHAPS County Coast Chicago delphia Control.
X
X








X



X


X







X








X





X

X
X






X
X
X







X


       Model Characteristics

       Each of the models has specific strengths and limitations.

       HEM/SHEAR—HEM (Human Exposure Model) is an exposure modeling system.
that internally includes national population data from the  Census Bureau at the block
group level6 and national meteorological ("STAR") data from the National Climatic Center
6  Reference is made within this report to census  data reported both  at the block
   group/enumeration district (BG/ED) and  block levels.   For exposure evaluations,
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(NCC).  HEM was designed to accept output from any dispersion model that produces
displays in a compatible format, although most urban air toxics studies using HEM have
employed the internal SHEAR model rather than conducting modeling separate from HEM.

       SHEAR (Systems Applications Human Exposure and Risk) is basically consistent
in many respects with the stand-alone EPA dispersion models shown in Table 4-2; it uses
similar treatments for dispersion coefficients, plume rise, and building downwash. Two
major differences  between  SHEAR  and the EPA-developed  dispersion models  are:
(1) SHEAR uses simplified box model treatments and prototype sources to represent area
sources (rather than using gridded area source data7) and (2) SHEAR does not contain a
mixing height term.

      HEM/SHEAR uses modeled  data as a basis to interpolate average concentrations
for all block groups within the modeling domain, such as from 30 to 50 km from a source.
Contributions from multiple sources are summed to estimate  total concentrations  for
population  exposure "within, each block group.   Since HEM/SHEAR  uses national
population data, it does not include the exact locations of residences within each block
group, and can therefore develop only approximations of MEI exposures.

       SCREAM—SCREAM  (South Coast Risk and Exposure Assessment Model)  is a
version of HEM/SHEAR as developed for use by the South Coast Study.  It involved  the
following changes to SHEAR:
       1.     The model's  code was  changed to  facilitate  the use of a 16-station
             meteorological data set that was  available for the study area.  The closest
             meteorological data set was selected when modeling each source.
   dispersion modeling usually uses population data resolved to the census tract level,
   apportioning that data to a modeling grid with cells that might typically be  1  to 5  km
   square.   BG/EDs, used in both HEM and GAMS,  are a smaller census reporting unit
   than a census tract.  Concentration estimates produced by HEM and GAMS are used to
   estimate concentrations at the centroids of individual BG/EDs. The SCREAM model,
   which was adapted from HEM, considers the smallest census unit, i.e., block-level
   data.
7  SHEAR uses prototype sources  for some area source components, such as gasoline
   marketing.  For example, rather than assume uniform emissions throughout a specified
   area,  specific hypothetical  sources are located on the modeling grid to match  the
   expected density of the service station coverage.
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        2.
       3.
Area  source modeling was done using  an adapted version of  the
Climatological  Dispersion Model (CDM)  rather than the box  model
treatment internal to SHEAR.
Population was resolved to the street block rather than the block group
level.8
       4.     City-specific population growth projections were used.

       The SCREAM computer model is being enhanced and will include options for
 population mobility, indoor  versus  outdoor exposure, and  noninhalation  routes  of
 exposure.  SCREAM will also be enhanced to provide better estimates of individual risk
 and community cancer burden in regions and subregions of the air basin.  (Barcikowski,
 1988)

       GAMS—GAMS (GEMS Atmospheric Modeling System) is an exposure modeling
 system similar to HEM/SHEAR, developed bylhe EPA Office of Toxic Substances as part
 of its Graphical Exposure Modeling System (GEMS).  It uses the ISCLT model for point
 sources and a box model .approach for area sources based on national meteorological
 ("STAR") data.9  GAMS integrates population data with concentration data in a similar
 manner to HEM, although the approaches differ .for close-in receptors, which could lead to
 minor differences on this basis between the  two models in exposure estimates across a
 study area.

       ISJ1LT—ISCLT (Industrial Source Complex-Long Term) is  designed to provide
 enhanced flexibility for evaluating complex industrial  sources.   It includes separate
treatments  for stack emissions, volume-source emissions, and area sources, as well as a
wide range of control options to tailor the model run to match the degree of specificity in
the available source and meteorological data.   ISCLT requires joint  frequency data (wind
speed, wind direction and stability class data) in the "STAR" format.  A prime limitation
of ISCLT  to support modeling goals in multi-pollutant, multi-facility studies  is its
   SCREAM used dispersion model estimates to predict concentrations at the centroid of
   the block or BG/ED, depending on the distance from the source.
   As noted below, at the time of the 35-County Study, GAMS used ATM as its internal
   Ql^ /11 OT^Q^OI f\m i-vm ns3 n"\  v***lv£ ovl> .Ln_.J,..Jj._il*	_i___l      1 .
   air dispersion model, which tended to bias study results.
                                      105

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relatively weak treatment of widespread urban area sources (e.g.,  mobile  sources,
residential heating, and distributed solvent use).

       CDM—CDM (Climatological Dispersion Model) does not provide the degree of
flexibility offered by ISCLT to address major point sources—which can be a significant
limitation for estimating MEI exposures.  Its greatest strength for modeling the urban soup
is its relatively detailed area source treatment. If matched with highly resolved emissions
data, for area sources, this model theoretically can provide the most representative
treatment available for area sources.  If MEI analyses are not required,  the resolution in
CDM  may be  suitable for most point or  area source treatments.   CDM  requires
meteorological data similar to  ISCLT, except that neutral stability is subdivided into
daytime and nighttime periods.

      , SHQRTZ/LONGZ—SHORTZ and LONGZ are  EPA-recommended second-level
screening models for estimating short- and long-term averages, respectively, in urban areas
haying complex terrain.  They .have two potentially useful features: (1) representative
treatment of urban area sources and (2) the  potential  to use site-specific measured
turbulence data to characterize dispersion rates.   This model requires meteorological data
in the form of a joint frequency distribution as in ISCLT. The user has the option of using
site-specific meteorological  data to characterize dispersion rates, however.

       Rationale for Model Selection

       Baltimore and Southeast Chicago—These studies used a combination of two
models to  reach their objectives: ISCLT (for its  strength in  modeling complex point
sources) and CDM (because of its relatively refined treatment of area  sources).  Model
selection was guided by adherence to the Guideline on Air Quality Models (revised) (EPA,
1986).

       Philadelphia and Santa  Clara—As the  first IEMP pilot study,  the Philadelphia
study tested two modeling  approaches. The CDM model  was used  early in the study for
all point and area sources, but the program later shifted to SHORTZ/LONGZ, primarily to
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 have the option of using site-specific meteorological data (collected over a one year period
 to document the project's 10-station air quality monitoring network) to define dispersion
 conditions.   SHORTZ/LONGZ  were noj. selected  because of their complex terrain
 capability.10

        Santa Clara  used  LONGZ primarily for consistency with  the concurrent
 Philadelphia study.  During the initial  design stages of the Santa Clara study, it was
 unclear if site-specific meteorological monitoring would be  done as in Philadelphia. If a
 comparable meteorological monitoring program were done in Santa Clara, LONGZ would
 have provided the  option of inputting this  data  set  as an alternative means  of
 characterizing dispersion.  Such a meteorological data set was not collected, however.

        Although Santa Clara has  substantial terrain variations within the study area, the
 terrain  features of LONGZ were not used, essentially because none of the releases in this
 study area were from  stacks (process vents and fugitive releases dominated the inventory)
 and terrain rise consequently was not a sensitive model input.11

        Kanawh^ Valley—Model selection for Kanawha Valley was controversial because
 of the  region's  complex  topographical  setting.   At  the  outset, the study made the
 following assumptions to simplify the modeling analysis and model selection:
        •      The modeling region was  confined to the valley floor,  thereby removing
              the complication of selecting models recommended for use in  complex
              terrain.                                                          ^
    The Philadelphia Study might have been strengthened by a more detailed comparison
    ot these two alternative modeling techniques. Although minor improvements were
    observed for a few pollutants in the model performance tests using the more specific
    dispersion treatment in SHORTZ (as opposed to the default stability class approach
    used by^CDM), the benefits of using SHORTZ/LONGZ over the more simplified CDM
    approach were not established.  Perhaps the most useful finding of the Philadelphia
    ?HnRT7§/TanS^1S Wal **"* t-heJU?e  Of the more sPecific evaluations possible with
    Sir  v     ?  Mu best SUlted for settings with complex topography and for all
n  applications where short-term averaging is a modeling goal.
    Sn^°fr£e?i*-n  the aUJho°rS'i.opinion,  an alternative modeling procedure, such as
    SantTciSa Stud101'6     Southeast Chicago, may have better served the needs of the
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       •      The valley was  subdivided into four relatively homogeneous zones, each
              to be modeled independently to minimize complications in flow analysis.

       •      Modeling objectives emphasized MEI concentrations in the areas near the
              major chemical  facilities; this was  expected to simplify the influence  of
              complex flows and minimize the need to consider reflection of plumes off
              the valley walls.


       Based on these simplifications, the project selected a reference model (ISCLT),

consistent with EPA's Guideline on Air Quality Models frevised! (EPA, 1986), and an

alternative model (LONGZ), essentially for research purposes.  The study's hypothesis

was that ISCLT, which uses generalized stability classes, may not adequately characterize

site-specific dispersion because of the Valley's  complicated  flow patterns.  It therefore

used LONGZ, which can accept measured site-specific turbulence data, as an alternative

model to ISCLT and compared the results.12   Overall, ISCLT met the project's  objectives

better—LONGZ substantially  underestimated .concentrations relative to  the  limited

measured data set, usually by a factor of 2-to 3.^3


       Because o'f ISCLT's simplified method for handling emissions  from large area

source emissions, the study also used a third model—a simple box model treatment—to

evaluate area source impacts.


     1  5 City  Controllability Study; NESHAPS Study— Several studies chose to utilize

EPA's Human Exposure Model  (HEM) because (1) it is consistent with what EPA has used

in its NESHAPS regulatory program and (2)  it  currently includes dispersion  modeling
12 LONGZ was used as an alternative model because it is not recommended within the
   EPA Guideline on Air Quality Models as a recommended model for this application.
   In this sense, it was used for research purposes.
13 Although LONGZ showed a consistent bias in its estimates, it also showed systematic
   differences in dispersion rates caused by site-specific turbulent intensity differences,
   as hypothesized  by the study.  There appeared to be a benefit in using LONGZ or
   SHORTZ if concentration bias could be removed.   It appears that  the empirical
   algorithms used by both LONGZ and SHORTZ to relate turbulence to dispersion rates
   work best with elevated sources.   Most of the  industrial sources of concern in this
   study, however, had low-level release points.
   The study concluded that the apparent bias in LONGZ's estimates could be removed
   by modifications  to the model code; currently proposed investigations of short-term
   ambient conditions in the Valley may therefore use a suitably modified version of
   SHORTZ to exploit available site-specific turbulent intensity data.
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 exposure and  risk characterization modules, all  within one system.  (HEM is being
 updated by EPA to give it much more capability.)

        The NESHAPS and 5 City Controllability Studies used the HEM/SHEAR model.
 The use of HEM/SHEAR appears to have met study goals, but the limitation of no mixing
 height treatment may act to significantly underestimate average concentrations for some
 applications.  It  is  also unclear if different conclusions may  have been reached if EPA
 recommended dispersion models were used in lieu of HEM.

        South Coast MATES—The South Coast adapted HEM/SHEAR for its own purposes
 by incorporating a modified version of the  CDM  model in place of SHEAR, but retained
 some of the assumptions of HEM/SHEAR, such as  not considering mixing height in the
 calculations (Liu, 1987).  The adapted model was named "SCREAM."  The motivation to
 develop an alternative model appears to have "been: (1) to conduct more sophisticated
 modeling techniques for area sources, (2) to take advantage of the extensive coverage of
 meteorological data in the study area,  and (3) to  obtain finer resolution in predicted
 concentrations.  Development of the SCREAM model appears to have produced a model
 that better met the goals of the South Coast study, while retaining much of the exposure
 modeling capability available within HEM.

       35-County Study—Model selection was difficult for this study.  An approach was
 needed  that could readily model the 600-plus point source and  35 metropolitan-scale area
 source treatments that make up this study.  The EPA GAMS exposure model, developed
 by the Office of Toxic Substances, was' selected over HEM primarily because:  (1)  model
 execution for this scale study was expected to be facilitated by the GAMS approach and
 (2) the analysts  were more familiar with. GAMS.  The tight project schedule resulted in the
 selection of GAMS.

       GAMS was successful in meeting the immediate objectives of this project,  and
appears to have provided more efficient execution and processing of this large data set
than would  have been possible with HEM.  A  limitation of this approach,  however,
observed two years after the completion of this project (Sullivan and Hlinka, 1986),  was
                                       109

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errors in the ATM model, which was the only Gaussian model available in GAMS at the
time.  An error in ATM's plume rise term, and questionable assumptions regarding
depletion of mass  from the plumes caused by wet and dry deposition (Sullivan,  1986),
were hypothesized to underestimate toxic air pollutant risk throughout the 35 counties
analyzed in  the  study.14   (Sullivan and Hlinka,  1986)   The  magnitude  of the
underestimates is mainly a function of the exhaust temperature. For sources with high
exhaust temperatures, (e.g., greater than 500° F), model underestimates could be in the
range of a factor  of 3  — 10,  in  the  authors' opinion; at near ambient temperatures,
                                                                  I     -  -
underestimates might be expected to be low by a factor of 2 -3.

4.4    Release Specifications for Emissions

       Model outputs  can, in some cases, be sensitive to the degree of resolution in
release specifications,15 with optimal resolution being a function of the scale of analysis.
For example, complex  facilities can be represented as a single release point to estimate
average  exposures -across   a  study  area without  necessarily  sacrificing  the
representativeness  of the average modeled concentrations.  On the other hand, the level of
detail in release specifications can be  an  important model input for MEI analyses—the
greatest differences in modeled concentrations occur within the first 1 to 2  kilometers of a
source, where MEIs are likely to be located.16

       With the exception of Kanawha Valley, and to a limited extent the Philadelphia
study, all modeling  analyses  used  simplified treatments  to characterize release
14  This problem points to the risk taken when selecting a dispersion model that has not
    been subject to the review process of recommended models in the Guideline on Air
    Quality Models (EPA, 1986).  The same risk appears to be present in using the SHEAR
    model routinely used within the HEM system.
15  Release specifications required as model input are as follows:
       Stack Source      release height, inner stack diameter, exit velocity, and exhaust
                        temperature.
       Area Source       horizontal dimensions of area, characteristic release height.
       Building Source   vent  specifications  (similar  to stack  data),  dimensions
                        horizontally and vertically of building (or structure).
16  This is especially true of air toxics  evaluations, where most sources of concern are
    low-level sources that produce MEI concentrations relatively close to  the facility
    itself.
                                       110

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specifications.  Often an assumed release height, such as 10 m, or one release point within
a  major facility, was  used to represent  a complicated industrial facility.   This was
consistent with the goals of the modeling analyses (see Table 4-1) because most studies
were primarily concerned  with average annual population exposures and areawide
incidence.

       The  Kanawha  Valley  study,  however, was primarily  concerned  with MEI
exposures and therefore needed greater detail in its  emissions specificity.  The available
emissions inventory, compiled by the  West Virginia Air Pollution Control Commission,
was coded  at  the release  point level—too  much detail  to  model  within resource
constraints.  The releases were therefore grouped into units that were more suitable  for
practical modeling purposes—up to 20  groups within each complex facility.  Stacks were
modeled individually.  Vents  on a common building were  modeled within one volume
source.  In some cases, fugitive sources emitted over an  area, such as tank farms, piping
arrays, and so forth, were modeled as area sources.  Figure 4-1 summarizes the approach
used in Kanawha to group sources; this may be of use to  future'studies with  similar
emphasis.

4-5    Selection of Receptor Network Array

       The receptor arrays used in these studies can be grouped into four categories:
(1) grid-based selection of receptors, (2) a Block Group/Enumeration District (BG/EDJ-based
approach, (3) use of special receptors to  match monitoring sites, and (4) complex terrain
receptors.
                                       Ill

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                                      FACIIUY X
©
          ptf*
         /?'*'
                                  , f
                  n  n n  n n n
                       ©
                                                                  ©
lir
n
n n n n
n
o
        Grouf
          1
          2
          3
 Source Type
    STACK
   STACK
VOLUME SOURCE
 (BUILDING)
VOLUME SOURCE
 (BUILDING)
 AREA SOURCE
Sped fie Rel ease  Points
        A
        B
        C,D,E,F,G,H

        I,J,K,L,M,N,

        O.P.Q
      Figure 4-1. Example of source grouping technique used for the Kanawha Valley Study.
                                            112

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        Grid-Based Approach—In a grid-based receptor array, estimates of impacts from all
 sources modeled are output to a single set of cells, with each cell including one or more
 discrete receptor points within or along its boundaries.  Since the grid is rectangular, all
 cells are the same size unless otherwise defined (some studies define subdivided cells in
 more densely populated areas).   Figure 4-2 provides an  example of a rectangular grid
 system used to define a receptor array.

        This approach was  used for the IEMP studies, where a rectangular grid was
 employed for all but the microscale  analyses.  First,  a standard grid spacing was
 established (such as a 2.5 to 5 km square17).  Average concentrations were then estimated
 for each grid cell by placing evenly spaced receptors (one to four per cell) within each cell
 and averaging all receptors within a cell to represent the average concentrations for all
 residents of the grid cell.  Population within "each grid  cell was allocated by  overlaying
 census  maps on the grid  and allocating population  from each census tract to the grid
 proportionally on- the basis of area (i.e., if half the area of a census tract lay within a cell,
 half the population of that tract is assigned to that cell).  The population thus assigned is
 assumed to experience exposures equal to the average concentrations estimated for that
 cell.

       Resolution tighter than 2.5 to 5 km grids is often needed to meet objectives other
than estimating average concentrations.  For example, the South Coast MATES used a
 1 km grid system to help select monitoring sites in the South Coast Air Basin. As another
example, the  Southeast Chicago  Study also used a 1  km grid system, albeit for only a
relatively small  (13 km x 13 km) receptor area.   Within the IEMP studies, supplemental
receptors within certain grid cells  were needed to  estimate  MEI concentrations—the
standard grid was complemented with specially selected locations (discrete receptors) that
*1 7 VH
   For core urban areas with relatively high population density, or sharp gradients in
   concentrations caused by major industrial releases,  a tighter (2.5 km)  grid  was
   generally used.                                                          6
                                        113

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Figure 4-2. Example of rectangular grid system.
               114

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 represented residential areas closest to major facilities.  The maximum concentration
 within residential areas was selected from the discrete receptor array to represent MEI
 concentrations..

       BG/ED-Based Approach—This method is used in the HEM and GAMS exposure
 modeling systems.  A polar  coordinate  grid (such as Figure 4-3) is used to  estimate
 concentrations around each facility. Unlike the rectangular grid-cell approach in which all
 facilities output to the same grid, every facility has its own (polar) grid.  Also unlike the
 rectangular grid-cell approach, cells in a polar array become larger the farther they lie from
 a source.

       To aggregate exposures from multiple sources, the HEM and GAMS systems assign
 concentrations from each grid  cell of each polar array to the appropriate BG/ED, and then
 aggregate exposures for each BG/ED.18  HEM and GAMS use somewhat different methods
 of assigning concentrations  to each BG/ED.  In GAMS, all BG/EDs whose  centroids lie
 within a particular cell are assigned the  same concentration. HEM interpolates values and
 assigns interpolated values to each BG/ED based on the location of its centroid in relation
 to each cell's assigned receptor points.

       An innovative approach along  the  same lines, developed  for the  South Coast
 MATES Study, was to consider distance  from the source as  a means of identifying the
 resolution to be  used for receptor coverage.  For example, for distances less than 2.5 km,
 the street block  level of detail was used.  BG/EDs were used  for distances 2.6 to  10 km.
 This permits the model to allocate concentrations more accurately, compensating for the
 variation in grid-cell size across the polar array.

       The  HEM  and GAMS exposure modeling  systems  are  quite similar in their
treatment  of receptors, although there are some differences between the two  in the
treatment of receptors within 3.5 km of a facility. Because of its finer detail, the BG/ED
   A block group (BG) is an area representing a combination of contiguous blocks having
   an average population of about 1,100. Enumeration districts (ED) are areas containing
   an average of about 800 people and are used when block groups are not defined.
                                       115

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                  NNW
WNW,
 WSW
          sw
                      SS'
                                                     NE
                                                                          ENE
                                                                           ESE
                                                        ONE SECTOR  SEGMENT
                    Figure 4-3.  Example of Polar Grid System
                                    116

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 approach should theoretically develop superior characterizations of average exposures for
 each cell than the rectangular grid approach, possibly leading to minor differences in
 estimates of total cancer incidence across a study area.   Intuitively, it does not appear,
 however, that their more refined resolution in population data necessarily improves the
 accuracy of the  exposure assessment on the metropolitan scale.  Since the goal is to
 estimate total exposure generally across a metropolitan area (e.g., 50 x 50 km), minor
 differences  in allocating population to model receptors  would not be expected to make
 major differences in exposure. Differences at the fine scale are smoothed  when producing
 an estimate of total exposure.  The major limitation of these two exposure  modeling
 systems is  their  relatively poor resolution for MEI exposures.  Supplemental receptor
 coverage to include  points  representing distances to property boundaries or nearest
 residences appears to be needed when using HEM or GAMS if MEI analyses are one of the
 modeling goals of a study.  (Refer to Chapter 5 for more detail on exposure assessment
 issues.)

       Special Receptors  Matched to Monitoring Sites—To evaluate the performance of
 models, the IEMP studies  and the South Coast Study identified additional receptor points
 representing the locations of monitoring sites in order to be able to compare monitored
 ambient concentrations with modeled concentrations.   Similar special sites are located
 where the study has  exact knowledge of potential MEI  locations,  such as the exact
 distances to the fenceline of houses nearest a major source.

       Selecting  receptors for this  purpose is obviously straightforward; the important
concern here is that the exact location of the site be entered into the modeling analysis in
order to accurately define the receptor locations.  Exact locations are especially important
when large point sources exist within 1 to 2 km of the monitoring or receptor sites.

       Characterizing  Terrain Rise  for Receptors—Modeling analyses should include
consideration  of terrain  differences between release  heights and receptor elevations
whenever terrain rise is expected to be a significant term in a modeling analysis.  This can
occur with  complex terrain or when the effective  height of the plume  is substantially
                                        117

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lowered with respect to the ground level.  For emissions from elevated stacks, such as
incinerators or power plants, terrain differences can be a significant issue.

       The majority of the air toxics emissions considered in these studies, however, were
from sources near ground level.  Consideration of terrain differences between sources and
receptors,  therefore, was generally not  a  sensitive issue, and none of  these studies
incorporated it.

       Of all the studies reviewed, the South  Coast and Kanawha Valley projects had the
greatest potential for terrain-related modeling complications. Both studies considered the
issue, but concluded that it was unimportant in meeting project goals.  In the South Coast
study, power plants were the source category expected to be most affected by terrain, but
these sources turned out to be located 10 km or more from areas with major terrain rise
(Shikiya, 1987).  In Kanawha Valley, flat terrain modeling was used even though large
terrain rise occurred within  1 km of the sources because:  (1) in nearly all cases, pollutants
were  emitted from near  ground  level;  (2)  the  modeling  goals emphasized  MEI
concentrations, which were essentially unaffected by terrain rise; and (3) the population
was densely  clustered along the valley floor.19

       Modeling receptors located in areas  with substantial terrain rise could, however,
be a major  limitation for exposure modeling systems such as HEM, SCREAM, and GAMS,
adversely affecting model results.  The automated  feature of these exposure  modeling
systems to  estimate concentrations without consideration of terrain rise can lead to large
model inaccuracies for source categories, such as power plants and incinerators,  that
release from  high stacks.  A bias to underestimate  impacts for tall stacks can occur on
this basis for areas with moderate to high terrain.
19 A limited special modeling analysis was done for Kanawha Valley to demonstrate the
   limited sensitivity of model  results to terrain rise for this application, based on the
   LONGZ model.
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 4.6
Meteorological Data
        In the ideal case, at least one year of hourly meteorological data collected at a
 representative location within a study  area would be available to support air toxics
 modeling.  The following parameters would be optimal for most of the models used in
 these studies:
        •      Wind speed/wind direction
        •      Atmospheric stability
        •      Mixing height specific to the study area
        •      Ambient temperature

        In the studies  reviewed, three different approaches were  used to obtain the
 required meteorological data: (1) use of available data from one existing site to represent
 the general study area, (2) use of available meteorological data from multiple sites, and
 (3) collection of site-specific  meteorological data at one or more sites within the study
 area.                                     •

        Studies That Used Existing Data from a  Singla Sitft—Mnst studies used available
 meteorological data from one site in the region to represent a study area.  These included
 the Southeast Chicago study, the NESHAPS study (within the Six Months Study), the
 Santa Clara study, the 35  County Study, and the 5 City Controllability study. Since all of
 these emphasized estimation  of areawide average concentrations, not MEIs, over annual
 averaging periods, reasonably representative annual meteorological data from a single site
 could be expected to satisfy the modeling objectives.

       Study That Used Meteorological Data from Multiple Sites  with Available Data—
 The South Coast Study is  the only one to use data from multiple existing sites within one
 defined study area.  Here, the closest meteorological station to each source was selected
 from among 16 available sites.   The incremental contributions from each source were
modeled on this basis.  This procedure was used to account for the differences in wind
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flow  across  the relatively large study region,  which included  substantial terrain
complexities in some subareas.

       gtudies Collecting Site-Specific Meteorological  Data—The  collection of site-
specific  meteorological data  was  only done in these  studies  to (1)  support  the
interpretation of  detailed air quality monitoring programs, as in Philadelphia and
Baltimore, or (2) support modeling within a complex topographical setting, i.e., Kanawha
Valley.

       The IEMP studies  in Philadelphia, Baltimore, and Kanawha Valley collected
meteorological data at one, two, and four monitoring sites, respectively.  Wind speed,
wind direction, and turbulence intensity data were collected, generally at  20  m  above
ground level, at sites selected to best represent the entire study area or subregions within
the study area.

       In the  Philadelphia and Baltimore studies, site-specific meteorological monitoring
was primarily done to support the interpretation of the 10-statiori air quality measured
data sets that  were collected in each city. For the Kanawha Valley study, meteorological
monitoring at  three new sites and one existing site was conducted because of the lack of
available data to account for the variability in wind flow and dispersion characteristics
across four delineated zones within this complex study area.

       Treatment of Meteorological Parameters

       Differences in  the treatment of meteorological  conditions can  make large
differences in  modeled concentrations:

       Wind Flow—Many of these study areas were relatively flat, such as Philadelphia,
Baltimore, and Southeast Chicago, where during most periods the differences in wind flow
across the study area would be expected to be relatively  small.  A likely  exception is
during evening hours under inversion conditions. During such periods, wind flow can be
highly variable and dispersion  rates  very small.  Meteorological data that best represent
receptors of greatest concern,  such  as the locations  of air quality monitoring stations,
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 areas with high population density, or MEI locations, are highly desirable for representing
 such conditions. This can be especially important when specific days are being modeled,
 such as to support a model performance evaluation.

       Stability—Atmospheric stability is  a term used within  a dispersion model to
 indicate the rate of horizontal and vertical  pollutant mixing within a plume.  Unstable
 conditions produce vigorous mixing, neutral conditions indicate moderate mixing, and
 stable conditions result in very limited mixing.

       Stability can be estimated based  on "default"  data  sources, such as available
 National Weather Service data, or site-specific data, such as turbulent intensity data.  For
 many applications,  default treatments likely provide reasonable estimates and are the
 only means of estimating concentration because more site-specific data are unavailable.
 There are applications, however, where default treatments may introduce considerable
 uncertainty into the modeling analysis.

       Research has shown that large differences in dispersion conditions can  occur
 within specific default stability  classes (Luna and Church, 1972). The use  of default •
 stability data to represent dispersion rates can therefore introduce substantial inaccuracies
 into the modeling analyses for some applications.  An alternative is to collect site-specific
turbulent intensity  data specifically for the  study, as was done in the ffiMP studies of
Philadelphia,  Baltimore, and  Kanawha Valley.20  Based on  their experience, two
 conclusions can be drawn:
       1.
       2.
The influence of terrain on dispersion rates appeared  to  be relatively
significant in the Kanawha  Valley study.   The benefit  of collecting
turbulent intensity data as site-specific  indicators of dispersion seems
greater in this setting, whether the averaging period be short term or annual
average.
For areas with flat terrain, the site-specific dispersion data appear to be
useful for characterizing conditions for short periods, such as for model
   These studies measured the standard deviation of horizontal and vertical wind speed
   which was used in conjunction with mean wind speed to estimate turbulent intensity.'
   These data were then used within LONGZ estimate average dispersion rates within
   each stability class.
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              performance evaluations or short-term ambient exposures.   For annual
              averaging periods, the default data appear to be a more reasonable indicator
              of dispersion.

       Mixing Height—Mixing  height refers to the vertical limit of turbulent mixing.
Because toxic air emissions are generally dominated by low-level sources, which are not
as significantly influenced by the mixing height term as elevated sources, none of the
studies collected site-specific mixing height data.  Regionally available mixing height data
were used as a default.  For relatively large study areas, however, or for modeling the
impacts of outlying major industrial  areas on  central business  district receptors, the
mixing height term could significantly affect model estimates.

       It  is important to note that there can be large  differences  between regionally
collected mixing height data and site-specific data. In fact, mixing heights can be quite
variable in time and space within a specific metropolitan area.  Differences can be most
pronounced during evening or early morning hours during low-level inversions.

       Although the collectibn~6f site-specific mixing heights can  be an expensive
addition to a meteorological monitoring program, it can at least theoretically improve the
representativeness of modeled air toxics concentrations in urban environments. The lack
of a mixing height term in the HEM/SHEAR and SCREAM models is a pertinent example in
which model estimates might be improved to some degree by inclusion of a representative
treatment  of mixing height in the modeling analyses.

       Treatment of Meteorological Variability

       The incorporation of diurnal or seasonal  differences in meteorological conditions
can be important when modeling industrial facilities or area source categories that have
distinct diurnal or seasonal patterns in emission rates.   A  number of studies took such
differences into account.

       Diurnal  Differences—For modeling purposes, separate daytime and  nighttime
model input files  can be  created,  containing meteorological  and emissions data
representative of each period.  This approach was used in the Philadelphia, Baltimore,
                                        122

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 Kanawha Valley, and South Coast MATES studies to minimize diurnal bias, although the
 results were reported without displaying diurnal differences.

       Hours of operation data within the National Emissions Data System (NEDS) are a
 default data source to aid in apportioning emissions on a day/night basis.  More detailed
 and accurate data on the hours of operation of processes and facilities may be available at
 the  local level.  Modeling daytime and nighttime conditions essentially doubles data
 processing  and requires additional quality control, but  ignoring  these differences can
 introduce substantial bias  in some situations, tending to overestimate concentrations of
 near-ground-level releases, and underestimating sources with high-level releases.  Routine
 model runs with HEM or GAMS do not consider diurnal differences in release.

       Seasonal Differences—Some emission source categories, such as wood burning,
 have distinct  seasonal patterns in their emission rates.   Some less  obvious  source
 categories can also  show distinct seasonal patterns.   For.example,  mobile  source
 emissions are highly" sensitive._to ambient temperature; winter periods show  higher
 emissions per  vehicle mile traveled because  of reduced combustion efficiencies  (Black,
 1986).

       If there are also large seasonal differences  in transport and dispersion within a
 study area, bias can be introduced to the modeling analyses if annual average  emissions
 and  annual meteorological data sets are used for modeling. This bias could be reduced by
 modeling on a seasonal basis and by linking seasonal emission rates with meteorological
 data representative  of each season.  Annual average concentrations  can then be averaged
 across all seasons.   None  of the studies used this level  of resolution in the  modeling
 analyses,  a  limitation  that most  significantly affects heating-related emissions (such as
wood and oil burning) and major industrial facilities with emission rates that are highly
variable on a seasonal basis.
                                        123

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4.7    Decay. Deposition, and Transformation

       The models used in all of these studies contain simplified treatments  for decay,
deposition, and transformation. These factors generally have not been significant issues in
most studies of urban air toxics,  but a number of issues  deserve at least some
consideration.

       Decay

       The half life values of most pollutants are long in relation to the travel times for
traversing the 50 km distances that are the limit of most of these modeling analyses.  Over
these distances, the exponential decay term contained in these dispersion models would
generally produce  only minor differences in model output.  A widely used, practical
simplification in these studies was to assume an essentially unlimited half life, such that
the decay term becomes zero and incremental  concentrations from each source can be
scaled by emission rates for different pollutants.

       Deposition    '                "

       Deposition  of pollutants removes pollutants from the ambient air  and therefore
lowers ambient concentrations.21  It can occur through three mechanisms: (1) gravitational
settling,  (2) dry deposition, and (3) wet deposition.  Consideration of these terms has not
been included in the modeling analyses under  review in this study.  Model-predicted
concentrations may therefore be biased upward on this basis,  but in many cases these.
terms would not substantially affect the results.

       Gravitational  Settling—Gravitational settling  has  a significant effect  only  on
pollutants that are bound or attached to particles that are relatively large,  such as
10 microns in diameter or larger.  Since most directly emitted, particulate phase toxic air
pollutants are released from combustion sources with relatively fine particles,  little
                                                                         !
accuracy is likely to be lost by assuming zero gravitational settling for  these pollutants.
21  Indirect effects of deposited pollutants, such as contamination of surface or ground
    water, uptake through the food chain, or  direct ingestion (pica), have  not been
    considered in any of the current studies and are therefore not discussed here.
                                         124

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The adsorption in the atmosphere  of gas phase pollutants onto participates is another

mechanism where gravitational settling could potentially be an issue, but such model

treatments  are beyond the current state of the art.  In addition, a major percentage of the

particles involved in the adsorption process likely are less than 10 microns in diameter,

and therefore have minimal settling velocities.


       Wet and Dry Deposition—Most of the pollutants emphasized in urban air toxics

studies to date are volatile organics that are minimally affected by wet or dry deposition

because the washout ratios and deposition velocities are relatively low.  All of these

studies  used modeling techniques that  assumed  that ambient  concentrations are  not

affected by wet or dry deposition.22 This assumption probably results in slightly higher

estimates of concentration than would be predicted if these terms were included.


       The wet and dry deposition terms can produce more significant reductions in

average concentrations at the urban scale for metals and high molecular weight organic

compounds.   The  current ,model treatments could have a bias to  overestimate

concentrations for metals and high molecular weight organics relative to volatile organics.

4.8    Model Execution


       For criteria pollutant modeling analyses, the specific procedures in the Guideline

on Air Quality Models  (Revised) (EPA, 1986) are followed.  This reference guides model

selection, as well as the manner in which the models are to be executed.   For example,
22 The one exception is the version of GAMS used for the 35 County Study. At that time
   the Atmospheric Transport Model (ATM) was used as the dispersion model in GAMS,
   rather than ISCLT.  ATM could model gravitational settling, dry deposition, and wet
   deposition. There were substantial limitations, however, noted with the use of these
   terms (Sullivan and Hlinka, 1986).  The wet deposition term was set to zero for the 35
   County Study, and the dry deposition term was set to its minimum value.  Still,
   concentrations were probably reduced on the basis of deposition in  the 35  County
   study to a greater extent than appropriate for most toxic air pollutants evaluated in
   that study.
   Another problem that was identified with ATM  is an error in the plume  rise term
   (Sullivan  and Hlinka,  1986).  For  sources with relatively high exhaust temperature,
   such as incinerators  or  power  plants,  the  35 County  Study would  underestimate
   concentration and risk on this basis.
                                       125

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guidance is provided regarding how to select model options for specific applications.
Most of these studies of noncriteria pollutants,  however, did  not use  procedures
recommended in the guideline.   Alternative model execution procedures  were used,
apparently because the goals of most studies were perceived to  be substantially different
from those of typical criteria pollutant modeling programs.  The three exceptions (i.e.,
where the modeling analyses were consistent with the guideline) are Baltimore, Kanawha
Valley, and Southeast Chicago.23        '

       In terms of the mechanics of executing the model runs, there were two primary
techniques used to execute the models: (1) one model run is made for each pollutant based
on using actual emission rates and no post processing,24 or (2) model outputs were made
for each source based on a normalized emission rate, such as 1 kkg/yr or 1 g/sec, with
post-processing used to sum concentrations across  all sources at each receptor.

       With  the  exception  of  the early analyses in the Philadelphia Study and the
NESHAPS Study/normalized, modeling techniques have been used in all urban scale toxic
air studies, for a variety of reasons.  The primary  consideration is the relatively large
number of pollutants modeled in air toxics studies; for criteria pollutant analyses, where
the number of pollutants is much more limited and multiple averaging periods frequently
need to be addressed, it is often a more common practice to make pollutant-specific model
runs. Another important factor that leads to  normalized  modeling is the often dynamic
nature of  emissions inventories that support analyses of the urban soup.   Since
normalized modeling techniques link modeled data to emissions during post-processing
rather than within the modeling program,25  effects of changes in emissions  terms  on
23 The Baltimore modeling was consistent with EPA guidance, with the exception that
   Urban Mode 1 dispersion coefficients were used in place of Urban Mode 3 coefficients.
   This departure from recommended practices was made because of the study managers'
   concern ^that  the  Urban Mode 3 dispersion coefficients  would introduce  a.bias,
   underestimating concentrations for the predominant, low-level heightis of release.
24 Post processing involves multiplying normalized concentrations for each source times
   emissions rates for each source, and then summing, at each receptor, the incremental
   concentrations across all sources.
25 Although some dispersion models, such as ISCLT allow for changing  modeled data for
   selected sources, the incorporation of updates and control scenarios  in a data base is
   much easier to implement.
                                       126

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 predicted concentrations can be displayed readily.  The same applies to displaying the

 benefits of various control scenarios.  Hence, changes in exposures and  risks can be

 linearly predicted from changes in emissions without having to rerun the models for each
 emission scenario.


        Although the normalized modeling approach is now almost universally adopted, it

 introduces several significant technical limitations that deserve attention:

        •      Potential Complications  with Atmospheric Decay—Normalized modeling
              techniques restrict consideration of pollutant decay because differences in
              the  treatment  of decay for different pollutants  results  in  the relation
              between emissions and concentrations for each source becoming nonlinear.
              As noted above, this is currently not considered an important  problem, but
              there may be situations where decay must be addressed. Although special
              normalized model runs can be made for pollutants with particularly short
              half lives, such as the pollutant-specific modeling of allyl chloride in the
              Kanawha Valley Study, this level of detail can be impractical for a study
              with wide pollutant coverage.

        •      Representativeness of Release Specifications—Whan estimates of ambient
              concentrations are_made for a source on a normalized basis, a set of release
              specifications "are used,  such as stack  specifications,  area  source
              dimensions, and so forth.  Some changes  in emissions may be associated
              with changes in release specifications, defying the assumption of linearity
              inherent in the normalized modeling approach.

4.9    Model Performance Evaluation


       Model performance evaluations are  only an option  for studies that have an

adequate measured  data set against which to compare the modeled values.  Where they are

possible, performance evaluations offer two major benefits: (1) they can help  evaluate the

uncertainties in modeling results—a prime concern when using modeled air toxics data to

support regulatory or policy developments, and (2) they can be used  as a basis to improve
future model performance.


       Evaluating Model Performance


       Procedures have been  established  to help support the standardization of model

performance evaluations; these include consideration of bias, noise, correlation, and other
                                       127

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statistical tests (Fox, 1981).  The Philadelphia study showed statistical analyses for each
of these tests.  The more limited model performance evaluations presented for the South
Coast MATES and Kanawha Valley studies emphasized tests of model bias.

       Improving model performance implies more than simply comparing statistical
analyses of alternative model formulations.26  Much can be learned about the strengths
and weaknesses of alternative model formulations through detailed review of the patterns
revealed by the raw measured data set.  The  measured data  can provide clues  for
improving model performance, such as:
       •     If measured  concentrations decrease with  distance  from  a source
             inconsistently with the rate of decrease predicted  by the model, perhaps
             the treatment of dispersion is suspect.
       •     If wide  variations are observed in measured concentrations ; for similar
             meteorological conditions, perhaps more specific emissions data are needed
             for key facilities to better describe emissions variability.

       For the Philadelphia,  Kanawha Valley, and South Coast  MATES studies, it appears
that the demonstration of model performance strengthened confidence in using modeled
data for the exposure assessments.  Particularly for studies with complex emissions or
topographical factors, model performance testing can help show the reasonableness of
predicted concentrations to  meet project goals, or demonstrate the failure of modeling
techniques to meet goals for some pollutants.  Kanawha Valley is a good example of
complicated sources located in complicated terrain.  Model performance testing in this
study, although relatively crude, was used to confirm that the modeled data were at least
the correct order of magnitude.27
26  Formulation refers to a dispersion model run with specific input data and control
    settings. A dispersion model can be run with multiple formulations.
27  For this study, the EPA Science Advisory Board (SAB) noted during the series of
    review meetings the benefits of evaluating model performance in this complex setting.
    This component of the study appeared to be instrumental in obtaining SAB acceptance
    of the modeling protocol
                                       128

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        Improving Model Performance

        It is a common misconception of reviewers of studies that have undertaken model
 performance testing that measured data are used to calibrate model output to reduce bias,
 thereby showing a better match with observed results.  This has not been done in any of
 these  studies,  principally because  of the  difficulty  of demonstrating  that  model
 performance on an annual average basis  is improved throughout a study area, and not just
 for the monitoring sites and specific days of the sampling program.

        Instead, model  performance  evaluation is usually used to  help reveal the
 limitations in the  physical aspects of a model, such as the strengths and weaknesses  of
 emissions for specific point sources  or source categories, weaknesses of treatments  of
 transport and dispersion, and so forth.  For example, the Philadelphia study used model
 performance testing as a basis for developing a-priority ranking for independently verifying
 emissions data.  Its procedures and results- illustrate many of the issues of concern with
 model performance evaluation:

       Philadelphia Model Performance' Evaluation—Four  model formulations  were
 tested in the Philadelphia Study, all  of which were based on the SHORTZ dispersion
 model.28 Different treatments of stability and different  methods of considering emissions
 variability were tested.

       Table 4-3  summarizes the means  and correlation coefficients across  the ten
pollutants and ten sites that were included in this program. Figures 4-4 and 4-5 display
examples of average modeled and measured concentrations for each of the 31 days of the
monitoring program at one of the sites  (Site 7) of the Philadelphia network: pollutant
coverage is for 1,2  dichloroethane and carbon tetrachloride, respectively.
   As already noted, the Philadelphia study used SHORTZ purely for model performance
   evaluations, not for evaluating complex terrain.  In the opinion of the authors, who
   participated in the Philadelphia study, it would have  been desirable  to have tested
   alternative models, such as CDM and RAM, in addition to SHORTZ.
                                       129

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                   Table 4-3.  Summary of Moans and Correlation
                Coefficients  for Modeling and Measured VOCs In the
                                Philadelphia  Study
Compound
Chloroform
Dichloroethane
Carbon Tetrachloride
Trichloroelhylene
Benzene
Dichloropropane
Toluene
Perchloroethylene
Ethyl Benzene
Xylene
Measured
0.3
0.4
1.8
1.6 .
6.0
1.2
12.0
4.7
4.7
12.3
Modeled
0.2
0.4
0.1
1.0
2.3
0.5
8.8
3.5
0.4
2.4
R Value
0.27
0.89
0.47
0.00
0.58
0.95
0.76
0.88
0.64
0.23
       The  IEMP model performance evaluations were performed based  on  the
Monitoring and Ambient Data Assessment Module (MADAM)29 of the PIPQUIC data
management system.  Figure 4-6 presents a summary of the inputs and outputs to this
system. The Philadelphia study focused on partitioning the measured and modeled data
by wind flow quadrant as a means of identifying systematic differences associated with
flow across" source regions. This approach provided a means of improving the verification
of emissions data. The following factors reduced bias, but did not significantly improve
correlations.
29
   MADAM is a software package within PIPQUIC that inputs measured concentrations,
   normalized modeled concentrations, emissions data, and meteorological data that is
   concurrent with work days of the monitoring program.  Model performance can be
   displayed through MADAM by partitioning the data by wind flow, stability,  or other
   parameters.  See Chapter 7 for a discussion of PIPQUIC.                 ;
                                       130

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Stability
V
Mixing
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                                                                    C O 0
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             Figure 4-6.  Summary of  MADAM Model  Performance
                             Evaluation Tool

         The study identified an order of magnitude error in the emission rate for a
         major facility on this basis.  Engineering review by an independent agency
         identified a process that was missing from the inventory—although process
         emissions  as  a category were included in the inventory,  the  solvent
         recovery system,  which was the dominant  source, was inadvertently
         omitted (Sullivan,  1985).

         Volatilization from industrial sewage en route to the wastewater treatment
         plant was flagged during the model performance review because of the
         relatively low concentrations for two key pollutants transferred from an
         industrial  facility to  the wastewater treatment plant via  the  sewage
         system.

         An area source term  was  also  added  for drinking water releases  of
         chloroform, essentially removing the bias for this compound.  Again, this
                                   133

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              assessment  was  done  by  an independent  analyst  based  on  water
              consumption rates and average chloroform concentrations in the drinking
              water, not by model calibration techniques.

       Only 31  days of monitoring  data were  available,  however,  which  was  an
insufficient sample to allow partitioning of the data set in greater detail.  It was not
possible, for instance, to evaluate performance as a function of mixing height, stability, or
similar factors.  More data rich, monitoring programs, such as the Staten Island study,
could provide a more robust data set and  permit more comprehensive model performance
evaluations for a broader range of toxic air pollutants.

       Results of Model Performance Evaluations for South Coast—Table 4-4 presents
the limited results of the model performance evaluation in the South. Coast area, and
Table 4-5 presents the results for the Kanawha Valley.  These tables show the ranges of
measured and modeled data to provide an indication of the magnitude of model bias. Note
that benzene and  chromium, which account for about 95  percent of-the risk in the South
Coast  Air Basin, show relatively  good agreement between measured and modeled
concentrations.            "     '
                                       134

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                  Table 4-4.   Comparison of  Measured and  Modeled
              Predicted Toxic Air Pollutants In the South Coast MATIES
                                       Project*
     Air Toxics
       Measured
               Model
               predicted
                          Predicted/
                          measured ratio
     ORGANIC GASES
     Benzene                 1.0-4.9          0.56-5.0
     Carbon Tetrachloride       0.10-0.12        1.1-24 x 10r5 -
     Chloroform               0.02-0.30        2.0-17 x 1Q-8
     Ethylene Dibromide        0-100            1.1-22 x 10"4
     Ethylene Dichtoride        0-18             1.7-10 x 1 (T3
     Parchlorethyiene          0.5-3.1          0.28-2.4
     Toluene                  2.5-6.7          0.80-3.7
     Trichtoroethylene          1.1-7.1          0.33-2.9
     Vinyl Chloride             0-2.0            5.1x10-5
                                           0.22-1.8
                                           1.0-25X10-4
                                           0.68-49 X10-7
                                           0.11-110x10-5
                                           1.2-52X10-4
                                           0.22-1.5
                                           0.16-0.66
                                           0.13-54
                                           2.6X10-5
TRACE METALS (ng/m3)
Arsenic
Beryllium
Cadmium.
Chromium
Lead
Nickel - - -
0-8.8
0-0.5
0-4.1
1.8-11
180-280
3.7-8.9
5.0-10 X10-4
~ -0-5.4x1 CT3
1.1-9.6
3.6-60
1,100-1.700
0.7-5.6
1. 5-2,3 X1CT4
0.003-3.4
0.71-1,200
1.06-8.6
3.9-9.4
0,08-7.3
     'This table shows the ranges in measured and modeled concentrations across all of the "existing
     sampling sites, which are more indicative of average/background concentrations than the
     SCAQMD's "new" sites, which were selected to reflect maximum exposures.

             Table 4-5.  Comparison of Measured and  Modeled  Data for
                      Kanawha Valley  Toxics  Screening  Study

                                  Concentration (ug/m3)
         Pollutant

         Chloroform

  Methylene Chloride

    "Ethylene Oxide"
(unknown compound)

Carbon Tetrachloride
   Site 1
Mod.    Mon.
 1.9

 0.6

 2.4



 0.0
3.6

3.3

8.4



1.4
            Site  2
        Mod.     Mon.
4.5

1.3

12.8



0.0
8.7

3.1

11.3



1.0
                    Site 3
                 Mod.    Mon.
                             Site 4
                          Mod.    Mon.
8.1

10.8

0.0



2.6
8.9

20.8


13.3



3.4
11.5


14.6


0.0

 i

3.3
8.0

12.3


12.4



2.2
             NOTE:  Ethylene oxide ("unknown compound") averages are based on very limited data-
                     Site 1 (n-10); Site 2 (n=14); Site 3 (n-3); and Site 4 (n=4).
                                          135

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       Conclusions Drawn from Available Model Performance Evaluations to Date— Ths

limited model performance evaluations completed to date in these three study areas tend

to support the following general conclusions:

       *      Models  tend to underestimate urban concentrations  of many toxic air
              pollutants.  It is obvious from review of Tables 4-3, 4-4, and 4-5 that, in
              general,  there is a bias to underestimate modeled concentrations relative to
              ambient concentrations  for volatile organics.  (From Table 4-4,  no clear
              trend  exists for metals.)  Similar  model underestimations  for volatile
              organics were found in other studies (EPA, 1988; Sullivan and Martini,
              1987J.  In many cases modeled results are low by a factor  of 2  or  3  or
              more.    Four  factors may be  responsible  for  this  tendency  toward
              underestimation:
              1.
             2.
                       of the model-based analyses assumed zero background.  For some
                    of these compounds, especially those with long atmospheric half
                    lives such as carbon tetrachloride, the background term is probably a
                    substantial portion of total concentrations.
                    Gaps in coverage in emissions inventories could be accounting for a
                    significant percentage of the model underestimates.
              .3.    Current models do not predict secondary pollutant formation (e.g.,
                    photochemical formaldehyde).
              4.    For the more recent  studies (EPA, 1988  and Sullivan and Martini,
                    1 98 7Jtnat_ evaluated model  performance .based on  the  EPA
  -                  recommended  dispersion coefficients  for urban applications  (i.e.
                    Urban  Model  3   dispersion coefficients), substantial  bias to
                    underestimate  concentrations  was observed for low-level sources
                    relative to the  more traditional Pasquill-Gifford coefficients that are
                    modified for urban applications.

              Model performance  annears to be strongly pollutant  df-pRnrifmt  These
              studies  appeared to be successful in separating pollutants with  relatively
              low bias (such as less than a factor of 2) from those with high bias, which
              in some cases was orders of magnitude.  These findings help support the
              use ol modeling for exposure  assessments  for the subset of pollutants
              found to be within reasonable accuracy limits.30


       The correlation of the average concentrations across all sites is another important

factor with which to judge  model performance,  particularly to evaluate the ability of a

model formulation to  identify gradients in concentration across  a study area.   The

Philadelphia study  is the  only study to  report correlation results;  its  correlation

coefficients (R),  presented in Table 4-3, indicate wide differences in correlation across
             ™         "I80 "  Con?ider1fble uncertainty in the measured data,  which
          be considered when assessing the confidence in the modeled data.
                                       136

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pollutants, ranging from 0 to 0.95. When considered in conjunction with bias, as shown
in Table 4-4, it is clear that  far more confidence should be placed in the results for some
compounds than for others.  For example, modeling predictions of carbon tetrachloride or
arsenic levels appear to be orders of magnitude less reliable than pollutants that appear to
be better characterized in emission inventories, such as perchloroethylene or toluene.

4.10   Insights into the Use of Modeling in Air Toxics Evaluations
                                                                        i
       The  experience gained through the air toxics studies conducted to date lead to
insights that can guide future use of dispersion models in multi-facility, multi-pollutant
studies.   Observations on  their experience are grouped below under four headings:
(1) Modeling Techniques, (2) Model Performance Considerations,  (3) Completeness of
Scope, and (4) Cost Saving Measures.

       Modeling Techniques            ....

       There are many similarities between routine modeling analyses done for permitting
or State Implementation Plan  (SIP) development for criteria pollutants, on the one hand,
and modeling done to support multi-pollutant, multi-facility urban air toxics studies, on
the other.  In many cases the  same  Gaussian dispersion models are applied, with similar
meteorological and release  specifications data.  There are also  some major differences.
The following  are issues that appear to  be of greatest concern for modeling noncriteria
pollutants:

        Background—All of these modeling analyses were limited to pollutants released
within the  study  area.  There was no inclusion  of a background term  to estimate
concentrations in the ambient air  (before transport into the  study area) prior to the
incremental contribution from the pollutants released in the study area.  This background
term is not necessarily represented by available data from remote rural sites, but should
be representative of concentrations at  the upwind fringes of the  modeling  domain.
Although in some cases, such  as the Philadelphia and Southeast Chicago  studies, the
boundary of the modeling domain was substantially larger than the receptor  area to
                                        137

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 partially account  for  local transport,  any regional background  was not  effectively
 considered.  Available national data bases, which cover a wide range of monitoring sites,
 suggest that some of the pollutants commonly reviewed may have background levels that
 are significant in relation to modeled concentrations (Sullivan and  Martini, 1987).  The
 increased ambient monitoring by EPA and others should improve information on pollutant
 background levels, at least in the urban context.  Little rural sampling is  under way; if
 attempted, it would probably yield nondetectable levels for some pollutants.

       Tendency to Underestimate Modeled Concentrations for Some Compounds—
 Although  limited  data are available, the  results in  Table 4-4  suggest that model
 underestimates can be relatively large  for pollutants such as carbon  tetrachloride,
 chloroform, ethylene dibromide, xylene, and ethyl benzene.  These  and other pollutants
 suspected  of being substantially  underestimated by modeling should be (1) pursued to
 evaluate cause or (2) flagged in the reported results.
       Use of Normalized Modeling TerhnifpiP.!— whpn normalized model runs are used
to support the  analysis of "control  options, there is a  potential for  mischaracterizing
release specifications  for alternative controls.  As processes change, and especially if
controls are added, the release specifications can substantially change, requiring follow-up
modeling of these sources to  replace outdated normalized model output.  For example, if
a scrubber is assumed to be added to enhance the control of emissions from a facility, the
release specifications  can be substantially altered.  In this  example, the benefits of a
control option could be greatly exaggerated because the lower effective release height of
the scrubber would not be considered if only the resulting emissions were adjusted in the
modeling analysis. (A scrubber can produce a substantially  lower exhaust temperature,
which can result in much lower plume rise and can increase the air quality impacts on a
unit mass release basis.) This issue can be a problem especially for data bases that are
used on a long-term basis as  a repository for emissions and modeled data.  Some  system
controls are needed to maintain the  currency of the data base, including  flagging major
process changes and changes in facility status through the permit process.
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       A  problem  related to the representativeness  of the release specifications is
maintaining the currency of the emissions data in a data base.  If a normalized modeling
approach is selected to meet the immediate project goals, and to become part of a data
base for future  uses,  a system is needed  to ensure  that emission rates and release
specifications remain current.

       Microscale Errors for Simplified Release Specifications—Most facilities in these
studies were modeled' with highly simplified release specifications.  In some cases this
likely was a reasonable approach, especially for estimating average concentrations across
a study area.  In the case of modeling MEI  concentrations for highly complex facilities,
however, major inaccuracies likely resulted from using simplified release specifications to
estimate maximum offsite  annual  average concentrations.  Depending on the layout of
each facility, it is possible that modeled  concentrations could  underestimate or
overestimate on this basis.  The study that appears to best represent MEI concentrations is
the Kanawha Valley study,  which is consistent  with the .emphasis provided on  the
modeling goals in Table 4-1. ___.__.

       Modeling Fugitive and Vent Releases—Fugitives and vent releases are dominant
source categories for many major and minor  industrial releases of toxic air pollutants. In
many  cases, stack emissions make minor  contributions, yet the emphasis on model
development within EPA has been on  releases of criteria pollutants from stacks. Errors
within area and building source treatments within models such as ISCLT and LONGZ can
introduce  bias  that  acts  to underestimate the risks from  air toxics (Sullivan  and
Hlinka, 1985). These two models were found to have an error in the smoothing term that
acts to underestimate concentration from area or building source treatments.  It is unclear,
based on Sullivan and Hlinka, 1986, if this error is present in other models, such as CDM.

       Coupling modeling uncertainties for fugitive/vent releases with the ioften  large
uncertainties in emission terms adversely affects  the confidence in modeling many major
point sources of air toxics.  Model performance testing within the composite plume (such
as 1 km from, the fenceline of a facility, as used in Kanawha Valley) provides a means of
displaying the overall acceptability of the modeling analyses for such sources.
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       Model Bias for Low-Level Sources in Urban Areas—It does not appear that any of
these modeling analyses are based on  the most recent  set of  dispersion coefficients
recommended for modeling in urban areas. This is largely because most of these studies
predate this guidance (EPA, 1986). The UNAMAP 6 version of the EPA dispersion models
provides regulatory guidance for using an adaptation of the McElroy-Pooler dispersion
coefficients for urban applications. These coefficients predict concentrations from low-
level sources that are substantially lower than predicted by. previous versions of the
UNAMAP models, which may result in significant underestimations of the magnitude of
air toxics  risks (Sullivan, 1985).  It  is  also likely that source culpability  will be
inaccurately apportioned between low-level and high-level releases (such as power plant
or incineration emissions) and low-level industrial and area sources. (EPA, 1987)

       Transformation  Products—Limited laboratory testing has  indicated that the
reaction products of some  pollutants have order of magnitude higher mutagenicity than
the parent compounds.  This is a research issue that is beyond the  scope of applied
modeling  studies "conducted, at. this  time,  and likely  in  the -near future.    Some
transformation products (e.g., secondary formaldehyde)  may be covered by aldehyde
monitoring, but others likely are missed by both modeling  and monitoring components of
current multi-pollutant, multi-facility studies.  Although little can be done to  fill this
void in present studies,  it is an  issue that should be  considered when interpreting the
magnitude  of the overall  results.

       Short-Term  Variability in Concentrations—There  has been no comprehensive
treatment of the effect of  the variability of emissions and meteorological  data on the
short-term  variability in  modeled concentrations in an urban area, or in the immediate
vicinity of major industrial sources.  For air toxics evaluations, the issue of most likely
potential future concern would be noncarcinogenic  health effects related to maximum
short-term concentrations. Studies based on annual average emissions rates and the use of
stability class data could underestimate peak concentrations by orders of magnitude (Luna
and Church, 1972).
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       To date,  it does  not appear that any studies  have adequately screened  the
magnitude of potential for exceedance of short-term noncarcinogenic thresholds, either
region-wide or within isolated subregions.   From a modeling perspective,  the  issue is
accurate representation of dispersion rates on  a short-term (hourly) basis and estimation of
hourly distributions of emission rates for key selected industrial processes (and possibly
area source categories).

       Two  EPA efforts currently  in  the  planning  stages may contribute to  the
understanding of short-term  variations in concentrations of air toxics.  EPA's Office of
Research and Development is initiating a study to characterize variations in concentrations
of a wide range of toxic air pollutants in urban areas.  In addition, a follow-up study is
planned in the Kanawha Valley to address the  distribution of concentrations of selected
toxic air  pollutants.  These  studies could help  show the relationship of peak hourly
concentrations to data compiled to estimate thresholds for toxic air pollutants.

       Absence of a.  Mixing Height Term in HEM/SHEAR  and SCREAM—The mixing
height term can result in significantly higher  predicted concentrations when modeling at
the scale of a metropolitan area, or when assessing exposure for major industrial facilities
that are located 10 to  15 km or more from high  population centers.  The lack; of a mixing
height  term  in  SHEAR  and  SCREAM appears to be a  simplification  that could
underestimate some concentrations and risks.

       Use of Turbulence Intensity Data to Characterize Dispersion Rates—The Kanawha
Valley study demonstrated the potential benefits that could be achieved if a dispersion
model, such as SHORTZ  or LONGZ,  were modified to include functional relationships
between turbulence intensity data and site-specific dispersion rates representative of near
ground-level releases.

       Availability of Terrain Data—The HEM and  GAMS exposure modeling systems
could be refined to include access to nationally  available terrain data. This could lead to
more accurate model  prediction, especially for tall stacks located in moderate to rough
terrain.   Without  consideration  for terrain,  the model estimates of power plants,
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 incinerators, and other sources with high effective stack heights may underestimate certain
 ambient concentrations.

        Network Size for Model  Performance Evaluations—A  common question  posed
 during the design stages of a study involves the optimal number of monitoring sites and
 the length of the field program that is needed to support a model performance evaluation.

        The  duration  of most  of these  monitoring studies  was  one season,  with
 intermittent  sampling.   As long as  meteorological and  emissions  data are available to
 compare this season to annual and climatological conditions, the single season approach
 appears to be a reasonable tradeoff between cost and data  needs. There also is precedence
 for single season model performance  testing for criteria pollutant applications.

        Appendix  B presents a  methodology^ to help address the  issue of the optimal
 number of monitoring sites, but ultimately..the.size of the network needs to be selected to
 match the goals of the model performance evaluation. For example, if only model bias is
 evaluated, as presented in the-South  Coast Study, a few carefully selected sites may meet
 this objective.  If spatial  correlation were also to be assessed, such as in the Philadelphia
 study, it would appear more reasonable to design an eight to ten site network.  If costs
 were  prohibitive, perhaps some of the cost-saving measures as presented below could be
 considered to stretch monitoring resources.

       Cost-Saving Measures

       Screening versus Refined Modeling—An obvious cost-saving measure for modeling
 analyses is to first do screening-level analysis prior  to performing refined analysis.  By
 using coarse modeling grids and  simplified release specifications, it is possible to identify
 the geographic areas and sources that require the greatest emphasis in the more detailed
 modeling to follow.  In this manner,  available  resources can be most efficiently allocated
to improving estimates in high impact areas.

       Normalized Emissions Modeling—KfnHgl ing of normalized, rather than actual,
emissions is discussed earlier in this chapter.  This technique saves resources insofar as it
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eliminates the  need to rerun the model(s) every time emissions change  or a different
control scenario is evaluated (assuming that the release specification for each facility does
not change significantly as emissions change).
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                                     Chapter 4
 References
 Barcikowski, ,W., 1988.  South  Coast  Air  Quality  Management District.   Letter and
 attachments to Tom Lahre, U.S. Environmental Protection Agency.  RTF, NC. October 25
 1988.

 Black, P., 1986.   Workshop to Support the Design of Denver Air Toxics Monitoring
 Program, Research Triangle Park, North Carolina, May 1986.

 Environmental Protection Agency.  1986.  Guideline on Air Quality Models  (Revised)
 EPA-450/2-78-027R, Office of Air Quality Planning  and Standards, Durham, North
 Carolina.

 Environmental Protection Agency.  1987. Integrated Environmental Management Project:
 Baltimore Phase H Report (In Progress].  Regulatory Integration Division, Washington D.C.

 Environmental  Protection  Agency.    1988.-   Baltimore Integrated Environmental
 Management Project: Phase  II, Draft Final Report, Regulatory Integration Division,  EPA
 Office of Policy, Planning and Evaluation, (in progress).

 Fox,  R.G., 198i:   Judging Aii-jQuality  Model  Performance, Bulletin  of the  American
 Meteorological Society.  Vol 62., No. 5, May 1981.. pps. 599-609.

 Liu,  C.,  1987.  California  South Coast Air Quality Management  District.  Personal
 Communication.

 Luna, R.E. and Church, H.W., 1972. "A  Comparison of Turbulent Intensity and Stability
 Ratio Measurements to Pasquill Stability Classes," Journal of Applied Meteorology, June
 j. y / £» t

 Shikiya, D., L. Chung, E.  Nelson, and R.  Rapoport, 1987.  The Magnitude of Ambient Air
 Toxics Impacts from Existing Sources in the South Coast  Air  Basin: 1987 Air Quality
 Management Plan Working Paper (Revision Number 3).  South  Coast  Air Quality
 Management District, El Monte, California.

 Sullivan,  D.  A.,  and J. Martini,  1987.  Evaluation of  Total Exposure  Assessment
 Methodology (TEAM) Data:  Review of  Ambient and Personal Data Sets (In Progress).
 U.S. Environmental Protection Agency,  Office of Air Policy Analysis and Review.

 Sullivan, D. A., 1985* >  Evaluation of the Performance of the Dispersion Model SHORTZ
 for Predicting Concentrations of Air Toxics in the U.S.  Environmental Protection Agency's
Philadelphia Geographic Study.   U.S.  Environmental Protection Agency, Integrated
Environmental Management Divisions, Washington, D.C.
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Sullivan, D.A. and D.J. Hlinka.  1986.  Air Quality Exposure Assessments:  Comparison
and Evaluation of the Human Exposure Model, Atmospheric Transport Model, Industrial
Source Complex Model, and LONGZ. U.S. Environmental Protection Agency, Office of
Air Policy and Review, Washington, D.C.

White, Fred D. (Editor), 1984.  "Review of the Attributes and Performance of Six Urban
Dispersion Models, U.S. Environmental  Protection Agency, Environmental Sciences
Research Laboratory, Research Triangle Park, North Carolina, EPA-600/3-84-089.
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                                    CHAPTERS
                       EXPOSURE AND RISK ASSESSMENT
5.1
The following topics will be discussed in this chapter:
•      The use of exposure and risk assessment in air toxics studies
•     .Methodological issues
•      Comparison of approaches to interpretation of exposure and risk
•      Comparison of field-oriented and research-oriented approaches to exposure
       and risk assessment
•      Insights into the use of exposure and risk assessment in air toxics studies

The Use of Exposure and Risk Assessment in Air Toxics Studies
       Estimating exposures and risks associated with air toxics is a complex issue that
most studies have addressed in a highly simplified manner.  Total human exposures to air
toxics include contributions from numerous residential, commercial, occupational, and
transportation-related microenvironments.   Fully describing these exposures and their
consequent risks is  beyond the scope of field-oriented studies  concerned with ambient
outdoor air concentrations  of air toxics. The Total  Exposure Assessment Methodology
(TEAM) program has attempted more comprehensive exposure  evaluations, but has not
attempted detailed examinations of individual microenvironments.   The Integrated Air
Cancer Project, on the other hand, is investigating microenvironments,  but  is not
conducting comprehensive exposure assessments, such as through the personal sampling
methods  employed  by TEAM.   Evaluation  of  all  risks associated  with air toxics  is
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hampered by  gaps in available cancer and noncancer potency  data; much additional
research must be completed before reasonably  comprehensive evaluations of all potential
air toxic risk can be attempted.

       All of the studies reviewed in this report evaluated, cancer risks from air toxics.
With the exception of the  TEAM  studies and the Integrated Air Cancer Project, all the
studies simplified their  exposure  and risk assessment  in a similar manner, essentially
reducing them to simple  multiplications of ambient concentration values times population
exposed times available cancer potency scores, as shown in the following formula:
Areawide
 Cancer -„
Incidence
 /  Model Predicted
'I       or
'I Measured Ambient
 \  Concentrations
                                                Number of      \
                                             Persons Exposed
                                                   to
                                          [ Ambient Concentrations^
Cancer^
 Unit
 Risk
Factor
       In this.formula, areawide, excess, additive (i.e., multi-pollutant) cancer Incidence is
 computed by summing over each subarea (e.g., 1x1 km2 grid square) and each pollutant.
 Cancer unit risk factors are, -with- some exceptions, drawn from current listings developed
 by the EPA Cancer Assessment Group and are accepted as constants. These factors are
 available from EPA's Integrated Risk  Information System (IRIS).  (EPA, 1988)  Some
 groups used other unit risk factors for  certain pollutants.  The South Coast MATES
 project, for example, used risk factors developed by the California Department of Health
 Services, as required under State law. (Barcikowski, 1988)  The issues of greatest practical
 concern are (1) estimating concentrations (usually  by monitoring  or  modeling) and
 (2) assigning population to these concentrations.  Both topics have  been discussed in
 earlier chapters.  This chapter therefore  deals primarily with the purposes to which
 exposure and risk  assessment have been put and how  their results have been interpreted,
 Of special interest are studies that compare results  obtained through monitoring  with
 those obtained through modeling.  It is also  important to discuss differences between
 typical field methods and more refined methods explored by research programs such as
 IACP or TEAM.

        At least two of the studies reviewed—the Baltimore and Santa Clara lEMPs—also
 evaluated noncancer health effects.  In the Baltimore IEMP, the investigators calculated a
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"health hazard index" to assess the potential hazards from cumulative exposures to the

target compounds. The index is based on the assumption of dose additivity and is defined

by the following equation:
              Where:
                                         fe       &
                                   AI_t  AL.2      ALj
                     HI =   Health index for a particular category of health effect
                     EJ  =   Ambient concentration of pollutant i
                     ALj     »      Threshold value for pollutant i for a particular category of
                     health effect


       After first calculating the ratio of the ambient concentration for the pollutant to
the threshold for  the health  effect  (Ei/ALi), these ratios are then summed across  all

pollutants with the same effect.             _ -


       The  health hazard index is a numerical indication  of  the  level  of  concern

associated with exposures to complex mixtures of pollutants in the- environment.  As the

index  approaches  unity, concern for  the potential  hazard  of  the chemical  mixture

increases. If the index exceeds 1, the concern is  the  same as if the no-effect threshold

were exceeded by the same amount by an individual pollutant.


       As noted in earlier chapters, the studies under review have varied in the types of

exposure and risk estimates they developed.  Most, for instance, emphasized areawide or

grid cell average annual population exposures; only a subset estimated values for "most

exposed individuals" (MEIs).   Beyond this, however, the interpretation of results follows

some common patterns.

       •      Ranking the relative importance of pollutants:  One .of the most common
              analyses has been to rank pollutants  in order of importance, either in terms
              of ambient concentrations or  in terms  of  potential  risk (ambient
              concentration times potency).  Where "threshold" (most noncarcinogenic)
              pollutants are concerned, the ranking  may simply distinguish between
              pollutants whose concentrations  fall above health thresholds and those
              that do not.

       •      Ranking the relative importance of sources or source categories: The other
              most common analysis is to  investigate the relative importance of sources
              or source categories — for single pollutants, for common  pollutant groups,
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             or for total risk.  Source categories can be broad (area sources versus point
             sources,  mobile  sources  versus  stationary  sources)  or  narrow
             (discrimination among industrial types).   In some  cases, total risks or
             pollutant-specific risks  are compared from source to source.  Any of these
             analyses can help set priorities for potential future source controls.

       •      Estimating "nulpabilitv" of particular sources or source BTOUDS in regard to
            . individual receptors;    Culpability analysis determines, for a particular grid
             cell or  receptor point, the  risks or pollutant-specific  ambient  air
             concentrations at that point which  are attributable to specific sources in
             the area.  This type of analysis may be most useful for.MEI analysis, where
             the immediate question after identifying the most exposed individual is to
             establish which sources are responsible for that exposure.

       •      Evaluating variations  in exposures and risks; For studies that estimate MEI
             exposures and risks,  the  ratio of MEI  to  average values may be of
             considerable interest.  For all studies, the ranges of exposures or risks are
             also of interest, particularly in terms of the number of people exposed to
             various levels.  Note that population  risks can be characterized in  two
             significantly different  ways: (1) as the total number of canjcer or other
             disease cases  estimated withia-the exposed  population,  a figure that is
             ..inherently dependent on the size of the exposed population (some studies
             based on population  normalize their incidence figures to  avoid  this
             dependency),  and (2)  as probabilistic risk  (i.e., ranging from 0  to 1)
             experienced by the average exposed person in the region or in each grid cell,
             a figure that is not dependent on the total number of people in the region.

       «      Evaluating relative effectiveness of alternative control  scenarios: The 5-
             City Controllability Study and several of the lEMPs projected future  risk
             reductions  as a function of alternative  combinations  of measures
             superimposed  to control air toxics emissions (see Chapter 6).


5.2    Methodological Issues


       Although many of the technical-issues involved in the initial  steps of exposure and

risk assessment have already been  discussed in earlier chapters, there are a number of

special topics that require  additional  clarification.  This section also briefly summarizes

risk assessment methods and available data.


       Exposure Assessments


       Most technical issues pertinent to conducting exposure assessments have already

been discussed  in previous  chapters. These included such factors as:

       •      Selection of study boundaries
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        •     Location of monitoring sites in relation to sources and population
        •     Design of receptor grids and special receptors for modeling analyses
              — Type (polar, rectangular)
              — Density
              — Special Points (monitoring station locations, known MEI locations)

        Various other exposure-related topics are discussed below.
       Linking  Population Data with Concentration Data— T^P South  Coast MATES
 study, which estimated exposure based on the direct use of measured data, employed the
 most detailed treatment for assigning concentration data to population of any study
 reviewed.  The study first plotted the locations  of the study's ten monitoring stations on
 its 4,200-cell grid,  each  cell being 1 km on a side.  It  then  estimated average
 concentrations for each grid cell  by averaging the reported  concentrations at  each
 monitoring station, weighted in inverse proportion to the square of the distance to the cell
 from the monitoring station.  In other words, concentrations at a monitoring station 2 km
 from a particular, cell  would be given one-fourth  the weight of concentrations 1 km from a
 cell.  All population within a grid cell was then assumed to  have the same average
 concentration, based on the distance-weighting procedure.  (SCAQMD, 1987)

       The  5 City Controllability Study and NESHAPS component of the Six Months
 Study utilized EPA's Human Exposure Model (HEM) to link population and concentration
 data.  HEM is an integrated modeling-exposure assessment system that first runs  a
 dispersion model to estimate ambient air concentrations around sources and then assigns
these concentrations  to BG/ED population centroids, based on  internally stored 1980
census data.  HEM calculates a polar  concentration array for 160 receptors around each
point source (ten  receptors extending radially out to 50 km along each of  16 wind
directions).  See Figure 4-3 in Chapter 4.  Then, depending on whether one  is running
HEM/SHED  or. HEM/SHEAR, HEM associates concentrations and populations in different
ways as a function of  distance from  the emission source.   Within  3.5 km, the polar grids
are smaller than typical BG/EDs, and  SHED apportions BG/ED populations to each  grid
point concentration based on proximity and area.  Beyond 3.5 km in SHED, and at all
radial distances in SHEAR, a log-log linear interpolation  is made among the polar  grid
points to estimate concentrations at each BG/ED centroid.  Incremental contributions from
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multiple facilities in an area are stored at the BG/ED level in SHEAR and summed across
all facilities.  Total exposures are then estimated in SHEAR across all BG/EDs in the study
area.  The SHED methodology is considered  more accurate than SHEAR for  estimating
maximum lifetime  risks, but not significantly more  accurate for estimating aggregate
incidence.

       Within HEM, area sources are handled only in the SHEAR module. The user does
not provide subcounty-allocated data to SHEAR; rather,  SHEAR performs this suhcounty
allocation in either of two user-specified ways—(1) uniformly,  assuming  no spatial
variation in emissions, or (2) by population.  In the latter case, a simplified box model is
run within hypothetical  boxes superimposed over each BG/ED.   The resulting
concentrations are therefore associated directly with population controls of each BG/ED.

        In the IEMP studies, a rectangular grid was used rather than a polar coordinate
system, as is used in HEM. The census tract maps  for the  metropolitan areas were then
overlayed onto the-modeling grid and population assigned to each.grid cell.  For census
tracts that extended into two or more grid cells, population  was assigned to  each affected
grid cell  on the basis  of the percentage of the area of the census tract within each cell.
Estimates of exposure for each grid cell were made by multiplying the resulting grid cell
populations by the model-predicted concentrations  for  each grid  cell.  In several of  the
DEMPs, up to four receptor points were defined within each grid cell; in these instances,
the average concentration  from  these multiple points  was multiplied by  the  grid cell
 population to estimate exposure. Total exposure was estimated by summing across all
 grid cells.

        A number of quite different approaches for relating populations and concentrations
 are planned for the Denver study.  As noted earlier, most toxic pollutants in the Denver
 area are believed to be mobile-source related, with concentration peaks near the urban
 center; hence, more  homogeneous concentrations are expected  here  than in a more
 industrialized area.  Because of this  anticipated regional homogeneity;  only three
 monitoring stations will be used, but,  as discussed in Chapter 2, pollutant  coverage at
 each site will  be extensive.   One goal of the study is to represent uncertainty in
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concentrations by estimating ranges  of exposures as well as best estimates.  Plans for

denning these ranges include:1

       1.     Calculating expected exposures by simply assigning measured values at
              each of the three monitoring stations to the population residing in each
              station's delineated zone.

       2.     Calculating exposures at distances away from the three monitoring stations
              through a variety of statistical correlations, one of which is expected to be
              multiple regression analysis involving (1)  available  conventional pollutant
              data and (2) distance from the center of the city.  CO data would serve as
              an indicator of all automotive-related toxics, which include all categories
              of pollutants  of concern in the study (metals, VOCs, semivolatiles, and
              aldehydes/ketones).  Fine particulate data  would be correlated with metals
              concentrations, and ozone data with aldehydes/ketones. Distance from the
              center of the city is significant because empirical evidence suggests that,
              despite regional mixing, air pollutant concentrations peak in the central
              urban area.

       3.     Estimating high and low average-concentrations across all three monitoring
              -sites for each  pollutant to. define endpoints  of ranges of exposures  in the
              region.


       Population MobiHty/MiGroenvironmental Exposure—Population mobility has  not

been widely addressed in studies to date.  Of the studies reviewed,  only the  Motor

Vehicle Study attempted to factor in non-outdoor exposures, using a modified version of

EPA's NAAQS Exposure Model (NEM) for CO.  (Ingalls, 1985) The NEM approach relies

on an activity pattern model that simulates a set of population groups called cohorts as

they go about their day-to-day activities.  Each of these  cohorts is assigned to a specific

location type during each hour of the day.  Each of several specific location types in the

urban area is  assigned  a  particular ambient pollutant "concentration based on fixed site

monitoring data. The model computes the hourly exposures for each cohort and then

sums up these values over the desired  averaging time  to arrive  at average population

exposure and exposure distributions.  Annual averages are possible because a full year's

data from fixed site monitors  are input to the model.


       It should be noted that indoor concentrations (and, therefore, exposure) caused by

ambient mobile source pollutants are also accounted for in the model.  A scaling factor of

0.85 was applied to the appropriate  neighborhood monitoring data  to  estimate indoor

1  These approaches may not necessarily be carried out as described here.
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exposures to the pollutant of interest in each, neighborhood. The scaling factor was based
on comparisons of indoor and outdoor CO levels of homes with no indoor CO sources
(e.g., gas stoves, smokers).

       The modified MEM does not account for photochemical reactions.  The exposure
levels predicted by the model are'those resulting from direct exhaust emissions, and do
not account for either the destruction or photochemical formation of the pollutant  in the
atmosphere.   The model also assumes  that  the pollutant  of  interest  has  emission
                                                                       !
formation and dispersion characteristics similar to those of CO.

       In contrast to the NEM  approach in the Motor  Vehicle Study,  most studies,
reviewed herein implicitly assume a fixed population defined  by the place of residence.
To evaluate the reasonableness of this assumption, Figure 5-1  summarizes the  results of
the work done by Rheingrover (1984), which helps shed light on the assumption of a
nonmobile population.  This figure applies to the Philadelphia Metropolitan area, as
represented by 85 five-km grid cells.
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                 Figure 5-1.  Comparison of Residential and Workday
                               Population  by Grid Cell
                                                       Core Central Business
                                                            District
              1357 9 11 131517192123252729313335373941434547495153555759616365676971 7375777981 8385
                                            Grid Cell Number
                                .— Residential Population — Workday Population
This figure shows population totals (1) under the assumption that all residents remain at
their  place  of residence, and  (2) as compared to workday estimates that account for
occupational mobility.  As shown, most grid cells had relatively equal  residential and
workday populations, reflecting an equal influx and outflux of people during the work
day.  The core central business district (see grid cells 47 and 48  in Figure 5-1) shows a
noticeable exception, where the influx of workers did substantially increase exposures
during the daytime over what would have been estimated based on considering residential
exposures only.

       If this is  representative  of other  metropolitan areas  (which has not been
demonstrated to  date),  the assumption of no  mobility for ambient-based exposure
assessments does  not appear to be  a major limitation, at least in terms of  outdoor air
exposures.   A more important factor likely is the mobility of subjects among different
micro environments  (e.g.,  indoors,  automobiles,  etc.).   Only personal sampling and
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microenvironment sampling are able to account for the mobility of the population beyond
outdoor air exposures.

       Risk Assessment

       The air toxics studies reviewed here used existing health effects data to estimate
potential air toxics risks based on their exposure estimates.  None of the applied studies
generated any original health effects  data.   Although technical  discussion of the
development and interpretation of dose-response data is beyond the scope of this study, a
brief review of the use of existing health effects data for air toxics studies is appropriate.
The use of potency  figures, for carcinogens and noncarcinogens is discussed separately
below.

       The factor that contributes significantly to the uncertainty in urban assessments is
the conversion'from  exposure to risk, especially when using unit cancer risk factors.  The
use of limited animal data to evaluate human risks, and the extrapolation from high to
low doses, are two contributors to uncertainties that can span orders of magnitude.  For
air studies, additional uncertainties also exist when oral ingestion effects data are used to
estimate potencies of the same substances when inhaled; in the absence of other evidence,
researchers must often  make the assumption  that oral and inhalation potencies are the
same.

       Carcinogens—Cancer has been the major emphasis of these studies, with nearly all
analyses being focused on this effect. . All of the studies that performed exposure/risk
assessments relied heavily on unit risk values developed by the EPA Cancer Assessment
Group (GAG), These are usually expressed  as "unit risk factors," a number that represents
the probability of contracting cancer from constant inhalation, over a nominal 70-year
lifetime, of 1 ug/m3 of the substance in question.   These are established  conservatively,
generally representing the 95th percentile  upper bound value based on laboratory
experiments.

       Studies that employ these dose-response functions explicitly or tacitly assume the
following:
                                        155

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       •      Risks calculated are theoretically valid only for constant exposures over a
              70-vear period.  Some studies have "annualized" cancer incidence or risk
              values by  dividing  lifetime risk  or the total number of  cases  by  70.
              Although this is intuitively useful for policy purposes, it is not, strictly
              speaking, a valid application of these numbers.

       •      There is  no threshold of exposure below which no carcinogenic effects are
              presumed to exist.  Any dose of a carcinogenic substance, no matter how
              small, is assumed to impose a finite risk of contracting disease. For most
              studies of carcinogenicity at the low levels encountered in ambient air, the
              shape of the dose-response function is assumed to be linear.2

       •      Cancer effects are  additive. Studies have assumed that it is valid to sum
              the risks of different carcinogenic substances. For instance, if substances A
              and B are each assumed to impose a risk of 10 cancer cases over 70 years to
              a given population, then the additive risk for the two together is 20 cases.3

       •      No interactions exist among chemicals that would cause  simultaneous
              exposures to multiple carcinogens to lead to either higher or lower disease
              incidence than would otherwise be presumed  to occur.  GAG potency
              values include  no  allowance" for synergistic  or  antagonistic  effects.
             -According to EPA guidance, all cancer incidence estimates  are assumed to
              be additive  unless  there is specific  evidence of  interaction among
              substances.  So far, no such evidence is available with reference to  the
              concentrations -of-carcinogens that may found in the ambient air.

       •      No cancer  occurs  from secondary or  transformation products or other
              compounds for which cancer potency scores are unavailable.


       Since all dose-response values published by GAG have undergone extensive peer

review, individual studies generally have not had to defend  the validity  of any

toxicological potency estimates developed by this group. Some studies, however, such as
   Obviously, risk levels cannot exceed 1, but the shape of dose-response curves as they
   approach 1 will vary. This issue is assumed to be unimportant in ambient air studies
   because measured or predicted ambient concentrations are almost always at the very
   low end of the observable range.
3  The theoretically more correct approach is to assume  that effects should be summed
   only for carcinogens affecting the same organ. For instance, risks from all carcinogens
   linked with lung cancer could be summed, but not risks from carcinogens affecting the
   lungs and, say, the liver.  Another more theoretically correct approach to cancer risk
   assessment is to  consider the  weight of  available  evidence on hazard in defining
   potential risks: risks from proven human carcinogens should, in such an  approach, be
   give more weight than risks  from substances for which evidence of carcinogenicity
   comes only from  laboratory experiments.  EPA uses a five-category  classification
   scheme (A through E) to distinguish among pollutants showing varying weights of
   evidence of carcinogenicity.  Group A includes definite human carcinogens, Group B
   includes probable human carcinogens,  etc.,  on down to  Group E, which includes
   compounds for which there is no evidence of carcinogenicity.  (EPA, 1987)
                                       156

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the Kanawha Valley and Southeast Chicago studies, have augmented the GAG list with
additional  unit risk values developed by other offices within  EPA.  For example, in
Kanawha Valley other unit risk values were used for formaldehyde and propylene oxide.

       Although  use of GAG  dose-response values has been  the  standard approach
throughout these studies,  at  least two alternative approaches have been used, or
suggested,  as methods of investigating the potential health effects  of the urban soup:
(1) the use  of structure-activity relationships and (2) comparative risk methods.

       Structure-Activity Methods—The Southeast Chicago study considered but rejected
the use of structure-activity data as a potential means of expanding the pollutants covered
in its risk  assessment (Summerhays, 1987).  In this approach, pollutants for which GAG
has not yet developed dose-response scores are compared to CAG-scored chemicals on the
basis of similar  chemical structures and activities.  Where chemical structures and
properties are sufficiently similar, risks from the matched chemical are presumed to be the
same as for the scored- chemical.  The benefit of this approach is to expand the number of
pollutants  evaluated; its  obvious disadvantage is its uncertainty.  Pollutants 'with closely
similar chemical structures and activities may, in fact, have entirely different potencies.

       Comparative Risk Method—The most fundamentally different risk assessment
approach suggested in the studies reviewed here is to estimate the risk of a suspect
carcinogen (e.g., diesel emissions), for which there are no epidemiological cancer data, by
comparing the potency of the agent in short-term mutagenicity and carcinogenicity
bioassays to those of known human carcinogens. The known human carcinogens are coke
oven, roofing tar, and cigarette smoke tar emissions. The following formula is used:
              Estimated         Known
              Human «*£--- Human Risk
                                            x
                                               Bioassay Potency untsMMMure
                                              Bioassay Potency TeMedCaicinoggn

       The  IACP  has used this biological approach to evaluate organic particulate
emissions from specific source categories—road vehicles, residential heating (oil, wood),
and utility power  plants.  These tests consider the relative potency in both skin tumor
initiation and short-term bioassays. (Lewtas, 1987)
                                        157

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       The advantage of this approach  is that it directly  evaluates complex organic
mixtures (e.g., POM), potentially taking synergistic or antagonistic effects into account.
This avoids potential errors in previous approaches (e.g., in the Six Months Study) of using
a single compound, often B(a)P, as a surrogate for all compounds within a class (typically
POM).   And,  because this approach uses, short-term tests, potency factors can be
developed more quickly and  cheaply  than developing human cancer data or long-term
animal data.  The disadvantage is that the short term tests commonly used (including in
vivo skin tumorigenicity bioassays and  various in vitro  bioassays  such as the  Ames
Salmonella test),  evaluate  mutagenicity  rather than  carcinogenicity.   Although most
known human carcinogens are also mutagens, the reverse is not true—evidence suggests
that many mutagens are not carcinogens.

       Comparative  potency factors were  used  in  the  Motor   Vehicle,  5  City
Controllability, and Baltimore IEMP studies.

       Noncancer- effects—Noncannftr effects  have been  considered in the  IEMP
Baltimore, Santa Clara, and Denver projects, but work in evaluating health endpoints
other than cancer is still  fairly  recent and not well  developed.   Although  some
pollutants—like, carcinogens—pose risks at  any dose, most noncancer  effects  are
presumed to  occur only above a definable  "no effect" level.  Defining this level, and
predicting whether this level is ever exceeded in the ambient environment, is the focus of
noncancer health effects evaluations of the urban soup.

       Although few studies have attempted to assess noncancer endpoints, those which
do must consider the following issues:
       •      Noncancer effects are assumed not to be additive for dissimilar endpoints:
              Where noncancer effects  have been demonstrated, their range of nature and
              severity is wide.  Some effects are temporary  and reversible (headaches,
              blurred vision); others permanent but nonfataJ  (birth defects, neurological
              disorders); and some  fatal or potentially fatal (heart disease).  It is clearly
              inappropriate  to sum  across such different effects.
       •      Threshold  values for noncancer effects may be time-sensitive:  For some
             pollutants, adverse  health effects are only  assumed to  exist if the
             threshold is exceeded for long periods of time (e.g., one year).  For others,
                                       158

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              the significant exceedance period may be minutes or hours.  Very few data
              are available on which to ground reasonable assessments of effects.
       •      Thresholds mav vary among individuals or across population groups: For
              instance, lead has much more severe effects on children than on adults.
              Individual  sensitivities   can  change  over time—sensitization to
              formaldehyde is an example.
       •      Dose-response relationships above threshold doses are a separate factor for
              evaluation; When thresholds are exceeded, dose-response relationships can
              follow different curves—linear, step-function, curves of various shapes.

       Studies that have addressed these issues typically make a number  of simplifying
assumptions. The most common is to  focus on the threshold level alone, disregarding the
shape of the dose-response curve above the threshold.  The statistic of interest is simply
whether or not a substance exceeds its threshold or some fixed fraction of its threshold,
such as 10 percent or 25 percent—the latter approach leaving room for other routes of
exposure,  such as residential indoor air, occupational exposures,  or nomnhalation routes.
As an example, the Santa Clara IEMP estimated that there  were 100,000 people in the
study area at some 'rislc of blood_effects from benzene exposures.

       Health  data on noncarcinogens  are  limited.   Although industrial exposure
standards have been established for many chemicals, EPA has not endorsed their use for
ambient air toxics  studies for a number  of reasons,  one of which is because they are
usually based on the  acute effects of relatively high exposures to healthy, adult  male
populations in work settings.  Effects of involuntary, long-term exposures at subacute
levels have not been  evaluated in equivalent detail, particularly on potentially  more
vulnerable subpopulations.    EPA  is in the  process of developing inhalation  reference
doses, which will facilitate the inclusion of noncarcinogenic effects in future studies.

       The IEMP Santa Clara, Baltimore, and Denver projects have,  however, provided
limited data on noncarcinogenic evaluations of the urban soup.

        Santa Clara—-Because the only available noncarcinogenic potency scores available
at the time  were based on using oral reference doses as a surrogate for inhalation values,
the  Santa  Clara  study stopped short  of actually  doing  a risk assessment for
noncarcinogens.  Instead, the  percentages of the population above the threshold were
                                        159

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 estimated  for the following effects:  immune system, blood, liver, and kidney.  These
 estimates were based solely  on chronic effects because only annual average modeled
 concentrations  were available, and  because of data limitations  for short-term health
 effects.

       It is possible that the exceedances of thresholds that were identified in the Santa
 Clara study could be translated into cases of noncarcinogenic risks in the future once more
 complete coverage of inhalation reference doses is available.

       Baltimore—Noncancer effects were determined in two ways.  First,  the increased
 risks of several noncancer  effects (liver  toxicity,  kidney toxicity,  reproductive,
 neurological, fetal, and blood) were  evaluated for specific compounds in  the modeling
 exercise.  In particular, the effect threshold(s) relevant to each pollutant was divided into
 the model-predicted ambient air concentration of that pollutant.  If the resulting ratio
 exceeded unity, a concern for noncancef effects was  identified.  Second, a hazard index
 (discussed earlier in this chapter) was developed that summed individual pollutant ratios
 by effect category.  This latter;analysis was aimed at examining the impact of exposure to
 complex chemical mixtures in the ambient air: if the hazard index exceeded unity,  the
 concern  for noncancer effects would be the same  as for the exceedance of a threshold
 value. The resulting  analysis suggested, preliminarily, some concern for  blood  effects
 from  benzene  exposures and  elevated  concern for  the  following effects  from xylene
 exposures: liver, kidney, reproductive, neurological, fetal, and blood.

       Denver: The IEMP Denver study collected  12- and  24-hour measured air quality
 data on days that include limited coverage of peak pollution  levels.  The study  anticipated
that the data gathered on peak exposure days may be of future use when additional short-
term health effects  data become available.  The project will evaluate these  effects to  the
extent feasible with currently available data, but is  deliberately gathering more data than
it can now evaluate in expectation of better future information—an interesting precedent
for other studies.
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5.3    Comparison of Approaches to Interpreting Exposure and Risk


       Table 5-1 compares the emphasis of the various studies in relation to| interpreting

exposure or risk information.4

                Table 5-1.   Comparison of Approaches to Interpreting
                           Exposure and  Risk  Information


Study
South Coast MATES
Motor Vehicle Study
Clark County
Southeast Chicago
lEMPs
Philadelphia
Baltimore
Santa Clara'
'Kanawha
Denver
NESHAPS*
35 County Study*
5 City Contrail. Study
Urban Air Toxics
Proqram
TEAM
IACP

Population
Exp or Risk
X
X
X
X

X
X
X
x" T" -
X
X
X
X
Goals vary

X
X

Maximum
Exp or Risk
X


X

X '
X
X
X


X

Goals vary

X

Rank
Exp or Risk,
by Source
Stat. v. Mobile


X

X
X
X
X
Area sources

X
X
Goals vary



Rank
Exp or Risk,
by Pollutant
X
X
X
X

X
X
X
X
X
X
X
X
Goals vary

X

Evaluate
Patterns of
Exp or Risk
X


X

! X
! x

X



X
Goals vary

X

 * Part of Six Months Study
 4  This information is taken, in some cases, only from published final reports and may
    therefore not capture all analyses conducted for each study.
                                        161

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        The purpose of this report is to present methods and approaches, not results.  The
 brief discussions below therefore cover only the most general types of exposure and risk
 conclusions, emphasizing problems of interpretation rather than statements of results.

        Ranking the Relative Importance of Pollutants

        Most studies evaluated the relative importance of pollutants in terms of potential
 population risks.  An important  general issue in ranking the relative significance of
 pollutants has been the accuracy of potencies or the assignment of default potencies to
 certain classes of pollutants.  For instance, in  the absence of data on  the speciation of
 airborne chromium between the hexavalent and the trivalent  forms, some studies have
 assumed, conservatively, that all chromium emissions are in the hexavalent form.  This
 significantly increases the calculated risks associated with chromium.  Actual chromium
 risks are likely far lower, since a substantial -portion of emissions likely occur in the
 trivalent form.' The Santa Clara IEMP assumed that only  10 percent of ambient chromium
 is Cr+6.  The 5 City .Controllability Study avoided this problem  by directly estimating
 both Cr+6 and total chromium- emissions and  modeling them separately.  Preliminary
 results from the 5  City Study suggest  that only  about one-third of total  chromium
 emissions will be as hexavalent chromium.

       The problem of speciation  has  occurred with beryllium, nickel, and products of
 incomplete combustion (PICs),  where some  investigators have assigned all species of a
 compound with the toxicity of a particular compound or chemical form for which data are
 available.

       Ranking the Relative Importance of Sources or Source Categories

       A general conclusion  of a number of the studies  reviewed was that area source-
related pollutants tend to dominate population-weighted average  risks, but that point
sources  often dominate MEI impacts.  Within area sources, mobile source-related
compounds—generally POM  and  VOC—tend to dominate exposures and risks.  Wood
stoves,  fireplaces, chrome platers, comfort  cooling towers, hospital sterilizers, small
                                       162

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degreasing operations,  and gasoline marketing are other area source contributors to
exposures and risks in most urban areas.

       The Santa Clara study investigated risks from some relatively unusual classes of
sources, such as volatilization of organics from drinking water treatment ;plants and
electronic component manufacturing.   Risks from these sources were found to be
relatively minor in comparison with more common sources of toxic air exposure.  This
study  also  did some  risk  analysis  of highways  to evaluate MEI  exposures near
intersections.                                                       ,

       For some point source emission categories, such as those investigated in  detail in
the Kanawha Valley study, the general conclusion has been that fugitive and vent releases
may often dominate air toxic risks, in large part because of their release characteristics.
Since emissions from these sources are at low-elevations, potential exposures and risks
                                                                        !
near the sites may be relatively high.  Stack-emissions of a particular air toxic tend to pose
lower risks than low level emissions of the same compound because greater dispersion is
likely-to occur prior to exposure.

       One  "source" that  has not been addressed in significant analytical detail is
background  contributions from adjoining  regions.  As  noted in Chapter £, modeled
exposures tend to be  consistently lower than monitoring concentrations of the  same
pollutants.  The inability of modeling studies to consider background is one of several
factors contributing to this underestimation.  Transport may be  most important for long-
lived substances such as carbon tetrachloride, but it can theoretically be a significant
factor for other pollutants as  well. "

       Evaluating Exposure and Risk Patterns

       Comparisons of MEI  risks to average individual risks  were done in the IEMP
studies (especially Philadelphia and Kanawha).  On the basis  of the  types of evidence
developed by these'programs, it appears  that the  ratios  of MEI to  average risks (or
concentrations) may be significant in some areas, possibly between 10 and  100.  This
ratio, however, is  strongly  dependent on how close residential areas are to major
                                        163

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industrial facilities or other significant point sources.   In Philadelphia, the  nearest
residences to industrial facilities were typically much farther away than they were in the
Kanawha Valley.  In relatively unindustrialized areas, such as Denver, ratios of MEIs to
average population exposures will typically be considerably lower.

       Using TEAM data, it is possible to look at ranges of average population exposures.
TEAM studies suggest that maximum exposures within a population are likely to be far
higher than  mean exposures, possibly by as much as three or more orders of magnitude.
Figure 5-2  presents examples of the wide range in concentrations  observed based on
personal sampling. (Wallace, 1987)   These plots  show the  differences in indoor  and
outdoor concentrations, based  on matched-pair TEAM samples, for several important
VOCs.  (The matched-pair values actually represent  the  differences between personal
monitoring samples and outdoor samples; however, since the samples represent the 7p.m.-
7a.m. time frame, the personal monitoring samples  largely  reflect indoor concentrations.)
All outdoor sampling was conducted in direct proximity to the indoor sampling locations.
                                       164

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      Figure 5-2. Distributions of Matched Pair Concentrations Differences, Based on Indoor and

               Outdoor TEAM Samples, from 7pm to 7am                  j
                                        165

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     Figure 5-2 (cont'dj
                                        166

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                 [Indoor] > [Outdoor]
             WINTER.
           FALL
 -80
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                                  167

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       Figure 5-2 (cont'd)
                                             168

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       Since TEAM compares outdoor ambient exposures with indoor exposures of all
types (occupational exposures, consumer product exposures, residential exposures), it is
not surprising that some fraction of the indoor sample concentrations exhibits  much
higher levels than the matched outdoor samples.  This effect is pronounced during the
winter for pollutants (e.g., p-dichlorobenzene)  associated with consumer products (e.g.,
mothballs)  or indoor activities.  On the other  hand, the middle range and low end  of
indoor and outdoor exposures documented by TEAM tend to be in much closer agreement.
Again, this is intuitively plausible—ambient air will be a proportionally more important
source of exposure for populations experiencing low  workplace or other indoor air toxics
concentrations, in part because of infiltration of  outdoor air to the indoors.   !

       In investigating patterns of exposure near large industrial complexes, the Kanawha
study apparently was successful in the level o"f  source disaggregation that it selected. As
discussed in Chapter 4, this study represented emissions within large chemical facilities as
a series of grouped -sources. Alternatives would have been, at one extreme, to model all
emissions' as a  single point source at the centroids  of each facility, or, at the other
extreme, to  attempt to model every release point individually.  Analysis suggested that,
results would have been significantly less accurate, especially for MEI exposures, if these
large complex sources had been modeled as  single points, but that not much additional
accuracy, if any, would have been gained if all sources had been modeled individually.

5.4    Insights into the Use of Exposure and  Risk Assessment in Multiple Air Toxics
       Studies

       The following insights  have been gained  from air  toxics exposure and risk
assessments done to date.                                               :
                                                                       i
       •      Perhaps the single most important  insight  that can be offered is that
              exposure and risk assessments  are very complex, integrating many data
              bases, procedures, and assumptions.   As such, the user should be very
              careful in interpreting the results  or comparing the results;  to those of
              another study. Major uncertainties are involved in all steps of the process,
              from monitoring, emission inventorying, modeling, assigning exposure
              levels to populations, and attributing health effects to exposure levels.
                                        169

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 Because of the errors, uncertainties, assumptions, and limitations involved
 in risk assessments, most investigators concede that their results should
 not be interpreted to represent absolute or precise predictions of cancer or
 other health risk.   At best, most studies  have labeled themselves  as
 screening or scoping  studies,  only yielding estimates  of  the relative
 importance of various sources, pollutants, etc., or the relative merits of
 alternative regulatory measures to control air toxics.

 Because cancer unit risk factors are considered to be  conservative for a
 number of reasons,  some studies have concluded that their results  are
 biased conservatively high.  In fact, there are other potential biases in these
 studies, some of which may lead to underpredictions of cancer risk.
 Missing source categories, unaddressed pollutants, underpredicting models,
 and underaccounting of pollutant transformation, all represent potential
 biases on the low side.   Conservative  unit risk factors,  conservative
 exposure assumptions, and the assumption of additivity  among different
 carcinogens all represent potential biases on the high side. The practice of
 considering all chromium, beryllium, and nickel to be  as carcinogenic  as
 particular forms  of these metals  (i.e., Cr+6, beryllium sulfate, and nickel
 carbonyl/subsulfide, respectively) also causes  significant biases on the high
 side, where this is done.

 The use of EPA's Human" Exposure Model frees the study manager from
 having to associate concentration data with population data in an exposure
 assessment,  as.this  is done  internally using stored  1980  U.S. Census
 Bureau data. This is a potential advantage for areas not wanting to develop
 their own exposure assessment modeling capabilities.

 Evolving methods being developed by the TEAM and IACP  studies both
 challenge the "standard" assessment methods  used in most studies to  date,
 and offer promise for improved methods in future studies. For example,
 the TEAM results challenge the standard assumption of  constant exposure
 to ambient outdoor air, and suggest that personal exposures may be much
 more influenced  by indoor, workplace, and product exposures than  by
 ambient air in many cases.  As another example, the IACP challenges the
 practice of evaluating pollutants individually, and is developing techniques
 to access the effects  of  complex  mixtures  through the use of source
 apportionment/bioassay  fractionization  of  ambient  samples and the
 development of comparative potency factors for complex organic mixtures.

 The study manager  should be  aware that the  data,  techniques, and
 assumptions in the field of cancer risk assessment have changed rapidly in
 the past five years and will probably continue to be dynamic as better data
 become available. Changes in key emission  factors and cancer unit risk
 factors, for example, can dramatically change  both  the absolute and
 relative contributions of particular pollutants,  source categories, etc.,  in
 one's study analysis.  The study manager should thus  either maintain a
 dynamic data base,  incorporating changes as they are  perceived, or be
prepared to be second-guessed if he/she "freezes" the study  at a certain
point in time and  certain data elements, assumptions, or techniques become
outdated.
                          170

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                                    Chapter 5
References
Barcikowski, W., 1988.  South  Coast  Air  Quality Management District.  Letter and
attachments to Tom Lahre, U.S. Environmental Protection Agency.  Research Triangle
Park, North Carolina., October 24, 1988.

EPA, 1987.  National Air Toxics Information Clearinghouse report entitled "Qualitative
and Quantitative Carcinogenic Risk Assessment," EPA 450/5-87-003, U.S. Environmental
Protection Agency and STAPPA/ALAPCO, pp. A-ll, A-12.

EPA, 1988.  Letter from W.H. Farland to Potential Risk Assessors announcing availability
of IRIS, and  accompanying  brochure.  EPA Office of  Research and  Development.
Washington, D.C., April 15, 1988.

Ingalls, M.N., 1985.   SWRI, Improved Mobile Source Exposure Estimation,  EPA-460/3-85-
002, March 1985.

Lewtas, J.,  and L.  Cupitt, 1987.  "Overview of the Integrated Air Cancer  Project,"
Proceedings of the 1987 EPA/APCA Symposium on Measurement of Toxics and Related
Air Pollutants, Research Triangle Park, North Carolina., pp.. 555-561.

Rheingrover, Scott, D. Wardi B Mazaache, and J. Thomas.1984.  "Draft Report: Estimates
of 1980 Work and Residential Populations for Philadelphia, Pa.," Contract No. 68-02-
3970,  General  Software  Corporation,  prepared for EPA Office of  Toxic Substances,
Washington, D.C., December 1984.                                      !

SCAQMD, 1987. MATES Working Paper #4. Urban Air Toxics Exposure Model:
Development and Application.  South Coast Air Quality Management District and SAI,
Inc.  October 1987.

Sullivan, D. A., and J. Martini, 1987. Evaluation of Total Exposure Assessment
Methodology (TEAM) Data: Review of Ambient and Personal Data Sets (In Progress).
U.S. Environmental Protection Agency, Office of Air Policy Analysis and Review.

Summerhays,  1987.  "Air Toxics Emission Inventory for  the Southeast Chicago Area."
U.S. Environmental Protection Agency Region V.

Wallace, Lance, 1987.  The Total Exposure Methodology  (TEAM)  Study: Summary and
Analysis (Vol. I), U.S. Environmental Protection  Agency, Office of Research and
Development,  Washington, D.C.
                                       171

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                                     CHAPTERS
                  CONTROL STRATEGY SIMULATION AND EVALUATION
        The following topics are discussed in this chapter:
        •      The use of control strategy simulation and evaluation in urban air toxics
               assessments
        •      Comprehensive vs. site-specific strategy simulation
        •      Control strategy, simulation procedures in the 5 City-Controllability Study
        •      Insights on control strategy simulation and evaluation
 6.1
The Use of Control Strategy Simulation and Evaluation in Urban Air Toxic
Assessments
       A primary objective of urban air toxics assessments has been to define the existing
levels of emissions, concentrations, exposures, and risks in the  study area.  This has
generally involved the assessment of current conditions  in what  is  often termed
"baseline" or "base  year" analyses.  Some studies have stopped at this point. Ambient air
monitoring studies can only assess current conditions because of their inherent inability to
project future ambient air levels.  Emission inventory/dispersion modeling studies, on the
other hand, are  suitable for control  analyses, because emissions  can reasonably  be
projected into the future both as a function of anticipated growth in an area and as a
function of alternative control measures that may be applied.  As indicated previously,
this  represents a significant advantage of using an emission inventory as a basis for
conducting an urban air toxics assessment.
                                        173

-------
       A principal objective in several  of the studies reviewed was the analysis  of the
potential for risk reductions achievable through alternative control measures. ; To do this,
specific control measures or combinations of control measures are superimpbsed on the
base case emission inventory, and the resulting emission projections are then modeled in
the same manner as in the base year analysis to estimate reductions in exposure and risk.
                                                                         !
Thus, the emission (inventory) projection becomes an important tool irt carrying out the
control strategy evaluation.                                        -      i

       •Once the control analysis is completed, the study manager can provide the local
policymakers (i.e., risk managers) with information needed to prioritize various alternative
measures, based on risk reduction potential. Cost-effectiveness can also be evaluated.  A
cost-effectiveness analysis of control  options can generate information highly useful to
local policymakers in setting control priorities.  Decision makers can get a sense of the
effectiveness o.f individual controls and total  control strategies by seeing their costs to
reduce cancer risk, either in  terms of  aggregate (population) incidence or risk  to the
maximum exposed individual,--This  allows them to allocate  their'community's limited
resources into activities that provide the greatest environmental health protection.

       Such analyses can also yield estimates  of "co-control" potential, i.e., the extent to
which measures designed for air toxics  control will also control ozone, PM-10, or other
criteria pollutants. This  is an important consideration as  air toxics control benefits may
help "sell" certain criteria pollutant control measures that may otherwise have marginal
acceptability on their own merits.  Similarly, the concept of co-control  may help the
acceptance of certain air toxics measures.                                  ;

6.2    Comprehensive vs. Site-specific Strategy Analyses

       Two types of control strategy analyses are  employed in the studies reviewed. One
can be best described as a comprehensive or "across-the-board" type of analysis, wherein
different combinations of controls are  applied (hypothetically) to  all facilities  within
many source categories. This type of analysis  was employed in the 5 City Controllability
Study. The second type of analysis is more limited and site specific.  In this latter type of
analysis, the technical feasibility of candidate control measures is first evaluated in detail
                                         174

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for particular facilities within the study area, followed by a simulated application of these
specific measures to determine  specific exposure and risk reductions.  The lEMPs
employed the more focused, site-specific type of analysis (to varying degrees).

       In both types of analyses, emissions projections are made corresponding to the
control scenarios simulated.  Fundamental differences are due to (1) the extent of source
coverage, (2) the consideration of multiple vs.  single  control strategies, and (3) the
incorporation of growth and plant retirement into  the future projections of emissions and
risk.

       Extent of Source Coverage

       The comprehensive coverage  in the 5 City  Study allowed  for the  simulated
application of controls  to all  sources  within any source  category  to which a  given
regulatory option may apply.  For example,  the  5  City  Controllability Study  could
simulate the incorporation of drift eliminator retrofits  on all industrial cooling towers or
scrubbers on all  hospital sterilizers within each study area,  without having to identify
specific candidate facilities for analysis. In contrast, the control measures superimposed
in the lEMPs typically targeted a handful of source categories or specific facilities for
analysis, albeit  categories thought to be more important.

       Multiple vs. Limited Control Options

       In the 5  City Controllability  Study and  Philadelphia IEMP, multiple control
options were analyzed for the sources under consideration. The idea behind this analysis
was to provide an assessment of different combinations of potential measures, from both
a technical and a cost standpoint.  In contrast, the Santa Clara IEMP considered only one
(or two) control options per source type based on a preliminary decision of what appeared
to be  the most cost-effective.

       Inclusion of Source Growth and Retirement

       Of the studies reviewed, only the 5 City Controllability Study projected emissions
and risks to a specific year in the future. This study used 1980 as its base year and 1995
                                        175

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as the yeax to which projections would be made. This time interval was considered long
enough that new source growth and old source retirement had to be factored into the
analysis.  This added considerable complexity to the analysis, as growth and retirement
rates had to be determined for each source category undergoing analysis, and separate
control efficiencies had to be  considered for existing sources  vs. new growth  and
replacement growth sources.

6.3    Control Strategy Simulation Procedures in the 5  Citv Controllability Study.

       The 5 City Controllability Study is both an analysis of existing conditions  in a
"base year" (defined as 1980)  and an analysis of conditions in a projection'year (1995),
reflecting alternative control scenarios.  The heart of this study is the  base year inventory
and a regulatory impact model (RIM) that operates on the base year inventory to simulate
different  combinations of control measures." "Emission projections made iby RIM are
subsequently used  in EPA's Human Exposure Model  (HEM) 'to estimate reductions in
cancer incidence.-  -   -        __                                .

       RIM Operation

       Figure 6-1 shows a schematic of the Regulatory Impact Model (RIM) that is used to
estimate future emissions and costs of emission control.  As shown in Figure 6-1, there are
two functional components of RIM:  an emissions projection module .and a  control cost
module.                                                               •

        Operation of RIM starts  with the baseline emissions inventory which, for  each
source type, contains information relating  to annual emissions and the existing level of
control in place on each source type.  From this inventory, the uncontrolled emission
rates can be determined by .back-calculation for the base year.

        To project changes in the baseline emissions to any future year, three pieces of
information, specific to each source type, must be established:
        1.    The rate at which old equipment will be replaced with new, less polluting
              equipment;
                                        176

-------
H
co

5
U


I
co
co
•W
                t
              CO

ffl

EW GROW
2

CO
EMISSION

H
§ M
W M
rj co
< &
^ S
& »



EXISTING



MISSIONS
w
S-vs
1 BASELINE
4
EMISSIONS
^
INVENTORY
>
UNCONTROLLED
EMISSIONS AND
EXISTING LEVEL
OF CONTROL
                                                        a
                                                        CO
                                                        H-4


                                                        I
Q
s
                                                         co
                                                         CO
                                                                   CO

                                                                    —
                                                                   w
PUT

                                                                            »"*»

                                                                            s
                                                                            
-------
       2.      The rate at which the industry (or emissions source category) is expected to
              experience growth in a geographic region; and
       3.      The constraints that existing and future environmental regulations impose
              on sources to reduce emissions from uncontrolled levels.

       Accordingly, three files are created in  RIM, each of which  operates on the
uncontrolled PM and VOC emission  levels of each source  category in the baseline
inventory.  (Note:  In this analysis, RIM calculates PM and VOC reductions and assumes
that particulate toxics and toxic  VOC are controlled to the same extent.)  The actual
calculations made by RIM cannot be described here in detail. The interested reader should
consult (EPA, 1985).
6.4
Control Strategies Evaluated in 5 Citv Controllability Study
       The following listing shows the kinds of control strategies evaluated in the 5 City
Controllability Study.

       Scenario 1:  Emissions projections'for 1995 under. existing and expected criteria
and NESHAP regulatory programs. Results of this scenario are considered representative
of the 1995 emissions picture assuming the anticipated regulatory agenda is accomplished.
This scenario would reflect toxics co-control benefits  of ozone and PM-10 SIPs.

       Scenario la:  Same as 1, plus the effects of new NESHAP initiatives. This scenario
might result if EPA focuses control of toxic air emissions on Section 112 of the Clean Air
Act, resulting in more NESHAPS.

       Scenario 2:  Same as 1,  with the addition  of the most stringent (reasonable)
controls  on new capacity emissions.  The incremental effect  of this scenario may be
described as requiring very stringent BACT on all new sources of air toxics.

       Scenario 2a:  Same as Scenario  2, with the addition of the most stringent controls
on road vehicle emissions.  This scenario imposes the control of mobile source emissions
required in Los Angeles to all study areas.
                                        178

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       Scenario 2b:  Same  as  2a, with the addition of the most  stringent controls

(reasonable)  on replacement emissions.   The  scenario  extends stringent BACT  to

replacement sources of toxic air emissions.


       Scenario 3: Same as 2b, with most stringent (reasonable) control on all (i.e., new,

replacement, existing, and retrofit) emissions.  This may best represent a requirement for

stringent BACT on all air toxics sources.


6.5    Assumptions Inherent in 5 City Controllability Study Control Analysis


       A number of important assumptions had to be made in the 5 City Controllability

Study in order to complete the analysis.  Assumptions that are made in a broad national

scoping  study,  because  of  insufficient   resources  to  analyze  individual

facilities/pollutants/controls, may not be acceptable to some  policymakers who need a

firmer basis  for taking regulatory action.-  Many of these assumptions, listed below,

should be reviewed carefully in more detailed, locale-specific studies.

       •      The baseline emissions aiid  control  efficiency data are assumed to  be
              accurate in the existing inventory.  Many of the projection algorithms are
              based on these two parameters, so it is essential  that they be as accurate as
              possible.   Because uncontrolled  emissions are  back-calculated  from
              existing controlled emissions  using the control device efficiency levels,
              this type of analysis  is  very sensitive to errors  in the control device
              efficiency, especially as it approaches 100 percent.

       •      Control levels  of toxic organics and toxic PM are assumed proportional to
              control levels  of VOC and PM, respectively.  This assumption should  be
              challenged in a more detailed study, at least for the more  important
              sources and pollutants.  Some metals, for example, may be present "in a
              gaseous state in a high temperature exhaust and may not be controlled at
              the same level as PM from some control hardware.

       •      Both new and replacement growth, as well as capacity retirement,  are
              assumed to occur at the same location as existing sources. A State or local
              agency may have more detailed, plant-specific data that could  obviate the
              need to make  projections  in this manner.  The locations of large plants
              expected to  open or close  in a few years is probably well known to local
              officials,  so growth and retirement could probably be handled without
              having  to make general projections based on published trends  data.

       •      Control measures are  assumed to  be applicable to the targeted sources,
              without regard for technical feasibility on a case-by-case basis.  Although
              the measures selected in this study  are generically applicable, there may  be
                                        179

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              specific technical reasons why they would not be appropriate in certain
              facilities.  Again, a State or local agency would be in a better position to
              make case-by-case projections.

6.6  -  Control Strategy Evaluation in the lEMPs

       The approach for simulating and evaluating controls in the lEMPs was much more
focused  on specific  sources,  pollutants, arid control measures  than in  the 5  City
Controllability Study discussed  above.   The Philadelphia and Baltimore  lEMPs were
somewhat more site specific than the Santa Clara IEMP.  All are  discussed separately
below.

       Philadelphia IEMP Control Analysis

       In the Philadelphia study, controls were evaluated for a "cluster" of point sources
centered in a particular section of the city.  Questionnaires and phone calls to industry
were used to help tailor the control options to specific space, power, and cost constraints
at each plant  site.' Even so, the analysis provided only a rough estimate  of costs and
effectiveness because a more comprehensive engineering analysis was beyond the scope of
the study.   A total of eight point sources  were  evaluated.  Control options included
charcoal adsorption,  solvent substitution, steam  stripping, and scrubbers.   Costs and
emission rates to all media were estimated  to address  risk reductions on a multimedia
basis.

       The Philadelphia  IEMP study also evaluated controls for  several  area sources
including dry cleaning, gasoline marketing, degreasing,  and miscellaneous solvent usage.
Control options included vapor recovery, solvent  substitution, and  Staige I  and Stage II
controls  for gasoline marketing. The feasibility of each option was  evaluated, as well as
the cost  and pollutant removal effectiveness.

       Table  6-1 summarizes the control options evaluated in the Philadelphia IEMP.
Multiple options were analyzed for each facility  or source category and then different
combinations of options  were evaluated for cost-effectiveness.  Table 6-2  shows the
projected cost-effectiveness of  various control simulations in terms of reducing annual,
aggregate cancer incidence.
                                        180

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Table 6-1 Control Ootions Evaluated  in  the  Philadelphia IE-IP
Source
0 eg r easing
























Refinery 8


























Control Option
Option 1







Option 2








Option 3





- -

'Option. 2.


Option 3


Option 4




Option 21






Option 23








Control Descriptions
Cold Cleaners: cover during idle time
(90 percent control efficiency); drain racks
with 30-second drains (50 percent control
efficiency)
Open Tap Vapor Oegreasers: cover during idle
time (90 percent control efficiency); increase
freeboard ratio during operation (27 percent
control efficiency)
Cold Cleaners: cover during idle time
(90 percent control efficiency); drain racks
with 30-second drain (50 percent control
efficiency)
Open Top Vapor Oegreasers: cover during idle
time (90 percent control efficiency); use
refrigerated freeboard device (60 percent
reduction in vapor losses, 29 percent reduction
of carry -out losses)
Cold Cleaners: cover during idle time (90 oer-
cent control efficiency); drain racks with
30-aecond drain (50 percent control
efficiency)
_0,pen Top Vapor Oegreasers: cover during idle
time (90 percent control efficiency); carbon
•adsorber (reduce 70 percent vapor losses,
30 percent carry-out losses)
Convert nonconfact . floating roofs to contact
floating roofs on benzene tanks only; leak
detection and repair methods (air)
Secondary seals on benzene ' tanks only; install
rupture disks for controlled degassing
reservoirs for compressors (air)
Convert noncontact floating roofs to contact
floating roofs for benzene tanks only; install
rupture disks for safety/relief- valves and seals
with controlled degassing reservoirs for com-
pressors (air)
Secondary seals on benzene tanks only; install
rupture disks for controlled degassing reser-
voirs for compressors; convert noncontact
floating roofs to contact floating roofs 'for
benzene tanks only; install dual mechanical
seals with a barrier fluid system and degassing
reservoir vents on light liquid pumps (air)
Secondary seals on benzene tanks only; install
rupture disks for controlled degassing reser-
voirs for compressors; install a thermal oxida-
tion system on. all benzene and gasoline tanks;
install dual mechanical seals with a barrier
fluid system and degassing reservoir vents on
light liquid pumps; require more frequent
inspection on the valves and addition of sealed
bellow valves (air)
                              181

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Table 6-1  Control  Options Evaluated in the Philadelphia, IEKP  (cont'd)
Source
Other Industrial

Industrial Dry Cleaner
Queen Lane and Selmont
Drinking Water Treatment
Plants

Baxter Drinking Water
Treatment Plant
Dry Cleaning


Chemical Manufacturer
.
•



.
Refinery A
•
Gas Marketing
•

Control Option
Option 1
Option 2
Option 1
Option 1
Option 2'
Option 3
Option 2
Option 3
Option 1
Option' 2
Option 3
Option 1
Option 2
Option 3
Option 4
'Option 5
Option 10
Option 11
Option 1
Option 2
Option 1
Option 2
Option 5
Control Descriptions
Enlarged condensation zone, waste recovery
facility, manual enclosure (air)
Same as Option 1, except automatic enclosure
Inspection and maintenance
Granular activated carbon (GAC) '•ugh-
sffectiveness
Granular activated carbon (GWC! medium-
effectiveness
Granular activated cartoon (GAC) lew-
effectiveness
GAC high-effectiveness
GAC medium-effectiveness
Inspection and maintenance (,air)
_ Same as Option 1, plus carbon adsorotisn For
commercial dry cleaners (air)
Same as Option 2, plus carbon adsorption for
coin-op and commercial dry cleaners (air)
Carbon adsorbers on each of three vents (PCS
emissions) :
Carbon adsorbers on each of ;bhree vents. (DC? •
emissions)
Option 2 plus Option 1 (air);
GAC of DCS and OCP waste streams (water)
Steam stripping of DCS and Ofc? waste streams
(water)
Option 2 plus Option 4
Option 2 plus Option 5
Secondary seals on internal 'floating roofs of
gasoline tanks (air)
Install secondary seals on internal floating
roof gasoline tanks only; leak detection and
repair methods (air)
Stage H, no enforcement inspection (air)
Stage II, plus enforcement inspections (air)
Option 2 plus onboard controls on all vehicles
                                   182

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 Table 6-2.  Estimated Cost Effectiveness  of Philadelphia IEMP Control Options
                            WAS II (CSUUIS »rO«D FW KJUICT

                     SMDUU or CWTWL OFTios rw «DOC»C MMUL cwen  WCOTNCE

                                                Atr

                                           (198* analysis)
C«s«« ftaducsd
  par Tsar

   0.0

   0.03
   0.09
   0.1S
              Jsreant Xoductlon
                 in Canear
               Inciasncs frasi
               Currant Control

                    0.0

                    1.4
                  •3f.7
                   *».»
  ' 0.25
 Tat»l Out
(n.OOO/vsar)
                                   .122
                                    323
                                    632
                                    900
                                  2,080
Avcrao* Coat- par
Case fttducsd frasj
 Currant Control
  (K.000/e»»«l
                                                  -3,7»
                                                   3,435
                                                   -4,074'
                                                   4,fZl
         l Ca«t
P«r :nc
  C*««
 (SI. OOP/MM)
                                                                   -3,7W
                                                                    7,417
                                                                    5,150
                                                                    •.TOO   '
                                                                   19,6*7
   0.30
                  77.1
                                  «,724
                                                  22,335
                                                    •ollution Controls !aelaa«nt
                                                Soure*
                                                                                Indwtriai dry ei»«i«
                                                                                Otn«r indu»tri«l
                                                                                <«fln«ry  9
                                                                      Control
                                                                                Industrial drv clt«n«r
                                                                                Other
                                                        3
                                                Dry elovning

                                                tndustri«l dry  el««n«r
                                                Otn*r  industrial
                                                Rsflntry 8
                                                        •anufaeturtr.

                                                tndustrisl dry  elcsncr
                                                Othsr  industrial
                                                Rafiniry 8
                                                0«(jr«sslng
                                                Dry elssning
                                                Cnsiiieal
                                                Industrial dry eltansr
                                                Otfisr industrial
                                                Osijrsssinq
                                                Dry elsaninq
                                                Rsrmsty 3
                                                »arin«ry A
                                                Cttcaueai •anu^aetursr

                                                Industrial dry eitsnsr
                                                Othar industrial
                                                Oaqrassinq
                                                »srtn«ry 3
                                                Dry elaanxng
                                                Caaelirw ssrkstinq
                                                              2
                                                              5

                                                              1
                                                              1
                                                              2
                                                              J
                                                              2
                                                              5

                                                              1
                                                              1
                                                              3
                                                             2
                                                            23
                                                             2
                                                            10

                                                             1
                                                             2
                                                             J
1        The unit risk factors used in this analysis are based on conservative assumptions that generally
produce upper bound estimates.  Because of limitations in data and methods in several areas of the
analyses, such as exposure calculations and pollutant selection, risk estimates were calculated as aids to
policy development, not as predictions of actual cancer risks in Philadelphia. Actual risks may be
significantly lower; in fact, they could be zero. The proper function of the estimates is to help local officials
select and evaluate issues, set priorities, and develop control  strategies for the topics examined.
2.       See Table 6-1 for definition of control options.

CAUTION:  All emission and risk reductions and cost estimates in this table are shown only for example
purposes, and are not intended to apply in other situations and locales.
                                                   183

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       Baltimore IEMP Control Analysis                                  \

       The Baltimore EEMP performed a control analysis for four point sources.  Sources
and pollutants were selected for the control options analysis if they contributed 1 percent
or more of the cancer incidence in the study area, or more than five in a million average
individual risk within a particular grid cell.  Each facility emitted metals (most notably
hexavalent chromium) and POMs, with one plant also being a major source! of benzene
emissions. Emission reductions and associated costs were determined for baghouses and
for air toxics co-control benefits of VOC and particulate controls.  Air pollution controls
were evaluated on a risk reduction basis.

       The study identified potentially available point source controls by first consulting
with Maryland Air Management Administration (AMA) staff responsible for monitoring
permit compliance of the  respective facilities.   Once potential control options were
identified, costs and removal efficiencies were calculated  based on data in the literature,
input from AMA staff, and estimates provided by vendors.

       Performing the site-specific assessment needed to accurately  quantify control
costs  and removal efficiencies for complex industrial facilities was beyond the scope of
this study, since neither the time or resources were available to identify and accurately
evaluate individual control options.  The intent of the study was to estimate relative costs
and effectiveness rather than to perform the much more  intensive engineering studies.
Results must be interpreted  accordingly, keeping  in mind that the actjial cost, of
implementing these controls, as well as the actual reductions that would be achieved,
might be significantly different from the generated estimates.

       The area source controls analysis for the Baltimore IEMP was more extensive than
for the Philadelphia EEMP since it also included a heating source and diesel road vehicles.
IEMP generated the cost estimates  and pollutant removal efficiencies for  heating, dry
cleaning,  degreasing, miscellaneous  industrial  solvent usage,  and diesel vehicles, while
Maryland AMA provided cost estimates and efficiencies for gasoline marketing.
                                        184

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         Santa Clara IEMP Control Analysis

         The Santa Clara IEMP was undertaken with the assumption that local efforts can
 complement the work of State and Federal agencies.  Therefore, this controllability study
 provided added  emphasis on unique, localized air toxics sources.  At the same time,
 sources of air toxics currently being considered for regulation at the State and Federal
 level were included in the study to add perspective and provide a point of reference from
 which to measure the cost-effectiveness of other control strategies.

        There are approximately 24 principal source categories (e.g., cement manufacturing,
 residential wood  combustion) of air  toxics in the Santa Clara Valley.  The following
 criteria were used to select source types for the controllability study.
         •      Estimated  health risk.   Do  the pollutants from this source category
               represent a significant health risk?
         •      "Feasibility of control. Is it technically feasible to control emissions in this
               source category?
  •-•-   .   *      Data  availability;-  Is there  easily accessible  information on costs and
               efficiencies of control measures?

        Often, regulatory development  programs examine  several control options for one
 source type.  The most stringent control option that is  economically achievable is chosen
 as the preferred option. Rather than compare several options for a given source type, the
 Santa Clara  IEMP chose for evaluation what appeared to be  the most cost-effective
 control  strategy.  In general, only one or two control methodologies were thus considered
 per source type.  In this way, numerous source types were considered in the analysis and
 compared with each other.   Where  a risk  reduction strategy appears desirable for a
 particular source  type, more in-depth control studies should be performed in which
• various  control options might be considered.

        Where possible, the capital and annualized costs of the control  strategies were
 evaluated.  Annualized costs consist of annual costs and capital recovery (depreciation
 and interest on capital). The estimated reductions in  excess cancer incidence were also
 estimated for each control strategy.  Generally, the reductions in the incidence rate were
 calculated from average risk values (i.e., the number of  excess cancer cases divided by the
                                         185

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overall study population).  For a few select source categories, the reduction 'in risk for a
hypothetical maximum exposed individual (MEI) was evaluated.

       Cost-effectiveness was measured in terms of the dollars per reduced cancer case.
More specifically, cost-effectiveness  was calculated by  dividing the. net present worth
(NPW) of the control strategy by the estimated number of cancer cases reduced.  The NPW
provides an indication of how much would theoretically have to be set aside in the
present to provide for the future services of the control option.  A time period of 30 years
was used; this is the maximum estimated lifetime of the control strategies considered.

       Table 6-3  summarizes the results of the cost-effectiveness  analysis  for cancer
incidence reduction in the Santa Clara IEMP.  Table 6-3 also shows the specific control
methods simulated on the source categories and pollutants selected for analysis.

       Analysis of Areawide vs. Most Exposed Individual fMEIl Risk Reduction
                                                                        |
       The primary controllability  emphasis  in the lEMPs (and 5  City Controllability
Study) is the reduction in areawide cancer incidence.  Assessing risks to the most exposed
individual (MEI) is another way to evaluate the effectiveness of control alternatives.  In
the management of risks, policymakers are often faced with value jiidgments between
control options that reduce risks to the overall population and  alternatives that reduce
risk to the most exposed individuals. Analysis that considers both measures of risks adds
clarity to the implications of policymakers' decisions and allows  fair consideration of the
risks  and control costs among different exposed groups of people.

        Cost-effective controls to reduce risk to the  most exposed individual  are not
necessarily the same as and may  be quite different from  the cost-effective controls
identified to reduce risk to the general population.  Though consideration of these two
different measures of risk in a cost-effectiveness framework may result in widely differing
control solutions, the contrasting quantitative information places  into sharper focus some
of the values policymakers must weigh before making public health protection decisions.
                                         186

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Table 6-3.  Estimated Cost Effectiveness of Santa Clara IEMP Control Options
Source Type
Dry Cleaning0








Solvent Degreasing

Residential Wood
Gcnfeusticn

Hospital
Sterilizatica
Mnufaeturing

Habile Sources
.





	
CtllLlUl Ifetlul
Converting perc
transfer to perc
dry-to-dry
Converting pare
transfer to
CPC 113 dry-to-dry
Converting pare
dry-to-dry to
CPC 113 dry-to-dry
Refrigerated
freeboard chillers

Riel efficient vccd
stores
Burning curtailttnts
Hydrolixing uot
scrubbers
Thensal incinerators
Ctt*lytic.incinarators
• Qq-gen sensor durability8

Hsdifier certified new.
vehicle registration1
Mass transit usuovcnnts
Catalyst retrofit for
heevy-duqr gaaoline
vehicles
I/H for heavy-duty
gasoline vehicle*
Pollutant
perchloroethylene


perchlcroethylena


parchlorouthylane


perchloroethylene
trichloroethylene
nethylene chloride
(e)
(e)
ethylene oxide
cellosolve
cellocolve
berscne and organic
paniculate
benzene and organic
particulate
batueue and organic
particulate
benzene and orgnaic
particulate

benzene and organic
particulate
Cancer
Incidence
Reduction
(incidence
in 30 years)
0.0019


0.00028


0.00044


0.069
0.021
0.12
0.9
0.74
0.33
Cfl
«) '
0.09

0.034
0.21
2.3


0.31
Hat Present
Value for
30 Tears of
service ($)
56,000


130.000


37,000


2.400,000
290,000
470,000
420.000
H/A
1.200,030


Oh •

°h
1,S» x 106
170,000,000


6, 600, COO
Oare
ZffactivcnesE
(nillian $/
reduced ,
incidence)
29


50


200


36
14
4.1
0.47
H/A
3.1
Cfl
(f)
•0

0
3.800
76


22

'
  Adopted by Ara in V85.

                                      187

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       Unfortunately,  population-averaged cost-effectiveness  cannot be  used in
determining MEI cost-effectiveness. If the size of the general population is large and the
risk to them low, the average cost-effectiveness of control will be poor regardless of the
risk  to the maximum  exposed  individual.  In addition, a  single, meaningful cost-
effectiveness value for the MEI is impossible to calculate without an estimate of the size
of the  affected population.  Thus, several lEMPs computed the total annualized cost of
control and cost per pound of pollutant for removal along with the risk reduction to the
MEI.   Together, these variables  were felt to offer qualitative insight into .the relative
merits  of the various controls.
                                                                        i
       Table 6-4 shows the results of the MEI analysis in the Santa Clara IEMP.

6.6    Insights on Control  Strategy Simulation and Evaluation
       Perhaps most important, the  study manager  should be  well aware of the
limitations of the controllability-analysis and should not use any conclusions to support
actions unwarranted by the accuracy of the underlying data base or the validity of the
assumptions made.  All of the studies reviewed were described as screening or scoping
studies, and were not intended to predict absolute reductions in risk associated  with any
particular control strategy.  Rather, they were intended to be used in a relative sense only
to begin to develop priorities.                                             !
                                                                        !
       Case-by-case emission and control projections at specific facilities are generally
more accurate  than more general types of projections based on surrogate measures  of
growth and plant  retirement.   The latter  type of  analysis,  used in jthe 5 City
Controllability  Study, gives only a very broad sense of control potential associated with
certain groups  of control measures,  and may not be appropriately  applied to determine
control potential and feasibility in a particular locale.                      i

       The study manager should plan at the outset of the controllability study just what
measures of risk reduction need to be quantified.  Generally, the studies reviewed focused
on areawide cancer incidence reductions; moreover, it was the easiest to develop cost-
                                        188

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effectiveness data for these reductions.  While MEI reductions can also readily be
projected, they are more difficult to evaluate from a cost-effectiveness standpoint.

       None of the studies documented how,  if at all, the resulting  controllability
                                                                        i
conclusions would be used in the risk management process,  or  broached the topic of
defining acceptable public risk.
                                        190

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                                                                                                    1
                                     CHAPTER 7
                          COMPUTERIZED DATA HANDLING
7.1
The following topics are presented in this chapter:
•      Data handling considerations in urban air toxics studies
•      -Data handling aspects of emission inventory/dispersion modeling studies
•      Data handling aspects of ambient air monitoring studies
•      Insights on computerized data handling

Data Handling Considerations in Urban Air Toxics Studies
       The development of exposure and risk estimates in multi-source, multi-pollutant
assessments involves  extensive data handling.  Because exposure and risk estimates are
made, both individually and collectively, for many pollutants and receptors across broad
geographical areas, computerized data handling is a virtual necessity in most  cases.
Hence, the study manager should consider the data handling aspects of carrying out an
urban air toxics study at the outset of his/her study as part of the overall study protocol.
The development of specialized data handling software is expensive and time consuming
and can potentially be avoided if existing software can be utilized.  Many of the studies
reviewed in this report adapted data handling capabilities already in existence rather than
developing new capabilities.
                                        191

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       Study Type
                                                                       I
       As discussed earlier, most urban air toxics studies have involved either emission
inventory/dispersion modeling studies or ambient air monitoring studies, albeit with some
hybridization common. Data handling is more complex for the former study type  because
one has to work with emission inventory data and dispersion models in order to predict
ambient air concentrations of air toxics, whereas  these concentrations are  measured
directly in the latter type of study. In both study types, ambient air concentrations must
be applied to population data, usually at some level of spatial disaggregation, to  estimate
population-averaged exposures.  These exposures are, in turn, adjusted by potency values
to estimate cancer risks or other health effects.

       Computer Accessibility

       An  obvious  consideration is  whether  the study has access  to a mainframe
computer.   Most State  and local agencies can access EPA's mainframe computers at
Research Triangle-Park, North-Garolina through remote terminals. "In some;cases, these
agencies also have access to their own States'  computer facilities.  EPA maintains an IBM
3090 computer and several DEC VAX computers. EPA's Univac computer, used in several
of the studies reviewed, is no longer on line as of October 1988.  Many of the dispersion,
exposure, and risk assessment models discussed earlier in this report reside on EPA's
mainframe computers.                                                   \

       Most agencies now have personal computers, and many agency personal untrained
in mainframe languages  prefer to operate in the  PC environment.  Some  of the  operations
involved in  urban air toxics assessments, such as dispersion modeling, cannot realistically
be carried out using today's PCs, but  some operations are possible, including inventory
compilation, exposure and risk estimation, and control strategy evaluation. The extent to
which these latter  operations can be accomplished on PCs will depend on the  number of
pollutants,  sources of pollution, and the  spatial resolution reflected in the analysis.
Regardless  of the extent of mainframe involvement in "number crunching,"; PCs can be
useful for analyzing summary data sets created by the mainframe and for tailoring special
                                        192

-------
reports and graphics.  Hence, PC data handling  should be considered to complement
mainframe analyses in many facets of a study.

       PCs are certainly capable of storing air quality monitoring data from one or several
sites.  Data summaries  can be produced using spreadsheets or data base management
programs commonly available for  most PCs.

7.2    Data Handling Aspects of Emission Inventory/Dispersion Modeling Studies

       PIPOUIC

       Many of the studies reviewed herein (the Philadelphia, Baltimore, Santa Clara, and
Kanawha Valley IEMPS; the Southeast Chicago Study; the 35 County Study within the Six
Months Study) used a computerized  data handling system called PIPQUIC to store their
emissions data and develop estimates of exposure  and risk. PIPQUIC was developed for
the lEMPs and resides on EPA's 1MB 3090 at Research Triangle  Park, North Carolina.  It
is accessible to "account holders .through any dialup terminal, including desktop PCs.
Various terminal  emulator software programs may be used  for accessing  PIPQUIC.
(PIPQUIC,  1989)

       PIPQUIC offers the user a "tool kit" to produce bar charts, tables, printouts, pie
charts, area maps, 3-D  maps, contour maps, and spreadsheets.  PIPQUIC creates charts,
maps, tables, and the like, using air toxics emissions data (or, as  a default, data generated
by PIPQUIC by  applying species factors to  NEDS data).  PIPQUIC executes two EPA
models—ISCLT and CDM—using gridded emissions data and appropriate meteorological
data for the area.  Optionally, the user can run his/her own models of choice in lieu of ISC
and CDM.  In either case, the model-predicted ambient concentrations are then  coupled
with population and cancer potency data to estimate individual cancer risks and aggregate
incidence.  PIPQUIC stores emissions, modeled concentrations, risk, and incidence data
for efficient retrieval and analysis to create tables, charts,  and maps.

       A discussion of specific PIPQUIC  tools is useful  as these tools were designed to
assist study area managers in answering  key questions regarding urban air toxics  risks.
                                       193

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Illustrations of example outputs from PIPQUIC are included to show the: reader the
various ways the data are handled to provide useful summary graphics.

       PIPQUIC's Tool 450 creates a broad range of study maps, and allows, the user to
overlay point sources, modeling grids, and area source emissions data on these maps.
Figures 7-1 and 7-2 show examples of several study area maps produced by PIPQUIC.
                                                                       I
       Tool 440 allows the user to rank order his/her source and emissions data in many
ways via bar charts, cross-tabulations, pie charts and printouts.  The user can select one or
more pollutants, facilities, counties, industries, source types or estimation  methods,  and
can  also define variables.  Whole values should appear as separate pages, rows, or
columns.  Figure 7-3 and Table 7-1, respectively, show example bar charts and cross-
tabulations created by Tool 440.                                          ;
                                        194

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       Figure 7-1.  Example of PIPQUIC Output for Southeast Chicago Study Area
            SOUTHEAST  CHICAGO  STUDY  AREA
TOOL  450A:  MAP OF AREA-SOURCE EMISSION SQUARES
                              195

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Table 7-1.  Example Cross-Tabulation Created by PIPQUIC Tool 440
                     EXAMPLE STUDY AREA
                     TOOL 44ft HUSSIONS IN HETHIC TONS PEfl YEAH
                     POLLUTANT: BENZENE

                     TABLE OF  INDUSTRY BY COUNTY

                     INDUSTRY          COUNTY

                     REOJEHCY       ADAMS CO JONES CO  LAKE CO  SAND CO    TOTAL

                   .  3318. STEEL MILLS  2401.68  642.493       0       0  3044.15

                     3989 AREA SOURCE  53.4081  740.087  35.5435  23.5138  852.553

                     9998 OTHffl POINT  4.25125  56.6334   3.695       0  84.5796

                      4952 ROWS             0  .725755       0       0  .785755

                      TOTAL            2459.32 1499.94  99.2985  23.5138  9962.01
                                                   198

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       PIPQUIC's  Tool  453  allows  the user  to  pinpoint  the  sites  of maximum
concentrations, individual risk, and aggregate cancer incidence and to assess the impact of
each pollutant and source at any receptor within the study area. Output options include
bar charts, 3-D maps, contour maps,  charts ranking receptors, charts ranking sources
culpable at the point of maximum individual exposure (or at any other point), and tables
of concentration and risk.  Tool 453 is perhaps PIPQUIC's most powerful analysis tool.
Figures 7-4 through 7-6 and Tables 7-2 and 7-3 show example  graphics created by Tool
453, which characterize emissions data and associated exposure and risks  in effective
ways to help understand the nature and magnitude of the air toxics problem in a given
area.

       PIPQUIC enables the user  to download maps, graphs,  and the like, to his/her
desktop computer and then to re-create and edit them without having to re-enter PIPQUIC.
Using various inexpensive PC  software packages, the user can edit titles, change colors,
assemble video .presentations,  route to color plotters, or  convert downloaded files  into
spreadsheet or graphics files for detailed editing.  As a specific  example, PIPQUIC's Tool
123 supports downloading of source, emissions, and aggregate incidence data for creating
LOTUS 1-2-3® spreadsheets to  evaluate control scenario  effectiveness.

       Other Data Handling Systems

       Several other emission inventory/dispersion modeling studies developed their  own
data handling capabilities.  The 5 City Controllability Study developed input/output
software around EPA's HEM/SHEAR model, whereas the South  Coast Study did likewise
around its modified version of HEM called SCREAM. The 5 City Study also produced a
series  of files containing regulatory projection information to  run a Regulatory Impact
Model (RIM).  RIM allows the user to project future emissions  and cancer incidence by
simulating various hypothetical control strategies.  HEM/SHEAR and SCREAM  are
mainframe models, whereas RIM is a PC-based model. As  of this writing, HEM/SHEAR is
being updated by EPA and converted to a VAX computer at Research Triangle Park, North
Carolina.  HEM/SHEAR will be accessible to users having an account on the EPA VAX
                                       199

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Figure 7-4 Example PIPQUIC Output - Incidence
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                                                                                          1
   Table 7-2. Example Cross-Tabulation Created by PIPQUIC Tool 453
XYZ STUDY AREA
TOOL 453: ANALYSIS  OF  MODELING RESULTS - FOR POLICY-MAKING ONLY
STANDARD (1-KM) GRID,  ESTIMATED 70-YEAR INCIDENCE
POLLUTANTS: ALL C.A.G.  POLLUTANTS
FACILITIES: ALL
RECEPTORS:  4616.00 454.00
SOURCES:    ALL

TABLE OF COMPOUND BY SOURCE

COMPOUND         SOURCE
FREQUENCY
  POINT    ROAD
SOURCES VEHICLES
COKE OVEN
ARSENIC
BENZENE
CADMIUM
1,3-BUTADIENE
GAS VAPORS
CHROMIUM HEXAVAL
METHYLENE CHLORIDE
FORMALDEHYDE
ETHYLENE OXIDE
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TOTAL
 5.9751  0.4982 0.11070 0.04100 0.0353 0.02210 0.0182 6.7007
                                      203

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Table 7-3 Example P1PQUIC  Output
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                                                  204

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 computer, and a user's  guide,  reflecting  the  updates made to the model, will  be
 forthcoming.  A user'sguide for the current version of HEM/SHEAR model is presently
 available.  (EPA,  1986)  The  SCREAM model  is applicable  only to the Los Angeles
 geographical area.

       Cost Saving Techniques

       Normalized Modeling—As mentioned in'Chapter 4, the practice of normalized
 modeling will minimize the number of dispersion model runs  necessary in an urban air
 assessment.   This practice will commensurately  save  on  data handling  expense.
 Normalized modeling was done in most of the emission inventory/dispersion  modeling
 studies reviewed in this report.

       For example, instead of running HEM/SHEAR separately for each pollutant and
 each emission.projection, the 5 City .Controllability Study used a single run,  assuming
 100 tons  per year of pollutant was emitted from each point source. For each point source,
 the output of  SHEAR  was-saved in an intermediate file containing the cumulative
 population exposure (microgram-persons/cubic meter-year) for each modeled point. These
 cumulative values were  thus  used to estimate population  exposures to individual
 pollutants by multiplying them by the ratio of actual-to-modeled emissions.

       Modeling Small  Point Sources as Area Sources—Because the cost and  execution
 time of modeling point sources is much greater than for modeling area sources, the 5 City
 Controllability Study opted to treat small point sources as area sources if they emitted
 below  a particular cutoff level.  This minimum cutoff level varied by pollutant and was
 determined to some extent to be a function of the number of small sources that emitted a
 particular pollutant within, the study area, as well as by the toxicity of that pollutant.

 7-3    Data Handling Aspects of Ambient Air Monitoring Studies

       Limited information is available on data handling specifics in the ambient air
monitoring studies reviewed herein.  Two of the studies for which some information  is
available—the Staten Island/Northern New Jersey Study and the Urban Air Toxics
Monitoring Program—are only in the data collection phases  at present and have not yet
                                       205

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completed any exposure or risk assessment.  Both of these studies are using Lotus 1-2-3
spreadsheets to store the  raw data and to develop summary reports.  Table 7-4 is an
example of a quarterly report generated in the Staten Island/Northern New Jersey Study.

7.4    Insights on Computerized Data Handling

       Data handling in urban  air assessment studies involving dispersioh  modeling,
exposure/risk assessment, and control scenario  evaluation can become quite complex and -
should be  carefully  considered when developing the  study protocol. The i agency may
want to consider contractual assistance in this area.

       The study area manager  should consider using available mainframe; software for
conducting his/her urban study.  EPA maintains various dispersion models  as well as
exposure/risk models that can perform many of ±he core data handling functions necessary
in an urban air toxics assessment.       	                            j   •     .
                                                                      !
       Many data handling functions can be performed efficiently and more! readily on a
PC than on a mainframe computer.  Specifically, the  preparation of  emissions data and
other data needed  to run dispersion models can be  done on  a PC in cases where
extraordinarily large  data bases are not  involved.    Additionally, the outputs  can
effectively be downloaded to desktop PCs for editing and analysis, and for the  creation of
summaries and graphics.

       Data handling complexity and costs  can be reduced by normalized modeling and
by modeling small point sources as  area sources.  Both  techniques result in fewer point
source modeling runs.  The reader is cautioned, however, that there are drawbacks to both
techniques. Normalized modeling assumes a linear relationship between emission changes
and model-predicted concentrations.  This assumption may be invalidated if  the release
specifications  change' as  emissions change (e.g.,  a control device may alter a plant's
stack/exhaust parameters  as well as its  emissions) and,  hence, may need to be carefully
examined in detailed assessments.  Also, the treatment of small point sources as area
sources may change the exposures resulting from those sources,  as their emissions will
subsequently be "smeared out" over entire grid cells and  could be assumed to be emitted
                                        206

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                                                                                                                                I
       Table 7-4 Example of Ambient Air Data Set Summary
Reduced Data from All Sampling Systems

Agency: College of Staten Island

pollutant:Chloroform

Quarter Beginning (Month, Year):       Jan88

H3L:      0.04 ppb low flow & 0.02 ppb high flow
                     (Quarterly Report)
                             CAS #:   67-66-3
                                      Units:  ppb
SAROAD t      Site
 Sampling  Analytical  * of    Arith.    Std.       1st
   Code       Code   Samples   Mean     Dev.       Max
                                                                                      2nd
                                                                                      Max
                                          Min
                                    * >
                                    HDL
                                                           FC
       8 Bayley Seton
       3 Eltingville
       6 Dongan Hills
Tenax      GC/HS
Tenax      GC/MS
Tenax      GC/MS
82    0.039
57    0.043
55    0.046
0.037
0.023
0.037
0.321
0.121
0.269
0.125
0.112
0.111
0.008       81
0.011       57
0.012       55
                                                        207

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at ground level.  Such a distribution of emissions from small sources could potentially
overemphasize the risks from these small point sources, especially if they are treated as
area sources that are subcounty-apportioned by population.
                                        208

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                                    Chapter 7
References
EPA, 1986. User's Manual For the Human Exposure Model (HEM).  Office of Air Quality
Planning and Standards, Research Triangle Park, North Carolina.

EPA, 1988. Staten Island New Jersey Urban Air Toxics Assessment Project, "Air Quality
Data Report. Volume I:  Quarterly Summary Reports. July 1987-March 1988."  U.S.
Environmental Protection Agency Region II.

PIPQUIC, 1989. Draft PIPQUIC User's Guide being prepared for T. Lahre of
U.S. Environmental Protection Agency, Research Triangle Park, North Carolina., by
American Management Systems.
                                      209

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                                    CHAPTER 8
                    EMERGING METHODS: RECEPTOR MODELING
                            AND BIOLOGICAL TESTING
       The following topics are presented in this chapter:                   I
                                                                       i
                                                                       !
       Receptor modeling                  ~"                           j

       •      Use of receptor modeling in urban assessments

       •      Pollutants and source categories addressed    -      '         '  .

       •      Source signature testing and tracer analysis

       •      Measured ambient air quality data sets                       :
                                                                       i
       •      Statistical techniques used for source apportionment          '•

       •      Spatial and temporal representativeness of results             :

       •      Comments on the use of receptor modeling

       Biological testing

       •      Approach to use of biological testing in urban air toxics studies

       •      Comments on the use of biological testing

8.1    The Use of Receptor Modeling in Urban Assessments

       Receptor modeling—also called source apportionment—is  an evolving science and
is not yet  widely implemented in urban air toxics assessments. Receptor modeling
techniques were originally developed to study sources of particulate matter, and have
                                       210

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been used to identify sources of certain toxic metals and extractable organic matter within
the  particulate catch (Lioy, 1988).  Recently,  investigators have  begun to use receptor
modeling to  study sources of VOC (O'Shea, 1988; Scheff, 1987), which can  yield
information on specific gaseous toxics such as benzene.  The Denver IEMP will attempt to
apportion gaseous  VOC if adequate data are acquired during the air monitoring phase of
the  study.  In addition, EPA's Integrated Air Cancer Project (IACP) is using receptor
modeling to apportion  the mutagenic activity of ambient particulate  matter between
mobile sources and wood smoke.

       The purpose  of  receptor modeling is  to  estimate  contributions of sources  to
monitored pollutant  concentrations at specific receptor sites.  The  process employs a
variety of statistical techniques to identify the  site-specific impacts of pollutant sources,
or source categories, on the basis of their emission "signatures" or "fingerprints."  Several
basic signature types  may be employed.. One type of signature is the specific mixture  of
chemical species  emitted from a  particular source,  identifying the ratio or relative
concentration of "each chemical species to the whole quantity of pollutant in an emission
stream.  For instance, if one source emits two grains of benzene  for every one gram  of
toluene,  its impacts  may be  distinguished, through statistical analysis,  from another
source that emits two grams of toluene for every gram of benzene.  Another signature type
involves  the use of unique "tracer" pollutant in sources' emissions.  For instance, lead and
bromine  emissions are associated with mobile source emissions, potassium and  iron with
wood burning emissions, and so forth.  These' tracers can be used in conjunction with
other source signature information to determine source contributions at a receptor point.
Since receptor modeling is dependent on the availability of source/emissions  chemical
composition data, it can be applied only to those pollutants for which adequate emissions
data are  available  for all sources, or source categories, in the study area emitting those
pollutants.

       Advantages of receptor modeling  are that it can (1) confirm source contributions
estimated through air dispersion modeling and (2) provide data on source contributions
where  air dispersion  modeling has not been done, where modeling results are suspect
because of terrain  or meteorological complexities, or where uncertainties exist regarding
                                        211

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atmospheric transformation mechanisms.  As  receptor modeling techniques improve—
particularly as hybrid techniques more comprehensively integrate emissions, dispersion
and transport, and measured data—receptor modeling may significantly improve the
reliability of policy conclusions about the nature of the urban soup problem.  !

       The disadvantages  of receptor modeling are its technical  complexity,  its data
requirements, and the inherent limitations of the monitoring data  (and' sometimes
modeling data) upon which it relies.

       Pollutants and Source Categories Addressed

       Past receptor modeling analyses have dealt almost exclusive^ with particulate
matter.  Little has been done on gaseous pollutants, although theoretically these could be
addressed  as long as  the  signatures  of sources, or source categories,  are j sufficiently
different to allow  for differentiation (Pace, 1987).  The current IACP and Denver IEMP
studies may further develop the appropriate techniques.                     j

       Both the  IACP and Denver IEMP studies are using receptor modeling primarily to
distinguish between  mobile source and  wood combustion contributions to  ambient
particulate concentrations.  The IACP Boise study will also include  the apportionment of
specific  gaseous pollutants  and pollutant  classes.  The Denver study will  attempt
apportionment of  gaseous  pollutants  only if the study's monitoring program develops
adequate data on the pollutants of concern. (Stevens, 1987)                 ',
                                                                       i
       At this time, neither of these studies plans to use receptor modeling to distinguish
between point and area source categories,  although some work may be done with power
plants and  refineries in the  Denver study.

       Source Signature Testing and Tracer Analysis

       The IACP  study is  performing source testing for wood combustion and mobile
sources to complement the  source signature data available in the literature.  Similarly, the
Denver IEMP will test mobile sources and power plants to compile sufficient signature
data for receptor modeling.  (Stevens, 1987)
                                        212

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       In certain kinds of receptor modeling, tracers are used as  unique signatures for
certain source categories.  The following tracers are commonly used to  identify source
categories:
              Mobile Sources:      Lead, bromine, carbon monoxide
              Power  Plants:       Sulfur, selenium, arsenic
              Wood Burning:       Potassium, iron
              Refineries:           Lanthanides
              Incinerators:         Zinc

       Measured Ambient Air Quality Data Sets

       Pollutant coverage and representativeness in time and space of the measured
ambient air quality data set are important considerations when drawing conclusions from
the receptor modeling analysis,  as discussed below:

       Diurnal Coverage—The  monitoring programs for the IACP and Denver studies are
very similar; the Denver  study, in fact, was patterned  after the IACP approach.  Each
study relied on 12-hour  sampling to  separate diurnal trends, although in Denver  a
combination of 12- and 24-hour samples was collected to reduce costs. In these studies,
it is important to be able  to resolve the chemical species emitted from residential wood
burning (predominantly a  nighttime activity) and mobile  source emissions (predominantly
a daytime activity). Hence, the 12-hour sampling  periods each day were extended from
7 a.m. to 7 p.m. and from 7 p.m. to 7 a.m.

       Pollutant Coverage—Chapter 2 discussed the pollutant coverage of these programs
in general terms. Pollutants  collected specifically to support the receptor modeling
include sulfates and nitrates, elemental and organic carbon, elemental analysis, and, in the
case of IACP, carbon-14 data to help separate wood from fossil fuel combustion sources.
CO  data are  collected  at  some sites, along  with  other  criteria  pollutants and
meteorological data, to provide  additional input on source contributions.  The IACP study
also analyzed samples for mutagenicity to apportion biological activity to appropriate
source categories.
                                        213

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        In certain kinds of receptor modeling, tracers are used as unique signatures for
 certain source categories.  The following tracers are commonly used to identify source
 categories:
               Mobile Sources:     Lead, bromine, carbon monoxide
               Power Plants:       Sulfur, selenium, arsenic
                                   Potassium, iron
                                   Lanthanides
                                   Zinc
Wood Burning:
Refineries:
Incinerators:
        Measured Ambient Air Quality Dafo Sets

        Pollutant coverage and representativeness in time and space of the measured
 ambient air quality data set are important considerations when drawing conclusions from
 the receptor modeling analysis, as discussed below:

    ""   Diurnal CovftragR—The" monitoring programs for the IACP and Denver studies are.
 very similar;  the Denver study, in fact, was patterned after the IACP approach.  Each
 study relied  on 12-hour sampling to separate diurnal trends, although in Denver  a
 combination of 12- and 24-hour samples was collected to reduce costs. In these studies,
 it is important to be able to resolve the chemical species emitted from residential wood
 burning (predominantly a nighttime activity) and mobile source emissions  (predominantly
 a daytime activity).  Hence, the 12-hour sampling  periods each day were extended from
 7 a.m. to 7 p.m. and from 7 p.m. to 7 a.m.

       Pollutant CovRrqgR—Chapter 2 discussed the pollutant coverage of these programs
 in general terms.   Pollutants collected specifically to support the receptor modeling
 include sulfates and nitrates, elemental and organic  carbon, elemental analysis, and, in the
 case of IACP,  carbon-14 data to help separate wood from fossil fuel combustion sources.
 CO data  are  collected  at some  sites, along  with  other criteria pollutants  and
meteorological data, to provide additional input  on source contributions. The IACP study
also analyzed  samples for mutagenicity to apportion biological activity  to  appropriate
source categories.
                                       213

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        Seasonal Coverage—The  results of a receptor modeling analysis are directly
 applicable to the monitoring period for the ambient air quality data set.  Both the IACP
 and the Denver study are performing monitoring in distinct seasonal blocks.  For the
 IACP, the major emphasis will be on the winter season.  The Denver study will provide a
 more balanced emphasis between the winter and summer seasons.

        The two-season coverage in Denver provides a more representative data set for
 estimating annual average source-receptor relationships. The inclusion of two  seasons in
 Denver provides more data for estimating source culpability on an annual average basis.

        Spatial Coverage—The Denver study has three ambient air quality monitoring sites
 in the air toxics monitoring network; a fourth (supplemental) site was available  during the
 winter season.  The IACP studies have used as many as seven fixed sites to support the
 receptor modeling analysis, although only two- sites were used in the earlier testing in
 Raleigh, North "Carolina and Albuquerque, New Mexico. With only a few sites there is a
 possibility that  the  results of source apportionment do not represent averages for the
 metropolitan area being evaluated.     •

        Statistical Techniques Used to Estimate Apportionment

        The statistical techniques used  in receptor modeling strive to  find the best  fit
 between the measured data at one or several receptors and  the source signature  data.  It is
 beyond the scope of this report to address these techniques in detail.  References such as
 EPA  1981,  1983,  1985 provide more comprehensive treatment of this subject.  The
 following is a capsule description of these techniques:

       Chemical Mass Balance—The CMB method is based on the assumptions that the
 mass  of material deposited on a filter at a receptor site is a  linear combination of the mass
 contributed from each of the sources and that the mass and  chemical speciation are
 conserved from the time of emission to the time  it is measured at a receptor  site.  The
 measured data and the data on source signatures  are used to form a set of simultaneous
 equations.  There are as many equations as there are chemical species being addressed.
The best fit for the set of equations is identified.
                                       214

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       Factor  Analysis—Whereas  CMB  methods  apply  knowledge  about  source
characteristics to a single filter data set to derive a source's contribution, multivariate
methods such as factor analysis extract information about a source's contribution on the
basis of the variability of elements measured on a large number of filters.  If two or more
chemical components originate from the same source, their variability as a function of
time as measured at a receptor site is assumed to be similar.                 j

       Multiple  Linear Regression Analyses—This analysis provides  a  means of
calculating the mass of emissions from a given source once  the tracer species from that
source are known.                                                       |

       Hybrid Receptor Modeling—The measured  ambient data and the dispersion term
between sources and monitoring sites are used as known values, and the emission rate
from each source is solved as the unknown.

       To date, the IACP has only used multiple linear regression because the studies are
reviewing only two source categories—wood combustion and mobile sources;—and more
complex models are not needed.  As more complex airsheds are studied, future IACP
studies may include more complex receptor modeling techniques such as chemical mass
balance, factor analysis, and hybrid receptor modeling.  For example, factor analysis may
be used in the IACP to provide groupings of chemical species (both organic and inorganic)
that are characteristic of the emissions and transformation products of sources within the
airshed under study.

        Spatial and Temporal Representativeness of Results                 ',
                                                                       i
        The Denver study plans to add a step in the data interpretation that will evaluate
the representativeness of the ambient air quality measured data set for areas in the city
beyond the  receptor  locations and for  periods  not covered during sampling.   Two
techniques—dispersion modeling and  use of CO data  as  a surrogate to toxic air
pollutants—will be used to extrapolate "the receptor modeling results to develop more
general conclusions regarding culpability.
                                        215

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        Dispersion Modeling—There are clear limitations to the use of dispersion models
 in Denver because of the city's topography. The complex drainage flows that occur during
 periods with peak concentrations are relatively difficult to model, considering  the
 available wind  data and the  cost of performing non-Gaussian modeling to address  the
 complex trajectories.  Confidence in modeled exposure estimates during peak days may be
 particularly low because of these factors. Dispersion modeling will, however, be used as
 input to the assessment of the spatial representativeness of the monitoring sites and  the
 representativeness  of the monitoring periods to typical concentrations.  This link will
 provide some limited input to support the extrapolation of the results.

        CO Measured Data as a Surrogate for Toxic Air Pollutants—As  already noted,  the
 Denver air toxics monitoring network has four sites; CO coverage is  available at these
 sites  as well as at three additional sites located in widely varying settings,  including a
 downtown site,  a residential site, and a relatively remote site.

        CO should be a valuable indicator of the magnitude of mobile  source impacts at
 each monitoring site because Cifniobile sources are the dominant source category for both
 CO and air toxics emitted in the Denver area (Machlin, 1986), and (2)  it is reasonable to
 expect that the measured  data for CO could be used to estimate the general magnitude of
 some air toxics for sites where CO data only were available.

&2	Comments on the Use of  Receptor Modeling for Urban Air Toxics Studies

       All receptor modeling analyses are  limited by the  representativeness of the
ambient air quality monitoring sites used to support  their general conclusions.  The
primary disadvantage  of receptor  modeling is  that  a limited  number of locations
(monitoring sites) are often used to support general conclusions  regarding urban-scale
impacts. For example, it  could be interpreted from a residential sample obtained during
the winter that 30 percent of the fine particulate  concentrations in an urban area are from
mobile  sources  and 70 percent are from wood combustion.  It is  always essential,
therefore, to place the receptor modeling results in proper spatial and  temporal  context.
Any extrapolation of the results beyond this point must be supported by a justification of
the representativeness of the data.
                                        216

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       Expanded use of dispersion modeling in conjunction with receptor modeling
analyses provides a means for assessing the representativeness of the measured data in
time and space, which should improve the interpretation of the results of: a receptor
modeling analysis.   Since the IACP and previous Denver studies do not perform this
interpretive step, receptor modeling could be strengthened in this area.        j

       It is assumed that the ratios  of signature pollutants will remain unchanged during
transport from the source to the receptor. If significant changes do occur during transport,
the apportionment of impacts among sources could be biased.                j

       The measurements need to be precise enough to distinguish among sources; that is,
if little difference is discerned among signatures of potential sources, the imprecise nature
                                                                        i
of the  measuring techniques could blur the differences among the sources under review.

        Ambient concentrations  should be. sufficiently  above the detection limits as to
allow  accurate quantification. This could be  a limiting factor for many compounds  and
could  be addressed by a complementary dispersion modeling analysis.          •
                                                                        i
        The study area in question should not contain other significant sources that are not
considered in a receptor modeling analysis.                                :

        The IACP methodology recommends that at least 40 daytime and 4Q nighttime
samples be available to characterize a season (Stevens, 1987),  which can be resource
intensive.  Similarly, research into hybrid receptor modeling techniques has also indicated
that relatively detailed temporal coverage is needed in  order for receptor modeling to be
effective.  (Draxler, 1987) There is some leeway in terms of the minimum number of
samples that is considered  reasonable for attempting source apportionment (Stevens,
1987); however, any study considering this approach should devote adequate resources to
the ambient monitoring and possibly to source testing to assure its effectiveness.

 8.3    The Use of Biological Testing in Urban Air Toxics Studies

        Testing of toxic air pollutants on living organisms has attracted  increased interest
 over the past several years, but this approach is still basically experimental.  Py exposing
                                         217

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 microorganism, whole animals, or  selected plant species to potential  genotoxicants,
 biological monitoring provides a method of assessing the potential biological effects  of
 previously untested chemicals or chemical mixtures.  Generally, bioassay techniques are
 limited to assessing direct mutagenic effects; by comparing the number of revertants  to
 known mutagens, bioassaying can  be used as  a relative indicator of mutagenic activity.
 These tests are not used to  identify tumor promoters or other nonmutagenic effects  in
 environmental monitoring  applications.

       The advantage of biological  testing as .part of multi-pollutant,  multi-facility
 studies is that such testing  can address interactions among pollutants in a complex
 mixture, thereby reducing  study uncertainties and characterizing sources and fractionated
 samples of ambient air in terms of their relative mutagenicity.

       Biological  testing has been conducted -primarily in the IACP  studies, although
 some testing  has  also been done in California, Connecticut, and New Jersey.  Current
 testing has been carried out  in research programs rather than in support of regulatory
 programs. For this reason, this chapter will provide only a brief overview of biological
 testing in order to introduce this technique as an approach to be considered in  future
 applications.  Providing details on biological testing is beyond the scope of this report.
 Details are available in (Claxton, 1987) and (Lioy, 1988).

       At present,  biological testing has  a number  of  practical  and theoretical
 disadvantages.  Because biological testing is still  experimental, there are a number of
 purely practical problems in attempting to apply it within current field-oriented air toxics
 surveys.   For instance, chemicals such as acetone and toluene/ethanol, which are used to
 extract samples, can produce artifacts that affect  the estimates  of mutagenicity.  The
 relatively small mass available from typical ambient air samples is another  important
 factor:  it limits the degree of fractionation and the subsequent chemical and biological
testing that can be performed.  Similar practical problems exist with  recovery rates of
pollutants from collection media  and  contamination and/or  loss of material  in  the
collection medium.
                                        218

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       The theoretical development of the technique  is also limited.  Fqr  instance,
experience with gas phase bioassay techniques is still limited (Claxton, 1985; Hughes,
1987).  Field  studies  to date generally do  not cover  gas phase  pollutants  in a
comprehensive  sense because of limitations in the sensitivity of the techniques.  Existing
studies to address gas phase pollutants have only been  carried out in the laboratory, at
higher than ambient conditions. At a more basic  level, additional research is needed on
evaluating relationships between mutagenic  activity and human or animal data on
carcinogenicity or other health impacts.                                   j

       Approach

       The IACP relies on short-term bioassay tests primarily because they ,require less
massive quantities of the sample, are rapid enough to guide the chemical speciation work,
and correlate well with known carcinogenesis studies  (Claxton, 1987).  Two types of
biological testing are used in the IACP:
       1.     Ames testing is  used for large samples,  i.e., when, more  than  10 mg of
              organic material are available.   Ames  test  procedures allow  for  the
              calculation  of dose-response relationships,  mutagenicity  slojpes, and so
              forth.  In the IACP studies, Ames  tests are used to compare combustion-
              impacted  ambient air samples to background (clean) ambient air samples,
              indoor ambient  air samples, and the combustion source emissions.  The
              IACP is currently examining wood  smoke-affected ambient air sites.
       2.     For relatively small samples, high performance liquid chromatography
               (HPLC)-coupled  liquid pre-incubation assay is used to provide a bioassay
               "fingerprint" of  the sample. Each  HPLC  fraction is characterised  as to its
               mutagenicity.  If the mutagenic fractions are not highly toxic, an  indication
               of  their relative mutagenicity is  available as revertants per  plate  per
               fraction recovered.
                                                                       i
 8.4   Comments on the Use of Biological Testing in Air Toxics Studies     \

       Relative Importance of  Gas Phase Pollutants                       ;

       Perhaps the most significant finding thus far  is that the mutagenicity  of the gas
 phase pollutants, after aging and transformation, can  be much  greater  than  either the
 "fresh" gaseous pollutants or  the particulate matter in urban air.  This cpnclusion is
 tentative, however, and more research  is needed  to corroborate initial' indications.
                                         219

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                                                                                                     1
Nevertheless, it suggests that if biological testing were used as a comprehensive screening
technique, future applications would have to address gaseous aged pollutants as well as
the more routinely studied particulate matter.

       Indoor/Outdoor Differences

       In another finding of the IACP  study, particulate mutagenicity levels inside a
subset of residences were found to be lower than those immediately outside the home.
The basis of this finding,  as well as the gas versus particle phase issue mentioned above,
will undoubtedly be subjects of future research.

       Use of Biological Testing to Set Priorities

       Biological testing could provide a broad check of genotoxic risks among different
metropolitan areas, though variations resulting from the use of different strains of the
same  species,  or varying  laboratory conditions, could  limit the  accuracy  of  these
comparisons.   Overall, however-, biological  testing could become  a useful means of
identifying urban or industrial areas where a more detailed review of source-receptor
relationships for specific pollutants is warranted.
                                        220

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                                     Chapter  8
References
Claxton, L.D., A.D. Stead, and D. Walsh. 1987.  "An Analysis by Chemical Class of
Salmonella Mutagenicity as Predictors of Animal Carcinogenicity."  Mutation Research (in
press).

Draxler, R. R. 1987. "Estimating Emissions from Air Concentration Measurements."
Journal of Air Pollution Control and Hazardous Waste Management, Volume 37, No. 6,
pp. 708-714.

EPA, 1981, 1983, 1985. Receptor Model Technical Series. Volume I. EPA-450/4-81-016a,
1981.  Volume II. EPA-450/4-81-0166, 1981, Volume III. EPA-450/4-83-014, 1983.
Volume IV. EPA-450/4-83-018, 1983. Volume V. EPA-450/4-85-007, 1985.

Lioy, P., et at., 1988. Toxic Air Pollution, A Comprehensive  Study of Non-Criteria Air
Pollutants, Chapter 5,  "Mutagenicity of Inhalable Particulate  Matter at Four Site in New
Jersey," Lewis Publishers, pp. 125-166.

Machlin,  Paula R., 1986.  "Denver Integrated  Environmental Management  Project:
Approach to Ambient Air Toxics Monitoring," Environmental Protection Agency Region 8,
Denver Colorado', November. 1986.

O'Shea, William J., and Peter A Sheff, 1988. "A Chemical Mass Balance for  Volatile
Orgnaics in Chicago,"  Journal of the Air Pollution Control Association, Volume 38, No. 8,
pp. 1020-1026, August 1988.

Pace, T.  1987, U.S. Environmental Protection Agency.  Personal Communication.

Scheff, Peter A. and Mardi Klevs, 1987, "Source-Receptor Analysis of Volatile
Hydrocarbons," Journal of Environmental Engineering, Volume 113, No. 5. October 1987.

Stevens, R. 1987.  U.S. Environmental Protection Agency.  Personal Communication,
1987.
                                       221

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                                    APPENDIX A

                                GLOSSARY OF TERMS
Acute exposure:
Additive risk/incidence:
Aggregate incidence:  -
Ambient (air) monitoring:
Annual incidence:
Area source:
Areawide average individual
risk:
Areawide incidence:
ATM:
One or a series of short-term exposures generally less than
24 hours.

Risk/incidence  due to the  interaction of two or more
chemicals in which the combined health effect is equal to
the sum of the effect of each chemical alone.

In an urban  air  toxics context, an  areawide, additive
incidence. This term sometimes refers'to areawide incidence
or additive incidence only, so one should pay attention to
the contextual use for the proper meaning.

The  collection of ambient  air samples and the analysis
thereof for air pollutant concentrations.

Lifetime cancer incidence  adjusted  to  a yearly  basis,
typically by dividing lifetime incidence by 70.

Any  source  too  small and/or numerous to  consider
individually as a point source in an emissions inventory.

Average individual risk to everyone in an area (but not
necessarily the actual risk to anyone). May be computed by
dividing lifetime aggregate incidence by  the  population
within the area.

Incidence over a broad area, such as a city or county, rather
than at a particular location, such as an individual grid cell.

Atmospheric  Transport Model.  A Gaussian point source
dispersion model used  in GAMS (before incorporation of
ISCLT for this purpose).
                                        A-4

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Averaging period:


B(a)P:




Background:




BACT:

BG/ED:
Bioassay:



Biological testing:

Box model:



GAG:


Cancer:




Carcinogenicity:


Catalyst:



COM:
The length of time over which concentrations are averaged,
such as 1 hour, 8 hours, or 24 hours.

Benzo(a)pyrene.  One of a  group of compounds called
polycyclic organic matter (POM).  B(a)P is sometimes used
as a  surrogate  for all  POM  in computing  emissions,
exposures, and risks.

A term used in dispersion modeling  representing  the
contribution to ambient  concentrations  from sources  not
specifically modeled in the analysis, including natural  and
man-made sources.

Best available  control technology.

Block  group/enumeration district, as designated by  the
Bureau of Census. A block group is  an area representing a
combination  of  contiguous blocks having  an  average
population of about 1,100.  An enumeration district is an
area containing an  average  of about  800 people and is
designated when-block groups are not defined.

A test  in living organisms, e.g., a test for  carcinogenicity in
laboratory animals, generally rats and mice, which includes
a near-lifelong exposure to the agent under test.

See Bioassay.

A simplified  modeling technique that  assumes uniform
emissions  within an  urban area, and  uniformly mixed
concentrations within a specified mixing depth.

Carcinogen assessment group.  EPA group that prepared
qualitative and quantitative carcinogenic risk assessments.

A cellular tumor, the natural course of which is  fatal.
Cancer cells,  unlike benign cells,  exhibit properties of
invasion and metastasis (malignancy).  Cancers are divided
into two broad categories: carcinoma and sarcoma.

The extent to which a substance is able to induce a cancer
response.

A substance that promotes  a chemical  reaction.   In  the
context of this report, a device installed on the tailpipe  of a
motor vehicle to control exhaust emissions.

Climatological Display Model.   A Gaussian  dispersion
model  whose particular strength is its detailed area source
                                       A-ii

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                            treatment.  Can  also handle  point sources, but not in as
                            detailed a manner as ISC.

Centroid (population, source): A single point whose coordinates represent the location of a
                            BG/ED, in the case of a population centroid, or the location
                            of an emission point, in the case of a complex source.

                            A type of receptor model,  employing  chemical methods for
                            source impact determinations.

                            Long-term exposure usually lasting six months to a lifetime.

                            In the context of air toxics,  co-control  represents the
                            simultaneous control or mitigation of air toxics and criteria
                            pollutants via  the same control measure.   For  example,  a
                            motor  vehicle  catalyst  would  reduce  VOC  and CO
                            emissions and also  reduce  benzene and other gaseous
                            toxics.

Comparative potency factor:  A cancer unit risk factor for a complex substance or mixture
                            that is extrapolated from human  risk data for a reference
                            substance based on  the ratio  of  short-term  bioassay
                            responses of  the  complex   substance  to the reference
                            substance. EPA is developing comparative  potency factors
                          . for_various sources of POM.
Chemical mass balance:


Chronic exposure:

Co-control:
Complex facility:



Complex terrain:

Composite plume:
                           A point source covering a large area and comprised of many,
                           generally different, kinds of emission points such as fugitive
                           equipment leaks, vents, stacks, volume sources, etc.

                           Terrain exceeding the height of a stack.

                           The result of merging of multiple plumes downwind of an
                           industrial complex.
Coverage (pollutant, source, The extent of inclusion of pollutants or sources in an
spatial):                    emission inventory or risk analysis or the amount of space
                           or area represented in a monitoring program or inventory or
                           risk analysis.
Cr+6:
Criteria pollutants:
                           Hexavalent  chromium, i.e., chromium in the +6 valence
                           state.

                           Pollutants defined pursuant to Section  108 of the Clean Air
                           Act and for which national ambient air  quality standards are
                           prescribed.  Current criteria pollutants include  particulate
                           matter, SOX, NOx, ozone, CO, and lead.
                                       A-iii

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Culpability:



Decay:


Deposition:



Detection limits:

Dispersion  coefficients:




Dispersion  modeling:
Dose-response assessment:
Effective stack height:



Excess cancer risk:

Exposure:


Exposure assessment:
The  extent to which something (generally a pollutant or
source) is responsible for some  effect (such as exposure or
risk).

A term  that represents  pollutant removal by physical or
chemical processes.

The  removal  of particulate matter  and gases, at  a land or
water body  surface, by  precipitation  or  dry removal
mechanisms, including surface reactions and filtering.

See minimum detection limits.

Parameters used  in Gaussian dispersion  modeling to
estimate plume  growth  through dispersion along the
horizontal  and vertical  axes.   These are computed as a
function of downwind distance and atmospheric stability.

A means of estimating ambient  concentrations at locations
(receptors)  downwind of a source, or an array of sources,
based  on  emission  rates, release  specifications,  and
meteorological factors such as wind speed, wind  direction,
atmospheric   stability,  mixing  height,  and  ambient
temperature.

The determination of the relation between the magnitude of
exposure and the  probability of occurrence of the health
effects in question.
Factor analysis:
                       Mltilial Uiid Is scBgial cartridge §
for monitoring formaldehyde and other aldehydes.

The height above ground level of the centerline of a plume.
It is the sum of the physical stack height, plume, and stack-
tip downwash (as applicable).

An increased risk of cancer above the normal background.

An event in which an organism comes  into contact with a
chemical or physical agent.

Measurement  or estimation of  the magnitude, frequency,
duration,  and route  of  exposure to  substances  in the
environment.  The exposure assessment also describes the
nature of exposure and the size and nature of the exposed
populations.

A type of  receptor model, employing  chemical methods for
source impact determination.
                                       A-iv

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Fenceline:


Fine particulate:

FMVCP:


Formulation:

Fugitive emission/release:


GAMS:



Gaussian model:
GC:



GC/MS:


GEMS:
Global buildup:
Grid:
A term  used to represent the property  boundary  of a
facility.

Particulate matter less than 2.5 microns in size.

Federal Motor Vehicle Control Program.  EPA's program to
control motor vehicle emissions.

(see Model formulation)

Emissions unconfined to a stack or duct, such as equipment
leaks  from valves, flanges, etc., or open spills.

GEMS Atmospheric Modeling System.   GAMS is  an
Exposure model, similar to HEM/SHEAR, developed  by
EPA's Office of Toxic Substances.

A Gaussian dispersion model represents the distribution of
concentrations within a plume by  assuming a normal
distribution along the horizontal and vertical axes.  In the
basic  form, predicted concentrations  are  estimated  as a
function of emission rate, horizontal and vertical dispersion
coefficients, and vertical and horizontal  distance from the
plume centerline.

Gas  chromatography.   A technique for  separating
compounds on a chromatographic column for  subsequent
analysis.

Gas chromatography coupled with mass spectrometry for
analysis of compounds.

Graphical  Exposure Modeling System.   An interactive,
multimedia information management system that contains
physiochemical parameters, fate  data,  and multimedia
exposure models (e.g., GAMS),  developed by EPA's Office
of Toxic Substances.

The  widespread  accumulation  of pollutants  in  the
atmosphere over the years.  Global buildup is generally
associated  with more  inert compounds  such as  halogens
(e.g., carbon tetrachloride).

A network of rectilinear or polar grid cells superimposed
over an area, generally for modeling analyses. A rectilinear
grid is defined  by a series  of perpendicular lines defining
rectangular or square  grid cells,  whereas  a polar grid is
defined by a series  of concentric circles  and straight lines
radiating from the center of the circles.
                                       A-v

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Grid cell:

Grid spacing:

Grid square:

Hazard identification:


HEM:
Highway vehicle source:


Hot spot:


HPLC:

Hybrid modeling:



IACP:
IEMP:
Incidence:
Individual risk:
 The smallest area resolved within a modeling grid.

 The dimensions of the grid cells within a grid.

 A grid cell whose sides are equal.

 The determination of whether a particular chemical is or is
 not casually linked to particular health effects.

 Human Exposure Model. EPA model used for exposure and
 risk analysis, which defines polar receptor grids around each
 point source.  Can also model area sources by apportioning
 county level emissions to each BG/ED and running a simple
 box model. HEM contains two component modules: SHED
 and SHEAR. (Note: HEM is  being upgraded by EPA.)

 Car, truck, or motorcycle.   Also called  road  vehicles or
 motor vehicles.

 A particular receptor, grid  cell, or localized area wherein
 exposure or risk-is high.

 High performance liquid  chromatography.

.-The- use of several different models, particularly the mixed
 use of dispersion and receptor modeling, for complementary
 analyses.

 Integrated Air Cancer Program.  EPA long-term research and
 development program to  develop methods  and conduct field
 and lab tests to learn what causes cancer in complex urban
 air mixtures and what sources are contributing to this cancer
 burden.

 Integrated  Environmental Management Project.  A series of
 studies conducted in Philadelphia, Baltimore, Santa Clara,
 Kanawha  Valley,  and  Denver  to  evaluate multimedia
 contributions to various health risks,  with emphasis on
 cancer.

 The  frequency of  occurrence  of  a certain  event or
 conditions, such as the  number of new cases of  a specific
 disease or tumor occurring during a certain period.  The
 incidence rate is the  number of new cases during a certain
 period  divided by the population size (e.g., 10  cases per
 100,000 exposed persons).

 The increased  risk  for a  person exposed to  a specific
 concentration of a toxicant.  May be expressed as  a lifetime
                                       A-vi

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IRIS:



ISC:



Lifetime:

LONGZ:
MADAM:
MEI:

Microenvironment:
Micron:
Microscale:
Minimum detection limit:
MIR:
individual risk or as an annual individual risk, the latter
usually computed as 1/70 of the lifetime risk.

Integrated Risk Information System.  EPA computer system
containing risk information (e.g., cancer unit risk factors) for
specific chemicals.

Industrial Source Complex model. EPA Gaussian dispersion
model designed to handle complex sources. ISC contains a
long-term module (ISCLT) and a short-term module (ISCST).

Considered to be 70 years in EPA health risk assessments.

A predecessor model of ISCLT, containing  many of the
source-specific features of ISCLT. A long-term (seasonal  or
annual average) Gaussian dispersion model that is used for
point and urban-wide area sources.  This model can accept
site-specific turbulence  data to estimate local dispersion
rates.

Monitoring of Ambient Data Assessment Module.   Module
within EPA.;s PIPQUIC  system used  to evaluate model
performance by comparing measured and model predicted
ambient air quality data and partitioning the results by
.various meteorological parameters.

Maximum exposed individual.

Localized environment in which one may be exposed to
pollutant concentrations that differ  considerably from
ambient  (outdoor)   air (e.g.,  indoor household  air,
occupational exposures, air within automobiles, etc.) EPA's
TEAM studies evaluate personal exposures as individuals
are exposed to air in different microenvironments  during
each day.

One millionth of a  meter.  A dimensional unit  used to
measure the diameters of particles.

The immediate vicinity  of a  source, e.g.,  within 1 to  2
kilometers.

The lowest level measurable by a monitoring technique at
some level of confidence.

Maximum individual risk, i.e., risk to  the most  exposed
individual.
                                      A—vii

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Mitigation:
The reduction or control of emissions, exposures, or risks
due to air toxics.
Mixing height:
(Or mixing depth)

Mobile source:
Model formulation:
Model performance:
Model(ing):
 Modeling protocol:
 Monitoring:
 Motor vehicle:

 Multiple linear
 regression analysis:
 Mutagenicity:
 NAAQS:
The height above the surface through which vertical mixing
occurs without suppression by an elevated stable layer.

Any motorized vehicle, such as cars, trucks, airplanes, or
trains.  Sometimes refers specifically to highway vehicle
sources.

A model formulation is determined by the model selected,
the specific input data, and options selected for a model
run.  A range of model formulations could be  made using
the same dispersion model. •

The evaluation of the performance of a dispersion model by
comparing modeled  concentrations to meeisured air quality
data, generally based on statistical tests such as measures
of bias, variance,  and correlation.

See  dispersion modeling or receptor modeling.  This term
can  also  be used in the context of emissions modeling,
referring to" the prediction  of emissions from  a source or
source category.

As used  in this report, a  modeling protocol provides  a
detailed account of the specific model formulation(s) that
will be used to perform a dispersion modeling analysis.  It
  fsnerally is used to obtain comment prior  to doing  a
  etailed analysis.
 The  collection  and  analysis  of ambient air  samples.
 Sometimes refers specifically to sampling alone and not to
 analysis.  Can also refer to source (stack) sampling.

 On-road or off-road cars, trucks, or motorcycles.
 A type of receptor model employing chemical methods for
 source impact determination.

 The extent to which a chemical or physical agent interacts
 with DNA to cause a permanent, transmissible change in the
 genetic material of a cell.

 National Ambient Air Quality Standard.  Set by EPA for
 criteria pollutants under  the Clean Air Act.
                                        A-viii

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NEDS:


NEM:



NESHAP:



Network:

Network enhancement:



Noncancer risk:

Nontraditional sources:
Normalized modeling:
NSR:
OPPE:
PAN:
National  Emissions  Data  System.   EPA's  centralized
emission inventory of criteria pollutant emissions.

NAAQS Emissions  Model.   EPA exposure  model that
considers  movement of  individuals through  various
microenvironments.

National Emission Standard for Hazardous Air  Pollutant.
Standards set by EPA for hazardous  air pollutants under
Section 112 of the Clean Air Act.

An array of ambient air monitors distributed over an area.

The enhancement of an ambient air  network to improve
spatial  or temporal  coverage.   Sometimes  done  on a
temporary basis.

Risk of a health effect other than cancer.

Sources not usually included in an emission inventory, such
as  wastewater -treatment plants,  ground-water aeration
facilities,  hazardous waste combustors,  landfills, etc.,
which are air emitters due to intermedia transfer from water
or solid waste.

Modeling of unit weights (e.g., 1 mg/yr) of emissions from
each source, rather than modeling of actual emissions, and
displaying incremental receptor concentrations or receptor
coefficients.  Thereafter, the resulting normalized receptor
coefficients  are  adjusted  by  actual  emission  rates to
simulate different emission scenarios rather than re-running
the model over  and  over with different emissions totals.
This  process  assumes linearity between  emissions and
modeled ambient air concentrations, which does not always
hold true if stack and exhaust parameters change.

New  Source  Review.   Permit process   for evaluating
emissions and need  for controls before construction and
operation  of  a proposed facility.   EPA, as  well  as many
States and local agencies,  has NSR requirements for  air
toxics sources.

EPA's Office of Policy, Planning, and Evaluation.  Initiator
of the Integrated Environmental Management Project, a
series of geographic,  multimedia studies in various cities.
(See IEMP.)

Peroxyacetyl Nitrate.   A photochemical oxidant formed in
urban atmospheres along with ozone.
                                      A-ix

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Personal Monitoring:
Photo chemically formed
pollutant:

PIC:
PIPQUIC:
PM:

Point source:



Polar grid:

POM:
 Population risk:

 Primary pollutant:


 PUF:


 QA/QC:

 Receptor:


 Receptor grid:


 Receptor modeling:
Sampling done in EPA's TEAM study  by individuals
wearing personal monitors.

A secondarily formed pollutant due to atmospheric
photochemistry, e.g., formaldehyde or PAN.

Products  of incomplete  combustion.    A term  used
somewhat loosely in various studies referring generally to
polycyclic organic matter.

Program Integration  Project—Queries Using Interactive
Commands.  A  data handling system developed  by EPA as
part of the IEMP to calculate exposures and risks from air
toxics emissions data. PIPQUIC  is being  used  in various
urban air toxics studies.

Particulate matter.

A source large enough for individual record to be kept in an
emission inventory, often emitting above  a certain cutoff
level or threshold.

See grid.

JPolycyclic organic  matter.  A broad class of compounds
that generally includes all organic structures having two or
more fused aromatic  rings (i.e.,  rings sharing  a common
border).  POM includes polynuclear aromatic hydrocarbons
(PAH or PNA).

Generally synonymous with areawide incidence.

One emitted directly from an emission source prior to any
secondary physical or chemical reaction.

Pblyurethane  foam.   An adsorbent material used  for
sampling semivolatile organic compounds.

Quality assurance/quality control.

A particular point  in space where a monitor is located or
where an exposure or risk is modeled.

An  array of  receptors.   Generally synonymous with
network.

A technique for inferring source culpability at a receptor(s)
by analysis of  the ambient sample composition.  There are
                                        A-x

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Release specifications:
Risk:
Risk assessment:




Risk characterization:


Risk management:
• -u



Road vehicle source:

Sample compositing:



Sampling:

Sampling duration:


Sampling frequency:


Sampling period:


SCAQMD:
 various receptor  models  employing  microscopic  and
 chemical methods for analysis.

 Used as model inputs to characterize the location, release
 height, and buoyant and momentum fluxes of each source.
 Required terms include stack height, exit velocity, inner
 stack diameter, exhaust temperature, and the dimensions of
 nearby structures.

 The probability of injury, disease, or death under specific
 circumstances.  In quantitative terms, risk is  expressed in
 values  ranging from zero (representing the certainty  that
 harm will not occur) to one (representing the certainty that
 harm will occur).

 The. use of the factual base to define the health effects of
 exposure  of individuals or  populations  to  hazardous
 materials and situations. May contain some  or all of the
 following four steps:

 The description of the  nature and often the magnitude of
 human risk, including attendant uncertainty.

 The decision-making process that uses the results of risk
.assessment to produce a decision -about environmental
 action.   Risk  management  includes  consideration  of
 technical, social, economic, and political information.

 See highway vehicle source.

 The combining of samples before analysis to increase  the
 temporal or spatial representativeness, while reducing
 analytical costs.

 See monitoring or ambient air monitoring

 The length of time (generally in hours) each sample is taken
 or drawn (e.g., 12 or 24 hours).

 The length of time between samples (1 hour, 1 day, 6 days,
 etc.).

 The length  of time (days,  months,  years) for which a
 sampling program is operational.

 South Coast Air Quality Management District.  The local air
 pollution control  agency in  California responsible for  the
 Los Angeles area.
                                       A—xi

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Scoping study:
SCREAM:



Screening study:

Secondary pollutant:




Semivolatile otganics:




SHEAR:
 SHORTZ:


 SIC:



 SIP:



 Source apportionment:

 Source grid:
 Also known as a screening study.  An  assessment or
 analysis using tentative or preliminary data whose results
 are not accepted as absolute indicators of risk or exposures,
 but rather are taken  as  an indication of  the  relative
 importance  of various sources, pollutants,  and control
 measures. Most urban air toxics assessments conducted to
 date have been considered to be scoping studies, useful for
 pointing  out where more  detailed work is needed prior to
 regulation.

 South Coast Risk and Exposure Model. An enhancement of
 EPA's HEM/SHEAR developed by SCAQMD that uses more
 detailed population and meteorological data.

 See scoping study.

 Also, "secondarily formed pollutant." A pollutant formed
 in the  atmosphere as a result of chemical reaction and/or
 condensation,  such as  PAN.   Some  pollutants  (e.g.,
 formaldehyde) are both primary and secondary pollutants.

 Compounds that have vapor pressures (in clean air) of 10"8
 to 10"4 torr, and  which readily adsorb  upon  particulate
 matter.  Not  clearly  gaseous  or  particulate  under all
. conditions.                         •               .

 Systems  Application Human Exposure and Risk.  A module
 within EPA's Human Exposure Model designed to  focus on
 multiple pollutant, multiple source exposures, including
 area source analyses. SHEAR uses a Gaussian dispersion
 model  for point sources and a box model for area sources.

 Short-term version (e.g., 1-hr to 24-hr averaging periods) of
 SHORTZ/LONGZ companion models. See LONGZ.

 Standard Industrial  Classification.  A series of  codes or
 classifications  to categorize industry, published  regularly
 by the Office of Management and Budget.

 State Implementation Plan.  Required by States under the
 Clean Air Act to indicate a plan of action to mee.t National
 Ambient Air Quality Standards for criteria pollutants.

 See receptor modeling.                        ;

 A grid defined to encompass all emission sources that one
 wants  to model.  The source  grid  is sometimes defined
 bigger than a corresponding receptor grid so that all local
 sources impacting on the receptor  grid will be considered.
                                       A—xii

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                            More typically, the source grid and receptor grid coincide in
                            most studies.
Spatial coverage:


Spatial resolution:
Species profile:



Stability:



Subchronic exposure:


Supersite:
Surrogate indicator:
TEAM:
Temporal resolution:
Tenax:
The area included or covered by a sampling network or a
source/receptor grid.

The extent to which emissions, monitoring or any other data
are  subdivided  or  resolved  in  space,  generally across a
geographical area.  For  example, emissions data may be
spatially resolved to 1. kilometer by 1 kilometer squares
within an urban area.

A set of apportioning factors that allow one to subdivide
VOC or PM emission totals  into individual chemicals or.
chemical classes.

A parameter to  describe the degree of turbulence in the
atmosphere, ranging  from- unstable (vigorous mixing) to
stable (suppressed mixing).

Exposure to a substance spanning approximately 10 percent
of the lifetime of an organism.

A monitoring site that alone, or in conjunction with  other
sites, best represents the scale of interest, such as suburban
neighborhoods,  central business district, or rural  areas.
Such sites can be inferred by statistical analysis .of modeled
data.

A variable  whose  spatial  or  temporal  distribution is
assumed to behave in the same manner as some variable of
interest.   Surrogate indicators  are used  for spatial and
temporal apportionment of emissions  data, especially for
area sources.

Total Exposure  Assessment Monitoring.   The type of
monitoring being conducted  by EPA's  Office of Research
and Development to measure total human  exposures of
individuals as they occupy various microenvironments, such
as outdoors, indoors, and commuting in motor vehicles.

The extent to which some variable, typically emissions and
monitoring data,  is subdivided or resolved in time. Data, for
example, may be resolved hourly or seasonally.

A porous polymer material often used for sample collection
of certain organic materials.
                                      A—xiii

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Tracer:
Transformation:



Transport:



UNAMAP model(s):



Unit cancer risk factors:



Urban soup:




VMT:


VOC:
 Tracer pollutants are used in receptor modeling to estimate
 the contribution of a source, or source category,  to total
 ambient levels of the pollutant  or pollutant class (such as
 PM-10) of interest.  Ideally, tracers are unique to a source or
 source category.

 The conversion, through chemical or physical processes, of
 one compound or several compounds into other compounds
 as a result of aging and irradiation in the atmosphere.

 The movement of pollutants by wind  flow.  Transport is
 characterized for modeling purposes  by wind speed and
 wind direction.

 User's Network for Applied Modeling of Air Pollution.  A
 set of dispersion models compiled by EPA that is  used to
 support regulatory and other needs for modeled data.

 The incremental  upper bound  lifetime risk estimated to
 result from a lifetime exposure to an agent if it is in the air
 at a concentration of 1 microgram. per cubic meter.

 An expression referring to the multi-source, multi-pollutant
 urban  air  toxics problem  resulting  from the complex
- interaction of many pollutants, sources, and  atmospheric
 transformation.

 Vehicle miles traveled.  Mobile source  emission factors are
 typically expressed in terms of grams per VMT.

 Volatile organic compounds.
                                       A—xiv

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                                     APPENDIX B
                EVALUATION OF OPTIMUM SIZE AND LOCATION OF AIR
                 TOXICS MONITORING NETWORKS BASED ON SPATIAL
                         CORRELATIONS OF CONCENTRATION
Introduction

       The purpose of this Appendix- is to  describe  a method to  select optimum
monitoring sites based on dispersion modeling and statistical analysis. This method uses
modelled  emissions, data to" design a  monitoring, network that best meets project
objectives within a specific study area.  The ultimate goal of this methodology is to select
a limited number of monitoring sites that yield relatively independent (i.e., noncorrelated)
air quality data, and to avoid selecting sites that provide relatively little new information.
Appendix B is organized to first describe this optimization method, and then to provide a
limited evaluation of its effectiveness based on the DEMP Philadelphia measured air toxics
data set.  This measured data set is used to evaluate how  well the modeling-based site
selection approach corresponds with the selection of optimal sites ("Supersites").

B.I    Method

       There are six steps, which lead to a quantitative  determination  of the sites that
best meet study objectives based on the best available emissions  data. The steps  can be
briefly summarized as  follows.

       Step 1 -  .Select a "long list"  of candidate monitoring sites.  Select  a relatively
broad range of candidate monitoring sites. In  many metropolitan areas this set could
include all existing criteria pollutant monitoring sites and other readily accessible
                                        B-i

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locations, such as schools,  state/county facilities, etc.  The goal of Step  1  could be to
provide broad coverage of candidate sites throughout the study area.

       Step 2 - Compile emissions data.  Obtain (or compile  as necessary) available
emissions data for point and area sources (such as mobile sources,  degreasing, etc.)

       Step 3 - Perform dispersion modeling analysis. Perform dispersion modeling using
emissions  data obtained in Step 2.   The meteorological data  ideally would be five1
separate years of sequential data to represent the season(s) proposed for the monitoring
program.  The averaging period should also match that proposed for the  monitoring
program, e.g., 24-hour periods.  If every 3rd of 6th day sample frequencies will be used for
the monitoring program, the same should be done for the modeling.  In short, the  goal of
the modeling is to compile an output data set comparable to the measured data set.

       Step 4-- Perform statistical analysis.  Correlation matrices for each pollutant are
compiled to show "R" values for all  site combinations.  The correlation considers all
"samples."  For example, if a-three-month sampling period is proposed, taking 24-hour
integrated samples on every third day, then there would be  30 modeled 24-hour averages
for each site and each pollutant.

       Step 5 - Group sites. Group sites by combining sites  correlated by 0.7 or higher
into one cluster.  (A 0.7 correlation coefficient was arbitrarily selected.  A higher or lower
value could be selected that best met project needs.)  There would be separate groups of
sites for each pollutant.  Assign a score of +1  to each cluster and subdivide the score
among the members of the cluster. This is done separately for each pollutant, and then
the scores at each site are summed across all pollutants.

       Step 6 - Successive elimination of  sites.   The site with the lowest score is
eliminated  and  scores recomputed (as was  initially done  in Step 5).  Another site is
eliminated as above and the scores recomputed,  and so  forth, until a core network (e.g., 3
sites) is described.
1   If multiple years of meteorological data are obtained, the model analysis is run for
   each receptor for each year to assess the influence of meteorological variability on site
    selection.
                                         B-ii

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B.2    Example Application:  Philadelphia IEMP

       This example application of the supersite concept first shows site selection based
on modeled data, followed, as a comparison, by sites that would be selected based on the
actual measured data set. Within the measured data set greater detail is provided in the
site elimination procedures of Step 6. (The reader is referenced to the model performance
study conducted for the BEMP Philadelphia Study 2 for background information on the
measured data set used for this example.]

       Siting Analysis Based on Modeled Data

       The IEMP Philadelphia Study operated  a ten-site monitoring network3 during the
period of 1983-1984.  Using these ten locations as "candidate sites," dispersion modeling
was used to select sites expected to be most independent.  Independence was defined as
having a correlation (R value) of less than or equal to 0.70 with all other "candidate sites."

       For benzene, ethylbenzene, trichloroethylene, and xylene all correlations in Step 1
were  above the  cutoff  of 0.7  (in fact, they  were above 0.84, 0.70,  0.73, and 0.79,
respectively), making all ten sites into one cluster. This is attributed to the dominance of
area sources in the  model and the inability to detect many  of the spatial variabilities
caused primarily by mobile sources and possibly by gasoline marketing.  The remaining
clusters are:

       Carbon tetrachloride         3,5, and 10; 4,7, and 10; 3,4,5,8, and 9
       Chloroform                 2 and 9; 3,4,5,7,8,9, and 10
       Ethyl-chloride               2 and 3; 5,,7,8, and 10; 5,8,9, and  10
       Perchloroethylen            1,2,3, and  5; 3,4,5,7,8,9, and 10
       1,2-dichloroethane           2 and 3; 5,7,8, and 10; 8 and 9

       Elimination of sites by Steps 5 and 6 of the site  selection procedure leads to:
2  Sullivan, D. A., 1985 ..  Evaluation  of the Performance of the Dispersion Model
   SHORTZ for  Predicting  Concentrations  of Air Toxics in the U.S. Environmental
   Protection Agency's Philadelphia Geographic Study.  U.S. Environmental Protection
   Agency, Integrated Environmental Management Divisions, Washington, D.C.
3  Nine sites had high enough data recovery to support the objectives of this analysis.
                                        B-iii

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Step
Stage 1
Stage 2
Stage 3
Stage 4
Stage 5
Stage 6
etc.
Site
Eliminated
7 out:
5 out:
8 out:
3 out:
10 out:
4 out:

Sites
Left
1,2,3,4
1,2,3,4
1,2,3,4
1,2,4,9
1*4,9
1,2,9


,5,8,9,10
,8,9,10
,9,10
,10



       This process  would thus prescribe sites  1,  2 and 9 as  yielding the  most
independent information, and thus represent the optimum location for 3 monitoring sites.
The process could be truncated or continued to  yield any number  of sites, down to a
single location.

       Siting Analysis Based on jvteasured Data

       As a test of the site selection approach described above, the measured data set
from the Philadelphia IEMP air toxics monitoring network was used  in place  of the
modeled data used in the previous subsections. The goal was to use the measured data set
to help evaluate  the  usefulness  of the model-based siting  procedure.   (The  following
discussion describes the site  elimination process in detail as it was carried out in
Phildelphia.)
                                        B-iv

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    The final clusters of Step 1 based on measured data are as follows:

    Compound	   Clusters
                    Number of Sites Needed
                    for Network4
    Benzene
    Carbon Tetrachloride
    Chloroform
    Ethylbenzene
    1,2-Dichloroethane
    Perchloroethylene
    Toluene
    Trichloroethylene
    Xylene
    1,2-Dichloropropane
9 and 10                   8
4 and 9                    8
1,3,4,8,9, and 10            4
1 and 5; 4,7, and 8           6
1 and 10                   8
4 and 10; 8 and 10           7
Sample size too small
3,4, and 8; 9 and 10          6
4 and 8; 7,9, and 10          6
1 and 4; 3 and 5             7
Considering one compound at a time.
                                   B-v

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       We note that for  perchloroethylene, site  10 is associated with sites  4 and 8,
although 4 and 8 are not highly correlated (0.58).  We then assigned a score of +1 to each
cluster and subdivided the score among the "members of each cluster.  Adding up the
scores across the nine chemicals produced the following values:

                     Site                               Score
                     1
                     2
                     3
                     4
                     5
                     7
                     8
                     .9
                     10
6.67
9.00
7.00
4.84
8.00
7.67
5.84
5.84
5.84
       From this analysis, site 4 clearly produced the least information, i.e., for seven of
the nine compounds considered,'site 4 was well correlated with other site(s) at  the  0.7
level or higher, and added marginal information to the network.   Consequently, if one
wanted to have a network of only nine sites, site 4 would be the first one to  eliminate
based on this approach.

       Next we assumed that station 4 was eliminated and repeated the above analysis.
This resulted in the information scores:
                                        B-vi

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                      Site                                Score

                     1                                   7.20
                     2                                   9.00
                     3                                   7.20
                     5                                   8.00
                     7                                   7.83
                    .8                                   6.70
                     9                                   6.53
                     10               .                   5.53

       Thus, the next site we would eliminate  would  be site 10.  Continuing  in this
manner we created networks with fewer  and-fewer sites until only three sites remained.
At that stage, "the sites were all  independent and so these represented a "core".  The
networks we obtained via this procedure  are as follows:

                                                         Remaining Sites

       Stage 1                      4 out:                1,2,3,5,7,8,9,10
       Stage 2                      10 out:               1,2,3,5,7,8,9
       Stage 3                      8 out:                1,2,3,5,7,9
       Stage 4                      3 out:                1,2,5,7,9
       Stage 5                      9 out:                1,2,5,7
       Stage 6.                      1 out:                2,5,7

       It should be noted that based on  the initial "value" ranking that sites 2,5, and 7
had  the highest scores:  Site 2 (9.0), site 5 (8.0) and site 7  (7.7).   This result is. not
surprising  when one considers the orientation of sources along the corridor in which the
monitoring network is located.   Site 2 is  located in  the downtown  area, which is
substantially different than the locations of the other sites that are in more industrialed
areas.  Site 5 is located in the heart of the industrial area of Bridesburg,  and as such would
be expected to be relatively independent.  Site 7 is located in an area north of the rest of
the network, and experienced substantially elevated concentrations for  automobile  related
                                        B-vii

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emissions with flow from the southwest, and as such, could be expected to be relatively
independent based on the measured data.

       Method Evaluation

       We note that at the end of the fourth stage of the modeling analysis, we have five
sites (1,2,4,9 and 10) in our proposed network, which include at least one representative
from each cluster for each of the nine chemicals.  In contrast, sites 1, 2,  5, 7 and 9 would
have been selected as optimum based on the actual measured data set.  It is interesting to
note that sites 5 and 7 were preferred sites based on the measured data  while those same
two sites were the first  to be eliminated for the theoretical model data.  On the other
hand, the model-based process selected sites 1,2, and 9 as important; these sites were
among the five most informative for the actual data.   Therefore, it appears that there is
some value in using dispersion models and spatial correlation analysis  as a guide to site
selection for air quality  studies.   Caution should be  used in selecting  only the top few
sites, because ast shown, the modeled data did well  in selecting the top  five sites, but
'resulted in significant differences  compared to the measured results when selecting a 2-3
site network. Further data needs to be evaluated to fully evaluate this approach.
                                        B-viii

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 1. REPORT NO.


        EPA-450/2-89-01Q
                           m n,    TECHNICAL REPORT DATA
                           (Please read Inunctions on the reverse before completing)
 4. TITLE AND SUBTITLE
                               ."' o .,

     Assessing Multiple Pollutant Multiple Source Cancer

     Risks From Urban Air Toxics  (Summary of Approaches

     and Insights From Completed and  Ongoing Urban Air
    ft te~int'~\  i^	
  '.AUTHORS  Toxics Assessment Studies)


     D.  Sullivan,  T. Lahre, M...Alford
 9. PERFORMING ORGANIZATION NAME AND ADDRESS
 12. SP
    fo^^e^aN7omi^?^rDoRge!lm Branch

    Air-Quality Management.Division-

    Office of Air Quality Planning and Standards

    Research Triangle Park,  N.  C.  27711


IS. SUPPLEMENTARY NOTES   '	
                                                       3. RECIPIENT'S ACCESSION NO
                                                       5. REPORT DATE
                                                            April 1Q«Q
                                                       6. PERFORMING ORGANIZATION CODE
                                                       8- PERFORMING ORGANIZATION REPORT NO
                                                        10. PROGRAM ELEMENT NO.
                                                        I. CONTRACT/GRANT NO.
                                                        3. TYPE OF REPORT AND PERIOD COVERED
                                                        «. SPONSORING AGE'NCY CODE
     techniques that  others  have  elected to employ and offers  insiahts

     that may  assist  the reader in selecting a  particular set  of
     techniques for use in a given locale.       Par^cuiar set  of



     Major topics covered include:  (1) a summary  of completed  and

     ongoing urban air toxics assessment studies,  (2?  SbieSt
     momtoring assessment approaches,  (3)  emission    amDient

     inventory/dispersion modeling assessment approaches  (4) aspects

     ?I^HP?SU;e ^  r±Sk aSSeSSment'   (5>  Control  stra?Igy evaStion
     (6)  data  handling,  and  (7)  evolving assessment technologies

     s2mpl?ngg reCept°r Deling,  personal  monitoring  S3 biSalsay
17.
                             KEY WORDS AND DOCUMENT ANALYSIS
                DESCRIPTORS
                                          b.IDENTIFIERS/OPEN ENDED TERMS
                                                                   c. COSATI Field/Gr<
    Air Toxics

    Assessment Methods, Air Toxics

    Air Toxics Cancer Studies

    Toxics, Air

    Urban Air Toxics Assessments

    Urban Soup
         ION STATEMEN1
                                          19. SECURITY CLASS (TinsReport)
                                                                   21. NO. OF PAGES

                                                                         237
                                          20. SECURITY CLASS (Tliispage/
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   Form 2220-1 (R«v. 4-77)   PREVIOUS EDITION is OBSOLETE

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