United States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park, NC 27711
EPA-454/R-98-012
May 1998
Air
r/EPA
GUIDANCE FOR USING
CONTINUOUS MONITORS
IN PM2 5 MONITORING
NETWORKS
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GUIDANCE FOR USING CONTINUOUS
MONITORS IN PM2.5 MONITORING NETWORKS
May 29, 1998
PREPARED BY
John G. Watson1
Judith C. Chow1
Hans Moosmiiller1
Mark Green1
Neil Frank2
Marc Pitchford3
PREPARED FOR
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
'Desert Research Institute, University and Community College System of Nevada, PO Box 60220, Reno, NV 89506
2U.S. EPA/OAQPS, Research Triangle Park, NC, 27711
3National Oceanic and Atmospheric Administration, 755 E. Flamingo, Las Vegas, NV 89119
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DISCLAIMER
The development of this document has been funded by the U.S. Environmental
Protection Agency, under cooperative agreement CX824291-01-1, and by the Desert
Research Institute of the University and Community College System of Nevada. Mention of
trade names or commercial products does not constitute endorsement or recommendation for
use. This draft has not been subject to the Agency's peer and administrative review, and
does not necessarily represent Agency policy or guidance.
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ABSTRACT
This guidance provides a survey of alternatives for continuous in-situ measurements
of suspended particles, their chemical components, and their gaseous precursors. Recent and
anticipated advances in measurement technology provide reliable and practical instruments
for particle quantification over averaging times ranging from minutes to hours. These
devices provide instantaneous, telemetered results and can use limited manpower more
efficiently than manual, filter-based methods. Commonly used continuous particle monitors
measure inertial mass, mobility, electron attenuation, light absorption, and light scattering
properties of fine particles. Sulfur and nitrogen oxides monitors can detect sulfate and nitrate
particles when the particles are reduced to a sulfur- or nitrogen-containing gas. The
measurement principles, as well as the operating environments, differ from those of the PM2.5
Federal Reference Method (FRM), and these differences vary between monitoring locations
and time of year. These variations are caused by the different properties quantified by a wide
array of measurement methods, modification of the aerosol by the sampling and analysis
train, and differences in calibration methods. When the causes of these discrepancies are
understood, they can be used advantageously to determine where and when: 1) equivalence
with FRMs is expected; 2) mathematical adjustments can be made to obtain a better
correlation; and 3) differences can be related to a specific particle or source characteristic.
in
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TABLE OF CONTENTS
Disclaimer ii
Abstract iii
Table of Contents iv
List of Tables vii
List of Figures viii
List of Acronyms xi
1. INTRODUCTION 1-1
1.1 Continuous Particle Monitors and Air Pollution 1-1
1.2 Federal Reference and Equivalent Methods 1-3
1.3 Relevant Documents 1-4
2. MEASURED PARTICLE PROPERTIES 2-1
2.1 Particle Size Distribution 2-1
2.2 Chemical Composition 2-5
2.3 Particle Interactions with Light 2-15
2.4 Mobility 2-16
2.5 Beta Attenuation 2-16
2.6 Summary 2-18
3. CONTINUOUS PARTICLE MEASUREMENT METHODS 3-1
3.1 Mass and Mass Equivalent 3-1
3.1.1 Tapered Element Oscillating Microbalance (TEOM®) 3-1
3.1.2 Piezoelectric Microbalance 3-12
3.1.3 Beta Attenuation Monitor (BAM) 3-13
3.1.4 Pressure Drop Tape Sampler (CAMMS) 3-14
3.2 Visible Light Scattering 3-14
3.2.1 Nephelometer 3-14
3.2.2 Optical Particle Counter (OPC) 3-19
3.2.3 Condensation Nuclei Counter (CNC) 3-19
3.2.4 Aerodynamic Particle Sizer (APS) 3-20
3.2.5 Light Detection And Ranging (LIDAR) 3-22
3.3 Visible Light Absorption 3-23
3.3.1 Aethalometer and Particle Soot/Absorption Photometer 3-24
3.3.2 Photoacoustic Spectroscopy 3-25
3.4 Electrical Mobility 3-26
3.4.1 Electrical Aerosol Analyzer (EAA) 3-26
3.4.2 Differential Mobility Particle Sizer (DMPS) 3-26
iv
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TABLE OF CONTENTS (continued)
3.5 Chemical Components 3-27
3.5.1 Single Particle Mass Spectrometers 3-27
3.5.2 Carbon Analyzer 3-28
3.5.3 Sulfur Analyzer 3-29
3.5.4 Nitrate Analyzer 3-30
3.5.5 Multi-Elemental Analyzer 3-31
3.5.5.1 Streaker 3-31
3.5.5.2 DRUM 3-31
3.6 Precursor Gases 3-32
3.6.1 Ammonia Analyzer 3-32
3.6.2 Nitric Acid Analyzer 3-33
3.6.3 Fourier Transform Infrared (FTIR) Spectroscopy 3-33
3.6.4 Other Nitric Acid Instruments 3-34
3.7 Summary 3-35
4. MEASUREMENT PREDICTABILITY, COMPARABILITY, AND
EQUIVALENCE 4-1
4.1 Measurement Comparability 4-3
4.2 Measurement Predictability 4-15
4.2.1 Particle Light Scattering and PM2.5 Concentration 4-15
4.2.2 Particle Light Absorption and Elemental Carbon Concentration 4-17
4.2.3 Mass Concentration and Optical Measurements 4-21
4.3 Summary 4-29
5. USES OF CONTINUOUS PM MEASUREMENTS 5-1
5.1 Diurnal Variations 5-2
5.2 Wind Speed and PM Relationships 5-4
5.3 Source Directionality 5-8
5.4 Receptor Zones of Representation and Source Zones of Representation 5-19
5.5 Summary 5-19
6. CONTINUOUS PARTICLE MONITORING IN PM2.5 NETWORKS 6-1
6.1 PM2.5 Network Site Types 6-1
6.2 Federal Reference and Equivalent Methods 6-2
6.3 Potential Tolerances for FEM and CAC Monitor Designation 6-4
7. REFERENCES 7-1
APPENDIX A: CONTINUOUS MEASUREMENT DATA SETS A-l
A.I Southern California Air Quality Study (SCAQS) A-l
A.2 1995 Integrated Monitoring Study (IMS95) A-2
A.3 1997 Southern California Ozone Study (SCOS97) - North American
Research Strategy for Tropospheric Ozone (NARSTO) A-2
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TABLE OF CONTENTS (continued)
A.4 San Joaquin Valley Compliance Network A-3
A.5 Imperial Valley/Mexicali Cross Border PMio Transport Study A-4
A.6 Washoe County (Nevada) Compliance Network A-5
A.7 Las Vegas PMio Study A-5
A.8 Northern Front Range Air Quality Study (NFRAQS) A-6
A.9 Mount Zirkel Visibility Study A-7
A. 10 Robbins Paniculate Study A-8
A. 11 Birmingham (Alabama) Compliance Network A-8
A. 12 Mexico City Aerosol Characterization Study A-8
VI
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LIST OF TABLES
Table 2-1
Table 3-1
Table 3-2
Table 3-3
Table 4-1
Table 4-2
Table 4-3
Table 4-4
Table 5-1
Table 5-2
Measured Aerosol Concentrations for the 19 Regions in the
IMPROVE Network from March 1988 to February 1991
Summary of Continuous Monitoring Technology
Operating Characteristics of Commercially Available Nephelometers
Comparison of Condensation Nuclei Counter Specifications
Test Specifications for PM2.5 Equivalence to Federal Reference
Method
Collocated Comparisons between Continuous and Filter-Based PM2.5
or PMio Monitors from Recent Aerosol Characterization Studies
PM2.5 Mass Scattering Efficiency (m2/g) as a Function of Relative
Humidity (%)
Relationships between Optical Measurements and PM Concentrations
Cross-Border Fluxes at the Calexico Site
2-12
3-2
3-17
3-22
4-2
4-6
4-18
4-22
5-12
Distibution of Hourly PMio and SO2 Concentrations at Robbins, IL, as
a Function of Wind Speed and Wind Direction 5-15
vn
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LIST OF FIGURES
Figure 2-1 Particle size range of aerosol properties and measurement instruments
- application range of aerosol instruments. 2-2
Figure 2-2 Size range of aerosol properties. 2-3
Figure 2-3 Example of particle number, surface area, and volume size distribution
in the atmosphere. 2-4
Figure 2-4 Representative mass size distribution with measured particle size
fractions and dominant chemical components. 2-6
Figure 2-5 Changes in liquid water content of sodium chloride, ammonium
nitrate, ammonium sulfate, and a combination of compounds at
different relative humidities. 2-9
Figure 2-6 Fraction of total nitrate as particulate ammonium nitrate at different
temperatures for various relative humidities and ammonia/nitrate
molar ratios. 2-11
Figure 2-7 Ammonium sulfate particle scattering efficiency as a function of
particle diameter. 2-17
Figure 2-8 Particle absorption efficiencies as a function of elemental carbon
particle diameter for several densities (first number in legend), real and
imaginary indices of refraction (second and third numbers in legend). 2-17
Figure 3-1 Particle scattering efficiency (osp) as function of particle diameter for
silica particles (5 = 2.2 g/cm3, n = 1.46 at 550 nm) and monochromatic
green light (A, = 550 nm). 3-19
Figure 4-1 Collocated comparisons of 24-hour-averaged TEOM and BAM with
high-volume SSI for PMio measurements acquired in Central
California between 1988 and 1993. 4-4
Figure 4-2 Collocated comparison of 24-hour-averaged TEOM and high-volume
SSI PMio during winter and summer at the Bakersfield and
Sacramento sites in Central California between 1988 and 1993. 4-5
Figure 4-3 Collocated comparison of three-hour PM2.5 SFS with PM2.5 TEOM at
the Bakersfield site, as well as PM2.5 and PMio SFS with BAM at the
Chowchilla site in California's San Joaquin Valley between 12/09/95
and 01/06/96. 4-8
Vlll
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LIST OF FIGURES (continued)
Figure 4-4 Collocated comparisons of 24-hour PMio with BAM versus
high-volume SSI, medium-volume SFS, low-volume dichotomous,
and mini-volume portable samplers in Imperial Valley, CA, between
03/13/92 and 08/29/93. 4-10
Figure 4-5 Collocated comparisons of 24-hour-averaged PMio BAM versus SFS
and portable samplers in Las Vegas Valley, NV, between 01/03/95 and
01/28/96. 4-12
Figure 4-6 Collocated comparison of 6- and 12-hour PMio BAM versus SFS in
north Denver, CO, during winter and summer 1996. 4-13
Figure 4-7 Collocated comparison of 24-hour PMio BAM versus high-volume SSI
and dichotomous samplers in southeastern Chicago, IL, between
10/12/95 and 09/30/96. 4-14
Figure 4-8 Relationship between hourly particle light scattering (bsp) measured by
nephelometer and ambient relative humidity in San Joaquin Valley,
CA, between 12/09/95 and 01/06/96. 4-16
Figure 4-9 Relationship between PM2.5 light absorption (bap) measured by
densitometer on Teflon-membrane filter and elemental carbon
measured by thermal/optical reflectance on a co-sampled quartz-fiber
filter for three-hour samples acquired in San Joaquin Valley, CA,
between 12/09/95 and 01/06/96. 4-19
Figure 4-10 Relationship between filter-based PM2.5 SFS elemental carbon
(measured by thermal/optical reflectance on quartz-fiber filter) and
aethalometer black carbon on three-hour samples acquired in the San
Joaquin Valley between 12/09/95 and 01/06/96. 4-20
Figure 5-1 Winter weekday and weekend patterns in PMio concentrations during
summer (April to September 1995) and winter (October 1995 to March
1996) periods at a mixed light industrial/suburban site in the Las
Vegas Valley, NV. 5-3
Figure 5-2 Weekday and weekend patterns for 50th percentile PMio
concentrations at the City Center and East Charleston (Microscale)
urban center sites and Bemis/Craig and Walter Johnson urban
periphery sites during Winter 1995 in the Las Vegas Valley, NV. 5-5
Figure 5-3 Diurnal variations of hourly BAM PMio concentrations at a
monitoring site in Calexico, CA, near the U.S./Mexico border during
03/12/92 to 08/29/93. 5-6
IX
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LIST OF FIGURES (continued)
Figure 5-4 Relationships between BAM PMio and wind speed for northerly flow. 5-7
Figure 5-5 Relationships between BAM PMio and wind speed for southerly flow. 5-7
Figure 5-6 Distribution of hourly BAM PMio as a function of wind speed at 14
meteorological sites in the Las Vegas Valley, NV, monitoring network
between 01/01/95 and 01/31/96. 5-9
Figure 5-7 Relationship between hourly averaged wind speed and
concentrations at a North Las Vegas, NV, site (Bemis) during the
period of 04/08/95 to 04/09/95. 5-10
Figure 5-8 Distribution of hourly PMio concentrations as a function of wind speed
at a southeastern Chicago, IL, site (Eisenhower) during the first three
quarters of 1996. 5-11
Figure 5-9 PMio concentrations at the 20th, 50th, and 80th percentiles at a
southeastern Chicago, IL, site (Eisenhower) during the first three
quarters of 1996. 5-13
Figure 5-10 Hourly PM2.5 elemental concentrations (ng/m3) at three southeastern
Chicago, IL, sites near Robbins, IL, averaged by wind sector for one
week apiece during the first three calendar quarters of 1996. 5-16
Figure 5-11 Five-minute-average aethalometer black carbon measurements at a
downtown (MER) site and a suburban (FED) site in Mexico City. 5-20
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LIST OF ACRONYMS
°C degrees Celsius
jig microgram
|im micron or micrometer
ACPM Ambient Carbon Particulate Monitor
ACS American Chemical Society
AIRS Aerometric Information Retrieval System
AMTIC Ambient Monitoring Technology Information Center
APNM Automated Particle Nitrate Monitor
APS Aerodynamic Particle Sizer
ASTM American Society for Testing Materials
ATOFMS Aerosol Time Of Flight Mass Spectrometry
babs light absorption
BAM Beta Attenuation Monitor
bap particle light absorption
BC black carbon
bext light extinction
bsp particle light scattering
CAC Correlated Acceptable Continuous
CAMMS Continuous Ambient Mass Monitoring System
CFR Code of Federal Regulations
CIMS Chemical lonization Mass Spectrometry
CMZ Community Monitoring Zone
CNC Condensation Nuclei Counter
XI
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LIST OF ACRONYMS (continued)
CO carbon monoxide
CO2 carbon dioxide
COH Coefficient Of Haze
CORE Community-Oriented site (or COmmunity-REpresentative site)
DIAL Differential Absorption Lidar
DIG DIChotomous sampler
DMPS Differential Mobility Particle Sizer
DOAS Differential Optical Absorption Spectroscopy
DRUM Davis Rotating-drum Universal-size-cut Monitoring impactor
EAA Electrical Aerosol Analyzer
EC elemental carbon
FEM Federal Equivalent Method
FPD Flame Photometric Detector
FRM Federal Reference Method
FTIR Fourier Transform InfraRed spectroscopy
H2SO4 sulfuric acid
HNOs nitric acid
1C Ion Chromatography
IMPROVE Interagency Monitoring of PROtected Visual Environments
km kilometer
L/min liter per minute
LAMMS Laser Microprobe Mass Spectrometer
LED Light Emitting Diode
xn
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LIST OF ACRONYMS (continued)
LIDAR
LPFF
m
m3
mm
Mm"1
MPA
MS
MSA
mW
NAAQS
NAMS
Nd:YAG
NFRAQS
NH4+
NH4HSO4
NFLJSTOs
NIST
nm
NO2
Light Detecting And Ranging
Laser Photolysis Fragment Fluorescence
meter
cubic meter
millimeter
inverse megameters
Metropolitan Planning Area
Mass Spectrometry
Metropolitan Statistical Area
milliwatt
National Ambient Air Quality Standards
National Air Monitoring Stations
Neodymium Yttrium Aluminum Garnet
Northern Front Range Air Quality Study [Colorado]
ammonia
ammonium
ammonium bisulfate
ammonium nitrate
ammonium sulfate
National Institute for Standards and Technology
nanometer
nitrogen dioxide
nitrate
xni
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LIST OF ACRONYMS (continued)
NOX
NFS
OAQPS
OC
OPC
ORD
PALMS
PM
PM2.5
ppb or ppbv
ppt or pptv
PSAP
RH
RSMS
S
SLAMS
SMPA
S02
SO4=
SPM
SSI
nitrogen oxides
National Park Service
Office of Air Quality Planning and Standards [U.S. Environmental Protection
Agency]
organic carbon
Optical Particle Counter
Office of Research and Development [U.S. Environmental Protection Agency]
Particle Analysis by Laser Mass Spectrometry
suspended Particulate Matter
suspended Particulate Matter with aerodynamic diameters less than 10
microns (|im)
suspended Particulate Matter with aerodynamic diameters less than 2.5
microns (|im)
parts per billion volume
parts per trillion volume
Particle Soot/Absorption Photometer
Relative Humidity
Rapid Single-particle Mass Spectrometer
sulfur
State/Local Air Monitoring Stations
Scanning Mobility Particle Analyzer
sulfur dioxide
sulfate
Special Purpose Monitor
Size-Selective Inlet
xiv
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LIST OF ACRONYMS (continued)
TDLAS Tunable Diode Laser Absorption Spectroscopy
TEOM Tapered Element Oscillating Microbalance
TSP Total Suspended Particles
U.S. EPA U.S. Environmental Protection Agency
WINS Well Impactor Ninety-Six PM2.5 inlet
xv
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1. INTRODUCTION
This guidance describes available continuous monitoring methods for suspended
particles. Some of these methods are candidates for Correlated Acceptable Continuous
(CAC) monitors that might be used in parallel with filter-based samplers to reduce sampling
frequencies for PM2.5 (fraction of particles with aerodynamic diameters less than 2.5 jim)
(U.S. EPA, 1997a, 1997b). The guidance describes: 1) properties of suspended particles that
can be measured; 2) available devices to measure these properties over durations of one-hour
or less; 3) conditions under which continuous monitor measurements might or might not be
correlated with or predictors of filter-based particle concentrations; and 4) how continuous
measurements can be used to attain a variety of monitoring objectives.
The relevant NAAQS are (U.S. EPA, 1997c, 1997d):
• Twenty-four hour average PM2.5 not to exceed 65 ng/m3 for a three-year average
of annual 98th percentiles at any population-oriented monitoring site in a
Metropolitan Planning Area (MPA).
• Three-year annual average PM2.5 not to exceed 15 ng/m3 at a single
community-oriented monitoring site or for the spatial average of eligible
community exposure sites in a MPA.
• Twenty-four hour average PMio (particles with aerodynamic diameters less than
10 jim) not to exceed 150 ng/m3 for
percentiles at any site in a monitoring area.
10 |im) not to exceed 150 ng/m3 for a three-year average of annual 99th
• Three-year average of three annual average PMio concentrations not to exceed 50
l|j,g/m3 at any site in a monitoring area.
This section states the background, federally specified monitoring methods, and
objectives of this continuous monitoring guidance document. Section 2 describes the
chemical and physical properties of particulate matter (PM) that are measured by different
continuous monitoring techniques. Section 3 specifies the measurement principles,
averaging periods, detection limits, and potential uses of existing continuous monitoring
instruments. Section 4 examines available collocated comparisons between continuous
particle monitors and filter samplers to determine the degree to which they are correlated in
different environments. Section 5 provides guidance and examples for using continuous
PM2.5 measurements to address source/receptor relationships. Section 6 describes how
continuous particle monitors might be used in PM2.5 networks to supplement filter
measurements. Cited references and resources that provide more detail on specific topics are
assembled in Section 7. Data bases, assembled from collocated filter and continuous PM2 5
and PMio measurements in past air quality studies, are provided in Appendix A.
1.1 Continuous Particle Monitors and Air Pollution
Continuous particle measurements have been made since the early days of air
pollution monitoring. The British Smoke Shade measurement was established as a
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continuous monitoring device in London during the 1920s to quantify the darkening of filter
material as air was drawn through it (Brimblecombe, 1987; Thornes, 1978). It evolved into a
more automated and reproducible particle concentration measurement during the ensuing
decades (Hill, 1936; Ingram and Golden, 1973). In the United States, the principle of light
absorption by particles was implemented in the form of Coefficient of Haze (COH) measured
by the American Iron and Steel Industry's paper tape sampler (Hemeon et al., 1953; ASTM,
1985;Herricketal., 1989).
In these early measurements, visible light (generated by an incandescent bulb) was
transmitted through (or reflected from in the case of the British Smoke method) a section of
filter paper before and after ambient air is drawn through it. The optical density of the
particle deposit was determined from the logarithm of the ratio of intensities measured on the
filter with and without the deposit. In its most advanced implementation, a clean portion of a
filter tape was periodically moved into the sampling position, thereby allowing diurnal
variations (typically hourly averages) in particle concentrations to be recorded. While these
methods provide a good measure of light absorption by suspended particles (Edwards et al.,
1984), they do not account for the portion of aerosol mass that does not absorb light (Ball and
Hume, 1977; Barnes, 1973; Waller, 1963; Waller et al., 1963; Lee et al., 1972; Lodge et al.,
1981). The particle size collection characteristics of the British Smoke Shade sampler were
not understood until the late 1970s (McFarland, 1979), when the instrument was found to
collect particles with aerodynamic diameters less than ~ 5 jam.
In spite of this specificity to light absorbing aerosol, the original epidemiological
associations between particles and health were established from these light absorption
measurements. Several of these health associations were used to justify the previous TSP
(Total Suspended Particles, particles with aerodynamic diameters less than 30 jim) (U.S.
Dept. HEW, 1969; Hemeon, 1973) and PMio NAAQS (U.S. EPA, 1982, 1987). These early
continuous measurements illustrate that a variety of particle indicators, including particle
mass and light absorption, can be associated with health end-points even though their
measured quantities are not the same. More recent health studies (U.S. EPA, 1996; Vedal,
1997) confirm positive correlations between a variety of particle indicators, several of which
derive from continuous measurement methods, and health end-points.
Continuous in-situ monitors have been used to acquire consecutive hourly-averaged
concentrations of mass, mass surrogates, chemical components, and precursor gases.
Continuous monitors contrast with "manual" measurements that draw air through an
absorbing substrate or filter medium that retains atmospheric pollutants for later laboratory
analysis. Since the promulgation of NAAQS in the early 1970s, continuous monitors have
been used to measure sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone gases.
Suspended particles, however, have typically been measured by filtration with subsequent
laboratory weighing or chemical analysis (Chow and Watson, 1998a).
Three continuous PMio monitors based on inertial mass (R&P Tapered Element
Oscillating Microbalance, U.S. EPA, 1990) and electron absorption (Andersen Instruments
and Wedding and Associates Beta Attenuation Monitors, U.S. EPA, 1990, 1991) have been
designated as equivalent methods that can be used to determine compliance with the PMio
NAAQS. The PMio equivalence designation (U.S. EPA, 1987) results from wind-tunnel test
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specifications for the PMio inlet and collocated sampling with filter-based PMio reference
methods at different test locations. Subsequent comparisons of these continuous monitors
with each other and with filters show very good and very poor agreement, mostly depending
on the aerosol being sampled (e.g., Allen et al., 1997; Arnold et al., 1992; Meyer et al., 1992;
Shimp, 1988; Tsai and Cheng, 1996; van Elzakker and van der Muelen, 1989).
New PM2.5 monitoring regulations (40 CFR 58, Appendix D, Section 2.8.1.3.8)
require continuous PM2.5 monitors to be operated in large U.S. metropolitan areas. These
regulations (40 CFR Section 58.13) also define a "CAC" monitor as an optional PM2.5
analyzer that can be used to supplement a PM2s reference or equivalent sampler at
community-oriented (CORE) monitoring sites to reduce sampling frequency from daily to
every third day (U.S. EPA, 1997b). This alternative sampling approach is intended to
provide state and local agencies with additional flexibility in designing and operating PM2.5
networks.
The potential uses of CAC measurements are to: 1) reduce site visits and network
operation costs; 2) identify the need to increase sampling frequency with a PM2.5 reference or
equivalent method in order to make better comparisons to the PM2.5 NAAQS; 3) evaluate
telemetered concentrations in real-time to issue alerts or to implement periodic control
strategies (e.g., burning bans, no-drive days); 4) evaluate diurnal variations in human
exposures to outdoor air; 5) define zones of representation of monitoring sites and zones of
influence of pollution sources; and 6) understand the physics and chemistry of high PM2.5
and PMio concentrations.
Unless a continuous analyzer is designated as an equivalent method, its data cannot
be used to determine NAAQS compliance. However, the potential discrepancies between
continuous PM and manual measurements can be determined. This will permit selection of
acceptable continuous methods that can be correlated with a federal reference or equivalent
method.
1.2 Federal Reference and Equivalent Methods
The revised PM NAAQS represent a major change from previous standards in terms
of the size fraction being measured, the averaging of concentrations over space and time, the
monitoring methods used, and network design strategies. Federal Reference Method (FRM)
or Federal Equivalent Method (FEM) samplers are to be used in PM2.s compliance
monitoring networks (i.e., State and Local Air Monitoring Stations [SLAMS] and National
Ambient Monitoring Stations [NAMS]). Interagency Monitoring of Protected Visual
Environments (IMPROVE) samplers may also be used at regional background or regional
transport sites in lieu of FRMs or FEMs. Continuous monitors can be tested and classified as
Class III FEM for compliance monitoring.
Sampler design, performance characteristics, and operational requirements for the
PM2.5 FRM are specified in 40 CFR part 50, Appendix L; 40 CFR part 53, Subpart E; and 40
CFR part 58, Appendix A (U.S. EPA, 1997a-d). The PM2.5 FRM is intended to acquire
deposits over a 24-hour period on a Teflon-membrane filter from air drawn at a controlled
flow rate through the Well Impactor Ninety Six (WINS) PM2 5 inlet. The inlet and size
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separation components, filter types, filter cassettes, and internal configurations of the filter
holder assemblies are specified by design, with drawings and manufacturing tolerances
published in 40 CFR part 53 (U.S. EPA, 1997b). Other sampler components and procedures
(such as flow rate control, operator interface controls, exterior housing, data acquisition) are
specified by performance characteristics, with specific test methods to assess that
performance.
Federal Equivalent Methods (FEMs) are divided into several classes in order to
encourage innovation and provide monitoring flexibility. Class I FEMs meet nearly all FRM
specifications, with minor design changes that permit sequential sampling without operator
intervention and different filter media in parallel or in series. Flow rate, inlets, and
temperature requirements are identical for FRMs and Class I FEMs. Particles losses in flow
diversion tubes are to be quantified and must be in compliance with Class I FEM tolerances
specified in 40 CFR part 53, Subpart E (U.S. EPA, 1997b).
Class II FEMs include samplers that acquire 24-hour integrated filter deposits for
gravimetric analysis, but that differ substantially in design from the reference-method
instruments. These might include dichotomous samplers, high-volume samplers with PM2.5
size-selective inlets, and other research samplers. More extensive performance testing is
required for Class II FEMs than for FRMs or Class I FEMs, as described in 40 CFR part 53,
Subpart F (U.S. EPA, 1997b).
Class III FEMs include samplers that do not qualify as Class I or Class II FEMS.
This category is intended to encourage the development of and to permit the evaluation of
new monitoring technologies that increase the specificity of PM2.5 measurements or decrease
the costs of acquiring a large number of measurements. Class III FEMs may be filter-based
integrated samplers or filter- or non-filter-based in-situ continuous or semi-continuous
samplers. Test procedures and performance requirements for Class III candidate instruments
will be determined on a case-by-case basis. Performance criteria for Class III FEMs will be
restrictive because equivalency to reference methods must be demonstrated over a wide
range of particle size distributions and aerosol compositions.
1.3 Relevant Documents
This continuous particle monitoring guidance is complemented by other U.S. EPA
documents:
• Guidance for Network Design and Optimum Site Exposure for PM2.5 and
Draft Version 3. Prepared under a cooperative agreement between U.S. EPA
Office of Air Quality Planning and Standards, Research Triangle Park, NC, and
Desert Research Institute, Reno, NV. December 15, 1997 (Watson et al.,1997a).
• Guideline on Speciated Particulate Monitoring, Draft 2. Prepared under a
cooperative agreement between U.S. EPA Office of Air Quality Planning and
Standards, Research Triangle Park, NC, and Desert Research Institute, Reno, NV.
February 9, 1998 (Chow and Watson, 1998a).
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• Prototype PM2.5 Federal Reference Method Field Studies Report - An EPA Staff
Report. U.S. EPA Office of Air Quality Planning and Standards, Las Vegas, NV.
July 9, 1997 (Pitchford et al., 1997).
• Revised Requirements for Designation of Reference and Equivalent Methods for
PM2.5 and Ambient Air Quality Surveillance for Particulate Matter - Final Rule.
40 CFR part 58. Federal Register, 62(138):38830-38854. July 18, 1997 (U.S.
EPA, 1997a).
• Revised Requirements for Designation of Reference and Equivalent Methods for
PM2.5 and Ambient Air Quality Surveillance for Particulate Matter - Final Rule.
40 CFR part 53. Federal Register, 62(138):38763-38830. July 18, 1997 (U.S.
EPA, 1997b).
• National Ambient Air Quality Standards for Particulate Matter - Final Rule. 40
CFR part 50. Federal Register, 62(138):38651-38760. July 18, 1997 (U.S. EPA,
1997c).
• National Ambient Air Quality Standards for Particulate Matter; Availability of
Supplemental Information and Request for Comments - Final Rule. 40 CFR part
50. Federal Register, 62(138):38761-38762. July 18, 1997 (U.S. EPA, 1997d).
1-5
-------
2. MEASURED PARTICLE PROPERTIES
Particles in the atmosphere vary in size, chemical composition, and optical properties.
Airborne particle diameters range over five orders of magnitude, from a few nanometers to
around 100 micrometers. Aerodynamic diameter (the diameter of spherical particles with
equal settling velocity and unit density of 1 g/cm3) is used in aerosol technology to
characterize air filtration, instrument performance, and respiratory deposition. Except for
spherical particles of unit density, the actual diameter or geometric mean diameter (which
accounts for actual particle density and shape factors) is smaller than the commonly
referenced aerodynamic diameter.
Different aerosol monitoring techniques have been developed to measure aerosol
properties in different size ranges. As shown in Figure 2-1, aerosol sizes between 0.001 and
100 |om can be quantified with continuous or manual aerosol sampling instruments (Hinds,
1982; Willeke and Baron, 1993). Figure 2-2 illustrates the particle size ranges that can be
measured in terms of aerosol number, surface area, volume and mass size distribution, mode
of aerosol, inhalation properties, deposition mechanism, and optical features. This section
discusses the chemical, physical (e.g., mobility), and optical (e.g., light scattering, light
absorption) properties of aerosol.
2.1 Particle Size Distribution
Particle size is one of the key parameters in determining emission sources,
atmospheric processes, formation mechanisms, deposition/removal processes, visibility
impairment, as well as interactions with the human respiratory system and associated health
effects. Aerosol particle sizes are often characterized by their size distributions. Figure 2-3
displays the multi-modal particle characteristics of the aerosol number, surface area, and
volume distributions. Size distributions like these have been found under a wide range of
environmental and emissions conditions.
The number of particles in the atmosphere can often exceed 107 or 108 for each cubic
centimeter of urban or non-urban air. The top panel of Figure 2-3 shows that the largest
number of particles is in the nuclei or ultrafine size fraction with particle diameters less than
0.1 |im. The number distribution exhibits a bimodal feature that peaks at -0.02 and -0.1 jim.
Ultrafine particles are often observed near emission sources and possess a very short lifetime,
with a duration of less than one hour. Ultrafine particles rapidly condense on or coagulate
with larger particles or serve as nuclei for fog or cloud droplets, forming particles in the
accumulation mode (0.08 to ~2 jim).
There is increasing concern regarding the potential health effects associated with
inhalation of ultrafine particles. Phalen et al. (1991) showed that lung deposition peaks at
60% for -0.03 jam particles. These high deposition levels in the upper respiratory system
may aggravate symptoms of rhinitis, allergies, and sinus infections, and are associated with
acute mortality (Oberdorster et al., 1995; Finlay et al., 1997). Continuous instruments that
can measure particle number concentrations include the condensation nuclei counter
2-1
-------
0.01
-*-
-*-Diffl
*
Mm 0.1
Electrical rr
sion batter
—s
Mm 1.0 Mm 10 Mm 100 Mm
obility — >-
Diff'
y— s-
„..
-e-Optical p;
article counts
itv
irs •&-
-« ^Impactors-j a-
-Opt
ical microsc
Centrifuges
-«= —
ope>-
~^-
— Coulter counter —
-^ —
3—
-Sieves —
0.001 0.01 0.1 1.0 10
Lognormal Particle Diameter (urn)
(a)
100
1000
Figure 2-1. Particle size range of aerosol properties and measurement instruments
application range of aerosol instruments (modified from Hinds, 1982).
2-2
-------
0.01
0.1
1.0
10
100
Settling
Charging
Kelvin
effect
Stokes with slip corrections"''^ Stokes .^ Transition
Diffusion
Combined
Field
Significant
Not significant
Atmospheric
aerosol Nuclei
modes ~
Accumulation / Coarse particle
Respiratory
deposition,
regions
Respiratory
deposition
mechanisms
J_
Alveolar
T.B./ Head /Not inhaled
Diffusion
Impaction and settling
Sampling Diffusion losses
Anisokinetic losses
Filtration Diffusion /^ D. + 1. / Impaction and interception
Size-
selective
sampling
Light
scattering
Fine
Coarse
Rayleigh
Mie
0.01
0.1 1.0
Particle diameter (/im)
10
100
Figure 2-2. Size range of aerosol properties (modified from Hinds, 1982).
2-3
-------
6000
§ 4000
2000 -
o
o
0.01 0.10 1.00
Diameter, jim
10.00
Figure 2-3. Example of particle number, surface area, and volume size distribution in the
atmosphere (based on Seinfeld and Pandis, 1997).
2-4
-------
(Pollak and Metnieks, 1959; Cheng, 1993), aerosol particle sizer (Wilson and Liu, 1980;
Baron et al., 1993), differential mobility analyzer (Yeh, 1993), diffusion battery (Fuchs,
1964; Cheng, 1993), electrical aerosol analyzer (Whitby and Clark, 1966), and optical
particle counter (Hodkinson, 1966; Whitby and Vomela, 1967; Sloane et al., 1991).
The surface area distribution in the middle panel of Figure 2-3 also exhibits a bimodal
feature that peaks at ~0.2 and ~1.3 jim. These particles, classified as accumulation range
(0.08 to ~2 |im), result from the coagulation of ultrafine particles, from condensation of
volatile species, from gas-to-particle conversion, and from finely ground dust particles.
Particles in the accumulation mode scatter and absorb light more efficiently than the larger
particles.
Particle surfaces are directly exposed to body fluids following inhalation or ingestion.
Potentially toxic trace metals and organic gases can be adsorbed onto these surfaces.
Characterizing the physics and chemistry of airborne particle surfaces is needed to
understand the biomechanisms of exposed populations. Electron spectroscopy (Lodge,
1989), electron probe microanalysis (Wernish, 1985), and time-of-flight mass spectrometry
(Prather et al., 1994) are applied for particle surface analysis.
Most aerosol measurements report integrals of the mass size distribution as shown in
Figure 2-4. Note that the nucleation and accumulation ranges constitute the PM2.5 size
fraction. The majority of sulfuric acid, ammonium bisulfate, ammonium sulfate, ammonium
nitrate, organic carbon, and elemental carbon is found in this size range. Particles larger than
2.5 |im are called "coarse" particles; they result from grinding activities and are dominated
by materials of geological origin. Pollen and spores are also found in the coarse mode.
Several continuous monitors measure chemical components dominating the
accumulation mode, such as the sulfur analyzer (e.g., Allen et al., 1984; Benner and
Stedman, 1989, 1990), automated nitrate analyzer (Hering, 1997), in-situ carbon analyzer
(Turpin et al., 1990a, 1990b; Turpin and Huntzicker, 1991; Rupprecht et al., 1995), and
time-of-flight mass spectrometer (Nordmeyer and Prather, 1994; Prather et al., 1994).
Continuous monitors that measure precursor gases (gases that transform into particles in the
atmosphere), including the ammonia analyzer (e.g., Rapsomanikis et al., 1988; Genfa et al.,
1989; Harrison and Msibi, 1994) and nitric acid analyzer (e.g., Burkhardt et al., 1988), can
also be used to address gas-to-particle transformation processes in the atmosphere.
2.2 Chemical Composition
Particle mass has been the primary property measured for compliance with PM
standards, mainly due to its practicality and cost-effectiveness. Epidemiological studies have
shown a relationship between increased ambient particle concentrations and adverse health
outcomes (U.S. EPA, 1996; Vedal, 1997). Attempts have been made to attribute observed
associations to specific compounds of airborne particles. The relative abundances of
chemical components in the atmosphere closely reflect the characteristics of emission
sources. These chemical compositions need to be quantified in order to establish causality
between exposure and health effects. Major chemical components of PM2.5 or PMio mass in
2-5
-------
Sulfate, Nitrate,
Ammonium, Organic
Carbon, Elemental
Carbon, Heavy Metals,
Fine Dust
0.01
0.1 1 10
Particle Aerodynamic Diameter (|Jm)
100
Figure 2-4. Representative mass size distribution with measured particle size fractions and
dominant chemical components.
2-6
-------
urban and non-urban areas consist of nitrate, sulfate, ammonium, carbon, geological material,
sodium chloride, and liquid water:
• Nitrate: Ammonium nitrate (NILtNOs) is the most abundant nitrate compound,
resulting from a reversible gas/particle equilibrium between ammonia gas (NHa),
nitric acid gas (HNOs), and particulate ammonium nitrate. Because this
equilibrium is reversible, ammonium nitrate particles can easily evaporate in the
atmosphere, or after they have been collected on a filter, owing to changes in
temperature and relative humidity (Stelson and Seinfeld, 1982a, 1982b; Allen et
al., 1989). Sodium nitrate (NaNOs) is found in the PM2.5 and coarse fractions
near sea coasts and salt playas (e.g., Watson et al., 1994b) where nitric acid vapor
irreversibly reacts with sea salt (NaCl).
• Sulfate: Ammonium sulfate ((NH4)2SO4), ammonium bisulfate ((NH4HSO4), and
sulfuric acid (H2SO4) are the most common forms of sulfate found in atmospheric
particles, resulting from conversion of gases to particles. These compounds are
water-soluble and reside almost exclusively in the PM2.5 size fraction. Sodium
sulfate (Na2SC>4) may be found in coastal areas where sulfuric acid has been
neutralized by sodium chloride (NaCl) in sea salt. Though gypsum (Ca2SC>4) and
some other geological compounds contain sulfate, these are not easily dissolved in
water for chemical analysis. They are more abundant in the coarse fraction than
in PM2.5, and are usually classified in the geological fraction.
• Ammonium: Ammonium sulfate ((NJL^SC^), ammonium bisulfate (NH4HSO4),
and ammonium nitrate (NJLiNOs) are the most common compounds. The sulfate
compounds result from irreversible reactions between sulfuric acid and ammonia
gas, while the ammonium nitrate can migrate between gases and particle phases
(Watson et al., 1994a). Ammonium ions may coexist with sulfate, nitrate, and
hydrogen ions in small water droplets. While most of the sulfur dioxide and
oxides of nitrogen precursors of these compounds originate from fuel combustion
in stationary and mobile sources, most of the ammonia derives from living beings,
especially animal husbandry practiced in dairies and feedlots.
• Organic Carbon: Particulate organic carbon consists of hundreds, possibly
thousands, of separate compounds. The mass concentration of organic carbon can
be accurately measured, as can carbonate carbon, but only about 10% of specific
organic compounds that it contains have been measured. Vehicle exhaust (Rogge
et al., 1993a), residential and agricultural burning (Rogge et al., 1998), meat
cooking (Rogge et al., 1991), fuel combustion (Rogge et al., 1993b, 1997), road
dust (Rogge et al., 1993c), and particle formation from heavy hydrocarbon (Cg to
C2o) gases (Pandis et al., 1992) are the major sources of organic carbon in PM2.5.
Because of this lack of molecular specificity, and owing to the semi-volatile
nature of many carbon compounds, particulate "organic carbon" is operationally
defined by the sampling and analysis method.
2-7
-------
• Elemental Carbon: Elemental carbon is black, often called "soot." Elemental
carbon contains pure, graphitic carbon, but it also contains high molecular weight,
dark-colored, non-volatile organic materials such as tar, biogenics, and coke.
Elemental carbon usually accompanies organic carbon in combustion emissions
with diesel exhaust (Watson et al., 1994c) being the largest contributor.
• Geological Material: Suspended dust consists mainly of oxides of aluminum,
silicon, calcium, titanium, iron, and other metals oxides (Chow and Watson,
1992). The precise combination of these minerals depends on the geology of the
area and industrial processes such as steel-making, smelting, mining, and cement
production. Geological material is mostly in the coarse particle fraction (Houck
et al., 1990), and typically constitutes -50% of PMio while only contributing 5 to
15% of PM2.5 (Chow et al., 1992a; Watson et al., 1994b).
• Sodium Chloride: Salt is found in suspended particles near sea coasts, open
playas, and after de-icing materials are applied. Bulk sea water contains 57+7%
chloride, 32+4% sodium, 8+1% sulfate, 1.1+0.1% soluble potassium, and
1.2+0.2% calcium (Pytkowicz and Kester, 1971). In its raw form (e.g., deicing
sand), salt is usually in the coarse particle fraction and classified as a geological
material (Chow et al., 1996). After evaporating from a suspended water droplet
(as in sea salt or when resuspended from melting snow), it is abundant in the
PM2.5 fraction. Sodium chloride is often neutralized by nitric or sulfuric acid in
urban air where it is often encountered as sodium nitrate or sodium sulfate (Pilinis
etal., 1987).
• Liquid Water: Soluble nitrates, sulfates, ammonium, sodium, other inorganic
ions, and some organic material (Saxena and Hildemann, 1997) absorb water
vapor from the atmosphere, especially when relative humidity exceeds 70% (Tang
and Munkelwitz, 1993). Sulfuric acid absorbs some water at all humidities.
Particles containing these compounds grow into the droplet mode as they take on
liquid water. Some of this water is retained when particles are sampled and
weighed for mass concentration. The precise amount of water quantified in a
PM2.5 depends on its ionic composition and the equilibration relative humidity
applied prior to laboratory weighing.
The liquid water and ammonium nitrate compositions of suspended particles are
especially important for continuous particle monitors. These are both volatile substances that
migrate between the gas and particle phase depending on the composition, temperature, and
relative humidity of the atmosphere. The presence of ionic species (such as sulfate and
nitrate compounds) enhances the liquid water uptake of suspended particles, as shown in
Figure 2-5. The sharp rise in liquid water content at relative humidities between 55% and
75% is known as deliquescence. The humidities at which soluble particles take on liquid
water depend on the chemical mixture and temperature, as explained in the caption to Figure
2-5.
2-8
-------
10000
1000
E
O)
i
100
NACL - NH4N03 - NH42SO4
30
40
50 60 70
Relative Humidity (%)
80
90
100
Figure 2-5. Changes in liquid water content of sodium chloride, ammonium nitrate,
ammonium sulfate, and a combination of compounds at different relative humidities. These
curves were generated from the SCAPE aerosol equilibrium model (Kim et al., 1993a; 1993b;
Kim and Seinfeld, 1995; Meng et al., 1995). The "NACL" case is for 3.83 |ig/m3 of sodium
ion and 6.24 |ig/m3 of gas phase hydrochloric acid (HC1). The "NH4NO3" case is for 10
|ig/m3 of gas phase nitric acid (HNOs) and 10 |ig/m3 of gas phase sulfuric acid (H2SO4). At a
temperature of 15 degrees Celsius, solid ammonium nitrate (NlrLJSTOs) is present for the
lower relative humidities. SCAPE shows a deliquescence relative humidity of 66.2%, within
4% of the measured value of 62% for 25 degrees Celsius (Pruppacher and Klett, 1978). The
"NH42SO4" case is for 10 |ig/m3 of gas phase ammonia (NH3) and 10 |ig/m3 of H2SO4; there
is sufficient ammonia to neutralize the available sulfate, and the gas-phase constituents are in
equilibrium with solid-phase ammonium sulfate for the lower relative humidities. The
deliquescence point of around 80% is expected (Tang et al., 1977a, 1977b). The "COMB"
(combination) case consists of 10 |ig/m3 of equivalent HNOs and H2SO4, 20 |ig/m3 of
equivalent NHa, 3.83 |ig/m3 of sodium ion, and 6.24 |ig/m3 of equivalent HC1. SCAPE
yields solid-phase sodium sulfate, ammonium sulfate, ammonium chloride, and ammonium
nitrate for the lower humidities, with a deliquescence relative humidity for the mixture of
approximately 57%. This is in agreement with the fact that the deliquescence point for a
mixture lies below the minimum deliquescence points for the individual salts (Wexler and
Seinfeld, 1991; Kim and Seinfeld, 1995), and is in agreement with a deliquescence relative
humidity of 56% found by Tang (1980) for a mixture of 45% by weight NH4NO3 and 55%
by weight (NH4)2SO4.
2-9
-------
Figure 2-6 shows how the fraction of nitrate in the particle phase changes with
temperature, relative humidity, and the amount of excess ammonia in the atmosphere. These
curves were generated from the same equilibrium model used to examine liquid water
content in Figure 2-5. Atmospheric particle nitrate can occur in atmospheric aerosol particles
as solid ammonium nitrate or as ionized ammonium nitrate in particles containing water. In
both the solid and ionized forms, ammonium nitrate is in equilibrium with gas-phase nitric
acid and ammonia. In Figure 2-6, the total sulfate concentration was set to 5 |ig/m3 of
equivalent H^SO^ and the total nitrate concentration was set to 20 |ig/m3 of equivalent
HNOs. The total ammonia concentration was varied to simulate different ammonium
enrichment regimes, and this is indicated in the legends as the molar ratio of total available
ammonia to the available sulfate and nitrate. When this "ion ratio" is unity, there is exactly
enough ammonium ion available to neutralize all available nitric and sulfuric acid.
For fixed relative humidity, increasing temperature decreases the particle nitrate
fraction and decreasing temperature increases the particle nitrate fraction. As temperatures
approach 0 °C, nearly all of the nitrate is in the particle phase, limited only by the availability
of ammonia. For higher temperatures, increasing relative humidity increases the particle
nitrate fraction. When there is sufficient ammonia present with 30% relative humidity, more
than 90% of the nitrate is in the particle phase for temperatures less than 20 °C. More than
half of the particle nitrate is gone at temperatures above 30 °C, and all of it disappears at
temperatures above 40 °C.
This has several implications for nitrate measurement by continuous monitors.
Particle nitrate concentrations are probably low in warm, arid environments, so it will not be
a large fraction of PM2.5 and will not influence mass measurements by continuous particle
monitors. However, ammonium nitrate can be a large fraction of PM2.5 in cool, moist
climates. Continuous monitors that require air streams to be heated from temperatures
exceeding 20 °C will cause ammonium nitrate in the sample to volatilize, thereby eliminating
that portion of the PM mass from detection.
PM concentration and chemical composition vary in time and space due to changes in
emission density, meteorology, and terrain features. Table 2-1 illustrates seasonal variations
of PM2.5 in different regions of the IMPROVE network for the three years between March
1988 and February 1991 (Malm et al., 1994). Only the Washington, DC site is situated in an
urban area, with the regional-scale background represented by the other areas.
Although PM2.5 mass concentrations were similar among different seasons, the PM2.5
chemical composition varied considerably with time of year at the Washington, DC site.
Ammonium sulfate concentrations were higher in summer (8.6 |ig/m3, accounting for 51% of
PM2.5 mass) and lower in winter (5.4 |ig/m3, 33.2% of PM2.5 mass). In contrast, ammonium
nitrate concentrations were lower in summer (1.2 |ig/m3, accounting for 7.4% of PM2.5 mass)
and higher in winter (3.4 |ig/m3, accounting for 20.9% of PM2.5 mass).
PM2.5 mass from the Appalachian Mountains region shows marked seasonal
differences, with 6.5 |ig/m3 during winter and 16.6 |ig/m3 during summer. These seasonal
differences are driven by ammonium sulfate levels, which ranged from 3.0 |ig/m3 (46% of
2-10
-------
Particle Nitrate Fraction for 30% RH
10 15 20 25 30 35 40 45
Temperature (Degress Celsius)
Particle Nitrate Fraction for 60% RH
\ \\ \ V
Temperature (Degress Celsius)
Particle Nitrate Fraction for 80% RH
15 20 25 30 35
Temperature (Degress Celsius)
Figure 2-6. Fraction of total nitrate as particulate ammonium nitrate at different
temperatures for various relative humidities and ammonia/nitrate molar ratios.
2-11
-------
Table 2-1
Measured Aerosol Concentrations for the 19 Regions3 in the IMPROVE Network
from March 1988 to February 1991b
Aerosol Concentration in ug/m3 (Percent Mass 1
Season
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Fine
Mass
1.6
2.4
2.7
1.2
1.9
6.5
10.6
16.6
9.7
10.9
5.2
5.4
6.2
4.3
5.3
3.8
5.2
6.7
5.3
5.1
2.9
3.4
4.1
3.2
3.4
2.0
3.4
4.8
2.9
3.3
5.6
4.2
4.5
5.7
5.0
Ammonium
Sulfate
0.7(42.1)
0.9(39.5)
0.5 (20.7)
0.4(32.1)
0.6 (32.6)
3.0 (45.8)
6.0 (56.8)
10.5 (63.5)
5.6(58.0)
6.3 (58.0)
2.0 (38.0)
2.6 (48.7)
2.2(35.8)
1.6(37.9)
2.0(38.9)
0.6(14.6)
1.4(26.7)
2.4 (35.7)
1.3 (24.6)
1.3(25.7)
0.9(33.0)
0.9 (27.9)
1.3(31.9)
1.2 (36.3)
1.1(31.9)
0.5 (27.8)
0.9 (27.6)
1.0(24.0)
0.8 (27.9)
0.8 (25.8)
0.9(16.8)
1.4(33.6)
1.9(43.4)
1.4(24.2)
1.4(28.5)
Nitrate Organics
Alaska
0.1(6.2) 0.6(36.5)
0.1(3.1) 0.7(30.5)
0.0(1.2) 1.5(57.9)
0.1(4.3) 0.6(49.2)
0.1(3.3) 0.9(43.9)
Appalachian
0.8(12.8) 2.0(31.3)
0.8(7.9) 2.7(25.1)
0.3 (2.0) 4.4 (26.5)
0.5(4.9) 2.7(28.1)
0.6 (5.7) 3.0 (27.2)
Boundary Waters
1.4(27.4) 1.4(27.0)
0.4(6.8) 1.8(32.6)
0.1 (2.1) 3.1 (50.6)
0.4(10.1) 1.8(40.9)
0.6(11.0) 2.1(39.5)
Cascades
0.1(3.5) 2.6(67.2)
0.2 (4.7) 2.7 (53.2)
0.4(6.1) 3.0(45.1)
0.2(3.7) 3.1(58.7)
0.2 (4.5) 2.8 (55.7)
Colorado Plateau
0.5(13.1) 1.1(37.3)
0.2(7.0) 1.0(29.9)
0.2(4.3) 1.6(39.0)
0.1(4.6) 1.2(38.4)
0.2(7.2) 1.2(36.3)
Central Rockies
0.2(11.2) 0.9(45.1)
0.3(7.8) 1.1(32.0)
0.1(3.2) 2.4(48.7)
0.1(4.5) 1.3(45.4)
0.2(5.9) 1.5(43.7)
Central Coast
1.9(29.3) 2.3(44.7)
0.8(18.7) 1.5(36.5)
0.8(17.1) 1.4(31.5)
1.0(16.3) 2.7(47.9)
1.1(21.1) 1.9(40.3)
Elemental
Carbon
0.1 (3.4)
0.1 (2.3)
0.1 (3.2)
0.1 (4.9)
0.1 (3.3)
0.4 (6.2)
0.5 (4.4)
0.5 (2.9)
0.5 (5.0)
0.5 (4.2)
0.2(3.8)
0.2 (3.6)
0.3 (4.2)
0.2 (4.6)
0.2(4.1)
0.5(12.0)
0.5 (8.8)
0.5(8.1)
0.5 (9.7)
0.5(9.5)
0.2(6.1)
0.1 (2.6)
0.2 (4.2)
0.2 (5.0)
0.2 (4.3)
0.1 (3.18)
0.1(2.1)
0.2 (4.6)
0.1 (4.3)
0.1 (3.9)
0.4 (6.3)
0.2(4.1)
0.1 (2.9)
0.4 (6.9)
0.3 (5.2)
Soil
0.2(11.8)
0.6 (24.6)
0.4(16.9)
0.1 (9.5)
0.3(17.0)
0.3(3.8)
0.6(5.8)
0.8(5.1)
0.4 (4.0)
0.5 (4.8)
0.2(3.9)
0.4(8.3)
0.5 (7.3)
0.3 (6.6)
0.3 (6.5)
0.1 (2.7)
0.3 (6.7)
0.3 (5.0)
0.2(3.3)
0.2 (4.5)
0.3(10.5)
1.1 (32.6)
0.9 (20.6)
0.5(15.7)
0.7 (20.3)
0.3 (12.2)
1.1 (30.5)
0.9(19.4)
0.5(18.0)
0.7 (20.7)
0.2 (2.9)
0.3(7.1)
0.2 (5.0)
0.3 (4.7)
0.2 (4.8)
Coarse
Mass
4.0
3.9
5.4
3.2
4.2
3.1
4.5
11.2
5.5
6.2
3.2
5.1
8.2
5.8
5.7
2.9
3.1
4.6
3.9
3.5
3.2
5.3
6.4
3.7
4.7
3.0
4.3
7.5
4.0
4.8
7.7
9.3
10.7
7.8
8.9
2-12
-------
Table 2-1 (continued)
Measured Aerosol Concentrations for the 19 Regions3 in the IMPROVE Network
from March 1988 to February 1991b
Aerosol Concentration in ug/m3 (Percent Mass )
Season
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Fine
Mass
5.5
7.7
9.1
6.9
7.1
1.1
2.4
4.5
3.1
2.8
4.0
3.6
1.6
3.4
3.2
6.6
6.1
8.6
5.6
6.7
3.4
5.0
5.6
4.0
4.5
5.3
4.6
5.4
6.7
5.5
4-6-
13.6
13.8
8.1
9.8
Ammonium
Sulfate
2.4 (43.3)
3.8(48.5)
2.5(27.1)
3.1 (45.8)
2.9 (40.9)
0.3 (25.9)
0.5(22.1)
0.7(14.9)
0.6(17.7)
0.5(18.3)
2.8 (70.8)
2.5 (67.8)
0.9 (56.7)
2.5 (72.0)
2.2 (68.5)
3.3 (50.6)
3.6(58.5)
4.5 (52.4)
3.0(53.5)
3.6(53.5)
1.2(34.5)
1.9(38.6)
1.8(32.1)
1.2(30.0)
1.5(34.0)
1.0(18.6)
1.1 (23.3)
0.9(17.1)
0.9(12.8)
1.0(17.7)
0.5(11.3)
1.7(12.2)
2.4 (17.2)
1.1(13.4)
1.4(13.9)
Nitrate Organics
Florida
0.7(12.5) 1.9(34.0)
0.9(11.2) 2.1(27.4)
0.5(5.9) 3.0(33.3)
0.5 (7.8) 2.3 (33.3)
0.7(9.2) 2.3(31.9)
Great Basin
0.1 (12.3) 0.5 (48.0)
0.1(5.9) 0.9(35.6)
0.1(2.5) 1.7(38.8)
0.1(4.6) 1.4(44.5)
0.1 (4.7) 1.1 (40.1)
Hawaii
0.1(1.6) 0.9(22.9)
0.1(2.2) 0.8(22.1)
0.1(5.3) 0.5(30.6)
0.1(1.6) 0.8(22.1)
0.1(2.2) 0.7(23.4)
Northeast
0.8(11.4) 1.8(27.8)
0.4(7.1) 1.5(24.4)
0.3(4.0) 3.0(35.1)
0.4(7.1) 1.6(29.4)
0.5 (7.2) 2.0 (29.8)
Northern Great Plains
0.6(16.6) 1.1(31.7)
0.6(11.8) 1.3(26.7)
0.2 (2.9) 2.2 (39.5)
0.2(5.2) 1.5(37.1)
0.4(8.5) 1.5(33.9)
Northern Rockies
0.6(10.6) 3.0(56.7)
0.2 (5.2) 2.4 (52.2)
0.2(3.1) 3.0(54.5)
0.3 (4.3) 4.3 (64.7)
0.3(5.7) 3.1(57.3)
Southern California
2.2(47.8) 1.2(26.2)
6.9(51.1) 3.2(23.5)
4.6 (33.4) 4.2 (30.6)
3.1(38.6) 2.0(24.3)
4.2 (43.0) 2.5 (25.9)
Elemental
Carbon
0.4 (6.9)
0.3 (3.7)
0.3 (3.4)
0.4 (6.2)
0.4 (5.0)
0.0(1.4)
0.0(1. 1)
0.1 (2.2)
0.1 (2.6)
0.1 (2.0)
0.1 (2.4)
0.1 (1.8)
0.0 (2.6)
0.1 (2.0)
0.1(2.1)
0.5 (7.2)
0.3(5.3)
0.4 (4.9)
0.4 (6.6)
0.4(5.9)
0.1 (3.6)
0.1 (2.4)
0.2 (3.2)
0.1 (3.6)
0.1(3.1)
0.5 (9.4)
0.3 (6.7)
0.3(6.1)
0.6 (9.4)
0.4 (7.9)
0.2(5.3)
0.6 (4.2)
0.8 (5.7)
0.4(5.1)
0.5 (4.9)
Soil
0.2 (3.2)
0.7(9.2)
2.7 (30.2)
0.5 (6.9)
0.9(13.0)
0.1 (12.3)
0.9(35.3)
1.9(41.6)
1.0(30.6)
1.0(34.9)
0.1 (2.4)
0.2(6.1)
0.1 (4.8)
0.1 (2.3)
0.1 (3.7)
0.2 (3.0)
0.3 (4.6)
0.3 (3.6)
0.2(3.5)
0.2 (3.7)
0.5(13.6)
1.0(20.5)
1.2(22.3)
1.0(24.1)
0.9 (20.6)
0.3 (4.8)
0.6(12.5)
1.0(19.2)
0.6(8.8)
0.6(11.4)
0.4 (9.4)
1.2(8.9)
1.8(13.1)
1.5(18.6)
1.2(12.3)
Coarse
Mass
8.5
8.0
13.6
8.6
9.6
1.0
3.7
8.2
5.1
5.0
3.0
7.4
10.3
9.3
8.1
3.1
4.1
6.7
4.1
4.5
3.9
6.0
9.7
5.8
6.3
2.5
4.2
9.2
5.7
5.5
4.2
9.8
15.2
13.2
10.4
2-13
-------
Table 2-1 (continued)
Measured Aerosol Concentrations for the 19 Regions3 in the IMPROVE Network
from March 1988 to February 1991b
Aerosol Concentration in ug/m (Percent Mass )
Season
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Winter
Spring
Summer
Autumn
Annual
Fine
Mass
3.2
4.4
5.6
4.5
4.4
2.5
4.3
7.2
4.4
4.5
1.7
3.0
4.0
2.8
2.9
16.3
16.8
16.7
15.3
16.2
3.6
6.4
6.6
4.8
5.4
Ammonium
Sulfate
1.2(38.6)
1.2(26.5)
2.1 (37.7)
1.7(37.5)
1.5(35.4)
0.4(14.9)
1.0(24.2)
1.7(23.4)
0.9 (20.6)
1.0(21.7)
0.2 (14.2)
0.6(18.6)
0.7(18.2)
0.4(15.5)
0.5(17.1)
5.4 (33.2)
7.3 (43.6)
8.6(51.4)
6.6 (43.3)
6.9 (42.4)
1.5(40.6)
2.2 (33.6)
2.5 (38.7)
2.3 (46.8)
2.1 (39.3)
Nitrate Organics
Sonora Desert
0.3(8.6) 1.1(34.6)
0.3(6.9) 1.3(29.8)
0.2(3.8) 1.8(33.0)
0.2(3.7) 1.7(37.1)
0.3(5.5) 1.5(33.4)
Sierra Nevada
0.7(27.1) 1.1(46.7)
0.6(14.3) 1.7(29.4)
0.6 (8.0) 3.6 (49.6)
0.6(13.2) 2.1(48.3)
0.6(13.6) 2.1(46.4)
Sierra-Humboldt
0.1(7.2) 1.0(56.6)
0.2(8.2) 1.4(48.5)
0.2(4.7) 2.2(55.1)
0.1(3.5) 1.7(59.9)
0.2(5.7) 1.6(54.7)
Washington B.C.
3.4 (20.9) 4.9 (29.9)
2.6(15.5) 4.2(24.9)
1.2(7.4) 4.4(26.1)
1.6(10.5) 4.4(28.5)
2.2(13.8) 4.5(27.5)
West Texas
0.2(6.2) 1.1(31.4)
0.3(4.7) 1.7(26.1)
0.3(4.7) 1.7(25.9)
0.2(3.4) 1.4(29.1)
0.3(4.7) 1.5(27.6)
Elemental
Carbon
0.2 (5.2)
0.1 (2.9)
0.2 (3.2)
0.2(5.1)
0.2(4.1)
0.1 (4.2)
0.2 (4.0)
0.5 (6.7)
0.3 (6.5)
0.3 (5.6)
0.1 (6.6)
0.1 (4.8)
0.3 (6.5)
0.2 (7.4)
0.2 (6.3)
2.0(12.4)
1.7(10.1)
1.6(9.8)
2.0(12.8)
1.8(11.4)
0.1 (3.8)
0.2 (2.5)
0.1 (2.0)
0.2(3.5)
0.1 (2.8)
Soil
0.4(13.0)
1.5(33.9)
1.2(22.3)
0.8(16.5)
0.9(21.6)
0.2 (7.2)
0.8(18.1)
0.9(12.2)
0.5(11.4)
0.6(12.7)
0.3(15.4)
0.6(19.9)
0.6(15.5)
0.4(13.7)
0.5(16.2)
0.6 (3.6)
1.0(5.9)
0.9(5.3)
0.8 (4.9)
0.8 (4.9)
0.6(18.0)
2.1 (33.0)
1.9(28.7)
0.8(17.2)
1.4(25.6)
Coarse
Mass
3.3
7.5
7.6
5.8
6.0
2.1
4.8
7.0
5.3
4.7
2.9
2.9
5.6
2.7
3.7
30.1
10.2
13.5
8.4
16.4
5.1
10.4
7.4
7.0
7.5
IMPROVE and NFS/IMPROVE protocol sites according to la Region:
Alaska
Denali National Park
Appalachian Mountains
Great Smoky Mountains National Park
Shenandoah National Park
Boundary Waters
Isle Royale National Park
Voyageurs National Park
Cascade Mountains
Mount Rainier National Park
Central Rocky Mountains
Bridger Wilderness Area
Great Sand Dunes National Monument
Rocky Mountain National Park
Weminuche Wilderness Area
Yellowstone National Park
(IMPROVE=Interagency Monitoring
b Based on Malm et al. (1994).
Coastal Mountains
Pinnacles National Monument
Point Reyes National Seashore
Redwood National Park
Colorado Plateau
Arches National Park
Bandelier National Monument
Bryce Canyon National Park
Canyonlands National Park
Grand Canyon National Park
Mesa Verde National Park
Petrified Forest National Park
Florida
Everglades
Great Basin
Jarbidge Wilderness Area
of Protected Visual Environments; NPS=National Park Service)
Hawaii
Hawaii Volcanoes National Park
Northeast
Acadia National Park
Northern Great Plains
Badlands National Monument
Northern Rocky Mountains
Glacier National Park
Sierra Nevada
Yosemite National Park
Sierra-Humboldt
Crater Lake National Park
Lassen Volcanoes National Park
Sonoran Desert
Chiricahua National Monument
Tonto National Monument
Southern California
San Gorgonio Wilderness Area
Washington, D.C.
Washington, D.C.
West Texas
Big Bend National Park
Guadalupe Mountains National Monument
2-14
-------
PM2.5 mass) in winter to 10.5 |ig/m3 (64% of PM2.5 mass) in summer. The region
represented by the San Gorgonio site in Southern California also reported significant
seasonal PM2.5 differences of 4.6 |ig/m3 during winter, 13.6 |ig/m3 during spring, and 13.8
|ig/m3 during summer. Spring and summer PM2.5 concentrations were driven by nitrate
concentrations, which were 2 to 3 times higher than during fall and winter. This site lies
along one of the ventilation pathways for California's South Coast Air Basin which produces
copious quantities of ammonium nitrate particles (Solomon et al., 1989; Chow et al., 1994a,
1994b).
In the IMPROVE network, carbonaceous aerosol accounts for 20% to 50% of PM2.5,
with elevated concentrations found during summer, reflecting possible contributions from
photochemical conversion of heavy hydrocarbon gases to particles. Crustal components
were also major PM2.5 components at these regionally representative sites, accounting for
20% to 30% of PM2.5 mass in the southwest and northwest.
Relative abundances of particulate chemical components in urban areas often differ
from the data presented in Table 2-1 due to the superposition of urban emissions on top of
regional background and particles transported from upwind sources. Chow et al. (1998a)
shows that PM2.5 organic carbon is enriched in residential neighborhoods during cold winter
periods, reflecting contributions from home heating and vehicle exhaust, especially cold
starts. Elevated nitrate concentrations are often found during the fall and winter owing to
lower temperatures and higher humidities, as described above.
2.3 Particle Interactions with Light
Visibility degrades when particle concentrations increase, but the nature of this
degradation has a complex dependence on particle properties and the atmosphere (Watson
and Chow, 1994). Visible light occupies a region of the electromagnetic spectrum with
wavelengths between 400 nm and 700 nm, similar to particle diameters in the accumulation
mode. Light falling on an object is reflected and absorbed as a function of its wavelength.
Light reflected from an object is transmitted through the atmosphere where its intensity is
attenuated when it is scattered and absorbed by gases and particles. The sum of these
scattering and absorption coefficients yields the extinction coefficient (bext) expressed in units
of inverse megameters (Mm"1=l/106 m). Typical extinction coefficients range from -10
Mm"1 in pollution-free air to -1,000 Mm"1 in extremely polluted air (Trijonis et al., 1988).
The inverse of bext corresponds to the distance (in 106 m) at which the original intensity of
transmitted light is reduced by approximately two-thirds.
Light is scattered when diverted from its original direction by matter (Malm, 1979).
The presence of atmospheric gases such as oxygen and nitrogen limits horizontal visual
range to -400 km and obscures many of the attributes of a target at less than half of this
distance. This "Rayleigh scattering" in honor of the scientist who elucidated this
phenomena, is the major component of light extinction in areas where pollution levels are
low, has a scattering coefficient of -10 Mm"1, and it can be accurately estimated from
temperature and pressure measurements (Edlen, 1953; Penndorf, 1957).
2-15
-------
Light is also scattered by particles suspended in the atmosphere, and the efficiency of
this scattering per unit mass concentration is largest for particles with sizes comparable to the
wavelength of light (-500 nm), as shown in Figure 2-7 for an ammonium sulfate particle.
Note the rapid change in scattering efficiency in the region between 0.1 and 1 jim that makes
light scattering measurements very sensitive to small changes in particle size within this
region. The degree to which particles scatter light depends on their size, shape, and index of
refraction (which depends on their chemical composition). Each |J,g/m3 of pure ammonium
sulfate or ammonium nitrate typically contributes 2 to 6 Mm"1. Each |J,g/m3 of soil particles
less than 2.5 |j,m in aerodynamic diameter contributes ~ 1 Mm"1. The sizes of most crustal
particles are several times the typical wavelengths of light and each ng/m3 of these particles
with diameters >2.5 |j,m contributes -0.5 Mm"1 to extinction (White et al., 1994).
Light is absorbed in the atmosphere by nitrogen dioxide (NO2) gas, by black
carbonaceous particles (Horvath, 1993a, 1993b), and by non-transparent geological material.
Each |J,g/m3 of nitrogen dioxide contributes -0.17 Mm"1 of extinction at -550 nm
wavelengths (Dixon, 1940), so NC>2 concentrations in excess of 60 |J,g/m3 (30 ppbv) are
needed to exceed Rayleigh scattering. This contribution is larger for shorter wavelengths
(e.g., blue light) and smaller for longer wavelengths (e.g., red light). Black carbon particles
are seldom found in emissions from efficient combustion sources, although they are abundant
in motor vehicle exhaust, fires, and residential heating emissions. Figure 2-8 shows that the
absorption efficiency of elemental carbon particles has a complex relationship to particle size
and assumptions about the particle composition. For the majority of particle types, Figure
2-8 shows that the theoretical scattering efficiency is substantially lower than that estimated
from ambient measurements, which are usually in the range of 5 to 20 m2/g (Hitzenberger
and Puxbaum, 1993; Jennings and Pinnick, 1980).
2.4 Mobility
A particle's mobility is defined as the ratio of particle velocity to the force that
accelerates the particle to that velocity. Mobility is related to mass by Newton's first law,
stating that its mass is equal to the force applied divided by the particles acceleration. A
constant velocity is easier to measure than a velocity that is changing during acceleration.
For this reason, detection devices submit individual particles to a force, usually aerodynamic
or electrostatic, for a short time period, then remove that force to measure the particle
velocity. They may also apply a force that counteracts the resistance of air to bring a particle
to a constant velocity; this force depends on the size and shape of the particle.
2.5 Beta Attenuation
Electrons (or "beta rays") having kinetic energies less than 1 million electron volts
collide with atoms they encounter, while higher energy electrons interact with the atomic
shell or the atomic nucleus. These collisions cause incremental losses in electron energy that
is somewhat proportional to the number of collisions. When a stream of electrons with a
given energy distribution is directed across a thin layer of material, the transmitted energy is
exponentially attenuated as the thickness of the sample, or the number and types of atoms it
encounters, increases.
2-16
-------
Scattering Efficiency for Ammonium Sulfate
Versus Particle Diameter
Scattering Efficiency (m2/g)
Wavelength = 0,53 urn
Refractive Index = 1.53
Density = 1,76 g/cm3
0.5
1 1.5
Diameter (urn)
2.5
Figure 2-7. Ammonium sulfate particle scattering efficiency as a function of particle
diameter. Note that the highest efficiency corresponds to particle sizes near the peak of the
accumulation mode.
Absorption Efficiency (Spherical Particles)
0.01
Diameter of Elemental Carbon Core (urn)
Figure 2-8. Particle absorption efficiencies as a function of elemental carbon particle
diameter for several densities (first number in legend), real and imaginary indices of
refraction (second and third numbers in legend). First three cases are for pure elemental
carbon. Fourth and fifth cases are for 10% and 1% carbon as the core of a sulfate particle.
2-17
-------
Beta ray attenuation is not the same for all atoms and varies with the ratio of atomic
number to atomic mass of each atom. Different elements in ambient air will have different
attenuation properties, so the relationship between attenuation and mass is not exact.
Jaklevic et al. (1981) show that the ratio of atomic number to mass for most of the atoms in
suspended particles is reasonably constant, with the exception of hydrogen.
2.6 Summary
Suspended particles are present in a large number of particle sizes and chemical
compositions. These vary from place to place and time to time, especially between the
eastern and western United States and between winter and summer. Some of the chemical
components of suspended particles, particularly water and ammonium nitrate, are in
equilibrium with gas-phase concentrations. This equilibrium changes with temperature,
relative humidity, and precursor gas concentrations. Particle mobility, light absorption, and
light scattering properties are also functions of chemical composition, size, and shape. These
particle properties must be considered, and to some extent defined, when different
measurement principles incorporated in filter-based and continuous in-situ monitors are
applied to their quantification.
2-18
-------
3. CONTINUOUS PARTICLE MEASUREMENT METHODS
This section surveys continuous in-situ instruments that measure different properties
of suspended particles. Instrument specifications, measurement properties, detection
thresholds, typical averaging times, development status, potential uses, and maintenance
needs are discussed when this information is available. Continuous monitors are classified
by the properties that they measure with respect to: 1) mass (i.e., inertial mass, beta-ray
attenuation, pressure drop); 2) interactions with light (i.e., particle light scattering, particle
light absorption); 3) mobility (i.e., electrical mobility, aerodynamic mobility); 4) chemical
components (i.e., single particle characteristics, nitrate, carbon, sulfur, and other elements);
and 5) precursor gases (i.e., ammonia, nitric acid). Table 3-1 shows that there are several
approaches to measuring the same properties as well as multiple providers for these
instruments. Instrument descriptions given here are brief, with emphasis on their
applicability to PM2.5 and PMio measurements. More detail is given by Baron et al. (1993),
Gebhart (1993), Rader and O'Hern (1993), Williams et al. (1993), and Pui and Swift (1995),
as well as in the cited references and the bibliography in Section 7.
3.1 Mass and Mass Equivalent
Particle mass is determined by its inertia, by its electron attenuation properties, and
by the decrease in pressure across small pores in a filter. Four different types of mass
measurement monitors are discussed in the following subsections.
3.1.1 Tapered Element Oscillating Microbalance (TEOM®)
The TEOM® (Patashnick and Hemenway, 1969; Patashnick, 1987; Patashnick and
Rupprecht, 1991; Rupprecht et al., 1992) draws air through a hollow tapered tube, with the
wide end of the tube fixed, while the narrow end oscillates in response to an applied electric
field. The narrow end of the tube carries the filter cartridge. The sampled air stream passes
from the sampling inlet, through the filter and tube, to a flow controller. The tube-filter unit
acts as a simple harmonic oscillator with
CD =(k/m)°'5 (3-1)
where: co = the angular frequency,
k = the restoring force constant, and
m = the oscillating mass.
As particles are collected on the filter, the oscillating mass changes and results in a
change of the oscillating frequency. An electronic control system maintains the tapered tube
in oscillation and continuously measures this oscillating frequency and its changes. To
calibrate the system, the restoring force constant (k in Equation 3-1) is determined by placing
a gravimetrically determined calibration mass on the filter and recording the frequency
change due to this mass.
3-1
-------
Table 3-1
Summary of Continuous Monitoring Technology
Instrument
Quantity Measured
Methodology
I. Mass and Mass Equivalent
Tapered Element Oscillating Microbalance
(TEOM) y
(Patashnick and Hemenway, 1969; Patashnick, 1987; Patashnick
and Rupprecht, 1991; Meyer et al., 1992; Rupprecht et al., 1992;
Allen etal., 1997)
Piezoelectric Microbalance d
(Olin and Sem, 1971); Sem et al., 1977; Fairchild and Wheat,
1984; Bowers and Chuan, 1989; Ward and Buttry, 1990; Noel and
Topart, 1994)
Beta Attenuation Monitor (BAM) ''af
(Nader and Allen, 1960; Spumy and Kubie, 1961; Lilienfeld and
Dulchinos, 1972; Husar, 1974; Cooper, 1975; Lilienfeld, 1975;
Sem andBorgos, 1975; Cooper, 1976; Macias, 1976; Jaklevic et
al., 1981; Courtney et al., 1982; Klein et al., 1984; Wedding and
Weigand, 1993; Speeretal., 1997)
Pressure Drop Tape Sampler (CAMMS) Mg
(Babich etal., 1997)
Particle mass. Detection limit
~ 5 |ig/m for a five minute
average.
Particle mass. Detection limit
~ 10 |ig/m3 for a one minute
average.
Particle mass. Detection limit
~ 5 |ig/m3 for a one hour
average
Particle mass. Detection linit
~ 2 |ig/m3
average
for a one hour
Particles are continuously collected on a filter mounted on the tip of a glass
element which oscillates in an applied electric field. The glass element is
hollow, with the wider end fixed; air is drawn through the filter and through
the element. The oscillation of the glass element is maintained based on the
feedback signal from an optical sensor. The resonant frequency of the
element decreases as mass accumulates on the filter, directly measuring
inertial mass. The typical signal averaging period is 10 minutes.
Temperatures are maintained at a constant value, typically 30°C or 50°C, to
minimize thermal expansion of the tapered element.
Particles are deposited by inertial impaction or electrostatic precipitation
onto the surface of a piezoelectric quartz crystal disk. The natural resonant
frequency of the crystal decreases as particle mass accumulates. The
changing frequency of the sampling crystal is electronically compared to a
clean reference crystal, generating a signal that is proportional to the
collected mass. The reference crystal also allows for temperature
compensation.
Beta rays (electrons with energies in the 0.01 to 0.1 MeV range) are
attenuated according to an approximate exponential (Beer's Law) function
of particulate mass, when they pass through deposits on a filter tape.
Automated samplers utilize a continuous filter tape, first measuring the
attenuation through the unexposed segment of tape to correct for blank
attenuation. The tape is then exposed to ambient sample flow, accumulating
a deposit. The beta attenuation measurement is repeated. The blank-
corrected attenuation readings are converted to mass concentrations, with
averaging times as short as 30 minutes.
CAMMS (continuous ambient mass monitor system) measures the pressure
drop across a porous membrane filter (Fluoropore). For properly chosen
conditions, the pressure drop is linearly correlated to the particle mass
deposited on the filter.
-------
Table 3-1 (continued)
Summary of Continuous Monitoring Technology
Instrument
Quantity Measured
Methodology
II. Visible Light Scattering
Nephelometer w™>™
(Mie, 1908; Koschmieder, 1924; Beuttell and Brewer, 1949;
Ahlquist and Charlson, 1967, 1969; Charlson et al, 1967, 1968,
1969; Quenzel, 1969a, 1969b; Horvath and Noll, 1969; Borho,
1970; Ensor and Waggoner, 1970; Garland and Rae, 1970;
Heintzenberg and Hanel, 1970; Rae and Garland, 1970; Rae,
1970a; Rae, 1970b; Ruppersberg, 1970; Covert et al., 1972; Ensor
et al., 1972; Thielke et al., 1972; Bhardwaja et al., 1973;
Heintzenberg and Quenzel, 1973a; Heintzenberg and Quenzel,
1973b; Rabinoff and Herman, 1973; Bhardwaja et al., 1974;
Charlson et al., 1974a, 1974b; Quenzel et al., 1975; Heintzenberg,
1975,1978; Heintzenberg and Bhardwaja, 1976; Harrison, 1977a,
1977b, 1979; Sverdrup and Whitby, 1977; Bodhaine, 1979;
Heintzenberg and Witt, 1979; Heintzenberg, 1980; Mathai and
Harrison, 1980; Waggoner and Weiss, 1980; Wiscombe, 1980;
Harrison and Mathai, 1981; Johnson, 1981; Malm et al., 1981;
Ruby and Waggoner, 1981; Waggoner et al., 1981; Winkler et al.,
1981; Larson et al., 1982; Hasan and Lewis, 1983; Heintzenberg
and Backlin, 1983; Waggoner et al., 1983; Hitzenberger et al.,
1984; Gordon and Johnson, 1985; Rood et al., 1985, 1987; Ruby,
1985; Wilson et al., 1988; Barber and Hill, 1990; Trijonis et al.,
1990; Bodhaine et al., 1991; Sloane et al., 1991; Nyeki et al., 1992;
Optec Inc., 1993; Eldering et al., 1994; Horvath and Kaller, 1994;
White et al., 1994; Mulholland and Bryner, 1994; Lowenthal et al.,
1995; Anderson et al., 1996; Heintzenberg and Charlson, 1996;
Watson et al., 1996; Rosen et al., 1997; Anderson and Ogren,
1998; Moosmilller et al., 1998)
In-situ, integrated light
scattering from particles and
gases; a direct estimate of the
aerosol light-scattering
coefficient, bscat; lower
detection limit ~ 1 Mm"1 for a
ten minute average.
Ambient gases and particles are continuously passed through an optical
chamber; the chamber is generally in the form of a long cylinder
illuminated from one side, perpendicular to the long axis of the chamber.
The light source is located behind a lambertian diffuser and illuminates the
aerosol at visible wavelengths. Light is scattered by particles in the
chamber over angles ranging from 0° to 180°; mounted behind a series of
baffles, a photomultiplier tube located at one end of the chamber detects
and integrates the light scattered over about 9° to 171°. The light detected
by the photomultiplier is usually limited by filters to wavelengths in the 500
to 600 nm range, corresponding to the response of the human eye. The
instrument is calibrated by introducing gases of known index of refraction,
which produce a known scattered energy flux. (For this purpose,
halocarbon gases must now be replaced by non-ozone-reactive alternatives.)
A typical signal averaging period is about 2 minutes.
-------
Table 3-1 (continued)
Summary of Continuous Monitoring Technology
Instrument
Quantity Measured
Methodology
Optical Particle Counter/Size Spectrometer j'u'v'aa
(Gucker et al., 1947a, 1947b; Gucker and Rose, 1954; Whitby and
Vomela, 1967; Whitby and Liu, 1968; Liu et al., 1974c;
Heintzenberg, 1975, 1980; Hindman et al., 1978; Makynen et al.,
1982; Chen et al., 1984; Robinson and Lamb, 1986; van der
Meulen and van Elzakker, 1986; Wen and Kasper, 1986; Buettner,
1990; Gebhart, 1991; Hering and McMurry, 1991; Kaye et al.,
1991; Sloane et al., 1991; Eldering et al., 1994; Kerker, 1997;
Fabiny, 1998)
Condensation Nuclei (CN) Counter aa
(Liu and Pui, 1974; Sinclair and Hoopes, 1975; Bricard et al.,
1976; Agarwal and Sem, 1980; Liu et al., 1982; Miller and
Bodhaine, 1982; Bartz et al., 1985; Ahn and Liu, 1990; Noone and
Hansson, 1990; Su et al., 1990; Zhang and Liu, 1990; Keston et al.,
1991; McDermottetal, 1991; Stolzenburg and McMurry, 1991;
Zhang and Liu, 1991; Saros etal., 1996)
Aerodynamic Particle Sizer aa
(Wilson and Liu, 1980; Kasper, 1982; Chen et al., 1985; Baron,
1986; Chen and Crow, 1986; Griffiths et al., 1986; Wang and John,
1987; Ananth and Wilson, 1988; Brockmann et al., 1988; Wang
and John, 1989; Brockmann and Rader, 1990; Chen et al., 1990;
Cheng et al., 1990, 1993; Lee et al., 1990; Rader et al., 1990;
Heitbrink et al., 1991; Marshall et al., 1991; Heitbrink and Baron,
1992; Peters etal., 1993)
Number of particles in the 0.1
to 50 |im size range.
Number of nucleating
particles (particles in the
-0.003 to 1 |im size range).
Number of particles in
different size ranges.
Light scattered by individual particles traversing a light beam is detected at
various angles; these signals are interpreted in terms of particle size via
calibrations.
Particles are exposed to high supersaturations (150% or greater) of a
working fluid such as alcohol; droplets are subsequently nucleated,
allowing detection of the particles by light scattering.
Parallel laser beams measure the velocity lag of particles suspended in
accelerating air flows.
-------
Table 3-1 (continued)
Summary of Continuous Monitoring Technology
Instrument
Quantity Measured
Methodology
LIDAR e f h ' s l
(Hitschfeld and Bordan, 1954; Fiocco et al., 1971; Femald, 1972;
Melfi, 1972; Rothe et al., 1974; Cooney, 1975; Woods and Jolliffe,
1978; Klett, 1981; Alden et al., 1982; Browell, 1982; Browell et
al., 1983; Measures, 1984; Force et al., 1985; McElroy and Smith,
1986; Ancellet et al., 1987, 1989; Edner et al., 1988; Galle et al.,
1988; Alvarez II et al., 1990; Beniston et al., 1990; de Jonge et al.,
1991; Grund and Eloranta, 1991; Ansmann et al., 1992; Kolsch et
al., 1992; McElroy and McGown, 1992; Milton et al., 1992; She et
al., 1992; Whiteman et al., 1992; Kovalev, 1993, 1995;
Moosmilller et al., 1993; Gibson, 1994; Kempfer et al., 1994;
Kovalev and Moosmilller, 1994; Piironen and Eloranta, 1994;
Zhao et al., 1994; Grant, 1995; Toriumi et al., 1996; Evans et al.,
1997; Hoff et al., 1997; Moosmiiller and Wilkerson, 1997)
Range resolved atmospheric
backscatter coefficient
(cm2/steradian) and gas
concentrations.
A short laser pulse is sent into the atmosphere, backscattered light from gas
and aerosols is detected as a function of time of flight for the light pulse.
This results in a range resolved measurement of the atmospheric backscatter
coefficient if extinction is properly accounted for. Special systems which
separate molecular and aerosol scattering have an absolute calibration and
extinction and backscatter ratio can be retrieved. Differential absorption
lidars use multiple wavelengths and utilize the wavelength dependent
absorption of atmospheric gases to retrieve their range resolved
concentrations.
///. Visible Light Absorption
Aethalometer m'z
(Hansen et al., 1984, 1988, 1989; Rosen et al., 1984; Hansen and
Novakov, 1989, 1990; Hansen and McMurry, 1990; Hansen and
Rosen, 1990; Parungo et al., 1994; Pirogov et al., 1994)
Particle Soot/Absorption Photometer (PSAP)
(Bond et al., 1998; Quinn et al., 1998)
Light absorption, reported as
concentration of elemental
carbon. Detection limit ~
0.1 |ig/m3 black carbon for a
one minute average.
Light absorption detection
limit-0.2 Mm"1 fora
five-minute average. For an
absorption efficiency of
10 m2/g, this would
correspond to 20 ng/m3 of
black carbon.
Ambient air is continuously passed through a quartz-fiber filter tape. Light-
absorbing particles such as black carbon cause attenuation of a light beam.
By assuming that all light-absorbing material is black carbon, and that the
absorption coefficient of the black carbon is known and constant, the net
attenuation signals can be converted into black carbon mass concentrations.
The time resolution of the aethelometer is on the order of a fraction of a
minute depending on ambient black carbon concentration.
The PSAP produces a continuous measurement of absorption by monitoring
the change in transmittance across a filter (Pallflex E70-2075W) for two
areas on the filter, a particle deposition area and a reference area. A light
emitting diode (LED) operating at 550 nm, followed by an Opal glass
serves as light source. The absorption reported by the PSAP is calculated
with a nonlinear equation correcting for the magnification of absorption by
the filter medium and for response nonlinearities as the filter is loaded.
-------
Table 3-1 (continued)
Summary of Continuous Monitoring Technology
Instrument
Quantity Measured
Methodology
Photoacoustic Spectroscopy g'ag
(Bruce and Pinnick, 1977; Pao, 1977; Terhune and Anderson,
1977; Lin and Campillo, 1985; Adams, 1988; Adams et al, 1989,
Arnott et al., 1995; Petzold and Niessner, 1995, 1996; Bijnen et aL
1996; Moosmilller and Arnott, 1996; Moosmilller et al., 1997;
Arnott et al., 1998; Moosmilller et al., 1998)
IV. Electrical Mobility
Electrical Aerosol Analyzer (EAA)aa
(Whitby and Clark, 1966; Liu et al., 1974a, 1974b; Liu and Pui,
1975; Helsper et al., 1982)
Light absorption, reported as
black carbon. Detection limit
~ 50 ng/m for a ten-minute
average.
Number of particles in the
sub-micrometer size range
(-0.01 to 1.0 |im).
Ambient air is aspirated through a resonant chamber, where it is illuminated
by modulated (chopped) laser light at a visible wavelength (e.g., 514.5 nm).
Light-absorbing particles, principally elemental carbon, absorb energy from
the laser beam and transfer it as heating of the surrounding air. The
expansion of the heated gas produces a sound wave at the same frequency
as the laser modulation. This acoustic signal is detected by a microphone;
its signal is proportional to the amount of absorbed energy. The
illumination must be carefully chosen to avoid atmospheric gaseous
absorption bands.
Particles are collected according to their size-dependent mobilities in an
electric field. The collected particles are detected by their deposition of
charge in an electrometer.
Differential Mobility Particle Sizer (DMPS)aa
(Knutson and Whitby, 1975a, 1975b; Hoppel, 1978; Alofs and
Balakumar, 1982; Hagen and Alofs, 1983; Fissan et al., 1983; ten
Brink et al., 1983; Kousaka et al., 1985; Reineking and
Porstendorfer, 1986; Wang andFlagan, 1990; Reischl, 1991;
Winklmayr et al., 1991; Zhang et al., 1995; Birmili et al., 1997;
Endoetal., 1997)
Number of nucleating
particles in different size
ranges (~ 0.01 to 1.0 |_im size
range).
Particles are classified according to their mobility in an electric field, which
is a function of their size; a condensation nuclei counter then counts the
population in a size "bin".
-------
Table 3-1 (continued)
Summary of Continuous Monitoring Technology
Instrument
Quantity Measured
Methodology
V. Chemical-Specific Particle Monitors
Single Particle Mass Spectrometer (RSMS,
PALMS, ATOFMS) ad'ae'af'as
(Thomson and Murphy, 1993; Mansoori et al., 1994; Murphy and
Thomson, 1994, 1995; Noble et al., 1994; Nordmeyer and Prather,
1994; Prather et al., 1994; Carson et al., 1995; Johnston and
Drexler, 1995; Mansoori et al., 1996; Neubauer et al., 1996; Noble
and Prather, 1996, 1997, 1998; Salt et al., 1996; Carson et al.,
1997; Gard et al., 1997, 1998; Liu et al., 1997; Middlebrook, 1997;
Murphy and Thomson, 1997a, 1997b; Murphy etal., 1997, 1998;
Silva and Prather, 1997; Thomson et al., 1997; Murphy and
Schein, 1998)
Ambient Carbon Particulate Monitor (ACPM) y
(Turpin et al., 1990a, 1990b; Chow et al., 1993a; Rupprecht et al.,
1995)
Sulfur Analyzer, Flame Photometric Detection
(FPD) Mg
(Dagnall et al., 1967; Stevens et al., 1969, 1971; Farwell and
Rasmussen, 1976; Coboum et al., 1978; Durham et al., 1978;
Huntzicker et al., 1978; Kittelson et al., 1978; Jaklevic et al., 1980;
Mueller and Collins, 1980; Tanner et al., 1980; Camp et al., 1982;
Benner and Stedman, 1989, 1990)
Particle sizes and single
particle compositions.
Concentrations of organic and
elemental carbon. Detection
limit ~ 0.2 |ig/m for a
two hour average.
Sulfur dioxide and sulfate.
Detection limit -0.1 |ig/m3
for a one hour average.
Particles in air are introduced into successively lower-pressure regions and
acquire high velocities due to gas expansion. Particle size is evaluated by
laser light scattering. The particles then enter a time-of-flight mass
spectrometer.
Measurement of carbon particulate by automatic thermal CO2method. The
carbon collected in a high-temperature impactor oxidized at elevated
temperatures after sample collection is complete. A CO2 meter measures
the amount of carbon released as result of sample oxidation. OC and EC
can be speciated by volatizing OC at an intermediate temperature.
Sulfur species are combusted in a hydrogen flame, creating excited sulfur
dimers (S2*). Fluorescence emission near 400 nm is detected by a
photomultiplier. The photomultiplier current is proportional to the
concentration of sulfur in all species. Four out of five FPD systems agreed
to within + 5% in a one-week ambient sampling intercomparison.
-------
Table 3-1 (continued)
Summary of Continuous Monitoring Technology
Instrument
Quantity Measured
Methodology
oo
oo
Nitrate Analyzer, Automated Particle Nitrate
Monitor (APNM) a'y'ag
(Winkler, 1974; Roberts and Friedlander, 1976; Hering and
Friedlander, 1982; Stein et al., 1994; Yamamoto and Kosaka,
1994; Hering, 1997; Chow and Watson, 1998b; Chow et al.,
1998b; Hering, 1998; Hering and Stolzenburg, 1998; Norton et al..
1998)
Streaker w
(Hudson et al., 1980; Bauman et al., 1987; Annegarn et al., 1990;
Chow, 1995)
Davis Rotating-Drum Universal-Size-Cut
Monitoring Impactor (DRUM)ac
(Raabe et al., 1988; Pitchford and Green, 1997)
Particle Nitrate. Detection
limit -0.5 |ig/m3 for a
12-minute average.
PM2 5 and PM10 elemental
composition.
Size-fractionated elemental
composition from 0.07 pm to
15 |jm in diameter for eight
size ranges.
Particle collection by impaction followed by flash vaporization and
detection of the evolved gases in a chemiluminescent NOX analyzer.
Particles are collected on two impaction stages and a Nuclepore
polycarbonate-membrane after-filter followed by particle-induced x-ray
emission (PIXE) analysis for multielements.
Particles are collected on grease-coated mylar substrates that cover the
outside circular surface of eight clock-driven slowly rotating cylinders or
drums (one for each stage). Mylar substrates are submitted for
focused-beam particle-induced x-ray emission (PIXE) analysis of
multielements.
VI. Precursor Gas Monitors
Ammonia Analyzer, Chemiluminescence z
(Breitenbach and Shelef, 1973; Braman et al., 1982; Keuken et al.,
1989; Langford et al., 1989; Wyers et al., 1993; Sorensen et al.,
1994; Chow et al., 1998b; Jaeschke et al., 1998)
Ammonia Analyzer, Fluorescence b'q
(Abbas and Tanner, 1981; Rapsomanikis et al., 1988; Genfa et al.,
1989; Harrison and Msibi, 1994)
Ammonia concentration.
Detection limit -10 ppb.
Ammonia concentration.
Detection limit — 0.1 ppb.
Ammonia concentrations are measured by first removing oxides of
nitrogen, then oxidizing ammonia to nitrogen oxide by thermal oxidation at
high temperature for detection by chemiluminescence.
Sampled ammonia is removed from the airstream by a diffusion scrubber,
dissolved in a buffered solution, and reacted with o-phtaldialdehyde and
sulfite. The resulting i-sulfonatatoisoindole fluoresces when excited with
365 nm radiation, and the intensity of the 425 nm emission is monitored for
quantification. The diffusion scrubber might be modified to pass particles
while excluding ammonia gas to continuously quantify ammonium ions.
-------
Table 3-1 (continued)
Summary of Continuous Monitoring Technology
Instrument
Quantity Measured
Methodology
Other ammonia analyzers"8
(Appel et al, 1988; Rooth et al., 1990; Wiebe et al., 1990;
Williams et al., 1992; Sauren et al., 1993; Schendel et al., 1990;
Platt, 1994; Mennen et al., 1996)
Ammonia concentration.
Other, less established methods to measure ammonia include photoacoustic
spectroscopy, vacuum ultraviolet/photofragmentation laser-induced
fluorescence, Differential Optical Absorption Spectroscopy (DOAS) in the
ultraviolet Differential Absorption Lidar (DIAL, see section 3.2.5), and
Fourier Transform Infrared (FTIR) spectroscopy (see section 3.6.3).
Nitric Acid Analyzer ag
(Ripley et al., 1964; Kelly et al., 1979, 1990; Burkhardt et al.,
1988; Fox et al., 1988; Hering et al., 1988; Fehsenfeld et al., 1990;
Gregory et al., 1990; Harrison, 1994; Harrison and Msibi, 1994)
Nitric acid concentration.
Detection limit -0.1 ppb for a
5-minute average.
Nitric acid can be reduced to NO2 prior
chemiluminescent and luminol methods.
to detection by the
Long Path Fourier Transform Infrared
Spectroscopy (FTIR) °
(White, 1976; Tuazon et al., 1978; Doyle et al., 1979; Tuazon et
al., 1980; Tuazon et al., 1981; Hanst et al., 1982; Biermann et al.,
1988; Hanst and Hanst, 1994)
Nitric acid and ammonia
concentrations. Detection
limit ~ 4 ppb for nitric acid
and -1.5 ppb for ammonia for
a 5-minute average.
Long path absorption spectroscopy.
folded into a 25-m long White cell.
A path length of more than 1 km is
Tunable Diode Laser Absorption Spectroscopy
(TOLAS)ab
(Schiff et al., 1983; Anlauf et al., 1985, 1988; Harris et al., 1987;
Fox et al., 1988; Hering et al., 1988; Mackay et al., 1988;
Schmidtke et al., 1988; Fehsenfeld et al., 1998)
Nitric acid concentation. Nitric acid concentrations are measured by high spectral resolution diode
Detection limit-0.3 ppb fora laser spectroscopy in the mid-infrared spectral region. The sample is
5-minute average. introduced into a reduced pressure White cell to reduce pressure broadening
and increase the path length.
Mist Chamber88
(Talbot et al., 1990)
Nitric acid concentrations. The mist chamber method samples nitric acid by efficiently scrubbing it
Detection limit ~ 0.01 ppb for from the atmosphere in a refluxing mist chamber followed by analysis of
a 10-minute average. the scrubbing solution for NO3~ by ion chromatography. A sensitivity of 10
pptv for a 10-minute integration period has been reported.
-------
Table 3-1 (continued)
Summary of Continuous Monitoring Technology
Instrument
Quantity Measured
Methodology
Laser-Photolysis Fragment-Fluorescence (LPFF)a
(Papenbrock and Stuhl, 1991)
Chemical lonization Mass Spectrometry (IMS)ag
(Huey et al, 1998; Mauldin et al., 1998; Fehsenfeld et al., 1998)
oo
o
Nitric acid concentrations.
Detection limit — 0.1 ppb for a
15-minute average.
Nitric acid concentrations.
Detection limit ~ 0.005 ppb
for a 10-second average.
Nitric acid concentrations have also been measured with the
Laser-Photolysis Fragment-Fluorescence (LPFF) Method, which irradiates
the air sample with ArF laser light (193 nm) resulting in the photolysis of
nitric acid. The resulting hydroxyl radical (OH) emits fluorescence at 309
nm which is taken as a measure of the nitric acid mixing ratio in air. A
sensitivity of 0.1 ppbv and a time constant of 15 minutes limited by surface
ad- and desorption have been reported. An intercomparison of this
technique with a denuder technique was also reported.
Chemical lonization Mass Spectrometry (CIMS) has been used for the
sensitive (few pptv) and fast (second response) measurement of
atmospheric nitric acid concentrations. Reagent ions formed by an ion
source are mixed with the sampled air and react selectively with nitric acid.
The ionic reaction product is detected with a mass spectrometer. Two
different CIMS instruments have been described and compared with an
older, more established filter pack technique.
-------
Table 3-1 (continued)
Summary of Continuous Monitoring Technology
" Aerosol Dynamics Inc.
2329 Fourth Street
Berkeley, CA 94710
Tel.: 510-649-9360
Fax: 510-649-9260
b Advanced Pollution Instrumentation Inc
6565 Nancy Ridge Dr.
San Diego, CA 92121
Tel.: 619-657-9800
Fax: 619-657-9816
email: api@cts.com
c Belfort Instrument Company
727 South Wolfe Street
Baltimore, MD 21231
Tel.: 410-342-2626
Fax: 410-342-7028
e-mail: belfortin@aol.com
www: http://www.belfortinst.com
d California Measurements Inc.
150 E. Montecito Ave.
Sierra Madre, CA 91024
Tel.: 818-355-3361
Fax: 818-355-5320
e Coherent Technologies Inc.
655 Aspen Ridge Drive
Lafayette, CO 80306
Tel.: 303-604-2000
e-mail: milton@ctilidar.com
f Corning OCA Corporation
Applied Optics
7421 Orangewood Avenue
P.O. Box 3115
Garden Grove, CA 92842-3115
Tel.: 714-895-1667
Fax: 714-891-4356
e-mail: clemensesa@corning.com
www: http://www.oca.com/lidar.htm
8 Desert Research Institute
Energy & Environmental Engineering
P.O. Box 60220
Reno,NV 89506
Tel.: 702-677-3194
Fax: 702-677-3157
www: http://www.dri.edu
h Blight Laser Systems GmbH
Potsdamer StraBe 18A
D-14513Teltow
Germany
Tel.: 49-3328-430-112
Fax:49-3328-430-115
e-mail: 100276.3673@compuserve.com
' Graseby-Andersen
500 Technology Court
Smyrna, GA 30082-5211
Tel.: 7703199999
Fax: 7703190336
e-mail: nutech@graseby.com
www: http://www.graseby.com
1 Grimm Technologies
9110CharltonPlace
Douglasville, GA 30135
Tel.: 770-577-0853
Fax: 770-577-0955
k Harvard University
School of Public Health
665 Huntington Ave.
Boston, MA 02115
www: http://www.hsph.harvard.edu
1 Kayser-Threde GmbH
Wolfratshauser StraBe 48
D-81379Miinchen
Germany
Tel.: 49-89-724-95-0
Fax: 49-89-724-95-291
e-mail: fy@kayser-threde.de
www: http://www.kayser-threde.de
Magee Scientific Company
1829 Francisco Street
Berkeley, CA 94703
Tel.: 510-845-2801
Fax: 510-845-7137
e-mail: Aethalometers@MageeSci.com
www: http://www.mageesci.holowww.com:80/'
Met One Instruments
1600 Washington Blvd.
Grants Pass, OR 97526
Tel.: 541-471-7111
Fax: 541-471-7116
Midac Corporation l
17911 Fitch Ave.
Irvine, CA 92714
Tel.: 714-660-8558
Fax: 714-660-9334
MIE Inc.
7 Oak Park
Bedford, MA 01730
Tel.: 617-275-1919
Fax: 617-275-2121
Opsis Inc.
1165 Linda Vista, Suite 112
San Marcos, CA 92069
Tel.: 760-752-3005
Fax: 760-752-3007
Optec, Inc.
199 Smith Street
Lowell, MI 49331
Tel.: 616-897-9351
Fax: 616-897-8229
e-mail: Opteclnc@aol.com
www: optecinc.com
Optech Inc.
100 Widcat Road
North York, Ontario M3J 2Z9
Canada
Tel.: 416-661-5904
Fax: 416-661-4168
ORCA Photonics Systems Inc.
14662 NE 95th St.
Redmond, WA 98052
Tel.: 425-702-8706
Fax: 425-702-8806
e-mail: info@willows.orcaphoton.com
www: http://www.orcaphoton.com/
Palas GmbH
Gresbachstr. 3b
D-76229 Karlsuhe
Germany
Tel.: 721-962-13-0
Fax: 721-962-13-33
www: http://gitverlag.com/laborjahrbuch/pis/
Palas/default.html
Particle Measuring Systems Inc.
1855 South 57th Court
Boulder, CO 80301
Tel.: 303-443-7100
Fax: 303-449-6870
PIXE Analytical Laboratories
P.O. Box 2744
Tallahassee, FL 32316
Tel.: 904-574-6469 or 800-700-7494
Radiance Research
535 N.W. 163rd St.
Seattle, WA 98177
Tel.: 206-546-4859
Fax: 206-546-8425
e-mail: radiance@cmc.net
-------
Table 3-1 (continued)
Summary of Continuous Monitoring Technology
y Rupprecht & Patashnick Co.
25 Corporate Circle
Albany, NY 12203
Tel.: 518-452-0065
Fax: 518-452-0067
email: info@rpco.com
www: http://www.rpco.com
z Thermo Environmental Instruments (TEI)
8 West Forge Parkway
Franklin, MA 02038
Tel.: 508-520-0430
Fax: 508-520-1460
aa TSIInc.
Particle Instruments Division
P.O. Box 64394
St. Paul, MN 55164
Tel.: 612-490-2833
Fax: 612-490-3860
e-mail: particle@tsii.com
www: http://www.tsi.com
ab Unisearch Associates Inc.
222 Snidercroft Road
Concord, Ontario
Tel.: 905-669-3547
Fax:905-669-8652
ac University of California
Air Quality Group
Crocker Nuclear Laboratory
Davis, CA 95616
ad University of California
Dept. of Chemistry
Riverside, CA 92521
www: http://www.ucr.edu
ae University of Delaware
Dept. of Mechanical Engineering
Newark, DE 19716
www: http://www.udel.edu
-------
Because the restoring force constant k is a function of temperature, the sampling
apparatus (tapered tube, filter) and sampled air are kept at a constant temperature. The
default value for this temperature setting is usually 50 °C to prevent the measurement of
particle-bound water. Filter lifetimes are usually two to four weeks. After a filter change,
the new filter is equilibrated for one-half to one hour prior to acquiring data.
In ambient particulate applications, the R&P TEOM® operates with an initial filter
mass of about 50 mg and a deposited aerosol mass of no greater than 10 mg. It is capable of
operating with flow rates through the filter from 0.5 to 5 L/min, with a typical flow rate of 3
L/min. The R&P TEOM® ambient particulate monitor provides for averaging times from 10
minutes to 24 hours and is available with a choice of sample inlets for TSP, PMio, PM2.5, or
PMi.o (particles with aerodynamic diameters less than 1 jim) monitoring. Collocated TEOM
instruments have reported a precision of ±2.8 |ig/m3 for hourly averaged PMio concentrations
and ±5.1 |ig/m3 for 10-minute-averaged PMio concentrations.
The default 50 °C temperature prevents water vapor condensation and provides a
standard sample condition, but it volatilizes most of the ammonium nitrate and some of the
volatile organic compounds in atmospheric particles. As a consequence, monitored sites and
seasons having high levels of ammonium nitrate and/or organic particulate mass do not
always yield a reasonable correspondence between time-integrated TEOM® and collocated
filter measurements (Allen et al., 1997). All instruments that apply high temperature
pre-conditioning, not just the TEOM®, are affected by this phenomena.
Large fluctuations in the TEOM® mass concentration can occur over several hours for
PM2.5 monitoring, but PMio TEOM® measurements do not seem to be as significantly
affected. It has been hypothesized that this noise is caused by changes in the equilibrium of
particles on the TEOM filter when ambient pollutants or moisture is changing rapidly (Allen
et al., 1997). Meyer et al. (1992) showed -30% more PMio mass was obtained with a 30 °C
TEOM® sampling beside a 50 °C TEOM® in the woodburning-dominated environment at
Mammoth Lakes, CA. R&P is currently testing modifications to the aerosol conditioning
process that approximate the nominal filter equilibration conditions (-21 °C and -35%
relative humidity) to which FRM filters will be subjected prior to laboratory weighing.
3.1.2 Piezoelectric Microbalance
Piezoelectric crystals have mechanical resonances that can be excited by applying an
alternating electrical voltage to the crystal. As the resonance frequencies are very well
defined, such crystals (quartz in particular) have found applications as secondary time and
frequency standards in clocks and watches. As for all mechanical resonators, the resonance
frequency is a function of mass. Therefore, by monitoring the resonance frequency in
comparison with a second crystal, one can continuously measure the mass deposited on the
crystal (Sem et al., 1977; Bowers and Chuan, 1989; Ward and Buttry, 1990; Noel and Topart,
1994). Comparison with a second crystal largely compensates for the effect of temperature
changes on the resonance frequency.
3-13
-------
The piezoelectric principle has been used to measure particle mass by depositing the
particles on the crystal surface either by electrostatic precipitation or by impaction (Olin and
Sem, 1971). The collection efficiency of either mechanism has to be determined as function
of particle size to achieve quantitative measurements. In addition, the mechanical coupling
of large particles to the crystal is uncertain. Both single and multi-stage impactors have been
used (Olin and Sem, 1971; Fairchild and Wheat, 1984). Quartz crystals have sensitivities of
several hundred hertz per microgram. This sensitivity results in the ability to measure the
mass concentration of a typical, 100 ng/m3, aerosol to within a few percent in less than one
minute (Olin and Sem, 1971).
3.1.3 Beta Attenuation Monitor (BAM)
Beta Attenuation Monitors (BAM) measure the loss of electrons as they penetrate a
filter on which particles have been deposited (Nader and Allen, 1960; Spurny and Kubie,
1961; Lilienfeld and Dulchinos, 1972; Husar, 1974; Lilienfeld, 1975; Macias, 1976; Jaklevic
et al., 1981; Klein et al., 1984; Wedding and Weigand, 1993). BAM technology has also
recently been used to measure the liquid water content of aerosols (Speer et al., 1997).
Particles are collected on a spot of a filter tape. A radioactive (beta) source emits
low-energy electrons. Electrons propagating through the tape are detected on the opposite
side. Their intensity is attenuated by inelastic scattering with atomic electrons, including
those of the particle deposit. The beta intensity is described, to a good approximation, by the
Beer-Lambert relationship (Sem and Borgos, 1975; Cooper, 1976). If the beta attenuation
coefficient per aerosol mass deposited on the filter is known, a continuous measurement of
aerosol mass concentration becomes possible. Movement of the filter tape is needed when
the aerosol loading on the deposition spot attenuates the beta intensity at the detector to near
background levels (Cooper, 1975).
Lilienfeld (1975) identifies 13 different electron-emitting isotopes with half-lives in
excess of one year, no significant emission of gamma radiation, and energies of less than
1 MeV. Carbon-14 (14C) sources are most commonly used. The beta attenuation coefficient
depends both on beta energy and on chemical composition of the aerosol. For a 14C source
and typical atmospheric aerosol, the attenuation coefficient is -0.26 cm2/mg (Macias, 1976).
The dependence of the attenuation coefficient on the chemical aerosol composition (Macias,
1976; Jaklevic et al., 1981) is commonly assumed to be within measurement precision, but it
may bias measurements when the composition of calibration standards differs substantially
from the composition of the ambient aerosol. Measurement resolution and lower detection
limit for modern instruments are on the order of a few |ig/m3 (Courtney et al., 1982).
Beta attenuation monitors are typically operated at ambient temperatures and relative
humidity. While these conditions preserve the integrity of volatile nitrates and organic
compounds, they also favor the sampling of liquid water associated with soluble species at
high humidities. Under these conditions BAM concentrations are often larger than those of
collocated filter samplers for which samples have been equilibrated at lower laboratory
relative humidities prior to gravimetric analysis. Sampled air can be preceded by a diffusion
dryer to remove water vapor, thereby encouraging the evaporation of liquid water associated
with soluble components of suspended particles.
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3.1.4 Pressure Drop Tape Sampler (CAMMS)
A continuous particle mass monitoring system, CAMMS (continuous ambient mass
monitor system), based on measuring the pressure drop across a porous membrane filter
(Fluoropore) has recently been developed at Harvard University (Babich et al., 1997). The
pressure drop is linearly correlated to the particle mass deposited on the filter.
The filter face velocity is chosen such that pore obstruction by interception is the
dominant cause of particle-related pressure drop change over time. The monitor consists of:
1) a Fluoropore filter tape to collect particles; 2) a filter tape transportation system to allow
for several weeks of unattended particle sampling; 3) a system to measure the pressure drop
across the filter; 4) a diffusion dryer to remove particle-bound water; and 5) an air sampling
pump. The monitor exposes a new segment of filter tape every 20 to 60 minutes for particle
collection. During this period, particles collected on the filter should remain in equilibrium
with the sample air, since the composition of ambient air does not usually vary substantially
over this short time period. Volatilization and adsorption artifacts are minimized because
measurements are made at ambient temperature for short time periods and at a low face
velocity. A diffusion dryer that removes water vapor could also be used to condition air,
thereby encouraging the evaporation of liquid water associated with soluble components of
suspended particles.
The CAMMS can detect concentrations as low as 2 |ig/m3 for hourly averages.
Results from a Boston, MA, field study conducted during the summer of 1996 show good
agreement between CAMMS and the Harvard Impactor, with a root mean square difference
of less than 3 |ig/m3. Unpublished data (P. Koutrakis, personal communication) comparing
CAMMS with the Harvard Impactor PM2.5 filter sampler show R2 for six individual sites
ranges from 0.86 to 0.97. CAMMS is believed to be relatively insensitive to changes in
aerosol composition between sites and over seasons.
3.2 Visible Light Scattering
Particle light scattering (bsp) is determined by illuminating particles, individually or as
a group, and measuring the scattered intensity at different orientations from the incident light
source. The intensity of scattered light is related to mass concentration by electromagnetic
theory or by comparison with a collocated filter measurement. Particle light scattering
measurements from five different types of instruments are discussed in the following
subsections.
3.2.1 Nephelometer
Nephelometers quantify particle light scattering. Integrating nephelometers quantify
particle light scattering integrated over all directions. The original application of integrating
nephelometers was to quantify airport visibility during World War II (Beuttell and Brewer,
1949). For visibility applications, scattering extinction serves as a surrogate for total light
extinction which is related to visibility (Koschmieder, 1924). The basic integrating
nephelometer design was developed by Beuttell and Brewer (1949), and has since been
further perfected and automated (e.g., Ahlquist and Charlson, 1967; Charlson et al., 1967;
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Quenzel, 1969a, 1969b; Ensor and Waggoner, 1970; Garland and Rae, 1970; Heintzenberg
and Hanel, 1970; Rae and Garland, 1970; Rae, 1970a; Rae, 1970b; Ruppersberg, 1970;
Heintzenberg and Quenzel, 1973a; Heintzenberg and Quenzel, 1973b; Rabinoff and Herman,
1973; Quenzel et al., 1975; Heintzenberg and Bhardwaja, 1976; Harrison, 1977a, 1977b;
Heintzenberg and Witt, 1979; Ruby and Waggoner, 1981; Hasan and Lewis, 1983;
Heintzenberg and Backlin, 1983; Hitzenberger et al., 1984; Gordon and Johnson, 1985;
Ruby, 1985; Rood et al., 1987; Bodhaine et al., 1991; Horvath and Kaller, 1994; Mulholland
and Bryner, 1994; Anderson et al., 1996; Rosen et al., 1997). A comprehensive review of
nephelometer designs and applications is provided by Heintzenberg and Charlson (1996).
The integrating nephelometer has been widely used to measure visibility in urban, non-urban,
and background areas (e.g., Horvath and Noll, 1969; Harrison, 1979; Johnson, 1981; Malm et
al., 1981; Ruby, 1985; White et al., 1994; Watson et al., 1996). Comparisons between
calculated and measured aerosol light scattering have been made including its humidity
dependence (e.g. Covert et al., 1972; Ensor et al., 1972; Winkler et al., 1981; Rood et al.,
1985; Wilson et al., 1988; Eldering et al., 1994). Table 3-2 summarizes the operating
characteristics of several available nephelometers.
Other applications of the integrating nephelometer include: 1) measurements of
Rayleigh scattering coefficients (e.g., Bhardwaja et al., 1973; Bodhaine, 1979);
2) determination of aerosol size distributions (e.g. Ahlquist and Charlson, 1969; Thielke et
al., 1972; Heintzenberg, 1975; Sverdrup and Whitby, 1977; Heintzenberg, 1980; Harrison
and Mathai, 1981; Sloane et al., 1991) and refractive indices (e.g., Bhardwaja et al., 1974;
Mathai and Harrison, 1980); 3) detection of sulfuric acid - ammonium sulfate aerosol (e.g.,
Charlson et al., 1974a; Charlson et al., 1974b; Larson et al., 1982; Waggoner et al., 1983);
and 4) estimation of particle mass concentrations (e.g., Charlson et al., 1967; Charlson et al.,
1968; Charlson et al., 1969; Borho, 1970; Thielke et al., 1972; Waggoner and Weiss, 1980;
Waggoner et al., 1981; Moosmuller et al., 1998).
Nephelometer sampling procedures depend on the intended uses of the data. To
determine visibility reduction, total light scattering is desired, including that caused by liquid
water associated with soluble particles and that from the molecules in clean air. For this
purpose the sensing chamber must have a minimal temperature differential with respect to
ambient air and a particle-free baseline of -10 Mm"1 is established. Internal ambient
temperatures are maintained by a measurement chamber that is thermally insulated from the
rest of the instrument and a large air inlet with motorized door that allows ambient air to flow
unmodified over a short distance. Closing the inlet door allows for the introduction of a
calibration gas. This passive sampling scheme alters the temperature of the air sample by
less than 0.5 °C (Optec Inc., 1993).
Light scattering is often very high at relative humidities exceeding 80% because small
particles grow to sizes that scatter light more efficiently as they acquire liquid water. As a
result, ambient temperature nephelometers overestimate mass concentrations at high
humidities when particles have a large soluble component. The air stream can be heated,
similar to the TEOM air conditioning (see Section 3.1.1), to remove liquid water when an
indicator of particle mass is desired (e.g., Waggoner and Weiss, 1980). Some systems
control both temperature and humidity to characterize the hygroscopic properties of
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Table 3-2
Operating Characteristics of Commercially Available
Manufacturer
Belfort
Optec
TSI
TSI
Radiance
Grimm
MIE
MIE
Met One
Instruments
R&P
TSI
Model
1590
NGN-2
3551
3563
M903
DustCheck
DataRam
personal
DataRam
GT-640
DustLite
Model 3000
DustTrack
Model 8520
Scattering
Type
Integrating
Integrating
Integrating
Integrating
Integrating
Sideways
Forward
Forward
Forward
Forward
Sideways
Sampling
Forced
Passive
Forced
Forced
Forced
Forced
Forced
Passive
Forced
Forced
Forced
Time
Resolution
2 min
> 2 min
> 2 sec
> 2 sec
> 10 sec
> 3 sec
> 1 sec
> 1 sec
> 1 min
> 3 sec
> 1 sec
Nephelometers
Center
Wavelength
530 nm
550 nm
550 nm
450, 550,
and 700 nm
530 nm
780 nm
880 nm
880 nm
781 nm
880 nm
780 nm
Backscatter
Feature
No
No
No
Yes
No
No
No
No
No
No
No
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suspended particles (e.g., Covert et al., 1972; Rood et al., 1985; Rood et al., 1987). Heating
prior to nephelometer sensing results in the same negative volatilization biases found for the
heated TEOM air stream; the higher the temperature, the greater the volatilization of
ammonium nitrate and volatile organic compounds.
Although light scattering is often highly correlated with mass concentrations, the
relationship depends on several variables and may be different from location to location and
for different seasons of the year. The light scattered per |ig/m3 (particle scattering efficiency,
asp, usually expressed in m2/g) depends on geometric particle diameter, real and imaginary
parts of the refractive index, and particle shape. Particle scattering (bsp in Mm"1) is the
product of the scattering efficiency and the particle concentration). For spherical particles of
known compositions, scattering efficiencies can be calculated (e.g., Wiscombe, 1980; Barber
and Hill, 1990; Lowenthal et al., 1995) based on Mie theory (Mie, 1908). An example of
scattering efficiency as a function of particle diameter is given in Figure 3-1. For particles
with diameters (d) less than the wavelength of light (k), the scattering efficiency increases as
function of diameter and reaches a maximum of ~4 m2/g at a particle diameter comparable to
the wavelength of the scattered light. For particles with diameters larger than the light
wavelength, the scattering efficiency decreases proportional to 1/d and is modulated by an
oscillation. This oscillation disappears as the particle diameter becomes much larger than the
wavelength. This oscillation attenuates if non-monochromatic light is used and when a
distribution of particle sizes and light wavelengths are present, as in ambient air.
Particle scattering measured by integrating nephelometers includes systematic errors
owing to: 1) non-monochromatic light sources; 2) limits of the integration angle; and 3) and
non-Lambertian light sources (e.g., Quenzel, 1969a, 1969b; Ensor and Waggoner, 1970;
Heintzenberg and Quenzel, 1973a, 1973b; Rabinoff and Herman, 1973; Quenzel et al., 1975;
Heintzenberg, 1978; Hasan and Lewis, 1983; Mulholland and Bryner, 1994; Anderson et al.,
1996; Heintzenberg and Charlson, 1996; Rosen et al., 1997; Anderson and Ogren, 1998).
Nephelometers can be designed to infer information about particle size, shape, and index of
refraction by using several wavelengths and detection geometries (e.g., Heintzenberg and
Bhardwaja, 1976; Bodhaine et al., 1991; Anderson et al., 1996; Heintzenberg and Charlson,
1996). Further modification of the nephelometer response can also be achieved by adding a
size-selective inlet to the nephelometer air intake (e.g., Nyeki et al., 1992; White et al.,
1994).
In practice, particle scattering efficiencies are empirically determined by collocating
nephelometers with filter-based samplers and comparing their measurements. This approach
has yielded a scattering efficiency of 2.6 m2/g for TSP with 90% of a large number of
measurements being within a factor of two from this value (e.g., Charlson et al., 1967;
Charlson et al., 1968; Charlson et al., 1969). For a variety of locations classified as pristine,
rural, residential, and industrial, the scattering efficiency was found to be 3.2 + 0.2 m2/g with
correlations >0.95 for fine particles (~ PM^.s) using a nephelometer with a heated air stream
(Waggoner and Weiss, 1980; Waggoner et al., 1981). The constant scattering efficiency is
due to the fact that the mass mean diameter of the fine particle mode is in the range of 0.4 to
1 |im where the scattering efficiency depends only weakly on the mass mean diameter.
Trijonis et al. (1990) found scattering efficiencies of ~3 m2/g for fine particles.
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1.00
Diameter (pm)
Figure 3-1. Particle scattering efficiency (osp) as function of particle diameter for silica
particles (5 = 2.2 g/cm3, n = 1.46 at 550 nm) and monochromatic green light (k = 550 nm).
3-19
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Husar and Falke (1996) examined relationships between urban bsp and PM2.5 for five
sites in Oregon, two sites in Missouri, and six sites in California, with R ranging from 0.22
to 0.93. Linear regression slopes of bsp vs. PM2.5 ranged from 4.1 to 11.9 m2/g. Most of the
relationships were straight line with a few notable outliers.
3.2.2 Optical Particle Counter (OPC)
Optical Particle Counters (OPC) use light scattering to detect the size and number of
individual particles (Gucker et al., 1947a; Gucker et al., 1947b; Gucker and Rose, 1954;
Kerker, 1997; Fabiny, 1998). OPCs have long been used in aerosol research (e.g., Liu et al.,
1974c; Heintzenberg, 1975; Heintzenberg, 1980; Eldering et al., 1994), thereby attaining a
degree of reliability and ease of operation that allow them to be deployed in long-term
monitoring networks. Some instruments analyze the spatial distribution of the scattered light
to derive a shape parameter (e.g., Kaye et al., 1991) that can be used to determine deviations
from sphericity.
In an OPC, a narrow air stream is directed through a small sensing zone, where it is
illuminated by an intensive light beam, commonly a visible laser beam. Light scattered by an
individual particle is sensed by a fast and sensitive detector, resulting in an electrical pulse.
Particle size is determined from the pulse amplitude, and particle number is determined from
the number of pulses (e.g., Whitby and Vomela, 1967; Makynen et al., 1982; Chen et al.,
1984; Robinson and Lamb, 1986; Wen and Kasper, 1986). The size of particles that can be
detected with OPCs ranges from about 0.05 to 50 |j,m (Fabiny, 1998), but it is more typically
0.2 to 30 urn.
Particle sizes and numbers are translated to mass concentration by assuming a
spherical particle shape and a particle density. The sum over all particle size bins can be
further related to mass loadings by comparison with a collocated filter sample. A few
recently developed units allow a 47-mm filter to be placed in the exhaust stream so that the
sensed particles can be collected for laboratory weighing and possible chemical
characterization. This would allow at least an average calibration to be obtained for
sampling periods that might last as long as one week.
The particle size measurement is commonly calibrated with a National Institute of
Standards and Technology (NIST)-traceable, monodisperse distribution of polystyrene latex
spheres (Whitby and Liu, 1968; Sloane et al., 1990). While size measurements with OPCs
can be very precise (van der Meulen and van Elzakker, 1986), their accuracy depends on
particle composition and shape (Buettner, 1990; Gebhart, 1991). These issues have been
explored for atmospheric aerosols (e.g., Hindman et al., 1978; Hering and McMurry, 1991).
Accuracy can be improved by simultaneous use of an integrating nephelometer with optical
particle counters (Sloane et al., 1991).
3.2.3 Condensation Nuclei Counter (CNC)
Continuous-flow Condensation Nuclei Counters (CNC) sense ultrafme particles by
causing them to grow to a size that is efficiently detected by light scattering (Sinclair and
Hoopes, 1975; Bricard et al., 1976). Particles in a sampled air stream enter a saturator where
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alcohol vapors at a temperature typically above ambient (e.g., 35 °C) create a saturated
atmosphere. Particles then pass into a condenser tube at a temperature sufficiently below that
of the saturator (e.g., 10 °C) (Miller and Bodhaine, 1982; Ahn and Liu, 1990). Alcohol
vapor condenses on the particles causing them to grow, and they are detected and classified
by an OPC. Table 3-3 summarizes the specifications of several representative CNCs.
CNCs detect particles with 0.003 to 1 jim diameters. For low particle concentrations,
the instrument operates in a counting mode, registering individual light pulses. For
concentrations above 1,000 particles/cm3, the simultaneous presence of more than one
particle in the viewing volume becomes frequent, and individual particles can no longer be
counted. At this point, the CNC switches to the photometric mode where the power of the
light scattered by all particles present in the viewing volume is measured (e.g., Agarwal and
Sem, 1980). In the counting mode, a CNC can be very precise (e.g., Liu et al., 1982), but the
counting efficiency for ultrafme particles depends substantially on the instrument design
(Bartz et al., 1985; Su et al., 1990; McDermott et al., 1991; Stolzenburg and McMurry, 1991;
Saros et al., 1996). In the photometric mode, the CNC must be calibrated with aerosol of
known concentration (for example, by using an electrostatic classifier) (Liu and Pui, 1974).
Response curves as a function of particle size, concentration, and different environmental
conditions have been determined for several different CNCs (Liu et al., 1982; Noone and
Hansson, 1990; Su et al., 1990; Zhang and Liu, 1990; Keston et al., 1991; Zhang and Liu,
1991).
CNCs are the most practical instruments for determining ultrafme particle
concentrations, but they are not as accurate as other continuous methods for determining
PM2.5 or larger size fractions owing to the low upper limit of their size range.
3.2.4 Aerodynamic Particle Sizer (APS)
The Aerodynamic Particle Sizer (APS) measures light scattering as well as the
time-of-flight of sampled particles (Wilson and Liu, 1980). The measured aerodynamic
diameter can be converted to volume-equivalent diameter or mobility-equivalent diameter
(Kasper, 1982).
The APS accelerates the air stream in a converging nozzle. Particles have a larger
inertia than the gaseous component, and therefore lag in acceleration and speed behind the air
stream. Particles with higher mass (as a result of higher density or larger size) achieve lower
velocities than those with lower mass. Each particle is detected by laser scattering at the
beginning and end of a fixed path length to determine the time taken to traverse this path (the
"time-of-flight"). The flight times are related to particle mass. The APS measures particles
with diameters of 0.5 to 30 jam (Peters et al., 1993).
The APS aerodynamic diameter differs from standard definition in Section 2 (the
diameter of the unit density sphere that has the same settling velocity in still air). The APS
aerodynamic diameter is adjusted (e.g., Rader et al., 1990) for particle density (Wilson and
Liu, 1980; Baron, 1986; Wang and John, 1987; Ananth and Wilson, 1988; Brockmann et al.,
1988; Wang and John, 1989; Chen et al., 1990), ambient gas density, and ambient air
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Table 3-3
Comparison of Condensation Nuclei Counter Specifications
TSI3010 TSI3022A TSI3025A
Minimum Particle Size (50% efficiency, nm) 10 7 3
Aerosol Flow Rate (cnrVmin) 1000 300 30
Upper Concentration Limit (particles/cm3) 104 107 105
Lower Concentration Sensitivity (particles/cm3) 000
False Count Rate (particles/cm3) < 0.0001 <0.01 <0.01
Response Time (95% response, sec) < 5 < 13 1
Vacuum Source External Internal Internal
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viscosity (Chen et al., 1985; Lee et al., 1990). During the acceleration process, calibration
and ambient particles can deform in different ways (i.e., flatten) depending on their viscosity
(Baron, 1986; Griffiths et al., 1986; Chen et al., 1990). Nonspherical particles behave
differently from spherical particles, necessitating additional adjustments (Chen and Crow,
1986; Brockmann and Rader, 1990; Cheng et al., 1990; Marshall et al., 1991; Cheng et al.,
1993). Phantom particle counts may result from the time-of-flight laser detection system
(Heitbrink et al., 1991; Heitbrink and Baron, 1992).
3.2.5 Light Detection And Ranging (LIDAR)
Llight Detection And Ranging (LIDAR) measures light scattered in the direction of
the light source ("backscattering") along a sight path (e.g., Measures, 1984; Grant, 1995).
Aerosol lidar determines aerosol distributions while Differential Absorption Lidar (DIAL)
can measure concentrations of several gases.
Basic single-wavelength aerosol lidar yields a semi-quantitative measurement of the
backscatter coefficient. High-spectral-resolution and Raman lidars provide quantitative
backscatter coefficients; these systems are very complex and currently not commercially
available. The connection between backscatter coefficient and PM concentration is indirect
and depends on particle size distribution and refractive index of the aerosol particles, similar
to nephelometers. Aerosol lidars are more suitable for determining the spatial distribution of
aerosol concentrations and its temporal development than for quantifying mass
concentrations.
A basic aerosol lidar system consists of a transmitter and a receiver located next to
each other. The transmitter, typically a pulsed laser, sends a short pulse of collimated light
into the atmosphere. A small part of this light pulse is scattered back into the receiver by
suspended particles and gas molecules. Light scattered at a distance r arrives at the receiver
after a round trip time, t = 2r/c, where c is the speed of light. The lidar return signal S as a
function of distance r depends both on the backscattering coefficient, B(r), at the distance r
and the path integrated extinction, a(r), between lidar location TO and range r. For each
measured signal, S(r), there are two atmospheric parameters, B(r,?i) and a(r,?i), that need to
be determined. The absolute system calibration is generally unknown. The lidar equation is,
therefore, under-determined and cannot be solved without additional assumptions or data.
One approach to obtain semi-quantitative estimates of range-resolved atmospheric
extinction and/or backscattering coefficients is to establish an empirical relationship between
the backscattering coefficient, B(r), and the extinction coefficient, a(r) (e.g., Gibson, 1994),
together with an estimate of a boundary condition for the extinction coefficient within the
measurement range (Hitschfeld and Bordan, 1954; Fernald et al., 1972; Klett, 1981). The
accuracy of the resulting backscattering and extinction data is highly dependent on the
assumptions used (e.g., Kovalev, 1993; Kovalev and Moosmiiller, 1994; Kovalev, 1995).
Despite the semi-quantitative nature of these data, they are useful for the evaluation of
aerosol sources and transport (e.g., McElroy and Smith, 1986; McElroy and McGown, 1992;
Hoffetal., 1997).
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Quantitative measurements of the atmospheric backscattering coefficient can be made
by separately measuring the aerosol (Mie) backscattering and the gaseous (Rayleigh or
Raman) backscattering. This dual measurement eliminates the extinction contribution to the
lidar signal by normalizing the aerosol to the gaseous signals. This has been done by
utilizing either inelastic gaseous scattering (i.e. Raman scattering) (Melfi, 1972; Cooney,
1975; Ansmann et al., 1992; Whiteman et al., 1992; Evans et al., 1997; Moosmuller and
Wilkerson, 1997) or near-elastic gaseous scattering (i.e., Rayleigh scattering) (Fiocco et al.,
1971; Alvarez II et al., 1990; Grund and Eloranta, 1991; She et al., 1992; Piironen and
Eloranta, 1994).
DIAL measurements (Measures, 1984) are made at two different wavelengths with
substantially different absorption coefficients for the gas of interest. DIAL may be a useful
complement to other continuous measurements of aerosol precursor gases described in
Section 3.6. The range resolved gas concentration is calculated from the ratio of the lidar
signals at the two wavelengths. The laser line (wavelength) with the larger (smaller)
absorption coefficient is referred to as "on-line" ("off-line").
DIAL has been used for the measurement of a number of relevant tropospheric trace
gases including ozone (Os) (e.g., Browell et al., 1983; Ancellet et al., 1989; Moosmuller et
al., 1993; Kempfer et al., 1994; Zhao et al., 1994), sulfur dioxide (SO2) (e.g., Woods and
Jolliffe, 1978; Browell, 1982; Ancellet et al., 1987; Beniston et al., 1990), nitric oxide (NO)
(e.g., Alden et al., 1982; Edner et al., 1988; Kolsch et al., 1992), nitrogen dioxide (NO2)
(Rothe et al., 1974; Galle et al., 1988; de Jonge et al., 1991; Toriumi et al., 1996), ammonia
(Force et al., 1985), and aromatic hydrocarbons (Milton et al., 1992). Though commercially
available, lidar systems are expensive and must be individually designed or modified for each
specific application.
3.3 Visible Light Absorption
Black carbon (BC) (sometimes termed "elemental carbon", "light-absorbing carbon",
or "soot") is the dominant visible light-absorbing particulate species in the troposphere and
mostly results from anthropogenic combustion sources (Horvath, 1993a). It is usually found
in the nucleation or accumulation mode for particles well under 1 jim in equivalent
dimensions (i.e., if chain aggregates were consolidated into a single sphere). Mass loadings
range from a few ng/m in remote pristine regions or over oceans distant from land, to a
fraction of 1 |ig/m3 in rural regions of the conti:
(Adams et al., 1990a, 1990b; Penner et al., 1993).
fraction of 1 |ig/m3 in rural regions of the continents, and exceed 1 |ig/m3 in many cities
Continuous methods that monitor particle light absorption (bap) can also be used to
measure the PM2.5 component consisting of light absorbing particles. Attenuation of light
through a filter and photoacoustic oscillation are detection principles used to quantify particle
absorption as a surrogate for black carbon. Particle light absorption measurements from
three different instruments are discussed in the following subsections.
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3.3.1 Aethalometer and Particle Soot/Absorption Photometer
Light absorbing aerosol (e.g., BC) deposited on a filter can be quantified through the
measurement of light transmission or reflection. As noted in Section 1, the British Smoke
measurement (Mage, 1995) was first used in the early 1950s to visually characterize the
reflectance of a filter sample. Coefficient of Haze (COH) measured by a paper tape sampler
(Herrick et al., 1989) was the United States counterpart to the British Smoke measurement.
A more quantitative method, the integrating sphere method (Fischer, 1973), measures aerosol
light absorption by placing the loaded filter in an integrating sphere (Fussell, 1974;
Labsphere Inc., 1994) and illuminating it. Light, both transmitted and scattered by the loaded
filter, first reaches the diffusely reflecting surface of the sphere where it is homogenized, and
then the light is detected by the photodetector. The difference between a clean filter and one
loaded with particles gives the amount of light absorbed by the particles. Simplifications of
the integrating sphere method, such as the integrating plate (Lin et al., 1973) or sandwich
(Clarke, 1982b) methods are most often used for routine measurements.
Integrating plate methods have been used extensively for the measurement of aerosol
light absorption as they are simple and cost-effective. Measurement accuracy is limited,
however, due to the interaction of the scattering and absorption properties of the concentrated
aerosol itself and of the aerosol with the filter medium. While some studies assure high
accuracy, others determine an overestimation of in-situ aerosol light absorption by a factor of
two to four (e.g., Szkarlat and Japar, 1981; Clarke, 1982a; Japar, 1984; Weiss and Waggoner,
1984; Horvath, 1993b; Campbell et al., 1995; Campbell and Cahill, 1996; Clarke et al., 1996;
Horvath, 1997; Moosmuller et al., 1998).
A real-time version of the integrating plate method, the aethalometer (Hansen et al.,
1984), continuously collects aerosol on a quartz-fiber filter tape. During the deposition
process, the light attenuation through the aerosol collection spot and an unloaded reference
spot are monitored. Their difference yields the absorption due to the integral of all
light-absorbing materials collected on a particular spot. The time derivative of this quantity
is a measure of the current aerosol light absorption. When the optical density of the aerosol
spot reaches a certain value, the filter tape advances automatically. Time resolution available
with the aethalometer varies from seconds or minutes in urban areas to ten minutes in rural
locations and longer in very remote locations. One filter tape is sufficient for approximately
700 aerosol collection spots corresponding to one or more months of operation in urban
areas, a year or more in rural areas.
The aethalometer converts the result of its filter attenuation measurement into BC
mass concentration by a conversion factor of 19.2 m2/g. Aethalometer BC agrees with
collocated filter samples analyzed for elemental carbon (Hansen and McMurry, 1990).
Applications of the aethalometer include air quality monitoring in urban (e.g., Hansen and
Novakov, 1989; Hansen and Novakov, 1990) and more remote locations (e.g., Pirogov et al.,
1994; Rosen et al., 1984), transport studies (e.g., Parungo et al., 1994), and source
characterization (Hansen and Rosen, 1990).
The Particle Soot/Absorption Photometer (PSAP) (Bond et al., 1998) gives a
filter-based, real-time measurement of aerosol light absorption. The PSAP produces
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a continuous measurement of absorption by monitoring the change in transmittance across a
filter (Pallflex E70-2075W) for two areas on the filter, a particle deposition area and
a reference area. A light emitting diode (LED) operating at 550 nm, followed by an Opal
glass, serves as light source. The absorption reported by the PSAP is calculated with a
nonlinear equation correcting for the magnification of absorption by the filter medium and
for response nonlinearities as the filter is loaded. Measurement time resolution can be as
short as a few seconds to five minutes, depending on ambient aerosol light absorption.
Applications of the PSAP include its use in ground-based monitoring by NOAA's Climate
Monitoring and Diagnostics Laboratory (CMDL) and in field campaigns such as the Aerosol
Characterization Experiment (ACE) of the International Global Atmospheric Chemistry
(IGAC) program (e.g., Quinn et al., 1998).
3.3.2 Photoacoustic Spectroscopy
At atmospheric pressures, electromagnetic energy absorbed by particles changes to
thermal energy, thereby heating the particles and the surrounding gases. Increased gas
temperatures surrounding light-absorbing particles cause thermal expansion of the gas.
When the light source power is modulated, the periodic expansion of the gas results in a
sound wave at the modulation frequency, which may be detected with a microphone (Pao,
1977). This "photoacoustic" detection of particle light absorption (Bruce and Pinnick, 1977;
Terhune and Anderson, 1977) can be related to the black carbon concentration.
Sensitive photoacoustic techniques use a power-modulated laser as light source. By
placing the aerosol-laden air into an acoustic resonator, and modulating the laser power at its
resonance frequency, the varying pressure disturbance (acoustic signal) is amplified by the
buildup of a standing acoustic wave in the resonator. Related, but more sophisticated
methods such as thermoacoustically amplified photoacoustic detection (Arnott et al., 1995;
Bijnen et al., 1996) and interferometric detection (Lin and Campillo, 1985; Moosmiiller and
Arnott, 1996) have been discussed by Moosmiiller et al. (1997).
Adams et al. (1989) used a water-cooled Argon ion laser operating at 514.5 nm
(green) with an output power of 1 W. The detection limit of this system was babs = 4.7 Mm"1
or about 0.5 |ig/m3 BC, limited by window absorption. More modern systems apply
solid-state lasers that greatly reduce system size and power consumption.
Petzold and Niessner (1995, 1996) developed a system with a detection limit of babs =
1.5 Mm"1. It uses a 802 nm laser diode with 450 mW output power. The equivalent black
carbon concentration was estimated to be 0.5 |ig/m3 (Petzold and Niessner, 1996).
Decreasing aerosol absorption efficiency with increasing wavelength does not favor this
design, nor does the fact that the 802-nm beam cannot be seen directly, which makes
alignment difficult.
Arnott et al. (1998) and Moosmiiller et al. (1998) use a diode-pumped,
frequency-doubled Nd:YAG laser operating at 532 nm (green) with an output power of about
100 mW. Using an advanced acoustic design, window noise and flow noise were greatly
reduced, resulting in a detection limit of about 0.5 Mm"1, corresponding to about 0.05 |ig/m3
BC. Increasing the laser power in this system will proportionally improve the detection limit.
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3.4 Electrical Mobility
Electrical mobility analyzers are applicable to particles smaller than 1 jim. They are
the only practical alternative to the CNC instrument for quantifying the ultrafine fraction of
the particle size distribution. The resulting particle size is known as mobility equivalent
diameter, which can be converted to volume equivalent diameter or aerodynamic diameter
(Kasper, 1982).
A basic electrical mobility analyzer consists of: 1) a charger to impart an electric
charge to the particles (a diffusion charger that exposes particles to unipolar positive ions is
commonly used); 2) a classifier that separates the particles by acting on their electrical charge
and mass; and 3) a detector to monitor the separated particles.
Electrical mobility analyzers are often used together with aerodynamic particle sizers
(see Section 3.2.4), with the electrical mobility analyzer capturing particles below 1 |im and
the aerodynamic particle sizers measuring the larger particles (Peters et al., 1993). Particle
number measurements with two different instruments are discussed in the following
subsections.
3.4.1 Electrical Aerosol Analyzer (EAA)
The Electrical Aerosol Analyzer (EAA) (Whitby and Clark, 1966) has been widely
applied and characterized in aerosol studies (Liu et al., 1974a, 1974b; Liu and Pui, 1975).
Methods to translate EAA outputs to particle sizes and numbers have been developed
(Helsper et al., 1982). EAAs are typically operated with about ten size channels covering the
range from 0.01 to 1.0 jim with a measurement time on the order of a few minutes.
Positively charged aerosol enters a mobility tube consisting of two coaxial cylinders.
The outer tube is grounded and a negative potential is applied to the inner tube. As the
aerosol flows down the mobility tube, its mobile fraction is precipitated on the inner tube by
electrical forces. The remaining aerosol is detected, commonly by an electrometer that
measures the electrical current of the remaining aerosol. The potential of the inner cylinder
is changed in steps. For each potential, a different fraction of the aerosol is precipitated. The
resulting current-versus-voltage curve for an aerosol can be converted into a
current-versus-size curve once the EAA has been calibrated with monodisperse aerosol of
known size. Calibration of the current sensitivity is done by grounding the inner cylinder and
measuring the aerosol current without precipitation losses.
3.4.2 Differential Mobility Particle Sizer (DMPS)
The Differential Mobility Particle Sizer (DMPS) (Knutson and Whitby, 1975a,
1975b) improves on the EAA by making measurements with much greater size resolution
(e.g., 100 channels). Several DMPS configurations have been reported (e.g., Hoppel, 1978;
Fissan et al., 1983; ten Brink et al., 1983; Kousaka et al., 1985) and data reduction has been
studied extensively (e.g., Alofs and Balakumar, 1982; Hagen and Alofs, 1983; Birmili et al.,
1997).
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The DMPS is a modification of the EAA. The basic difference is that the DMPS
produces a flow of aerosol consisting of particles with an electrical mobility between two
closely spaced values (i.e., differential), while the EAA produces a flow consisting of
particles with an electrical mobility above some value (i.e., integral). Instead of measuring
the flow of particles missing the inner tube as in the EAA, a sample flow of aerosol is
extracted through a slot in the inner tube. Only particles with mobilities within a limited
range enter the sample stream for detection. As in the EAA, the voltage of the inner tube is
stepped through a number of values and the DMPS directly yields the electrical mobility
distribution without further differentiation.
Measurement times for DMPS with electrometers as detectors can be on the order of
one hour. Operation with a CNC as detector can reduce the measurement time by an order of
magnitude. A further order of magnitude reduction in averaging time can be achieved by
scanning the inner tube voltage instead of stepping it discreetly (Wang and Flagan, 1990;
Endo et al., 1997). This modification is referred to as Scanning Mobility Particle Analyzer
(SMPA).
The conventional DMPS utilizing a cylindrical geometry is limited for ultrafme
particles (< 0.020 jim) as its transmission efficiency drops dramatically below 0.02 |im due
to diffusion losses (Reineking and Porstendorfer, 1986). The accessible size range can be
extended down to 0.001 |im by either modifying the cylindrical DMPS (Reischl, 1991;
Winklmayr et al., 1991) or by changing to a radial geometry (Zhang et al., 1995).
3.5 Chemical Components
If the carbonaceous, nitrate, sulfate, ammonium, and geological components of
suspended particles could be determined continuously and in situ, a reliable speciated
estimate of PM2.5 or PMio mass concentration could be derived. While most of these
chemical-specific particle monitors are currently experimental, rapid technology advances
will make them more available and more widely used within coming years. The following
subsections introduce various versions of single particle mass spectrometers that measure
particle size and chemical composition, along with single compound instruments to measure
carbon, sulfur, and nitrate.
3.5.1 Single Particle Mass Spectrometers
Continuous versions of the LAser Microprobe Mass Spectrometer (LAMMS) have
been developed as the Rapid Single particle Mass Spectrometer (RSMS) (Mansoori et al.,
1994; Carson et al., 1995; Johnston and Drexler, 1995), Particle Analysis by Laser Mass
Spectrometry (PALMS) (Murphy and Thomson, 1994, 1995), and Aerosol Time Of Flight
Mass Spectrometry (ATOFMS) (Noble et al., 1994; Nordmeyer and Prather, 1994; Prather et
al., 1994; Noble and Prather, 1996, 1998). These devices measure the size and chemical
composition of individual particles. Portable instruments for ground based monitoring are
becoming available (e.g., Gard et al., 1997), and an airborne instrument for measurements in
the troposphere and lower stratosphere is being developed (Murphy and Schein, 1998).
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Particles are introduced into a vacuum by a nozzle. The presence of particles is
detected through light scattered from a visible laser beam. This scattering process is also
used as an OPC (see Section 3.2.2) or APS (see Section 3.2.4) to determine the size and
number of particles passing through the instrument. The presence of a particle triggers a
high-energy pulsed laser which, with a single pulse, ablates particle material and ionizes part
of it. The ions are detected and analyzed by a time-of-flight mass spectrometer. The time
particles spend in the vacuum is on the order of microseconds, minimizing condensation,
evaporation, and reactions.
The analysis rate is limited by the repetition rate of the pulsed laser. The presence of
the OPC makes it possible to analyze a size selected fraction of the particles, however, it also
imposes a lower limit on the particle size being analyzed, as very small particles are not
detected by the OPC. Running a pulsed laser without triggering can acquire smaller
particles, but at the expense of a lower duty cycle and no size selection. Because of the
complicated ionization process, the technique is currently used to survey the chemical
composition of particles than to yield quantitative mass concentrations of particle
components.
These instruments have been recently developed and characterized with respect to
ionization thresholds of aerosol particles (Thomson and Murphy, 1993; Thomson et al.,
1997), trade-offs between aerodynamic particle sizing and OPC sizing coupled with mass
spectroscopy (Salt et al., 1996), and the ability to determine surface and total composition of
the aerosol particles (Carson et al., 1997). Applications of this technique have included
characterizing aerosol composition in support of the 1993 OH experiment at Idaho Hill, CO
(Murphy and Thomson, 1997a, 1997b), examining the purity of laboratory-generated sulfuric
acid droplets (Middlebrook et al., 1997), determining halogen, (Murphy et al., 1997),
speciating sulfur (Neubauer et al., 1996), studying matrix-assisted laser desorption/ionization
(Mansoori et al., 1996), monitoring pyrotechnically derived aerosol in the troposphere (Liu et
al., 1997), characterizing automotive emissions (Silva and Prather, 1997), measuring marine
aerosols (Noble and Prather, 1997) and their radiative properties (Murphy et al., 1998), and
observing heterogeneous chemistry (Gard et al., 1998).
3.5.2 Carbon Analyzer
The differentiation of organic carbon (OC) and elemental carbon (EC) based on their
thermal properties followed by their detection as carbon dioxide (CO2) or methane (CH4)
after combustion is commonly applied to filter deposits (e.g., Chow et al., 1993a). Turpin et
al. (1990a, 1990b) pioneered the continuous thermal/optical carbon analyzer which provides
in-situ time-resolved OC and EC measurements. Another version of the thermal method, the
Ambient Carbon Particulate Monitor (ACPM, R&P Series 5400) (Rupprecht et al., 1995),
consists of two aerosol collectors; one collector operates in the collection mode while the
other one operates in the analysis mode at any given time. In the collection mode, particles
are drawn through a size-selective inlet and deposited onto an impactor. The temperature of
the collector in collection mode can be set either at or above ambient temperature. Once the
pre-specified sampling period is achieved (typically one or more hours), the collector is
switched into the analysis mode, while the second collector is switched from analysis to
collection mode.
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In the analysis mode, the ACPM first purges the analysis loop with filtered ambient
air and measures a base line CO2 concentration. The furnace surrounding the collector then
heats the sample to the intermediate temperature level of 250 °C (default) while an infrared
CO2 detector measures the increase in CO2 concentration due to carbon oxidation. An
afterburner ensures that all volatile materials are fully oxidized. Once the temperature has
stabilized and the CO2 concentration has been measured, the oven heats to the final burn
temperature of 750 °C (default) in the closed loop. The ACPM computes the OC and EC
concentrations by dividing the measured amount of carbon released from the intermediate
and final burn, respectively, by the air volume that passed through the instrument during
sample collection.
The ACPM provides a continuous measure of OC and EC concentrations. However,
its operational definitions of OC and EC and the selected combustion temperatures differ
from those of commonly applied laboratory filter analysis protocols (e.g., Chow et al.,
1993a).
3.5.3 Sulfur Analyzer
Continuous methods for the quantification of aerosol sulfur compounds first remove
gaseous sulfur (e.g., SO2, H^S) from the sample stream by a diffusion tube denuder followed
by the analysis of particulate sulfur (Cobourn et al., 1978; Durham et al., 1978; Huntzicker et
al., 1978; Mueller and Collins, 1980; Tanner et al., 1980). Another approach is to measure
total sulfur and gaseous sulfur separately by alternately removing particles from the sample
stream, and aerosol sulfur is obtained as the difference between the total and gaseous sulfur
(Kittelson et al., 1978). The total sulfur content is measured by a flame photometric detector
(FPD) by introducing the sampling stream into a fuel-rich hydrogen-air flame (e.g., Stevens
et al., 1969; Farwell and Rasmussen, 1976) that reduces sulfur compounds and measures the
intensity of the 82* chemiluminescence.
Because the formation of 82* requires two sulfur atoms, the intensity of the
chemiluminescence is theoretically proportional to the square of the concentration of
molecules that contain a single sulfur atom. In practice, the relationship is between linear
and square and depends on the sulfur compound being analyzed (Dagnall et al., 1967;
Stevens et al., 1971). Calibrations are performed using both particles and gases as standards.
The FPD can also be replaced by a chemiluminescent reaction with ozone that minimizes the
potential for interference with a faster time response (Benner and Stedman, 1989, 1990).
Capabilities added to the basic system include in-situ thermal analysis (Cobourn et
al., 1978; Huntzicker et al., 1978) and sulfuric acid (H2SO/t) speciation (Tanner et al., 1980).
Sensitivities for sulfur aerosols as low as 0.1 |ig/m3 with time resolution ranging from 1 to 30
minutes have been reported. Continuous measurements of aerosol sulfur content have also
been obtained by on-line x-ray fluorescence analysis with a time resolution of 30 minutes or
less (Jaklevic et al., 1980). During a field-intercomparison study of five different sulfur
instruments, Camp et al. (1982) reported four out of five FPD systems agreed to within ±5%
during a one-week sampling period.
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3.5.4 Nitrate Analyzer
The Automated Particle Nitrate Monitor (APNM) is a new method being developed
to provide high-time-resolution measurements of particle nitrate concentration (Hering, 1997;
Chow et al., 1998b; Hering and Stolzenburg, 1998). It uses an integrated collection and
vaporization cell whereby particles are collected by a humidified impaction process, and
analyzed in place by flash vaporization. The approach is similar to the manual method for
measuring the size distribution of sulfate aerosols (Roberts and Friedlander, 1976; Hering
and Friedlander, 1982). The difference is that the particle collection and analysis has been
combined into a single cell, allowing the system to be automated. Although the automated
method that has been recently tested is specific to nitrate, the same technology could be
applied for continuous sulfate measurements by using a sulfur detector instead of a nitric
oxide detector.
Particles are humidified, and collected onto a metal strip by means of impaction. The
humidification eliminates particle bounce from the collection surface without the use of
grease (Winkler, 1974; Stein et al., 1994). Interference from vapors such as nitric acid is
minimized with a denuder upstream of the humidifier. At the end of the 10-minute particle
sampling period, a valve is switched to stop particle collection and to pass a carrier gas
through the cell and into a gas analyzer. For nitrate, the deposited particles are analyzed by
flash-vaporization in a nitrogen carrier gas, with quantification of the evolved gases by a
chemiluminescent analyzer operated in NOX mode (Yamamoto and Kosaka, 1994). The flow
system is configured such that there are no valves on the aerosol sampling line. Time
resolution of the instrument is on the order of 12 minutes, corresponding to a ten-minute
collection followed by an analysis step of less than two minutes.
Field calibration and validation procedures include on-line checks of particle
collection efficiency, calibration of aqueous standards, and determination of blanks by
measurements of filtered ambient air. Particle collection efficiencies have been checked
against an optical particle counter that operated between the collection cell and the pump.
The analysis step of the monitor has been calibrated by application of aqueous standards (i.e.,
sodium nitrate and ammonium nitrate) directly onto the metal collection substrate. To ensure
the absence of response to ammonium ion, standards of ammonium sulfate have also been
applied. Field blanks are determined by placing a Teflon filter at the inlet of the system,
collecting for the 10-minute sampling period, and then analyzing the strip exactly as done for
a normal sample.
During the Northern Front Range Air Quality Study in Colorado (Watson et al.,
1998a), the automated nitrate monitor captured the 12-minute time variability in fine particle
nitrate concentrations with a precision of approximately ±0.5 |ig/m3 (Chow et al., 1998b). A
comparison with denuded filter measurements followed by ion chromatographic analysis
(Chow and Watson, 1998b) showed agreement within ±0.6 |ig/m3 for most of the
measurements, but exhibited a discrepancy of a factor of two for the elevated nitrate periods.
More recent intercomparisons took place during the 1997 Southern California Ozone
Study (SCOS97) in Riverside, CA. Comparisons with 14 days of 24-hour denuder-filter
sampling gave a correlation coefficient of R2 = 0.87, and showed no significant bias (i.e., the
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regression slope is not significantly different from 1). As currently configured, the system
has a detection limit of 0.7 |ig/m3, and a precision of 0.2 |ig/m3. Field operations with the
system in Riverside showed that it was robust, providing nearly uninterrupted data over the
six-week study period (S. Hering, personal communication).
3.5.5 Multi-Elemental Analyzer
Both streaker (PIXE International, Tallahassee, FL) and DRUM (Davis
Rotating-drum Universal-size-cut Monitoring impactor) (University of California, Davis,
CA) samplers provide continuous particle collection on filter substrates followed by
laboratory elemental analysis with Particle-Induced X-Ray Emission (PIXE). This is a
continuous, but not an in-situ real-time monitoring method due to the lag time between
sample collection and chemical analysis in a laboratory. These samplers have a time
resolution of- one hour, and can supply high-time-resolution elemental concentrations.
3.5.5.1 Streaker
Ambient particles in the streaker sampler are collected on two impaction stages and
an after-filter (Hudson et al., 1980; Bauman et al., 1987; Annegarn et al., 1990). The first
impaction stage has a 10-|im cutpoint and collects particles on an oiled frit that does not
move. The particles collected on this stage are discarded. The second impaction stage has a
2.5-|im cutpoint and collects coarse particles (PMio minus PM2.5) on a rotating Kapton
substrate that is coated with Vaseline to minimize particle bounce. The second impaction
stage is followed by a 0.4-|im-pore-size Nuclepore polycarbonate-membrane filter (Chow,
1995) that has an 8-mm-long negative pressure orifice behind it to collect fine particles
(nominally PM2.5). The air flow rate through the streaker sampler is primarily controlled by
the porosity of the filter and the area of the sucking orifice. A 1-mm-wide orifice can be set
up to result in a flow rate of approximately 1 L/min and produce an annular deposit of 8 mm
in width, with any point on the deposit collected during a one-hour time period.
The body of the streaker sampler has a cylindrical form with a diameter of
approximately 10 cm and a length of about 20 cm. It contains a clock motor that advances
two particle collection substrates mounted within the streaker. The streaker is mounted in the
open air with the sample air inlet at the bottom to keep out very large particles (e.g., rain and
drizzle). Sample air flow rates can be verified by a flow meter, temporarily attached to the
inlet of the streaker sampler at the beginning and end of sampling on each substrate, or at
times in between. Substrates of 168 mm in length had the capacity to accommodate a
seven-day sampling period.
3.5.5.2 DRUM
The Davis Rotating-drum Universal-size-cut Monitoring impactor (DRUM) is an
eight-stage cascade impactor. It collects particles on grease-coated mylar substrates that
cover the outside circular surface of eight clock-driven slowly rotating cylinders or drums
(one for each stage) (Raabe et al., 1988). The advantages of the DRUM sampler are its
capability to operate for up to thirty days unattended and its use of the location of particle
deposits along the drum substrate as a means to determine the time of their collection. The
DRUM collects aerosol from 0.07 |im to 15 jim in diameter for eight size ranges (0.07 to
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0.24, 0.24 to 0.34, 0.34 to 0.56, 0.56 to 1.15, 1.15 to 2.5, 2.5 to 5, 5 to 10, and 10 to 15 |im),
followed by focused-beam PIXE analysis. DRUM samplers have been used in several
visibility field studies to confirm assumptions about mass scattering efficiency dependence
on sulfur size distributions (e.g., Pitchford and Green, 1997).
The DRUM sampler, and other Lundgren-type rotating drum impactors, are unique
among size-segregating samplers in that they generate a continuous time history of aerosol
component size distributions with short time resolution (e.g., hourly). Unlike the
conventional aerosol size distribution data sets with at most a few dozen distributions that
can be individually scrutinized, the DRUM produces hundreds of distributions. In addition,
time series analysis, summary statistics, and multivariate analysis can be applied to
concentrations measured on any or all stages of the DRUM sampler.
3.6 Precursor Gases
Continuous sulfur dioxide and oxides of nitrogen monitors have been available for
decades, largely because these are regulated pollutants. These gases are important precursors
to particulate sulfate and nitrate. Ammonia and nitric acid are other precursor gases for
which concentrations are essential for understanding the equilibrium of particulate
ammonium nitrate (Watson et al., 1994a). The following subsections discuss the instruments
available to continuously measure ammonia and nitric acid.
3.6.1 Ammonia Analyzer
Ammonia gas can be quantified by fluorescence or by chemiluminescence methods.
For the fluorescence method, sampled ammonia is removed from the airstream by a diffusion
scrubber, dissolved in a buffered solution, and reacted with o-phtaldialdehyde and sulfite.
The resulting i-sulfonatatoisoindole fluoresces when excited with 365-nm radiation, and the
intensity of the 425-nm emission is monitored for quantification. The diffusion scrubber
might be modified to pass particles while excluding ammonia gas to continuously quantify
ammonium ions (Abbas and Tanner, 1981; Rapsomanikis et al., 1988; Genfa et al., 1989;
Harrison and Msibi, 1994).
For the chemiluminescence method, oxides of nitrogen (NOX) are first removed from
an airstream, and ammonia is then oxidized to nitrogen oxide (NO) for detection by the same
chemiluminscent detectors that are used to monitor ambient nitrogen oxide and nitrogen
dioxide (NO2) (e.g.,Breitenbach and Shelef, 1973; Braman et al., 1982; Keuken et al., 1989;
Langford et al., 1989; Wyers et al., 1993; S0rensen et al., 1994; Jaeschke et al., 1998).
Chemiluminescent ammonia analyzers convert ammonia to NOX by thermal oxidation using a
catalytic technique at high temperature. This type of continuous ammonia monitor has been
used mostly in source emission testing rather than ambient monitoring in the past.
Laboratory tests of TEI Model 42 (Thermo Environmental Instruments, Franklin, MA)
during the Northern Front Range Air Quality Study (Chow et al., 1998b) show that:
1) typical response time is on the order of two or more hours for concentrations of 40 ppb to
400 ppb; 2) the instrument's detection limit is approximately 10 ppb (ambient concentration);
3) oxidizer efficiency is in the range of 50% to 75% and lower for NHa concentrations less
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than 40 ppb; and 4) no effects upon ammonia concentrations could be identified by the
changes in ambient relative humidity.
Other, less established methods to measure ammonia include photoacoustic
spectroscopy (Rooth et al., 1990; Sauren et al., 1993), vacuum ultraviolet/photofragmentation
laser-induced fluorescence (Schendel et al., 1990), Differential Optical Absorption
Spectroscopy (DOAS) in the ultraviolet (Platt, 1994), Differential Absorption Lidar (DIAL,
see section 3.2.5), and Fourier Transform Infrared (FTIR) spectroscopy (see section 3.6.3).
Intercomparisons between different ammonia measurement methods have been conducted
(Appel et al., 1988; Wiebe et al., 1990; Williams et al., 1992; Mennen et al., 1996).
3.6.2 Nitric Acid Analyzer
Atmospheric nitric acid (HNOs) concentrations can be monitored by conversion of
nitric acid to nitrogen dioxide (NO2), followed by detection with a chemiluminescent
analyzer (Kelly et al., 1979; Burkhardt et al., 1988; Harrison and Msibi, 1994). While
conversion methods from nitric acid to nitrogen dioxide are not very selective, nylon filters
have been used to remove nitric acid from a gas stream. The nitric acid concentration is
therefore determined by the difference in NO2 measurements with and without a nylon filter
in the gas stream.
The sampled gas is initially passed through a Teflon filter to remove particles. This is
followed by the nylon filter that removes more than 99% of the nitric acid and can be
bypassed for using the difference method. Conversion from nitric acid to NO and NO2 is
achieved with a glass bead converter operating at high temperature (i.e., 350 to 400 °C). This
is followed by a CrOs impregnated filter used to convert NO to NO2 (Ripley et al., 1964). A
chemiluminescence instrument (Fehsenfeld et al., 1990; Gregory et al., 1990) is used to
measure the resulting NO2 concentration, with luminol based instruments being particularly
sensitive (Kelly et al., 1990).
These instruments have a sensitivity around 0.1 ppb with a time response of about 5
minutes (Harrison, 1994). Limitations of the technique are due to the sticking of nitric acid
to walls and to the possible interference of high humidity and other nitrogen species with
conversion processes (e.g., Burkhardt et al., 1988). These instruments have been tested in
intercomparison studies (e.g., Fox et al., 1988; Hering et al., 1988; Gregory et al., 1990).
3.6.3 Fourier Transform Infrared (FTIR) Spectroscopy
Fourier Transform Infrared (FTIR) spectroscopy detects molecular gases (with the
exception of homonuclear diatomic gases) by measuring the light absorption due to their
rotational-vibrational transitions (Hanst and Hanst, 1994). For the measurement of the very
polar nitric acid, this open-path technique mitigates the associated adsorption and desorption
sampling problems. Nitric acid and ammonia have been measured with 1 to 1.5 km
absorption path length folded into a 25-m long White cell (White, 1976) at wavenumbers of
879 and 967 cm"1 (nitric acid) and 932 and 967 cm"1 (ammonia) (Tuazon et al., 1978; Doyle
et al., 1979; Tuazon et al., 1980; Tuazon et al., 1981; Hanst et al., 1982; Biermann et al.,
1988). Sensitivities of 4 ppbv for nitric acid and 1.5 ppbv for ammonia have been achieved
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with averaging times of about 5 minutes. This technique is not as sensitive as tunable diode
laser techniques, but its sensitivity is sufficient for use in urban environments.
3.6.4 Other Nitric Acid Instruments
Tunable Diode Laser Absorption Spectroscopy (TDLAS) takes advantage of the high
monochromaticity and rapid tunability of a lead salt diode laser to measure absorptions from
single rotational-vibrational lines in the middle infrared spectrum of a molecule, since most
gases absorb radiation in this spectral region. High spectral resolution is required to prevent
interferences from other gases in the sampled air. The atmospheric sample is pumped rapidly
at reduced pressure through a White cell, which also provides the long optical path lengths
required to achieve the desired detection limits.
The tunable diode laser is a small lead crystal with variable amounts of tin, selenium,
tellurium, or sulfur. The wavelength region at which the laser emits radiation is governed by
the proportions of the three elements in the crystal. Techniques of measuring nitric acid by
TDLAS at 1721 cm"1 and at 1314 cm"1 have been described (Schiff et al., 1983; Harris et al.,
1987; Mackay et al., 1988; Schmidtke et al., 1988). The sensitivity of this technique is about
0.3 ppbv for 5-minute time resolution (a factor of 10 better than the long-path FTIR method).
The time resolution is mostly limited by nitric acid sticking to surfaces as it is a very polar
gas. The accuracy depends on the ability to measure the various flows and to determine the
mixing ratio of the calibration standard. Several studies have compared TDLAS
measurements with other nitric acid measurements (Anlauf et al., 1985, 1988; Fox et al.,
1988; Hering et al., 1988; Fehsenfeld et al., 1998) and differences of about 30% have been
reported. The lack of a direct nitric acid standard combined with sampling difficulties makes
the interpretation of these intercomparisons difficult.
The mist chamber method samples nitric acid by efficiently scrubbing it from the
atmosphere in a refluxing mist chamber followed by analysis of the scrubbing solution for
NOs by ion chromatography (Talbot et al., 1990). A sensitivity of 10 pptv for a 10-minute
integration period has been reported.
Nitric acid concentrations have also been measured with the Laser-Photolysis
Fragment-Fluorescence (LPFF) Method, which irradiates the air sample with ArF laser light
(193 nm) resulting in the photolysis of nitric acid (Papenbrock and Stuhl, 1991). The
resulting hydroxyl radical (OH) emits fluorescence at 309 nm which is taken as a measure of
the nitric acid mixing ratio in air. A sensitivity of 0.1 ppbv and a time constant of 15 minutes
limited by surface ad- and desorption have been reported. An intercomparison of this
technique with a denuder technique was also reported.
Chemical lonization Mass Spectrometry (CIMS) has been used for the sensitive (few
pptv) and fast (second response) measurement of atmospheric nitric acid concentrations.
Reagent ions formed by an ion source are mixed with the sampled air and react selectively
with nitric acid. The ionic reaction product is detected with a mass spectrometer. Two
different CIMS instruments have been described (Huey et al., 1998; Mauldin et al., 1998) and
compared with an older, more established filter pack technique (Fehsenfeld et al., 1998).
3-35
-------
3.7 Summary
All of the measurement methods identified in Table 3-1, and described genetically
above, show merit for particle monitoring. The most commonly available and widely used
units range in cost from ~$10K to $3OK for the TEOM, BAM, nephelometer, aethalometer,
and carbon analyzer. Some continuous light scattering instruments can be purchased for as
little as ~3K. These are comparable to the cost of continuous gas monitors for other criteria
pollutants. These instruments are reliable and have benefited from feedback of a relatively
large number of users. Their operating manuals and operating procedures are established.
Most of these instruments have been evaluated in collocated tests with filter samplers, though
the majority of these evaluations are for the PMio rather than PM2.5 size fraction.
Well-established research monitors include the APS, the OPC, the EAA, the DMPS,
and the CNC. These are in the cost range of ~$20K to $50K. They are reliable and
well-documented, but substantial skill is required to maintain and operate the equipment and
to reduce the resulting data. Most of these instruments have also been evaluated in
collocated tests with each other and with filter samplers, though once again there is a dearth
of comparisons with PM2.5 measurements.
The remaining devices are specialized technologies for which only a few units exist
for application in special studies. Their costs range from ~$50K to $300K, their operating
procedures are still being developed, and in many cases only the developer is currently
competent to apply them. This situation will change in coming years, however, as the utility
of these instruments is borne out in specialized studies. The information they acquire will be
essential to accomplishing several of the objectives for continuous monitoring stated in
Section 1.
The state of technology shows that several of the major chemical components can be
adequately measured by continuous monitors. Organic and elemental carbon, sulfate, and
nitrate each have continuous in-situ methods that have been proven, but need to be evaluated,
accepted, and packaged. Continuous in-situ measurement methods for ammonium, crustal
elements, and liquid water are still lacking (with the possible exception of the single particle
mass spectrometers). Consideration needs to be given to ways in which these components
might be practically quantified at intervals of one-hour duration or less. Continuous
measurement methods for particle size, based on inertial, optical, electrical, and condensation
properties, are also adequate to characterize the ultrafine as well as the accumulation modes
of suspended particles.
The remainder of this document focuses on the equivalence and utility of the most
commonly and widely used monitors that can be procured and operated within the fiscal and
expertise constraints of most air pollution surveillance agencies at the present time. These
are the most viable candidates for designation as Correlated Acceptable Continuous (CAC)
monitoring status.
3-36
-------
4. MEASUREMENT PREDICTABILITY, COMPARABILITY, AND
EQUIVALENCE
This section provides example comparisons of collocated continuous particle
monitors with each other and with filter-based aerosol samplers. Only a few of these are
specific to the PM2.5 size fraction, since few comparisons have been reported for this size
fraction; most relate to PMio sampling. Comparisons of continuous PM2.5 monitors with
PM2.5 Federal Reference Method (FRM) samplers were unavailable for this guidance. These
comparisons are intended to be illustrative rather than comprehensive. They are drawn from
recent studies for which collocated measurements were readily available and quality assured.
PM2.5 measurement predictability, comparability, and equivalence are defined as
follows:
• Predictability: A consistent and reliable relationship can be established between
a continuous particle monitor and a collocated FRM or FEM measurement that
allows the FRM concentration to be consistently estimated from the continuous
monitor measurements within acceptable precision intervals. Predictability does
not require traceability to a PM2 5 or PMio mass concentration but it does require
sufficient collocated measurements to establish the predictive relationship
between the measured quantity and an FRM or FEM concentration. Light
scattering or light absorption measurements are examples of continuously
measured particle properties from which PM2.5 concentrations might be predicted.
• Comparability: The predictability of a continuous particle monitor is established
and the measurement is traceable to a PM2 5 or PMio mass calibration standard. A
comparable monitor should provide readings in units of mass concentration, be
equipped with a standardized size-selective inlet, and yield measurements that are
the same as collocated FRM or FEM measurements. TEOM, BAM, and CAMMS
are examples of instruments that are calibrated in units of PM2 5 mass and are
equipped with characterized inlets. Several of the particle sizing and light
extinction methods could be comparable when particle shape, index of refraction,
and density can be defined. A suite of the chemical specific monitors could also
eventually estimate mass concentrations.
• Equivalence: The predictability and comparability of a continuous particle
monitor is established and the requirements in Table 4-1 have been attained.
Equivalence requires designation of the monitor as a Federal Equivalent Method,
or FEM. Equivalence is more demanding than predictability or comparability in
that it requires demonstration of comparability within high tolerances over a wide
range of concentration loadings and measurement environments.
The following sub-sections examine the comparability and predictability of particle
concentrations from selected field studies in order to determine the situations under which
these attributes might be established for different types of monitors. They also examine
different methods of quantifying comparability and predictability.
4-1
-------
Table 4-1
Test Specifications for PM2.s Equivalence to Federal Reference Method3
Criteria
Specifications
Concentration Range 10 to 200 |ig/nr
Number of Test Sites
One for "Class I" monitors, two for "Class II" monitors. (More
varied environments will be required for "Class III" continuous
particle monitors.)
Number of Samplers Three FRMs, three candidate samplers
Number of Samples
Class I 24-hour samples: Rjb > 40 |ig/m3 and Rj < 40 |ig/m3
Class I 48-hour samples: Rj > 30 |ig/m3 and Rj < 30 |ig/m3
Class II 24-hour samples:
a. for PM2.5/PMio ratio > 0.75: Rj > 40 |ig/m3 and Rj < 40 |ig/m3,
b. for PM2.5/PMio ratio < 0.40: Rj > 30 |ig/m3 and Rj < 30 |ig/m3,
Class II 48-hour samples:
a. for PM2.5/PMio ratio > 0.75: Rj > 30 |ig/m3 and Rj < 30 |ig/m3,
b. for PM2.5/PMio ratio < 0.40: Rj > 20 |ig/m3 and Rj < 20 |ig/m3
Collocated Precision 2 |ig/m or 5% (largest)
Regression Slope
1±0.05
Intercept
0 ± 1 |ig/m3
Correlation
>0.97
a U.S. EPA(1997a).
b RJ = the minimum number of acceptable sample sets per site for PM2 5. Rj must be equal to
or greater than 3.
4-2
-------
4.1 Measurement Comparability
There is a dearth of data available in the compliance monitoring network to evaluate
the comparability between the U.S. EPA designated PMio reference and equivalent methods
and continuous PM monitors. Watson et al. (1997b) evaluated the quality and comparability
of PM measurements from different networks between 1988 and 1993 in Central California.
Figure 4-1 compares the high-volume size-selective-inlet (SSI) PMio measurements with
TEOM and BAM in Central California. The SSI measurements were 30% higher than those
measured with the TEOM samplers, but they were nearly the same as those measured with
the BAM samplers. There are notable outliers in each of these comparison plots, especially
for the highest concentrations.
The TEOM data in Figure 4-1 show several values that compare well with the SSI,
with nearly a one-to-one correspondence. These data were further divided for comparison
into winter/fall and spring/summer subsets at the Bakersfield and Sacramento sites as shown
in Figure 4-2. The spring/summer comparisons are good, with nearly a one-to-one
correspondence. However, during the fall and winter, the TEOM read -30% lower
concentrations than the corresponding SSI PMio. This is due to the higher content of volatile
ammonium nitrate in fall and winter samples. The TEOM used in this example heated
samples to 50 °C, well above the temperature at which ammonium nitrate can evaporate.
The TEOM appears to be a good measurement of PMio during other than fall and winter
months when the aerosol is more stable.
Allen et al. (1995) reported similar TEOM versus SSI comparison results for the 61
data pairs collected at Rubidoux, California, with TEOM concentrations -30% lower than the
corresponding SSI PMio concentrations. The Rubidoux site is well-known for having the
highest nitrate levels in southern California (Solomon et al., 1989; Chow et al., 1992b; Chow
et al., 1994a, 1994b), especially during fall and winter.
Several empirical and statistical approaches could have been used to make these
comparisons (Mathai et al., 1990), although conclusions about sampler comparability are
often subjective. Table 4-2 summarizes the collocated comparison between continuous and
filter-based PMio or PM2.5 monitors from recent aerosol characterization studies.
Linear regression can be used to evaluate comparability between the X and Y
samplers as well as predictability of one sampler's measurements from that of the other
sampler (King, 1977). Regression slopes and intercepts with effective variance weighting
(Watson et al., 1984) for each sampler pair, along with their standard errors, are given in
Table 4-2. For each comparison, the X-sampler measurement was the independent variable
and the Y-sampler measurement was the dependent variable. When the slope equals unity
within three standard errors, when the intercept does not significantly differ from zero within
three standard errors, and when the correlation coefficient also exceeds 0.9, the selection of
independent and dependent variables is interchangeable (Berkson, 1950; Madansky, 1959;
Kendall, 1951). When the correlation coefficient is greater than 0.9 but the slope and
intercept criteria are not met, the dependent variable is predictable from the independent
4-3
-------
SSI(l)vsTEOM
zuu -
o
o
w
H
n
c
Slope = 0.71
Intercept = 5.7
r 2 = 0.33
•
• mm
jfiB* "*•** •
) 5
S
•
f "-
% m ^-"^ *
•
f
0 1C
S-
•** ^^^
)0 If
^^
0 2C
SSI 1 PMio (|ig/m3)
Collocated Comparison
SSI(l)vsBAM
i ^n
o
S
50 -
n _
Slope = 0.96
Intercept = 3.29
r 2 = 0.84
4
>**
Clovis-908 N
•
• .• ,/
•»
Villa Avenue
• jS
50
100
150
SSI 1 PMio (ng/m3)
200
Figure 4-1. Collocated comparisons of 24-hour-averaged TEOM and BAM with
high-volume SSI for PMio measurements acquired in Central California between 1988 and
1993.
4-4
-------
Collocated Comparison
SSI 1 and TEOM (Winter/Fall)
200
150 --
o
w
H
100 --
50 --
Bakersfield
Oct. - F
xX»
eb.
/
./•
•/"^
r
y ./
/ S
/
r
Slope
Interc
r = 0.9
/
/ /
/
= 0.85
ept = 2.7
5
50 100 150
SSI 1 PM10 (ng/m3)
N = 35
I i i i i I
I I I I
200 0 50 100 150
SSI 1 PM10 (ng/m3)
200
Figure 4-2. Collocated comparison of 24-hour-averaged TEOM and high-volume SSI
PMio during winter and summer at the Bakersfield and Sacramento sites in Central California
between 1988 and 1993.
4-5
-------
Table 4-2
Collocated Comparisons between Continuous and Filter-Based PM2.5 or PMi0 Monitors from Recent Aerosol Characterization Studies
Percent Distribution Average
Intercept Correlation
Study Name
IMS95'
IMS95
IMS95
IMS95
IMS95
U.S./Mexico Transboundary Studyc
U.S./Mexico Transboundary Study
U.S./Mexico Transboundary Study
U.S./Mexico Transboundary Study
Las Vegas PM10 Study8
Las Vegas PM10 Study
Las Vegas PM1D Study
Las Vegas PM1D Study
NFRAQS0
NFRAQS
NFRAQS
NFRAQS
Robbins Particulate Study'
Robbins Particulate Study
Study Area
San Joaquin Valley, CA
San Joaquin Valley, CA
San Joaquin Valley, CA
San Joaquin Valley, CA
San Joaquin Valley, CA
Calexico, CA
Calexico, CA
Calexico, CA
Calexico, CA
Las Vegas, NV
Las Vegas, NV
Las Vegas, NV
Las Vegas, NV
Northern Front Range, CO
(Winter 1995-96,6-hr)
(Winter 1995-96, 12-hr)
Northern Front Range, CO
(Summer 1996, 6-hr)
Northern Front Range, CO
(Summer 1996, 12-hr)
Robbins, IL
Robbins, IL
Species
PM25
PM25
PM25
PM1D
PM25
PM10
PM10
PM1D
PM1D
PM10
PM10
PM1D
PM1D
PM10
PM10
PM10
PM10
PM10
PM10
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Mass
Sampler Y
Bakersfield TEOM
Bakersfield Collocated TEOM
Chowchilla BAM
Chowchilla BAM
Bakersfield Collocated TEOM
Calexico BAM
Calexico BAM
Calexico BAM
Calexico BAM
Bemis BAM
Bemis BAM
East Charleston BAM
East Charleston BAM
Welby PM10 BAM
Welby PM10 BAM
Welby PM10 BAM
Welby PM10 BAM
Eisenhower BAM
Eisenhower BAM
Sampler X
Bakersfield 3-hr SFS
Bakersfield 3-hr SFS
Chowchilla 3-hr SFS
Chowchilla 3-hr SFS
Bakersfield TEOM
Calexico 24-hr SFS
Calexico 24-hr POR
Calexico 24-hr DIC
Calexico 24-hr SSI
Bemis 24-hr SFS
Bemis 24-hr POR
East Charleston 24-hr SFS
East Charleston 24-hr POR
Welby 6-hr and 12-hr SFS
Welby 6-hr and 12-hr SFS
Welby 6-hr and 12-hr SFS
Welby 6-hr and 12-hr SFS
Eisenhower 24-hr DICHOT
Eisenhower 24-hr SSI
Slope
0.78±0.04
0.93±0.04
0.32±0.03
0.57±0.03
0.85±0.01
1.10±0.06
0.86±0.09
1.00±0.08
0.89±0.06
0.66±0.14
1.78±0.59
0.71 ±0.09
0.54±0.19
0.82±0.04
0.62±0.07
0.62±0.07
0.81 ±0.09
0.847±0.153
0.760±0.107
(uq/m3)
-1.8±13.5
-6.2±13.6
2.3±4.6
1.1±7.3
3.9±9.3
3.29±2.95
14.47±5.13
6.34±4.48
4.21±4.21
23.92±5.28
1.65±15.00
16.40±3.67
19.97±5.61
1.87±1.27
3.73±1.44
9.02±1.99
5.53±1.85
1.339±4.277
0.108±3.574
Coefficient
0.82°
0.86°
0.69°
0.78°
0.94°
0.94°
0.91°
0.91°
0.93°
0.44'
0.52'
0.64'
0.5'
0.93°
0.84°
0.72°
0.80°
0.661
0.751
of
Pairs
200
200
181
183
902
45
21
34
33
91
26
93
25
63
37
85
45
41
42
Average
Ratio
0.65±1.8
0.59±1.9
0.51 ±0.49
0.64±0.36
0.99±6.71
1.20±0.27
1.34±0.40
1.14±0.26
0.97±0.16
1.61 ±0.92
2.06±1.78
1.30±0.51
1.70±0.64
1.01 ±0.44
0.94±0.42
1.03±0.36
1.14±0.36
0.93±0.40
0.78±0.34
<1(T 1-2(7 2-3(7 >3(T
73
75
59
62
88
47
24
56
58
54
73
72
92
17
15
25
27
8
8
20
21
39
36
6
31
52
32
33
28
19
18
8
13
10
21
25
17
22
6
3
3
2
3
4
10
3
9
8
0
2
0
8
11
9
13
7
10
1
1
0
0
3
18
14
9
0
10
8
8
0
62
64
45
35
68
60
Difference
(uq/m3)
10.0
8.8
14.4
13.4
0.43
7.63
7.78
6.43
2.5
-13.6
-19.3
-6.2
-9.3
2.6
3.3
0.9
-1.8
2.7
7.4
Collocated RMS
14.4
13.7
10.2
9.9
8.9
9.63
13.76
10.82
10.16
29.3
34.7
20.0
18.6
6.889
5.199
10.290
3.861
9.1
8.5
17.6
16.2
17.6
16.6
10.4
6.63
7.65
7.56
9.26
5.7
6.4
11.0
4.8
5.61
2.45
4.28
2.27
2.9
4.3
P>ITI
0.000
0.000
0.000
0.000
0.212
0.0001
0.02
0.0017
0.17
0.000
0.009
0.004
0.019
0.0035
0.0005
0.422
0.003
0.066
0.000
Chow and Egami (1997), Chow et al. (1998a).
° See Figure 4-3.
' Chow and Watson (1997a).
° See Figure 4-4.
'- Chow and Watson (1997b).
1 See Figure 4-5.
" Chow et al. (1998b), Watson et al. (1998a).
° See Figure 4-6.
Watson etal.(1997c).
1 See Figure 4-7.
-------
variable. These regression criteria for comparability are less stringent than those required for
equivalence specified in Table 4-1
Table 4-2 also presents the average ratios and standard deviations of Y to X and the
distribution differences (X minus Y) for 3a precision
intervals. Here, a is the propagated precision of X minus Y, which is the square root of the
sum of the squared uncertainties (a2x+a2y), where ax and ay are the reported precisions for
the X or Y samples. Individual measurement uncertainties are calculated from replicate
analyses, blank variabilities, and flow rate performance tests for each filter-based sampler.
When hourly concentrations are integrated to calculate 24-hour average concentrations for
comparison, the standard error of the mean is used to represent their associated uncertainties.
Table 4-2 gives the average of the paired differences (X-Y) between the X and Y
samplers; the collocated precision, which is the standard deviation of the paired differences;
and the root mean squared (RMS) precision (the square root of the mean squared precisions),
which is essentially the average measurement uncertainty of "X-Y."
The average differences and collocated precisions can be used to test the statistical
hypothesis that the difference between samplers X and Y is zero. A parametric test
(Student's T-test) is performed for each pair of samplers to illustrate the paired differences.
Table 4-2 gives the probability (P) for a greater absolute value of Student's T statistic. If P is
less than 0.05, one can infer that one of the samplers gives a concentration that is larger or
smaller than the other, depending on the sign of the average difference.
Chow and Egami (1997) and Chow et al. (1998a) compared three-hour filter-based
PM2.5 and PMio samples with hourly TEOM and BAM measurements during winter, 1995 in
California's San Joaquin Valley. Ammonium nitrate and woodburning have been found to
be large wintertime particle contributors in this area (Chow et al., 1992a), and there were
high humidities with fogs, cloud, and rain during the study period. None of these collocated
measurements met the slope, intercept, and correlation criteria for comparability, as shown in
the first five rows of Table 4-2.
Figure 4-3 shows that the collocated TEOMs operating with 30 °C heating of the air
stream exhibit some data scatter, especially for PM2.5 concentrations less than 50 |ig/m3.
This conditioning temperature was purposefully set below the 50 °C temperature
recommended by the manufacturer to minimize particle volatilization. The
three-hour-average TEOM PM2.5 mass fell below zero on several occasions. This probably
occurred because some aerosol liquid water was present for high humidities during one
three-hour period that subsequently evaporated when relative humidity decreased. On
average, the 30 °C TEOM registered -10 |ig/m3 less than the corresponding filter
measurements in this challenging environment. The average ratio of TEOM versus
filter-based (i.e., medium-volume Sequential Filter Sampler [SFS]) PM2.5 was 0.65 ± 1.8 and
0.59 ±1.9 for the two collocated TEOMs, implying that PM2.s mass concentrations acquired
with the TEOMs were approximately 35% to 40% lower than those of gravimetric masses
during this test. Ammonium nitrate constituted one-third to half of the PM2.5 mass measured
on a subset of samples during this test (Chow et al., 1998a).
4-7
-------
160
Bakersfield PM2.5 TEOM vs. SFS
20
40
60
80
100
SFS PM2.s (M9/m )
OTEOM PM2.5 Dcollocated TEOM PM2.5
120
140
Chowchilla PM2.5 BAM vs. SFS
10
20
30
40
SFS PM2.s
50
60
70
80
60
Chowchilla PM10 BAM vs. SFS
50 -
20 -
10 -
c) Chowchilla PM
10
1
« 4
10
20 30 40 , 50
SFS PM10 (Mg/m )
60
70
80
Figure 4-3. Collocated comparison of three-hour PM2.5 SFS with PM2.5 TEOM at the
Bakersfield site, as well as PM2.5 and PMio SFS with BAM at the Chowchilla site in
California's San Joaquin Valley between 12/09/95 and 01/06/96.
4-8
-------
Similar observations were found for the PM2.5 and PMio BAM versus Sequential
Filter Sampler (SFS) comparison. The average ratio of BAM versus SFS was 0.51 ± 0.49 for
PM2.5 and 0.64 ± 0.36 for PMio measurements (Table 4-2). The correlations were moderate
(0.69
-------
BAM vs. Dichot
BAM vs. SFS
IOU
?r
6)
H.^*
o 100
i
Q.
»
o>
1 50
O
ji
"5
CO
n
Y = 1.00X + 6.34 x;^"
r = 0.91 X?'
N = 34 X^'X
"X^"
•/ '''
' • *X^"'
-•*" x ^ •
x'^V" *
/•''*"'
x'*~ *^«
/ ' ' i i
1OU
_£
s^
o 100
i
Q.
O
m
2 50
n
O
(8
"S
CD
A
X /* ^
Y = 1.10X + 3.29 x/X'"
•^
r = 0.94 X X
N = 45 X'''''"
• X"" , ^
» X x' '
1 •x->x'" "
•X""1 jn ^
• ff ^ »
i V-- *
x >*
x*^-"' ^
'X^*"^*
J^"'' "
-^4 '
50 100
Dichot PM10 (ug/m3)
BAM vs. Portable
150
50 100
SFS PM10(ug/m3)
BAM vs. SSI
150
1 OU
"DJ
o 100
T~
Q.
0)
O)
2 50
n
O
0
•Iwl
0)
CD
0
c
Y = 0.86X + 14.47 X>X
r = 0.91 X"'"^
N = 21 . ,X'"
'^.X
* X^
x'^'" "
x^x'' "
« ^
) 50 100 16
1
TO
3,100
o
Q.
H)
TO
3
0 50
(3
a>
m
0
0 c
,/•
Y = 0,89X + 4,2 s''^'
r = 0,99 x"'X
N = 33 -""X"'
' x'X''
• ^'X
B ^ ^^
jX>
*-*X" H m ,
y^' .
X'
j^
^H,X *
I i
! 50 100 15
_ . . , _-..,_, . _, SSI PM10(ug/m3)
Portable PM10 (ug/m3)
Figure 4-4. Collocated comparisons of 24-hour PMio with BAM versus high-volume SSI, medium-volume SFS, low-v
dichotomous, and mini -volume portable samplers in Imperial Valley, CA, between 03/13/92 and 08/29/93.
-------
For Las Vegas, collocated comparisons from 01/03/95 and 01/28/96 at the Bemis and
East Charleston sites exhibit correlation coefficients less than 0.9, and filter measurements
are not equivalent to continuous BAM measurements. The intercepts are quite large, ranging
from 1.7 ± 15 |J,g/m3 from the BAM versus portable PMio survey sampler (FOR) to 23.9 ±
5.3 ng/m3 for the BAM versus SFS at the Bemis site. Figure 4-5 confirms that the BAM
concentrations were generally higher than those obtained from the SFS or portable PMio
survey samplers, with average ratios ranging from 1.3 ± 0.51 (BAM versus SFS at the East
Charleston site) to 2.06 ± 1.78 (BAM versus portable at the Bemis site). Table 4-2 shows
that over 90% of all the pair comparisons lie within ±la for BAM versus FOR at the East
Charleston site, with more than 90% of the pair comparisons lying within ±2a for BAM
versus FOR at the Bemis site. In all cases, over 80% of the paired differences lie within ±2o,
and over 90% of the measurements differ by no more than ±3a. That is, in most cases, the
differences between samplers are within the measurement errors. Table 4-2 indicates
statistical comparability based on the pair-difference test is only valid for BAM versus FOR
at the East Charleston site. Overall, this comparison shows that BAM PMio was
systematically higher than the SFS and FOR data (in the order of 30% to 100% on average)
in the Las Vegas PMio Study, even though the aerosol composition and climate was similar
to that of the Imperial Valley, California.
At the Northern Front Range Air Quality Study (NFRAQS) Welby site just north of
Denver, Table 4-2 shows the PMio BAM measurements were lower than corresponding filter
measurements, with slopes of 0.62 to 0.82 and large intercepts of 1.9 ± 1.3 to 9.0 ± 2.0
|ig/m3. The average ratios of BAM versus SFS PMio were reasonable, ranging from 0.94 ±
0.42 to 1.14 ± 0.4. The correlations were variable (between 0.72 and 0.93), with higher
correlations for measurements acquired during winter 1996. Figure 4-6 shows the extent of
data scattering. The percent distribution in Table 4-2 shows that only 36% to 65% of the
measurement differences fell within a ±3a interval, however.
At the Eisenhower School site near Robbins IL, Table 4-2 shows correlation
coefficients less than 0.9, and PMio measurements from the collocated samplers are not
equivalent to each other based on the pairwise comparison. While the regression statistics
show that the slopes are equal to unity within three standard errors, the intercepts are variable
(ranging from 0.11 ± 3.6 |J,g/m3 for the BAM versus SSI pairs to 1.3 ± 4.2 |J,g/m3 for the
BAM versus DICHOT pairs). The correlations among these comparisons were low, being
0.66 and 0.75. Figure 4-7 confirms that the SSI PMio concentrations were generally higher
than those obtained from the BAM sampler in this Illinois study, in contrast to those
observed in Central California. The BAM versus DICHOT comparisons meet the
comparability criteria based on the parametric test. This comparison shows that over 70% of
all the pair comparisons lie within a ±3a interval. This percent distribution could be biased
due to the estimated (rather than error-propagated) measurement uncertainties. The standard
deviations associated with the average ratios are quite high, however, indicating some
amount of data scattering. The average differences in PMio measurements were 7.4 |ig/m3
for BAM versus SSI pairs and 2.7 |ig/m3 for BAM versus DICHOT pairs. The RMS
precisions for PMio mass comparisons are all less than 5 |ig/m3 in these comparisons.
4-11
-------
BAM vs. SFS
BAM vs. Portable
0 20 40 60 80 100 120 140 160 180 200
SFS PM10 (fig/m3)
Slope = 1.78 ±0.59
Intercept =1.65 ±15.0 fig/m3
r=0.52
Y/X = 2.06± 1.'
0 20 40 60 80 100 120 140 160 180 200
Portable PM10 (fig/m3)
BAM vs. SFS
BAM vs. Portable
hj) Slope = 0.71 ± 0.09
Intercept =16.40 ±3.67 ng/m3
r = 0.64
40 60 80 100
SFSPMio (fig/in3)
Slope = 0.54 ±0.19
Intercept = 19.97 ± 5.61 pg/ffl3-
r = 0.50
n = 25
40 60
Portable PMjo
80
Figure 4-5. Collocated comparisons of 24-hour-averaged PMio BAM versus SFS and
portable samplers in Las Vegas Valley, NV, between 01/03/95 and 01/28/96.
4-12
-------
Welby 6-hr
100
Welby 12-hr
100
40 60
Sequential Filter Sampler PM10 (ug/m3)
80
40 60
Sequential Filter Sampler PM10 (ug/m3)
80
100
100
Figure 4-6. Collocated comparison of 6- and 12-hour PMio BAM versus SFS in north
Denver, CO, during winter and summer 1996.
4-13
-------
Eisenhower
DO
DICHOT PM10 Mass (|jg/mj)
Eisenhower
SSI PM10 Mass (|jg/nr)
Figure 4-7. Collocated comparison of 24-hour PMio BAM versus high-volume SSI and
dichotomous samplers in southeastern Chicago, IL, between 10/12/95 and 09/30/96.
4-14
-------
These comparisons show that comparability among the continuous BAM or TEOM
measurements with 3-, 6-, 12-, or 24-hour filter measurements from MiniVol (i.e., portable
PMio survey sampler), low-volume (i.e., dichotomous sampler), medium-volume (i.e., SFS),
or high-volume (i.e., SSI) samplers are highly variable, depending on the operating
environments, and probably on the operating procedures. Reasonably good comparisons
were found for TEOM-SSI during spring and summer only and BAM-SSI in Sacramento,
Bakersfield, and Calexico, CA; and BAM-SFS, BAM-DICHOT, and BAM-portable PMio in
Calexico, CA; but comparability was not evident in Las Vegas, NV; Welby, CO; or Robbins,
IL. The observed relationship between TEOM and filter-based methods varied widely
depending on site location, time of year, range of particle concentrations, and conditioning
temperature. TEOM measurements correlated well with filter-based measurements in urban
areas along the East Coast during summer, but yielded much lower concentrations (and
correlations) during winter (Allen et al., 1995). These measurement discrepancies could
either be due to differences in inlet cleanliness, which affects the sampling efficiency
(Watson et al., 1983); to differences in sampler calibration, which controls the sample
volume; or to differences in filter handling and weighing for the conventional samplers.
These examples of sampler comparisons also show large discrepancies between
different filter-based manual samplers for PMio. In general, comparisons between the TEOM
and BAM are no better or worse than comparisons among collocated PMio filter samplers
(Chow, 1995), many of which are designated PMio reference methods.
4.2 Measurement Predictability
Though light scattering and absorption do not directly measure mass, they may
provide reliable surrogates from which mass can be predicted once a correspondence has
been established. As noted earlier, this empirical relationship is highly dependent on the
consistency of aerosol composition measured at the monitoring site.
4.2.1 Particle Light Scattering and PMi.5 Concentration
Chow and Egami (1997) examine relationships between particle scattering (bsp)
measured with the ambient temperature OPTEC NGN-2 nephelometer and a PM2.5
Sequential Filter Sampler (SFS) in the high nitrate, foggy, and moist environment of the
wintertime San Joaquin Valley, CA. When sampling during foggy conditions without
preheating the sample stream, water vapor can condense on the NGN-2's optics, thereby
affecting its response. Since water-soluble ammonium nitrate and ammonium sulfate
particles can grow to many times their original size under high relative humidities, it is
difficult to estimate the relationship between bsp and PM2.5 mass during these foggy
conditions. It should also be noted that the nephelometer without a PM2.5 size-selective inlet
will also account for scattering by particles larger than 2.5 jam in diameter, though these were
determined to be less than 20% of PMio during the comparison. Figure 4-8 shows that
particle scattering becomes dominated by liquid water at relative humidity (RH) above 80%,
and that increased particle scattering can be detected at RH above 60%. These figures also
show limitations of the relative humidity sensors that often are inaccurate at humidities above
90%; these limitations are especially noticeable at the Kern Wildlife Refuge site
4-15
-------
6000
6000
5000 -
4000 -
3000 -
o.
w
CO
2000 -
50
60 70 80
Relative Humidity (%)
90
100
Fresno
60 70 80
Relative Humidity (%)
6000
5000 -
4000 -
3000 -
2000 -
1000 -
Kern Wildlife Refuge
4000 -
3000 -
a.
VI
m
2000 -
1000 -
Chowchilla
50
60 70 80
Relative Humidity (%)
90
100
0
40
50 60 70 80
Relative Humidity (%)
• •
Figure 4-8. Relationship between hourly particle light scattering (bsp) measured by nephelometer and ambient relative humidity in
San Joaquin Valley, CA, between 12/09/95 and 01/06/96.
-------
The PM2.5 mass scattering efficiency is computed from the ratio of bsp to PM2.5
concentration, assuming that all particle light scattering is caused by particles smaller than
2.5 jim. While the hygroscopic growth properties of aerosols collected during the study are
not known specifically, mass scattering efficiencies in similar environments change with
1/(1-RH) (Zhang et al., 1994). This is why bsp and mass scattering efficiencies exhibit a
similar upward pattern as RH above 80% or 90%.
Table 4-3 presents mass scattering efficiencies averaged by site as a function of
relative humidity. Average mass scattering efficiencies ranged from 4.3 to 6.4 m2/g for RH
below 80%. Approximately 10% to 20% higher scattering efficiencies were estimated at the
Kern Wildlife Refuge and Chowchilla sites for RH < 80%. These differences probably result
from the higher proportion of ammonium sulfate and ammonium nitrate in PM2.5 at the rural
Kern Wildlife Refuge and Chowchilla sites. While ammonium nitrate and organic carbon are
the dominant components of PM2.5 at the urban sites, the relative abundance of organic
carbon, which may be less hygroscopic than ammonium nitrate, is greatly diminished at the
non-urban sites (Chow et al., 1998a).
Except at the Kern Wildlife Refuge site, mass scattering efficiency increased by
-30% to -45% for RH between 80% and 90%, and increased by four- to ninefold for RH
exceeding 90%. As noted earlier, high RH measurements at the Kern Wildlife Refuge site
appear to be imprecise. Table 4-3 shows that recalculating mass scattering efficiency with
adjusted RH at the two non-urban sites reduces the difference among the four sites. This
analysis implies the importance of accurate on-site RH measurements and demonstrates that
light scattering efficiency can only be derived for RH less than 80% or 90%.
4.2.2 Particle Light Absorption and Elemental Carbon Concentration
Chow and Egami (1997) measured light absorption (bap) on PM2.5 Teflon-membrane
filters with a densitometer standardized with photographers' neutral density filters. For each
sample, the bap measurement on the Teflon-membrane filter was compared with light
absorbing carbon or elemental carbon (EC) measured with Thermal/Optical Reflectance
(TOR) analysis on a co-sampled quartz-fiber filter (Chow et al., 1993a), as shown in Figure
4-9. The overall correlation coefficient between bap and elemental carbon was 0.94. This
suggests that nearly all of the fine-particle light absorption was due to elemental carbon. The
average ratios and standard deviations of bap divided by elemental carbon concentration were
9.9 ±2.1 m2/g and 9.4 ± 2.6 m2/g at the Bakersfield and Fresno urban sites, respectively; and
12.5 ± 3.2 m2/g and 12.4 ± 4.1 m2/g at the Kern Wildlife Refuge and Chowchilla non-urban
sites, respectively. Note that in Figure 4-9, the standard deviations of the average ratios
(which are influenced by the variations of individual data pairs) are approximately a factor of
10 higher than those derived with the effective variance weighted regression. Nevertheless,
these absorption coefficients are within one standard deviation of the commonly accepted
value of 10 m2/g for the mass absorption efficiency of elemental carbon (Trijonis et al.,
1988), but they differ from the most likely theoretical values shown in Section 2.
Three-hour average aethalometer BC concentrations are compared with
Thermal/Optical Reflectance EC concentrations at the Bakersfield site in Figure 4-10a. The
4-17
-------
Table 4-3
.s Mass Scattering Efficiency (m2/g) as a Function of Relative Humidity (%)
Site
Bakersfield
Fresno
Kern Wildlife Refuge
Site Type RH<80% 80%90%
Urban
Urban
Chowchilla
Chowchilla (RH+5%)
4.9±1.2
5.4±1.2
Non-urban 6.4±2.7
Kern Wildlife Refuge (RH+10%) Non-urban 4.3±0.7
Non-urban 5.7±0.9
Non-urban 5.1±0.6
6.6±1.3
7.7±2.2
71±132
7.2±2.7
8.2±3.9
6.8±1.8
19.2±30.7
27±42
87±73
71±130
56±106
49±99
4-18
-------
Q.
Rf
U 4D
BsterefisW
Bap = 9.4 ± 0.3 EC
N = 63
r = 0.97
Ave. Bap/EC = 9.9 ± 2.1
0.
U
Ff*s no
Bap = 8.5 ± 0.3 EC
N = 62
r = 0.97
Ave. Bap/EC = 9.4 ± 2.6
ID IE
EC
31 IE
VO
Kern WiW life R«fug»
Bap =11.6 ±0.3 EC
N = 62
r = 0.97
Ave. Bap/EC = 12.5± 3.2
E
O.
Chowohilh
Bap =11.2 ±0.4 EC
N = 39
r = 0.97
Ave. Bap/EC = 12.4 ± 4.1
Figure 4-9. Relationship between PM2.5 light absorption (bap) measured by densitometer on Teflon-membrane filter and elemental
carbon measured by thermal/optical reflectance on a co-sampled quartz-fiber filter for three-hour samples acquired in San Joaquin
Valley, CA, between 12/09/95 and 01/06/96.
-------
a) Assumed value of 10 m /g absorption efficiency:
15
12
n
_
oi
O
w
in
u.
w
Slope = 1.16±0.10
Intercept = 0.81 ±0.21
N =36
r = 0,89
Avg. EC/BC = 1.68±0.56
0369
Aethalometei BC (ug/m3)
b) Calculated value of 9.4 m2/g absorption efficiency:
15
12
15
CO
E
O
LU
U.
U)
Slope = 1.11 ±0.10
Intercept = 0.81 ±0.21
N =36
r = 0,89
Avg, EC/BC = 1.58±0.53
6 9
Aethalometer BC (ug/m3)
12
15
Figure 4-10. Relationship between filter-based PM2.5 SFS elemental carbon (measured by
thermal/optical reflectance on quartz-fiber filter) and aethalometer black carbon on
three-hour samples acquired in the San Joaquin Valley between 12/09/95 and 01/06/96.
4-20
-------
regression line was obtained using effective variance weighting assuming a 10% uncertainty
in the BC measurements. The two measurements are well correlated (r = 0.89), but EC is
systematically larger. This is indicated not only by the regression results (slope = 1.18 ±
0.10) and average ratio of EC/BC (1.68 ± 0.56), but also by a paired-difference t-test
(Probability>|T| = 0.0001). This systematic difference is most likely due to bias built into the
19.2 m2/g absorption efficiency that the aethalometer software uses to convert particle
absorption to black carbon than to the variations in the chemical measurements. Figure
4-10b shows that average BC concentrations are within ±10% of EC measurements if 9.4
m2/g (absorption efficiency derived at the Bakersfield site based on filter measurements of
bap versus EC) instead of 10 m2/g were used in this calculation.
4.2.3 Mass Concentration and Optical Measurements
Table 4-4 compares the comparability and predictability of particle light scattering
measurements from nephelometers and particle light absorption measurements from
aethalometers with collocated PM2.5 concentrations for several environments.
The relationships between three-hour PM2.5 and bsp were well-defined at the four San
Joaquin Valley (JJVIS95) sites with correlation coefficients (r) exceeding 0.93. The regression
slopes of PM2.5 versus bsp were also consistent among the four sites (0.17 to 0.18). The
correlations between bsp and PMio were reduced to 0.86 even though a majority (69% to
78%) of the PMio was in the PM2.5 fraction. The regression slopes for PMio versus bsp were
similar to those derived from PM2.5, in the range of 0.14 to 0.21. This reconfirms the fact
that particle light scattering induced by coarse particles (PMio minus PM2.5) is insignificant
during wintertime in the San Joaquin Valley, CA (Chow et al., 1993b).
There were many periods when relative humidity exceeded 90% in the San Joaquin
Valley, CA, during the winter study, however. Because of increased bsp due to liquid water
growth under these conditions, the relationship between PM and bsp breaks down over any
sample averaging period containing periods of high relative humidity.
Table 4-4 indicates that relationships between bsp and PM are less consistent with
lower correlations for longer sample averaging times. For example, the correlations of bsp
with 12-hour PM2.5 and PMio concentrations at the Chowchilla, CA, site were 0.57 and 0.60,
respectively. This deterioration is related to the inclusion of a few hours with high (>80%)
JAHs in the averages, even though the average humidities were <80%.
The relationships between bsp and PM2.5 were not as consistent for measurements in
northern Colorado, with correlation coefficients ranging from 0.81 to 0.88 for 6- and 12-hour
samples. The regression slope of 6-hour PM2.s versus bsp varied a factor of two among these
sites, ranging from 0.12 to 0.24. These values include the 0.17 or 0.18 slopes derived from
three-hour IMS95 measurements.
The same relationships were not found for the Mt. Zirkel Wilderness Area in
northwestern Colorado during the winter, summer, and fall seasons from 02/06/95 through
11/30/95 (Watson et al., 1996). The effects of liquid water on light scattering are apparent
4-21
-------
Table 4-4
Relationships between Optical Measurements
Sampling
and PM Concentrations
Duration
Study
IMS95d
IMS95
IMS95
IMS95
IMS95
IMS95
_^ IMS95
to IMS95
to
IMS95
IMS95
IMS95
IMS95
IMS95
IMS95
IMS95
IMS95
IMS95
IMS95
IMS95
IMS95
(hours)
3
3
3
3
12f
12
12
12
24g
24
24
24
3
3
3
3
12
12
12
12
Site
Bakersfield, CA
Fresno, CA
Kern Wildlife Refuge, CA
Chowchilla, CA
Bakersfield, CA
Fresno, CA
Kern Wildlife Refuge, CA
Chowchilla, CA
Bakersfield, CA
Fresno, CA
Bakersfield, CA
Fresno, CA
Bakersfield, CA
Fresno, CA
Kern Wildlife Refuge, CA
Chowchilla, CA
Bakersfield, CA
Fresno, CA
Kern Wildlife Refuge, CA
Chowchilla, CA
X
bspe for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80% and n>5
bsp for RH<80% and n>5
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
Y
PM25
PM25
PM2.5
PM25
PM25
PM25
PM25
PM2.5
PM25
PM25
PM25
PM25
PM10
PM10
PM10
PM10
PM10
PM10
PM10
PM10
Slope3
0.165 ± 0.005
0.179 ± 0.008
0.166 ±0.012
0.177 ±0.010
0.135 ±0.007
0.146 ±0.012
0.032 ± 0.051
0.055 ± 0.079
0.106 ± 0.024
0.140 ±0.031
0.106 ± 0.024
0.14 ±0.031
0.191 ±0.014
0.197 ±0.012
0.141 ±0.015
0.21 ± 0.02
0.165 ± 0.026
0.166 ±0.014
0.038 ± 0.051
0.067 ± 0.090
Intercept3
(Mg/m3)
2.2 ±0.3
0.120 ± 1.00
0.74 ± 1.05
0.32 ± 0.75
2.8 ±0.4
2.2 ±2.5
11.4 ± 13.7
9.7 ± 16.4
4.5 ± 1.6
3.1 ± 11.7
4.5 ± 1.6
3.1 ± 11.7
6.9 ± 1.0
6.9 ± 1.5
9.2 ± 1.6
5.2 ± 1.9
2.5 ± 1.8
8.0 ±2.9
14.8 ± 13.6
13.3 ± 18.6
rb
0.98
0.94
0.93
0.97
0.98
0.95
0.84
0.57
0.93
0.86
0.93
0.86
0.89
0.91
0.86
0.89
0.88
0.95
0.95
0.60
nc
51
58
33
19
14
18
7
3
5
9
5
9
51
58
33
19
14
18
7
3
PM, ,/PMln
0.75 ± 0.42
0.78 ± 0.12
0.74 ± 0.22
0.69 ± 0.15
0.75 ± 0.42
0.78 ± 0.12
0.74 ± 0.22
0.69 ±0.15
0.75 ± 0.42
0.78 ±0.12
0.75 ± 0.42
0.78 ±0.12
0.75 ± 0.42
0.78 ±0.12
0.74 ± 0.22
0.69 ±0.15
0.75 ± 0.42
0.78 ±0.12
0.74 ± 0.22
0.69 ±0.15
-------
-^
to
Sampling
Table 4-4 (continued)
Relationships between Optical Measurements and PM Concentrations
Duration
Study
IMS95
IMS95
IMS95
IMS95
NFRAQSh
NFRAQS
NFRAQS
NFRAQS
NFRAQS
NFRAQS
NFRAQS
NFRAQS
NFRAQS
NFRAQS
Mt. Zirkelk
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
(hours)
24
24
24
24
6
6
6
12
12
12
6
6
12
12
6
6
6
6
6
Site
Bakersfield, CA
Fresno, CA
Bakersfield, CA
Fresno, CA
Brighton, CO
Evans, CO
Welby, CO
Brighton, CO
Evans, CO
Welby, CO
Brighton, CO
Welby, CO
Brighton, CO
Welby, CO
BAG1 BAGZ, CO
BUF1 BUFZ, CO
SEW1 SEWZ, CO
VOR1 VORZ, CO
JUN1 JUNZ, CO
X
bsp for RH<80% and n>5
bsp for RH<80% and n>5
bsp for RH<80%
bsp for RH<80%
bsp
bsp
bsp
bsp
bsp
bsp
bsp2,]
bsp2.5
bsp2.5
bsp2.5
bsp for RH<50%
bsp for RH<50%
bsp for RH<50%
bsp for RH<50%
bsp for RH<50%
Y
PM10
PM10
PM10
PM10
PM25
PM25
PM2.5
PM25
PM25
PM25
PM25
PM2.5
PM25
PM25
PM25
PM25
PM2.5
PM25
PM25
Slope3
0.139 ±0.065
0.172 ± 0.028
0.139 ±0.065
0.172 ± 0.028
0.21 ± 0.02
0.123 ± 0.009
0.24 ± 0.01
0.148 ±0.014
0.088 ± 0.009
0.189 ±0.017
0.23 ± 0.02
0.26 ± 0.02
0.188 ±0.019
0.191 ±0.020
0.34 ± 0.04
0.33 ± 0.05
0.32 ± 0.04
0.35 ± 0.06
0.23 ± 0.06
Intercept3
^g/m3)
11.0 ±3.3
2.8 ± 10.4
11.0 ±3.3
2.8 ± 10.4
2.0 ±0.3
2.8 ±0.4
2.3 ±0.4
1.35 ±0.24
1.73 ±0.42
1.43 ±0.43
2.5 ±0.3
3.0 ±0.4
1.41 ±0.30
2.1 ±0.05
0.59 ±0.39
1.38 ±0.34
0.76 ± 0.44
2.0 ±0.5
1.72 ±0.49
rb
0.78
0.92
0.78
0.92
0.83
0.81
0.85
0.88
0.85
0.87
0.83
0.83
0.87
0.82
0.67
0.73
0.73
0.60
0.54
nc
5
9
5
9
78
98
97
36
39
40
72
99
32
44
73
38
47
58
42
PM, ./PMin
0.75 ± 0.42
0.78 ±0.12
0.78 ±0.12
0.78 ±0.12
NA1
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
-------
-^
to
Table 4-4 (continued)
Relationships between Optical Measurements and PM Concentrations
Sampling
Duration
Study
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
MOHAVE111
MOHAVE
MOHAVE
MOHAVE
MOHAVE
(hours)
12
12
12
6
6
6
6
6
12
12
12
6
6
12
12
12
12
12
12
12
Site
BUF2 BUFZ, CO
JUN2 JUNZ, CO
GLC2 GLCZ, CO
BAG1 BAGZ, CO
BUF1 BUFZ, CO
SEW1 SEWZ, CO
VOR1 VORZ, CO
JUN1 JUNZ, CO
BUF2 BUFZ, CO
JUN2 JUNZ, CO
GLC2 GLCZ, CO
BUF3 BUFZ, CO
BUF3 BUFZ, CO
BUF6 BUFZ, CO
BUF6 BUFZ, CO
M2 (Summer 1992), AZ
M4 (Winter 1992), AZ
Ml (Summer 1992), AZ
M3 (Summer 1992), AZ
M5 (Winter 1992), AZ
X
bsp for RH<50%
bsp for RH<50%
bsp for RH<50%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bsp for RH<80%
bspg2.51 for RH<50% (heated
bspg2 5 for RH<80% (heated
bspg25 for RH<50% (heated
bspg2 5 for RH<80% (heated
bsp
bsp
bsp2.5
bsp
bsp
Y
PM25
PM25
PM25
PM2.5
PM25
PM25
PM25
PM25
PM2.5
PM25
PM25
inlet) PM2 5
inlet) PM2 5
inlet) PM2 5
inlet) PM2 5
PM25
PM25
PM25
PM10
PM10
Slope8
0.33 ± 0.04
0.123 ± 0.056
0.37 ± 0.09
0.162 ± 0.029
0.31 ± 0.04
0.194 ± 0.025
0.149 ± 0.042
0.043 ± 0.02
0.0022 ± 0.0041
-0.000021 ± 0.00089
0.0066 ± 0.0028
0.31 ± 0.04
0.32 ± 0.05
0.31 ±0.13
0.31 ±0.13
0.25 ± 0.03
0.32 ± 0.07
0.29 ± 0.04
0.78 ±0.15
0.8 ±0.12
Intercept3
(ug/m3)
1.06 ±0.19
1.97 ±0.47
0.29 ± 0.83
1.69 ±0.31
1.11 ±0.25
1.77 ±0.33
3.2 ± 0.4
2.9 ± 0.3
2.6 ±0.1
2.5 ± 0.2
2.9 ± 0.2
1.22 ±0.37
1.16 ±0.41
0.76 ± 0.80
0.76 ± 0.80
2.6 ± 0.4
-0.0112 ±0.37
2.8 ±0.3
5.3 ± 1.6
-0.39 ± 0.58
rb
0.80
0.38
0.46
0.46
0.69
0.61
0.33
0.24
0.06
0.00
0.20
0.59
0.56
0.53
0.53
0.62
0.51
0.64
0.48
0.66
nc
34
31
62
124
73
107
102
75
73
61
138
93
95
16
16
89
61
85
88
61
PM,
-------
Sampling
Table 4-4 (continued)
Relationships between Optical Measurements and PM Concentrations
Duration
Study
Phoenix"
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Mexico0
Mexico
Mexico
Mexico
IMS95
IMS95
IMS95
IMS95
IMS95
IMS95
IMS95
IMS95
(hours)
6
6
6
6
6
6
6
6
6
6
24
6
24
3
12
24
24
3
12
24
24
Site
Cl CA, AZ
SI SC,AZ
W1WP,AZ
VI VA, AZ
C3 CA, AZ
C2 CA, AZ
S2 SC, AZ
W2 WP, AZ
V2 VA, AZ
Ml MER, MX
PI FED, MX
M2MER,MX
P2 FED, MX
Bakersfield, CA
Bakersfield, CA
Bakersfield, CA
Bakersfield, CA
Bakersfield, CA
Bakersfield, CA
Bakersfield, CA
Bakersfield, CA
X
bsp
bsp
bsp
bsp
bsp:
bsp
bsp
bsp
bsp
bsp
bsp
bsp
bsp
for RH<80%
for RH<80%
for RH<80%
for RH<80%
15 for RH<80%
for RH<80%
for RH<80%
for RH<80%
for RH<80%
for RH<80%
for RH<80%
for RH<80%
for RH<80%
bapp for RH<80%
bap
bap
bap
bap
bap
bap
bap
for RH<80%
for RH<80% and n>5
for RH<80%
for RH<80%
for RH<80%
for RH<80% and n>5
for RH<80%
Y
PM25
PM25
PM25
PM2.5
PM25
PM10
PM10
PM10
PM10
PM25
PM25
PM10
PM10
PM2.5
PM25
PM25
PM25
PM10
PM10
PM10
PM10
Slope'
0.115 ±0.009
0.115 ±0.01
0.089 ± 0.007
0.104 ± 0.008
0.194 ± 0.021
0.33 ± 0.02
0.29 ± 0.03
0.192 ± 0.022
0.22 ± 0.03
0.083 ± 0.003
0.16 ±0.031
0.09 ± 0.005
0.24 ± 0.08
0.71 ± 0.08
0.69 ±0.11
0.58 ± 0.20
0.58 ± 0.20
0.86 ±0.11
0.83 ±0.19
0.69 ± 0.25
0.69 ± 0.25
Intercept*
(ug/m3)
6.2 ± 0.2
4.7 ± 0.6
6.5 ±0.7
6 ±0.5
7.1 ±0.8
17.9 ± 1.3
13.7 ± 1.5
22 ±2
17.2 ± 1.7
17.8 ±2.4
58 ±2
37 ±5
20 ±6
1.40 ± 1.20
0.89 ± 2.2
2.4 ±4.8
2.4 ±4.8
5.8 ± 1.8
5.1 ±3.8
6.3 ±6.1
6.3 ±6.1
rb
0.68
0.68
0.68
0.69
0.56
0.72
0.61
0.54
0.47
0.62
0.87
0.41
0.72
0.81
0.89
0.86
0.86
0.76
0.81
0.85
0.85
nc
194
178
180
172
201
189
171
181
172
66
11
67
11
46
40
5
5
46
12
5
5
PM,
-------
Sampling
Table 4-4 (continued)
Relationships between Optical Measurements and PM Concentrations
Duration
Study
NFRAQS
NFRAQS
NFRAQS
NFRAQS
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
to Mt. Zirkel
Oi
Phoenix
Phoenix
Mexico
Mexico
Mexico
Mexico
IMS95
IMS95
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
Mt. Zirkel
(hours)
6
6
12
12
6
6
12
12
6
6
6
24
6
24
24
24
6
12
6
12
Site
Brighton, CO
Welby, CO
Brighton, CO
Welby, CO
BUF4 BUFZ, CO
BUF4 BUFZ, CO
BUF7 BUFZ, CO
BUF7 BUFZ, CO
C6 CA, AZ
C7 CA, AZ
M3 MER, MX
P3 FED, MX
M4 MER, MX
P4 FED, MX
Bakersfield, CA
Bakersfield, CA
BUF5 BUFZ, CO
BUF8 BUFZ, CO
BUF5 BUFZ, CO
BUF8 BUFZ, CO
X
bap
bap
bap
bap
bap
bap
bap
bap
bap
bap
bap
bap
bap
bap
bext'
bext
bext
bext
bext
bext
for RH<50% h
for RH<80%
for RH<50%
for RH<80%
for RH<80%
for RH<80%
* 19.2 for RH<80%
* 19.2 for RH<80%
* 19.2forRH<80%
* 19.2 for RH<80%
q = bsp + bap for RH<80%
= bsp + bap for RH<80%
= bsp + bap for RH<50%
= bsp + bap for RH<50%
= bsp + bap for RH<80%
= bsp + bap for RH<80%
Y
PM25
PM25
PM25
PM25
PM2.5
PM25
PM25
PM25
PM25
PM10
PM25
PM25
PM10
PM10
PM2.5
PM10
PM25
PM25
PM25
PM25
Slope3
0.41 ± 0.05
0.28 ± 0.06
0.38 ±0.09
0.155 ±0.075
1.33 ±0.28
1.25 ±0.2
1.19 ±0.32
1.02 ±0.18
0.56 ± 0.09
1.3 ±0.2
0.175 ±0.034
0.28 ± 0.08
0.174 ±0.059
0.36 ±0.17
0.091 ± 0.021
0.117 ±0.047
0.22 ± 0.05
0.26 ± 0.05
0.25 ± 0.03
0.166 ± 0.026
Intercept3
to
2.6
4.0
1.41
5.2
0.54
0.76
0.70
1.00
3.3
9.6
18.2
7
36
18.7
4.3
9.8
1.69
0.81
1.19
1.24
;/m3)
±0.6
± 1.2
±0.69
± 1.4
±0.73
±0.50
±0.47
±0.25
± 1.6
±2.9
±2.6
± 3.9
± 5
±8.2
±2.3
± 3.4
±0.52
±0.26
±0.37
±0.19
rb
0.64
0.67
0.58
0.53
0.61
0.60
0.63
0.66
0.71
0.73
0.47
0.60
0.30
0.42
0.93
0.82
0.62
0.77
0.69
0.69
nc
82
29
37
13
41
67
22
47
39
38
96
23
95
23
5
5
37
22
62
45
PM, ,/PMln
NA
NA
NA
NA
NA
NA
NA
NA
0.38 ±0.21
0.38 ±0.21
0.63 ±0.18
0.56 ±0.12
0.63 ±0.18
0.56 ±0.12
0.75 ± 0.42
0.78 ±0.12
NA
NA
NA
NA
-------
-^
to
Sampling
Relationships
Table 4-4 (continued)
between Optical Measurements and PM Concentrations
Duration
Study
Phoenix
Phoenix
Mexico
Mexico
Mexico
Mexico
(hours)
6
6
6
24
6
24
Site
C4 CA, AZ
C5 CA, AZ
M5 MER, MX
P5 FED, MX
M6 MER, MX
P6 FED, MX
X
bext = bsp H
bext = V
bext = V
bext = V
bext = bsp H
bext = bsp H
- bap for RH<80%
- bap for RH<80%
- bap for RH<80%
- bap for RH<80%
- bap for RH<80%
- bap for RH<80%
Y
PM25
PM10
PM25
PM25
PM10
PM10
Slope8
0.107 ±0.009
0.2 ± 0.04
0.065 ±0.01
0.122 ±0.025
0.079 ±0.019
0.178 ±0.054
Intercept3
(ug/m3)
2.6 ±0.9
10.2 ±0.3
16.4 ±2.6
3.6 ±3.0
34 ±5
16.8 ±8.7
rb
0.91
0.71
0.61
0.85
0.45
0.73
nc
32
32
66
11
67
11
PM,
-------
for RH above 80%, resulting in a wide range of regression slopes. As the analyses were
limited to samples with average RH < 50% for 6- and 12-hour measurements, the slope for
PM2.5 versus bsp were 0.23 to 0.34 for 6-hour samples and 0.12 to 0.37 for 12-hour samples.
Table 4-4 shows that correlations are quite variable, ranging from 0.54 to 0.73 for 6-hour
averages and 0.38 to 0.80 for 12-hour averages. Because the multiwavelength nephelometer
has a PM2.5 inlet with elevated chamber temperature, its measurements (bspg2.5 in Table 4-4)
have a higher slope (0.31 to 0.32). Similar PM2.5 versus bsp slopes of 0.25 to 0.32 were found
for the 12-hour samples acquired during the summer and winter of 1992 at a pristine location
(Meadview, AZ, near the Grand Canyon) for Project MOHAVE (Watson et al., 1993), with
moderate (0.51 < r < 0.64) correlations. Table 4-4 shows reasonable agreement between
PM2.5 and bsp (slope of 0.09 to 0.19, correlation of 0.56 to 0.69) among the four urban sites in
Arizona during the 1989/90 winter visibility study (Watson et al., 1991). Similar PM2.5
versus bsp slopes (0.08 to 0.16) were found in Mexico City's urban environment (Watson et
al., 1998b).
Scattering by particles of all sizes (bsp) and scattering by PM2.5 (bsp2.5, determined by
installing a PM2.5 inlet on the nephelometer) were compared in northern Colorado and central
Arizona, and there was little difference between their outputs. This is due to low coarse
particle concentrations during winter as well as a low scattering efficiency for those coarse
particles that are present.
This analysis demonstrates the feasibility of using hourly nephelometer light
scattering measurements to predict PM2.5 concentrations as long as samples are taken at
relative humidities less than 80% and site or region specific relationships can be established
by collocation with filter samplers. Data from a variety of urban, non-urban, and pristine
environments imply that each 100 Mm"1 of light scattering could potentially be associated
with 8 to 34 ng/m3 PM2.5 in the atmosphere for 3- to 12-hour sampling durations. Preceding
the nephelometer sensing chamber with a heatless dryer, such as a Nafion or Drierite
denuder, and a PM2.5 size-selective inlet may improve the nephelometer's utility as a
surrogate for continuous PM2.5 measurements.
Relationships between 3-hour PM2.s and PMio and particle absorption (bap) measured
with an aethalometer were good at the Bakersfield site, with correlation coefficients ranging
from 0.81 to 0.89 for all but one sample-averaging periods (r = 0.76). The regression slopes
of mass versus bap vary from 0.58 to 0.71 for PM2.5 and 0.69 to 0.86 for PMio. The
relationships for the 6- and 12-hour measurements in northern and northwestern Colorado
and central Arizona were not as consistent, with correlations ranging from 0.53 to 0.73. The
regression slopes of PM2.5 versus bap differed substantially from those derived at Bakersfield,
CA, ranging from 0.16 to 1.3. Longer sample averaging times or ambient liquid water
content appear to have little effect on the bap versus PM relationship, which is expected since
relative humidity and particle growth have small effects on particle absorption. Higher
correlations between light absorption and PM2.5 at the Bakersfield site are mainly due to
higher proportions of elemental carbon than those found at other locations.
Table 4-4 also compares the relationship between light extinction (derived from
collocated nephelometers and aethalometers) and PM2.s. A more narrow range of slopes
4-28
-------
(between 0.09 and 0.26) and higher correlations (>0.7 in most cases) were found. Because
the proportions of scattering and absorption may vary from sample to sample, more data is
needed for comparison.
4.3 Summary
Comparisons with collocated filter samplers show that the TEOM and BAM have the
capability of providing measurements comparable to filter samplers when air is dry and the
sampled aerosol is stable. Inconsistencies found by some comparisons in similar
environments are probably caused by differences in operating procedures rather than by
inherent deficiencies of the instruments. Biases are evident when the sampled aerosol is
volatile and when humidities are high, and these conditions often occur together.
Elevated sulfate and nitrate concentrations are often found in moist environments and
absorb copious amounts of liquid water. TEOM heating evaporates volatile components, but
lower temperatures allow liquid water to be collected along with particles. BAM monitors
that sample at ambient temperatures and relative humidities may overestimate particle
concentrations at high humidities owing to the liquid water associated with sampled particles.
Sample conditioning that is similar to laboratory filter equilibration temperature and relative
humidity conditions may alleviate these biases.
Relative humidity also affects the predictability of PM2.5 from light scattering
measured with a nephelometer because scattering increases substantially as RH climbs above
80%. Light absorption measured with an aethalometer is primarily sensitive to black carbon,
and it will only be a good predictor of PM2.5 when black carbon is a constant fraction of
mass. The sum of nephelometer (bsp) and aethalometer (bap) measurements, which is an
indicator of particle light extinction (bext), gives slightly better correlations with PM2.5 than
bsp and bap do individually. More comparison is needed before a reasonable relationship can
be inferred. Again, sample conditioning that is similar to laboratory filter equilibration
temperature and relative humidity conditions may alleviate these biases.
4-29
-------
5. USES OF CONTINUOUS PM MEASUREMENTS
This section provides examples of how continuous particle measurements can be
applied to achieve several of the objectives specified in Section 1. These purposes include:
• Reduce site visits and network operation costs: When a continuous particle
monitor achieves FEM status, it can be used in place of a manual FRM or FEM,
thereby reducing the frequency of site visits and the need for laboratory analysis.
A proven CAC monitor can be operated alongside a manual FRM or FEM at a
CORE site to reduce sampling frequency from daily to every third day (U.S. EPA,
1997b).
• Identify the need to increase FRM/FEM sampling frequency: Many PM2.5
sites will have less than daily sampling frequency. A CAC monitor collocated
with a manual PM2.5 sampler at this site can be used to evaluate the extent to
which this periodic sampling affects the annual average and 98th percentile PM2.5
concentrations. The CAC data can be used to justify the lower sampling
frequency or to demonstrate that more frequent manual sampling is needed to
represent population exposures to outdoor air.
• Evaluate real-time data to issue alerts or implement control strategies: CAC
measurements can be relayed via phone lines or satellites to a central facility
where they can be evaluated as part of a public health strategy. These are
particularly useful to detect pollution buildups under adverse meteorology or
during unusual pollution events. Several communities currently use continuous
particle and meteorological monitors to curtail residential wood combustion.
• Evaluate diurnal variations in human exposures to outdoor air: Although
values for a 24-hour averaging time are required for comparison with standards,
data bases with shorter time resolutions will provide better information for
scientists investigating relationships to health end-points.
• Define zones of representation of monitoring sites and zones of influence of
pollution sources: The portability and low cost of several continuous particle
monitors, specifically a few of the nephelometers, facilitates their use in
middle-scale and neighborhood-scale special purpose monitoring sites around
CORE sites and at different downwind distances from suspected particle emitters.
These data can be used to evaluate how well a CORE site represents population
exposure and the distances at which individual source emissions significantly
affect PM2.5 levels. The width of PM2.5 pulses in continuous measurements at a
long-term PM2.5 monitoring site also provides insight into the zone of
representation. Short-duration (1 to 5 minutes) pulses often originate from nearby
emitters and can distinguished from longer sample durations (>1 hour) that
originate from urban and regional source mixtures.
• Understand the physics, chemistry, and sources of high particle
concentrations: Short-duration particle measurements, especially those that are
chemically specific, can be examined in conjunction with short-term
5-1
-------
meteorological and gaseous air quality measurements to infer source origins,
transport properties, and chemical transformation mechanisms.
The final continuous monitoring purpose offers a large range of possibilities. During
a 24-hour day, wind directions and wind speed may change significantly. Some areas may
experience several cycles of meteorological patterns with durations of a few hours. For
example, the daytime sea-breeze and nighttime land-breeze are common occurrences in
coastal areas, as are mountain-valley circulations in mountainous areas. With integrated
24-hour averaged PM data, changes in transport directions during the sampling period make
it difficult to establish source-receptor relationships. Use of short-term or hourly particle,
along with hourly meteorological observations, allows for a better understanding of receptor
zones of representation and source zones of influence. High correlations of PM2.5 with
carbon monoxide and nitrogen oxide might indicate a large vehicle exhaust contribution,
especially when coupled with wind directions from a major highway. Elevated elemental
concentrations from a specific wind direction might indicate the location of a pollution
source.
Sources that are sufficiently close to receptors often affect the measured
concentrations on a scale of a few hours or less. These diurnal and episodic characteristics of
PM2.5 can also be used to investigate exposure patterns. A significant limitation of
high-time-resolution PM data from instruments such as the BAM and TEOM and other
continuous mass measurement methods is that they do not provide real-time chemical
composition information. However, when used in conjunction with chemically speciated
data averaged over longer time periods, these measurements can add to the understanding of
the sources and behavior of atmospheric PM.
In arid regions, disturbances of the land surface can cause high concentrations of PM,
especially during periods of high wind. Short-term PM and wind data can be used to
determine "threshold velocities" above which high amounts of crustal material can be
suspended. These threshold wind velocities could be used in control strategy development
by mitigating the effects of certain activities when high winds are expected or are occurring.
For example, clearing of land for construction activities or agricultural tilling could be
restricted during periods of high winds.
Several possible uses of continuous particle measurements are illustrated by examples
presented in the following sub-sections.
5.1 Diurnal Variations
By considering the diurnal pattern for PM, information may be obtained about factors
that affect PM concentration. These diurnal patterns are also useful for better estimating
human exposure, especially when they are available from locations near where people live,
work, and play, as well as for periods when they are expected to be outdoors during
commuting or exercise.
Figure 5-1 shows the 20th, 50th, and 80th percentile PMio BAM data for a mixed
light industrial/suburban site in the Las Vegas Valley, NV, during winter and summer
5-2
-------
jj 150
0 2 4 6 8 10 12 14 16 18 20 22 0
Hour
• 20%tile 50%tile -w- 80%tile
160
140
ar- 120
E
0 2 4 6 8 10 12 14 16 18 20 22 0
Hour
. 20%tile -»- 50%tile -v- 80%tile
Q_
160
140
^ 120
| 100
| 80
60
40
20
10 12 14 16 18 20 22 0
Hour
. 20%tile 50%tile -*- 80%tile
- 20%tile -•- 50%tile
Figure 5-1. Winter weekday and weekend patterns in PMio concentrations during summer
(April to September 1995) and winter (October 1995 to March 1996) periods at a mixed light
industrial/suburban site in the Las Vegas Valley, NV.
5-3
-------
seasons, with comparisons of weekends and weekdays. Two distinguishable peaks occur on
correspond to periods of high traffic volume and low mixing heights. In the middle of the
afternoon, deeper mixing depths caused by solar surface heating, along with somewhat
reduced traffic volumes, result in a period with lower concentrations than during the morning
and evening rush hours.
During summer weekdays, a large peak occurs in the early morning around 0600 to
0700 PST, and a smaller peak occurs in the early evening around 2000 PST. Lower
concentrations occur during the intervening daytime hours due to dispersion associated with
the deep mixing depths in summer. In fact, the evening peak in PMio occurs well after peak
traffic volumes, because during the rush hours deep mixing compensates for the increased
traffic. The weekend patterns show a substantially reduced size of the morning peak. This
may be explained by having fewer commuters traveling to work on weekend mornings.
Time-resolved PM data can also be used to understand differences between sites
within the same urban area. Figure 5-2 shows diurnal patterns of winter 50th-percentile
PMio BAM concentrations for four sites in the Las Vegas Valley. The East Charleston and
City Center sites are near the center of the Las Vegas urban area, while the Bemis and Walter
Johnson sites are at the edge of the urban area. The two urban sites exhibit high PMio
concentrations until late in the evening (especially on weekends) due to higher traffic
activities, while the two suburban sites have decreased concentrations after the evening rush
hours. Each of these sites shows a characteristic morning traffic peak on weekdays that is
attenuated on weekends. The apparent relationships between traffic level and PMio
concentration in Las Vegas are consistent with contributions from emissions along roadways,
including both direct vehicle emissions and dust resuspended by vehicles traveling on
(mostly) paved roads.
Figure 5-3 illustrates the diurnal cycle of PMio concentrations at a site in Calexico,
CA, along the U.S./Mexico border, a location close to vehicle exhaust and paved road dust
sources. These patterns have many of the same features as the Las Vegas measurements, but
the timing is different. The median and 95th percentile data show peaks at about 0800 PST
as well as 1900 to 2000 PST. The evening peak at the 95th percentile level is about twice as
high as the morning peak. In Las Vegas, the morning peak is associated with increased
traffic and low mixing heights. The evening peak is consistent with a collapse in the mixing
depth at sunset that traps vehicle emissions at the end of the evening rush hours. Traffic is
heavy at the nearby border crossing late into the evening, and this is reflected in diurnal
maxima that occur a few hours later in the evening.
5.2 Wind Speed and PM Relationships
Figures 5-4 and 5-5 show average PMio corresponding to different wind speed
categories at the Calexico, CA, site for winds from the north (Figure 5-4) and from the south
(Figure 5-5). Higher PMio concentrations occur for wind flow from the south (i.e., Mexico)
at all wind speeds. Higher concentrations occur at very low and high wind speeds as
5-4
-------
<
DO
100
80
60
40
20
a) Weekdays
Winter
0 2 4 6 8 10 12 14 16 18 20 22 0
Hour
Craig/Bemis
East Charleston
City Center
Walter Johnson
100
b) Weekends, Winter
8 10 12 14 16 18 20 22
Craig/Bemis
East Charleston
City Center
Walter Johnson
Figure 5-2. Weekday and weekend patterns for 50th percentile PMio concentrations at the
City Center and East Charleston (Microscale) urban center sites and Bemis/Craig and Walter
Johnson urban periphery sites during Winter 1995 in the Las Vegas Valley, NV.
5-5
-------
(a) All Seasons (03/12/92 to 08/29/93)
(b) Spring Season
400
5th Percentile + Median *95th Percentile
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hour (PST)
500
— 5th Percentile + Median * 95th Percentile
012345678 91011121314151617181920212223
Hour (PST)
(c) Summer Season
(d) Winter Season
5th Percentile + Median *95th Percentile
012345678 91011121314151617181920212223
Hour (PST)
400
350 -
— 5th Percentile + Median *95th Percentile
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hour (PST)
Figure 5-3. Diurnal variations of hourly BAM PMio concentrations at a monitoring site in Calexico, CA, near the U.S./Mexico
border during 03/12/92 to 08/29/93.
-------
275 -
250 -
_ 225 -
"J 200 -
|] 175 -
^ 150 -
i. 125 r-
§ 100 T"
2 75 r-
50 t-
25 -•
0 —
2345
hourly wind speed (mis)
7
Figure 5-4. Relationships between BAM PMio and wind speed for northerly flow.
275 -
250 -
_ 225 --
"E 200 T-
175
Q_
^
<
150
125 *-
25 r
100 T-i--^
75 T '
50 4-
0
3458
hourly wind speed (m/s)
8
Figure 5-5. Relationships between BAM PMio and wind speed for southerly flow.
5-7
-------
compared to intermediate wind speeds. At low wind speeds, stagnation allows buildup of
locally generated pollutants. As wind speed increases, increased transport and dispersion
leads to lower concentrations. At yet higher wind speeds, especially above ~7 m/s, PMio
increases rapidly with increased wind speed due to resuspended dust.
A similar pattern between wind speed and PMio concentrations is found in Las Vegas
(Chow and Watson, 1997b), as shown in Figure 5-6. Concentrations are relatively high at
very low wind speeds, reach a minimum at about 4 m/s, and begin increasing above ~6 m/s,
with a sharp increase above 10 m/s.
Figure 5-7 shows the hour-by-hour relationship between PMio and wind speed at a
Las Vegas Valley site on a day with high PMio concentrations. A rise in wind speed from 4
to 14 m/s is accompanied by an increase in PMio from less than 50 |ig/m3 to over 1,000
|ig/m3 during an hour, clearly demonstrating the effect of increased wind speed on dust
suspension. Figure 5-7 also shows that peak PMio concentrations dropped rapidly even
though wind speeds stayed high (>10 m/s) for several hours. This suggests that the
"reservoir" of material available for suspension was largely depleted by the initial gusts
(Chow and Watson, 1994).
Figure 5-8 shows similar variations with wind speed for data acquired at a
southeastern Chicago (Eisenhower) site near Robbins, IL. At moderate wind speeds of 1.5 to
4 m/s, PMio concentrations reach their lowest levels due to greater dispersion. When wind
speeds exceed 7 m/s, PMio concentrations increase due to wind-raised particulate matter.
The third quarter reported higher PMio levels because lower wind speeds were more frequent
than during the other quarters. It appears that threshold suspension velocities may be ~2 m/s
lower during the third quarter (summer) than during the other two quarters, possibly due to
drier conditions that allow dust to be more easily suspended by wind.
5.3 Source Directionality
The Imperial Valley examples in Figures 5-4 and 5-5 (Chow and Watson, 1997a)
demonstrate how continuous data can be coupled with wind direction measurements to
determine transport fluxes. This will be an important use of continuous PM2.5 at transport
sites that are intended to determine the effect of one Metropolitan Planning Area (MPA) on
other MPAs (Watson et al., 1997a). Cross-border fluxes of PMio at the Calexico site are
shown in Table 5-1. Mean fluxes were calculated by multiplying average wind speed with
average PMio concentration for each flow direction (U.S to Mexico and Mexico to U.S). For
periods of flow from Mexico, cross-border fluxes were three times as large as for flows from
the U.S. However, because flow was more frequently from the U.S., the total flux from
Mexico was only about 45% higher than the total flux from the U.S.
Figure 5-9 shows PMio concentrations at various percentiles as a function of wind
direction for the Eisenhower site in southeastern Chicago, IL (Watson et al., 1997c). In the
first calendar quarter of 1996, PMio concentrations were highest for transport from the east
and south, although directional differences were not pronounced. In the second calendar
quarter of 1996, PMio concentrations were highest with transport from the southwest and
5-8
-------
1000
S
A
100
M
10
~l » I I I I I
6 8 10
Wind Speed (m/s)
20% tile 50% tile -A- 80% tile
14
Figure 5-6. Distribution of hourly BAM PMio as a function of wind speed at 14
meteorological sites in the Las Vegas Valley, NV, monitoring network between 01/01/95 and
01/31/96.
5-9
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-800
-600
1200
- 1000
E
"DJ
-400
1-200
-200
12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hour of Day (for 04/08/95-04/09/95)
•Hourly Averaged Wind Speed
•PM10BAM
Figure 5-7. Relationship between hourly averaged wind speed and PMio concentrations at
a North Las Vegas, NV, site (Bemis) during the period of 04/08/95 to 04/09/95.
5-10
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First Quarter 1996
0.7 1.2 1.7 2.2 2.7 3.2 3.7 4.2 4.7 5.2 5.7 6.3 7.2
Second Quarter 1996
/
z
z
^^
0.6 1.2 1.7 2.2 2.7 3.2 3.7 4.2 4.7 5.4 6.4 7.4 8.3
Hourly Wind Speed (mis)
-20%tile -«-50%tile -*-80%tile
Third Quarter 1996
0.3 0.7
1.2 1.7 2.2 2.6 3.2 3.7
Hourly Wind Speed (mis)
-•—20 percentile U 50 percentile A 80 percentile
Figure 5-8. Distribution of hourly PMio concentrations as a function of wind speed at a
southeastern Chicago, IL, site (Eisenhower) during the first three quarters of 1996.
5-11
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Table 5-1
Cross-Border Fluxes at the Calexico Site
Northerly Flow Southerly Flow
(from the United States) (from Mexico)
Mean Flux 52 |ig/m2-s 156 |ig/m2-s
Total Flux (|ig-hr/m2-s) 213,367 |ig-hr/m2-s 308,430 |ig-hr/m2-s
5-12
-------
EIS 1st quarter
NW
sw
NW
SW
SE
EIS 3rd quarter
SE
20 percentile
50 percentile
80 percentile
20 percentile
50 percentile
80 percentile
NE 20 percentile
50 percentile
80 percentile
Figure 5-9. PMio concentrations at the 20th, 50th, and 80th percentiles at a southeastern
Chicago, IL, site (Eisenhower) during the first three quarters of 1996.
5-13
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south, then from the east, and lowest for northerly transport. PMio concentrations during the
second quarter were higher than those from the first quarter in most directions except for the
north. The third quarter exhibited the highest PMio concentrations with transport from the
south.
Table 5-2 shows how continuous particle measurements can be associated with wind
direction and continuous precursor gas measurements, in this case sulfur dioxide (802).
Average wind speeds in each wind direction were highest in the first quarter and lowest in
the third quarter. At the 50th percentile, PMio concentrations during the third quarter of 1996
were 10 to 15 |ig/m3 higher than during the first two quarters of 1996. The higher PMio
concentrations in the third quarter are associated with lower wind speeds that limit dilution of
local emissions.
Higher PMio concentrations during the third quarter may also be due to more
photochemically produced PM. Sulfate and some of the organic carbon components are
derived from this photochemistry, and these may originate in emissions from other populated
areas. Major population centers, as well as large sulfur dioxide emitters in the Ohio River
Valley, are in the northeastern to southern direction with respect to northeastern Illinois.
During all three quarters, average hourly 862 concentrations were highest when transport
was from the south. The highest SC>2 values were observed with a narrow range of wind
directions (about 200 degrees), which is precisely the direction of a nearby oil refinery.
When continuous particle chemistry measurements are available, even more
specificity can be obtained from the correspondence between concentration and wind
direction. Figure 5-10 shows the directionality of selected elemental concentrations
determined from a streaker sampler at three southeastern Chicago sites along a north/south
line of-10 km length (the Meadowlane [MEA] site is -1.5 km north of the Eisenhower [EIS]
site, and the Breman [BRE] site is -8 km south of the Eisenhower site). A large number of
industrial sources are located between these sites, and even larger complexes are located to
the east and northeast of the Robbins, IL, neighborhood.
Wind-sector averages of PM2.5 aluminum (Al), silicon (Si), calcium (Ca), potassium
(K), iron (Fe), manganese (Mn), lead (Pb), sulfur (S), zinc (Zn), chromium (Cr), calcium
(Ca), and bromine (Br) are plotted in Figure 5-10. A marker on each axis in these figures
represents the average concentration for the 45-degree sector from which the designated
element originates. Similar directionality and magnitude for an element at all sites indicates
transport from sources in that direction outside of the study domain. Lack of directionality at
any site indicates a widespread area-type source. Large directionality and magnitude at a
single site that is not observed at the other sites indicates a nearby emitter with a small (~<5
km) zone of influence. A high directionality in a northerly or southerly direction at some
sites, but in the opposite direction at other sites, indicates a source within the domain
between the sites. The source is probably closer to the site with the highest directional
average. Each sector average is a combination of contributions from areawide, distant point,
and nearby emitters. Contributions from sources near an individual site manifest themselves
as larger concentration increments over those measured from the same wind direction at
other sites.
5-14
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Table 5-2
Distribution of Hourly PMio and SOi Concentrations at Robbins, IL,
as a Function of Wind Speed and Wind Direction
Distribution of PMm Concentrations (ue/m3)
Wind
Direction
First Quarter
N
NE
E
SE
S
SW
w
NW
20th
Percentile
1996
6.2
7.0
12.0
7.0
14.0
10.0
8.0
7.0
50th
Percentile
15.0
17.0
23.0
16.0
22.0
19.0
17.0
16.0
80th
Percentile
30.0
31.0
37.0
29.0
32.0
29.0
31.0
30.0
Average
Wind Speed
(m/s)
3.9
3.3
2.3
3.1
3.8
4.1
6.7
3.5
Average
PMio
(|ig/m3)
20.1
22.1
25.9
19.7
25.7
21.1
21.1
19.0
Average
SO2
(ppb)
1.3
2.3
7.1
6.7
15.1
10.2
5.0
4.7
Second Quarter 1996
N
NE
E
SE
S
SW
w
NW
4.0
6.0
13.6
11.0
17.0
13.4
8.0
7.0
10.0
13.0
25.0
22.0
27.0
27.0
16.0
14.0
21.0
26.0
42.4
38.6
47.0
48.0
30.0
24.6
2.4
2.2
2.2
2.2
3.4
4.0
3.4
2.9
13.6
19.3
30.3
25.3
32.6
33.9
20.2
18.2
1.0
1.5
5.8
4.0
11.5
5.9
2.1
0.8
Third Quarter 1996
N
NE
E
SE
S
SW
w
NW
8.4
11.0
10.0
8.0
20.0
8.0
10.0
9.0
15.0
19.0
28.0
24.0
36.5
24.0
19.0
17.0
32.6
46.0
47.2
46.4
57.4
42.0
34.0
35.0
1.8
1.5
1.9
1.6
2.3
2.3
2.3
1.9
23.3
28.8
32.1
29.2
40.6
26.5
23.5
22.8
3.7
4.7
9.3
5.1
10.3
6.3
4.2
3.7
5-15
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NW<"
'->, NE
SE
PM2.6 Al (ng/m3)
NW<"
"> NE
SE
PM2.6 Si (ng/m3)
'>, NE
SE
PM2.6 Ca (ng/m3)
->NE
SE
PM2.6 K (ng/m3)
NW
•> NE
SE
PM2.6 Fe (ng/m3)
NW
•> NE
SE
PM2.6 Mn (ng/m3)
Figure 5-10. Hourly PM2.5 elemental concentrations (ng/m3) at three southeastern Chicago,
IL, sites near Robbins, IL, averaged by wind sector for one week apiece during the first three
calendar quarters of 1996.
5-16
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NW
w
NW<-"
NE
'SE
S (ng/m3)
NW<"
NE
"->, NE
'SE
PM26Cr(ng/m3)
">,NE
'SE
s Cu (ng/m3)
NW<-"
NE
W
Figure 5-10. (continued)
5-17
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Silicon (Si) is a good indicator for suspended road dust and windblown geological
sources that are ubiquitous in most urban areas. The PM2.5 Si homogeneity in all wind
directions at the residential (Breman and Meadowlane) sites is consistent with an areawide
source. This homogeneity also indicates that average dilution of emissions does not
significantly differ as a function of wind direction. At the Eisenhower site, however, Si
levels are slightly higher for the southern and southeastern directions, while PM2.5 Si
concentrations from remaining directions are comparable to those at the other sites. Even the
Meadowlane site shows slightly higher Si levels from the south. This indicates a Si point
source lying south of the Eisenhower and Meadowlane sites that is not south of the Breman
site.
PM2.5 aluminum (Al) concentrations show a distinct southerly source contributing to
the Eisenhower and Meadowlane sites, but not to the Breman site. A slight southeasterly
aluminum source affects the Breman location.
The Eisenhower site also shows higher particulate sulfur (S) concentrations deriving
from the southern and southwestern directions that are not evident at the other two sites. For
the most part, average particle sulfur concentrations are about twice as high from the
northeastern to southeastern directions as they are from the remaining sectors, consistent with
the locations of large sulfur emitters in the Midwest. The incremental particle sulfur
measured at the Eisenhower site is most probably from a nearby oil refinery.
PM2.5 calcium (Ca), potassium (K), iron (Fe), manganese (Mn), and lead (Pb) each
have strong northeasterly to easterly components. A steel mill is located ~6 km to the east
and there are other steel mills further to the northeast and east of the study network.
Sector-averaged iron concentrations are comparable at the Breman and Meadowlane sites, as
are the manganese concentrations, consistent with contributions from more distant Indiana
sources. Large increments in iron and manganese concentrations at the Eisenhower site are
consistent with contributions from a steel mill.
Lead (Pb) also appears to derive from distant and nearby sources to the east and
northeast. Lead concentrations at the Meadowlane site represent contributions from the more
distant sources. A nearby lead source to the east of Eisenhower affects that site and the
Breman site, but not the Meadowlane site. The Eisenhower site also measures large
incremental calcium and potassium concentrations in the eastern sector with respect to the
other sites, while there is no indication of excessive calcium or other minerals from the
southern sector at the Meadowlane site, even though stone cutting and polishing take place
within 100 m south of the sampler.
Zinc (Zn), chromium (Cr), and copper (Cu) patterns are variable between the
elements and the measurement locations. Concentrations are highest in the eastern, western,
and southwestern sectors. There is a strong local copper influence to the southeast of the
Eisenhower site that may originate from several nearby plating and metal handling industries.
The common directionality for these metals at the different sites indicates that a major
fraction of their contributions originate from sources outside of the study domain.
5-18
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5.4 Receptor Zones of Representation and Source Zones of Representation
The directional analyses shown in Figure 5-10 demonstrates similarities and
differences among nearby sites. The proximity of a pollution source can also be estimated
from the width of a pulse received at a receptor. This is illustrated in Figure 5-11 for which
five-minute average black carbon concentrations are plotted for a downtown site (MER) in
Mexico City located on a single-story rooftop near a heavily traveled roadway and for a
suburban residential site (FED) in Mexico City located in a schoolyard more than 200 m
from a major highway. These sites are separated by -15 km, typical of an urban-scale
network.
The overall diurnal pattern represents a daily buildup starting at 0500 CST at the
downtown (MER) site and at -0800 CST at the suburban (FED) site. The concentrations
become similar after the morning surface inversion breaks at -1000 CST. The short-duration
spikes at the downtown (MER) site are probably from passing trucks and buses that emit
substantial quantities of black carbon.
Smaller spikes are evident at the suburban (FED) site, but they are not as prominent
or as frequent as those detected at the downtown (MER) site. The integral of these spikes at
the downtown (MER) site constitutes a substantial fraction of the 24-hour black carbon
concentration. These could be filtered out of the time-resolved measurements to obtain a
larger zone of representation of this site. Comparison of the 0500 to 1000 CST
concentrations at the downtown (MER) and suburban (FED) sites shows that nearly half of
the black carbon accumulates within the neighborhood surrounding the downtown (MER)
site, probably owing to traffic emissions into a stable morning layer. Aside from the spikes
at the downtown (MER) site, the black carbon measured at either site appears to represent
black carbon concentrations over a large portion of Mexico City. This analysis indicates that
when continuous monitors are operated and have sufficient sensitivity over short monitoring
periods, they can acquire high-resolution measurements that allow a single site to quantify
the superposition of particles from middle-scale, neighborhood-scale, and urban-scale
particle influences.
5.5 Summary
The examples in the previous subsections show that continuous particle monitoring
provides perspectives on particle concentrations that cannot be inferred from 24-hour average
filter samplers. Hourly particle measurements elucidate the times of day when high and low
concentrations occur, and these can often be related to emissions patterns, such as those from
traffic, and to atmospheric mixing characteristics, such as the breakup of a morning
inversion.
In addition to indicating the origins of particles, these diurnal cycles show those
periods when people are most likely to be exposed to the highest and lowest PM
concentrations. Relationships to changes in wind direction and wind speed help to locate
source areas and to determine interactions between the atmosphere and emissions. This is
especially important for intermittent sources, such as windblown dust, that are not well
5-19
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16000
14000
Hour
Figure 5-11. Five-minute-average aethalometer black carbon measurements at a downtown
(MER) site and a suburban (FED) site in Mexico City. The short-term spikes correspond to
contributions from very nearby sources, such as diesel exhaust plumes from trucks and buses.
5-20
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represented in emissions inventories. With chemical-specific methods, more source
specificity can be obtained. Very short averaging times (~5 minutes) allow estimates to be
placed on contributions from nearby, local, or distant emitters from measurements at one or
two sites. This information will be especially important for evaluating Community
Monitoring Zones (CMZ) within which PM2.5 concentrations may be averaged to determine
compliance with the annual standard.
These examples show how measurements from widely used monitors can be used for
fairly simple analyses. More complex combinations of instruments described in Section 3
will allow the science of particle formation and transport to be understood. Condensation
nuclei counter (CNC), electrical aerosol analyzer (EAA), and differential mobility particle
sizer (DMPS) monitors can quantify the ultrafme fraction that may be a future indicator of
adverse health effects (Oberdorster et al., 1995).
Simultaneous continuous measurements of sulfate, nitrate, nitric acid, and ammonia
will provide more accurate estimates of changes in equilibrium with changes in temperature
and relative humidity than are currently possible with filter/denuder sampling and analysis.
The time-width of nitrate, sulfate, sulfur dioxide, and oxides of nitrogen pulses will permit
distant and nearby sources of secondary aerosol contributions to be distinguished from each
other, similar to the example for primary carbon shown in Figure 5-11. This concept has
been tested to some extent in recent PM2.5 studies (Watson et al., 1996, 1998a), and shows
large potential once the operation of these instruments is perfected.
As noted above, the examples given here should be considered illustrative, but not
comprehensive. They show the potential of continuous particle monitors to address certain
problems. Creative combinations of continuous measurements and data analysis methods are
needed to address specific particle pollution issues.
5-21
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6. CONTINUOUS PARTICLE MONITORING IN PM2 5 NETWORKS
This section discusses how continuous particle measurements complement, and
possibly substitute for, filter sampling in PM2.5 networks. It examines the issues involved in
designating Correlated Acceptable Continuous (CAC) monitors and specifies reasonable tests
for a CAC monitor. CAC monitors are not required to attain FEM designation, but they
should show comparability to FRM or FEM measurements in the monitored environment.
The CAC monitor's predictability for PM2.5 concentrations may be considered sufficient for
certain situations, but traceability to PM2.5 mass concentration standards is preferred.
6.1 PM2.5 Network Site Types
CAC monitors are a subset of continuous particle monitors that are to be used at
Community Representative (CORE) PM2 5 monitoring sites. These CORE sites are located
where people live, work, and play rather than at the expected maximum impact point for
specific source emissions. CORE sites are used to determine NAAQS compliance for both
annual and 24-hour PM2.5 standards within a Community Monitoring Zone (CMZ), the
spatial zone of representation of the site. PM2 5 concentrations may be spatially averaged
among several sites within a CMZ when the annual average PM2 5 at each Core site is within
±20% of the spatial average on a yearly basis.
CORE sites have a zone of representation of at least neighborhood scale (> 0.5 km).
For a neighborhood scale, this means that the 24-hour concentrations should vary by no more
than ±10% within an area whose diameter is between 0.5 and 4 km. For urban scale, the
concentrations would be similar for distances greater than 4 km. In some monitoring areas, a
site with a smaller spatially representative scale (microscale [-100 m] or middle scale [a few
hundred m]) may represent many such small scale sites in the general area.
At least two CORE sites in each metropolitan area with more than 500,000 people are
required to sample every day with Federal Reference Method (FRM) or Federal Equivalent
Method (FEM) samplers. The regulations allow sampling frequency to be reduced to once in
three days if the sampler is collocated with a continuous analyzer whose measurements are
correlated with those of the FRM. A continuous monitor can be used as a CAC monitor for
this purpose when the FRM PM2 5 is predicable from the CAC measurements.
Hot spot sites do not represent community-oriented monitoring and would be located
near an emitter with a microscale or middle-scale zone of influence. Data from
population-oriented hot spot sites would only be compared to the 24-hour standard.
Everyday sampling is not specifically required at hot spot locations. CAC monitors will not
be routinely operated at hot spot PM2 5 sites.
Special Purpose Monitors (SPM) may be used to understand the nature and causes of
excessive concentrations measured at core or hot spot compliance monitoring sites. Any or
all of the continuous particle measurement methods described in Section 3, or ones that may
be invented in the future, qualify as SPMs as long as they contributes to knowledge about the
nature, sources, and/or health effects of suspended particles. SPM sites do not need to use
FRMs, FEMs, or CACs. They do not necessarily need to show comparability or
6-1
-------
predictability of PM2.5 mass concentrations, though they must have sufficient accuracy,
precision, validity, and reliability to meet special purpose monitoring objectives. SPMs may
be operated over short periods of time at different locations, and they can be discontinued
within their first two years of operation without prejudice when monitoring purposes have
been achieved.
6.2 Federal Reference and Equivalent Methods
The Federal Reference Method (FRM) specifies sampler design, performance
characteristics, operational requirements, and quality assurance applicable to the PM2.5 FRM
in 40 CFR part 50, Appendix L; 40 CFR part 53, Subpart E; and 40 CFR part 58, Appendices
A and C. PM2.5 FRMs are intended to acquire deposits over 24-hour periods on
Teflon-membrane filters from air drawn at a controlled flow rate through the WINS (Well
Impactor Ninety Six) PM2.5 inlet. The inlet and size separation components, filter types,
filter cassettes, and internal configurations of the filter holder assemblies are specified by
design, with drawings and manufacturing tolerances published in 40 CFR part 53 (U.S. EPA,
1997b).
Other sampler components and procedures, such as flow rate control, operator
interface controls, exterior housing, data acquisition) are specified by performance
characteristics, with specific test methods to assess that performance. Chow and Watson
(1998a) provide more detail on FRM and equivalent filter sampling systems.
Design specifications of the FRM samplers include a modified SA-246 PMio inlet
that has previously been wind tunnel tested and approved for PMio compliance monitoring.
The inlet cover has been extended by 2.5 inches and bent 45° downward to minimize water
penetration during rainstorms. Sample air enters the inlet and is drawn through the WINS
inlet that removes particles with aerodynamic diameters greater than 2.5 |im by impacting
them on the bottom of an open-topped aluminum cylindrical container.
Impacted particles are trapped at the bottom of the well on an oil-impregnated filter
(35 to 37 mm borosilicate glass-fiber) impregnated with a low vapor-pressure oil (tetramethyl
tetraphenyltrisiloxane, maximum vapor pressure 2xlO"8 mm Hg, density 1.06 to 1.07 g/cm3,
32 to 40 centistoke viscosity at 25 °C). More than 50% of the particles with aerodynamic
diameters less than 2.5 jim follow the air flow through the WINS, which turns up and out of
the well and is directed back down to a Teflon-membrane filter where the particles are
removed by filtration. Internal surfaces exposed to sample air prior to the Teflon-membrane
filter are treated electrolytically in a sulfuric acid bath to produce a clear, uniformly anodized
coating (at least 1.08 mg/cm2 in accordance with military standard specifications).
CAC monitors should be adapted, to the greatest extent possible, to comply with
these inlet characteristics so that the size fraction they measure is equivalent to that measured
by the FRM. This may not always be possible, owing to differences in flow rate and sample
configuration. In this case, other inlets that have been demonstrated as equivalent (as
described below) should receive first preference.
6-2
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FRM performance specifications require constant volumetric flow rates (16.67 ± 0.83
L/min) to be monitored and recorded continuously with temperature and pressure of the
sample air entering the inlet and near the filter. FRMs are required to maintain the
temperature of the filter during and after sampling within ±5 °C of concurrent ambient
temperatures regardless of heating and cooling from direct sun or shade during and after
sampling. This specification intends to minimize losses from volatile particles such as
ammonium nitrate and some organic compounds. Potential FRM designs use active
ventilation of the enclosure that surrounds the filter holder and WINS impactor to attain these
temperature performance specifications.
FRMs from different manufacturers may vary in appearance, but their principles of
operation and resulting PM2.5 mass measurements should be the same within reasonable
measurement precisions. Though they may follow the published design specifications, PM2.5
samplers are not FRMs until they have demonstrated attainment of the published
specifications (U.S. EPA, 1997b) and assigned an FRM number published in the Federal
Register.
As stated in Section 1, Federal Equivalent Methods (FEMs) are divided into several
classes in order to encourage innovation and provide monitoring flexibility. This is
especially important for continuous particle monitors, as it provides a means for them to be
accepted as CAC monitors, or even as FEMs.
Class I FEMs meet nearly all FRM specifications, with minor design changes that
permit sequential sampling without operator intervention and different filter media in parallel
or in series. Flow rate, inlets, and temperature requirements are identical for FRMs and Class
I FEMs. Particles losses in flow diversion tubes are to be quantified and must be in
compliance with Class I FEM tolerances specified in 40 CFR part 53, Subpart E.
Class II FEMs include samplers that acquire 24-hour integrated filter deposits for
gravimetric analysis, but that differ substantially in design from the reference-method
instruments. These might include dichotomous samplers, and high-volume samplers with
PM2.5 size-selective inlets. More extensive performance testing is required for Class II FEMs
than for FRMs or Class I FEMs, as described in 40 CFR part 53, Subpart F. Key
requirements for Class I and Class II FEM equivalence tests are summarized in Table 4-1.
Class III FEMs include samplers that do not qualify as Class I or Class II FEMS.
This category is intended to encourage the development of and to evaluate new monitoring
technologies that increase the specificity of PM2.5 measurements or decrease the costs of
acquiring a large number of measurements. Class III FEMs may either be filter-based
integrated samplers or filter- or non-filter-based in-situ continuous or semi-continuous
samplers, including many of those described in Section 3. Test procedures and performance
requirements for Class III candidate instruments will be determined on a case-by-case basis.
Performance criteria for Class III FEMs will be the most restrictive, because equivalency to
reference methods must be demonstrated over a wide range of particle size distributions and
aerosol compositions.
6-3
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FEM applications require the following:
• A detailed description of measurement principles and procedures, manufacturer's
name, model number, schematic diagrams of components, design drawings, and
apparatus description;
• A comprehensive instrument manual documenting operational, maintenance, and
calibration procedures;
• A statement of the method, analyzer, or sampler being tested; and
• A description of quality systems that will be utilized as well as the durability
characteristics of the sampler.
Candidate sampler inlets differing from those already tested are to be evaluated in a
wind tunnel test at wind speeds of 2 and 24 km/hr for monodisperse aerosol between 1.5 ±
0.25 |im and 4.0 ± 0.5 jim. In addition, tests for inlet aspiration, static fractionation, loading,
and volatility are also specified in 40 CFR part 53, Subpart F, as procedures to test
performance characteristics of Class II equivalent method for PM2.5.
Requirements for PM2.5 Class III FEMs have not been specified owing to ". . . the
wide range of non-filter-based measurement technologies that could be applied and the
likelihood that these requirements will have to be specifically adapted for each such type of
technology. Specific requirements will be developed as needed and may include selected
requirements from Subpart C, E, or F as described for Class II FEM . . ." (U.S. EPA, 1997b).
6.3 Potential Tolerances for FEM and CAC Monitor Designation
Given the comparison results shown in Section 4, Class III FEM designation will be
difficult to attain for all sampling sites and monitoring periods likely to be encountered in
PM2.5 monitoring networks. The specifications in Table 4-1 should be attained for Class III
FEMs, but the types of measurement environments needs to be more than two. These
environments should include: 1) stable, non-hygroscopic aerosol in low relative humidity,
such as that found in summertime Las Vegas, NV or Phoenix, AZ; 2) stable, hygroscopic
aerosol in moderate to high relative humidity, such as that found in summertime
Birmingham, AL or Chicago, IL; 3) unstable, hygroscopic aerosol, under variable humidity,
such as that found in wintertime Fresno, CA or Washington D.C.; 4) unstable,
non-hygroscopic aerosol, such as that found in wood-smoke-dominated environments such as
Medford, OR, or Mammoth Lakes, CA; and 5) a complex photochemical environment with a
combination of stable and unstable aerosol within and outside of a moist marine layer, such
as that found during early fall in Riverside, CA.
Aerosol volatility and liquid water sampling are the major impediments for FEM
designation of continuous paniculate monitors. Until methods are perfected to compensate
for these interferences, such as equilibrating the sampled air stream to conditions comparable
to those of the FRM filter equilibration, continuous particle monitors will not be universally
equivalent to filter-based measurements. A filter/gravimetric PM2.5 mass fraction has been
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established as the NAAQS, and continuous FEMs must be equivalent, within acceptable
bounds, to these concentrations as operationally defined by an FRM.
It is also evident from the comparisons in Section 4 and from other published
comparisons (e.g. Husar and Falke, 1996) that reasonable PM2.5 estimates can be obtained
from certain continuous particle monitors, and that these monitors can be used as CAC
monitors. The main criteria selecting a CAC monitor should be: 1) measurement precision;
2) correlation with FRMs and FEMs; 3) consistency of relationships with FRMs; and
4) frequency of deviation. The performance measures applied in Section 4 address each of
these issues.
With respect to measurement precision, CAC monitors, cannot be expected to
compare with FRMs or FEMs better than collocated PM2.5 FRM monitors. Several PM2.5
samplers using the WINS and other PM2.5 inlets were collocated in a variety of environments
from November 1996 through May 1997 (Pitchford et al., 1997) and demonstrated collocated
precisions of -0.5 to 1.0 |ig/m3 among the WINS samplers. A reasonable lower bound on
collocated precision is, therefore, ±1 |ig/m3.
As an upper limit, the annual PM2.5 NAAQS allow measurements at a single site in a
Community Monitoring Zone to deviate from the spatial average of Core sites within that
zone by up to 20%. For PM2.5 concentrations near the annual standard of 15 |ig/m3, this is a
maximum difference of ±3 |ig/m3. This tolerance is not as stringent as the tolerances for
collocated samplers because annual averages can be quite similar even though specific
samples in those averages may be markedly different.
A precision requirement for collocated CAC monitors should be between these
extremes. Table 4-1 allows a collocated precision of ±2 |ig/m3 for Class I or Class II FEMs,
and this (or its equivalent) should be retained as precision requirement for collocated CAC
monitors.
CAC monitors are intended to be used alongside FRMs or FEMs, so sufficient data
sets will be acquired from PM2.5 networks to evaluate correlations between their output and
24-hour PM2.5 concentrations in a variety of environments. Table 4-1 specifies 0.97 as the
correlation coefficient required for FEM status. This is intended for specialized testing under
controlled conditions. Section 4 demonstrates that a correlation coefficient in excess of 0.90
is probably more realistic for routine operating conditions. This is more consistent with
correlation coefficients found for collocated PMio samplers.
The linear relationship between CAC monitor output and collocated FRMs or FEMs
needs to be consistent, although Section 4 shows that this relationship may differ for different
environments. The 1.0 ±0.05 slope requirement in Table 4-1 will probably not be attained by
potential CAC monitors that are not calibrated or traceable to PM2.5 mass. The ±5% standard
error on the slope that is attained should be retained for the environments to which the
relationship is to apply. Similarly the 0±1 |ig/m3 intercept is not applicable to all potential
CAC monitors, but its |ig/m3 equivalent should be.
6-5
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CAC monitors can be used to reduce FRM monitoring frequencies after sufficient
daily data have been acquired to establish their PM2.5 comparability or predictability at a
CORE sites. The initial reduction could be from every day to every third day. In some
cases, FRM sampling might be less frequent, possibly every sixth day, during spring and
summer in the California's San Joaquin Valley when the arid climate and non-volatile
aerosol allow TEOM and BAM measurements to approximate filter PM2.5 samples.
In areas that are shown to have low humidities and low abundances of volatile aerosol
throughout the year, such as Las Vegas, NV, and California's Imperial Valley, CAC
monitors might be shown to meet the FEM requirements all of the time and could completely
replace filter/gravimetric samplers. This might also be found in certain parts of the
midwestern and eastern U.S., such as Robbins, IL, where the sulfate aerosol is stable, and
heating of the sampled airstream to evaporate liquid water will not necessarily volatilize
significant fractions of the PM2.5. It is more likely, however, that the Class III FEM
designation will be reserved for monitors that meet Class II FEM requirements for a large
number of environments.
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APPENDIX A: CONTINUOUS MEASUREMENT DATA SETS
This appendix provides a brief description of current aerosol characterization studies
with collocated filter and continuous PM measurements, preferably chemical speciation. A
description of study periods, sampling sites, sources affecting sites, composition of sampled
aerosol, and measurements available for comparison are summarized.
A.1 Southern California Air Quality Study (SCAQS)
The SCAQS was conducted during the summer (06/19/87 to 09/03/87, with 5
episodes, 11 days total) and fall (11/11/87 to 12/11/87, with 3 episodes, 6 days total) periods.
Meteorological, air quality, and visibility measurements were acquired simultaneously at more
than 40 locations throughout California's South Coast Air Basin (Lawson, 1990). Aerosol
and gaseous measurements at nine sites (termed "B sites") included:
• Hourly gaseous data (O3, NO/NOX, CO, and SO2);
• Hourly meteorological data (temperature, dew point, wind speed, wind direction,
and ultraviolet radiation intensity);
• Nephelometer measurements of light scattering;
• Five samples per episode day (4-, 5-, and 7-hour durations during summer and 4-
and 6-hour durations during fall) for PM2.5 and PMio mass, chemistry, and
precursor gases (HNO3, NH3, SO2) (Chow et al., 1994a, 1994b); and
• Three samples per day during the summer and fall periods for carbonyl compounds
and hydrocarbons (Ci to Ci2).
Continuous measurements of aerosol mass and chemistry included:
• Hourly PMio mass measurements by beta gauge sampler;
• Hourly sulfur and sulfate measurements by continuous sulfur analyzer with flame
photometric detector (FPD);
• Hourly organic and elemental carbon measurements by in-situ carbon analyzer;
• Hourly black carbon measurements by photoacoustic spectroscopy; and
• Hourly black carbon measurements by aethalometer.
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A.2 1995 Integrated Monitoring Study (IMS95)
The IMS95 Study domain in central California extends from the city of Merced to
south of Bakersfield and from the coastal mountains to 1,000 m elevation in the Sierra
Nevadas. IMS95 included a 11/01/95 to 11/14/95 saturation monitoring network (the fall
study) in the Corcoran/Hanford area and a 12/09/95 to 01/06/96 monitoring network (the
winter study) that included saturation sampling for aerosols and reactive aerosol precursor
gases, high-time-resolution aerosol measurements, fog chemistry measurements, surface- and
upper-air measurements, and micrometeorological surface measurements. PM2.5 tapered
element oscillating microbalance (TEOM), PMio and PM2.5 beta attenuation monitor (BAM),
5-minute aethalometer, 5-minute nephelometer, 3-hour-average PM2.5 and PMio filter
measurements under high-nitrate and high- and low-relative-humidity (RH) conditions with
meteorological data are available (Chow and Egami, 1997).
• Hourly Optec NGN-2 nephelometer measurements (SWC, BFC, FBI and KWR)
for particle light scattering (bsp) from 12/09/95 through 01/06/96.
• Hourly meteorological data (12/09/95 to 01/06/96).
• Hourly visibility data (12/09/95 to 01/06/96).
• Hourly PM2.5 TEOM at Bakersfield during winter study (12/09/95 to 01/06/96).
• Hourly collocated PMio and PM2.5 BAM, at Chowchilla during winter study
(12/09/95 to 01/06/96).
• Daily 3-hour PM2.5 and PMio mass and babs (12/09/95 to 01/06/96).
• 3-hour PM2.5 mass, light absorption (babs), and chemistry data for the three core
sites (on selected 9 days).
• 3-hour PMio mass, babs, chemistry data for the three core sites (on selected 9 days).
A.3 1997 Southern California Ozone Study (SCOS97) - North American Research
Strategy for Tropospheric Ozone (NARSTO)
The SCOS97-NARSTO was conducted from 06/16/97 through 10/15/97 and
consisted of extensive monitoring of emissions activity, meteorology, and air quality in
Southern California (Motallebi et al., 1998). Filter-pack and continuous measurements of fine
particles and precursor gases included:
• PM2.5 and PMio mass and chemistry measurements taken 5 times/day at 6 sites;
• PM2.5 organic species measurements taken 5 times/day at 6 sites;
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• Measurements of precursor gases (NH3, HNOs) with filter packs taken 5 times/day
at 6 sites;
• Size-resolved measurements of aerosol (mass, ions, and carbon) by 8-stage
Micro-Orifice Uniform Deposit Impactor (MOUDI) at 6 sites;
• Continuous PM2.s mass measurements by beta attenuation monitor (BAM),
tapered element oscillating microbalance (TEOM, sandwich and desiccation
prototypes) and continuous ambient mass monitoring system (CAMMS) at 1 site;
• Continuous particle number and size measurements by electrical aerosol analyzer
(EAA), differential mobility particle sizer (DMPS), and optical particle counter
(OPC) at 6 sites;
• Continuous size and chemical composition measurements by aerosol time-of-flight
mass spectrometer (ATOFMS) at 6 sites;
• Continuous nitric acid and ammonia measurements by TECO 42CV analyzer,
denuder diffusion method, and long-path Fourier transform spectrometer at 5 sites;
• Continuous light absorption measurements by aethalometer and continuous light
scattering measurements with nephelometer at 3 sites; and
• Continuous nitrate measurements with automated particle nitrate monitor (APNM)
at 1 site.
A.4 San Joaquin Valley Compliance Network
Bakersfield and Fresno sites with PMio TEOM and BAM collocated with PMio
high-volume size-selective inlet (SSI), dichotomous, and California Acid Deposition
Monitoring Program (CADMP) measurements (Watson et al., 1997b). San Joaquin Valley
meteorological data other than 1995 is currently being processed and will be available from
California Air Resources Board (CARB), Sacramento, CA.
• Hourly BAM PMio network at four sites from 1994 to 1996.
• Hourly TEOM PMio data at 30 sites (currently) from 1992 to 1996.
• Hourly nephelometer (bscat) network data at 11 sites from 1991 to 1996.
• Hourly Coefficient of Haze data arranged by quarter at 43 sites during 1995.
• Hourly meteorological data averaged by quarter at 40 sites for 1995.
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• Every-sixth-day 24-hour compliance PMio SSI mass and chemistry data at 58 sites
from 1991 to 1996.
• Every-sixth-day 12-hour mass, ion, and precursor gas data from California Acid
Deposition Monitoring Program network at 11 sites from 1990 to 1996.
A.5 Imperial Valley/Mexicali Cross Border PMio Transport Study
The ambient sampling program acquired PMio measurements at two base sites in the
U.S. and Mexico near the international border between 03/13/92 and 08/29/93, on an
every-sixth-day sampling schedule. A saturation monitoring network consisting of 20 to 30
sampling sites was operated for the summer (08/21/92 to 08/27/92), winter (12/11/92 to
12/20/92), and spring (05/13/93 to 05/19/93) periods. Source emissions for fugitive dust,
motor vehicle exhaust, field burning, charcoal cooking, and industrial sources were sampled
and chemically characterized. The data were supplemented with: 1) hourly average BAM
PMio mass measured on the U.S. side of the border by the CARB, 2) high-volume SSI PMio
at sites in the cities of Brawley, El Centre, and Calexico, operated by the Imperial County Air
Pollution Control District (ICAPCD), and 3) meteorological data from the California
Irrigation Management Information System (CEVIIS) and several industrial permitting stations
located in Imperial County, California. Collocated PMio SSI, BAM, dichotomous, sequential
filter sampler (SFS), and Minivol portable PMio survey sampler with meteorology was
measured at the Calexico site (Chow and Watson, 1997a).
• Hourly BAM PMio at the Grant Fire Station site from 03/07/92 to 08/29/93.
• Every-sixth-day dichotomous fine, coarse, and PMio at the El Centre and Grant
Fire Station sites from 01/01/92 to 08/29/92.
• Every-sixth-day TSP mass at the Laidlaw Environmental Services site from
01/04/89 to 06/29/91.
• Every-sixth-day high-volume SSI PMio mass at the Brawley, Grant Fire Station, El
Centra, and Calexico Police and Fire Station sites from 01/02/92 to 08/29/93.
• Every-sixth-day 24-hour portable PMio mass, babs, and elements at the Calexico
(Grant Fire Station) and Mexicali (SEDESOL) sites from 03/18/92 to 08/29/92.
• Every-sixth-day 24-hour SFS PMio mass, babs, elements, and ions at the Calexico
(Grant Fire Station) and Mexicali (SEDESOL) sites from 02/19/92 to 08/29/92,
and daily during summer (08/21/92 to 08/27/92), winter (12/11/92 to 12/20/92),
and spring (05/13/93 to 05/19/93) periods.
• Daily 24-hour PMio mass, babs, and elements at the Calexico (Grant Fire Station)
and Mexicali (SEDESOL) sites, and at 20 satellite sites during summer (08/21/92
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to 08/27/92), winter (12/11/92 to 12/20/92), and spring (05/13/93 to 05/19/93)
periods.
• Daily 6-hour, 4-times-per-day, SFS PMio mass, babs, elements, organic and
elemental carbon, and ions collected at the Calexico (Grant Fire Station) and
Mexicali (SEDESOL) sites during summer (08/21/92 to 08/27/92), winter
(12/11/92 to 12/20/92), and spring (05/13/93 to 05/19/93) periods.
A.6 Washoe County (Nevada) Compliance Network
Collocated SSI and BAM PMio with meteorology at the Sparks (NV) Post Office site.
This site was selected for its high wood smoke influence during the winter.
• Hourly meteorology and photochemical data from 1995 to 1997.
• Hourly BAM PMio and collocated 24-hour high-volume SSI PMio data from 1995
to 1997.
A.7 Las Vegas PMio Study
The Las Vegas Valley PMio Study was conducted during 1995 and 1996 to determine
the contributions to PMio aerosol from fugitive dust, motor vehicle exhaust, residential wood
combustion, and secondary aerosol sources. In addition to monitoring with BAMs, 24-hour
samples were taken at two neighborhood-scale sites every sixth day. Five week-long intensive
saturation studies were conducted over a middle-scale subregion that contained many
construction projects emitting fugitive dust (Chow and Watson, 1997b).
• Hourly PMio data from 14 sites (including one compliance monitoring site with
PMio high-volume SSI, six compliance monitoring sites with BAMs, and seven
special purpose sites with BAMs) from 01/03/95 to 01/28/96.
• Hourly meteorological data from 10-m towers at 17 sites from 01/03/95 to
01/28/96.
• Every-sixth-day 24-hour SFS PMio data from two sites (one in Las Vegas, NV
[East Charleston] and the other in North Las Vegas [Bemis]) from 01/03/95 to
01/28/96.
• Daily 24-hour battery-powered mini-volume portable survey sampler PMio data
from 30 satellite sites from 01/03/95 to 01/28/96 during the intensive monitoring
period.
elements, ions, and carbon on a selected subset of samples.
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A.8 Northern Front Range Air Quality Study (NFRAQS)
The Northern Front Range Air Quality Study (Watson et al., 1998a, Chow et al.,
1998b) was executed as three intensive field campaigns. These three field campaigns were;
• Winter 96 Campaign: This 44 day intensive field campaign was conducted
between 01/16/96 and 02/29/96. This was a pilot study that was used to design
the Winter 97 study. It also provides a contrast between different wintertime
periods along the Northern Front Range. Measurements taken during this
campaign include:
- Hourly Optec NGN-2 measurements at one site;
- Hourly particle light absorption by aethalometer at one site;
- Upper air and surface meteorological measurements at seven sites; and
- Daily PM2.5, PMio, nitric acid, and ammonia measurements of 3- to 24-hour
durations at one site.
• Summer 96 Campaign: This 45-day intensive field campaign was conducted
between 07/16/96 and 08/31/96. It was intended to provide baseline
measurements for summer PM2.s and to provide a contrast to wintertime levels.
Few detailed summertime particulate measurements are available from the
Northern Front Range. Measurements taken during this campaign include:
- Hourly PMio BAM at one site;
- Hourly Optec NGN-2 measurements at one site;
- Hourly particle light absorption by aethalometer at one site;
- Upper air and surface meteorological measurements at seven sites; and
- Daily PM2.5, PMio, nitric acid, and ammonia measurements of 3- to 24-hour
durations at three sites.
• Winter 97 Campaign: This 60-day intensive field campaign was conducted
between 12/09/96 and 02/07/97 over a large domain along the Northern Front
Range in Colorado. The most complete set of measurements was acquired during
this period. Measurements taken during this campaign include:
- Hourly particle light absorption by aethalometer at three sites;
- Hourly light extinction by transmissometer at two sites;
- Hourly nitrate measurements by automated particle nitrate monitor at one site;
- Hourly total particle light absorption by photoacoustic spectrometer at one
site;
- Continuous scene measurements at five sites;
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- Hourly PMio BAM measurements at three sites;
- Hourly Optec NGN-2 measurements at five sites;
- Hourly PM2 5 size cut NGN-2 measurements at two sites;
- Hourly PM2 5 particle light scattering by TSI three-color nephelometer at one
site; and
- Daily PM2.5, ammonia, and nitric acid measurements of 3- to 24-hour durations
at nine sites.
A.9 Mount Zirkel Visibility Study
Ambient measurements were taken for a one-year period from 12/01/94 through
11/30/95. Twelve-hour-average (0600 to 1800 MST) aerosol and sulfur dioxide filter
sampling did not commence until 02/06/95 and continued every day through 11/30/95 at the
Buffalo Pass, Gilpin Creek, and Juniper Mountian sites (Watson et al., 1996). Embedded in
the annual measurements were three intensive monitoring periods. These included winter
(02/06/95 to 03/02/95), summer (08/03/95 to 09/02/95), and fall (09/15/95 to 10/15/95).
Morning (0600 to 1200 MST) and afternoon (1200 to 1800 MST) aerosol and sulfur dioxide
measurements were taken a the Buffalo Pass, Juniper Mountain, Baggs, Hayden VOR, and
Hayden Waste Water sites during these periods. Morning and afternoon denuder-difference
filter-based measurements of nitric acid and ammonia precursor gases were acquired at the
Buffalo Pass, Juniper Mountian, and Hayden VOR sites. Continuous sulfur dioxide, sulfate,
and optical absorption measurements were taken during the intensives at the Buffalo Pass site.
• Hourly Optec NGN-2 nephelometer measurements from 12/01/94 to 11/30/95 at
six sites.
• Hourly Magee Scientific aethelometer measurements from 12/01/94 to 11/30/95 at
one site.
• Hourly meteorological measurements from 12/01/94 to 11/30/95 at eight sites.
• Hourly TSI three-color nephelometer measurements during summer (08/03/95 to
09/02/95) and fall (09/15/95 to 10/15/95) intensive monitoring periods at two
sites.
• 12-hour PM2.s filter measurements for particle mass, light absorption, elements,
ions, and carbon during annual period on selected days at three sites.
• 6-hour PM2.5 filter measurements for particle mass, light absorption, elements,
ions, and carbon during intensive monitoring periods on selected days at five sites.
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A. 10 Robbins Particulate Study
The Robbins Particulate Study (RPS) began in October 1996 will continue for five
years in order to characterize PM2.s and PMio mass and chemical concentrations, as well as
source contributions in neighborhoods surrounding the Robbins Waste-to-Energy (WTE)
power station (south Chicago, IL) (Watson et al., 1997c). BAM PMio, collocated SSI PMio,
and dichotomous data at the Eisenhower site were assembled. Meteorological data was
measured from the nearby Alsip site.
• Hourly wind measurements at the Alsip site from 10/01/95 to 09/30/96.
• Hourly BAM PMio at the Eisenhower site from 01/0196 to 09/30/96.
• Every-sixth-day dichotomous and high-volume SSI PMio and PM2.s at the
Eisenhower site from 10/12/95 to 09/30/96.
• PM2.s and coarse elements, ions, and carbon on a selected subset of samples from
the Alsip, Breman, Meadowlane, and Eisenhower sites.
A. 11 Birmingham (Alabama) Compliance Network
• Hourly wind speed and wind direction data at the Birmingham Airport site from
1989 to 1992.
• Hourly TEOM PMio and collocated SSI PMio at the North Birmingham site during
1993.
• Every-sixth-day high-volume SSI PMio data at the North Birmingham site from
1990 to 1995.
A. 12 Mexico City Aerosol Characterization Study
The Mexico City Aerosol Characterization Study is a two-year study with a duration
from 01/01/97 through 12/31/98. The first year consisted of planning and executing a major
field study in Mexico City from 02/23/97 through 03/22/97. This study is a cooperative
project sponsored by PEMEX through the Institute Mexicano del Petroleo (IMP) and by U.S.
Department of Energy (DOE) through national laboratories and universities (Watson et al.,
1998b).
• Five-minute aethalometer measurements from 02/23/97 to 03/22/97 at two sites
(Merced and Pedregal).
• Hourly nephelometer measurements from 02/23/97 to 03/22/97 at two sites
(Merced and Pedregal).
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Hourly TEOM PMio from 02/2/97 to 03/22/97 at ten RAMA particulate
monitoring sites (Merced, Xalostoc, Pedregal, Tlalnepantla, Nezahuatcoyotl,
Cerro de Estrella, Tultitlan, La Villa, Coacalco, and Tlahuac).
Hourly temperature, relative humidity, and wind measurements from 02/23/97 to
03/22/97 at ten RAMA meteorological monitoring sites (Merced, Xalostoc,
Pedregal, Tlalnepantla, Cerro de Estrella, ENAP Acatalan, Hangares, San
Augustin, and Plateros).
Daily 24-hour portable PMio mass, babs, elements, ions, carbon, and ammonia from
03/02/97 to 03/19/97 at 25 satellite sites.
Daily 6-hour PMio mass, babs, elements, ions, and carbon from 03/02/97 to
03/19/97 at three super-core sites.
Daily 24-hour PMio mass, babs, elements, ions, and carbon from 03/02/97 to
03/19/97 at three core sites.
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