United States Office of Air Quality EPA-454/R-98-012

Environmental Protection Planning and Standards May 1998

Agency Research Triangle Park, NC 27711

Air

GUIDANCE FOR USING

 CONTINUOUS MONITORS 

 IN PM2.5 MONITORING

 NETWORKS
GUIDANCE FOR USING CONTINUOUS

MONITORS IN PM2.5 MONITORING NETWORKS

May 29, 1998

PREPARED BY

John G. Watson1

Judith C. Chow1

Hans Moosmüller1

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

1Desert 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
ii

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.
iii

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.
TABLE OF CONTENTS

Page

iv

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
TABLE OF CONTENTS (continued)

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v

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-1

A.1 Southern California Air Quality Study (SCAQS) .............................................A-1

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
TABLE OF CONTENTS (continued)

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vi

A.4 San Joaquin Valley Compliance Network ........................................................A-3

A.5 Imperial Valley/Mexicali Cross Border PM10 Transport Study.........................A-4

A.6 Washoe County (Nevada) Compliance Network ..............................................A-5

A.7 Las Vegas PM10 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 Particulate Study ................................................................................A-8

A.11 Birmingham (Alabama) Compliance Network .................................................A-8

A.12 Mexico City Aerosol Characterization Study ...................................................A-8
LIST OF TABLES

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vii

Table 2-1 Measured Aerosol Concentrations for the 19 Regions in the

IMPROVE Network from March 1988 to February 1991 2-12

Table 3-1 Summary of Continuous Monitoring Technology 3-2

Table 3-2 Operating Characteristics of Commercially Available Nephelometers 3-17

Table 3-3 Comparison of Condensation Nuclei Counter Specifications 3-22

Table 4-1 Test Specifications for PM2.5 Equivalence to Federal Reference

Method 4-2

Table 4-2 Collocated Comparisons between Continuous and Filter-Based PM2.5

or PM10 Monitors from Recent Aerosol Characterization Studies 4-6

Table 4-3 PM2.5 Mass Scattering Efficiency (m2/g) as a Function of Relative

Humidity (%) 4-18

Table 4-4 Relationships between Optical Measurements and PM Concentrations 4-22

Table 5-1 Cross-Border Fluxes at the Calexico Site 5-12

Table 5-2 Distibution of Hourly PM10 and SO2 Concentrations at Robbins, IL, as

a Function of Wind Speed and Wind Direction 5-15
LIST OF FIGURES

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viii

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 (σsp) as function of particle diameter for

silica particles (δ = 2.2 g/cm3, n = 1.46 at 550 nm) and monochromatic

green light (λ = 550 nm). 3-19

Figure 4-1 Collocated comparisons of 24-hour-averaged TEOM and BAM with

high-volume SSI for PM10 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 PM10 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 PM10 SFS with BAM at the

Chowchilla site in California’s San Joaquin Valley between 12/09/95

and 01/06/96. 4-8
LIST OF FIGURES (continued)

Page

ix

Figure 4-4 Collocated comparisons of 24-hour PM10 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 PM10 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 PM10 BAM versus SFS in

north Denver, CO, during winter and summer 1996. 4-13

Figure 4-7 Collocated comparison of 24-hour PM10 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 PM10 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 PM10

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 PM10 concentrations at a

monitoring site in Calexico, CA, near the U.S./Mexico border during

03/12/92 to 08/29/93. 5-6
LIST OF FIGURES (continued)

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x

Figure 5-4 Relationships between BAM PM10 and wind speed for northerly flow. 5-7

Figure 5-5 Relationships between BAM PM10 and wind speed for southerly flow. 5-7

Figure 5-6 Distribution of hourly BAM PM10 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 PM10

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 PM10 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 PM10 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 (PED) site in Mexico City. 5-20
LIST OF ACRONYMS

xi

°C degrees Celsius

μg microgram

μm 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 Ionization Mass Spectrometry

CMZ Community Monitoring Zone

CNC Condensation Nuclei Counter
LIST OF ACRONYMS (continued)

xii

CO carbon monoxide

CO2 carbon dioxide

COH Coefficient Of Haze

CORE Community-Oriented site (or COmmunity-REpresentative site)

DIAL DIfferential Absorption Lidar

DIC 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

HNO3 nitric acid

IC 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
LIST OF ACRONYMS (continued)

xiii

LIDAR LIght Detecting And Ranging

LPFF Laser Photolysis Fragment Fluorescence

m meter

m

3

cubic meter

mm millimeter

Mm–1 inverse megameters

MPA Metropolitan Planning Area

MS Mass Spectrometry

MSA Metropolitan Statistical Area

mW milliwatt

NAAQS National Ambient Air Quality Standards

NAMS National Air Monitoring Stations

Nd:YAG Neodymium Yttrium Aluminum Garnet

NFRAQS Northern Front Range Air Quality Study [Colorado]

NH3 ammonia

NH4

+

ammonium

NH4HSO4 ammonium bisulfate

NH4NO3 ammonium nitrate

(NH4)2SO4 ammonium sulfate

NIST National Institute for Standards and Technology

nm nanometer

NO2 nitrogen dioxide

NO3

–

nitrate
LIST OF ACRONYMS (continued)

xiv

NOX nitrogen oxides

NPS National Park Service

OAQPS Office of Air Quality Planning and Standards [U.S. Environmental Protection

Agency]

OC organic carbon

OPC Optical Particle Counter

ORD Office of Research and Development [U.S. Environmental Protection Agency]

PALMS Particle Analysis by Laser Mass Spectrometry

PM suspended Particulate Matter

PM10 suspended Particulate Matter with aerodynamic diameters less than 10

microns (μm)

PM2.5 suspended Particulate Matter with aerodynamic diameters less than 2.5

microns (μm)

ppb or ppbv parts per billion volume

ppt or pptv parts per trillion volume

PSAP Particle Soot/Absorption Photometer

RH Relative Humidity

RSMS Rapid Single-particle Mass Spectrometer

S sulfur

SLAMS State/Local Air Monitoring Stations

SMPA Scanning Mobility Particle Analyzer

SO2 sulfur dioxide

SO4

=

sulfate

SPM Special Purpose Monitor

SSI Size-Selective Inlet
LIST OF ACRONYMS (continued)

xv

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
1-1

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 μm)

(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 μg/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 μg/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 PM10 (particles with aerodynamic diameters less than

10 μm) not to exceed 150 μg/m3 for a three-year average of annual 99th

percentiles at any site in a monitoring area.

• Three-year average of three annual average PM10 concentrations not to exceed 50

lμg/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 PM10 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
1-2

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; Herrick et al., 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 μm.

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 μm) (U.S.

Dept. HEW, 1969; Hemeon, 1973) and PM10 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 PM10 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 PM10

NAAQS. The PM10 equivalence designation (U.S. EPA, 1987) results from wind-tunnel test
1-3

specifications for the PM10 inlet and collocated sampling with filter-based PM10 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 PM2.5 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 PM10 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 PM2.5 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
1-4

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 PM10 –

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).
1-5

• 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).
2-1

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 μm 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 μm. The number distribution exhibits a bimodal feature that peaks at ∼0.02 and ∼0.1 μm.

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 μm).

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 μm 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 (Oberdörster et al., 1995; Finlay et al., 1997). Continuous instruments that

can measure particle number concentrations include the condensation nuclei counter
2-2

Lognormal Particle Diameter (μm)

Optical particle counters

Differential mobility

Figure 2-1. Particle size range of aerosol properties and measurement instruments –

application range of aerosol instruments (modified from Hinds, 1982).

0.001 1000
2-3

Fine Coarse

Figure 2-2. Size range of aerosol properties (modified from Hinds, 1982).
2-4

Figure 2-3. Example of particle number, surface area, and volume size distribution in the

atmosphere (based on Seinfeld and Pandis, 1997).

n

v

e (μm

3/cm

3

) ns

e (μm

2/cm

3

) n

N

e (1/cm

3

)
2-5

(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 μm. These particles, classified as accumulation range

(0.08 to ~2 μm), 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 μm 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 PM10 mass in
2-6

0

2

4

6

8

10

0.01 0.1 1 10 100

Particle Aerodynamic Diameter (μm)

Relative Concentration

Accumulation Coarse

PM 10

PM 2.5

TSP

PM 0.1

Geological Material, 

Pollen 

Sulfate, Nitrate, 

Ammonium, Organic 

Carbon, Elemental 

Carbon, Heavy Metals, 

Carbon Fine Dust

Nucleation/Ultrafine

Figure 2-4. Representative mass size distribution with measured particle size fractions and

dominant chemical components.
2-7

urban and non-urban areas consist of nitrate, sulfate, ammonium, carbon, geological material,

sodium chloride, and liquid water:

• Nitrate: Ammonium nitrate (NH4NO3) is the most abundant nitrate compound,

resulting from a reversible gas/particle equilibrium between ammonia gas (NH3),

nitric acid gas (HNO3), 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 (NaNO3) 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 (Na2SO4) may be found in coastal areas where sulfuric acid has been

neutralized by sodium chloride (NaCl) in sea salt. Though gypsum (Ca2SO4) 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 ((NH4)2SO4), ammonium bisulfate (NH4HSO4),

and ammonium nitrate (NH4NO3) 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 (C8 to

C20) 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.
2-8

• 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 PM10 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

et al., 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.
2-9

T = 15 Deg C

0.1

1

10

100

1000

10000

30 40 50 60 70 80 90 100

Relative Humidity (%)

Liquid Water (μg/m

3

)

 NACL NH4NO3 NH42SO4 COMB

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 μg/m3 of sodium

ion and 6.24 μg/m3 of gas phase hydrochloric acid (HCl). The “NH4NO3” case is for 10

μg/m3 of gas phase nitric acid (HNO3) and 10 μg/m3 of gas phase sulfuric acid (H2SO4). At a

temperature of 15 degrees Celsius, solid ammonium nitrate (NH4NO3) 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 μg/m3 of gas phase ammonia (NH3) and 10 μg/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 μg/m3 of equivalent HNO3 and H2SO4, 20 μg/m3 of

equivalent NH3, 3.83 μg/m3 of sodium ion, and 6.24 μg/m3 of equivalent HCl. 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.
2-10

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 μg/m3 of

equivalent H2SO4, and the total nitrate concentration was set to 20 μg/m3 of equivalent

HNO3. 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 μg/m3, accounting for 51% of

PM2.5 mass) and lower in winter (5.4 μg/m3, 33.2% of PM2.5 mass). In contrast, ammonium

nitrate concentrations were lower in summer (1.2 μg/m3, accounting for 7.4% of PM2.5 mass)

and higher in winter (3.4 μg/m3, accounting for 20.9% of PM2.5 mass).

PM2.5 mass from the Appalachian Mountains region shows marked seasonal

differences, with 6.5 μg/m3 during winter and 16.6 μg/m3 during summer. These seasonal

differences are driven by ammonium sulfate levels, which ranged from 3.0 μg/m3 (46% of
2-11

Particle Nitrate Fraction for 30% RH

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35 40 45 50

Temperature (Degress Celsius)

Particle Nitrate Fraction

 0.5to1 0.75to1 1to1

 2to1 5to1 10to1

Particle Nitrate Fraction for 60% RH

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35 40 45 50

Temperature (Degress Celsius)

Particle Nitrate Fraction

 0.5to1 0.75to1 1to1

 2to1 5to1 10to1

Particle Nitrate Fraction for 80% RH

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35 40 45 50

Temperature (Degress Celsius)

Particle Nitrate Fraction

 0.5to1 0.75to1 1to1

 2to1 5to1 10to1

Figure 2-6. Fraction of total nitrate as particulate ammonium nitrate at different

temperatures for various relative humidities and ammonia/nitrate molar ratios.
2-12

Table 2-1

Measured Aerosol Concentrations for the 19 Regionsa in the IMPROVE Network

from March 1988 to February 1991b

 Aerosol Concentration in μg/m3 (Percent Mass ) 

Fine Ammonium Elemental Coarse

Season Mass Sulfate Nitrate Organics Carbon Soil Mass

Alaska

Winter 1.6 0.7 (42.1) 0.1 (6.2) 0.6 (36.5) 0.1 (3.4) 0.2 (11.8) 4.0

Spring 2.4 0.9 (39.5) 0.1 (3.1) 0.7 (30.5) 0.1 (2.3) 0.6 (24.6) 3.9

Summer 2.7 0.5 (20.7) 0.0 (1.2) 1.5 (57.9) 0.1 (3.2) 0.4 (16.9) 5.4

Autumn 1.2 0.4 (32.1) 0.1 (4.3) 0.6 (49.2) 0.1 (4.9) 0.1 (9.5) 3.2

Annual 1.9 0.6 (32.6) 0.1 (3.3) 0.9 (43.9) 0.1 (3.3) 0.3 (17.0) 4.2

Appalachian

Winter 6.5 3.0 (45.8) 0.8 (12.8) 2.0 (31.3) 0.4 (6.2) 0.3 (3.8) 3.1

Spring 10.6 6.0 (56.8) 0.8 (7.9) 2.7 (25.1) 0.5 (4.4) 0.6 (5.8) 4.5

Summer 16.6 10.5 (63.5) 0.3 (2.0) 4.4 (26.5) 0.5 (2.9) 0.8 (5.1) 11.2

Autumn 9.7 5.6 (58.0) 0.5 (4.9) 2.7 (28.1) 0.5 (5.0) 0.4 (4.0) 5.5

Annual 10.9 6.3 (58.0) 0.6 (5.7) 3.0 (27.2) 0.5 (4.2) 0.5 (4.8) 6.2

Boundary Waters

Winter 5.2 2.0 (38.0) 1.4 (27.4) 1.4 (27.0) 0.2 (3.8) 0.2 (3.9) 3.2

Spring 5.4 2.6 (48.7) 0.4 (6.8) 1.8 (32.6) 0.2 (3.6) 0.4 (8.3) 5.1

Summer 6.2 2.2 (35.8) 0.1 (2.1) 3.1 (50.6) 0.3 (4.2) 0.5 (7.3) 8.2

Autumn 4.3 1.6 (37.9) 0.4 (10.1) 1.8 (40.9) 0.2 (4.6) 0.3 (6.6) 5.8

Annual 5.3 2.0 (38.9) 0.6 (11.0) 2.1 (39.5) 0.2 (4.1) 0.3 (6.5) 5.7

Cascades

Winter 3.8 0.6 (14.6) 0.1 (3.5) 2.6 (67.2) 0.5 (12.0) 0.1 (2.7) 2.9

Spring 5.2 1.4 (26.7) 0.2 (4.7) 2.7 (53.2) 0.5 (8.8) 0.3 (6.7) 3.1

Summer 6.7 2.4 (35.7) 0.4 (6.1) 3.0 (45.1) 0.5 (8.1) 0.3 (5.0) 4.6

Autumn 5.3 1.3 (24.6) 0.2 (3.7) 3.1 (58.7) 0.5 (9.7) 0.2 (3.3) 3.9

Annual 5.1 1.3 (25.7) 0.2 (4.5) 2.8 (55.7) 0.5 (9.5) 0.2 (4.5) 3.5

Colorado Plateau

Winter 2.9 0.9 (33.0) 0.5 (13.1) 1.1 (37.3) 0.2 (6.1) 0.3 (10.5) 3.2

Spring 3.4 0.9 (27.9) 0.2 (7.0) 1.0 (29.9) 0.1 (2.6) 1.1 (32.6) 5.3

Summer 4.1 1.3 (31.9) 0.2 (4.3) 1.6 (39.0) 0.2 (4.2) 0.9 (20.6) 6.4

Autumn 3.2 1.2 (36.3) 0.1 (4.6) 1.2 (38.4) 0.2 (5.0) 0.5 (15.7) 3.7

Annual 3.4 1.1 (31.9) 0.2 (7.2) 1.2 (36.3) 0.2 (4.3) 0.7 (20.3) 4.7

Central Rockies

Winter 2.0 0.5 (27.8) 0.2 (11.2) 0.9 (45.1) 0.1 (3.18) 0.3 (12.2) 3.0

Spring 3.4 0.9 (27.6) 0.3 (7.8) 1.1 (32.0) 0.1 (2.1) 1.1 (30.5) 4.3

Summer 4.8 1.0 (24.0) 0.1 (3.2) 2.4 (48.7) 0.2 (4.6) 0.9 (19.4) 7.5

Autumn 2.9 0.8 (27.9) 0.1 (4.5) 1.3 (45.4) 0.1 (4.3) 0.5 (18.0) 4.0

Annual 3.3 0.8 (25.8) 0.2 (5.9) 1.5 (43.7) 0.1 (3.9) 0.7 (20.7) 4.8

Central Coast

Winter 5.6 0.9 (16.8) 1.9 (29.3) 2.3 (44.7) 0.4 (6.3) 0.2 (2.9) 7.7

Spring 4.2 1.4 (33.6) 0.8 (18.7) 1.5 (36.5) 0.2 (4.1) 0.3 (7.1) 9.3

Summer 4.5 1.9 (43.4) 0.8 (17.1) 1.4 (31.5) 0.1 (2.9) 0.2 (5.0) 10.7

Autumn 5.7 1.4 (24.2) 1.0 (16.3) 2.7 (47.9) 0.4 (6.9) 0.3 (4.7) 7.8

Annual 5.0 1.4 (28.5) 1.1 (21.1) 1.9 (40.3) 0.3 (5.2) 0.2 (4.8) 8.9
2-13

Table 2-1 (continued)

Measured Aerosol Concentrations for the 19 Regionsa in the IMPROVE Network

from March 1988 to February 1991b

 Aerosol Concentration in μg/m3 (Percent Mass ) 

Fine Ammonium Elemental Coarse

Season Mass Sulfate Nitrate Organics Carbon Soil Mass

Florida

Winter 5.5 2.4 (43.3) 0.7 (12.5) 1.9 (34.0) 0.4 (6.9) 0.2 (3.2) 8.5

Spring 7.7 3.8 (48.5) 0.9 (11.2) 2.1 (27.4) 0.3 (3.7) 0.7 (9.2) 8.0

Summer 9.1 2.5 (27.1) 0.5 (5.9) 3.0 (33.3) 0.3 (3.4) 2.7 (30.2) 13.6

Autumn 6.9 3.1 (45.8) 0.5 (7.8) 2.3 (33.3) 0.4 (6.2) 0.5 (6.9) 8.6

Annual 7.1 2.9 (40.9) 0.7 (9.2) 2.3 (31.9) 0.4 (5.0) 0.9 (13.0) 9.6

Great Basin

Winter 1.1 0.3 (25.9) 0.1 (12.3) 0.5 (48.0) 0.0 (1.4) 0.1 (12.3) 1.0

Spring 2.4 0.5 (22.1) 0.1 (5.9) 0.9 (35.6) 0.0 (I. 1) 0.9 (35.3) 3.7

Summer 4.5 0.7 (14.9) 0.1 (2.5) 1.7 (38.8) 0.1 (2.2) 1.9 (41.6) 8.2

Autumn 3.1 0.6 (17.7) 0.1 (4.6) 1.4 (44.5) 0.1 (2.6) 1.0 (30.6) 5.1

Annual 2.8 0.5 (18.3) 0.1 (4.7) 1.1 (40.1) 0.1 (2.0) 1.0 (34.9) 5.0

Hawaii

Winter 4.0 2.8 (70.8) 0.1 (1.6) 0.9 (22.9) 0.1 (2.4) 0.1 (2.4) 3.0

Spring 3.6 2.5 (67.8) 0.1 (2.2) 0.8 (22.1) 0.1 (1.8) 0.2 (6.1) 7.4

Summer 1.6 0.9 (56.7) 0.1 (5.3) 0.5 (30.6) 0.0 (2.6) 0.1 (4.8) 10.3

Autumn 3.4 2.5 (72.0) 0.1 (1.6) 0.8 (22.1) 0.1 (2.0) 0.1 (2.3) 9.3

Annual 3.2 2.2 (68.5) 0.1 (2.2) 0.7 (23.4) 0.1 (2.1) 0.1 (3.7) 8.1

Northeast

Winter 6.6 3.3 (50.6) 0.8 (11.4) 1.8 (27.8) 0.5 (7.2) 0.2 (3.0) 3.1

Spring 6.1 3.6 (58.5) 0.4 (7.1) 1.5 (24.4) 0.3 (5.3) 0.3 (4.6) 4.1

Summer 8.6 4.5 (52.4) 0.3 (4.0) 3.0 (35.1) 0.4 (4.9) 0.3 (3.6) 6.7

Autumn 5.6 3.0 (53.5) 0.4 (7.1) 1.6 (29.4) 0.4 (6.6) 0.2 (3.5) 4.1

Annual 6.7 3.6 (53.5) 0.5 (7.2) 2.0 (29.8) 0.4 (5.9) 0.2 (3.7) 4.5

Northern Great Plains

Winter 3.4 1.2 (34.5) 0.6 (16.6) 1.1 (31.7) 0.1 (3.6) 0.5 (13.6) 3.9

Spring 5.0 1.9 (38.6) 0.6 (11.8) 1.3 (26.7) 0.1 (2.4) 1.0 (20.5) 6.0

Summer 5.6 1.8 (32.1) 0.2 (2.9) 2.2 (39.5) 0.2 (3.2) 1.2 (22.3) 9.7

Autumn 4.0 1.2 (30.0) 0.2 (5.2) 1.5 (37.1) 0.1 (3.6) 1.0 (24.1) 5.8

Annual 4.5 1.5 (34.0) 0.4 (8.5) 1.5 (33.9) 0.1 (3.1) 0.9 (20.6) 6.3

Northern Rockies

Winter 5.3 1.0 (18.6) 0.6 (10.6) 3.0 (56.7) 0.5 (9.4) 0.3 (4.8) 2.5

Spring 4.6 1.1 (23.3) 0.2 (5.2) 2.4 (52.2) 0.3 (6.7) 0.6 (12.5) 4.2

Summer 5.4 0.9 (17.1) 0.2 (3.1) 3.0 (54.5) 0.3 (6.1) 1.0 (19.2) 9.2

Autumn 6.7 0.9 (12.8) 0.3 (4.3) 4.3 (64.7) 0.6 (9.4) 0.6 (8.8) 5.7

Annual 5.5 1.0 (17.7) 0.3 (5.7) 3.1 (57.3) 0.4 (7.9) 0.6 (11.4) 5.5

Southern California

Winter 4-6- 0.5 (11.3) 2.2 (47.8) 1.2 (26.2) 0.2 (5.3) 0.4 (9.4) 4.2

Spring 13.6 1.7 (12.2) 6.9 (51.1) 3.2 (23.5) 0.6 (4.2) 1.2 (8.9) 9.8

Summer 13.8 2.4 (17.2) 4.6 (33.4) 4.2 (30.6) 0.8 (5.7) 1.8 (13.1) 15.2

Autumn 8.1 1.1 (13.4) 3.1 (38.6) 2.0 (24.3) 0.4 (5.1) 1.5 (18.6) 13.2

Annual 9.8 1.4 (13.9) 4.2 (43.0) 2.5 (25.9) 0.5 (4.9) 1.2 (12.3) 10.4
2-14

Table 2-1 (continued)

Measured Aerosol Concentrations for the 19 Regionsa in the IMPROVE Network

from March 1988 to February 1991b

 Aerosol Concentration in μg/m3 (Percent Mass ) 

Fine Ammonium Elemental Coarse

Season Mass Sulfate Nitrate Organics Carbon Soil Mass

Sonora Desert

Winter 3.2 1.2 (38.6) 0.3 (8.6) 1.1 (34.6) 0.2 (5.2) 0.4 (13.0) 3.3

Spring 4.4 1.2 (26.5) 0.3 (6.9) 1.3 (29.8) 0.1 (2.9) 1.5 (33.9) 7.5

Summer 5.6 2.1 (37.7) 0.2 (3.8) 1.8 (33.0) 0.2 (3.2) 1.2 (22.3) 7.6

Autumn 4.5 1.7 (37.5) 0.2 (3.7) 1.7 (37.1) 0.2 (5.1) 0.8 (16.5) 5.8

Annual 4.4 1.5 (35.4) 0.3 (5.5) 1.5 (33.4) 0.2 (4.1) 0.9 (21.6) 6.0

Sierra Nevada

Winter 2.5 0.4 (14.9) 0.7 (27.1) 1.1 (46.7) 0.1 (4.2) 0.2 (7.2) 2.1

Spring 4.3 1.0 (24.2) 0.6 (14.3) 1.7 (29.4) 0.2 (4.0) 0.8 (18.1) 4.8

Summer 7.2 1.7 (23.4) 0.6 (8.0) 3.6 (49.6) 0.5 (6.7) 0.9 (12.2) 7.0

Autumn 4.4 0.9 (20.6) 0.6 (13.2) 2.1 (48.3) 0.3 (6.5) 0.5 (11.4) 5.3

Annual 4.5 1.0 (21.7) 0.6 (13.6) 2.1 (46.4) 0.3 (5.6) 0.6 (12.7) 4.7

Sierra-Humboldt

Winter 1.7 0.2 (14.2) 0.1 (7.2) 1.0 (56.6) 0.1 (6.6) 0.3 (15.4) 2.9

Spring 3.0 0.6 (18.6) 0.2 (8.2) 1.4 (48.5) 0.1 (4.8) 0.6 (19.9) 2.9

Summer 4.0 0.7 (18.2) 0.2 (4.7) 2.2 (55.1) 0.3 (6.5) 0.6 (15.5) 5.6

Autumn 2.8 0.4 (15.5) 0.1 (3.5) 1.7 (59.9) 0.2 (7.4) 0.4 (13.7) 2.7

Annual 2.9 0.5 (17.1) 0.2 (5.7) 1.6 (54.7) 0.2 (6.3) 0.5 (16.2) 3.7

Washington D.C.

Winter 16.3 5.4 (33.2) 3.4 (20.9) 4.9 (29.9) 2.0 (12.4) 0.6 (3.6) 30.1

Spring 16.8 7.3 (43.6) 2.6 (15.5) 4.2 (24.9) 1.7 (10.1) 1.0 (5.9) 10.2

Summer 16.7 8.6 (51.4) 1.2 (7.4) 4.4 (26.1) 1.6 (9.8) 0.9 (5.3) 13.5

Autumn 15.3 6.6 (43.3) 1.6 (10.5) 4.4 (28.5) 2.0 (12.8) 0.8 (4.9) 8.4

Annual 16.2 6.9 (42.4) 2.2 (13.8) 4.5 (27.5) 1.8 (11.4) 0.8 (4.9) 16.4

West Texas

Winter 3.6 1.5 (40.6) 0.2 (6.2) 1.1 (31.4) 0.1 (3.8) 0.6 (18.0) 5.1

Spring 6.4 2.2 (33.6) 0.3 (4.7) 1.7 (26.1) 0.2 (2.5) 2.1 (33.0) 10.4

Summer 6.6 2.5 (38.7) 0.3 (4.7) 1.7 (25.9) 0.1 (2.0) 1.9 (28.7) 7.4

Autumn 4.8 2.3 (46.8) 0.2 (3.4) 1.4 (29.1) 0.2 (3.5) 0.8 (17.2) 7.0

Annual 5.4 2.1 (39.3) 0.3 (4.7) 1.5 (27.6) 0.1 (2.8) 1.4 (25.6) 7.5

___________________________________

a

IMPROVE and NPS/IMPROVE protocol sites according to 1a Region:

Alaska Coastal Mountains Hawaii Southern California

Denali National Park Pinnacles National Monument Hawaii Volcanoes National Park San Gorgonio Wilderness Area

Appalachian Mountains Point Reyes National Seashore Northeast Washington, D.C.

Great Smoky Mountains National Park Redwood National Park Acadia National Park Washington, D.C.

Shenandoah National Park Colorado Plateau Northern Great Plains West Texas

Boundary Waters Arches National Park Badlands National Monument Big Bend National Park

Isle Royale National Park Bandelier National Monument Northern Rocky Mountains Guadalupe Mountains National Monument

Voyageurs National Park Bryce Canyon National Park Glacier National Park

Cascade Mountains Canyonlands National Park Sierra Nevada

Mount Rainier National Park Grand Canyon National Park Yosemite National Park

Central Rocky Mountains Mesa Verde National Park Sierra-Humboldt

Bridger Wilderness Area Petrified Forest National Park Crater Lake National Park

Great Sand Dunes National Monument Florida Lassen Volcanoes National Park

Rocky Mountain National Park Everglades Sonoran Desert

Weminuche Wilderness Area Great Basin Chiricahua National Monument

Yellowstone National Park Jarbidge Wilderness Area Tonto National Monument

(IMPROVE=Interagency Monitoring of Protected Visual Environments; NPS=National Park Service)

b Based on Malm et al. (1994).
2-15

PM2.5 mass) in winter to 10.5 μg/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 μg/m3 during winter, 13.6 μg/m3 during spring, and 13.8

μg/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=1/106 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).
2-16

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 μm 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 μg/m3 of pure ammonium

sulfate or ammonium nitrate typically contributes 2 to 6 Mm-1. Each μg/m3 of soil particles

less than 2.5 μm in aerodynamic diameter contributes ~ 1 Mm-1. The sizes of most crustal

particles are several times the typical wavelengths of light and each μg/m3 of these particles

with diameters >2.5 μm 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 μg/m3 of nitrogen dioxide contributes ~0.17 Mm-1 of extinction at ~550 nm

wavelengths (Dixon, 1940), so NO2 concentrations in excess of 60 μg/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.
2-17

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.

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.
2-18

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.
3-1

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 PM10 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

ω =(k/m)0.5 (3-1)

where: ω = 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.
Table 3-1

Summary of Continuous Monitoring Technology

3-2

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 et al., 1997)

Particle mass. Detection limit 

∼ 5 μg/m3 for a five minute

average.

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.

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)

Particle mass. Detection limit 

∼ 10 μg/m3 for a one minute

average.

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 Attenuation Monitor (BAM) i,af

(Nader and Allen, 1960; Spurny and Kubie, 1961; Lilienfeld and

Dulchinos, 1972; Husar, 1974; Cooper, 1975; Lilienfeld, 1975;

Sem and Borgos, 1975; Cooper, 1976; Macias, 1976; Jaklevic et

al., 1981; Courtney et al., 1982; Klein et al., 1984; Wedding and

Weigand, 1993; Speer et al., 1997)

Particle mass. Detection limit 

∼ 5 μg/m3 for a one hour

average

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 blankcorrected attenuation readings are converted to mass concentrations, with

averaging times as short as 30 minutes.

Pressure Drop Tape Sampler (CAMMS) k,ag

(Babich et al., 1997)

Particle mass. Detection linit 

∼ 2 μg/m3 for a one hour

average

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

3-3

Instrument Quantity Measured Methodology

II. Visible Light Scattering

Nephelometer c,n,p,r,x,y,aa

(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 Hänel, 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 Bäcklin, 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; Moosmüller 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

3-4

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; Mäkynen 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)

Number of particles in the 0.1

to 50 μm size range.

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.

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; McDermott et al., 1991; Stolzenburg and McMurry, 1991;

Zhang and Liu, 1991; Saros et al., 1996)

Number of nucleating

particles (particles in the

~0.003 to 1 μm size range).

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.

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 et al., 1993)

Number of particles in

different size ranges.

Parallel laser beams measure the velocity lag of particles suspended in

accelerating air flows.
Table 3-1 (continued)

Summary of Continuous Monitoring Technology

3-5

Instrument Quantity Measured Methodology

LIDAR e,f,h,l,s,t

(Hitschfeld and Bordan, 1954; Fiocco et al., 1971; Fernald, 1972;

Melfi, 1972; Rothe et al., 1974; Cooney, 1975; Woods and Jolliffe,

1978; Klett, 1981; Aldén 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; Kölsch et

al., 1992; McElroy and McGown, 1992; Milton et al., 1992; She et

al., 1992; Whiteman et al., 1992; Kovalev, 1993, 1995;

Moosmüller et al., 1993; Gibson, 1994; Kempfer et al., 1994;

Kovalev and Moosmüller, 1994; Piironen and Eloranta, 1994;

Zhao et al., 1994; Grant, 1995; Toriumi et al., 1996; Evans et al.,

1997; Hoff et al., 1997; Moosmüller 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.

III. 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)

Light absorption, reported as

concentration of elemental

carbon. Detection limit ∼

 0.1 μg/m3 black carbon for a

one minute average.

Ambient air is continuously passed through a quartz-fiber filter tape. Lightabsorbing 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.

Particle Soot/Absorption Photometer (PSAP) x

(Bond et al., 1998; Quinn et al., 1998)

Light absorption detection

limit ~0.2 Mm–1 for a

five-minute average. For an

absorption efficiency of

10 m2/g, this would

correspond to 20 ng/m3 of

black carbon.

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

3-6

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; Moosmüller and Arnott, 1996; Moosmüller et al., 1997;

Arnott et al., 1998; Moosmüller et al., 1998)

Light absorption, reported as

black carbon. Detection limit 

∼ 50 ng/m3 for a ten-minute

average.

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.

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)

Number of particles in the

sub-micrometer size range

(~ 0.01 to 1.0 μm).

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

Porstendörfer, 1986; Wang and Flagan, 1990; Reischl, 1991;

Winklmayr et al., 1991; Zhang et al., 1995; Birmili et al., 1997;

Endo et al., 1997)

Number of nucleating

particles in different size

ranges (~ 0.01 to 1.0 μm 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

3-7

Instrument Quantity Measured Methodology

V. Chemical-Specific Particle Monitors

Single Particle Mass Spectrometer (RSMS,

PALMS, ATOFMS) ad,ae,af,ag

(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 et al., 1997, 1998;

Silva and Prather, 1997; Thomson et al., 1997; Murphy and

Schein, 1998)

Particle sizes and single

particle compositions.

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.

Ambient Carbon Particulate Monitor (ACPM) y

(Turpin et al., 1990a, 1990b; Chow et al., 1993a; Rupprecht et al.,

1995)

Concentrations of organic and

elemental carbon. Detection

limit ∼ 0.2 μg/m3 for a

two hour average.

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 Analyzer, Flame Photometric Detection

(FPD) k,ag

(Dagnall et al., 1967; Stevens et al., 1969, 1971; Farwell and

Rasmussen, 1976; Cobourn 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)

Sulfur dioxide and sulfate.

Detection limit ∼ 0.1 μg/m3

for a one hour average.

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

3-8

Instrument Quantity Measured Methodology

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)

Particle Nitrate. Detection

limit ∼ 0.5 μg/m3 for a

12-minute average.

Particle collection by impaction followed by flash vaporization and

detection of the evolved gases in a chemiluminescent NOx analyzer.

Streaker w

(Hudson et al., 1980; Bauman et al., 1987; Annegarn et al., 1990;

Chow, 1995)

PM2.5 and PM10 elemental

composition.

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.

Davis Rotating-Drum Universal-Size-Cut

Monitoring Impactor (DRUM) ac

(Raabe et al., 1988; Pitchford and Green, 1997)

Size-fractionated elemental

composition from 0.07 μm to

15 μm in diameter for eight

size ranges.

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 concentration.

Detection limit ~ 10 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.

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 ~ 0.1 ppb.

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

3-9

Instrument Quantity Measured Methodology

Other ammonia analyzersag

(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 to detection by the

chemiluminescent and luminol methods.

Long Path Fourier Transform Infrared

Spectroscopy (FTIR) o

(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. A path length of more than 1 km is

folded into a 25-m long White cell.

Tunable Diode Laser Absorption Spectroscopy

(TDLAS) 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.

Detection limit ∼ 0.3 ppb for a

5-minute average.

Nitric acid concentrations are measured by high spectral resolution diode

laser spectroscopy in the mid-infrared spectral region. The sample is

introduced into a reduced pressure White cell to reduce pressure broadening

and increase the path length.

Mist Chamberag

(Talbot et al., 1990)

Nitric acid concentrations.

Detection limit ~ 0.01 ppb for

a 10-minute average.

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 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

3-10

Instrument Quantity Measured Methodology

Laser-Photolysis Fragment-Fluorescence (LPFF)ag

(Papenbrock and Stuhl, 1991)

Nitric acid concentrations.

Detection limit ~ 0.1 ppb for a

15-minute 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 Ionization Mass Spectrometry (IMS)ag

(Huey et al., 1998; Mauldin et al., 1998; Fehsenfeld et al., 1998)

Nitric acid concentrations.

Detection limit ~ 0.005 ppb

for a 10-second average.

Chemical Ionization 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

3-11

a 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

g 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 Elight Laser Systems GmbH

Potsdamer Straße 18A

D-14513 Teltow

Germany

Tel.: 49-3328-430-112

Fax: 49-3328-430-115

e-mail: 100276.3673@compuserve.com

i Graseby-Andersen

500 Technology Court

Smyrna, GA 30082-5211

Tel.: 770 319 9999

Fax: 770 319 0336

e-mail: nutech@graseby.com

www: http://www.graseby.com

j Grimm Technologies

9110 Charlton Place

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

l Kayser-Threde GmbH

Wolfratshauser Straße 48

D-81379 München

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

m 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/

n Met One Instruments

1600 Washington Blvd.

Grants Pass, OR 97526

Tel.: 541-471-7111

Fax: 541-471-7116

o Midac Corporation

17911 Fitch Ave.

Irvine, CA 92714

Tel.: 714-660-8558

Fax: 714-660-9334

p MIE Inc.

7 Oak Park

Bedford, MA 01730

Tel.: 617-275-1919

Fax: 617-275-2121

q Opsis Inc.

1165 Linda Vista, Suite 112

San Marcos, CA 92069

Tel.: 760-752-3005

Fax: 760-752-3007

r Optec, Inc.

199 Smith Street

Lowell, MI 49331

Tel.: 616-897-9351

Fax: 616-897-8229

e-mail: OptecInc@aol.com

www: optecinc.com

s Optech Inc.

100 Widcat Road

North York, Ontario M3J 2Z9

Canada

Tel.: 416-661-5904

Fax: 416-661-4168

t 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/

u

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

v

Particle Measuring Systems Inc.

1855 South 57th Court

Boulder, CO 80301

Tel.: 303-443-7100

Fax: 303-449-6870

w

PIXE Analytical Laboratories

P.O. Box 2744

Tallahassee, FL 32316

Tel.: 904-574-6469 or 800-700-7494

x 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

3-12

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 TSI Inc.

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
3-13

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, PM10, PM2.5, or

PM1.0 (particles with aerodynamic diameters less than 1 μm) monitoring. Collocated TEOM

instruments have reported a precision of ±2.8 μg/m3 for hourly averaged PM10 concentrations

and ±5.1 μg/m3 for 10-minute-averaged PM10 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 PM10 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 PM10 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.
3-14

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 μg/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 μg/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.
3-15

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 μg/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 μg/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;
3-16

Quenzel, 1969a, 1969b; Ensor and Waggoner, 1970; Garland and Rae, 1970; Heintzenberg

and Hänel, 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 Bäcklin, 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; Moosmüller 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
3-17

Table 3-2

Operating Characteristics of Commercially Available Nephelometers

Manufacturer Model

Scattering

 Type Sampling

Time

Resolution

Center

Wavelength

Backscatter

 Feature

Belfort 1590 Integrating Forced 2 min 530 nm No

Optec NGN-2 Integrating Passive ≥ 2 min 550 nm No

TSI 3551 Integrating Forced ≥ 2 sec 550 nm No

TSI 3563 Integrating Forced ≥ 2 sec 450, 550,

and 700 nm

Yes

Radiance M903 Integrating Forced ≥ 10 sec 530 nm No

Grimm DustCheck Sideways Forced ≥ 3 sec 780 nm No

MIE DataRam Forward Forced ≥ 1 sec 880 nm No

MIE personal

DataRam

Forward Passive ≥ 1 sec 880 nm No

Met One

Instruments

GT-640 Forward Forced ≥ 1 min 781 nm No

R&P DustLite

Model 3000

Forward Forced ≥ 3 sec 880 nm No

TSI DustTrack

Model 8520

Sideways Forced ≥ 1 sec 780 nm No
3-18

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 μg/m3 (particle scattering efficiency,

σsp, 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 (λ), 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 (~ PM2.5) 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 μm 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.
3-19

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

0.01 0.10 1.00 10.00 100.00

Diameter (μm)

Figure 3-1. Particle scattering efficiency (σsp) as function of particle diameter for silica

particles (δ = 2.2 g/cm3, n = 1.46 at 550 nm) and monochromatic green light (λ = 550 nm).

σsp, (m

2/g)
3-20

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 R2 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; Mäkynen 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 μm (Fabiny, 1998), but it is more typically

0.2 to 30 μm.

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 ultrafine 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
3-21

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 μm 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 ultrafine 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 ultrafine 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 μm (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
3-22

Table 3-3

Comparison of Condensation Nuclei Counter Specifications

TSI 3010 TSI 3022A TSI 3025A

Minimum Particle Size (50% efficiency, nm) 10 7 3

Aerosol Flow Rate (cm3/min) 1000 300 30

Upper Concentration Limit (particles/cm3) 104107105

Lower Concentration Sensitivity (particles/cm3) 0 0 0

False Count Rate (particles/cm3) < 0.0001 < 0.01 < 0.01

Response Time (95% response, sec) < 5 < 13 1

Vacuum Source External Internal Internal
3-23

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)

LIight 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, ß(r), at the distance r

and the path integrated extinction, σ(r), between lidar location r0 and range r. For each

measured signal, S(r), there are two atmospheric parameters, ß(r,λ) and σ(r,λ), 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, ß(r), and the extinction coefficient, σ(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 Moosmüller, 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;

Hoff et al., 1997).
3-24

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; Moosmüller 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 (O3) (e.g., Browell et al., 1983; Ancellet et al., 1989; Moosmüller 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., Aldén et al., 1982; Edner et al., 1988; Kölsch 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 μm in equivalent

dimensions (i.e., if chain aggregates were consolidated into a single sphere). Mass loadings

range from a few ng/m3 in remote pristine regions or over oceans distant from land, to a

fraction of 1 μg/m3 in rural regions of the continents, and exceed 1 μg/m3 in many cities

(Adams et al., 1990a, 1990b; Penner et al., 1993).

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.
3-25

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; Moosmüller 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
3-26

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; Moosmüller and

Arnott, 1996) have been discussed by Moosmüller 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 μg/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 μg/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 Moosmüller 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 μg/m3

BC. Increasing the laser power in this system will proportionally improve the detection limit.
3-27

3.4 Electrical Mobility

Electrical mobility analyzers are applicable to particles smaller than 1 μm. 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 μm 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 μm 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).
3-28

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 ultrafine

particles (< 0.020 μm) as its transmission efficiency drops dramatically below 0.02 μm due

to diffusion losses (Reineking and Porstendörfer, 1986). The accessible size range can be

extended down to 0.001 μm 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 PM10 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).
3-29

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.
3-30

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, H2S) 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 S2

∗

 chemiluminescence.

Because the formation of S2

∗

 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 (H2SO4) speciation (Tanner et al., 1980).

Sensitivities for sulfur aerosols as low as 0.1 μg/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.
3-31

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 μg/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 μg/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
3-32

regression slope is not significantly different from 1). As currently configured, the system

has a detection limit of 0.7 μg/m3, and a precision of 0.2 μg/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-μm 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-μm cutpoint and collects coarse particles (PM10 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-μm-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 μm to 15 μm in diameter for eight size ranges (0.07 to
3-33

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 μm),

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; Sørensen 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 NH3 concentrations less
3-34

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 (HNO3) 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 CrO3 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
3-35

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

NO3

–

 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 Ionization 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).
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3.7 Summary

All of the measurement methods identified in Table 3-1, and described generically

above, show merit for particle monitoring. The most commonly available and widely used

units range in cost from ~$10K to $30K 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 PM10 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.
4-1

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 PM10 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 PM10 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 PM10 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.
4-2

Table 4-1

Test Specifications for PM2.5 Equivalence to Federal Reference Methoda

Criteria Specifications

Concentration Range 10 to 200 μg/m3

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: Rj

b

 > 40 μg/m3 and Rj < 40 μg/m3

Class I 48-hour samples: Rj > 30 μg/m3 and Rj < 30 μg/m3

Class II 24-hour samples:

a. for PM2.5/PM10 ratio > 0.75: Rj > 40 μg/m3 and Rj < 40 μg/m3,

b. for PM2.5/PM10 ratio < 0.40: Rj > 30 μg/m3 and Rj < 30 μg/m3,

Class II 48-hour samples:

a. for PM2.5/PM10 ratio > 0.75: Rj > 30 μg/m3 and Rj < 30 μg/m3,

b. for PM2.5/PM10 ratio < 0.40: Rj > 20 μg/m3 and Rj < 20 μg/m3

Collocated Precision 2 μg/m3 or 5% (largest)

Regression Slope 1 ± 0.05

Intercept 0 ± 1 μg/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.
4-3

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 PM10 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) PM10 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 PM10. 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 PM10 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 PM10 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 PM10 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
4-4

Figure 4-1. Collocated comparisons of 24-hour-averaged TEOM and BAM with

high-volume SSI for PM10 measurements acquired in Central California between 1988 and

1993.

Collocated Comparison

SSI (1) vs BETA

2/17/91 to 12/27/93

SSI 1 (μg/m3)

0 50 100 150 200

BETA (μg/m

3

)

0

50

100

150

200

Slope = 0.96

Intercept = 3.29

r 2 = 0.84

Clovis-908 N Villa Avenue

SSI (1) vs TEOM

SSI 1

0 50 100 150 200

TEOM

0

50

100

150

200

Slope = 0.71

Intercept = 5.75

r 2 = 0.33

Collocated Comparison

SSI (1) vs BAM

SSI (1) vs TEOM

TEOM PM10 BAM PM10

SSI 1 PM10 (μg/m3)

SSI 1 PM10 (μg/m3)
4-5

SSI 1 ( μg/m3 )

0 50 100 150 200

TEOM ( μg/m3 

)

0

50

100

150

200

Bakersfield

Oct. - Feb.

SSI 1 ( μg/m3 )

0 50 100 150 200

0

50

100

150

200

Sacramento

Oct. - Feb.

Collocated Comparison

SSI 1 and TEOM (Winter/Fall)

Slope = 0.61

Intercept = 7.4

r = 0.82

N = 14

Slope = 0.65

Intercept = 3.0

r = 0.84

N = 58

SSI 1 ( μg/m3 )

0 50 100 150 200

TEOM ( μg/m3 

)

0

50

100

150

200

Bakersfield

March - Sept.

(outliers removed)

SSI 1 ( μg/m3 )

0 50 100 150 200

0

50

100

150

200

Sacramento

March - Sept.

Collocated Comparison

SSI 1 and TEOM (Summer/Spring)

Slope = 1.1

Intercept = 0.92

r = 0.97

N = 23

Slope = 0.85

Intercept = 2.7

r = 0.95

N = 35

Figure 4-2. Collocated comparison of 24-hour-averaged TEOM and high-volume SSI

PM10 during winter and summer at the Bakersfield and Sacramento sites in Central California

between 1988 and 1993. TEOM PM10 TEOM PM10 (μg/m

3

)

SSI 1 PM10 (μg/m3) SSI 1 PM10 (μg/m3)

SSI 1 PM10 (μg/m3) SSI 1 PM10 (μg/m3)
4-6

Table 4-2

Collocated Comparisons between Continuous and Filter-Based PM2.5 or PM10 Monitors from Recent Aerosol Characterization Studies

Study Name Study Area Species Sampler Y Sampler X

IMS95a

San Joaquin Valley, CA PM2.5 Mass Bakersfield TEOM Bakersfield 3-hr SFS

IMS95 San Joaquin Valley, CA PM2.5 Mass Bakersfield Collocated TEOM Bakersfield 3-hr SFS

IMS95 San Joaquin Valley, CA PM2.5 Mass Chowchilla BAM Chowchilla 3-hr SFS

IMS95 San Joaquin Valley, CA PM10 Mass Chowchilla BAM Chowchilla 3-hr SFS

IMS95 San Joaquin Valley, CA PM2.5 Mass Bakersfield Collocated TEOM Bakersfield TEOM

U.S./Mexico Transboundary Studyc

Calexico, CA PM10 Mass Calexico BAM Calexico 24-hr SFS

U.S./Mexico Transboundary Study Calexico, CA PM10 Mass Calexico BAM Calexico 24-hr POR

U.S./Mexico Transboundary Study Calexico, CA PM10 Mass Calexico BAM Calexico 24-hr DIC

U.S./Mexico Transboundary Study Calexico, CA PM10 Mass Calexico BAM Calexico 24-hr SSI

Las Vegas PM10 Studye

Las Vegas, NV PM10 Mass Bemis BAM Bemis 24-hr SFS

Las Vegas PM10 Study Las Vegas, NV PM10 Mass Bemis BAM Bemis 24-hr POR

Las Vegas PM10 Study Las Vegas, NV PM10 Mass East Charleston BAM East Charleston 24-hr SFS

Las Vegas PM10 Study Las Vegas, NV PM10 Mass East Charleston BAM East Charleston 24-hr POR

NFRAQSg Northern Front Range, CO 

(Winter 1995-96, 6-hr) PM10 Mass Welby PM10 BAM Welby 6-hr and 12-hr SFS

NFRAQS Northern Front Range, CO 

(Winter 1995-96, 12-hr) PM10 Mass Welby PM10 BAM Welby 6-hr and 12-hr SFS

NFRAQS Northern Front Range, CO 

(Summer 1996, 6-hr) PM10 Mass Welby PM10 BAM Welby 6-hr and 12-hr SFS

NFRAQS Northern Front Range, CO 

(Summer 1996, 12-hr) PM10 Mass Welby PM10 BAM Welby 6-hr and 12-hr SFS

Robbins Particulate Studyi

Robbins, IL PM10 Mass Eisenhower BAM Eisenhower 24-hr DICHOT 

Robbins Particulate Study Robbins, IL PM10 Mass Eisenhower BAM Eisenhower 24-hr SSI

x

 Chow and Egami (1997), Chow et al. (1998a).

b

 See Figure 4-3.

c

 Chow and Watson (1997a).

d

 See Figure 4-4.

e

 Chow and Watson (1997b).

f

 See Figure 4-5.

g

 Chow et al. (1998b), Watson et al. (1998a).

h

 See Figure 4-6.

i

 Watson et al. (1997c).

j

 See Figure 4-7.

No. Percent Distribution Average Precision

Intercept Correlation of Average Difference

Slope (μg/m3

) Coefficient Pairs Ratio <1σ 1-2σ 2-3σ >3σ (μg/m3) Collocated RMS P>|T|

0.78±0.04 -1.8±13.5 0.82b200 0.65±1.8 73 20 6 1 10.0 14.4 17.6 0.000

0.93±0.04 -6.2±13.6 0.86b

200 0.59±1.9 75 21 3 1 8.8 13.7 16.2 0.000

0.32±0.03 2.3±4.6 0.69b

181 0.51±0.49 59 39 3 0 14.4 10.2 17.6 0.000

0.57±0.03 1.1±7.3 0.78b

183 0.64±0.36 62 36 2 0 13.4 9.9 16.6 0.000

0.85±0.01 3.9±9.3 0.94b

902 0.99±6.71 88 6 3 3 0.43 8.9 10.4 0.212

 1.10±0.06 3.29±2.95 0.94d45 1.20±0.27 47 31 4 18 7.63 9.63 6.63 0.0001

 0.86±0.09 14.47±5.13 0.91d21 1.34±0.40 24 52 10 14 7.78 13.76 7.65 0.02

 1.00±0.08 6.34±4.48 0.91d34 1.14±0.26 56 32 3 9 6.43 10.82 7.56 0.0017

 0.89±0.06 4.21±4.21 0.93d

33 0.97±0.16 58 33 9 0 2.5 10.16 9.26 0.17

0.66±0.14 23.92±5.28 0.44f

91 1.61±0.92 54 28 8 10 -13.6 29.3 5.7 0.000

1.78±0.59 1.65±15.00 0.52f26 2.06±1.78 73 19 0 8 -19.3 34.7 6.4 0.009

0.71±0.09 16.40±3.67 0.64f93 1.30±0.51 72 18 2 8 -6.2 20.0 11.0 0.004

0.54±0.19 19.97±5.61 0.5f25 1.70±0.64 92 8 0 0 -9.3 18.6 4.8 0.019

0.82±0.04 1.87±1.27 0.93h63 1.01±0.44 17 13 8 62 2.6 6.889 5.61 0.0035

0.62±0.07 3.73±1.44 0.84h 37 0.94±0.42 15 10 11 64 3.3 5.199 2.45 0.0005

0.62±0.07 9.02±1.99 0.72h 85 1.03±0.36 25 21 9 45 0.9 10.290 4.28 0.422

0.81±0.09 5.53±1.85 0.80h 45 1.14±0.36 27 25 13 35 -1.8 3.861 2.27 0.003

0.847±0.153 1.339±4.277 0.66j

41 0.93±0.40 8 17 7 68 2.7 9.1 2.9 0.066

0.760±0.107 0.108±3.574 0.75j

42 0.78±0.34 8 22 10 60 7.4 8.5 4.3 0.000
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 <1σ, 1σ to 2σ, 2σ to 3σ, and >3σ precision

intervals. Here, σ is the propagated precision of X minus Y, which is the square root of the

sum of the squared uncertainties (σ

2

x+σ

2

y), where σx and σy 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 PM10 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 μg/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 μg/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 PM2.5 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).
4-8

Bakersfield PM2.5 TEOM vs. SFS

-40

0

40

80

120

160

0 20 40 60 80 100 120 140

SFS PM2.5 (μg/m3)

TEOM and collocated TEOM 

(μg/m

3

)

TEOM PM2.5 collocated TEOM PM2.5

a) Bakersfield

Chowchilla PM2.5 BAM vs. SFS

0

10

20

30

40

50

60

0 10 20 30 40 50 60 70 80

SFS PM2.5 (μg/m3)

BAM PM2.5 (μg/m

3

)

b) Chowchilla PM2.5

Chowchilla PM10 BAM vs. SFS

0

10

20

30

40

50

60

0 10 20 30 40 50 60 70 80

SFS PM10 (μg/m3)

BAM PM10 (μg/m

3

)

c) Chowchilla PM10

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 PM10 SFS with BAM at the Chowchilla site in

California’s San Joaquin Valley between 12/09/95 and 01/06/96.
4-9

Similar observations were found for the PM2.5 and PM10 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 PM10 measurements (Table 4-2). The correlations were moderate

(0.69 <r < 0.78) for these measurements. The percent distribution in Table 4-2 shows that

over 90% of the measurement differences fell within a ±2σ interval, however. The two

collocated PM2.5 TEOM comparisons yielded a collocated precision of 8.9 μg/m3. The

correlation coefficient was 0.94, but the slope and intercept criteria were not met, indicating

the Y sampler was predictable from the X sampler with 97% of measurement differences

falling within a ±3σ interval.

Chow and Watson (1997a), compared several PM10 filter samplers with BAM

measurements at a site in California’s Imperial Valley along the U.S./Mexican border from

spring of 1992 through summer of 1993. Most of the suspended particles were composed of

suspended dust and vehicle exhaust, with small quantities of nitrate. The high-volume

size-selective inlet (SSI) and the dichotomous sampler (DIC) are designated PM10 reference

methods while the BAM is a PM10 equivalent method (Chow, 1995). The SFS uses was

similar, but not identical, to the Oregon Sequential Filter Sampler PM10 reference method.

The Airmetrics portable (POR) PM10 monitor is used to determine zones of representation of

fixed measurement locations. The Imperial Valley weather is warm throughout the year at

this site.

As shown in the sixth through ninth rows of Table 4-2, the slope equals unity within

three standard errors, the intercept is equal to zero within three standard errors, and the

correlation coefficient is greater than 0.9. Table 4-2 shows that in all cases, 70% of the

measurement differences lie within ±2σ, and more than 80% of the measurement differences

lie within ±3σ. As shown in Figure 4-4, the differences between samplers are within the

measurement error. Statistical comparability based on the pair-difference test is only valid

for the BAM-SSI case. However, this rigorous test does not account for measurement

uncertainty (i.e., if the samplers are different to within some multiple of the measurement

uncertainty, they cannot realistically be considered different). Testing the hypothesis that the

average ratio of “Y to X” (shown in Table 4-2) is equal to one indicates that for all sampler

pairs, this ratio is different from unity at the 95% confidence interval except for BAM-SSI,

which yielded an average ratio of 1.06 ± 0.18.

In this case, the BAM is as comparable to any filter-based sampler as these samplers

are with each other. The sampled aerosol was relatively stable, non-hygroscopic, and

humidities were low, providing stable conditions that favor a good comparison.

The remaining examples in Table 4-2 refer to: 1) year-long sampling in Las Vegas,

NV, which is dry, dusty and hot (Chow and Watson, 1997a); 2) winter and summer sampling

in the vicinity of Denver, CO (NFRAQS, Northern Front Range Air Quality Study) (Watson

et al., 1998a; Chow et al., 1998b), where it is cold and moist during the winter and hot and

dry during the summer; and 3) year-long sampling in southeastern Chicago, IL, where it is

humid and warm in the summer and cold and wet during the winter (Watson et al., 1997c).
4-10

BAM vs. SSI

BAM vs. Dichot BAM vs. SFS

BAM vs. Portable

Figure 4-4. Collocated comparisons of 24-hour PM10 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.
4-11

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 μg/m3 from the BAM versus portable PM10 survey sampler (POR) to 23.9 ±

5.3 μg/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 PM10

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 ±1σ for BAM versus POR at the East

Charleston site, with more than 90% of the pair comparisons lying within ±2σ for BAM

versus POR at the Bemis site. In all cases, over 80% of the paired differences lie within ±2σ,

and over 90% of the measurements differ by no more than ±3σ. 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 POR

at the East Charleston site. Overall, this comparison shows that BAM PM10 was

systematically higher than the SFS and POR data (in the order of 30% to 100% on average)

in the Las Vegas PM10 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 PM10 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

μg/m3. The average ratios of BAM versus SFS PM10 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 ±3σ interval, however.

At the Eisenhower School site near Robbins IL, Table 4-2 shows correlation

coefficients less than 0.9, and PM10 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 μg/m3 for the BAM versus SSI pairs to 1.3 ± 4.2 μg/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 PM10 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 ±3σ 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 PM10 measurements were 7.4 μg/m3

for BAM versus SSI pairs and 2.7 μg/m3 for BAM versus DICHOT pairs. The RMS

precisions for PM10 mass comparisons are all less than 5 μg/m3 in these comparisons.
4-12

Figure 4-5. Collocated comparisons of 24-hour-averaged PM10 BAM versus SFS and

portable samplers in Las Vegas Valley, NV, between 01/03/95 and 01/28/96.

BAM vs. SFS

0

20

40

60

80

100

120

140

160

180

200

0 20 40 60 80 100 120 140 160 180 200

SFS PM10 (μg/m3)

BAM PM10 (μg/m

3

)

Slope = 0.66 ± 0.14

Intercept = 23.92 ± 5.28 μg/m3

r = 0.44

n = 91

Y/X = 1.61 ± 0.92

BAM vs. Portable

0

2 0

4 0

6 0

8 0

100

120

140

160

180

200

0 2 0 4 0 6 0 8 0 100 120 140 160 180 200

Portable PM10 (μg/m3)

BAM PM10 (μg/m 3

)

S lope = 1.78 ± 0.59

Intercept = 1.65 ± 15.0 μg/m3

 r = 0.52

n = 26

Y/X = 2.06 ± 1.78

BAM vs. Portable

0

20

40

60

80

100

120

0 20 40 60 80 100 120

Portable PM10 (μg/m3)

BAM PM10 (μg/m

3

)

Slope = 0.54 ± 0.19

Intercept = 19.97 ± 5.61 μg/m3

r = 0.50

n = 25

Y/X = 1.70 ± 0.64

BAM vs. SFS

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120 140

SFS PM10 (μg/m3)

BAM PM10 (μg/m

3

)

Slope = 0.71 ± 0.09

Intercept = 16.40 ± 3.67 μg/m3

r = 0.64

n = 93

Y/X = 1.30 ± 0.51
4-13

Welby 6-hr

0

20

40

60

80

100

0 20 40 60 80 100

Sequential Filter Sampler PM10 (μg/m3)

Beta Attenuation Monitor PM10 (μg/m

3

)

Welby 12-hr

0

20

40

60

80

100

0 20 40 60 80 100

Sequential Filter Sampler PM10 (μg/m3)

Beta Attenuation Monitor PM10 (μg/m

3

)

Figure 4-6. Collocated comparison of 6- and 12-hour PM10 BAM versus SFS in north

Denver, CO, during winter and summer 1996.
4-14

Eisenhower

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70

DICHOT PM10 Mass (μg/m3)

BAM

Eisenhower

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70

SSI PM10 Mass (μg/m3)

BAM

Figure 4-7. Collocated comparison of 24-hour PM10 BAM versus high-volume SSI and

dichotomous samplers in southeastern Chicago, IL, between 10/12/95 and 09/30/96.
4-15

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

PM10 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 PM10 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 PM10. In general, comparisons between the TEOM

and BAM are no better or worse than comparisons among collocated PM10 filter samplers

(Chow, 1995), many of which are designated PM10 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 PM2.5 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 μm in diameter, though these were

determined to be less than 20% of PM10 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
4-16

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.
4-17

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 μm. 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
4-18

Table 4-3

PM2.5 Mass Scattering Efficiency (m2/g) as a Function of Relative Humidity (%)

Site Site Type RH<80% 80%≤RH≤90% RH>90%

Bakersfield Urban 4.9±1.2 6.6±1.3 19.2±30.7

Fresno Urban 5.4±1.2 7.7±2.2 27±42

Kern Wildlife Refuge Non-urban 6.4±2.7 71±132 87±73

Kern Wildlife Refuge (RH+10%) Non-urban 4.3±0.7 7.2±2.7 71±130

Chowchilla Non-urban 5.7±0.9 8.2±3.9 56±106

Chowchilla (RH+5%) Non-urban 5.1±0.6 6.8±1.8 49±99
4-19

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.

Bap = 11.6 ± 0.3 EC

N = 62

r = 0.97

Ave. Bap/EC = 12.5 ± 3.2

Bap = 11.2 ± 0.4 EC

N = 39

r = 0.97

Ave. Bap/EC = 12.4 ± 4.1

Bap = 8.5 ± 0.3 EC

N = 62

r = 0.97

Ave. Bap/EC = 9.4 ± 2.6

Bap = 9.4 ± 0.3 EC

N = 63

r = 0.97

Ave. Bap/EC = 9.9 ± 2.1
4-20

a) Assumed value of 10 m2/g absorption efficiency:

b) Calculated value of 9.4 m2/g absorption efficiency:

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.
4-21

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

m

2

/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 (IMS95) 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 PM10 were reduced to 0.86 even though a majority (69% to

78%) of the PM10 was in the PM2.5 fraction. The regression slopes for PM10 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 (PM10 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 PM10 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%)

RHs 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 PM2.5 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
4-22

Table 4-4

Relationships between Optical Measurements and PM Concentrations

Sampling

Duration Intercepta

Study (hours) Site X Y Slopea

(μg/m3) r

b

n

c

PM2.5/PM10

IMS95d

3 Bakersfield, CA bsp

e

 for RH<80% PM2.5 0.165 ± 0.005 2.2 ± 0.3 0.98 51 0.75 ± 0.42

IMS95 3 Fresno, CA bsp for RH<80% PM2.5 0.179 ± 0.008 0.120 ± 1.00 0.94 58 0.78 ± 0.12

IMS95 3 Kern Wildlife Refuge, CA bsp for RH<80% PM2.5 0.166 ± 0.012 0.74 ± 1.05 0.93 33 0.74 ± 0.22

IMS95 3 Chowchilla, CA bsp for RH<80% PM2.5 0.177 ± 0.010 0.32 ± 0.75 0.97 19 0.69 ± 0.15

IMS95 12f

Bakersfield, CA bsp for RH<80% PM2.5 0.135 ± 0.007 2.8 ± 0.4 0.98 14 0.75 ± 0.42

IMS95 12 Fresno, CA bsp for RH<80% PM2.5 0.146 ± 0.012 2.2 ± 2.5 0.95 18 0.78 ± 0.12

IMS95 12 Kern Wildlife Refuge, CA bsp for RH<80% PM2.5 0.032 ± 0.051 11.4 ± 13.7 0.84 7 0.74 ± 0.22

IMS95 12 Chowchilla, CA bsp for RH<80% PM2.5 0.055 ± 0.079 9.7 ± 16.4 0.57 3 0.69 ± 0.15

IMS95 24gBakersfield, CA bsp for RH<80% and n≥5 PM2.5 0.106 ± 0.024 4.5 ± 1.6 0.93 5 0.75 ± 0.42

IMS95 24 Fresno, CA bsp for RH<80% and n≥5 PM2.5 0.140 ± 0.031 3.1 ± 11.7 0.86 9 0.78 ± 0.12

IMS95 24 Bakersfield, CA bsp for RH<80% PM2.5 0.106 ± 0.024 4.5 ± 1.6 0.93 5 0.75 ± 0.42

IMS95 24 Fresno, CA bsp for RH<80% PM2.5 0.14 ± 0.031 3.1 ± 11.7 0.86 9 0.78 ± 0.12

IMS95 3 Bakersfield, CA bsp for RH<80% PM10 0.191 ± 0.014 6.9 ± 1.0 0.89 51 0.75 ± 0.42

IMS95 3 Fresno, CA bsp for RH<80% PM10 0.197 ± 0.012 6.9 ± 1.5 0.91 58 0.78 ± 0.12

IMS95 3 Kern Wildlife Refuge, CA bsp for RH<80% PM10 0.141 ± 0.015 9.2 ± 1.6 0.86 33 0.74 ± 0.22

IMS95 3 Chowchilla, CA bsp for RH<80% PM10 0.21 ± 0.02 5.2 ± 1.9 0.89 19 0.69 ± 0.15

IMS95 12 Bakersfield, CA bsp for RH<80% PM10 0.165 ± 0.026 2.5 ± 1.8 0.88 14 0.75 ± 0.42

IMS95 12 Fresno, CA bsp for RH<80% PM10 0.166 ± 0.014 8.0 ± 2.9 0.95 18 0.78 ± 0.12

IMS95 12 Kern Wildlife Refuge, CA bsp for RH<80% PM10 0.038 ± 0.051 14.8 ± 13.6 0.95 7 0.74 ± 0.22

IMS95 12 Chowchilla, CA bsp for RH<80% PM10 0.067 ± 0.090 13.3 ± 18.6 0.60 3 0.69 ± 0.15
4-23

Table 4-4 (continued)

Relationships between Optical Measurements and PM Concentrations

Sampling

Duration Intercepta

Study (hours) Site X Y Slopea

(μg/m3) r

b

n

c

PM2.5/PM10

IMS95 24 Bakersfield, CA bsp for RH<80% and n≥5 PM10 0.139 ± 0.065 11.0 ± 3.3 0.78 5 0.75 ± 0.42

IMS95 24 Fresno, CA bsp for RH<80% and n≥5 PM10 0.172 ± 0.028 2.8 ± 10.4 0.92 9 0.78 ± 0.12

IMS95 24 Bakersfield, CA bsp for RH<80% PM10 0.139 ± 0.065 11.0 ± 3.3 0.78 5 0.78 ± 0.12

IMS95 24 Fresno, CA bsp for RH<80% PM10 0.172 ± 0.028 2.8 ± 10.4 0.92 9 0.78 ± 0.12

NFRAQSh

6 Brighton, CO bsp PM2.5 0.21 ± 0.02 2.0 ± 0.3 0.83 78 NAi

NFRAQS 6 Evans, CO bsp PM2.5 0.123 ± 0.009 2.8 ± 0.4 0.81 98 NA

NFRAQS 6 Welby, CO bsp PM2.5 0.24 ± 0.01 2.3 ± 0.4 0.85 97 NA

NFRAQS 12 Brighton, CO bsp PM2.5 0.148 ± 0.014 1.35 ± 0.24 0.88 36 NA

NFRAQS 12 Evans, CO bsp PM2.5 0.088 ± 0.009 1.73 ± 0.42 0.85 39 NA

NFRAQS 12 Welby, CO bsp PM2.5 0.189 ± 0.017 1.43 ± 0.43 0.87 40 NA

NFRAQS 6 Brighton, CO bsp2.5

j

PM2.5 0.23 ± 0.02 2.5 ± 0.3 0.83 72 NA

NFRAQS 6 Welby, CO bsp2.5 PM2.5 0.26 ± 0.02 3.0 ± 0.4 0.83 99 NA

NFRAQS 12 Brighton, CO bsp2.5 PM2.5 0.188 ± 0.019 1.41 ± 0.30 0.87 32 NA

NFRAQS 12 Welby, CO bsp2.5 PM2.5 0.191 ± 0.020 2.1 ± 0.05 0.82 44 NA

Mt. Zirkelk

6 BAG1 BAGZ, CO bsp for RH<50% PM2.5 0.34 ± 0.04 0.59 ± 0.39 0.67 73 NA

Mt. Zirkel 6 BUF1 BUFZ, CO bsp for RH<50% PM2.5 0.33 ± 0.05 1.38 ± 0.34 0.73 38 NA

Mt. Zirkel 6 SEW1 SEWZ, CO bsp for RH<50% PM2.5 0.32 ± 0.04 0.76 ± 0.44 0.73 47 NA

Mt. Zirkel 6 VOR1 VORZ, CO bsp for RH<50% PM2.5 0.35 ± 0.06 2.0 ± 0.5 0.60 58 NA

Mt. Zirkel 6 JUN1 JUNZ, CO bsp for RH<50% PM2.5 0.23 ± 0.06 1.72 ± 0.49 0.54 42 NA
4-24

Table 4-4 (continued)

Relationships between Optical Measurements and PM Concentrations

Sampling

Duration Intercepta

Study (hours) Site X Y Slopea

(μg/m3) r

b

n

c

PM2.5/PM10

Mt. Zirkel 12 BUF2 BUFZ, CO bsp for RH<50% PM2.5 0.33 ± 0.04 1.06 ± 0.19 0.80 34 NA

Mt. Zirkel 12 JUN2 JUNZ, CO bsp for RH<50% PM2.5 0.123 ± 0.056 1.97 ± 0.47 0.38 31 NA

Mt. Zirkel 12 GLC2 GLCZ, CO bsp for RH<50% PM2.5 0.37 ± 0.09 0.29 ± 0.83 0.46 62 NA

Mt. Zirkel 6 BAG1 BAGZ, CO bsp for RH<80% PM2.5 0.162 ± 0.029 1.69 ± 0.31 0.46 124 NA

Mt. Zirkel 6 BUF1 BUFZ, CO bsp for RH<80% PM2.5 0.31 ± 0.04 1.11 ± 0.25 0.69 73 NA

Mt. Zirkel 6 SEW1 SEWZ, CO bsp for RH<80% PM2.5 0.194 ± 0.025 1.77 ± 0.33 0.61 107 NA

Mt. Zirkel 6 VOR1 VORZ, CO bsp for RH<80% PM2.5 0.149 ± 0.042 3.2 ± 0.4 0.33 102 NA

Mt. Zirkel 6 JUN1 JUNZ, CO bsp for RH<80% PM2.5 0.043 ± 0.02 2.9 ± 0.3 0.24 75 NA

Mt. Zirkel 12 BUF2 BUFZ, CO bsp for RH<80% PM2.5 0.0022 ± 0.0041 2.6 ± 0.1 0.06 73 NA

Mt. Zirkel 12 JUN2 JUNZ, CO bsp for RH<80% PM2.5 -0.000021 ± 0.00089 2.5 ± 0.2 0.00 61 NA

Mt. Zirkel 12 GLC2 GLCZ, CO bsp for RH<80% PM2.5 0.0066 ± 0.0028 2.9 ± 0.2 0.20 138 NA

Mt. Zirkel 6 BUF3 BUFZ, CO bspg2.5

l

 for RH<50% (heated inlet) PM2.5 0.31 ± 0.04 1.22 ± 0.37 0.59 93 NA

Mt. Zirkel 6 BUF3 BUFZ, CO bspg2.5 for RH<80% (heated inlet) PM2.5 0.32 ± 0.05 1.16 ± 0.41 0.56 95 NA

Mt. Zirkel 12 BUF6 BUFZ, CO bspg2.5 for RH<50% (heated inlet) PM2.5 0.31 ± 0.13 0.76 ± 0.80 0.53 16 NA

Mt. Zirkel 12 BUF6 BUFZ, CO bspg2.5 for RH<80% (heated inlet) PM2.5 0.31 ± 0.13 0.76 ± 0.80 0.53 16 NA

MOHAVEm

12 M2 (Summer 1992), AZ bsp PM2.5 0.25 ± 0.03 2.6 ± 0.4 0.62 89 0.40 ± 0.11

MOHAVE 12 M4 (Winter 1992), AZ bsp PM2.5 0.32 ± 0.07 -0.0112 ± 0.37 0.51 61 0.41 ± 0.25

MOHAVE 12 M1 (Summer 1992), AZ bsp2.5 PM2.5 0.29 ± 0.04 2.8 ± 0.3 0.64 85 0.40 ± 0.11

MOHAVE 12 M3 (Summer 1992), AZ bsp PM10 0.78 ± 0.15 5.3 ± 1.6 0.48 88 0.40 ± 0.11

MOHAVE 12 M5 (Winter 1992), AZ bsp PM10 0.8 ± 0.12 -0.39 ± 0.58 0.66 61 0.41 ± 0.25
4-25

Table 4-4 (continued)

Relationships between Optical Measurements and PM Concentrations

Sampling

Duration Intercepta

Study (hours) Site X Y Slopea

(μg/m3) r

b

n

c

PM2.5/PM10

Phoenixn

6 C1 CA, AZ bsp for RH<80% PM2.5 0.115 ± 0.009 6.2 ± 0.2 0.68 194 0.38 ± 0.21

Phoenix 6 S1 SC, AZ bsp for RH<80% PM2.5 0.115 ± 0.01 4.7 ± 0.6 0.68 178 0.38 ± 0.15

Phoenix 6 W1 WP, AZ bsp for RH<80% PM2.5 0.089 ± 0.007 6.5 ± 0.7 0.68 180 0.37 ± 0.20

Phoenix 6 V1 VA, AZ bsp for RH<80% PM2.5 0.104 ± 0.008 6 ± 0.5 0.69 172 0.41 ± 0.16

Phoenix 6 C3 CA, AZ bsp2.5 for RH<80% PM2.5 0.194 ± 0.021 7.1 ± 0.8 0.56 201 0.38 ± 0.21

Phoenix 6 C2 CA, AZ bsp for RH<80% PM10 0.33 ± 0.02 17.9 ± 1.3 0.72 189 0.38 ± 0.21

Phoenix 6 S2 SC, AZ bsp for RH<80% PM10 0.29 ± 0.03 13.7 ± 1.5 0.61 171 0.38 ± 0.15

Phoenix 6 W2 WP, AZ bsp for RH<80% PM10 0.192 ± 0.022 22 ± 2 0.54 181 0.37 ± 0.20

Phoenix 6 V2 VA, AZ bsp for RH<80% PM10 0.22 ± 0.03 17.2 ± 1.7 0.47 172 0.41 ± 0.16

Mexicoo

6 M1 MER, MX bsp for RH<80% PM2.5 0.083 ± 0.003 17.8 ± 2.4 0.62 66 0.63 ± 0.18

Mexico 24 P1 PED, MX bsp for RH<80% PM2.5 0.16 ± 0.031 58 ± 2 0.87 11 0.56 ± 0.12

Mexico 6 M2 MER, MX bsp for RH<80% PM10 0.09 ± 0.005 37 ± 5 0.41 67 0.63 ± 0.18

Mexico 24 P2 PED, MX bsp for RH<80% PM10 0.24 ± 0.08 20 ± 6 0.72 11 0.56 ± 0.12

IMS95 3 Bakersfield, CA bap

p

 for RH<80% PM2.5 0.71 ± 0.08 1.40 ± 1.20 0.81 46 0.75 ± 0.42

IMS95 12 Bakersfield, CA bap for RH<80% PM2.5 0.69 ± 0.11 0.89 ± 2.2 0.89 40 0.75 ± 0.42

IMS95 24 Bakersfield, CA bap for RH<80% and n≥5 PM2.5 0.58 ± 0.20 2.4 ± 4.8 0.86 5 0.75 ± 0.42

IMS95 24 Bakersfield, CA bap for RH<80% PM2.5 0.58 ± 0.20 2.4 ± 4.8 0.86 5 0.75 ± 0.42

IMS95 3 Bakersfield, CA bap for RH<80% PM10 0.86 ± 0.11 5.8 ± 1.8 0.76 46 0.75 ± 0.42

IMS95 12 Bakersfield, CA bap for RH<80% PM10 0.83 ± 0.19 5.1 ± 3.8 0.81 12 0.75 ± 0.42

IMS95 24 Bakersfield, CA bap for RH<80% and n≥5 PM10 0.69 ± 0.25 6.3 ± 6.1 0.85 5 0.75 ± 0.42

IMS95 24 Bakersfield, CA bap for RH<80% PM10 0.69 ± 0.25 6.3 ± 6.1 0.85 5 0.78 ± 0.12
4-26

Table 4-4 (continued)

Relationships between Optical Measurements and PM Concentrations

Sampling

Duration Intercepta

Study (hours) Site X Y Slopea

(μg/m3) r

b

n

c

PM2.5/PM10

NFRAQS 6 Brighton, CO bap PM2.5 0.41 ± 0.05 2.6 ± 0.6 0.64 82 NA 

NFRAQS 6 Welby, CO bap PM2.5 0.28 ± 0.06 4.0 ± 1.2 0.67 29 NA 

NFRAQS 12 Brighton, CO bap PM2.5 0.38 ± 0.09 1.41 ± 0.69 0.58 37 NA 

NFRAQS 12 Welby, CO bap PM2.5 0.155 ± 0.075 5.2 ± 1.4 0.53 13 NA 

Mt. Zirkel 6 BUF4 BUFZ, CO bap for RH<50% h

PM2.5 1.33 ± 0.28 0.54 ± 0.73 0.61 41 NA

Mt. Zirkel 6 BUF4 BUFZ, CO bap for RH<80% PM2.5 1.25 ± 0.2 0.76 ± 0.50 0.60 67 NA

Mt. Zirkel 12 BUF7 BUFZ, CO bap for RH<50% PM2.5 1.19 ± 0.32 0.70 ± 0.47 0.63 22 NA

Mt. Zirkel 12 BUF7 BUFZ, CO bap for RH<80% PM2.5 1.02 ± 0.18 1.00 ± 0.25 0.66 47 NA

Phoenix 6 C6 CA, AZ bap for RH<80% PM2.5 0.56 ± 0.09 3.3 ± 1.6 0.71 39 0.38 ± 0.21

Phoenix 6 C7 CA, AZ bap for RH<80% PM10 1.3 ± 0.2 9.6 ± 2.9 0.73 38 0.38 ± 0.21

Mexico 6 M3 MER, MX bap * 19.2 for RH<80% PM2.5 0.175 ± 0.034 18.2 ± 2.6 0.47 96 0.63 ± 0.18

Mexico 24 P3 PED, MX bap * 19.2 for RH<80% PM2.5 0.28 ± 0.08 7 ± 3.9 0.60 23 0.56 ± 0.12

Mexico 6 M4 MER, MX bap * 19.2 for RH<80% PM10 0.174 ± 0.059 36 ± 5 0.30 95 0.63 ± 0.18

Mexico 24 P4 PED, MX bap * 19.2 for RH<80% PM10 0.36 ± 0.17 18.7 ± 8.2 0.42 23 0.56 ± 0.12

IMS95 24 Bakersfield, CA bext

q

 = bsp + bap for RH<80% PM2.5 0.091 ± 0.021 4.3 ± 2.3 0.93 5 0.75 ± 0.42

IMS95 24 Bakersfield, CA bext = bsp + bap for RH<80% PM10 0.117 ± 0.047 9.8 ± 3.4 0.82 5 0.78 ± 0.12

Mt. Zirkel 6 BUF5 BUFZ, CO bext = bsp + bap for RH<50% PM2.5 0.22 ± 0.05 1.69 ± 0.52 0.62 37 NA

Mt. Zirkel 12 BUF8 BUFZ, CO bext = bsp + bap for RH<50% PM2.5 0.26 ± 0.05 0.81 ± 0.26 0.77 22 NA

Mt. Zirkel 6 BUF5 BUFZ, CO bext = bsp + bap for RH<80% PM2.5 0.25 ± 0.03 1.19 ± 0.37 0.69 62 NA

Mt. Zirkel 12 BUF8 BUFZ, CO bext = bsp + bap for RH<80% PM2.5 0.166 ± 0.026 1.24 ± 0.19 0.69 45 NA
4-27

Table 4-4 (continued)

Relationships between Optical Measurements and PM Concentrations

Sampling

Duration Intercepta

Study (hours) Site X Y Slopea

(μg/m3) r

b

n

c

PM2.5/PM10

Phoenix 6 C4 CA, AZ bext = bsp + bap for RH<80% PM2.5 0.107 ± 0.009 2.6 ± 0.9 0.91 32 0.38 ± 0.21

Phoenix 6 C5 CA, AZ bext = bsp + bap for RH<80% PM10 0.2 ± 0.04 10.2 ± 0.3 0.71 32 0.38 ± 0.21

Mexico 6 M5 MER, MX bext = bsp + bap for RH<80% PM2.5 0.065 ± 0.01 16.4 ± 2.6 0.61 66 0.63 ± 0.18

Mexico 24 P5 PED, MX bext = bsp + bap for RH<80% PM2.5 0.122 ± 0.025 3.6 ± 3.0 0.85 11 0.56 ± 0.12

Mexico 6 M6 MER, MX bext = bsp + bap for RH<80% PM10 0.079 ± 0.019 34 ± 5 0.45 67 0.63 ± 0.18

Mexico 24 P6 PED, MX bext = bsp + bap for RH<80% PM10 0.178 ± 0.054 16.8 ± 8.7 0.73 11 0.56 ± 0.12

_____________________

a

 Effective variance weighted regression.

b

 Correlation coefficient.

c

 Number of sample pairs in the analysis.

d

 Integrated Monitoring Study (Chow and Egami, 1997; Chow et al., 1998a).

e

 Total particle light scattering.

f

 Averaging periods 0600–1800 and 1800–0600 PST.

g

 Averaging period 0000–2400 PST.

h

 Northern Front Range Air Quality Study (Chow et al., 1998b, Watson et al., 1998a).

i

 PM10 measurements were not acquired.

j

 PM2.5 particle light scattering.

k

 Mt. Zirkel Visibility Study (Watson et al., 1996).

l

 Three-color nephelometer with PM2.5 inlet (Chow et al., 1998b).

m

 Project MOHAVE Study (Watson et al., 1993).

n

 Phoenix Urban Haze Study (Watson et al., 1991).

o

 Mexico City Aerosol Characterization Study (Watson et al., 1998b).

p

 PM2.5 particle light absorption.

q

 Particle light extinction = the sum of particle light scattering and particle light absorption.
4-28

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 μg/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 PM2.5 and PM10 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 PM10. 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 PM2.5. A more narrow range of slopes
4-29

(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.
5-1

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
5-2

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 PM10 BAM data for a mixed

light industrial/suburban site in the Las Vegas Valley, NV, during winter and summer
5-3

0 

20 

40 

60 

80 

100 

120 

140 

160 

PM10 BAM (μg/m3)

0 2 4 6 8 10 12 14 16 18 20 22 0

Hour

20%tile 50%tile 80%tile

a) Weekdays, Winter (Oct-Mar)

0 

50 

100 

150 

200 

250 

PM10 BAM (μg/m3)

0 2 4 6 8 10 12 14 16 18 20 22 0

Hour

20%tile 50%tile 80%tile

b) Weekends, Summer (Apr- Sep)

0 

20 

40 

60 

80 

100 

120 

140 

160 

PM10 BAM (μg/m3)

0 2 4 6 8 10 12 14 16 18 20 22 0

Hour

20%tile 50%tile 80%tile

b) Weekends, Winter (Oct-Mar)

0 

50 

100 

150 

200 

250 

PM10 BAM (μg/m3)

0 2 4 6 8 10 12 14 16 18 20 22 0

Hour

20%tile 50%tile 80%tile

a) Weekdays, Summer (Apr- Sep)

BAM PM10 (μg/m

3

) BAM PM10 (μg/m

3

)

BAM PM10 (μg/m

3

) BAM PM10 (μg/m

3

)

Figure 5-1. Winter weekday and weekend patterns in PM10 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.
5-4

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 PM10 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

PM10 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 PM10

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 PM10

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 PM10 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 PM10 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 PM10 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
5-5

0 

20 

40 

60 

80 

100 

PM10 BAM (μg/m3)

0 2 4 6 8 10 12 14 16 18 20 22 0

Hour

Craig/Bemis East Charleston City Center Walter Johnson

a) Weekdays, Winter

(Oct-Mar)

0 

20 

40 

60 

80 

100 

PM10 BAM (μg/m3)

0 2 4 6 8 10 12 14 16 18 20 22 0

Hour

Craig/Bemis East Charleston City Center Walter Johnson

b) Weekends, Winter

(Oct-Mar)

BAM PM10 (μg/m3

) BAM PM10 (μg/m3

)

Figure 5-2. Weekday and weekend patterns for 50th percentile PM10 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.
5-6

Figure 5-3. Diurnal variations of hourly BAM PM10 concentrations at a monitoring site in Calexico, CA, near the U.S./Mexico

border during 03/12/92 to 08/29/93.
5-7

Figure 5-4. Relationships between BAM PM10 and wind speed for northerly flow.

Figure 5-5. Relationships between BAM PM10 and wind speed for southerly flow. BAM PM10 (μg/m

3

) BAM PM10 (μg/m

3

)
5-8

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, PM10

increases rapidly with increased wind speed due to resuspended dust.

A similar pattern between wind speed and PM10 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 PM10 and wind speed at a

Las Vegas Valley site on a day with high PM10 concentrations. A rise in wind speed from 4

to 14 m/s is accompanied by an increase in PM10 from less than 50 μg/m3 to over 1,000

μg/m3 during an hour, clearly demonstrating the effect of increased wind speed on dust

suspension. Figure 5-7 also shows that peak PM10 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, PM10 concentrations reach their lowest levels due to greater dispersion. When wind

speeds exceed 7 m/s, PM10 concentrations increase due to wind-raised particulate matter.

The third quarter reported higher PM10 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 PM10 at the Calexico site are

shown in Table 5-1. Mean fluxes were calculated by multiplying average wind speed with

average PM10 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 PM10 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, PM10 concentrations were highest for transport from the east

and south, although directional differences were not pronounced. In the second calendar

quarter of 1996, PM10 concentrations were highest with transport from the southwest and
5-9

10

100

1000

0 2 4 6 8 10 12 14

Wind Speed (m/s)

PM10 BAM (μg/m

3

)

20% tile 50% tile 80% tile

Figure 5-6. Distribution of hourly BAM PM10 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. BAM PM10 (μg/m

3

)
5-10

0

2

4

6

8

10

12

14

16

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 (m/s)

-200

0

200

400

600

800

1000

1200

PM10 BAM (μg/m

3

)

Hourly Averaged Wind Speed PM10 BAM

Figure 5-7. Relationship between hourly averaged wind speed and PM10 concentrations at

a North Las Vegas, NV, site (Bemis) during the period of 04/08/95 to 04/09/95.

BAM PM10 (μg/m

3

)
5-11

First Quarter 1996

0

10

20

30

40

50

60

70

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 9.0

Hourly Wind Speed (m/s)

PM10 BAM (μg/m

3

)

20%tile 50%tile 80%tile

Second Quarter 1996

0

20

40

60

80

100

120

140

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 8.9

Hourly WInd Speed (m/s)

PM10 BAM (μg/m

3

)

20%tile 50%tile 80%tile

Third Quarter 1996

0

10

20

30

40

50

60

70

0.3 0.7 1.2 1.7 2.2 2.6 3.2 3.7 4.2 5.0

Hourly Wind Speed (m/s)

PM10 BAM (μg/m

3

)

20 percentile 50 percentile 80 percentile

Figure 5-8. Distribution of hourly PM10 concentrations as a function of wind speed at a

southeastern Chicago, IL, site (Eisenhower) during the first three quarters of 1996. BAM PM10 (μg/m

3

) BAM PM10 (μg/m

3

) BAM PM10 (μg/m

3

)
5-12

Table 5-1

Cross-Border Fluxes at the Calexico Site

Northerly Flow

(from the United States)

Southerly Flow

(from Mexico)

Mean Flux 52 μg/m2-s 156 μg/m2-s

Total Flux (μg-hr/m2-s) 213,367 μg-hr/m2-s 308,430 μg-hr/m2-s
5-13

EIS 1st quarter

0

1 0

2 0

3 0

4 0

5 0

6 0

N 

NE

E

SW SE

W

NW 20 percentile

50 percentile

80 percentile

EIS 2nd quarter

0

1 0

2 0

3 0

4 0

5 0

6 0

N 

NE

E

SW SE

W

NW 20 percentile

50 percentile

80 percentile

EIS 3rd quarter

0

1 0

2 0

3 0

4 0

5 0

6 0

N 

NE

E

SE

S

SW

W

NW 20 percentile

50 percentile

80 percentile

Figure 5-9. PM10 concentrations at the 20th, 50th, and 80th percentiles at a southeastern

Chicago, IL, site (Eisenhower) during the first three quarters of 1996.
5-14

south, then from the east, and lowest for northerly transport. PM10 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 PM10 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 (SO2).

Average wind speeds in each wind direction were highest in the first quarter and lowest in

the third quarter. At the 50th percentile, PM10 concentrations during the third quarter of 1996

were 10 to 15 μg/m3 higher than during the first two quarters of 1996. The higher PM10

concentrations in the third quarter are associated with lower wind speeds that limit dilution of

local emissions.

Higher PM10 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 SO2 concentrations were highest when transport

was from the south. The highest SO2 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.
5-15

Table 5-2

Distribution of Hourly PM10 and SO2 Concentrations at Robbins, IL,

as a Function of Wind Speed and Wind Direction

Distribution of PM10 Concentrations (μg/m3) Average Average Average

Wind

Direction

20th

Percentile

50th

Percentile

80th

Percentile

Wind Speed

 (m/s)

PM10

 (μg/m3)

SO2

 (ppb)

First Quarter 1996

N 6.2 15.0 30.0 3.9 20.1 1.3

NE 7.0 17.0 31.0 3.3 22.1 2.3

E 12.0 23.0 37.0 2.3 25.9 7.1

SE 7.0 16.0 29.0 3.1 19.7 6.7

S 14.0 22.0 32.0 3.8 25.7 15.1

SW 10.0 19.0 29.0 4.1 21.1 10.2

W 8.0 17.0 31.0 6.7 21.1 5.0

NW 7.0 16.0 30.0 3.5 19.0 4.7

Second Quarter 1996

N 4.0 10.0 21.0 2.4 13.6 1.0

NE 6.0 13.0 26.0 2.2 19.3 1.5

E 13.6 25.0 42.4 2.2 30.3 5.8

SE 11.0 22.0 38.6 2.2 25.3 4.0

S 17.0 27.0 47.0 3.4 32.6 11.5

SW 13.4 27.0 48.0 4.0 33.9 5.9

W 8.0 16.0 30.0 3.4 20.2 2.1

NW 7.0 14.0 24.6 2.9 18.2 0.8

Third Quarter 1996

N 8.4 15.0 32.6 1.8 23.3 3.7

NE 11.0 19.0 46.0 1.5 28.8 4.7

E 10.0 28.0 47.2 1.9 32.1 9.3

SE 8.0 24.0 46.4 1.6 29.2 5.1

S 20.0 36.5 57.4 2.3 40.6 10.3

SW 8.0 24.0 42.0 2.3 26.5 6.3

W 10.0 19.0 34.0 2.3 23.5 4.2

NW 9.0 17.0 35.0 1.9 22.8 3.7
5-16

0

10

20

N

NE

E

SE

S

SW

W

NW

BRE

EIS

MEA PM2.5 Al (ng/m3

)

0

200

400

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NE

E

SE

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EIS

MEA PM2.5 Si (ng/m3

)

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SE

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EIS

MEA PM2.5 K (ng/m3

)

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SE

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EIS

MEA PM2.5 Ca (ng/m3

)

0

250

500

N

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E

SE

S

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EIS

MEA PM2.5 Fe (ng/m3

)

0

10

20

N

NE

E

SE

S

SW

W

NW

BRE

EIS

MEA PM2.5 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.
5-17

0

30

60

N

NE

E

SE

S

SW

W

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BRE

EIS

MEA PM2.5 Pb (ng/m3

)

0

1000

2000

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NE

E

SE

S

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EIS

MEA PM2.5 S (ng/m3

)

0

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100

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MEA PM2.5 Zn (ng/m3

)

0

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MEA PM2.5 Cr (ng/m3

)

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10

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MEA PM2.5 Cu (ng/m3

)

0

10

20

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SE

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W

NW

BRE

EIS

MEA PM2.5 Br (ng/m3

)

Figure 5-10. (continued)
5-18

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.
5-19

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 (PED) 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 (PED) 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 (PED) 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 (PED) 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
5-20

0

2000

4000

6000

8000

10000

12000

14000

16000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Hour

EC (ng/m3)

PED MER

Figure 5-11. Five-minute-average aethalometer black carbon measurements at a downtown

(MER) site and a suburban (PED) site in Mexico City. The short-term spikes correspond to

contributions from very nearby sources, such as diesel exhaust plumes from trucks and buses.
5-21

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 ultrafine 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.
6-1

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
6-2

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 PM10 inlet

that has previously been wind tunnel tested and approved for PM10 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 μm 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 2×10-8 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 μm 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.
6-3

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.
6-4

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 μm and 4.0 ± 0.5 μm. 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 particulate 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
6-5

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 μg/m3 among the WINS samplers. A reasonable lower bound on

collocated precision is, therefore, ±1 μg/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 μg/m3, this is a

maximum difference of ±3 μg/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 μg/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 PM10 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 μg/m3

 intercept is not applicable to all potential

CAC monitors, but its μg/m3 equivalent should be.
6-6

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.
7-1

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A-1

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 PM10 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 (C1 to C12).

Continuous measurements of aerosol mass and chemistry included:

• Hourly PM10 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.
A-2

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), PM10 and PM2.5 beta attenuation monitor (BAM),

5-minute aethalometer, 5-minute nephelometer, 3-hour-average PM2.5 and PM10 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, FEI 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 PM10 and PM2.5 BAM, at Chowchilla during winter study

(12/09/95 to 01/06/96).

• Daily 3-hour PM2.5 and PM10 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 PM10 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 PM10 mass and chemistry measurements taken 5 times/day at 6 sites;

• PM2.5 organic species measurements taken 5 times/day at 6 sites;
A-3

• Measurements of precursor gases (NH3, HNO3) 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 PM2.5 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 PM10 TEOM and BAM collocated with PM10

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 PM10 network at four sites from 1994 to 1996.

• Hourly TEOM PM10 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.
A-4

• Every-sixth-day 24-hour compliance PM10 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 PM10 Transport Study

The ambient sampling program acquired PM10 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

PM10 mass measured on the U.S. side of the border by the CARB, 2) high-volume SSI PM10

at sites in the cities of Brawley, El Centro, and Calexico, operated by the Imperial County Air

Pollution Control District (ICAPCD), and 3) meteorological data from the California

Irrigation Management Information System (CIMIS) and several industrial permitting stations

located in Imperial County, California. Collocated PM10 SSI, BAM, dichotomous, sequential

filter sampler (SFS), and Minivol portable PM10 survey sampler with meteorology was

measured at the Calexico site (Chow and Watson, 1997a).

• Hourly BAM PM10 at the Grant Fire Station site from 03/07/92 to 08/29/93.

• Every-sixth-day dichotomous fine, coarse, and PM10 at the El Centro 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 PM10 mass at the Brawley, Grant Fire Station, El

Centro, and Calexico Police and Fire Station sites from 01/02/92 to 08/29/93.

• Every-sixth-day 24-hour portable PM10 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 PM10 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 PM10 mass, babs, and elements at the Calexico (Grant Fire Station)

and Mexicali (SEDESOL) sites, and at 20 satellite sites during summer (08/21/92
A-5

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 PM10 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 PM10 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 PM10 and collocated 24-hour high-volume SSI PM10 data from 1995

to 1997.

A.7 Las Vegas PM10 Study

The Las Vegas Valley PM10 Study was conducted during 1995 and 1996 to determine

the contributions to PM10 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 PM10 data from 14 sites (including one compliance monitoring site with

PM10 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 PM10 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 PM10 data

from 30 satellite sites from 01/03/95 to 01/28/96 during the intensive monitoring

period.

• PM10 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, PM10, 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 PM2.5 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 PM10 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, PM10, 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 PM10 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 PM2.5 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 PM2.5 and PM10 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 PM10, collocated SSI PM10,

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 PM10 at the Eisenhower site from 01/0196 to 09/30/96.

• Every-sixth-day dichotomous and high-volume SSI PM10 and PM2.5 at the

Eisenhower site from 10/12/95 to 09/30/96.

• PM2.5 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 PM10 and collocated SSI PM10 at the North Birmingham site during

1993.

• Every-sixth-day high-volume SSI PM10 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 Instituto 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 PM10 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 PM10 mass, babs, elements, ions, carbon, and ammonia from

03/02/97 to 03/19/97 at 25 satellite sites.

• Daily 6-hour PM10 mass, babs, elements, ions, and carbon from 03/02/97 to

03/19/97 at three super-core sites.

• Daily 24-hour PM10 mass, babs, elements, ions, and carbon from 03/02/97 to

03/19/97 at three core sites.