United States October 2004 Environmental Protection 

Agency EPA EPA/600/P-99/002aF

Air Quality Criteria for 

Particulate Matter 

Volume I of II
EPA/600/P-99/002aF 

October 2004 

Air Quality Criteria for Particulate Matter 

Volume I 

National Center for Environmental Assessment-RTP Office 

Office of Research and Development 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 
DISCLAIMER 

This document has been reviewed in accordance with U.S. Environmental Protection 

Agency policy and approved for publication. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 

PREFACE 

National Ambient Air Quality Standards (NAAQS) are promulgated by the United States 

Environmental Protection Agency (EPA) to meet requirements set forth in Sections 108 and 109 

of the U.S. Clean Air Act (CAA). Sections 108 and 109 require the EPA Administrator (1) to 

list widespread air pollutants that reasonably may be expected to endanger public health or 

welfare; (2) to issue air quality criteria for them that assess the latest available scientific 

information on nature and effects of ambient exposure to them; (3) to set “primary” NAAQS to 

protect human health with adequate margin of safety; (4) to set “secondary” NAAQS to protect 

against welfare effects (e.g., effects on vegetation, ecosystems, visibility, climate, manmade 

materials, etc.); and (5) to periodically (every 5 years) review and revise, as appropriate, the 

criteria and NAAQS for a given listed pollutant or class of pollutants. 

The original NAAQS for particulate matter (PM), issued in 1971 as “total suspended 

particulate” (TSP) standards, were revised in 1987 to focus on protecting against human health 

effects associated with exposure to ambient PM less than 10 microns (#10 μm) that are capable 

of being deposited in thoracic (tracheobronchial and alveolar) portions of the lower respiratory 

tract. Later periodic reevaluation of newly available scientific information, as presented in the 

last previous version of this “Air Quality Criteria for Particulate Matter” document published in 

1996, provided key scientific bases for PM NAAQS decisions published in July 1997. More 

specifically, the PM10 NAAQS set in 1987 (150 μg/m3

, 24-h; 50 μg/m3, annual average) were 

retained in modified form and new standards (65 μg/m3

, 24-h; 15 μg/m3, annual average) for 

particles # 2.5 μm (PM2.5) were promulgated in July 1997. 

This final version of revised Air Quality Criteria for Particulate Matter assesses new 

scientific information that has become available (published or accepted for publication) mainly 

I-ii 
between early 1996 through April 2002, although a few important new studies published through 

2003 are also considered. Several previous successive drafts of this document were released for 

public comment and review by the Clean Air Scientific Advisory Committee (CASAC), to 

obtain comments on the organization and structure of the document, the issues addressed, the 

approaches employed in assessing and interpreting the newly available information on PM 

exposures and effects, and the key findings and conclusions arrived at by this assessment. Public 

comments and CASAC review recommendations were taken into account in making revisions to 

this document for incorporation into this final draft. Evaluations contained in this document will 

be drawn on to provide inputs to associated PM Staff Paper analyses prepared by EPA’s Office 

of Air Quality Planning and Standards (OAQPS) to pose alternatives for consideration by the 

EPA Administrator with regard to proposal and, ultimately, promulgation of decisions on 

potential retention or revision of the current PM NAAQS. 

The document describes the nature, sources, distribution, measurement, and concentrations 

of PM in outdoor (ambient) environments. It also evaluates the latest data on human exposures 

to ambient PM and consequent health effects in exposed human populations, to support decision 

making regarding primary, health-related PM NAAQS. The document also evaluates ambient 

PM environmental effects on vegetation and ecosystems, visibility, and man-made materials, as 

well as atmospheric PM effects on climate change processes, to support decision making bearing 

on secondary PM NAAQS. 

Preparation of this document was coordinated by EPA’s National Center for 

Environmental Assessment in Research Triangle Park (NCEA-RTP). NCEA-RTP scientific 

staff, together with experts from other EPA/ORD laboratories and academia, contributed to 

writing of document chapters. The NCEA of EPA acknowledges the contributions provided by 

authors, contributors, and reviewers and the diligence of its staff and contractors in the 

preparation of this document. 

I-iii 
I-iv

Air Quality Criteria for Particulate Matter

VOLUME I

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

2. PHYSICS, CHEMISTRY, AND MEASUREMENT OF

OF PARTICULATE MATTER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

APPENDIX 2A: Techniques for Measuring of Semivolatile 

Organic Compounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2A-1

APPENDIX 2B: Analytical Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2B-1

3. CONCENTRATIONS, SOURCES, AND EMISSIONS OF

ATMOSPHERIC PARTICULATE MATTER . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

APPENDIX 3A: Composition of Particulate Matter Source Emissions . . . . 3A-1

APPENDIX 3B: Organic Composition of Particulate Matter . . . . . . . . . . . . 3B-1

APPENDIX 3C: Aerosol Composition Data from the Speciation 

Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3C-1

APPENDIX 3D: Spatial and Temporal Variability of the Nationwide 

AIRS PM2.5 and PM10-2.5 Data Sets . . . . . . . . . . . . . . . . . . 3D-1

APPENDIX 3E: Characterization of PM2.5, PM10, and PM10-2.5

Concentrations at IMPROVE Sites . . . . . . . . . . . . . . . . . . . 3E-1

4. ENVIRONMENTAL EFFECTS OF AIRBORNE PARTICULATE 

MATTER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

APPENDIX 4A: Common and Latin Names . . . . . . . . . . . . . . . . . . . . . . . . 4A-1

5. HUMAN EXPOSURE TO PARTICULATE MATTER AND

ITS CONSTITUENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1
I-v

Air Quality Criteria for Particulate Matter

(cont’d)

VOLUME II

6. DOSIMETRY OF PARTICULATE MATTER . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

7. TOXICOLOGY OF PARTICULATE MATTER IN HUMANS AND

LABORATORY ANIMALS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

APPENDIX 7A: Rat-to-Human Dose Extrapolation . . . . . . . . . . . . . . . . . . 7A-1

APPENDIX 7B: Ambient Bioaerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7B-1

8. EPIDEMIOLOGY OF HUMAN HEALTH EFFECTS ASSOCIATED 

WITH AMBIENT PARTICULATE MATTER . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1

APPENDIX 8A: Short-Term PM Exposure-Mortality Studies:

Summary Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8A-1

APPENDIX 8B: Particulate Matter-Morbidity Studies: 

Summary Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8B-1

9. INTEGRATIVE SYNTHESIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1
Table of Contents 

Page 

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-xiii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-xix

Authors, Contributors, and Reviewers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-xxxii

U.S. Environmental Protection Agency Project Team for Development of 

Air Quality Criteria for Particulate Matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-xl 

U.S. Environmental Protection Agency Science Advisory Board (SAB) 

Staff Office Clean Air Scientific Advisory Committee (CASAC) 

Particulate Matter Review Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-xliii 

Abbreviations and Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-xlvi 

1. INTRODUCTION ...................................................... 1-1

1.1 BACKGROUND ................................................. 1-1

1.1.1 Legislative Requirements .................................. 1-1

1.1.2 Criteria and NAAQS Review Process ........................ 1-2

1.1.3 History of Earlier PM Criteria and NAAQS Reviews ............ 1-4

1.1.4 The 1997 PM NAAQS Revision ............................. 1-5

1.1.5 Coordinated PM Research Program .......................... 1-7

1.2 CURRENT PM CRITERIA AND NAAQS REVIEW ................... 1-12

1.2.1 Key Milestones and Procedures for Document Preparation ....... 1-12

1.2.2 Assessment Approaches .................................. 1-16

1.3 DOCUMENT ORGANIZATION ................................... 1-18

REFERENCES ....................................................... 1-19

2. PHYSICS, CHEMISTRY, AND MEASUREMENT OF 

PARTICULATE MATTER .............................................. 2-1

2.1 PHYSICS AND CHEMISTRY OF PARTICULATE MATTER ............ 2-2

2.1.1 Basic Concepts .......................................... 2-2

2.1.2 Physical Properties and Processes ........................... 2-3

2.1.2.1 Definitions of Particle Diameter .................... 2-3

2.1.2.2 Aerosol Size Distributions ........................ 2-5

2.1.2.3 Ultrafine Particles .............................. 2-28

2.1.3 Chemistry of Atmospheric Particulate Matter ................. 2-37

2.1.3.1 Chemical Composition and Its Dependence on 

Particle Size .................................. 2-37

2.1.3.2 Primary and Secondary Particulate Matter ........... 2-38

2.1.3.3 Particle-Vapor Partitioning ....................... 2-39

2.1.3.4 Atmospheric Lifetimes and Removal Processes ....... 2-49

2.1.4 Comparison of Fine and Coarse Particles ..................... 2-51

2.2 MEASUREMENT OF PARTICULATE MATTER ..................... 2-51

2.2.1 Particle Measurements of Interest ........................... 2-51

2.2.2 Issues in Measurement of Particulate Matter .................. 2-54

2.2.2.1 Artifacts Due to Chemical Reactions ............... 2-55

I-vi 
Table of Contents 

(cont’d) 

Page 

2.2.2.2 Treatment of Semivolatile Components of 

Particulate Matter .............................. 2-56

2.2.2.3 Upper Cut Point ............................... 2-57

2.2.2.4 Cut Point for Separation of Fine and Coarse 

Particulate Matter .............................. 2-58

2.2.2.5 Treatment of Pressure, Temperature, and 

Relative Humidity .............................. 2-58

2.2.2.6 Time Resolution ............................... 2-59

2.2.2.7 Accuracy and Precision ......................... 2-59

2.2.3 Measurement of Semivolatile Particulate Matter ............... 2-62

2.2.3.1 Particle-Bound Water ........................... 2-62

2.2.3.2 Nitrate and Organic Species ...................... 2-68

2.2.3.3 Continuous Measurement of Semivolatile and 

Nonvolatile Mass .............................. 2-75

2.2.4 U.S. Environmental Protection Agency Monitoring Methods ..... 2-79

2.2.4.1 The Federal Reference Methods for Measurement

of Equilibrated Mass for PM10, PM2.5, and PM10-2.5 ..... 2-79

2.2.5 Speciation Monitoring ................................... 2-93

2.2.6 Inorganic Elemental Analyses ............................. 2-96

2.2.7 Elemental and Organic Carbon in Particulate Matter ............ 2-97

2.2.8 Ionic Species ........................................... 2-99

2.2.9 Continuous Monitoring ................................... 2-99

2.2.10 Measurements of Individual Particles ....................... 2-101

2.2.11 Low Flow Filter Samples for Multiday Collection of 

Particulate Matter ...................................... 2-103

2.3 SUMMARY AND KEY POINTS .................................. 2-104

2.3.1 Chemistry and Physics of Atmospheric Particles .............. 2-104

2.3.2 Measurement of Atmospheric Particles ..................... 2-107

2.3.3 Key Points ............................................ 2-111

REFERENCES ...................................................... 2-113

APPENDIX 2A: TECHNIQUES FOR MEASURING OF SEMIVOLATILE

ORGANIC COMPOUNDS . . . . . . . . . . . . . . . . . . . . . . . . . . . 2A-1

APPENDIX 2B: ANALYTICAL TECHNIQUES . . . . . . . . . . . . . . . . . . . . . . . . . 2B-1

2B.1 INORGANIC ELEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2B-1

2B.1.1 Energy Dispersive X-Ray Fluorescence . . . . . . . . . . . . . . . . . . . 2B-1

2B.1.2 Synchrotron Induced X-ray Fluorescence . . . . . . . . . . . . . . . . . 2B-2

2B.1.3 Proton (or Particle) Induced X-ray Emission . . . . . . . . . . . . . . . 2B-2

2B.1.4 Proton Elastic Scattering Analysis . . . . . . . . . . . . . . . . . . . . . . . 2B-3

2B.1.5 Total Reflection X-Ray Fluorescence . . . . . . . . . . . . . . . . . . . . . 2B-3

2B.1.6 Instrumental Neutron Activation Analysis . . . . . . . . . . . . . . . . . 2B-4

I-vii 
Table of Contents 

(cont’d) 

Page 

2B.1.7 Atomic Absorption Spectrophotometry . . . . . . . . . . . . . . . . . . . 2B-5

Emission Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2B-6

Mass Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2B-6

Elemental Carbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2B-21

2B.1.8 Inductively Coupled Plasma with Atomic 

2B.1.9 Inductively Coupled Plasma with 

2B.1.10 Scanning Electron Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . 2B-7

2B.2 ORGANIC AND INORGANIC CARBON . . . . . . . . . . . . . . . . . . . . . . . . . . 2B-8

2B.3 CONTINUOUS METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2B-17

2B.3.1 Continuous Measurement of Mass . . . . . . . . . . . . . . . . . . . . . . 2B-17

2B.3.2 Continuous Measurement of Organic and/or 

2B.3.3 Continuous Measurements of Nitrate and Sulfate . . . . . . . . . . 2B-24

2B.4 OTHER CONTINUOUS MEASUREMENTS . . . . . . . . . . . . . . . . . . . . . . 2B-26

3. CONCENTRATIONS, SOURCES, AND EMISSIONS OF ATMOSPHERIC

PARTICULATE MATTER .............................................. 3-1

3.1 INTRODUCTION ................................................ 3-1

3.2 PATTERNS AND TRENDS IN AMBIENT PM CONCENTRATIONS ..... 3-2

3.2.1 Seasonal Variability in PM Concentrations ................... 3-13

3.2.2 Diurnal (Circadian) Variability in PM Concentrations .......... 3-30

3.2.3 Relationships Among Particulate Matter in Different 

Size Fractions .......................................... 3-32

3.2.4 Relationships Between Mass and Chemical 

Component Concentrations ................................ 3-34

3.2.5 Spatial Variability in Particulate Matter and its Components ..... 3-40

3.3 SOURCES OF PRIMARY AND SECONDARY PM ................... 3-59

3.3.1 Chemistry of Secondary PM Formation ...................... 3-62

3.3.2 Source Contributions to Ambient PM Determined by 

Receptor Models ........................................ 3-72

3.3.3 Background Concentrations of PM in the United States ......... 3-82

3.3.4 Emissions Estimates for Primary PM, and Precursors 

to Secondary PM (SO2, NOx, VOCs, and NH3) in the 

United States ........................................... 3-92

3.3.5 Uncertainties of Emissions Inventories ...................... 3-98

3.4 SUMMARY AND KEY CONCLUSIONS ........................... 3-101

REFERENCES ...................................................... 3-106

PM

APPENDIX 3A: Spatial and Temporal Variability of the Nationwide AIRS 

2.5 and PM10-2.5 Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-1 

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-3 

I-viii 
Table of Contents 

(cont’d) 

Page 

APPENDIX 3B: Aerosol Composition Data from the Speciation Network . . . . . . . 3B-1

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3B-3

APPENDIX 3C: Organic Composition of Particulate Matter . . . . . . . . . . . . . . . . . . 3C-1

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3C-14

APPENDIX 3D: Composition of Particulate Matter Source Emissions . . . . . . . . . 3D-1

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-25

APPENDIX 3E: Characterization of PM2.5, PM10, and PM10-2.5

Concentrations at IMPROVE Sites . . . . . . . . . . . . . . . . . . . . . . . . 3E-1

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3E-6

4. ENVIRONMENTAL EFFECTS OF AIRBORNE PARTICULATE MATTER ...... 4-1

4.1 INTRODUCTION ................................................ 4-1

4.2 EFFECTS OF AMBIENT AIRBORNE PARTICULATE MATTER ON

VEGETATION AND NATURAL ECOSYSTEMS ...................... 4-1

4.2.1 Ecological Attributes ..................................... 4-2

4.2.2 Ecosystem Exposures – Particle Deposition .................... 4-4

4.2.2.1 Fine and Coarse Particulate Matter .................. 4-5

4.2.2.2 Diversity of Deposition Modes ..................... 4-8

4.2.2.3 Magnitude of Deposition ........................ 4-44

4.2.3 Assessment of Atmospheric PM Deposition Effects ............ 4-55

4.2.3.1 Effects on Vegetation and Ecosystems .............. 4-61

4.2.3.2 Ecosystem Response to Stresses ................... 4-79

4.2.4 Urban Ecosystems ...................................... 4-148

4.3 AIRBORNE PARTICLE EFFECTS ON VISIBILITY ................. 4-152

4.3.1 Introduction ........................................... 4-152

4.3.2 Factors Affecting Atmospheric Visibility .................... 4-154

4.3.2.1 Optical Properties of the Atmosphere and 

Atmospheric Particles .......................... 4-154

4.3.2.2 Relative Humidity Effects on Particle Size and 

Light-Scattering Properties ...................... 4-161

4.3.3 Relationships Between Particles and Visibility ............... 4-165

4.3.4 Photographic Modeling of Visibility Impairment ............. 4-173

4.3.5 Visibility Monitoring Methods and Networks ................ 4-174

4.3.6 Visibility Impairment: Trends and Current Conditions ......... 4-177

4.3.6.1 Trends in Visibility Impairment .................. 4-177

4.3.6.2 Current Conditions ............................ 4-185

4.3.7 Societal Impacts of Particulate Matter Visibility Effects ........ 4-186

4.3.7.1 Economic Studies ............................. 4-186

4.3.7.2 Public Perception and Attitude Studies ............ 4-188

4.4 PARTICULATE MATTER EFFECTS ON MATERIALS .............. 4-191

4.4.1 Corrosive Effects of Particles and Sulfur Dioxide on 

Man-Made Surfaces .................................... 4-191

I-ix 
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4.4.1.1 Metals ...................................... 4-192

4.4.1.2 Painted Finishes .............................. 4-193

4.4.1.3 Stone and Concrete ............................ 4-196

4.4.2 Soiling and Discoloration of Man-Made Surfaces ............. 4-202

4.4.2.1 Stones and Concrete ........................... 4-203

4.4.2.2 Household and Industrial Paints .................. 4-203

4.5 ATMOSPHERIC PARTICULATE MATTER, CLIMATE CHANGE, 

AND EFFECTS ON SOLAR UV-B RADIATION TRANSMISSION ..... 4-204

4.5.1 Atmospheric Particle Interactions with Solar and Terrestrial 

Radiation Related to Climate Change ....................... 4-206

4.5.1.1 The Projected Impacts of Global Climate Change .... 4-206

4.5.1.2 Airborne Particle Relationships to Global 

Warming and Climate Change ................... 4-209

4.5.2 Atmospheric Particulate Matter Effects on the Transmission 

of Solar Ultraviolet Radiation Transmission: Impacts on 

Human Health and the Environment ........................ 4-220

4.5.2.1 Potential Effects of Increased Ultraviolet

Radiation Transmission ........................ 4-220

4.5.2.2 Airborne Particle Effects on Atmospheric 

Transmission of Solar Ultraviolet Radiation ........ 4-223

4.6 SUMMARY AND KEY CONCLUSIONS ........................... 4-227

4.6.1 Particulate Matter Effects on Vegetation and Ecosystems ....... 4-227

4.6.2 Particulate Matter-Related Effects on Visibility ............... 4-235

4.6.3 Particulate Matter-Related Effects on Materials ............... 4-238

4.6.4 Effects of Atmospheric Particulate Matter on Global 

Warming Processes and Transmission of Solar 

Ultraviolet Radiation .................................... 4-239

REFERENCES ...................................................... 4-241

APPENDIX 4A: Common and Latin Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4A-1

5. HUMAN EXPOSURE TO PARTICULATE MATTER AND 

ITS CONSTITUENTS .................................................. 5-1

5.1 INTRODUCTION ................................................ 5-1

5.1.1 Purpose ................................................ 5-1

5.1.2 Particulate Matter Mass and Constituents ..................... 5-2

5.1.3 Relationship to Past Documents ............................. 5-3

5.1.4 Chapter Structure ........................................ 5-4

5.2 BASIC CONCEPTS OF EXPOSURE ................................ 5-5

5.2.1 The Concept of Exposure .................................. 5-5

5.2.2 Components of Exposure .................................. 5-6

5.2.3 Quantification of Exposure ................................. 5-9

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5.2.4 Methods To Estimate Personal Exposure ..................... 5-10

5.2.4.1 Direct Measurement Methods .................... 5-11

5.2.4.2 Indirect or Modeling Methods .................... 5-15

5.2.4.3 Methods for Estimating Personal Exposure to 

Ambient Particulate Matter ....................... 5-22

5.3 SUMMARY OF PARTICULATE MATTER MASS DATA .............. 5-29

5.3.1 Types of Particulate Matter Exposure Measurement Studies ...... 5-29

5.3.2 Available Data ......................................... 5-30

5.3.2.1 Personal Exposure Data ......................... 5-30

5.3.2.2 Microenvironmental Data ........................ 5-35

5.3.2.3 Traffic-Related Microenvironments ................ 5-43

5.3.2.4 Reanalyses of Previously-Reported Particulate 

Matter Exposure Data ........................... 5-43

5.3.3 Factors Influencing and Key Findings on Particulate

Matter Exposures ....................................... 5-46

5.3.3.1 Relationship of Personal/Microenvironmental 

Particulate Matter with Ambient Particulate Matter .... 5-46

5.3.3.2 Factors That Affect Relationships Between

Personal Exposure and Ambient PM ............... 5-66

5.3.3.3 Effect of Ambient Sources on Exposures to 

Particulate Matter .............................. 5-87

5.3.3.4 Correlations of Particulate Matter with 

Other Pollutants ............................... 5-89

5.4 SUMMARY OF PARTICULATE MATTER CONSTITUENT DATA ..... 5-94

5.4.1 Introduction ............................................ 5-94

5.4.2 Monitoring Studies That Address Particulate Matter

Constituents ........................................... 5-94

5.4.3 Key Findings ........................................... 5-94

5.4.3.1 Correlations of Personal and Indoor Concentrations

with Ambient Concentrations of Particulate Matter

Constituents .................................. 5-94

5.4.4 Factors Affecting Correlations Between Ambient 

Measurements and Personal or Microenvironmental 

Measurements of Particulate Matter Constituents ............. 5-105

5.4.5 Limitations of Available Data ............................. 5-106

5.5 IMPLICATIONS OF USING AMBIENT PM CONCENTRATIONS

IN TOXICOLOGICAL AND EPIDEMIOLOGICAL STUDIES OF

PM HEALTH EFFECTS ......................................... 5-107

5.5.1 Toxicology ........................................... 5-107

5.5.2 Potential Sources of Error Resulting from Using Ambient 

Particulate Matter Concentrations in Epidemiological

Analyses ............................................. 5-107

I-xi 
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5.5.2.1 Associations Between Personal Exposures and 

Ambient Particulate Matter Concentrations ......... 5-110

5.5.2.2 Role of Compositional Differences in Exposure

Characterization for Epidemiology ................ 5-114

5.5.2.3 Role of Spatial Variability in Exposure 

Characterization for Epidemiology ................ 5-115

5.5.3 Analysis of Exposure Measurement Error Issues in Particulate 

Matter Epidemiology ................................... 5-116

5.5.3.1 Time-Series Analyses .......................... 5-117

5.5.3.2 Studies of Chronic Effects ...................... 5-117

5.5.4 Conclusions from Analysis of Exposure Measurement Errors 

on Particulate Matter Epidemiology ........................ 5-120

5.6 SUMMARY OF OBSERVATIONS AND LIMITATIONS ............. 5-121

REFERENCES ...................................................... 5-129

I-xii 
List of Tables 

Number Page 

1-1 Key Scientific Uncertainties Related to the Source-To-Response 

Framework ..................................................... 1-10

1-2 Research Topics and Questions Recommended by National Research 

Council to be Addressed by Expanded M Research Program .............. 1-11

1-3 Key Milestones in the Development of This Document ................... 1-13

2-1 Correlations Between TEOM Measurements in Phoenix .................. 2-26

2-2 Comparison of Ambient Particles, Fine Particles (ultrafine plus 

accumulation-mode) and Coarse Particles ............................. 2-52

2-3 Particulate Matter Components/Parameters of Interest for Health, 

Ecological, or Radiative Effects; for Source Category Apportionment 

Studies; or for Air Quality Modeling Evaluation Studies ................. 2-53

2-4 Summary of Sensitivity Studies of WINS Impactor Performance ........... 2-84

2-5 PM2.5 Samplers Currently Designated as FRMs for PM2.5 Mass 

Concentrations .................................................. 2-85

2-6 Measurement Methods for Inorganic Elements ......................... 2-97

2-7 Methods for Continuous Measurement of PM Mass, PM 

Components, etc. ................................................ 2-100

3-1 Distribution of Ratios of PM2.5 to PM10 and Correlations Between PM2.5

and PM10, PM2.5 and PM10-2.5, and PM10-2.5 and PM10 Found at Collocated 

Monitoring Sites in Seven Aerosol Characteristic (EPA/HEI) Regions 

in 1999 ........................................................ 3-33

3-2 Concentrations (ng/m3

) of PM2.5, PM10-2.5, and Selected Elements (ng/m3) 

in the PM2.5 and PM10-2.5 Size Ranges with Standard Deviations (SD) and

Correlations Between Elements and PM2.5 Mass in Philadelphia, PA ........ 3-35

PM

3-3 Concentrations (in ng/m3

) of PM2.5, PM10-2.5, and Selected Elements in the

2.5 and PM10-2.5 Size Range with Standard Deviations (SD) and 

Correlations (r) Between Elements and PM2.5 and PM10-2.5 Mass in 

Phoenix, AZ .................................................... 3-36

3-4a-d Measures of the Spatial Variability of PM2.5 Concentrations Within 

Selected Metropolitan Statistical Areas ............................... 3-41

I-xiii 
List of Tables 

(Cont’d) 

Number Page 

3-5 Measures of the Spatial Variability of PM10-2.5 Concentrations Within 

Selected Metropolitan Statistical Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-49 

3-6 Correlation Coefficients for Spatial Variation of PM2.5 Mass and Different 

Sources for Pairs of Sampling Sites in the South Coast Air Basin (1986) . . . . . 3-57 

3-7 Correlation Coefficients for Spatial Variation of PM2.5 Mass and Different 

Components for Pairs of Sampling Sites in Philadelphia (1994) . . . . . . . . . . . . 3-57 

3-8 Constituents of Atmospheric Particles and their Major Sources . . . . . . . . . . . . 3-60 

3-9 Receptor Model Source Contributions to PM2.5 . . . . . . . . . . . . . . . . . . . . . . . . . 3-76 

3-10 Receptor Model Source Contributions to PM10 . . . . . . . . . . . . . . . . . . . . . . . . . 3-77 

3-11 Emissions of Primary PM2.5 by Various Sources in 1999 . . . . . . . . . . . . . . . . . . 3-94 

3-12 Emissions of Precursors to Secondary PM2.5 Formation by Various Sources 

in 1999 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-95 

3-13 Nationwide Changes in Ambient Concentrations and Emissions of PM10 and 

Gaseous Precursors to Secondary Particulate Matter from 1992 to 2001 . . . . . 3-97 

3A-1 Performance Metrics for PM2.5 from Collocated Samplers . . . . . . . . . . . . . . . 3A-4 

3B-1 PM2.5 Speciation Samplers by Location: Sites Selected for Summary in 

2004 PM AQCD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3B-4 

3B-2a Burlington, VT Summary Data (October 2001 to September 2002) . . . . . . . . . 3B-5 

3B-2b Philadelphia, PA Summary Data (October 2001 to September 2002) . . . . . . . . 3B-6 

3B-2c Atlanta, GA Summary Data (October 2001 to September 2002) . . . . . . . . . . . . 3B-7 

3B-2d Detroit, MI Summary Data (October 2001 to September 2002) . . . . . . . . . . . . 3B-8 

3B-2e Chicago, IL Summary Data (October 2001 to September 2002) . . . . . . . . . . . . 3B-9 

3B-2f St. Louis, MI Summary Data (October 2001 to September 2002) . . . . . . . . . . 3B-10 

3B-2g Houston, TX Summary Data (October 2001 to September 2002) . . . . . . . . . . 3B-11 

I-xiv 
List of Tables 

(Cont’d) 

Number Page 

3B-2h Minneapolis, MN Summary Data (October 2001 to September 2002) . . . . . . 3B-12 

3B-2i Boulder, CO Summary Data (October 2001 to September 2002) . . . . . . . . . . 3B-13 

3B-2j Phoenix, AZ Summary Data (October 2001 to September 2002) . . . . . . . . . . 3B-14 

3B-2k Seattle, WA Summary Data (October 2001 to September 2002) . . . . . . . . . . . 3B-15 

3B-2l Sacramento, CA Summary Data (October 2001 to September 2002) . . . . . . . 3B-16 

3B-2m Riverside-Rubidoux, CA Summary Data (October 2001 to 

September 2002) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3B-17 

3B-3 Blank Corrections for Elemental Carbon, Organic Carbon, and Total 

Carbon in the Speciation Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3B-18 

3C-1 Particulate Organic and Elemental Carbon Concentrations (in μg C/m3

) 

Based on Studies Published After 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3C-2 

3C-2 Particulate Organic Compound Concentrations (in ng C/m3

) Based on 

Studies Published After 1990 at Selected Sites . . . . . . . . . . . . . . . . . . . . . . . . . 3C-5 

3D-1 Average Abundances of Major Elements in Soil and Crustal Rock . . . . . . . . . 3D-2 

3D-2 Summary of Particle-Phase Organic Compounds Present in Fine Particle 

Road Dust Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-5 

3D-3 Composition of Fine Particles Released by Various Stationary Sources in 

the Philadelphia Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-7 

3D-4a Organic and Elemental Carbon Fractions of Diesel and Gasoline Engine 

Particulate Matter Exhaust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-12 

3D-4b Contribution of Organic Carbon to Particulate Matter Carbon Emissions 

in Motor Vehicle Exhaust Collected from Vehicles Operated on Chassis 

Dynamometers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-13 

3D-5 Emission Rates (mg/mi) for Constituents of Particulate Matter from 

Gasoline and Diesel Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-14 

3D-6 Summary of Particle-Phase Organic Compounds Emitted from 

Motor Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-17 

I-xv 
List of Tables 

(Cont’d) 

Number Page 

3D-7 Mass Emissions, Organic Carbon, and Elemental Carbon Emissions from 

Residential Combustion of Wood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-19 

3D-8 Summary of Particle-Phase Organic Compounds Emitted from the 

Combustion of Wood in Fireplaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-21 

3D-9 Mean Aerosol Composition at Tropical Site (Sriwijaya University, 

Sumatra, Indonesia) Affected Heavily by Biomass Burning Emissions . . . . 3D-22 

3E-1 Ranges of Annual Mean PM Concentrations at IMPROVE Monitoring 

Sites (μg/m3

) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3E-3 

3E-2 Ranges of Annual 90th Percentile Values of Daily PM Concentrations at 

IMPROVE Monitoring Sites (μg/m3

) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3E-4 

4-1 Essential Ecological Attributes and Reporting Categories . . . . . . . . . . . . . . . . . . 4-3

4-2 Types and Determinants of Particulate Deposition and Impact 

to Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9 

4-3 Relative Importance of Wet, Dry, Particulate, and Total Deposition to 

Three Forest Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12 

4-4 Reported Mean Deposition Velocities for Sulfate, Chlorine, Nitrate, 

and Ammonium-Ion-Containing Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-37 

4-5 Representative Empirical Measurements of Deposition Velocity for 

Particulate Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-38 

4-6 Reported Mean Deposition Velocities for Potassium, Sodium, Calcium, 

and Magnesium Base Cation Containing Particles . . . . . . . . . . . . . . . . . . . . . . . 4-38 

4-7 Mean Annual Nitrogen Deposition (equivalents/ha/year) from Fine and 

Coarse Particles Compared to Total Nitrogen Deposition from all Sources 

to a Variety of Forest Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-46 

4-8 Mean Annual Sulfate Deposition (equivalents/ha/year) from Fine and 

Coarse Particles Compared to Total Sulfur Deposition from all Sources 

to a Variety of Forest Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-47 

I-xvi 
List of Tables 

(Cont’d)

Number Page

4-9 Mean Annual Base Cation Deposition (equivalents/ha/year) from Fine 

and Coarse Particles Compared to Total Base Cation Deposition from

all Sources to a Variety of Forest Ecosystems .......................... 4-48

4-10 Mean Particle Size, Deposition Rates, and Derived Deposition Velocities 

for Heavy Metal Deposition to the Upper Canopy (inert plates or leaves) 

of an Upland Oak Forest ........................................... 4-52

4-11 Total Heavy Metal Deposition to Temperate Latitude Forests .............. 4-52

4-12 Primary Goods and Services Provided by Ecosystems ................... 4-58

4-13 Ecosystem Functions Impacted by Air Pollution Effects on Temperate 

Forest Ecosystems ................................................ 4-84

4-14 Nitrogen-Saturated Forests in North America, Including Estimated 

N Inputs and Outputs ............................................ 4-102

4-15 Visibility Measurement Techniques ................................. 4-171

4-16 Residential Visibility Contingent Valuation Study Results ............... 4-189

4-17 Residential Visibility Valuation Study Results for Los Angeles and 

San Francisco .................................................. 4-190

4-18 Corrosive Effects of Particulate Matter and Sulfur Dioxide on Metals ...... 4-194

4-19 Corrosive Effects of Particulate Matter and Sulfur Dioxide on Stone ....... 4-197

4-20 Examples of Impacts Resulting from Projected Changes in Extreme

Climate Events ................................................. 4-208

4-21 Effects of Reactive Nitrogen ....................................... 4-230

5-1 Types of Particulate Matter Used in Exposure and Concentration Variables . . . 5-7

5-2 Activity Pattern Studies Included in the Consolidated Human 

Activity Database ................................................ 5-18

5-3 Personal Exposure Models for Particulate Matter ....................... 5-19

5-4 Summary of Recent PM Personal Exposure Studies ..................... 5-31

I-xvii 
List of Tables 

PM

(cont’d)

Number Page

5-5 Summary of Recent Microenvironmental PM Measurement Studies ........ 5-36

5-6 Papers Reporting Reanalyses of Particulate Matter Exposure Studies ........ 5-44

5-7 Personal Monitoring Studies for Particulate Matter: Measured 

Concentrations and Correlation Coefficients ........................... 5-49

5-8 Mean Concentration for PM Mass Reported for the Baltimore (Williams

et al., 2000a,b,c) and the Fresno (Evans et al., 2000) Studies .............. 5-58

5-9 Daily-average Correlation Coefficients Reported for the Baltimore 

(Williams et al., 2000a,b,c) and the Fresno (Evans et al., 2000) Studies ...... 5-58

5-10 Regression Analysis Reported for Indoor/Outdoor Relationships for 

2.5 in the Baltimore (Williams et al., 2000a,b,c) and Fresno (Evans 

et al., 2000) Studies ............................................... 5-58

5-11 Volume Mean Diameter and Maximum PM2.5 Concentrations of Indoor

Particle Sources .................................................. 5-84

5-12 Correlations Between Personal PM2.5 and Ambient Pollutant 

Concentrations .................................................. 5-91

5-13 Correlations Between Hourly Personal PM2.5 and Gaseous Pollutants ....... 5-93

5-14 Studies That have Measured Particulate Matter Constituents in Personal 

Exposure Samples ................................................ 5-96

5-15 Studies That Have Measured Particulate Matter Constituents in

Microenvironmental Samples ....................................... 5-97

5-16 Summary Statistics for Personal, Indoor, and Outdoor Concentrations of 

Selected Aerosol Components in Two Pennsylvania Communities .......... 5-99

5-17 Regression Analysis of Indoor Versus Outdoor Concentrations ........... 5-101

5-18 Mixed Model Analysis of Personal Versus Outdoor Concentrations ........ 5-103

5-19 Regression Analysis of Indoor Versus Outdoor Concentrations ........... 5-103

5-20 Regression Analysis of Indoor Versus Outdoor Concentrations ........... 5-104

I-xviii 
List of Figures 

Number Page 

1-1 A general framework for integrating PM research ........................ 1-9

2-1 Distribution of coarse (c), accumulation (a), and nuclei (n) mode particles

by three characteristics: (a) number, N; (b) surface area, S; and (c) volume,

V for the grand average continental size distribution ...................... 2-8

2-2 Particle size distributions by number: (a) number concentrations are shown

on a logarithmic scale to display the wide range by site and size; (b) number

concentrations for the average urban distribution are shown on a linear scale . . 2-9

2-3 Size distribution by volume for the averaged (a) rural and urban-influenced

rural number distribution shown in Figure 2-2a and a distribution from

south central New Mexico, and (b) for the averaged urban and freewayinfluenced urban number distributions shown in Figure 2-2a .............. 2-10

2-4 Volume size distribution, measured in traffic, showing fine and coarse 

particles and the nuclei and accumulation modes of fine particles ........... 2-11

2-5 Sub-micrometer number size distribution observed in a boreal forest in 

Finland showing the tri-modal structure of fine particles .................. 2-12

2-6 An idealized size distribution, that might be observed in traffic, showing 

fine and coarse particles and the nucleation, Aitken, and accumulation 

modes that comprise fine particles ................................... 2-13

2-7 Specified particle penetration (size-cut curves) through an ideal (no-particleloss) inlet for five different size-selective sampling criteria ............... 2-16

2-8 An idealized distribution of ambient particulate matter showing fine-mode 

particles and coarse-mode particles and the fractions collected by sizeselective samplers ................................................ 2-18

2-9 Comparison of penetration curves for two PM10 beta gauge samplers using

cyclone inlets ................................................... 2-19

2-10 Three examples of impactor size distributions and distributions resulting 

from fitting several log normal distributions to the impactor data ........... 2-24

2-11 Particulate matter concentrations in Spokane, WA, during the August 30, 

1996 dust storm .................................................. 2-27

2-12 Typical engine exhaust size distribution ............................... 2-32

I-xix 
List of Figures 

(cont’d) 

Number Page 

2-13 Number size distributions showing measurement of a nucleation burst 

mode in a boreal forest in Finland ................................... 2-33

2-14 Examples of the measured 1-h average particle number size distributions 

and the log normal fits to the modes of the data ......................... 2-34

2-15a-f Fitted multi-model particle size distribution at different sampling distances

from Freeway 405: (a) 30 m downwind, (b) 60 m downwind, (c) 90 m

downwind, (d) 150 m downwind, (e) 300 m downwind, (f) 300 m upwind . . . 2-35

2-15g Combination of Figures 2-15(a-e), with dN/d logDp scale ................. 2-36

2-16a Particle growth curves showing fully reversible hygroscopic growth of 

sulfuric acid (H2SO4) particles, deliquescent growth of ammonium sulfate 

[(NH4)2 SO4] particles at the deliquescent point (A, about 80% relative 

humidity [RH]), reversible hygroscopic growth of ammonium sulfate 

solution droplets at RH greater than 80%, and hysteresis (the droplet 

remains supersaturated as the RH decreases below 80%) until the 

crystallization point (B, about 38% RH) is reached ...................... 2-42

2-16b Theoretical predictions and experimental measurements of growth of 

NH4HSO4 particles at relative humidities between 95 and 100% ........... 2-43

2-17 Concentration of the dissolved gas in the particle normalized by its initial 

concentration as a function of airway generation for H = 104

 M atm!1

for particle diameters of 0.1, 0.3, 0.7, and 1 μm ......................... 2-46

2-18 Schematic showing major nonvolatile and semivolatile components of PM2.5 . 2-57

2-19 Aerosol water content expressed as a mass percentage, as a function of 

relative humidity ................................................. 2-65

2-20 Amount of ammonium nitrate volatilized from Teflon filters, expressed 

as a percentage of the measured PM2.5 mass, for the SCAQS and CalTech

studies, for spring and fall sampling periods ........................... 2-69

2-21 Average concentration (μg/m3

) of nonvolatile and semivolatile PM 

components in three cities .......................................... 2-76

2-22 Comparison of mass measurements with collocated RAMS (real-time 

data), PC-BOSS (1-h data), FRM PM2.5 sampler (average of 24-h data), 

and a conventional TEOM monitor (real-time data) ..................... 2-77

I-xx 
List of Figures 

(cont’d)

Number Page

2-23 Schematic diagram of the sample collection portion of the PM2.5

FRM sampler ................................................... 2-81

diesel exhaust, shows three traces that correspond to temperature, filter 

transmittance, and Flame Ionization Detector (FID) detector response . . . . . . 2B-10

and by (b) the NIOSH protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2B-12

2-24 Schematic view of the final design of the WINS ........................ 2-82

2-25 Evaluation of the final version of the WINS ........................... 2-83

2-26 Schematic diagram showing the principle of virtual impaction ............. 2-91

2-27 Size distribution of particles divided by chemical classification into 

organic, marine, and soil (or crustal) ................................ 2-102

2B-1 Thermogram for a sample containing rock dust (a carbonate source) and 

2B-2 Examples of thermograms obtained by (a) the IMPROVE protocol, 

3-1a 1999-2001 county-wide average annual mean PM10 concentrations 

(μg/m3

) for counties with PM10 monitors ............................... 3-7

3-1b 1999-2001 highest county-wide 98th percentile 24-h average PM10

concentrations (μg/m3

) for counties with PM10 monitors ................... 3-7

3-2 Nationwide trend in ambient PM10 concentration from 1992 through 2001 .... 3-8

3-3 Trend in PM10 annual mean concentrations by EPA region, 1992 through

2001 (μg/m3

) ..................................................... 3-9

3-4a 1999-2001 county-wide average annual mean PM2.5 concentrations 

(μg/m3

) for counties with PM2.5 monitors .............................. 3-10

3-4b 1999-2001 highest county-wide 98th percentile 24-h average PM2.5

concentrations (μg/m3

) for counties with PM2.5 monitors .................. 3-10

3-5 Collection of annual distribution of 24-h average PM2.5 concentrations 

observed in U.S. and Canadian health studies conducted during the 

1980’s and early 1990’s ........................................... 3-11

I-xxi 
List of Figures 

(cont’d) 

Number Page 

3-6a 1999-2000 estimated county-wide average annual mean PM10-2.5

concentrations (μg/m3

) for counties with collocated PM2.5 and 

PM10 monitors ................................................... 3-14

PM

3-6b 1999-2000 estimated county-wide highest 98th percentile 24-h 

10-2.5 concentrations (μg/m3

) for counties with collocated PM2.5

and PM10 monitors ............................................... 3-15

3-7a,b Quarterly distribution of 24-h average PM2.5 concentrations for selected

monitors in the (a) Philadelphia, PA and (b) Cleveland, OH MSAs ......... 3-16

3-7c,d Quarterly distribution of 24-h average PM2.5 concentrations for selected 

monitors in the (c) Dallas, TX and (d) Los Angeles, CA MSAs ............ 3-17

3-8 Seasonal concentrations of PM2.5 and PM10 measured in the four

MAACS cities ................................................... 3-20

3-9a,b Quarterly distribution of 24-h average PM10-2.5 concentrations for selected 

sites in the (a) Cleveland, OH; (b) Dallas, TX MSAs .................... 3-22

3-9c Quarterly distribution of 24-h average PM10-2.5 concentrations for selected 

sites in the Los Angeles, CA MSAs .................................. 3-23

3-10 Frequency distribution of 24-h average PM2.5 concentrations measured 

at the Presbyterian home (PBY) monitoring site in southwestern 

Philadelphia from 1992 to 1995 ..................................... 3-25

3-11 Concentrations of 24-h average PM2.5 measured at the EPA site in 

Phoenix, AZ from 1995 to 1997 ..................................... 3-26

3-12 Frequency distribution of 24-h average PM2.5 concentrations measured 

at the EPA site in Phoenix, AZ from 1995 to 1997 ...................... 3-27

3-13 Frequency distribution of 24-h average PM2.5 measurements obtained from

all California Air Resources Board dichotomous sampler sites from 1989 

to 1998 ........................................................ 3-27

3-14 Frequency distribution of 24-h average PM10-2.5 concentrations obtained 

from all California Air Resource Board Dichotomous sampler sites from

1989 to 1998 .................................................... 3-28

I-xxii 
List of Figures 

(cont’d)

Number Page

3-15 Concentrations of 24-h average PM2.5 measured at the Riverside-Rubidoux 

site from 1989 to 1998 ............................................ 3-28

3-16 Frequency distribution of 24-h average PM2.5 concentrations measured

at the Riverside-Rubidoux site from 1989 to 1994 ....................... 3-29

3-17 Intraday variability of hourly average PM2.5 concentrations across the 

United States .................................................... 3-31

3-18 Occurrence of differences between pairs of sites in three MSAs ............ 3-47

3-19 Intersite correlation coefficients for PM2.5, PM10, and PM10-2.5 .............. 3-51

3-20 PM2.5 chemical components in downtown Los Angeles and Burbank (1986)

have similar characteristics ......................................... 3-54

3-21 Concentrations of PM2.5 chemical components in Rubidoux and downtown

Los Angeles (1986) ............................................... 3-55

3-22 Monthly average Saharan dust components in all size fractions of the 

aerosol sampled in Miami, FL (from 1974 to 1996) ...................... 3-85

3-23 PM2.5 and PM10 concentrations measured at Chilliwack Airport, located in 

southwestern British Columbia, just before and during the Asian desert

dust episode of April and May 1998 .................................. 3-86

3-24 Time series of 24-h average PM10 concentrations observed in the 

Rio Grande Valley during May 1998 ................................. 3-88

3-25 PM10 concentrations observed in St. Louis, MO, during May 1998 .......... 3-89

3A-1 Philadelphia, PA-NJ metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . 3A-5

3A-2 Washington, DC metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . 3A-6

3A-3 Norfolk, VA metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-7

3A-4 Columbia, SC metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-8

3A-5 Atlanta, GA metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-9

I-xxiii 
List of Figures 

(cont’d) 

Number Page 

3A-6 Birmingham, AL metropolitan statistical area ........................ 3A-10 

3A-7 Tampa, FL metropolitan statistical area ............................. 3A-11 

3A-8 Cleveland, OH metropolitan statistical area .......................... 3A-12 

3A-9 Pittsburgh, PA metropolitan statistical area .......................... 3A-13 

3A-10 Steubenville, OH-Weirton, WV metropolitan statistical area ............ 3A-14 

3A-11 Detroit MI metropolitan statistical area ............................. 3A-15 

3A-12 Grand Rapids, MI metropolitan statistical area ....................... 3A-16 

3A-13 Milwaukee, WI metropolitan statistical area ......................... 3A-17 

3A-14 Chicago, IL metropolitan statistical area ............................ 3A-18 

3A-15 Gary, IN metropolitan statistical area ............................... 3A-19 

3A-16 Louisville, KY metropolitan statistical area .......................... 3A-20 

3A-17 St. Louis, MO metropolitan statistical area .......................... 3A-21 

3A-18 Baton Rouge, LA metropolitan statistical area ........................ 3A-22 

3A-19 Kansas City, KS-MO metropolitan statistical area ..................... 3A-23 

3A-20 Dallas, TX metropolitan statistical area ............................. 3A-24 

3A-21 Boise, ID metropolitan statistical area .............................. 3A-25 

3A-22 Salt Lake City, UT metropolitan statistical area ....................... 3A-26 

3A-23 Seattle, WA metropolitan statistical area ............................ 3A-27 

3A-24 Portland, OR metropolitan statistical area ........................... 3A-28 

3A-25 Los Angeles-Long Beach, CA metropolitan statistical area .............. 3A-29 

3A-26 Riverside-San Bernadino, CA metropolitan statistical area .............. 3A-30 

I-xxiv 
List of Figures 

(cont’d)

Number Page

3A-27 San Diego, CA metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-31

3A-28 Columbia, SC metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-32

3A-29 Tampa, FL metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-33

3A-30 Cleveland, OH metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-34

3A-31 Steubenville, OH metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . 3A-35

3A-32 Detroit, MI metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-36

3A-33 Milwaukee, WI metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . 3A-37

3A-34 Chicago, IL metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-38

3A-35 Gary, IN metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-39

3A-36 Louisville, KY metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-40

3A-37 St. Louis, MO metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-41

3A-38 Baton Rouge, LA metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . 3A-42

3A-39 Dallas, TX metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-43

3A-40 Salt Lake City, UT metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . 3A-44

3A-41 Portland, OR metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-45

3A-42 Los Angeles, CA metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . 3A-46

3A-43 Riverside, CA metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-47

3A-44 San Diego, CA metropolitan statistical area . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-48

3D-1 Size distribution of particles generated in a laboratory resuspension 

chamber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-2

3D-2 Size distribution of California source emissions, 1986 . . . . . . . . . . . . . . . . . . 3D-4

I-xxv 
List of Figures 

(cont’d) 

Number Page 

3D-3 Chemical abundances for PM2.5 emissions from paved road dust in 

Denver, CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-5 

3D-4 Chemical abundances for PM2.5 emissions from wood burning in 

Denver, CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3D-18 

3E-1a,b Interannual variability in 24-h average PM2.5 concentrations observed at 

selected IMPROVE sites: (a) Bridger Wilderness, WY; (b) Yellowstone 

National Park, WY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3E-7 

3E-2a,b Seasonal variability in 24-h average PM2.5 concentrations observed at 

selected IMPROVE sites: (a) Bridger Wilderness, WY; (b) Yellowstone 

National Park, WY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3E-8 

3E-3a,b Seasonal variability in 24-h average PM10-2.5 concentrations observed at 

selected IMPROVE sites: (a) Bridger Wilderness, WY; (b) Yellowstone 

National Park, WY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3E-9 

4-1 The diversity of fine PM from sites in the western and eastern 

United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6 

4-2 Relative importance of three modes of deposition of nitrate (A) and 

sulfate (B) at high elevation sites (Unsworth and Wilshaw, 1989; 

Fowler et al., 1989; Mueller, 1988; Aneja and Murthy, 1994) . . . . . . . . . . . . . . 4-11 

4-3 A simplified resistance catena representing the factors controlling 

deposition of particles to the surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-16 

4-4 The relationship between deposition velocity of selected particulate 

materials and the distribution of the material between the coarse- and 

fine-aerosol fractions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-17 

4-5 The relationship between particle diameter and deposition velocity 

for particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-18 

4-6 Vertical stratification of diverse, chemically speciated particles in a 

mixed oak forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-33 

4-7 The relationship between particle size and concentration below a spruce 

canopy with wind velocity at a height of 16.8 m equaling 5 m/s . . . . . . . . . . . . 4-34 

I-xxvi 
List of Figures 

(cont’d) 

Number Page 

4-8 Range of percent of total deposition delivered in the dry particulate 

fraction, across the sites of the Integrated Forest Study ................... 4-40

4-9 Contribution of particulate deposition to total deposition of nitrogen, 

sulfur, and base cations ............................................ 4-40

4-10 Mean percent of total nitrogen, sulfur, or base cation deposition 

contributed by fine plus coarse particles ............................... 4-49

4-11 Annual total deposition of heavy metals to Hubbard Brook Experimental 

Forest, NH ...................................................... 4-53

4-12 Sample stressors and the essential ecological attributes they affect 

(after Science Advisory Board, 2002) ................................ 4-56

4-13 Linkages among various ecosystem goods and services (food, water, 

biodiversity, forest products) and other driving forces (climate change) ...... 4-60

4-14 Effects of environmental stress on forest trees are presented on a 

hierarchial scale for the leaf, branch, tree, and stand levels of organization . . . 4-80

4-15 Illustration of the nitrogen cascade showing the movement of the 

human-produced reactive nitrogen (Nr) as it cycles through the various

environmental reservoirs in the atmosphere, terrestrial ecosystems, and 

aquatic ecosystems ............................................... 4-97

4-16 Nitrogen cycle (dotted lines indicate processes altered by 

nitrogen saturation) ............................................... 4-99

4-17 Diagrammatic overview of excess nitrogen in North America ............ 4-103

4-18 Schematic of sources and sinks of hydrogen ions in a forest .............. 4-117

4-19 Key elements of proposed framework for determining critical loads for 

nitrogen and sulfur in the United States .............................. 4-124

4-20 Calcium deposition in > 2-μm particles, < 2-μm particles, and wet 

forms (upper bars) and leaching (lower bars) in the Integrated Forest 

Study sites ..................................................... 4-128

I-xxvii 
List of Figures 

(cont’d) 

Number Page 

4-21 Magnesium deposition in > 2-μm particles, < 2-μm particles, and wet 

forms (upper bars) and leaching (lower bars) in the Integrated Forest 

Study sites ..................................................... 4-129

4-22 Potassium deposition in > 2-μm particles, < 2-μm particles, and wet 

forms (upper bars) and leaching (lower bars) in the Integrated Forest 

Study sites ..................................................... 4-129

4-23 Base cation deposition in > 2-μm particles, < 2-μm particles, and wet 

forms (upper bars) and leaching (lower bars) in the Integrated Forest 

Study sites ..................................................... 4-130

4-24 Total cation leaching (total height of bar) balanced by sulfate and nitrate 

estimated from particulate deposition (assuming no ecosystem retention, 

particulate sulfur and nitrogen) and by other sources (both deposition and

internal) of sulfate and nitrate (other sulfur and nitrogen sources) and by

other anions in the Integrated Forest Study sites ....................... 4-131

4-25 Soil exchangeable Ca2+ pools and net annual export of Ca2+ (deposition 

minus leaching times 25 years) in the Integrated Forest Study sites ........ 4-133

4-26 Soil exchangeable Mg2+ pools and net annual export of Mg2+ (deposition 

minus leaching times 25 years) in the Integrated Forest Study sites ........ 4-133

4-27 Soil exchangeable K+

 pools and net annual export of K+ (deposition

minus leaching times 25 years) in the Integrated Forest Study sites ........ 4-134

4-28a Simulated soil solution mineral acid anions in the red spruce site with 

no change, 50% N and S deposition, and 50% base cation deposition ....... 4-138

4-28b Simulated soil solution base cations in the red spruce site with no change, 

50% N and S deposition, and 50% base cation deposition ................ 4-139

4-29a Simulated soil solution Al in the red spruce site with no change, 

50% N and S deposition, and 50% base cation deposition ................ 4-140

4-29b Simulated soil solution soil base saturation in the red spruce site with 

no change, 50% N and S deposition, and 50% base cation deposition ....... 4-141

4-30a Simulated soil solution mineral acid anions in the Coweeta site with 

no change, 50% N and S deposition, and 50% base cation deposition ....... 4-142

I-xxviii 
List of Figures 

(cont’d) 

Number Page 

4-30b Simulated soil solution base cations in the Coweeta site with no change, 

50% N and S deposition, and 50% base cation deposition ................ 4-143

4-31 Relationship of plant nutrients and trace metals with vegetation ........... 4-147

4-32 Light reflected from a target toward an observer ....................... 4-156

4-33 Light-scattering efficiency factor (per cross-sectional area), Q, for a 

homogeneous sphere with an index of refraction of 1.50 as a function 

of the size parameter, " = BD/8 .................................... 4-159

4-34 Volume-specific light-scattering efficiency as a function of particle

diameter Dp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-161

4-35 Particle growth curve as a function of relative humidity (RH) showing 

deliquescent growth of ammonium sulfate [(NH4)2 SO4] particles at the 

deliquescent point (A, about 80% RH), reversible hygroscopic growth

of ammonium sulfate solution droplets at RH > 80%, and hysteresis

(the droplet remains supersaturated as the RH decreases below 80%)

until the crystallization point (B, about 38% RH) is reached .............. 4-162

4-36 Comparison of extinction (Mm-1) and visual range (km) ................. 4-167

4-37 Proportionality of observed daytime haziness to fine particle mass 

concentration in Los Angeles ...................................... 4-168

4-38 Relative humidity adjustment factor, f(RH), for ammonium sulfate as a 

function of relative humidity ...................................... 4-170

4-39a Aggregate visibility trends (in deciviews) for 10 eastern Class 1 areas ...... 4-180

4-39b Aggregate visibility trends (in deciviews) for 26 western Class 1 areas ..... 4-180

4-40 (a) Eastern class I area aggregate trends in aerosol light extinction on 

the 20% haziest days, including trends by major aerosol component; 

(b) Western class I area aggregate trends in aerosol light extinction on 

the 20% haziest days, including trends by major aerosol component ....... 4-182

4-41a Light extinction trends in Tucson, Arizona from 1993 to 2002 ............ 4-184

4-41b Light extinction trends in Phoenix, Arizona from 1994 to 2001 ........... 4-184

I-xxix 
List of Figures 

(cont’d) 

Number Page 

4-42 Estimated global mean radiative forcing exerted by gas and various 

particle phase species for the year 2000, relative to 1750 ................ 4-213

5-1 Hypothetical exposure time profile: pollutant exposure as a function of 

time showing how the averaged exposure, integrated exposure, and peak 

exposure relate to the instantaneous exposure ........................... 5-9

5-2 Distribution of FINF (a) and " (b) for daytime as estimated from PTEAM 

study data ...................................................... 5-25

5-3 Comparison of correlation coefficients for longitudinal analyses of 

personal exposure versus ambient concentrations for individual subjects 

for PM2.5 and sulfate .............................................. 5-61

5-4 Personal exposure versus ambient concentrations for PM2.5 and sulfate for

well, moderately, and poorly ventilated indoor environments .............. 5-64

5-5 Regression analyses of aspects of daytime personal exposure to PM10

estimated using data from the PTEAM study: (a) total personal exposure 

to PM, T, regressed on ambient concentration, C; (b) personal exposure 

to ambient PM, A, regressed on C; and (c) personal exposure to 

nonambient PM, N, regressed on C .................................. 5-65

5-6 Air exchange rates measured in homes throughout the United States ........ 5-72

5-7 Box plots of hourly air exchange rates stratified by season in Boston, MA 

during 1998 ..................................................... 5-73

5-8 Geometric mean infiltration factor (indoor/outdoor ratio) for hourly 

nighttime, nonsource data for two seasons ............................. 5-74

5-9 Regression of air exchange rate on absolute indoor-outdoor temperature 

difference ...................................................... 5-76

5-10 Comparison of deposition rates from Long et al. (2001a) with literature 

values (from Abt et al., 2000b) ...................................... 5-78

5-11 Penetration efficiencies and deposition rates from models of nightly 

average data .................................................... 5-79

5-12 Mean hourly indoor/outdoor particle concentration ratio from an 

unoccupied residence in Fresno, CA during spring 1999 .................. 5-85

I-xxx 
List of Figures 

(cont’d) 

Number Page 

5-13 Personal versus outdoor SO4

2!

 in State College, PA . . . . . . . . . . . . . . . . . . . . 5-100

5-14 Plots of nonambient exposure to PM10 showing (a) daily individual 

daytime values from PTEAM data and (b) daily-average values from 

THEES data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-113

I-xxxi 
Authors, Contributors, and Reviewers 

CHAPTER 1. INTRODUCTION 

Principal Authors 

Dr. Lester D. Grant—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. Dennis J. Kotchmar—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

CHAPTER 2. PHYSICS, CHEMISTRY, AND MEASUREMENT 

OF PARTICULATE MATTER 

Principal Authors 

Dr. William Wilson—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. Candis S. Claiborn—Washington State University, Laboratory for Atmospheric Research, 

Department of Civil and Environmental Engineering, P.O. Box 642910, Pullman, WA 99164 

Dr. Brooke L. Hemming—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Contributing Authors 

Dr. Tom Cahill, University of California, Davis, One Shields Ave., Davis, CA 95616 

Dr. Judith C. Chow, Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512 

Max R. Peterson, Research Triangle Institute, P.O. Box 12194, Research Triangle Park, NC 

Dr. James J. Schauer, University of Wisconsin, College of Engineering, Department of Civil and 

Environmental Engineering, Madison, WI 53706 

Dr. Barbara J. Turpin, The State University of New Jersey, Rutgers, Department of 

Environmental Sciences and Rutgers Cooperative Extension, New Brunswick, NJ 08901-8551 

Dr. John G. Watson, Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512 

I-xxxii 

27709 
Authors, Contributors, and Reviewers 

(cont’d) 

Contributors and Reviewers 

Dr. Edward O. Edney—National Exposure Research Laboratory (E205-02) 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. R.R. Eldred, Crocker Nuclear Laboratory, University of California, Davis, Davis, CA 95616 

Mr. William Ewald—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

James B. Flanagan—Research Triangle Institute, P.O. Box 12194, Research Triangle Park, NC 

27709 

Dr. Judith Graham—American Chemistry Council, 1300 Wilson Boulevard, Arlington, VA 

22207 

Dr. Lester D. Grant—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Mr. Jim Homolya—Office of Air Quality Planning and Standards (C339-02), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

R.K.M. Jayanty—Research Triangle Institute, P.O. Box 12194, Research Triangle Park, NC 

27709 

Mr. Scott Mathias—Office of Air Quality Planning and Standards (C539-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Mr. Frank McElroy—National Exposure Research Laboratory (MD-46), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. Joseph Pinto—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Joann Rice—Office of Air Quality Planning and Standards (C339-02), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27709 

Ed E. Rickman—Research Triangle Institute, P.O. Box 12194, Research Triangle Park, NC 

27709 

Dr. Bret Schichtel—Colorado State University, Cooperative Institute for Research in the 

Atmosphere, Foothills Campus, Fort Collins, CO 80523-1375 

I-xxxiii 
Authors, Contributors, and Reviewers 

(cont’d) 

Contributors and Reviewers 

(cont’d) 

Dr. John J. Vandenberg—National Center for Environmental Assessment (8601D), U.S. 

Environmental Protection Agency, 1200 Pennsylvania Avenue, NW, Washington, DC 20460 

Dr. Russell Wiener—National Exposure Research Laboratory (D205-03) 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

CHAPTER 3. CONCENTRATIONS, SOURCES, AND 

EMISSIONS OF ATMOSPHERIC PARTICULATE MATTER 

Principal Author 

Dr. Joseph P. Pinto—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Contributing Authors 

Dr. Allen S. Lefohn—A.S.L. & Associates, 111 North Last Chance Gulch, Helena, MT 59601 

Dr. Barbara J. Turpin—The State University of New Jersey, Rutgers, Department of 

Environmental Sciences and Rutgers Cooperative Extension, New Brunswick, NJ 08901-8551 

Dr. James J. Schauer—University of Wisconsin, College of Engineering, Department of Civil 

and Environmental Engineering, Madison, WI 53706 

Dr. Robert D. Willis, ManTech Environmental, Inc., Research Triangle Park, NC 27711 

Contributors and Reviewers 

Dr. JoEllen Bandemeyer—Research Triangle Institute, PO Box 12194, Research Triangle Park, 

NC 27709 

Dr. Lyle Chinkin—Sonoma Technology, 1360 Redwood Way, Suite C, Petaluma, CA 94549 

Dr. Candis S. Claiborn—Washington State University, Laboratory for Atmospheric Research, 

Department of Civil and Environmental Engineering, P.O. Box 642910, Pullman, WA 99164 

I-xxxiv 
Authors, Contributors, and Reviewers 

(cont’d) 

Contributors and Reviewers 

(cont’d)

Dr. Steven Colome—Integrated Environmental Services, 5319 University Drive, #430, Irvine,

CA 92612

Mr. Tom Coulter—National Exposure Research Laboratory (D234-01),

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. Edward O. Edney—National Exposure Research Laboratory (E205-02), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Mr. Terence Fitz-Simons—Office of Air Quality Planning and Standards (C304-01), 

U. S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. Sheldon Friedlander—University of California at Los Angeles, Department of Chemical 

Engineering, 5531 Boelter Hall, Los Angeles, CA 90095 

Dr. Lester D. Grant—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. Lynn Hildemann—Civil and Environmental Engineering Department, Stanford University, 

Stanford, CA 94305 

Mr. Phil Lorang—Office of Air Quality Planning and Standards (D205-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Ms. Karen Magliano—California Air Resources Board, 2020 L Street, Sacramento, CA 95814 

Mr. Scott Mathias—Office of Air Quality Planning and Standards (C539-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Ms. Nehzat Mottallebi—California Air Resources Board, 2020 L Street, Sacramento, CA 95814 

Mr. Tom Pace—Office of Air Quality Planning and Standards (D205-01), U.S. Environmental 

Protection Agency, Research Triangle Park, NC 27711 

Dr. Richard Poirot—VT Air Program, Building 3 South, 103 South Main Street, Waterbury, VT 

Mr. Win Setiawan—California Air Resources Board, 2020 L Street, Sacramento, CA 95814 

I-xxxv 

05671 
Authors, Contributors, and Reviewers 

(cont’d) 

Contributors and Reviewers 

(cont’d) 

Dr. John J. Vandenberg—National Center for Environmental Assessment (8601D), U.S. 

Environmental Protection Agency, 1200 Pennsylvania Avenue, NW, Washington, DC 20460 

Mr. Dane Westerdahl—California Air Resources Board, 2020 L Street, Sacramento, CA 95814 

CHAPTER 4. ENVIRONMENTAL EFFECTS OF PARTICULATE MATTER 

Principal Authors 

Ms. Beverly Comfort—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Mr. William Ewald—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. J.H.B. Garner—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. Lester D. Grant—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. David A. Grantz—University of California/Riverside, Kearney Agricultural Center, 

9240 South Riverbend Avenue, Parlier, CA 93648 

Dr. Brooke L. Hemming—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. Dale W. Johnson—Environmental and Resource Science, 1000 Valley Road, University of 

Nevada, Reno, NV 89512 

Dr. William Malm—National Park Service, Air Resources Division, CIRA, Colorado State 

University, Fort Collins, CO 

Dr. Joseph P. Pinto—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

I-xxxvi 
Authors, Contributors, and Reviewers 

(cont’d) 

Contributing Authors 

Dr. Paul J. Hanson—Environmental Sciences Division, Oak Ridge National Laboratory, 

P.O. Box 2008, Bethel Valley Road, Building 1059, Oak Ridge, TN 37831-6422 

Dr. William H. Smith—Professor Emeritus, Yale University School of Forestry and 

Environmental Studies, 370 Prospect Street, New Haven, CT 06511 

Contributors and Reviewers 

Dr. Larry T. Cupitt—National Exposure Research Laboratory (D305-01), 

U. S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Mr. Rich Damberg—Office of Air Quality Planning and Standards (C539-02), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. Russell R. Dickerson—University of Maryland, Department of Meteorology,

Stadium Drive, College Park, MD 20742

Dr. Anne Grambsch—National Center for Environmental Assessment (8601D),

U. S. Environmental Protection Agency, Washington, DC 20036

Dr. Sagar V. Krupa—University of Minnesota, Department of Plant Pathology,

495 Borlaug Hall, 1991 Upper Buford Circle, St. Paul, MN 55108

Dr. Alan J. Krupnick—Quality of the Environment Division, Resources for the Future,

1616 P Street, NW, Washington, DC 20036

Mr. Paul T. Roberts—Sonoma Technology, Inc.,1360 Redwood Way - Suite C,

Petaluma, CA 94954

Mr. John Spence–Cary, NC 27511

Dr. John J. Vandenberg—National Center for Environmental Assessment (8601D), U.S.

Environmental Protection Agency, 1200 Pennsylvania Avenue, NW, Washington, DC 20460 

Ms. Debra Meyer Wefering—Duckterather Weg 61, Bergisch Gladbach, Germany 54169 

Ms. Kay Whitfield—Office of Air Quality Planning and Standards (C243-02), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 

Dr. Richard Zepp—National Exposure Research Laboratory (IOD), 

U. S. Environmental Protection Agency, Athens, GA 

I-xxxvii 
Authors, Contributors, and Reviewers 

(cont’d) 

CHAPTER 5. HUMAN EXPOSURE TO PARTICULATE MATTER 

AND ITS CONSTITUENTS 

Principal Authors 

Dr. William E. Wilson—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Linda S. Sheldon—National Exposure Research Laboratory (E205-02), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Halûk Özkaynak—National Exposure Research Laboratory (E205-02), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Contributing Authors 

Dr. Janet Burke—National Exposure Research Laboratory (E205-02),

U. S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Roy Fortmann—National Exposure Research Laboratory (E205-02), 

U. S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. David T. Mage—Institute for Survey Research, Temple University,

Philadelphia, PA 19122-6099 

Mr. Thomas McCurdy—National Exposure Research Laboratory (E205-02), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Gary Norris—National Exposure Research Laboratory (E205-03),

U. S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Anne Rea—National Exposure Research Laboratory (E205-04),

U. S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Alan Vette—National Exposure Research Laboratory (E205-04),

U. S. Environmental Protection Agency, Research Triangle Park, NC 27711

I-xxxviii 
Authors, Contributors, and Reviewers 

(cont’d) 

Contributors and Reviewers 

Dr. Lester D. Grant—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. John Langstaff—Office of Air Quality Planning and Standards (C539-01) 

U.S. Environmental Protection Agency, Research Triangle park, NC 27711

Dr. Joseph Pinto—National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Harvey Richmond—Office of Air Quality Planning and Standards (C539-01), 

U. S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. John J. Vandenberg—National Center for Environmental Assessment (8601D), U.S.

Environmental Protection Agency, 1200 Pennsylvania Avenue, NW, Washington, DC 20460

I-xxxix 
27711 

U.S. ENVIRONMENTAL PROTECTION AGENCY

PROJECT TEAM FOR DEVELOPMENT OF AIR QUALITY CRITERIA

FOR PARTICULATE MATTER 

Executive Direction 

Dr. Lester D. Grant—Director, National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Scientific Staff 

Dr. Robert W. Elias—PM Team Leader, Health Scientist, National Center for Environmental 

Assessment (B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. William E. Wilson—Air Quality Coordinator, Physical Scientist, National Center for

Environmental Assessment (B243-01), U.S. Environmental Protection Agency, Research

Triangle Park, NC 27711

Dr. James Brown—Health Scientist, National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Beverly Comfort—Health Scientist, National Center for Environmental Assessment

(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. William Ewald—Health Scientist, National Center for Environmental Assessment

(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. J.H.B. Garner—Ecological Scientist, National Center for Environmental Assessment

(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Brooke Hemming—Health Scientist, National Center for Environmental Assessment

(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Dennis J. Kotchmar—Medical Officer, National Center for Environmental Assessment

(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Joseph P. Pinto—Physical Scientist, National Center for Environmental Assessment 

(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Lori White—Health Scientist, National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

I-xl 
U.S. ENVIRONMENTAL PROTECTION AGENCY

PROJECT TEAM FOR DEVELOPMENT OF AIR QUALITY CRITERIA

FOR PARTICULATE MATTER 

(cont’d) 

Technical Support Staff 

Ms. Nancy Broom—Information Technology Manager, National Center for Environmental

Assessment (B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 

27711

Mr. Douglas B. Fennell—Technical Information Specialist, National Center for Environmental

Assessment (B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 

27711

Ms. Emily R. Lee—Management Analyst, National Center for Environmental Assessment 

(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Diane H. Ray—Program Specialist, National Center for Environmental Assessment

(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Donna Wicker—Administrative Officer, National Center for Environmental Assessment

(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Richard Wilson—Clerk, National Center for Environmental Assessment (B243-01), 

U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Document Production Staff 

Ms. Carolyn T. Perry—Manager/Word Processor, Computer Sciences Corporation, 2803 Slater

Road, Suite 220, Morrisville, NC 27560

Dr. Barbara Liljequist—Technical Editor, Computer Sciences Corporation, 2803 Slater Road,

Suite 220, Morrisville, NC 27560

Dr. Carol A. Seagle—Technical Editor, Computer Sciences Corporation, 2803 Slater Road,

Suite 220, Morrisville, NC 27560

Ms. Jessica Long—Graphic Artist, Computer Sciences Corporation, 2803 Slater Road,

Suite 220, Morrisville, NC 27560

Mr. Matthew Kirk—Graphic Artist, Computer Sciences Corporation, 2803 Slater Road,

Suite 220, Morrisville, NC 27560

I-xli 
U.S. ENVIRONMENTAL PROTECTION AGENCY

PROJECT TEAM FOR DEVELOPMENT OF AIR QUALITY CRITERIA

FOR PARTICULATE MATTER 

(cont’d) 

Document Production Staff 

(cont’d) 

Mr. John A. Bennett—Technical Information Specialist, Library Associates of Maryland, 

11820 Parklawn Drive, Suite 400, Rockville, MD 20852 

Ms. Sandra L. Hughey—Technical Information Specialist, Library Associates of Maryland, 

11820 Parklawn Drive, Suite 400, Rockville, MD 20852 

Ms. Rebecca Caffey—Records Management Technician, Reference Retrieval and Database 

Entry Clerk, InfoPro, Inc., 8200 Greensboro Drive, Suite 1450, McLean, VA 22102 

I-xlii 
___________________________ 

U.S. ENVIRONMENTAL PROTECTION AGENCY

SCIENCE ADVISORY BOARD (SAB) STAFF OFFICE 

CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE (CASAC) 

PARTICULATE MATTER REVIEW PANEL* 

CASAC Chair 

Dr. Philip Hopke—Bayard D. Clarkson Distinguished Professor, Department of Chemical 

Engineering, Clarkson University, Box 5708, Potsdam, NY 13699-5708 

CASAC Members 

Dr. Ellis Cowling—University Distinguished Professor At-Large, North Carolina State 

University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina 

State University, 1509 Varsity Drive, Raleigh, NC 27695-7632 

Dr. James D. Crapo—Chairman, Department of Medicine, National Jewish Medical and 

Research Center, 1400 Jackson Street, Denver, CO, 80206, and Chief Executive Officer (CEO) 

of Aeolus Pharmaceuticals, Inc. 

Dr. Frederick J. Miller—Vice President for Research, CIIT Centers for Health Research, 6 Davis 

Drive, P.O. Box 12137, Research Triangle Park, NC 27709 

Mr. Richard L. Poirot—Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Bldg. 3 South, 103 South 

Main Street, Waterbury, VT 05671-0402 

Dr. Frank Speizer—Edward Kass Professor of Medicine, Channing Laboratory, Harvard 

Medical School, 181 Longwood Avenue, Boston, MA 02115-5804 

Dr. Barbara Zielinska—Research Professor , Division of Atmospheric Science, Desert Research 

Institute, 2215 Raggio Parkway, Reno, NV 89512-1095 

* Members of this CASAC Panel consist of: 

a. CASAC Members: Experts appointed to the statutory Clean Air Scientific Advisory Committee by 

the EPA Administrator; and 

b. CASAC Consultants: Experts appointed by the SAB Staff Director to serve on one of the 

CASAC’s National Ambient Air Quality Standards (NAAQS) Panels for a particular criteria air pollutant. 

I-xliii 
U.S. ENVIRONMENTAL PROTECTION AGENCY

SCIENCE ADVISORY BOARD (SAB) STAFF OFFICE

CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE (CASAC)

PARTICULATE MATTER REVIEW PANEL*

(cont’d)

CASAC Consultants 

Dr. Jane Q. Koenig—Professor, Department of Environmental Health, School of Public Health 

and Community Medicine, University of Washington, Box 357234, Seattle, WA 98195-7234 

Dr. Petros Koutrakis—Professor of Environmental Science, Environmental Health , School of 

Public Health, Harvard University, HSPH, 401 Park Dr., Room 410 West, Boston, MA 02215 

Dr. Allan Legge—President, Biosphere Solutions, 1601 11th Avenue NW, Calgary, Alberta, 

CANADA T2N 1H1 

Dr. Paul J. Lioy—Associate Director and Professor, Environmental and Occupational Health 

Sciences Institute, UMDNJ - Robert Wood Johnson Medical School, 170 Frelinghuysen Road, 

Rm 301, Piscataway, NJ 08854 

Dr. Morton Lippmann—Professor, Nelson Institute of Environmental Medicine, New York 

University School of Medicine, 57 Old Forge Road, Tuxedo, NY 10987 

Dr. Joe Mauderly—Vice President, Senior Scientist, and Director, National Environmental 

Respiratory Center, Lovelace Respiratory Research Institute, 2425 Ridgecrest Drive SE, 

Albuquerque, NM, 87108 

Dr. Roger O. McClellan—Consultant, 1370 Quaking Aspen Pine, Albuquerque, NM, 87111 

Dr. Gunter Oberdorster—Professor of Toxicology, Department of Environmental Medicine, 

School of Medicine and Dentistry, University of Rochester, 575 Elmwood Avenue, Box 850, 

Rochester, NY 14642 

Dr. Robert D. Rowe—President, Stratus Consulting, Inc., , PO Box 4059, Boulder, CO 

80306-4059 

Dr. Jonathan M. Samet—Professor and Chair, Department of Epidemiology, Bloomberg School 

of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Suite W6041, Baltimore, MD 

21205-2179 

Dr. Sverre Vedal—Professor of Medicine, School of Public Health and Community Medicine 

Department of Environmental and Occupational Health Sciences, University of Washington, 

4225 Roosevelt Way NE, Suite 100, Seattle, WA 98105-6099 

I-xliv 
21205 

U.S. ENVIRONMENTAL PROTECTION AGENCY

SCIENCE ADVISORY BOARD (SAB) STAFF OFFICE

CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE (CASAC)

PARTICULATE MATTER REVIEW PANEL*

(cont’d)

CASAC Consultants 

(cont’d) 

Mr. Ronald White—Research Scientist, Epidemiology, Bloomberg School of Public Health, 

Room W6035, The Johns Hopkins University, 615 N. Wolfe St., Rm W6035, Baltimore, MD 

Dr. Warren H. White—Visiting Professor, Crocker Nuclear Laboratory, University of 

California-Davis, , Davis, CA 95616-8569 

Dr. George T. Wolff—Principal Scientist, General Motors Corporation, 300 Renaissance Center 

(MC482-C27-B76), Detroit, MI 48265-3000 

EPA Science Advisory Board Staff 

Dr. Vanessa Vu—SAB Staff Office Director, EPA Science Advisory Board Staff Office 

(Mail Code 1400F), 1200 Pennsylvania Avenue, N.W., Washington DC, 20460 

Mr. Fred Butterfield—CASAC Designated Federal Officer, EPA Science Advisory Board Staff 

Office (Mail Code 1400F), 1200 Pennsylvania Avenue, N.W., Washington, DC 20460 

I-xlv 
Abbreviations and Acronyms 

AAS atomic absorption spectrophotometry 

AC air conditioning 

ACGIH American Conference of Governmental Industrial Hygienists 

ACS American Cancer Society 

ADS annular denuder system 

AES atomic emission spectroscopy 

AIRS Aerometric Information Retrieval System 

AM alveolar macrophage 

ANC acid neutralizing capacity 

APS aerodynamic particle sizer 

AQCD Air Quality Criteria Document 

AQI Air Quality Index 

ARIES Aerosol Research and Inhalation Epidemiology Study 

ASOS Automated Surface Observing System 

ATOFMS aerosol time-of-flight mass spectrometry 

AWOS Automated Weather Observing System 

BaP benzo(a)pyrene 

BASE Building Assessment and Survey Evaluation 

BC black carbon 

BNF bacterial nitrogen fertilization 

BOSS Brigham Young University Organic Sampling System 

BYU Brigham Young University 

CAA Clean Air Act 

CAAM continuous ambient mass monitor 

CAMNET Coordinated Air Monitoring Network 

CARB California Air Resources Board 

I-xlvi 
CASAC Clean Air Scientific Advisory Committee 

CASTNet Clean Air Status and Trends Network 

CC carbonate carbon 

CCPM continuous coarse particle monitor 

CCSEM computer-controlled scanning electron microscopy 

CEN European Standardization Committee 

CFCs chlorofluorocarbons 

CFR Code of Federal Regulations 

CHAD Consolidated Human Activity Database 

CI confidence interval 

CIF charcoal-impregnated cellulose fiber; charcoal-impregnated filter 

CMAQ Community Model for Air Quality 

CMB chemical mass balance 

CMSA Consolidated Metropolitan Statistical Area 

CO AQCD Air Quality Criteria Document for Carbon Monoxide 

COD coefficient of divergence 

COH coefficient of haze 

CONCDEP average of unitless regional deposition and precipitation-weighted 

D

concentrations 

COPD chronic obstructive pulmonary disease 

CPC condensation particle counter 

CRP Coordinated Research Program 

CSIRO Commonwealth Scientific and Industrial Research Organisation 

CSMCS Carbonaceous Species Methods Comparison Study 

CSS coastal sage scrub 

CTM chemistry-transport model 

CV coefficient of variation 

CVM contigent valuation method 

a aerodynamic diameter 

I-xlvii 
DAQ Department of Air Quality 

DMPS differential mobility particle sizer 

DMS dimethyl sulfide 

Dp Stokes particle diameter 

dv deciview 

EAD electrical aerosol detector 

EC elemental carbon 

EDXRF energy dispersive X-ray fluorescence 

EEA essential ecological attribute 

ENSO El Niño-Southern Oscillation 

EPEC Ecological Processes and Effects Committee 

ESP electrostatic precipitator 

ETS environmental tobacco smoke 

EXPOLIS Air Pollution Exposure Distributions within Adult Urban Populations in 

Europe 

FID flame ionization detection 

FRD flame photometric detector 

FRM Federal Reference Method 

GAM general additive model 

GC gas chromatography 

GCM General Circulation Model 

GCVTC Grand Canyon Visibility Transport Commission 

GC/MSD gas chromatography/mass-selective detection 

GHG greenhouse gas 

GSD geometric standard deviation 

HAPs hazardous air pollutants 

H Henry’s law constant 

HBEF Hubbard Brook Experimental Forest 

HDS honeycomb denuder/filter pack sampler 

I-xlviii 
HEADS Harvard-EPA Annular Denuder System 

HEI Health Effects Institute 

HI Harvard Impactor 

hivol high volume sampler 

HPEM Harvard personal environmental monitor 

HTGC/MS high temperature gas chromotography/mass spectrometry 

HVAC heating, ventilation, or air conditioning 

IC ion chromatography 

ICP inductively coupled plasma 

IFS Integrated Forest Study 

IMPROVE Interagency Monitoring of Protected Visual Environments 

INAA instrumental neutron activation analysis 

IOVPS integrated organic vapor/particle sampler 

IP inhalable particle 

IPCC Intergovernmental Panel on Climate Change 

IPM inhalable particulate matter 

IPN Inhalable Particulate Network 

ISO International Standards Organization 

LAI leaf area indices 

LOD level of detection 

LWC liquid water content 

LWCA liquid water content analyzer 

MAA mineral acid anion 

MAACS Metropolitan Acid Aerosol Characterization Study 

MADPro Mountain Acid Deposition Program 

MAQSIP Multiscale Air Quality Simulation Platform 

MCM mass concentration monitor 

MHVOC more highly volatile organic compound 

I-xlix 
MDL minimum detection level 

MOUDI microorifice uniform deposit impactor; multiple orifice uniform deposit 

impactor 

MS mass spectroscopy 

MSA methane sulfonic acid 

MSA metropolitan statistical area 

mv motor vehicle 

NAAQS National Ambient Air Quality Standards 

NAMS National Ambient Monitoring Stations 

NAPAP National Acid Precipitation Assessment Program 

NARSTO North American Research Strategy for Tropospheric Ozone 

NCEA National Center for Environmental Assessment 

NDDN National Dry Deposition Network 

NERL National Exposure Research Laboratory 

NESCAUM Northeast States for Coordinated Air Use Management 

NFRAQS North Frontal Range Air Quality Study 

NHAPS National Human Activity Pattern Survey 

NIOSH National Institute for Occupational Safety and Health 

NIR near infared radiation 

NIST National Institute of Standards and Technology 

NOx nitrogen oxides 

Nr reactive nitrogen 

NRC National Research Council 

NuCM nutrient cycling model 

OAQPS Office of Air Quality Planning and Standards 

OC organic carbon 

ORD Office of Research and Development 

PAH polycyclic aromatic hydrocarbon; polynuclear aromatic hydrocarbon 

PAN peroxyacetyl nitrate 

I-l 
PAR photosynthetically active radiation 

PBL planetary boundary layer 

PBPs primary biological particles 

PBW particle-bound water 

PBY Presbyterian Home 

PC particle concentrator 

PC pyrolitic carbon 

PC-BOSS Particulate Concentrator-Brigham Young University Organic Sampling 

PM

PM

PM

PM

System 

PCA principal component analysis 

PCBs polychloronated biphenyls 

PCDD polychlorinated dibenzo-p-dioxins 

PCDF polychlorinated dibenzofurans 

PCM particle composition monitor 

pDR personal DataRAM 

PEM personal exposure monitor 

PESA proton (or particle) elastic scattering analysis 

PIXE proton (or particle) induced X-ray emission 

PM particulate matter 

2.5 fine particulate matter 

10 combination of coarse and fine particulate matter 

10-25 coarse particulate matter 

90 90th percentile difference in concentration 

PM ACQD Air Quality Criteria Document for Particulate Matter 

PMF positive matrix factorization 

POP persistent organic pollutant 

PRB policy-relevant background 

PTEAM Particle Total Exposure Assessment Methodology 

PTEP PM10 Technical Enhancement Program 

I-li 
PTFE polytetrafluoroethylene (Teflon) 

PUF polyurethane foam 

PWC precipitation-weighted concentrations 

r Pearson correlation coefficient 

RAMS Regional Air Monitoring Study 

RAMS real-time total ambient mass sampler 

RAPS Regional Air Pollution Study 

RAS Roll-Around System 

RCS Random Component Superposition 

RFO residual fuels oils 

RH relative humidity 

RIVM Dutch National Institute of Public Health and the Environment 

ROS reactive oxygen species 

RPM reconstructed particulate mass 

RPM respirable particulate matter 

RRMS relatively remote monitoring sites 

RSP respirable particulate matter 

RSP respirable suspended particles 

RTI Research Triangle Institute 

RTP Research Triangle Park 

RUE radiation use efficiency 

SA Sierra Anderson 

SAB Science Advisory Board 

SCAQS Southern California Air Quality Study 

SCENES Subregional Cooperative Electric Utility, Department of Defense, 

National Park Services, and Environmental Protection Agency Study 

SCOS97 1997 Southern California Ozone Study 

sd, SD standard deviation 

sec secondary 

I-lii 
SEM scanning electron microscopy 

SES sample equilibration system 

SEV Sensor Equivalent Visibility 

SHEDS Stochastic Human Exposure and Dose Simulation 

SIP State Implementation Plan 

SLAMS State and Local Air Monitoring Stations 

SMPS scanning mobility particle sizer 

SMSAs Standard Metropolitan Statistical Areas 

SoCAB South Coast Air Basin 

SOPM secondary organic particulate matter 

SOx sulfur oxides 

SRM standard reference method 

STN Speciation Trends Network 

SUVB solar ultraviolet B radiation 

SVOC semivolatile organic compound 

SVM semivolatile material 

S-XRF synchrotron induced X-ray fluoroescence 

TAR Third Assessment Report 

TC total carbon 

TDMA Tandem Differential Mobility Analyzer 

TEO trace element oxides 

TEOM tapered element oscillating microbalance 

THEES Total Human Environmental Exposure Study 

TMO thermal manganese oxidation 

TNF tumor necrosis factor 

TO thermal-optical 

TOFMS time-of-flight mass spectroscopy 

TOR thermal-optical reflectance 

I-liii 
TOT thermal-optical transmission 

TPM thoracic particulate matter 

TRM traffic-related microenvironment 

TRXRF total reflection X-ray fluorescence 

TSP total suspended particulate 

TVOC total volatile organic compounds 

UAM-IV Urban Airshed Model Version IV 

UCM unresolved complex mixture 

UNEP United Nations Environment Programme 

URG University Research Glassware 

VAPS Versatile Air Pollution Sampler 

VMD volume mean diameter 

VOC volatile organic compound 

WMO World Meteorological Organization 

VR visual range 

WINS Well Impactor Ninety-Six 

WRAC Wide Range Aerosol Classifier 

WTA willing to accept 

WTP willing to pay 

XAD polystyrene-divinyl benzene 

XRF X-ray fluorescence 

I-liv 
1. INTRODUCTION

This document is an update of “Air Quality Criteria for Particulate Matter” published by 

the U.S. Environmental Protection Agency (EPA) in 1996, and it will serve as the basis for 

Congressionally-mandated periodic review by EPA of the National Ambient Air Quality 

Standards for Particulate Matter (PM NAAQS). The present document critically assesses the 

latest scientific information relative to determining the health and welfare effects associated with 

the presence of various concentrations of PM in ambient air. It builds upon the assessment in the 

previous 1996 EPA Air Quality Criteria Document for Particulate Matter (1996 PM AQCD; U.S. 

Environmental Protection Agency, 1996a) by focusing on assessment and integration of 

information most relevant to PM NAAQS criteria development, based on pertinent peerreviewed literature published or accepted for publication mainly through 2002, as well as a few 

more recent important studies. This introductory chapter presents (1) background information 

that summarizes legislative requirements, the criteria and NAAQS review process, the history of 

PM NAAQS reviews including the chronology of changes in key elements of the standards, and 

the coordinated PM research program that has guided much of the more recent research in this 

area; (2) an overview of the current PM criteria and NAAQS review (including key milestones), 

as well as assessment approaches and procedures used in preparing this document; and (3) an 

orientation to the general organizational structure of this document. 

.1.1 BACKGROUND 

.1.1.1 Legislative Requirements 

As indicated in U.S. Code (1991), Sections 108 and 109 of the U.S. Clean Air Act (CAA) 

(42 U.S.C. Sections 7408 and 7409) govern the establishment, review, and revision of NAAQS. 

Section 108(a) directs the EPA Administrator to list pollutants, which, in the Administrator’s 

judgment, cause or contribute to air pollution that may reasonably be anticipated to endanger 

public health or welfare and to issue air quality criteria for them. The air quality criteria are to 

1-1
reflect the latest scientific information useful in indicating the kind and extent of all identifiable 

effects on public health and welfare that may be expected from the presence of the pollutant in 

ambient air. 

Section 109 directs the Administrator of EPA to propose and promulgate “primary” and 

“secondary” NAAQS for criteria air pollutants listed under Section 108. Section 109(b)(1) 

defines a primary standard as a level of air quality, the attainment and maintenance of which, 

in the judgment of the Administrator, based on the criteria and allowing for an adequate margin 

of safety, is requisite to protect the public health. Section 109(b)(2) defines a secondary standard 

as one which, in the judgment of the Administrator, based on the criteria, is requisite to protect 

public welfare from any known or anticipated adverse effects associated with the presence of the 

pollutant in ambient air. Welfare effects, defined in Section 302(h), include (but are not limited 

to) effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, 

visibility, and climate; damage to and deterioration of property; hazards to transportation; and 

effects on economic values, personal comfort, and well-being. Section 109(d)(1) requires the 

periodic review and, as appropriate, revision of existing criteria and standards. Also, 

Section 109(d)(2) requires an independent committee of non-EPA experts, the Clean Air 

Scientific Advisory Committee (CASAC), to provide advice and recommendations to the EPA 

Administrator regarding the scientific soundness and appropriateness of criteria and NAAQS 

for PM and other criteria air pollutants (i.e., ozone, nitrogen dioxide, sulfur oxides, carbon 

monoxide, lead). 

.1.1.2 Criteria and NAAQS Review Process 

The EPA’s periodic reviews of criteria and NAAQS for a given criteria air pollutant 

progress through a number of steps, beginning with the preparation of an Air Quality Criteria 

Document (AQCD) by EPA’s National Center for Environmental Assessment Division in 

Research Triangle Park, NC (NCEA-RTP), which critically assesses the scientific information 

upon which the NAAQS are to be based. Building upon the AQCD, staff within EPA’s Office of 

Air Quality Planning and Standards (OAQPS) prepare a Staff Paper that evaluates the policy 

implications of the key studies and scientific information contained in the AQCD and presents 

1-2
staff conclusions and recommendations of options for the Administrator to consider. The Staff 

Paper is intended to help “bridge the gap” between the scientific assessment contained in the 

AQCD and the judgments required of the Administrator in determining whether it is appropriate 

to retain or to revise the NAAQS. Iterative drafts of both of the AQCD and the Staff Paper 

(as well as any other analyses supporting the Staff Paper, such as exposure and/or risk 

assessments) are made available for public comment and are reviewed by CASAC. Following 

CASAC review of these documents, the Administrator proposes decisions on whether to retain 

or revise the NAAQS based on the information in these documents, taking into account CASAC 

advice and recommendations and public comments. The Administrator’s proposed decisions are 

published in the Federal Register, with a preamble that presents the rationale for the decisions 

and solicits public comment. After taking into consideration comment received on the proposed 

decisions, the Administrator’s final decisions are promulgated in a final Federal Register notice 

that addresses significant comments received on the proposal. 

Decisions on the NAAQS involve consideration of the four basic elements of a standard: 

indicator, averaging time, form, and level. The indicator defines the pollutant to be measured in 

the ambient air for the purpose of determining compliance with the standard. The averaging 

time defines the time period over which air quality measurements are to be obtained and 

averaged, considering evidence of effects associated with various time periods of exposure. 

The form of a standard defines the air quality statistic that is to be compared to the level of the 

standard (i.e., an ambient concentration of the indicator pollutant) in determining whether an 

area attains the standard. The form of the standard specifies the air quality measurements that 

are to be used for compliance purposes (e.g., the 98th percentile of an annual distribution of 

daily concentrations; the annual arithmetic average), the monitors from which the measurements 

are to be obtained (e.g., one or more population-oriented monitors in an area), and whether the 

statistic is to be averaged across multiple years. These basic elements of a standard are the 

primary focus of the staff conclusions and recommendations in the Staff Paper and in the 

subsequent rulemaking, building upon the policy-relevant scientific information assessed in the 

AQCD and on the policy analyses contained in the Staff Paper. These four elements taken 

together determine the degree of public health and welfare protection afforded by the NAAQS. 

1-3
.1.1.3 History of Earlier PM Criteria and NAAQS Reviews 

Selection of appropriate indicator(s) for the PM NAAQS has long posed a unique 

challenge, in that unlike the other criteria air pollutants, PM is the generic term for a broad class 

of physically and chemically diverse substances that exist in ambient air as discrete particles 

(liquid droplets or solids) over a wide range of sizes. These airborne particles originate from a 

variety of stationary and mobile sources. Primary particles are emitted directly into ambient air; 

whereas secondary particles are formed in the atmosphere by transformation of gaseous 

emissions such as sulfur oxides (SOx), nitrogen oxides (NOx), and volatile organic compounds 

(VOCs). The physical and chemical properties of PM vary greatly with time, region, 

meteorology, and source category, thus complicating assessment of ambient PM health and 

welfare effects. Particles in ambient air are usually distributed bimodally in two somewhat 

overlapping size categories: (1) fine (diameter generally < 2.5 μm) and (2) coarse (diameter 

generally > 1.0 μm). Particles in these two size fractions tend to differ in terms of formation 

mechanisms, sources of origin, composition, and behavior in the atmosphere and human 

respiratory tract. 

EPA first promulgated primary and secondary NAAQS for PM on April 30, 1971 (Federal 

Register, 1971). These standards measured PM as “total suspended particulate” (TSP), which 

refers to ambient PM up to a nominal size of 25 to 45 μm. The primary standards for PM 

(measured as TSP) were 260 μg/m3

 (24-h average), not to be exceeded more than once per year, 

and 75 μg/m3

 (annual geometric mean). The secondary standard (measured as TSP) was 

150 μg/m3 (24-h average), not to be exceeded more than once per year. 

EPA completed the next review of PM air quality criteria and standards in July 1987, 

revising the 1971 standards to protect against adverse health effects of inhalable airborne 

particles that can be deposited in the lower (thoracic) regions of the human respiratory tract, with 

“PM10” (i.e., those particles collected by a sampler with a specified penetration curve yielding an 

upper 50% cut-point of 10-μm aerodynamic diameter) as the indicator (Federal Register, 1987). 

EPA established identical primary and secondary PM10 standards for two averaging times: 

150 μg/m3

 (24-h average, with no more than one expected exceedance per year) and 50 μg/m3 

(expected annual arithmetic mean, averaged over three years). 

1-4
.1.1.4 The 1997 PM NAAQS Revision 

The last previous review of the air quality criteria and standards for PM was initiated in 

April 1994 by EPA announcing its intention to develop a revised Air Quality Criteria for 

Particulate Matter. Several workshops were held in November 1994 and January 1995 to 

discuss important new health effects information useful in preparing initial PM AQCD draft 

materials. Also, plans for review of the PM criteria and standards under a highly accelerated, 

court-ordered schedule were presented by EPA at a CASAC public meeting in December 1994. 

A court order entered in American Lung Association v. Browner, CIV-93-643-TUC-ACM (U.S. 

District Court of Arizona, 1995), as subsequently modified, required publication of EPA’s final 

decision on the review of the PM NAAQS by July 19, 1997. 

Several external review drafts of the revised PM AQCD were prepared by NCEA-RTP and 

made available for public comment and CASAC review (at public meetings held in August 

1995, December 1995, and February 1996). The CASAC completed its review of the PM 

AQCD in early 1996, advising the EPA Administrator in a March 15, 1996 letter (Wolff, 1996) 

that “although our understanding of the health effects of PM is far from complete, a revised 

Criteria Document which incorporates the Panel’s latest comments will provide an adequate 

review of the available scientific data and relevant studies of PM.” Revisions made in response 

to public and CASAC comments were then incorporated by NCEA-RTP, as appropriate, into the 

final 1996 PM AQCD (U.S. Environmental Protection Agency, 1996a). The associated PM 

Staff Paper, prepared by OAQPS staff, drew upon the 1996 PM AQCD and other assessments to 

pose options for the Administrator to consider in making PM NAAQS decisions. Drafts of the 

PM Staff Paper also underwent public comment and CASAC review, which informed the final 

1996 version (U.S. Environmental Protection Agency, 1996b). 

The 1996 PM AQCD and PM Staff Paper provide detailed information on atmospheric 

formation, ambient concentrations, and health effects of ambient air PM, as well as quantitative 

estimates of human health risks associated with exposure to ambient PM. The main focus of 

these documents was on recent epidemiologic evidence reporting associations between ambient 

concentrations of PM and a range of serious health effects. Special attention was given to 

several size-specific classes of particles, including PM10 and the principal fractions of PM10, 

1-5
referred to as the fine (PM2.5) and coarse (PM10-2.5) fractions. PM2.5 refers to those particles 

collected by samplers having penetration curves yielding an upper 50% cut point of 2.5 μm 

aerodynamic diameter. PM10-2.5 refers to those particles in an aggregate sample having an upper 

50% cut point of 10 μm and a lower 50% cut point of 2.5 μm aerodynamic diameter. In other 

words, the coarse fraction (PM10-2.5) refers to inhalable particles that remain if fine (PM2.5) 

particles are removed from a sample of PM10 particles. As discussed in the 1996 PM AQCD, 

fine and coarse fraction particles can be differentiated by their sources and formation processes, 

by their chemical and physical properties, and by their behavior in the atmosphere. 

Taking into account information and assessments presented in the 1996 PM AQCD and 

Staff Paper, CASAC advice and recommendations, and public comments received on proposed 

revisions to the PM NAAQS published in December 1996 (Federal Register, 1996), the EPA 

Administrator promulgated significant revisions to the PM NAAQS in July 1997 (Federal 

Register, 1997). In that decision, the PM NAAQS were revised in several respects. While it was 

determined that the PM NAAQS should continue to focus on particles less than or equal to 

10 μm in diameter, it was also determined that the fine and coarse fractions of PM10 should be 

considered separately. New standards were added, using PM2.5 as the indicator for fine particles; 

and PM10 standards were retained for the purpose of regulating coarse-fraction particles. Two 

new PM2.5 standards were set: an annual standard of 15 μg/m3

, based on the 3-year average of 

annual arithmetic mean PM2.5 concentrations from single or multiple community-oriented 

monitors; and a 24-h standard of 65 μg/m3

, based on the 3-year average of the 98th percentile of 

24-h PM2.5 concentrations at each population-oriented monitor within an area. To continue to 

address coarse-fraction particles, the annual PM10 standard was retained, and the form, but 

not the level, of the 24-h PM10 standard was revised to be based on the 99th percentile of 24-h 

PM10 concentrations at each monitor in an area. The secondary standards were revised by 

making them identical in all respects to the PM2.5 and PM10 primary standards. 

Following promulgation of the revised PM NAAQS, legal challenges were filed by a large 

number of parties, addressing a broad range of issues. In May 1998, the U.S. Court of Appeals 

for the District of Columbia Circuit issued an initial opinion that upheld EPA’s decision to 

establish fine particle standards, holding that such standards were amply justified by the growing 

1-6
body of empirical evidence demonstrating a relationship between fine particle pollution and 

adverse health effects. American Trucking Associations v. Browner, 175 F. 3d 1027, 1055-56 

(D.C. Cir. 1999a) (rehearing granted in part and denied in part, 195 F. 3d 4 (D.C. Cir. 1999b),

affirmed in part and reversed in part, Whitman v. American Trucking Associations, 531 U.S. 457 

(2001). Further, the court found “ample support” for EPA’s decision to regulate coarse fraction 

particles, although it vacated the revisions to the 1987 PM10 standards on the basis of PM10 being 

a “poorly matched indicator for coarse particulate pollution” because PM10 includes fine 

particles (Id. at 1053-55). As a result of this aspect of the court’s ruling, which EPA did not 

appeal, the 1987 PM10 standards remain in effect. 

In addition, the court broadly held that EPA’s approach to establishing the level of the 

standards in its 1997 decisions on both the PM and Ozone NAAQS (which were promulgated on 

the same day and considered together by the court in this aspect of its opinion) effected “an 

unconstitutional delegation of legislative authority” (Id. at 1034-40). EPA appealed this aspect 

of the court’s ruling to the U.S. Supreme Court. In February 2001, the U.S. Supreme Court 

unanimously reversed the Court of Appeals’ ruling on the constitutional issue and sent the case 

back to the Court of Appeals for resolution of any remaining issues that had not been addressed 

in that court’s earlier rulings. Whitman v. American Trucking Associations, 531 U.S. 457, 

475-76 (2001). In March 2002, the Court of Appeals rejected all remaining challenges to the 

standards, finding that the 1997 PM2.5 standards were reasonably supported by the record and 

were not “arbitrary or capricious.” American Trucking Associations v. EPA, 283 F. 3d 355, 

369-72 (D.C. Cir. 2002). Thus, the 1997 PM2.5 standards also remain in effect. 

.1.1.5 Coordinated PM Research Program 

Shortly after promulgation of the 1997 PM NAAQS decisions, NCEA-RTP published a 

PM Health Risk Research Needs Document (U.S. Environmental Protection Agency, 1998a) that 

identified research needed to improve scientific information supporting future reviews of the 

PM NAAQS. The document provided a foundation for PM research coordination among Federal 

agencies and other research organizations, as well as input to National Research Council (NRC) 

deliberations on PM research. The Office of Research and Development (ORD) of EPA also 

1-7
moved quickly to broaden its ongoing PM research activities by developing, in partnership with 

other Federal agencies, a coordinated interagency PM research program. This interagency 

program has focused and continues to focus mainly on expanding scientific knowledge of 

ambient PM exposure and health effects, as well as on developing improved monitoring methods 

and cost-effective mitigation strategies. The interagency effort also promotes substantially 

expanded coordination with other research organizations, including the Health Effects Institute 

(HEI) and other state-, university-, and industry-sponsored research groups. Beginning in the 

fall of 1997, public participation was and continues to be encouraged through workshops and 

review of EPA’s PM Research Program documentation. 

In response to Congressional requirements in EPA’s Fiscal Year 1998 Appropriation, the 

NRC established its Committee on Research Priorities for Airborne Particulate Matter in January 

1998. This NRC PM Research Committee’s charge was to identify the most important research 

priorities relevant to setting primary (health-based) PM NAAQS, to develop a conceptual plan 

for PM research, and to monitor EPA’s research progress toward improved understanding of the 

relationship between PM and public health. The NRC PM Research Committee issued a series 

of reports (National Research Council, 1998, 1999, 2001) which recommended that expanded 

PM research efforts be planned and carried out in relation to a general conceptual framework, 

as shown in Figure 1-1. That framework essentially calls for research aimed at (a) identifying 

sources of airborne particles or gaseous precursor emissions and characterization of processes 

involved in atmospheric transformation, transport, and fate of ambient PM; (b) delineating 

temporal and spatial patterns of air quality indicators (e.g., PM2.5, PM10-2.5, PM10 mass 

concentrations) of ambient PM and apportionment of observed variations in such ambient PM 

indicators to various emission sources; (c) characterizing human exposures to ambient PM as 

one important component of total personal exposure to particles, as modified by time-activity 

patterns and varying microenvironmental exposure to particles of indoor or ambient origin; 

(d) characterizing resulting respiratory tract deposition, clearance, retention, and disposition of

inhaled particles, as determinants of dose to target tissues (e.g., locally in the lungs or via 

systemic translocation to the heart or other organs); and (e) delineating of mechanisms of 

damage and repair plausibly leading to (f) human health responses, as extrapolated from or 

1-8
Figure 1-1. A general framework for integrating PM research. Note that this figure is 

not intended to represent a framework for research management. Such a 

framework would include multiple pathways for the flow of information. 

Source: National Research Council (2001). 

quantified by experimental animal or human exposure (toxicology) studies and/or observational 

(epidemiology) studies. 

Research conducted under a PM Research Program structured in relation to the conceptual 

framework shown in Figure 1-1 would be expected to (a) reduce key scientific uncertainties 

regarding interrelationships between PM sources, ambient concentrations, exposures, dose to 

target tissues, and resulting health effects and thereby (b) improve the scientific underpinnings 

for both current and future periodic PM NAAQS reviews. Table 1-1 highlights some types of 

key uncertainties identified by the NRC PM Research Committee in relation to elements of the 

source-to-response conceptual framework illustrated in Figure 1-1. The NRC Committee went 

on to delineate a series of 10 research topics that they recommended be addressed by an 

expanded PM research program aimed at answering a set of broadly stated questions, as shown 

in Table 1-2. 

The EPA PM Research Program, structured to address topics shown in Table 1-2, has 

encompassed studies to improve understanding of the formation and composition of fine PM, 

to improve the measurement and estimation of population exposures to ambient PM, to delineate 

characteristics or components of PM that are responsible for its health effects, to evaluate factors 

that increase susceptibility to PM effects in some subpopulations, and to elucidate mechanisms 

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TABLE 1-1. KEY SCIENTIFIC UNCERTAINTIES RELATED TO THE

SOURCE-TO-RESPONSE FRAMEWORK

Source Concentration (or other indicator) 

• Contribution of various emission sources to ambient and indoor particulate matter concentrations 

• Relative contribution of various sources to the most toxic components of particulate matter 

Concentration (indicator) Exposure 

• Relationship between ambient (indoor) particulate matter and the composition of particles to 

which people are exposed 

• Contribution of ambient particulate matter to total personal exposure for: 

– Susceptible subpopulations 

– General population 

• Variation in relationship of ambient particulate matter concentrations to human exposure by place 

• Variation in contribution of ambient particulate matter concentrations to total human exposure 

over time 

• Covariance of particulate matter exposures with exposures to other pollutants 

• Relationships between outdoor ambient and personal exposures for particulate matter 

and co-pollutants 

Exposure Dose 

• Relationship between inhaled concentration and dose of particulate matter and constituents at 

the tissue level in susceptible subjects 

– Asthma

– Chronic obstructive pulmonary disease (COPD)

– Heart disease 

– Age: infants and elderly

– Others 

Dose Response 

• Mechanisms linking morbidity and mortality to particulate matter dose to or via the lungs

 – Inflammation – Host defenses – Neural mechanisms 

Source: National Research Council (2001). 

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TABLE 1-2. RESEARCH TOPICS AND QUESTIONS RECOMMENDED BY

NATIONAL RESEARCH COUNCIL (NRC) TO BE ADDRESSED BY EXPANDED

PM RESEARCH PROGRAM

RESEARCH TOPIC 1. OUTDOOR MEASURES VERSUS ACTUAL HUMAN EXPOSURES 

• What are the quantitative relationships between concentrations of particulate matter and gaseous 

co-pollutants measured at stationary outdoor air monitoring sites and the contributions of these 

concentrations to actual personal exposures, especially for subpopulations and individuals? 

RESEARCH TOPIC 2. EXPOSURES OF SUSCEPTIBLE SUBPOPULATIONS TO TOXIC 

PARTICULATE MATTER COMPONENTS 

• What are the exposures to biologically important constituents and specific characteristics of particulate 

matter that cause responses in potentially susceptible subpopulations and the general population? 

RESEARCH TOPIC 3. CHARACTERIZATION OF EMISSION SOURCES 

• What are the size distribution, chemical composition, and mass-emission rates of particulate matter emitted 

from the collection of primary-particle sources in the United States, and what are the emissions of reactive 

gases that lead to secondary particle formation through atmospheric chemical reactions? 

RESEARCH TOPIC 4. AIR-QUALITY MODEL DEVELOPMENT AND TESTING 

• What are the linkages between emission sources and ambient concentrations of the biologically important 

components of particulate matter? 

RESEARCH TOPIC 5. ASSESSMENT OF HAZARDOUS PARTICULATE MATTER 

COMPONENTS 

• What is the role of physicochemical characteristics of particulate matter in eliciting adverse health effects? 

RESEARCH TOPIC 6. DOSIMETRY: DEPOSITION AND FATE OF PARTICLES IN THE 

RESPIRATORY TRACT 

• What are the deposition patterns and fate of particles in the respiratory tract of individuals belonging to 

presumed susceptible subpopulations? 

RESEARCH TOPIC 7. COMBINED EFFECTS OF PARTICULATE MATTER AND GASEOUS 

POLLUTANTS 

• How can the effects of particulate matter be disentangled from the effects of other pollutants? How can the 

effects of long-term exposure to particulate matter and other pollutants be better understood? 

RESEARCH TOPIC 8. SUSCEPTIBLE SUBPOPULATIONS 

• What subpopulations are at increased risk of adverse health outcomes from particulate matter? 

RESEARCH TOPIC 9. MECHANISMS OF INJURY 

• What are the underlying mechanisms (local pulmonary and systemic) that can explain the epidemiologic 

findings of mortality/morbidity associated with exposure to ambient particulate matter? 

RESEARCH TOPIC 10. ANALYSIS AND MEASUREMENT 

• To what extent does the choice of statistical methods in the analysis of data from epidemiologic studies 

influence estimates of health risks from exposures to particulate matter? Can existing methods be 

improved? What is the effect of measurement error and misclassification on estimates of the association 

between air pollution and health? 

Source: National Research Council (2001). 

1-11 
by which these effects are produced. The results from these efforts, and related efforts by other 

Federal agencies and the general scientific community during the past several years, have 

substantially enhanced the scientific and technical bases for future decisions on the PM NAAQS. 

.1.2 CURRENT PM CRITERIA AND NAAQS REVIEW 

.1.2.1 Key Milestones and Procedures for Document Preparation 

As with other NAAQS reviews, critical assessment of relevant scientific information is 

presented in this updated PM AQCD. Key milestones in the development of the present PM 

AQCD are shown in Table 1-3 and discussed below. It is important to note that development of 

the document involved substantial external peer and public review through (a) public workshops 

involving the general aerosol scientific community, (b) iterative reviews of successive drafts by 

CASAC, and (c) comments from the public on successive drafts. This final document reflects 

extensive external input received through these reviews and serves to ensure that the review of 

the PM standards is based on critical assessment of the latest available pertinent science. 

The procedures for developing this updated PM AQCD built on the experience derived 

from the most recent previous PM, Ozone, and CO AQCD preparation efforts. Briefly, the 

respective responsibilities for production of the present PM AQCD are as follows. An NCEARTP PM Team was responsible for developing and implementing a project plan for preparing the 

PM AQCD, taking into account inputs from individuals in other EPA program and policy offices 

identified as part of the EPA PM Work Group. The resulting project plan (i.e., the PM 

Document Development Plan) was discussed with CASAC in May 1998 and was appropriately 

revised. A literature search was ongoing throughout the preparation of this document to identify, 

to the extent possible, all pertinent PM literature published since early 1996. Additionally, EPA 

published in the Federal Register (1) a request for information asking for recently available 

research information on PM that may not yet be published and (2) a request for individuals with 

the appropriate type and level of expertise to contribute to the writing of PM AQCD materials to 

identify themselves (U.S. Environmental Protection Agency, 1998b). The specific authors of 

chapters or sections of the proposed document were selected on the basis of their expertise on 

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TABLE 1-3. KEY MILESTONES IN THE DEVELOPMENT OF THIS DOCUMENT

Key Milestones Dates 

PM NAAQS Review Plan to CASAC 

Prepare AQCD Development Plan 

Begin Literature Search 

Federal Register Call for Information/Sources Sought 

CASAC Meeting on PM AQCD Development Plan 

Prepare Workshop Drafts of Chapters 

Peer Review Workshop 

Prepare First External Review Draft PM AQCD 

First External Review Draft PM AQCD 

Public Comment Period on First Draft 

CASAC Meeting on First Draft 

Second External Review Draft PM AQCD 

Public Comment Period on Second Draft 

CASAC Meeting on Second Draft 

Third External Review Draft PM AQCD 

Public Comment Period on Third Draft 

CASAC Meeting on Third Draft 

Fourth External Review Draft PM AQCD 

Public Comment Period on Fourth Draft 

CASAC Meeting on Fourth Draft 

CASAC Consultation on Proposed Revisions to Chapter 9 

CASAC Consultation on Revisions to Chapters 7 and 8 

Revised Draft Chapters 7 and 8 

Public Comment Period on Revised Draft Chapters 7 and 8 

CASAC Teleconference Review of Revised Draft Chapters 7 and 8 

Revised Draft Chapters 7, 8 and 9 

Public Comment Period on Revised Draft Chapters 7, 8 and 9 

CASAC Meeting on Revised Draft Chapters 7, 8 and 9 

Revised Draft Chapter 9 

Public Comment Period on Revised Chapter 9 

CASAC Review of Revised Chapter 9 

Final PM AQCD 

October 1997 

November 1997 to January 1998 

February 1998 

April 1998 

May 1998 

May to December 1998 

April 1999 

March to September 1999 

October 1999 

October 1999 to January 2000 

December 1999 

March 2001 

April to July 2001 

July 2001 

April 2002 

May to July 2002 

July 2002 

June 2003 

June to August 2003 

August 2003 

October 2003 

November 2003 

December 2003 

January 2004 

February 2004 

June 2004 

July 2004 

July 2004 

August 2004 

September 2004 

September 2004 

October 2004 

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the subject areas and their familiarity with the relevant literature; and these included both EPA 

and non-EPA scientific experts. The project team defined critical issues and topics to be 

addressed by the authors and provided direction in order to emphasize evaluation of those 

studies most clearly identified as important for standard setting. It should be noted that materials 

contributed by non-EPA authors were, at times, modified by EPA PM Team staff in response to 

internal and/or external review comments, e.g., by the public or CASAC, and that EPA is 

responsible for the ultimate content of this PM AQCD. 

The main focus of this document is the evaluation and interpretation of pertinent 

atmospheric science information, air quality data, human exposure information, and health and 

welfare effects information newly published since that assessed in the 1996 PM AQCD. Initial 

draft versions of AQCD chapters were evaluated via expert peer-review workshop discussions 

and/or written peer reviews that focused on the selection of pertinent studies to be included in 

the chapters, the potential need for additional information to be added to the chapters, and the 

quality of the characterization and interpretation of the literature. The authors of the draft 

chapters then revised them on the basis of workshop and/or written expert review comments. 

These and other integrative materials were incorporated into the First External Review Draft of 

the PM AQCD (October 1999), which was made available for public comment and was the 

subject of consultation with CASAC at a December 1999 public meeting. 

In order to foster timely presentation and publication of newly emerging PM research 

findings, EPA cosponsored and helped to organize an Air and Waste Management Association 

International Speciality Conference, entitled “PM 2000: Particulate Matter and Health,” held in 

January 2000 in Charleston, SC. The conference was cosponsored in cooperation with several 

other government agencies and/or private organizations that also fund PM research. Topics 

covered included new research results concerning the latest advances in PM atmospheric 

sciences (e.g., PM formation, transport, transformation), PM exposure, PM dosimetry and 

extrapolation modeling, PM toxicology (e.g., mechanisms, laboratory animal models, human 

clinical responses), and PM epidemiology. The main purpose of the conference was to facilitate 

having the latest scientific information available in time for incorporation as quickly as possible 

into the Second External Review Draft of the PM AQCD. Hence, arrangements were made for 

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scientists to submit written manuscripts on papers or posters presented at the PM 2000 

Conference for expedited peer-review by several major journals, so that decisions on acceptance 

for publication could be made by mid-2000. The evaluations and findings set forth in the 

Second External Review Draft (March 2001) included consideration of such published PM 2000 

papers, as well as extensive additional information published elsewhere since that addressed in 

the First External Review Draft. The Second External Review Draft was made available for 

public comment and was reviewed by CASAC at a July 2001 public meeting. 

Further revisions were then incorporated into the Third External Review Draft (April 2002) 

to reflect both public comment and CASAC review of the Second Draft, as well as assessment of 

additional extensive new information published since that addressed in the Second Draft. 

Shortly after EPA released the Third External Review Draft in May 2002 for public comment 

and CASAC review, the HEI announced that researchers at Johns Hopkins University had 

discovered problems with applications of statistical software used in a number of important 

epidemiologic studies on links between ambient air PM and mortality and morbidity effects. 

The Third External Review Draft was reviewed by CASAC at a July 2002 public meeting, 

although it was recognized that discussion of the epidemiology information would need to be 

further revised after the newly surfaced statistical issues were appropriately addressed. 

In response to the surfacing of such statistical issues, which affected numerous PM time-series 

studies that used General Additive Models (GAM) and were published post-1995, EPA took 

steps in consultation with CASAC to identify particularly policy-relevant studies and to 

encourage researchers to reanalyze affected studies and to submit them expeditiously for peer 

review by a special expert panel convened by HEI. The results of reanalyses for more than 

30 studies were peer-reviewed and published, along with commentary by the HEI expert peerreview panel, in an HEI Special Report (Health Effects Institute, May 2003). 

Discussion of the newly addressed statistical issues and reanalyses results was incorporated 

into the Fourth External Review Draft (June 2003), which was made available for public 

comment and was reviewed by CASAC at an August 2003 public meeting. The Fourth Draft 

also incorporated changes made in response to earlier public comments and CASAC reviews, 

including pertinent peer-reviewed literature published or accepted for publication mainly 

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through April 2002. CASAC completed its review of Chapters 1 through 6 of the Fourth Draft 

PM AQCD at the August 2003 meeting, but comments on the remaining chapters (7, 8, and 9) 

were judged to warrant further revision and review of those chapters. EPA subsequently 

consulted with CASAC and made available revised Chapters 7, 8, and 9 for public comment and 

CASAC review, as indicated in Table 1-3. The CASAC completed its review in September, 

2004, as indicated in an October 4, 2004 letter (Hopke, 2004) to the EPA Administrator. 

Revisions made in response to final CASAC and public comments were then incorporated by the 

NCEA-RTP PM team, as appropriate, into this final 2004 PM AQCD (U.S. Environmental 

Protection Agency, 2004) released in October, 2004. 

.1.2.2 Assessment Approaches 

The assessment presented in this document is framed by: (1) the selection of pertinent 

issues to be addressed; (2) the selection of relevant studies and an approach to the presentation of 

information drawn from those studies; and (3) the selection of an approach to interpreting and 

integrating the body of evidence evaluated in the document. 

As an initial matter, the NCEA-RTP PM team focused on selecting pertinent issues to be 

addressed in this assessment. Preliminary issues were drawn from among those highlighted in 

the 1996 PM AQCD and Staff Paper and in CASAC and public reviews of those documents, 

the 1997 PM NAAQS promulgation process, and the 1998 PM Health Risk Research Needs 

Document (U.S. Environmental Protection Agency, 1998a). Further identification and 

refinement of issues resulted from the NRC review and reports on PM research priorities, 

as discussed in Section 1.1.5 above. The CASAC review of the PM AQCD Development Plan 

and public comments on early draft AQCD materials at various stages of their development also 

provided important inputs regarding issue identification. The issues selected are reflected 

throughout this document and are most concisely identified in the introductory section at the 

beginning of each of the ensuing chapters. 

The selection of relevant studies to be included in this assessment was based on a detailed 

review of new research published in the peer-reviewed literature since early 1996, including 

materials accepted for publication mainly through April 2002 (and, thus, appearing mostly 

1-16
during 2002). Limited coverage of some more recent studies is also included as deemed 

appropriate in light of their special importance. For example, information derived from the HEI 

Special Report (Health Effects Institute, May 2003), discussed above in Section 1.2.1, has been 

integrated into this assessment. Literature discussed in detail in the 1996 PM AQCD (U.S. 

Environmental Protection Agency, 1996a) generally is not discussed in depth in this document; 

rather key findings from the 1996 review are concisely summarized as appropriate. Limited 

treatment is included, however, for some earlier studies judged to be of particular importance in 

this review of the PM NAAQS. Key literature is summarized in tables, whereas text discussions 

focus mainly on evaluation and integration of the literature, including discussion of alternative 

points of view where scientific controversy exists. This approach to study selection and 

presentation reflects recommendations from CASAC aimed at development of a more concise 

document than the 1996 PM AQCD. 

The scientific assessments presented in this document are primarily driven by the large 

body of available epidemiologic evidence evaluating associations between ambient PM, alone 

and in combination with other air pollutants, and various health endpoints. In such a case, an 

approach to interpreting the evidence most fundamentally focuses on characterizing the causal 

significance of observed associations. In so doing, it is appropriate to consider various aspects 

of the evidence of associations, in particular drawing from those presented earlier in Hill’s 

classic monograph (Hill, 1965) and extensively used by the scientific community in conducting 

such evidence-based reviews, e.g., in the 2004 Surgeon General’s Report on smoking (Centers 

for Disease Control and Prevention, 2004). As discussed in Chapters 8 and 9, a number of these 

aspects are judged to be particularly salient in evaluating the body of evidence available in this 

review, including the aspects described by Hill as strength, consistency, temporality, biologic 

gradient, experiment, plausibility, and coherence. These interrelated aspects are considered in 

the evaluation of epidemiologic evidence presented in Chapter 8 and are also more broadly 

addressed in the Chapter 9 integrative synthesis. 

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.1.3 DOCUMENT ORGANIZATION 

This document is basically organized to assess information related to topics along the same 

flow of issues presented in the NRC conceptual framework shown in Figure 1-1, including 

information related to effects on both human health and the environment. The document 

consists of nine chapters presented in two volumes. Volume I contains this general introduction 

(Chapter 1), as well as Chapters 2 through 5. Chapters 2 and 3 provide background information 

on the physical and chemical properties of PM and related compounds; sources and emissions; 

atmospheric transport, transformation, and fate of PM; methods for the collection and 

measurement of PM; and U.S. ambient air PM concentrations. Chapter 4 assesses welfarerelated PM effects on vegetation and ecosystems, visibility, and man-made materials, as well as 

climate-related effects (including effects on atmospheric transmission of solar radiation), and it 

includes limited information on economic impacts of some welfare effects. Chapter 5 discusses 

factors affecting exposure of the general population to ambient PM. 

The second volume contains Chapters 6 through 9. Chapter 6 assesses information 

concerning dosimetry of inhaled particles in the respiratory tract. Chapter 7 assesses the 

toxicology of specific types of PM constituents and potential mechanisms of action, based 

primarily on laboratory animal studies and controlled human exposure studies. Chapter 8 

assesses the epidemiologic literature. Lastly, Chapter 9 integrates key information on PMrelated health effects, drawing from assessments in prior chapters of the literature on exposure, 

dosimetry, toxicology, and epidemiology, as well as highlighting key information regarding 

important welfare effects associated with ambient PM. 

1-18
.REFERENCES 

Centers for Disease Control and Prevention. (2004) The health consequences of smoking: a report of the Surgeon 

General. Atlanta, GA: U.S. Department of Health and Human Services, National Center for Chronic Disease 

Prevention and Health Promotion, Office on Smoking and Health. Available: 

http://www.cdc.gov/tobacco/sgr/sgr_2004/chapters.htm (18 August, 2004). 

Federal Register. (1971) National primary and secondary ambient air quality standards. F. R. (April 30) 

36: 8186-8201.

Federal Register. (1987) Revisions to the national ambient air quality standards for particulate matter. F. R. (July 1) 

52: 24,634-24,669.

Federal Register. (1996) National ambient air quality standards for particulate matter; proposed rule. F. R. 

(December 13) 61: 65,638-65,713. 

Federal Register. (1997) National ambient air quality standards for particulate matter; final rule. F. R. (July 18) 

62: 38,652-38,752.

Health Effects Institute. (2003) Revised analyses of time-series studies of air pollution and health. Boston, MA: 

Health Effects Institute; special report. 

Hill, A. B. (1965) The environment and disease: association or causation? Proc. R. Soc. Med. 58: 295-300. 

Hopke, P. K. (2004) Clean Air Scientific Advisory Committee (CASAC) Particulate Matter (PM) Review Panel's 

ongoing peer review of the agency's draft Air Quality Criteria for Particulate Matter [letter to Michael O. 

Leavitt, Administrator, U.S. EPA]. Washington, DC: U.S. Environmental Protection Agency, Clean Air 

Scientific Advisory Committee; EPA-SAB-CASAC-04-009; October 4. 

National Research Council. (1998) Research priorities for airborne particulate matter. I. Immediate priorities and a 

long-range research portfolio. Washington, DC: National Academy Press. Available: 

http://www.nap.edu/catalog/6131.html (4 June 2003). 

National Research Council. (1999) Research priorities for airborne particulate matter. II. Evaluating research 

progress and updating the portfolio. Washington, DC: National Academy Press. Available: 

http://www.nap.edu/books/0309066387/html/ (4 June 2003). 

National Research Council. (2001) Research priorities for airborne particulate matter. III. Early research progress. 

Washington, DC: National Academy Press. Available: http://www.nap.edu/books/0309073375/html/ 

(4 June 2003). 

U.S. Code. (1991) Clean Air Act, §108, air quality criteria and control techniques, §109, national ambient air quality 

standards. U. S. C. 42: §§7408-7409. 

U.S. Court of Appeals for the District of Columbia. (1999a) American Trucking Associations, Inc. vs. U.S. 

Environmental Protection Agency. 175 F3d 1027 (D.C. Cir. 1999). 

U.S. Court of Appeals for the District of Columbia. (1999b) American Trucking Associations, Inc. vs. U.S. 

Environmental Protection Agency. 195 F.3d 4 (D.C. Cir. 1999), affirmed in part, reversed in part, and 

remanded.. 

U.S. Court of Appeals for the District of Columbia. (2002) American Trucking Associations, Inc. vs. U.S. 

Environmental Protection Agency. 283 F.3d 355, 378-79 (D.C. Cir. 2002). 

U.S. District Court of Arizona. (1995) American Lung Association v. Browner. West's Federal Supplement 

884 F.Supp. 345 (No. CIV 93-643 TUC ACM). 

U.S. Environmental Protection Agency. (1996a) Air quality criteria for particulate matter. Research Triangle Park, 

NC: National Center for Environmental Assessment-RTP Office; report nos. EPA/600/P-95/001aF-cF. 3v. 

U.S. Environmental Protection Agency. (1996b) Review of the national ambient air quality standards for particulate 

matter: policy assessment of scientific and technical information. OAQPS staff paper. Research Triangle 

Park, NC: Office of Air Quality Planning and Standards; report no. EPA/452/R-96-013. Available from: 

NTIS, Springfield, VA; PB97-115406REB. 

U.S. Environmental Protection Agency. (1998a) Particulate matter research needs for human health risk assessment 

to support future reviews of the national ambient air quality standards for particulate matter. Research 

Triangle Park, NC: National Center for Environmental Assessment; report no. EPA/600/R-97/132F. 

U.S. Environmental Protection Agency. (1998b) Review of national ambient air quality standards for particulate 

matter. Commer. Bus. Daily: February 19. Available: http://cbdnet.access.gpo.gov/index.html 

[1999, November 24]. 

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U.S. Supreme Court. (2001) Whitman v. American Trucking Association. 531 U.S. 457 (nos. 99-1257 and 99-1426). 

Wolff, G. T. (1996) Closure by the Clean Air Scientific Advisory Committee (CASAC) on the draft Air Quality 

Criteria for Particulate Matter [letter to Carol M. Browner, Administrator, U.S. EPA]. Washington, DC: 

U.S. Environmental Protection Agency, Clean Air Scientific Advisory Committee.; EPA-SAB-CASAC-LTR96-005; March 15. 

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2. PHYSICS, CHEMISTRY, AND MEASUREMENT

OF PARTICULATE MATTER

Chapter 3 of the 1996 EPA document Air Quality Criteria for Particulate Matter (1996 PM 

AQCD; U.S. Environmental Protection Agency, 1996a) contained an extensive review of the 

physics and chemistry of airborne particulate matter (PM). Chapter 2 of this revised version of 

the PM AQCD also provides background information on the physics and chemistry of 

atmospheric particles, information useful in understanding the subsequent chapters. The 

chapters in this document are organized to generally follow the sequence of key elements that 

make up the risk assessment framework described in Chapter 1 (Section 1.2.2) beginning with 

sources and continuing to effects as shown in Figure 1-1. Thus, this chapter discusses new 

background information useful in evaluating PM effects on human health and welfare and in 

preparing related risk assessments used to support PM standard-setting decisions. Information 

important for implementation of PM standards, but not essential to the standard setting process, 

is not the focus in this chapter. The reader is referred to the NARSTO (North American 

Research Strategy for Tropospheric Ozone) Fine Particle Assessment (NARSTO, 2003) for 

information relevant to air quality management for PM. 

Unlike other criteria pollutants (O3, CO, SO2, NO2, and Pb), PM is not a specific chemical 

entity but is a mixture of particles from different sources and of different sizes, compositions, 

and properties. Emphasis is placed here on discussion of differences between fine and coarse 

particles and differences between ultrafine particles and accumulation-mode particles within fine 

particles. 

Since PM is defined quantitatively by measurement techniques, it will be useful to discuss 

our understanding of the relationships between PM suspended in the atmosphere, PM inhaled by 

people, and PM measured by various sampling and analytical techniques. Chapter 4 of the 1996 

PM AQCD (U.S. Environmental Protection Agency, 1996a) contained a review of the state of 

the art of PM measurement technology. Since that time, considerable progress has been made in 

understanding problems in the measurement of PM mass, chemical composition, and physical 

parameters. Progress has also been made in developing new and improved measurement 

techniques, especially for continuous measurements. Therefore, a more extensive survey on 

2-1
measurement problems and on newly developed measurement techniques is included here in 

Section 2.2. For more detail and older references, the reader is referred to Chapters 3 and 4 of 

the 1996 PM AQCD (U.S. Environmental Protection Agency, 1996a). 

2.1 PHYSICS AND CHEMISTRY OF PARTICULATE MATTER 

2.1.1 Basic Concepts 

Atmospheric particles originate from a variety of sources and possess a range of 

morphological, chemical, physical, and thermodynamic properties. Examples of atmospheric 

particles include combustion-generated particles, such as diesel soot or fly ash; photochemically 

produced particles, such as those found in urban haze; salt particles formed from sea spray; and 

soil-like particles from resuspended dust. Some particles are liquid; some are solid. Others may 

contain a solid core surrounded by liquid. Atmospheric particles contain inorganic ions, metallic 

compounds, elemental carbon, organic compounds, and crustal compounds. Some atmospheric 

particles are hygroscopic and contain particle-bound water. The organic fraction is especially 

complex, containing hundreds (probably thousands) of organic compounds. (See Appendix 3C 

for information on the composition of the organic fraction and the concentration of specific 

organic compounds.) Primary particles are emitted directly from sources; whereas secondary 

particles are formed from gases through chemical reactions in the atmosphere involving 

atmospheric oxygen (O2) and water vapor (H2O); reactive species such as ozone (O3); radicals 

such as the hydroxyl (COH) and nitrate (CNO3) radicals; and pollutants such as sulfur 

dioxide (SO2), nitrogen oxides (NOX), and organic gases from natural and anthropogenic 

sources. The particle formation process includes nucleation of particles from low-vapor pressure 

gases emitted from sources or formed in the atmosphere by chemical reactions, condensation of 

low-vapor pressure gases on existing particles, and coagulation of particles. Thus, any given 

particle may contain PM from many sources. Because a particle from a given source is likely to 

be composed of a mixture of chemical components and because particles from different sources 

may coagulate to form a new particle, atmospheric particles may be considered a mixture of 

mixtures. The composition and behavior of particles are fundamentally linked with those of the 

surrounding gas. An aerosol may be defined as a suspension of solid or liquid particles in air. 

The term aerosol includes both the particles and all vapor or gas phase components of air. 

2-2
However, the term aerosol is sometimes used to refer to the suspended particles only. In this 

document, “particulate” is used only as an adjective, as in particulate matter. 

A complete description of the atmospheric aerosol would include an accounting of the 

chemical composition, morphology, and size of each particle, as well as the relative abundance 

of each particle type as a function of particle size (Friedlander, 1970). However, the physical 

and chemical characteristics of particles are usually measured separately. Size distributions by 

particle number used to calculate surface area and volume distributions often are determined by 

physical means, such as electrical mobility, aerodynamic behavior, or light scattering. Chemical 

composition usually is determined by analysis of collected samples, although some species can 

be measured in situ. The mass and average chemical composition of particles segregated 

according to aerodynamic diameter by cyclones or impactors can also be determined. However, 

recent developments in single particle analysis techniques by electron microscopy with X-ray 

analysis of single particles (but not agglomerates) collected on a substrate or by mass 

spectroscopy of individual suspended particles provide elemental composition of individual 

particles by particle size and, thus, are bringing the description envisioned by Friedlander closer 

to reality. 

2.1.2 Physical Properties and Processes 

2.1.2.1 Definitions of Particle Diameter 

The diameter of a spherical particle may be determined by optical or electron microscopy, 

by light scattering and Mie theory, by its electrical mobility, or by its aerodynamic behavior. 

However, atmospheric particles often are not spherical. Therefore, their diameters are described 

by an “equivalent” diameter (i.e., the diameter of a sphere that would have the same physical 

behavior). An optical diameter is the diameter of a spherical particle, with the same refractive 

index as the particle used to calibrate the optical particle sizer, that scatters the same amount of 

light into the solid angle measured. Diffusion and gravitational settling are important physical 

behaviors for particle transport, collection, and removal processes, including deposition in the 

respiratory tract. Different equivalent diameters are used depending on which process is more 

important. For smaller particles, diffusion is more important and the Stokes diameter is often 

used. For larger particles, gravitational setting is more important and the aerodynamic diameter 

is often used. 

2-3
The Stokes diameter, Dp, describes particle size based on the aerodynamic drag force 

imparted on a particle when its velocity differs from that of the surrounding fluid. For a smooth, 

spherically shaped particle, Dp exactly equals the physical diameter of the particle. For 

irregularly shaped particles, Dp is the diameter of an equivalent sphere that would have the same 

aerodynamic resistance. Electrical mobility analyzers classify particles according to their 

electrical mobility. Particles of equal Stokes diameters that carry the same electric charge will 

have the same electrical mobility. Hence, for spherical particles, the electrical mobility diameter 

would equal the Stokes diameter. The mobility diameter can be considered the diameter of a 

spherical particle that would have the same electrical mobility. The particle mobility can be 

related to the particle diffusion coefficient and Brownian diffusion velocity through the StokesEinstein equation. Thus, the Stokes diameter is the appropriate parameter for particle behavior 

governed by diffusion. The Stokes diameter, Dp, is used in size distributions based on light 

scattering and mobility analysis. The Stokes diameter is independent of density. 

The aerodynamic diameter, Da, however, depends on particle density. It is defined as the 

diameter of a spherical particle with an equal gravitational settling velocity but a material density 

of 1 g/cm3

. Cascade impactors separate particles based on their aerodynamic diameter, and 

aerodynamic particle sizers measure the aerodynamic diameter. Respirable, thoracic, and 

inhalable sampling and PM2.5 and PM10 sampling are based on particle aerodynamic diameter. 

For particles greater than about 0.5 μm, the aerodynamic diameter is generally the quantity of 

interest. For smaller particles, the Stokes diameter may be more useful. Particles with the same 

physical size and shape but different densities will have the same Stokes diameter but different 

aerodynamic diameters. 

The aerodynamic diameter, Da, is related to the Stokes diameter, Dp, by: 

(2-1) 

where D is the particle density, and Cp and Ca are the Cunningham slip factors evaluated for the 

particle diameters Dp and Da respectively. The slip factor is a function of the ratio between 

particle diameter and mean free path of the suspending gas (0.066 μm for air at one atmosphere 

pressure and 20 °C). C is an empirical factor that accounts for the reduction in the drag force on 

2-4
particles due to the “slip” of the gas molecules at the particle surface. C is an important factor 

for particles less than 1 :m in diameter, for which the surrounding air cannot be modeled by a 

continuous fluid. For large particles (Dp > 5 μm) C = 1; while for smaller particles C > 1. 

For particles with diameters greater than the mean free path, 8, the aerodynamic diameter 

given by equation (2-1) is approximated by: 

(2-2) 

This expression, which shows that aerodynamic diameter is directly proportional to the square 

root of the particle density, is often used for particles as small as 0.5 μm. For particles with 

diameters much smaller than the mean free path, the slip factor must be taken into account. 

In this case, the aerodynamic diameter is directly proportional to the particle density, 

(2-3) 

Detailed definitions of the various sizes and their relationships are given in standard aerosol 

textbooks (e.g., Friedlander [2000], Reist [1984, 1993], Seinfeld and Pandis [1998], Hinds 

[1999], Vincent [1989, 1995], Willeke and Baron [1993], Baron and Willeke [2002], and Fuchs 

[1964, 1989]). 

2.1.2.2 Aerosol Size Distributions 

Particle size, as indexed by one of the “equivalent” diameters, is an important parameter in 

determining the properties, effects, and fate of atmospheric particles. The atmospheric 

deposition rates of particles and, therefore, their residence times in the atmosphere are a strong 

function of their Stokes and aerodynamic diameters. Particle diameters also influence the 

deposition patterns of particles within the lung. Because light scattering is strongly dependent 

on the optical particle size, the amount of light scattering per unit PM mass will be dependent on 

the size distribution of atmospheric particles. Therefore, the effects of atmospheric particles on 

visibility, radiative balance, and climate will be influenced by the size distribution of the 

2-5
particles. Studies using cascade impactors or cyclones measure the particle-size distribution 

directly in aerodynamic diameter. The diameters of atmospheric particles range from 1 nm to 

100 μm, spanning 5 orders of magnitude. A variety of different instruments, measuring a variety 

of equivalent diameters, are required to cover this range. 

Older particle counting studies used optical particle counters to cover the range of 0.3 to 

30 μm diameter. Diameters of particles below 0.5 μm were measured as mobility diameters. 

The particle diameters used in size distribution graphs from these studies usually are given as 

physical or Stokes diameters rather than aerodynamic diameters. In recent years, aerodynamic 

particle sizers have been developed that give a direct measurement of the aerodynamic diameter 

in the range of approximately 0.7 to 10 μm diameter. These instruments have been used with 

electrical mobility analyzers that measure the mobility diameter of particles from 3 nm to 

approximately 0.5 μm (McMurry, 2000). Unfortunately, there is no agreed-upon technique for 

combining the various equivalent diameters. Some workers use various assumptions to combine 

the various measurements into one presentation; others report each instrument separately. 

Therefore, the user of size distribution data should be careful to determine exactly which 

equivalent diameter is reported. 

Particle Size Distribution Functions 

The distribution of particles with respect to size is an important physical parameter 

governing particle behavior. Because atmospheric particles cover several orders of magnitude in 

particle size, size distributions often are expressed in terms of the logarithm of the particle 

diameter on the X-axis and the measured differential concentration on the Y-axis: 

)N/)(logDp) = the number of particles per cm3 of air having diameters in the size range from 

log Dp to log(Dp + )Dp). Because logarithms do not have dimensions, it is necessary to think 

of the distribution as a function of log(Dp/Dp0), where the reference diameter Dp0 = 1 μm is 

not explicitly stated. If )N/)(logDp) is plotted on a linear scale, the number of particles 

between D and D + )Dp is proportional to the area under the curve of )N/)(logDp) versus p p

logDp. Similar considerations apply to distributions of surface, volume, and mass. When 

approximated by a function, the distributions are usually given as dN/d(log Dp) rather than 

)N/)(log Dp). 

2-6
Atmospheric Aerosol Size Distributions 

Whitby (1978) published an analysis of over 1,000 particle size distributions measured at 

various locations in the United States. Figure 2-1 shows the number, surface area, and volume 

distributions for the grand average continental size distribution. Volume, surface area, and 

number distributions are plotted on an arithmetic scale such that the volume, surface area, or 

number of particles in any specified size range is proportional to the corresponding area under 

the curve. These distributions show that most of the particles are quite small, below 0.1 μm; 

whereas most of the particle volume (and therefore most of the mass) is found in particles 

> 0.1 μm. Other averaged atmospheric size distributions are shown in Figures 2-2 and 2-3

(Whitby, 1978; Whitby and Sverdrup, 1980). Figures 2-2a and 2-2b describe the number of 

particles as a function of particle diameter for rural, urban-influenced rural, urban, and freewayinfluenced urban aerosols. For some of the same data, the particle volume distributions are 

shown in Figures 2-3a and 2-3b. Whitby (1978) observed that the size distributions typically 

had three peaks which he called “modes.” The entire size distribution could be characterized 

well by a trimodal model consisting of three additive log-normal distributions. The mode with a 

peak between 5 and 30 μm diameter formed by mechanical processes was called the coarse 

particle mode; the mode with a peak between 0.15 and 0.5 μm formed by condensation and 

coagulation was called the accumulation mode; and the mode with a peak between 0.015 and 

0.04 μm whose size was influenced by nucleation as well as by condensation and coagulation 

was called the transient nuclei or Aitken nuclei range, subsequently shortened to the nuclei 

mode. The nuclei mode could be seen in the number and surface distribution but only in special 

situations was it noticeable in the mass or volume distributions. The accumulation and nuclei 

modes taken together were called fine particles. An experimental size distribution showing 

modes and formation mechanisms is given in Figure 2-4. This size distribution was measured in 

traffic. Therefore, the nuclei mode is clearly separated from the accumulation mode and larger 

than it would be in size-distributions measured farther from sources of nuclei-mode particles. 

Whitby (1978) concluded 

“The distinction between ‘fine particles’ and ‘coarse particles’ is a fundamental 

one. There is now an overwhelming amount of evidence that not only are two 

modes in the mass or volume distribution usually observed, but that these fine and 

coarse modes are usually chemically quite different. The physical separation of 

2-7
Figure 2-1. Distribution of coarse (c), accumulation (a), and nuclei (n) mode particles by 

three characteristics: (a) number, N; (b) surface area, S; and (c) volume, V 

for the grand average continental size distribution. DGV = geometric mean 

diameter by volume; DGS = geometric mean diameter by surface area; 

DGN = geometric mean diameter by number; Dp = particle diameter. 

Source: Whitby (1978). 

2-8 
Figure 2-2. Particle size distributions by number: (a) number concentrations are shown 

on a logarithmic scale to display the wide range by site and size; (b) number 

concentrations for the average urban distribution are shown on a linear scale. 

For the linear scale, the area under any part of the curve is proportional to 

particle number in that size range. 

Source: Whitby (1978); Whitby and Sverdrup (1980). 

the fine and coarse modes originates because condensation produces fine particles 

while mechanical processes produce mostly coarse particles . . . the dynamics of 

fine particle growth ordinarily operate to prevent the fine particles from growing 

larger than about 1 μm. Thus, the fine and coarse modes originate separately, are 

transformed separately, are removed separately, and are usually chemically 

different . . . practically all of the sulfur found in atmospheric aerosol is found in 

the fine particle fraction. Thus, the distinction between fine and coarse fractions is 

of fundamental importance to any discussion of aerosol physics, chemistry, 

measurement, or aerosol air quality standards.” 

Whitby’s (1978) conclusions were based on extensive studies of size distributions in a 

number of western and midwestern locations during the 1970s (Whitby et al., 1974; Willeke 

and Whitby, 1975; Whitby, 1978; Wilson et al., 1977; Whitby and Sverdrup, 1980). No size 

2-9 
Figure 2-3. Size distribution by volume for the averaged (a) rural and urban-influenced 

rural number distribution shown in Figure 2-2a and a distribution from 

south-central New Mexico, and (b) urban and freeway-influenced urban 

number distributions shown in Figure 2-2a. 

Source: Whitby and Sverdrup (1980); Kim et al. (1993) and south-central New Mexico. 

distribution studies of similar scope have been published since then. Newer results from particle 

counting and impactor techniques, including data from Europe (U.S. Environmental Protection 

Agency, 1996a) and Australia (Keywood et al., 1999, 2000), show similar results for the 

accumulation and coarse modes. Extensive measurements of particle size distributions, as part 

of the EPA’s Supersites program, are providing considerable new data for analysis. 

Whitby’s (1978) conclusions have held up remarkably well. However, ideas about the 

sub-0.1 μm diameter range have changed somewhat as newer instruments provided 

measurements extending to smaller sizes and with greater resolution in size and time (McMurry 

et al., 2000). Depending on the source, temperature, saturated vapor pressure of the components, 

2-10
Figure 2-4. Volume size distribution, measured in traffic, showing fine and coarse 

particles and the nuclei and accumulation modes of fine particles. DGV 

(geometric mean diameter by volume, equivalent to volume median diameter) 

and Fg (geometric standard deviation) are shown for each mode. Also shown 

are transformation and growth mechanisms (e.g., nucleation, condensation, 

and coagulation). 

Source: Adapted from Wilson et al. (1977) and Wilson and Suh (1997). 

and the age of the aerosol, size distributions have been observed with peaks (including multiple 

peaks) throughout the sub-0.1 μm diameter size range. Sub-0.1 μm diameter peaks have been 

observed in rural areas (O’Dowd, 2002) as well as for brief periods (nucleation bursts) in urban 

areas (Woo et al., 2001a). Based on these and other observations, discussed in detail in 

Section 2.1.2.3, aerosol scientists now classify particles in the sub-0.1 μm size range as ultrafine 

particles and divide this size range into a nucleation region (< 10 nm) and an Aitken (nuclei) 

region (10 to 100 nm), as shown in Figure 2-5. Other studies, discussed in detail in the 1996 PM 

2-11
Figure 2-5. Sub-micrometer number size distribution observed in a boreal forest in 

Finland showing the trimodal structure of fine particles. The total particle 

number concentration was 1,011 particles/cm3

 (10-min average). 

Source: Mäkelä et al. (1997). 

AQCD (U.S. Environmental Protection Agency, 1996a), have shown that in fog or clouds or at 

very high relative humidities the accumulation mode may split into a larger size (more 

hygroscopic or droplet) submode and a smaller size (less hygroscopic or condensation) submode. 

Definitions of Particle Size Fractions 

In the preceding discussion several subdivisions of the aerosol size distribution were 

identified. Aerosol scientists use several different approaches or conventions in the 

classification of particles by size. These include: (1) modes, based on the observed size 

distributions and formation mechanisms; (2) dosimetry or occupational health sizes, based on the 

entrance into various compartments of the respiratory system; and (3) cut point, usually based on 

the 50% cut point of the specific sampling device, including legally specified, regulatory cut 

points for air quality standards. 

2-12
Modal. The modal classification as first proposed by Whitby (1978) is shown in 

Figures 2-1 and 2-4. The newer modes introduced since 1978 are shown in Figure 2-5. 

An idealized distribution showing all four modes is shown in Figure 2-6. The nucleation and 

Aitken modes are best observed in the number distribution. However, the Aitken mode can be 

seen in the volume distribution in traffic or near traffic or other sources of ultrafine particles 

(Figures 2-3b and 2-4). The observed modal structure is frequently approximated by several 

lognormal distributions. Definitions of terms used to describe size distributions in modal terms 

are given below. 

Figure 2-6. An idealized size distribution, that might be observed in traffic, showing fine 

and coarse particles and the nucleation, Aitken, and accumulation modes that 

comprise fine particles. Also shown are the major formation and growth 

mechanisms of the four modes of atmospheric particles. 

2-13
Nucleation Mode: Freshly formed particles with diameters below about 10 nm, observed 

during active nucleation events. The lower limit, where particles and molecular clusters or 

large molecules overlap, is uncertain. Current techniques limit measurements to particles 

3 nm or greater. 

Aitken Mode: Larger particles with diameters between about 10 and 100 nm. The Aitken 

mode may result from growth of smaller particles or nucleation from higher concentrations 

of precursors. 

Accumulation Mode: Particles with diameters from about 0.1 μm to just above the 

minimum in the mass or volume distributions which usually occurs between 1 and 3 μm. 

Fine Particles: Fine particles include the nucleation, Aitken, and accumulation modes, 

i.e., particles from the lowest measurable size, currently about 3 nm, to just above the 

minimum in the mass or volume distribution which generally occurs between 1 and 3 μm. 

Coarse Mode or Coarse Particles: Particles with diameters mostly greater than the 

minimum in the particle mass or volume distributions, which generally occurs between 

1 and 3 μm. 

Ultrafine Particles: Ultrafine particles are not a mode. In the air pollution literature, they 

are generally defined by size alone, i.e., particles with diameters of 0.1 μm (100 nm) or 

less. They include the nucleation mode and much of the Aitken mode. They may also be 

defined as particles whose properties differ from those of the bulk material because of their 

small size. 

Modes are defined primarily in terms of their formation mechanisms but also differ in 

sources, composition, transport and fate, as well as in size. Nucleation mode applies to newly 

formed particles which have had little chance to grow by condensation or coagulation. Aitken 

mode particles are also recently formed particles that are still actively undergoing coagulation. 

However, because of higher concentrations of precursors or more time for condensation and 

2-14
coagulation, Aitken particles have grown to larger sizes. Fine particles grow by coagulation 

(two particles combining to form one) or by condensation (low-equilibrium vapor pressure gas 

molecules condensing on a particle). As the particle size increases, the rate of growth by 

coagulation and condensation decreases and particles “accumulate” in the accumulation mode 

size range. Thus, accumulation-mode particles normally do not grow into the coarse particle 

size range. However, during conditions of high relative humidity, hygroscopic accumulation 

mode particles grow in size, increasing the overlap of fine and coarse particles. The 

accumulation mode may split into a (hygroscopic) droplet mode and a (non-hygroscopic) 

condensation mode. In addition, gas-phase pollutants may dissolve and react in the particlebound water of hygroscopic particles, leading to an increase in the dry size. The combination of 

nucleation, Aitken, and accumulation modes are called fine particles (or sometimes fine-mode 

particles). Fine particles are formed primarily by combustion or chemical reactions of gases 

yielding products with low saturated vapor pressures. Fine particles are composed of metals 

(and metal oxides), black or elemental carbon, primary and secondary organic compounds, and 

sulfate, nitrate, ammonium and hydrogen ions. 

The coarse mode refers to particles formed by the mechanical breakdown of minerals, 

crustal material, and organic debris. In addition to primary minerals and organic material, the 

coarse mode may include sea salt, nitrate formed from the reaction of nitric acid with sodium 

chloride, and sulfate formed from the reaction of sulfur dioxide with basic particles. The 

accumulation mode and the coarse mode overlap in the region between 1 and 3 μm (and 

occasionally over an even larger range). In this region, the chemical composition of individual 

particles can usually, but not always, allow identification of a source or formation mechanism, 

permitting identification of a particle as belonging to the accumulation or coarse mode. 

Occupational Health or Dosimetric Size Cuts. The occupational health community has 

defined size fractions in terms of their entrance into various compartments of the respiratory 

system. This convention classifies particles into inhalable, thoracic, and respirable particles 

according to their upper size cuts. Inhalable particles enter the respiratory tract, beginning with 

the head airways. Thoracic particles travel past the larynx and reach the lung airways and the 

gas-exchange regions of the lung. Respirable particles are a subset of thoracic particles that are 

more likely to reach the gas-exchange region of the lung. In the past, exact definitions of these 

2-15
terms have varied among organizations. As of 1993, a unified set of definitions was adopted by 

the American Conference of Governmental Industrial Hygienists (ACGIH, 1994), the 

International Standards Organization (ISO), and the European Standardization Committee 

(CEN). The curves which define inhalable (IPM), thoracic (TPM), and respirable (RPM) 

particulate matter are shown in Figure 2-7. These curves should not be taken to indicate that 

particles > 4 μm Da do not reach the gas exchange regions or that particles < 4 μm Da do not 

deposit in the bronchi. See Figure 6-13 in Chapter 6 for a graphical characterization of particle 

deposition in regions of the respiratory system as a function of particle size. 

Figure 2-7. Specified particle penetration (size-cut curves) through an ideal (no-particleloss) inlet for five different size-selective sampling criteria. Regulatory size 

cuts are defined in the Code of Federal Regulations (PM2.5 [2001c], PM10 

[2001a]). PM2.5 is also defined in the Federal Register (1997). Size-cut curves 

for inhalable particulate matter (IPM), thoracic particulate matter (TPM) 

and respirable particulate matter (RPM) size cuts are computed from 

definitions given by American Conference of Governmental and Industrial 

Hygienists (1994). 

2-16
Size-Selective Sampling. Another set of definitions of particle size fractions arises from 

considerations of size-selective sampling. Size-selective sampling refers to the collection of 

particles below or within a specified aerodynamic size range. Size fractions are usually specified 

by the 50% cut point size; e.g., PM2.5 refers to particles collected by a sampling device that 

collects 50% of 2.5 μm particles and rejects 50% of 2.5 μm particles. However, size fractions 

are defined not merely by the 50% cut point, but by the entire penetration curve. Examples of 

penetration curves are given in Figure 2-7. Thus, as shown by Figure 2-7, a PM2.5 sampler, as 

defined by the Federal Reference Method, rejects 94% of 3 μm particles, 50% of 2.5 μm 

particles, and 16% of 2 μm particles. Samplers with the same 50% cut point, but differently 

shaped penetration curves, would collect different fractions of PM. Size-selective sampling has 

arisen in an effort to measure particle size fractions with some special significance (e.g., health, 

visibility, source apportionment, etc.), to measure mass size distributions, or to collect sizesegregated particles for chemical analysis. Dichotomous samplers split the particles into smaller 

and larger fractions that may be collected on separate filters. However, some fine particles 

(.10%) are collected with the coarse particle fraction. Cascade impactors use multiple size cuts 

to obtain a distribution of size cuts for mass or chemical composition measurements. One-filter 

samplers with a variety of upper size cuts are also used, e.g., PM2.5, PM10. 

PM

An idealized particle size distribution with the normally observed division of ambient 

aerosols into fine and coarse particles and the size fractions collected by the WRAC, TSP, PM10, 

2.5 and PM10-2.5 samplers is shown in Figure 2-8. PM10 samplers, as defined in Appendix J to 

Title 40 Code of Federal Regulations (40 CFR), Part 50 (Code of Federal Regulations, 2001a; 

Federal Register, 1987), collect all of the fine-mode particles and part of the coarse-mode 

particles. The upper cut point is defined as having a 50% collection efficiency at 10 ± 0.5 μm 

aerodynamic diameter. The slope of the collection efficiency curve is defined in amendments to 

40 CFR, Part 53 (Code of Federal Regulations, 2001b). 

An example of a PM2.5 size-cut curve is also shown in Figure 2-7. The PM2.5 size-cut 

curve, however, is defined by the design of the Federal Reference Method (FRM) sampler. The 

basic design of the FRM sampler is given in the Federal Register (1997, 1998) and in 40 CFR, 

Part 50, Appendix L (Code of Federal Regulations, 2001c). Additional performance 

specifications are given in 40 CFR, Parts 53 and 58 (Code of Federal Regulations, 2001b,d). 

In order to be used for measurement of PM2.5 to determine compliance with the PM2.5 NAAQS, 

2-17
Figure 2-8. An idealized distribution of ambient particulate matter showing fine-mode 

particles and coarse-mode particles and the fractions collected by size-selective 

samplers. (WRAC is the Wide Range Aerosol Classifier which collects the 

entire coarse mode [Lundgren and Burton, 1995].) 

Source: Adapted from Wilson and Suh (1997) and Whitby (1978). 

each specific sampler design and its associated manual of operational procedures must be 

designated as a reference method under 40 CFR, Part 53 in Section 1.2 of Appendix L (Code of 

Federal Regulations, 2001c). Thus PM2.5 FRM samplers may have somewhat different designs 

(see Table 2-5 in Section 2.2.4.1.2). 

Papers discussing PM10 or PM2.5 frequently insert an explanation such as “PMx (particles 

less than x μm diameter)” or “PMx (nominally, particles with aerodynamic diameter # x μm).” 

While these explanations may seem easier than “PMX (particles collected with an upper 50% cut 

point of x μm aerodynamic diameter and a specified penetration curve),” they are not entirely 

correct and may be misleading, because they imply an upper 100% cut point of x μm. Some 

2-18
countries use PM10 to refer not to samplers with a 50% cut at 10 μm Da, but to samplers with 

100% rejection of all particles greater than 10 μm Da. Such samplers miss a fraction of coarse 

thoracic PM. An example is shown in Figure 2-9. 

Figure 2-9. Comparison of penetration curves for two PM10 beta gauge samplers using 

cyclone inlets. The Wedding PM10 sampler uses the EPA definition of PMx as 

x = 50% cut point. The Kimoto PM10 defines PMx as x = the 100% cut point 

(or zero penetration). 

Source: Tsai and Cheng (1996). 

PM10, as defined by EPA, refers to particles collected by a sampler with an upper 50% cut 

point of 10 μm Da and a specific, fairly sharp, penetration curve. PM2.5 is analogously defined. 

Although there is not yet an FRM, PM10-2.5 refers either to particles collected by a sampler with 

an upper 50% cut point of 10 μm Da and a lower 50% cut point of 2.5 μm Da or to the difference 

between the particle concentration measured by a PM10 monitor and a PM2.5 monitor. In all 

2-19
PM

cases, the fraction of PM collected depends on the entire penetration curve (or curves); i.e., for 

2.5 some particles > 2.5 μm Da are collected and not all particles < 2.5 μm Da are collected. 

A PM10-2.5 size fraction may be obtained from a dichotomous sampler or by subtracting 

the mass collected by a PM2.5 sampler from the mass collected by a PM10 sampler. The 

resulting PM10-2.5 mass, or PM10-2.5, is sometimes called “coarse” particles or “thoracic coarse” 

particles. However, it would be more correct to call PM10-2.5 an indicator of the thoracic 

component of coarse particles (because it excludes some coarse particles below 2.5 μm Da and 

above 10 μm Da). Also, PM2.5 should be considered an indicator of fine particles (because it 

contains some coarse particles). It would also be appropriate to call PM10 an indicator of 

thoracic particles. PM10 and thoracic PM, as shown in Figure 2-7, have the same 50% cut point. 

However, the thoracic cut is not as sharp as the PM10 cut; therefore, thoracic PM contains some 

particles between 10 and 30 μm diameter that are excluded from the PM10 fraction. 

Over the years, the terms fine and coarse, as applied to particles, have lost the precise 

meaning given in Whitby’s (1978) definition. In any given article, therefore, the meaning of fine 

and coarse, unless defined, must be inferred from the author’s usage. In this document, “fine 

particles” means all particles in the nucleation, Aitken, and accumulation modes; and “coarse 

particles” means all particles in the coarse mode. Fine particles and PM2.5 are not equivalent 

terms. 

Selection of Cut Points for Regulatory Size Cuts 

TSP. Regulatory size cuts are a specific example of size-selective sampling. Prior to 

1987, the indicator for the NAAQS for PM was TSP. TSP is defined by the design of the High 

Volume sampler (hivol) that collects all of the fine particles but only part of the coarse particles 

(Figure 2-8). The upper cut-off size of the hivol depends on the wind speed and direction and 

may vary from 25 to 40 μm. Newer PM samplers are usually designed to have an upper cut 

point and its standard deviation that are independent of wind direction and relatively independent 

of wind speed. 

PM10. In 1987, the NAAQS for PM were revised to use PM10, rather than TSP, as the 

indicator for the NAAQS for PM (Federal Register, 1987). The use of PM10 as an indicator is an 

example of size-selective sampling based on a regulatory size cut (Federal Register, 1987). The 

2-20
selection of cut point characteristics depends upon the application for the sampling device. 

A separation that simulates the removal of particles by the human upper respiratory system 

would appear to be a good choice for both health risk and regulatory monitoring (i.e., it would 

measure what gets into the lungs). The ACGIH-ISO-CEN penetration curve for thoracic 

particles (particles able to pass the larynx and penetrate into the bronchial and alveolar regions of 

the lung) has a 50% cut point at 10 μm aerodynamic diameter (Da). The selection of PM10 as an 

indicator was based on health considerations and was intended to focus regulatory concern on 

those particles small enough to enter the thoracic region of the human respiratory tract. The 

PM10 is an indicator of thoracic particles and is a compromise between the desire to collect all 

particles that might enter the thoracic regions and the need to design a sampler with a collection 

efficiency independent of particle size or wind speed. As shown in Figure 2-7, the PM10 

penetration efficiency curve matches the definition for thoracic PM given by the American 

Conference of Government and Industrial Hygienists (1994) very well except for a slight undercollection of particles between 10 and 30 μm in diameter. While the U.S. PM10 separation curve 

is sharper than the thoracic penetration curve, it has the advantage of reducing the problem of 

maintaining the finite collection efficiency specified by the thoracic penetration curve for 

particles larger than 10 μm Da. (See Section 2.1.2.2 and Figure 2-7.) 

Current PM10 samplers have upper cut points that are stable under normal operating 

conditions. However, problems may occur under unusual or adverse conditions. Ono et al. 

(2000) reported the results of a study in which several PM10 samplers were collocated and 

operated at various sites at Owens Lake, CA, a location with high concentrations of coarse PM. 

Samplers included the Partisol, the dichotomous, the Wedding high-volume, and the Graseby 

high-volume samplers in addition to the tapered element oscillating microbalance (TEOM) 

monitor. They found that the TEOM and Partisol samplers agreed to within 6%, on average. 

The dichotomous sampler and the Graseby and Wedding high-volume samplers, however, 

measured significantly lower PM10 concentrations than the TEOM (on average 10, 25, and 35% 

lower, respectively). These lower concentrations were attributed to a decrease in the cut point at 

higher wind speeds and to a dirty inlet. Since the 10 μm cut point is on a part of the size 

distribution curve where the concentration is changing rapidly, the amount of PM collected is 

sensitive to small changes in the cut point. Therefore, the cut point needs to be specified very 

2-21
precisely and the balance between a design standard and a performance standard will need to be 

considered to deal with this problem. 

PM2.5: Cut Point for Separation of Fine and Coarse Particles. The PM2.5 standard set in 

1997 is also an example of size-selective sampling based on a regulatory size cut (Federal 

Register, 1997). The PM2.5 standard was based primarily on epidemiologic studies using 

concentrations measured with PM2.5 samplers as an exposure index. However, the PM2.5 sampler 

was not designed to collect all respirable particles; it was designed to separate fine and coarse 

particles and to collect fine particles because of their different sources and properties (Whitby, 

1978; Miller et al., 1979). Thus, the need to attain a PM2.5 standard tends to focus regulatory 

concern on control of the sources of fine particles. 

Fine and coarse particles differ not only in size but also in formation mechanisms, sources, 

and chemical, physical, and biological properties. They also differ in concentration-exposure 

relationships, dosimetry (deposition and retention in the respiratory system), toxicity, and health 

effects as observed in epidemiologic studies. Thus, it is desirable to measure fine and coarse PM 

separately in order to properly allocate health effects to either fine or coarse PM and to correctly 

determine sources by receptor modeling approaches. For example, sulfates in fine particles are 

associated with hydrogen or ammonium ions, while sulfates in coarse particles are associated 

with basic metal ions. Transition metals in coarse particles are likely to be associated with soil 

and tend to be less soluble (and presumably less bioavailable) than transition metals in fresh 

combustion particles found in fine particles. 

In the early 1970s, aerosol scientists were beginning to recognize the existence of a 

minimum between 1 and 3 μm in the distribution of particle size by volume (Whitby et al., 

1974). However, the limited size distribution information available at that time (two 

distributions with a minimum near 1 μm and two with a minimum near 2.5 μm) did not permit a 

unambiguous definition of the appropriate cut point size for the separation of the two modes. 

A cut point of 2.5 μm was chosen for a new dichotomous sampler (Loo et al., 1976; Jaklevic 

et al., 1977) designed for use in the Regional Air Pollution Study in St. Louis, MO. The 2.5 μm 

cut point was subsequently used as an indicator of fine particles in a number of studies including 

the Harvard Six-City Studies of the relationships between mortality and PM concentrations 

(Dockery et al., 1993; Schwartz et al., 1996). In an analysis reported in 1979, EPA scientists 

2-22
endorsed the need to measure fine and coarse particles separately (Miller et al., 1979). Based on 

the availability of a dichotomous sampler with a separation size of 2.5 μm Da, they 

recommended 2.5 μm Da as the cut point between fine and coarse particles. Because of the wide 

use of this cut point, the PM2.5 fraction is frequently referred to as “fine” particles. However, 

although the PM2.5 sample will contain most of the fine particles, except to a lesser extent during 

conditions of high relative humidity, it may also collect a small fraction of the coarse particles, 

especially in dry areas or during dry conditions. A 2.5 μm cut point was also used in the 

Inhalable Particle Network (Suggs and Burton, 1983), which provided data for another major 

epidemiologic study of PM-mortality relationships using an American Cancer Society cohort 

(Pope et al., 1995). Therefore, at the time of the last previous review of the NAAQS for PM 

(U.S. Environmental Protection Agency, 1996a,b), a number of epidemiologic studies 

demonstrating a statistical relationship between PM2.5 concentrations and mortality were 

available. 

During the previous review of the PM standards, EPA conducted an extensive review of 

the cut point to be used for a fine particle standard, including consideration of PM1 as an 

alternative to PM2.5. The 1996 PM AQCD (U.S. Environmental Protection Agency, 1996a) 

contains a review of the available information on size distributions. As shown in Figure 2-10

(adapted from the 1966 PM AQCD; U.S. Environmental Protection Agency, 1996a), published 

size distributions exhibit considerable variability in the intermodal region (1 to 3.0 μm 

diameter). In Figure 2-10a, Philadelphia, very little mass is found in the intermodal region and it 

is not clear whether it should be considered fine or coarse. In Figure 2-10b, Phoenix, a tail of the 

coarse mode extends to 1 μm or below. These two size distributions were fit with three 

lognormal distributions. However, the intermodal mode probably does not have physical 

significance. In Figure 2.10c, Claremont, South Coast Basin, a size distribution is shown with 

the accumulation mode split into a condensation mode and a droplet mode, which extends to 

3.0 μm or above (John et al., 1990). The droplet mode occurs under high relative humidity (RH) 

conditions, usually with very high fine PM concentrations, and is believed to result from 

reactions involving gases dissolving and reacting in the particle-bound water of the particle 

droplets (Hering and Friedlander, 1982; McMurry and Wilson, 1983). Although Figure 2-10c 

shows nitrate concentrations, similar distributions have been observed for sulfate (John et al., 

1990). 

2-23
Figure 2-10. Three examples of impactor size distributions and distributions resulting 

from fitting several lognormal distributions to the impactor data. These 

distributions show the variation in the particle size range of the minimum in 

mass between the accumulation mode and the coarse mode. 

Source: a and b, adapted from Lundgren and Hausknecht (1982); c, John et al. (1990). 

2-24 
It is now understood that the size range between 1.0 and 3.0 μm, sometimes called the 

intermodal region, may contain either accumulation-mode or coarse-mode material or both, i.e., 

the two modes may overlap in this region (Kegler et al., 2001). The experimental information on 

the composition and source of the intermodal mass was discussed extensively in the 1996 PM 

AQCD (U.S. Environmental Protection Agency, 1996a). Depending largely on the RH, a 

significant amount of either accumulation- or coarse-mode material may be found in the 

intermodal region between 1.0 and 3.0 μm. The analysis demonstrated the important role of 

relative humidity in influencing the size of particles in both the accumulation and coarse modes. 

As the RH increases, hygroscopic accumulation-mode particles will increase in size due to 

accumulation of particle-bound water. At high RH, some originally submicrometer-sized 

accumulation-mode PM may be found with a Da above 1 μm. At an RH of 100%, such as is 

found in fog and clouds, accumulation-mode PM may extend above 2.5 μm Da. What is not well 

understood is whether such particles will shrink to diameters below 1 μm as the RH decreases or 

whether reactions occurring in the wet particles will result in an increase in nonaqueous mass, so 

that, even at low RH, the diameters would exceed 1 μm. On the other hand, at very low RH, 

coarse particles may be fragmented into smaller sizes, and small amounts of coarse PM may be 

found with an Da < 2.5 μm (Lundgren et al., 1984; Lundgren and Burton, 1995). Thus, a PM2.5 

sample will contain all of the accumulation-mode PM except during periods of high RH. 

However, under low-RH conditions, it may also contain a small fraction of the coarse PM. 

Considerations that led the EPA to choose to retain 2.5 μm as the cut point for the 

separation of fine and coarse particles included the following three points. First, epidemiologic 

data showing statistical relationships between fine PM and health outcomes were based largely 

on PM2.5. Second, while PM1 would exclude a tail of the coarse mode in some locations, in other 

locations it would miss a portion of the fine PM. Since the growth of the droplet mode is 

associated with very high fine PM concentrations, this would result in falsely low fine PM 

measurements on days with the highest fine PM concentrations. Third, only limited data on the 

concentration and composition of the intermodal PM mass was available. The selection of a cut 

point of 2.5 μm as a basis for the EPA’s 1997 NAAQS for fine particles (Federal Register, 1997) 

and its continued use in many health effects studies reflect the importance placed on a more 

complete inclusion of accumulation-mode particles, while recognizing that the intrusion of 

coarse particles can occur under some conditions with this cut point. 

2-25
Since the 1966 PM AQCD (U.S. Environmental Protection Agency, 1996a), several papers 

have addressed the issue of intermodal mass in terms of PM2.5-PM1. Kegler et al. (2001) 

analyzed data from Phoenix including TEOM measurements of PM1, PM2.5, and PM10 as well as 

filter measurements of PM2.5 and concluded that while PM2.5-1 was dominated by soil 

components, it also contained some nonsoil components. Their analysis suggested that there 

were two sources of the coarse mass found in PM2.5-1: (1) resuspension of soil dust by natural 

wind (windblown dust), which would be prominent at high wind speeds, and (2) resuspension 

due to roadway turbulence generated by motor vehicles (road dust), which would occur at all 

natural wind speeds. Correlations among the various PM size ranges measured in Phoenix are 

given in Table 2-1. The high correlation found between PM2.5 and PM1 (r = 0.97) suggests that 

the use of PM1 instead of PM2.5 would not significantly change epidemiologic relationships 

between PM mass and health outcomes in Phoenix. 

TABLE 2-1. CORRELATIONS BETWEEN TEOM MEASUREMENTS IN PHOENIX 

PM1 PM2.5-1 PM2.5 PM10-2.5 PM10 

PM1 1 0.69 0.97 0.65 0.81 

PM2.5-1 0.69 1 0.84 0.84 0.89 

PM2.5 0.97 0.84 1 0.75 0.89 

PM10-2.5 0.65 0.84 0.75 1 0.97 

PM10 0.81 0.89 0.89 0.97 1 

In areas where winds cause high concentrations of windblown soil, there is evidence that a 

significant amount of coarse-mode PM may be found below 2.5 μm. An example, taken from 

data collected during the August 1996 dust storm in Spokane, WA, is shown in Figure 2-11. 

Note that the PM10 scale is 10 times that of the other size fractions. PM1, although high in the 

morning, goes down as the wind increases and PM10, PM2.5, and PM2.5-1 go up. During the peak 

of the dust storm, around 9:00 p.m., PM2.5-1 was 88% of PM2.5. For the 24-h period, PM2.5-1 was 

54% of PM2.5. However, PM1 was not affected by the intrusion of coarse particles. Similar 

2-26
Figure 2-11. Particulate matter concentrations in Spokane, WA, during the August 30, 

1996 dust storm. 

Source: Claiborn et al. (2000). 

considerations probably apply to short-term intrusions of dust transported from distant sources 

such as the Sahara and Gobi deserts (Husar et al., 2001). In Spokane, the correlations 

between PM1 and PM2.5-1 (Haller et al., 1999) were lower, r = !0.37 (spring) to 0.26 (winter), 

than those observed in Phoenix (Kegler et al., 2001), r = 0.69. 

Pope et al. (1999) found that removing days with high PM10 values due to windblown dust 

resulted in the observation of a statistically significant relationship between PM10 and mortality 

in Salt Lake City. This relationship was not observed when days with windblown dust events 

were included in the PM10 concentration time series. The elevated values of PM2.5, but not PM1, 

observed during the windblown dust events in Spokane (Claiborn et al., 2000) suggest that 

a PM2.5 time series would also be impacted by windblown dust while a PM1 time series would 

not. Thus, the contribution of soil to PM2.5 could possibly contribute to erroneous conclusions 

from some epidemiologic analyses. 

2-27
A cut point of 1.0 μm could reduce the misclassification of coarse-mode material as fine, 

especially in areas with high levels of windblown soil, but under high RH conditions could result 

in some accumulation-mode material being misclassified as coarse. A reduction in RH, either 

intentionally or inadvertently, will reduce the mass mean diameter of the accumulation- mode 

particles. Studies of the changes in particle size with changes in relative humidity suggest that 

only a small fraction of accumulation-mode particles will be above 1.0 μm in diameter at RH 

below 60%, but a substantial fraction will grow to above 1.0 μm at RH above 80% (Hitzenberger 

et al., 1997; McMurry and Stolzenburg, 1989; U.S. Environmental Protection Agency, 1996a). 

Studies in Europe (Berner, 1989; see Figure 3-31 in the 1966 PM AQCD) show that 

dehumidification of ambient PM by heating will reduce the size of accumulation mode particles, 

leaving little accumulation mode PM above 1 μm in aerodynamic diameter. However, heating 

will also remove ammonium nitrate and semivolatile organic compounds. Currently, the data are 

insufficient to determine how much dehumidification by diffusion drying would reduce the size 

of accumulation mode particles. 

Under high RH circumstances, a monitor using a 1.0 μm Da cut point can achieve better 

modal separation if the air stream is dehumidified to some fixed humidity that would remove all 

or most particle-bound water without evaporating semivolatile components. New techniques 

requiring the reduction of RH by diffusion drying prior to collection have been developed for 

measuring fine PM minus particle-bound water but including semivolatile nitrate and organic 

compounds. With such techniques, measurements with a 1 μm (or slightly higher) cut point, in 

conjunction with concurrent PM2.5 measurements, would be useful for exposure, epidemiologic, 

and source-apportionment studies, especially in areas where intrusion of coarse-mode particles 

into the intermodal range is likely. 

2.1.2.3 Ultrafine Particles 

As discussed in Chapter 7 (Toxicology of Particulate Matter in Humans and Laboratory 

Animals) and in Chapter 8 (Epidemiology of Human Health Effects Associated with Ambient 

Particulate Matter), some scientists argue that ultrafine particles may pose potential health 

problems and that some health effects may be associated with particle number or particle surface 

area as well as, or more closely than, with particle mass. Some additional attention will be given 

2-28
here to ultrafine particles, because they contribute the major portion of particle number and a 

significant portion of particle surface area. 

Formation and Growth of Fine Particles 

Several processes influence the formation and growth of particles. New particles may be 

formed by nucleation from gas phase material. Particles may grow by condensation as gas phase 

material condenses on existing particles; particles may also grow by coagulation as two particles 

combine to form one. Gas phase material condenses preferentially on smaller particles, and the 

rate constant for coagulation of two particles decreases as the particle size increases. Therefore, 

ultrafine particles grow into the accumulation mode; but accumulation-mode particles do not 

normally grow into the coarse mode (see Figure 2-6). More information and references on the 

formation and growth of fine particles can be found in the 1996 AQCD PM (U.S. Environmental 

Protection Agency, 1996a). 

Equilibrium Vapor Pressures 

An important parameter in particle nucleation and in particle growth by condensation is the 

saturation ratio, S, defined as the ratio of the partial pressure of a species, p, to its equilibrium 

vapor pressure above a flat surface at a specified temperature, po: S = p/po. For either 

condensation or nucleation to occur, the species vapor pressure must exceed its equilibrium 

vapor pressure. For particles, the equilibrium vapor pressure is not the same as po. Two effects 

are important: (1) the Kelvin effect, which is an increase in the equilibrium vapor pressure 

above the surface due to its curvature (very small particles have higher vapor pressures and will 

not be stable to evaporation until they attain a critical size) and (2) the solute effect, which is a 

decrease in the equilibrium vapor pressure of the liquid due to the presence of other compounds 

in solution. Organic compounds may also be adsorbed onto ultrafine carbonaceous particles. 

For an aqueous solution of a nonvolatile salt, the presence of the salt decreases the 

equilibrium vapor pressure of the water vapor around the droplet. This effect is in the opposite 

direction of the Kelvin effect, which increases the equilibrium water vapor pressure around a 

droplet because of its curvature. The existence of an aqueous solution will also influence the 

vapor pressure of water-soluble species. The vapor pressure behavior of mixtures of several 

liquids or of liquids containing several solutes is complex. 

2-29
New Particle Formation 

When the vapor concentration of a species exceeds its equilibrium concentration 

(expressed as its equilibrium vapor pressure), it is considered condensable. Condensable species 

can either condense on the surface of existing particles or can nucleate to form new particles. 

The relative importance of nucleation versus condensation depends on the rate of formation of 

the condensable species and on the surface or cross-sectional area of existing particles (McMurry 

and Friedlander, 1979). In ambient urban environments, the available particle surface area is 

usually sufficient to rapidly scavenge the newly formed condensable species. Formation of new, 

ultrafine particles is usually not observable in mass or volume distributions except near sources 

of condensable species. Wilson et al. (1977) reported observations of the Aitken nuclei mode in 

traffic. However, bursts of new particle formation have been observed in urban areas in the 

number distribution (Woo et al., 2001a; McMurray et al., 2000). New particle formation also 

can be observed in cleaner, remote regions. Bursts of new particle formation in the atmosphere 

under clean conditions usually occur when aerosol surface area concentrations are low (Covert 

et al., 1992). High concentrations of nuclei mode particles have been observed in regions with 

low particle mass concentrations, indicating that new particle formation is inversely related to 

the available aerosol surface area (Clarke, 1992). 

Sources of Ultrafine Particles 

Ultrafine particles are the result of nucleation of gas phase species to form condensed 

phase species with a very low equilibrium vapor pressure. In the atmosphere, four major classes 

of substances yield PM with equilibrium vapor pressures low enough to form nuclei mode 

particles: 

(1) Particles containing heavy metals. Nuclei mode particles of metal oxides or other metal 

compounds are generated when metallic impurities in coal or oil are vaporized during 

combustion and the vapor undergoes nucleation. Metallic ultrafine particles also may be 

formed from metals in lubricating oil or fuel additives that are vaporized during 

combustion of gasoline or diesel fuels. Ultrafine metallic particles were discussed in 

Section 6.9 of the 1996 PM AQCD (U.S. Environmental Protection Agency, 1996a). 

(2) Elemental carbon (EC) or soot. Elemental carbon particles are formed primarily by the 

condensation of C2 molecules generated during the combustion process. Because EC 

has a very low equilibrium vapor pressure, ultrafine EC particles can nucleate even at 

high temperatures (Kittelson, 1998; Morawska et al., 1998). 

2-30
(3) Organic carbon (OC). Recent smog chamber studies and indoor experiments show that 

atmospheric oxidation of certain organic compounds often found in the atmosphere can 

produce highly oxidized organic compounds with an equilibrium vapor pressure 

sufficiently low to result in nucleation (Kamens et al., 1999; Weschler and Shields, 

1999). 

(4) Sulfates. Sulfuric acid (H2SO4) molecules are generated in the atmosphere by 

conversion of sulfur dioxide (SO2) to H2SO4. As H2SO4 is formed, it can either nucleate 

to form new ultrafine particles, or it can condense on existing ultrafine or accumulation 

mode particles (Clark and Whitby, 1975; Whitby, 1978). Nucleation theory allows the 

calculation of nucleation rates for both binary nucleation, involving water and sulfuric 

acid (Easter and Peters, 1994) or ternary nucleation, which requires sulfuric acid, 

ammonia (NH3), and water (Korhonen et al., 1999). Kulmala et al. (2000) compared 

binary and ternary nucleation rates and concluded that the ternary rate is 1,000 times the 

binary rate. Results from an aerosol dynamics model with a ternary nucleation scheme 

indicate that nucleation in the troposphere should be ubiquitous and yield a reservoir of 

thermodynamically stable sulfate clusters 1 to 3 nm in diameter. The growth of these 

clusters to a detectable size (> 3 nm diameter) is limited by the availability of 

condensable vapor. Observations of atmospheric particle formation and growth from a 

continental and a coastal site suggest that a growth process including ternary nucleation 

is responsible for the formation of cloud condensation nuclei. Nucleation processes in 

the atmosphere may also involve organic compounds as well as sulfuric acid, ammonia, 

and water. However, current formulations of nucleation theory only include the three 

inorganic components. (The possible formation of ultrafine NH4NO3 by reaction of NH3 

and nitric acid [HNO3] vapor has not been investigated.) 

Vehicle engine exhaust may include all the substances described above. Ultrafine particles 

are observed in the emissions from spark, diesel, and jet engines (Kittelson, 1998). In these 

cases, it seems likely that EC, organic compounds, ammonia and sulfuric acid from sulfur in the 

fuel, as well as metal additives in the fuel or fuel oil, may contribute to the formation of ultrafine 

particles (Tobias et al., 2001). An example of particles from a diesel engine showing number 

and volume distributions is shown in Figure 2-12. 

Recent Measurements of Ultrafine Particles 

Instruments developed during the past decade permit measurement of size distributions of 

particles down to 3 nm in diameter. The use of these techniques has led to new information on 

the formation of new particles by nucleation. Such measurements have been carried out during 

intensive field measurement campaigns, during continuous measurements in urban areas in 

several European cities, and in the United States as a part of the Supersite program (McMurry 

2-31
Figure 2-12. Typical engine exhaust size distribution. 

Source: Kittelson (1998). 

et al., 2000; Woo et al., 2001a). Nucleation has been observed in the free troposphere (Weber 

et al., 1999; Clarke, 1992; Schröder and Ström, 1997; Raes et al., 1997), in outflows of 

evaporating convective clouds (Clarke et al., 1998; Hegg et al., 1990, 1991; Radke and Hobbs, 

1991; Perry and Hobbs, 1994), in the marine boundary layer (Covert et al., 1992; Hoppel et al., 

1994; Van Dingenen et al., 1995; Weber et al., 1998; Clarke et al., 1998), downwind of coastal 

regions during low tide (McGovern et al., 1996; McGovern, 1999), on mountains (Weber et al., 

1995, 1997; Raes et al., 1997; Wiedensohler et al., 1997), over forests (Mäkelä et al., 1997; 

Kulmala et al., 1998; O’Dowd et al., 2002), downwind of certain biogenic emissions (Weber 

et al., 1998), in urban areas (Birmili and Wiedensohler, 1998; McMurry et al., 2000; Woo et al., 

2001a), near freeways (Zhu et al., 2002a,b), in engine exhaust (Kittelson, 1998; Tobias et al., 

2001), and in homes (Wallace and Howard-Reed, 2002). Nucleation events in outdoor air 

almost always occur during daylight, indicating that photochemistry plays a role in producing 

the gas phase precursors of new particles. 

The number size distributions observed over a boreal forest in Finland before and during 

the initial stages of a nucleation event are shown in Figure 2-13. The Aitken and accumulation 

modes can be seen clearly before the nucleation event. The nucleation mode, with a peak 

2-32
Figure 2-13. Number size distributions showing measurement of a nucleation burst mode 

in a boreal forest in Finland. 

Source: O’Dowd et al. (2002). 

between 3 and 7 nm, appears during the event. Figure 2-14 shows the variety of size

distributions that may be observed as nuclei are formed and grow, based on size distributions 

measured in the Arctic marine boundary layer (Covert et al., 1996). These distributions all show 

a trimodal distribution within the fine-particle size range. The changes in size distribution due to 

coagulation (and dilution) immediately downwind of a freeway (Zhu et al., 2002b) are shown in 

Figures 2-15(a-f) and 2-15(g). At 30 m downwind, the nucleation mode number concentration is 

larger than that of the Aitken mode, but by 60 m downwind that has reversed, because 

coagulation removes particles from the nucleation mode and adds particles to the Aitken mode. 

Strong evidence suggests that sulfuric acid vapor often participates in nucleation. 

However, condensation of sulfuric acid and its associated water and ammonium ions typically 

accounts for only 10 to 20% of the observed growth rates for freshly nucleated particles. 

Therefore, organic compounds may account for much of the formation and growth of freshly 

nucleated particles. Evidence of nucleation of organic particles comes from smog chamber 

studies (Kamens et al., 1999) and from field studies over forests (Mäkelä et al., 1997; Kulmala 

et al., 1998; O’Dowd et al., 2002). Nucleation of organic particles may also occur indoors due to 

the reaction of infiltrated ozone with indoor terpenes from air fresheners or cleaning solutions 

(Weschler and Shields, 1999). The observation of bursts of nuclei-mode particles in Atlanta 

2-33
Figure 2-14. Examples of the measured 1-h average particle number size distributions 

and the log normal fits to the modes of the data. Squares are measured data, 

solid lines are the fitted lognormal modes determined by DistFitTM. These 

modes (nucleation between 3 and 20 nm, Aitken between 20 and 100 nm, and 

accumulation above 100 nm) can be observed in most examples. 

Source: Covert et al. (1996). 

(Woo et al., 2001a), perhaps due to unusually high rates of production of condensable species, 

suggests that high concentrations of ultrafine particles may be a normal occurrence in polluted 

urban areas. 

2-34
2-35
Figure 2-15g. Combination of Figures 2-15(a-e), with dN/d logDp scale. Ultrafine 

particle size distribution at different sampling locations near Freeway 405 

in Los Angeles, CA. 

Source: Zhu et al. (2002b). 

Concentration of Ultrafine Particles: A Balance Between Formation and Removal 

Nuclei-mode particles may be removed by dry deposition or by their growth into the 

accumulation mode. Such growth takes place as other low vapor pressure material condenses 

on the particles or as nuclei-mode particles coagulate with themselves or with accumulation 

mode particles. Because the rate of coagulation would vary with the concentration of 

accumulation-mode particles, it might be expected that the concentration of nuclei-mode 

particles would increase with a decrease in accumulation-mode mass. On the other hand, the 

concentration of particles would be expected to decrease with a decrease in the rate of generation 

of particles by reduction in emissions of metal and carbon particles or a decrease in the rate of 

generation of H2SO4 or condensable organic vapor. The rate of generation of H2SO4 depends on 

the concentrations of SO2 and the hydroxyl radical (COH), which is generated primarily by 

reactions involving ozone (O3). Thus, reductions in SO2 and O3 would lead to a decrease in the 

2-36
rate of generation of H2SO4 and condensable organic vapor and to a decrease in the 

concentration of nuclei-mode particles. The balance between formation and removal is 

uncertain. However, these processes can be modeled using a general dynamic equation for 

particle size distribution (Friedlander, 2000) or by aerosol dynamics modules in newer air 

quality models (Binkowski and Shanker, 1995; Binkowski and Ching, 1995). 

2.1.3 Chemistry of Atmospheric Particulate Matter 

The major constituents of atmospheric PM are sulfate, nitrate, ammonium, and hydrogen 

ions; particle-bound water; elemental carbon; a great variety of organic compounds; crustal 

material; and (at coastal locations) sea salt. Atmospheric PM also contains a large number of 

elements in various compounds and concentrations. More information and references on the 

composition of PM measured in a large number of studies in the United States, may be found in 

the 1996 PM AQCD (U.S. Environmental Protection Agency, 1996a). Also, in this document, 

the concentrations and composition of ambient PM are discussed in Chapter 3 (Section 3.1), and 

ambient data for concentrations and composition of PM2.5 are given in Appendices 3A, 3B, 

and 3C. 

2.1.3.1 Chemical Composition and Its Dependence on Particle Size 

Studies conducted in most parts of the United States indicate that sulfate, ammonium, and 

hydrogen ions; elemental carbon, secondary organic compounds, and primary organic species 

from cooking and combustion; and certain transition metals are found predominantly in the fine 

particle mode. Crustal materials such as calcium, aluminum, silicon, magnesium, and iron are 

found predominately in the coarse particles. Some primary organic materials such as pollen, 

spores, and plant and animal debris are also found predominantly in the coarse mode. Certain 

components such as potassium and nitrate may be found in both the fine and coarse particle 

modes, but they originate from different sources or mechanisms. Potassium in coarse particles 

comes from soil. Potassium in fine particles originates in emissions from burning wood or 

cooking meat. Nitrate in fine particles comes primarily from the reaction of gas-phase nitric acid 

with gas-phase ammonia forming particulate ammonium nitrate. Nitrate in coarse particles 

comes primarily from the reaction of gas-phase nitric acid with preexisting coarse particles. 

2-37
2.1.3.2 Primary and Secondary Particulate Matter 

Particulate matter can be primary or secondary. PM is called “primary” if it is in the same 

chemical form in which it was emitted into the atmosphere, but it is called “secondary” if it is 

formed by chemical reactions in the atmosphere. Primary coarse particles are usually formed by 

mechanical processes and include windblown dust, sea salt, road dust, and combustion-generated 

particles such as fly ash and soot. Primary fine particles are emitted from sources either directly 

as particles or as vapors that rapidly condense to form ultrafine or nuclei-mode particles. This 

includes soot from diesel engines, a great variety of organic compounds condensed from 

incomplete combustion or cooking, and compounds of As, Se, Zn, etc. that condense from vapor 

formed during combustion or smelting. The concentration of primary particles depends on their 

emission rate, transport and dispersion, and removal rate from the atmosphere. 

Secondary PM is formed by chemical reactions of free, adsorbed, or dissolved gases. Most 

secondary fine PM is formed from condensable vapors generated by the chemical reactions of 

gas-phase precursors. Secondary formation processes can result in either the formation of new 

particles or the addition of PM to preexisting particles. Most of the sulfate and nitrate and a 

portion of the organic compounds in atmospheric particles are formed by chemical reactions that 

occur in the atmosphere. Secondary aerosol formation depends on numerous factors, including 

the concentrations of precursors; the concentrations of other gaseous reactive species such as 

ozone, hydroxyl radical, peroxy radicals, or hydrogen peroxide; atmospheric conditions 

including solar radiation and RH; and the interactions of precursors and preexisting particles 

within cloud or fog droplets or in the liquid film on solid particles. As a result, it is considerably 

more difficult to relate ambient concentrations of secondary species to sources of precursor 

emissions than it is to identify the sources of primary particles. A significant effort is currently 

being directed toward the identification and modeling of organic products of photochemical 

smog, including the conversion of gases to PM. More information of the transformation of 

precursor gases into secondary PM is given in Chapter 3 (Section 3.3.1). 

Particle strong acidity is due almost entirely to the presence of H2SO4 or NH4HSO4. Thus, 

the acidity of atmospheric particles depends on both the amount of SO2 that is oxidized to SO3 

and subsequently forms H2SO4 as well as the amount of ammonia available to react with the 

sulfuric acid. Nitric acid is more volatile than sulfuric acid. Thus, if gas phase SO3 or sulfuric 

acid or particles containing H2SO4 or NH4HSO4 contact particles containing NH4NO3, nitric acid 

2-38
gas will be released with the remaining ammonia contributing to the further neutralization of the 

acid. Little NH4NO3 is found in atmospheres containing significant particle strong acidity. 

However, as SO2 emissions are reduced to the point that there is more than enough ammonia to 

neutralize the sulfuric acid, NH4NO3 particles will begin to form. Thus, ammonia emissions and 

concentrations relative to those of SO2 and H2SO4 are important in determining the strong acidity 

in the atmosphere and the concentration of particulate NH4NO3. Therefore, once SO2 emissions 

have been reduced to the point that ammonia and sulfate are in balance to form (NH4)2SO4, 

further reductions in SO2 will not result in an equivalent reduction in airborne PM, because 

one (NH4)2SO4 unit will be replaced by two NH4NO3 units. 

2.1.3.3 Particle-Vapor Partitioning 

Several atmospheric aerosol species, such as water, ammonium nitrate and certain organic 

compounds, exist in an equilibrium between gaseous and condensed phases and are called 

semivolatile. The equilibrium between water vapor and particle-bound water is well understood 

and can be modeled accurately. A variety of thermodynamic models have also been developed 

to predict the temperature and relative humidity dependence of the ammonium nitrate equilibria 

with gaseous nitric acid and ammonia. However, under some atmospheric conditions (such as 

cool, cold, or very clean air), the relative concentrations of the gas and solid phases are not 

accurately predicted by equilibrium considerations alone, and transport kinetics can be 

important. The gas-particle distribution of semivolatile organic compounds depends on the 

equilibrium vapor pressure of the compound, total particle surface area, particle composition, 

atmospheric temperature, and relative humidity. Although it generally is assumed that the gasparticle partitioning of semivolatile organics is in equilibrium in the atmosphere, neither the 

equilibria nor the kinetics of redistribution are well understood. Diurnal temperature fluctuations 

cause gas-particle partitioning to be dynamic on a time scale of a few hours and can cause 

semivolatile compounds to evaporate during the sampling process. The pressure drop across the 

filter can also contribute to the loss of semivolatile compounds. The dynamic changes in 

gas-particle partitioning caused by changes in temperature, pressure, and gas-phase 

concentration, both in the atmosphere and after collection, cause serious sampling problems that 

are discussed in Section 2.2.3, Measurement of Semivolatile Particulate Matter. 

2-39
Equilibria with Water Vapor 

As a result of the equilibrium of water vapor with liquid water in hygroscopic particles, 

many ambient particles contain liquid water (particle-bound water). Unless removed, this 

particle-bound water will be measured as a component of the particle mass. Particle-bound 

water plays an important role in atmospheric chemistry and physics. It influences the size of 

particles, and in turn, the light scattering and aerodynamic properties of particles. Particle 

aerodynamic properties are important in dry deposition to surfaces, deposition to airway surfaces 

during breathing, and deposition in sampling instrumentation. The aqueous solution provides a 

medium for reactions of dissolved gases, including reactions that do not take place in the gas 

phase. The aqueous solutions also may act as a carrier to convey soluble toxic species to the 

gas-exchange regions of the respiratory system, including species that would be removed by 

deposition in the upper airways if no particles were present. An extensive review of the 

equilibrium of water vapor with particle-bound water as it pertains to ambient aerosols was 

given in Chapter 3 of the 1996 PM AQCD (U.S. Environmental Protection Agency, 1996a). 

The interaction of particles with water vapor may be described briefly as follows. 

As relative humidity increases, particles of crystalline soluble salts, such as (NH4)2SO4, 

NH4HSO4, or NH4NO3, undergo a phase transition to become aqueous solution particles. 

According to the phase rule, for particles consisting of a single component, this phase transition 

is abrupt, taking place at a relative humidity that corresponds to the vapor pressure of water 

above the saturated solution (the deliquescence point). With a further increase in relative 

humidity, the solution particle adds water (and the concentration of the solute decreases) so that 

the vapor pressure of the solution is maintained equal to that of the surrounding relative 

humidity; thus, the solution particle tends to follow the equilibrium growth curve. As relative 

humidity decreases, the solution particle follows the equilibrium curve to the deliquescence 

point. However, rather than crystallizing at the deliquescence relative humidity, the solute 

remains dissolved in a supersaturated solution to considerably lower relative humidities. 

Ultimately, the solution particle abruptly loses its water vapor (efflorescence) and typically 

returns to the initial crystalline form. 

For particles consisting of more than one component, the solid to liquid transition will take 

place over a range of RHs, with an abrupt onset at the lowest deliquescence point of the several 

components. Subsequent growth will occur as crystalline material in the particle dissolves 

2-40
according to the phase diagram for the particular multicomponent system. Under such 

circumstances, a single particle may undergo several more or less abrupt phase transitions until 

the soluble material is fully dissolved. At decreasing relative humidity, such particles tend to 

remain in solution to relative humidities well below the several deliquescence points. In the case 

of the sulfuric acid-ammonium sulfate-water system, the phase diagram is fairly well 

understood. For particles of composition intermediate between NH4HSO4 and (NH4)2SO4, this 

transition occurs in the range from 40% to below 10% relative humidity, indicating that for 

certain compositions the solution cannot be fully dried in the atmosphere. At low relative 

humidities, particles of this composition would likely be present in the atmosphere as 

supersaturated solution droplets (liquid particles) rather than as solid particles. Thus, they would 

exhibit hygroscopic rather than deliquescent behavior during relative humidity cycles. 

Other pure compounds, such as sulfuric acid, are hygroscopic (i.e., they form aqueous 

solutions at any relative humidity and maintain a solution vapor pressure over the entire range of 

relative humidity). Soluble organic compounds may also contribute to the hygroscopicity of the 

atmospheric aerosol (Saxena et al., 1995; Saxena and Hildeman, 1996), but the equilibria 

involving organic compounds and water vapor, and, especially for mixtures of salts, organic 

compounds, and water, are not so well understood. These equilibrium processes may cause an 

ambient particle to significantly increase its diameter at relative humidities above about 40% 

(Figure 2-16a). A particle can grow to five times its dry diameter as the RH approaches 100% 

(Figure 2-16b). The Federal Reference Methods, for filter measurements of PM2.5 and PM10 

mass, require equilibration at a specified, low relative humidity after collection (for PM2.5, 

between 30 and 40% RH with control of ± 5% RH [Code of Federal Regulations, 2001a]). 

This equilibration removes much of the particle-bound water and provides a relatively stable PM 

mass for gravimetric measurements (see Section 2.2 for details and references). Otherwise, 

particle mass would be a function of relative humidity, and the particle mass would be largely 

particle-bound water at higher relative humidities. However, some particle-bound water may be 

retained even after equilibration. Recent studies have shown that significant amounts of particlebound water are retained in particles collected on impaction surfaces even after equilibration and 

that the amount of retained particle-bound water increases with relative humidity during 

collection (Hitzenberger et al., 1997). 

2-41
Figure 2-16a. Particle growth curves showing fully reversible hygroscopic growth of 

sulfuric acid (H2SO4) particles, deliquescent growth of ammonium sulfate 

[(NH4)2 SO4] particles at the deliquescent point (A, about 80% relative 

humidity [RH]), reversible hygroscopic growth of ammonium sulfate 

solution droplets at RH greater than 80%, and hysteresis (the droplet 

remains supersaturated as the RH decreases below 80%) until the 

crystallization point (B, about 38% RH) is reached. 

Source: National Research Council (1993) adapted from Tang (1980). 

The retention of particle-bound water is a greater problem for continuous monitors, which 

measure changes in mass collected on a filter over long sampling times. If particle-bound water 

is not removed, changes in relative humidity would cause changes in the mass of PM collected 

over previous hours or days. These changes could be much greater than amount of PM mass 

added in one hour. Therefore, continuous monitoring techniques generally attempt to remove 

particle-bound water before measurement either by heating or dehumidification. However, other 

semivolatile materials (e.g., ammonium nitrate and organic compounds) that may be partially 

2-42
Figure 2-16b. Theoretical predictions and experimental measurements of growth of 

NH4HSO4 particles at relative humidities between 95 and 100%. 

Source: Li et al. (1992). 

lost during sampling or equilibration of an unheated filter are certainly lost when the collected 

sample is heated above ambient temperature. These changes in particle size with relative 

humidity also mean that particle measurements such as surface area or volume, or composition 

as a function of size, should be made at the same RH in order for the results to be comparable. 

These problems are addressed in more detail in Section 2.2, Measurement of Particulate Matter. 

Particle-Bound Water as a Carrier 

Water vapor is not a pollutant, and particle-bound water (PBW) is not included in the mass 

of PM subject to regulation and control. However, while water is not itself a pollutant, PBW 

may act as a carrier for pollutants, as stated by Wilson (1995): 

2-43
“It is possible that water-soluble gases, which would be removed by deposition to 

wet surfaces in the upper respiratory system during inhalation, could dissolve in 

particle-bound water and be carried with the particles into the deep lung. Watersoluble gases in polluted air include oxidants such as O3, H2O2, and organic 

peroxides; acid gases such as SO2, HCl, HNO3, HONO, and formic acid; and polar 

organic species such as formaldehyde. Some of these species may have biological 

effects, but current techniques do not measure particle-bound water or the species 

dissolved in it.” 

Kao and Friedlander (1995) also suggest that aerosols could carry adsorbed free radicals or 

dissolved H2O2 into the lung. Friedlander and Yeh (1998) point out that the aqueous component 

of the atmospheric aerosol carries short-lived, reactive chemical species, including hydrogen 

peroxide and organic peroxides. Equilibrium calculations and limited experimental data suggest 

that aerosol-phase concentrations of hydrogen peroxide will fall within a range (around 1 mM) 

in which significant biochemical effects have been observed when respiratory tract epithelial 

cells were treated with hydrogen peroxide solutions. 

Wexler and Sarangapani (1998) used a physical model of “gas-particle-mucus heat and 

mass transport in the human airways,” developed in order to investigate the hypothesis that 

endogenous ammonia neutralizes aerosol acidity (Sarangapani and Wexler, 1996), to investigate 

the transport by particles of soluble vapors to the tracheobronchial and air exchange (also called 

alveolar or pulmonary) regions of the lung. Wexler and Sarangapani (1998) provides a concise 

description of this process: 

“Air pollutants are deposited in the human airway via two pathways — particle 

and vapor deposition. In the absence of particles, vapors deposit at different 

locations in the lung depending on their solubility in mucus, which is over 99% 

water. High-solubility compounds, such as nitric acid or hydrogen peroxide, are 

rapidly removed in the upper airways while low-solubility compounds, such as 

oxygen or ozone, are less well removed and so penetrate to the alveoli. Pollutant 

deposition in the upper airways is less harmful than in the lower airways because 

upper airways clearances is more rapid and the epithelium is protected by a mucus 

layer. As a result, low-solubility pollutants, such as ozone, may harm the alveoli 

while high-solubility pollutants, such as nitric acid, do not reach these tissues. 

“In the presence of aerosol particles, this scenario changes. Under most ambient 

conditions, aerosol particles contain some liquid water so that soluble compounds 

are partitioned between the gas phase and the aerosol liquid-water phase. The 

degree of deposition via the gas compared to that via the particles is a function of a 

number of factors including the solubility of the compound and the liquid-water 

content of the aerosol. Since highly soluble compounds deposit in the upper 

2-44
airways, particles may provide a vector for deposition of these compounds in the 

lower airways. Lower-solubility compounds may persist in the vapor phase and so 

may deposit in lower airway segments. 

“When both a water-soluble vapor and an aerosol liquid phase are present together, 

some of the vapor will dissolve in the liquid-water phase until an equilibrium is 

established. The equilibrium condition is given by the Henry’s law coefficient, 

H (M atm–1), which gives the relationship between the partial pressure of the 

vapor, typically in units of atmospheres, and the concentration of the dissolved 

compound in the aqueous phase, typically in units of molal. 

“Direct deposition of the vapor is primarily a function of its solubility. Since the 

airways are lined with mucus, deposition is higher for compounds with a higher 

solubility than for those with lower solubility. Compounds with Henry’s law 

constant (H) greater than 100 M atm–1 deposit relatively uniformly over the first 

10 generations (e.g., formaldehyde and hydrogen peroxide [and sulfur (IV) {SO2 

(aq) plus H2SO3 (aq) plus HSO3 

G

 (aq)}], so they affect health via interactions with 

the upper airway epithelia. All the vapor is deposited if H is greater than about 

0.1. Low-solubility compounds do not deposit effectively in the mucus until past 

the 20th generation so are still present in significant concentrations in the 

pulmonary region. These compounds (e.g., ozone, nitrogen dioxide) affect human 

health via interactions in the air-exchange regions. 

“As aerosols are inhaled, soluble vapors deposit on the mucus, disrupting the 

gas-particle equilibrium, and the compound begins to evaporate from the aerosol 

particles. If the evaporation is rapid, the pattern of deposition of the compound 

will not be influenced by the presence of the particles, i.e., the deposition pattern is 

essentially that of the vapor alone. If the evaporation is very slow compared to a 

breathing cycle, a significant amount of the compound will remain in the particle 

phase and the pattern of deposition may be shifted toward the pulmonary region by 

the particle.” 

Hygroscopic particles, present in the inhaled aerosol disrupt the gas-particle equilibria. 

The soluble gas will deposit on the mucus, reducing the gas phase vapor pressure, and reducing 

the equilibrium concentration of the gas in solution. However, in the higher relative humidity of 

the lung, hygroscopic particles will add PBW and grow in volume. Therefore, even though the 

concentration of the soluble gas may be lower, there will be a greater amount of the soluble gas 

in solution. The amount of the soluble gas in the particle will depend on the Henry’s Law 

coefficient of the gas, the size of the particle, and the position of the particle in the respiratory 

tract. Figure 2-17 shows how the concentration of the soluble gas (H = 104

 M atm!1) in the PBW 

of the aerosol relative to that in the gas phase changes as the particle moves deeper into the lung. 

2-45
Figure 2-17. Concentration of the dissolved gas in the particle normalized by its initial 

concentration as a function of airway generation for H = 104

 M atm!1 for 

particle diameters of 0.1, 0.3, 0.7, and 1 μm. 

Source: Wexler and Sarangpani (1998). 

In similar figures for other values of H, Wexler and Sarangapani (1998) show that, 

regardless of particle size, the greatest relative increase in the amount of soluble gas dissolved in 

PBW (a factor of 10 to 100) occurs for lower solubility gases (H < 0 M atm!1

). However, the 

deposition of such gases by particles will be small since the concentration of dissolved gas will 

be low. For higher solubility gases (H = 102

 M atm!1), the enhancement may be 2- to 20-fold, 

depending on particle size. There may be a sufficient amount of the soluble gas in the PBW to 

influence the deposition pattern, with more soluble gas being transported to higher generations 

(deeper into the lung). For the highest solubility gases, the enhancement in less than 2, but a 

larger amount of gas is dissolved in the PBW. The highest solubility gases (H > 104

 M atm!1) 

will evaporate from particles < 0.1 μm before the particles reach the air exchange region of the 

lung. However, as shown in Figure 2-17, particles > 0.3 μm can efficiently transport high 

solubility gases into the air exchange region. 

2-46
Wexler and Sarangapani (1998) observed that even for a high liquid water content 

(PBW = 100 μg/m3) and for a highly soluble gas such as H2O2 (H = 105 M atm!1), only about 1% 

of the soluble gas will dissolve in the PBW. Therefore, any influence of the particles on the gas 

concentration will cause little change in the quantity of soluble gas deposited on the upper 

airways. The Wexler and Sarangapani (1998) analysis was conducted under the assumption that 

equilibrium with the gas phase is obtained quickly and considered only the “physical” solubility 

of the soluble gas. For example, their discussion of SO2 assumes an H of 1 M atm!1

 which is 

correct for the equilibrium of SO2 (aq) with SO2 gas. However, SO2 reacts chemically with 

– water to yield SO2 (aq), H2SO3 (aq), and HSO 3 (aq). The H for sulfur (IV) in solution in 

equilibrium with SO2 gas is dependent on the pH of the solution but may reach 104 M atm!1 at a 

– pH of 6 (Schwartz, 1984). Thus, if SO2 (aq), H2SO3 (aq), and HSO 3 (aq) are in equilibrium in 

the liquid phase, and any one of the three species is toxic, significantly more toxic sulfur (IV) 

will be carried to the air exchange region than predicted by Wexler and Sarangapani (1998). 

Friedlander and Yeh (1998) point out that H2O2 forms adducts called peroxohydrates with many 

substances in which it acts like water of hydration (Elvers et al., 1991). If such adducts form in 

the PBW of atmospheric aerosols, a much higher percentage of the atmospheric peroxide would 

dissolve in the PBW and be carried into the air exchange region of the lungs. 

Thus, soluble gases, like H2O2, SO2, and formaldehyde, which would be completely 

removed in the upper airways in the absence of particles, can dissolve in the PBW of 

hygroscopic particles and be transported by the particles into the air exchange (alveolar or 

pulmonary) region of the lungs. 

Friedlander and Yeh (1998) also suggested that the epidemiologic associations found 

between adverse health effects and sulfate or PM mass may represent a response to atmospheric 

peroxides, or other toxic substances dissolved in PBW. Friedlander and Yeh (1998) stated, 

“This hypothesis supports reduction of the total submicron aerosol mass as a way 

to reduce adverse health effects, because the total submicron mass is closely linked 

to the aqueous component that carries the reactive species. To test this hypothesis, 

studies are needed of the effects of exposures of cellular layers and/or animals to 

submicron hydrogen peroxide-containing aerosols that also contain salts such as 

ammonium sulfate.” 

2-47
Morio et al. (2001) conducted an inhalation study in which rats were exposed to (NH4)2SO4 

(429 or 215 μg/m3; 0.3 to 0.4 μm mass median diameter) or H2O2 (10, 20, or 100 ppb) alone or in 

combination for 2 h. The authors summarized the study as follows. 

“In summary, the present studies find that exposure of rats to (NH4)2SO4 + H2O2 

results in tissue damage and altered alveolar macrophage activity. Our findings 

suggest that macrophage-derived TNF-" and peroxynitrite are potential factors 

contributing to the toxicity of (NH4)SO4 + H2O2. It is possible that alterations in 

macrophage functioning following inhalation of (NH4)2SO4 + H2O2 underlie 

increases in individual susceptibility to infection following the fine PM exposure.” 

The potential for PBW to carry toxic gases into the air exchange region of the lung 

suggests that it would be useful to measure both the amount of PBW in the atmospheric aerosol 

and the composition of the material dissolved in it. Although measurements have been made of 

the amount of PBW (discussed in 2.2.3.1), there is little information on the composition or 

concentration of soluble species that would evaporate as the PBW evaporates. Hung and Wang 

(2001) measured the concentrations of reactive oxygen species (ROS) in various size fractions of 

atmospheric aerosols and vehicle emissions in Taipei using a technique which would respond to 

ROS adsorbed on particle surfaces or dissolved in PBW. The ROS were not identified, but the 

ROS concentration was correlated with the atmospheric ozone concentration. The ROS 

concentrations were highest in the 0.18 to 1.0 μm particle size range and decreased by over 50% 

between analyses at 1 h and 115 h after collection. While this study does not demonstrate that 

PBW carries H2O2, it does demonstrate the ability of particles to act as carriers of reactive 

species and offers a technique for the measurement of ROS absorbed on particles or dissolved in 

PBW. 

PBW Effects on Visibility 

Light scattering, and the resultant effects on visibility, depend on the size and refractive 

index of the particle. PBW contributes to light scattering just like any other component of fine 

PM. As discussed in detail in Chapter 4, the light scattering due to a given mass of PM can be 

estimated from the composition and the relative humidity using mass scattering coefficients for 

the chemical components that are higher for hygroscopic components. 

2-48
PBW Effects on Dosimetry 

As particles are inhaled, they will be exposed to the high relative humidities of the 

respiratory tract. Although supersaturation may occur in the early generations of the airways, by 

the time the aerosol reaches the air exchange region, the relative humidity will have reached 

99.5% and the particles will have attained their equilibrium size (Sarangapani and Wexler, 

1996). As discussed in Chapter 6, these size changes can have significant effects on the 

fractions of inhaled particles deposited in the various portions of the respiratory tract. 

PBW as a Reaction Medium 

If the air becomes saturated with water vapor, hygroscopic particles will grow into cloud 

droplets. The liquid water content of cloud droplets is much higher than that of aerosols at 

relative humidities below 100%. This liquid water provides a reaction medium where reactions 

can take place that do not take place in the air. For example, dissolved SO2 can react with 

dissolved O3 or dissolved H2O2 to form H2SO4 in cloud droplets (Lazrus et al., 1983; Schwartz, 

1984, 1986; Martin, 1994; Hegg and Hobbs, 1982, 1986). 

The chemistry of fog and cloud droplets is important in the formation of secondary 

pollutants. An extensive discussion was given in Section 3.3.1.4 of the 1996 PM AQCD (U.S. 

Environmental Protection Agency, 1996a). The possibility of chemical reactions in PBW is 

controversial because of the much lower quantity of liquid water at lower relative humidities 

(Brock and Durham, 1984; McMurray and Wilson, 1982, 1983; McMurray et al., 1981). 

2.1.3.4 Atmospheric Lifetimes and Removal Processes 

The lifetimes of particles vary with size. Nuclei-mode particles rapidly grow into the 

accumulation mode. However, the accumulation mode does not grow into the coarse mode. 

Accumulation-mode fine particles are kept suspended by normal air motions and have very low 

deposition rates to surfaces. They can be transported thousands of km and remain in the 

atmosphere for a number of days. Coarse particles can settle rapidly from the atmosphere 

(within hours) and normally travel only short distances. However, when mixed high into the 

atmosphere, as in dust storms, the smaller-sized coarse-mode particles have longer lives and 

travel greater distances. Dry deposition rates are expressed in terms of a deposition velocity that 

varies with particle size, reaching a minimum between 0.1 and 1.0 μm aerodynamic diameter 

2-49
(e.g., Lin et al., 1994). Accumulation-mode particles are removed from the atmosphere 

primarily by cloud processes. Fine particles, especially particles with a hygroscopic component, 

grow as the relative humidity increases, serve as cloud condensation nuclei, and grow into cloud 

droplets. If the cloud droplets grow large enough to form rain, the particles are removed in the 

rain. Falling rain drops impact coarse particles and remove them. Ultrafine or nuclei-mode 

particles are small enough to diffuse to the falling drop, be captured, and be removed in rain. 

Falling rain drops, however, are not nearly as effective in removing accumulation-mode particles 

as the cloud processes mentioned above. A more detailed discussion of particle deposition, 

including acid deposition, especially as it applies to deposition to vegetation, soil, and water 

surfaces, is given in Chapter 4 (Environmental Effects of Airborne Particulate Matter). Acid 

deposition and PM are intimately related, first, because particles contribute to the acidification of 

rain and, secondly, because the gas-phase species that lead to dry deposition of acidity are also 

precursors of particles. Therefore, reductions in SO2 and NOx emissions will decrease both 

acidic deposition and PM concentrations. 

Sulfate, nitrate, and some partially oxidized organic compounds are hygroscopic and act as 

nuclei for the formation of cloud droplets. These droplets serve as chemical reactors in which 

(even slightly) soluble gases can dissolve and react. Thus, SO2 can dissolve in cloud droplets 

and be oxidized to sulfuric acid by dissolved ozone or hydrogen peroxide. These reactions take 

place only in aqueous solution, not in the gas phase. Sulfur dioxide may also be oxidized by 

dissolved oxygen. This process will be faster if metal catalysts such as iron or manganese are 

present in solution. If the droplets evaporate, larger particles are left behind. If the droplets 

grow large enough, they will fall as rain; and the particles will be removed from the atmosphere 

with potential effects on the materials, plants, or soil on which the rain falls. (Similar 

considerations apply to dew.) Atmospheric particles that nucleate cloud droplets also may 

contain other soluble or nonsoluble materials such as metal salts and organic compounds that 

may add to the toxicity of the rain. Sulfuric acid, ammonium nitrate, ammonium sulfates, and 

organic particles also are deposited on surfaces by dry deposition. The utilization of ammonium 

by plants leads to the production of acidity. Therefore, dry deposition of particles can also 

contribute to the ecological impacts of acid deposition. These effects are discussed in Chapter 4 

(Section 4.1). 

2-50
2.1.4 Comparison of Fine and Coarse Particles 

The physical and chemical properties of fine particles (including ultrafine particles and 

accumulation-mode particles) and coarse particles are summarized for comparison purposes in 

Table 2-2. These include important differences in sources, formation mechanisms, composition, 

atmospheric residence time, removal processes, and travel distances. Ensuing chapters in this 

document will also show that fine and coarse particles differ in aspects of concentrations, 

exposure, dosimetry, toxicology, and epidemiology. Collectively, these differences continue to 

warrant consideration of fine particles as a separate air pollutant class from coarse particles and 

the setting of separate standards for fine and coarse particles. 

2.2 MEASUREMENT OF PARTICULATE MATTER 

The 1996 PM AQCD (U.S. Environmental Protection Agency, 1996a) summarized the 

sampling and analytical techniques for PM and acid deposition that had appeared in the literature 

since the earlier 1982 PM AQCD (U.S. Environmental Protection Agency, 1982). Excellent 

reviews have also been published by Chow (1995) and McMurry (2000). This section discusses 

problems in measuring PM; new techniques that attempt to alleviate these problems or measure 

problem species; Federal Reference Methods, speciation monitors, analytical methods for 

inorganic elements, organic and elemental carbon, and ionic species; and continuous and 

multiday monitors. 

2.2.1 Particle Measurements of Interest 

There are many PM components and parameters that are of interest across the various 

types of uses to which PM measurement data are applied. These uses include analyses of 

compliance with air quality standards and trends; source category apportionment studies related 

to the development of pollution reduction strategies and the validation of air quality models; 

studies related to health, ecological, and radiative effects; and characterization of current air 

quality for presentation to the public in the context of EPA’s Air Quality Index. Particulate 

matter measurement components and parameters of specific interest for these various purposes 

are noted below and summarized in Table 2-3.

2-51
TABLE 2-2. COMPARISON OF AMBIENT PARTICLES,

FINE PARTICLES (Ultrafine plus Accumulation-Mode) AND COARSE PARTICLES

Fine 

Ultrafine Accumulation Coarse 

Formation 

Processes: 

Combustion, high-temperature 

processes, and atmospheric reactions 

Break-up of large solids/droplets 

Formed by: Nucleation 

Condensation 

Coagulation 

Condensation 

Coagulation 

Reactions of gases in or

 on particles 

Evaporation of fog and cloud 

droplets in which gases have 

dissolved and reacted 

Mechanical disruption (crushing,

 grinding, abrasion of surfaces) 

Evaporation of sprays 

Suspension of dusts 

Reactions of gases in or on particles 

Composed 

of: 

Sulfate 

Elemental carbon 

Metal compounds 

Organic compounds

 with very low

 saturation vapor

 pressure at ambient

 temperature 

Sulfate, nitrate, ammonium,

 and hydrogen ions 

Elemental carbon 

Large variety of organic

 compounds 

Metals: compounds of Pb, Cd,

 V, Ni, Cu, Zn, Mn, Fe, etc. 

Particle-bound water 

Suspended soil or street dust 

Fly ash from uncontrolled combustion

 of coal, oil, and wood 

Nitrates/chlorides/sulfates from

 HNO3/HCl/SO2 reactions with

 coarse particles 

Oxides of crustal elements

 (Si, Al, Ti, Fe) 

CaCO3, CaSO4, NaCl, sea salt 

Pollen, mold, fungal spores 

Plant and animal fragments 

Tire, brake pad, and road wear debris 

Solubility: Probably less soluble

 than accumulation

 mode 

Largely soluble, hygroscopic,

 and deliquescent 

Largely insoluble and nonhygroscopic 

Sources: Combustion 

Atmospheric

 transformation of

 SO2 and some

 organic compounds 

High temperature

 processes 

Combustion of coal, oil,

 gasoline, diesel fuel, wood 

Atmospheric transformation

 products of NOx, SO2, and

 organic compounds,

 including biogenic organic

 species (e.g., terpenes) 

High-temperature processes,

 smelters, steel mills, etc. 

Resuspension of industrial dust and

 soil tracked onto roads and streets 

Suspension from disturbed soil (e.g.,

 farming, mining, unpaved roads) 

Construction and demolition 

Uncontrolled coal and oil combustion 

Ocean spray 

Biological sources 

Atmospheric 

half-life: 

Minutes to hours Days to weeks Minutes to hours 

Removal 

Processes: 

Grows into

 accumulation mode 

Diffuses to raindrops 

Forms cloud droplets and

 rains out 

Dry deposition 

Dry deposition by fallout 

Scavenging by falling rain drops 

Travel 

distance: 

< 1 to 10s of km 100s to 1000s of km < 1 to 10s of km (small size tail, 

100s to 1000s in dust storms) 

Source: Adapted from Wilson and Suh (1997). 

2-52 
TABLE 2-3. PARTICULATE MATTER COMPONENTS/PARAMETERS OF

INTEREST FOR HEALTH, ECOLOGICAL, OR RADIATIVE EFFECTS;

FOR SOURCE CATEGORY APPORTIONMENT STUDIES;

OR FOR AIR QUALITY MODELING EVALUATION STUDIES

• Particle number 

• Particle surface area 

• Particle size distribution 

• PM mass (fine PM mass [PM2.5] and coarse thoracic PM mass [PM10-2.5]) including both 

nonvolatile mass as measured by the current Federal Reference method and total mass 

(including semivolatile components such as ammonium nitrate and semivolatile organic 

compounds, but not particle-bound water) 

• Ions (sulfate, nitrate, and ammonium) 

• Strong acidity (H+

) 

• Elemental carbon 

• Organic carbon (total, nonvolatile, and semivolatile; functional groups and individual species) 

• Transition metals (water soluble, bioavailable, oxidant generation) 

• Specific toxic elements and organic compounds 

• Crustal elements 

• Bioaerosols 

• Particle refractive index (real and imaginary) 

C Particle density 

• Particle size change with changes in relative humidity 

Particle measurements are needed to determine if a location is in compliance with air 

quality standards, to determine long-term trends in air quality patterns, and for use in 

epidemiologic studies. For these purposes, the precision of the measurements made by a variety 

of measurement instruments in use is a critical consideration. Therefore, the intercomparisons of 

various samplers under a variety of atmospheric and air quality conditions are important. 

2-53
In order to reduce pollution to attain a standard, pollution control agencies and national 

research organizations need measurements to identify source categories and to develop and 

validate air quality models. For these purposes, PM parameters other than mass, such as 

chemical composition and size distribution, must also be measured. Moreover, measurements 

are needed with shorter time resolutions in order to match changes in pollution with the diurnal 

changes in the boundary layer. 

A number of PM measurements are needed for use in epidemiologic and exposure studies 

and to determine components of PM to guide the planning and interpretation of toxicologic 

studies. Thus, size and chemical composition measurements are important, as are measurement 

across different time intervals. For epidemiologic studies of acute (i.e., short-term) PM 

exposures, 1-h or continuous measurements can provide important information beyond that 

provided by 24-h measurements. However, for epidemiologic studies of chronic PM exposures, 

measurements that permit integration over longer intervals (e.g., a week to a month) are more 

relevant. For dosimetric studies and modeling, information will be needed on the particle size 

distribution and on the behavior of particles as the relative humidity and temperature changes 

found in the atmosphere are increased to those found in the respiratory system. 

For studies of ecological effects and materials damage, measurements of particles and of 

the chemical components of PM in rain, fog, and dew are needed to understand the contributions 

of PM to the soiling of surfaces and damage to materials and to understand the wet and dry 

deposition of acidity and toxic substances to surface water, soil, and plants. Some differentiation 

into particle size is needed to determine dry deposition. 

For studies of visibility impairment and radiative effects, information is needed regarding 

how particles scatter and absorb light, including data on refractive index, ratio of scattering to 

absorption, size distribution, and change in particle size with change in relative humidity. 

2.2.2 Issues in Measurement of Particulate Matter 

The EPA decision to revise the PM standards by adding daily and yearly standards 

for PM2.5 has led to a renewed interest in the measurement of atmospheric particles and to a 

better understanding of the problems in developing precise and accurate measurements of 

particles. It is very difficult to measure and characterize particles suspended in the atmosphere; 

however, numerous improvements in PM monitoring are in use and others are in development. 

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EPA’s PM standards are based, in part, on epidemiologic relationships between health effects 

and PM concentrations as measured with existing monitoring methods. As understanding of 

suspended PM has advanced and new monitoring information has become available, EPA has 

changed the indicator for the PM NAAQS from TSP to PM10, and added PM2.5. During the 

current PM NAAQS review, consideration will be given to a standard for coarse thoracic PM. 

The U.S. Federal Reference Methods (FRM) for PM2.5 and PM10 provide relatively precise 

(± 10%) methods for determining the mass of material remaining on a Teflon filter after 

equilibration. However, numerous uncertainties remain as to the relationship between the mass 

and composition of material remaining on the filter as determined by the FRM measurement 

procedure and the mass and composition of material that existed in the atmosphere as suspended 

PM. As a result, the EPA defines accuracy for PM measurements in terms of agreement of a 

candidate sampler with a reference sampler. Therefore, intercomparison of samplers is very 

important in determining how well various samplers agree and how various design choices 

influence what is actually measured. 

There are eight general areas where choices are made in the design and use of an aerosol 

sampler. These include (1) consideration of positive artifacts due to chemical reaction or 

adsorption, (2) treatment of semivolatile components, (3) selection of particle size cut 

characteristics for the upper cut point, (4) separation of fine and coarse PM, (5) treatment of 

pressure, temperature, and relative humidity, (6) time resolution, (7) assessment of the reliability 

of the measurement technique, and (8) operation and maintenance procedures needed to maintain 

consistent measurements over time. In many cases, choices have been made without adequate 

recognition of their consequences. As a result, measurement methods developed by different 

organizations may give different results when sampling the same atmosphere even though the 

techniques appear to be similar. 

2.2.2.1 Artifacts Due to Chemical Reactions 

When TSP was collected on glass-fiber filters, the reaction of SO2 (and other acid gases) 

with basic sites on the glass fiber or with basic coarse particles on the filter led to the formation 

of sulfate (or other nonvolatile salts, e.g., nitrate, chloride). These positive artifacts led to the 

overestimation of mass, sulfate, and probably also of nitrate. The metal impurities in the glass 

fiber resulted in high background levels that led to low precision in the measurement of trace 

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metals. These problems were largely overcome by changing to quartz fiber or Teflon filters and 

by the separate collection of PM2.5. However, the possible reaction of acidic gases with basic 

coarse particles remains a possibility, especially with PM10 and PM10-2.5 measurements. The 

reaction of NH3 with acidic particles, either during sampling or during transportation, storage, 

and equilibration remains a problem in areas such as the eastern United States where PM is 

frequently acidic. One technique that has been developed to overcome this problem makes use 

of a denuder to remove NH3 during sampling and to protect the collected PM from NH3 (Suh 

et al., 1992, 1994; Brauer et al., 1991; Koutrakis et al., 1988a,b). However, this technique has 

been applied primarily for measurement of particle strong acidity, not for the measurement of 

artifact-free ammonium or mass concentrations. In the measurement of particle strong acidity, 

basic coarse particles must be separated from acidic fine particles (Koutrakis et al., 1992). 

2.2.2.2 Treatment of Semivolatile Components of Particulate Matter 

Current filtration-based mass measurements can result in significant evaporative losses, 

during and possibly after collection, of a variety of semivolatile components (i.e., species that 

exist in the atmosphere in dynamic equilibrium between the condensed phase and gas phase). 

Important examples include ammonium nitrate, semivolatile organic compounds, and particlebound water. This problem is illustrated in Figure 2-18. 

The following approaches, that have been used to address the problem of potentially lost 

semivolatile components, will be discussed in more detail in subsequent sections. 

(1) Collect/measure all components present in the atmosphere in the condensed phase except 

particle-bound water. Examples: Brigham Young absorptive sampler and Harvard 

pressure drop monitor. Both require preconcentration of the accumulation mode and 

reduction of ambient humidity by diffusion denuder techniques. 

(2) Stabilize PM at a specified temperature high enough to remove all, or almost all, particlebound water. This results in loss of much of the semivolatile PM. Examples: tapered 

element oscillating microbalance (TEOM) operated at 50 oC beta gauge with heated inlet. 

(3) Equilibrate collected material at fixed, near-room temperature and moderate relative 

humidity to reduce particle-bound water. Accept the loss of an unknown but possibly 

significant fraction of semivolatile PM. Examples: U.S. Federal Reference Method and 

most filter-weighing techniques. Equilibration originally was designed to remove 

adsorbed water vapor from glass fiber filters in order to maintain a stable filter weight. 

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Figure 2-18. Schematic showing major nonvolatile and semivolatile components of PM2.5. 

Semivolatile components are subject to partial-to-complete loss during 

equilibration or heating. The optimal technique would be to remove all 

particle-bound water but no ammonium nitrate or semivolatile organic PM. 

The designated RH (35 ± 5%) was a compromise. If the RH is too low, electrostatic 

charging becomes a problem. The equilibration process does help provide a stable and 

reproducible mass. The equilibrium process reduces the amount of particle-bound water 

but may not remove all particle-bound water. Moreover, the equilibration process may 

lead to the loss of other semivolatile PM components. 

2.2.2.3 Upper Cut Point 

The upper cut point of the high volume sampler varies with wind speed and direction. 

However, it is usually desirable to have an upper cut point that is independent of these factors. 

Considerations in selecting an upper cut point are discussed in Section 2.1.2.2. 

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2.2.2.4 Cut Point for Separation of Fine and Coarse Particulate Matter 

As shown in Table 2-2, fine and coarse particles differ not only in size but also in 

formation mechanisms, sources, and chemical, physical, and biological properties. They also 

differ in concentration-exposure relationships, dosimetry (deposition and retention in the 

respiratory system), toxicity, and health effects as observed in epidemiologic studies. Thus, it is 

desirable to measure fine and coarse PM separately in order to properly allocate health effects to 

either fine or coarse PM and to correctly determine sources by receptor modeling approaches. 

Considerations in selecting a cut point to separate fine and coarse particles are discussed in 

Section 2.1.2.2. 

2.2.2.5 Treatment of Pressure, Temperature, and Relative Humidity 

There are a variety of techniques for defining (or ignoring) the pressure, temperature, and 

relative humidity during and after sampling. For example, the sample volume may be based on 

the mass or volumetric flow corrected to standard temperature and pressure (298 K and 1 atm) 

(as in the current FRM for PM10), or it may be based on the volumetric flow at ambient 

conditions of temperature and pressure (current FRM for PM2.5). 

There are also a variety of options for the control of temperature during collection. The 

particles may be heated enough to remove much of the particle-bound water (i.e., TEOM at 

50 °C); the particles may be heated several degrees, just enough to prevent condensation of 

water in the sampling system; the particles and the sampler may be maintained near ambient 

temperature (± 5 °C of ambient temperature is required for EPA FRM samplers for PM2.5); 

or the particles and sampler may be maintained at a constant temperature inside a heated or air 

conditioned shelter. There are also options for controlling of temperature after collection: 

(a) no control (room temperature) or (b) ship and store at cool temperature (4 °C is the current 

EPA FRM requirement). 

Consideration must also be given to relative humidity (RH). Changes in RH cause changes 

in particle size of hygroscopic or deliquescent particles. Changing RH by adding or removing 

water vapor affects particle number, particle surface area, and particle size distribution 

measurements and the amount of overlap of accumulation-mode and coarse-mode particles. 

Changing RH by intentional or inadvertent changes in temperature also affects the relative loss 

of ammonium nitrate and semivolatile organic compounds. Monitoring personnel should be 

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aware of the various options for the treatment of pressure, temperature, and RH factors; make 

appropriate selections; and document which options are used. 

Studies of relationships between personal/indoor/outdoor measurements present special 

problems. Indoor environments are typically dryer than outdoors and may be warmer or, if 

air-conditioned, cooler. These differences may change particle size and the amount of 

volatilization of semivolatile components. Such changes between indoors and outdoors will 

complicate the comparison of indoor to outdoor PM concentrations, the modeling of personal 

exposure to all particles, and apportionment of personal exposure into particles of ambient 

origin, particles of indoor origin, and particles originating from personal activity. 

2.2.2.6 Time Resolution 

The classic 24-h filter collection technique is being supplemented by a variety of 

continuous monitors for various PM constituents. This process is being accelerated by the lower 

operational cost of continuous monitors and the availability of new continuous monitors for 

mass, number, and certain chemical components, as well as refinements of older methods based 

on beta attenuation or light scattering. Most epidemiologic studies have used 24-hour 

concentrations as exposure indicators. However, one epidemiologic study of chronic effects 

uses a filter sampler with a 2-week collection period (Gauderman et al., 2000). Another recent 

study used 1 to 2 h concentrations (see Peters et al., 2000). Continuous methods are discussed in 

Section 2.2.5. 

2.2.2.7 Accuracy and Precision 

Precision is typically determined by comparing measurements obtained with collocated 

samplers or through replicate analyses; whereas accuracy is determined through the use of 

traceable calibration standards. Unfortunately, no standard reference calibration material or 

procedure has been developed for suspended, atmospheric PM. It is possible to determine the 

accuracy of certain components of the PM measurement system (e.g., flow control, inlet 

aspiration, PM2.5 cut, weighing, etc.). The absolute accuracy for collecting a test aerosol can also 

be determined by isokinetic sampling in a wind tunnel. However, it is not currently feasible to 

provide a simulated atmospheric aerosol with naturally occurring semivolatile components. It is 

particularly challenging to develop an atmospheric aerosol calibration standard suitable for 

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testing samplers in the field. Therefore, it is not possible at the present time to establish the 

absolute accuracy of a PM monitoring technique. Intercomparison studies may be used to 

establish the precision of identical monitors and the extent of agreement between different types 

of monitors. Such studies are important for establishing the reliability of PM measurements. 

Intercomparison studies have contributed greatly to our understanding of the problems in PM 

measurement. Such studies will be discussed as they apply to specific measurement problems, 

monitoring instruments, or analytical techniques. 

Some measurement errors of concern in PM10 sampling, including those that arise due to 

uncertainty tolerances in cut point, particle bounce and reentrainment; impactor surface 

overloading; and losses to sampler internal surfaces, were discussed in detail in the 1996 PM 

AQCD (U.S. Environmental Protection Agency, 1996a). Other measurement errors of concern 

in PM2.5 sampling arise because of our inability to assess accuracy in an absolute sense due to a 

lack of an atmospheric aerosol calibration standard, the inclusion in PM2.5 of a small amount of 

coarse particles as discussed in Section 2.2.1.3, and problems associated with the definition 

of PM2.5 as what remains on a filter after collection and equilibration, rather than as the mass of 

particles as they exist in the air. Still, it is possible to measure PM indicators with high 

precision. Detailed information on precision and quality assurance may be found on the EPA’s 

Technology Transfer Network website (U.S. Environmental Protection Agency, 2002a). See 

discussion in Section 2.2.4. 

Because of the difficulties associated with determining the accuracy of PM measurements, 

EPA has sought to make FRM measurements equivalent by specifying operating conditions and, 

in the case of PM2.5 samplers, by specifying details of the sampler design. Thus, both the PM10 

and the PM2.5 standards are defined with consistency of measurement technique rather than with 

the accuracy of the true mass concentration measurement in mind (McMurry, 2000). It is 

acknowledged in the Federal Register (1997) that, “because the size and volatility of the particles 

making up ambient particulate matter vary over a wide range and the mass concentration of 

particles varies with particle size, it is difficult to define the accuracy of PM2.5 measurements in 

an absolute sense. . . .” Thus, accuracy is defined as the degree of agreement between a 

field PM2.5 sampler and a collocated PM2.5 reference method audit sampler (McMurry, 2000). 

The Federal Reference Method for PM2.5 is discussed in Section 2.2.3.3. As mentioned earlier, 

volatilization of organic compounds and ammonium nitrate during sampling or post-sampling 

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handling can lead to significant underestimation of the fine particulate mass concentration in 

some locations. Sources of error in the measurement of mass of PM2.5 suspended in the 

atmosphere also arise because of adsorption or desorption of semivolatile vapors onto or from 

collected PM, filter media, or other sampler surfaces; neutralization of acid or basic vapors on 

either filter media or collected PM; and artifacts associated with particle-bound water. 

During the past 25 years, there have been advancements in the generation and 

classification of monodisperse aerosols as well as in the development of electron microscopy and 

imaging analyses that have contributed to the advancement in aerosol calibration (Chen, 1993). 

Still, one of the limitations in PM sampling and analysis remains the lack of primary calibration 

standards for evaluating analytical methods and for intercomparing laboratories. Klouda et al. 

(1996) examined the possibility of resuspending the National Institute of Standards and 

Technology (NIST) Standard Reference Material 1649 (Urban Dust) in air for collection on up to 

320 filters simultaneously using SRI’s dust generation and collection system. However, the fine 

component does not resuspend and the semivolatile component evaporates. Consequently, this 

material is not a suitable standard for suspended PM. NIST is continuing work in this area with 

EPA support. 

Method validation was discussed in the 1996 PM AQCD (U.S. Environmental Protection 

Agency, 1996a), and the usefulness of intercomparisons and “internal redundancy” was 

emphasized. For example, a number of internal consistency checks are applied to the IMPROVE 

network (Malm et al., 1994). These include mass balances, sulfur measurements by both 

proton-induced X-ray emission (PIXE) and ion chromatography (IC), and comparison of organic 

matter by combustion and by proton elastic scattering (PESA) of hydrogen. Mass balances 

compare the gravimetrically determined mass with the mass calculated from the sum of the 

major chemical components (i.e., crustal elements plus associated oxygen, organic carbon, 

elemental carbon, sulfate, nitrate, ammonium, and hydrogen ions). Mass balances are useful 

validation techniques; however, they do not check for, or account for, artifacts associated with 

the absorption of gases during sampling, the loss of semivolatile material during sampling, or 

errors in assumptions regarding unmeasured “associated species.” The mass balance check may 

appear reasonable even if such artifacts are present, because only the material collected on the 

filter is included in the balance. Mass balance checks may also suffer from errors due to some 

particle-bound water remaining in the PM even after equilibration and from the use of an 

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arbitrary factor to account for the amount of oxygen and hydrogen atoms per carbon atom in the 

organic carbon fractions (Andrews et al., 2000). 

2.2.3 Measurement of Semivolatile Particulate Matter 

Some atmospheric species, such as ammonium nitrate, water, and certain organic 

compounds have vapor pressures such that, for typical ambient concentrations and temperatures, 

they may exist in an equilibrium state with some material in the gas phase and some in the 

condensed (particulate) phase. Such species are known as semivolatile materials (SVM) and 

may partition to varying degrees between the gas and particle phases (Pankow, 1994a). Organic 

compounds with vapor pressure < 10!6

 Torr are nonvolatile, i.e., primarily in the particle phase. 

Organic compounds with vapor pressure > 10!5

 Torr will be primarily in the gas phase. Organic 

compounds with vapor pressures on the order of 10!5

 to 10!6 are semivolatile. Semivolatile 

material, originally in the atmosphere in the particulate phase and collected on a filter, may 

subsequently be lost from the filter. For example, SVM may evaporate during sampling due to a 

reduction in its concentration in the atmosphere being sampled or due to the pressure drop across 

the filter. Or, SVM may evaporate after sampling; during intentional equilibration at a low 

relative humidity; or during transport, handling, and storage if exposed to an atmosphere in 

which the vapor pressure of one or more semivolatile components is lower than in the 

atmosphere sampled. Since water is not a pollutant, it is necessary to remove most of the 

particle-bound water before weighing (Chow, 1995). However, the collection and measurement 

of ammonium nitrate and semivolatile organic compounds in suspended atmospheric PM 

represents a major analytical challenge (McMurry, 2000). 

2.2.3.1 Particle-Bound Water 

It is generally desirable to collect and measure ammonium nitrate and semivolatile organic 

compounds. However, for many measurements of suspended particle mass, it is desirable to 

remove the particle-bound water before determining the mass. The mass of particle-bound water 

is strongly dependent on the relative humidity and the particle composition. However, the 

dependence on relative humidity is not linear, because there is significant hysteresis in the water 

adsorption-desorption pathways (Seinfeld and Pandis, 1998). Water vapor cannot be controlled, 

and particle-bound water is not included in the mass of PM subject to regulation and control. 

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components, a measurement of PM mass including particle-bound water would depend more on 

relative humidity than pollution. For all these reasons, it is usually desirable to remove most, 

if not all, particle-bound water before weighing collected PM. On the other hand, in some 

situations it may be important to know how much of the suspended particle mass or volume 

results from particle-bound water. Figures 2-16a and 2-16b show the change in diameter of 

sulfate particles as a function of relative humidity. Figure 2-16a also shows hysteresis resulting 

from the difference between deliquescence and crystallization points. 

Pilinis et al. (1989) calculated the water content of atmospheric PM above and below the 

deliquescent point. They predicted that aerosol water content is strongly dependent on 

composition and concluded from their calculations that liquid water could represent a significant 

mass fraction of aerosol concentration at relative humidities above 60%. Since then, a few 

researchers have attempted to measure the water content of atmospheric aerosols. Most 

techniques have focused on tracking changes in the particle mass as the relative humidity is 

changes. Only a few demonstrations have been carried out using atomospheric particles. 

Of particular interest is the development of the Tandem Differential Mobility Analyzer (TDMA) 

and its applications in investigations of the effects of relative humidity on particle growth. 

Lee et al. (1997) examined the influence of relative humidity on the size of atmospheric 

aerosols using a TDMA coupled with a scanning mobility particle sizer (SMPS). They reported 

that the use of the TDMA/SMPS system allowed for the abrupt size changes of aerosols at the 

deliquescence point to be observed precisely. They also reported that at relative humidities 

between 81 and 89% the water content of ammonium sulfate aerosols (by mass) ranged from 

47 to 66%. 

Andrews and Larson (1993) investigated the interactions of single aerosol particles coated 

with an organic film within a humid environment. Using an electrodynamic balance, they 

conducted laboratory experiments in which sodium chloride and carbon black particles were 

coated with individual organic surfactants (intended to simulate the surface-active, organic films 

that many atmospheric aerosol particles may exhibit) and their water sorption curves were 

examined. Their results showed that when ordinarily hydrophobic carbon black particles were 

coated with an organic surfactant, they sorbed significant amounts of water (20 to 40% of the dry 

mass of the particle). 

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Liang and Chan (1997) developed a fast technique using the electrodynamic balance to 

measure the water activity of atmospheric aerosols. In their technique, the mass of a levitated 

particle is determined as the particle either evaporates or grows in response to a step change in 

the relative humidity. Their technique was demonstrated using laboratory experiments with 

NaCl, (NH4)2SO4, NaNO3, and (NH4)2SO4/NH4NO3 solutions. They concluded that one of the 

advantages of their fast method is the ability to measure the water activity of aerosols containing 

volatile solutes such as ammonium chloride and some organics. 

McInnes et al. (1996) measured aerosol mass concentration, ionic composition, and the 

associated water mass of marine aerosol over the remote Pacific Ocean. The mass of PBW was 

determined by taking the difference between the mass obtained at 48% RH and at 19% RH, 

assuming the aerosol particles were dry at 19% RH. Based on a comparison of the remote 

Pacific aerosol to an aerosol collected at a site at the marine/continental interface of the 

Washington coast, the amount of water associated with the aerosol was observed to be a function 

of the ammonium to sulfate ratio. They found that the amount of water associated with the 

submicrometer aerosol comprised 29% of the total aerosol mass collected at 47% RH and 9% of 

the total mass at 35% RH. 

Ohta et al. (1998) characterized the chemical composition of atmospheric fine particles 

(50% cut point of 2 μm) in Sapporo, Japan and, as part of their measurements, determined the 

water content using the Karl Fischer method (Meyer and Boyd, 1959). After exposing a Teflon 

filter, a portion of the filter was equilibrated at 30% RH for 24 h. Then the filter piece was 

placed in a water evaporator and heated at 150 °C, vaporizing the particle-bound water. The 

evolved vapor was analyzed for water in an aqua-counter where it was titrated coulometrically in 

Karl Fischer reagent solution (containing iodine, sulfur, and methanol). The accuracy of the 

aqua-counter is ± 1 mg. Using this technique, they determined that the water content of the 

particles ranged from 0.4 to 3.2% of the total particulate mass (at RH < 30%). This represents a 

smaller portion of water compared to their previous reported values (Ohta and Okita, 1990), 

which were determined by calculation at 50% RH. 

Speer et al. (1997) developed an aerosol liquid water content analyzer (LWCA) in which 

aerosol samples are collected on Teflon filters and then placed in a closed chamber in which 

the relative humidity is closely controlled. The aerosol mass is monitored using a beta-gauge, 

first as the relative humidity is increased from low RH to high RH, and then as the RH is 

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decreased again. They demonstrated the LWCA on a laboratory-generated aerosol and on an 

ambient PM2.5 sample collected in Research Triangle Park, NC. The ambient aerosol sample 

was also analyzed for chemical constituents. It is interesting to note that, although their 

laboratory-generated (NH4)2SO4 aerosol demonstrated a sharp deliquescent point, their 

atmospheric aerosol, which was essentially (NH4)2SO4, did not show a sharp deliquescent point. 

Hygroscopic properties of aerosols have been studied from the viewpoint of their ability to 

act as condensation nuclei. The hygroscopic properties of fresh and aged carbon and diesel soot 

particles were examined by Weingartner et al. (1997) who found that fresh, submicron-size 

particles tended to shrink with increasing relative humidity because of a structural change. 

Lammel and Novakov (1995) found, in laboratory studies, that the hygroscopicity of soot 

particles could be increased by chemical modification and that the cloud condensation nucleation 

characteristics of diesel soot were similar to those of wood smoke aerosol. 

The results of several of the above studies in which aerosol water content was determined 

as a function of relative humidity are summarized in Figure 2-19, which includes the results of

Lee et al. (1997), McInnes et al. (1996), and Ohta et al. (1998). Relative humidity ranged from 

9%, at which the aerosol water content was assumed to be zero (McInnes et al., 1996), to 89%, at 

which the aerosol water content was determined to be 66% by mass (Lee et al., 1997). Koutrakis 

et al. (1989) and Koutrakis and Kelly (1993) also have reported field measurements of the 

equilibrium size of atmospheric sulfate particles as a function of relative humidity and acidity. 

Figure 2-19. Aerosol water content expressed as a mass percentage, as a function of 

relative humidity. 

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The effects of relative humidity on particle growth were also examined in several studies. 

Fang et al. (1991) investigated the effects of flow-induced relative humidity changes on particle 

cut sizes for aqueous sulfuric acid particles in a multi-nozzle microorifice uniform deposit 

impactor (MOUDI). Laboratory experiments were conducted in which polydisperse sulfuric 

acid aerosols were generated and the RH was adjusted. The aerosols were analyzed by a 

differential mobility analyzer. Fang et al. (1991) observed that for inlet RH less than 80%, the 

cut sizes for the sulfuric acid aerosols were within 5% of that for nonhygroscopic particles 

except at the stage for which the cut size was 0.047 μm, (10.7% larger than the nonhygroscopic 

particle cut size). They concluded that flow-induced RH changes would have only a modest 

effect on MOUDI cut sizes at RH < 80%. 

Hitzenberger et al. (1997) collected atmospheric aerosol in the size range of 0.06 to 15 μm 

in Vienna, Austria using a nine-stage cascade impactor and measured the humidity-dependent 

water uptake when the individual impaction foils were exposed to high RH. They observed 

particle growth with varying growth patterns. Calculated extinction coefficients and single 

scattering albedo increased with humidity. 

The hygroscopic properties, along with the mixing characteristics, of the submicrometer 

particles sampled in Los Angeles, CA during the summer of 1987 SCAQS study and at the 

Grand Canyon, AZ during the 1990 Navajo Generating Station Visibility Study were reported by 

Zhang et al. (1993). They used a tandem differential mobility analyzer (TDMA; McMurry and 

Stolzenburg, 1989) to measure the hygroscopic properties for particles in the 0.05 to 0.5 μm 

range. In their experimental technique, monodisperse particles of a known size are selected from 

the atmospheric aerosol with the first analyzer. Then, the relative humidity of the monodisperse 

aerosol is adjusted, and the new particle size distribution is measured with the second analyzer. 

At both sites, they observed that monodisperse particles could be classified according to “more” 

hygroscopic and “less” hygroscopic. Aerosol behavior observed at the two sites differed 

markedly. Within the experimental uncertainty (± 2%) the “less” hygroscopic particles sampled 

in Los Angeles did not grow when the RH was increased to 90%; whereas at the Grand Canyon, 

the growth of the “less” hygroscopic particles varied from day to day, but ranged from near 0 to 

40% when the RH was increased to 90%. The growth of the “more” hygroscopic particles in 

Los Angeles was dependent on particle size (15% at 0.05 μm to 60% at 0.5 μm); whereas at the 

Grand Canyon, the “more” hygroscopic particles grew by about 50% with the growth not 

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varying significantly with particle size. After comparing the TDMA data to impactor data, 

Zhang et al. (1993) surmised that the more hygroscopic particles contained more sulfates and 

nitrates, while the less hygroscopic particles contained more carbon and crustal components. 

Although most of the work to date on the hygroscopic properties of atmospheric aerosols 

has focused on the inorganic fraction, the determination of the contribution of particle-bound 

water to atmospheric particulate mass is greatly complicated by the presence of organics. The 

effect of RH on the adsorption of semivolatile organic compounds is discussed elsewhere in this 

chapter. Saxena et al. (1995) observed that particulate organic compounds can also affect the 

hygroscopic behavior of atmospheric particles. They idealized the organic component of aerosol 

as containing a hydrophobic fraction (e.g., high-molecular weight alkanes, alkanoic acids, 

alkenoic acids, aldehydes, and ketones) and a hydrophilic fraction (e.g., lower molecular weight 

carboxylic acids, dicarboxylic acids, alcohols, aldehydes, etc.) that would be likely to absorb 

water. They then analyzed data from a TDMA in conjunction with particle composition 

observations from an urban site (Claremont, CA) and from a nonurban site (Grand Canyon) to 

test the hypothesis that, by adding particulate organics to an inorganic aerosol, the amount of 

water absorbed would be affected, and the effect could be positive or negative, depending on the 

nature of the organics added. They further presumed that the particulate organic matter in 

nonurban areas would be predominantly secondary, and thus hydrophilic, compared to the urban 

aerosol that was presumed to be derived from primary emissions and thus hydrophobic in nature. 

Their observations were consistent with their hypothesis, in that at the Grand Canyon, the 

presence of organics tended to increase the water uptake by aerosols; whereas at the Los Angeles 

site, the presence of organics tended to decrease water uptake. 

Peng and Chan (2001) also recently studied the hygroscopic properties of nine water 

soluble organic salts of atmospheric interest using an electrodynamic balance operated at 25 °C. 

Salts studied included sodium formate, sodium acetate, sodium succinate, sodium pyruvate, 

sodium methanesulfonate, sodium oxalate, ammonium oxalate, sodium malonate, and sodium 

maleate. They observed that hygroscopic organic salts have a growth factor of 1.76 to 2.18 from 

RH = 10 to 90%, which is similar to that of typical hygroscopic inorganic salts such as NaCl 

and (NH4)2SO4. 

Nonequilibrium issues may be important for the TDMA, as well as for other methods of 

measuring water content. Although the approach to equilibrium as the RH is increased is 

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expected to be rapid for pure salts, it may be much slower for aerosols containing a complex mix 

of components (Saxena et al., 1995). For example, if an aerosol contains an organic film or 

coating, that film may impede the transport of water across the particle surface, thus increasing 

the time required for equilibrium (Saxena et al., 1995). Insufficient time to achieve equilibrium 

in the TDMA could result in underestimation of the water content. 

2.2.3.2 Nitrate and Organic Species 

Particulate Nitrates 

It is now well known that volatilization losses of particulate nitrates occur during sampling 

on Teflon filters (e.g., Zhang and McMurry [1992]; see also Hering and Cass [1999] and Babich 

et al. [2000]). The effect on the accuracy of atmospheric particulate measurements from these 

volatilization losses is more significant for PM2.5 than for PM10. The FRM for PM2.5 will likely 

suffer a loss of nitrates similar to that experienced with other simple filter collection systems. 

Sampling artifacts resulting from the loss of particulate nitrates represents a significant problem 

in areas such as southern California that experience high amounts of nitrates. Hering and Cass 

(1999) reported on errors in PM2.5 mass measurements due to the volatilization of particulate 

nitrate (Figure 2-20). They examined data from two field measurement campaigns that were 

conducted in southern California: (1) the Southern California Air Quality Study (SCAQS) 

(Lawson, 1990) and (2) the 1986 CalTech study (Solomon et al., 1992). In both these studies, 

side-by-side sampling of PM2.5 was conducted. One sampler collected particles directly onto a 

Teflon filter. The second sampler consisted of a denuder to remove gaseous nitric acid followed 

by a nylon filter that absorbed the HNO3 as it evaporated from ammonium nitrate. In both 

studies, the denuder consisted of MgO-coated glass tubes (Appel et al., 1981). Fine particulate 

nitrate collected on the Teflon filter was compared to fine particulate nitrate collected on the 

denuded nylon filter. In both studies, the PM2.5 mass lost because of ammonium nitrate 

volatilization represented a significant fraction of the total PM2.5 mass. The fraction of mass lost 

was higher during summer than during fall (17% versus 9% during the SCAQS study, and 21% 

versus 13% during the CalTech study; Figure 2-20). In regard to percentage loss of nitrate, as 

opposed to percentage loss of mass discussed above, Hering and Cass (1999) found that the 

amount of nitrate remaining on the Teflon filter samples was on average 28% lower than that on 

the denuded nylon filters. 

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Figure 2-20. Amount of ammonium nitrate volatilized from Teflon filters, expressed as a 

percentage of the measured PM2.5 mass, for the SCAQS and CalTech studies, 

for spring and fall sampling periods. 

Source: Hering and Cass (1999). 

Hering and Cass (1999) also analyzed these data by extending the evaporative model 

developed by Zhang and McMurry (1987). The extended model used by Hering and Cass (1999) 

takes into account the dissociation of collected particulate ammonium nitrate on Teflon filters 

into nitric acid and ammonia via three mechanisms: (1) the scrubbing of nitric acid and 

ammonia in the sampler inlet (John et al. [1988] showed that clean PM10 inlet surfaces serve as 

an effective denuder for nitric acid); (2) the heating of the filter substrate above ambient 

temperature by sampling; and (3) the pressure drop across the Teflon filter. For the sampling 

systems modeled, the flow-induced pressure drop was measured to be less than 0.02 atm, and the 

corresponding change in vapor pressure was 2%, so losses driven by pressure drop were not 

considered to be significant in this work. Losses from Teflon filters were found to be higher 

during the summer compared to the winter, higher during the day compared to night, and 

reasonably consistent with modeled predictions. 

Finally, during the SCAQS (Lawson, 1990) study, particulate samples also were collected 

using a Berner impactor and greased Tedlar substrates in size ranges from 0.05 to 10 μm in 

aerodynamic diameter. The Berner impactor PM2.5 nitrate values were much closer to those 

2-69
from the denuded nylon filter than those from the Teflon filter, the impactor nitrate values being 

~2% lower than the nylon filter nitrate for the fall measurements and ~7% lower for the summer 

measurements. When the impactor collection was compared to the Teflon filter collection for a 

nonvolatile species (sulfate), the results were in agreement. Chang et al. (2000) discuss reasons 

for reduced loss of nitrate from impactors. 

It should be noted that filters or collection surfaces were removed immediately after 

sampling and placed into vials containing a basic extraction solution during these 

intercomparison studies to preclude losses that might occur during their handling, storage, and 

equilibration. The loss of nitrate observed from Teflon filters and impaction surfaces in this 

study, therefore, is a lower limit compared to losses that might occur during the normal 

processes involved in equilibration and weighing of filters and impaction surfaces. Brook and 

Dann (1999) observed much higher nitrate losses during a study in which they measured 

particulate nitrate in Windsor and Hamilton, Ontario, Canada, by three techniques: (1) a single 

Teflon filter in a dichotomous sampler, (2) the Teflon filter in an annular denuder system (ADS), 

and (3) total nitrate including both the Teflon filter and the nylon back-up filter from the ADS. 

The Teflon filter from the dichotomous sampler averaged only 13% of the total nitrate, whereas 

the Teflon filter from the ADS averaged 46% of the total nitrate. The authors concluded that 

considerable nitrate was lost from the dichotomous sampler filters during handling, which 

included weighing and X-ray fluorescence (XRF) measurement in a vacuum. 

Kim et al. (1999) also examined nitrate sampling artifacts by comparing denuded and 

undenuded quartz and nylon filters, during the PM10 Technical Enhancement Program (PTEP) in 

the South Coast Air Basin of California. They observed negative nitrate artifacts (losses) for 

most measurements; however, for a significant number of measurements, they observed positive 

nitrate artifacts. Kim et al. (1999) pointed out that random measurement errors make it difficult 

to measure true amounts of nitrate loss. 

Diffusion denuder samplers, developed primarily to measure particle strong acidity 

(Koutrakis et al., 1988a,b, 1992), also can be used to study nitrate volatilization. Such 

techniques were used to measure loss of particulate nitrate from Teflon filters in seven U.S. 

cities (Babich et al., 2000). Measurements were made with two versions of the Harvard-EPA 

Annular Denuder System (HEADS). Nitric acid vapor was removed by a Na2CO3-coated 

denuder. Particulate nitrate was the sum of nonvolatile nitrate collected on a Teflon filter and 

2-70
volatized nitrate collected on a Na2CO3-coated filter downstream of the Teflon filter (full 

HEADS) or on a Nylon filter downstream of the Teflon filter (Nylon HEADS). It was found that 

the full HEADS (using a Na2CO3 filter) consistently underestimated the total particulate nitrate 

by approximately 20% compared to the nylon HEADS. Nonvolatilized nitrate was also 

measured on a Teflon filter from collocated Harvard Impactors (HIs). The PM2.5 HI sampler, 

like the PM2.5 FRM, uses impactors with a 50% cut at 2.5 μm. The HI uses a 37-mm filter and a 

flow rate of 10 L/min (0.465 L/min/cm2

), while the FRM uses a 47-mm filter and a flow rate of 

16.7 L/min (0.481 L/min/cm2

). Therefore, the flow rate and pressure drop across the filters are 

similar and the loss of nitrate should be similar for both types of samples. Babich et al. (2000) 

found significant nitrate losses in Riverside, CA; Philadelphia, PA; and Boston, MA but not in 

Bakersfield, CA; Chicago, IL; Dallas, TX; or Phoenix, AZ where measurements were made only 

during the winter. Tsai and Huang (1995) used a diffusion denuder to study the positive and 

negative artifacts on glass and quartz filters. They found positive artifacts attributed to SO2 and 

HNO3 reaction with basic sites on glass fibers and basic particles and negative artifacts attributed 

to loss of HNO3 and HCl due to volatilization of NH4NO3 and NH4Cl and reaction of these 

species with acid sulfates. 

Eatough et al. (1999a) developed a high-volume diffusion denuder system that combined 

diffusion denuder and particle concentrator techniques (see Section 2.2.3.2). In this system, the 

particle concentrator reduces the flow through the denuder so that the denuder can be operated 

for weeks without a loss of collection efficiency, making the sampler suitable for routine field 

sampling. The system was evaluated for the collection of fine particulate sulfate and nitrate in 

Riverside, CA (Eatough et al., 1999b). Concentrations of PM2.5 nitrate obtained from the PCBOSS agreed with those obtained using the Harvard-EPA Annular Denuder Sampler, HEADS 

(Koutrakis et al., 1988b). 

Particulate Organic Compounds 

Many semivolatile organic compounds (SVOCs) are of interest because of their possible 

health effects. Semivolatile organic compounds include products of incomplete combustion 

such as polycyclic aromatic hydrocarbons (PAHs) and polycyclic organic matter, which has been 

identified as a hazardous air pollutant. Polycyclic aromatic hydrocarbons also have been 

suggested as alternative particulate tracers for automobile emissions, because lead is no longer a 

2-71
good tracer for automobile emissions sources due to the phase-out of organo-lead additives in 

gasoline (Venkataraman et al., 1994). PAHs also are emitted during biomass burning, i.e., 

burning of cereal crop residues and wood fuels (Jenkins et al., 1996; Roberts and Corkill, 1998). 

A number of studies have used absorbing material following quartz filters to determine 

phase equilibria of specific organic compounds (Liang et al., 1997; Gundel et al., 1995; Kamens 

et al., 1995). Much work has also gone into the development of a theory to help understand the 

phase relationships (Yamasaki et al., 1982; Rounds and Pankow, 1990; Pankow, 1987, 1994a,b; 

Pankow et al., 1993; Rounds et al., 1993; Odum et al., 1994). The development of a theory 

describing phase equilibria of SVOC and the gas/particle partitioning of SVOC on inorganic, 

organic, and ambient smog aerosols is ongoing (Liang et al., 1997; Jang et al., 1997; Strommen 

and Kamens, 1997; Jang and Kamens, 1998, 1999, 2001; Leach et al., 1999; Kamens et al., 

1999; Kamens and Jaoui, 2001; Chandramouli and Kamens, 2001; Chandramouli et al., 2003). 

The mass of OC and EC is usually determined by the analysis of PM collected on a quartz 

filter. However, quartz fiber filters have a large specific surface area on which adsorption of 

gases can also occur. Possible artifacts associated with adsorption of organic gases onto quartz 

filters have been examined in experiments in which two quartz-fiber filters were deployed in 

series. The second quartz filter may indicate gaseous volatile organic compounds (VOCs) 

adsorbed on both filters (positive artifact), SVOCs evaporated from particles on the first filter 

and subsequently adsorbed on the second filter (negative artifact) or a combination of both 

effects. Unless the individual compounds are identified, the investigator does not know what to 

do with the loading value on the second filter (i.e., to add or subtract from the first filter loading 

value). Moreover, even if the individual compounds were identified on the back-up filter, the 

decision concerning adding or subtracting the back-up filter loading would not be 

straightforward. 

The positive quartz filter artifact has been discussed by Gundel et al. (1995) and Turpin 

et al. (2000). It is also possible that some SVOCs may desorb from the filter resulting in a 

negative artifact (Eatough et al., 1993, 1995; Tang et al., 1994; Gundel et al., 1995; Cui et al., 

1998; Pang et al., 2001; Finn et al., 2001). Semivolatile organic compounds can similarly be lost 

from Teflon filters because of volatilization, causing the PM2.5 mass to be significantly 

underestimated (negative artifact). Like particulate nitrates, the FRM for PM2.5 will suffer loss 

of SVOC similar to the losses experienced with other simple filter collection systems. Most 

2-72
studies that have focused on the positive and negative sampling artifacts associated with SVOCs 

have used either diffusion denuder technology (Eatough et al., 1995; Mader et al., 2001) or 

placed an adsorbent media, such as a back-up quartz filter or a polyurethane foam adsorbent, 

behind the main filter (Wallace and Hites, 1995). Further information on denuder techniques is 

given in Appendix 2A. 

Using their multichannel diffusion denuder sampling system (BOSS), Eatough et al. (1995) 

reported that, for samples collected at the South Coast Air Quality Management District 

sampling site at Azusa, CA, changes in the phase distribution of SVOCs could result in an 

average loss of 35% of the particulate organic material. Cui et al. (1998) found that losses of 

SVOCs from particles in the Los Angeles Basin during the summer were greater during the night 

(average = 62%) than during the day (average = 42%). 

The percent SVOC lost from the front filter in a filter-denuder system may be greater than 

that lost in a filter-only system such as the FRM. In a filter-denuder system, the gas-phase 

component of the SVOCs is removed. The absence of the gas-phase causes the gas-particle 

equilibrium to shift, so the SVOCs collected on the filter may evaporate more rapidly in a filterdenuder system than in a filter-only collection system. To determine the fraction of SVOCs lost 

from a Teflon filter in a filter-only system, it is necessary to compare the amount measured by a 

nondenuder system with that measured by a denuder system. (Similar considerations apply to 

the collection of ammonium nitrate. However, in the case of ammonium nitrate, the total 

particulate nitrate is easily obtainable from the sum of nitrate on the Teflon front filter and the 

back-up Nylon filter. In the case of SVOCs, the existence of the positive artifact [since the 

organic denuder is less efficient than the nitric acid denuder], makes it much more difficult to 

determine the total OC.) At present, little information is available on the volatilization losses of 

SVOCs. However, in one study (Pang et al., 2001), the total mass on denuded and undenuded 

filters were compared and found to be identical within error limits (R2

 = 0.816, slope = 0.961 ± 

0.027 for total mass compared to R2

 = 0.940, slope = 0.986 ± 0.020 for sulfate). Pang et al. 

(2001) interpreted this result as suggesting that the major cause of loss of SVOCs is the pressure 

drop across the filter. 

Positive artifacts may occur during sample collection because of the adsorption of gases 

onto the filter materials (e.g., Gundel et al., 1995). Using a quartz filter behind a Teflon filter, 

Kim et al. (2001a) estimated that on an annual average basis 30% of the PM2.5 organic carbon 

2-73
concentration resulted from positive artifacts. Quartz fiber filters have a larger positive artifact 

than Teflon filters because of a greater adsorption of organic vapor onto quartz fibers (Turpin 

et al., 1994; Chow et al., 1994a,b, 1996; Eatough et al., 1996; Finn et al., 2001; Kirchstetter 

et al., 2001). 

Kirchstetter et al. (2001) reported that adsorptive properties of quartz fiber filters vary with 

lot number; therefore, front and back-up filters should be taken from the same lot. The literature 

suggests that a Teflon filter followed by a quartz back-up filter provides a better estimate of the 

adsorption of gases on a quartz fiber front filter than does a quartz filter followed by a quartz 

backup, and that the difference between these two adsorption estimates can be substantial for 

short durations (Novakov et al., 1997; Kirchstetter et al., 2001; Turpin et al., 2000). The 

typically lower organic carbon loadings on concurrently collected quartz followed by quartz 

filters relative to Teflon followed by quartz filters are believed to occur because adsorption on 

the quartz front filter acts to reduce the gas-phase concentration downstream until adsorption 

equilibrium has been achieved in the vicinity of the front quartz filter surface. Because Teflon 

filters have little affinity for organic vapors, this equilibrium occurs almost instantaneously for 

Teflon filters; and the Teflon-quartz back-up filter is exposed to the ambient concentration of 

organic vapors from the beginning of the sampling period. It might be expected that the quantity 

of organic vapor adsorbed on quartz filters would depend on the organic composition and would 

vary by season and location. However, it is also possible that the quartz possesses a limited 

number of adsorption sites that are rapidly occupied, so that the quantity of OC on the back up 

filter would be relatively constant and depend on the pretreatment of the quartz. 

Combined Measurement of Semivolatile Nitrate and Organic Carbon and Nonvolatile 

(Organic Carbon, Elemental Carbon, Nitrate, and Sulfate) 

Fine particles in urban atmospheres contain substantial quantities of semivolatile material 

(e.g., NH4NO3 and SVOCs) that are lost from particles during collection on a filter. Several 

diffusion denuder samplers have been developed for the determination of both NO3 

!

 and organic 

semivolatile fine particulate components as well as nonvolatile nitrate, organic compounds, and 

nonvolatile sulfate (Pang et al., 2001; Eatough et al., 1993). The combination of technology 

used in the BOSS diffusion denuder sampler and the Harvard particle concentrator has resulted 

in the Particle Concentrator-Brigham Young University Organic Sampling System (PC-BOSS) 

for the 24-h integrated collection of PM2.5, including NH4NO3 and SVOC. Modifications of the 

2-74
BOSS sampler allow for the determination of these same species on a time scale from a few 

hours to weekly (Ding et al., 2002; Eatough et al., 1999a,b, 2001). Episodic studies have been 

conducted in Riverside, CA, and Bakersfield, CA, (Obeidi et al., 2002) and Provo, UT (Obeidi 

and Eatough, 2002). The average concentration of semivolatile and nonvolatile components in 

the three cities, during the study periods, are shown in Figure 2-21.

2.2.3.3 Continuous Measurement of Semivolatile and Nonvolatile Mass 

Techniques for the continuous measurement of PM mass are needed both to provide 

real-time information on pollution levels (Long et al., 2002) and to reduce the costs involved in 

visiting sites to change filters and in the equilibration and weighing of filters. Two techniques 

are currently in use. The TEOM is normally operated at 50 °C in order to remove particle-bound 

water. However, at 50 °C most semivolatile material is also evaporated. Therefore, the TEOM, 

operated at 50 °C, may be considered to measure the mass of nonvolatile PM. Since the filter in 

the beta gauge mass monitor is changed more frequently than the filter in the TEOM, the beta 

gauge is less sensitive to changes in mass caused by changes in relative humidity. However, 

most beta gauge monitors heat the inlet, but do not otherwise control the temperature at the filter. 

This heating causes evaporation of a substantial fraction of the particle-bound water and an 

unknown fraction of the semivolatile PM. Thus, the beta gauge may be considered to measure 

the nonvolatile PM plus a small fraction of the particle-bound water and an unknown fraction of 

the semivolatile PM. Three new techniques have been developed to address the issue of lost 

semivolatile PM mass: the real-time ambient mass sample (RAMS), the differential TEOM, and 

the continuous ambient mass monitor (CAMM). 

Real-Time Total Ambient Mass Sampler (RAMS) 

The RAMS, a monitor based on diffusion denuder and TEOM monitor technology, has 

been developed, validated, and field tested for the real-time determination of total fine PM mass, 

including semivolatile PM (Eatough et al., 1999a; Obeidi and Eatough, 2002; Obedi et al., 2002; 

Pang et al., 2001). The RAMS measures the total mass of collected particles including 

semivolatile species with a TEOM monitor using a “sandwich filter.” The sandwich contains a 

Teflon coated particle collection filter followed by a charcoal-impregnated filter (CIF) to collect 

any semivolatile species lost from the particles during sampling. Because the instrument 

2-75
Figure 2-21. Average concentration (μg/m3

) of nonvolatile and semivolatile PM 

components in three cities. 

Source: Obeidi and Eatough (2002); Obeidi et al. (2002). 

2-76 
measures total mass collected by the sandwich filter, all gas phase compounds that can be 

adsorbed by a CIF must be removed from the sampling stream prior to the TEOM monitor. 

Laboratory and field validation data indicate that the precision of fine PM mass determination is 

better than 10%. The RAMS uses a Nafion dryer to remove particle-bound water from the 

suspended particles and a particle concentrator to reduce the amount of gas phase organics that 

must be removed by the denuder. Examples of PM2.5 mass as measured by the RAMS, the 

TEOM, the PC-BOSS, and an FRM sampler are shown in Figure 2-22. This figure also shows 

that the TEOM recorded the PM2.5 mass as being negative for the hours of 16 to 19. This likely 

results from the loss of volatile materials from the heated filter. 

Figure 2-22. Comparison of mass measurements with collocated RAMS (real-time data), 

PC-BOSS (1-h data), FRM PM2.5 sampler (average of 24-h data), and a 

conventional TEOM monitor (real-time data). The semivolatile fine 

particulate matter is sampled with the RAMS and PC-BOSS, but not with 

the TEOM monitor or the FRM PM2.5 single filter sampler. The PC-BOSS 

provides information on both the nonvolatile component (NV) and the 

semivolatile organic component (SVOC). 

Source: Eatough et al. (1999). 

2-77 
Differential TEOM 

Patashnick et al. (2001) developed a differential TEOM system that is based on a pair of 

TEOM sensors, each of which is preceded by its own electrostatic precipitator (ESP) and 

downstream from a common size selective inlet. By alternately switching the ESPs on and off 

and out of phase with each other, the two sensors measure “effective mass” that includes both 

the nonvolatile component and the volatile component sampled by the TEOM, less the volatile 

component that vaporized during the sampling interval. On the sensor side with the ESP turned 

on, there is no particle collection on that filter so that only volatilization of previously collected 

particles continues. This allows a correction for the effective mass as measured by the first 

sensor by subtracting out the volatilization artifact and leaving the nonvolatile and volatile 

components of the PM. This system has yet to be well characterized for other biases or 

interferences such as reactions on the filters, particle collection efficiency of the ESPs, and 

particle and semivolatile material losses. 

Continuous Ambient Mass Monitor (CAMM) 

Koutrakis and colleagues (Koutrakis et al., 1996; Wang, 1997) developed CAMM, 

a technique for the continuous measurement of ambient particulate matter mass concentration 

based on the measurement of pressure drop increase with particle loading across a membrane 

filter. Recently, Sioutas et al. (1999) examined the increase in pressure drop with increasing 

particle loading on Nucleopore filters. They tested filters with two pore diameters (2 and 5 μm) 

and filter face velocities ranging from 4 to 52 cm s!1

 and examined the effects of relative 

humidity in the range of 10 to 50%. They found that, for hygroscopic ammonium sulfate 

particles, the change in pressure drop per unit time and concentration was a strong function of 

relative humidity, decreasing with increasing relative humidity. These results suggest that 

particulate concentration measurements made with the pressure drop method (Koutrakis et al., 

1996) may be subject to additional uncertainties if used in an environment where the ambient 

RH is quite variable and the RH where the particles are measured cannot be controlled 

accurately. The current version of the CAMM (Wang, 1997) uses a particle concentrator and a 

Nafion dryer and frequently moves the filter tape to avoid artifacts due to evaporation of 

semivolatile components from the active portion of the filter tape which would occur if the 

atmospheric concentration of the semivolatile components decreased. 

2-78
The CAMM was recently operated alongside a gravimetric PM method (the Harvard 

Impactor, or HI) in seven U.S. cities selected for their distinctly different ambient particulate 

compositions and densities. The correlation between the two methods was high, with an 

overall r2

 of 0.90 and average CAMM/HI ratio of 1.07 (Babich et al., 2000). 

2.2.4 U.S. Environmental Protection Agency Monitoring Methods 

PM

2.2.4.1 The Federal Reference Methods for Measurement of Equilibrated Mass for PM10, 

2.5, and PM10-2.5 

In 1997, the EPA promulgated new standards for PM2.5 to address fine-fraction thoracic 

particles and retained with minor revisions, the 1987 PM10 standards to continue to address 

coarse-fraction thoracic particles (Federal Register, 1997). In partial response to numerous 

challenges to these standards, the U.S. Court of Appeals for the District of Columbia Circuit in 

American Trucking Association v. EPA, 175 F. 3d 1027 (U.S. Court of Appeals, D.C. Cir. 1999) 

found “ample support” for regulating coarse-fraction particles. However, the court revoked the 

revised PM10 standards (leaving in effect the 1987 PM10 standards) on the basis of PM10 being a 

“poorly matched indicator for coarse particulate pollution” because PM10 includes fine particles. 

Consistent with this specific aspect of the Court’s ruling, which the EPA did not appeal, the EPA 

is now considering use of PM10-2.5 as the indicator for coarse-fraction thoracic particles, in 

conjunction with PM2.5 standards that address fine-fraction thoracic particles. Thus, the EPA is 

now developing a Federal Reference Method for the measurement of PM10-2.5. 

2.2.4.1.1 PM10 

The FRM for measuring PM10 is specified in the Code of Federal Regulations (2001a,b). 

The PM10 FRM defines performance specifications for samplers in which particles are inertially 

separated with a penetration efficiency of 50% at an aerodynamic diameter (Da) of 10 ± 0.5 μm. 

The collection efficiency increases to .100% for smaller particles and drops to .0% for larger 

particles. Particles are collected on filters and mass concentrations are determined 

gravimetrically. Instrument manufacturers are required to demonstrate through field tests a 

measurement precision for 24-h samples of ± 5 μg/m3

 for PM10 concentrations below 80 μg/m3 

and 7% above this value. A number of samplers have been designated as PM10 reference 

samplers. The TEOM and several beta gauge samplers with 1-h time resolution have been 

designated as automated equivalent methods (U.S. Environmental Protection Agency, 2001). 

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2.2.4.1.2 PM2.5 

In contrast to the performance-based FRM standard for PM10, the FRM for PM2.5 (Code of 

Federal Regulations, 2001a) specifies certain details of the sampler design, as well as of sample 

handling and analysis, whereas other aspects have performance specifications (Noble et al., 

2001). The PM2.5 FRM sampler consists of a PM10 inlet/impactor, a PM2.5 impactor with an oilsoaked impaction substrate to remove particles larger than 2.5 μm Da, and a 47-mm PTFE filter 

with a particle collection efficiency greater than 99.7%. The sample duration is 24 h, during 

which time the sample temperature is not to exceed ambient temperatures by more than 5 °C. 

A schematic diagram of the PM2.5 FRM sample collection system is shown in Figure 2-23. After 

collection, samples are equilibrated for 24 h at temperatures in the range of 20 to 23 °C (± 2 °C) 

and at relative humidities in the range of 30 to 40% (± 5%). The equilibration tends to reduce 

particle-bound water and stabilizes the filter plus sample weight. Filters are weighed before and 

after sampling under the same temperature and relative humidity conditions. For sampling 

conducted at ambient relative humidity < 30%, mass measurements at relative humidities down 

to 20% are permissible (Code of Federal Regulations, 2001a). 

The PM10 inlet specified for the PM2.5 FRM is modified from a previous low flow 

rate PM10 inlet that was acceptable in both EPA-designated reference and equivalent PM10 

methods. The modification corrects a flaw that was reported for the previous sampler in that 

under some meteorological conditions the inlet may allow precipitation to penetrate the inlet. 

The modification includes a larger drain hole, a one-piece top plate, and louvers. Tolocka et al. 

(2001a) evaluated the performance of this modified inlet in a series of wind tunnel experiments. 

The modified inlet was found to provide a size cut comparable to the original inlet, for 

both PM2.5 and PM10 sampling. Because the modification did not change the characteristics of 

the size cut, the modified inlet may be substituted for the original inlet as part of a reference or 

equivalent method for PM10 and PM2.5 (Tolocka et al., 2001a). 

WINS Impactor. Design and calibration of the EPA PM2.5 Well Impactor Ninety-Six 

(WINS) is given by Peters et al. (2001a). The WINS impactor was designed to be deployed 

downstream of the Graseby-Andersen 246B PM10 inlet as part of a sampler operating at a 

flow rate of 16.7 L/m. A schematic diagram of the WINS is shown in Figure 2-24. The PM2.5 

inlet consists of a single jet directed toward a round hole with a jet exit impaction surface made 

2-80
Figure 2-23. Schematic diagram of the sample collection portion of the PM2.5

FRM sampler.

Source: Noble et al. (2001). 

up of a 37-mm diameter glass fiber filter immersed in 1 ml of low volatility diffusion pump oil 

(i.e., the well). Particles not having enough inertia to be removed by the impactor are captured 

downstream on the sample collection filter. This design was selected to minimize impactor 

overloading that would otherwise result in particle bounce. The oil wicks through the particulate 

deposit on the impactor to provide a continuously wetted surface for impaction. The penetration 

curve indicated a 50% cutpoint of 2.48 μm Da with a geometric standard deviation of 1.18 

(Figure 2-25). 

The WINS separator was evaluated for its loading characteristics (Vanderpool et al., 2001) 

by monitoring the performance after repeated operation in an artificially generated, high 

concentration, coarse-mode aerosol composed of Arizona Test Dust, as well as in the field in 

Rubidoux, Phoenix, Philadelphia, Research Triangle Park, and Atlanta. In the wind tunnel 

experiments, the WINS performance was found to be a monotonic function of loading. 

2-81
Figure 2-24. Schematic view of the final design of the WINS. 

Source: Peters et al. (2001a). 

A !5% bias in the PM2.5 measurement resulted from a coarse particulate loading of 

approximately 16 mg. This negative bias was due to a slight reduction in the separator cut point. 

It was also found that the predictable results from the controlled laboratory experiments could 

not be extrapolated to field settings and that the WINS performance was more sensitive to the 

impactor loading in the field than it was in experiments with the single component aerosol. 

Significant particle bounce was not observed in either the laboratory or the field experiments. 

Vanderpool et al. (2001) concluded that their study supports the recommendation that the FRM 

WINS wells should be replaced after every 5 days of 24-h operation (U.S. Environmental 

Protection Agency, 1998). 

A detailed sensitivity study of the WINS impactor was conducted (Vanderpool et al., 2001) 

in which the effects on the impactor performance of a number of parameters were examined. 

The results of this study are summarized in Table 2-4. 

2-82
Figure 2-25. Evaluation of the final version of the WINS. 

Source: Peters et al. (2001a). 

The regulations also allow for Class I, II, and III equivalent methods for PM2.5 (Code of 

Federal Regulations, 2001c). Class I equivalent methods use samplers with relatively small 

deviations from the sampler described in the FRM. Class II equivalent methods include “all 

other PM2.5 methods that are based upon 24-h integrated filter samplers that are subjected to 

subsequent moisture equilibration and gravimetric mass analysis.” Class III equivalent methods 

include non-filter-based methods such as beta attenuation, harmonic oscillating elements, or 

nephelometry (McMurry, 2000). As of July 2001, 11 PM2.5 samplers (listed in Table 2-5) had 

been tested, leading to the conclusion that the PM10 sampling systems can be designed to 

produce concentration measurements that are precise to ± 10%. Cut point tolerances are not 

expected to affect the mass concentration for PM2.5 as much as for PM10, because the 2.5 μm Da 

cut point generally occurs near the minimum in a mass distribution (e.g., Figure 2-6). 

2-83
TABLE 2-4. SUMMARY OF SENSITIVITY STUDIES OF

WINS IMPACTOR PERFORMANCE

PM2.5 mass 

Parameter Amount of variance Cutpoint variation concentration bias 

Manufacturing tolerances Specified tolerances 0.05 micrometers < 1% 

on WINS components 

Flow control biases 4% 0.05 micrometers Cutpoint shift partially offset 

volume bias 

T and P measurement Allowable ambient ± 0.02 micrometers ± 0.4% 

Diffusion oil volume 0.75 mL to 3 mL No effect 

Impactor loading After 5 24 h events !0.07 micrometers < 1.5% 

Ambient P variations Negligible Negligible 

Air Properties 0 °C 2.40 micrometers NA 

Impactor oil crystallization No effect No effect 

Impactor oil viscosity !20 °C No effect No effect 

!35 °C Need to change WINS more frequently than every

 5 days 

Source: Vanderpool et al. (2001). 

On the other hand, the PM2.5 mass concentration will be affected by other sampling issues 

mentioned, but not discussed extensively, in the previous 1996 PM AQCD (U.S. Environmental 

Protection Agency, 1996a). These issues (described earlier in this chapter) include gas/particle, 

particle/particle, and particle/substrate interactions for sulfates and nitrates (e.g., Appel et al., 

1984), volatilization losses of nitrates (Zhang and McMurry, 1992), SVOC artifacts (e.g., 

Eatough et al., 1993), and relative humidity effects (e.g., Keeler et al., 1988). 

Several studies have been reported in which the FRM was collocated with other PM2.5 

samplers in intercomparison studies. During the Aerosol Research and Inhalation Epidemiology 

Study (ARIES), several PM2.5 samplers were collocated at a mixed industrial-residential site near 

Atlanta, GA (Van Loy et al., 2000). These samplers included a standard PM2.5 FRM, a TEOM 

with Nafion drier, a particulate composition monitor (PCM; Atmospheric Research and 

Analysis, Cary, NC), a medium-volume (113 L/min flow rate) fine particle (PM2.5) and SVOC 

sampler (i.e., a filter followed by a solid adsorbent) operated by the Desert Research Institute, 

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PM

TABLE 2-5. PM2.5 SAMPLERS CURRENTLY DESIGNATED AS FRMs FOR 

2.5 MASS CONCENTRATIONS 

Sampler Manufacturer Description Federal Register Reference 

RAAS2.5-100 Andersen Instruments FRM single Vol. 63, p. 31991, 6/11/98 

RAAS2.5-300 Andersen Instruments FRM sequential Vol. 63, p. 31991, 6/11/98 

RAAS2.5-200 Andersen Instruments FRM audit Vol. 64, p. 12167, 3/11/99 

Partisol 2000 Rupprecht & Patashnick FRM single Vol. 63, p. 18911, 4/16/98 

Partisol-Plus 2025 Rupprecht & Patashnick FRM sequential Vol. 63, p. 18911, 4/16/98 

Partisol 2000 audit Rupprecht & Patashnick FRM audit Vol. 64, p. 19153, 4/19/99 

PQ 200 BGI, Inc. FRM single Vol. 63, p. 18911, 4/16/98 

PQ 200A BGI, Inc. FRM audit Vol. 63, p. 18911, 4/16/98 

605 CAPS ThermoEnvironmental Instruments FRM single Vol. 63, p. 58036, 10/29/98 

MASS 100 URC FRM single Vol. 65, p. 26603, 05/08/00 

MASS 300 URC FRM sequential Vol. 65, p. 26603, 05/08/00 

Source: Peters et al. (2001b); U.S. Environmental Protection Agency (2001). 

a HEADS sampler, and a dichotomous sampler for coarse PM. The PCM sampler has three 

channels, all of which have PM10 cyclone inlets. The first two channels have two denuders 

preceding a 2.5-μm WINS impactor and filter packs. The first denuder is coated with Na2CO3 

to remove acid gases, and the second is coated with citric acid to remove ammonia. The 

third channel has a carbon-coated, parallel-plate denuder preceding the WINS impactor. 

Measurements of 24-h mass from the FRM, PCM, and TEOM samplers, as well as 

reconstructed PM2.5 mass (RPM), were compared for a 12-month period. The slopes for the 

TEOM-FRM, PCM-FRM, and RPM-FRM correlations were 1.01, 0.94, and 0.91, respectively; 

whereas the y-intercepts for each were 0.68, 0.04, and 0.98. Particulate sulfate measurements 

on the FRM Teflon filter, the PCM Teflon filter, and PCM Nylon filter were nearly identical. 

Nitrate results from the three filters were much less consistent, with the FRM collecting 

substantially less nitrate than was collected on either the denuded nylon filter or denuder 

followed by a Teflon-nylon filter sandwich. Particulate ammonia measurements were also 

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compared and showed more scatter than the sulfate measurements but less than the nitrate 

measurements. 

An intercomparison of both PM10 and PM2.5 mass measurements was conducted during the 

1998 Baltimore PM Study (Williams et al., 2000). PM monitors were collocated at a residential 

indoor, residential outdoor, and ambient monitoring site within Baltimore County, MD. PM 

samplers included TEOMs, PM2.5 FRMs, cyclone-based inlets manufactured by University 

Research Glassware (URG), and Versatile Air Pollution Samplers (VAPSs). The VAPS is a 

dichotomous sampler operating at 33 L/min (one coarse particle channel at 3 L/min, and two fine 

particle channels at 15 L/min, each). In the configuration employed during this study, one fine 

particle channel was operated with a Teflon filter backed by a nylon filter and preceded by 

a Na2CO3-coated annular denuder; the second fine particle channel had a quartz filter preceded 

by a citric acid-coated annular denuder; and the coarse particle channel had a polycarbonate 

filter followed by a Zefluor filter for flow distribution. Differences in PM2.5 mass concentrations 

between the samplers, although not large, were attributed to potential particle nitrate losses, 

denuder losses, and losses of SVOCs for some samplers. Differences between coarse PM 

concentrations, on the other hand, varied widely between the instruments. 

In another intercomparison study, Tolocka et al. (2001b) examined the magnitude of 

potential sampling artifacts associated with the use of the FRM by collocating FRMs alongside 

other chemical speciation samplers at four U.S. cities. The locations included a high-nitrate, 

high-carbon, low-sulfate site (Rubidoux, CA); a high-crustal, moderate-carbon, moderate-nitrate 

site (Phoenix); a high-sulfate, moderate-carbon, low-nitrate site (Philadelphia); and a low-PM2.5 

mass site (Research Triangle Park, NC). The use of Teflon versus heat-treated quartz filters was 

also examined in this study. The Teflon filters collected less nitrate than the heat-treated quartz 

filters. Filters in samplers using denuders to remove organic gases collected less organic PM 

than filters in samplers without denuders. 

Peters et al. (2001b) compiled the results of several field studies in which a number of 

FRM and other PM2.5 samplers were intercompared. In addition to the FRM samplers listed in 

Table 2-5, other PM2.5 samplers included in the evaluation were the Sierra Instruments 

dichotomous sampler, the Harvard Impactor, the IMPROVE sampler, and the Air Metrics 

saturation monitor. Results were compiled from PM2.5 field studies conducted in Birmingham, 

Denver, Bakersfield, Phoenix, Research Triangle Park, Atlanta, and Rubidoux. Limited studies 

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on precision for the non-FRM samplers suggest that the Harvard Impactor and dichotomous 

samplers had the lowest coefficients of variation (CVs), with both under 10%. The CV for this 

study was calculated by dividing the sample standard deviation by the average concentration. 

The IMPROVE samplers had CVs between 10 and 12%, and the Air Metrics samplers had the 

highest observed CVs, over 15%. In intercomparisons with FRM samplers, the non-FRM 

samplers showed strong linear relationships with the FRM sampler; however, none of the 

comparisons passed the current EPA Subpart C equivalent method criteria, which EPA is in the 

process of revising. 

Detailed information on precision of PM samplers used in monitoring networks may be 

found at EPA’s Technology Transfer Network website (U.S. Environmental Protection Agency, 

2002a). 

2.2.4.1.3 PM10-2.5 

Measurement techniques for PM10-2.5 are somewhat more complex than those for PM2.5 

or PM10, because it is necessary to isolate a size fraction between an upper 50% cut point of 

10 μm Da and a lower 50% cut point of 2.5 μm Da for PM10-2.5. EPA is currently developing an 

FRM for PM10-2.5. Several candidate techniques are discussed below. 

The Difference Method. One approach to measuring PM10-2.5 is to make separate 

measurements of PM10 and PM2.5 and take the difference of the resulting equilibrated masses. 

One problem is that, if either the PM2.5 or the PM10 sampler fails, no PM10-2.5 measurement can be 

obtained. In addition, errors in cut point, flow rate, and filter weights (both before use and after 

collection and equilibration of particles) and uncertainties due to the loss of semivolatile 

components of PM may occur for each size cut. Careful control of flow rate and equivalent 

treatment of PM10 and PM2.5 filters in terms of pressure drop across the filter and temperature of 

the filter during and after collection can improve precision and accuracy. Allen et al. (1999a) 

summarized several sampling issues that should be considered in measuring coarse particulate 

mass by difference, including the use of identical instrumentation (except cut points), filter 

media, filter face velocity, and ambient-filter temperature differences; common flow 

measurement devices; use of higher sampler flow rates (a 10 L/min minimum for 24-h sample is 

recommended); and avoiding excessive filter loading. The concern, expressed by Allen et al. 

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(1999a), that the “pie-plate” inlet required by the final version of the PM2.5 FRM might have a 

different cut point than the “flat-top” inlet of the PM10 FRM, has been addressed by a wind 

tunnel study, which found each to have an appropriate PM10 cut point (Tolocka et al., 2001a). 

Since the difference method requires weighing two filters, the key to obtaining high 

precision in the coarse mass measurement is precise measurements of filter weights. Allen et al. 

(1999a) discussed techniques for increasing the precision of the difference method by reducing 

errors in filter weights. These include proper temperature and humidity controls, use of a high 

quality microbalance, 100% replicate weighings, control of static charge, aging of new filters, 

weighing of a sufficient number of laboratory blank filters, and accounting for buoyancy errors 

caused by variability in barometric pressure. Allen et al. (1999a) emphasized the necessity of 

replicate weighing of filters and of using a third weighing if the difference between the first two 

weights exceeds a specified minimum. Lawless et al. (2001) investigated the magnitude of 

uncertainties attributed to fluctuations in some of these parameters (humidity, temperature, 

drafts, vibration, and electrostatic charges) and recommended methods for improving their 

control. Koistinen et al. (1999) and Hänninen et al. (2002) gave an excellent discussion of the 

procedures developed to overcome problems associated with gravimetric measurements of PM2.5 

mass in the EXPOLIS (Air Pollution Exposure Distributions Within Adult Urban Populations in 

Europe) Study. They discussed factors such as corrections for buoyancy, elimination of static 

charge, and increases in the mass of blank filters with time. The establishment of a temperature 

and humidity controlled room required for the equilibration and weighing of filters for the FRM 

is expensive. Allen et al. (2001) described a more cost-effective technique that uses a chamber 

with relative humidity controlled at 34% relative humidity by a saturated aqueous solution 

of MgCl2. 

Allen et al. (1999a) recommended that, in reporting precision from collocated samplers 

both the CV and the square of the correlation coefficient (r2

) be reported. For a study in Boston 

with 27 pairs of mass data from collocated PM10 and PM2.5 using standard weighing methods, 

they obtained a CV of 4.7% with an r2

 of 0.991 for PM2.5, a CV of 4.4% with an r2 of 0.994 

for PM10, and a CV of 15% with an r2

 of 0.88 for PM10-2.5. By using duplicate weighings and 

other techniques suggested for improving precision, they obtained a CV of 1.3% with an r2

 of 

0.998 for PM2.5 in a study in Chicago with 38 collocated measurements. On the basis of the 

improvement in the CV for PM2.5, they estimated that use of the recommended techniques 

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for PM10-2.5 by difference would have yielded a CV of 3.8% with an r2

 of 0.98 if they had been 

applied in the Chicago study. 

Multistage Impaction. A second technique involves the use of impaction to isolate the 

size fraction between 2.5 and 10 μm Da. In the impaction process, the air stream is first 

accelerated through a small hole (nozzle) or slit. The air stream is directed so that it “impacts” 

on a surface. Depending on the velocity and pressure of the air stream, particles smaller than a 

certain size will follow the air stream around the impactor surface. Larger particles will impact 

on the surface. In practice, impactors have 50% cut points similar to the 50% cut point for the 

rejection of larger particles in PM2.5 and PM10 samples (see Figure 2-7). 

Multistage impactors are used to separate particles into several size fractions for the 

determination of mass and chemical composition as a function of size (Wang and John, 1988; 

Marple et al., 1991). The major problem with the use of impactors to separate the PM10-2.5 μm Da 

fraction of coarse particles (thoracic coarse PM) is bounce. Coarse particles tend to be dry, solid 

particles. When they hit a hard surface, they can bounce and be carried away with the air stream 

(e.g., Dzubay et al., 1976; Wesolowski et al., 1977; Rao and Whitby, 1978; Cheng and Yeh, 

1979; Wang and John, 1987; John and Sethi, 1993). Various techniques have been used to 

reduce bounce. One technique is to use a porous substance such as a glass- or quartz-fiber filter 

(Chang et al., 1999) material or a polyurethane foam (Breum, 2000; Kavouras and Koutrakis, 

2001). However, this technique may result in less precise separation and yield a sample that 

must be extracted before chemical analyses can be performed. Another technique is to coat the 

impactor with a soft wax or grease (Rao and Whitby, 1977; Turner and Hering, 1987; Pak et al., 

1992). This can cause problems with weighing and chemical analyses, and as the deposit of 

particles builds up, incoming particles may not hit the soft surface, but instead hit a previously 

collected hard particle and bounce off of it. The WINS impactor discussed earlier uses a filter in 

a well of low volatility oil to ensure a wetted surface at all times. However, such a technique, 

while appropriate for removing unwanted particles, would not yield a particle sample suitable for 

weighing or for chemical analyses. 

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Virtual Impaction. The problems of bounce and blow off of particles from impactors, 

especially for the collection of large quantities of particles, was addressed by aerosol scientists in 

the mid-1960s by the development of what is now known as “virtual” impaction (Hounam and 

Sherwood, 1965; Conner, 1966). 

In a virtual impactor, a hole is placed in the impaction plate just below the accelerating jet. 

Two controlled flows allow a fraction, e.g., 10% (or another predetermined fraction, typically 5 

to 20%), of the air containing the coarse particles to go through the hole and through a filter 

(minor flow). A 10% minor flow gives a coarse channel enrichment factor of 10. The remaining 

fraction (e.g., 90% of the airflow) containing the fine particles follows a different path and goes 

through a second filter (major flow). The upper cut point is usually set by the inlet (e.g., 

10 μm Da). The flow rates, pressures, and distance from the nozzle to the virtual impactor 

surface can be varied to direct particles with an Da greater than the lower cut point (i.e., 

> 2.5 μm) to go through the hole and be collected on the first filter and to direct smaller particles 

(i.e., < 2.5 μm) to flow around the impactor and be collected on the second filter. Large particles 

“impact” into the hole with a small amount of the air flow. The smaller particles follow the 

major air flow around the impactor plate. This technique overcomes the problem of bounce. 

An example of the separation into fine and coarse particles is shown in Figure 2-26.

The usefulness of this technique for collecting samples of fine and coarse particles for 

chemical analysis was recognized by the EPA in the mid-1970s and led to the development of 

the now well-known “dichotomous sampler” (a virtual impactor that separates particles into two 

size fractions) and an associated XRF analyzer (Dzubay and Stevens, 1975; Loo et al., 1976; 

Jaklevic et al., 1977; Dzubay et al., 1977). The dichotomous sampler was originally developed 

for use in the Regional Air Monitoring Study (RAMS), part of the Regional Air Pollution Study 

(RAPS), conducted in St. Louis, MO in the mid-1970s. Dichotomous samplers were operated at 

10 RAMS sites from March 1975 to March 1977; and 33,695 filters were collected and analyzed 

by XRF with an overall sampling efficiency of 97.25% (Strothmann and Schiermeier, 1979; Loo 

et al., 1976; Loo et al., 1978; Dzubay, 1980; Lewis and Macias, 1980). Dichotomous samplers 

were a novel concept at that time. Concern over particle losses and other problems at cut point 

sizes < 2.5 μm Da influenced the decision to choose 2.5 instead of 1.0 as the cut point diameter. 

Since the use of the dichotomous sampler in RAPS, considerable progress has been made 

in the theory and practice of designing virtual impactors, especially in how to reduce losses and 

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Figure 2-26. Schematic diagram showing the principle of virtual impaction. The initial 

flow, Q0, is split into a minor flow, Q1, which carries the larger particles that 

impact into the hole, to the coarse particle filter and a major flow, Q2, which 

carries the smaller particles that can follow the airflow, to the fine particle 

filter. 

Source: Loo et al. (1976). 

provide a sharp cut (Masuda et al., 1979; Marple and Chien, 1980; Chen at al., 1985, 1986; Loo 

and Cork, 1988). Now virtual impactors, with rectangular slits or round holes, are used to 

(a) provide cut point sizes as low as 0.15 μm Da and (b) concentrate coarse, accumulation, and

ultrafine mode particles for use in health studies (Solomon et al., 1983; Marple et al., 1990;

Sioutas et al., 1994a,b,c). Dichotomous samplers were also used in a national network to

measure PM2.5 and PM10-2.5 in the Harvard Six City Study (Spengler and Thurston, 1983;

Dockery et al., 1993) and the Inhalable Particulate Network (Suggs and Burton, 1983). 

A trichotomous high volume sampler has also been developed that provides samples of particles

< 1.0 μm, 1.0 μm to 2.5 μm, and > 2.5 μm (Marple and Olsen, 1995). This sampler was

intended to aid in the study of the composition of the intermodal mass in the range of 1.0 to

2.5 μm Da. 

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More recently, two dichotomous sequential PM air samplers were collocated with a manual 

FRM air sampler and operated for over a year at a waterfront site on Tampa Bay (Poor et al., 

2002). The FRM sampler was alternately configured as a PM2.5, then as a PM10 sampler. For the 

dichotomous sampler measurements, daily 24-h integrated PM2.5 and PM10-2.5 ambient air 

samples were collected at a total flow rate of 16.7 L/min. As was the case for earlier versions of 

the dichotomous sampler, a virtual impactor split the air into flow rates of 1.67 and 15.01 L/min 

and collected PM10-2.5 and PM2.5 on 47-mm diameter Teflon filters. Between the two 

dichotomous air samplers, the average concentration, relative bias, and relative precision 

for PM2.5 were 13.3 μg/m3

, 0.02% and 5.2% (n = 282), and for PM10-2.5 were 12.3 μg/m3, 

3.9% and 7.7% (n = 282). Federal Reference Method measurements were alternate-day 24-h 

integrated PM2.5 or PM10 ambient air samples collected onto 47-mm diameter PTFE filters at a 

flow rate of 16.71 L/min. Between a dichotomous and a PM2.5 FRM air sampler, the average 

concentration, relative bias, and relative precision were 12.4 μg/m3

, !5.6%, and 8.2% (n = 43). 

Between a dichotomous and a PM10 FRM air sampler, the average concentration, relative bias 

and relative precision were 25.7 μg/m3

, !4.0%, and 5.8% (n = 102). The PM2.5 concentration 

measurement standard errors for the two dichotomous and one FRM samplers were 0.95, 0.79, 

and 1.02 μg/m3

; and for the PM10 sampler, the standard errors were 1.06, 1.59, and 1.70 μg/m3. 

The authors (Poor et al., 2002) concluded that their results indicated that “the dichotomous 

samplers have superior technical merit” and demonstrate “the potential for the dichotomous 

sequential air sampler to replace the combination of the PM2.5 and PM10 FRM air samplers, 

offering the capability of making simultaneous, self-consistent determinations of these 

particulate matter fractions in a routine ambient monitoring mode.” 

The dichotomous sampler provides two separate samples. However, a fraction of the 

smaller particles, equal to the minor flow, will go through the virtual impaction opening with the 

air stream and be collected on the coarse particle filter. In the dichotomous sampler used in the 

RAPS program, 10% of the fine particles were collected with the coarse particles. Thus, in order 

to determine the mass or composition of the coarse particles, it is necessary to determine the 

mass and composition of the fine particles and subtract the appropriate fraction from the mass or 

composition of the particles collected on the coarse particle filter. Allen et al. (1999b) discussed 

potential errors in the dichotomous sampler caused by uncertainties in the coarse mass channel 

enrichment factor. Virtual impactors have also been designed with a clean air jet in the center of 

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the round nozzle. This reduces the contamination of coarse particles by fine particles, while 

maintaining low losses and sharp cuts (Chen and Yeh, 1987; Chein and Lundgren, 1993). The 

fine particle intrusion into the coarse particle sample can also be reduced by operating two 

virtual impactors in series (Dzubay and Stevens, 1975). 

Aerosol physicists have also conducted theoretical and experimental investigations of 

virtual impaction using slits instead of round holes (Forney et al., 1978, 1982; Ravenhall et al., 

1978; Masuda and Nakasita, 1988; Sioutas et al., 1994b,c,d; Ding and Koutrakis, 2000). The slit 

virtual impactor permits a much higher flow rate than round hole virtual impactors and resolves 

problems that occur with multihole virtual impactors (Marple et al., 1990; Fang et al., 1991). 

The slit technique has been used to develop virtual impaction systems for concentrating particles 

in the size range 0.1 to 2.5 μm Da for exposure studies using animals and humans (Sioutas et al., 

1995a, b). The slit impactor can also be used to concentrate coarse particles for measurement 

(Misra et al., 2001) or exposure studies (Chang et al., 2002). In addition, ultrafine particles 

(> 0.1 μm) can be concentrated by first separating ultrafine particles from larger particles, adding 

water vapor to saturate the air containing the ultrafine particles, cooling the air to cause 

supersaturation and growth of the ultrafine particles into the 1.0 to 4.0 μm size range, then 

concentrating these particles with a slit virtual impactor, and finally, heating the air to return the 

particles to their original size (Sioutas and Koutrakis, 1996; Sioutas et al., 1999; Sioutas et al., 

2000; Kim et al., 2001b,c; Geller et al., 2002). 

2.2.5 Speciation Monitoring 

Speciation Network and Monitoring 

In addition to FRM sampling to determine compliance with PM standards, the EPA 

requires states to conduct chemical speciation sampling primarily to determine source categories 

and trends (Code of Federal Regulations, 2001b). Source category apportionment calculations 

are discussed in Chapter 3. A PM2.5 chemical Speciation Trends Network (STN) has been 

deployed that consists of 54 core National Ambient Monitoring Stations (NAMS) and 

approximately 250 State and Local Air Monitoring Stations (SLAMS). In addition, over 

100 IMPROVE (Interagency Monitoring of Protected Visual Environments) samplers located at 

regional background and transport sites can be used to fulfill SLAMS requirements. The overall 

goal of the speciation program is “to provide ambient data that support the Nation’s air quality 

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program objectives” (U.S. Environmental Protection Agency, 1999). Information and reports on 

the EPA’s speciation monitoring program may be found on the EPA’s Technology Transfer 

Network at http://www.epa.gov/ttn/amtic/pmspec.html. The NAMS speciation sites will provide 

routine chemical speciation data to be used in developing annual and seasonal aerosol 

characterization, air quality trends analysis, and emission control strategies. The SLAMS 

speciation sites will further support the NAMS network and provide information for 

development of state implementation plans (SIPs). 

At both NAMS and SLAMS sites, aerosol samples will be collected for the analysis of 

trace elements, ions (sulfate, nitrate, ammonium, sodium, and potassium), and total carbon (TC). 

The NAMS speciation sites will operate on a 1-in-3-day schedule, with 10 of these sites 

augmented with continuous speciation analyses for everyday operation. The SLAMS speciation 

sites will generally operate on a 1-in-6-day basis; however, many sites may be operated on a 1-

in-3-day basis in locations where increased data collection is needed. There are several samplers 

that are suitable for use in the NAMS/SLAMS network. These samplers include an inlet cut 

point with size cut characteristics comparable to the WINS FRM, proven denuder technology for 

nitrate, and sampler face velocity and sample volume similar to that of the FRM. The current 

samplers include three filters: (1) Teflon for equilibrated mass and elemental analysis by energy 

dispersive X-ray fluorescence (EDXRF), (2) a nitric acid denuded nylon filter for ion analysis 

(ion chromatography), (3) a quartz-fiber filter for elemental and organic carbon. Elemental 

carbon and OC are determined by thermal-optical analysis via a modification of the NIOSH 

(National Institute for Occupational Safety and Health) method 5040 (thermal-optical 

transmission [TOT]). However, no corrections are made for positive artifacts caused by 

adsorption on organic gases or the quartz filters or negative artifacts due to the evaporation of 

SVOCs from collected particles. 

Since 1987, the IMPROVE network has provided measurements of ambient PM and 

associated light extinction in order to quantify PM chemical components that affect visibility at 

Federal Class 1 areas that include designated national parks, national monuments, and 

wilderness areas. Management of this network is a cooperative effort between the EPA, 

federal land management agencies, and state governments. The IMPROVE program has 

established analytical protocols for measurements of ambient concentrations of PM10, PM2.5, 

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sulfates, nitrates, organic and elemental carbon, crustal material, and a number of other elements. 

Information on the IMPROVE program may be found at http://vista.cira.colostate.edu/ improve. 

IMPROVE aerosol monitoring consists of a combination of particle sampling and sample 

analysis. The IMPROVE sampler, which collects two 24-h samples per week, simultaneously 

collects one sample of PM10 on a Teflon filter, and three samples of PM2.5 on Teflon, nylon, and 

quartz filters. PM10 mass concentrations are determined gravimetrically from the PM10 filter 

sample, while PM2.5 mass concentrations are determined gravimetrically from the PM2.5 Teflon 

filter sample. The PM2.5 Teflon filter sample is also used to determine concentrations of selected 

elements using PIXE, XRF, and PESA. The PM2.5 nylon filter sample, which is preceded by a 

denuder to remove acidic gases, is analyzed to determine nitrate and sulfate aerosol 

concentrations using IC. Finally, the PM2.5 quartz filter sample is analyzed for OC and EC using 

the thermal-optical reflectance (TOR) method. Corrections are made for positive artifacts but 

not for negative artifacts. 

The STN and the IMPROVE networks represent a major advance in the measurement of 

nitrate, because the combination of a denuder to remove nitric acid vapor and a Nylon filter to 

adsorb nitric acid vapor volatilizing from the collected ammonium nitrate particles overcomes 

the loss of nitrate from Teflon filters. However, different techniques used for measurement of 

OC and EC lead to significant differences between their measurements when the two techniques 

are intercompared (Chow et al., 2001). IMPROVE yields higher EC and lower OC, although 

there is good agreement for TC. Another difference arises from the treatment of the positive 

artifacts due to the absorption of organic gases by the quartz filters used in IMPROVE and STN 

samplers. More information on these differences is given in Section 2.2.7 and details are 

discussed in Appendix 2B. 

Several of the PM2.5 size selectors developed for use in the EPA National PM2.5 STN were 

recently evaluated by comparing their penetration curves under clean room experiments with 

that of the WINS impactor (Peters et al., 2001c). The corresponding speciation monitors were 

then compared to the FRM in four cities. The PM2.5 inlets tested were the SCC 2.141 cyclone 

(6.7 L/min) that is in the Met One Instruments SASS sampler, the SCC 1.829 cyclone 

(5.0 L/min) that is proposed for use in the Rupprecht and Patashnik real-time sulfate/nitrate 

monitor, the AN 3.68 cyclone (24.0 L/min) that is in the Andersen RAAS, and the spiral 

separator (7.0 L/min) that was previously in the Met One SASS. The cutpoints of the SCC 

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cyclones compared reasonably well with the WINS (2.52 and 2.44 μm for the SCC 2.141 and 

SCC 1.829, respectively, at their design flow rates), but both demonstrated a tail extending into 

the coarse-particle mode. The AN inlet had the sharpest cut point curve, but the 50% cut point 

diameter was 2.7 μm Da at its design flow rate. The spiral inlet had the shallowest cut point 

curve: the 50% cut point was 2.69 and 2.67 μm Da for an ungreased and greased inlet, 

respectively. The speciation samplers were also compared to the FRM sampler with WINS inlet 

under ambient conditions in four cities. The Andersen RAAS equipped with the AN 3.68 

cyclone compared well to the FRM in all four cities when compared on the basis of PM2.5 mass, 

sulfate, and crustal concentrations. Greasing the spiral inlet in the Met One sampler improved 

the performance of that sampler, which tended to give much higher PM2.5 concentrations than the 

FRM in cities with high crustal PM. 

2.2.6 Inorganic Elemental Analyses 

In addition to the lighter elements (hydrogen, carbon, oxygen and nitrogen), the following 

40 heavier elements are commonly found in ambient air samples: sodium, magnesium, 

aluminum, silicon, phosphorus, sulfur, chlorine, potassium, calcium, titanium, vanadium, 

chromium, manganese, iron, cobalt, nickel, copper, zinc, gallium, arsenic, selenium, bromine, 

rubidium, strontium, yttrium, zirconium, molybdenum, palladium, silver, cadmium, indium, tin, 

antimony, barium, lanthanum, gold, mercury, thallium, lead, and uranium. These heavier 

elements are often indicators of air pollution sources, and several of them are considered to be 

toxic (such as transition metals, water-soluble metals, and metals in certain valence states [e.g., 

Fe(II), Fe(III), Cr(III), Cr(VI), As(III), As(V)]). Various measurement methods for inorganic 

elements are listed in Table 2-6. These methods differ with respect to detection limits, sample 

preparation, and cost (Chow, 1995). EDXRF and PIXE are the most commonly applied methods 

because they quantify more than 40 detectable elements, are nondestructive, and are relatively 

inexpensive. Both were discussed in the previous 1996 PM AQCD (U.S. Environmental 

Protection Agency, 1996a). TRXRF and S-XRF are newer techniques with lower detection 

limits. AAS, ICP-AES, and ICP-MS are also appropriate for ionic measurements of elements 

that can be dissolved. PESA provides a means for measuring elements with lower atomic 

numbers (i.e., the elements from hydrogen to carbon). More detailed information on each 

technique is given in Appendix 2B.1. 

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TABLE 2-6. MEASUREMENT METHODS FOR INORGANIC ELEMENTS

# Acronym Full Name Comments

 1 EDXRF Energy Dispersive X-ray Fluorescence Heavier elements

 2 S-XRF Synchrotron Induced X-ray Fluorescence Heavier elements; lower detection

 limits than EDXRF

 3 PIXE Proton Induced X-ray Emission Heavier elements; lower detection

 limits than EDXRF

 4 PESA Proton (or particle) Elastic 

 Scattering Analysis 

Lighter elements

 5 TRXRF Total Reflection X-ray Fluorescence Heavier elements; lower detection

 limits than EDXRF

 6 INAA Instrumental Neutron Activation Analysis Many elements; sensitivity different 

than EDXRF

 7 AAS Atomic Absorption Spectrophotometry Many elements that can be dissolved

 8 ICP-AES Inductively Coupled Plasma with

 Atomic Emission Spectroscopy 

Many elements that can be dissolved

 9 ICP-MS Inductively Coupled Plasma with

 Mass Spectroscopy 

Many elements that can be dissolved 

10 SEM Scanning Electron Microscopy Heavier elements 

2.2.7 Elemental and Organic Carbon in Particulate Matter 

Ambient particles from combustion sources contain carbon in several chemically and 

optically distinct forms. Health- and visibility-related studies of these particles require 

information about the relative contributions to total particle mass by these different forms of 

carbon. With the exception of carbonate-based carbon, however, a clear classification scheme 

has not yet been established to distinguish OC, light-absorbing carbon, black carbon, soot and 

EC. The absence of clear, physically-based definitions results in confusion in the interpretation 

of speciation data. For example, depending on the radiation wavelength specified, “lightabsorbing” carbon can include compounds that volatilize without oxidation. “Black” carbon 

includes various mixtures containing “elemental” (graphitic) carbon; partially degraded, 

oxidized graphitic fragments; and partially oxidized amorphous aromatic carbon. For studying 

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visibility reduction, a measurement of light-absorbing carbon may be more useful than one of 

EC. For source apportionment by receptor models, several consistent, but distinct, fractions of 

carbon in both source and receptor samples are desired, regardless of their light-absorbing or 

chemical properties. Differences in the ratios of the carbon concentrations in these fractions 

form part of the source profiles that distinguish the contribution of one source from another 

(Watson et al., 1994a,b). 

Three method-dependent operational classes of carbon are commonly measured in ambient 

aerosol samples collected on quartz fiber filters: (1) semivolatile organic or non-visible lightabsorbing carbon, termed “organic carbon (OC)”; (2) elemental carbon, soot, black carbon, or 

light-absorbing carbon, termed “elemental carbon (EC)”; and (3) carbon present as K2CO3, 

Na2CO3, MgCO3, CaCO3, termed “carbonate carbon (CC).” The sum of OC, EC, and CC in PM 

gives the total carbon (TC). 

The thermal-optical reflectance (TOR), thermal-optical transmission (TOT), and thermalmanganese oxidation (TMO) methods are most commonly used for the analysis of OC and EC in 

atmospheric PM. In thermal separation methods, OC is vaporized and the EC remaining on the 

filter is then oxidized to CO2 and quantified by nondispersive infrared detection, by 

electrochemical techniques or by reducing the CO2 to CH4 and the quantifying CH4 via flame 

ionization detection (FID). Organic carbon that does not vaporize below 550 °C can pyrolyze at 

higher temperatures to form additional black carbon. Thermal optical methods must correct for 

this effect in order to correctly distinguish OC from EC. The various methods give similar 

results for TC, but not for EC or OC, due to differing assumptions and analytical procedures 

regarding the thermal behavior of ambient aerosol carbon. These methods are discussed in detail 

in Appendix 2B.2. 

Carbonate carbon can be determined thermally, or on a separate filter section by measuring 

the carbon dioxide (CO2) evolved after acidification (Johnson et al., 1980). It is usually on the 

order of 5% or less of TC for ambient particulate samples collected in urban areas (Appel, 1993). 

The forms of carbon present in natural materials that may lead to the formation of 

atmospheric aerosol tend to be poorly defined. Thus, the pyrolysis products of these materials 

during thermal-optical analysis cannot be predicted. The Geochemical Society convened an 

international steering committee in 1999 to define a set of representative black carbon and black 

carbon-containing benchmark materials to be used to support ambient aerosol sample analysis. 

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Such materials can be used to provide thermal-optical “fingerprints” for deducing primary 

aerosol sources, and to establish characteristic analytic interferences or artifacts associated with 

such sources. The materials recommended to date include n-hexane soot, lignocellulosic chars, 

soils, marine sediments, and the NIST urban dust standard reference material (SRM 1649a). The 

committee has also recommended a set of standard materials that may potentially interfere with 

black carbon analyses: shale, natural organic matter, coals, and melanoidin (an amino 

acid-based material). These recommendations are discussed on the steering committee’s 

website: http://www.du.edu/~dwismith/bcsteer.html. 

2.2.8 Ionic Species 

Ion chromatography is widely used for analyzing ionic species in the water-soluble portion 

of suspended PM. Ion chromatography is the method of choice for the measurement of sulfate, 

nitrate, ammonium, sodium, and potassium ions for the NAMS program. Aerosol strong 

acidity, H+

, is determined by titrating on aqueous solution of PM that is collected after passage 

through a series of annular denuders to remove acid and basic gases with back-up filters to 

collect the NH3 and HNO3 that volatilize from the PM during collection. The 1996 PM AQCD 

(U.S. Environmental Protection Agency, 1996a) discussed measurement of ions by ion 

chromatography (Section 4.3.3.1) and of strong acidity (Sections 3.3.1.1 and 4.3.3.1); so, 

no further details will be discussed here. 

2.2.9 Continuous Monitoring 

The EPA expects that many local environmental agencies will operate continuous PM 

monitors. All currently available continuous measurements of suspended particle mass share the 

problem of dealing with semivolatile PM components. So as not to include particle-bound water 

as part of the mass, the particle-bound water must be removed by heating or dehumidification. 

However, heating also causes the loss of ammonium nitrate and SVOCs. A variety of potential 

candidates for the continuous measurement of particle mass and related properties are listed in 

Table 2-7. These techniques are discussed in more detail in Appendix 2B.3. 

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TABLE 2-7. METHODS FOR CONTINUOUS MEASUREMENT OF PM MASS,

PM COMPONENTS, ETC.

# Acronym Name Comments

 1 TEOM Tapered Element Measures only nonvolatile components of PM.

Oscillating Microbalance 

2 TEOM TEOM with Sample By using dehumidification instead of heat for drying and a lower 

with SES Equilibration System temperature for stabilization, includes some of the semivolatile 

components of PM.

 3 — Differential TEOM Research instrument designed to measure both the nonvolatile 

and semivolatile components of PM.

 4 FDMS Filter Dynamics Commercial version of the differential TEOM.

Measurement System 

5 RAMS Real-time total Ambient Research instrument designed to measure total PM using 

Mass Sampler denuders and an adsorptive filter.

 6 CAMM Continuous Ambient Measures total PM by pressure drop across a frequently 

Mass Monitor changed filter.

 7 BAM Beta gauge Measures PM mass by beta attenuation. Unless dried by heat or 

dehumidification will also measure particle-bound water.

 8 — Piezoelectric Measures mass by change in resonance frequency when particles 

microbalance are deposited on a crystal.

 9 CCPM Continuous Coarse Virtual impaction is used to concentrate PM10-2.5 which is then 

Particle Monitor measured by a TEOM. 

10 — Semi-continuous EC/OC Several commercially available instruments automate the thermal 

technique for EC/OC and provide hourly measurements. 

11 — Semi-continuous nitrate Collection of PM followed by flash vaporization and 

determination of NOx provides 10 minute measurements of 

particulate nitrate. 

12 — Semi-continuous sulfate Several techniques are available in which particulate sulfate is 

converted to SO2 which is measured by a pulsed fluorescence 

analyzer. 

13 — Continuous ion Particles are grown by mixing with water vapor, collected in 

chromatography of water, and injected into an automatic ion chromatography. 

water-soluble ions 

14 — Mass spectroscopy of Single particles are evaporated, ionized and the components 

individual particles analyzed by mass spectroscopy. Several different systems are in 

use in research studies. 

15 EAD Electrical Aerosol This instrument measures charge collected by particles and gives 

Detector a continuous signal that is proportional to the integral of the 

particle diameter. 

16 — Integrating nephelometer Light scattering by suspended particles, collected over a large 

solid angle, provides an indicator for particle mass including 

particle-bound water unless the air sample is dried by heating or 

dehumidification. 

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2.2.10 Measurements of Individual Particles 

Recently, several researchers have developed instruments for real-time in situ analysis of 

single particles (e.g., Noble and Prather, 1996; Gard et al., 1997; Johnson and Wexler, 1995; 

Silva and Prather, 1997; Thomson and Murphy, 1994). Although the technique varies from one 

laboratory to another, the underlying principle is to fragment each particle into ions, using either 

a high-power laser or a heated surface and, then, a time-of-flight mass spectrometer (TOFMS) to 

measure the ion fragments in a vacuum. Each particle is analyzed in a suspended state in the air 

stream (i.e., without collection), avoiding sampling artifacts associated with impactors and 

filters. The technique is called aerosol time-of-flight mass spectrometry (ATOFMS). 

By measuring both positive and negative ions from the same particle, information can be 

obtained about the composition of individual particles of known aerodynamic diameter. This 

information is especially useful in determining the sources of particles. Noble and Prather 

(1996) used ATOFMS to provide compositionally resolved particle-size distributions. Their 

instrument is capable of analyzing size and chemical composition of 50 to 100 particles/min at 

typical ambient concentrations and up to 600/min at high particle concentrations. Four systems 

for measurement of single particles using mass spectroscopy are reviewed by Middlebrook et al. 

(2003). An example of the type of information that can be determined is shown in Figure 2-27. 

Because particles are analyzed individually, biases in particle sampling (including biases in 

the efficiency of particle transmission into the sensor chamber as a function of size; particle size 

measurement, and detection of particles prior to fragmentation) represent a major challenge for 

these instruments. Moreover, laser ablation has a relatively large variability in ion yields, i.e., 

identical samples would yield relatively large differences in mass spectrometer signals 

(Thomson and Murphy, 1994). Therefore, quantitation is inherently difficult (Murphy and 

Thomson, 1997). Quantitation will be even more challenging for complex organic mixtures 

because of the following two reasons: (1) a large number of fragments are generated from each 

molecule, and (2) ion peaks for organics can be influenced or obscured by inorganic ions 

(Middlebrook et al., 1998). Nonetheless, scientists have been successful in using these 

techniques to identify the presence of organics in atmospheric particles and laboratory-generated 

particles (i.e., as contaminants in laboratory-generated sulfuric acid droplets) as well as the 

identification of specific compound classes such as PAHs in combustion emissions (Castaldi and 

Senkan, 1998; Hinz et al., 1994; Middlebrook et al., 1998; Murphy and Thomson, 1997; 

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Figure 2-27. Size distribution of particles divided by chemical classification into organic, 

marine, and soil (or crustal). 

Source: Noble and Prather (1998). 

Neubauer et al., 1998; Noble and Prather, 1998; Reilly et al., 1998; Silva and Prather, 1997). 

A new multivariate technique for calibrating of ATOFMS using microorifice impactors shows 

promise for simplifying the calibration process (Fergenson et al., 2001). This calibration 

technique has been applied to gasoline and diesel particles to demonstrate the feasibility of using 

this technique for the source apportionment of gasoline and diesel particles in an atmospheric 

mixture (Song et al., 2001). 

Previously, ATOFMS systems have only been able to characterize particles larger than 

~0.2 to 0.3 μm in diameter. Recently, Wexler and colleagues (Carson et al., 1997; Ge et al., 

1998) developed an ATOFMS instrument that is able to size, count, and provide chemical 

composition on individual particles ranging in size from 10 nm to 2 μm. 

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2.2.11 Low Flow Filter Samples for Multiday Collection of 

Particulate Matter 

PM

For some purposes, such as demonstrating the attainment of an annual standard or as an 

exposure indicator for epidemiologic studies of chronic health effects, 24-h measurements are 

not essential. Annual or seasonal averages may be adequate and multiday sampling techniques 

can result in lower costs for weighing, chemical analysis, and travel time to change filters. The 

multiday sampler serves a second purpose. Most commercially available samplers are optimized 

for collecting 24-h samples of PM concentrations as found in the United States, Europe, or 

Japan, but many other parts of the world have significantly higher PM concentrations. Under 

such conditions, the 16.7 L/min flow through 37- or 47-mm diameter filters may overload the 

filter, preventing the sampler from maintaining the prescribed flow rate for 24 h. A low-flow 

sampler with a 0.4 L/min flow rate and a 47-mm diameter filter has been designed by Aerosol 

Dynamics, Inc. With this sampler, the sample collection time can be chosen to suit the ambient 

concentration level. This sampler, with a 1-week collection period, has been used to characterize 

2.5 in Beijing, PRC (He et al., 2001). It is also being used with a 2-week collection period in a 

chronic epidemiologic study in southern California (Gauderman, et al., 2000). 

The low-flow sampler, as described by He et al. (2001), has three PM2.5 channels. One 

channel collects PM on a Teflon filter for gravimetric mass measurement and elemental analysis 

by XRF. A second channel collects PM on a quartz filter for OC and EC analysis. A denuder 

may be added to the second channel to remove organic gases, as may a backup filter to collect 

SVOCs. The third channel uses a carbonate denuder to remove acid gases such as HNO3 and 

SO2, a Teflon filter to collect PM for analysis of ions by IC, and a nylon filter to collect 

volatilized nitrate. The Teflon filter can also be weighed prior to extraction; thus, the multiday 

sampler can provide the information needed for source apportionment by chemical mass balance 

techniques (Watson et al., 1990a,b; U.S. Environmental Protection Agency, 2002b). 

For monitoring sites with high day-to-day variability in PM concentrations, a sample 

integrated over a week may provide a more accurate measurement of the annual average than 

can be obtained by 1-in-3- or 1-in-6-day sampling schedules. Daily PM data from Spokane, WA 

were resampled to simulate common sampling schedules and the error due to less-than-daily 

sampling was computed (Rumburg et al., 2001). The error in the annual mean concentration 

for PM2.5, expressed as a percentage difference from the daily sampling mean, was 1.7, 3.4, and 

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7.7% for 1-in-2-, 1-in-3, and 1-in-6-day sampling, respectively. These results may or may not be 

representative of other locations and time periods. 

2.3 SUMMARY AND KEY POINTS 

2.3.1 Chemistry and Physics of Atmospheric Particles 

Atmospheric particles originate from a variety of sources and possess a range of 

morphological, chemical, physical, and thermodynamic properties. The composition and 

behavior of aerosols are linked with those of the surrounding gas. An aerosol may be defined as 

a suspension of solid or liquid particles in air and includes both the particles and all vapor or gas 

phase components of air. However, the term aerosol is also often used to refer to the suspended 

particles only. 

A complete description of atmospheric PM would include an accounting of the chemical 

composition, morphology, and size of each particle, and the relative abundance of each particle 

type as a function of particle size. Recent developments in single particle analysis techniques 

are bringing this description closer to reality. 

The diameter of a spherical particle may be determined geometrically, from optical or 

electron microscopy, by light scattering and Mie theory, or by a particle’s behavior (e.g., 

electrical mobility or its aerodynamic behavior). However, the various types of diameters may 

be different, and atmospheric particles often are not spherical. Therefore, particle diameters are 

described by an “equivalent” diameter. Aerodynamic diameter, Da (the diameter of a unit 

density sphere that would have the same terminal settling velocity as the real particle), and the 

Stokes diameter, Dp (the diameter of a sphere of the same density as the particle that would have 

the same aerodynamic resistance or drag), are the most widely used equivalent diameters. 

Atmospheric size distributions show that most atmospheric particles are quite small, 

< 0.1 μm in diameter; whereas most of the particle volume (and therefore most of the mass) is 

found in particles > 0.1 μm. An important feature of the mass or volume size distributions of 

atmospheric particles is their multimodal nature. Volume distributions, measured in ambient air 

in the United States, almost always have a minimum between 1.0 and 3.0 μm. The portion of the 

size distribution that contains particles that are mostly larger than the minimum is called 

“coarse” particles or the “coarse” mode. The portion of the size distribution that contains 

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particles that are mostly smaller than the minimum is called “fine” particles and includes several 

modes. “Accumulation mode” refers to that portion of fine particles with diameters above about 

0.1 μm. The portion of fine particles with diameters < 0.1 μm are usually called “ultrafine” 

by toxicologists and epidemiologists and “nanoparticles” by aerosol physicists and material 

scientists. In the number distribution of ultrafine particles, particles in the size range < 0.01 are 

called the nucleation mode and particles between 0.01 and 0.1 are called the Aitken mode. The 

Aitken mode can be observed as a separate mode in mass or volume distributions only in clean 

or remote areas or near sources of new particle formation by nucleation. 

The aerosol community uses three different approaches or conventions in the classification 

of particles by size: (1) modes, based on the observed size distributions and formation 

mechanisms; (2) dosimetry or occupational sizes, based on the entrance into various 

compartments of the respiratory system; and (3) cut point, usually based on the 50% cut point of 

the specific sampling device, including legally specified, regulatory sizes for air quality 

standards. Over the years, the terms fine and coarse as applied to particle sizes have lost their 

original precise meanings. In any given article, therefore, the meaning of fine and coarse, unless 

defined, must be inferred from the author’s usage. In particular, PM2.5 and fine particles are not 

equivalent. PM2.5 refers to the aggregate sample of PM that is collected following a sizeselective inlet with a specified penetration as a function of size and a 50% cutpoint of 2.5 μm Da. 

It may also be used to refer to the number (or other measure) of particles suspended in the 

atmosphere that would be collected by such a sampler). PM10 is defined similarly. PM10-2.5 

refers to the sample that would be collected if the PM2.5 component could be removed from 

a PM10 sample. 

Several processes influence the formation and growth of particles. New particles may be 

formed by nucleation from gas phase material. Particles may grow by condensation as gas phase 

material condenses onto existing particles. Particles may also grow by coagulation as two 

particles combine to form one. Gas phase material condenses preferentially on smaller particles, 

and the rate constant for coagulation of two particles decreases as the particle size increases. 

Therefore, nucleation-mode particles grow into Aitken-mode particles and Aitken-mode 

particles grow into the accumulation mode, but growth of accumulation-mode particles into the 

coarse mode size range is unusual. 

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The major constituents of atmospheric PM are sulfate, nitrate, ammonium, and hydrogen 

ions; particle-bound water; elemental or black carbon; a great variety of organic compounds; and 

crustal material. Atmospheric PM contains a large number of elements in various compounds 

and concentrations and hundreds of specific organic compounds. Particulate material can be 

primary or secondary. It is designated primary if it is in the same chemical form in which it was 

emitted into the atmosphere. It is called secondary PM if it is formed by chemical reactions in 

the atmosphere. Primary coarse particles are usually formed by mechanical processes; whereas 

primary fine particles are emitted from sources either directly as particles or as vapors that 

rapidly condense to form particles. Water-soluble and potentially toxic gases such as SO2, H2O2, 

and formaldehyde, which in the absence of particles would be removed by deposition to the 

mucus on the upper airways of the respiratory tract, may dissolve in particle-bound water and be 

carried into the air exchange regions of the lungs. 

Most of the sulfate and nitrate and a portion of the organic compounds in atmospheric 

particles are secondary. Secondary aerosol formation depends on numerous factors including the 

concentrations of precursors; the concentrations of other gaseous reactive species such as ozone, 

hydroxyl radical, peroxy radicals, and hydrogen peroxide; atmospheric conditions, including 

solar radiation and relative humidity; and the interactions of precursors and preexisting particles 

within cloud or fog droplets or on or in the liquid film on solid particles. As a result, it is 

considerably more difficult to relate ambient concentrations of secondary species to sources of 

precursor emissions than it is to identify the sources of primary particles. 

The lifetimes of particles vary with particle size. Coarse particles can settle rapidly from 

the atmosphere within minutes or hours and normally travel only short distances. However, 

when mixed high into the atmosphere, as in dust storms, the smaller-sized, coarse-mode particles 

may have longer lives and travel greater distances. Accumulation-mode particles are kept in 

suspension by normal air motions and have a lower deposition velocity than coarse-mode 

particles; they can be transported thousands of kilometers and remain in the atmosphere for a 

number of days. They are removed from the atmosphere primarily by cloud processes. Dry 

deposition rates are expressed in terms of a deposition velocity that varies with the particle size, 

reaching a minimum between 0.1 and 1.0 μm Da. 

Particulate matter is a factor in acid deposition. Particles serve as cloud condensation 

nuclei and contribute directly to the acidification of rain. In addition, the gas-phase species that 

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lead to the dry deposition of acidity are also precursors of particles. Therefore, reductions in SO2 

and NOx emissions will decrease both acid deposition and PM concentrations, but not 

necessarily in a linear fashion. Sulfuric acid, ammonium nitrate, and organic particles also are 

deposited on surfaces by dry deposition and can contribute to ecological effects. 

2.3.2 Measurement of Atmospheric Particles 

The decision by the EPA to revise the PM standards by adding daily and yearly standards 

for PM2.5 has led to a renewed interest in the measurement of atmospheric particles and to a 

better understanding of the problems in their precise and accurate measurements. Unfortunately, 

it is very difficult to measure and characterize particles suspended in the atmosphere. 

Particulate matter monitoring is used to develop information to guide the implementation 

of standards (i.e., to identify particles sources, to determine whether or not a standard has been 

attained) and to determine health, ecological, and radiative effects. Federal Reference Methods 

(FRM) specify techniques for measuring PM10 and PM2.5. Particles are collected on filters and 

mass concentrations are determined gravimetrically. The PM10 FRMs consist of low flow rate 

(1 L/h) and high flow rate (67.92 L/h) samplers, each with a PM10 inlet/impactor. The high flow 

rate sampler uses a 20.3 × 35.4 cm2

 quartz filter whereas the low flow rate sampler uses a 47-mm 

Teflon filter, each with a collection efficiency of $ 99% as described in Appendix M to 40 CFR, 

Part 50 (Code of Federal Regulations, 2001e). The PM2.5 FRM is similar to the low flow rate 

PM10 FRM except that it includes a PM2.5 impactor with an oil-covered impaction substrate to 

remove particles > 2.5 μm and a filter with a collection efficiency of greater than 99.7% as 

described in Appendix L, 40 CFR, Part 50 (Code of Federal Regulations, 2001c). Both 

techniques provide relatively precise (± 10%) methods for determining the mass of material 

remaining on a filter after equilibration. 

Despite considerable progress in measuring the atmospheric PM mass concentration, 

numerous uncertainties continue to exist as to the relationship between the mass and composition 

of material remaining on the filter as measured by the FRMs and the mass and composition of 

material that exists in the atmosphere as suspended PM. There is no reference standard for 

particles suspended in the atmosphere, nor is there an accepted way to remove particle-bound 

water without losing some of the semivolatile components of PM such as ammonium nitrate and 

semivolatile organic compounds. It also is difficult to cleanly separate fine and coarse PM. As a 

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result, EPA defines accuracy for PM measurements in terms of agreement of a candidate sampler 

with a reference sampler. Therefore, intercomparisons of samplers become very important in 

determining how well various samplers agree and how various design choices influence what is 

actually measured. 

Current filtration-based mass measurements lead to significant evaporative losses of a 

variety of semivolatile components (i.e., species that exist in the atmosphere in dynamic 

equilibrium between the condensed phase and gas phase) during and possibly after collection. 

Important examples include ammonium nitrate, SVOCs, and particle-bound water. Loss of these 

components may significantly affect the quality of the measurement and can lead to either 

positive or negative sampling artifacts. Negative artifacts resulting from loss of ammonium 

nitrate and SVOC may occur during sampling because of changes in temperature, relative 

humidity, or the composition of the aerosol, or because of the pressure drop across the filter. 

Negative artifacts also may occur during handling and storage because of evaporation. Positive 

artifacts occur when gas-phase compounds (H2O, HNO3, SO2, and organic compounds) absorb 

onto or react with filter media or collected PM, or when some particle-bound water is not 

removed. 

Sampling systems for semivolatile PM components make use of denuders to remove the 

gas-phase fraction and absorptive filters to remove the condensed phase while retaining any 

material that subsequently evaporates from the collected PM. The loss of particulate nitrate may 

be determined by comparing nitrate collected on a Teflon filter to that collected on a nylon filter 

(which absorbs nitric acid as it evaporates from ammonium nitrate particles) preceded by a 

denuder to remove gas-phase nitric acid. In two studies in southern California, the PM2.5 mass 

lost because of volatilization of ammonium nitrate was found to represent 10 to 20% of the 

total PM2.5 mass and almost a third of the nitrate. Denuder/absorptive filter sampling systems 

also have been developed for measuring particulate phase organic compounds. This technique is 

an improvement over the filter/adsorbent collection method. However, the denuder systems 

currently discussed in the literature are not straightforward in their use, and the user must have a 

thorough understanding of the technology. The FRM for PM2.5 will likely suffer loss of 

particulate nitrates and SVOCs, similar to the losses experienced with other single-filter 

collection systems. 

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It is generally desirable to collect and measure ammonium nitrate and SVOCs as part of 

PM mass. However, it is usually desirable to remove the particle-bound water before 

determining the mass. In some situations, it may be important to know how much of the 

suspended particle’s mass or volume results from particle-bound water. Calculations and 

measurements indicate that aerosol water content is strongly dependent on relative humidity and 

composition. Particle-bound water can represent a significant mass fraction of the PM 

concentration at relative humidities above 60%. A substantial fraction of accumulation-mode 

PM is hygroscopic or deliquescent. The more hygroscopic particles tend to contain more 

sulfates, nitrates, and secondary organic compounds, while the less hygroscopic particles tend to 

contain more elemental carbon, primary organic compounds, and crustal components. Fresh, 

submicrometer-size soot particles may tend to shrink with increasing relative humidity because 

of a structural change. The effects of relative humidity on the sorption of semivolatile organic 

compounds on particles are not well understood. The amount of water sorbed to an atmospheric 

aerosol may be affected by the presence of an organic film on the particle, which can impede the 

transport of water across the surface. 

Fine and coarse particles differ not only in formation mechanisms and size, but also in 

sources, composition, and chemical, physical, and biological properties. Fine and coarse 

particles overlap in the intermodal size range (1 to 2.5 μm Da). As relative humidity increases, 

fine particles grow into this size range; as relative humidity decreases, more coarse particles may 

be suspended in this size range. It is desirable to measure fine PM and coarse PM separately in 

order to properly allocate health effects to either fine PM or coarse PM as well as to correctly 

determine sources by factor analysis or chemical mass balance. The selection of a cut point of 

2.5 μm as a basis for EPA’s 1997 NAAQS for fine particles (Federal Register, 1997) and its 

continued use in many health effects studies reflects the importance placed on more complete 

inclusion of accumulation-mode particles, while recognizing that intrusion of coarse-mode 

particles can occur under some conditions with this cut point. 

In addition to FRM sampling of equilibrated mass to determine compliance with PM 

standards, EPA requires states to conduct speciation sampling, primarily to determine source 

categories and trends. The current speciation samplers collect PM2.5 on three filters: 

(1) a Teflon filter for gravimetric determination of mass and for analysis of heavy elements by 

X-ray fluorescence; (2) a nylon filter preceded by a nitric acid denuder for artifact-free 

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determination of nitrate and measurement of other ionic species by ion chromatography; and 

(3) a quartz filter for measurement of elemental carbon (EC) and organic carbon (OC). 

In addition, IMPROVE samplers provide information on regional PM background and transport. 

IMPROVE samplers, in addition to the three types of filters collected by the speciation samplers, 

also collect a PM10 sample. The IMPROVE and speciation networks use slightly different 

methods for determination of EC and OC. The two methods agree on total carbon (TC) but 

differ in the split of TC into EC and OC. The two methods also differ in their correction for 

positive artifacts due to absorption of volatile organic compounds on the quartz filters. Neither 

EC/OC method provides for any correction for negative artifacts due to the evaporation of 

SVOCs from the collected particles. 

The EPA expects that monitoring agencies will operate continuous PM monitors and is in 

the process of providing guidance regarding appropriate continuous monitoring techniques. 

All currently available techniques for continuous measurements of suspended particle mass, e.g., 

the integrating nephelometer, the beta-absorption monitor, and the Tapered Element Oscillating 

Microbalance (TEOM), share the problem of dealing with semivolatile PM components: that is, 

in order to not include particle-bound water as part of the mass, the particle-bound water must be 

removed by heating or dehumidification; however, heating causes ammonium nitrate and 

SVOCs to evaporate. The TEOM monitor operates at a constant, but higher than ambient, 

temperature to remove particle-bound water, whereas the FRM is required to operate at no more 

than 5 °C above the ambient temperature. Subsequently, much of the particle-bound water is 

removed during equilibration at 40% relative humidity. This difference in techniques for the 

removal of particle-bound water causes differences in the measured mass concentration between 

the TEOM and FRMs. 

Several new techniques for continuous PM mass measurements are currently being field 

tested. The RAMS measures the total mass of collected particles including semivolatile species 

with a TEOM monitor using a “sandwich filter.” The sandwich contains a Teflon-coated 

particle-collection filter followed by a charcoal-impregnated filter to collect any semivolatile 

species lost from the particles during sampling. The RAMS uses a Nafion dryer to remove 

particle-bound water from the suspended particles and a particle concentrator to reduce the 

quantity of gas phase organic compounds that must be removed by the denuder. The CAMM 

estimates ambient PM mass by measuring of the increase in the pressure drop across a 

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membrane filter caused by particle loading. It also uses a Nafion dryer to remove particle-bound 

water. A new differential TEOM offers the possibility of measuring both nonvolatile and 

semivolatile PM mass. In addition to continuous mass measurement, a number of techniques for 

continuous measurement of sulfate, nitrate, or elements are being tested. 

2.3.3 Key Points 

Fine and Coarse Particles 

Particle size distributions show that atmospheric particles exist in two classes, fine 

particles and coarse particles. Fine and coarse particles are defined primarily in terms of their 

formation mechanisms and size; and they also differ in sources, chemical composition, and 

removal processes (see Table 2-2). Subsequent chapters will show that fine and coarse particles 

also differ with regard to aspects of concentration, exposure, dosimetry, toxicology, and 

epidemiology. 

These differences support the setting of separate standards for fine and coarse particles. 

Fine and coarse particles overlap in the size range between 1 and 3 μm Da where PM 

concentrations are at a minimum. Coarse particles are generally larger than this minimum and 

are generally formed by mechanical processes. Coarse particles and coarse-mode particles are 

equivalent terms. Fine particles are generally smaller than the minimum and are generally 

formed by coagulation and condensation of gases. Fine particles are subdivided into 

accumulation, Aitken, and nucleation modes. In earlier texts, nuclei mode referred to the size 

range now split into the Aitken and nucleation modes (see Figures 2-5 and 2-6). 

Measurement of Mass and Composition 

Nonvolatile Particulate Matter. Analytical techniques exist for measurement of the mass 

and chemical composition of PM retained on a filter (nonvolatile mass) in terms of elements 

(except carbon) and certain key ions (sulfate, nitrate, hydrogen, and ammonium). Acceptable 

measurements can be made of the TC retained on a filter. However, the split into OC and EC 

depends on the operational details of the analytical methods and varies somewhat among 

methods. Determination of the various organic compounds in the organic carbon fraction 

remains a challenge. 

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Semivolatile Particulate Matter. Important components of atmospheric PM (particlebound water, ammonium nitrate, and many organic compounds) are termed semivolatile because 

significant amounts of both the gaseous and condensed phases may exist in the atmosphere in 

equilibrium. While particle-bound water is not considered to be a pollutant, it can significantly 

influence the size and light scattering and absorbing properties of particles and may also act as a 

carrier to convey dissolved gases or reactive species into the lungs. Most of the particle-bound 

water is removed by heating the particles or by equilibration of the collected particles at a low 

relative humidity (40%) for 24 hours. However, these processes also cause the loss of other 

semivolatile components. Semivolatile components also evaporate from the filter during 

sampling due to the pressure drop across the filter or due to a reduction in the atmospheric 

concentration during the sampling time. Filter collection and equilibration techniques for PM, 

such as prescribed by the FRM, lose a fraction of the semivolatile PM. Continuous methods 

must dry the PM to remove particle-bound water. If heating is used to dry the particles, more of 

the semivolatile components may be removed than are lost in filter sampling. Collection and 

retention of ammonium nitrate and SVOCs represents a major challenge in the effort to move to 

the continuous measurement of PM mass. The use of diffusion dryers, which dehumidify the air 

stream without heating, represents a promising approach. Uncertainty in the efficiency of the 

retention of ammonium nitrate and organic compounds on filters also impacts source category 

attribution and epidemiologic studies. 

Separation of Fine and Coarse Particulate Matter 

Satisfactory techniques are available to separate fine particles from coarse particles and to 

collect the fine particles on a filter. However, no consensus exists as yet on the best technique 

for collecting a coarse particle sample for determination of mass and composition. Candidates 

include multistage impaction, virtual impaction, and difference (subtracting PM2.5 mass or 

composition from PM10 mass or composition). Advances in the theory and practice of virtual 

impaction suggest that it would be possible to design virtual impactors with much less than the 

10% of fine PM collected in the coarse PM sample as is now the case for the dichotomous 

samplers used in air quality studies and with penetration curves as sharp as those used in the 

current FRM for PM2.5. 

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APPENDIX 2A. TECHNIQUES FOR MEASURING OF SEMIVOLATILE

ORGANIC COMPOUNDS 

Use of Denuder Systems To Measure Semivolatile Organic Compounds 

Phase distribution of semivolatile organic species has been the subject of several studies 

that have employed denuder technology (see Gundel et al., 1995; Gundel and Lane, 1999) to 

directly determine the phase distributions while avoiding some of the positive and negative 

sampling artifacts associated with back-up quartz filters. In an ideal system with a denuder that 

is 100% efficient, the gas phase would be collected in the denuder and the particle phase would 

be the sum of the material collected on the filter and the adsorbent downstream. Denuder 

collection efficiency depends on the denuder surface area (+), the diffusivity (+) and vapor 

pressure (!) of the compound, the temperature (!) and flow rate (!) of the air stream, and the 

presence of competing species (!), including water vapor (Cui et al., 1998; Kamens and Coe, 

1997; Lane et al., 1988). (The + and – symbols in parentheses indicate qualitatively the effect 

increasing each parameter would have on efficiency). In a system with a denuder collection 

efficiency < 100%, the collection efficiency must be known to accurately attribute adsorbed 

organics from denuder breakthrough to the gas phase and adsorbed organics volatilized from 

collected particles to the particle phase. In calculating the overall phase distributions of SVOC 

PAH from a denuder system, the collection efficiency for each compound is needed. 

The efficiency of silicone-grease-coated denuders for the collection of polynuclear 

aromatic hydrocarbons was examined by Coutant et al. (1992), who examined the effects of 

uncertainties in the diffusion coefficients and in the collisional reaction efficiencies on the 

overall phase distributions of SVOC PAH calculated using denuder technology. In their study, 

they used a single stage, silicone-grease-coated aluminum annular denuder with a filter holder 

mounted ahead of the denuder and an XAD trap deployed downstream of the denuder. In a 

series of laboratory experiments, they spiked the filter with a mixture of perdeuterated PAH, 

swept the system with ultra-high purity air for several hours, and then analyzed the filter and the 

XAD. They found that the effects of these uncertainties, introduced by using a single compound 

as a surrogate PAH (in their case, naphthalene) for validation of the denuder collection 

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efficiency, are less significant than normal variations because of sampling and analytical effects. 

Field study results using their sampling system have not been published. 

For measuring particulate phase organic compounds, the denuder-based sampling system 

represents an improvement over the filter/adsorbent collection method (Turpin et al., 1993). 

Some researchers, however, have reported that denuder coatings themselves can introduce 

contamination (Mukerjee et al., 1997) and that the adsorbed species may be difficult to remove 

from the coating (Eatough et al., 1993). 

In a study conducted in southern California (Eatough et al., 1995), the Brigham Young 

University Organic Sampling System (BOSS; Eatough et al., 1993) was used to determine POM 

composition, and a high-volume version (BIG BOSS; flow rate 200 L/min) was used to 

determine the particulate size distribution and the chemical composition of SVOC in fine 

particles. The BOSS, a multi-channel diffusion denuder sampling system, consists of two 

separate samplers (each operating at 35 L/min). The first sampler consists of a multi-parallel 

plate diffusion denuder with charcoal-impregnated filter papers as the collection surfaces 

followed by a two-stage quartz filter pack and a two-stage charcoal-impregnated filter pack. The 

second sampler operating in parallel with the first consists of a two-stage quartz filter pack, 

followed by the parallel plate denuder, followed by the two-stage charcoal-impregnated filter 

pack. The filter samples collected by the BOSS sampler were analyzed by temperatureprogrammed volatilization analysis. The second channel allows calculations of the efficiency of 

the denuder in removing gas-phase specifics that would be absorbed by the charcoal impregnated 

filter. Eatough et al. (1995) also operated a two-stage quartz filter pack alongside the BOSS 

sampler. The BIG BOSS system (Tang et al., 1994) consists of four systems (each with a flow 

rate of 200 L/min). Particle size cuts of 2.5, 0.8, and 0.4 μm are achieved by virtual impaction, 

and the sample subsequently flows through a denuder, then is split, with the major flow 

(150 L/min) flowing through a quartz filter followed by an XAD-II bed. The minor flow is 

sampled through a quartz filter backed by a charcoal-impregnated filter paper. The samples 

derived from the major flow (quartz filters and XAD-II traps) were extracted with organic 

solvents and analyzed by gas chromatography (GC) and GC-mass spectroscopy. The organic 

material lost from the particles was found to represent all classes of organic compounds. 

Eatough et al. (1996) operated the BOSS sampler for a year at the IMPROVE site at 

Canyonlands National Park, UT alongside the IMPROVE monitor and a separate sampler 

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consisting of a two-stage quartz filter pack. They found that concentrations of particulate carbon 

determined from the quartz filter pack sampling system were lower by an average 39%, which 

was attributed to volatilization losses of SVOC from the quartz filters. In another study 

conducted with the BOSS in southern California, losses of 35% of the POM, on average, were 

found and attributed to losses of the SVOC during sampling (Eatough et al., 1995). 

The denuder used in the various BOSS samplers consists of charcoal-impregnated cellulose 

fiber filter material. Denuder collection efficiencies of greater than 95% have been reported for 

organic gases that adsorb on quartz and charcoal-impregnated filters (Eatough et al., 1999a; Ding 

et al., 2002; Lewtas et al., 2001). However, because the mass concentration of gas phase species 

that adsorb on quartz and charcoal-impregnated filters is so much greater than the mass of 

semivolatile organic material in the particulate phase, it is necessary to measure and account for 

the inefficiency of the denuder in the BOSS samplers. To address this problem, Brigham Young 

University (BYU) developed a particle-concentrator (PC)-BOSS system (Ding et al., 2002; 

Eatough et al., 1999b; Lewtas et al., 2001; Modey et al., 2001; Pang et al., 2001, 2002a,b). The 

PC-BOSS includes a virtual impactor upstream of the denuder to improve the denuder collection 

efficiency by removing a majority of the gases from the aerosol flow. With this system, denuder 

collection efficiencies of > 99% have been reported for organic gases and other species, such as 

SO2(g), HNO3(g), etc., that adsorb on quartz and charcoal-impregnated filters (Pang et al., 2001). 

Since the concentrations of semivolatile organic and other gases are not altered by the virtualimpaction concentration of accumulation mode particles (except possibly by slight changes in 

pressure), it is anticipated that the gas-particle distribution will not be significantly altered by the 

concentration process. The virtual impactor has a 50% cut point at 0.1 μm Da. As a result, some 

particles in the 0.05 to 0.2 μm diameter size range will be removed in the major flow along with 

the majority of the gases. Therefore, the mass collection efficiency of the virtual impactor 

concentrator will be a function of the particle size distribution in the 0.05 to 0.1 μm size range. 

This collection efficiency is measured by comparing the concentration of nonvolatile 

components measured in the concentrated sample with that measured in an unconcentrated 

sample. The concentration efficiency varies from 50 to 75%. It is relatively constant over 

periods of weeks, but varies by season and by site, presumably as the particle size distribution 

changes. Previous studies at Harvard (Sioutas et al., 1995a,b) have shown that the composition 

of the sampled aerosol is little changed by the concentration process. The BYU studies listed 

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above have shown that the concentration efficiencies for sulfate, OC, and EC are comparable for 

given sampling locations. Furthermore, the concentrations of these species and of fine 

particulate nitrate determined using the PC-BOSS have been shown to be comparable to those 

determined using more conventional samplers for sulfate or EC or using simpler denuder 

systems for OC and nitrate. 

Ding et al. (1998a) developed a method for the determination of total n-nitroso compounds 

in air samples and used the method to examine organic compounds formed from NOx chemistry 

in Provo, UT (Ding et al., 1998b). In their method, n-nitroso compounds are selectively 

decomposed to yield nitric oxide, which is then detected using chemiluminescence. Using the 

Provo samples, they found that the majority of the n-nitroso and nitrite organic compounds that 

were present in fine particulate matter were SVOCs that could be evaporated from the particles 

during sampling. They found particulate n-nitroso compound concentrations ranging between 

< 1 and 3 nmoles/m3

 and gas-phase n-nitroso compound concentrations in the same range. 

Particulate organic nitrite concentrations were found in the range of <1 to .5 nmoles/m3

, and 

gas-phase concentrations as high as 10 nmoles/m3

 were found. 

Turpin et al. (1993) developed a sampling system that corrects for the loss of SVOCs 

during sampling by removing most of the gas phase material from the particles in a diffusion 

separator sampling system. Unlike the previously mentioned systems, wherein the particulate 

phase is measured directly, in the system of Turpin et al. (1993), the gas-phase is measured 

directly. In the laminar flow system, ambient, particle-laden air enters the sampler as an annular 

flow. Clean, particle-free air is pushed through the core inlet of the separator. The clean air and 

ambient aerosol join downstream of the core inlet section, and flow parallel to each other 

through the diffusion zone. Because of the much higher diffusivities for gases compared to 

particles, the SVOC in the ambient air diffuses to the clean, core flow. The aerosol exits the 

separator in the annular flow, and the core flow exiting the separator now contains a known 

fraction of the ambient SVOC. Downstream of the diffusion separator, the core exit flow goes 

into a polyurethane foam (PUF) plug where the SVOC is collected. The adsorbed gas phase on 

the PUF plug is extracted with supercritical fluid CO2 and then analyzed by gas 

chromatography/mass-selective detection (GC/MSD). The gas-phase SVOC is thus determined. 

Ultimately, to determine particulate phase SVOC concentrations, the total compound 

concentration also has to be measured and the particulate phase obtained by difference. 

2A-4
The system was tested for the collection of PAHs. The diffusional transport of gas-phase PAHs 

and particle concentrations agreed well with theory. Breakthrough was problematic for low 

molecular weight PAHs (mol. wt. < 160). Detection limits ranged from 20 to 50 pg of injected 

mass for all PAHs. 

Gundel et al. (1995) recently developed a technique for the direct determination of phase 

distributions of semivolatile polycyclic aromatic hydrocarbons using annular denuder 

technology. The method, called the integrated organic vapor/particle sampler (IOVPS), uses a 

cyclone inlet with a 50% cut point of 2.5 μm at a sampling rate of 10 L/min. The airstream then 

goes through two or three sandblasted glass annular denuders that are coated with ground 

adsorbent resin material (XAD-4 was initially examined) that traps vapor-phase organics. The 

airstream subsequently passes through a filter, followed by a backup denuder. The denuder 

collection efficiency is high and compares well with predictions based on the diffusivity of the 

compounds. The denuder can also be extracted to obtain gas-phase concentrations directly 

(Gundel and Lane, 1999). Particle-phase PAHs are taken to be the sum of material on the filter 

and XAD adsorbent downstream after correction for denuder collection efficiency. The IOVPS 

was tested for sampling semivolatile PAH in laboratory indoor air and in environmental tobacco 

smoke (ETS). After exposure, the denuders, filters, and sorbent traps were extracted with 

cyclohexane (Gundel et al., 1995) and analyzed for PAHs from naphthalene to chrysene using 

dual-fluorescence detection (Mahanama et al., 1994). Recoveries from both denuders and filters 

were ~70% for 30 samples. Detection limits (defined as three times the standard deviation of the 

blanks) for gas-phase SVOC PAHs ranged from 0.06 ng for anthracene to 19 ng for 

2-methylnaphthalene. The 95% confidence interval (CI) for reproduction of an internal standard 

concentration was 6.5% of the mean value. Relative precision, from a propagation of errors 

analysis or from the 95% CI from replicate analyses of standard reference material SRM 1649 

(urban dust/organics), was 12% on average (8% for naphthalene to 22% for fluorene). Sources 

of error included sampling flow rate, internal standard concentration, and coeluting peaks. 

Gundel and Lane (1999) reported that roughly two-thirds of particulate PAH fluoranthene, 

pyrene, benz[a]anthracene, and chrysene were found on the postfilter denuders, so that it is 

likely that considerable desorption from the collected particles took place. 

2A-5
Solid adsorbent-based denuder systems have been investigated by other researchers as 

well. Bertoni et al. (1984) described the development of a charcoal-based denuder system for 

the collection of organic vapors. Risse et al. (1996) developed a diffusion denuder system to 

sample aromatic hydrocarbons. In their system, denuder tubes with charcoal coating and 

charcoal paper precede a filter pack for particulate collection and an adsorption tube to capture 

particle blow-off from the filter sample. Breakthrough curves for benzene, toluene, orthoxylene, and meta-xylene were developed for 60-, 90-, and 120-cm denuder tubes. The effects of 

relative humidity on the adsorption capacities of the denuder system were examined, and it was 

found that the capacity of the charcoal was not affected significantly by increases in relative 

humidity. The feasibility of outdoor air sampling with the system was demonstrated. 

Krieger and Hites (1992) designed a diffusion denuder system that uses capillary gas 

chromatographic columns as the tubes for SVOC collection. The denuder was followed by a 

filter to collect particles, which in turn was followed by a PUF plug to collect organic material 

volatilizing off the filter. Denuder samples were analyzed by liquid solvent extraction (CH2Cl2) 

followed by GC-mass spectrometric analysis. The PUF plugs and filters were fluid extracted 

with supercritical N2O. Using this system, an indoor air sample was found to contain primarily 

chlorinated biphenyls, ranging from trichlorobiphenyls (vapor pressures 10!3

 to 10!4 Torr at 

25 °C) to octachlorobiphenyls (10!6

 – 10-7 Torr). This demonstrated that the sampler collects 

compounds with a wide range of volatility. They also found that on-line desorption is successful 

in maintaining good chromatographic peak shape and resolution. The entire method, from 

sample collection to the end of the chromatographic separation, took 2 hours. 

Organic acids in both the vapor and particulate phases may be important contributors to 

ambient acidity, as well as representing an important fraction of organic PM. Lawrence and 

Koutrakis (1996a,b) used a modified Harvard/EPA annular denuder system (HEADS) to sample 

both gas and particulate phase organic acids in Philadelphia, PA in the summer of 1992. The 

HEADS sampler inlet had a 2.1-μm cut point impactor (at 10 L/min), followed by two denuder 

tubes, and finally a Teflon filter. The first denuder tube was coated with potassium hydroxide 

(KOH) to trap gas phase organic acids. The second denuder tube was coated with citric acid to 

remove ammonia to avoid neutralizing particle phase acids collected on the filter. The KOHcoated denuder tube was reported to collect gas phase formic and acetic acids at better than 

98.5% efficiency and with precisions of 5% or better (Lawrence and Koutrakis, 1994). It was 

2A-6
noted that for future field measurements of particulate organic acids, a Na2CO3-coated filter 

should be deployed downstream of the Teflon filter to trap organic acids that may evaporate 

from the Teflon filter during sampling. 

Role of the Collection Media 

The role of the collection media was recently examined in a study conducted in Seattle 

(Lewtas et al., 2001). In that study, the influence of denuder sampling methods and filter 

collection media on the measurement of SVOC associated with PM2.5 was evaluated. Activated 

carbon and XAD collection media were used in diffusion denuders and impregnated back-up 

filters in two different samplers, the Versatile Air Pollution Sampler (VAPS) and the PC-BOSS. 

XAD-coated glass annular denuders and charcoal-impregnated cellulose fiber (CIF) filter 

denuders also were used. CIF filters were also compared to XAD-coated quartz filters as backup 

filter collection media. Lewtas et al. (2001) found that the two denuder types resulted in an 

equivalent measurement of particulate OC and particle mass. The carbon-coated denuders in the 

BOSS sampler were more efficient than the XAD-coated denuders for the collection of the more 

highly volatile organic compounds (MHVOCs). Lewtas et al. (2001) concluded that this 

MHVOC fraction that is collected in the carbon-coated BOSS denuder does not contribute 

substantially to the particle mass or to the SVOC measured as OC on quartz filters. However, 

this MHVOC fraction would be captured in the carbon-impregnated filters placed behind the 

quartz filters, so that, in the XAD denuder configuration, the captured MHVOC would cause a 

higher OC concentration and an overestimation of the SVOC. 

Some of the recent research in denuder technology has also focused on reducing the size of 

the denuder, optimizing the residence time in the denuder, understanding the effect of diffusion 

denuders on the positive quartz filter artifact, identifying changes in chemical composition that 

occur during sampling, determining the effects of changes in temperature and relative humidity, 

and identifying possible losses by absorption in coatings. 

Reducing the Size of Denuders 

The typical denuder configuration is an annular diffusion denuder tube of significant length 

(e.g., 26.5 cm for 10 L/min [Koutrakis et al., 1988a,b]). A more compact design based on a 

honeycomb configuration was shown to significantly increase the capacity (Koutrakis et al., 

2A-7
1993). However, in intercomparisons with an annular denuder/filter pack system (Koutrakis 

et al., 1993), significant losses of ammonia and nitric acid were observed for the honeycomb 

configuration and were attributed to the large inlet surface area and long sample residence time 

of the honeycomb design relative to the annular denuder system. Sioutas et al. (1996a) 

subsequently designed a modified glass honeycomb denuder/filter pack sampler (HDS) with an 

inlet that minimizes vapor losses on the inlet surfaces. The modified HDS has reduced inlet 

surfaces and decreased residence time for sampled gases (NH3 and HNO3) compared to its 

predecessor (Sioutas et al., 1994d). Sioutas et al. (1996b) also tested various inlet materials 

(glass, PFA, and polytetrafluoroethylene [PTFE]) in laboratory tests and found that a PTFE 

Teflon coated inlet minimized loss of sampled gases (1 to 8% loss of HNO3 observed, and !4 to 

2% loss of NH3 observed). The highest inlet losses were observed for HNO3 lost to PFA 

surfaces (14 to 25%). The modified HDS was tested in laboratory and field tests and found to 

agree within 10% with the annular denuder system. 

Residence Time in the Denuder 

The efficiency of a diffusion denuder sampler for the removal of gas phase material can be 

improved by increasing the residence time of the sampled aerosol in the denuder. However, the 

residence time can only be increased within certain limits. Because the diffusion denuder 

reduces the concentration of gas-phase semivolatile organic material, semivolatile organic matter 

present in the particles passing through the denuder will be in a thermodynamically unstable 

environment and will tend to outgas SVOC during passage through the denuder. The residence 

time of the aerosol in the denuder, therefore, should be short enough to prevent significant loss 

of particulate-phase SVOC to the denuder. Various studies have suggested that the residence 

time in the denuder should be less than about 2 s (Gundel and Lane, 1999; Kamens and Coe, 

1997; Kamens et al., 1995). The residence times in the various denuder designs described by 

Gundel and Lane (1999) are from 1.5 to 0.2 s. The equilibria and evaporation rates are not as 

well understood for organic components as they are for NH4NO3 (Zhang and McMurry, 1987, 

1992; Hering and Cass, 1999). 

2A-8
Effect of Diffusion Denuders on the Positive Quartz Filter Artifact 

The adsorption of organic compounds by a second quartz filter has been shown to be 

reduced, but not eliminated, in samples collected in the Los Angeles Basin using a multi-channel 

diffusion denuder with quartz filter material as the denuder collection surface preceding the 

quartz filters (Fitz, 1990). This artifact can be further reduced by using activated charcoal as the 

denuder surface and using a particle concentrator to reduce the amount of gas phase organic 

compounds relative to condensed phase organic compounds (Cui et al., 1997, 1998; Eatough, 

1999). Other experiments (Cotham and Bidleman, 1992; Cui et al., 1998; Eatough et al., 1995, 

1996) have shown that the quartz filter artifact can result both from the collection of gas phase 

organic compounds and from the collection of SVOCs lost from particles during sampling. 

Thus, available results suggest that both a “positive” and a “negative” artifact can occur with the 

determination of particulate phase organic compounds using two tandem quartz filters. 

The importance of the adsorption of organic vapors on PM or filters relative to the 

volatilization of organic compounds from PM collected on a filter continues to be a topic of 

active debate. The relative importance of positive and negative artifacts differ for denuded and 

undenuded filters; depend on face velocity, sample loading, and the vapor pressures of the 

compounds of interest; and may vary with season and location because of variations in the 

composition of volatile and semivolatile organic material. Evidence exists for substantial 

positive and negative artifacts in the collection of organic PM. 

Changes in Chemical Composition During Sampling 

The use of sampling systems designed to correctly identify the atmospheric gas and 

particulate phase distributions of collected organic material has been outlined above. 

An additional sampling artifact that has received little consideration in the collection of 

atmospheric samples is the potential alteration of organic compounds by the sampling process 

itself. These alterations appear to result from the movement of ambient air containing oxidants 

and other reactive compounds past the collected particles. The addition of NO2 (< 1 ppm) or O3 

(< 200 ppb) to the sampled air stream (at 0 to 5 °C) for a high-volume sampler reduced the 

concentrations of benzo[a]pyrene and benzo[a]anthracene from a few percent to 38%, with the 

observed reduction increasing with increased concentration of the added gases (Brorström et al., 

1983). Spiking a filter with an amine increased the measured concentrations of nitrosamines in 

2A-9
both the filter and following XAD sorbent bed for a mid-volume sampler (Ding et al., 1998a,b). 

Similar results have been obtained for the exposure of a deuterated amine on a filter to NOx 

(Pellizzari and Krost, 1984). When Tenax columns spiked with deuterated styrene and 

cyclohexene were exposed to ppm concentrations of ozone or halogens, oxygenated and 

halogenated compounds were formed (Pellizzari and Krost, 1984). Similar oxidation of 

aldehydes and peroxyacetylnitrate (PAN) during sampling has been observed (Grosjean and 

Parmar, 1990). Collected PAH compounds can be oxygenated or nitrated on a filter (Davis 

et al., 1987; Lindskog et al., 1987), but 1-nitropyrene has been shown to be resistant to additional 

nitration (Grosjean, 1983). These various chemical transformations of collected organic 

compounds can be eliminated by removing the gas phase oxidants, NOx, HNO3, etc., by reaction 

or adsorption prior to particle collection (Ding, 1998a,b; Grosjean and Parmar, 1990; Parmar and 

Grosjean, 1990; Pellizzari and Krost, 1984; Williams and Grosjean, 1990). The BOSS denuder 

should be effective in eliminating most of the chemical transformation artifacts because reactive 

gases are removed by the charcoal denuder that precedes the particle collection filter. 

Temperature and Relative Humidity Effects 

The problems of sampling artifacts associated with SVOC adsorption and evaporation are 

compounded by temperature and relative humidity (RH) effects (Pankow and Bidleman, 1991; 

Pankow et al., 1993; Falconer et al., 1995; Goss and Eisenreich, 1997). The effects of 

temperature on the partitioning of PAHs were examined by Yamasaki et al. (1982), who found 

that the partition coefficient (PAHvapor/PAHpart) was inversely related to temperature and could be 

described using the Langmuir adsorption concept. The dissociation of ammonium nitrate aerosol 

is also a function of temperature. Bunz et al. (1996) examined the dissociation and subsequent 

redistribution of NH4NO3 within a bimodal distribution using a nine-stage low-pressure Berner 

impactor followed by ion chromatography analysis and found a strong temperature dependency 

on the redistribution. Bunz et al. (1996) found that at lower temperatures (below 10 °C) there 

was little change in the aerosol size distribution. At temperatures between 25 and 45 °C, 

however, the lifetime of NH4NO3 particles decreased by more than a factor of 10, and size 

redistribution, as measured by average ending particle diameter, increased more for higher 

temperatures than for lower temperatures. 

2A-10
The effects of relative humidity on the sorption of SVOC on particles are not well 

understood. In a series of laboratory experiments, Goss and Eisenreich (1997) examined the 

sorption of both nonpolar (hydrocarbons and chlorinated hydrocarbons) and polar (ethyl ether 

and acetone) VOCs onto combustion soot particles as a function of temperature and relative 

humidity. The soot particles used in their experiments were collected from oil furnaces and 

contained 60% (w/w) iron sulfate (water-soluble fraction) and 9% (w/w) EC and OC. They 

found that, for all compounds, the sorption of VOCs onto soot particles decreased with 

increasing relative humidity over the range of 10 to 95%. They also observed hysteresis in the 

relative humidity dependency, with sorption coefficients at a given relative humidity higher 

when the RH is being increased than when the RH is being decreased. The sorption coefficients 

were fit with an exponential function to the RH so that the slope of the regression line would 

provide a measure of the influence of relative humidity. Based on the magnitude of the slope, 

they concluded that the RH-dependency of sorption was stronger for water-soluble organic 

compounds. 

In another study by Jang and Kamens (1998), humidity effects on SVOC gas-particle 

partitioning were examined using outdoor environmental chambers and the experimentally 

determined partitioning coefficients were compared to theoretical values. They examined the 

partitioning of SVOC onto wood soot, diesel soot, and secondary aerosols and concluded that 

“the humidity effect on partitioning was most significant for hydrophobic compounds adsorbing 

onto polar aerosols.” Although these two studies seem to be contradictory, on closer 

examination, it is difficult to compare the two studies for several reasons. The experiments 

conducted by Jang and Kamens (1998) were conducted in outdoor chambers at ambient 

temperatures and humidities. Their model was for absorptive partitioning of SVOC on 

liquid-like atmospheric PM. In contrast, the results of Goss and Eisenreich (1997) were obtained 

using a GC system operated at 70 °C higher than ambient conditions. Goss and Eisenreich 

(1997) modeled adsorptive partitioning of VOC on solid-like atmospheric PM. In the study of 

Jang and Kamens (1998), calculated theoretical values for water activity coefficients for diesel 

soot were based on an inorganic salt content of 1 to 2%; whereas, the combustion particles 

studied by Goss and Eisenreich (1997) contained 60% water-soluble, inorganic salt content. 

Jang and Kamens (1998) obtained their diesel soot from their outdoor chamber, extracted it with 

organic solvent (mixtures of hexane and methylene chloride), and measured the organic fraction. 

2A-11
The resulting salt content of 2% of the PM studied in Jang and Kamens (1998) is enough to 

affect water uptake but presumably not enough to affect the sorption partitioning of organics. 

Impactor Coatings 

Impactors are used to achieve a size cut point and to collect particles on surfaces. Particles 

collected on impactors are exposed to smaller pressure drops than filter-collected particles, 

making them less susceptible to volatile losses (Zhang and McMurry, 1987). However, size 

resolution can be affected by bounce when samples are collected at low humidities (Stein et al., 

1994). Other sources of error inherent in some currently acceptable practices could potentially 

affect PM concentration measurements and will likely become even more important as more 

emphasis is placed on chemical speciation. Allen et al. (1999a) reported that the practice of 

greasing impaction substrates may introduce an artifact from the absorption of semivolatile 

species from the gas phase by the grease, because the grease could artificially increase the 

amount of PAHs and other organic compounds attributed to the aerosol. Allen et al. (1999b) 

offered several criteria to ensure that this absorption artifact is negligible, including selecting 

impaction oils in which analytes of interest are negligibly soluble and ensuring that species do 

not have time to equilibrate between the vapor and oil phases (this criterion is met for 

nonvolatile species). They recommend using oiled impaction substrates only if the absorption 

artifact is negligible as determined from these criteria. The application of greases and impaction 

oils for preventing or reducing bounce when sampling with impactors is not suitable for carbon 

analysis, because the greases contain carbon (Vasilou et al., 1999). 

Kavouras and Koutrakis (2001) investigated the use of polyurethane foam (PUF) as a 

substrate for conventional inertial impactors. The PUF impactor substrate is not rigid like the 

traditional impactor substrate, so particle bounce and reentrainment artifacts are reduced 

significantly. Kavouras and Koutrakis (2001) found that the PUF impaction substrate resulted in 

a much smaller 50% cut point at the same flow rate and Reynolds number. Moreover, the lower 

50% cut point was obtained at a lower pressure drop than with the conventional substrate, which 

could reduce artifact vaporization of semivolatile components. 

2A-12
APPENDIX 2B. ANALYTICAL TECHNIQUES 

2B.1 INORGANIC ELEMENTS 

2B.1.1 Energy Dispersive X-Ray Fluorescence (EDXRF) 

EDXRF has usually been the method of choice for analysis of trace elements on filters. 

EDXRF is preferred for aerosol analysis over wavelength dispersive X-ray fluorescence (XRF) 

because its fast analysis over the total spectrum allows simultaneously analysis of numerous 

elements. EDXRF can accommodate small sample sizes and requires little sample preparation 

or operator time after the samples are placed in the analyzer. It also leaves the sample intact 

after analysis, making further analysis possible. XRF irradiates a uniform particle deposit on the 

surface of a membrane filter with 1 to 50 kev X-rays that eject inner shell electrons from the 

atoms of each element in the sample (Dzubay and Stevens, 1975; Jaklevic et al., 1977; Billiet 

et al., 1980; Potts and Webb, 1992; Piorek, 1994; Bacon et al., 1995; deBoer et al., 1995; 

Holy1⁄2ska et al., 1997; Török et al., 1998; Watson et al., 1999). When a higher energy electron 

drops into the vacant lower energy orbital, a fluorescent x-ray photon is released. The energy of 

this photon is unique to each element, and the number of photons is proportional to the 

concentration of the element. Concentrations are quantified by comparing photon counts for a 

sample with those obtained from thin-film standards of known concentration (Dane et al., 1996). 

The previous 1996 PM AQCD (U.S. Environmental Protection Agency, 1996a) included a 

detailed discussion of EDXRF. 

Emitted x-rays with energies less than ~4 kev (affecting the elements sodium, magnesium, 

aluminum, silicon, phosphorus, sulfur, chlorine, and potassium) are absorbed by the filter, by a 

thick particle deposit, or even by large particles in which these elements are contained. Very 

thick filters also scatter much of the excitation radiation or protons, thereby lowering the 

signal-to-noise ratio for XRF and proton (or particle) induced X-ray emission (PIXE). For this 

reason, thin membrane filters with deposits in the range of 10 to 50 μg/cm2

 provide the best 

accuracy and precision for XRF and PIXE analysis (Davis et al., 1977; Haupt et al., 1995). 

2B-1
2B.1.2 Synchrotron Induced X-ray Fluorescence (S-XRF) 

S-XRF is a form of EDXRF in which the exciting X-rays are generated in a synchrotron. 

Bremmstrahlung X-rays are generated when energetic electrons (generally several GeV in 

energy) are forced by a magnetic field to make a bend in their path. The advantages of the 

technique are that an extremely high flux of X-rays may be obtained and that the X-rays are 

100% polarized in the plane of the electron beam. The former allows for X-ray beams generally 

of 50 to 500 μm in diameter. However, the beams can be focused into X-ray microprobes, with 

spot sizes on the order of 1 μm diameter. The X-ray polarization allows for removal of most of 

the background normally found under characteristic X-ray peaks, greatly improving sensitivity 

compared to other XRF techniques. The primary disadvantages are the limited number of 

synchrotrons, and that few synchrotrons have S-XRF capabilities. Thus, the technique has been 

rarely used for PM, and then generally only for special problems, such as for analysis of the 

smoke from the Kuwaiti oil fires (Cahill et al., 1992; Reid et al., 1994). However, with the 

increasing availability of S-XRF facilities dedicated to PM analysis (the first of which was the 

Advanced Light Source opened at Lawrence Berkeley National Laboratory last year), utilization 

of S-XRF for PM analysis is increasing. 

2B.1.3 Proton (or Particle) Induced X-ray Emission (PIXE) 

PIXE differs from XRF analysis in the excitation source for producing fluorescence. The 

filter deposit is bombarded with high-energy protons to remove inner shell electrons, and the 

resulting characteristic X-rays are analyzed as in XRF (Johansson, 1970; Cahill, 1981, 1985, 

1990; Zeng et al., 1993). Small accelerators, generally Van de Graaffs, generate intense beams 

of low energy protons, usually of a few MeV in energy. These have the ability to remove 

electrons from the inner shells of atoms of any element. Thus, PIXE can measure a very wide 

range of elements in a single analysis. The cross section for producing X-rays using protons of a 

few MeV in energy tends to favor lighter elements, Na through Ca, but sensitivities for 

equivalent PIXE and multi-wavelength XRF analysis are roughly comparable. The technique 

has been widely used in the United States (Flocchini et al., 1976; Malm et al., 1994) and around 

the world, as many universities have the small accelerators needed for the method. Like S-XRF, 

the proton beams can be focused into μm size beams, but these have not been widely used for 

PM. However, the mm-size beams used in both S-XRF and PIXE are well suited to analyzing 

2B-2
the limited mass and small deposits that result from detailed particle size measurements by 

impactors (Perry et al., 1999). 

2B.1.4 Proton Elastic Scattering Analysis (PESA) 

With the routine availability of elemental analyses for all elements sodium and heavier, 

organic components remain the major unmeasured species for mass balance. For programs like 

IMPROVE (Malm et al., 1994), parallel filters are collected for separate OC and EC 

determinations. Aerosol programs that use PIXE can directly measure hydrogen simultaneously 

by scattering protons from Teflon filters, which lack hydrogen (Cahill et al., 1989, 1992). After 

correction for hydrogen in ammonium sulfate and nitrate, the remaining hydrogen can be used to 

infer the OC mass. Generally, analyses of organic matter by carbon combustion from quartz 

filters and analysis of organic matter by hydrogen in particles on Teflon filters are in agreement, 

assuming certain assumptions about the chemical states of sulfates and nitrates are met (Malm 

et al., 1994; Cahill et al., 1996). 

2B.1.5 Total Reflection X-Ray Fluorescence (TRXRF) 

One of the limitations of the EDXRF method is its minimum detection limit, which may be 

high due to high background values (Streit et al., 2000). However, by using implementation of 

X-ray optical geometries that use the total reflection of the primary radiation on flat surfaces, 

scattering on the substrate is lessened, thus reducing detection limits. This is the basis for the 

total reflection X-ray fluorescence (TRXRF) method (Aiginger and Streli, 1997). This 

modification to the EDXRF technique improves detection limits and eliminates the need to 

correct for matrix effects. Despite its apparent advantages, TRXRF has not yet become widely 

used for atmospheric aerosol analysis, although it has been used in the analysis of marine aerosol 

(Stahlschmidt et al., 1997) and at a high elevation site (Streit et al., 2000). Streit et al. (2000) 

sampled ambient air at the High Alpine Research Station (3,580 m above sealevel) in the 

Bernese Alps, Switzerland, using a nine-stage, single-jet, low-pressure, cascade impactor 

equipped with quartz impactor plates that were coated with silicon oil diluted in 2-propanol. The 

typical sample volume for a weekly sample was 10 m3

. The quartz plates were analyzed directly 

by TRXRF. Streit et al. reported that the minimum detection limits, defined by the 3F values of 

the blanks, ranged from 25 ng for S, decreased monotonically with increasing atomic number 

2B-3
down to 5 pg for Rb, and decreased after that. The use of TRXRF is expected to increase as 

EDXRF users become more aware of the method. A relatively low-cost, add-on unit has been 

developed that would allow EDXRF users to test the TRXRF technique (Aiginger, 1991). 

2B.1.6 Instrumental Neutron Activation Analysis (INAA) 

INAA irradiates a sample in the core of a nuclear reactor for few minutes to several hours, 

depending on the elements being quantified (Dams et al., 1970; Zoller and Gordon, 1970; 

Nadkarni, 1975; Landsberger, 1988; Olmez, 1989; Ondov and Divita, 1993). The neutron 

bombardment transforms many elements into radioactive isotopes. The energies of the gamma 

rays emitted by these isotopes identify them and, therefore, their parent elements. Furthermore, 

the intensity of these gamma rays is proportional to the amount of the parent element present in 

the sample. Different irradiation times and cooling periods are used before counting with a 

germanium detector. In source apportionment studies, it is possible to use a combination of XRF 

and INAA to develop a relatively complete set of elemental measurements. Between these two 

analytical techniques, good sensitivity is possible for many elements, including most of the toxic 

metals of interest. In general, XRF provides better sensitivity for some metals (Ni, Pb, Cu, and 

Fe); whereas INAA provides better sensitivity for others (Sb, As, Cr, Co, Se, and Cd). Both 

methods provide similar detection limits for still other elements (V, Zn, and Mn). INAA does 

not quantify some of the abundant species in ambient PM such as silicon, nickel, tin, and lead. 

While INAA is technically nondestructive, sample preparation involves folding the sample 

tightly and sealing it in plastic, and the irradiation process makes the filter membrane brittle and 

radioactive. These factors limit the use of the sample for subsequent analyses. 

INAA has been used to examine the chemical composition of atmospheric aerosols in 

several studies either as the only method of analysis or in addition to XRF (e.g., Yatin et al., 

1994; Gallorini, 1995). INAA has higher sensitivity for many trace species, and it is particularly 

useful in analyzing for many trace metals. Landsberger and Wu (1993) used INAA to analyze 

air samples collected near Lake Ontario for Sb, As, Cd, In, I, Mo, Si, and V using INAA. They 

and found that using INAA in conjunction with epithermal neutrons and Compton suppression 

produces very precise values with relatively low detection limits. 

Enriched rare-earth isotopes have been analyzed via INAA and used to trace sources of PM 

from a coal-fired power plant (Ondov et al., 1992), from various sources in the San Joaquin 

2B-4
Valley (Ondov, 1996), from intentially tagged (iridium) diesel emissions from sanitation trucks 

(Suarez et al., 1996; Wu et al., 1998), and from iridium-tagged emissions from school buses 

(Wu et al., 1998). 

An intercomparison was conducted in which 18 pairs of filters were sent to participants in 

the Coordinated Research Program (CRP) on Applied Research on Waste Using Nuclear Related 

Analytical Techniques (Landsberger et al., 1997). As part of that study, participants used PIXE, 

INAA, XRF, or AAS to analyze the samples. Many of the results for XRF and PIXE in the 

coarse fraction were observed to be biased low compared to INAA. The authors speculated that 

self-attenuation of the X-rays resulting from the particle size effect could create a systematic 

error. 

2B.1.7 Atomic Absorption Spectrophotometry (AAS) 

AAS is applied to the residue of a filter extracted in a strong solvent to dissolve the solid 

material; the filter or a portion of it is also dissolved during this process (Ranweiler and Moyers, 

1974; Fernandez, 1989; Jackson and Mahmood, 1994; Chow et al., 2000). A few milliliters of 

the extract are injected into a flame where the ions are reduced to elements and vaporized. 

Elements absorb light at certain wavelengths in the visible spectrum, and a light beam with 

wavelengths specific to the elements being measured is directed through the flame to be detected 

by a monochrometer. The light absorbed by the flame containing the extract is compared with 

the absorption from known standards to quantify the elemental concentrations. AAS requires an 

individual analysis for each element, and a large filter or several filters are needed to obtain 

concentrations for a large variety of elements. AAS is a useful complement to other methods 

(such as XRF and PIXE) for species such as beryllium, sodium, and magnesium, which are not 

well quantified by these methods. Airborne particles are chemically complex and do not 

dissolve easily into complete solution, regardless of the strength of the solvent. Thus, insoluble 

residues may be left behind, and soluble species may co-precipitate on them or on container 

walls. 

AAS was used to characterize the atmospheric deposition of trace elements Zn, Ni, Cr, Cd, 

Pb, and Hg to the Rouge River watershed by particulate deposition (Pirrone and Keeler, 1996). 

The modeled deposition rates were compared to annual emissions of trace elements that were 

estimated from the emissions inventory for coal and oil combustion utilities, iron and steel 

2B-5
manufacturing, metal production, cement manufacturing, and solid waste and sewage sludge 

incinerators. They found generally good agreement between the trend observed in atmospheric 

inputs to the river (dry + wet deposition) and annual emissions of trace elements, with R2

s 

varying from .0.84 to 0.98. Both atmospheric inputs and emissions for Pb showed downward 

trends. For the period of 1987 to 1992, steady increases were observed for Cd (major sources 

are municipal solid waste incineration, coal combustion, sludge incineration, and iron and steel 

manufacturing), Cr and Ni (major sources are iron and steel production and coal combustion), 

and Hg (major sources are coal, the contribution from which had decreased from 53 to 45%, as 

well as municipal, solid, and medical waste incineration, the contribution from which has 

increased). 

2B.1.8 Inductively Coupled Plasma with Atomic Emission Spectroscopy 

(ICP-AES) 

ICP-AES introduces an extracted sample into an atmosphere of argon gas seeded with free 

electrons induced by high voltage from a surrounding Tesla coil (Fassel and Kniseley, 1974; 

McQuaker et al., 1979; Lynch et al., 1980; Harman, 1989; Tyler, 1992; Baldwin et al., 1994). 

The high temperatures in the induced plasma raise valence electrons above their normally stable 

states. When these electrons return to their stable states, a photon of light is emitted that is 

unique to the element that was excited. This light is detected at specified wavelengths to identify 

the elements in the sample. ICP-AES can determine a large number of elemental concentrations 

using small sample volumes with acceptable detection limits for atmospheric samples. As with 

AAS, this method requires complete extraction and destruction of the sample. 

2B.1.9 Inductively Coupled Plasma with Mass Spectroscopy (ICP-MS) 

ICP-MS has been applied in the analysis of personal exposure samples (Tan and Horlick, 

1986; Gray and Williams, 1987a,b; Nam et al., 1993; Munksgaard and Parry, 1998; Campbell 

and Humayun, 1999). Ion species generated from ICP and from the sample matrix can produce a 

significant background at certain masses resulting in the formation of polyatomic ions that can 

limit the ability of ICP-MS to determine some elements of interest. Cool plasma techniques 

have demonstrated the potential to detect elements at the ultra-trace level (Nham et al., 1996) 

and to minimize common molecular ion interferences (Sakata and Kawabata, 1994; Turner, 

2B-6
1994; Plantz, 1996). Detection limits of ICP-MS using a one-second scan are typically in the 

range of 10-3 ng/m3, which is an order of magnitude lower than other elemental analysis methods. 

The instrument can also be set up to analyze a wide dynamic range of aerosol concentrations. 

Isotope analysis can also be performed with ICP-MS. Intercomparison studies are needed to 

establish the comparability of ICP-MS with other nondestructive filter analysis methods. 

Keeler and Pirrone (1996) used ICP-MS to determine trace elements Cd, Mn, V, As, Se, 

and Pb in atmospheric fine particulate (PM2.5) and total suspended particulate samples collected 

in two Detroit sites. The results were used in a deposition model with additional measurements 

using AAS to estimate the dry deposition flux of trace elements to Lake Erie. 

2B.1.10 Scanning Electron Microscopy (SEM) 

Mamane et al. (2001) investigated the use of computer-controlled scanning electron 

microscopy (CCSEM) as a way of supplementing XRF analysis and providing automated 

analysis of particle size, chemistry, and particle classification. An ambient coarse particulate 

sample from Baltimore was collected on a polycarbonate filter for this analysis. CCSEM 

analyses were conducted for 2,819 particles in 78 randomly selected fields of view during an 

unattended 8-h run. Mamane et al. (2001) confirmed the stability of the CCSEM instrument 

over several hours of operation. The physical properties of the sample such as particle diameter, 

mass loading per field, and particle number per field were well represented by analyzing 

approximately 360 particles, and little additional information was gained by analyzing more 

particles. Teflon filters are not well suited for SEM analyses. Unfortunately, analysis of fine 

PM is expected to pose analytical challenges not addressed in the present study (Mamane et al., 

2001). 

Nelson et al. (2000) applied Raman chemical imaging and SEM (Raman/SEM) to study the 

size, morphology, elemental and molecular composition, and molecular structure of fine PM. 

In their study, filter compatibility was examined, and Raman/SEM chemical imaging was 

conducted for several standard materials as well as for ambient PM2.5 samples. Polycarbonate 

was determined to be a suitable substrate for both SEM and Raman chemical imaging analysis. 

Conner et al. (2001) used CCSEM with individual X-ray analysis to study the chemical and 

physical attributes of indoor and outdoor aerosols collected around a retirement home in 

Baltimore. The CCSEM technique was demonstrated to be capable of identifying spherical 

2B-7
particles typical of combustion or other high temperature (presumably industrial) processes as 

well as pollens and spores. Indoor particles originating from cosmetics were also identified. 

2B.2 ORGANIC AND INORGANIC CARBON 

Large scale efforts to characterize carbonaceous aerosol require cost effective methods that 

can analyze samples rapidly. Commercial thermal-optical (TO) instruments were developed to 

serve this need. The IMPROVE and STN networks have employed these instruments to 

accumulate large data sets, including measurements taken over the past 18 years by the 

IMPROVE network and over the past 3 years by STN. In addition to the protocols developed for 

the IMPROVE and STN networks, a number of alternative TO-based protocols and techniques 

have been employed by the academic research community. Protocols vary in temperature range 

and step size, in the duration of heating at each step, in the timing for introduction of oxygen for 

the conversion of black carbon into CO2, in the use of a catalyst to reduce pyrolysis, and in the 

assignment of OC and EC fractions. These operational differences have complicated efforts to 

compare and combine data sets from studies using different TO protocols. 

Thermal-Optical Reflectance 

The thermal-optical reflectance (TOR) method of carbon analysis developed by Huntzicker 

et al. (1982) has been adapted by several laboratories for the quantification of OC and EC in PM 

collected on quartz-fiber filters. Although the principle applied by these laboratories is identical 

to that of Huntzicker et al. (1982), the details differed with respect to calibration standards, 

analysis time, temperature ramping, and volatilization/combustion temperature. The IMPROVE 

network employs a version of the TOR method for its OC/EC analyses. 

In the most commonly used version of the TOR method (Chow et al., 1993), a punch from 

the filter sample is heated to temperatures ranging from ambient levels to 550 °C in a pure 

helium atmosphere. In principle, the OC fraction of the PM contained in the filter punch will 

vaporize, leaving behind only refractory EC. The organic carbon that evolves at each 

temperature step is first oxidized to CO2, then converted to methane and finally quantified with a 

flame ionization detector (FID). The filter punch is incubated at 550 °C for a period sufficient to 

allow the flame ionization signal to return to its baseline value. The punch is then exposed to a 

2B-8
2% oxygen and 98% helium atmosphere and heated from 550 °C to 800 °C with several 

temperature ramping steps. The reflectance from the deposit side of the filter punch is 

monitored throughout the analysis. This reflectance decreases during OC volatilization in the 

helium atmosphere owing to the pyrolysis of nonvolatile organic material present in the filter 

punch. When oxygen is added, the reflectance increases as light-absorbing carbon is combusted 

and removed. It is assumed that the first light-absorbing carbon compounds to combust are 

pyrolysis artifacts from the first phase of the analysis. Therefore, the OC mass is defined as that 

fraction that evolves up to the introduction of oxygen plus the oxidized carbon that evolves up to 

the point when the filter reflectance signal is restored to its preanalysis value. Once the original 

reflectance level is re-attained, all further carbon evolving from the sample punch is assigned to 

the EC fraction. Accordingly, “organic carbon” (OC) is carbon that does not absorb light at the 

laser wavelength (632.8 nm) typically used by TOR instruments, and all other carbon is defined 

as “elemental carbon” (EC). 

Thermal-Optical Transmission 

The primary difference between TOR and thermal-optical transmission (TOT) methods is 

in the choice of absorption detection: light transmission through the filter punch, rather than its 

reflectance, is monitored throughout the analysis. The TOT method of Birch and Cary (1996) 

also uses a pure helium atmosphere for volatilizing OC, but the second stage involves a higher 

oxygen/helium (10%) gas mixture to oxidize the black carbon remaining on the filter punch. 

The temperature is raised to approximately 820 °C in the helium phase, during which both 

organic and carbonate carbon are volatilized from the filter. In the second stage, the oven 

temperature is reduced, then raised to about 860 °C. During this stage, pyrolysis correction and 

the EC measurement is made. Figure 2B-1 is an example of a TOT thermogram, showing 

temperature, transmittance, and FID response traces. The peaks that correspond to the 

concentrations of CO2 that evolve from the filter punch during the course of the analysis are 

assigned to OC, carbonate carbon (CC), pyrolitic carbon (PC), and EC. The high temperature in 

the first stage of the TOT thermal profile is included in order to decompose CC and to volatilize 

very high-boiling organic compounds. However, many organic carbon compounds will pyrolyze 

at this temperature to generate PC. The ability to quantify PC is particularly important in high 

OC/EC regions such as wood smoke-impacted air sheds. Wood smoke aerosol contains many 

2B-9
Figure 2B-1. Thermogram for a sample containing rock dust (a carbonate source) and 

diesel exhaust, showing three traces that correspond to temperature, filter 

transmittance, and Flame Ionization Detector (FID) detector response. 

Peaks correspond to organic (OC), carbonate (CC), pyrolytic (PC), and 

elemental (EC) carbon. The final peak is a methane calibration peak. 

Source: Birch and Cary (1996). 

complex compounds that generate substantial PC. Significant error in the EC fraction can result 

in the absence of a careful PC correction. 

Informal intercomparisons among different filter transmission methods have shown high 

correlations of absorption, but with differences of up to a factor of two in absolute values 

(Watson et al., 1988a,b). These differences are functions of the type of filter, filter loading, the 

chemical and physical nature of the deposit, the wavelengths of light used, calibration standards, 

and light diffusing methods. Currently, there is no agreement on which combination most 

accurately represents light absorption in the atmosphere. 

The National Institute for Occupational Safety and Health (NIOSH) Method 5040 is based 

on the TOT method (Birch and Cary, 1996). The NIOSH protocol consists of a two-stage 

process with the first stage being conducted in a pure helium atmosphere at temperatures of 250, 

2B-10
500, 650, and 850 °C for a total of 4.5 min and the second stage conducted in a 2% oxygen/98% 

helium mix at temperatures of 650, 750, 850, and 940 /C for 4 min. A pyrolysis base correction 

is made based on the transmittance measurement. The EPA adopted a modified version of the 

NIOSH Method 5040 protocol for use in its STN. 

IMPROVE versus NIOSH protocols 

Although the IMPROVE protocol and the NIOSH protocol in use by the STN network 

yield closely comparable TC values, the differences in thermal profiles and operational 

definitions of organic versus black carbon result in very different mass assignments to these 

fractions. Other methodological differences may make data comparisons difficult, including the 

different choice of absorption detection, e.g. reflectance versus transmission, the different 

temperature ranges and incubation periods, and the different approaches used to account for 

background OC. Examples of thermograms obtained with the IMPROVE and NIOSH protocols 

are shown in Figure 2B-2. 

Using both the IMPROVE and NIOSH protocols, Chow et al. (2000) analyzed 60 quartz 

filter samples on a prototype reflectance/transmittance analyzer that represented a wide variety 

of aerosol compositions and concentrations. The two TC data sets possessed no statistically 

significant differences. However, marked differences were found in the fraction of TC that was 

attributed to EC as determined by the IMPROVE versus NIOSH thermal evolution protocols. 

The IMPROVE EC measurements were typically higher than the NIOSH EC measurements. 

When the NIOSH protocol was modified to exclude the helium-only 850 oC temperature step, 

however, the OC/EC ratios came into agreement between the two methods. Because OC and EC 

are operationally defined parameters, Chow et al. (2000) pointed out that it is important to retain 

ancillary information when reporting EC and OC by these analytical methods, so that 

comparisons can be made among measurements taken at different sites using these two methods. 

The NIOSH and IMPROVE protocols both require correction for positive organic artifacts 

resulting from the absorption of background organic vapor by the heat-treated quartz filters used 

for OC/EC measurements. Both the IMPROVE and STN science teams have evaluated the 

presence of carbon artifacts in their measurements. The IMPROVE team has established that 

heat-treated quartz filters adsorb OC vapors up to a saturation threshold over the course of a few 

days in the field. The STN science team observed that positive carbon artifacts can vary with 

2B-11
Figure 2B-2. Examples of thermograms obtained by (a) the IMPROVE protocol, and by 

(b) the NIOSH protocol. Flame ionization detection is used in both examples. 

FID denotes the observed signal intensity. The intervals denoted ECR/ECT, 

i.e., elemental carbon reflectance/transmittance, refer to the time and 

temperature intervals during which evolved CO2 is assumed to come from 

oxidized elemental carbon (EC). Laser Reflect and Laser Trans refer to 

laser reflectance or transmission measurement of filter “blackness” at the 

He-Ne wavelength, 633 nm. 

Source: Birch and Cary (1996). 

2B-12 
sampler type. Total carbon artifacts for the samplers used by the STN range from 9.5 to 33.9% 

of the carbon collected during a typical ambient measurement. Documents describing the issues 

regarding blank subtraction and the current protocols used by the IMPROVE and STN networks 

are available on the network websites: 

IMPROVE – http://vista.cira.colostate.edu/improve 

STN – http://www.epa.gov/ttn/amtic/files/ambient/pm25/spec 

Pyrolitic Carbon (PC) and Other Confounders 

In a methods comparison study, Countess (1990) determined that it is necessary to 

minimize or correct for the PC and OC found in wood smoke and automobile exhaust samples 

that pyrolyze to create interferences during the course of some thermal-optical protocols. 

Pyrolysis of organic compounds occurs during analysis by both TO methods, although the 

greater temperature to which samples are exposed in the NIOSH protocol is likely to produce 

larger quantities of pyrolysis-derived EC. During the initial heating phase, pyrolysis is indicated 

by an increase in optical density (blackness) in the filter sample. Both methods distinguish 

artifact pyrolysis-derived EC from ambient EC at the point at which the transmittance or 

reflectance signal is restored to the pre-heating level. The assumption made is that heating does 

not alter the absorption properties of the material collected on the filter. This assumption is 

reasonable if the only light absorbing species of carbon is strictly a graphite-like EC that is 

unlikely to undergo a change in its absorption properties over the temperature ranges used by TO 

methods. The effects of heat-induced changes to the light absorption and chemical properties of 

atmospheric organic compounds undergoing TO analysis are being evaluated by NIST. NIST 

has identified three assumptions that must be met in order for TO methods to reliably measure 

EC: (1) absorptivity of carbonaceous PM remains constant up to the point of pyrolysis; (2) once 

formed, pyrolyzed carbon (char) absorbs at the analytic wavelength and its absorptivity remains 

constant within the high temperature step; (3) pyrolyzed OC has the same absorptivity as the EC 

that is native to the sample. Using urban dust, forest fire emissions, and ambient laboratory 

aerosol, they observed changes in the absorptivities of these materials during heating before and 

during the formation of pyrolysis artifacts, up to the OC/EC split point. NIST, therefore, 

recommends that standard TOR/T protocols be developed that account for these changes. 

2B-13
Light-absorbing carbon or black carbon (BC) includes a large number of compounds that 

may be altered, not only in their light-absorbing properties but also in their oxidation chemistry. 

The materials responsible for defining the original blackness of the filter sample may be altered 

during the initial heating phase of TO analysis, so that the mix present during the oxidation cycle 

may not be representative of the original, atmospherically derived BC. An error in determining 

the mass of EC would arise, for example, if a heavy, but weakly light-absorbing, compound were 

transformed into a material that possessed a higher absorption coefficient and higher resistance 

to oxidation than absorbing compounds collected from the atmosphere. The pyrolized form of 

this compound would resist oxidation past the OC-EC split point in the analysis, leading to a 

falsely high EC fraction. 

Chow et al. (2000) noted that neither the IMPROVE nor NIOSH methods were able to 

accurately detect further blackening on optically dense, i.e., very black, filters that are typical of 

source profile samples. Predictions of artifact pyrolysis carbon by TOR and TOT differed 

widely. Note that both transmittance and reflectance methods rely on derivations of optics laws 

(i.e., the Beer-Lambert and Lambert Laws) that predict a linear transmittance/reflectance signal 

response with species concentration, but only for optically thin samples (Strobel and Heineman, 

1989). Very black filters exceed this limitation; thus, the signal response of these methods may 

not be a linear or otherwise predictable function of BC concentration. 

Another important source of error in any TO measurement of aerosol OC/EC arises when 

samples contain transition metal oxides, such as iron oxide. Many transition metal oxides are 

found in crustal material. Fung et al. (2002) reported that such oxides can serve as oxidizing 

agents for BC at high temperatures. The consequence of such an effect is an elevation of the 

signal usually assigned to OC and corresponding reduction in apparent BC. 

Thermal Manganese Oxidation 

The thermal manganese oxidation (TMO) method (Mueller et al., 1982; Fung, 1990) uses 

manganese dioxide in contact with the sample throughout the analysis as the oxidizing agent. 

Temperature is relied upon to distinguish between OC and EC. Carbon evolving at 525 °C is 

classified as OC, and carbon evolving at 850 °C is classified as EC. TMO does not correct for 

pyrolized OC, which may lead to the overestimation of EC. This method has been used in the 

SCENES (Subregional Cooperative Electric Utility, Department of Defense, National Park 

2B-14
Services, and Environmental Protection Agency Study); (Sutherland and Bhardwaja, 1987; 

Mueller et al., 1986) visibility network, as well as in the SCAQS (Southern California Air 

Quality Study) (Chow et al., 1994a,b; Watson et al., 1993, 1994a,b). 

OC/EC Methods Intercomparisons 

Chow et al. (1993) documented several variations of the thermal (T), thermal-optical 

reflectance (TOR), thermal-optical transmission (TOT), and thermal manganese oxidation 

(TMO) methods for OC and EC. Comparisons among the results of the majority of these 

methods show that they yield comparable quantities of TC in aerosol samples, but the 

distinctions between OC and EC are quite different (Cadle and Groblicki, 1982; Cadle and 

Mulawa, 1990; Countess, 1990; Hering et al., 1990; Birch, 1998; Schmid et al., 2001). For the 

sum of OC and EC, these methods reported agreement within 5 to 15% for ambient and source 

samples (Houck et al., 1989; Kusko et al., 1989; Countess, 1990; Shah and Rau, 1990) and 

within 3% on carefully prepared standards. Evaluation of these methods is thus a matter of 

assessing how they differentiate between OC and EC. The EC:TC ratio for samples is method 

dependent. 

An international methods intercomparison study on the analysis of carbonaceous aerosols 

on quartz fiber filters was organized by the Vienna University of Technology and involved 17 

laboratories and 9 different thermal and optical methods (Schmid et al., 2001). All of the 

participating laboratories were sent punches from three 150-mm quartz-fiber filters that had been 

exposed for 24 h near a high-traffic street in Berlin. Five laboratories employed the German 

official standard VDI 2465 methods. Two of these laboratories used the VDI 2465/1 method 

that determines extractable OC, non-extractable OC, and EC using a solvent-based extraction 

protocol. Other laboratories participating in the intercomparison used variations of the VDI 

2465 standard that rely upon differences in thermal stability to accomplish the separation of 

carbonaceous aerosol fractions. A range of thermal protocols, TC determination techniques 

and CO2 detection schemes were employed by the participating laboratories. 

Good agreement of the TC results was obtained by all laboratories, with only two outliers 

in the complete data set. The relative standard deviations between laboratories for the TC 

results, were 6.7, 10.6, and 8.8% for the three samples. In contrast, the EC results were much 

more variable. The relative standard deviations between laboratories for the EC results, were 

2B-15
36.6, 24.4, and 45.5% for the three samples. The VDI methods, especially the VDI 2465/2, were 

found to give generally higher amounts of EC than the thermal-optical methods. This trend was 

detected for all samples. The authors recognized that uncorrected thermal methods are prone to 

positive artifacts by charring during pyrolysis. They also noted that when using solvent 

extraction methods, the dissolution of polymeric aerosol constituents may not be successful. 

Both of these effects would lead to an overestimation of the EC fraction. When the laboratories 

were grouped according to their methods, the relative standard deviation between laboratories 

was much smaller. This study demonstrated that the TC measurement can yield similar results 

from a variety of methods, but the EC measurement is highly dependent upon the method used. 

The problems associated with the determination of EC are exacerbated by the lack of a standard 

reference material and consistent definitions of EC. 

Measuring Black Carbon (BC) Instead of EC 

Light-absorbing or black carbon (BC) can be measured by optical techniques (Penner and 

Novakov, 1996). Both EC and BC define a similar fraction of aerosol; but EC is defined in 

terms of both the thermal and light-absorption properties of the sample, whereas BC is based on 

solely on its light-absorption properties. The aethalometer, the integrating sphere 

sunphotometer, and photoacoustic spectroscopy (described in Section 2B.3) are example 

techniques for determining BC. 

Hitzenberger et al. (1996) investigated the feasibility of using an integrating sphere 

photometer as an adequate measurement system for the BC content and the absorption 

coefficient. In another study (Hitzenberger et al., 1999), the integrating sphere method was 

compared to an aethalometer (Hansen et al., 1984), the thermal method of Cachier et al. (1989), 

and the thermal-optical method of Birch and Cary (1996). The absorption coefficients that were 

obtained from both the integrating sphere and the aethalometer were comparable. The BC mass 

concentrations obtained from the aethalometer were 23% of those obtained from the integrating 

sphere. Compared to the thermal method, the integrating sphere method overestimated the BC 

mass concentrations by 21%. Compared to the thermal-optical method, the integrating sphere 

was within 5% of the 1:1 line. However, the data were not well correlated. Thermal EC and 

aethalometer BC measurements have also been compared by Lavanchy et al. (1999). 

2B-16
The Carbonaceous Species Methods Comparison Study (CSMCS), as mentioned in the 

1996 PM AQCD (U.S. Environmental Protection Agency, 1996a) was conducted in Los Angeles 

in 1986. Hansen and McMurry (1990) compared the thermal manganese method with the 

aethalometer for aerosol EC. The first method involved collection of impactor samples backed 

by a quartz-fiber after-filter followed by EC analysis by oxidation in helium over a MnO2 

catalyst; the other method involved conducting real-time measurements using an aethalometer 

(an optical absorption technique). They found good agreement between these two very different 

methods. The CSMCS interlaboratory precision for TC was 4.2% (Turpin et al., 2000). 

However, because the split between OC and EC is operationally defined, there was substantial 

interlaboratory variability in OC and EC (e.g., 34% for EC [Turpin et al., 1990]). 

EC/OC Summary. With the limitations and precautions described above, laboratory 

analyses for the carbonaceous properties of collected particles have matured to the point at 

which they can be performed with commercially available instruments following established 

standard operating procedures. However, carbon analysis continues to be a subject of active 

research within the atmospheric sciences community and EPA, and carbon speciation methods 

comparisons are being undertaken in such studies as the Atlanta Supersite. The state of the art 

for carbonaceous PM measurements continues to advance; and, although progress is being made, 

the definitions of OC, EC, and BC continue to be operationally defined in reference to the 

method employed. Reports of EC/OC measurements should therefore include mention of the 

method with which the species were determined. Finally, if possible, all ancillary data should be 

retained, to allow later comparison to other methods. 

2B.3 CONTINUOUS METHODS 

2B.3.1 Continuous Measurement of Mass 

Tapered Element Oscillating Microbalance (TEOM) 

The advantages of continuous PM monitoring and the designation of the TEOM as an 

equivalent method for PM10, have led to the deployment of the TEOM at a number of air 

monitoring sites. The TEOM also is being used to measure PM2.5. The TEOM differs from the 

federal reference methods for PM in that it does not require equilibration of the samples at a 

2B-17
specified temperature and RH. The TEOM samples at a constant temperature and is typically 

heated to some temperature higher than the ambient temperature (Meyer et al., 1995; Meyer and 

Rupprecht, 1996), whereas the FRM samples at the ambient temperature. Thus, the TEOM does 

not provide data equivalent to the FRM because of losses of volatile species. Volatilization 

losses in the TEOM sampler can be reduced by operating the instrument at 30 °C, rather than the 

typical 50 °C specified, or by using a Nafion diffusion dryer instead of heating to dehumidify the 

particles (with a 30 °C temperature). 

This difference in operation and implications for fine particle measurements was examined 

by researchers at Commonwealth Scientific and Industrial Research Organization (CSIRO) 

Atmospheric Research in Australia (Ayers et al., 1999). That group compared 24-h mean PM2.5 

mass concentrations as determined by a TEOM and by two manual, gravimetric samplers (a lowvolume filter sampler and a MOUDI sampler) in four Australian cities, on 15 days in the 

wintertime. The TEOM was operated at 50 °C at one location and at 35 °C at the other three 

locations. A systematically lower TEOM response in comparison to the integrated gravimetric 

methods was observed. In a comprehensive study, Allen et al. (1997) reported results in which 

TEOM data collected at 10 urban sites in the United States and Mexico were compared with 

24-h integrated mass concentrations for both PM10 and PM2.5. They collected a large data set that 

included both winter and summer seasons. Allen et al. (1997) concluded that, especially for 

urban areas, a significant fraction of PM10 may be semivolatile compounds that could be lost 

from the heated filter in the TEOM leading to a systematic difference between the TEOM and 

the EPA FRM for PM10. They suggested that this difference is likely to be larger for PM2.5 than 

it is for PM10 (Allen et al., 1997). 

In a similar study conducted in Vancouver, British Columbia the effect of equilibration 

temperature on PM10 concentrations from the TEOM was examined. Two collocated TEOM 

monitors, operated at 30 and 50 °C, respectively, collected data for a period of ~17 months in the 

Lower Fraser Valley in British Columbia (Mignacca and Stubbs, 1999). A third TEOM 

operating at 40 °C was operated for 2 months during this period. They found that, on average, 

the 1-h average PM10 from the TEOM operating at 30 °C was consistently greater than that from 

the TEOM operated at 50 °C. For the period during which the third TEOM was operated (at 

40 °C), the PM10 from that instrument was between the values for the other two instruments. 

They also found that the differences in masses were proportional to the PM10 loading, and more 

2B-18
strongly correlated to the PM10 from the TEOM operated at the lower temperature. They 

recommended that the TEOM monitors be operated at 40 oC in summer and 30 °C in winter, 

in order to avoid introducing a methodological seasonal bias. 

A new sample equilibration system (SES) was developed to reduce losses of semivolatile 

species from the PM2.5 TEOM by conditioning the sample stream to a lower RH and temperature 

(Meyer et al., 2000). The SES utilizes humidity sensors and a Nafion dryer designed for low 

particle loss. The dryer fits between the flow splitter that follows the size-selective inlet and the 

sensor unit. A dry purge gas flows over the exterior of the Nafion tubing and allows for selfregeneration. A TEOM with PM2.5 inlet and equipped with an SES was operated at 30 °C 

alongside another TEOM operating at 50 °C without the SES in Albany, NY, over a 6-day period 

during a summertime high-temperature, high-relative-humidity episode. The SES maintained 

the sample air relative humidity under 30%, and the TEOM with the SES generally measured 

more mass than the other TEOM. The TEOM with SES also was operated alongside an FRMtype sampler for the period of June 6 through September 25, 1999. The correlation between the 

FRM and TEOM/SES showed a slope of 1.0293 and R2

 of 0.9352; whereas the correlation 

between the FRM and the TEOM without SES and operating at 50 oC showed a slope of 0.8612 

and R2

 of 0.8209. The SES can be installed on existing TEOM monitors. 

Beta-Gauge Techniques 

Beta radiation absorption, as a indicator of particle mass, has been used effectively to 

measure the mass of equilibrated PM collected on Teflon filters (Jaklevic et al., 1981a; Courtney 

et al., 1982). The technique also has been used to provide near real-time measurements with 

time intervals on the order of an hour (Wedding and Weigand, 1993). However, real-time betagauge monitors experience the same problems as other continuous or near real-time PM mass 

monitoring techniques. Particle-bound water (PBW) must be removed to reduce the sensitivity 

of the indicated mass to relative humidity. However, the simplest technique, mild heating, will 

remove a portion of the ammonium nitrate and the semivolatile organic compounds as well as 

the PBW. 

An intercomparison study of two beta gauges at three sites indicated that the Wedding beta 

gauge and the Sierra Anderson (SA) 1200 PM10 samplers were highly correlated, r > 0.97 (Tsai 

and Cheng, 1996). The Wedding beta gauge was not sensitive to relative humidity but yielded 

2B-19
results approximately 7% lower. This suggests that the mild heating in the beta gauge causes 

losses comparable to those caused by equilibration, although the differences could result from 

slight differences in the upper cut points. The Kimoto beta gauge that was operated at ambient 

temperature was sensitive to relative humidity yielding significantly higher mass concentrations 

relative to the SA 1200 for RH > 80% than for RH < 80% even though the correlation with the 

SA 1200 was reasonable (r = 0.94 for RH > 80% and 0.83 for RH < 80%). 

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 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; Noël and Topart, 1994). Comparison with a second 

crystal largely compensates for the effect of temperature changes on the resonance frequency. 

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). 

Coarse Particle Mass 

The RAMS and CAMM are only appropriate for fine particle measurements (e.g., PM2.5 

or PM1). However, the TEOM, beta gauge, and piezoelectric microbalance may be used to 

measure either PM2.5 or PM10 (or a sample with any specified upper 50% size cut). A pair of 

such samplers may be used to measure thoracic coarse PM mass concentration (PM10-2.5) by 

2B-20
difference between the PM10 and PM2.5 concentrations. However, concerns have been raised 

concerning the quality of the data from such difference calculations and the resulting potential 

biases in exposure assessment and risk determinations (Wilson and Suh, 1997; White, 1998). 

Misra et al. (2001) describe the development and evaluation of a continuous coarse particle 

monitor (CCPM) that may provide direct measurements of coarse mode PM mass concentrations 

at short time intervals (on the order of 5-10 min). The basis of the CCPM is enrichment of the 

coarse particle concentrations through use of virtual impaction while maintaining fine particle 

concentrations at ambient levels. The resulting aerosol mixture is analyzed using a standard 

TEOM for which the response is now dominated by the enriched coarse PM mass. The coarse 

PM concentrations determined from the CCPM were compared to those obtained with a 

MOUDI, operating with only the 10- and 2.5-μm cut point stages, and a Partisol dichotomous 

sampler. The CCPM coarse particulate concentrations were highly correlated with both the 

MOUDI (R2 = 0.88) and the Partisol (R2

 = 0.88) coarse PM concentrations. By operating the 

CCPM at a coarse particle enrichment factor of 25, the coarse PM concentration can be 

determined a priori without determination of the fine particle concentration, so long as the fineto-coarse particle concentration ratios are not unusually high (i.e., 4 to 6). Misra et al. (2001) 

also concluded from field experiments that the coarse particulate concentrations determined from 

the CCPM were independent of the ambient fine-to-coarse particulate concentration ratio due to 

the decrease in particle mass median diameter that should accompany fine-to-coarse particulate 

concentration ratios during stagnation conditions. 

2B.3.2 Continuous Measurement of Organic and/or Elemental Carbon 

Testing and refinement of models that simulate aerosol concentrations from gas and 

particle emissions require air quality measurements of approximately 1-h time resolution to 

reflect the dynamics of atmospheric transport, dispersion, transformation, and removal. Below 

instruments are described that have been used to collect and analyze atmospheric organic PM 

with better than 2-h resolution. These instruments were all present at the Atlanta Supersite 

experiment during the summer of 1999, and an intercomparison of results is underway. 

2B-21
Automated Carbon Analyses 

Turpin et al. (1990) describe an in situ, time-resolved analyzer for particulate organic and 

EC that can operate on a time cycle as short as 90 min. This analyzer collects PM on a quartzfiber filter mounted in a thermal-optical transmittance carbon analyzer (Turpin et al., 1990). 

A second quartz-fiber filter behind a Teflon filter in a second sampling port may also be 

analyzed to provide an estimate of the positive sampling artifact (i.e., gas adsorption on the 

quartz sampling filter). The organic material in the collected PM is thermally desorbed from the 

filter at 650 °C and oxidized at 1000 °C over a MnO2 catalyst bed. The evolved CO2 is 

converted to methane over a nickel catalyst, and the methane is measured in a flame ionization 

detector. Then the EC is oxidized at 350 °C in a 98% He-2% O2 atmosphere. Correction is 

made for pyrolytic conversion of some of the organic PM. The instrument was operated with a 

2-h resolution during SCAQS in 1987 (Turpin and Huntzicker, 1991, 1995), as well as during 

CSMCS in 1986 (Turpin et al., 1990). By using EC as a tracer for primary, combustiongenerated OC, these authors estimated the contributions of primary sources (i.e., material 

emitted in particulate form) and secondary sources (i.e., PM formed in the atmosphere) to the 

total atmospheric particulate OC concentrations in these locations. 

An automated carbon analyzer with 15-min to 1-h resolution is now commercially 

available (Rupprecht et al., 1995) and has been operated in several locations including the 

Atlanta Supersite. It collects samples on a 0.1 μm impactor downstream of an inlet with a 

2.5 μm cut point. Use of an impactor eliminates the gas adsorption that must be addressed when 

filter collection is used. However, this collection system may experience substantial particle 

bounce and loss of a sizable fraction of EC since some EC is in particles < 0.2 μm. It is possible 

that ongoing research, in which particle size is increased by humidification prior to impaction, 

may result in an improvement in collection efficiency. In the analysis step, carbonaceous 

compounds are removed by heating in filtered ambient air. Carbonaceous material removed 

below 340 °C is reported as OC, and material removed between 340 and 750 °C is reported as 

EC. Turpin et al. (2000) commented that it would be more appropriate to report carbon values 

obtained by this method as “low-” and “high-temperature” carbon, because some organics are 

known to evolve at temperatures greater than 340 °C (e.g., organics from woodsmoke). 

2B-22
Aethalometer for Elemental Carbon 

As discussed earlier, black carbon (BC), a carbon fraction very similar to EC, is most 

commonly measured using an aethalometer, a commercially available, automated, time-resolved 

instrument (i.e., 5- to 15-min sample duration) that measures the light attenuation of aerosol 

particles collected on a filter tape (Hansen et al., 1984). The concentration of EC is derived from 

the light absorption measured on a filter using an estimate of the specific absorption (m2

/g) of 

EC on the filter; the specific absorption value is derived from laboratory and atmospheric tests 

and is specified by the manufacturer. The specific absorption value could be expected to vary 

with location, season, and source mix. Comparisons in atmospheric experiments at some 

locations with EC values measured by thermal methods confirm that the aethalometer provides a 

statistically meaningful estimate of EC concentration (Allen et al., 1999c; Liousse et al., 1993). 

For instance, Allen et al. (1999c) found the following statistical relationship for Uniontown, PA 

during summer 1990: black carbon (aethalometer) = 0.95*EC (thermal) – 0.2 (r2

 = 0.925, n not 

specified but appears to be > 50, EC range from 0 to 9 μg/m3

). Another source of error in 

aethalometer measurements arises from the sampling procedure. Particles are trapped within a 

three-dimensional filter matrix. Therefore, scattering of transmitted and reflected light may 

erroneously be attributed to absorption, thus causing errors in the BC calculation. Ballach et al. 

(2001) investigated immersing the filter in oil of a similar refractive index as a means to 

minimize the interferences due to light scattering effects from the filter, similar to a procedure 

common in microscopy. Black carbon measurements determined using the oil immersion 

technique were compared to those from an integrating sphere, a polar photometer, and Mie 

calculations. Aerosol tests included several pure carbon blacks from different generating 

procedures that were used to calibrate the immersion technique, pure ammonium sulfate aerosol, 

and external and internal mixtures of ammonium sulfate with varying amounts of carbon blacks. 

The oil immersion technique was also tested on ambient air samples collected at two different 

sites in the cities of Frankfurt am Main and Freiburg, Germany. Optical measurements, both of 

blank and loaded filters, showed that the oil immersion technique minimizes scattering losses. 

Ballach et al. (2001) found that site-related effects were reduced and that there was reasonably 

good agreement with the other optical techniques as well as with the Mie calculations. 

2B-23
Photoacoustic Measurement of Elemental Carbon 

Measurement of aerosol light absorption utilizing photoacoustic spectroscopy has been 

examined as a continuous method for measuring EC mass concentrations (Petzold and Niessner, 

1996; Petzold et al., 1997; Arnott et al., 1999, 2000). Like the aethalometer, this method 

measures light absorption; however, unlike most other light absorption methods, the 

photoacoustic technique does not require a filter. The photoacoustic spectrometer of Arnott and 

coworkers was demonstrated during the Northern Front Range Air Quality Study and compared 

to an aethalometer (Moosmüller et al., 1998). Neither the aethalometer nor the photoacoustic 

spectrometer measure EC mass directly. Because the photoacoustic spectrometer measures the 

absorption coefficient directly, the specific absorption efficiency must be known or assumed in 

order to determine EC mass. Assuming a light absorption efficiency of 10 m2

/g, Arnott et al. 

(1999) reported a lower detection limit for light absorption of 0.4 M/m corresponding to a mass 

concentration of EC of approximately 40 ng/m3

. 

2B.3.3 Continuous Measurements of Nitrate and Sulfate 

Nitrate 

An integrated collection and vaporization cell was developed by Stolzenburg and Hering 

(2000) that provides automated, 10-min resolution monitoring of fine-particulate nitrate. In this 

system, particles are collected by a humidified impaction process and analyzed in place by flash 

vaporization and chemiluminescent detection of the evolved nitrogen oxides. In field tests in 

which the system was collocated with two FRM samplers, the automated nitrate sampler results 

followed the results from the FRM, but were offset lower. The system also was collocated with 

a HEADS and a SASS speciation sampler (MetOne Instruments). In all these tests, the 

automated sampler was well correlated to other samplers with slopes near 1 (ranging from 0.95 

for the FRM to 1.06 for the HEADS) and correlation coefficients ranging from 0.94 to 0.996. 

During the Northern Front Range Air Quality Study in Colorado (Watson et al., 1998), the 

automated nitrate monitor captured the 12-min variability in fine-particle nitrate concentrations 

with a precision of approximately ± 0.5 μg/m3

 (Chow et al., 1998). A comparison with denuded 

filter measurements followed by ion chromatographic (IC) analysis (Chow and Watson, 1999) 

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 

2B-24
during the 1997 Southern California Ozone Study (SCOS97) in Riverside, CA. Comparisons 

with 14 days of 24-h denuder-filter sampling gave a correlation coefficient of R2 = 0.87 and 

showed no significant bias (i.e., the 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. 

Sulfate 

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. 

Particulate 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 

chemiluminescence from electronically excited sulfur molecules (S2*). 

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 exponent is between 1 and 2 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 and provides 

a faster response time (Benner and Stedman, 1989, 1990). 

Capabilities added to the basic system include in situ thermal analysis and sulfuric acid 

speciation (Cobourn et al., 1978; Huntzicker et al., 1978; Tanner et al., 1980; Cobourn and 

Husar, 1982). Sensitivities for particulate sulfur as low as 0.1 μg/m3

, with time resolution 

ranging from 1 to 30 min, have been reported. Continuous measurements of particulate sulfur 

content have also been obtained by on-line XRF analysis with resolution of 30 min or less 

(Jaklevic et al., 1981b). 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 1-week sampling period. 

2B-25
2B.4 OTHER CONTINUOUS MEASUREMENTS 

Continuous Ion Chromatography of Water-Soluble Ions 

Two particle collection systems that grow particles by increasing the relative humidity and 

collect the particles in an aqueous solution suitable for injection into an ion chromatography 

have been developed independently (Simon and Dasgupta, 1995; Khlystov et al., 1995). 

Automation of these systems yield semi-continuous monitors for those ions that can be 

determined using ion chromatography. A similar system using a particle size magnifier has been 

reported by Weber et al. (2001). 

Determination of Aerosol Surface Area in Real Time 

Aerosol surface area is an important aerosol property for health effects research. However, 

methods for on-line measurement of surface area are not widely available. Woo et al. (2001b) 

used three continuous aerosol sensors to determine aerosol surface area. They used a 

condensation particle counter (CPC, TSI, Inc., Model 3020), an aerosol mass concentration 

monitor (MCM, TSI, Inc., Model 8520), and an electrical aerosol detector (EAD, TSI, Inc., 

Model 3070) to measure integral moments of the particle size distribution. The three sensor 

signals were inverted to obtain the aerosol size distribution, using a lognormal size distribution 

model (by minimizing the difference between the measured signals and the theoretical values 

based upon a size distribution model, the instrument calibration, and its theoretical responses). 

The lognormal function was then integrated to calculate the total surface area concentration. 

Woo et al. (2001b) demonstrated that this method can give near real-time measurements of 

aerosol surface area. 

Light Scattering 

A variety of types of nephelometers that integrate aerosol light scattering over various solid 

angles are available (McMurry, 2000). When used to measure visibility, e.g., to provide pilots 

with real-time data on visual range, it is desirable to include the light scattering due to PBW. 

However, when used as an indicator of fine particle mass, it is desirable to exclude PBW. This 

is frequently done by heating the ambient aerosol to a low reference relative humidity of 40%. 

However, this heating has the potential of also causing the loss of semivolatile components of 

the aerosol. The evaporation of ammonium nitrate aerosol in a heated nephelometer has been 

2B-26
examined. Bergin et al. (1997) conducted laboratory experiments at low relative humidity 

(~10%) and as a function of temperature (27 to 47 °C), mean residence time in the nephelometer, 

and initial particle-size distribution. The evaporation of ammonium nitrate aerosol was also 

modeled for comparison and was found to describe accurately the decrease in aerosol scattering 

coefficient as a function of aerosol physical properties and nephelometer operating conditions. 

Bergin et al. (1997) determined an upper limit estimate of the decrease in the aerosol light 

scattering coefficient at 450 nm due to evaporation for typical field conditions. The model 

estimates for their worst-case scenario suggest that the decrease in the aerosol scattering 

coefficient could be roughly 40%. Under most conditions, however, they estimate that the 

decrease in aerosol scattering coefficient is generally expected to be < 20%. 

2B-27
3. CONCENTRATIONS, SOURCES, AND EMISSIONS

OF ATMOSPHERIC PARTICULATE MATTER

3.1 INTRODUCTION 

This chapter discusses topics that were covered in Chapter 5 (Sources and Emissions of 

Atmospheric Particles) and Chapter 6 (Environmental Concentrations) of the previous document, 

Air Quality Criteria for Particulate Matter or “1996 PM AQCD” (U.S. Environmental 

Protection, 1996) and updates the subject information based on new data, as available. 

Information about concentrations, the composition, and the spatial and temporal variability 

of ambient particulate matter (PM) across the United States is presented in Section 3.2. Ambient 

concentration data obtained during the first 3 years of operation of the recently deployed 

nationwide network of Federal Reference Method (FRM) PM2.5 monitors in 27 metropolitan 

statistical areas (MSAs) are presented and analyzed in Appendix 3A. Initial data from the pilot 

method evaluation study for the national speciation network are presented in Appendix 3B. 

Results of field studies that help to characterize the composition of organic compounds in 

ambient air particles are summarized in Appendix 3C, to complement data on the inorganic 

composition of ambient particles presented in Appendix 6A of the 1996 PM AQCD and 

Appendix 3B of this document. Data useful for characterizing the daily and seasonal variability 

of PM2.5 concentrations are discussed in Section 3.2.1, and the intraday variability of PM2.5 

concentrations is discussed in Section 3.2.2. Relationships among different size fractions are 

discussed in Section 3.2.3; interrelationships and correlations among PM components are 

discussed in Section 3.2.4; and the spatial variability of various PM components is discussed in 

Section 3.2.5. 

Atmospheric PM, unlike gaseous criteria pollutants (SO2, NO2, CO, O3) which are 

well-defined chemical entities, is composed of a variety of particles differing in size and 

chemical composition. Therefore, sources of each component of the atmospheric aerosol must 

be considered in turn. Differences in the composition of particles emitted by different sources 

also lead to spatial and temporal heterogeneity in the composition of the atmospheric aerosol. 

The nature of the sources and the composition of the emissions from these sources are discussed 

in Section 3.3. The chemistry of formation of secondary PM from gaseous precursors is 

3-1
discussed in Section 3.3.1. Estimates of contributions of various sources to ambient PM levels 

given by source apportionment studies also are presented in Section 3.3.2. More detailed 

information about the composition of emissions from various sources is given in Appendix 3D. 

The determination of “policy-relevant background” (PRB) concentrations, including 

contributions from natural local sources and the long-range transport of PM from sources outside 

the United States, is discussed in Section 3.3.3. Reviews of transport of PM and its precursors 

within the United States can be found in the North American Research Strategy for Tropospheric 

Ozone (NARSTO) Fine Particle Assessment (NARSTO, 2002). More detailed information 

regarding sulfur and nitrogen species can be found in Hidy (1994). Further information about 

PM concentrations observed at relatively remote monitoring sites (RRMS), i.e., monitoring sites 

located in relatively remote areas that are not obviously influenced by local anthropogenic 

sources, is given in Appendix 3E. Because PM is composed of both primary and secondary 

constituents, emissions of both the primary components and the gaseous precursors of secondary 

PM are considered here. Nationwide emissions estimates of primary PM and precursors to 

secondary PM are discussed in Section 3.3.4, and uncertainties in emissions estimates are 

discussed in Section 3.3.5. 

The organization of topics in this chapter (ambient measurements, source characterization 

and apportionment, and emissions inventories) reflects, in a broad sense, the order in which these 

topics are addressed in scientific studies and, arguably, the increasing levels of uncertainty that 

are associated with these different topics. 

3.2 PATTERNS AND TRENDS IN AMBIENT PM CONCENTRATIONS 

Considerable data exists for characterizing PM10 mass concentrations and trends, and those 

available at the time were presented in the 1996 PM AQCD. In contrast, less extensive data sets 

were available for characterizing PM2.5 and PM10-2.5 mass or trends. Sources of data for PM2.5 

(fine) and PM10-2.5 (coarse) discussed in the 1996 PM AQCD include: EPA’s Aerometric 

Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2000a); 

Interagency Monitoring of Protected Visual Environments (IMPROVE; Eldred and Cahill, 1994; 

Cahill, 1996); the California Air Resources Board (CARB) Data Base (California Air Resources 

Board, 1995); the Harvard Six-Cities Data Base (Spengler et al., 1986; Neas, 1996); and the 

3-2
Harvard-Philadelphia Data Base (Koutrakis, 1995). The Inhalable Particulate Network (IPN) 

(Inhalable Particulate Network, 1985; Rodes and Evans, 1985) provided TSP, PM15, and PM2.5 

data but only a small amount of PM10 data. 

New sources of PM data include the recently deployed nationwide PM2.5 compliancemonitoring network, which provides mass measurements using a FRM. This section 

summarizes data obtained by this network from 1999 to 2001 and provides an approximate 

characterization of nationwide PM10-2.5 concentrations by comparing PM10 to PM2.5 data from 

sites where both types of compliance monitors are located. Various aspects of these data are 

presented in greater detail in Appendix 3A. In addition, a small number of recent studies in 

which daily mass and composition measurements are available for extended periods are 

discussed in this section. The results of quality-assured aerosol composition data obtained by 

X-ray fluorescence (XRF) and by analyses of organic carbon and elemental carbon for 13 urban 

areas from the methods evaluation study for the national PM2.5 speciation network are presented 

in Appendix 3B. The terms organic carbon (OC) and elemental carbon (EC) are subject to some 

ambiguity, and the meanings of these terms as discussed in Section 2.2.7 and Appendix 2.B.2 are 

applied here. 

Organic compounds contribute from 10 to 70% of the dry fine particle mass in the 

atmosphere (see Appendix 3C). However, concentrations and the composition of organic PM 

are poorly characterized, as are its formation mechanisms. Particulate organic matter is an 

aggregate of hundreds of individual compounds spanning a wide range of chemical and 

thermodynamic properties (Saxena and Hildemann, 1996). Some of the organic compounds are 

“semivolatile” (i.e., they have atmospheric concentrations and saturation vapor pressures such 

that both gaseous and condensed phases exist in equilibrium in the atmosphere). The presence 

of semivolatile or multiphase organic compounds complicates the sampling process. Organic 

compounds originally in the gas phase may be absorbed on glass or quartz filter fibers and create 

a positive artifact. Conversely, semivolatile compounds originally present in the condensed 

phase may evaporate from particles collected on glass, quartz, or Teflon filters creating a 

negative artifact. In addition, no single analytical technique is currently capable of analyzing the 

entire range of organic compounds present in atmospheric PM. Rigorous analytical methods are 

able to identify only 10 to 20% of the organic PM mass on the molecular level (Rogge et al., 

1993), and only about 50% of the condensed phase compounds can be identified in smog 

3-3
chamber studies of specific compounds (Forstner et al., 1997a,b). Measurement techniques and 

associated artifacts are discussed in Section 2.2.3.2. Information on the identification and 

concentration of the many different organic compounds identified in atmospheric samples 

obtained during the 1990s is given in Appendix 3C. 

Summary tables giving the results of 66 field studies that obtained data for the composition 

of particles in the PM2.5, PM10-2.5, or PM10 size ranges were presented in Appendix 6A of the 

1996 PM AQCD. The summary tables included data for mass, OC, EC, nitrate, sulfate, and trace 

elements. Data from the studies were presented for the eastern, western, and central 

United States. It should be noted that these studies took place at various times and lasted for 

various durations over a 20-year period, and there may have been significant changes in the 

concentrations of many species between the times when these studies were conducted and now. 

These changes resulted from a number of factors (e.g., pollution controls, technological 

advances, land use changes, etc). 

PM

There were a number of discernible differences in the composition of particles across the 

United States evident in the data sets listed in Appendix 6A in the 1996 PM AQCD (cf., 

Figures 6-85a to 6-85c). However, these differences can only be discussed in the context of the 

uncertainties in the measurements of the main components (sulfate, OC, EC, crustal material, 

ammonium, and nitrate). Sulfate, followed by crustal materials, has the smallest uncertainty 

associated with its measurement among all the components listed. Sulfate constituted ~38% of 

2.5 in the aerosol composition studies in the eastern United States and was the major 

identifiable component of PM2.5. However, it constituted only ~11% of PM2.5 in the studies 

listed for the western United States. The contribution of crustal materials to PM2.5 ranged from 

~4% in the East to ~15% in the West. The contribution of unidentified material (possibly 

consisting mostly of water of hydration) ranged ~23% in the East to 0% in the West. The 

contribution of EC to PM2.5 ranged from ~4% in the East to ~15% in the West. Organic 

compounds constituted ~21% of PM2.5 in the eastern United States, reaching to ~39% for the 

studies listed in the western United States. However, uncertainties for OC, EC, ammonium, and 

nitrate are larger than for sulfate and crustal material. Concentrations of OC were multiplied by 

a factor of 1.4 when calculating mass to account for the presence of oxygen, nitrogen, and 

hydrogen in the organic compounds. This factor may vary among different areas and may 

represent the lowest reasonable estimate for an urban aerosol (Turpin and Lim, 2001). 

3-4
In addition, the samples collected in the studies were subject to a number of sampling artifacts 

involving the adsorption of gases and the evaporation of volatile components that either formed 

on the filters or were present in the ambient particles. The values reported for OC and EC in 

filter samples depend strongly on the specific analysis method used (Chow et al., 2002), as 

discussed extensively in Chapter 2. 

of PM

Crustal materials constitute from ~52% of PM10-2.5 in the eastern United States to ~70% 

10-2.5 in the western United States, as indicated by studies in Appendix 6A of the 1996 PM 

AQCD. The fraction of unidentified material in PM10-2.5 varied from 41% in the eastern United 

States to 27% in the western United States. However, in the vast majority of these studies, no 

attempt was made to characterize organic components or nitrate in the PM10-2.5 size fraction. 

It should also be remembered that a small fraction (typically about 10%) of PM2.5 is entrained 

into the flow of the channel of the dichotomous sampler that collects the PM10-2.5 sample and that 

errors may occur during the procedure used to account for this entrainment. Even if analyses of 

total OC were available, they would not be able to distinguish between primary biological 

particles (PBPs), which include microorganisms and fragments of living things, and organic 

compounds of miscellaneous origin in surface deposits on filters. A clear distinction should be 

made between PBP and primary OC emitted by organisms (e.g., waxes coating the surfaces of 

leaves, seeds, fruits, stems, pollen, fungi, and insects). Indeed, the fields of view of many 

photomicrographs of PM samples obtained by scanning electron microscopy are often dominated 

by large numbers of pollens, plant and insect fragments, and microorganisms. Bioaerosols such 

as pollens, fungal spores, and most bacteria are expected to be found mainly in the coarse size 

fraction. However, allergens from pollens can also be found in respirable particles (Monn, 2001; 

Taylor et al., 2002). Matthias-Maser (1998) summarized information about the size distribution 

of PBP in and around Mainz, Germany in what is perhaps the most complete study of this sort. 

Matthias-Maser found that PBP constituted up to 30% of total particle number and volume in the 

size range from about 0.35 μm to about 50 μm on an annual basis. Additionally, whereas the 

contribution of PBP to the total aerosol volume did not change appreciably with season, the 

contribution of PBP to total particle number ranged from about 10% in December and March to 

about 25% in June and October. 

Data for the chemical composition of particles in a number of national parks and remote 

areas have been collected for a number of years by the IMPROVE network. Concentrations 

3-5
have been reported for sulfate, nitrate, light-absorbing carbon, OC, and soil components. 

With the collection of compositional data by the speciation network, more synoptic (i.e., 

concurrent) coverage will be obtained for these constituents from relatively remote to urban 

environments across the United States. 

PM10 Concentrations and Trends 

Nationwide PM10 annual mean concentrations on a county-wide basis from the AIRS 

database for calendar years 1999, 2000, and 2001 are shown in Figure 3-1a. Concentrations in 

most areas of the country from 1999 through 2001 were below the PM10 annual standard of 

50 μg/m3. Further information about the attainment status of different areas can be found in 

EPA’s Air Quality Trends Reports. The nationwide median value of county-wide annual 

average PM10 concentrations for this three year period (1999 to 2001) was ~23 μg/m3

. Those 

concentrations flagged as natural events (e.g., resulting from high winds, wildfires, volcanic 

eruptions) or exceptional events (e.g., construction, prescribed burning) were not included in the 

calculations. Procedures used for calculating the annual means at the site level followed 

40 Code of Federal Regulations (CFR) Part 50 Appendix K (requiring 75% completeness per 

quarter for all three years). The 98th percentile concentrations from the PM10 monitor showing 

the highest value in a given county over the three-year period are shown in Figure 3-1b. 

As shown by the blank areas on the maps, the picture is not complete, because some monitoring 

locations did not record valid data for all four quarters, or recorded fewer than 11 samples in one 

or more quarters, or some counties simply did not have monitors. Similar considerations apply 

to the maps shown later for PM2.5 and PM10-2.5. It should also be noted that the area of counties 

can be much greater in the West than in the East. As a result, the density of monitors may 

appear to be greater in the West and air quality may appear to be worse over much larger areas in 

the West than in the East. Concentrations are shown at the county level because this is the 

typical scale used in many health outcome studies. Metropolitan statistical area (MSA) or multicounty scales have also been used in some number of epidemiologic studies, e.g., Schwartz et al. 

(1996) or the NMMAPS studies discussed in Chapter 8. 

Nationwide trends in annual mean PM10 concentrations from 1992 through 2001 (based on 

data obtained at 153 rural sites, 297 suburban sites, and 316 urban sites reporting to AIRS) 

are shown in Figure 3-2 (U.S. Environmental Protection Agency, 2002a). Although average 

3-6
Figure 3-1a. 1999-2001 county-wide average annual mean PM10 concentrations (μg/m3

) 

for counties with PM10 monitors. 

Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b). 

Figure 3-1b. 1999-2001 highest county-wide 98th percentile 24-h average PM10 

concentrations (μg/m3) for counties with PM10 monitors. 

Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b). 

3-7 
Figure 3-2. Nationwide trend in ambient PM10 concentration from 1992 through 2001. 

Source: U.S. Environmental Protection Agency (2002a). 

concentration levels differ among types of sites with higher levels at urban and suburban sites 

the nationwide data set shows a decrease of 14% that occurred mainly during the first half of the 

record. Concentrations at suburban and urban sites show a consistent decline from 1992 to 2001. 

However, concentrations at rural sites appear to have leveled off since about 1996. Figure 3-3 

shows the annual mean PM10 trend summarized by EPA region. Decreases in annual average 

PM10 concentrations from 1990 to 1999 were largest in the Northwest (9.6 μg/m3

) and smallest 

in the south-central United States (1.3 μg/m3

). Analyses of available TSP measurements 

obtained since 1950 indicate that mean TSP concentrations appear to have declined by about 

2- to 3-fold in urban areas between 1950 and 1980 (Lipfert, 1998). 

PM2.5 Concentrations and Trends 

Nationwide annual mean PM2.5 concentrations obtained from data collected during 1999, 

2000, and 2001 are shown in Figure 3-4a; and 98th percentile concentrations are shown in

Figure 3-4b. Quantities shown in Figure 3-4a and 3-4b were calculated for individual counties. 

3-8
Figure 3-3. Trend in PM10 annual mean concentrations by EPA region, 1992 through 

2001 (μg/m3

). 

Source: U.S. Environmental Protection Agency (2002a). 

Annual mean concentrations shown in Figure 3-4a were calculated on the basis of the latest 

8 consecutive quarters containing at least 11 samples per quarter, and results from the highest 

monitor were used to produce Figure 3-4b. Data meeting these completeness criteria were 

obtained at 897 sites in 592 counties. The median PM2.5 concentration nationwide was about 

13 μg/m3

. Annual mean PM2.5 concentrations were above 17 μg/m3 at 5% of the sites, mainly in 

California and in the southeastern United States. The 98th percentile 24-h average 

concentrations (as defined earlier for PM10) were below 47 μg/m3

 at 95% of the sites sampled. 

Most of the sites with levels above this value are located in California. 

Annual average PM2.5 concentrations obtained as part of several health studies conducted 

in various locations in the United States and Canada from the late 1980s to the early 1990s are 

shown in Figure 3-5 (Bahadori, 2000a). These studies include the Harvard Six-Cities Study 

(Steubenville, OH; Watertown, MA; Portage, WI; Topeka, KS; St. Louis, MO; and Kingston3-9
Figure 3-4a. 1999-2001 county-wide average annual mean PM2.5 concentrations (μg/m3

) 

for counties with PM2.5 monitors. 

Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b). 

Figure 3-4b. 1999-2001 highest county-wide 98th percentile 24-h average PM2.5 

concentrations (μg/m3) for counties with PM2.5 monitors. 

Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b). 

3-10 
3-11 

Figure 3-5. Collection of annual distribution of 24-h average PM2.5 concentrations observed in U.S

and Canadian health studies conducted during the 1980’s and early 1990’s. 

Source:: Bahadori et al. (2000a). 
Harriman, TN); Particle Total Exposure Assessment Methodology (PTEAM) study (Riverside, 

CA); Metropolitan Acid Aerosol Characterization Study (MAACS; Philadelphia, PA; 

Washington, DC; and Nashville, TN); South Boston Air Quality and Source Apportionment 

Study (Boston, MA); and NPMRMN (Phoenix, AZ). The remaining sites were part of the 

24-Cities Study (Spengler et al., 1996). 

Sufficient data are not yet available to permit the calculation of nationwide trends of PM2.5 

and PM10-2.5; however, some general emerging patterns can be discerned. Darlington et al. 

(1997) proposed that the consistent reductions in PM10 concentrations found in a wide variety of 

environments ranging from urban to rural may have resulted from common factors or controls 

that affected fine particles more strongly than coarse particles. This is because fine particles 

have longer atmospheric lifetimes than coarse particles and can be transported over longer 

distances and, hence, can affect larger areas. Apart from the IMPROVE network of monitoring 

sites located mainly in national parks, the longest time series of PM2.5 concentration and 

composition data have been obtained by the California Air Resources Board (CARB). Their 

data show that annual average PM2.5 concentrations decreased from 1990 to 1995 by ~50% in the 

South Coast Air Basin, 35% in the San Joaquin Valley, 30% in the San Francisco Bay Area, and 

35% in the Sacramento Valley (Dolislager and Motallebi, 1999). PM2.5 data were collected 

continuously from 1994 to 1998 as part of the Children’s Health Study in 12 southern California 

communities (Taylor et al., 1998). Data obtained at all sites show decreases in PM2.5 ranging 

from 2% at Santa Maria to 37% at San Dimas/Glendora from 1994 through 1998. These 

decreases were accompanied by decreases in major components such as nitrate, sulfate, 

ammonium, and acids. Based on the analysis of PM2.5 data sets collected prior to 1990, Lipfert 

(1998) found that PM2.5 concentrations appear to have decreased by about 5% per year from 

1970 to 1990 in a number of urban areas. These declines were also found to be consistent with 

decreases in emissions from combustion sources over that time period. 

PM10-2.5 Concentrations 

By using AIRS data for 1999, 2000, and 2001 obtained by the PM10 and PM2.5 compliance 

networks, it is possible to construct a picture of the distribution of coarse PM across the country. 

This is accomplished by pairing data from 208 compliance monitoring sites in 196 counties 

where PM10 and PM2.5 monitors are collocated and subtracting the mass concentrations of PM2.5 

3-12
from PM10. Annual mean concentrations were calculated on the basis of the latest 8 consecutive 

quarters containing at least 11 samples per quarter. Nationwide annual mean PM10-2.5 values 

calculated by this approach are shown in Figure 3-6a. Annual mean PM10-2.5 concentrations 

ranged from ~1 to ~50 μg/m3

; the nationwide median concentration was ~10 μg/m3; and 5% of 

the sites had mean concentrations > 28 μg/m3

. The higher values occur mainly in the western 

United States, particularly in California. The highest county-wide 98th percentile PM10-2.5 

concentrations based on this same data set are shown in Figure 3-6b. Highest values in the 

western United States are caused by dust raised locally either by natural means or by 

anthropogenic activity. It is not clear what the contribution of PBP to these values may be. 

Elevated dust levels are also found in southern Florida as the result of dust storms in North 

Africa (Section 3.3.3) and trans-Atlantic transport. In many areas, combined errors in the PM2.5 

and PM10 measurements may be similar to or even greater than the calculated PM10-2.5 

concentrations. Because of this and other potential problems with this approach (Section 3.2.1), 

these results should be viewed with caution. 

3.2.1 Seasonal Variability in PM Concentrations 

PM2.5 

Aspects of the spatial and temporal variability of PM2.5 concentrations for 1999, 2000, 

and 2001 in a number of metropolitan areas across the United States are presented in this and 

following subsections. Data for multiple sites in 27 urban areas across the United States were 

obtained from the AIRS data base and analyzed for their seasonal variations and for their spatial 

correlations and spatial uniformity in concentrations. Selection of these 27 MSAs was based on 

the criteria that data be available for at least 15 days in each calendar quarter of either a 3-year 

period (1999, 2000, and 2001) or a 2-year period (2000 and 2001) at three or more sites within 

that MSA. In addition, a maximum of 11 sites per MSA were included for analysis. (In the 

Chicago and St. Louis MSAs, the 11 sites having the most observations were selected from a 

greater number of qualifying sites.) A number of aspects of the spatial and temporal variability 

of the 1999 PM2.5 data set were presented in Rizzo and Pinto (2001), based in part on analyses 

given in Fitz-Simons et al. (2000). 

Information regarding the seasonal variability in PM2.5 concentrations in four U.S. MSAs 

(Philadelphia, PA; Cleveland, OH; Dallas, TX; and Los Angeles-Long Beach, CA) 

3-13
Figure 3-6a. 1999-2000 estimated county-wide average annual mean PM10-2.5 

concentrations (μg/m3) for counties with collocated PM2.5 and PM10 monitors. 

Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b). 

is summarized in Figures 3-7a through 3-7d. These four urban areas were chosen to illustrate 

some general features of the spatial and temporal variability found in the United States. The 

figures show lowest, lower quartile, median, upper quartile, and highest concentrations for each 

calendar quarter of 1999, 2000, and 2001 for the Cleveland, OH MSA and for 2000 and 2001 for 

Philadelphia, Dallas, and Los Angeles MSAs. For each monitoring site, the AIRS ID numbers, 

annual mean concentrations, the number of observations, and standard deviations are also 

shown. Data for multiple sites within these MSAs are shown to provide an indication of the 

degree of inter-site variability. Data for these MSAs and an additional 23 MSAs, criteria used 

for site selection, and additional descriptions of the data are given in Appendix 3A. 

3-14
Figure 3-6b. 1999-2000 estimated county-wide highest 98th percentile 24-h PM10-2.5 

concentrations (μg/m3) for counties with collocated PM2.5 and PM10 monitors. 

Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b). 

Annual mean PM2.5 concentrations at individual monitoring sites in the MSAs examined 

ranged from ~6 to ~30 μg/m3

. The lowest values were found in rural portions of the MSAs 

examined, typically near the perimeter of the MSA. The two highest mean concentrations were 

found in the Riverside and Los Angeles-Long Beach MSAs in southern California, whereas the 

three lowest means were found in the Northwest ( Portland, OR; Boise, ID; Seattle, WA). These 

MSAs along the Eastern seaboard (Washington, DC; Philadelphia, PA; Norfolk, VA) tend to 

have lower mean PM2.5 concentrations than those in the north-central United States 

(Steubenville, OH; Cleveland, OH; Pittsburgh, PA; Chicago, IL; Detroit, MI; Gary, IN; 

Appendix 3A). Also, average PM2.5 concentrations tended to be lower in 1999, 2000, and 

2001 in the urban areas given in Appendix 3A compared to concentrations observed during 

3-15
Figure 3-7a,b. Quarterly distribution of 24-h average PM2.5 concentrations for selected 

monitors in the (a) Philadelphia, PA and (b) Cleveland, OH MSAs. Values 

for the lowest, lower quartile, median, upper quartile and highest 

concentrations are shown in the figures. The AIRS site ID number, annual 

mean concentration (μg/m3

), number of observations, and standard 

deviation of the data are shown above the figures for each site. 

3-16 
Figure 3-7c,d. Quarterly distribution of 24-h average PM2.5 concentrations for selected 

monitors in the (c) Dallas, TX and (d) Los Angeles, CA MSAs. Values 

for the lowest, lower quartile, median, upper quartile and highest 

concentrations are shown in the figures. The AIRS site ID number, 

annual mean concentration (μg/m3

), number of observations, and 

standard deviation of the data are shown above the figures for each site. 

3-17 
pollution-health outcome studies conducted in those five urban areas where these overlap 

(Figure 3-5). It should be noted that there are no data demonstrating the comparability of the 

monitors used in the studies shown in Figure 3-5 and the FRM. 

The patterns of seasonal variability observed in the MSAs examined are complex. In the 

Philadelphia, PA MSA, highest median concentrations occur at all but one site in the first quarter 

(cf., Figure 3-7a). First quarter maxima are also observed at all sites in the Detroit, MI and 

Chicago, IL MSAs (cf., Figures 3A-11 and 3A-14). The Cleveland, OH MSA (Figure 3-7b), 

in contrast, does not have a clear seasonal pattern. In several MSAs examined in the southern 

and eastern United States (Atlanta, GA; Baton Rouge, LA; Birmingham, AL; Columbia, SC; 

and Washington, DC), highest median concentrations occur at all sites during the third calendar 

quarter (i.e., summer months; cf., Appendix 3A). Sites in Dallas, TX, (Figure 3-7c), as in the 

other southern cities mentioned above, generally exhibit third quarter median concentration 

maxima. Highest median concentrations occur during the fourth calendar quarter in MSAs in the 

western United States as in the Los Angeles, CA MSA (Figure 3-7d), although there are 

exceptions at individual sites in the Riverside, CA MSA (Figure 3A-26). 

Lowest median concentrations occur mainly during the first or fourth quarters at most sites 

in the eastern United States, as well as in Cleveland, OH and Dallas, TX (Figures 3-7b and 3-7c), 

whereas some occur during the second quarter (Philadelphia, PA; Figure 3-7a). Moving 

westward, the seasonal pattern is not as distinct: lowest median concentrations occur in any 

quarter, but usually in the second or third quarter, as in the Chicago, Detroit, and Los AngelesLong Beach (Figure 3-7d) MSAs. With the exception of Los Angeles, CA and Riverside, CA, 

sites in the West show lowest median concentrations in the second quarter. In most of the MSAs 

examined, seasonal variations follow a similar pattern at all of the sites within the MSA; but in a 

few MSAs, there are noticeable differences in the seasonal pattern between sites. The largescale differences in seasonal variability between MSAs tend to follow differences in the major 

categories of PM sources affecting the monitoring sites. Local heating by wood burning during 

the colder months is practiced more widely in the western United States than in the eastern 

United States. Prolonged winter stagnation events are also more common in western mountain 

valleys during winter than in sections of the eastern United States located in relatively flat 

topography. These conditions are also conducive to the formation of ammonium nitrate, which 

is a major and sometimes the dominant contributor to PM2.5 in western valleys during the winter 

3-18
and in southwestern coastal areas during the fall (Kim et al., 2000). Hence, winter maxima and 

greater variability in PM2.5 concentrations across sites are expected in the West due to the 

influence of these local sources. On the other hand, photochemical production of secondary PM, 

especially sulfate, occurs over wide areas in relatively homogeneous air masses during the 

summer months in the eastern United States. Because sulfates (along with associated cations 

and water) constitute the major fraction of summertime PM2.5 in the East, there is greater 

uniformity in third quarter PM concentrations within eastern MSAs (cf., Appendix 3A). 

Maximum 24-h average concentrations shown in the box plots in Figures 3-7a to 3-7d and 

in Figures 3A-1 to 3A-27 do not necessarily follow the same seasonal pattern as the median 

concentrations. There is no clear relationship between the maximum and the median 

concentrations evident in the Philadelphia, PA data set (Figure 3-7a). In Cleveland, OH 

(Figure 3-7b), maximum concentrations occur during the second or fourth quarters, and highest 

median concentrations generally occur during the third or first quarters. In Dallas, TX 

(Figure 3-7c), maximum concentrations generally occur during the fourth quarter, but highest 

median concentrations tend to occur during the second or third quarter. In the Los Angeles-Long 

Beach MSA (Figure 3-7d), the maximum and highest median concentrations occur together in 

the fourth quarter with the exception of one site. Peak individual concentrations likely reflect 

the occurrence of transient events such as forest fires (mainly in the West) or episodes of 

secondary PM production (mainly in the East). However, chemical analyses of filter samples or 

other evidence should be used to determine specific causes in particular locations. 

There have been a few studies that have characterized PM2.5 and PM10 concentrations in 

major urban areas. The Metropolitan Aerosol Acidity Characterization Study (MAACS) 

(Bahadori et al., 2000b) characterized the levels and the spatial and temporal variability 

of PM2.5, PM10, and acidic sulfate concentrations in four cities in the eastern United States 

(Philadelphia, PA; Washington, D.C.; Nashville, TN; and Boston, MA). Seasonal variations 

in PM2.5 and PM10 concentrations obtained during the course of this study are shown in 

Figure 3-8. The data for the four cities included in MAACS are presented in a box plot showing 

the lowest, lowest tenth percentile, lowest quartile, median, highest quartile, highest tenth 

percentile, and highest PM2.5 and PM10 values. Mean and highest PM2.5 and PM10 concentrations 

peak during the summer in all four cities (in contrast with Figure 3-7a), although the seasonal 

pattern in Boston appears to be more nearly bimodal with an additional winter peak. 

3-19
Figure 3-8. Seasonal concentrations of PM2.5 and PM10 measured in the four MAACS 

cities. The data show the lowest, lowest tenth percentile, lowest quartile, 

median highest quartile, highest tenth percentile, and highest PM2.5 24-h 

average values. The dashed line shows the level of the annual PM2.5 standard. 

SP = spring; SU = summer; F = fall; W = winter. 

Source: Bahadori et al. (2000b). 

PM10-2.5 

Of the 27 MSAs selected for analysis of PM2.5 data (Appendix 3A), 17 have multiple sites 

satisfying the PM10-2.5 data completeness criteria. A subset of 53 qualifying sites were selected 

for analysis of PM10-2.5 data. Each qualifying site has 11 or more observations per calendar 

quarter for 12 consecutive quarters (1999 to 2001, 2 MSAs), eight quarters (1999 + 2000 or 

2000 + 2001, 7 MSAs) or four quarters (2000 or 2001, 8 MSAs). In addition, data from sites 

within the same MSA represent the same year (or years) of observations, so that intersite 

comparisons within an MSA are based on the same reporting period. However, comparisons 

between different MSAs can involve different annual reporting periods. As can be seen by 

3-20
comparing Figures 3-7a,b,c,d and Figures 3-9a,b,c, the number of observations used for 

summarizing PM10-2.5 data is much lower than that measured for PM2.5. Unfortunately, fewer 

qualifying MSAs for PM10-2.5 result in geographical coverage that is more spotty than with PM2.5. 

Five of the seven MSAs representing the eastern United States for PM2.5, for example, did not 

meet completeness criteria for PM10-2.5, leaving only Tampa, FL and Columbia, SC to represent 

the East. Secondly, it can readily be seen, e.g., for Cleveland, OH (Figure 3-9a) and, also, in 

Figures 3A-28 through 3A-44, that a number of PM10-2.5 concentrations are negative. For 

example, in 2001, about one-fourth of all PM10-2.5 concentrations at the three qualifying sites in 

the Gary, IN MSA were negative. (The negative estimates have been included in the calculation 

of mean concentrations.) There are a number of reasons for the negative concentration 

estimates, many of which arise because the ratios of PM2.5 to PM10 are based on two independent 

measurements. Measurement imprecision plays a role when the ratios are large and 

concentrations are small. Differences in the behavior of semivolatile components in the two 

samplers could occur; and the results may also reflect errors in sampler placement, field, 

laboratory, or data processing procedures. Thus, caution should be exercised when attempting to 

interpret results for PM10-2.5 based on current network collocated PM2.5 and PM10 monitors. 

Annual mean PM10-2.5 concentrations at individual monitoring sites range from ~6 μg/m3 

(Portland, OR) to ~33 μg/m3 (Riverside, CA). (Gary, IN sites were excluded because of 

numerous negative PM10-2.5 concentrations, and one site in Riverside, CA affected by a local dust 

event was excluded.) The three highest annual mean concentrations are observed in Riverside, 

CA; Los Angeles-Long Beach, CA; and Salt Lake City, UT; and the lowest (excluding Gary) 

were observed in Portland, OR. For the remaining MSAs, there does not appear to be a 

significant geographical trend associated with the annual mean PM10-2.5 concentration. Within 

MSAs, the lowest concentrations are frequently observed at sites near the perimeter of the MSA, 

although it must be noted that the number of sites is limited. 

Within each MSA, collocated PM2.5 and PM10-2.5 concentrations, averaged over the same 

years, were compared. The mean PM2.5:PM10-2.5 ratio was calculated for each of the 17 MSAs 

using as many sites as possible. The median mean PM2.5:PM10-2.5 ratio for the 17 MSAs was 1.2. 

For eight MSAs (Tampa, FL; Columbia, SC; Louisville, KY; Chicago, IL; Gary, IN; Milwaukee, 

WI; Steubenville, OH; Portland, OR), the mean PM2.5 concentration exceeded the mean PM10-2.5 

concentration. For an additional eight MSAs, the PM2.5 and PM10-2.5 concentration means were 

3-21
Figure 3-9a,b. Quarterly distribution of 24-h average PM10-2.5 concentrations for selected 

sites in the (a) Cleveland, OH; (b) Dallas, TX MSAs. Values for the lowest, 

lower quartile, median, upper quartile and highest concentrations are 

shown in the figures. The AIRS site ID number, annual concentration, 

number and standard deviation are shown above the figures for each site. 

3-22 
Figure 3-9c. Quarterly distribution of 24-h average PM10-2.5 concentrations for selected 

sites in the Los Angeles, CA MSAs. Values for the lowest, lower quartile, 

median, upper quartile and highest concentrations are shown in the figures. 

The AIRS site ID number, annual concentration, number and standard 

deviation are shown above the figures for each site. 

Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b). 

the same (within one SD). Salt Lake City was the only MSA for which the mean PM10-2.5 

concentration exceeded the mean PM2.5 concentration. 

Information regarding the seasonal variability in PM10-2.5 concentrations in three MSAs 

(Cleveland, OH; Dallas, TX; Los Angeles-Long Beach, CA) is summarized in Figures 3-9a 

through 3-9c. The figures show lowest, lower quartile, median, upper quartile, and highest 

concentrations for each calendar quarter of 2000 and 2001 for the Cleveland, OH MSA (six 

sites); and of 2001 for the Dallas, TX and Los Angeles, CA MSAs (four sites each). Data for 

3-23
PM

these and an additional 13 MSAs, as well as additional descriptions of the data, are given in 

Appendix 3A. The seasonal pattern for PM10-2.5 median concentrations is different from that 

of PM2.5 (Figures 3-7a,b,c,d, and Appendix 3A). Most seasonal median maxima in PM10-2.5 occur 

during the second or third calendar quarters, i.e., spring and summer months (45% and 36% of 

all sites, respectively) as seen, for example, at most sites in Figures 3-9. Contributions from 

bioaerosols during the spring blooming season, which mostly affect PM10-2.5 concentrations, 

might be a significant contributing factor in the second quarter PM10-2.5 median maxima in some 

regions of the United States. Lowest median concentrations occur mainly in the first or fourth 

quarters 62% and 23% of all sites, respectively). Cleveland, OH (Figure 3-9a) and Tampa, FL, 

where lowest medians generally are observed in the third quarter, are exceptions (Figure 3A-29). 

At no site was the lowest median PM10-2.5 concentration observed in the second quarter. In 

comparison to PM10-2.5, seasonal PM2.5 median maxima mostly occurred in the first or third 

quarters and PM2.5 median minima are mostly observed in the second and fourth quarters. 

With few exceptions, collocated PM2.5 and PM10-2.5 seasonal medians at individual sites peak in 

different quarters. Likewise, at a given site, the lowest median concentrations for PM2.5 and 

10-2.5 rarely occur in the same quarter. In MSAs with several PM10-2.5 sites, the seasonal 

patterns are typically seen at all sites within the MSA. In the Dallas, TX MSA, the maximum 

and minimum estimated PM10-2.5 concentrations both occur in the first quarter at all four sites. 

The ratio of PM2.5:PM10-2.5 seasonal median concentrations peak in the first quarter for 

MSAs in the central and north-central United States and tend to peak in the fourth quarter for 

western states. The largest ratios of PM2.5:PM10-2.5 seasonal median concentrations are observed 

in the central and north-central MSAs (Chicago, Cleveland, Detroit, and St. Louis), whereas 

smaller ratios are found in the western and southern United States. 

As can be seen from Figure 3-9a and Figures 3A-40 and 3A-43, the maxima for PM10-2.5 

concentrations exceeded 100 μg/m3 in Cleveland, OH and Salt Lake City, UT, and 500 μg/m3 in 

Riverside, CA. This latter value was related to a dust storm. In several urban areas (Cleveland, 

OH; Detroit, MI; Chicago, IL; Dallas, TX; and Riverside, CA) maxima in PM10-2.5 concentrations 

were larger than those for PM2.5. However, there is no clear geographic pattern. 

The results described above should be viewed with caution because of inherently large 

errors in the technique used to derive them. 

3-24
Frequency Distributions for PM2.5 Data 

Frequency distributions for PM2.5 concentrations obtained in Philadelphia from 1992 

through 1995 are shown in Figure 3-10 (data obtained by Bahadori et al., 2000b). Also shown 

are concentrations predicted from the log-normal distribution, using geometric mean values and 

standard deviations derived from the data. In Philadelphia, the highest PM2.5 values were 

observed when winds were from the southwest during sunny but hazy high pressure conditions. 

In contrast, the lowest values were found after significant rainstorms during all seasons of the 

year. Mean ± SD day-to-day concentration differences in the data set are 6.8 ± 6.5 μg/m3 

for PM2.5 and 8.6 ± 7.5 μg/m3 for PM10. Maximum day-to-day concentration differences are 

54.7 μg/m3

 for PM2.5 and 50.4 μg/m3 for PM10. 

Figure 3-10. Frequency distribution of 24-h average PM2.5 concentrations measured at the 

Presbyterian home (PBY) monitoring site in southwestern Philadelphia from 

1992 to 1995. Log-normal distribution fit to the data shown as solid line. 

Source: Bahadori et al. (2000b). 

3-25 
Different patterns are observed in data collected elsewhere in the United States. PM2.5 

concentrations obtained in Phoenix, AZ from 1995 through 1997 (Zweidinger et al., 1998) are 

summarized in Figure 3-11; and frequency distributions of PM2.5 concentrations obtained in 

Phoenix are shown in Figure 3-12. Mean ± SD day-to-day concentration differences in this data 

set are 2.9 ± 3.0 μg/m3

; the maximum day-to-day concentration difference was 23 μg/m3. PM2.5 

and PM10-2.5 data were obtained with dichotomous samplers at a number of sites in California on 

a sampling schedule of every 6 days from 1989 through 1998. Histograms showing the 

frequency distribution of the entire set of PM2.5 and PM10-2.5 concentrations obtained by the 

CARB network of dichotomous samplers from 1989 to 1998 are shown in Figures 3-13 and 

3-14. Also shown are log-normal distributions generated by using geometric means and 

standard deviations derived from the data as input. Although the data for both size fractions 

appear to be reasonably well simulated by the function, data obtained at individual locations may 

not be. Data showing the seasonal variability of PM2.5 obtained at Riverside-Rubidoux are 

summarized in box plot form in Figure 3-15. The frequency distribution of PM2.5 concentrations 

obtained at Riverside-Rubidoux from 1989 to 1994 is shown in Figure 3-16. It can be seen that 

the data are not as well fit by a log-normal distribution as are data shown in Figure 3-10, partly 

as the result of a significant number of days when PM2.5 values exceed 100 μg/m3

. 

Figure 3-11. Concentrations of 24-h average PM2.5 measured at the EPA site in Phoenix, 

AZ from 1995 to 1997. The data show the lowest, lowest tenth percentile, 

lowest quartile, median (black circles), highest quartile, highest tenth 

percentile, and highest PM2.5 values. 

Source: Zweidinger et al. (1998). 

3-26 
Figure 3-12. Frequency distribution of 24-h average PM2.5 concentrations measured 

at the EPA site in Phoenix, AZ from 1995 to 1997. 

Source: Zweidinger et al. (1998). 

Figure 3-13. Frequency distribution of 24-h average PM2.5 measurements obtained 

from all California Air Resources Board dichotomous sampler sites 

from 1989 to 1998. 

3-27
Figure 3-14. Frequency distribution of 24-h average PM10-2.5 concentrations obtained 

from all California Air Resource Board Dichotomous sampler sites from 

1989 to 1998. 

Figure 3-15. Concentrations of 24-h average PM2.5 measured at the Riverside-Rubidoux 

site from 1989 to 1998. The data show the lowest, lowest tenth percentile, 

lowest quartile, median (black squares), highest quartile, highest tenth 

percentile, and highest PM2.5 values. 

3-28
Figure 3-16. Frequency distribution of 24-h average PM2.5 concentrations measured 

at the Riverside-Rubidoux site from 1989 to 1994. 

An examination of the data from the four MAACS cities, Phoenix, AZ, and Riverside, CA, 

indicates that substantial differences exist in aerosol properties between the eastern cities 

(MAACS) and the western cities (Phoenix, AZ; Riverside, CA). Fine-mode particles account for 

most of the PM10 mass observed in the MAACS cities and appear to drive the daily and seasonal 

variability in PM10 concentrations in the East, whereas coarse-mode particles represent a larger 

fraction of PM10 mass in Phoenix and Riverside and drive the seasonal variability in PM10 seen in 

the West. The average ratio of PM2.5 to PM10 concentrations is much larger in the MAACS cities 

of Philadelphia, PA (0.72), Washington, DC (0.74), and Nashville, TN (0.63) than in either 

Phoenix, AZ (0.34) or Riverside, CA (0.49). Differences between median and maximum 

concentrations in any size fraction are much larger at the Riverside site than at either the 

MAACS or Phoenix sites. Many of these differences could reflect the more sporadic nature of 

dust suspension at Riverside. In addition, the seasonal variability of PM2.5 concentrations seen in 

Phoenix and Riverside appears to be different from that observed in the MAACS cities. These 

considerations demonstrate the hazards in attempting to extrapolate conclusions about the nature 

of variability in aerosol characteristics inferred at one location to another. 

3-29
3.2.2 Diurnal (Circadian) Variability in PM Concentrations 

The variability of PM concentrations on time scales shorter than a day can, in principle, be 

characterized by measurements made by continuous samplers (e.g, TEOMs and $-gauge 

monitors that are currently used to provide Air Quality Index [AQI] information to the public). 

A description of these methods was provided in Section 2.2.9. However, as shown in Chapter 2, 

continuous methods are subject to artifacts in large part because of the heating of their inlets to 

remove water, which results in the loss of components such as ammonium nitrate and, also 

semivolatile organic compounds (see Sections 2.2.2.1 and 2.2.3 for further details concerning the 

chemistry of volatilizable components). Consequently, caution should be used in interpreting 

results obtained by these techniques. It should be remembered that the FRMs are also subject to 

artifacts; therefore, caution should also be exercised in interpreting results obtained by them. 

The composite diurnal variation of PM2.5 concentrations obtained across the continental 

United States by 31 TEOM and $-gauge monitors reporting to AIRS in 1999 is shown in 

Figure 3-17. As can be seen in that figure, there is a distinct pattern with maxima occurring 

during the morning and evening. Notable exceptions to this pattern occur in California, where 

broad nighttime maxima and daytime minima occur, which may be related to the use of 

$-gauges with unheated inlets there. It should be noted that, in examining the diurnal variations 

shown in Figure 3-17, there is substantial day-to-day variability in the diurnal profile of PM2.5 

measured at the same location, which is smoothed out after a suitably long averaging period is 

chosen. The large ratio of the interquartile range to the median values supports the view that 

there is substantial variability in the diurnal profiles. 

The diurnal variability of PM components is determined by interactions between variations 

in emissions, the rates of photochemical transformations, and the vertical extent and intensity of 

turbulent mixing near the surface. Wilson and Stockburger (1990) characterized the diurnal 

variability of sulfate and lead (Pb) in Philadelphia. At that time, Pb was emitted mainly by 

motor vehicles. Pollutants emitted mainly by motor vehicles, such as carbon monoxide (CO), 

show two distinct peaks occurring during the morning and evening rush hours (see Chapter 3, 

U.S. Environmental Protection Agency, 2000b). Pollutants, such as sulfate, which are 

transported long distances in the free troposphere (i.e., above the planetary boundary layer), tend 

to be mixed downward and have their highest concentrations during the afternoon when the 

intensity and vertical extent of turbulent mixing (and chemical oxidation) are greatest. 

3-30
Figure 3-17. Intraday variability of hourly average PM2.5 concentrations across the 

United States. Interquartile ranges, median and mean (+) values are shown. 

Values above the box plots refer to the number of observations during 1999. 

Source: Fitz-Simons et al. (2000). 

Secondary aerosol components (such as secondary organic compounds) that are produced by 

photochemical reactions may have a daily maximum in the afternoon, similar to ozone. PM 

concentrations produced by residential heating (e.g., from wood burning), on the other hand, 

reach maximum levels during the night when mixing heights are much lower than during the 

day. 

Although the interquartile ranges for hour-to-hour changes in PM2.5 concentrations shown 

in Figure 3-17 encompass several μg/m3

, extreme values for the hour-to-hour variations can be 

much larger (Fitz-Simons et al., 2000). The 98th percentile values for positive and negative 

excursions in concentration are all less than 20 μg/m3

. Maximum positive excursions were much 

larger, ranging from 27 μg/m3

 in the Northeast up to 220 μg/m3 in the Southwest and with 

maximum excursions in other regions all less than 125 μg/m3

. It should be borne in mind that 

the hour-to-hour changes that are reported reflect the effects of a number of processes occurring 

during passage through the sampler inlets and on the TEOM measurement elements. These 

3-31
factors add uncertainty to the interpretation of the hour-to-hour changes that are observed, as 

discussed in Chapter 2. However, because of the tendency of these monitoring instruments to 

lose material by evaporation, the concentrations reported during excursions probably represent 

lower limits for the true values that were present. 

3.2.3 Relationships Among Particulate Matter in Different Size Fractions 

Relations Among PM2.5, PM10-2.5, and PM10 

Data obtained in 1999 by collocated PM2.5 and PM10 FRM monitors have been used to 

calculate the ratio of PM2.5 to PM10 concentrations and correlations among PM2.5, PM10-2.5, 

and PM10 concentrations. Results are shown in Table 3-1 for each of the seven aerosol 

characteristic regions identified in Chapter 6 of the 1996 PM AQCD. As can be seen from the 

table, the ratio of PM2.5 to PM10 concentrations tends to be higher in the eastern United States 

than in the western United States. This general pattern and the values are consistent with that 

found for the studies included in Appendix 6A of 1996 PM AQCD. In that compilation based on 

the results of studies using dichotomous samplers, the mean ratio of PM2.5 to PM10 was 0.75 in 

the East, 0.52 in the central United States, and 0.53 in the western United States. Although a 

large number of paired entries have been included in Table 3-1, seasonal variations and annual

averages in a number of regions could not be determined from the data set because of data 

sparseness, mainly during the early part of 1999. It also can be seen in Table 3-1 that the ratio 

of PM2.5 to PM10 was > 1 for a few hundred measurements. There are a number of reasons for 

these results, as mentioned in Section 3.2.1 in the discussion on PM10-2.5 concentrations. 

Ultrafine Particle Concentrations 

Data for characterizing the concentrations of ultrafine particles (< 0.10 μm Da) and the 

relations between ultrafine particles and larger particles are sparse. Although ultrafine particles 

dominate particle number concentrations, they make very minor contributions to PM2.5 mass. 

For example, Cass et al. (2000) found that particles between 0.056 and 0.1 μm Da contributed 

only 0.55 to 1.16 μg/m3

 at several sites in southern California. Perhaps the most extensive data 

set for ultrafine particle properties is that described by Woo et al. (2001) for a site located 10 km 

to the northwest of downtown Atlanta, GA. Size distributions from 3 to 2000 nm were measured 

every 12 min for 24 months beginning in August 1998. Approximately 89% of the total number 

3-32
PM

TABLE 3-1. DISTRIBUTION OF RATIOS OF PM2.5 TO PM10 AND CORRELATIONS BETWEEN 

2.5 AND PM10-2.5, AND PM10-2.5 AND PM10 FOUND AT COLLOCATED MONITORING SITES IN S

CHARACTERISTIC (EPA/HEI) REGIONS IN 1999 

Percentiles Correla

Region Mean Sites Values 95 90 75 50 25 10 5 PM2.5:PM10 PM2.5:PM

Northeast 0.70 45 1433 0.97 0.95 0.77 0.67 0.60 0.51 0.48 0.72a 0.02 Southeast 0.70 76 2823 1.27 1.06 0.74 0.63 0.54 0.46 0.43 0.69a !0.04a Industrial Midwest 0.70 92 4827 1.09 0.88 0.78 0.68 0.59 0.51 0.47 0.71a 0.17a Upper Midwest 0.53 39 1446 0.92 0.84 0.62 0.49 0.44 0.34 0.24 0.35a !0.02 Southwest 0.38 23 701 0.51 0.51 0.47 0.40 0.31 0.23 0.23 0.63a 0.49a Northwest 0.50 73 3300 0.67 0.65 0.56 0.49 0.44 0.39 0.36 0.69a 0.07a Southern California 0.47 36 1813 0.70 0.57 0.55 0.48 0.44 0.31 0.24 0.70a 0.19a 

Total: 384 16343 

3-33

a Results considered to be significantly different from zero at the " = 0.01 level.

Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b).
of particles were found to be smaller than 100 nm; whereas 26% were < 10 nm. Concentrations 

tend to be lower during the summer than during the winter. No correlation was found between 

number concentration and either volume or surface area for particle sizes up to 2 μm. Because 

the total number of particles is concentrated in the smallest size ranges, these results also 

indicate that fine particle mass does not correlate with the number of ultrafine particles. The 

high time resolution of the measurements allows some inferences to be made about the possible 

sources of the ultrafine particles. The number of particles > 10 nm tends to peak during the 

morning rush hour around 8:00 am and then to decrease through the day and to increase again 

after 6:00 pm, consistent with a traffic-related source. Particles < 10 nm tend to peak during the 

mid-afternoon, consistent with nucleation involving products of active photochemistry 

(McMurry et al., 2000). More direct relationships between particle mass observed in different 

size ranges can be obtained using multi-stage impactors. Keywood et al. (1999) found a 

correlation between PM2.5 and PM0.15 of ~0.7, whereas they found correlations of ~0.96 

between PM1 and PM2.5 and between PM2.5 and PM10 based on samples collected by MOUDIs 

(Multiple Orifice Uniform Deposit Impactors) in six Australian cities. 

3.2.4 Relationships Between Mass and Chemical Component Concentrations 

Time series of elemental composition data for PM2.5 based on X-ray fluorescence (XRF) 

analyses have been obtained at a number of locations across the United States. Time series of 

components of the OC fraction of the aerosol have not yet been obtained. The results of XRF 

analyses for the composition of the inorganic fraction of PM2.5 and PM10-2.5 are presented in 

Table 3-2 for Philadelphia, PA and in Table 3-3 for Phoenix, AZ. Frequency distributions 

for PM2.5 concentration data collected at these sites were shown in Figures 3-10 and 3-11. 

All XRF analyses were performed at the same U.S. EPA X-ray spectrometry facility in Research 

Triangle Park, NC. Data shown in the first column of Table 3-2 are based on analyses of filters 

collected over 3 years (April 1992 to April 1995, labeled a) at the Castor Avenue Laboratory at 

the PBY site in southwestern Philadelphia. These data and data for PM10 were collected using 

Harvard impactors. Data for PM2.5 and PM 10-2.5 shown in the second and third columns were 

obtained by the City of Philadelphia from July 25 to August 14, 1994, using a modified 

dichotomous sampler (VAPS). The samples at the Phoenix site were collected in 1996 and 1997 

using the same type of dichotomous sampler used in the shorter term study in Philadelphia. 

3-34
PM

TABLE 3-2. CONCENTRATIONS (ng/m3) OF PM2.5, PM10-2.5, AND SELECTED ELEMENTS (ng

2.5 AND PM10-2.5 SIZE RANGES WITH STANDARD DEVIATIONS (SD) AND CORRELATION

ELEMENTS AND PM2.5 MASS IN PHILADELPHIA, PA* 

3-35

n = 1105 Conc (ng/m3) ± SD (unc) r n = 20 Conc (ng/m3) ± SD (unc) r n = 20 Conc (ng/m

PM2.5 1 17 ± 0.9 (0.8) × 103 1 PM2.5 2 29.8 ± 14.7 (1.1) × 103 1 PM10-2.5 2 8.4 ± 2.9 

Al 4.0 ± 56 (31) 0.1 Al 109 ± 61 (21) 0.15 Al 325 ± 

Si 116 ± 107 (21) 0.51 Si 191 ± 134 (26) 0.22 Si 933 ± 6

P 8.6 ± 14 (10) 0.31 P 15 ± 4.3 (2.7) 0.72 P 28 ± 9S 2100 ± 1610 (143) 0.92 S 3190 ± 1920 (207) 0.91 S 38 ± 

Cl 5.1 ± 35 (3.4) !0.01 Cl 23 ± 28 (5.5) 0.19 Cl 47 ± 4

K 60.4 ± 45 (4.7) 0.5 K 68 ± 21 (6.4) 0.31 K 100 ± 

Ca 47 ± 33 (4.2) 0.39 Ca 63 ± 33 (9.0) !0.02 Ca 421 ± 

Ti 4.9 ± 5.2 (4.1) 0.44 Ti 8.7 ± 4.7 (9.0) 0.47 Ti 30 ± 

V 8.8 ± 8.7 (1.8) 0.37 V 9.7 ± 7.1 (2.9) 0.38 V 3.2 ± 2

Cr 0.7 ± 1.1 (0.7) 0.15 Cr 1.4 ± 1.2 (2.9) 0.09 Cr 1.0 ± 5

Mn 3.1 ± 2.2 (0.8) 0.39 Mn 3.2 ± 1.5 (1.6) 0.43 Mn 6.3 ± 4

Fe 109 ± 71 (10.5) 0.5 Fe 134 ± 49 (0.5) 0.48 Fe 352 ± 

Co 0.1 ± 1.8 (1.4) 0.04 Co 0.8 ± 0.7 (8.5) 0.58 Co !0.2 ± 

Ni 7.3 ± 8.4 (1.4) 0.22 Ni 8.5 ± 5.6 (0.3) 0.61 Ni 2.0 ± 

Cu 4.8 ± 4.9 (1.1) 0.25 Cu 7.7 ± 3.8 (0.7) 0.22 Cu 14 ± 

Zn 36.9 ± 44 (3.7) 0.21 Zn 56 ± 37 (4.8) 0.22 Zn 52 ± 4As 0.6 ± 1.4 (1.2) 0.18 As 0.4 ± 1.0 (1.0) !0.02 As 0 ± 0

Se 1.5 ± 1.3 (0.6) 0.63 Se 1.3 ± 0.8 (0.4) 0.65 Se !0.1 ± 

Br 5.0 ± 11.7 (0.9) 0.11 Br 14 ± 12 (1.3) 0.21 Br 3.0 ± 2

Pb 17.6 ± 22 (2.5) 0.19 Pb 28 ± 24 (2.4) 0.26 Pb 13 ± 

1Data obtained at the Presbyterian home (PBY) site in Philadelphia from April 1992 to April 1995 with Harvard impactors.2Data obtained at the Castor Avenue Laboratory, North Central Philadelphia, from July 25 to August 14, 1994 with a modified dichoto*Note: Values in parentheses refer to analytical uncertainty (unc) in X-ray fluorescence determinations. 
PM

TABLE 3-3. CONCENTRATIONS (in ng/m3) OF PM2.5, PM10-2.5, AND SELECTED 

ELEMENTS IN THE PM2.5 AND PM10-2.5 SIZE RANGE WITH STANDARD 

DEVIATIONS (SD) AND CORRELATIONS (r) BETWEEN ELEMENTS AND 

2.5 AND PM10-2.5 MASS IN PHOENIX, AZ * 

n = 164 Conc (ng/m3) ± SD (unc) r n = 164 Conc (ng/m3) ± SD (unc) r 

PM2.5 11.2 ± 0.6 (0.6) × 103

 1 PM10-2.5 27.6 ± 14.8 × 103 1 

Al 125 ± 77 (30) 0.23 Al 1879 ± 979 (547) 0.92 

Si 330 ± 191 (48) 0.35 Si 5350 ± 2825 (1347) 0.92 

P 11 ± 7.8 (5.7) 0.52 P 37 ± 20 (17) 0.58 

S 487 ± 254 (40) 0.16 S 131 ± 47 (26) 0.77 

Cl 19 ± 44 (3.0) 0.13 Cl 208 ± 204 (24) 0.28 

K 110 ± 63 (9.2) 0.67 K 561 ± 298 (62) 0.92 

Ca 129 ± 72 (11) 0.51 Ca 1407 ± 755 (124) 0.9 

Ti 11 ± 7.1 (2.7) 0.44 Ti 130 ± 71 (20) 0.9 

V 0.7 ± 2.0 (2.2) !0.28 V 2.0 ± 2.0 (1.5) 0.51 

Cr 0.6 ± 0.9 (0.7) 0.41 Cr 2.6 ± 1.7 (0.7) 0.76 

Mn 5.7 ± 4.3 (0.7) 0.64 Mn 29 ± 16 (3.0) 0.91 

Fe 177 ± 113 (16) 0.8 Fe 1211 ± 674 (133) 0.9 

Co !0.4 ± 1.0 (1.0) !0.01 Co 1.2 ± 2.2 (1.9) 0.38 

Ni 0.6 ± 0.9 (0.5) 0.38 Ni 1.8 ± 1.4 (0.7) 0.7 

Cu 5.2 ± 6.1 (1.5) 0.69 Cu 10.3 ± 9.0 (1.5) 0.58 

Zn 17 ± 14.7 (1.8) 0.64 Zn 25 ± 16 (3.2) 0.64 

As 1.9 ± 3.2 (0.6) 0.5 As 0.6 ± 0.8 (0.6) 0.41 

Se 0.4 ± 0.8 (0.4) 0.4 Se !0.02 ± 0.3 (0.3) 0.21 

Br 3.8 ± 2.0 (0.6) 0.57 Br 0.8 ± 0.6 (0.4) 0.48 

Pb 6.6 ± 6.6 (1.0) 0.69 Pb 4.6 ± 3.8 (1.1) 0.59 

* Values in parenthesis refer to analytical uncertainty (unc) in X-ray fluorescence determinations. 

Source: Calculations based on data obtained by Zweidinger et al. (1998). 

These data are shown to give an idea of the range of concentrations found in studies conducted 

more recently than those shown in Appendix 6A of the 1996 PM AQCD. The speciation 

network should provide more thorough coverage of the composition of particles in the PM2.5 size 

3-36
range across the United States. Results from the pilot study for the speciation network are given 

in Appendix 3B. 

As can be seen from inspection of Tables 3-2 and 3-3, the analytical uncertainty (given in 

parentheses next to concentrations) as a fraction of the absolute concentration is highly variable. 

It exceeds the concentration for a number of trace metals whose absolute concentrations are low, 

whereas it is very small for abundant elements such as sulfur. 

Sulfur (S) is the major element analyzed in the PM2.5 size fraction in the two Philadelphia 

studies and is highly correlated with PM2.5; however, its abundance is roughly two orders of 

magnitude lower in the PM10-2.5 size range and is negatively correlated with PM10-2.5. The 

concentrations of the crustal elements Al, Si, K, Ca, and Fe are much higher in the PM10-2.5 size 

range than in the PM2.5 size range and are well correlated with PM10-2.5. A number of trace 

elements (e.g., Cr, Co, Ni, Cu, Zn, As, Se, and Pb) are detectable in the two PM2.5 data sets, and 

the concentrations of many of these elements are much greater than the uncertainty in their 

determination. Except for Co, As, and Se (which are not detected in the PM10-2.5 samples), the 

concentrations of many elements (Cr, Zn, and Pb) are comparable in the PM2.5 and PM10-2.5 size 

ranges. The concentration of Cu is significantly higher in the PM10-2.5 size range, whereas the 

concentration of Ni is smaller in the PM10-2.5 size range than in the PM2.5 size range. Problems 

with detecting and quantifying concentrations of Co, As, and Se can result in artificial reduction 

of their correlation coefficients with PM2.5. 

There are a number of distinct differences between the PM2.5 sets for Philadelphia and 

Phoenix. For instance, sulfate and associated cations and water that would be expected to 

correspond to the measurement of S appear to constitute a major fraction of the composition of 

the PM in the Philadelphia data set; whereas they appear to constitute a much smaller fraction of 

the PM in the Phoenix data set. The highest PM2.5 values were observed in Philadelphia during 

episodes driven by high sulfate abundances, whereas those in Phoenix were driven by raised soil 

dust. The concentration of S in Phoenix is much lower in the Phoenix PM2.5 data set than in 

either Philadelphia PM2.5 data set, even though it represents the most abundant element and it is 

only weakly correlated with PM2.5. This is in marked contrast to the data shown for Philadelphia 

and also to data found at other eastern sites. It is not clear what the reasons are for this finding. 

As in Philadelphia, the concentration of S in Phoenix is higher in the PM2.5 size range than in 

the PM10-2.5 size range. Trace metals (e.g., Cr, Co, Ni, Cu, Zn, As, and Pb) are not well 

3-37
correlated (0.04 < r < 0.25) with PM2.5 in the Philadelphia data set, whereas they are more 

variably correlated (0.01 < r < 0.69) with PM2.5 in the Phoenix data set. The uncertainty in the 

concentration measurement most probably plays a role in determining a species’ correlation 

with PM2.5, especially when the analytical uncertainty is high relative to concentration as it is for 

a number of elements in the data shown in Tables 3-2 and 3-3. Concentrations of Al, Si, K, Ca, 

and Fe are again much higher in the PM10-2.5 size range than in the PM2.5 size range and are 

strongly correlated with PM10-2.5 in both data sets. 

There are also similarities in the PM2.5 data sets for Philadelphia and Phoenix. Crustal 

elements are not as well correlated with PM2.5 as they are with PM10-2.5 in both data sets. The 

concentrations of trace metals (Cr, Ni, Cu, and Zn) in PM2.5 are similar in Philadelphia and 

Phoenix. It can also be seen that their concentrations are of the same order of magnitude in 

both PM2.5 and PM10-2.5. Concentrations of Cu are noticeably higher in PM10-2.5 than in PM2.5 in 

both Philadelphia and Phoenix. These results are consistent with those of many monitoring 

studies shown in Appendix 6A of the 1996 PM AQCD, which also show that concentrations of 

these metals are of the same order of magnitude in both size fractions and that concentrations of 

Cu tend to be higher in PM10-2.5 than in PM2.5. 

One study suggests that the partitioning of trace metals between the fine and coarse 

fractions varies with PM concentration. Salma et al. (2002) determined the size distribution of a 

number of trace elements at four sites characterizing environments ranging from the urban 

background to an urban traffic tunnel in Budapest, Hungary. Although S, K, V, Ni, Cu, Zn, As, 

and Pb were found mainly in the fine fraction at the urban background site, their mass median 

aerodynamic diameters increased with increasing PM concentrations until they were all found 

mainly in the coarse fraction in the traffic tunnel. They also found that Na, Mg, Al, Si, P, Ca, Ti, 

Fe, Ga, Sr, Zr, Mo, and Ba were concentrated mainly in the coarse fraction at all four sites and 

that their mass median aerodynamic diameters increased with increasing PM concentrations. 

The mean concentration of Pb observed in the methods evaluation study for the speciation 

network was only about 5 ng/m3

 in Philadelphia during the first half of 2000 (Appendix 3B), 

whereas its concentration was about three times higher during studies conducted during the early 

1990s (Table 3-3). In a study conducted in the greater Philadelphia area during the summer of 

1982, Dzubay et al. (1988) found concentrations of Pb of about 250 ng/m3

, or about fifty times 

higher than observed in 2000. The mean Pb concentration was about 3 ng/m3

 at the Phoenix site 

3-38
included as part of the same methods evaluation study for the speciation network; however, the 

mean Pb concentration was 39 ng/m3 during an earlier study conducted during 1989 and 1990 in 

Phoenix (Chow et al., 1991). These changes in Pb concentrations are consistent with those in 

many other urban areas for which monitoring studies have been conducted during the late 1970s 

and 1980s (cf., Appendix 6A of the 1996 PM AQCD) and for which there are data given in 

Appendix 3B. It should be remembered that the older studies were conducted while Pb was still 

used as a gasoline additive. The ratio of Pb in PM2.5 to Pb in PM10-2.5 was also much higher in 

the older studies than in the more recent ones, reflecting the importance of combustion as its 

source. Smaller decreases are apparent in the concentrations of other trace metals such as Cu, 

Ni, and Zn between studies conducted in the early 1980s and in the methods evaluation study for 

the speciation network conducted in 2000. 

PM

Some indication of the sources of metals such as Pb, Cu, Cd, and Zn in current ambient 

2.5 and PM10-2.5 samples can be obtained by examining their sources in urban runoff. The 

sources of these elements in urban runoff were found to be the weathering of building surfaces, 

motor vehicle brake and tire wear, engine oil and lubricant leakage and combustion, and wet and 

dry atmospheric deposition (Davis et al., 2001). Once deposited on the ground, these elements 

can be resuspended with other material as PM2.5 and PM10-2.5, although research is needed into 

the mechanisms of how this is accomplished. Wind-abrasion on building siding and roofs 

(coatings such as Pb paint and building material such as brick, metal, and wood siding); brake 

wear (brake pads contain significant quantities of Cu and Zn); tire wear (Zn is used as a filler in 

tire production); and burning engine oil could all produce particles containing these metals, 

especially Zn. 

Data for the chemical composition of ambient ultrafine particles are sparse. In a study 

conducted at several urban sites in Southern California, Cass et al. (2000) found that the 

composition of ultrafine particles ranged from 32 to 67% organic compounds, 3.5 to 17.5% 

elemental carbon, 1 to 18% sulfate, 0 to 19% nitrate, 0 to 9% ammonium, 1 to 26% metal oxides, 

0 to 2% sodium, and 0 to 2% chloride. Thus carbon, in various forms, was found to be the major 

contributor to the mass of ultrafine particles. However, ammonium was found to contribute 33% 

of the mass of ultrafine particles at one site in Riverside. Iron was the most abundant metal 

found in the ultrafine particles. Chung et al. (2001) found that carbon was the major component 

of the mass of ultrafine particles in a study conducted during January of 1999 in Bakersfield, 

3-39
CA. However, in the study of Chung et al., the contribution of carbonaceous species (OC and 

EC; typically 20 to 30%) was much lower than that found in the cities in Southern California. 

They found that calcium was the dominant cation, accounting for about 20% of the mass of 

ultrafine particles in their samples. Sizable contributions from silicon (0 to 4%) and aluminum 

(6 to 14%) were also found. Further studies, including scanning electron microscopy, may be 

needed to quantify the role of coarse particle bounce from the upper stages of their MOUDI 

impactor. 

Gone et al. (2000) measured the size distribution of trace elements from 0.056 μm to 

1.8 μm Da in Pasadena, CA and in the Great Smoky Mountains National Park, TN. They found 

that elements identified as being of anthropogenic origin had mass median diameters < 1 μm 

PM; whereas elements of crustal origin generally had a mass median diameter > 1 μm. 

Concentrations of trace metals were much higher in the accumulation mode than in the ultrafine 

mode in both study areas. In PM1, 76% of Cr, 95% of Fe, 94% of Zn, 89% of As, and 79% of 

Cd at the Tennessee site were found in the accumulation mode; and 70% of Fe, 85% of Zn, 92% 

of As, and 84% of Cd were found in the accumulation mode in Pasadena. Fe was the most 

abundant metal found in the ultrafine particles. The abundance of crustal elements, such as Al, 

declined rapidly with decreasing particle size at both locations; and Al in PM1 probably 

represented the lower tail of the coarse PM mode. However, on two days at Pasadena there were 

increases in the concentration of Al in ultrafine particles that were associated with increases in 

Sc and Sm. The latter two elements originate exclusively from crustal material (Gone et al., 

2000). 

3.2.5 Spatial Variability in Particulate Matter and its Components 

PM2.5 

Aspects of the spatial variability of PM2.5 concentrations on the urban scale are examined 

in this section. Intersite correlation coefficients for PM2.5 can be calculated based on the results 

of FRM monitors placed at multiple sites within MSAs across the United States. Pearson 

correlation coefficients (r) calculated for pairs of monitoring sites in the Philadelphia, PA; 

Cleveland, OH; Dallas, TX; and Los Angeles, CA MSAs are shown in Table 3-4. The 90th 

percentile value, P90, of the absolute differences (in μg/m3

) between the two sites is shown in 

parentheses below r along with the coefficient of divergence (COD), and the number of 

3-40
PM

TABLE 3-4a-d. MEASURES OF THE SPATIAL VARIABILITY OF 

2.5 CONCENTRATIONS WITHIN SELECTED METROPOLITAN 

STATISTICAL AREAS 

(a) Philadelphia, PA

Site I.D. # 340071007 340155001 420170012 420450002 420910013 421010004 421010136 

340071007 1 0.91 0.93 0.87 0.88 0.94 0.93 

(6.3, 0.14) 

170 

(5.2, 0.15) 

167 

(6.9, 0.19) 

183 

(5.0, 0.16) 

176 

(4.6, 0.15) 

163 

(5.1, 0.14) 

166 

340155001 1 0.84 0.88 0.83 0.89 0.85 

(7.5, 0.19) 

176 

(7.4, 0.18) 

194 

(7.1, 0.18) 

184 

(7.4, 0.17) 

169 

(6.9, 0.18) 

173 

420170012 1 0.85 0.88 0.94 0.89 

(7.5, 0.16) 

199 

(4.7, 0.13) 

200 

(4.9, 0.11) 

177 

(5.3, 0.13) 

180 

420450002 1 0.87 0.94 0.88 

(6.1, 0.15) 

208 

(5.1, 0.11) 

187 

(4.0, 0.12) 

193 

420910013 

421010004 

Key 

Airs Site I.D. # 

Pearson r 

(90th %-tile difference in concentration, 

coefficient of divergence) 

number of observations 

1 0.90 

(4.6, 0.11) 

181 

1 

0.87 

(4.5, 0.11) 

185 

0.96 

(3.3, 0.08) 

550 

421010136 1 

Mean 14.78 14.59 14.11 15.92 14.2 15.72 15.81 

Obs 197 208 217 230 221 610 616 

SD 9.2 8.65 8.47 8.65 8.93 9.18 9.26 

(b) Cleveland, OH 

Site I.D. # 390350013 390350038 390350060 390350065 390350066 390351002 390851001 390932003 

390350013 1 0.91 0.96 0.94 0.92 0.88 0.89 0.92 

(7.1, 0.13) (3.3, 0.12) (5.4, 0.10) (7.2, 0.16) (9.0, 0.18) (10.7, 0.21) (8.1, 0.17) 

320 322 314 300 308 308 265 

390350038 1 0.92 0.89 0.85 0.84 0.84 0.892 

(6.9, 0.14) (9.4, 0.15) (13.1, 0.21) (12.9, 0.21) (14.3, 0.23) (11.2, 0.18) 

306 296 290 304 294 256 

390350060 1 0.93 0.90 0.87 0.88 0.90 

(5.1, 0.14) (8.15, 0.19) (8.6, 0.20) (10.8, 0.22) (8.9, 0.18) 

309 300 310 307 256 

390350065 1 0.96 0.91 0.90 0.91 

(4.7, 0.14) (5.4, 0.16) (7.8, 0.20) (7.7, 0.18) 

295 310 306 264 

390350066 1 0.88 0.91 0.91 

(4.9, 0.16) (5.8, 0.15) (5.8, 0.13) 

304 295 247 

390351002 Key 1 0.89 0.87 

Airs Site I.D. # 

Pearson r 

(6.0, 0.18) 

303 

(5.3, 0.18) 

261 

390851001 (90th %-tile difference in concentration, 

coefficient of divergence) 

number of observations 

1 0.90 

(6.6, 0.15) 

275 

390932003 1 

Mean 18.34 20.16 18.39 17.47 14.74 15 14 15.22 

Obs 368 931 353 340 332 351 342 298 

SD 9.59 11.5 9.9 8.95 8.45 8.16 8.44 8.8 

3-41 
PM

TABLE 3-4a-d (cont’d). MEASURES OF THE SPATIAL VARIABILITY OF 

2.5 CONCENTRATIONS WITHIN SELECTED METROPOLITAN 

STATISTICAL AREAS. 

(c) Dallas, TX 

Site I.D. # 480850005 481130020 481130035 481130050 481130057 480850005 481130020 

480850005 1 0.92 

(3.5, 0.11) 

220 

0.94 

(3.6, 0.11) 

204 

0.94 

(4.3, 0.13) 

213 

0.89 

(6.3, 0.15) 

195 

0.94 

(3.7, 0.10) 

218 

0.94 

(3.1, 0.10) 

189 

481130020 1 0.95 

(3.2, 0.08) 

212 

0.94 

(3.3, 0.09) 

603 

0.92 

(4.1, 0.11) 

205 

0.95 

(2.5, 0.07) 

635 

0.97 

(2.3, 0.07) 

207 

481130035 1 0.97 

(2.0, 0.06) 

203 

0.93 

(3.9, 0.09) 

191 

0.97 

(1.9, 0.06) 

207 

0.94 

(3.6, 0.10) 

185 

481130050 1 0.94 

(2.7, 0.08) 

199 

0.98 

(2.2, 0.06) 

608 

0.94 

(4.2, 0.12) 

196 

481130057 

480850005 

Key 

Airs Site I.D. # 

Pearson r 

(90th %-tile difference in concentration, 

coefficient of divergence) 

number of observations 

1 0.95 

(3.1, 0.08) 

198 

1 

0.91 

(5.5, 0.14) 

182 

0.96 

(3.0, 0.09) 

198 

481130020 1 

Mean 11.54 12.42 12.83 13.34 13.67 12.68 11.67 

Obs 234 677 222 644 215 687 216 

SD 5.62 5.69 5.79 5.79 6.14 5.67 5.43 

(d) Los Angeles, CA 

Site I.D. # 60370002 60371103 60371201 60371301 60372005 60374002 

60370002 1 0.87 0.76 0.68 0.95 0.60 

(10.7, 0.18) (14.6, 0.23) (17.9, 0.25) (6.2, 0.14) (18.1, 0.26) 

581 208 229 212 553 

60371103 1 0.86 0.89 0.93 0.80 

(12.8, 0.20) (10.1, 0.12) (7.1, 0.11) (13.6, 0.17) 

205 222 207 563 

60371201 1 0.76 0.85 0.66 

(18.1, 0.24) (12.1, 0.18) (18.2, 0.24) 

212 197 197 

60371301 

Key 

Airs Site I.D. # 

Pearson r 

1 0.78 

(13.2, 0.18) 

214 

0.95 

(8.1, 0.11) 

216 

60372005 (90th %-tile difference in concentration, 1 0.62 

coefficient of divergence) 

number of observations (15, 0.20) 

204 

60374002 1 

Mean 20.91 22.48 18.32 23.73 20.09 20.31 

Obs 641 656 217 237 220 621 

SD 13.35 13.47 11.65 13.43 11.92 12.42 

3-42 
observations used in the calculation of r, P90 and COD is given on the third line. The COD was 

used by Wongphatarakul et al. (1998) as a measure of the degree of similarity between aerosol 

data sets1

. The annual mean concentrations, the number of observations used to calculate the 

annual average, and the standard deviation are shown directly beneath the correlation tables for 

each site. These analyses and those for another 23 MSAs are given along with maps in 

Appendix 3A. As the concentrations of PM2.5 at two sampling sites become more alike, the 

COD approaches zero; as the concentrations diverge, the COD approaches one. 

The four MSAs shown in Table 3-4(a-d) were chosen to illustrate different patterns of 

spatial variability across the United States. In addition, air-pollution health-outcome studies 

have been performed in a few of these MSAs. It can be seen from inspection of Table 3-4 that 

correlation coefficients vary over a wide range in the MSAs shown. Correlations between sites 

in the Philadelphia, PA, Cleveland, OH, and Dallas, TX MSAs are all high and span a relatively 

narrow range (0.82 to 0.97). However, correlations between sites in the Los Angeles-Long 

Beach MSA are lower than in the three other MSAs and span a wider range of values (0.60 to 

0.95). If the monitoring site in Lancaster, CA were included, correlations would be even lower. 

This site was omitted because it did not meet completeness criteria for 2001. The extension of 

these analyses to include the relevant consolidated MSAs (CMSAs) would also produce a 

number of sites that are even less well correlated with each other, in part, because some included 

sites are located outside of urban airsheds. 

Correlation coefficients between pairs of sites in the other 23 MSAs given in Appendix 3A 

for the most part fall within the range of values given in Table 3-4. In four MSAs (Columbia, 

SC; Norfolk, VA; Grand Rapids, MI; and Baton Rouge, LA), intersite correlations are all greater 

than 0.9. In nine others (Cleveland, OH; Louisville, KY; Chicago, IL; Milwaukee, WI; 

Philadelphia, PA; Detroit, MI; Kansas City, MO; Dallas, TX; and Salt Lake City, UT), they are 

1 The COD for this purpose is defined as follows: 

COD p 

x x 

x x jk 

i 

p ij 

ij 

= − 

+ 

⎛

⎝

⎜

⎜

⎞

⎠

⎟

⎟ = 

∑1 

1 

2 

ik 

ik 

(3-1) 

where xij and xik represent the 24-h average PM2.5 concentration for day i at site j and site k and p is the number 

of observations. 

3-43 
all greater than 0.8. Correlations between sites in the other MSAs examined tend to be lower 

and span a broader range than for the MSAs mentioned above. 

Seven pairs of collocated monitors in seven MSAs (Columbia, SC; Dallas, TX; Detroit, 

MI; Grand Rapids, MI; Louisville, KY; Steubenville, OH; Washington, DC) provide an 

indication of the performance of collocated monitors (see Table 3A-1). Mean values of r, P90, 

and COD for these seven pairs of monitors are 0.986, 1.63 μg/m3

, and 0.060, thus suggesting that 

most of the intersite variability seen is not due to sampler imprecision. 

There are no strong regional patterns evident in the data given in Appendix 3A except that 

correlations tend to be higher between monitoring sites in MSAs in the southeastern United 

States than between monitoring sites in other regions. 

A number of factors affect intersite correlations within MSAs. These include field 

measurement and laboratory analysis errors, placement of monitors close to active sources, 

placement of monitors in outlying areas, placement of monitors in locations that are isolated 

topographically from other monitors, placement of monitors in areas outside of local 

atmospheric circulation regimes (e.g., land-sea breezes), and transient local events (e.g., 

thunderstorms or sporadic emissions). In several MSAs (i.e., Atlanta, GA, Seattle, WA, and 

Los Angeles-Long Beach, CA) at least one site is remote from the others (by at least 100 km), 

is physically separated from them by mountains, and is really neither part of the urban area nor 

the urban airshed. Correlations between concentrations at these sites and others tend to be lower 

than among the other sites, and concentration differences tend to be larger. It should be noted 

that outlying sites such as these are included in many epidemiologic time-series studies without 

any differential weighting (e.g., with respect to the exposed population or spatial differences in 

susceptibility or with regard to compositional differences). Although it is frequently the case 

that distance between sites in urban areas is largely responsible for the spatial variability that is 

observed, there are a few instances for which correlations are higher and differences in 

concentrations are lower for sites that are located farthest apart. This situation may arise because 

these sites are influenced more by the regional background of secondary PM rather than by local 

sources, but there are not any consistent distances evident below which correlations and 

differences in concentrations tend towards some limiting values. Still, it is generally the case 

that outlying sites are characterized by lower annual mean concentrations. 

3-44
Indications of land use (commercial, industrial, residential, agricultural, forest) and 

location of sites (urban/city center, suburban, rural) are given in the AIRS data base. Categories 

such as urban/city center can refer to very different conditions in Columbia, SC, and Chicago, 

IL. Also, it should not be automatically assumed that concentrations measured at sites 

categorized as industrial are dominated by local emissions. The PM2.5 monitoring sites are 

generally deployed to capture potential population exposures in a variety of environments as 

opposed to monitoring for compliance as it exists around local sources. It should be 

remembered that much of PM2.5 is secondary in origin. The widespread formation of secondary 

PM, coupled with the long atmospheric lifetime of PM2.5, ensures some measure of uniformity in 

the correlations of PM2.5 across urban areas. Correlations between many site pairs classified as 

industrial can be high, even though they are separated by large distances (as in the Seattle MSA). 

Some indication of the variability of primary PM2.5 produced by local sources can be 

obtained by examining the variability of CO, which is produced mainly by mobile sources (U.S. 

Environmental Protection Agency, 2000b), and by the variability in EC concentrations (Kinney 

et al., 2000). Carbon monoxide is relatively inert on the urban scale, and its distribution is 

governed by the spatial pattern of its emissions and the subsequent dispersion of these emissions 

not by photochemistry. Carbon monoxide concentrations are at least a factor of three higher 

near urban centers than in surrounding rural areas within the four consolidated metropolitan 

statistical areas examined in the EPA document, Air Quality Criteria for Carbon Monoxide (CO 

AQCD; U.S. Environmental Protection Agency, 2000b). The correlations of CO within the 

urban areas examined in that document were all low to moderate. Therefore, it might be 

expected that primary PM2.5 produced by local traffic should be at least as heterogeneous as CO 

in a given urban area. Elemental carbon is a significant component of diesel exhaust (cf., 

Appendix 3D). Kinney et al. (2000) measured EC and PM2.5 concentrations at four sites located 

on sidewalks of streets characterized by varying exposures to diesel emissions in upper 

Manhattan (Harlem, NY). Whereas the mean PM2.5 concentrations varied by about one-third 

from 37 to 47 μg/m3

 at the four sites, mean EC concentrations varied by a factor of four from 

1.5 to 6.2 μg/m3

. The corresponding ratios of EC to PM2.5 ranged from 0.039 to 0.14. Although 

EC constituted a relatively small fraction of PM2.5 in this study, spatial variability in its sources 

(diesel and gasoline fueled vehicles, resuspended road dust, and cooking) contributed, on 

average, about one-third of the spatial variability observed in PM2.5 concentrations. Further 

3-45
analyses are needed to determine whether the remaining variability could be attributed to other 

local and city-wide sources. Because the effects of emissions from local point sources on 

receptor sites depend strongly on wind direction, correlations involving contributions from local 

sources can be much lower than from area sources (much as motor vehicle traffic) or from 

regionally dispersed sources (such as the photochemical production of secondary organic PM 

and sulfate). 

The difference in mean PM2.5 concentrations between the site with the lowest and the site 

with the highest mean concentration range in all MSAs included in Appendix 3A ranges from 

0.4 μg/m3

 (Baton Rouge) to about 8 μg/m3 (Pittsburgh). Six MSAs (Chicago, Seattle, Cleveland, 

St. Louis, Detroit, and Pittsburgh) show maximum intersite differences in the annual mean larger 

than 6 μg/m3

. In the Seattle MSA, there is one monitoring site (Figure 3A-23a) that is separated 

from the remaining sites by topography and has much lower mean PM2.5 concentrations, much 

smaller seasonal variability in concentrations, and much lower maximum concentrations than 

these other sites. However, the annual mean concentrations at all the other sites within the 

Seattle MSA are within 3 μg/m3

 of each other. Differences in annual mean concentrations are 

also larger between sites located in different MSAs but within the same CMSA. For example, 

in the consolidated MSA of Los Angeles-Riverside, the range of annual mean PM2.5 values is 

extended from ~20 μg/m3

 in the urban area of Los Angeles county to ~29 μg/m3 in Riverside 

County. Large differences in annual mean concentrations within a given area reflect differences 

in source or meteorological or unique topographic characteristics affecting sites, whereas very 

small differences found in some areas may mainly be the result of measurement imprecision. 

While high correlations of PM2.5 provide an indication of the spatial uniformity in temporal 

variability (directions of changes) in PM2.5 concentrations across urban areas, they do not imply 

uniformity in the PM2.5 concentrations themselves. The 90th percentile difference in 

concentrations (P90) and the coefficient of divergence (COD) are used here to give a more 

quantitative indication of the degree of spatial uniformity in PM2.5 concentrations across urban 

areas. A COD of zero implies that both data sets are identical, and a COD of one indicates 

that two data sets are completely different. The calculation of the Pearson correlation coefficient 

(r), P90, and COD allows for distinctions between pairs of sites to be made based on various 

combinations of these parameters. Figure 3-18 shows examples of the varying degree of 

heterogeneity in concentrations between pairs of sites that are highly correlated (r > 0.9 for all 

3-46
Figure 3-18. Occurrence of differences between pairs of sites in three MSAs. The 

absolute differences in daily average PM2.5 concentrations between sites are 

shown on the x-axis and the number of occurrences on the y-axis. The MSA, 

years of observations, AIRS site I.D. numbers for the site pairs, Pearson 

correlation coefficients (r), coefficients of divergence (COD), 90th percentile 

(P90) difference in concentration between concurrent measurements are 

also shown. 

3-47
three site pairs). The increase in the spread of concentrations between the chosen site-pairs is 

reflected in increases in both P90 and COD. Pairs of sites showing high correlations and 

CODs < 0.1 and P90s < 4 μg/m3

 (as in Columbia, SC, Figure 3-7a) indicate homogeneity in 

both PM2.5 concentrations and in their temporal variations. Presumably, sites such as these are 

more strongly affected by regional than local sources. Pairs of sites showing low correlations, 

values of P90 > 10 μg/m3

 and CODs > 0.2, as in Los Angeles, CA (Table 3-5), indicate 

heterogeneity in both PM2.5 concentrations and in their temporal variations. Note that the 

extended urban area or the CMSA includes Riverside County, as well as Los Angeles County. 

Even lower correlations and a greater degree of heterogeneity in PM2.5 concentrations were 

found in the extended CMSA. Pairs of sites showing high correlations (r > 0.9) and CODs > 0.2 

and P90s > 10 μg/m3

 indicate heterogeneity in concentrations but homogeneity in their day-to-day 

changes. Selected pairs of sites in the Cleveland MSA show moderate to high correlations 

coupled with CODs > 0.2 and P90s $ 10 μg/m3

 (Table 3-4), suggesting moderate homogeneity in 

day-to-day changes but significant spatial heterogeneity in concentrations. 

The effect of local point sources on intersite variability can be seen at several sites among 

those listed in Table 3-4(a-d) and Appendix 3A. Sites 39-035-0038 (Cleveland, OH; Table 3-4 

and Figure 3A-8), 18-089-0022 (Gary, IN; Figure 3A-15), 55-079-0043 (Milwaukee, WI; 

Figure 3A-13), and 17-119-0023 (St. Louis, MO; Figure 3A-17) are designated as “source 

oriented” in the AIRS data base in contrast to the “population exposure” objective associated 

with most of the MSA sites. PM2.5 concentrations at these sites are weakly correlated with other 

sites within the MSA as evidenced by low correlation coefficients and large P90s and CODs even 

though some of the neighboring sites may be located short distances away. Other sites 

designated as “source oriented” in Chicago, Milwaukee, and St. Louis do not show clear 

evidence that local sources are contributing to intersite variability. Conversely, in the Tampa, 

FL MSA, pairs of sites are only moderately correlated (0.7 < r < 0.87), but the distribution of 

concentrations is rather homogeneous (COD < 0.14 and P90 < 5 μg/m3

; Figure 3A-7). Thus, a 

number of different combinations of spatial uniformity in PM2.5 concentrations, and correlations 

of these concentrations are found. 

Values of P90 for absolute differences in concentrations between sites span a wide range in 

the data set given in Appendix 3A. In many instances they can be quite low, only about a 

few μg/m3

. Such cases are found mainly in the eastern United States. The largest P90 values 

3-48
PM

TABLE 3-5. MEASURES OF THE SPATIAL VARIABILITY OF 

10-2.5 CONCENTRATIONS WITHIN SELECTED METROPOLITAN 

STATISTICAL AREAS 

(a) Cleveland, OH 

Site 390350013 390350038 390350045 390350060 390350065 390851001 

390350013 1 0.67 

(23.2, 0.26) 

182 

0.67 0.73 0.62 0.41 

(28.5, 0.28) (17.9, 0.22) (27.0, 0.31) (40.0, 0.60) 

95 97 98 94 

390350038 1 0.65 

(16.1, 0.22) 

90 

0.73 0.69 0.44 

(11.7, 0.18) (13.9, 0.62) (24.9, 0.53) 

93 90 89 

390350045 1 0.66 0.71 0.49 

(18.1, 0.23) (10.6, 0.31) (19.9, 0.50) 

94 102 99 

Key 390350060 Airs Site I.D. # 1 0.74 0.31 

(15.4, 0.38) (28.0, 0.59) Pearson r 

93 94 (90th %-tile difference in concentration, 

coefficient of divergence) 1 0.22 

number of observations 

390350065 

(20.4, 0.55) 

99 

390851001 1 

MEAN 26.36 18.63 16.76 21.35 16.79 7.15 

Obs 216 614 112 113 111 109 

SD 17.38 11.6 8.96 16.39 9.49 4.94 

(b) Dallas, TX 

Site 481130020 481130035 481130050 481130057 

481130020 1 0.79 0.71 

(4.5, 0.17) (9.3, 0.22) 

54 55 

481130035 1 0.69 

(7.8, 0.18) 

50 

481130050 1 

481130057 

MEAN 11.22 

Obs 60 

SD 5.35 

12.86 14.46 

55 56 

6.66 6.44 

(c) Los Angeles, CA 

0.66 

(16.5, 0.32) 

54 

0.60 

(13.2, 0.30) 

50 

0.69 

(13.5, 0.24) 

50 

1 

19.12 

55 

10.55 

Key 

Airs Site I.D. #

Pearson r

(90th %-tile difference in

concentration, 

coefficient of divergence)

number of observations

Site 60370002 60371002 60371103 60374002 

60370002 1 0.82 0.63 0.58 

(19.0, 0.24) (15.5, 0.18) (17.3, 0.27) 

49 49 45 

60371002 1 0.74 0.54 

(11.5, 0.21) (11.5, 0.25) 

49 47 

60371103 1 0.57 

(12.5, 0.22) 

45 

60374002 1 

MEAN 24.1 15.33 21.44 16.08 

Obs 56 56 57 53 

SD 11.67011257 6.68 8.65 6.61 

Key 

Airs Site I.D. #

Pearson r

(90th %-tile difference in

concentration, 

coefficient of divergence)

number of observations

3-49
were associated with a single site in Pittsburgh and reached as high as 21 μg/m3

 (Figure 3A-9). 

Excluding this site, large P90 values are found mainly in the western United States. Values of P90 

> 18 μg/m3 are found in the Riverside and Los-Angeles-Long Beach MSAs. Maximum 

differences in concentrations between sites can be much larger than shown in Figure 3-18 and 

have been larger than 100 μg/m3

 on several occasions in the Atlanta, GA and Los Angeles-Long 

Beach, CA MSAs. Rizzo and Pinto (2001) and Fitz-Simons et al. (2000) examined correlations 

between sites located even farther apart than those examined here based on the 1999 AIRS data 

set for PM2.5. They found that in a number of MSAs, PM2.5 concentrations are still well 

correlated (r > 0.7) up to distances of 100 km or more. Leaderer et al. (1999) found r = 0.49 

between sites outside of homes and a regional background monitor located from 1 to 175 km 

away in southwestern Virginia. PM2.5 tends to be correlated over much larger areas in the East 

than in the West, mainly because the terrain tends to be flatter over wider areas in the East 

(Rizzo and Pinto, 2001). As a result, there is a greater opportunity for mixing of emissions 

among dispersed source regions. Many large urban areas in the West are surrounded by 

mountains. The presence of more rugged terrain in the West leads to greater confinement of 

emissions from large urban areas. Other factors such as differences in the composition and 

amount of emissions of precursors and in the rates of photochemical oxidation of these 

emissions in the atmosphere also play a role. 

There is also evidence for inter-annual variability in the spatial variability in PM2.5 

concentrations. The median year-to-year changes in intersite r (0.03), P90 (!0.75 μg/m3), and 

COD (!0.015) from 1999 to 2000 do not differ significantly from zero for all the site pairs 

considered in Appendix 3A. The year-to-year changes in the spatial variability of PM2.5 

concentrations in a number of MSAs (e.g., the Columbia, SC; Grand Rapids, MI; Milwaukee, 

WI; Baton Rouge, LA; Kansas City, MO; Boise, ID; and Portland, OR MSAs) are similar and 

are smaller than those found in the Cleveland, OH; Salt Lake City, UT; and San Diego, CA 

MSAs. The ranges in these parameters are largest for a number of individual site-pairs, 

especially those involving sites that are remote from the others in their MSAs. In these MSAs 

(e.g., Atlanta, GA; Los Angeles, CA; and Seattle, WA MSAs), some sites may be located in 

different airsheds from the remaining sites. Year-to-year changes in parameters describing 

spatial variability in PM2.5 concentrations tend to be larger when sites in different counties within 

a given MSA are considered rather than when sites in the same county are considered. There are 

3-50
a number of factors that can account for inter-annual variability in these parameters, such as 

changes in patterns in the emissions of primary PM2.5; in the transport and rates of 

transformation of secondary PM2.5 precursors in field measurement; and analysis procedures. 

Some additional data for indicating the stability with respect to year-to-year changes in 

spatial variability are available from earlier studies. For example, a comparison between data 

obtained during the summers of 1992 and 1993 (Wilson and Suh, 1997), shown in Figure 3-19,

and data obtained during the summer of 1994 (Pinto et al., 1995) (cf., Table 3-8) in Philadelphia, 

PA, suggests that inter-site correlations of PM2.5 have remained high and have changed very little 

between the two study periods. 

Figure 3-19. Intersite correlation coefficients for PM2.5, PM10, and PM10-2.5. 

Source: Wilson and Suh (1997). 

3-51 
PM10-2.5 

Intersite correlations of PM10-2.5 concentrations obtained during the summers of 1992 and 

1993 in Philadelphia, PA (Wilson and Suh, 1997) are shown in Figure 3-19. As can be seen, 

correlations of PM10-2.5 are substantially lower than those for PM2.5. 

Intersite correlation coefficients can also be calculated for PM10-2.5 based on the AIRS data 

set as shown in Table 3-5 for the Cleveland, OH, Dallas, TX, and Los Angeles, CA MSAs. 

However, data for analyzing the spatial variability of PM10-2.5 are more limited than for PM2.5; 

therefore, fewer urban areas could be characterized in Appendix 3A (Figures 3A-28 to 3A-44). 

Whereas PM2.5 concentrations were found to be highly correlated between sites in the Detroit, 

MI MSA (Table 3-4), estimated PM10-2.5 concentrations are noticeably less well correlated. 

Likewise, correlations of PM10-2.5 in the Chicago, IL MSA are also lower than those for PM2.5. 

In contrast, correlations of PM10-2.5 concentrations between several pairs of sites in the 

Los Angeles-Long Beach partial MSA are higher than those for PM2.5. 

PM

The interpretation of these results is not straightforward, as concentrations of PM10-2.5 are 

generated by taking the difference between collocated PM2.5 and PM10 monitors. Consequently, 

caution must be exercised when viewing them. Errors in the measurement of PM2.5 and PM10 

may play a large role in reducing apparent correlations of PM10-2.5 such that collocated 

10-2.5“measurements” may be expected to be poorly correlated (White, 1998). Indeed, several 

estimates of concentrations are negative. Negative PM10-2.5 concentrations also lead to artifacts 

in the calculation of CODs. In cases where these artifacts cause a division by zero or a very 

small number in the calculation of CODs, dashes are used in Figures 3A-28 through 3A-44. 

These results imply that negative concentrations can be almost equal in absolute magnitude to 

positive concentrations in the same MSA. The possible causes of these errors are essentially the 

same as those discussed in Section 3.2.1 with regard to the occurrence of PM2.5 to PM10 ratios 

greater than one. There are also physical bases for expecting that PM10-2.5 concentrations may be 

more variable than those for PM2.5. PM10-2.5 is mainly primary in origin, and its emissions are 

spatially and temporally heterogenous. Similar considerations apply to primary PM2.5, but much 

of PM2.5 is secondary, and sources of secondary PM are much less spatially and temporally 

variable. Dry deposition rates of particles depend strongly on particle size. Whereas all particles 

may be brought to the surface by turbulent motions in the atmosphere, gravitational settling 

becomes more important with increasing particle size. Gravitational settling can effectively 

3-52
limit the horizontal distance a particle can travel. For example, 10 μm Da particles suspended in 

a hypothetical 1 km deep planetary boundary layer can be removed within a few hours, but 1 μm 

Da particles can remain suspended in the atmosphere for up to 100 to 1,000 times longer before 

being dry deposited. (Estimated atmospheric lifetimes were based on deposition velocities given 

in Lin et al. [1994] for typical wind speeds.) The findings of larger correlations of PM10-2.5 

between several site pairs in the Los Angeles basin (cf., Figures 3A-25/26 and Figures 3A-42/43) 

and one other site pair in the St. Louis, MO-IL MSA (cf., Figures 3A-17 and 3A-37) are 

anomalous in light of the discussion above. However, these findings could have resulted from 

differences between the spatial and temporal behavior of sources of PM2.5 and PM10-2.5 in these 

locations. Because of negative values, CODs were not calculated. 

PM Components 

Three methods for comparing the chemical composition of aerosol databases obtained at 

different locations and times were discussed by Wongphatarakul et al. (1998). Log-log plots of 

chemical concentrations obtained at pairs of sampling sites accompanied by the coefficient of 

divergence (COD) were examined as a way to provide an easily visualized means of comparing 

two data sets2

. Examples comparing downtown Los Angeles with Burbank and with 

Riverside-Rubidoux are shown in Figures 3-20 and 3-21. As the composition of two sampling 

sites become more similar, the COD approaches zero; as their compositions diverge, the COD 

approaches one. Correlation coefficients calculated between components can be used to show 

the degree of similarity between pairs of sampling sites. 

In addition to calculating correlation coefficients for total mass or for individual 

components, correlation coefficients for characterizing the spatial variation of the contributions 

from given source types can also be calculated by averaging the correlation coefficients of the 

set of chemical components that represent the source type. Correlation coefficients showing the 

2 The COD for two sampling sites is defined as follows: 

COD p 

x x 

x x jk 

i 

p ij 

ij ik 

= − 

+ 

⎛

⎝

⎜

⎜

⎞

⎠

⎟

⎟ = 

∑1 

1 

2 

ik (3-2) 

where xij represents the average concentration for a chemical component i at site j, j and k represent two sampling 

sites, and p is the number of chemical components. 

3-53 
Figure 3-20. PM2.5 chemical components in downtown Los Angeles and Burbank (1986) 

have similar characteristics. The spread in the data is shown by the bars. 

Source: Wongphatarakul et al. (1998). 

3-54 
Figure 3-21. Concentrations of PM2.5 chemical components in Rubidoux and downtown 

Los Angeles (1986). The diagram shows a significant spread in the 

concentrations for the two sites compared with downtown Los Angeles and 

Burbank (Figure 3-20). 

Source: Wongphatarakul et al. (1998). 

3-55 
spatial relationships among PM2.5 (total) and contributions from different source categories 

obtained at various sites in the South Coast Air Basin (SoCAB) Study are shown in Table 3-6. 

In Wongphatarakul et al. (1998), crustal material (crustal), motor vehicle exhaust (mv), residual 

oil emissions (residual oil), and secondary PM (sec) were considered as source categories. Also, 

in that study, Al, Si, Fe, and Ca were used as markers for crustal material (crustal); V and Ni 

were used as markers for fuel oil combustion (residual oil); and Pb, Br, and Mn were used as 

markers for motor vehicle exhaust (mv), based on the lack of other, perhaps more suitable, 

tracers. NO3 

!

, NH4

+

, and SO4

2!

 represent secondary PM components. The averages of the 

correlation coefficients of marker elements within each source category are shown in Table 3-6. 

Values of rsec and rmv are much higher than those for rcrustal and rresidual oil throughout the SoCAB, 

suggesting a more uniform distribution of the contributions from secondary PM formation and 

automobiles than from crustal material and localized stationary sources. 

Correlation coefficients in Philadelphia air based on data obtained at four sites for PM2.5 

(total), crustal components (Al, Si, Ca, and Fe), the major secondary component (sulfate), 

organic carbon (OC), and elemental carbon (EC) are shown in Table 3-7. Because these data 

were obtained after Pb had been phased out of gasoline, a motor vehicle contribution could not 

be estimated from the data. Pb also is emitted by discrete point sources, such as the Franklin 

smelter. Concentrations of V and Ni were often beneath detection limits; so, the spatial 

variability in PM due to residual oil combustion was not estimated. Sulfate in aerosol samples 

collected in Philadelphia arises mainly from long-range transport from regionally dispersed 

sources (Dzubay et al., 1988). This conclusion is strengthened by the high correlations in sulfate 

between different monitoring sites and the uniformity in sulfate concentrations observed among 

the sites. Widespread area sources (e.g., motor vehicle traffic) also may emit pollutants that are 

correlated between sites provided that traffic patterns and emissions are similar throughout the 

area under consideration. 

Landis et al. (2001) found relatively high correlations between PM2.5 (r = 0.97), sulfate 

(r = 0.99), OC (r = 0.97), EC (r = 0.83), NaCl (r = 0.83), and nitrate (r = 0.83) measured at two 

sites located several km apart in the Baltimore, MD area. Concentrations of crustal material 

(r = 0.63) and the sum of total metal oxides (r = 0.76) were not as well correlated. These results 

are consistent with those for another eastern city, Philadelphia, PA, given in Table 3-7. The 

results presented above for Philadelphia, PA; Baltimore, MD; and Los Angeles, CA, indicate 

3-56
PM

TABLE 3-6. CORRELATION COEFFICIENTS FOR SPATIAL VARIATION OF 

2.5 MASS AND DIFFERENT SOURCES FOR PAIRS OF SAMPLING 

SITES IN THE SOUTH COAST AIR BASIN (1986) 

Sampling Site rtotal rcrustal rsec rmv rresidual oil 

Hawthorne and Rubidoux !0.027 

Long Beach and Rubidoux 0.051 

Anaheim and Rubidoux 0.066 

Downtown Los Angeles and Rubidoux 0.095 

Burbank and Rubidoux 0.12 

Hawthorne and Anaheim 0.76 0.034 0.768 0.492 0.17 

Long Beach and Anaheim 0.852 0.075 0.888 0.504 0.15 

Burbank and Anaheim 0.77 0.105 0.749 0.579 0.161 

Downtown Los Angeles and Anaheim 0.827 0.143 0.804 0.556 0.233 

Downtown Los Angeles and Hawthorne 0.808 0.568 0.854 0.669 0.533 

Burbank and Hawthorne 0.704 0.599 0.79 0.688 0.491 

Long Beach and Burbank 0.731 0.633 0.737 0.714 0.295 

Long Beach and Hawthorne 0.88 0.649 0.909 0.861 0.482 

Downtown Long Angeles and Long Beach 0.842 0.653 0.817 0.719 0.378 

Downtown Los Angeles and Burbank 0.928 0.825 0.96 0.871 0.606 

Source: Wongphatarakul et al. (1998). 

PM

TABLE 3-7. CORRELATION COEFFICIENTS FOR SPATIAL VARIATION OF 

2.5 MASS AND DIFFERENT COMPONENTS FOR PAIRS OF SAMPLING 

SITES IN PHILADELPHIA (1994) 

Sampling Site rtot rcrustal rsec rOC rEC rPb 

Castor Ave. and Roxboro 0.92 0.52 0.98 0.88 0.84 0.43 

Castor Ave. and NE Airport 0.93 0.47 0.99 0.88 0.77 !0.07 

Castor Ave. and Broad St. 0.93 0.57 0.99 0.85 0.89 0.11 

Roxboro and NE Airport 0.98 0.67 0.98 0.83 0.82 0.2 

Roxboro and Broad St. 0.95 0.9 0.98 0.86 0.79 0.47 

NE Airport and Broad St. 0.95 0.69 0.99 0.84 0.63 0.11 

Source: Pinto et al. (1995). 

3-57 
that secondary PM components are more highly correlated than primary components and may be 

more highly correlated than total PM2.5. These results suggest that the correlation of PM 

concentrations across an urban area may depend on the relative proportions of primary and 

secondary components of PM at individual sites. Sampling artifacts affecting the measurement 

of nitrate and organic carbon can obscure these relations and may depress correlations between 

sites. 

Kao and Friedlander (1995) examined the statistical properties of a number of PM 

components in the South Coast Air Basin (Los Angeles area). They found that, regardless of 

source type and location within their study area, the concentrations of nonreactive, primary 

components of PM10 had approximately log-normal frequency distributions with constant values 

of the geometric standard deviations (GSDs). However, aerosol constituents of secondary origin 

(e.g., SO4

2!

, NH4

+

, and NO3 

!

) were found to have much higher GSDs. Surprisingly, the GSDs of 

organic (1.87) and elemental (1.74) carbon were both found to be within 1 SD (0.14) of the mean 

GSD (1.85) for nonreactive primary species, compared to GSDs of 2.1 for sulfate, 3.5 for nitrate, 

and 2.6 for ammonium. These results suggest that most of the OC seen in ambient samples in 

the South Coast Air Basin was of primary origin. Pinto et al. (1995) found similar results for 

data obtained during the summer of 1994 in Philadelphia. Further studies are needed to 

determine if these relations are valid at other locations and to what extent the results might be 

influenced by sampling artifacts such as the evaporation of volatile constituents during or after 

sampling. 

The use of correlations between OC and EC and between OC to EC ratios based on a 

comparison between values measured in source emissions and ambient observations has also 

been suggested as a means to distinguish between secondary and primary sources of OC (Turpin 

and Huntzicker, 1995; Strader et al., 1999). Ratios of OC to EC from combustion sources are 

typically < 3 and may even be < 1 in diesel emissions (cf., Appendix 3D). Cabada et al. (2002) 

concluded that secondary organic PM can contribute from 10 to 35% of total organic PM on an 

annual basis, with values > 50% during the summer and 0% during the winter months in 

Pittsburgh, PA based on chemistry-transport model (CTM) results and comparison with 

emissions inventory values of OC to EC ratios. All of these inferences are subject to 

considerable uncertainty in the methods for measuring OC, as discussed in Chapter 2. 

Ambiguity also arises in the ratio method, as the ratios may change due to chemical reactions 

3-58
occurring during aging of the particles. However, little work has been done on this issue. The 

ratio can be greater than nine in emissions from wildfires, and transport from distant fires can 

skew results unless this transport is taken into account (cf., Appendix 3D). Modeling studies 

that rely on OC to EC ratios in emissions inventories to predict the amount of secondary OC may 

be biased towards higher ratios of secondary OC because emissions of primary biologic particles 

are not included in the inventories. Additional concerns arise from uncertainties in the 

mechanism of formation of secondary OC from gaseous biogenic and anthropogenic precursor 

emissions and the uncertainty in those emissions (Section 3.3.1). It is clear, however, that 

secondary organic PM is being formed in the atmosphere (Blando et al., 1998 and Appendix 3C). 

Few studies have compared aerosol composition in urban areas to that in nearby rural 

areas. One exception is Tanner and Parkhurst (2000), which found that sulfate constituted a 

larger fraction of fine particle mass at rural sites in the Tennessee Valley PM2.5 monitoring 

network than did OC. For urban sites, the situation was largely reversed: OC constituted a 

larger fraction of aerosol mass than sulfate. Future systematic comparisons of urban-rural 

differences in aerosol properties should be facilitated with implementation of the national 

speciation network and continued operation of the IMPROVE network. 

3.3 SOURCES OF PRIMARY AND SECONDARY PM 

Information about the nature and relative importance of sources of ambient PM is 

presented in this section. Table 3-8 summarizes anthropogenic and natural sources for the major 

primary and secondary aerosol constituents of fine and coarse particles. Anthropogenic sources 

can be further divided into stationary and mobile sources. Stationary sources include those such 

as: fuel combustion for electrical utilities, residential space heating, and industrial processes; 

construction and demolition; metals, minerals, and petrochemicals; wood products processing; 

mills and elevators used in agriculture; erosion from tilled lands; waste disposal and recycling. 

Mobile or transportation-related sources include direct emissions of primary PM and secondary 

PM precursors from highway vehicles and non-road sources as well as fugitive dust from paved 

and unpaved roads. In addition to fossil fuel combustion, biomass in the form of wood is burned 

for fuel. Vegetation is burned to clear new land for agriculture and for building construction, to 

dispose of agricultural and domestic waste, to control the growth of animal or plant pests, and 

3-59
TABLE 3-8. CONSTITUENTS OF ATMOSPHERIC PARTICLES AND THEIR MAJOR SO

Sources 

Primary (PM < 2.5 μm) Primary (PM > 2.5 μm) Secondary PM Pre

Aerosol species Natural Anthropogenic Natural Anthropogenic Natural 

3-60

Sulfate Sea spray (SO42!) 

Nitrate —

! (NO3 )

Minerals Erosion and 

reentrainment 

Ammonium —

+ (NH4 )

Organic Wildfires carbon (OC) Elemental Wildfires carbon (EC) 

Metals Volcanic 

activity 

Bioaerosols Viruses and 

bacteria

Fossil fuel combustion 

—

Fugitive dust from paved and unpaved roads, agriculture, forestry, construction, and demolition 

—

Prescribed burning, 

wood burning, motor vehicle exhaust, and cooking 

Motor vehicle exhaust, 

wood burning, and cooking

Fossil fuel combustion, smelting, and brake wear 

— 

Sea spray

 —

Erosion and reentrainment 

—

Soil humic matter 

— 

Erosion, reentrainment, and organic debris 

Plant and insect 

fragments, pollen, fungal spores, and bacterial agglomerates

 — 

— 

Fugitive dust, paved and unpaved road dust, agriculture, forestry, construction, and demolition

 — 

Tire and asphalt wear 

and paved road dust 

Tire and asphalt wear 

and paved road dust

 —

 —

Oxidation of reduced sulfur 

gases emitted by the oceans wetlands and SO2 and H2S 

emitted by volcanism and ffires 

Oxidation of NOx produced 

soils, forest fires, and lighti

—

Emissions of NH3 from wild 

animals, and undisturbed so

Oxidation of hydrocarbons emitted by vegetation (terpewaxes) and wild fires 

—

 —

 —

1Dash (–) indicates either very minor source or no known source of component. 
to manage forest resources (prescribed burning). Also shown are sources for precursor gases, 

the oxidation of which forms secondary PM. The atmospheric chemical processes producing 

secondary PM are described in Section 3.3.1. 

In general, the sources of fine PM are very different from those for coarse PM. Some of 

the mass in the fine size fraction forms during combustion from material that has volatilized in 

combustion chambers and then recondensed before emission into the atmosphere. Some 

ambient PM2.5 forms in the atmosphere from photochemical reactions involving precursor gases. 

That PM formed by the first mechanism is referred to as primary, and the PM formed by the 

second mechanism is referred to as secondary. PM10-2.5 is mainly primary in origin, as it is 

produced by the abrasion of surfaces or by the suspension of biological material. Because 

precursor gases undergo mixing during transport from their sources, it is difficult to identify 

individual sources of secondary PM constituents. Transport and transformations of precursors 

can occur over distances of hundreds of kilometers. The coarse PM constituents have shorter 

lifetimes in the atmosphere; so their effects tend to be more localized. Only major sources for 

each constituent within each broad category shown at the top of Table 3-8 are listed. Not all 

sources are equal in magnitude. Chemical characterizations of primary particulate emissions for 

a wide variety of natural and anthropogenic sources (as shown in Table 3-8) were given in 

Chapter 5 of the 1996 PM AQCD. Summary tables of the composition of source emissions 

presented in the 1996 PM AQCD and updates to that information are provided in Appendix 3D. 

The profiles of source composition were based in large measure on the results of various studies 

that collected signatures for use in source apportionment studies. 

Natural sources of primary PM include windblown dust from undisturbed land, sea spray, 

and plant and insect debris. The oxidation of a fraction of terpenes emitted by vegetation and 

reduced sulfur species from anaerobic environments leads to secondary PM formation. 

Ammonium (NH4

+

) ions, which play a major role in regulating the pH of particles, are derived 

from emissions of ammonia (NH3) gas. Source categories for NH3 have been divided into 

emissions from undisturbed soils (natural) and emissions that are related to human activities 

(e.g., fertilized lands, domestic and farm animal waste). There is ongoing debate about 

characterizing emissions from wildfires (i.e., unwanted fire) as either natural or anthropogenic. 

Wildfires have been listed in Table 3-8 as natural in origin, but land management practices and 

other human actions affect the occurrence and scope of wildfires. For example, fire suppression 

3-61
practices allow the buildup of fire fuels and increase the susceptibility of forests to more severe 

and infrequent fires from whatever cause, including lightning strikes. Similarly, prescribed 

burning is listed as anthropogenic, but can be viewed as a substitute for wildfires that would 

otherwise occur eventually on the same land. 

The transformations that gaseous precursors to secondary PM undergo after being emitted 

from the sources shown in Table 3-8 are described in Section 3.3.1. Aspects of the transport of 

primary PM and secondary PM, including the transport of material from outside the United 

States, are described in Section 3.3.2. A brief introduction to the deposition of particles is also 

given in Section 3.3.2, and a more detailed discussion of deposition processes is presented in 

Chapter 4. Methods to infer contributions from different source categories to ambient PM using 

receptor models and the results of these modeling efforts are given in Section 3.3.3. Estimates of 

emissions of primary PM and precursors to secondary PM from major sources are presented in 

Section 3.3.4. A discussion of the uncertainties associated with these emissions is given in 

Section 3.3.5. 

3.3.1 Chemistry of Secondary PM Formation 

Precursors to secondary PM have natural and anthropogenic sources, just as primary PM 

has natural and anthropogenic sources. The major atmospheric chemical transformations leading 

to the formation of particulate nitrate and sulfate are relatively well understood; whereas those 

involving the formation of secondary aerosol OC are less so and are still subject to much current 

investigation. A large number of organic precursors are involved and many of the kinetic details 

still need to be determined. Also, many of the actual products of the oxidation of hydrocarbons 

have yet to be identified. 

Formation of Sulfates and Nitrates 

A substantial fraction of the fine particle mass, especially during the warmer months of the 

year, is secondary sulfate and nitrate formed as the result of atmospheric reactions. Such 

reactions involve the gas phase conversion of SO2 to H2SO4 (which forms liquid particles) 

initiated by reaction with OH radicals and aqueous-phase reactions of SO2 with H2O2, O3, or O2 

(catalyzed by Fe and Mn). These heterogeneous reactions may occur in cloud and fog droplets 

or in films on atmospheric particles. NO2 can be converted to gaseous HNO3 by reaction with 

3-62
OH radicals during the day. At night, NO2 also is oxidized to nitric acid by a sequence of 

reactions initiated by O3 that produce nitrate radicals (NO3) and dinitrogenpentoxide (N2O5) as 

intermediates. Both H2SO4 and HNO3 react with atmospheric ammonia (NH3). Gaseous NH3 

reacts with gaseous HNO3 to form particulate NH4NO3. Gaseous NH3 reacts with H2SO4 to form 

- acidic HSO4 (in NH4 HSO4) as well as SO42!

 in (NH4)2SO4. In addition, acid gases such as SO2 

and HNO3 may react with coarse alkaline particles to form coarse secondary PM containing 

sulfate and nitrate. Examples include reactions with basic compounds resulting in neutralization 

(e.g., CaCO3 + 2HNO3 6 Ca(NO3)2 + H2CO38) or with salts of volatile acids resulting in release 

of the volatile acid (e.g., SO2 + 2NaCl + H2O 6 Na2SO3 + 2HCl8). 

If particulate NH4NO3 coagulates with an acidic sulfate particle (H2SO4 or HSO4 

! ), 

gaseous HNO3 will be released, and the NH3 will increase the neutralization of the acidic sulfate. 

Thus, in the eastern United States, where PM tends to be acidic, sulfate usually constitutes a 

larger fraction of PM mass than nitrate. However, in the western United States, where 

higher NH3 and lower SO2 emissions permit complete neutralization of H2SO4, the concentration 

of nitrate could be higher than that of sulfate as it is in areas such as the Los Angeles Basin and 

the San Joaquin Valley. As SO2 concentrations in the atmosphere in the eastern United States 

are reduced, the NH3 left in the atmosphere after neutralization of H2SO4 will be able to react 

with HNO3 to form NH4NO3. Therefore, a reduction in SO2 emissions, especially without a 

reduction in NOx emissions, could lead to an increase in NH4NO3 concentrations (West et al., 

1999; Ansari and Pandis, 1998). Thus, the sources, trends, and possible environmental effects 

of NH4NO3 are of interest for both the western and eastern United States. 

2! Chemical reactions of SO2 and NOx within plumes are an important source of H+

, SO4 , 

- and NO3 . These conversions can occur by gas-phase and aqueous-phase mechanisms. 

In power-plant or smelter plumes containing SO2 and NOx, the gas-phase chemistry depends on 

plume dilution, sunlight, and VOCs either in the plume or in the ambient air mixing into and 

diluting the plume. For the conversion of SO2 to H2SO4 in the gas-phase in such plumes during 

summer midday conditions in the eastern United States, the rate typically varies between 1 and 

3% /h, but in the cleaner western United States the rate rarely exceeds 1% /h. For the conversion 

of NOx to HNO3, the gas-phase rates appear to be approximately three times faster than the SO2 

conversion rates. Winter rates for SO2 conversion are approximately an order of magnitude 

lower than summer rates. 

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The contribution of aqueous-phase chemistry to particle formation in point-source plumes 

is highly variable, depending on the availability of the aqueous phase (wetted aerosols, clouds, 

fog, and light rain) and the photochemically generated gas-phase oxidizing agents, especially 

H2O2 for SO2 chemistry. The in-cloud conversion rates of SO2 to SO4

2!

 can be several times 

2! larger than the gas-phase rates given above. Overall, it appears that SO2 oxidation rates to SO4 

by gas-phase and aqueous-phase mechanisms may be comparable in summer, but aqueous-phase 

chemistry may dominate in winter. Further details concerning the chemistry of SO2 and NOx in 

power plant plumes can be found in Hewitt (2001). 

In the western United States, markedly higher SO2 conversion rates have been reported in 

smelter plumes than in power plant plumes. The conversion occurs predominantly by a gasphase mechanism. This result is attributed to the lower NOx in smelter plumes. In power plant 

plumes, NO2 depletes OH radicals and competes with SO2 for OH radicals. 

In urban plumes, the upper limit for the gas-phase SO2 conversion rate appears to be about 

5% /h under the more polluted conditions. For NO2, the rates appear to be approximately three 

times faster than the SO2 conversion rates. Conversion rates of SO2 and NOx in background air 

are comparable to the peak rates in diluted plumes. Neutralization of H2SO4 formed by SO2 

conversion increases with plume age and background NH3 concentration. If the NH3 levels are 

more than sufficient to neutralize H2SO4 to (NH4)2SO4, the HNO3 formed from NOx conversions 

may be converted to NH4NO3. 

Formation of Secondary Organic Particulate Matter (SOPM) 

Atmospheric reactions involving VOCs such as alkanes, alkenes, aromatics, cyclic olefins, 

and terpenes (or any reactive organic gas that contains at least seven carbon atoms) yield organic 

compounds with low-saturation vapor pressures at ambient temperature. Such reactions may 

occur in the gas phase, in fog or cloud droplets (Graedel and Goldberg, 1983; Faust, 1994), or 

possibly in aqueous aerosols (Aumont et al., 2000). Reaction products from the oxidation of 

reactive organic gases also may nucleate to form new particles or condense on existing particles 

to form SOPM. Organic compounds with two double bonds or cyclic olefins may react to form 

dicarboxylic acids, which, with four or more carbon atoms, also may condense. Both biogenic 

and anthropogenic sources contribute to primary and secondary organic PM (Grosjean, 1992; 

3-64
Hildemann et al., 1996; Mazurek et al., 1997; Schauer et al., 1996). Oxalic acid was the most 

abundant organic acid found in PM2.5 in California (Poore, 2000). 

Although the mechanisms and pathways for forming inorganic secondary PM are fairly 

well known, those for forming SOPM are not as well understood. Ozone and the OH radicals 

are thought to be the major initiating reactants. However, HO2 and NO3 radicals also may 

initiate reactions; and organic radicals may be nitrated by HNO2, HNO3, or NO2. Pun et al. 

(2000) discuss formation mechanisms for highly oxidized, multifunctional organic compounds. 

The production of such species has been included in a photochemical model by Aumont et al. 

(2000), for example. Understanding the mechanisms of formation of SOPM is important 

because SOPM can contribute in a significant way to ambient PM levels, especially during 

photochemical smog episodes. Experimental studies of the production of secondary organic PM 

in ambient air have focused on the Los Angeles Basin. Turpin and Huntzicker (1991, 1995) and 

Turpin et al. (1991) provided strong evidence that secondary PM formation occurs during 

periods of photochemical ozone formation in Los Angeles and that as much as 70% of the OC 

in ambient PM was secondary in origin during a smog episode in 1987. Schauer et al. (1996) 

estimated that 20 to 30%, on an annually averaged basis, of the total organic carbon PM in the 

< 2.1 μm size range in the Los Angeles airshed is secondary in origin. 

Pandis et al. (1992) identified three mechanisms for formation of SOPM: (1) condensation 

of oxidized end-products of photochemical reactions (e.g., ketones, aldehydes, organic acids, and 

hydroperoxides), (2) adsorption of semivolatile organic compounds (SVOCs) onto existing solid 

particles (e.g., polycyclic aromatic hydrocarbons), and (3) dissolution of soluble gases that can 

undergo reactions in particles (e.g., aldehydes). The first and third mechanisms are expected to 

be of major importance during the summer when photochemistry is at its peak. The second 

pathway can be driven by diurnal and seasonal temperature and humidity variations at any time 

of the year. With regard to the first mechanism, Odum et al. (1996) suggested that the products 

of the photochemical oxidation of reactive organic gases are semivolatile and can partition 

themselves onto existing organic carbon at concentrations below their saturation concentrations. 

Thus, the yield of SOPM depends not only on the identity of the precursor organic gas but also 

on the ambient levels of OC capable of adsorbing the oxidation products. 

Haagen-Smit (1952) first demonstrated that hydrocarbons irradiated in the presence of NOx 

produce light scattering aerosols. The aerosol-forming potentials of a wide variety of individual 

3-65
anthropogenic and biogenic hydrocarbons were compiled by Pandis et al. (1992), based mainly 

on estimates made by Grosjean and Seinfeld (1989) and on data from Pandis et al. (1991) for 

$-pinene and from Izumi and Fukuyama (1990) for aromatic hydrocarbons. Zhang et al. (1992) 

examined the oxidation of "-pinene. Pandis et al. (1991) found no aerosol products formed in 

the photochemical oxidation of isoprene, although they and Zhang et al. (1992) found that the 

addition of isoprene to reaction mixtures increased the reactivity of the systems studied. Further 

details about the oxidation mechanisms and SOPM yields from various reactive organic gases 

are given in the above studies. Estimates of the production rate of SOPM in the Los Angeles 

airshed are provided in the 1996 PM AQCD (U.S. Environmental Protection Agency, 1996). 

More recently, Odum et al. (1997a,b) have found that the aerosol-formation potential of 

whole gasoline vapor can be accounted for solely by summing the contributions of the individual 

aromatic compounds in the fuel. In general, data for yields for SOPM formation can be broken 

into two distinct categories. The oxidation of toluene and aromatic compounds containing ethyl 

or propyl groups (i.e., ethylbenzene, ethyltoluene, n-propylbenzene) produced higher yields of 

SOPM than did the oxidation of aromatic compounds containing two or more methyl groups 

(i.e., xylenes, di-, tri-, tetra-methylbenzenes). Yields in the first group ranged from about 7 to 

10%; in the second group, yields ranged from 3 to 4% for OC concentrations between 13 and 

100 μg/m3

. Reasons for the differences in SOPM yields found between the two classes of 

compounds are not clear. 

There have been a few recent studies that have examined the composition of secondary 

organic PM. Edney et al. (2001) carried out a smog chamber study to investigate the formation 

of multifunctional oxygenates from photooxidation of toluene. The experiments were carried 

out by irradiating toluene/propylene/NOx/air mixtures in a smog chamber operated in the 

dynamic mode and analyzing the collected aerosol by positive chemical ionization GC-MS after 

derivatization of the carbonyl oxidation products. The results of the GC-MS analyses were 

consistent with the formation of semivolatile multifunctional oxygenates, including hydroxy 

diones as well as triones, tetraones, and pentaones. The authors also suggested that some of 

these compounds could be present in SOPM in the form of polymers. 

Jang and Kamens (2001a) employed a number of analytical approaches, including GC-MS 

detection of volatile derivatives of carbonyl, hydroxy, and acid compounds in SOPM formed in 

the irradiation of toluene/NOx mixtures. A wide range of substituted aromatics, nonaromatic 

3-66
ring-retaining and ring-opening products were detected. Newly identified ring-opening 

oxycarboxylic acids detected included glyoxylic acid; methylglyoxylic acid; 4-oxo-2-butenoic 

acid; oxo-C5-alkenoic acids; dioxopentenoic acids; oxo-C7-alkadienoic acids; dioxo-C6-alkenoic 

acids; hydroxydioxo-C7-alkenoic acids; and hydroxytrioxo-C6-alkanoic acids. Other newly 

identified compounds included methylcyclohexenetriones; hydroxymethylcyclohexenetriones; 

2-hydroxy-3-penten-1,5-dial, hydroxyoxo-C6-alkenals; hydroxy-C5-triones, hydroxydioxo-C7-

alkenals; and hydroxy-C6-tetranones. Included among these compounds were a number of the 

hydroxy polyketones detected by Edney et al. (2001). 

Recent laboratory and field studies support the concept that nonvolatile and semivolatile 

oxidation products from the photooxidation of biogenic hydrocarbons contribute significantly to 

ambient PM concentrations in both urban and rural environments. The oxidation of a variety of 

biogenic hydrocarbons emitted by trees and plants, such as terpenes ("-pinene, $-pinene, 

)3-carene, sabinene, "-terpinene, (-terpinene, terpinolene, myrcene, and ocimene) and 

sesquiterpenes ($-caryophyllene and "-humulene) could form SOPM. Vegetation also emits 

oxygenated organic compounds such as alcohols, acetates, aldehyde, ketones, ethers, and esters 

(Winer et al., 1992). However, their contribution to SOPM remains uncertain. Hoffmann et al. 

(1997) found SOPM yields of .5% for open-chain biogenic hydrocarbons such as ocimene and 

linalool; 5 to 25% for monounsaturated cyclic monoterpenes such as "-pinene, )-3 carene and 

terpinene-4-ol; and .40% for a cyclic monoterpene with an endocyclic and an exocyclic double 

bond such as d-limonene. Secondary organic PM yields of close to 100% were observed during 

the photochemical oxidation of one sesquiterpene, trans-caryophyllene. These results were all 

obtained for initial hydrocarbon mixing ratios of 100 ppb, which are much higher than found in 

the atmosphere. 

Kamens et al. (1999) observed SOPM yields of 20 to 40% for "-pinene. Using information 

on the composition of secondary PM formed from "-pinene (Jang and Kamens, 1999), they 

were able to calculate formation rates with a kinetic model including formation mechanisms 

for O3 + "-pinene reaction products. 

Griffin et al. (1999) introduced the concept of incremental aerosol reactivity, the change in 

the secondary organic aerosol mass produced (in μg/m3

) per unit change of parent organic 

reacted (in ppb), as a measure of the aerosol-forming capability of a given parent organic 

compound in a prescribed mixture of other organic compounds. They measured the incremental 

3-67
aerosol reactivity for a number of aromatic and biogenic compounds for four initial mixtures. 

Incremental aerosol reactivity ranged from 0.133 to 10.352 μg/m3/ppb and varied by almost a 

factor of two depending on the initial mixture. 

A number of multifunctional oxidation products produced by the oxidation of biogenic 

hydrocarbons have been identified in laboratory studies (Yu et al., 1998; Glasius et al., 2000; 

Christoffersen et al., 1998; Koch et al., 2000; and Leach et al., 1999). Many of these compounds 

have subsequently been identified in field investigations (Yu et al., 1999; Kavouras et al., 1998, 

1999a,b; Pio et al., 2001; and Castro et al., 1999). Most studies of the formation of secondary 

organic aerosol formation from terpenes have focused on their reactions with ozone. There have 

been many fewer studies dealing with the oxidation of terpenes initiated by OH radicals. Larsen 

et al. (2001) found that the major aerosol products produced ultimately from the reaction of OH 

radicals with monoterpenes with endocyclic double bonds ("-pinene, 3-carene) were C10 

keto-carboxylic acids (such as pinonic and caronic acids); whereas the major products from the 

oxidation of monoterpenes with exocyclic double bonds ($-pinene) were C9-dicarboxylic acids 

(such as pinic acid), and the major product from the oxidation of limonene (which has both 

endo- and exocyclic double bonds) was 3-acetyl-6-oxo-heptanal (keto-limonaldehyde). A large 

number of related aldehydes, ketones and acids were also found in their experiments. However, 

the total yields of condensable products are much lower than for the corresponding reactions 

with ozone. For example, yields of C9-dicarboxylic acids, C10-hydroxy-keto-carboxylic acids, 

and C10-hydroxy-keto-aldehydes from the reaction of ozone with mono-terpenes with endocyclic 

double bonds ranged form 3 to 9%, whereas they ranged only from 0.4 to 0.6% in the reaction 

with OH radicals. Likewise, the reaction of monoterpenes with exocyclic double bonds with O3 

produced much higher yields (1 to 4%) of C8- and C9-dicarboxylic acids than did their reaction 

with OH radicals (0.2 to 0.3%). Apart from the complex products noted above, it should be 

remembered that much simpler products, such as formaldehyde and formic acid, are also formed 

in much larger yields from the same reactants (e.g., Winterhalter et al., 2000). Compounds such 

as these also contribute to the formation of secondary organic aerosol according to the 

mechanisms given in Pandis et al. (1992) and mentioned earlier in this section. 

It is worth noting that the dicarboxylic acids and hydroxy-keto-carboxylic acids have very 

low vapor pressures and may act as nucleating species in OH- and O3-terpene reactions (Larsen 

et al., 2001). The rate coefficient for reaction of "-pinene with OH radicals is approximately a 

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factor of 106

 greater than for its reaction with O3, based on data given in Atkinson (1994). The 

daytime average concentration of O3 is typically a factor of 106

 greater than that for OH radicals 

in polluted boundary layers, whereas the above-mentioned yields of aerosol products are roughly 

a factor of ten greater in the O3-initiated reaction than in the corresponding OH radical reaction. 

The foregoing analysis suggests that the O3-initiated reaction may be more important than the 

OH-initiated reaction for the formation of aerosol products. During the day, new particles may 

be generated indoors through the infiltration of ambient O3; and, because ambient O3 is also 

present at night in lower concentrations, new particles may be generated under these conditions 

at lower rates. For example, Wainman et al. (2000) found that O3 can react with limonene 

released by air fresheners in indoor environments to produce substantial quantities of submicron 

particles. The corresponding reaction involving OH radicals outdoors at night is expected to be 

negligible by comparison because of the very low OH concentrations present. Sarwar et al. 

(2002) estimated indoor OH radical concentrations and suggested that OH in indoor 

environments is produced mainly by reactions of O3 transported from outdoors and terpenes 

emitted from indoor sources. They reported that indoor OH levels (1-5 × 105

 OH/cm3) are 

usually lower than typical urban outdoor daytime OH levels (1-5 × 106

 OH/cm3). However, they 

can be greater than typical urban outdoor night time OH levels (1-5 × 104

 OH/cm3). Although 

much progress has been made in determining the importance of anthropogenic and biogenic 

hydrocarbons for the formation of SOPM, further investigations are needed to accurately assess 

their overall contributions to PM2.5 concentrations. 

Reactions of organic compounds either in particles or on the surface of particles have only 

come under study during the past 20 years. Tobias and Ziemann (2000) reported evidence for 

the formation of relatively stable low-volatility peroxy hemiacetals from reactions of 

hydroperoxides with aldehydes and ketones on the surface of secondary organic particles. 

Not long after the publication of these results, Jang and Kamens (2001a) suggested, based on 

results of their outdoor Teflon chamber studies of SOPM formation from irradiation of 

toluene/propylene/NOx /air mixtures, that carbonyls and hydroxy compounds (either within or on 

the surface of aromatic SOPM) could react together to form larger and less volatile hemiacetals 

and acetals. They also proposed that dissolved carbonyls could undergo further reactions 

leading to the formation of a polymer, a mechanism that has also been suggested by Edney et al. 

(2001). Jang and Kamens (2001b) carried out a series of screening experiments to assess 

3-69
whether volatile carbonyl compounds adsorbed onto particles could undergo heterogeneous 

reactions forming low vapor pressure compounds. Experiments were carried out in which 

aldehydes were introduced in Teflon bags in the dark in the presence of a seed aerosol containing 

either ammonium sulfate or a mixture of ammonium sulfate and sulfuric acid. The increase in 

the aerosol volume was then measured using a scanning mobility particle sizer. The aldehydes 

employed for the study included glyoxal, butanal, hexanal, octanal, and decanal. Increased 

organic aerosol yields were found in the presence of the ammonium sulfate seed aerosol for each 

of the carbonyls; the highest yield was found for hexanal followed in decreasing order by 

glyoxal and then octanal. The presence of the acidified sulfate salt significantly increased the 

yields even further. In a number of other experiments, 1-decanol was added to the carbonylaerosol system to investigate the possible formation of hemiacetals and/or acetals. Again, the 

volume of aerosol increased in both the presence of ammonium sulfate aerosol and the acidified 

salt with a significantly larger yield found in the presence of acidity. 

To explain their findings for acid-catalyzed carbonyl reactions, Jang and Kamens (2001a,b) 

proposed a chemical mechanism in which the dissolved carbonyl first undergoes a protonization 

reaction forming an adduct that can react with water to form its hydrate (1,1-dihydroxy gemdiol). The adducts can then react with OH groups of the gem-diol, forming higher molecular 

weight and less volatile dimers that are subject to further reactions. In principal, this process, 

which the authors refer to as a “zipping reaction” can lead to the formation of polymers. 

However, because the individual reactions are reversible, the process can be reversed by an 

unzipping reaction. The zipping process could serve as an important mechanism for SOPM 

formation by converting volatile oxidation products, including glyoxal and methyl glyoxal, into 

low-volatility compounds. On the other hand, the unzipping process that could take place during 

the workup of the aerosol samples could be responsible for the detection of highly volatile 

oxidation products in SOPM (including glyoxal and methyl glyoxal) reported by Edney et al. 

(2001), Cocker et al. (2001), and Jang and Kamens (2001a). While these processes may take 

place in the absence of significant acidity, the experimental results suggest that the processes are 

likely enhanced by acid-catalyzed reactions. Further research is needed to determine the 

importance of the mechanisms proposed above for the ambient atmosphere. 

Sampling and characterizing PM in the ambient atmosphere and in important 

microenvironments is required to address important issues in exposure, toxicology, and 

3-70
compliance. Currently, it is not possible to fully quantify the concentration, composition, or 

sources of the organic components. Many of the secondary organic aerosol components are 

highly oxidized, difficult-to-measure multifunctional compounds. Additional laboratory studies 

are needed to identify such compounds, strategies need to be developed to sample and measure 

such compounds in the atmosphere, and models of secondary organic aerosol formation need to 

be improved and added to air quality models in order to address issues related to human 

exposure. 

A high degree of uncertainty is associated with all aspects of the calculation of secondary 

organic PM concentrations. This is compounded by the volatilization of OC from filter 

substrates during and after sampling as well as potential positive artifact formation from the 

absorption of gaseous hydrocarbon on quartz filters. Significant uncertainties always arise in the 

interpretation of smog chamber data because of wall reactions, sampling artifacts, and the use of 

unrealistically high concentrations of reactants. Limitations also exist in extrapolating the 

results of smog chamber studies to ambient conditions found in urban airsheds and forest 

canopies. Concentrations of terpenes and NOx are much lower in forest canopies (Altshuller, 

1983) than the levels commonly used in smog chamber studies. The identification of aerosol 

products of terpene oxidation has seldom been a specific aim of field studies, making it difficult 

to judge the results of model calculations of secondary organic PM formation. 

Uncertainties also arise because of the methods used to measure biogenic hydrocarbon 

emissions. Khalil and Rasmussen (1992) found much lower ratios of terpenes to other 

hydrocarbons (e.g., isoprene) in forest air than were expected based on their relative emissions 

strengths and rate coefficients for reaction with OH radicals and O3. In many cases, reactions 

with these species are capable of reducing the concentrations of monoterpenes to beneath 

minimum detection levels as has been found by others in a wide range of North American forest 

ecosystems (Guenther et al., 1996; Helmig et al., 1998; Geron et al., 2000). Thus, making 

judgments about the importance of additional loss processes can be a highly problematic 

exercise, given uncertainties in obtaining relevant OH radical and O3 concentrations and in the 

reaction rate coefficients. The ocimenes and sesquiterpenes are estimated to have half lives of 

seconds to minutes in the presence of ambient O3 levels, while the pinenes and isoprene have 

lifetimes ranging from several hours to days. Reaction with hydroxyl radicals is the major sink 

for isoprene. Khalil and Rasmussen offered two explanations for their findings: (1) either the 

3-71
terpenes were being removed rapidly by some heterogeneous process, or (2) emissions were 

enhanced artificially by feedbacks caused by the bag enclosures they used. The isoprene 

emission rates observed by Khalil and Rasmussen (1992) are reasonably consistent with those 

found by Geron et al. (2000a and 2001), given inevitable self-shading of much of the foliage 

within the bag enclosures and also the lower position of the foliage within the accessible portion 

of the sampled tree crowns. Temperatures were sufficiently warm ($30 °C) and sunny to induce 

high isoprene emission, although cloudiness from a passing thunderstorm could have markedly 

reduced these emissions from some of the species. Disturbance or elevated temperatures may 

also have induced elevated monoterpene emissions from several of the species sampled by 

Khalil and Rasmussen, because emissions rates reported by them are in many cases 2 to 20 times 

higher than those reported by others (including those who performed enclosure studies that did 

not cause elevated temperatures, as well as micrometeorological flux studies that did not disturb 

the forest canopy). Thus, the somewhat reduced isoprene emissions, combined with elevated 

monoterpene emissions, can indeed affect the comparison of ambient isoprene versus 

monoterpene emissions. However, monoterpene compounds recently have been found to 

undergo heterogenous reactions on the surface of acid aerosol particles. Further work is needed 

to assess the importance of these reactions for ambient monoterpene concentrations and for the 

rate of production of secondary organic PM in forest ecosystems. 

3.3.2 Source Contributions to Ambient PM Determined by Receptor Models 

Receptor models are perhaps the primary means used to estimate the contributions of 

different source categories to PM concentrations at individual monitoring sites. Dispersion 

models (i.e., three-dimensional chemistry and transport models) are formulated in a prognostic 

manner (i.e., they attempt to predict species concentrations using a tendency equation that 

includes terms based on emissions inventories, atmospheric transport, chemical transformations, 

and deposition). Receptor models are diagnostic in their approach (i.e., they attempt to derive 

source contributions based either on ambient data alone or in combination with data from the 

chemical composition of sources). These methods have the advantage that they do not invoke all 

of the uncertainties inherent in emissions inventories or in parameterizing atmospheric transport 

processes in grid point models. 

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There are two main approaches to receptor modeling. Receptor models such as the 

chemical mass balance (CMB) model (Watson et al., 1990a) relate source category contributions 

to ambient concentrations based on analyses of the composition of ambient PM and source 

emissions samples. This technique has been developed for apportioning source categories of 

primary PM and was not formulated to include the processes of secondary PM formation. In the 

second approach, various forms of factor analysis are used that rely on the varying mix of 

species present in observations of compositional data to derive both the composition of sources 

and the source contributions. Few of these techniques actually utilize the information contained 

in the temporal sequences of the data. Standard approaches such as factor analysis or Principal 

Component Analysis (PCA) can apportion only the variance, but not the mass, in an aerosol 

composition data set. The other techniques described below, PMF and UNMIX, do apportion 

mass, however. Positive matrix factorization (PMF) is a recently developed multivariate 

technique (Paatero and Tapper, 1993, 1994) that overcomes many of the limitations of standard 

techniques, such as PCA, by allowing for the treatment of missing data and data near or below 

detection limits. This is accomplished by weighting elements inversely according to their 

uncertainties. Standard methods such as PCA weight elements equally regardless of their 

uncertainty. Solutions also are constrained to yield non-negative factors. Both the CMB and the 

PMF approaches find a solution based on least squares fitting and minimize an object function. 

Both methods provide error estimates for the solutions based on estimates of the errors in the 

input parameters. It should be remembered that the error estimates often contain subjective 

judgments. For a complete apportionment of mass, all of the major sources affecting a 

monitoring site must be sampled for analysis by CMB, whereas there is no such restriction in the 

use of PMF. 

Among other approaches, the UNMIX model takes a geometric approach that exploits the 

covariance of the ambient data to determine the number of sources, the composition and 

contributions of the sources, and the uncertainties (Henry, 1997). A simple example may help 

illustrate the approach taken by UNMIX. In a two-element scatter plot of ambient Al and Si, a 

straight line and a high correlation for Al versus Si can indicate a single source for both species 

(soil), while the slope of the line gives information on the composition of the soil source. In the 

same data set, Fe may not plot on a straight line against Si, indicating other sources of Fe in 

addition to soil. More importantly, the Fe-Si scatter plot may reveal a lower edge. The points 

3-73
defining this edge represent ambient samples collected on days when the only significant source 

of Fe was soil. Success of the UNMIX model hinges on the ability to find these “edges” in the 

ambient data from which the number of source types and the source compositions are extracted. 

UNMIX uses principal component analysis to find edges in m-dimensional space, where m is the 

number of ambient species. The problem of finding edges is more properly described as finding 

hyperplanes that define a simplex. The vertices at which the hyperplanes intersect represent pure 

sources from which source compositions can be determined. However, there are measurement 

errors in the ambient data that “fuzz” the edges, making them difficult to find. UNMIX employs 

an “edge-finding” algorithm to find the best edges in the presence of error. UNMIX does not 

make explicit use of errors or uncertainties in the ambient concentrations, unlike the methods 

outlined above. This is not to imply that the UNMIX approach regards data uncertainty as 

unimportant, but rather that the UNMIX model results implicitly incorporate error in the ambient 

data. The underlying philosophy is that the uncertainties are often unquantifiable and, hence, 

it is best to make no a priori assumptions about what they are. 

In addition to chemical speciation data, Norris et al. (1999) showed that meteorological 

indices could prove useful in identifying sources of PM responsible for observed health effects 

(specifically asthma) associated with exposure to ambient PM. They examined meteorology 

associated with elevated pollution events in Spokane and Seattle, WA and identified a 

“stagnation index” that was associated with low wind speeds and increases in concentrations of 

combustion-related pollutants. Their factor analysis also identified a meteorological index (low 

relative humidity and high temperatures) that was associated with increases in soil-derived PM 

as well as a third factor (high temperatures and low relative humidity) that was associated with 

increases in concentrations of particulate sulfate and nitrate species (Norris, 1998). In addition 

to these variables, past investigations have also used cooling and heating degree days. 

Ondov (1996) examined the feasibility of using sensitive isotopic and elemental tracer 

materials to determine the contributions of petroleum-fueled sources of PM10 in the San Joaquin 

Valley, CA. Costs of these experiments are affected not only by the expense for tracer materials, 

but also by the sensitivities of the analytical methods for each tracer, as well as their background 

levels. Suarez et al. (1996) used iridium as a tracer to tag emissions from diesel-burning 

sanitation trucks in Baltimore and determined the size distribution of soot from the trucks. 

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A number of specialty conference proceedings, review articles, and books have been 

published that provide greater detail about source category apportionment receptor models than 

described in the 1996 PM AQCD. A review of the various methods used to apportion PM in 

ambient samples among its source categories was given in Section 5.5.2 of the 1996 PM AQCD. 

The collection of the source category characterization profiles shown in Appendix 3D has been 

motivated in many cases by the need to use them in receptor modeling applications. 

The results of several source apportionment studies are discussed in this section to provide 

an indication of the relative importance of different sources of ambient PM across the United 

States. First, results obtained mainly by using the CMB approach for estimating contributions 

to PM2.5 from different source categories at monitoring sites in the United States are discussed 

and presented in Table 3-9. More recent results using the PMF approach are included for 

Phoenix, AZ. Results obtained at a number of monitoring sites in the central and western United 

States by using the CMB model for PM10 are shown in Table 3-10. The sampling sites represent 

a variety of different source characteristics within different regions of Arizona, California, 

Colorado, Idaho, Illinois, Nevada, and Ohio. Definitions of source categories also vary from 

study to study. The results of the PM10 source apportionment studies were given in the 1996 PM 

AQCD and are presented here to allow easy comparison with results of PM2.5 source 

apportionment studies. Chow and Watson (2002) present a detailed comparison of numerous 

studies using the CMB model performed mainly after 1995. Their comprehensive review 

includes the results of a number of studies that have been published as reports, such as the 

Northern Front Range Air Quality Study, in addition to those that have appeared in peerreviewed journals. 

There are several differences between the broadly defined source categories shown at the 

tops of Tables 3-9 and 3-10. These differences reflect the nature of sources that are important 

for producing the fine and coarse PM shown in Table 3-8. They also are related to 

improvements in the ability to distinguish between sources of similar nature (e.g., diesel and 

gasoline vehicles, meat cooking, and vegetation burning). The use of organic tracers allows 

motor vehicle emissions to be broken down into contributions from diesel and gasoline vehicles. 

In studies where this distinction cannot be made, the source type is listed as ‘total motor 

vehicles’ in the tables. The studies that were reported to be able to distinguish gasoline- from 

diesel-fueled vehicles reported mixed results for the contributions from gasoline and diesel 

3-75
TABLE 3-9. RECEPTOR MODEL SOURCE CONTRIBUTIONS TO PM2.5 

% Contribution 

Measured Total 

PM2.5 Motor Gasoline Road Vegetation Secondary Secondary Misc. Misc. 

Sampling Site Concentration Vehicles Diesel Vehicles Dust, Soil Burning Sulfate Nitrate Source 1 Source 2 

Pasadena, CA 19821 28.2 — 18.8 5.7 12.4 9.6 20.9 7.4 5.3a 9.2b 

Downtown LA, CA 19821 32.5 — 35.7 6.5 11.1 5.8 20.3 9.2 3.7a 9.2b 

West LA, CA 19821 24.5 — 18 5.7 12.2 11 24.1 7.8 4.1a 9.4b Rubidoux, CA 19821 42.1 — 12.8 0.7 13.1 1.2 13.8 24.7 4.5a 12.1b 

Sacramento, CA 39.5 24.5 — — 1.2 18.1 4.5 36.6 — — Winter 1991-962 

Bakersfield, CA 52 16 — — < 3 20 7 34 — — Winter 19963 Fresno, CA Winter 19963 63 13 — — < 3 19 5 32 — — Philadelphia, PA Summer 19824 27 8.5 — — 4.4 — 81.9f — 2.2g 1.9h 

Camden, NJ Summer 19824 28.3 9.2 — — 3.2 — 81.3f 0.4 2.5g 2.5h Clarksboro, NJ Summer 19824 26 5.8 — — 2.7 — 84.6f — 0.8g 1.5h Grover City, IL ENEj 

1986-875 

— — — 2.3 — 83.2f — 9.7k 3.0l 

Grover City, IL SSWj 

1986-875 

— — — — — 59.0f — 11.6k 11.9l 

Grover City, IL WNWj 1986-875 2.4 — 5.1 — 88.5f — 2.8k — 

Grover City, IL NNWj 1986-875 — — — 3.1 — 86.6f — 3.4l 3.0n 

Reno, NV Summer 19986 7.8 68 — — 14.5 4 11 2 0.6q — 

Phoenix, AZ Summer 1995-987 8.3 — 10.9 36.2 1.8 15 — — 20.8n 4.9r 

Phoenix, AZ Winter 1995-987 13.8 — 14.5 38.9 1.1 8.9 — — 9.5n 4.5r 

3-76

1Schauer et al. (1996) 2Motallebi (1999) 3Magliano et al. (1998) 4Dzubay et al (1988) 5Glover et al. (1991) 6Gillies et al. (2000) 7Ramadan et al. (2000) 

aSecondary and other organic compounds bSecondary ammonium cMeat cooking dVegetative detritus eValue represents sum of diesel and gasoline 

vehicle exhaust fIncluding associated cations and water 

gIncinerators hOil fly ash iFluidized catalyst cracker jWind direction kLead smelter lIron works mCopper smelter 

nCoal powoAs ammopAs ammoqSea salt rWood busNonferro
TABLE 3-10. RECEPTOR MODEL SOURCE CONTRIBUTIONS TO PM10 

% Contribution 

Primary 

Measured Motor Primary Secondary Secondary 

PM10 Primary Primary Vehicle Vegetative Ammonium Ammonium Misc. Misc. 

Sampling Site Concentration Geological Construction Exhaust Burning Sulfate Nitrate Source 1 Source 2 

Craycroft, AZ Winter 1989-19901 23.4 55.6 0 35.5 0.0 3 2.6 5.1a 0.0 

Hayden 1, AZ 19861 105.0 4.8 1.9b 0.0 0.0 3.8 0.0 70.5c 4.8d 

Hayden 2, AZ 19861 59.0 35.6 6.8b 0.0 0.0 6.8 0.0 47.5c 0.0 

Rillito, AZ 19882 79.5 53.7 17.4b 1.5f 0.0 0 0 14.6g 0.0 Bakersfield, CA 1988-19893 79.6 53.9 2 9.7 8.2 6.9 16 1.3m 1.9n 

Crows Landing, CA 1988-19893 52.5 61.3 0.0 4.2 6.5 5.3 12.4 1.0m 1.9n Fellows, CA 1988-19893 54.6 53.1 2.6 3.8 6.2 9.3 13.7 12.8m 2.6n Fresno, CA 1988-19893 71.5 44.5 0.0 9.5 7.1 5 14.5 0.4m 1.9n Indio, CA4 58.0 56.9 5.2 7.6 12.2 6.2 7.1 0.3j 1.7h 

Kern Wildlife Refuge, CA 47.8 31.6 4.2 4.6 8.4 6.9 3.1 1.0m 3.1n 1988-19893 

Long Beach, CA 19865 51.9 39.9 0.0 9.8i 0.0 15.4 17.7 0.2j 3.9h Long Beach, CA Summer 19876 46.1 24.1 0.0 13.7 0.0 23.6 1.7 0.2j 4.8h 

Long Beach, CA Fall 19876 96.1 11.8 0.0 44.5 0.0 4 24.1 0.0j 2.8h Riverside, CA 19887 64 50.9 0.0 10.9 0.0 7.5 33.4 0.5j 2.0h 

Rubidoux, CA 19865 87.4 49.3 4.6 6.4i 0.0 7.3 24.4 0.3j 1.1h Rubidoux, CA Summer 19876 114.8 30.4 3.9 15.1 0 8.3 23.9 0.0j 4.4h 

3-77
TABLE 3-10 (cont’d). RECEPTOR MODEL SOURCE CONTRIBUTIONS TO PM10 

% Contribution 

Primary 

Measured Motor Primary Secondary Secondary 

PM10 Primary Primary Vehicle Vegetative Ammonium Ammonium Misc. Misc. 

Sampling Site Concentration Geological Construction Exhaust Burning Sulfate Nitrate Source 1 Source 2 

Rubidoux, CA Fall 19876 112 17.1 14.4 27.1 0.0 1.9 28.2 0.0j 1.0h 

Rubidoux, CA 19887 87 55.2 0.0 11.7 0.0 6.1 24.9 0.6j 1.7h 

San Nicolas Island, CA Summer 19876 17.4 9.2 0 5.2 0.0 21.3 2.9 0.0j 24.7h 

Stockton, CA 19893 62.4 55.1 0.8 8.3 7.7 5 11.2 1.1m 2.9n 

Pocatello, ID 19908 100.0 8.3 7.5q 0.1 0.0 0 0 0 0.0 S. Chicago, IL 19869 80.1 34 3 3.5 0.0 19.2s — 18.9t 2.7u S.E. Chicago, IL 41 35.9v 0.0 2.2f 0.0 18.8 — 2.0t 0.7h 198810 

Reno, NV 1986-8711 30.0 49.7 0.0 33.3 6.3 4.3 2 0.0 0.0 

Sparks, NV 1986-8711 41 36.8 0.0 28.3 32.7 6.6 2.2 0.0 0.0 

Follansbee, WV 199112 66.0 15.2 0.0 53 0.0 24.2 — 14.1t 0.0 

Mingo, OH 199112 60.0 20 0.0 23.3 6.8 25 — 5.7t 18.3x 

Steubenville, OH 46 18 0.0 30.4 1.7 30.4 — 8.3t 10.9x 199112 

3-78

1Chow et al. (1992a)2Garfield; Ryan et al. (1988)3Jail; Ryan et al. (1988)4Thanukos et al. (1992)5Chow et al. (1992b)6Kim et al. (1992)7Gray et al. (1988)8Watson et al. (1994)9Chow et al. (1992c)

10Houck et al. (1992) cCopper ore iMotor vehicle exhaust from11Hopke et al. (1988) dCopper tailings diesel and leaded gasoline 12Vermette et al. (1992) eCopper smelter building jResidual oil combustion 13Chow et al. (1988) fHeavy-duty diesel exhaust kSecondary organic carbon 14Skidmore et al. (1992) emission lBiomass burning aSmelter background aerosol gBackground aerosol mPrimary crude oil bCement plant sources, including hMarine aerosol, road salt, and nNaCl + NaNO3 

 kiln stacks, gypsum pile, and sea salt plus sodium nitrate oLime 

kiln area pRoad sanding material 

qAsphrPhospsRegiotSteel uRefuvLoca

 dust, 

wIncinxUnex
vehicles. In the study in southern California, diesels made a larger contribution to PM2.5 than did 

gasoline-fueled vehicles. On the other hand, the study in Phoenix, AZ, found the opposite result. 

Meat cooking is also distinguished from vegetation burning in more recent studies although both 

are considered to be part of biomass burning. Vegetation burning consists of contributions from 

residential fuel wood burning, wildfires, prescribed burning, and burning of agricultural and 

other biomass waste. Miscellaneous sources of fine particles include contributions from 

combustion sources, whereas miscellaneous sources of coarse particles consist of contributions 

from soil and sea spray and industrial processing of geological material (e.g., cement 

manufacturing). Although a large number of elements and chemical components are used to 

differentiate among source categories and although there can be a large number of source types 

affecting a given site, only a few broadly defined source types are needed to account for most of 

the mass of PM2.5 and PM10. At any given site, # 5 source types account for > 65% of the mass 

of PM2.5 (Table 3-9); and #5 source types account for > 65% of the mass of PM10 (Table 3-10). 

Secondary sulfate is the dominant component of PM2.5 samples collected in the studies of 

Dzubay et al. (1988) and Glover et al. (1991). Both studies found that sulfate at their monitoring 

site arose from regionally dispersed sources. Sulfate also represented the major component 

of PM2.5 found in monitoring studies in the eastern United States shown in Appendix 6A of the 

1996 PM AQCD. Primary and secondary organic components also make major contributions 

to PM2.5 and in many areas may represent a larger component than sulfate. Contributions from 

road dust and soils are relatively minor, typically constituting < 10% of PM2.5 in the studies 

shown in Table 3-9. Studies in the western United States shown in Table 3-9 have found larger 

contributions from motor vehicles, fugitive dust, and ammonium nitrate. The most notable 

difference in the relative importance of major source categories of PM2.5 shown in Table 3-9 and 

PM10 shown in Table 3-10 involves crustal material, (e.g., soil, road dust), which represents 

about 40% on average of the total mass of PM10 in the studies shown in Table 3-10. The fraction 

is higher at sites located away from specific sources, such as sea spray or smelters. Emissions of 

crustal material are concentrated mainly in the PM10-2.5 size range. 

In Table 3-10, the magnitude of primary motor vehicle exhaust contributions is highly 

variable and ranges from 5 to 40% of average PM10. Vehicle exhaust contributions are also 

variable at different sites within the same study area. The mean value and the variability of 

motor vehicle exhaust contributions reflects the proximity of sampling sites to roadways and 

3-79
traffic conditions during the time of sampling. Many studies were conducted during the late 

1980s when a portion of the vehicle fleet still used leaded gasoline. Pb and Br in motor vehicle 

emissions facilitated the distinction of motor vehicle contributions from other sources. Vehicles 

using leaded fuels have higher emission rates than vehicles using unleaded fuels. Pb also 

poisons automobile exhaust catalysts and produces adverse human health effects. As a result, Pb 

has been eliminated from vehicle fuels. Organic species such as n-pentacosane through 

n-nonacosene, cholestanes, ergostanes, sitostanes, and hopanes have replaced Pb as a source 

marker for motor vehicle emissions (e.g., Schauer and Cass, 2000). In their comprehensive 

review of CMB modeling studies undertaken since 1995, Chow and Watson (2002) note that, 

in 22 studies, fossil fuel combustion was found to be a large contributor to PM2.5 and PM10 

concentrations, with most of the contributions to primary PM originating from the exhaust of 

diesel and gasoline vehicles. 

Marine aerosol is found, as expected, at coastal sites such as Long Beach (average 3.8% of 

total mass) and San Nicolas Island (25%). These contributions to PM10 are relatively variable 

and are larger at the more remote sites. Individual values reflect proximity to local sources. 

Of great importance are the contributions from secondary ammonium sulfate in the eastern 

United States and ammonium nitrate in the western United States. Secondary ammonium sulfate 

is especially noticeable at sites in California’s San Joaquin Valley (Bakersfield, Crows Landing, 

Fellows, Fresno, and Stockton) and in the Los Angeles area. 

Because many source apportionment studies address problems in compliance with the 

National Ambient Air Quality Standards (NAAQS) and other air quality standards, samples 

selected for chemical analysis are often biased toward the highest PM10 mass concentrations in 

the studies shown in Table 3-10. Thus, the average source contribution estimates shown in 

Table 3-10 are probably not representative of annual averages and may not be representative of 

a large spatial area for some source-dominated studies. For example, the study by Motallebi 

(1999) considered only days when the PM10 concentration was greater than 40 μg/m3

. Quoted 

uncertainties in the estimated contributions of the individual sources shown in Tables 3-9 and 

3-10 range from 10 to 50%. Errors can be much higher when the chemical source profiles for 

different sources are highly uncertain or are too similar to distinguish one source from another. 

Very few source apportionment studies using the CMB modeling technique have examined 

the spatial variability of source contributions at different sites within an urban area. As can be 

3-80
seen from Table 3-9, Dzubay et al. (1988) found a uniform distribution of sulfate at the NE 

Airport in Philadelphia, PA, downtown Camden, NJ, and Clarksboro, NJ during the summer of 

1982. The farthest distance between two monitoring sites (NE Airport and Clarksboro) was 

~40 km. Magliano et al. (1998) examined the spatial variability of PM10 source contributions at 

a number of sites in Fresno and Bakersfield, CA during the winter of 1995-1996 and reported 

values for 1 day (December 27, 1995). During that day, mobile sources contributed from 13.0 to 

15.8 μg/m3

, vegetation burning 5.1 to 11.1 μg/m3, ammonium sulfate 2.4 to 3.4 μg/m3, and 

ammonium nitrate 19.3 to 24.6 μg/m3

 to PM10 at the sites in Bakersfield. Mobile sources 

contributed 13.9 to 22.5 μg/m3

, vegetation burning 8.2 to 15.7 μg/m3, ammonium sulfate 1.8 to 

2.3 μg/m3

, and ammonium nitrate 14.5 to 18.9 μg/m3 at the sites in Fresno. All of these 

components are expected to be found mainly in the PM2.5 size fraction. As can be seen, source 

contributions at different sites varied by factors of 1.2 to 2.2 in Bakersfield and by factors of 

1.3 to 1.9 in Fresno on that day.

The receptor modeling methods outlined above do not explicitly include consideration of 

the distances between PM sources and the receptor site. Information about the relative 

importance of sources as a function of distance may be available from examination of data 

obtained by continuous monitoring methods. For example, concentration spikes are expected to 

be the result of transport from nearby sources because turbulent mixing in the atmosphere would 

not allow them to persist for very long. Short duration spikes in the time series of concentrations 

are assumed to result from emissions from local sources (0.1 to 1 km away) in this method. 

Contributions from sources located farther away are determined by comparisons between 

baselines measured at different sites. Details such as these are also lost in integrated 24-h 

samples. Watson and Chow (2001) used time series of black carbon (BC) obtained by 

aetholometers over 5-min intervals to estimate the contributions from sources located < 1 km 

away, 1 to 5 km away, and > 5 km away from a monitoring site in downtown Mexico City. 

They found that most of the BC was produced by sources scattered throughout the city and that 

sources located less than 1 km away from the site contributed only about 10% to BC 

concentrations even in the presence of local sources such as buses and trucks. 

3-81
3.3.3 Background Concentrations of PM in the United States 

This section contains information about the concentrations of “background” PM that are 

relevant for policy setting. For the purposes of this document (and consistent with the 1996 

PM AQCD), policy relevant background (PRB) concentrations are those that would result in the 

United States from emissions from natural sources worldwide plus anthropogenic sources 

outside of North America. In other words, they are the concentrations that would exist if 

anthropogenic emissions in North America were zero. PRB concentrations of PM2.5, PM10-2.5, 

and PM10 may be viewed as coming from two conceptually separate components: a reasonably 

consistent “baseline” component and an episodic component. The baseline component consists 

of the contribution from natural sources within North America and from transport of natural and 

anthropogenic sources outside of North America that is generally well characterized by a 

reasonably consistent distribution of daily values each year, although there is variability by 

region and season. The episodic component consists of infrequent, sporadic contributions from 

natural high-concentration events over shorter periods of time (e.g., hours to several days ) both 

within North America (e.g., volcanic eruptions, large forest fires, dust storms) and outside of 

North America (e.g., transport related to dust storms from deserts in North Africa and China and 

storms at sea). These episodic natural events, as well as events like the uncontrolled biomass 

burning in Central America and southern Mexico, are essentially uncontrollable and do not 

necessarily occur in all years. The same processes that are responsible for transporting these 

emissions also transport anthropogenic emissions from outside North America. 

It is impossible to reliably estimate PRB concentrations solely by examining mass 

measurements of PM2.5, PM10-2.5, and PM10. Rather, this section examines available monitoring 

data, source apportionment analysis, and information about various source contributions to 

inform a characterization of these two components of PRB PM concentrations and the associated 

uncertainties and limitations in estimating PRB concentrations. Appendix 3E presents data from 

relatively remote IMPROVE sites to provide some rough upper limits on PRB concentrations for 

the United States. 

Annual average natural background concentrations of PM10 have been estimated to range 

from 4 to 8 μg/m3

 in the western United States and 5 to 11 μg/m3 in the eastern United States. 

Corresponding PM2.5 levels have been estimated to range from 1 to 4 μg/m3

 in the western 

United States and from 2 to 5 μg/m3

 in the eastern United States (U.S. Environmental Protection 

3-82
Agency, 1996). The range of the natural background concentration of PM10-2.5 can be roughly 

estimated by subtracting the minimum PM2.5 value [1 μg/m3

 (western U.S.) and 2 μg/m3 (eastern 

U.S.)] from the maximum PM10 value [8 μg/m3

 (western U.S.) and 11 μg/m3 (eastern U.S.)] and 

by subtracting the maximum PM2.5 value [4 μg/m3

 (western U.S.) and 5 μg/m3 (eastern U.S.)] 

from the minimum PM10 value [4 μg/m3

 (western U.S.) and 5 μg/m3 (eastern U.S.)]. These 

calculations indicate that the average PM10-2.5 concentration is # 1 to 7 μg/m3

 in the western 

United States and # 1 to 9 μg/m3

 in the eastern United States. The estimated natural background 

concentrations given above do not include contributions from long-range transport from sources 

outside North America. [The range of annual mean PM2.5 concentrations at IMPROVE network 

sites in the western United States (cf., Appendix E) is consistent with the range of values in the 

lowest 5th percentile annual mean PM2.5 concentrations for specific sites in the AIRS data base 

of PM

(2.8 μg/m3

 to 6.9 μg/m3)]. However, PM2.5 concentrations are much higher at sites in the eastern 

United States than at sites in the western United States. At most IMPROVE sites in the western 

United States, the annual mean concentration of PM10-2.5 is higher than that of PM2.5, and daily 

average PM2.5 concentrations are moderately correlated (r = 0.72) with PM10-2.5 concentrations. 

In contrast, PM2.5 concentrations are higher than those of PM10-2.5 at IMPROVE sites in the 

eastern United States, and PM2.5 concentrations are only weakly correlated (r = 0.26) with those 

10-2.5. As discussed above, peak 24-h average concentrations due to natural sources may be 

substantially higher than the annual or seasonal average concentrations from natural sources, 

especially within areas affected by wildfires and dust storms. 

Long-Range Transport from Outside North America 

Windblown dust from dust storms in the Sahara desert has been observed in satellite 

images as plumes crossing the Atlantic Ocean and reaching the southeast coast of the United 

States (e.g., Ott et al., 1991). Dust transport from the deserts of Asia across the Pacific Ocean 

also occurs (Prospero, 1996). Most dust storms in the deserts of China occur in the spring 

following the passage of strong cold fronts after the snow has melted and before a surface 

vegetation cover has been established. Strong winds and unstable conditions result in the rapid 

transport of dust to altitudes of several kilometers, where it is transported by strong westerly 

winds out over the Pacific Ocean (Duce, 1995). Satellite images were used to track the progress 

3-83
of a dust cloud from the Gobi desert to the northwestern United States during the spring of 1998 

(Husar et al., 2000). 

Satellite images obtained at visible wavelengths cannot track mineral dust across the 

continents because of a lack of contrast between the plume and the underlying surface. Other 

means must be used to track the spread of North African dust through the eastern United States. 

Perry et al. (1997) used two criteria (PM2.5 soil concentration > 3 μg/m3

 and Al:Ca > 3.8) to 

distinguish between soil of local origin from soil originating in North Africa in characterizing 

the sources of PM in aerosol samples collected in the IMPROVE network. North African dust 

has been tracked as far north as Illinois (Gatz and Prospero, 1996) and Maine (Perry et al., 

1997). The analysis of Perry et al. (1997) indicates that incursions of Saharan dust into the 

continental United States have occurred, on average, about three times per year from 1992 to 

1995. These events persist for ~10 days on average, mainly during the summer. Large-scale 

dust events typically cover from 15 to 30% of the area of the continental United States and result 

in ~8.7 ± 2.3 μg/m3

 increases of PM2.5 levels throughout the affected areas during these events, 

with mean maximum dust contributions of 19.7 ± 8.4 μg/m3

 and a peak contribution of 32 μg/m3 

to 24-h average PM2.5 levels. 

As can be expected, the frequency of dust events is highest in the southeastern United 

States. About half of these events are observed only within the state of Florida and are 

associated with dense hazes in Miami (Figure 3-22) during the summer (Prospero et al., 1987). 

North African dust is the dominant aerosol constituent in southern Florida during the summer, 

whereas soil dust constitutes only a minor fraction of PM during the remainder of the year 

(Prospero, 1999). Approximately 33 to 50% of the mass of the particles reaching southern 

Florida have aerodynamic diameters < 2.5 μm (Prospero et al., 2001). During episodes when 

daily total dust concentrations ranged up to 100 μg/m3

, it can be seen that daily PM2.5 values of 

up to 50 μg/m3

 could have resulted in Miami, FL. 

Husar et al. (2001) documented the transport of dust from the Gobi and Taklimakan deserts 

to North America during April 1998. The PM10 concentration averaged over 150 stations in 

Washington, Oregon, California, Nevada, and Idaho reporting data to AIRS was 65 μg/m3 

between April 26 and May 1, compared to about 20 μg/m3 during the rest of April and May. 

Data from several networks indicated that PM10 concentrations were over 100 μg/m3

 in central 

British Columbia, Washington State, and Oregon. The highest PM concentrations observed 

3-84
Figure 3-22. Monthly average Saharan dust components in all size fractions of the aerosol 

sampled in Miami, FL (from 1974 to 1996). Approximately one-third to 

one-half of Saharan dust is in the PM2.5 size range. 

Source: Prospero (1999). 

were 120 μg/m3 for PM10 and 50 μg/m3 for PM2.5 at Chilliwack Airport in northwestern 

Washington State (Figure 3-23). Aircraft measurements made over the northwestern United 

States were consistent with a mass median diameter of the dust being between 2 and 3 μm. 

Desert dust deposited over oceans provides nutrients to marine ecosystems (Savoie and 

Prospero, 1980), and such dust deposited on nutrient-depleted soils also provides nutrients to 

terrestrial ecosystems, e.g., Hawaiian rain forests (Chadwick et al., 1999). Microorganisms, 

including fungi and bacteria, have been found attached to African dust particles in the U.S. 

Virgin Islands (Griffin et al., 2001). The fungus, Aspergillus sydowii, which has been connected 

to the death of coral reefs, has been identified in air samples collected in the Caribbean during 

African dust transport events (Smith et al., 1996; Shinn et al., 2000). Measurements of the 

3-85
Figure 3-23. PM2.5 and PM10 concentrations measured at Chilliwack Airport, located in 

southwestern British Columbia, just before and during the Asian desert dust 

episode of April and May 1998. 

Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b). 

composition of Saharan dust in Miami indicate enhancements of nitrate, non-sea salt sulfate, 

ammonium, and trace metals above concentrations expected for clean marine air, suggesting 

pollution emitted in Europe and North Africa as sources (Prospero, 1999). It is likely that many 

other constituents will be found associated with dust from outside North America as more 

measurements are made. It should be noted that, as North African dust and associated material 

are transported northward through the United States during the summer, they are added to the 

mixture of primary and secondary PM generated domestically. 

The transport of PM from uncontrolled biomass burning in Central America and southern 

Mexico resulted in anomalously high PM levels observed in southern Texas and generally 

elevated PM concentrations throughout the entire central and southeastern United States during 

3-86
the spring and early summer of 1998. Biomass burning for agricultural purposes occurs 

normally during the spring of each year in Central America and southern Mexico. During the 

spring of 1998, fires burned uncontrollably because of abnormally hot and dry conditions 

associated with the intense El Niño of 1997-1998. PM10 concentrations observed in the southern 

Rio Grande Valley were elevated substantially during the passage northward of the biomass 

burning plume produced by these fires, as shown in Figure 3-24. Elevated PM10 concentrations 

also were found as far north as St. Louis, MO (Figure 3-25). As can be seen from Figure 3-24 

and Figure 3-25, the elevations in PM concentrations were limited in duration. Uncontrolled 

wildfires occur in the United States every year, but their effects on air quality throughout the 

United States still need to be evaluated systematically. These fires can be widespread, and the 

frequency of their occurrence can vary markedly from year to year. For example, ~26,000 km2

 of 

forested land were consumed during 2000, but only a small fraction of this area was burnt during 

2001 in the western United States. Wildfires also occur throughout the boreal forests of Canada. 

Wotawa and Trainer (2000) suggested that the plume from fires occurring in the Northwest 

Territories of Canada in early July 1995 may have extended throughout most of the eastern 

United States, resulting in elevated levels of CO and O3. Simple scaling of their calculated 

excess CO concentrations because of the fires, by the ratio of emission factors of PM2.5 to CO, 

indicates that the excess PM2.5 concentrations in the plume may have ranged from ~5 μg/m3

 in 

the Southeast and to close to 100 μg/m3

 in the northern Plains States. 

Sources Within North America 

It is much more difficult to determine 24-h PRB concentrations in the absence of specific 

events such as those noted above, because contributions from anthropogenic sources located 

either nearby or elsewhere within North America can contribute substantially to observed values 

and perhaps overwhelm the contributions from PRB sources. Source-apportionment modeling 

techniques (described in Section 5.5 of the 1996 PM AQCD and updated in Section 3.3.2 of this 

document) can be helpful for this purpose. It is more likely that PRB concentrations can be 

more accurately determined in the West than in the East because contributions from pollution 

sources can overwhelm those from background sources in the East, and background source 

contributions can be lost within the errors of their source contributions. However, it should be 

noted that source apportionment techniques such as PMF and UNMIX are not able to distinguish 

3-87
Figure 3-24. Time series of 24-h average PM10 concentrations observed in the Rio Grande 

Valley during May 1998. 

Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b). 

between bioaerosol and organic PM from whatever source, mainly because of analytical 

limitations. In addition, background PM source contributions are contaminated by contributions 

from pollution sources during transport from source to receptor monitoring site. Thus, 

background source contributions derived by these methods should be regarded as upper limits on 

the true values. 

Eastern United States 

PM

The use of data for PM2.5, PM10-2.5, and PM10 from IMPROVE sites in the East will 

generally result in gross overestimates of PRB concentrations because of extensive 

contamination by anthropogenic sources. Song et al. (2001) derived source contributions to 

2.5 concentrations measured at Washington, DC, Brigantine, NJ, and Underhill, VT using 

PMF. They found that woodsmoke (consisting of contributions from wildfires and residential 

wood burning) could contribute 0.93 ± 0.97 μg/m3

; soil 0.11 ± 0.22 μg/m3; and sea spray 

0.90 ± 1.0 μg/m3

 on an annual basis at Brigantine, NJ throughout the 1989 to 1999 sampling 

period. They also derived contributions of 1.2 ± 0.9 μg/m3

 from woodsmoke; 0.32 ± 0.61 μg/m3 

3-88
 Figure 3-25. PM10 concentrations observed in St. Louis, MO, during May 1998.

 Source: Aerometric Information Retrieval System (AIRS; U.S. Environmental Protection Agency, 2002b). 

from soil; and 0.05 ± 0.05 μg/m3 from sea spray at Underhill, VT from 1989 through 1999. The 

“background” sources contribute about 7% to annual average PM2.5 concentrations at Brigantine 

and about 12% at Underhill. The daily time series at the NJ and VT sites show striking 

variability in background components, characterized by spikes. Maximum daily values during 

these spikes are in the range of several μg/m3

. Song et al. concluded that such spikes in 

concentrations are likely caused by transient meteorological events, e.g., storms or transport of 

dust from northern Africa or other distant regions or by events such as wild fires. Contributions 

from all of these sources should be regarded as upper limits because of entrainment of pollutant 

emissions during transport from source to receptor of the background source emissions. Care 

should also be taken to ensure that distinctions are made between contributions from wildfires 

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versus those from residential wood burning on the basis of seasonality or observations of smoke 

plumes when using these results. 

Pun et al. (2002) used a regional scale chemistry-transport model (CMAQ) to simulate O3 

and PM2.5 concentrations arising from natural sources alone in model domains centered over 

Tennessee and over the mid-Atlantic region for several days in July 1995. These calculations 

were performed for meteorological conditions that resulted in high ambient ozone concentrations 

in the eastern United States. They found that natural sources contributed about 1.7 μg/m3

 to 

Washington, DC, ( ranging from about 0.6 to 3.1 μg/m3

 in the mid-Atlantic domain) and about 

1.2 μg/m3

 to Nashville, TN, on a 24 h average basis. The formation of secondary organic PM 

from biogenic precursors may be expected to be maximized for these conditions; however, their 

contribution was estimated to be at most 15% of natural PM2.5 or less than 1 μg/m3

. The largest 

contributions in both cases came from natural PM2.5 that was advected in from other regions of 

the United States. In addition to the sources considered above, contributions to both primary and 

secondary PM from events such as volcanic eruptions or geothermal activity are highly sporadic. 

The spatial and temporal distributions of secondary PM produced by background sources shown 

in Table 3-8 still remain to be investigated. 

Western United States 

As mentioned earlier, it is impossible to obtain reliable estimates of PRB concentrations 

solely on the basis of measurements of PM2.5, PM10-2.5, or PM10. It is preferable to quantify 

contributions from both background and non-background sources by using compositional data in 

techniques such as source apportionment modeling. However, of those measured throughout the 

United States, the concentrations observed at several RRMS in the western United States 

probably come closest to what PRB concentrations might be in the West. In addition to looking 

at PM2.5, PM10, and PM10-2.5, the PM2.5 and PM10 mass with the sulfate component removed was 

examined to obtain better insight into non-anthropogenic background. However, it should be 

noted at the outset that an undetermined fraction of the sulfate will be PRB in nature as the result 

of long range transport from Asia, more local crustal sources or volcanic emissions, or from seaspray (especially at coastal sites). Data from 19 IMPROVE sites are examined in Appendix 3E 

(including two eastern sites). The ranges of annual average non-sulfate PM2.5 and non-sulfate 

PM10 as well as PM10-2.5 (calculated as the difference between PM10 and PM2.5) concentrations are 

3-90
shown in Table 3-E1 and the corresponding ranges for the 90th percentile concentrations are 

summarized in Table 3-E2. The IMPROVE monitors did not measure coarse fraction sulfate. 

It must be recognized at the outset that these concentrations will only provide upper limits, and it 

is not clear over what spatial scale these concentrations can be extrapolated. However, at nine of 

the 16 sites in the western United States summarized in Appendix 3E, annual mean 

concentrations are within the range of PRB values estimated in the 1996 PM AQCD and given 

earlier in this section. At the other seven sites in the western United States, they are consistent 

with, although slightly above, the annual average values defined earlier in the 1996 PM AQCD. 

The annual mean sulfate concentrations at these western IMPROVE sites range significantly 

above the PRB values estimated in the 1996 PM AQCD. 

Some screening should be performed to rule out transport from urban areas or other 

sources of anthropogenic PM on a day-by-day basis. It is important to note that there is much 

smaller relative variability in PM2.5 concentrations (from the minimum to the P90 level) on a 

year-to-year basis at the western IMPROVE sites than at the eastern IMPROVE sites. This may 

be related to a lack of discrete transport events affecting most samples at the western sites 

compared to the eastern sites; but does not rule out the possibility that the western sites are 

receiving highly diluted contributions from many distant urban sources. Further inquiry is 

needed to address these issues. Likewise, data for PM10-2.5 concentrations at RRMS can be used 

for similar purposes. 

Summary 

It can be concluded from the above discussion that 24-h policy relevant background (PRB) 

concentrations are highly variable both spatially and temporally. Contributions to PRB 

concentrations from external sources (e.g., Asian and North African dust storms and Central 

American wildfires) can be significant on an episodic, but probably not on an annual basis. 

More local sources of primary PRB PM are also likely to be episodic, reflecting the occurrence 

of volcanic eruptions, wildfires, and storms that raise dust and sea spray. The influence from 

events such as these can be felt over thousands of square kilometers. Very little work has been 

done to quantify the magnitude and variability of contributions from the production of secondary 

PM. However, the one modeling study cited above found values < 1 μg/m3

 as secondary PM. 

Perhaps the greatest possibility for estimating these concentrations comes from the application of 

3-91
source apportionment techniques such as PMF (positive matrix factorization) to time series of 

species compositional data obtained at relatively remote monitoring sites to minimize 

interference from anthropogenic sources. In the absence of such results, some useful estimates 

may be obtained by examining the time series of PM concentrations at such sites with screening 

to eliminate days when concentrations are influenced by anthropogenic sources. 

The above discussions do not explicitly consider the contributions from PBP. However, 

it should be noted there is very little data available for concentrations of PBP, and their 

contributions are likely to be highly spatially and temporally variable, especially on a seasonal 

basis. For example, one study conducted in Mainz, Germany (Mathias-Maser, 1998) found that 

PBP could contribute from about 17 to 20% of total aerosol volume and from 9 to 30% of total 

particle number in the size range from 0.35 μm to 50 μm, depending on season. Also, pollen 

can, at times, be a dominant contributor to ambient PM particles larger than 10 μm. However, 

rupture of pollen grains (especially during warm temperature and high humidity or rainfall 

conditions) can result in release of many smaller-size airborne cytoplasmic fragments ranging 

down to around 0.3 to 0.4 μm (see Appendix 7B of Chapter 7 for more information on pollen 

and other bioaerosols). 

3.3.4 Emissions Estimates for Primary PM, and Precursors to Secondary 

PM (SO2, NOx, VOCs, and NH3) in the United States 

In principle, source contributions to ambient PM also could be estimated on the basis of 

predictions made by chemistry-transport models (CTMs) or even on the basis of emissions 

inventories alone. Uncertainties in emissions inventories have arguably been regarded as 

representing the largest source of uncertainty in CTMs (Calvert et al., 1993). Apart from 

uncertainties in emission inventories, a number of other factors limit the ability of an emissions 

inventory-driven CTM to determine the effects of various sources on particle samples obtained 

at a particular location. CTM predictions represent averages over the area of a grid cell, which 

in the case of CMAQ (Community Model for Air Quality) and MAQSIP (Multiscale Air Quality 

Simulation Platform), ranges from 16 km2

 (4 km × 4 km) to 1296 km2 (36 km × 36 km). CMAQ 

and MAQSIP constitute the CTMs within the overall Models3 framework, which also includes 

emissions processors, the meteorological model, and decision-support modules. The 

contributions of sources to pollutant concentrations at a monitoring site are controlled strongly 

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by local conditions that cannot be resolved by an Eulerian grid-cell model. Examples would be 

the downward mixing of tall-stack emissions and deviations from the mean flow caused by 

buildings. The effect of local sources at a particular point in the model domain may not be 

predicted accurately because their emissions would be smeared over the area of a grid cell or if 

the local wind fields at the sampling point deviated significantly from the mean wind fields 

calculated by the model. CTMs also have problems in predicting pollutant concentrations 

because of uncertainties in vertical mixing and in predicting concentrations of pollutants from 

stationary combustion sources resulting from uncertainties in estimates of plume rise. CTMs are 

an integral part of air quality management programs and are reviewed in the NARSTO Fine 

Particle Assessment (NARSTO, 2002). 

Estimated emissions of primary PM2.5 from different sources in the United States are 

summarized in Table 3-11, and estimated emissions of precursors to secondary PM2.5 (SO2, NOx, 

VOCs, and NH3) are summarized in Table 3-12. These estimates provide a rough overview of 

the relative importance of major PM sources in the United States. The emissions estimates are 

based on information presented in the EPA National Air Pollutant Emission Trends Report, 

1990-1999 (U.S. Environmental Protection Agency, 2001), to which the reader is referred for 

detailed tables showing trends in PM2.5 emissions from a number of source categories from 1990 

to 1999. Detailed descriptions of the methodology for constructing emissions inventories for 

criteria pollutants, quality assurance procedures, and examples of calculations of emissions can 

be found in U.S. Environmental Protection Agency (1999). Although uncertainties associated 

with the estimates in the National Air Pollutant Emission Trends Report are not given therein, a 

discussion of uncertainties in emissions estimates is given in Section 3.3.5. 

For the sake of completeness, an attempt was made to supplement the information given 

in the emissions tables in the Trends Report (which concentrates mainly on anthropogenic 

emissions) with information about emissions from natural sources. Details regarding the 

composition of the emissions of primary PM2.5 from the source categories shown in Table 3-11 

are summarized in Appendix 3D, where available. Fugitive dust emissions are estimated to 

constitute over 50% of nationwide primary PM2.5 emissions according to Table 3-11. However, 

there are a number of issues concerning the methods for obtaining relevant emissions factor data 

for fugitive dust in field studies, as discussed in Section 3.3.5. An estimate of the production 

of PM2.5 from wind erosion on natural surfaces was not included in Table 3-11, because this 

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TABLE 3-11. EMISSIONS OF PRIMARY PM2.5 BY VARIOUS SOURCES IN 1999 

Source 

Emissions 

(109

 kg/y) 

Major PM 

Components Notes 

On-road vehicle 0.21 Organic compounds, Exhaust emissions from diesel (72%) and gasoline 

exhaust elemental carbon vehicles (28%). 

Non-road vehicle 0.37 Organic compounds, Exhaust emissions from off-road diesel (57%) and 

exhaust elemental carbon gasoline vehicles (20%); ships and boats (10%); 

aircraft (7%); railroads (6%). 

Fossil fuel 0.36 Crustal elements, Fuel burning in stationary sources such as power 

combustion trace metals plants (33%); industries (39%); businesses and 

institutions (25%); residences (3%). 

Industrial 0.35 Metals, crustal material, Metals processing (29%); mineral products (27%); 

processes organic compounds chemical mfg. (11%); other industries (33%). 

Biomass burning 1.2 Organic compounds, Managed burning (47%); residential wood burning 

elemental carbon (28%); agricultural burning (7%); wildfires (18%). 

Waste disposal 0.48 Organic compounds, Open burning (91%); incineration (9%). 

trace metals 

Fugitive dust 3.3 Crustal elements Dust raised by vehicles on paved (19%) and 

unpaved roads (40%); construction (15%), 

dust from raising crops (24%) and livestock (2%). 

Windblown dust NA1 Crustal elements Dust raised by wind on bare land. 

Other 0.02 Organic compounds, Structural fires. 

elemental carbon 

Total 6.2 

1

NA = not available. 

Source: Adapted from U.S. Environmental Protection Agency (2001). 

source is highly sporadic, occurs during periods of high winds, and the resulting emissions are 

too highly uncertain to be included here. As can be seen from a comparison of entries in Tables 

3-11 and 3-12, estimates of emissions of potential precursors to secondary PM formation are 

considerably larger than those for estimates of primary PM2.5 emissions in the United States. 

The emissions of SO2, NOx, and NH3 that are converted to PM should be multiplied by factors of 

1.5, 1.35, and 1.07, respectively, to account for their chemical form in the aerosol phase. 

Estimating a factor for VOCs is somewhat less straightforward. Turpin and Lim (2001) 

recommend factors ranging from 1.4 to 2 to account for the conversion of OC to oxygen- and 

3-94
TABLE 3-12. EMISSIONS OF PRECURSORS TO SECONDARY PM2.5 FORMATION 

BY VARIOUS SOURCES IN 1999 

Precursor 

Emissions 

(109

 kg/y) 

Secondary PM 

Component Notes 

SO2 17 Sulfate Exhaust from on-road (2%) and non-road (5%) engines 

and vehicles; fossil fuel combustion by electrical utilities, 

industries, other sources (85%); various industrial 

processes (7%); and other minor sources (1%). 

NOx 

1,2 26 Nitrate Exhaust from on-road (34%) and non-road (22%) engines 

and vehicles; fossil fuel combustion by electrical utilities, 

industries, other sources (39%); lightning (4%); soils 

(4%); and other minor sources (5%). 

Anthropogenic 

VOCs 

16 Various mainly 

unidentified 

compounds of ‘OC’ 

Evaporative and exhaust emissions from on-road (29%) 

and non-road (18%) vehicles; evaporation of solvents and 

surface coatings (27%); biomass burning (9%); storage 

and transport of petroleum and volatile compounds (7%); 

chemical and petroleum industrial processes (5%); other 

sources (5%). 

Biogenic 

VOCs1 

44 Various mainly 

unidentified 

compounds of ‘OC’ 

Approximately 98% emitted by vegetation. Isoprene 

(35%); monoterpenes (25%); all other reactive and 

non-reactive compounds (40%). 

NH3 45 Ammonium Exhaust from on-road and non-road engines and vehicles 

(4%); chemical manufacturing (3%); waste disposal, 

recycling, and other minor sources (4%); livestock (73%); 

and fertilizer application (16%). 

1

Includes estimates of natural sources from Guenther et al. (2000). 2

Emissions expressed in terms of NO2.

Source: Adapted from U.S. Environmental Protection Agency (2001). 

nitrogen-containing compounds in the aerosol phase. There is some additional uncertainty in 

assigning a factor for the fraction of VOCs that are converted into OC in the aerosol phase. 

These factors are all greater than 1 and further underscore the potential importance of secondary 

PM precursor emissions relative to primary PM emissions. However, the emissions of 

precursors cannot be translated directly into rates of PM formation. Dry deposition and 

precipitation scavenging of some of these gaseous precursors and their intermediate oxidation 

products occur before they are converted to PM in the atmosphere, and most of the VOCs are 

oxidized to CO2 rather than PM. In addition, some fraction of these gases are transported outside 

of the domain of the continental United States before being oxidized. Likewise, emissions of 

3-95
these gases from areas outside the United States can result in the transport of their oxidation 

products into the United States. 

As discussed in Section 3.3.1, the photochemical oxidation of SO2 leads to the production 

of sulfate, whereas that of NOx leads ultimately to particulate-phase nitrite and nitrate. Due to 

uncertainties it is difficult to calculate the rates of formation of SOPM from the emissions of 

VOC precursors. Smog chamber and laboratory studies discussed in Section 3.3.1 indicate that 

anthropogenic aromatic compounds and biogenic terpenoid compounds have the highest 

potential for forming SOPM; and, as can be seen from Table 3C-1, the dominant compounds 

tend to be those derived from these categories. Each of the source categories capable of emitting 

VOCs shown in Table 3-12 has components capable of forming SOPM, although in small yields 

(ranging typically up to several percent, cf., Section 3.3.1). The oxidation of lighter organic 

compounds leads ultimately to the formation of CO and CO2. As discussed by Pandis et al. 

(1991) and in Section 3.3.1, soluble gas phase compounds, such as formaldehyde (CH2O), other 

aldehydes, organic acids, etc., formed during the oxidation of a wide variety of hydrocarbons, 

can be incorporated into suspended particles. Although isoprene is a major component of 

biogenic emissions, its oxidation has not been found to result in the formation of new particles; 

whereas the oxidation of monoterpenes has. However, it should be remembered that soluble gas 

phase species such as CH2O are formed during the oxidation of isoprene. 

The emissions estimates shown in this section are based on averaged annual totals. 

However, annual averages do not reflect the variability of a number of emissions categories on 

shorter time scales. Residential wood burning in fireplaces and stoves, for example, is a 

seasonal practice that reaches its peak during cold weather. Cold weather also affects motor 

vehicle exhaust PM emissions, both in terms of chemical composition and emission rates (e.g., 

Watson et al., 1990b; Huang et al., 1994). Agricultural activities such as planting, fertilizing, 

and harvesting are also seasonal. Forest fires occur mainly during the local dry season and 

during periods of drought. Maximum dust production by wind erosion in the United States 

occurs during the spring, whereas the minimum occurs during the summer (Gillette and Hanson, 

1989). Efforts are being made to account for the seasonal variations of emissions in the 

nationwide emissions inventories. Techniques for calculating emissions of criteria pollutants on 

a seasonal basis are given in U.S. Environmental Protection Agency (1999). 

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Trends in nationwide, annual average concentrations of PM10 and precursor gases (SO2, 

NO2, and VOC) during the 10 years from 1992 to 2001 are shown in Table 3-13. As can be seen 

from Table 3-13, there have been substantial decreases in the ambient concentrations of PM10, 

SO2, and NO2. Not enough data are available to define trends in VOC concentrations. There 

also have been substantial decreases in the emissions of all the species shown in Table 3-13, 

except for NO2, although its average ambient concentration has decreased by 11%. These entries 

suggest that decreases in the average ambient concentration of PM10 could have been produced 

by both decreases in emissions of primary PM10 and in the formation of secondary PM10. The 

large reductions in ambient SO2 concentrations have resulted in reductions in sulfate formation 

that would have been manifest in PM2.5 concentrations on the regional scale in the East and 

Midwest, where sulfate has constituted a larger fraction of PM2.5 than in the West. Likewise, 

reductions in NO2 concentrations would have had a more noticeable effect on PM2.5 

concentrations in the West than in the East, because nitrate is a larger component of the aerosol 

in the West. 

TABLE 3-13. NATIONWIDE CHANGES IN AMBIENT CONCENTRATIONS AND 

EMISSIONS OF PM10 AND GASEOUS PRECURSORS TO SECONDARY 

PARTICULATE MATTER FROM 1992 TO 2001 

% Change 1992-2001 

Ambient Concentration Emissions 

PM

PM10 !14% !13%* 

2.5 — !10%* 

SO2 !35% (SO2) !24% (SO2) 

NOx !11% (NO2) !3% (NOx) 

VOC — !8% 

*Includes only primary PM.

Source: U.S. Environmental Protection Agency (2002a).

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Trends in aerosol components (i.e., nitrate, sulfate, carbon, etc.) are needed for a more 

quantitative assessment of the effects of changes in emissions of precursors. Aerosol nitrate and 

sulfate concentrations obtained at North Long Beach and Riverside, CA tracked downward 

trends in NOx concentrations. SO2 and sulfate concentrations have both decreased; however, the 

rate of decline of sulfate has been smaller than that of SO2, indicating the long range transport of 

sulfate from outside the airshed may be an important source in addition to the oxidation of 

locally generated SO2. There are a number of reasons why pollutant concentrations do not track 

well estimated reductions in emissions. Some of these reasons are related to atmospheric effects 

such as meteorological variability and secular changes in the rates of photochemical 

transformations and deposition (U.S. Environmental Protection Agency, 2000c). Other reasons 

are related to uncertainties in ambient measurements and in emissions inventories. 

3.3.5 Uncertainties of Emissions Inventories 

As described in the 1996 PM AQCD, it is difficult to quantitatively assign uncertainties to 

entries in emissions inventories. Methods that can be used to verify or place constraints on 

emissions inventories are sparse. In general, the overall uncertainty in the emissions of a given 

pollutant includes contributions from all of the terms used to calculate emissions (i.e., activity 

rates, emissions factors, and control device efficiencies). Additional uncertainties may arise 

during the compilation of an emissions inventory due to missing sources and computational 

errors. The variability of emissions can cause errors when annual average emissions are applied 

to applications involving shorter time scales. 

Activity rates for well-defined point sources (e.g., power plants) should have the smallest 

uncertainty associated with their use, because emissions are monitored continuously in many 

cases and accurate production records need to be kept. On the other hand, activity rates for a 

number of very disperse fugitive sources are difficult to quantify. Emissions factors for easily 

measured fuel components that are released quantitatively during combustion (e.g., CO2, SO2) 

should be the most reliable. Emissions of components formed during combustion are more 

difficult to characterize, as the emissions rates are dependent on factors specific to individual 

combustion units and on combustion stage (i.e., smoldering or active). Although the AP-42 

emissions factors (U.S. Environmental Protection Agency, 1995) contain extensive information 

for a large number of source types, these data are very limited in the number of sources sampled. 

3-98
The efficiency of control devices is determined by their design, age, maintenance history, and 

operating conditions. It is virtually impossible to assign uncertainties in control device 

performance because of these factors. It should be noted that the largest uncertainties occur for 

those devices that have the highest efficiencies (> 90%). This occurs because the efficiencies are 

subtracted from one, and small errors in assigning efficiencies can lead to large errors in 

emissions. 

Ideally, an emissions inventory should include all major sources of a given pollutant. This 

may be an easy task for major point sources. However, area sources of both primary PM and 

precursors to secondary PM formation are more difficult to characterize than point sources; and, 

thus, they require special emphasis when preparing emission inventories. Further research is 

needed to better characterize the sources of pollutants to reduce this source of uncertainty. 

Errors also can arise from the misreporting of data, and arithmetic errors can occur in the course 

of compiling entries from thousands of individual sources. A quality assurance program is 

required to check for outliers and arithmetic errors. Because of the variability in emissions rates, 

there can be errors in the application of inventories developed on an annually averaged basis (as 

are the inventories shown in Tables 3-11 and 3-12) to episodes occurring on much shorter time 

scales. As an example, most modeling studies of air pollution episodes are carried out for 

periods of a few days. 

Uncertainties in annual emissions were estimated to range from 4 to 9% for SO2 and from 

6 to 11% for NOx in the 1985 NAPAP inventories for the United States (Placet et al., 1991). 

Uncertainties in these estimates increase as the emissions are disaggregated both spatially and 

temporally. The uncertainties quoted above are minimum estimates and refer only to random 

variability about the mean, assuming that the variability in emissions factors was adequately 

characterized and that extrapolation of emissions factors to sources other than those for which 

they were measured is valid. The estimates do not consider the effects of weather or variations 

in operating and maintenance procedures. 

Fugitive dust sources, as mentioned above, are extremely difficult to quantify; and stated 

emission rates may represent only order-of-magnitude estimates. Although crustal dust 

emissions constitute about 50% of the total primary PM2.5 inventory, they constitute less than 

about 15% of the source strengths inferred from the receptor modeling studies shown in 

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Table 3-9. However, it should be remembered that secondary components (sulfate, nitrate, and 

some fractions of OC) often account for most of the mass of ambient PM2.5 samples. 

Although mineral dust sources represent the major category in Table 3-11, their 

contributions are distributed much more widely than are those from combustion sources. 

Watson and Chow (2000) reexamined the methodology used to determine emissions of fugitive 

dust. The standard methods use data obtained by particle monitors stacked at several elevations 

from 1 to 2 m up to 7 to 10 m above the surface. However, small-scale turbulent motions and 

variable winds characterize atmospheric flow patterns immediately adjacent to the surface in this 

height range (Garratt, 1994). The depth of this turbulent layer is determined by surface 

roughness elements, and there is a high probability of particles being entrained in turbulent 

eddies and redepositing on the ground within a very short distance. In addition to the sourcesampling problem referred to above, it should be remembered that dust often is raised in remote 

areas far removed from population centers. Precipitation or scavenging by cloud droplets and 

dry deposition removes particles during transport from the source area. In addition, gravitational 

settling can be an important loss mechanism for particles larger than a few micrometers in 

aerodynamic diameter. 

As rough estimates, uncertainties in emissions estimates could be as low as 10% for the 

best characterized source categories; whereas emissions figures for windblown dust should be 

regarded as order-of-magnitude estimates. The application of emissions inventories to the 

estimation of source contributions at monitoring sites is also limited by the effects of local 

topography and meteorology. For example, Pinto et al. (1998) found that the contribution of 

power plants and residential space heating to PM2.5 concentrations in northwestern Bohemia are 

comparable on the basis of CMB receptor modeling. However, according to the emissions 

inventories, the contribution from power plants should have been roughly an order of magnitude 

larger than that from residential space heating. The difference between the two methods can be 

explained by noting that mixing of the emissions from the power plants downward to the surface 

is inhibited by strong surface inversions that develop during the winter season in this area. 

There have been few field studies designed to test emissions inventories observationally. 

The most direct approach would be to use aircraft to obtain cross sections of pollutants upwind 

and downwind of major urban areas. The computed mass flux through a cross section of the 

urban plume can then be equated to emissions from the chosen city. This approach has been 

3-100
attempted on a few occasions, but results have been ambiguous because of contributions from 

fugitive sources, variable wind flows, and logistic difficulties. 

3.4 SUMMARY AND KEY CONCLUSIONS 

The recently deployed PM2.5 FRM network has returned data for a large number of sites 

across the United States. Annual mean PM2.5 concentrations range from ~5 μg/m3

 to ~30 μg/m3. 

In the eastern United States, the data from 1999 to 2001 indicate that highest quarterly mean 

concentrations and maximum concentrations most often occur during the summer. In the 

western United States, highest quarterly mean values and maximum values occur mainly during 

the winter at a number of sites, although there were exceptions to these general patterns. Sites 

affected strongly by sources of primary PM are expected to show winter maxima. Sources 

emitting precursors of secondary ammonium nitrate are also contributors to winter PM maxima. 

These findings are generally consistent with those based on longer term data sets such as these 

form the Metropolitan Acid Aerosol Characterization Study (MAAQS) in the eastern United 

States and the California Air Resources Board (CARB) network of dichotomous samplers in 

California. PM2.5 and PM10 concentrations in a number of urban areas have generally declined 

over the past few decades, and they appear to have leveled off in the past few years. 

Differences in annual mean PM2.5 concentrations between monitoring sites in urban areas 

examined are typically less than 6 or 7 μg/m3

. However, on individual days, differences in 24-h 

average PM2.5 concentrations can be much larger. Some sites in metropolitan areas are highly 

correlated with each other but not with others, due to the presence of local sources, topographic 

barriers, etc. Although PM2.5 concentrations at sites within a MSA can be highly correlated, 

significant differences in their concentrations can occur on any given day. Consequently, 

additional measures should be used to characterize the spatial variability of PM2.5 concentrations. 

The degree of spatial uniformity in PM2.5 concentrations in urban areas varies across the country. 

These factors should be considered in using data obtained by the PM2.5 FRM network to estimate 

community-scale human exposure, and caution should be exercised in extrapolating conclusions 

obtained in one urban area to another. PM2.5 to PM10 ratios were generally higher in the East 

than in the West, and values for this ratio are consistent with those found in numerous earlier 

studies presented in the 1996 PM AQCD. 

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Data for PM10-2.5 are not as abundant as they are for PM2.5, and their interpretation is 

complicated by the difference method used to determine their concentrations. The more 

sporadic nature of sources of PM10-2.5 and its shorter atmospheric lifetime tend to result in lower 

spatial correlations for PM10-2.5 than for PM2.5 concentrations. Errors in the measurement 

of PM2.5 and PM10 also result in calculations of lower spatial correlations of PM10-2.5. Calculated 

concentrations of PM10-2.5 are occasionally negative as reflected by PM2.5 to PM10 ratios greater 

than one. Because analytical errors are generally larger for individual species than for total 

mass, similar problems arise in their determination in PM10-2.5 samples by the difference 

approach. Some, but not all of these problems could be resolved by the use of dichotomous 

samplers that also provide a direct sample of PM10-2.5 for compositional analyses. 

Estimates of concentrations of individual species in PM10-2.5 samples used in this chapter 

were limited to those obtained by dichotomous samplers. Generally, concentrations of most 

elements differ for PM2.5 and PM10-2.5. However, the available data suggest that concentrations of 

many metals are of the same order of magnitude in both size fractions. This is in marked 

contrast to the situation 20 years ago, when uncontrolled combustion sources were prevalent. 

At that time, concentrations of many metals, especially Pb, were much higher than today in 

fine-mode particles, and their concentrations were much higher in the fine mode than in the 

coarse mode. No substantive conclusions about contemporary concentrations and composition 

of ultrafine particles (< 0.1 μm Da) can be drawn for the nation as a whole because of a lack of 

data. 

Ambient PM contains both primary and secondary components. The results of ambient 

monitoring studies and receptor modeling studies indicate that PM2.5 is dominated by secondary 

components in the eastern United States. General statements about the origin of organic carbon 

(OC) in ambient PM2.5 samples cannot yet be made; and, so, the contribution of secondary 

components throughout the rest of the United States is still highly uncertain. Primary 

constituents represent smaller but still important components of PM2.5. Crustal materials, which 

are primary constituents, constitute the largest measured fraction of PM10-2.5 throughout the 

United States. Data for the concentration of bioaerosols in both the PM2.5 and PM10-2.5 size 

ranges are sparse. Data collected in several airsheds, including the Los Angeles Basin, 

Bakersfield and Fresno, CA and Philadelphia, PA airsheds, suggest that secondary PM 

3-102
components are more uniformly distributed than are primary PM components. Compositional 

data obtained at multiple sites in other urban areas are sparse. 

Because of the complexity of the composition of ambient PM2.5 and PM10-2.5, sources 

are best discussed in terms of individual constituents of both primary and secondary PM2.5 

and PM10-2.5. Each of these constituents can have anthropogenic and natural sources as shown 

in Table 3-8. The distinction between natural and anthropogenic sources is not always obvious. 

Although windblown dust might seem to be the result of natural processes, highest emission 

rates are associated with agricultural activities in areas that are susceptible to periodic drought. 

Examples include the dust bowl region of the midwestern United States and the Sahel of Africa. 

There is also ongoing debate about characterizing wildfires as either natural or anthropogenic. 

Land management practices and other human actions affect the occurrence and scope of 

wildfires. Similarly, prescribed burning can be viewed as anthropogenic or as a substitute for 

wildfires that would otherwise eventually occur on the same land. 

During the past decade, a significant amount of research has been carried out to improve 

our understanding of the atmospheric chemistry of secondary organic PM (SOPM) formation. 

Although additional sources of SOPM might still be identified, there appears to be a general 

consensus that biogenic compounds (monoterpenes, sesquiterpenes) and aromatic compounds 

(toluene, ethylbenzene) are the most significant SOPM precursors. A large number of 

compounds have been detected in biogenic and aromatic SOPM, although the chemical 

composition of these two categories has not been fully established, especially for aromatic 

SOPM. Transformations that occur during the aging of particles are still inadequately 

understood. There still exist large gaps in our current understanding of a number of key 

processes related to the partitioning of semivolatile compounds between the gas phase and 

ambient particles containing organic compounds, liquid water, and inorganic salts and acids. 

In addition, there is a general lack of reliable analytical methods for measuring multifunctional 

oxygenated compounds in the gas and aerosol phases. 

The results of receptor modeling studies throughout the United States indicate that the 

combustion of fossil and biomass fuels is the major source of measured ambient PM2.5. Fugitive 

dust, found mainly in the PM10-2.5 range size, represents the largest source of measured ambient 

PM10 in many locations in the western United States. Quoted uncertainties in the source 

apportionment of constituents in ambient aerosol samples typically range from 10 to 50%. It is 

3-103
apparent that a relatively small number of source categories, compared to the total number of 

chemical species that typically are measured in ambient monitoring-source receptor model 

studies, are needed to account for the majority of the observed mass of PM in these studies. 

The application of any of the source apportionment techniques is still limited by the 

availability of source profile data as well as by the level of detail and the quality of ambient 

measurement data. Whereas the chemical mass balance (CMB) approach relies directly on 

source profile data, solutions from the positive matrix factorization (PMF) technique yield 

profiles for the factors that contribute to PM. However, there is some rotational ambiguity 

present in the solutions. Source profile data obtained by PMF must still be verified by 

comparison with observational data and these data can be used in techniques such as PMF to 

improve the solutions. Serious limitations still exist with regard to source profiles for organic 

compounds. The complexity of reactions involving organic compounds in particles adds to the 

difficulties of finding stable species that could be used as tracers. 

As seen in Table 3-8, emissions of mineral dust, organic debris, and sea spray are 

concentrated mainly in the coarse fraction of PM10 (> 2.5 μm Da). A small fraction of this 

material is in the PM2.5 size range (< 2.5 μm Da). Still, PM2.5 concentrations of crustal material 

can be appreciable, especially during dust events. It also should be remembered that from onethird to one-half of the Saharan dust reaching the United States is in the PM2.5 size range. 

Emissions from combustion sources (mobile and stationary sources and biomass burning) are 

also predominantly in the PM2.5 size range. 

A number of sources contribute to policy relevant background (PRB) concentrations. Data 

obtained at relatively remote monitoring sites (RRMS) in the western United States could be 

used to place reasonable upper limits on PRB concentrations. More definitive results for both 

annual average and daily average concentrations could potentially be obtained from the 

application of source-receptor models and/or the application of large-scale chemistry transport 

models. Many areas in the East are affected by dust transported from northern Africa, and it has 

recently become apparent that many areas, especially, but not limited to the Northwest, are 

affected by dust transported from the deserts of Asia. In addition to crustal material, pollutants 

and primary biological aerosol particles (PBPs) are also transported during intercontinental 

transport events. Many areas are also affected by smoke from wildfires occurring within the 

United States or in Canada, Mexico, or Central America. Storms, in which the winds can 

3-104
suspend material from the surface of the land or seas, also contribute soil, sea spray and PBP. 

Contributions of primary PM from natural sources and sources outside Northern America as 

given above are all episodic. Because concentrations of PBP are so poorly quantified, even 

though they can constitute significant portions of the organic fraction of the atmospheric aerosol, 

estimates of PRB concentrations will remain highly uncertain. Estimates of annually averaged 

PRB concentrations or their range have not changed from the 1996 PM AQCD. 

3-105
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APPENDIX 3A

PM

Spatial and Temporal Variability of the Nationwide AIRS 

2.5 and PM10-2.5 Data Sets 

Aspects of the spatial and temporal variability of 24-h average PM2.5 concentrations for 

1999, 2000, and 2001 in a number of metropolitan statistical areas (MSAs) across the United 

States are presented in this Appendix. PM2.5 data for multiple sites in 27 urban areas have been 

obtained from the AIRS data base and analyzed for their seasonal variations, for their spatial 

correlations, and for their spatial uniformity. A number of aspects of the spatial and temporal 

variability of the PM2.5 data set from 1999 were presented in Rizzo and Pinto (2001) based in 

part on analyses given in Fitz-Simons et al. (2000). 

Information about seasonal and spatial variability in PM2.5 concentrations within 27 MSAs 

across the United States are provided in the accompanying figures (Figures 3A-1 to 3A-27). 

Underneath the value for r, the 90th percentile (P90) values of the absolute difference in PM2.5 

concentrations (in μg/m3) and the coefficient of divergence (COD) are provided in parentheses. 

Beneath these two measures of spatial variability, the number of observations used in the 

calculations of the statistics in part c of each figure is given. 

Quality assured measurements from four monitoring sites for at least fifteen days during 

each calendar quarter for 1999, 2000, and 2001 (preferably) or for 2000 and 2001 at a minimum 

in a given MSA were required for their inclusion in the analyses presented in this appendix. The 

Columbia, SC and Baton Rouge, LA MSAs, which had only three sites meeting this criterion, 

are exceptions. Typically, at least 200 measurements were available for each monitoring site 

chosen. Monitoring sites were chosen without consideration of the land use type used to 

characterize their locations. 

Because of changes in monitoring strategies, funding levels etc., there were year to year 

changes in monitoring sites meeting the above criteria in a number of MSAs. Data for the MSAs 

in Philadelphia, PA, Norfolk, VA, Pittsburgh, PA, Detroit, MI, Chicago, IL, Louisville, KY, 

St. Louis, MO, and the Dallas, TX have been analyzed only for 2000 and 2001 because of a lack 

of consistent coverage in 1999. 

3A-1
Information about seasonal and spatial variability in PM10-2.5 concentrations within 

17 MSAs across the United States are provided in the accompanying figures (Figures 3A-28 to

3A-44). Underneath the value for r, the 90th percentile values of the absolute difference in PM2.5 

concentrations (in μg/m3) and the CODs are given in parentheses. Beneath these two measures 

of spatial variability, the number of observations used in the calculations of the statistics in part c 

of each figure is given. In order to maximize coverage, data were calculated for a number of 

sampling periods. Only Milwaukee, WI, and Salt Lake City, UT, had enough data for a 3 year 

average (1999 to 2001). Tampa, FL; Cleveland, OH; Steubenville, OH; Baton Rouge, LA; 

Portland, OR; and Riverside, CA had data for a 2 year average (2000 to 2001), as did Chicago, 

IL and Pittsburgh, PA (1999 to 2000). Other MSAs had only one year of data (for 2000 or 

2001). 

The COD was defined mathematically and used earlier in Chapter 3 as a measure of the 

degree of similarity between two data sets. A COD of zero implies that values in both data sets 

are identical, and a COD of one indicates that two data sets are completely different. Values 

of P90 provide a measure in absolute terms of differences in concentrations between sites, and 

CODs provide a relative measure of these differences. The maximum number of days of 

coincident data from paired sites were used to calculate correlation coefficients, values for P90, 

and CODs. The correlation coefficients were also calculated by using only concurrent 

measurements obtained at all of the monitoring sites within urban areas meeting the above 

selection criteria. The correlation coefficients that were calculated differed only in the third 

significant figure between the two methods. Metrics used above for characterizing differences 

between separated monitors are applied to collocated monitors in Table 3A-1. 

Figures 3A-28 to 3A-44 summarize information about the spatial and temporal variability 

of 24-h average PM10-2.5 concentrations. Data are shown for a subset of MSAs included in the 

analyses for PM2.5. Not all MSAs could be included, because of a lack of data for some. 

A schematic map showing locations of sampling sites within each MSA is given in part a, at the 

top of each figure. Also included in the map are major highways and a distance scale. A key 

giving the AIRS site ID numbers (#’s) is shown alongside each map. Box plots showing lowest, 

lower quartile, median, upper quartile and highest PM2.5 concentrations for each calendar quarter 

are shown in part b of each figure. AIRS site ID #’s, annual mean concentrations, the number of 

observations, and the standard deviation of the data are shown above the box plots. Finally, 

3A-2
in part c of each figure, statistics characterizing the spatial variability in PM2.5 concentrations are 

given. For each site-pair, the Pearson correlation coefficient (r) is provided. Underneath each 

value for r, the 90th percentile of the absolute difference in PM10-2.5 concentrations, the COD, and 

number of observations is given. In some cases, because of negative concentration values, the 

COD may not be calculated. Dashes are shown for such cases. 

It should be noted that the vertical axes for each MSA are drawn to different scales. 

Therefore, care should be taken in attempting to compare values in different MSAs. For PM2.5, 

the maxima on the y-axis vary by up to a factor of four, from 40 μg/m3

 to 150 μg/m3. The use of 

a single range of values would result in compression of the figures and loss of detail in a number 

of MSAs which have a lower range of values. Comparisons between MSAs can more easily be 

made by inspection of Figures 3-4a,b and 3-6a,b. 

REFERENCES 

Fitz-Simons, T. S.; Mathias, S.; Rizzo, M. (2000) Analyses of 1999 PM data for the PM NAAQS review. Research 

Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards; 

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Federal Reference Monitoring Network. Presented at: 94th annual conference & exhibition of the Air & 

Waste Management Association; June, Orlando, FL. Pittsburgh, PA: Air & Waste Management Association. 

3A-3
TABLE 3A-1. PERFORMANCE METRICS FOR PM2.5 FROM 

COLLOCATED SAMPLERS (Mean concentrations, the number of samples, the 

standard deviation at each site, and the Pearson correlation coefficient and the 

coefficient of divergence for the site pair are shown; concentrations are given in μg/m3

). 

Columbia, SC Sampler 1 Sampler 2 

45-079-0019 45-079-0019 

MEAN 15.3 15.5 

N 226 211 

SD 6.5 6.5 

r 0.995 

COD 0.022 

Dallas, TX Sampler 1 Sampler 2 

48-113-0069 48-113-0069 

MEAN 12.7 13.1 

N 687 116 

SD 5.7 6 

r 0.996 

COD 0.032 

Detroit, MI Sampler 1 Sampler 2 

26-163-0001 26-163-0001 

MEAN 16.5 16.2 

N 637 111 

SD 9.2 8.8 

r 0.986 

COD 0.041 

Grand Rapids, MI Sampler 1 Sampler 2 

26-081-0020 26-081-0020 

MEAN 14 14.3 

N 1050 181 

SD 8.6 8.3 

r 0.992 

COD 0.059 

Louisville, KY Sampler 1 Sampler 2 

18-043-1004 18-043-1004 

MEAN 15.8 16 

N 196 104 

SD 8.2 7.8 

r 0.997 

COD 0.027 

Steubenville, OH Sampler 1 Sampler 2 

54-029-0011 54-029-0011 

Mean 16.5 16.6 

Obs 342 325 

SD 10.2 10.3 

r 0.985 

COD 0.087 

Washington DC Sampler 1 Sampler 2 

51805 51805 

Mean 16.8 17.5 

Obs 600 132 

SD 9.7 11.1 

r 0.953 

COD 0.15 

3A-4
Figure 3A-1. Philadelphia, PA-NJ MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) for 

2000-2001; (c) Intersite correlation statistics for each data pair given as the 

correlation coefficient, (P90, COD), and number of measurements. 

3A-5 
Figure 3A-2. Washington, DC MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-6 
Figure 3A-3. Norfolk, VA MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-7 
Figure 3A-4. Columbia, SC MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-8 
Figure 3A-5. Atlanta, GA MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-9 
Figure 3A-6. Birmingham, AL MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-10 
Figure 3A-7. Tampa, FL MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-11 
Figure 3A-8. Cleveland, OH MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-12 
Figure 3A-9. Pittsburgh, PA MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-13 
Figure 3A-10. Steubenville, OH-Weirton, WV MSA. (a) Locations of sampling sites by 

AIRS ID#; (b) Quarterly distribution of 24-h average PM2.5 concentrations 

(μg/m3

) for 2000-2001; (c) Intersite correlation statistics for each data pair 

given as the correlation coefficient, (P90, COD), and number of 

measurements. 

3A-14 
Figure 3A-11. Detroit MI MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-15 
Figure 3A-12. Grand Rapids, MI MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-16 
Figure 3A-13. Milwaukee, WI MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-17 
Figure 3A-14. Chicago, IL MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-18 
Figure 3A-15. Gary, IN MSA. (a) Locations of sampling sites by AIRS ID#; (b) Quarterly 

distribution of 24-h average PM2.5 concentrations (μg/m3

) for 2000-2001; 

(c) Intersite correlation statistics for each data pair given as the correlation

coefficient, (P90, COD), and number of measurements. 

3A-19 
Figure 3A-16. Louisville, KY MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-20 
Figure 3A-17. St. Louis, MO MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-21 
Figure 3A-18. Baton Rouge, LA MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-22 
Figure 3A-19. Kansas City, KS-MO MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-23 
Figure 3A-20. Dallas, TX MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-24 
Figure 3A-21. Boise, ID MSA. (a) Locations of sampling sites by AIRS ID#; (b) Quarterly 

distribution of 24-h average PM2.5 concentrations (μg/m3

) for 2000-2001; 

(c) Intersite correlation statistics for each data pair given as the correlation

coefficient, (P90, COD), and number of measurements. 

3A-25 
Figure 3A-22. Salt Lake City, UT MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-26 
Figure 3A-23. Seattle, WA MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-27 
Figure 3A-24. Portland, OR MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-28 
Figure 3A-25. Los Angeles-Long Beach, CA MSA. (a) Locations of sampling sites by AIRS 

ID#; (b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-29 
Figure 3A-26. Riverside-San Bernadino, CA MSA. (a) Locations of sampling sites by 

AIRS ID#; (b) Quarterly distribution of 24-h average PM2.5 concentrations 

(μg/m3

) for 2000-2001; (c) Intersite correlation statistics for each data pair 

given as the correlation coefficient, (P90, COD), and number of 

measurements. 

3A-30 
Figure 3A-27. San Diego, CA MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation statistics for each data pair given 

as the correlation coefficient, (P90, COD), and number of measurements. 

3A-31 
Figure 3A-28. Columbia, SC MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2000; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-32 
Figure 3A-29. Tampa, FL MSA (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-33 
Figure 3A-30. Cleveland, OH MSA (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-34 
Figure 3A-31. Steubenville, OH MSA (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-35 
Figure 3A-32. Detroit, MI MSA (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2000; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-36 
Figure 3A-33. Milwaukee, WI MSA (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 1999-2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-37 
Figure 3A-34. Chicago, IL MSA (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 1999-2000; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-38 
Figure 3A-35. Gary, IN MSA (a) Locations of sampling sites by AIRS ID#; (b) Quarterly 

distribution of 24-h average PM10-2.5 concentrations (μg/m3

) for 2001; 

(c) Intersite correlation coefficients, (P90, COD), and number of 

observations. 

3A-39 
Figure 3A-36. Louisville, KY MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-40 
Figure 3A-37. St. Louis, MO MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2000; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-41 
Figure 3A-38. Baton Rouge, LA MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-42 
Figure 3A-39. Dallas, TX MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-43 
Figure 3A-40. Salt Lake City, UT MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 1999-2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-44 
Figure 3A-41. Portland, OR MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-45 
Figure 3A-42. Los Angeles, CA MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-46 
Figure 3A-43. Riverside, CA MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2000-2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-47 
Figure 3A-44. San Diego, CA MSA. (a) Locations of sampling sites by AIRS ID#; 

(b) Quarterly distribution of 24-h average PM10-2.5 concentrations (μg/m3

) 

for 2001; (c) Intersite correlation coefficients, (P90, COD), and number 

of observations. 

3A-48 
APPENDIX 3B

Aerosol Composition Data from the Speciation Network 

The U.S. Environmental Protection Agency (EPA), working with state and local air quality 

agencies, began implementing an ambient air monitoring network in 1999/2000 to provide a 

consistent data set for the characterization and evaluation of trends in PM components (chemical 

species). The network was designed to include about 52 core trends sites across the United 

States and to provide a stable ongoing national perspective. In 1999, an initial thirteen sites were 

installed and operated to be used as a model for the deployment of the more comprehensive 

network consisting of the 52 core trends sites and roughly 200 additional locally relevant sites. 

Data from the initial 13 sites were presented in the Third External Review Draft of this 

document, released by the EPA in 2001 for public comment and CASAC review. These sites 

were designed and operated to evaluate the suitability of various aerosol sampling devices for 

obtaining PM2.5 composition data, and data from those sites were summarized in Appendix 3B of 

the 2001 Third External Review Draft. Three types of collocated aerosol sampling devices were 

used to collect the data from February through July 2000, and data obtained from the three 

sampling devices were shown for each site. A complete description of the data, techniques used 

to analyze the filters, and the results of the evaluation of the performance of the sampling 

devices (including a number of caveats regarding the data) can be found in Coutant and Stetzer 

(2001) and the analyses of data in Coutant et al. (2001). 

More recent measurements of ambient levels of PM2.5 constituents are presented in the 

tables provided below. Table 3B-1 presents the locations and general sampling schedules for 

13 monitoring sites included for various reasons and representing a cross section of the country. 

Many of these sites were chosen because they are located in (or close to) MSAs in which risk 

assessments are to be performed. Sacramento, CA and Riverside, CA, for example, are located 

near San Francisco and Los Angeles, respectively. The data reported here are from the period 

October 2001 to September 2002. For this time period, a total of 51 sites (including both 

“Trends” and “non-Trends” sites) across the country have complete data (as defined by 50% of 

3B-1
observations available for every quarter for each of the major chemical species: sulfate, 

ammonium, nitrate, elemental carbon, organic carbon, and the five trace elements that go into 

the calculation of the crustal contribution to PM2.5 (Al, Fe, Ca, Ti, and Si). 

Summary statistics for concentrations of PM2.5 and chemical species are given for each of 

the 13 sites in Table 3B-2. The number of samples (n) and the AIRS site code are given above 

each table. Entries in the tables provide the mean, minimum, and maximum component 

concentrations, and minimum detection limits for each component. Minimum detection levels 

(MDL) differ among the various sampling methods; these limits were estimated by Research 

Triangle Institute (RTI) in July of 2001 and are subject to review, revision, and reinterpretation. 

Anions and cations (ammonium, nitrate, sodium, potassium, and sulfate) were determined by ion 

chromatography; carbonaceous species were determined by the thermal optical transmittance 

method (NIOSH method); and trace elements (aluminum through zirconium) were determined 

by X-ray fluorescence spectrometry. The sulfate (calculated) entry is based on the XRF 

determination of sulfur. In general, relatively good agreement is found between the 

reconstructed mass and the PM2.5 concentration measured by the collocated FRM monitor at 

each site. However, there are exceptions at several locations, as can be seen from inspection of 

Table 3B-2. Somewhat different sampling trains are used in the different sampling systems. 

These differences can result in differences in performance metrics and in differences in the 

entities that are measured. All samplers use a denuder in front of the filter for species to be 

analyzed by ion chromatography. In the RAAS and SASS samplers the denuder is followed by a 

nylon filter, whereas in the MASS samplers (Chicago, Houston, Seattle) a teflon filter is 

followed by a nylon filter. Particulate nitrate is collected on the teflon filter and is referred to as 

nonvolatile nitrate. However, there may be volatilization of nitrate containing compound from 

the teflon filter which is then adsorbed on the nylon filter. Nitrate extracted from the nylon filter 

is referred to as volatile nitrate. 

Organic carbon (OC) concentrations are multiplied by a factor of 1.4 when calculating 

mass to account for the presence of H, N, and O in organic compounds on all samplers. 

Carbonate carbon has never been detected in any of the samples. Field blank corrections that 

could be applied to elemental carbon (EC) and OC concentrations are shown in Table 3B-3 for 

different samplers. Blank corrections for OC and EC shown in Table 3B-3 were applied to

concentrations shown in Table 3B-2. However, subtracting blank corrections from OC 

concentrations results in negative values in several cases. A possible cause for results such as 

3B-2
these could have been that not enough blank samples were obtained to fully characterize the 

blank levels. Although the concentrations of 47 elements could be obtained by X-ray 

fluorescence spectrometry, the concentrations of many of these elements are beneath MDLs and 

are not shown. The same elements shown in Appendix 6A of the 1996 PM AQCD are shown 

here. The usual practice of denoting table entry values below MDL by (—) is followed here. 

Missing data for PM2.5 are also indicated by (—). Often, environmental data below MDL are 

represented by 0.5 x MDL for various purposes such as risk assessments and for mass closure. 

Unfortunately, speciation data are not available for the PM10-2.5 size fraction. 

REFERENCES 

Coutant, B.; Stetzer, S. (2001) Evaluation of PM2.5 speciation sampler performance and related sample collection and 

stability issues: final report. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of 

Air Quality Planning and Standards; report no. EPA-454/R-01-008. Available: 

http://www.epa.gov/ttn/amtic/pmspec.html [5 April, 2002]. 

Coutant, B.; Zhang, X.; Pivetz, T. (2001) Summary statistics and data displays for the speciation minitrends study: 

final report. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality 

Planning and Standards; contract no. 68-D-98-030. 

3B-3
TABLE 3B-1. PM2.5 SPECIATION SAMPLERS BY LOCATION: SITES SELECTED 

FOR SUMMARY IN 2004 PM AQCD 

Sampler Began Reason for 

Type Location_Name State Operation AIRS_Code POC Schedule Inclusion* 

SASS Burlington VT 12/14/2000 000500070012 5 1-in-3 3 

SASS Philadelphia PA 9/10/2001 000421010004 7 1-in-3 3 

(AMS Laboratory) 

RAAS Atlanta GA 3/2/2001 000130890002 5 1-in-3 2 

(South DeKalb) 

SASS Detroit (Allen Park) MI 12/14/2000 000261630001 5 1-in-3 1 

MASS Chicago (Com ED) IL 5/22/2001 000170310076 5 Alt 1-in-3 3 

RAAS St. Louis (Blair Street) MO 2/9/2000 000295100085 6 1-in-3 1 

MASS Houston (Deer Park) TX 2/9/2000 000482011039 37778 1-in-3 3 

SASS Minneapolis (Philips) MN 12/14/2000 000270530963 5 Alt 1-in-3 2 

SASS Boulder CO 2/18/2001 000080010006 5 Alt 1-in-3 3 

(Commerce City) 

SASS Phoenix (Supersite) AZ 2/21/2000 000040139997 7 1-in-3 1 

MASS Seattle (Beacon Hill) WA 2/9/2000 000530330080 6 1-in-3 3 

SASS Sacramento CA 2/9/2000 000060670006 5 1-in-3 2 

(Del Paso Manor) 

SASS Riverside-Rubidoux CA 5/13/2001 000060658001 37746 1-in-3 1 

* Reason for Inclusion:

1 = Completeness of geographic coverage 

2 = PM10 risk assessment city 

3 = PM2.5 and PM10 risk assessment city 

3B-4
TABLE 3B-2a. BURLINGTON, VT SUMMARY DATA

(October 2001 to September 2002).

All concentrations are given in μg/m3

; n = 201; AIRS Site Code: 500070012

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 10.0 61.0 1.0 — 

PM2.5 (Reconstructed Mass) 10.9 63.0 1.6 — 

Sulfate (Calculated) 2.6 17.7 0.001 — 

Sulfate (by I.C.) 2.8 18.8 0.17 0.012 

Ammonium (by I.C.) 1.1 6.8 — 0.017 

Sodium Ion (by I.C.) 0.22 2.1 — 0.030 

Potassium (by I.C.) 0.035 0.38 — 0.014 

Nitrate 1.3 8.8 — 0.008 

Volatile Nitrate — — — — 

Nonvolatile Nitrate — — — — 

Elemental Carbon 0.26 0.84 — 0.15 

Carbonate Carbon — — — 0.15 

Organic Carbon 2.5 31.4 !0.43 0.15 

Aluminum 0.020 0.28 — 0.011 

Arsenic — 0.005 — 0.002 

Barium — 0.12 — 0.059 

Bromine 0.002 0.007 — 0.002 

Cadmium — 0.014 — 0.011 

Calcium 0.035 0.19 — 0.003 

Chlorine — 0.12 — 0.006 

Chromium 0.002 0.060 — 0.002 

Copper 0.002 0.014 0.001 0.001 

Iron 0.041 0.390 0.007 0.002 

Lead — 0.016 — 0.006 

Magnesium — 0.19 — 0.018 

Manganese 0.002 0.007 — 0.002 

Molybdenum — 0.012 — 0.005 

Nickel 0.002 0.022 0.001 0.001 

Phosphorous — 0.092 — 0.006 

Potassium 0.041 0.41 — 0.003 

Rubidium — 0.003 — 0.002 

Selenium — 0.005 — 0.002 

Silicon 0.069 0.54 — 0.008 

Sodium 0.10 0.40 — 0.051 

Strontium — 0.004 — 0.003 

Sulfur 0.88 5.9 — 0.007 

Tin — 0.033 — 0.018 

Titanium 0.004 0.023 — 0.002 

Vanadium — 0.006 — 0.002 

Zinc 0.008 0.057 0.007 0.002 

3B-5
TABLE 3B-2b. PHILADELPHIA, PA SUMMARY DATA (October 2001 to

September 2002). All concentrations are given in μg/m3

; n = 262; 

AIRS Site Code: 421010004

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 14.2 86.8 2.1 — 

PM2.5 (Reconstructed Mass) 16.0 239.0 0.0 — 

Sulfate (Calculated) 4.3 29.0 0.004 — 

Sulfate (by I.C.) 4.4 30.5 0.020 0.012 

Ammonium (by I.C.) 2.0 11.4 — 0.017 

Sodium Ion (by I.C.) 0.21 1.6 — 0.030 

Potassium (by I.C.) 0.042 0.83 — 0.014 

Nitrate 2.1 8.7 0.050 0.008 

Volatile Nitrate 0.84 4.0 0.030 — 

Nonvolatile Nitrate 0.61 5.6 0.020 — 

Elemental Carbon 0.66 2.3 — 0.15 

Carbonate Carbon — — — 0.15 

Organic Carbon 3.1 50.5 !1.13 0.15 

Aluminum 0.019 0.54 — 0.011 

Arsenic — 0.006 — 0.002 

Barium — 0.12 — 0.059 

Bromine 0.004 0.013 — 0.002 

Cadmium — 0.017 — 0.011 

Calcium 0.037 0.18 — 0.003 

Chlorine 0.011 0.68 — 0.006 

Chromium 0.002 0.018 — 0.002 

Copper 0.004 0.025 0.003 0.001 

Iron 0.084 0.53 — 0.002 

Lead 0.005 0.025 — 0.005 

Magnesium — 0.30 — 0.018 

Manganese 0.002 0.009 — 0.002 

Molybdenum — 0.012 — 0.005 

Nickel 0.006 0.13 0.001 0.001 

Phosphorous — 0.039 — 0.006 

Potassium 0.053 1.1 — 0.003 

Rubidium — 0.003 — 0.002 

Selenium 0.002 0.005 — 0.002 

Silicon 0.086 1.1 — 0.008 

Sodium 0.087 0.63 — 0.051 

Strontium — 0.016 — 0.003 

Sulfur 1.45 9.6 — 0.007 

Tin — 0.035 — 0.018 

Titanium 0.006 0.049 — 0.002 

Vanadium 0.004 0.035 — 0.002 

Zinc 0.015 0.095 0.007 0.001 

3B-6
TABLE 3B-2c. ATLANTA, GA SUMMARY DATA (October 2001 to September 2002). 

All concentrations are given in μg/m3

; n = 183; AIRS Site Code: 130890002 

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) — — — — 

PM2.5 (Reconstructed Mass) 16.3 40.0 4.7 — 

Sulfate (Calculated) 4.8 14.5 0.27 — 

Sulfate (by I.C.) 4.8 15.2 0.88 0.011 

Ammonium (by I.C.) 1.3 4.2 — 0.015 

Sodium Ion (by I.C.) 0.27 1.7 — 0.028 

Potassium (by I.C.) 0.044 0.31 — 0.013 

Nitrate 0.70 3.5 0.16 0.008 

Volatile Nitrate — — — — 

Nonvolatile Nitrate — — — — 

Elemental Carbon 0.90 3.5 — 0.13 

Carbonate Carbon — — — 0.13 

Organic Carbon 4.3 11.2 0.8 0.13 

Aluminum 0.028 0.65 — 0.004 

Arsenic 0.001 0.014 — 0.001 

Barium — 0.054 — 0.024 

Bromine 0.003 0.009 — 0.001 

Cadmium — 0.006 — 0.004 

Calcium 0.037 0.28 0.006 0.001 

Chlorine 0.003 0.049 — 0.002 

Chromium — 0.003 — 0.001 

Copper 0.002 0.011 — 0.001 

Gold — 0.004 — 0.002 

Iron 0.084 0.47 0.013 0.001 

Lead 0.003 0.008 — 0.002 

Magnesium 0.008 0.16 — 0.007 

Manganese 0.002 0.011 — 0.001 

Molybdenum — 0.003 — 0.002 

Nickel — 0.002 — 0.001 

Phosphorous — 0.017 — 0.003 

Potassium 0.060 0.36 0.015 0.001 

Rubidium 0.005 0.002 — 0.001 

Selenium 0.001 0.004 0.000 0.001 

Silicon 0.11 1.4 0.010 0.003 

Sodium 0.056 0.31 — 0.021 

Strontium 0.001 0.005 0.001 0.001 

Sulfur 1.6 4.8 0.089 0.003 

Tin — 0.019 — 0.007 

Titanium 0.006 0.043 — 0.001 

Vanadium 0.001 0.003 — 0.001 

Zinc 0.008 0.034 0.001 0.001 

3B-7
TABLE 3B-2d. DETROIT, MI SUMMARY DATA (October 2001 to September 2002). 

All concentrations are given in μg/m3

; n = 189; AIRS Site Code: 261630001 

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 16.6 53.1 2.6 

PM2.5 (Reconstructed Mass) 18.0 52.9 3.2 — 

Sulfate (Calculated) 4.4 22.0 0.010 

Sulfate (by I.C.) 4.6 24.4 0.040 0.012 

Ammonium (by I.C.) 2.2 9.3 0.009 0.017 

Sodium Ion (by I.C.) 0.27 1.7 0.015 0.030 

Potassium (by I.C.) 0.061 1.2 0.007 0.014 

Nitrate 3.1 15.2 0.004 0.008 

Volatile Nitrate — — — — 

Nonvolatile Nitrate — — — — 

Elemental Carbon 0.68 3.7 — 0.15 

Carbonate Carbon — — — 0.15 

Organic Carbon 3.2 11.2 !0.46 0.15 

Aluminum 0.025 0.53 — 0.011 

Arsenic 0.002 0.010 — 0.002 

Barium — 0.11 — 0.059 

Bromine 0.003 0.011 — 0.002 

Cadmium — 0.014 — 0.011 

Calcium 0.069 0.33 — 0.003 

Chlorine 0.017 0.65 — 0.006 

Chromium 0.002 0.033 — 0.002 

Copper 0.006 0.043 0.001 0.001 

Iron 0.12 0.60 0.003 0.002 

Lead 0.006 0.034 — 0.005 

Magnesium 0.020 0.30 — 0.018 

Manganese 0.004 0.025 — 0.002 

Molybdenum — 0.013 — 0.005 

Nickel 0.002 0.022 0.001 0.001 

Phosphorous — 0.028 — 0.006 

Potassium 0.078 1.3 — 0.003 

Rubidium — 0.003 — 0.002 

Selenium 0.002 0.011 — 0.002 

Silicon 0.11 0.84 — 0.008 

Sodium 0.098 0.53 — 0.051 

Strontium — 0.021 — 0.003 

Sulfur 1.5 7.3 — 0.007 

Tin — 0.032 — 0.018 

Titanium 0.007 0.079 — 0.002 

Vanadium 0.002 0.012 — 0.002 

Zinc 0.025 0.19 0.001 0.001 

3B-8
TABLE 3B-2e. CHICAGO, IL SUMMARY DATA (October 2001 to September 2002). 

All concentrations are given in μg/m3

; n = 139; AIRS Site Code: 170310076 

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 15.7 42.7 3.6 — 

PM2.5 (Reconstructed Mass) 15.0 44.5 3.4 — 

Sulfate (Calculated) 4.3 22.8 0.64 — 

Sulfate (by I.C.) 4.2 21.2 0.56 0.005 

Ammonium (by I.C.) 1.9 8.8 0.17 0.007 

Sodium Ion (by I.C.) 0.052 0.26 — 0.012 

Potassium (by I.C.) 0.068 3.1 — 0.006 

Nitrate 2.0 10.2 0.12 0.003 

Volatile Nitrate 0.71 3.6 0.04 0.003 

Nonvolatile Nitrate 1.3 9.9 0.03 0.003 

Elemental Carbon 0.61 1.6 — 0.059 

Carbonate Carbon — — — 0.059 

Organic Carbon 2.8 7.4 0.31 0.059 

Aluminum 0.03 1.1 — 0.004 

Arsenic 0.001 0.006 0.001 0.001 

Barium — 0.22 — 0.024 

Bromine 0.003 0.011 — 0.001 

Cadmium — 0.007 — 0.004 

Calcium 0.058 0.55 0.10 0.001 

Chlorine 0.017 0.67 — 0.002 

Chromium 0.001 0.005 — 0.001 

Copper 0.004 0.056 — 0.001 

Iron 0.091 0.53 0.014 0.001 

Lead 0.006 0.040 — 0.002 

Magnesium 0.017 0.69 — 0.007 

Manganese 0.003 0.014 — 0.001 

Molybdenum — 0.006 — 0.002 

Nickel 0.001 0.007 — 0.001 

Phosphorous 0.002 0.015 — 0.003 

Potassium 0.085 3.1 0.005 0.001 

Rubidium — 0.001 — 0.001 

Selenium 0.001 0.004 0.001 0.001 

Silicon 0.11 2.2 0.010 0.003 

Sodium 0.048 0.58 — 0.021 

Strontium 0.001 0.061 0.001 0.001 

Sulfur 1.4 7.6 0.21 0.003 

Tin — 0.023 — 0.007 

Titanium 0.004 0.070 — 0.001 

Vanadium 0.001 0.002 — 0.001 

Zinc 0.023 0.093 0.001 0.001 

3B-9
TABLE 3B-2f. ST. LOUIS, MO SUMMARY DATA (October 2001 to September 2002). 

All concentrations are given in μg/m3

; n = 324; AIRS Site Code: 295100085 

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 15.6 52.1 3.8 — 

PM2.5 (Reconstructed Mass) 16.4 53.2 2.8 — 

Sulfate (Calculated) 4.3 22.5 0.61 — 

Sulfate (by I.C.) 4.3 20.6 0.64 0.011 

Ammonium (by I.C.) 1.9 7.2 0.14 0.015 

Sodium Ion (by I.C.) 0.19 1.6 0.014 0.028 

Potassium (by I.C.) 0.069 5.9 0.007 0.013 

Nitrate 2.3 14.0 0.20 0.008 

Volatile Nitrate — — — — 

Nonvolatile Nitrate — — — — 

Elemental Carbon 0.72 2.7 — 0.13 

Carbonate Carbon — — — 0.13 

Organic Carbon 3.5 11.5 — 0.13 

Aluminum 0.044 2.5 — 0.004 

Arsenic 0.002 0.011 0.001 0.001 

Barium 0.024 0.38 — 0.024 

Bromine 0.004 0.16 — 0.001 

Cadmium — 0.010 — 0.004 

Calcium 0.13 1.1 0.010 0.001 

Chlorine 0.027 0.57 — 0.002 

Chromium 0.002 0.034 — 0.001 

Copper 0.018 0.72 — 0.001 

Iron 0.24 1.3 0.012 0.001 

Lead 0.014 0.09 — 0.002 

Magnesium 0.011 0.79 — 0.007 

Manganese 0.014 0.13 — 0.001 

Molybdenum — 0.007 — 0.002 

Nickel 0.002 0.04 — 0.001 

Phosphorous 0.004 0.095 — 0.003 

Potassium 0.099 5.6 0.016 0.001 

Rubidium — 0.002 — 0.001 

Selenium 0.001 0.006 — 0.001 

Silicon 0.17 4.0 0.011 0.003 

Sodium 0.045 0.43 — 0.021 

Strontium 0.001 0.12 0.001 0.001 

Sulfur 1.4 7.5 0.20 0.003 

Tin 0.008 0.028 — 0.007 

Titanium 0.008 0.14 — 0.001 

Vanadium 0.001 0.011 — 0.001 

Zinc 0.033 0.48 0.002 0.001 

3B-10
TABLE 3B-2g. HOUSTON, TX SUMMARY DATA (October 2001 to September 2002). 

All concentrations are given in μg/m3

; n = 229; AIRS Site Code: 482011039 

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 12.4 21.9 5.9 — 

PM2.5 (Reconstructed Mass) 11.0 48.0 0.0 — 

Sulfate (Calculated) 3.4 24.5 0.004 — 

Sulfate (by I.C.) 3.5 26.0 0.030 0.005 

Ammonium (by I.C.) 1.2 8.8 — 0.007 

Sodium Ion (by I.C.) 0.22 1.1 — 0.012 

Potassium (by I.C.) 0.051 0.38 — 0.006 

Nitrate 0.68 5.8 0.040 0.003 

Volatile Nitrate 0.37 3.5 0.010 0.003 

Nonvolatile Nitrate 0.32 4.2 0.020 0.003 

Elemental Carbon 0.30 1.1 — 0.059 

Carbonate Carbon — — — 0.059 

Organic Carbon 2.2 12.1 — 0.059 

Aluminum 0.092 1.6 — 0.004 

Arsenic 0.001 0.006 0.001 0.001 

Barium — 0.059 — 0.024 

Bromine 0.003 0.022 — 0.001 

Cadmium — 0.008 — 0.004 

Calcium 0.055 0.44 0.001 0.001 

Chlorine 0.050 0.70 — 0.002 

Chromium 0.001 0.009 — 0.001 

Copper 0.003 0.024 — 0.001 

Iron 0.073 0.83 — 0.001 

Lead 0.002 0.007 — 0.002 

Magnesium 0.020 0.38 — 0.007 

Manganese 0.002 0.013 — 0.001 

Molybdenum 0.002 0.022 — 0.002 

Nickel 0.002 0.047 — 0.001 

Phosphorous 0.002 0.021 — 0.003 

Potassium 0.072 0.64 0.001 0.001 

Rubidium — 0.002 — 0.001 

Selenium 0.001 0.001 0.001 0.001 

Silicon 0.23 2.8 — 0.003 

Sodium 0.17 1.1 — 0.021 

Strontium 0.001 0.009 0.001 0.001 

Sulfur 1.1 8.2 — 0.003 

Tin — 0.019 — 0.007 

Titanium 0.007 0.091 — 0.001 

Vanadium 0.003 0.011 — 0.001 

Zinc 0.006 0.030 — 0.001 

3B-11
TABLE 3B-2h. MINNEAPOLIS, MN SUMMARY DATA (October 2001 to

September 2002). All concentrations are given in μg/m3

; n = 163; 

AIRS Site Code: 270530963

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 10.3 33.4 1.8 — 

PM2.5 (Reconstructed Mass) 12.4 37.7 2.2 — 

Sulfate (Calculated) 2.3 8.6 0.12 — 

Sulfate (by I.C.) 2.4 8.9 0.24 0.012 

Ammonium (by I.C.) 1.2 6.0 — 0.017 

Sodium Ion (by I.C.) 0.27 3.7 — 0.030 

Potassium (by I.C.) 0.059 1.7 — 0.014 

Nitrate 2.3 16.4 0.098 0.008 

Volatile Nitrate — — — — 

Nonvolatile Nitrate — — — — 

Elemental Carbon 0.39 1.8 — 0.15 

Carbonate Carbon — — — 0.15 

Organic Carbon 2.6 9.9 — 0.15 

Aluminum 0.028 0.67 — 0.011 

Arsenic 0.002 0.012 — 0.002 

Barium — 0.15 — 0.059 

Bromine 0.002 0.008 — 0.002 

Cadmium — 0.028 — 0.011 

Calcium 0.071 0.46 — 0.003 

Chlorine 0.008 0.17 — 0.006 

Chromium 0.002 0.016 — 0.002 

Copper 0.003 0.045 0.001 0.001 

Iron 0.065 0.37 0.016 0.002 

Lead 0.005 0.066 — 0.005 

Magnesium 0.018 0.52 — 0.018 

Manganese 0.002 0.009 — 0.002 

Molybdenum — 0.011 — 0.005 

Nickel 0.002 0.014 0.001 0.001 

Phosphorous — 0.11 — 0.006 

Potassium 0.068 1.8 0.005 0.003 

Rubidium — 0.002 — 0.002 

Selenium — 0.004 — 0.002 

Silicon 0.12 1.3 — 0.008 

Sodium 0.093 0.69 — 0.051 

Strontium — 0.026 — 0.003 

Sulfur 0.78 2.9 0.040 0.007 

Tin — 0.034 — 0.018 

Titanium 0.006 0.043 — 0.002 

Vanadium 0.002 0.021 — 0.002 

Zinc 0.008 0.039 0.001 0.001 

3B-12
TABLE 3B-2i. BOULDER, CO SUMMARY DATA (October 2001 to September 2002). 

All concentrations are given in μg/m3

; n = 161; AIRS Site Code: 080010006 

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 9.5 26.9 2.1 — 

PM2.5 (Reconstructed Mass) 11.3 28.2 3.7 — 

Sulfate (Calculated) 1.5 5.3 0.31 — 

Sulfate (by I.C.) 1.6 5.9 0.29 0.012 

Ammonium (by I.C.) 0.79 4.3 0.009 0.017 

Sodium Ion (by I.C.) 0.24 1.7 0.015 0.030 

Potassium (by I.C.) 0.032 0.29 0.007 0.014 

Nitrate 1.5 7.9 0.14 0.008 

Volatile Nitrate — — — — 

Nonvolatile Nitrate — — — — 

Elemental Carbon 1.0 4.4 0.15 0.15 

Carbonate Carbon — — — 0.15 

Organic Carbon 3.2 13.8 0.28 0.15 

Aluminum 0.092 0.41 — 0.011 

Arsenic — 0.005 — 0.002 

Barium — 0.12 — 0.059 

Bromine 0.002 0.011 — 0.002 

Cadmium — 0.016 — 0.011 

Calcium 0.12 0.55 0.008 0.003 

Chlorine 0.019 0.66 — 0.006 

Chromium 0.002 0.009 — 0.002 

Copper 0.004 0.013 0.001 0.001 

Iron 0.13 0.47 0.016 0.002 

Lead 0.005 0.036 — 0.005 

Magnesium 0.020 0.15 — 0.018 

Manganese 0.003 0.015 — 0.002 

Molybdenum — 0.010 — 0.005 

Nickel 0.001 0.010 0.001 0.001 

Phosphorous 0.006 0.096 — 0.006 

Potassium 0.063 0.33 — 0.003 

Rubidium — 0.003 — 0.002 

Selenium — 0.004 — 0.002 

Silicon 0.28 0.97 0.027 0.008 

Sodium 0.075 0.28 — 0.051 

Strontium — 0.005 — 0.003 

Sulfur 0.49 1.8 0.10 0.007 

Tin — 0.033 — 0.018 

Titanium 0.010 0.031 — 0.002 

Vanadium — 0.004 — 0.002 

Zinc 0.023 0.53 0.001 0.001 

3B-13
TABLE 3B-2j. PHOENIX, AZ SUMMARY DATA (October 2001 to September 2002). 

All concentrations are given in μg/m3

; n = 275; AIRS Site Code: 040139997 

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 9.9 32.1 3.3 — 

PM2.5 (Reconstructed Mass) 12.0 158.0 0.8 — 

Sulfate (Calculated) 1.2 2.9 0.001 — 

Sulfate (by I.C.) 1.3 3.8 — 0.012 

Ammonium (by I.C.) 0.5 2.6 — 0.017 

Sodium Ion (by I.C.) 0.28 1.9 — 0.030 

Potassium (by I.C.) 0.062 0.64 — 0.014 

Nitrate 1.1 6.9 — 0.008 

Volatile Nitrate — — — — 

Nonvolatile Nitrate — — — — 

Elemental Carbon 0.75 5.0 — 0.15 

Carbonate Carbon — — — 0.15 

Organic Carbon 4.0 21.0 !0.66 0.15 

Aluminum 0.12 0.87 — 0.011 

Arsenic 0.002 0.009 — 0.002 

Barium — 0.13 — 0.059 

Bromine 0.003 0.009 — 0.002 

Cadmium — 0.024 — 0.011 

Calcium 0.15 0.82 0.005 0.003 

Chlorine 0.047 0.40 — 0.006 

Chromium 0.002 0.021 — 0.002 

Copper 0.006 0.038 0.001 0.001 

Iron 0.17 0.95 0.003 0.002 

Lead — 0.020 — 0.005 

Magnesium 0.025 0.38 — 0.018 

Manganese 0.004 0.027 — 0.002 

Molybdenum — 0.011 — 0.005 

Nickel 0.003 0.13 — 0.001 

Phosphorous 0.006 0.13 — 0.006 

Potassium 0.11 0.69 — 0.003 

Rubidium — 0.003 — 0.002 

Selenium — 0.005 — 0.002 

Silicon 0.36 2.2 — 0.008 

Sodium 0.10 0.52 — 0.051 

Strontium 0.003 0.10 — 0.003 

Sulfur 0.39 0.98 — 0.007 

Tin — 0.038 — 0.018 

Titanium 0.013 0.058 — 0.002 

Vanadium — 0.005 — 0.002 

Zinc 0.009 0.080 0.001 0.001 

3B-14
TABLE 3B-2k. SEATTLE, WA SUMMARY DATA (October 2001 to September 2002). 

All concentrations are given in μg/m3

; n = 314; AIRS Site Code: 530330080 

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 8.2 29.1 1.9 — 

PM2.5 (Reconstructed Mass) 8.0 28.5 1.5 — 

Sulfate (Calculated) 1.4 4.9 0.057 — 

Sulfate (by I.C.) 1.3 4.6 0.14 0.005 

Ammonium (by I.C.) 0.47 2.0 — 0.007 

Sodium Ion (by I.C.) 0.16 0.76 0.012 0.012 

Potassium (by I.C.) 0.045 2.3 — 0.006 

Nitrate 0.67 2.9 0.12 0.003 

Volatile Nitrate 0.25 1.6 0.010 0.003 

Nonvolatile Nitrate 0.41 2.5 0.020 0.003 

Elemental Carbon 0.60 2.7 — 0.059 

Carbonate Carbon — — — 0.059 

Organic Carbon 2.6 11.0 — 0.059 

Aluminum 0.015 0.24 — 0.004 

Arsenic 0.001 0.007 0.001 0.001 

Barium — 0.15 — 0.024 

Bromine 0.002 0.014 — 0.001 

Cadmium — 0.007 — 0.004 

Calcium 0.029 0.20 0.001 0.001 

Chlorine 0.055 0.84 — 0.002 

Chromium 0.002 0.016 — 0.001 

Copper 0.003 0.045 — 0.001 

Iron 0.053 0.29 0.001 0.001 

Lead 0.004 0.078 — 0.002 

Magnesium 0.014 0.19 — 0.007 

Manganese 0.003 0.024 — 0.001 

Molybdenum 0.002 0.011 — 0.002 

Nickel 0.002 0.020 — 0.001 

Phosphorous — 0.028 — 0.003 

Potassium 0.055 2.3 0.007 0.001 

Rubidium — 0.002 — 0.001 

Selenium 0.001 0.008 0.001 0.001 

Silicon 0.049 0.52 — 0.003 

Sodium 0.15 0.84 — 0.021 

Strontium 0.001 0.044 0.001 0.001 

Sulfur 0.45 1.6 0.019 0.003 

Tin 0.007 0.030 — 0.007 

Titanium 0.003 0.026 — 0.001 

Vanadium 0.003 0.029 — 0.001 

Zinc 0.009 0.048 — 0.001 

3B-15
TABLE 3B-2l. SACRAMENTO, CA SUMMARY DATA (October 2001 to September 

2002). All concentrations are given in μg/m3

; n = 265; AIRS Site Code: 060670006 

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 9.4 78.0 3.0 — 

PM2.5 (Reconstructed Mass) 15.0 120.0 3.0 — 

Sulfate (Calculated) 1.2 5.5 0.14 — 

Sulfate (by I.C.) 1.3 4.1 0.06 0.012 

Ammonium (by I.C.) 0.74 7.4 0.009 0.017 

Sodium Ion (by I.C.) 0.38 3.5 — 0.030 

Potassium (by I.C.) 0.077 3.6 — 0.014 

Nitrate 2.3 23.4 — 0.008 

Volatile Nitrate — — — — 

Nonvolatile Nitrate — — — — 

Elemental Carbon 0.66 8.4 — 0.15 

Carbonate Carbon — — — 0.15 

Organic Carbon 5.2 54.1 0.18 0.15 

Aluminum 0.040 0.52 — 0.011 

Arsenic 0.002 0.006 — 0.002 

Barium — 0.26 — 0.059 

Bromine 0.002 0.013 — 0.002 

Cadmium — 0.018 — 0.011 

Calcium 0.043 0.56 0.005 0.003 

Chlorine 0.052 1.8 — 0.006 

Chromium 0.002 0.043 — 0.002 

Copper 0.006 0.16 0.001 0.001 

Iron 0.079 0.64 0.005 0.002 

Lead — 0.044 — 0.005 

Magnesium 0.021 0.88 — 0.018 

Manganese 0.002 0.013 — 0.002 

Molybdenum — 0.015 — 0.005 

Nickel 0.010 0.61 0.001 0.001 

Phosphorous 0.006 0.11 — 0.006 

Potassium 0.10 3.3 — 0.003 

Rubidium — 0.003 — 0.002 

Selenium — 0.004 — 0.002 

Silicon 0.12 1.1 0.011 0.008 

Silver — 0.015 — 0.010 

Sodium 0.19 1.7 — 0.010 

Strontium — 0.077 — 0.051 

Sulfur 0.41 1.8 0.045 0.003 

Tin — 0.035 0.009 0.007 

Titanium — 0.052 — 0.018 

Vanadium — 0.006 — 0.002 

Zinc 0.005 0.11 — 0.002 

3B-16
TABLE 3B-2m. RIVERSIDE-RUBIDOUX, CA SUMMARY DATA (October 2001 to

September 2002). All concentrations are given in μg/m3

; n = 161;

AIRS Site Code: 060658001

Parameter Mean Max Min MDL 

PM2.5 (FRM Mass) 28.6 78.2 2.6 — 

PM2.5 (Reconstructed Mass) 30.5 79.2 3.1 — 

Sulfate (Calculated) 3.6 9.6 0.23 — 

Sulfate (by I.C.) 3.7 10.0 0.28 0.012 

Ammonium (by I.C.) 4.8 16.8 — 0.017 

Sodium Ion (by I.C.) 0.49 1.8 — 0.030 

Potassium (by I.C.) 0.075 1.3 — 0.014 

Nitrate 12.3 40.3 0.18 0.008 

Volatile Nitrate — — — — 

Nonvolatile Nitrate — — — — 

Elemental Carbon 1.2 4.3 0.12 0.15 

Carbonate Carbon — — — 0.15 

Organic Carbon 6.0 13.0 0.38 0.15 

Aluminum 0.057 0.32 — 0.011 

Arsenic 0.002 0.006 — 0.002 

Barium — 0.17 — 0.059 

Bromine 0.006 0.016 — 0.002 

Cadmium — 0.017 — 0.011 

Calcium 0.17 1.0 0.012 0.003 

Chlorine 0.072 0.71 — 0.006 

Chromium 0.003 0.024 — 0.002 

Copper 0.006 0.037 0.001 0.001 

Iron 0.17 0.58 0.019 0.002 

Lead 0.006 0.026 — 0.005 

Magnesium 0.032 0.31 — 0.018 

Manganese 0.004 0.015 — 0.002 

Molybdenum — 0.024 — 0.005 

Nickel 0.002 0.014 0.006 0.001 

Phosphorous 0.007 0.067 — 0.006 

Potassium 0.11 1.31 0.017 0.003 

Rubidium — 0.003 — 0.002 

Selenium — — — 0.006 

Silicon 0.20 0.88 0.023 0.008 

Silver — 0.015 — 0.010 

Sodium 0.20 0.94 — 0.051 

Strontium — 0.026 — 0.003 

Sulfur 1.2 3.2 0.078 0.007 

Tin 0.014 0.081 — 0.018 

Titanium 0.012 0.041 — 0.002 

Vanadium 0.006 0.017 — 0.002 

Zinc 0.023 0.21 0.001 0.001 

3B-17
TABLE 3B-3. BLANK CORRECTIONS FOR ELEMENTAL CARBON (EC),

ORGANIC CARBON (OC), AND TOTAL CARBON (TC)

IN THE SPECIATION NETWORK

Elemental Carbon Organic Carbon Total Carbon 

Sampler 

Type 

EC Mass 

(μgC/filter) 

EC Conc 

(μgC/m3

) 

OC Mass 

(μgC/filter) 

OC Conc 

(μgC/m3

) 

TC Mass 

(μgC/filter) 

TC Conc 

(μgC/m3

) 

URG MASS 0.63 0.03 7.08 0.29 7.71 0.32 

R & P 2300 3.21 0.22 12.93 0.9 16.13 1.12 

Anderson 0.97 0.09 12.54 1.19 13.51 1.29 

RAAS 

R & P 2025 1.67 0.07 18.42 0.77 19.91 0.83 

MetOne SASS 1.03 0.11 13.75 1.42 14.78 1.53 

3B-18
APPENDIX 3C

Organic Composition of Particulate Matter 

Although organic compounds typically constitute approximately 10 to 70% of the total dry 

fine particle mass in the atmosphere, organic PM concentrations, composition, and formation 

mechanisms are poorly understood. This is because organic particulate matter is an aggregate of 

hundreds of individual compounds spanning a wide range of chemical and thermodynamic 

properties (Saxena and Hildemann, 1996). The presence of multiphase or “semivolatile” 

compounds complicates collection of organic particulate matter. Furthermore, no single 

analytical technique currently is capable of analyzing the entire range of compounds present. 

Rigorous analytical methods frequently identify only 10 to 20% of the organic mass on the 

molecular level (Rogge et al., 1993). The data shown in Appendix 3C are meant to complement 

the data given for the inorganic components of particles in Appendix 6A of the 1996 PM AQCD 

(U.S. Environmental Protection Agency, 1996). Table 3C-1 lists a number of recent urban 

and some rural measurements of particulate organic and elemental carbon in μg of carbon/m3 

(μg C/m3). Emphasis is placed on measurements published after 1995. The analysis method and 

artifact correction procedure, if any, are indicated. Table 3C-2 presents information on recent 

(post-1990) studies concerning concentrations (in ng C/m3

) of particulate organic compounds 

found at selected U.S. sites. 

3C-1
TABLE 3C-1. PARTICULATE ORGANIC AND ELEMENTAL CARBON CONCENTRATION

BASED ON STUDIES PUBLISHED AFTER 1995 

3C-2

OC Mean EC Mean TC Mean Avg. 

Reference Location Dates (Max) (Max) (Max) Time 

URBAN PM2.5 

Offenberg and Baker Chicago, IL July 1994; 2.2 (3.8) 12 h (2000) Jan 1995

 1.7 

Allen et al. (1999) Uniontown, PA July-Aug 1990 (0.8-8.4)a (0.4-3.5)a 3 h 

1.3 (3.1) 10 min 

Pedersen et al. (1999) Boston, MA Jan-Dec 1995 5.8 1.7 24 h 

Reading, MA (suburban) 4.0 0.7 Quabbin, MA (rural) 2.8 0.5 Rochester, NY (urban) 3.3 0.7 Brockport, NY (rural) 2.7 0.5

IMPROVE (2000) Washington, DC 1994-1998 3.4 1.1 24 h 

Seattle, WA 1.8 0.3

Lewtas et al. (2001) Seattle, WA Apr-May 1999 8 1.4 23 h 

Khwaja (1995) Schenectady, NY Oct 24-26, 1991 23.2 (49.9) 6 h 

Christoforou et al. Azusa, CA Jan-Dec 1993 9.4 1.3 24 h (2000) Long Beach, CA 8.9 1.8 

Central, LA 12.3 2.7 Rubidoux, LA 9.7 1.5 San Nicolas, LA 1.6 1.5

Turpin and Huntzicker Claremont, CA Jun-Sept na (29.4) na (9.0) 2 h (1995) Long Beach, CA Nov-Dec 1987 na (62.6) na (24.6) 2-6 h 
TABLE 3C-1 (cont’d). PARTICULATE ORGANIC AND ELEMENTAL CARBON CONCENTRATI

BASED ON STUDIES PUBLISHED AFTER 1995 

3C-3

OC Mean EC Mean TC Mean Avg. 

Reference Location Dates (Max) (Max) (Max) Time 

RURAL PM2.5 

Klinedinst and Currie Welby, CO Dec 1996-Jan 5.6 (13.4) 3.3 (8.1) 6 h (1999) Brighton, CO 1997 3.6 (6.4) 1.9 (3.6)

Andrews et al. (2000) Look Rock, Smoky Mountains, 

TN 

July-Aug 1995 2.2 

2.7 

0.4 0.1 

12 h (day) 

1.2 0.2 

Malm and Gebhart Tahoma Wood, WA June-Aug 1990 2.6 (7.4) 0.7 (2.2) 12 h (1996) 

IMPROVE (2000) Three Sisters Wilderness, OR 1994-1998 0.9 0.2 24 h 

Rocky Mountains, CO 1.0 0.2 Brigantine, NJ 2.0 0.5 Acadia, MA 1.2 0.2 Jefferson: James River Face 3.8 0.7  Wilderness, VA Glacier, MT 2.4 0.4

Hegg et al. (1997) 150 km East of Mid-Atlantic July 1996 2.9 (5.4) 

Coast (0.02-4 km altitude) 

Cui et al. (1997) Meadview, AZ Aug 6-15, 1992 3 12 h Chow et al. (1996) Point Reyes, CA July-Aug 1990 1.5 (2.7) 0.4 (0.6) 5-7 h 

Altamont Pass, CA 4.8 (7.2) 2.6 (3.9)Pacheco Pass, CA 3.2 (6.1) 1.0 (1.3)Crows Landing, CA 7.4 1.8 (2.5)Academy, CA (12.7) 1.4 (2.4)Button-Willow, CA 5.9 (8.7) 1.9 (2.7)Edison, CA 6.4 2.9 (4.1)Caliente, CA (10.6) 3.3 (4.4)Sequoia, CA 10.0 1.6 (3.0)Yosemite, CA (12.8) 1.9 (3.5)

 7.4 

(10.7)

 5.3 (7.0)

 12.1 

(25.8)
TABLE 3C-1 (cont’d). PARTICULATE ORGANIC AND ELEMENTAL CARBON CONCENTRAT

BASED ON STUDIES PUBLISHED AFTER 1995 

Reference Location Dates 

OC Mean 

(Max) 

EC Mean 

(Max) 

TC Mean 

(Max) 

Avg

Tim

RURAL PM2.5 

Malm and Day (2000) Grand Canyon, AZ July-Aug 1998 1.1 (1.6) 0.10 (0.3) 24 h PM10 Omar et al. (1999) Bondville, IL Jan-Dec 1994 2.6 0.2 24-48 

Gertler et al. (1995) Bullhead City, AZ Sept 1988-Oct 1989 6.0 (16.0) 1.9 (4.0) 24 h Chow et al. (1996) Santa Barbara, CA (urban) 

Santa Maria, CA (urban) Santa Ynez, CA (airport) Gaviota, CA (rural SB) Watt Road, CA (rural SB) Anacapa Island, CA 

Jan-Dec 1989 8.8 

4.6 3.5 3.4 2.1 3.1

 24 h 

Lioy and Daisey (1987) 

Newark, NJ 

1982: Summer Winter 

4.1 5.9 

3.0 3.3 

Elizabeth, NJ Summer 

Winter 

2.1 7.1 

1.7 2.3 

Camden, NJ Summer 

Winter 

2.2 5.2 

1.3 2.0 

3C-4

A limited amount of rural data is presented. In some cases, total carbon (TC = OC + EC) is reported. OC concentrations must be multiplied by the carbon weight to convert to mass of particulate organic compounds. The location and dates over which sampling occurred are provided. Averaging duration. Sampling method: Q – quartz fiber filter; QQ – two quartz fiber filters in series; Q+TQ – a quartz fiber filter in one port and a Teflon follparallel port; Imp – cascade impactor; DQQ – denuder followed by two quartz fiber filters; DQA – denuder followed by quartz fiber filter and adsorimpactor inlet followed by denuder, quartz filter, and adsorbent. Analysis method is reported as follows: TOR – thermal optical reflectance; TOT – transmittance; TMO – thermal MnO2 oxidation; EGA – evolved gas analysis; Th – Thermal analysis; Aeth – Aethalometer. na – data not available. aRange is provided. It should be noted that samples were collected only during elevated pollution episodes and are not representative of average conbParticulate OC was considered to be the sum of front and back quartz fiber filters.cSum of adsorbent and filter after correction for inlet losses and denuder efficiency.dCorrected for adsorption by subtracting the Teflon-quartz back-up filter.eReported concentrations are corrected for adsorption by subtracting the quartz (TQ or QQ) back-up filter.fSampler contained two quartz fiber filters in series, but publication did not indicate whether the quartz back-up filter was subtracted to correct for agCorrected for adsorption using Micro-Orifice Uniform Deposit Impactor (MOUDI) data from a collocated sampler.
TABLE 3C-2. PARTICULATE ORGANIC COMPOUND CONCENTRATIONS (in ng C/m3) BASE

PUBLISHED AFTER 1990 AT SELECTED SITES 

Rogge et al. (1993)a Schauer and Cass (2000) Khwaja (1995) Allen et al. 

Jan-Dec 1982 Dec 26-28, 1995 Veltkamp et al. (1996) October 1991 Summer 1

(annual average) (pollution episode) July 24-Aug 4, 1989 (semiurban) (urban

PM2.1 PM2.5 no precut no precut PM1.9 

Los Angeles, 

CA 

Pasadena, 

CA 

Fresno, 

CA 

Bakersfield, 

CA 

Niwot Ridge, 

CO 

Schenectady, 

NY 

Kenmore Sq

Boston, M

n-Alkanes n-tricosane 6.7 5.4 19.23 (57.7) n-tetracosane 6.4 4.7 42.3 12.7 6.04 (21.1) n-pentacosane 11.2 9.5 41.2 14.2 7.77 (21.3) n-hexacosane 8.2 4.3 29.9 10.7 2.08 (12.7) n-heptacosane 6.7 5.6 25.0 10.8 5.62 (15.1) n-octacosane 3.1 2.5 12.3 5.24 1.26 (9.0) n-nonacosane 7.1 4.7 33.8 23.6 7.70 (20.6) n-triacontane 2.7 2.5 7.39 4.27 0.76 (4.6) n-hentriacontane 12.6 9.6 16.1 9.66 5.24 (17.9) n-dotriacontane 1.5 1.5 2.61 3.50 0.41 (2.1) n-tritriacontane 2.1 2.3 5.02 3.31 1.49 (5.5) n-tetratriacontane 0.58 0.68 Total n-alkanes 68.9 53.3 215.6 98.0 57.9 n-Alkanoic Acids n-nonanoic acid 6.6 5.3 n-decanoic acid 2.0 2.4 0.711 0.164 n-undecanoic acid 2.8 6.0 n-dodecanoic acid 5.3 7.0 0.905 0.803 n-tridecanoic acid 4.3 4.9 6.17 1.78 n-tetradecanoic acid 19.7 22.2 9.42 4.01 n-pentadecanoic acid 5.3 6.1 33.7 5.63 n-hexadecanoic acid 140.5 127.4 166 54.4 (palmitic acid) n-heptadecanoic acid 4.7 5.2 13.6 3.77 n-octadecanoic acid 59.2 50.0 60.0 24.1 (stearic acid) n-nonadecanoic acid 1.1 1.1 10.7 2.58 n-eicosanoic acid 5.1 6.1 41.2 10.4 n-heneicosanoic acid 2.1 2.3 20.8 6.46 

3C-5
TABLE 3C-2 (cont’d). PARTICULATE ORGANIC COMPOUND CONCENTRATIONS (in ng C/m3) B

PUBLISHED AFTER 1990 AT SELECTED SITES 

3C-6

Rogge et al. (1993)a Schauer and Cass (2000) Khwaja (1995) Allen et al. (

Jan-Dec 1982 Dec 26-28, 1995 Veltkamp et al. (1996) October 1991 Summer 1

(annual average) (pollution episode) July 24-Aug 4, 1989 (semiurban) (urban

PM2.1 PM2.5 no precut no precut PM1.9 

Los Angeles, Pasadena, Fresno, Bakersfield, Niwot Ridge, Schenectady, Kenmore Sq

CA CA CA CA CO NY Boston, M

n-Alkanoic Acids (cont’d) n-docosanoic acid 8.7 9.9 160 43.1 n-tricosanoic acid 2.0 2.5 32.1 9.71 n-tetracosanoic acid 11.8 16.5 205 78.0 n-pentacosanoic acid 1.3 1.6 15.4 6.59 n-hexacosanoic acid 5.6 9.3 174 81.3 n-heptacosanoic acid 0.49 0.81 2.56 2.38 n-octacosanoic acid 2.7 4.9 21.3 9.65 n-nonacosanoic acid 0.33 0.57 1.46 2.11 n-triacontanoic acid 1.0 2.2 4.32 5.79 Total n-alcanoic acids 292.6 294.3 979.3 352.7 n-Alkenoic Acids n-9-hexadecenoic acid 18.8 3.96 n-9-octadecenoic acid 24.8 26.0 27.1 3.96 n-9,12-octadecane-dienoic 13.6 1.83 acid Total n-alkenoic acids 24.8 26.0 59.5 9.75 n-Alkanals 1-octanal 3.26 (14.4) n-nonanal 5.7 9.5 19.4 3.01 29.01 (62.8) n-decanal 23.58 (71.2) n-dodecanal 6.01 (16.4) n-tridecanal 6.50 (25.8) n-tetradecanal 9.62 (30.7) n-pentadecanal 12.47 (113.6) n-hexadecanal 17.45 (49.3) n-heptadecanal 24.09 (88.9) n-octadecanal 1.84 (11.7) Total n-alkanals 5.7 9.5 19.4 3.01 133.8 
TABLE 3C-2 (cont’d). PARTICULATE ORGANIC COMPOUND CONCENTRATIONS (in ng C/m3) B

PUBLISHED AFTER 1990 AT SELECTED SITES 

3C-7

Rogge et al. (1993)a Schauer and Cass (2000) Khwaja (1995) Allen et al

Jan-Dec 1982 Dec 26-28, 1995 Veltkamp et al. (1996) October 1991 Summer 

(annual average) (pollution episode) July 24-Aug 4, 1989 (semiurban) (urba

PM2.1 PM2.5 no precut no precut PM1

Los Angeles, Pasadena, Fresno, Bakersfield, Niwot Ridge, Schenectady, Kenmore S

CA CA CA CA CO NY Boston, 

n-Alkanols 1-decanol 8.66 (64.1) 1-dodecanol 21.29 (61.7) 1-tetradecanol 13.59 (41.4) 1-pentadecanol 4.50 (30.1) 1-hexadecanol 27.42 (141.1) Total n-alkanols 75.5 Aliphatic Dicarboxylic Acids oxalic acid (C2) 198 (360) 

malonic acid 32.7 44.4 84 (107) (propanedioic) methylmalonic acid 2.13 nd (methylpropanedioic) malonic acid 0.66 1.3 (2-butenedioic) succinic acid 66.5 51.2 102 (167) (butanedioic) methylsuccinic acid 18 15.0 24.0 8.80 (methylbutanedioic) glutaric acid 32.3 28.3 21.3 10.5 (pentanedioic) methylglutaric acid 19.3 16.6 (methylpentanedioic) hydroxybutanedioic acid 14.3 16.0 adipic acid 14.1 14.1 3.39 3.07 (hexanedioic) pimelic acid (heptanedioic) 2.22 1.03 suberic acid 3.4 4.1 4.41 13.4 (octanedioic) axelaic acid (nonanedioic) 29.0 22.8 19.9 8.22 Total aliphaitc 230.3 213.8 77.4 45.0 384 dicarboxylic acids 
TABLE 3C-2 (cont’d). PARTICULATE ORGANIC COMPOUND CONCENTRATIONS (in ng C/m3) 

PUBLISHED AFTER 1990 AT SELECTED SITES 

3C-8

Rogge et al. (1993)a Schauer and Cass (2000) Khwaja (1995) Allen et 

Jan-Dec 1982 Dec 26-28, 1995 Veltkamp et al. (1996) October 1991 Summ

(annual average) (pollution episode) July 24-Aug 4, 1989 (semiurban) (ur

PM2.1 PM2.5 no precut no precut P

Los Angeles, Pasadena, Fresno, Bakersfield, Niwot Ridge, Schenectady, Kenmor

CA CA CA CA CO NY Bosto

Ketocarboxylic Acids pyruvic acid (C3) 59 (103) 

glyoxylic acid (C2) 44 (68) 

Total ketocarboxylic acids 103 Diterpenoid/Resin Acids dehydroabietic acid 23.6 22.6 98.5 8.01 abietic acid 30.4 0.784 13-isopropyl-5"-podocarpa- 0.63 1.2 

6,8,11,13-tetraen-16-oic acid 8,15-pimaradien-18-oic acid 0.44 0.57 0.48 0.03 pimaric acid 2.3 4.8 9.97 0.735 isopimaric acid 1.3 2.3 127 7.95 7-oxodehydroabietic acid 3.4 4.1 6.68 1.43 abieta-6,8,11,13,15-pentaen- 11.8 2.43 18-oic acid abieta-8,11,13,15-tetraen- 2.62 0.251 18-oic acid sandaracopimaric acid 1.6 2.2 8.91 0.525 Total diterpenoid acids 33.3 37.6 296.4 22.15 Aromatic Polycarboxylic Acids 1,2-benzene-dicarboxylic 60.0 55.7 9.16 6.78 acid (phthalic acid) 1,3-benzene-dicarboxylic 3.4 2.9 3.41 1.98 acid 
TABLE 3C-2 (cont’d). PARTICULATE ORGANIC COMPOUND CONCENTRATIONS (in ng C/m3) B

PUBLISHED AFTER 1990 AT SELECTED SITES 

3C-9

Rogge et al. (1993)a Schauer and Cass (2000) Khwaja (1995) Allen et a

Jan-Dec 1982 Dec 26-28, 1995 Veltkamp et al. (1996) October 1991 Summe

(annual average) (pollution episode) July 24-Aug 4, 1989 (semiurban) (urb

PM2.1 PM2.5 no precut no precut PM

Los Angeles, Pasadena, Fresno, Bakersfield, Niwot Ridge, Schenectady, Kenmore 

CA CA CA CA CO NY Boston

Aromatic Polycarboxylic Acids (cont’d) 1,4-benzene-dicarboxylic 2.8 1.5 5.16 4.48 acid benzene tricarboxylic acids 14.4 8.77 4-methyl-1,2- 27.8 28.8 benzenedicarboxylic acid 1,2,4-benzene-tricarboxylic 0.52 0.84 acid (trimellitic acid) 1,3,5-benzene-tricarboxylic 20.6 17.2 acid (trimesic acid) 1,2,4,5-benzene- 0.74 0.80 tetracarboxylic acid (pyromellitic acid) Total aromatic 115.9 107.7 32.1 22.0 polycarboxylic acids Polycyclic Aromatic Hydrocarbons retene 0.07 0.06 6.02 0.563 fluoranthene 0.15 0.13 2.52 0.553 acephenanthrylene 0.834 0.302 pyrene 0.26 0.17 3.28 0.564 C1-202 MW PAH 11.7 3.80 

C2-202 MW PAH benz[a]anthracene 0.29 0.25 13.8 2.49 

cyclopenta[cd]pyrene 0.23 0.41 1.90 0.496 benzo[ghi]-fluoranthene 0.39 0.30 6.05 1.25 

C1-226 MW PAH 10.1 1.48 

chrysene/triphenyline 0.61 0.43 7.70 1.50 
TABLE 3C-2 (cont’d). PARTICULATE ORGANIC COMPOUND CONCENTRATIONS (in ng C/m3) 

PUBLISHED AFTER 1990 AT SELECTED SITES 

3C-10

Rogge et al. (1993)a Schauer and Cass (2000) Khwaja (1995) Allen et a

Jan-Dec 1982 Dec 26-28, 1995 Veltkamp et al. (1996) October 1991 Summe

(annual average) (pollution episode) July 24-Aug 4, 1989 (semiurban) (urb

PM2.1 PM2.5 no precut no precut PM

Los Angeles, Pasadena, Fresno, Bakersfield, Niwot Ridge, Schenectady, Kenmore 

CA CA CA CA CO NY Boston

Polycyclic Aromatic Hydrocarbons (cont’d) C1 -228 MW PAH 17.6 5.35 

C2 -228 MW PAH benz[e]acephen-anthrylene benzo[k]fluoranthene 1.15 1.20 8.69 2.13 

benzo[b]fluoranthene 1.23 0.85 10.7 2.48 benzo[j]fluoranthene 3.62 0.499 

benzo[e]pyrene 0.97 0.93 7.20 1.98 

benzo[a]pyrene 0.42 0.44 8.23 1.77 perylene 1.50 0.246 methyl-substituted 252 MW PAH indeno[1,2,3-cd]-pyrene 0.37 0.42 6.84 2.56 indeno[1,2,3-cd]-fluoranthene 1.05 1.09 1.36 0.764 benzo[ghi]perylene 4.47 4.43 9.75 3.49 

anthanthrene 0.180 0.131 coronene Total polycyclic aromatic 11.66 11.10 139.57 34.40 hydrocarbons 

Oxygenated PAHs/ Polycyclic Aromatic Ketones/Quinones 1,4-naphthoquinone 1-acenaphthenone 9-fluorenone 1,8-naphthalic anhydride phenanthrenequinone phenalen-9-one anthracene-9,10-dione methylanthracene-9,10-dione 11H-benzo[a]fluoren-11-one 
TABLE 3C-2 (cont’d). PARTICULATE ORGANIC COMPOUND CONCENTRATIONS (in ng C/m3) 

PUBLISHED AFTER 1990 AT SELECTED SITES 

3C-11

Rogge et al. (1993)a Schauer and Cass (2000) Khwaja (1995) Allen et a

Jan-Dec 1982 Dec 26-28, 1995 Veltkamp et al. (1996) October 1991 Summe

(annual average) (pollution episode) July 24-Aug 4, 1989 (semiurban) (urb

PM2.1 PM2.5 no precut no precut PM

Los Angeles, Pasadena, Fresno, Bakersfield, Niwot Ridge, Schenectady, Kenmore 

CA CA CA CA CO NY Boston

Oxygenated PAHs/ Polycyclic Aromatic Ketones/Quinones (cont’d) 7H-benzo[c]fluoren-7-one 0

11H-benzo[b]fluoren-11-one 0

1H-phenalen-1-one 7.96 0.588 benzanthrone 15,12-naphthacene-quinone 07H-benz[de]-anthracen-7-one 0.81 0.84 7.80 1.48 

benz[de]anthracene-7-dione 

benz[a]anthracene-7,12-dione 0.21 0.25 

cyclopenta[def]phen-anthrone 

benzo[cd]pyren-6-one 0.80 1.24 6H-benzo[cd]pyrene-6-one 1benzo[a]pyrene-6,12-dione 0Total polycyclic aromatic 1.82 2.33 15.76 2.07 9ketones/quinones 

Steroids cholesterol nd 1.9 Substituted Phenols p-benzenediol 3.46 nd m-benzenediol 7.59 nd hydroxybenzaldehydes 2.64 0.604 Total substituted phenols 13.69 0.604 
TABLE 3C-2 (cont’d). PARTICULATE ORGANIC COMPOUND CONCENTRATIONS (in ng C/m3) 

PUBLISHED AFTER 1990 AT SELECTED SITES 

Rogge et al. (1993)a Schauer and Cass (2000) Khwaja (1995) Allen et 

Jan-Dec 1982 Dec 26-28, 1995 Veltkamp et al. (1996) October 1991 Summ

(annual average) (pollution episode) July 24-Aug 4, 1989 (semiurban) (ur

PM2.1 PM2.5 no precut no precut PM

Los Angeles, 

CA 

Pasadena, 

CA 

Fresno, 

CA 

Bakersfield, 

CA 

Niwot Ridge, 

CO 

Schenectady, 

NY 

Kenmor

Bosto

Guaiacol and Substituted Guaiacols guaiacol 0.889 0.832 4-methylguaiacol 0.606 0.387 trans-isoeugenol 1.45 1.04 vanillin 26.8 6.05 acetovanillone 3.23 0.705 guaiacyl acetone 10.8 4.29 coniferyl aldehyde 47.0 nd Total guaiacol and 90.78 13.30 substituted guaiacols Syringol and Substituted Syringols syringol 1.16 0.845 4-methylsyringol 1.72 1.77 4-ethylsyringol 2.28 2.39 4-propylsyringol 0.871 nd 4-propenylsyringol 4.38 1.40 syringaldehyde 135 44.5 acetosyringone 171 55.7 acetonylsyringol 406 68.1 propionylsyringol 32.1 16.2 butyrylsyringol 15.3 6.18 sinapyl aldehyde 15.9 Total syringol and 785.7 197.1 substituted syringols Sugars levoglucosan 7590 1100 other sugars 1070 171 Total sugars 8660 1271 

3C-12
TABLE 3C-2 (cont’d). PARTICULATE ORGANIC COMPOUND CONCENTRATIONS (in ng C/m

STUDIES PUBLISHED AFTER 1990 AT SELECTED SITES 

3C-13

Rogge et al. (1993)a Schauer and Cass (2000) Khwaja (1995) Allen et a

Jan-Dec 1982 Dec 26-28, 1995 Veltkamp et al. (1996) October 1991 Summe

(annual average) (pollution episode) July 24-Aug 4, 1989 (semiurban) (urb

PM2.1 PM2.5 no precut no precut PM

Los Angeles, Pasadena, Fresno, Bakersfield, Niwot Ridge, Schenectady, Kenmore 

CA CA CA CA CO NY Boston

Other Compounds divanillyl 19.4 3.18 divanillyl methane 2.39 nd vanillylmethylguaiacol 3.24 0.568 Total other 25.0 3.75 N-Containing Compounds 3-methoxypyridine 0.86 1.4 isoquinoline 1.1 1.1 1-methoxypyridine 0.27 0.24 1,2-dimethoxy-4-nitro-benzene 1.8 3.9 dihydroxynitrobenzene Total N-containing 4.03 6.64 compounds Total Quantified Organic 789 764 11410 2075 267 487 10 Compound Mass Total Organic Compound 55700 18700 Mass Percent of Organic Mass 8-15% (a) 8-15% (a) 20% 11% <3% QuantifiedPercent of Organic Mass 45-60% (a) 45-60% (a) 30% 21% Extractable and ElutableMean values are provided with maximum concentrations in parentheses. 

a Rogge et al. (1993) summarized these percentages for all four Los Angeles Basin sampling sites (West LA, Downtown LA, Pasadena, and Rubidoux). Only Downtow

 are shown here. 
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oxygenated polycyclic aromatic hydrocarbons associated with a size-segregated urban aerosol. Environ. Sci. 

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Allen, G. A.; Lawrence, J.; Koutrakis, P. (1999) Field validation of a semi-continuous method for aerosol black 

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Andrews, E.; Saxena, P.; Musarra, S.; Hildemann, L .M.; Koutrakis, P.; McMurry, P. H.; Olmez, I.; White, W. H. 

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Chow, J. C.; Watson, J. G.; Lu, Z.; Lowenthal, D. H.; Frazier, C. A.; Solomon, P. A.; Thuillier, R. H.; Magliano, K. 

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Christoforou, C. S.; Salmon, L. G.; Hannigan, M. P.; Solomon, P. A.; Cass, G. R. (2000) Trends in fine particle 

concentration and chemical composition in southern California. J. Air Waste Manage. Assoc. 50: 43-53. 

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during the project MOHAVE summer intensive study. J. Air Waste Manage. Assoc. 47: 357-369. 

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organics. 5. C6-C22 nonpolar and semipolar aromatic compounds. Environ. Sci. Technol. 32: 1760-1770. 

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Klinedinst, D. B.; Currie, L. A. (1999) Direct quantification of PM2.5 fossil and biomass carbon within the northern 

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3C-14
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organic aerosols at Niwot Ridge, Colorado. J. Geophys. Res. [Atmos.] 101: 19,495-19,504. 

3C-15
APPENDIX 3D

Composition of Particulate Matter Source Emissions 

This appendix includes discussions of the elemental composition of emissions from various 

source categories discussed in Table 3-8. Discussions in this appendix incorporate material 

dealing with the inorganic components of source emissions from Chapter 5 of the 1996 PM 

AQCD (U.S. Environmental Protection Agency, 1996), provides updates to that material, and 

adds material describing the composition of organic components in source emissions. Primary 

emphasis is placed in the discussions on the composition of PM2.5 sources. 

Soil and Fugitive Dust 

The compositions of soils and average crustal material are shown in Table 3D-1 (adapted

from Warneck, 1988). Two entries are shown as representations of average crustal material. 

Differences from the mean soil composition shown can result from local geology and climate. 

Major elements in both soil and crustal profiles are Si, Al, and Fe, which are found in the form 

of various minerals. In addition, organic matter constitutes a few percent, on average, of soils. 

In general, the soil profile is similar to the crustal profiles, except for the depletion of soluble 

elements such as Ca, Mg, Na, and K. It should be noted that the composition of soils from 

specific locations can vary considerably from these global averages, especially for elements like 

Ca, Mg, Na, and K. 

Fugitive dust emissions arise from paved and unpaved roads, building construction and 

demolition, parking lots, mining operations, storage piles, feed lots, grain handling, and 

agricultural tilling in addition to wind erosion. Figure 3D-1 shows examples of size distributions 

in dust from paved and unpaved roads, agricultural soil, sand and gravel, and alkaline lake bed 

sediments, which were measured in a laboratory resuspension chamber as part of a study in 

California (Chow et al., 1994). This figure shows substantial variation in particle size among 

some of these fugitive dust sources. The PM1.0 abundance (6.9%) in the total suspended PM 

(TSP) from alkaline lake bed dust is twice its abundance in paved and unpaved road dust. 

Approximately 10% of the TSP is in the PM2.5 fraction and ~50% of TSP is in the PM10 fraction. 

3D-1
TABLE 3D-1. AVERAGE ABUNDANCES OF MAJOR ELEMENTS IN SOIL AND

CRUSTAL ROCK

Elemental Abundances (ppm) 

Element 

Soil 

(1) 

Crustal Rock 

(2) (3) 

Si 330,000 277,200 311,000 

Al 71,300 81,300 77,400 

Fe 38,000 50,000 34,300 

Ca 13700 36,300 25,700 

Mg 6,300 20,900 33,000 

Na 6,300 28,300 31,900 

K 13,600 25,900 29,500 

Ti 4,600 4,400 4,400 

Mn 850 950 670 

Cr 200 100 48 

V 100 135 98 

Co 8 25 12 

Source: (1) Vinogradov (1959); (2) Mason (1966); (3) Turekian (1971), Model A; as quoted in Warneck (1988). 

Figure 3D-1. Size distribution of particles generated in a laboratory resuspension 

chamber. 

Source: Chow et al. (1994). 

3D-2 
The sand/gravel dust sample shows that 65% of the mass is in particles larger than the PM10 

fraction. The PM2.5 fraction of TSP is ~30 to 40% higher in alkaline lake beds and sand/gravel 

than in the other soil types. The tests were performed after sieving and with a short (< 1 min) 

waiting period prior to sampling. It is expected that the fraction of PM1.0 and PM2.5 would 

increase with distance from a fugitive dust emitter, as the larger particles deposit to the surface 

faster than do the smaller particles. 

The size distribution of samples of paved road dust obtained from a source characterization 

study in California is shown in Figure 3D-2. As might be expected, most of the emissions are in 

the coarse size mode. The chemical composition of paved road dust obtained in Denver, CO 

during the winter of 1987-1988 is shown in Figure 3D-3. The chemical composition of paved 

road dust consists of a complex mixture of PM from a wide variety of sources. Hopke et al. 

(1980) found that the inorganic composition of urban roadway dust in samples from Urbana, IL 

could be described in terms of contributions from natural soil, automobile exhaust, rust, tire 

wear, and salt. Automobile contributions arose from: exhaust emissions enriched in Pb; Fe as 

rust; tire wear particles enriched in Zn; brake linings enriched in Cr, Ba, and Mn; and cement 

particles derived from roadways by abrasion. In addition to organic compounds from 

combustion and secondary sources, road dust also contains biological material such as pollen 

and fungal spores. 

Very limited data exist for characterizing the composition in organic compounds found in 

resuspended paved road dust and soil dust. The only reported measurements are from Rogge 

et al. (1993a) and Schauer and Cass (2000), which consist of data for the fine particle fraction. 

The resuspended road dust sample analyzed by Rogge et al. (1993a) was collected in Pasadena, 

CA during May of 1988. The sample analyzed by Schauer and Cass (2000) is a composite 

sample collected at several sites in the Central Valley of California in 1995. In both cases, road 

dust samples were resuspended in the laboratory. Samples were drawn through a PM2.0 cyclone 

upstream of the collection substrate to remove particles with aerodynamic diameters > 2.0 μm. 

It is unclear if these samples are representative of road dust in other locations of the United 

States. Table 3D-2 summarizes the organic compounds measured in these road dust samples. 

3D-3
 Figure 3D-2. Size distribution of California source emissions, 1986. 

Source: Houck et al. (1989, 1990). 

3D-4
Figure 3D-3. Chemical abundances for PM2.5 emissions from paved road dust in Denver, 

CO. Solid bars represent fractional abundances, and the error bars 

represent variability in species abundances. Error bars represent detection 

limits when there are no solid bars. 

Source: Watson and Chow (1994). 

TABLE 3D-2. SUMMARY OF PARTICLE-PHASE ORGANIC COMPOUNDS 

PRESENT IN FINE PARTICLE ROAD DUST SAMPLE 

Source Compound Class 

Contribution to 

Particulate Mass (%) 

Dominant Contributors to 

Emissions of Compound Class 

Pasadena Road Dust 

(Rogge et al., 1993a) 

n-Alkanes 

n-Alkanoic acids 

0.13 

0.37 

C17, C19, C21 

Palmitic acid and stearic acid 

n-Alkenoic acids 0.028 Oleic acid and linoleic Acid 

Petroleum biomarkers 0.017 Hopanes and steranes 

PAH 0.0059 No dominant compounds 

n-Alkanals 0.046 Octacosanol and triacontanal 

n-Alkanols 0.021 Hexacosanol and octacosanol 

San Joaquin Valley 

Road Dust (Schauer 

and Cass, 2000) 

n-Alkanes 

n-Alkanoic acids 

n-Alkenoic acids 

0.023 

0.23 

0.095 

No dominant compounds 

Palmitic acid and stearic acid 

Oleic acid, linoleic acid, and 

hexadecenoic acid 

3D-5
Stationary Sources 

The elemental composition of primary PM emitted in the fine fraction from a variety of 

power plants and industries in the Philadelphia area is shown in Table 3D-3 as a representative

example of emissions from stationary fossil-fuel combustion sources (Olmez et al., 1988). 

Entries for the coal fired power plant show that Si and Al, followed by sulfate, are the major 

primary constituents produced by coal combustion, whereas fractional abundances of elemental 

carbon (EC) were much lower and organic carbon (OC) species were not detected. Sulfate is the 

major particulate constituent released by the oil-fired power plants examined in this study; and, 

again, EC and OC are not among the major species emitted. Olmez et al. (1988) also compared 

their results to a number of similar studies and concluded that their data should have much wider 

applicability to receptor model studies in other areas with some of the same source types. The 

high temperature of combustion in power plants results in the almost complete oxidation of the 

carbon in the fuel to CO2 and very small amounts of CO. Combustion conditions in smaller 

boilers and furnaces allow the emission of unburned carbon and sulfur in more reduced forms 

such as thiophenes and inorganic sulfides. A number of trace elements are greatly enriched over 

crustal abundances in different fuels, such as Se in coal and V, Zn, and Ni in oil. In fact, the 

higher V content of the fuel oil than in coal could help account for the higher sulfate seen in the 

profiles from the oil-fired power plant compared to the coal-fired power plant, because V at 

combustion temperatures found in power plants is known to catalyze the oxidation of reduced 

sulfur species. During combustion at lower temperatures, the emission of reduced sulfur species 

also occurs. For example, Huffman et al. (2000) identified sulfur species emitted by the 

combustion of several residual fuel oils (RFO) in a fire tube package boiler that is meant to 

simulate conditions in small institutional and industrial boilers. They found that sulfur was 

emitted not only as sulfate (26 to 84%), but as thiophenes (13 to 39%) with smaller amounts of 

sulfides and elemental S. They also found that Ni, V, Fe, Cu, Zn, and Pb are present mainly as 

sulfates in emissions. Linak et al. (2000) found, when burning RFO, that the fire tube package 

boiler produced particles with a bimodal size distribution in which ~0.2% of the mass was 

associated with particles < 0.1 μm AD, with the rest of the mass lying between 0.5 and 100 μm. 

Miller et al. (1998) found that larger particles consisted mainly of cenospheric carbon; whereas 

trace metals and sulfates were found concentrated in the smaller particles in a fire tube package 

boiler. In contrast, when RFO was burning in a refractory-lined combustor that is meant to 

3D-6
TABLE 3D-3. COMPOSITION OF FINE PARTICLES RELEASED BY VARIOUS STATIONAR

IN THE PHILADELPHIA AREA

Eddystone Oil-Fired Power Plants 

Species Coal-Fired Secondary Fluid Cat. (Units) Power Plant N Eddystone N Schuylkill N Al Plant N Cracker N 

C-v (%) ND 2.7 ± 1.2 3 0.75 ± 0.63 4 1.6 ± 1.5 2 ND C-e (%) 0.89 ± 0.12 3 7.7 ± 1.5 3 0.22 ± 0.17 4 0.18 ± 0.10 2 0.16 ± 0.05 3 NH4 (%) 1.89 ± 0.19 3 3.5 ± 1.6 3 3.7 ± 1.7 4 2.2 ± 0.9 2 0.43 ± 0.22 3 

Na (%) 0.31 ± 0.03 3 3.0 ± 0.8 3 3.3 ± 0.8 3 16.3 ± 0.8 1 0.38 ± 0.05 3 Al (%) 14 ± 2 3 0.45 ± 0.09 3 0.94 ± 0.08 3 1.74 ± 0.09 1 6.8 ± 1.2 3 Si (%) 21.8 ± 1.6 9 1.9 ± 0.6 9 2.6 ± 0.4 11 3.1 ± 2.2 2 9.8 ± 20.0 9 P (%) 0.62 ± 0.10 9 1.5 ± 0.4 9 1.0 ± 0.2 11 0.45 ± 0.27 2 ND S (%) 3.4 ± 0.6 9 11 ± 2 9 13 ± 1 11 3 ± 4 2 4.2 ± 12.6 9 SO4 (%) 11.9 ± 1.2 3 40 ± 4 3 45 ± 7 4 5.9 ± 2 2 38 ± 4 3 

Cl (%) 0.022 ± 0.11 3 0.019 ± 0.009 2 ND 21 ± 4 1 ND K (%) 1.20 ± 0.09 9 0.16 ± 0.05 9 0.21 ± 0.03 11 10.9 ± 1.5 2 0.031 ± 0.005 9 Ca (%) 1.4 ± 0.5 3 3.6 ± 1.0 3 2.3 ± 1.0 3 0.12 ± 0.09 2 0.030 ± 0.004 9 Sc (ppm) 42 ± 2 3 0.17 ± 0.02 3 0.47 ± 0.02 3 0.092 ± 0.039 1 2.7 ± 0.4 3 Ti (%) 1.1 ± 0.2 3 0.040 ± 0.044 9 0.12 ± 0.02 11 0.024 ± 0.003 2 0.38 ± 0.1 3 V (ppm) 550 ± 170 3 11,500 ± 3000 3 20,000 ± 3000 3 36 ± 7 1 250 ± 70 3 Cr (ppm) 390 ± 120 3 235 ± 10 3 230 ± 70 3 410 ± 20 1 59 ± 8 3 Mn (ppm) 290 ± 15 3 380 ± 40 3 210 ± 50 3 120 ± 15 1 14 ± 3 3 Fe (%) 7.6 ± 0.4 3 1.6 ± 0.2 3 1.7 ± 0.4 3 0.31 ± 0.02 1 0.20 ± 0.03 9 Co (ppm) 93 ± 10 3 790 ± 150 3 1100 ± 200 3 13 ± 2 1 15 ± 2 3 Ni (ppm) 380 ± 50 9 15,000 ± 5000 9 19,000 ± 2000 11 300 ± 100 2 220 ± 30 9 

3D-7
TABLE 3D-3 (cont'd). COMPOSITION OF FINE PARTICLES RELEASED BY VARIOUS STA

SOURCES IN THE PHILADELPHIA AREA 

Eddystone Oil-Fired Power Plants 

Species Coal-Fired Secondary Fluid Cat. (units) Power Plant N Eddystone N Schuylkill N Al Plant N Cracker N 

Cu (ppm) 290 ± 20 9 980 ± 320 9 1100 ± 500 11 450 ± 200 2 14 ± 8 9 Zn (%) 0.041 ± 0.005 3 1.3 ± 0.3 3 0.78 ± 0.30 3 0.079 ± 0.006 1 0.0026 ± 0.0007 3 As (ppm) 640 ± 80 3 33 ± 6 1 50 ± 16 3 15 ± 6 1 ND Se (ppm) 250 ± 20 3 26 ± 9 3 23 ± 7 3 66 ± 3 1 15 ± 1 3 Br (ppm) 35 ± 8 3 90 ± 60 9 45 ± 17 11 630 ± 70 2 5.6 ± 1.8 9 Rb (ppm) 190 ± 80 1 ND ND 97 ± 38 1 ND Sr (ppm) 1290 ± 60 9 160 ± 50 9 280 ± 70 11 ND 36 ± 6 9 Zr (ppm) 490 ± 190 9 140 ± 180 9 100 ± 120 11 ND 130 ± 50 2 Mo (ppm) 170 ± 60 2 930 ± 210 3 1500 ± 300 3 ND ND Ag (ppm) ND ND ND ND ND Cd (ppm) ND ND ND ND ND In (ppm) 0.71 ± 0.04 2 ND ND ND ND Sn (ppm) ND 320 ± 230 9 200 ± 80 11 550 ± 540 2 ND Sb (ppm) 

a 

370 ± 410 3 1020 ± 90 3 6100 ± 300 1 7.7 ± 1.5 3 

Cs (ppm) 9.2 ± 0.9 2 ND ND ND ND Ba (ppm) ND 1960 ± 100 3 2000 ± 500 3 ND 290 ± 90 2 La (ppm) 120 ± 10 3 130 ± 30 3 450 ± 30 3 19 ± 2 1 3300 ± 500 3 Ce (ppm) 180 ± 10 2 89 ± 23 3 360 ± 20 3 ND 2700 ± 400 3 Nd (ppm) 80 ± 26 3 28 ± 5 2 230 ± 20 3 ND 1800 ± 250 3 Sm (ppm) 23 ± 2 3 3.7 ± 0.7 3 20.5 ± 1.5 3 ND 170 ± 20 3 

3D-8
TABLE 3D-3 (cont'd). COMPOSITION OF FINE PARTICLES RELEASED BY VARIO

STATIONARY SOURCES IN THE PHILADELPHIA AREA 

Eddystone Oil-Fired Power Plants 

Species Coal-Fired Secondary Fluid Cat. (units) Power Plant N Eddystone N Schuylkill N Al Plant N Cracker N 

Eu (ppm) 5.1 ± 0.5 3 ND 0.65 ± 0.23 3 ND 4.9 ± 0.7 3 Gd (ppm) ND ND ND ND 71 ± 10 3 Tb (ppm) 3.3 ± 0.3 3 ND 0.90 ± 0.29 3 ND 8.9 ± 1.3 3 Yb (ppm) 10.3 ± 0.5 1 ND ND ND 3.7 ± 0.4 3 Lu (ppm) ND ND ND ND 0.59 ± 0.17 3 Hf (ppm) 5.8 ± 0.8 3 0.39 ± 0.07 1 ND ND 0.99 ± 0.08 3 Ta (ppm) ND ND ND ND 0.56 ± 0.10 3 W (ppm) 20 ± 8 1 60 ± 5 2 ND ND ND Au (ppm) ND 0.054 ± 0.017 2 ND ND ND Pb (%) 0.041 ± 0.004 9 1.8 ± 0.6 9 1.0 ± 0.2 11 0.081 ± 0.014 2 0.0091 ± 0.0021 9 Th (ppm) 24 ± 2 3 1.9 ± 0.5 2 ND ND 6.2 ± 0.7 3 % mass 24 ± 2 6 93.5 ± 2.5 6 96 ± 2 6 81 ± 10 2 97 ± 2 7 

3D-9

a Omitted because of sample contamination. N = Number of samples.ND = Not detected.The “% mass” entries give the average percentage of the total emitted mass found in the fine fraction.1 ppm = 10-4%

Source: Adapted from Olmez et al. (1988). 
simulate combustion conditions in a large utility residual oil fired boiler, Linak et al. (2000) 

found that particles were distributed essentially unimodally, with a mean diameter of about 

0.1 μm. 

Apart from emissions in the combustion of fossil fuels, trace elements are emitted as the 

result of various industrial processes such as steel and iron manufacturing and nonferrous metal 

production (e.g., for Pb, Cu, Ni, Zn, and Cd). As may be expected, emissions factors for the 

various trace elements are highly source-specific (Nriagu and Pacyna, 1988). Inspection of 

Table 3D-3 reveals that the emissions from the catalytic cracker and the oil-fired power plant are 

greatly enriched in rare-earth elements (e.g., La) compared to other sources. 

Emissions from municipal waste incinerators are heavily enriched in Cl, arising mainly 

from the combustion of plastics and metals that form volatile chlorides. The metals can 

originate from cans or other metallic objects, and some metals such as Zn and Cd are also used 

as additives in plastics or rubber. Many elements such as S, Cl, Zn, Br, Ag, Cd, Sn, In, Sb, and 

Pb are enormously enriched compared to their crustal abundances. A comparison of the trace 

elemental composition of incinerator emissions in Philadelphia, PA (shown in Table 3D-3) with 

those in Washington, DC and Chicago, IL (Olmez et al., 1988) shows agreement for most 

constituents to within less than a factor of two. 

Very limited data exist for characterizing the chemical composition of organic compounds 

present in particulate emissions from industrial-scale stationary fuel combustion. Oros and 

Simoneit (2000) have reported on the abundance and distribution of organic constituents in coal 

smokes burned under laboratory conditions. Their work provides the basis for further 

investigation addressing the emissions of coal fired boilers. 

Rogge et al. (1997a) measured the composition of the organic constituents in the PM 

emissions from a 50 billion kj/h boiler that was operating at 60% capacity and was burning 

Number 2 distillate fuel oil. The fine carbon PM emissions from this boiler across five tests 

were composed of an average of 14% OC and 86% EC (Hildemann et al., 1991). Significant 

variability in the distribution of organic compounds present in the emissions from two separate 

tests was observed. Most of the identified organic mass consisted of n-alkanonic acids, aromatic 

acids, n-alkanes, PAH, oxygenated PAH, and chlorinated compounds. It is unclear if these 

emissions are representative of typical fuel oil combustion units in the United States. Rogge 

3D-10
et al. (1997b) measured the composition of hot asphalt roofing tar pots, and Rogge et al. (1993b) 

measured the composition of emissions from home appliances that use natural gas. 

Motor Vehicles 

Particulate emissions in exhaust from gasoline- and diesel-powered vehicles have changed 

significantly over the past 25 years (Sawyer and Johnson, 1995; Cadle et al., 1999). These 

changes have resulted from reformulation of fuels, the wide application of exhaust-gas treatment 

in gasoline-powered motor vehicles, and changes in engine design and operation. Because of 

these evolving tailpipe emissions, along with the wide variability of emissions between vehicles 

of the same class (Hildemann et al., 1991; Cadle et al., 1997; Sagebiel et al., 1997; Yanowitz 

et al., 2000), well-defined average emissions profiles for the major classes of motor vehicles 

have not been established. Two sampling strategies have been employed to obtain motor vehicle 

emissions profiles: (1) the measurement of exhaust emissions from vehicles operating on 

dynamometers and (2) the measurement of integrated emissions of motor vehicles driving 

through roadway tunnels. Dynamometer testing can be used to measure vehicle emissions 

operating over an integrated driving cycle and allows the measurement of emissions from 

individual vehicles. However, dynamometer testing requires considerable resources and usually 

precludes testing a very large number of vehicles. In contrast, a large number of vehicles can be 

readily sampled in tunnels; however, vehicles driving through tunnels operate over limited 

driving conditions, and the measurements represent contributions from a large number of vehicle 

types. As a result, except in a few cases, tunnel tests have not been effective at developing 

chemically speciated PM emissions profiles for individual motor vehicle classes. Rather, several 

studies have measured the contribution of both OC and EC to the PM emissions from different 

classes of motor vehicles operating on chassis dynamometers. 

The principal PM components emitted by diesel- and gasoline-fueled vehicles are OC and 

EC as shown in Tables 3D-4a and 4b. As can be seen, the variability among entries for an 

individual fuel type is large and overlaps that found between different fuel types. On average, 

the abundance of EC is larger than that of OC in the exhaust of diesel vehicles, whereas OC is 

the dominant species in the exhaust of gasoline-fueled vehicles. Per vehicle mile, total carbon 

emissions from light and heavy duty diesel vehicles can range from 1 to 2 orders of magnitude 

higher than those from gasoline vehicles. 

3D-11
TABLE 3D-4a. ORGANIC AND ELEMENTAL CARBON FRACTIONS OF DIESEL 

AND GASOLINE ENGINE PARTICULATE MATTER EXHAUST 

Engine Type Organic Carbon Elemental Carbon 

Heavy-duty diesela 19 ± 8% 75 ± 10% 

Heavy-duty diesel (SPECIATE) b 21 - 36% 52 - 54% 

Light-duty diesel c 30 ± 9% 61 ± 16% 

Light-duty diesel (SPECIATE) b 22 - 43% 51 - 64% 

Gasoline (hot stabilized) a 56 ± 11% 25 ± 15% 

Gasoline (“smoker” and “high emitter”) a,c 76 ± 10% 7 ± 6% 

Gasoline (cold start) a 46 ± 14% 42 ± 14% 

a Fujita et al. (1998) and Watson et al. (1998).

b SPECIATE database (U.S. Environmental Protection Agency, 1999).

c Norbeck et al. (1998).

Source: U.S. Environmental Protection Agency (2002). 

As might be expected, most of the PM emitted by motor vehicles is in the PM2.5 size range. 

Particles in diesel exhaust are typically trimodal (consisting of a nuclei mode, an accumulation 

mode, and a coarse mode) and are log-normal in form (Kittelson, 1998). More than 90% of the 

total number of particles are in the nuclei mode, which contains only about 1 to 20% of the 

particle mass with a mass median diameter of about 0.02 μm, whereas the accumulation mode 

(with a mass median diameter of about 0.25 μm) contains most of the mass with a smaller 

fraction (5 to 20%) contained in the coarse mode. Kerminin et al. (1997), Bagley et al. (1998), 

and Kleeman et al. (2000) also have shown that gasoline- and diesel-fueled vehicles produce 

particles that are mostly < 2.0 μm in diameter. Cadle et al. (1999) found that 91% of PM emitted 

by in-use gasoline vehicles in the Denver area was in the PM2.5 size range, which increased to 

97% for “smokers” (i.e., light-duty vehicles with visible smoke emitted from their tailpipes) and 

98% for light-duty diesels. Durbin et al. (1999) found that about 92% of the PM was < 2.5 μm 

for smokers and diesels. The mass median diameter of the PM emitted by the gasoline vehicles 

sampled by Cadle et al. (1999) was ~0.12 μm and increased to ~0.18 μm for smokers and 

diesels. Corresponding average emissions rates of PM2.5 found by Cadle et al. (1999) were 

552 mg/mile for diesels; 222 mg/mile for gasoline smokers; and 38 mg/mile for other gasoline 

3D-12
TABLE 3D-4b. CONTRIBUTION OF ORGANIC CARBON TO PARTICULATE 

MATTER CARBON EMISSIONS IN MOTOR VEHICLE EXHAUST COLLECTED 

FROM VEHICLES OPERATED ON CHASSIS DYNAMOMETERS 

GASOLINE POWERED VEHICLES 

Light-duty vehicles 

High-CO/VOC-emitting smokers 

High-CO/VOC-emitting nonsmokers 

Catalyst-equipped vehicles 

Noncatalyst vehicles 

DIESEL VEHICLES 

Light-duty diesel vehicles 

Medium-duty diesel vehicles 

Heavy-duty diesel vehicles 

Heavy-duty diesel vehicles 

Year of Test Number of OC % of 

Tests Cycle Vehicles Total Carbon Notes 

1996-97 FTP 195 a 70 A 

1994 IM-240 7 91 B 

1994 IM-240 15 76 B 

Mid-1980s FTP 7 69 C 

Mid-1980s FTP 6 89 C 

1996-1997 FTP 195 a 40 A 

1996 FTP 2 50 b D 

1992 c 6 42 E 

Mid-1980s c 

2 45 C 

Notes: 

A. From Cadle et al. (1999). Average of summer and winter cold start emissions. 

B. From Sagebiel et al. (1997). Hot start testing of vehicles identified as either high emitters of carbon

monoxide or volatile organic compounds (VOCs).

C. From Hildemann et al. (1991). Cold start tests. 

D. From Schauer et al. (1999). Hot start tests of medium duty vehicles operating on an FTP cycle. 

E. From Lowenthal et al. (1994). Only includes measurement of vehicles powered by diesel fuel operated

without an exhaust particulate trap. 

a A total of 195 light-duty vehicles were tested that include both gasoline- and diesel-powered vehicles. b Fraction of PM consisting of OC was measured with and without an organics denuder upstream of particulate

 filter. Results reported here represent measurement without an organics denuder for consistency with other

 measurements. Using an organics denuder, the OC comprised 39% of the PM carbon. c Driving cycle comprised of multiple idle, steady acceleration, constant speed, deceleration steps (see reference

 for more details). 

vehicles. The values for gasoline smokers and for diesels appear to be somewhat lower than 

those given in Table 3D-5, whereas the value for other gasoline vehicles falls in the range given

for low and medium gasoline vehicle emissions. 

Examples of data for the trace elemental composition of the emissions from a number of 

vehicle classes obtained December 1997 in Colorado, as part of the North Frontal Range Air 

3D-13
TABLE 3D-5. EMISSION RATES (mg/mi) FOR CONSTITUENTS OF PARTICULATE 

MATTER FROM GASOLINE AND DIESEL VEHICLES 

Gasoline Vehicles 

Low Medium High Smoker 

TC 9.07 ± 0.75 41.30 ± 1.68 207.44 ± 7.29 456.38 ± 16.80 373.43 ± 13.75 1570.69 ± 58.24 

OC 6.35 ± 0.54 26.02 ± 1.31 95.25 ± 4.28 350.24 ± 15.27 132.01 ± 5.82 253.94 ± 16.12 

EC 2.72 ± 0.52 15.28 ± 0.99 112.19 ± 5.82 106.14 ± 5.42 241.42 ± 12.11 1316.75 ± 55.33 

NO3 

! 0.039 ± 0.027 0.057 ± 0.028 0.141 ± 0.031 0.964 ± 0.051 1.474 ± 0.071 1.833 ± 1.285 

SO4 

2! 0.158 ± 0.036 0.518 ± 0.043 0.651 ± 0.052 2.160 ± 0.137 2.902 ± 0.165 3.830 ± 1.286 

Na 0.060 ± 0.063 0.023 ± 0.111 0.052 ± 0.092 0.000 ± 0.000 0.000 ± 0.000 1.288 ± 2.160 

Mg 0.036 ± 0.022 0.068 ± 0.027 0.041 ± 0.033 0.000 ± 0.000 0.000 ± 0.000 1.061 ± 0.729 

Al 0.083 ± 0.016 0.078 ± 0.016 0.057 ± 0.014 0.000 ± 0.000 0.000 ± 0.000 0.321 ± 0.543 

Si 0.066 ± 0.008 0.279 ± 0.011 0.714 ± 0.012 0.000 ± 0.000 0.000 ± 0.000 8.018 ± 0.221 

P 0.035 ± 0.004 0.152 ± 0.007 0.113 ± 0.007 0.000 ± 0.000 0.000 ± 0.000 0.407 ± 0.136 

S 0.085 ± 0.006 0.442 ± 0.009 0.822 ± 0.022 2.515 ± 0.116 2.458 ± 0.124 3.717 ± 0.111 

Cl 0.024 ± 0.012 0.038 ± 0.012 0.081 ± 0.020 0.140 ± 0.117 0.228 ± 0.114 0.881 ± 0.221 

K 0.010 ± 0.009 0.019 ± 0.009 0.031 ± 0.035 0.033 ± 0.386 0.000 ± 0.426 0.064 ± 0.248 

Ca 0.060 ± 0.010 0.212 ± 0.011 0.210 ± 0.030 0.362 ± 0.250 0.150 ± 0.304 0.716 ± 0.107 

Fe 0.143 ± 0.004 0.756 ± 0.005 1.047 ± 0.010 2.438 ± 0.054 0.515 ± 0.057 0.376 ± 0.055 

Ni 0.001 ± 0.004 0.005 ± 0.004 0.011 ± 0.005 0.008 ± 0.017 0.014 ± 0.018 0.002 ± 0.057 

Cu 0.002 ± 0.004 0.016 ± 0.003 0.021 ± 0.005 0.071 ± 0.018 0.024 ± 0.021 0.001 ± 0.062 

Zn 0.048 ± 0.003 0.251 ± 0.004 0.265 ± 0.023 0.188 ± 0.272 0.000 ± 0.299 0.707 ± 0.032 

Br 0.001 ± 0.002 0.016 ± 0.002 0.079 ± 0.003 0.047 ± 0.012 0.003 ± 0.014 0.012 ± 0.050 

Ba 0.013 ± 0.136 0.009 ± 0.138 0.011 ± 0.299 0.380 ± 2.175 0.428 ± 2.390 0.493 ± 3.108 

Pb 0.007 ± 0.006 0.085 ± 0.005 0.255 ± 0.008 0.345 ± 0.032 0.153 ± 0.033 0.008 ± 0.154 

Diesel Vehicles 

Light Duty Heavy Duty 

Source: Lawson and Smith (1998). 

Quality Study (NFRAQS), are shown in Table 3D-5. As can be seen from Table 3D-5, 

emissions of total carbon (TC), which is equal to the sum of organic carbon (OC) and elemental 

carbon (EC), from gasoline vehicles are highly variable. Gillies and Gertler (2000) point out that 

there is greater variability in the concentrations of trace elements and ionic species than for OC 

3D-14
and EC among different source profiles, e.g., SPECIATE (U.S. Environmental Protection 

Agency, 1999; Lawson and Smith, 1998; Norbeck et al., 1998). They suggest that this may arise 

because emissions of trace elements are not related only to the combustion process, but also 

to their abundances in different fuels and lubricants and to wear and tear during vehicle 

operation. Emissions from gasoline smokers are comparable to those from light-duty diesel 

vehicles. Thus, older, poorly maintained gasoline vehicles could be significant sources of PM2.5 

(Sagebiel et al., 1997; Lawson and Smith, 1998), in addition to being significant sources of 

gaseous pollutants (e.g., Calvert et al., 1993). Durbin et al. (1999) point out that although 

smokers constitute only 1.1 to 1.7% of the light-duty fleet in the South Coast Air Quality 

Management District in California, they contribute roughly 20% of the total PM emissions from 

the light-duty fleet. In general, motor vehicles that are high emitters of hydrocarbons and CO 

also will tend to be high emitters of PM (Sagebiel et al.,1997; Cadle et al., 1997). Particle 

emission rates, even in newer vehicles, also are correlated with vehicle acceleration; and 

emissions occur predominantly during periods of heavy acceleration (Maricq et al., 1999). 

Although the data shown in Table 3D-5 indicate that sulfur (mainly in the form of sulfate) 

is a minor component of PM2.5 emissions, sulfur (S) may be the major component of the ultrafine 

particles emitted by either diesel or internal combustion engines (Gertler et al., 2000). It is not 

clear what the source of the small amount of Pb seen in the auto exhaust profile is. It is very 

difficult to find suitable tracers for automotive exhaust, because Pb has been removed from 

gasoline. 

Several tunnel studies have measured the distribution of OC and EC in the integrated 

exhaust of motor vehicle fleets comprising several classes of motor vehicles (Pierson and 

Brachaczek, 1983; Weingartner et al., 1997a; Fraser et al., 1998a). The study by Fraser et al. 

(1998a) found that OC constituted 46% of the carbonaceous PM emissions from the vehicles 

operating in the Van Nuys tunnel in Southern California in the Summer of 1993. Although 

diesel vehicles constituted only 2.8% of the vehicles measured by Fraser et al. (1998a), the 

contribution of OC to the total particulate carbon emissions obtained in the Van Nuys tunnels is 

in reasonable agreement with the dynamometer measurements shown in Table 3D-4b. 

Very few studies have reported comprehensive analyses of the organic composition of 

motor vehicle exhaust. The measurements by Rogge et al. (1993c) are the most comprehensive 

but are not expected to be the best representation of current motor vehicle emissions, because 

3D-15
these measurements were made in the mid-1980s. Measurements reported by Fraser et al. 

(1999) were made in a tunnel study conducted in 1993 and represent integrated gasoline and 

diesel powered vehicle emissions. In addition, exhaust emissions from two medium-duty diesel 

vehicles operating over an FTP cycle were analyzed by Schauer et al. (1999). A unique feature 

of both the measurements by Fraser et al. (1999) and Schauer et al. (1999) is that they include 

the quantification of unresolved complex mixture (UCM), which comprises aliphatic and cyclic 

hydrocarbons that cannot be resolved by gas chromatography (GC) (Schauer et al., 1999). 

Schauer et al. (1999) have shown that all of the organic compound mass in their diesel exhaust 

samples could be extracted and eluted by CG/MS techniques even though not all of the organic 

compound mass can identified on a single compound basis. Table 3D-6 summarizes the 

composition of motor vehicle exhaust measured by Fraser et al. (1999) and Schauer et al. (1999). 

Several studies have measured the distribution of polycyclic aromatic hydrocarbons 

(PAHs) in motor vehicle exhaust from on-road vehicles (Westerholm et al., 1991; Lowenthal 

et al., 1994; Venkataraman et al., 1994; Westerholm and Egeback, 1994; Reilly et al., 1998; 

Cadle et al., 1999, Weingartner et al., 1997b; Marr et al., 1999). Cadle et al. (1999) found that 

high molecular weight PAHs (PAHs with molecular weights greater than or equal to 202 g/mole) 

constitute 0.1 to 7.0% of the PM emissions from gasoline- and diesel-powered light-duty 

vehicles. It is important to note, however, that PAHs with molecular weights of 

202 (fluoranthene, acephenanthrylene, and pyrene), 226 (benzo[ghi]fluoranthene and 

cyclopenta[cd]pyrene), and 228 (benz[a]anthracene, chrysene, and triphenylene) exist in both the 

gas-phase and particle-phase at atmospheric conditions (Fraser et al., 1998b), although those 

with molecular weight of 228 are predominantly associated with particles, with only traces in the 

gas-phase (Arey et al., 1987). Excluding these semivolatile PAHs, the contribution of 

nonvolatile PAHs to the PM emitted from the light-duty vehicles sampled by Cadle et al. (1999) 

ranges from 0.013 to 0.18%. These measurements are in good agreement with the tunnel study 

conducted by Fraser et al. (1999) and the heavy-duty diesel truck and bus exhaust measurements 

by Lowenthal et al. (1994), except that the nonvolatile PAH emissions from the heavy-duty 

diesel vehicles tested by Lowenthal et al. (1994) were moderately higher, making up ~0.30% of 

the PM mass emissions. 

3D-16
TABLE 3D-6. SUMMARY OF PARTICLE-PHASE ORGANIC COMPOUNDS

EMITTED FROM MOTOR VEHICLES

Source Compound Class 

Contribution to 

Particulate Mass (%) 

Dominant Contributors to 

Emissions of Compound Class 

Gasoline and dieselpowered vehicles 

driving through the 

Van Nuys Tunnel 

(Fraser et al., 1999) a 

n-Alkanes 

Petroleum biomarkers 

PAHs 

0.009 

0.078 

0.38 

C21 through C29 

Hopanes and steranes 

No dominant compound 

Aromatic acids 0.29 Benzenedicarboxylic acids 

Aliphatic acids 0.21 Palmitic and stearic acids 

Substituted aromatic 0.042 No dominant compound 

UCM b 23.0 

Medium-duty diesel 

vehicles operated over 

an FTP Cycle 

(Schauer et al., 1999) 

n-Alkanes 

Petroleum biomarkers 

PAHs 

0.22 

0.027 

0.54 

C20 through C28 

Hopanes and steranes 

No dominant compound 

Aliphatic acids 0.24 n-Octadecanoic acid 

Aromatic acids 0.014 Methylbenzoic acid 

Saturated cycloalkanes 0.037 C21 through C25 

UCM b 22.2 

a Includes emissions of brake wear, tire wear, and resuspension of road dust associated with motor vehicle traffic. b Unresolved complex mixture. 

Biomass Burning 

In contrast to the mobile and stationary sources discussed earlier, emissions from biomass 

burning in wood stoves and forest fires are strongly seasonal and can be highly episodic within 

their peak emissions seasons. The burning of fuelwood is confined mainly to the winter months 

and is acknowledged to be a major source of ambient air PM in the northwestern United States 

during the heating season. Forest fires occur primarily during the driest seasons of the year in 

different areas of the country and are especially prevalent during prolonged droughts. 

Particulate matter produced by biomass burning outside the United States (e.g., in Central 

America during the spring of 1988) also can affect ambient air quality in the United States. 

3D-17
An example of the composition of fine particles (PM2.5) produced by wood stoves is shown 

in Figure 3D-4. These data were obtained in Denver during the winter of 1987-1988 (Watson 

and Chow, 1994). As was the case for motor vehicle emissions, OC and EC are the major 

components of particulate emissions from wood burning. It should be remembered that the 

relative amounts shown for OC and EC vary with the type of stove, the stage of combustion, and 

the type and condition of the fuelwood. Fine particles are dominant in smoke studies of wood 

burning emissions. For instance, the mass median diameter of wood particles was found to be 

~0.17 μm in a study of the emissions from burning hardwood, softwood, and synthetic logs 

(Dasch, 1982). 

Figure 3D-4. Chemical abundances for PM2.5 emissions from wood burning in Denver, CO. 

Solid bars represent fractional abundances, and the error bars represent 

variability in species abundances. Error bars represent detection limits 

when there are no solid bars. 

Source: Watson and Chow (1994). 

3D-18 
Kleeman et al. (1999) showed that the particles emitted by the combustion of wood in 

fireplaces are predominately < 1.0 μm in diameter, such that the composition of fine PM (PM2.5) 

emitted from fireplace combustion of wood is representative of the total PM emissions from this 

source. Hildemann et al. (1991) and McDonald et al. (2000) reported that fireplace and wood 

stove smoke consists of 48 to 71% OC and 2.9 to 15% EC. Average OC and EC contents for 

these measurements are shown in Table 3D-7. It should be noted that the two methods used for 

the measurements shown in Table 3D-7 have been reported to produce different relative amounts 

of OC and EC for wood smoke samples but show good agreement for total carbon (OC + EC) 

measurements (Chow et al., 1993). 

TABLE 3D-7. MASS EMISSIONS, ORGANIC CARBON, AND ELEMENTAL 

CARBON EMISSIONS FROM RESIDENTIAL COMBUSTION OF WOOD 

Average Mass 

Emission Rate Percent Percent 

Wood Type 

Combustion 

Type 

(g/kg of wood 

burned) 

Number of 

Tests 

Organic 

Carbon a 

Elemental 

Carbon a References 

Softwood Fireplace 13.0 2 48.4 5.2 Hildemann et al. 

(1991) 

Softwood Fireplace 5.14 5 58.5 15.0 McDonald et al. 

(2000) 

Hardwood Fireplace 5.28 3 48.4 2.9 Hildemann et al. 

(1991) 

Hardwood Fireplace 5.66 5 63.2 7.0 McDonald et al. 

(2000) 

Hardwood Wood Stove 3.96 8 71.2 9.0 McDonald et al. 

(2000) 

a Hildemann et al. (1991) used the method described by Birch and Cary (1996) to measure EC; 

 and McDonald et al. (2000) used the method reported by Chow et al. (1993) to measure OC. 

Hawthorne et al. (1988) and Hawthorne et al. (1989) measured gas-phase and particlephase derivatives of guaiacol (2-methoxyphenol), syringol (2,6-dimethoxyphenol), phenol, and 

catechol (1,2-benzenediol) in the downwind plume of 28 residential wood stoves and fireplaces. 

Rogge et al. (1998) reported a broad range of particle-phase organic compounds in the wood 

3D-19
smoke samples collected by Hildemann et al. (1991), which include n-alkanes, n-alkanoic acids, 

n-alkenoic acids, dicarboxylic acids, resin acids, phytosterols, PAHs, and the compounds 

reported by Hawthorne et al. (1989). Supplementing these measurements, McDonald et al. 

(2000) reported the combined gas-phase and particle-phase emissions of PAHs and the 

compounds quantified by Hawthorne et al. (1989). The measurements by Rogge et al. (1998), 

which represent a comprehensive data set of the organic compounds present in wood smoke 

aerosol, are summarized in Table 3D-8. It should be noted, however, that these nearly 200 

compounds account for only ~15 to 25% of the OC particle mass emitted from the residential 

combustion of wood. Simoneit et al. (1999) have shown that levoglucosan constitutes a 

noticeable portion of the organic compound mass not identified by Rogge et al. (1998). In 

addition, Elias et al. (1999) used high-temperature gas chromatography/mass spectrometry 

(HTGC-MS) to measure high-molecular-weight organic compounds in smoke from South 

American leaf and stem litter biomass burning. These compounds cannot be measured by the 

analytical techniques employed by Rogge et al. (1998) and, therefore, are strong candidates to 

make up some of the unidentified organic mass in the wood smoke samples analyzed by Rogge 

et al. (1998). These compounds, which include triterpenyl fatty acid esters, wax esters, 

triglycerides, and high-molecular-weight n-alkan-2-ones, are expected to be present in North 

American biomass smoke originating from agricultural burning, forest fires, grassland fires, and 

wood stove/fireplace emissions. 

Measurements of aerosol composition, size distributions, and aerosol emissions factors 

have been made in biomass burning plumes, either on towers (Susott et al., 1991) or aloft on 

fixed-wing aircraft (e.g., Radke et al., 1991) or helicopters (e.g., Cofer et al., 1988). As was 

found for wood stove emissions, the composition of biomass burning emissions is strongly 

dependent on the stage of combustion (i.e., flaming, smoldering, or mixed), and the type of 

vegetation (e.g., forest, grassland, scrub). Over 90% of the dry mass in particulate biomass 

burning emissions is composed of OC (Mazurek et al., 1991). Ratios of OC to EC are highly 

variable, ranging from 10:1 to 95:1, with the highest ratio found for smoldering conditions and 

the lowest for flaming conditions. Emissions factors for total particulate emissions increase by 

factors of two to four in going from flaming to smoldering stages in the individual fires studied 

by Susott et al. (1991). 

3D-20
TABLE 3D-8. SUMMARY OF PARTICLE-PHASE ORGANIC COMPOUNDS 

EMITTED FROM THE COMBUSTION OF WOOD IN FIREPLACES* 

Biomass Type Compound Class 

Contribution to 

Particulate Mass (%) 

Dominant Contributors to Emissions 

of Compound Class 

Fireplace 

combustion 

of softwood 

n-Alkanes 

n-Alkanoic acids 

0.039 

0.45 

C21 through C31 

C16, C18, C20, C21, C22, C24 

n-Alkenoic acids 0.12 Oleic and linoleic acid 

Dicarboxylic acids 0.36 Malonic acid 

Resin acids 1.28 Abietic, dehydroabietic, isopimaric, 

pimaric, and sandaracopimaric acids 

Substituted phenols 3.30 Benzenediols and guaiacols 

Phytosterols 0.37 $-Sitosterol 

PAHs 0.092 Fluoranthene and pyrene 

Oxygenated PAH 0.019 1H-phenalen-1-one 

Fireplace 

combustion 

of hardwood 

n-Alkanes 

n-Alkanoic acids 

0.044 

1.33 

C21 through C29 

C16, C22, C24, C26 

n-Alkenoic acids 0.049 Oleic and linoleic acid 

Dicarboxylic acids 0.42 Succinic acid 

Resin acids 0.11 Dehydroabietic acid 

Substituted phenols 8.23 Benzediols, guaiacols, and syringols 

Phytosterols 0.21 $-sitosterol 

PAHs 0.13 No dominant compounds 

Oxygenated PAHs 0.020 1H-phenalen-1-one 

*

Note: Measurements were made using a dilution sampler and no semivolatile organic compound sorbent. 

Source: Rogge et al. (1998). 

Particles in biomass burning plumes from a number of different fires were found to have 

three distinguishable size modes: (1) a nucleation mode, (2) an accumulation mode, and 

(3) a coarse mode (Radke et al., 1991). Based on an average of 81 samples, ~70% of the mass 

was found in particles < 3.5 μm in aerodynamic diameter. The fine particle composition was 

found to be dominated by tarlike, condensed hydrocarbons; and the particles were usually 

3D-21
spherical in shape. Figure 3D-2 shows additional information for the size distribution of 

particles produced by vegetation burning. 

An example of ambient data for the composition of PM2.5 collected at a tropical site that 

was heavily affected by biomass burning is shown in Table 3D-9. The samples were collected 

during November 1997 on the campus of Sriwijaya University, located in a rural setting on the 

island of Sumatra in Indonesia (Pinto et al., 1998). The site was subjected routinely to levels 

of PM2.5 well in excess of the U.S. NAAQS as a result of biomass fires in Indonesia from 

summer 1997 through spring 1998. As can be seen from a comparison of the data shown in 

Table 3D-9 with those shown in Figure 3D-4, there are a number of similarities and differences 

(especially with regard to the heavy metal content) in the abundances of many species. The 

abundances of some crustal elements (e.g., Si, Fe) are higher in Table 3D-9 than in Figure 3D-4, 

perhaps reflecting additional contributions of entrained soil dust. 

TABLE 3D-9. MEAN AEROSOL COMPOSITION AT TROPICAL SITE 

(SRIWIJAYA UNIVERSITY, SUMATRA, INDONESIA) AFFECTED 

HEAVILY BY BIOMASS BURNING EMISSIONS a 

Component Abundance (%) Component Abundance (%)

 OC 76 Cr BD b

 EC 1.2 Mn BD b

 SO4 

2! 11 Fe 3.9 × 10!2

 Al BD b Ni < 3.8 × 10!5

 Si 9.3 × 10!2 Cu 4.8 × 10!4

 Cl 4.4 Zn 3.1 × 10!3

 K 0.7 As 6.4 × 10!4

 Ca 4.5 × 10!2 Se 2.8 × 10!4

 Ti 4.2 × 10!3 Br 3.6 × 10!2

 V BD b Pb 3.1 × 10!3 

a The mean PM2.5 concentration during the sampling period (November 5 through 11, 1997) was 264 μg/m3

. b Beneath detection limit. 

Source: Pinto et al. (1998). 

3D-22 
Limited emissions data that include organic compound speciation information have been 

reported for agricultural burning (Jenkins et al., 1996), forest fires (Simoneit, 1985), and 

grassland burning (Standley and Simoneit, 1987). Jenkins et al. (1996) present PAH emissions 

factors for the combustion of cereals (barley, corn, rice, and wheat), along with PAH emissions 

factors for wood burning. Profiles of organic compounds in emissions from meat cooking 

(Rogge et al., 1991) and cigarette smoke (Rogge et al., 1994) also have been obtained. 

Natural Sources 

Although sea-salt aerosol production is confined to salt water bodies, it is included here 

because many marine aerosols can exert a strong influence on the composition of the ambient 

aerosol in coastal areas. In some respects, the production of sea-salt aerosols is like that of 

windblown dust, in that both are produced by wind agitation of the surface. The difference 

between the two categories arises because sea-salt particles are produced from the bursting of air 

bubbles rising to the sea surface. Air bubbles are formed by the entrainment of air into the water 

by breaking waves. The surface energy of a collapsing bubble is converted to kinetic energy in 

the form of a jet of water that can eject drops above the sea surface. The mean diameter of the 

jet drops is about 15% of the bubble diameter (Wu, 1979). Bubbles in breaking waves range in 

size from a few μm to several mm in diameter. Field measurements by Johnson and Cooke 

(1979) of bubble size spectra show maxima in diameters at around 100 μm, with the bubble size 

distribution varying as (d/d0) with d0 = 100 μm. 

Because sea-salt particles receive water from the surface layer, which is enriched in 

organic compounds, aerosol drops are composed of this organic material in addition to sea salt 

(about 3.5% by weight in seawater). The major ionic species by mass in seawater are: Na+ 

(30.7%), Cl! (55.0%), SO4

2!

 (7.7%), Mg2+ (3.6%), Ca2+ (1.2%), K+ (1.1%), HCO3 

!

 (0.4%), 

and Br!

 (0.2%) (Wilson, 1975). The composition of the marine aerosol also reflects the 

occurrence of displacement reactions that enrich sea-salt particles in SO4

2!

 and NO3 

!

 while 

depleting them of Cl!

 and Br!. 

Sea salt is concentrated in the coarse mode, with a mass median diameter of ~7 μm for 

samples collected in Florida, the Canary Islands, and Barbados (Savoie and Prospero, 1982). 

The size distribution of sulfate is distinctly bimodal. Sulfate in the coarse mode is derived from 

sea water, but sulfate in the submicron aerosol arises from the oxidation of dimethyl sulfide 

3D-23
(CH3SCH3) or DMS. DMS is produced during the decomposition of marine micro-organisms. 

DMS is oxidized to methane sulfonic acid (MSA), a large fraction of which is oxidized to sulfate 

(e.g., Hertel et al., 1994). 

Apart from sea spray, other natural sources of particles include the suspension of organic 

debris and volcanism. Profiles of organic compounds in vegetative detritus have been obtained 

by Rogge et al. (1993d). Particles released from plants in the form of seeds, pollen, spores, leaf 

waxes, and resins, range in size from ~1 to 250 μm (Warneck, 1988). Fungal spores and animal 

debris, such as insect fragments, also are to be found in ambient aerosol samples in this size 

range. Although material from all the foregoing categories may exist as individual particles, 

bacteria usually are found attached to other dust particles (Warneck, 1988). Smaller bioaerosol 

particles include viruses, individual bacteria, protozoa, and algae (Matthias-Maser and Jaenicke, 

1994). In addition to natural sources, other sources of bioaerosols include industry (e.g., textile 

mills), agriculture, and municipal waste disposal (Spendlove, 1974). Size distributions for 

bioaerosols have not been as well characterized as for other categories of airborne particles. 

Trace metals are emitted to the atmosphere from a variety of sources such as sea spray, 

wind-blown dust, volcanoes, wildfires, and biotic sources (Nriagu, 1989). Biologically mediated 

volatilization processes (e.g., biomethylation) are estimated to account for 30 to 50% of the 

worldwide total Hg, As, and Se emitted annually; whereas other metals are derived principally 

from pollens, spores, waxes, plant fragments, fungi, and algae. It is not clear, however, how 

much of the biomethylated species are remobilized from anthropogenic inputs. Median ratios of 

the natural contribution to globally averaged total sources for trace metals are estimated to be 

0.39 (As), 0.15 (Cd), 0.59 (Cr), 0.44 (Cu), 0.41 (Hg), 0.35 (Ni), 0.04 (Pb), 0.41 (Sb), 0.58 (Se),

0.25 (V), and 0.34 (Zn), suggesting a significant natural source for many trace elements. 

It should be noted, however, that these estimates are based on emissions estimates that have 

uncertainty ranges of an order of magnitude. 

3D-24
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3D-28 
APPENDIX 3E

Characterization of PM2.5, PM10, and PM10-2.5 

Concentrations at IMPROVE Sites 

In this appendix, concentrations of PM2.5, PM10-2.5 and PM10 are presented based on daily 

values in the PM data set obtained at 18 sites in the IMPROVE network. The purpose of these 

analyses is to provide summaries of concentrations of PM2.5 and PM10-2.5 that could be used for 

characterizing human and ecosystem exposures to PM under the least polluted conditions that 

are found in the United States and to provide upper limits on policy relevant background (PRB) 

PM concentrations (cf., Section 3.3.3). This is accomplished by summarizing nonsulfate PM2.5 

and PM10-2.5 concentrations and their variability at relatively remote monitoring sites (RRMS) 

that are not obviously influenced by local and/or regional anthropogenic pollution sources in 

the United States. The rationale for looking at nonsulfate PM2.5 is that the sulfate component 

of PM2.5 is almost all anthropogenic in origin and, therefore, nonsulfate PM2.5 provides a better 

estimate of upper limits on PRB PM concentrations. 

The potential for receiving contributions from anthropogenic sources exists at every 

monitoring site. Thus, sites were chosen for this analysis where it was judged that anthropogenic 

sources do not contribute extensively to the monitored concentrations. These sites are 

characterized by lower interannual variability and smaller ranges of values across the percentile 

distribution in concentrations and by lower probabilities of being affected by transport from 

regional pollution sources than other sites. Such sites are almost all in the western United States. 

This analysis includes only two sites in the eastern United States and two areas in the midwest. 

The 24-h average data available at the selected 18 IMPROVE sites from 1990 through 

2002 were summarized on an annual basis; and annual mean values and annual 90th percentiles 

of the 24-h concentrations were calculated. The data capture requirement for these summaries 

required that each site have least 11 observations in each calendar quarter, and only years in 

which this requirement was satisfied for all four quarters were used. The monitoring site in 

Yellowstone National Park was relocated in 1996; thus, the designations for the two locations 

are Yellowstone National Park 1 and Yellowstone National Park 2. The Voyageurs National 

3E-1
Park site was also relocated. The designations for the two locations are analogous to those used 

for Yellowstone National Park. 

Both PM2.5 and PM10 mass were examined with the sulfate component removed to obtain 

better insight into non-anthropogenic background. Daily fine sulfate concentrations were 

calculated by 4.125 * S, where S is the measured mass of fine sulfur (Malm, et al., 1994). Since 

sulfate is not a significant component of coarse fraction particles and measurements are not 

available, we calculated an estimate of the coarse PM (PM10-2.5) by the difference between PM10 

and PM2.5. 

Information about the range of annual mean nonsulfate (and total) PM2.5 and PM10 mass 

concentrations, and PM10-2.5 mass concentrations, is summarized in Table 3E-1, and the range of 

90th percentile concentrations is summarized in Table 3E-2. The 90th percentile was chosen to 

reduce the impact of infrequent, episodic events that can result in exceptionally high daily 

values. As can be seen from Table 3E-1, the lowest annual average nonsulfate PM2.5 

concentrations among all sites examined were recorded at Denali National Park (AK), with 

concentrations there ranging from an annual mean of 0.7 to 2.4 μg/m3

. The site with the next 

lowest observed annual mean (among the 18 sites analyzed) was Bridger Wilderness (WY), 

where annual means ranged from 1.5 to 2.2 μg/m3

. The highest annual mean nonsulfate PM2.5 

concentrations at the sites examined were observed in Glacier National Park (MT), where values 

ranged from 3.8 to 5.5 μg/m3

. Annual means of coarse PM ranged from a low of 1.1 to 

5.6 μg/m3

 at Denali National Park to a high of 5.6 to 10.8 μg/m3 at Big Bend National Park, TX. 

Over the period of the observational record, a substantial range in annual mean concentrations in 

all three size ranges is observed, indicating the importance of examining a number of years in 

attempting to characterize long-term averages. Inspection of Table 3E-1 shows that the range in 

annual average PM10-2.5 concentrations was generally larger than for PM2.5. However, it should 

be noted that PM10-2.5 concentrations are obtained by a difference technique and, so, are more 

strongly affected by errors in the determination of both PM2.5 and PM10. 

As can be seen in Table 3E-2, 90th percentile 24-h nonsulfate PM2.5 levels ranged from 

1.5 to 10.5 μg/m3

. The 90th percentiles of 24-h coarse PM ranged from 1.9 to 27.6 μg/m3. 

As can be seen from Table 3E-2, concentrations of PM in all three size fractions at the 

90th percentile level are often several times higher than the mean concentrations at all sites. 

In addition, the range of values at the 90th percentile level is much greater than that at lower 

3E-2
TABLE 3E-1. RANGES OF ANNUAL MEAN PM CONCENTRATIONS AT IMPROV

MONITORING SITES (μg/m3) 

PM2.5 PM10 

Site Nonsulfate (Total) Nonsulfate (Total) Acadia National Park, ME 2.6-4.7 (4.9-8.2) 4.6-11.3 (7.3-15.0Big Bend National Park, TX 2.7-4.9 (5.0-7.8) 8.8-15.7 (11.3-18.6Boundary Waters Canoe Area, MN 2.6-3.9 (4.4-5.8) 5.0-10.2 (7.0-12.0Bryce Canyon National Park, UT 1.7-2.4 (2.6-3.4) 4.4-7.6 (5.3-8.5) Bridger Wilderness, WY 1.5-2.2 (2.1-2.9) 3.7-6.5 (4.3-7.3) Canyonlands National Park, UT 1.9-3.2 (2.8-4.0) 5.1-10.5 (6.3-11.7Denali National Park, AK 0.7-2.4 (1.1-3.2) 2.0-7.5 (2.4-8.3) Gila Wilderness, NM 2.4-3.4 (3.4-4.5) 4.9-7.9 (6.0-9.2) Glacier National Park, MT 3.8-5.5 (4.8-6.5) 7.6-14.2 (8.5-15.2Lassen Volcanic National Park, CA 1.7-4.5 (2.1-5.1) 4.0-8.1 (4.6-8.5) Lone Peak Wilderness, UT 3.1-5.3 (4.1-6.9) 7.1-10.9 (8.1-12.5Lye Brook Wilderness, VT 2.3-4.8 (4.5-8.8) 4.2-9.7 (7.0-13.6Redwood National Park, CA 2.8-4.6 (3.6-5.4) 6.0-10.6 (7.2-11.7Three Sisters Wilderness, OR 2.0-5.4 (2.7-6.5) 4.0-8.1 (4.6-9.1) Voyageurs National Park 1, MN 3.2-3.5 (5.1-5.9) 5.7-11.2 (8.1-13.1Voyageurs National Park 2, MN 2.6-5.4 (4.1-7.2) 5.2-10.8 (7.0-12.5Yellowstone National Park 1, WY 2.0-3.0 (2.6-3.6) 6.0-9.2 (6.6-9.9) Yellowstone National Park 2, WY 1.7-4.1 (2.3-4.7) 3.6-9.0 (4.2-9.6) 

3E-3
TABLE 3E-2. RANGES OF ANNUAL 90th PERCENTILE VALUES OF DAILY PM CONCENTR

IMPROVE MONITORING SITES (μg/m3) 

PM2.5 PM10 

Site Nonsulfate (Total) Nonsulfate (Total) Acadia National Park, ME 4.8-10.5 (8.8-16.5) 9.0-20.2 (12.7-26.0Big Bend National Park, TX 4.7-9.5 (8.7-12.8) 15.9-29.0 (18.5-34.5Boundary Waters Canoe Area, MN 5.5-8.0 (8.1-12.0) 9.4-13.8 (13.6-17.1Bryce Canyon National Park, UT 2.9-5.0 (4.4-6.1) 7.4-13.8 (8.9-14.8) Bridger Wilderness, WY 2.8-4.6 (3.9-5.8) 7.3-14.5 (8.3-15.1) Canyonlands National Park, UT 3.2-7.3 (4.5-7.9) 9.5-16.1 (10.9-16.9Denali National Park, AK 1.5-5.1 (2.2-7.4) 3.6-17.2 (4.3-17.9) Gila Wilderness, NM 4.1-6.3 (5.6-8.0) 8.3-14.4 (10.2-15.9Glacier National Park, MT 7.2-10.0 (8.0-11.0) 16.2-36.5 (16.5-36.8Lassen Volcanic National Park, CA 3.7-7.7 (4.0-9.1) 7.6-15.6 (8.5-16.0) Lone Peak Wilderness, UT 5.4-9.8 (6.7-11.1) 11.7-19.6 (13.4-21.5Lye Brook Wilderness, VT 5.4-9.1 (11.4-18.2) 8.5-15.8 (14.4-22.9Redwood National Park, CA 5.2-9.4 (6.3-9.9) 11.8-21.8 (13.2-23.7Three Sisters Wilderness, OR 4.5-8.0 (5.8-10.0) 7.3-16.2 (8.5-18.0) Voyageurs National Park 1, MN 5.7-6.4 (7.7-11.3) 13.9-22.0 (17.9-23.1Voyageurs National Park 2, MN 5.1-5.5 (7.4-8.1) 9.6-11.9 (12.0-15.5Yellowstone National Park 1, WY 3.9-5.7 (4.7-6.6) 11.7-20.6 (12.3-21.3Yellowstone National Park 2, WY 3.4-4.9 (4.1-6.0) 6.7-10.3 (7.4-10.8) 

3E-4
levels. These findings indicate that extreme events are important for shaping the frequency 

distribution of PM that is observed. Wildfires play a major role in defining the year-to-year 

variability at specific sites, especially in the West. Notable examples include the massive forest 

fire in 1988 in Yellowstone National Park and those that occurred in 2000 in many western 

states. Dust storms also play a role in arid climates. The variability in annual average PM10-2.5 

concentrations is again related to several factors. Variability in factors that govern the 

production of soil dust from the surface and the production of primary biological aerosol 

particles may be largely responsible. Wildfires also produce PM in this size range (cf., 

Appendix 3D). 

Examples of the interannual variability in the percentile distributions of PM2.5 

concentrations for 1997 through 2001 are shown in Figure 3E-1(a,b) for two sites. As can be 

seen, at concentrations less than the 90th percentile (P90) level, year-to-year differences in PM2.5 

can be less than 1 μg/m3 at sites such as Bridger, WY. Differences at the P90 level are much 

larger, both on an absolute and on a relative basis at eastern sites. 

Percentile distributions by season are shown in Figure 3E-2(a,b) for PM2.5 and 

Figure 3E-3(a,b) for PM10-2.5. For most sites, the variability in concentrations on a quarterly 

basis increases substantially beyond the P90 level, as can be seen from inspection of 

Figure 3E-2(a,b) for PM2.5 and Figure 3E-3(a,b) for PM10-2.5. Perhaps the most striking features 

seen in Figure 3E-2(a,b) and Figure 3E-3(a,b) are the concentration changes associated with the 

95th (P95) and 99th (P99) percentile events, which represent extreme value events. Most of these 

events occur in the third calendar quarter; however, high concentrations also do occur at some 

sites during the second and fourth calendar quarters. If locally derived climatologic seasons 

were used instead of calendar quarters, a more accurate depiction of the seasonal variability 

of these events may have been obtained. In most cases, there is consistency in the behavior 

of PM2.5 and PM10, which suggests that the episodes of higher concentrations could be associated 

with sources that produce mainly PM2.5 such as wildfires and/or anthropogenic combustion 

sources. Wildfires are limited to hotter and drier times of the year, but anthropogenic sources 

can contribute to high concentrations during other seasons. Additional factors which would tend 

to produce a third quarter maximum include: the enhanced production of secondary PM from 

anthropogenic and biogenic precursors during summer months; wildfires that are located in the 

East or elsewhere during summer; and surface dust produced locally and/or in northern Africa. 

3E-5
REFERENCES 

Malm, W. C.; Sisler, J. F.; Huffman, D.; Eldred, R. A.; Cahill, T. A. (1994) Spatial and seasonal trends in particle 

concentration and optical extinction in the United States. J. Geophys. Res. [Atmos.] 99: 1347-1370. 

3E-6
Figure 3E-1a,b. Interannual variability in 24-h average PM2.5 concentrations observed at 

selected IMPROVE sites: (a) Bridger Wilderness, WY; (b) Yellowstone 

National Park, WY. 

3E-7 
Figure 3E-2a,b. Seasonal variability in 24-h average PM2.5 concentrations observed at 

selected IMPROVE sites: (a) Bridger Wilderness, WY; (b) Yellowstone 

National Park, WY. 

3E-8
Figure 3E-3a,b. Seasonal variability in 24-h average PM10-2.5 concentrations observed at 

selected IMPROVE sites: (a) Bridger Wilderness, WY; (b) Yellowstone 

National Park, WY. 

3E-9 
4. ENVIRONMENTAL EFFECTS OF AIRBORNE

PARTICULATE MATTER

4.1 INTRODUCTION 

This chapter assesses information providing inputs to U.S. EPA decision making on 

secondary National Ambient Air Quality Standards (NAAQS) aimed at protecting against 

welfare effects of ambient airborne particulate matter (PM). Specifically, it assesses the effects 

of atmospheric PM on the environment, including: (a) direct and indirect effects on vegetation 

and natural ecosystem integrity, (b) effects on visibility, and (c) effects on man-made materials, 

as well as (d) relationships of atmospheric PM to climate change processes. 

4.2 EFFECTS OF AMBIENT AIRBORNE PARTICULATE MATTER ON 

VEGETATION AND NATURAL ECOSYSTEMS 

Introduction 

The effects of airborne particles are manifested via physical and chemical effects exerted at 

the individual plant level. However, plants are key members of ecosystems, which are 

structurally complex communities comprised of populations of plants, animals (including 

humans), insects, and microorganisms that interact with one another and with their non-living 

(abiotic) chemical and physical environment in which they exist (Odum, 1989; U.S. 

Environmental Protection Agency, 1993). All life on Earth is dependent on chemical energy in 

the form of carbon compounds to sustain their life processes. Terrestrial vegetation, via the 

process of photosynthesis, provides approximately half of the carbon that annually cycles 

between the Earth and the atmosphere (Chapin and Ruess, 2001). 

Ecosystems respond to stresses through their constituent organisms. The responses of 

plant species and populations to environmental perturbations (such as those caused by 

atmospheric PM) depend on their genetic constitution (genotype), their life cycles, and their 

microhabitats. Stresses that produce changes in their physical and chemical environment apply 

selection pressures on individual organisms (Treshow, 1980). The changes that occur within 

populations and plant communities reflect these new and different pressures. A common 

4-1
response in a community under stress is the elimination of the more sensitive populations and an 

increase in abundance of species that tolerate or are favored by stress (Woodwell, 1970, 

Guderian et al., 1985). 

The present section is organized to discuss: (1) factors affecting deposition of airborne 

PM on plants and ecosystems and then (2) the effects of PM deposition on individual plants, 

plant populations, forest trees and terrestrial ecosystems. As such, the section is organized to 

follow, in rough outline, the Framework for Assessing and Reporting on Ecological Condition 

recommended in a report by the Ecological Processes and Effects Committee (EPEC) of the 

EPA’s Science Advisory Board (Science Advisory Board, 2002), which states: 

“The purpose of this report is to provide the Agency with a sample framework that 

may serve as a guide for designing a system to assess, and then report on, ecological 

condition at local, regional, or national scale. The sample framework is intended as 

an organizing tool that may help the Agency decide what ecological attributes to 

measure and how to aggregate those measurements into an understandable picture of 

ecological integrity.” This framework is not actually a risk assessment per se, but it 

can be used to “construct a report of ecological condition” that characterizes the 

ecological integrity of an ecosystem based on “the relationship between common 

anthropogenic stressors and one or more of the six Essential Ecological Attributes.” 

The EPEC report provides a useful approach for organizing discussions of stressor effects on 

ecosystem components at successive levels of complexity. 

4.2.1 Ecological Attributes 

The EPEC Framework provides a checklist of generic ecological attributes that should be 

considered when evaluating the integrity of ecological systems (see Table 4-1). The six generic 

ecological attributes, termed Essential Ecological Attributes (EEAs), represent groups of related 

ecological characteristics (Science Advisory Board, 2002; Harwell et al., 1999) and include: 

landscape conditions, biotic conditions, chemical and physical characteristics, ecological 

processes, hydrology and geomorphology, and natural disturbance regimes. All of the EEAs 

are interrelated (i.e., changes in one EEA may directly or indirectly affect other EEAs). 

The first three ecological attributes listed in Table 4-1 are primarily “patterns,” whereas the 

last three are “processes.” Ecological science has used “patterns” and “processes” as terms to 

describe features of ecological systems for many years (e.g., Bormann and Likens, 1979). 

4-2
TABLE 4-1. ESSENTIAL ECOLOGICAL ATTRIBUTES AND

REPORTING CATEGORIES

Landscape Condition 

• Extent of Ecological System/Habitat Types 

• Landscape Composition 

• Landscape Pattern and Structure 

Biotic Condition 

• Ecosystems and Communities 

– Community Extent 

– Community Composition 

– Trophic Structure

– Community Dynamics 

– Physical Structure

• Species and Populations 

– Population Size

– Genetic Diversity

– Population Structure

– Population Dynamics 

– Habitat Suitability

• Organism Condition 

– Physiological Status

– Symptoms of Disease or Trauma 

– Signs of Disease

Chemical and Physical Characteristics 

(Water, Air, Soil, and Sediment) 

• Nutrient Concentrations 

– Nitrogen

– Phosphorus

– Other Nutrients

• Trace Inorganic and Organic Chemicals 

– Metals

– Other Trace Elements 

– Organic Compounds 

• Other Chemical Parameters 

– pH

– Dissolved Oxygen

– Salinity

– Organic Matter

– Other

• Physical Parameters 

Ecological Processes 

• Energy Flow 

– Primary Production 

– Net Ecosystem Production 

– Growth Efficiency

• Material Flow 

– Organic Carbon Cycling

– Nitrogen and Phosphorus Cycling

– Other Nutrient Cycling

Hydrology and Geomorphology 

• Surface and Groundwater flows 

– Pattern of Surface flows

– Hydrodynamics 

– Pattern of Groundwater flows

– Salinity Patterns 

– Water Storage 

• Dynamic Structural Characteristics 

– Channel/Shoreline Morphology, Complexity 

– Extent/Distribution of Connected Floodplain

– Aquatic Physical Habitat Complexity 

• Sediment and Material Transport 

– Sediment Supply/Movement 

– Particle Size Distribution Patterns

– Other Material Flux

Natural Disturbance Regimes 

• Frequency 

• Intensity 

• Extent 

• Duration 

Source: Science Advisory Board (2002). 

Of main concern in this chapter are relationships between a certain class of diverse airborne 

stressors from anthropogenic sources (i.e., PM) and one or more of the EEAs. Changes in 

patterns resulting from responses of vegetation and ecosystems to the effects of fine and coarse 

4-3
PM deposition, along with known or possible effects on ecological processes associated with 

changes in the patterns, are discussed in the subsections that follow. 

The reader is also referred to several other sources for more detailed discussions of several 

topics only briefly alluded to or addressed here. For example, an extensive discussion of various 

types of effects of acidic deposition is presented in the U.S. National Acid Precipitation 

Assessment Program (NAPAP) Biennial Report to Congress: An Integrated Assessment 

Program (National Science and Technology Council, 1998). Additionally, ecological effects of 

acidic precipitation and nitrate deposition on aquatic systems are discussed in the EPA document 

Air Quality Criteria Document for Nitrogen Oxides (U.S. Environmental Protection Agency, 

1993); and sulfate deposition and effects, as related to wetlands and aquatic habitats, are 

discussed in U.S. Environmental Protection Agency (1982). Effects of lead on crops, vegetation, 

and ecosystems are assessed in the EPA document Air Quality Criteria for Lead (U.S. 

Environmental Protection Agency, 1986). Lastly, effects of “certain pesticides, metal 

compounds, chlorinated organic compounds, and nitrogen compounds” are discussed in 

Deposition of Air Pollutants to the Great Waters, Third Report to Congress (U.S. Environmental 

Protection Agency, 2000a). 

4.2.2 Ecosystem Exposures – Particle Deposition 

Airborne particles, their precursors, and their transformation products are removed from 

the atmosphere by wet and dry deposition processes. This atmospheric cleansing process 

fortunately lowers the long-term buildup of lethal concentrations of these pollutants in the air 

and moderates the potential for direct human health effects caused by their inhalation. 

Unfortunately, these deposition processes also mediate the transfer of PM pollutants to other 

environmental media where they can and do alter the structure, function, diversity, and 

sustainability of complex ecosystems. 

The potential effects of PM deposition on vegetation and ecosystems encompass the full 

range, scales, and properties of biological organization listed under Biotic Condition, Table 4-1. 

Exposure to a given mass concentration of airborne PM, however, may lead to widely differing 

responses, depending on the particular mix of deposited particles. Particulate matter is not a 

single pollutant, but rather a heterogeneous mixture of particles differing in size, origin, and 

chemical composition. This heterogeneity exists across individual particles within samples from 

4-4
individual sites and, to an even greater extent, between samples from different sites. Thus far, 

atmospheric PM has been defined, for regulatory purposes, mainly by size fractions and less 

clearly so in terms of chemical nature, structure, or source. While size is related to the mode and 

magnitude of deposition to vegetated landscapes and may be a useful surrogate for chemical 

constitution, PM size classes do not necessarily have specific differential relevance for 

vegetation effects (Whitby, 1978; U.S. Environmental Protection Agency, 1996a); i.e., both fineand coarse-mode particles may affect plants. Much of the burden of sulfates (SO4

2!

), nitrates 

(NO3 

!

), ammonium salts (NH4

+

), and hydrogen ions (H+) resides in the atmosphere either 

dissolved in fog water or as liquid or solid aerosols. Therefore, assessment of atmospheric PM 

deposition and effects on vegetation unavoidably include discussion of NO3 

!

 and SO4

2!

 and 

associated compounds involved in acidic and acidifying deposition. Other important issues 

relate to trace elements and heavy metals often found in ambient airborne PM. 

4.2.2.1 Fine and Coarse Particulate Matter 

Fine and coarse PM have a number of contrasting properties that affect their impact on 

vegetated systems (see Chapter 2, Table 2-1 of this document). The model results of Wiman and 

Agren (1985) and the measurements of Lovett and Lindberg (1993) addressing the complexity of 

deposition processes in patchy forested landscapes and vertical stratification within canopies 

reveal clear distinctions between the deposition behavior of fine and coarse particles. For one, 

coarse particles settle nearer their site of formation than do fine particles. Also, the chemical 

constitution of individual particles is strongly correlated with size (i.e., most S and much N is 

present on fine particles, whereas much of the base cation and heavy metal burden is present on 

coarse particles) and influences the predicted landscape loading of specific elements. 

Atmospheric PM may also act as a carrier for other directly phytotoxic materials (e.g., 

herbicides). Fine PM dominates the surface area of particles suspended in the atmosphere, while 

coarse PM dominates the mass of such airborne particles. Surface area may become more 

central to ecological impact assessment as recognition of the oxidizing capacity of fine particles, 

their interactions with other pollutants such as ozone (O3), and their adsorption of phytoactive 

organic compounds such as herbicides become more fully appreciated. Fine and coarse particles 

respond to changes in atmospheric humidity, precipitation, and wind through different 

mechanisms, differentially altering their deposition characteristics. 

4-5
Fine PM may exhibit similar mass concentrations at different sites (Figure 4-1) and yet be

composed of very different constituents. In eastern North America, sulfate is typically the major 

component of this fraction, in contrast to the West where nitrate is a key component (Figure 4-1; 

cf., the eastern Appalachian site and the western California site). On the other hand, in the urban 

center of Mexico City (Hidy et al., 2000), an environment more similar to the western than 

eastern U.S., concentrations of fine PM of about 300 μg/m3

 are found, and sulfate concentrations 

are three times that of nitrate. In contrast to sulfur and nitrogen, the contributions of organic and 

elemental carbon to the eastern and western U.S. sites were similar (Figure 4-1), although soilderived geologic material was greater at the more arid western site. 

Figure 4-1. The diversity of fine PM from sites in the western and eastern United States. 

Source: Modified from Sisler and Malm (2000). 

Fine PM is typically more diverse than coarse PM and is secondary in nature, having 

condensed from the vapor phase or been formed by chemical reaction from gaseous precursors 

4-6
in the atmosphere (see Chapter 2). Fine PM derives from atmospheric gas-to-particle conversion 

reactions involving nucleation, condensation, and coagulation, and from evaporation of water 

from contaminated fog and cloud droplets. Sulfur and nitrogen oxides (SOx and NOx) are often 

oxidized to their respective acids and neutralized with ammonium cations as particulate salts. 

Fine PM may also contain condensates of volatile organic compounds, volatilized metals, and 

products of incomplete combustion (see Chapter 3). 

Coarse PM, in contrast, is mainly primary in nature, having been emitted from area, point, 

or line sources as fully formed particles derived from abrasion and crushing processes, soil 

disturbances, desiccation of marine aerosol from bursting bubbles, hygroscopic fine PM 

expanding with humidity to coarse mode, and/or gas condensation directly onto preexisting 

coarse particles (see Chapters 2 and 3). Suspended primary coarse PM may contain iron, silica, 

aluminum, and base cations from soil, plant and insect fragments, pollen, fungal spores, bacteria, 

and viruses, as well as fly ash, brake linings, debris, and automobile tire fragments. Coarsemode particles can be altered by chemical reactions and/or physical interactions with gaseous 

or liquid contaminants. 

The coating of coarse particles with semivolatile materials can substantially affect their 

deposition and potential for biological effects. For example, nitrogen exhibits a strongly 

bimodal size distribution: the peak above 1 μm can be attributed to nitric acid (HNO3) 

adsorption onto coarse alkaline particles; and that below 1 μm can be attributed to gas phase 

condensation of ammonia with either sulfuric or nitric acid yielding either (NH4)2SO4 or 

NH4NO3 aerosol. HNO3 has an extremely high deposition velocity, nearly independent of the 

physiography of the surface. Therefore, formation of ammonium nitrate reduces local nitrogen 

deposition, because the deposition velocity of these particles is much less than that of HNO3 gas. 

Similarly, anthropogenic emissions of sulfur are mostly as sulfur dioxide (SO2), which is 

hydrophilic, rapidly hydrated, and subsequently oxidized to sulfate (SO4

2!

), which is about 

30-fold less phytotoxic than SO2. The ratio of SO4

2!

:SO2 increases with aging of the air mass 

and, therefore, with distance from the source. Sulfate is thus a widespread regional/global 

pollutant and is sufficiently hygroscopic that, in humid air, it exists significantly in the coarse 

PM fraction. It is unusual for injurious levels of particulate sulfate to be deposited upon 

vegetation in the vicinity of emission sources, while direct injury due to SO2 is commonly 

observed near uncontrolled point sources. In this case, gas-to-particle conversion is of benefit 

4-7
to vegetation. The chemical composition of gaseous precursors of PM and the formation of 

sulfates and nitrates is discussed in Section 2.1.3 of Chapter 2. 

Since enactment of the 1990 Clean Air Act Amendments, the atmospheric mix of PM 

precursors in the United States has changed substantially. That is, as emissions of SO2 have 

declined, emissions of oxides of nitrogen (NOx) have remained about the same, but emissions of 

cations have decreased (Hedin et al., 1994; Likens et al., 1996). This is almost certainly due to 

increased suspension of wind-borne geologic material from exposed soils. 

For characterization of tropospheric chemistry, the deposition of O3, NOx, peroxides, and 

ammonia are first-order problems, followed by deposition of organics, SO2, and particulate 

sulfate and nitrate (Wesely and Hicks, 2000). For impact on vegetation, however, the order may 

be different and may include different species — notably SO2, fluoride (where it still exists as a 

problem), particulate heavy metals, base cations, sulfate and nitrate. In spite of the current 

regulatory focus on non-speciated PM, exposure to a given mass concentration of PM may lead 

to widely differing phytotoxic outcomes depending upon the particular mix of PM constituents 

involved. This variability has not been characterized adequately. Though effects of specific 

chemical fractions of PM have been described, there has been relatively little research aimed at 

defining the effects of unspeciated PM on plants or ecosystems. 

4.2.2.2 Diversity of Deposition Modes 

Atmospheric deposition of particles to ecosystems takes place via both wet and dry 

processes, through three major routes: (1) wet, by precipitation scavenging in which particles 

are deposited in rain and snow; (2) much slower dry deposition; and (3) occult deposition 

(so named because it was hidden from measurements which determined the previous two types 

of deposition) by fog, cloud-water, and mist interception (Table 4-2). Unlike gaseous dry 

deposition, neither the solubility of the particle nor the physiographical activity of the surface are 

likely to be of first order importance in determining particulate dry deposition velocity (Vd). 

Factors that contribute to surface wetness or stickiness may be critical determinants of deposition 

efficiency. Available tabulations of deposition velocity are highly variable and suspect. Recent 

evidence indicates that all three modes of deposition (wet, occult, and dry) must be considered in 

determining inputs to watersheds or ecosystems, because each may dominate over specific 

4-8
TABLE 4-2. TYPES AND DETERMINANTS OF PARTICULATE DEPOSITION

AND IMPACT TO VEGETATION

Type of Deposition Determinant of Deposition Quantifiable Factors 

Dry deposition Ambient Concentration Distance from Source 

Emission Strength 

Atmospheric Conditions Wind Speed 

Stability 

Mixing Height 

Temperature 

Humidity 

Dew Formation 

Aerosol Properties Chemical Reactivity 

Particle Solubility 

Aerodynamic Diameter 

Biological Availability 

Hygroscopicity 

Surface Roughness Terrain Discontinuity 

Leaf Pubescence 

Leaf Shape 

Plant Density 

Branch Spacing 

Tissue Flexibility 

Vegetation Condition Surface Wetness 

Salt Exudates 

Organic Exudates 

Insect Excreta 

Wet deposition Ambient Concentration Distance from Source 

Emission Strength 

Atmospheric Conditions Mixing Height 

Timing of Precipitation 

Intensity of Precipitation 

Duration of Precipitation 

Aerosol Properties Chemical Reactivity 

Particle Solubility 

Biological Availability 

Surface Roughness Terrain Discontinuity 

Leaf Pubescence 

Leaf Area Index 

Nature of Exposed Bark and Stem 

Occult deposition As Above Combination of Above Factors 

4-9
intervals of time or space and ultimately, by interception and impaction, onto vegetation or other 

rough elements in the landscape. 

The distribution of deposition between wet, dry, and occult modes varies substantially 

between locations for both nitrogen and sulfur (Figure 4-2). Clearly, rainfall and snowfall will 

determine the magnitude of wet deposition. Precipitation events clean the air so that dry 

deposition is eliminated or reduced during subsequent dry periods. Occult deposition depends 

upon landscape interception of the cloud base (Cape, 1993). This may occur at high elevation 

sites, in coastal areas subject to onshore advection, or in low-lying interior areas subject to 

radiation fogs. Thus, ecosystem exposure is determined by the mode, and to some extent the 

magnitude, of deposition. Total deposition among mountain sites is related to the magnitude of 

the occult deposition, particularly for nitrogen (Figure 4-2). Topography and vegetation 

characteristics influence the deposition modes differently (Table 4-3). In general, dry deposition 

is the most sensitive to features of the vegetated surface, and wet deposition is the least sensitive. 

Comparison of micrometeorological and other methods for estimating particle deposition 

velocity (Erisman et al., 1997) suggests that there is little discrepancy between contrasting 

methodologies for estimating particle deposition and that this conclusion holds for both anions 

and cations, with the exception of nitrogenous species, which appear to interact with foliage in 

more complex ways. These comprehensive studies in the Speulder forest in the Netherlands 

indicated that aerosol deposition represents a considerable fraction of total deposition to the 

landscape. At this location, occult deposition was relatively insignificant, but dry deposition 

accounted for about one-fourth of alkaline-earth cation deposition. 

Wet Deposition 

Wet deposition results from the incorporation of atmospheric particles and gases into cloud 

droplets and their subsequent precipitation as rain or snow, or from the scavenging of particles 

and gases by raindrops or snowflakes as they fall (Lovett, 1994). Precipitation scavenging, in 

which particles are incorporated in hydrometers and deposited in the resulting rain and snow, 

includes rainout (within-cloud incorporation by nucleation) and washout (below-cloud 

scavenging by impaction). Wet deposition generally is confounded by fewer factors than dry or 

occult deposition and has been easier to quantify. Total inputs from wet deposition to vegetative 

canopies can be significant (Table 4-3), although not all wet deposition involves particle 

4-10
Figure 4-2. Relative importance of three modes of deposition of nitrate (A) and sulfate (B) 

at high elevation sites (Unsworth and Wilshaw, 1989; Fowler et al., 1989; 

Mueller, 1988; Aneja and Murthy, 1994). 

4-11
TABLE 4-3. RELATIVE IMPORTANCE OF WET, DRY, PARTICULATE, AND 

TOTAL DEPOSITION TO THREE FOREST SITES a 

Deposition 

Total Nitrogenb Total Sulfurc 

Wet Dry Particle Total Wet Dry Particle Total 

Site (%) (%) (%) (kg/ha) (%) (%) (%) (kg/ha) 

Duke Forest 75 25 0.11 9.87 64 33 2.7 17.20 

Cary Forest 71 20 0.94 5.80 76 20 4.2 7.60 

Austin Forest 71 29 0.58 6.57 83 13 4.3 7.79 

a Data from Allen et al. (1994). Sampling was by triple filter pack so that fine-mode particles could be sampled

 preferentially. An average particle deposition velocity of 0.9 cm/s was derived as in Hicks et al. (1987). b Wet nitrogen consists of NO3 

!

 and NH4

+

; dry nitrogen consists of vapor phase HNO3 and NO2; and particulate ! nitrogen consists of NO3 . c Wet sulfur consists of SO !2.

4

2!

, dry sulfur consists of vapor phase SO2, and particulate sulfur consists of pSO4 

scavenging because gaseous pollutants also dissolve in raindrops during precipitation events 

(Lovett, 1994). This contribution is obscured during measurements, because wet deposition is 

measured simply by chemical analysis of total precipitation collected in clean, nonreactive 

buckets. Exclusion of dry deposited material (as opposed to dissolved gaseous species) requires 

closure or covering of the vessels except during precipitation. 

Wet deposition is largely a function of precipitation amount and ambient pollutant 

concentrations. Surface properties (Table 4-2) have little effect on wet deposition, compared to 

dry or occult deposition, although leaves (depending on their surface properties of wettability, 

exposure, and roughness) retain liquid and solubilized PM. Extensive vegetative canopies 

typically develop leaf area indices (LAI; ratio of projected leaf area to ground area) much greater 

than 1. Thus, any material deposited via precipitation to the upper stratum of foliage is likely to 

be intercepted by several foliar surfaces before reaching the soil. 

Landscape characteristics may also affect wet deposition. Forested hillsides often receive 

four- to six-fold greater inputs of wet deposition than short vegetation in nearby valleys. This 

effect is due to a variety of orographic effects (Unsworth and Wilshaw, 1989) and the closer 

aerodynamic coupling to the atmosphere of tall forest canopies than of the shorter canopies in 

the valleys. This leads to more rapid foliar drying in forest camopies, which reduces the 

4-12
residence time but concentrates the solubilized particulate materials available for foliar uptake 

on the cuticular surface more quickly; and concentration increases the thermodynamic driving 

force for foliar uptake (Fowler et al., 1991; Unsworth, 1984; Schönherr and Huber, 1977). 

Following wet deposition, humidity and temperature conditions strongly influence the extent of 

biological effects, because of the competing effects of drying versus concentrating the solutions, 

which influence the rate of metabolic uptake of surface solutes (Swietlik and Faust, 1984). The 

net consequence of these factors on direct physical effects of wet deposited PM on leaves is not 

known. 

Rainfall introduces new wet deposition and redistributes throughout the canopy previously 

dry-deposited PM, particularly coarse particles that were preferentially deposited in the upper 

foliage (Peters and Eiden, 1992). Both effects scale the likelihood of foliar contact and potential 

direct PM effects on vegetation nearly linearly with canopy leaf area. The concentrations of 

suspended and dissolved materials are typically highest at the onset and decline with duration of 

individual precipitation events (Lindberg and McLaughlin, 1986; Hansen et al., 1994). 

Sustained rainfall removes much of the accumulation of dry-deposited PM from foliar surfaces, 

reducing direct foliar effects and combining the associated chemical burden with the wetdeposited material (Lovett and Lindberg, 1984; Lovett, 1994) for transfer to the soil. Intense 

rainfall may contribute substantial total particulate inputs to vegetated land surfaces, mostly via 

the soil, but is less effective as a source of directly bioavailable or injurious pollutants to foliar 

surfaces. This washing effect, combined with differential foliar uptake and foliar leaching of 

different chemical constituents of PM, alters the composition of the rainwater that reaches the 

soil. Low intensity precipitation events, in contrast, may deposit significantly more particulate 

pollutants to foliar-surfaces than high intensity precipitation events. Because of the short 

duration and limited atmospheric cleansing, the concentration of PM in the final precipitation 

that remains in contact with foliar surfaces may be high. Additionally, low-intensity events may 

enhance foliar uptake through the hydrating of some previously dry-deposited particles. The 

combination of dry deposition to foliage and subsequent wet removal increases the potential for 

PM to exert effects via soil pathways: first by enhancing dry deposition relative to adjacent 

unvegetated surfaces; and second by accelerating passage along with wet deposited PM material 

by throughfall and stemflow to the soil. Once, in the soil, PM may affect the important 

ecosystem-level biogeochemical cycles of major, minor, and trace elements. 

4-13
Dry Deposition 

Dry particulate deposition is a complex, poorly characterized process controlled primarily 

by atmospheric stability, macro- and micro-surface roughness, particle diameter, and surface 

characteristics (Table 4-2; Hosker and Lindberg, 1982). The range of particle sizes, the diversity 

of canopy surfaces, and the variety of chemical constituents in airborne PM have slowed 

progress in both prediction and measurement of dry particulate deposition. Deposition of 

particles suspended regionally and throughout the full depth of the planetary boundary layer 

(PBL) is controlled by different mechanisms within the three distinct atmospheric transport 

zones above the surface. In the lower atmosphere, fine particles are transported by turbulent 

eddies of mechanical and convective origin. In the relatively unstirred, laminar boundary layer 

surrounding individual surface elements, Brownian diffusion dominates. Near the surface, 

actual deposition and contact with the surface is mediated by impaction (El-Shobokshy, 1985). 

Dry deposition of atmospheric particles to plant and soil is a much slower process than wet 

or occult deposition, but it occurs nearly continuously and affects all exposed surfaces (Hicks, 

1986). In dry deposition, particles at the large end of the spectrum (i.e., > 5 μm diameter) are 

deposited mainly by gravitational sedimentation and inertial impaction. Smaller particles, 

especially those with diameters between .0.2 and 2 μm, are not readily dry-deposited and tend 

to travel long distances in the atmosphere until their eventual deposition, most often via 

precipitation. This long-distance transport of fine aerosols is largely responsible for the regional 

nature of acid deposition (Lovett, 1994). A major conclusion from atmospheric deposition 

research is the realization that dry deposition is usually a significant and, in some cases, a 

dominant portion of total atmospheric deposition to an ecosystem (Lovett, 1994). Plant parts of 

all types, including those not currently physiologically active, along with exposed soil and water 

surfaces, receive steady deposits of dry dusts, elemental carbon encrustations, grease films, tarry 

acidic coatings, and heterogeneous secondary particles formed from gaseous precursors (U.S. 

Environmental Protection Agency, 1982). 

Deposition fluxes may be calculated from measurements, estimates, or modeled values of 

mass concentration, C, at a specified measurement height and the total conductance or deposition 

velocity, Vd, from this height to the surface (equation 4-1; Hicks et al., 1987). These modeling 

techniques are closely allied with the micrometeorological techniques used to measure such 

fluxes. The flux, F, may be inferred as: 

4-14
(4-1) 

where F is flux to the surface, Cz is the particle concentration at measurement height z, Co is the 

particle concentration at receptor sites in the canopy (usually assumed equal to 0), and Vd is the 

overall deposition velocity. The flux is controlled by Vd and Cz. 

Vertical transport of particles through the lower atmosphere to the vicinity of the 

vegetation elements is by turbulence and sedimentation, such that: 

(4-2) 

in which Vt

 (inner, left-hand pathway of Figure 4-3) is a turbulent diffusion term, and Vs is a 

sedimentation term that dominates deposition of very coarse particles (Figure 4-4) and increases 

with particle size (Figure 4-5; dotted line). Sedimentation may be considered a pathway parallel 

to turbulent transport (Figure 4-4), but this is an oversimplification. Vs affects the concentration 

of particles near the surface where eddy transport may occur and also governs the redeposition 

of some fraction of the particles that were lost to resuspension or rebound following deposition 

by impaction. For this reason, Vs is included (Figure 4-3) in the composite surface resistance 

term (RaR Vs) as well as in the parallel sedimentation term. cp

For submicron particles for which sedimentation is negligible (Hicks et al., 1987; Monteith 

and Unsworth, 1990; Wesely, 1989), the resistance catena Ohm’s Law Analogy analogous to the 

law used to describe transport of heat, momentum, or gases may be adequate, as: 

(4-3) 

where Vt

 is the deposition velocity due to turbulent transport of particles or other entities through 

the atmosphere; ra is aerodynamic resistance (inverse of conductance or velocity) associated with 

the efficiency of turbulent transport above the canopy; rb is the boundary layer resistance 

associated with diffusional transport through the still air layer immediately adjacent to canopy 

elements; and rc is canopy resistance associated with physiological control of leaf porosity via 

stomata in the leaf surface. Significant departures from the analogy arise near the surface 

4-15
Figure 4-3. A simplified resistance catena representing the factors controlling deposition 

of particles to the surface. Vegetation-specific parameters are not explicitly 

considered. 

Source: Modified after Hicks et al. (1987). 

(Chamberlain, 1975; Sehmel, 1980), where particles that were transported efficiently by 

turbulent eddies are slowed substantially in the laminar boundary layer, reducing the efficiency 

of impaction. The preservation of momentum in this zone declines with decreasing diameter; 

however, this is offset by an increase in Brownian diffusivity with decreasing diameter 

(Figure 4-3). Aerodynamic streamlines are parallel to the surface of each roughness element, 

so that deposition ultimately depends on diffusion to the surface. The transition from impaction 

to diffusion is likely blurred in the presence of leaf pubescence extending beyond the boundary 

layer. These conflicting trends lead to a broad range over which empirical measurements of Vd 

and particle size are relatively independent (Figure 4-3), further demonstrating the importance of 

the quasilaminar boundary layer (Lamaud et al., 1994; Shinn, 1978). 

4-16
Figure 4-4. The relationship between deposition velocity of selected particulate materials 

and the distribution of the material between the coarse- and fine-aerosol 

fractions. 

Source: Data from Foltescu et al. (1994). Ranges for Mn and Fe are from Davidson and Wu (1989). 

The aerodynamic term (ra) decreases with increasing wind speed, turbulence, and friction 

velocity and increases with measurement height and atmospheric stability. It describes the 

capacity of turbulent eddies to transport material, momentum, and heat between the 

measurement height and the roughness height of the surface. Coarse particles may not be carried 

efficiently by the high frequency eddies near the surface and may fall more rapidly than they 

diffuse by either Brownian or turbulent processes. Thus, the relevance of ra breaks down as Vs 

increases. Indeed because Vs (equation 4-2) is independent of a concentration gradient, the 

electrical analogy is a theoretically flawed approximate approach (Venkatram and Pleim, 1999). 

4-17
Figure 4-5. The relationship between particle diameter and deposition velocity for 

particles. Values measured in wind tunnels by Little and Wiffen (1977) over 

short grass with wind speed of 2.5 m/s closely approximate the theoretical 

distribution determined by Peters and Eiden (1992) for a tall spruce forest. 

These distributions reflect the interaction of Brownian diffusivity (descending 

dashed line), which decreases with particle size and sedimentation velocity 

(ascending dotted line from Stokes Law), which increases with particle size. 

Intermediate-sized particles (.0.1 to 1.0 μm) are influenced strongly by both 

particle size and sedimentation velocity, and deposition is independent of size. 

Deposition Velocity 

Because the final stage of deposition for particles involves either impaction following 

deceleration through a quasilaminar boundary layer or diffusion through this boundary layer, its 

effective depth is a critical determinant of Vd (Wiman et al., 1985; Peters and Eiden, 1992). The 

term corresponding to the boundary layer resistance for gases (rb; equation 4-3) incorporates the 

absence of form drag for gases. This parameter decreases with increasing turbulence and 

particle diffusivity; it is poorly characterized for gases (depending critically on canopy 

morphology, vertical wind profiles, and gust penetration) and is of extremely limited usefulness 

for particles. 

4-18
Once delivered by turbulent transport or sedimentation to the vicinity of vegetative surface 

elements, a variety of particle size-dependent mechanisms come into play, some differing 

substantially from those governing gaseous deposition. The concepts of rb (the still air or 

boundary layer resistance) and rc (the canopy or surface resistance) are not generally applicable 

to deposition of polydisperse particles. Because of the roles of momentum and bounce-off and 

the complication by reentrainment back into the airstream following deposition of a particle to 

the surface, the factors determining the effective rb and rc for particle deposition are not as 

independent as for gases. They are replaced in some resistance formulations (e.g., Hicks et al., 

1987) by the term, rcp, that combines near-surface and surface effects and by a mathematically 

derived composite term, RaR Vs, that combines atmospheric, surface, and sedimentation effects cp

(Figure 4-4). This latter term was insignificant for the submicron sulfate component considered 

originally in its derivation (Hicks et al., 1987); however, it scales with the square of particle 

diameter, so that its general applicability to polydisperse particles is unclear. In general, 

transport between the turbulent air column and the leaf surface through the laminar boundary 

layer remains difficult to describe (Lindberg and McLaughlin, 1986). 

Current estimates of regional particulate dry deposition (e.g., Edgerton et al., 1992; Brook 

et al., 1999) infer fluxes from the product of variable and uncertain measured or modeled 

particulate concentrations and even more variable and uncertain measured or modeled estimates 

of dry deposition velocity parameterized for a variety of specific surfaces (e.g., Brook et al., 

1999). However, even for specific sites and well defined particles, uncertainties in F are largest 

in the values of Vd, which are typically characterized by the large ranges and variances described 

in other sources (e.g., Bytnerowicz et al., 1987a,b and Hanson and Lindberg, 1991, for nitrogencontaining particles; McMahon and Denison, 1979 and Hicks et al., 1987, for general treatment). 

The nature of the vegetative cover to which particulate deposition occurs has a moderate to 

substantial effect on the components of Vd. The surface resistance (Hicks et al., 1987) is a 

significant and highly site-specific component of total resistance that is difficult to predict along 

with site, seasonal, and diurnal effects on the atmospheric components of total resistance. 

Early models of dry particulate deposition to vegetation (e.g., U.S. Environmental 

Protection Agency, 1982; Chamberlain, 1975; Davidson and Friedlander, 1978; Garland, 1978; 

Little and Wiffen, 1977; McMahon and Denison, 1979; Sehmel, 1980; Sehmel and Hodgson, 

1976; Slinn, 1977, 1978) used this paradigm (e.g., equation 4-3) to deal with transport to the 

4-19
near-surface regime explicitly including conventional micrometeorological and particle size 

considerations. Alternative modeling treatments have attempted to parameterize the geometry of 

vegetative receptor surfaces and within-canopy micrometeorology (Wiman and Ågren, 1985; 

Peters and Eiden, 1992). Chemical reactivity, particle shape and density, rates of physiological 

sequestration, and reentrainment by gusts of wind remain to be addressed. Modeling the 

deposition of particles to vegetation is at a relatively early stage of development, and it is not 

currently possible to identify a best or most generally applicable modeling approach. These 

approaches have been further elaborated with canopy-specific choices among the available 

models and with specific incorporation of capture efficiencies by Brook et al. (1999). 

Methods of Measuring Dry Deposition 

Methods of measuring dry deposition of particles are more restricted than for gaseous 

species and fall into two major categories: surface analysis methods, which include all types of 

measurements that examine contaminant accumulation or surfaces of interest, and atmospheric 

flux methods, which measure contaminants in the atmosphere from which one may estimate the 

flux (Davidson and Wu, 1990). Surface extraction or washing methods characterize the 

accumulation of particles on natural receptor surfaces of interest or on experimental surrogate 

surfaces. These techniques rely on methods designed specifically to remove only 

surface-deposited material (Lindberg and Lovett, 1985). Total surface rinsate may be equated to 

accumulated deposition or to the difference in concentrations in rinsate between exposed and 

control (sheltered) surfaces and may be used to refine estimates of deposition (John et al., 1985; 

Dasch, 1987). In either case, foliar extraction techniques may underestimate deposition to leaves 

because of uptake and translocation processes that remove pollutants from the leaf surface 

(Taylor et al., 1988; Garten and Hanson, 1990). Foliar extraction methods also cannot 

distinguish sources of chemicals (e.g., N) deposited as gases from those deposited as particles 

(e.g., HNO3 or NO3 

!

 from nitrogen dioxide [NO2], or NH3 from NH4

+

 [Bytnerowicz et al., 

1987a,b; Dasch, 1987; Lindberg and Lovett, 1985; Van Aalst, 1982]). Despite these limitations, 

these methods are often used in the development of in-canopy deposition models (McCartney 

and Aylor, 1987). 

Deposition of pollutants by wet deposition is relatively straightforward to determine 

through analysis of precipitation samples. Dry deposition of pollutants, on the other hand, is 

4-20
more difficult to measure. A significant limitation on current capacity to estimate regional 

impacts of PM is inadequate knowledge of the mechanism and factors governing particle dry 

deposition to diverse surfaces. This has hindered efforts to develop robust measurement 

techniques for particle deposition (distinct from atmospheric concentration) and has 

compromised efforts to develop generally applicable deposition models for particles. The 

National Dry Deposition Network (NDDN) was established in 1986 to document the magnitude, 

spatial variability, and trends in dry deposition across the United States. Currently, the network 

operates as a component of the Clean Air Status and Trends Network (CASTNet) (Clarke et al., 

1997). 

Dry deposition is not measured directly, but is determined by a inferential approach (i.e., 

fluxes are calculated as the product of measured ambient concentration and a modeled deposition 

velocity). This method is appealing and widely used because atmospheric concentrations are 

relatively easy to measure when compared to dry deposition fluxes, and models have been 

developed to calculate deposition velocities (Lovett, 1994). Ambient pollutant concentrations, 

meteorological conditions, and land use data required for the inferential model are routinely 

collected at CASTNet dry deposition sites. Monitored chemical species include ozone, sulfate, 

nitrate, ammonium, sulfur dioxide, and nitric acid. The temporal resolution for the ambient 

concentration measurements and dry deposition flux calculations is hourly for ozone and weekly 

for the other chemical substances (Clarke et al., 1997). Isotopic labeling of dry deposited PM 

(e.g., sulfate with 35S) prior to experimental surface exposures and extractions (Garten et al., 

1988) can provide more precise differentiation between the deposition rates of related chemical 

species (e.g., SO4

2!

 from SO2). 

At the whole-canopy level, natural surface washing by rainfall may be used to estimate dry 

deposition of PM and gases during the preceding dry period (Cape et al., 1992; Davidson and 

Wu, 1990; Draaijers and Erisman, 1993; Erisman, 1993; Fahey et al., 1988; Lindberg and Lovett, 

1992; Lovett and Lindberg, 1993; Reiners and Olson, 1984; Sievering, 1987). Collection and 

analysis of stem flow and throughfall provides useful estimates of particulate deposition when 

compared to directly sampled precipitation. The method is most precise for strictly PM 

deposition when gaseous deposition is a small component of the total dry deposition and when 

leaching or uptake of compounds of interest out of or into the foliage (i.e., N, S, base cations) is 

not a significant fraction of the total depositional flux (Davidson and Wu, 1990; Draaijers and 

4-21
Erisman, 1993; Lindberg and Lovett, 1992; Lovett and Lindberg, 1993). Throughfall sampling 

of sulfate deposition (Garten et al., 1988; Lindberg and Garten, 1988; Lindberg et al., 1990) 

often suggests substantial foliar exchange. Other throughfall studies (e.g., Erisman, 1993; Fahey 

et al., 1988) may lack sufficient specificity for evaluating dry particle deposition. 

Careful timing of throughfall and stemflow measurements after prolonged dry periods, 

with simultaneous direct measurement of unchanged precipitation, can be used to determine the 

magnitude of dry deposition between precipitation events. Indeed, this foliar washing technique, 

whether using subsequent precipitation or experimental lavage, is one of the best available 

methods to determine dry deposition of PM to vegetated ecosystems. Major limitations include 

the extreme site specificity of the measurements, the substantial labor requirement that normally 

precludes regional coverage, and the restriction to elements that are conserved within the 

vegetative system (thereby excluding semivolatile organics, ammonium salts, and gases such as 

ozone). 

Surrogate surfaces have not been found that can adequately replicate essential features of 

natural surfaces; and therefore the surfaces currently used do not produce reliable estimates of 

particle deposition to the landscape. Deposition to surrogate surfaces deployed in extensive 

plant canopies may provide a measure of particle deposition to the surrounding foliage or soil 

surfaces. For example, a uniform population of 0.8 μm gold colloid particles deposited with 

similar coverage to leaves of Phaseolus vulgaris and to upward-facing inert surfaces (Klepper 

and Craig, 1975). However, dry deposition of particles to foliage and to flat inert surrogate 

surfaces (polycarbonate Petri dishes) in a deciduous forest resulted in greater accumulation on 

the inert surfaces, although both surfaces accumulated particles of a similar range of sizes 

(Lindberg and Lovett, 1985). These differences in deposition/accumulation remain to be fully 

characterized and hinder the surrogate techniques in providing quantitative deposition estimates. 

Micrometeorological methods employ an eddy covariance, eddy accumulation, or flux 

gradient protocol in contrast to washing or extracting of receptor surfaces for quantifying dry 

deposition. These techniques require measurements of PM concentrations and of atmospheric 

transport processes. They are currently well developed for ideal conditions of flat, 

homogeneous, and extensive landscapes and for chemical species for which accurate and rapid 

sensors are available. Additional studies have expanded the variety of such species and extended 

4-22
these techniques to more complex terrain (McMillen, 1988; Hicks et al., 1984; Wesely and 

Hicks, 1977). 

The eddy covariance technique measures vertical fluxes of gases and fine particles directly 

from calculations of the mean covariance between the vertical component of wind velocity and 

pollutant concentration (Wesely et al., 1982). This technique is particularly limited by its 

requirement for sensors capable of acquiring concentration data at 5 to 20 Hz. For the fluxgradient or profile techniques, vertical fluxes are calculated from a concentration difference and 

an eddy exchange coefficient determined at discrete heights (Erisman et al., 1988; Huebert et al., 

1988). Businger (1986), Baldocchi (1988), and Wesely and Hicks (1977) evaluated the benefits 

and pitfalls of these micrometeorological flux measurements for gases. Most measurements of 

eddy transport of PM have used chemical sensors (rather than mass or particle counting) to focus 

on specific PM components. These techniques have not been well developed for generalized 

particles and, for the same reasons that limit mathematical description of particle deposition, 

may be less suitable for coarse particles that are transported efficiently in high frequency eddies 

(Gallagher et al., 1988). 

Factors Affecting Dry Deposition 

Ambient Concentration. The ambient concentration of particles (Cz; equation 4-1), the 

parameter for which there is the most data (for example, see Chapter 3, this document), is at best 

an indicator of exposure. However, it is the amount of PM actually entering the immediate plant 

environment that determines the biological effect. The linkage between ambient concentration 

and delivery to vegetation is the deposition velocity (Vd; equation 4-1). Cz is determined by 

regional and local emission sources, regional circulation, and weather. It may be locally 

sensitive to removal from the atmosphere by deposition, but this effect is generally small. 

Average annual concentrations for NO3 

!

 exhibit much more variability than SO4 

!2 and a definite 

pattern of higher concentrations in the Midwest than elsewhere. The highest concentrations are 

observed (i.e, > 2 μg m!3

) in the agricultural areas of the Midwest, while the lowest are seen at 

forested sites in New England and the southern Appalachian Mountains. Annual average 

concentrations SO4

2!

 of 5.0 μg m!3 are observed over nearly the entire eastern United States from 

New York and Michigan to northern Mississippi and Alabama (Edgerton et al., 1992). 

4-23
Deposition increases linearly with the concentration of many materials over a broad range. 

This allows atmospheric cleansing to take place and accounts for the greater surface impact of 

pollutant particles during pollution episodes. A serious limitation of the Vd formulation used to 

infer deposition of specific chemical species that exist in a range of particle sizes is an 

appropriate specification of their concentration. Most sulfur emissions are readily oxidized to 

2! sulfite, bisulfite, and SO4 . In the presence of atmospheric ammonia, particulate ammonium 

sulfate is formed. However, this material is hygroscopic and will increase in mass and diameter 

in the presence of high humidity, altering its deposition behavior. Similarly, coalescence of 

small particles into larger aggregates and adsorption of gaseous pollutants onto existing coarse 

particles complicate the association of particle size with concentration of individual chemical 

species. 

Distance from the source, and the resulting residence time in the atmosphere, control the 

relative concentrations of surface-reactive materials (NO, SO2) of secondary particles that take 

some time to form in the atmosphere and of coarse particles that exhibit high rates of deposition 

by sedimentation near the source. These interacting processes affect the time required for the 

formation of secondary particles by gas-to-particle conversion reactions, resulting in a greater 

ratio of dry to wet deposition near emission sources where gaseous sulfur dioxide (gSO2) 

2! deposition predominates than at greater distances where rainout of particulate SO ) 42!

 (pSO4 

may dominate (Barrie et al., 1984) and where dry deposition of pSO4

2!

 may be greater than that 

of gSO2. The effect of gas-to-particle conversion on dry deposition of a specific chemical 

species can be substantial because Vd for SO2 is approximately 0.33 ± 0.17 cm/s; whereas it is 

2! approximately 0.16 ± 0.08 cm/s for SO4 . These phase conversions affect both Cz and the 

effective Vd which together control dry depositional fluxes (equation 4-1). The neutralization of 

acidic gaseous and particulate species by alkaline coarse particles has been described in arid 

regions, but it may be more prevalent in urban New York where coarse particles are observed to 

be neutral because alkaline cations approximately balance gaseous acidic species (Lovett et al., 

2000). The deposition of the acidic materials in the urban environment is likely enhanced by 

incorporation into these previously formed coarse particles. 

Similarly, the ratio of coarse to fine particle concentrations determines the effective Vd for 

chemically speciated particles (Figure 4-4). This ratio reflects the size-dependent deposition 

2! processes that govern delivery of PM to receptor surfaces (Figure 4-5). For example, SO4 was 

4-24
found predominantly on fine submicron particles; whereas potassium (K+

), calcium (Ca2+), and 

nitrate (NO3 

!

) ions were associated most often with coarse particles larger than 2 μm (Lindberg 

and Lovett, 1985). However, concentrations of particulate S and K+

 within a coniferous canopy 

were strongly correlated (Wiman and Lannefors, 1985), suggesting a primary source of 

coarse-mode sulfur particles. Many researchers reported biomodal mass-size distributions of 

NO3 

!

 with one peak in the fine mode and the other peak in the coarse mode (Wu and Okada, 

1994). The behavior of NO3 

!

 in ambient aerosols depends not only on the concentrations of 

gaseous nitric acid and ammonia, but also on the chemical composition of the particles and 

atmospheric conditions (Tang, 1980). When sea-salt particles were transported atmospherically 

from the Pacific Ocean in the Nagoya, Japan area, the amount of nitrate in the coarse particle 

size range increased. Coarse particle formation on sea salt under these conditions becomes a 

major pathway for nitrate. The heterogeneous reaction of NaCl with gaseous HNO3 is 

considered to be the dominating pathway for nitrate formation (Wu and Okada, 1994). As a 

result, marine and continental particle size spectra for both N and S differ substantially: a peak 

in the coarse mode is generally apparent near marine sources (Milford and Davidson, 1987). 

The issue for NO3 

!

 is further confounded by uncertain discrimination between gaseous and 

particulate N species by current sampling methods. The substantial effect of particle size on Vd 

(Figure 4-5) implies a need for size resolution as well as chemically speciated ambient PM 

concentrations, even within the PM10 fraction. 

Particle Effects on Vd. Particle size is a key determinant of Vd as noted above; but, 

unfortunately, the size spectra may be quite complex. The particles in the study of Lindberg and 

Lovett (1985) at Walker Branch Watershed had median diameters ranging from 3 to 5 μm; but 

approximately 25% of the particles had diameters < 1 μm (0.2 to 0.3 μm), and 5 to 20% of the 

particles were much larger aggregates. The aggregated particles are significant in that 

chemically they reflect their fine particle origins, but physically they behave like large particles 

and deposit by sedimentation. Direct observations with SEM demonstrate that particle 

morphology can be highly variable. Many submicron particles can be observed on trichomes 

(leaf hairs), although most particles are in the 5 to 50 μm diameter range. Large aggregated 

particles in excess of 100 μm also are seen; larger carbonaceous aggregate particles are 

4-25
especially common (Smith, 1990a). Trichomes are efficient particle receptors; however, they are 

reduced in size by “weathering” and occasionally break off during the growing season. 

In the size range around approximately 0.1 to 1.0 μm, where Vd is relatively independent of 

particle diameter (Figure 4-5), deposition is controlled by macroscopic roughness properties of 

the surface and by the stability and turbulence of the atmospheric surface layer. The resistance 

catena (Figure 4-3) is less useful in this size range and, in some treatments, has been abandoned 

entirely (e.g., Erisman et al., 1994; equation 4-4). Impaction and interception dominate over 

diffusion, and the Vd is considerably (up to two orders of magnitude; Figure 4-2) lower than for 

particles either smaller or larger (Shinn, 1978). The deposition velocity may be parameterized in 

this size range as a function of friction velocity, 

(4-4) 

where a depends on atmospheric stability and b depends on surface roughness (Wesely et al., 

1985; Erisman et al., 1994). Similar formulations have been presented in terms of turbulence 

(standard deviation of wind direction) and wind speed (e.g., Wesely et al., 1983), both 

determinants of u*. 

Deposition of particles between 1 and 10 μm in diameter, including the coarse mode 

of PM10, is strongly dependent on particle size (Shinn, 1978). Larger particles within this size 

range are collected more efficiently at typical wind speeds than are smaller particles (Clough, 

1975), suggesting the importance of impaction. Impaction is related to wind speed, the square of 

particle diameter, and the inverse of receptor diameter as a depositing particle fails to follow the 

streamlines of the air in which it is suspended around the receptor. When particle trajectory 

favors a collision, increasing either wind speed or the ratio of particle size to receptor cross 

section makes collision nearly certain. Similarly, as these parameters become very small, the 

probability of collision becomes negligible. However, the shape parameter for the more 

common range of situations between these extremes remains poorly characterized (Peters and 

Eiden, 1992; Wiman and Ågren, 1985). 

As particle size increases above 1 μm, deposition is governed increasingly by 

sedimentation (Figure 4-5) and decreasingly by turbulence and impaction. Particles between 

~10 and 24 μm (Gallagher et al., 1988) are both small enough to be transported efficiently by 

4-26
turbulent eddies to the surface and large enough to impact with sufficient momentum to 

overcome boundary layer effects. These particles deposit highly efficiently and relatively 

independently of particle size. Deposition of the largest suspended particles (e.g., > 50 μm) is 

governed, through sedimentation and the corresponding terminal settling velocity, Vs, almost 

entirely by size. These particles are not transported efficiently by small-scale eddies near the 

surface. 

Theoretically based models for predicting particle deposition velocities have been 

published by Bache (1979a,b), Davidson et al. (1982), Noll and Fang (1989), Slinn (1982), and 

Wiman (1985). These models deal primarily with low canopies or individual elements of 

canopy surfaces. Wiman and Ågren (1985) developed an aerosol deposition model that 

specifically treats the problem of particle deposition to forests where turbulence plays a 

particularly important role, especially at roughness transitions such as forest edges. They found 

that deposition of supermicron particles is controlled by complex interactions among particle 

size and concentration, forest structure, and aerodynamics; whereas deposition of fine 

(submicron) particles is controlled by particle concentration and forest structure. 

Empirical measurements of Vd for fine particles under wind tunnel and field conditions are 

often several-fold greater than predicted by available theory (Unsworth and Wilshaw, 1989). 

A large number of transport phenomena, including streamlining of foliar obstacles, turbulence 

structure near surfaces, and various phoretic transport mechanisms remain poorly parameterized 

in current models. The discrepancy between measured and predicted values of Vd may reflect 

such model limitations or experimental limitations in the specification of the effective size and 

number of receptor obstacles, as suggested by Slinn (1982). 

Available reviews (Davidson and Wu, 1990; McMahon and Denison, 1979; Nicholson, 

1988; Sehmel, 1980; Slinn, 1982; U.S. Environmental Protection Agency, 1982, 1996a) suggest 

the following generalizations: (1) particles > 10 μm exhibit a variable Vd between .0.5 and 

1.1 cm/s depending on friction velocities; whereas a minimum particle Vd of 0.03 cm/s exists for 

particles in the size range 0.1 to 1.0 μm; (2) the Vd of particles is approximately a linear function 

of friction velocity; and (3) deposition of particles from the atmosphere to a forest canopy is 

from 2 to 16 times greater than deposition in adjacent open terrain (i.e., grasslands or other 

vegetation of low stature). 

4-27
Leaf Surface Effects on Vd. The term rc (Equation 4-3) reflects the chemical, physical, or 

physiographical characteristics of the surface that govern its ability to capture, denature, or 

otherwise remove particulate material from the atmospheric surface layer. For gases, relevant 

surface properties involve the physiological state of the vegetation, including stomatal opening 

and mesophyll antioxidant activity, as well as the chemical reactivity of the exposed surface with 

the specific gas. For particles, relevant surface properties involve stickiness, microscale 

roughness, and cross-sectional area. These properties determine the probability of impaction and 

bounce (e.g., Shinn, 1978). The chemical composition of PM is not usually a primary 

determinant of deposition velocity. At the microscopic scale where Van der Waals forces may 

determine particle bounce and reentrainment, the chemical properties of both surface and particle 

may be significant but remain poorly understood. 

Stickiness may itself depend on previous deposition of deliquescent particles that prolong 

leaf wetness, on the wettability of foliar surfaces, and on the presence of sticky residues such as 

honeydew deposited by aphids. These factors increase deposition by decreasing bounce-off, 

wind reentrainment, and, to some extent, wash-off by precipitation. 

The distribution of particles on, and the efficiency of deposition to, vegetation also varies 

with leaf shape and plant part. Particles are more prevalent on the adaxial (upper surface facing 

twig) surface than on the abaxial (lower surface away from the twig). Peripheral leaf areas tend 

to be the cleanest, with most particles accumulating in the midvein, central portion of leaves. 

The rough area surrounding the stomatal pores was not found to be a preferential site for particle 

deposition or retention (Smith and Staskawicz, 1977). Most particles were located near veins 

with smaller particles localized on the trichomes. The greatest particle loading on 

dicotyledonous leaves is frequently on the adaxial (upper) surface at the base of the blade, just 

above the petiole junction. Precipitation washing probably plays an important role in this 

distribution pattern. Lead particles accumulated to a greater extent on older than younger 

needles and twigs of white pine, indicating that wind and rain were insufficient to fully wash the 

foliage. Fungal mycelia (derived from windborne spores) were frequently observed in intimate 

contact with other particles on other leaves, which may reflect minimal reentrainment of the 

spore due to shelter by the particles, mycelia development near sources of soluble nutrients 

provided by the particles, or simply codeposition (Smith and Staskawicz, 1977). This pattern is 

significant and could yield further insight into deposition mechanisms. 

4-28
Leaves with complex shapes collect more particles than those with regular shapes, but 

conifer needles are more effective than broad leaves in accumulating particles. The edge to area 

ratio (Woodcock, 1953) is also a key determinant of salt deposition to individual artificial leaves. 

A strong negative correlation was observed under wind tunnel conditions between the area of 

individual leaves and the deposition of coarse particles (Little, 1977). Small twigs and branches 

were more effective particle collectors than were large branches and trunks of trees (Smith, 

1984). Lead particles accumulated 20-fold more on woody stems than on needles of white pine 

(Pinus strobus), even though needles displayed a 10-fold greater total surface area (Heichel and 

Hankin, 1976). Deposition has heaviest at the tips of individual leaves. 

Rough, pubescent broadleaf discs collected coarse (5.0 μm) particles up to 7-fold more 

efficiently than glabrous leaf discs (Little, 1977). Laminae, petioles, and stems all differed in 

collection efficiency. Pubescent leaves of sunflower (Helianthus annuus) collected coarse 

particles nearly an order of magnitude more efficiently than the glabrous leaves of tulip poplar 

(Liriodendron tulipifera) under wind tunnel conditions (Wedding et al., 1975). The rough 

pubescent leaves of nettle (Urtica dioica) were more effective in capturing coarse PM10 than 

were the densely tomentose leaves of poplar (Populus alba) or smooth leaves of beech 

(Fagus sylvatica). 

Conifer needles are more efficient than broad leaves in collecting particles by impaction. 

This reflects the small cross section of the needles relative to the larger leaf laminae of 

broadleaves and the greater penetration of wind into conifer than broadleaf canopies. Conifers 

were more effective in removing coarse (.20 μm) particles of ragweed pollen from the 

atmosphere than were broadleaf trees (Steubing and Klee, 1970) and in intercepting even coarser 

rain particles (Smith, 1984). Conifers were also more effective in retaining and accumulating 

particles against reentrainment by wind and removal by rain, particularly on foliar surfaces 

where they are likely to be most biologically active. Seedlings of white pine (Pinus strobus) and 

oak (Quercus rubra) initially retained between a quarter (pine) and a third (oak) of very coarse 

(88 to 175 μm) 134Cs-tagged quartz particles applied under field conditions (Witherspoon and 

Taylor, 1971). After 1 h, the pine retained over 20% of the 134Cs-tagged particles; whereas the 

oak retained only approximately 3%. Long-term retention of the particles was concentrated at 

the base of the fascicles in pine and near the surface roughness caused by the vascular system on 

leaves of oak. The sheltered locations available in the conifer foliage contribute substantially to 

4-29
greater retention of particles. For similar reasons, grasses also are efficient particle collectors 

(Smith and Staskawicz, 1977) with long-term retention mostly in the ligule and leaf sheath. 

Because of the strong relationship between particle size and deposition, the sharply 

increasing humidity gradient near transpiring foliar surfaces may cause hygroscopic particles to 

behave, at the immediate surface, as larger particles than reflected in ambient measurements at 

reference heights. This needs to be further considered (Wesely and Hicks, 2000). Recent 

deposition models (Ruijgrok et al., 1997; Zhang et al., 2001) have introduced the role of ambient 

humidity but lack sufficient emphasis on the role of vegetation itself in modifying humidity near 

the surface. This may be significant, as the size of a dry, 0.5 μm particle of ammonium sulfate 

may increase to about 3.5 μm at saturated humidity (Ruijgrok et al., 1997). Kinetic analyses 

have suggested that full (95 to 100%) equilibration to the new diameter will occur during the 

deposition process in ambient humidity. 

Wind tunnel studies also demonstrated equivalent deposition properties for 3.4 μm 

particles of dense lead chloride and the 6.8 μm particles of the less dense uranine dye. These 

particles were shown to be aerodynamically equivalent, substantiating the use of aerodynamic 

diameter as a classification parameter for particle deposition. 

Canopy Surface Effects on Vd. In general, surface roughness increases particulate 

deposition. As a result, Vd is typically greater for a forest than for a field or nonwoody wetland 

and greater for a field than for a water surface. The contrasting transport properties and 

deposition velocities of different size particles lead to predictable patterns of deposition. 

For coarse particles, the upwind leading edges of forests, hedge rows, and individual plants, as 

well as of individual leaves, are primary sites of deposition. Impaction at high wind speed and 

the sedimentation that follows the reduction in wind speed and carrying capacity of the air in 

these areas lead to preferential deposition of larger particles. 

Air movement is slowed in proximity to vegetated surfaces. Resulting log profiles of wind 

and pollutant concentrations in the near-surface turbulent boundary layer above canopies reflect 

surface characteristics of roughness length, friction velocity, and displacement height. Plasticity, 

streamlining, and oscillations of foliar elements also alter the aerodynamic roughness and the 

level of within-canopy turbulence. Canopies of uneven age or with a diversity of species are 

typically aerodynamically rougher and receive larger inputs of pollutants than do smooth, low, or 

4-30
monoculture vegetation (Garner et al., 1989; Wiman and Ågren, 1985). Canopies on slopes 

facing the prevailing winds and individual plants on the windward edges of discontinuities in 

vegetative cover over which roughness increases receive larger inputs of pollutants than more 

sheltered, interior canopy regions. For example, some 80% of coarse particulate sea salt was 

deposited on the upwind edge of a hedgerow (Edwards and Claxton, 1964), and the ragweed 

pollen concentration was reduced by 80% within 100 m of the upwind edge of a forest 

(Neuberger et al., 1967). 

Beier et al. (1992) and Beier (1991) discussed two methods for estimating the dry 

deposition of base cations to forest edges: (1) a difference method between measured 

precipitation and throughfall concentrations of base cations, and (2) a calculation method based 

on known ratios of Na+

 deposition in wet and dry forms (Ulrich, 1983). A combination of these 

two approaches has produced the best estimates of SO4

2!

, Ca2+, Mg2+, and K+ particle deposition. 

Using these methods, particulate SO4

2!

 (Beier, 1991) and particulate Ca2+, Mg2+, and K+ (Beier 

et al., 1992) were found to decrease by an order of magnitude from the forest edge to the forest 

interior. A number of authors also have shown that particle deposition is elevated at forest edges 

when compared to a uniform forest canopy (Draaijers et al., 1988; Grennfelt, 1987; Lindberg and 

Owens, 1993), and Draaijers et al. (1992) reported that differences are likely to exist between 

forest types because of variable canopy structure. Draaijers et al. (1988) further emphasized that 

enhanced particle deposition at or near forest edges is strongly dependent on the velocity and 

wind direction during observations. 

The factors leading to horizontal gradients are confounded by time- and distance-related 

sedimentation. For example, geologic dust (mostly around 7 μm aerodynamic diameter) collects 

on stems of wild oats (Avena spp.; Davidson and Friedlander, 1978) and on eastern white pine 

(Heichel and Hankin, 1972; Smith 1973) downwind of roadways. Rapid sedimentation of coarse 

crustal particles suggests that potential direct effects may be restricted to roadway margins, 

forest edges, and, because of the density of unpaved roads in agricultural areas, crop plants. 

Simulated deposition to an ecologically complex, mixed canopy was considerably higher 

than to a pure spruce stand in which most of the leaf area was concentrated in regions of low 

wind speed. Limitations to the application of these models to predict deposition over large 

regions include an incomplete understanding both of the nature of microscopic particle-surface 

4-31
interactions and of the effects of complex terrain and species composition on macroscopic 

transport processes. 

Macroscopic turbulent transport processes, related to ra at successive layers through the 

canopy, can be separated from microscopic processes related to rb and rc (or rcp) at each 

deposition surface (e.g., Peters and Eiden, 1992; Wiman and Lannefors, 1985). The 

macroscopic approach deals with deposition as the product of a turbulent diffusion coefficient 

and a given concentration (Cz) at each canopy layer, both of which vary with particle size and 

with height (z) in the canopy. The microscale parameters involve those factors that determine 

absorption of a particle at each surface as captured imperfectly by rc. Shelter effects caused by 

the crowding of foliar elements within the canopy can be ignored if the wind speed within each 

canopy layer is specified. This approach requires knowledge of the vertical distribution of 

particle concentration and foliage density in the canopy airspace along with profiles of wind 

speed or turbulence. 

Once introduced into a forest canopy, elements associated with coarse particles tend to 

decrease markedly with canopy depth; whereas elements associated with fine particles do not 

(Lovett and Lindberg, 1992). Trace elements and alkaline-earth elements are enriched below the 

canopies of both southern (Lindberg et al., 1986) and northern (Eaton et al., 1973) hardwood 

forests. Vertical gradients in concentration of coarse particles and of elements associated with 

coarse particles were observed in a mixed conifer/birch forest canopy (Wiman and Lannefors, 

1985; Wiman et al., 1985) and in a mixed oak forest (e.g., Ca2+, Figure 4-6a; Lovett and

Lindberg, 1992). The highly reactive gas, HNO3, also exhibited a vertical gradient but with a 

steep decline at the top of the canopy (Figure 4-6b). Lovett and Lindberg (1992) studied 

concentration profiles of various gases and particles within an closed canopy forest and 

concluded that coarse particle concentrations associated with elements like Ca2+ would decrease 

markedly with depth in the canopy, but they found only minor reductions with depth in the 

concentrations of fine aerosols containing SO4

2!

, NH4

+

, and H+. These data suggest that all foliar 

surfaces within a forest canopy are not equally exposed to particle deposition: upper canopy 

foliage tends to receive maximum exposure to coarse and fine particles, but foliage within the 

canopy tends to receive primarily fine aerosol exposures. Fine-mode particles (e.g., sulfate, 

Figure 4-6c) and unreactive gases typically do not exhibit such vertical profiles, suggesting that 

their uptake is smaller in magnitude and more evenly distributed throughout the canopy. 

4-32
Figure 4-6. Vertical stratification of diverse, chemically speciated particles in a mixed oak 

forest. Nitric acid vapor, as a highly reactive, nonparticulate species, is shown 

for comparison. The horizontal dotted line indicates canopy height, and 

) indicates the percent depletion from above to below the canopy. 

Source: Based on data from Lovett and Lindberg (1992). 

In multilayer canopies, simultaneous reentrainment and deposition may effectively uncouple 

deposition from local concentration. Polydisperse size distributions of many chemical species 

effectively prevent the use of a single estimate of Vd for any element if highly accurate results 

are required. 

Although gradients (Figures 4-6 and 4-7) may be related to local Vd within the canopy 

(Bennett and Hill, 1975), the absence of a gradient may reflect either low or very high rates of 

4-33
Figure 4-7. The relationship between particle size and concentration below a spruce 

canopy with wind velocity at a height of 16.8 m equaling 5 m/s. 

Source: Modified from Peters and Eiden (1992). 

deposition relative to turbulent replenishment from above the canopy (Tanner, 1981). Below- or 

within-canopy emissions may confound interpretation of vertical gradients. Linear gradients of 

the gaseous pollutants hydrogen fluoride (HF) and O3 reflected large uptake rates; whereas small 

gradients in NO suggested little uptake by foliage (Bennett and Hill, 1973, 1975). However, soil 

efflux of NO could have complicated the latter interpretation. The lack of a vertical gradient and 

a peak near the top of the active canopy in particulate K+

 (Figure 4-6d) was interpreted as 

evidence for a biogenic source within the deciduous forest canopy with moderate rates of 

deposition (Lindberg et al., 1986; Lovett and Lindberg, 1992). 

The size dependence of this vertical stratification of particulate concentration (see 

Figure 4-7) is reflected in current simulation models (Wiman et al., 1985; Peters and Eiden, 

1992). The model of Wiman and Ågren (1985) predicts a uniform vertical distribution of 

fine-mode particles and a pronounced vertical gradient of coarse-mode particles which is in 

agreement with observations (Lovett and Lindberg, 1992). 

4-34
Simulation of the horizontal deposition patterns at the windward edge of a spruce forest 

downwind of an open field with the canopy between 1 and 25 m above the ground indicated that 

deposition was maximal at the forest edge, where wind speed and impaction were greatest. 

Simulation of the vertical deposition pattern was more complex. Deposition was not greatest at 

the top of the canopy where wind speed was highest, but at z = 0.75 h, where the balance 

between leaf area (obstacles for impaction) and wind speed (momentum for impaction) was 

optimal, although neither parameter alone was maximal. Simulated deposition in this spruce 

forest increased considerably with increasing LAI at the forest edge, where wind speed was 

insensitive to LAI but the number of obstacles increased. Inside the forest, where both wind 

speed and impaction increasingly were attenuated by increasing LAI, deposition increased only 

marginally in spite of the increase in obstacle frequency. 

To scale surface-specific measurements of particle deposition to forest or crop canopies, 

conversions of the following type have been suggested: 

(4-5) 

with empirical scaling factors proposed by Lindberg et al. (1988). 

To appropriately scale surface-specific measurements of particle deposition to landscapes, 

one must consider the complexity of grassland, crop, and forest canopies in order to avoid 

serious over- or underestimates of particle deposition. Individual species exposed to similar 

ambient concentrations may receive a range of particulate loading that is more closely related to 

foliar damage than the ambient concentration (Vora and Bhatnagar, 1987). 

Both uptake and release of specific constituents of PM may occur within a single canopy 

(e.g., K+

; Lovett and Lindberg, 1992). The leaf cuticular surface is a region of dynamic 

exchange processes occurring through leaching and uptake. Exchange occurs with epiphytic 

microorganisms and bark and through solubilization and erosion of previously deposited PM. 

Vegetation emits a variety of particles and particulate precursor materials. Terpenes and 

isoprenoids predominate and, on oxidation, become condensation nuclei for heterogeneous 

particle formation. Salts and exudates on leaves and other plant parts are continually abraded 

and suspended as particles, as are plant constituents from living and dead foliage (Rogge et al., 

1993a). Soil minerals, including radioactive strontium, nutrient cations and anions, and trace 

4-35
metals are transferred to the active upper foliage and then to the atmosphere in this way. 

Although not representing a net addition to an ecosystem, particle release from vegetation is a 

mechanism for redistributing chemical pollutants derived from the soil or prior deposition within 

a canopy, potentially enhancing direct effects and confounding estimates of Vd. 

Range of Deposition Velocity 

As noted in an earlier criteria document (U.S. Environmental Protection Agency, 1982) 

and in McMahon and Denison (1979), estimates of Vd for PM10 particles to vegetation are 

variable and suggest a minimum between 0.1 and 1.0 μm, as predicted using first principles 

(Monteith and Unsworth, 1990; Sehmel, 1980). Determinations in wind tunnels with passive 

collectors and micrometeorological methods tend to converge in this range. The range of Vd for 

sulfate from passive collectors was found to be from 0.147 to 0.356 cm/s; and, from eddy 

covariance techniques, a mean Vd of 0.27 cm/s was observed (Dolske and Gatz, 1984). 

Micrometeorological techniques over grass (Wesely et al., 1985); indirect, inert collector 

techniques within an oak forest (Lindberg and Lovett, 1985); and many other empirical 

determinations (e.g., McMahon and Denison, 1979; Table 4-4) generally support this range. 

Over aerodynamically smooth snow (Duan et al., 1988; Table 4-5), measurements of Vd were 

an order of magnitude smaller. Very coarse particles, often non-size-specified primary geologic 

material, frequently exhibit Vd greater than 1.0 cm/s (e.g., Clough, 1975). The increase in Vd 

with decreasing size below 0.1 μm is probably hidden in most empirical determinations of Vd 

because the total mass in this fraction is very small despite the large number of individual 

particles. Table 4-6 shows published estimates of Vd with variability estimates for fine particles 

of specified aerodynamic diameters dominated by a range of chemical species. 

Ibrahim et al. (1983) evaluated the deposition of ammonium sulfate particles to a range of 

surfaces and found that particles having a mean diameter of 0.7 μm had deposition velocities 

ranging from 0.039 to 0.096 cm/s. Larger particles (having mean diameters of 7 μm) had greater 

deposition velocities (between 0.096 and 0.16 cm/s). The authors further concluded that the 

hygroscopic nature of the sulfate particle could increase its size and enhance deposition near 

sources of water (e.g., snow). Using eddy correlation approaches, Hicks et al. (1989) found a 

mean daily Vd for sulfur-containing PM to be 0.16 cm/s. However, they suggested that the Vd 

value could be as high as 1 cm/s during the day and near zero at night. 

4-36
TABLE 4-4. REPORTED MEAN DEPOSITION VELOCITIES (Vd) FOR SULFATE, 

CHLORINE, NITRATE, AND AMMONIUM-ION-CONTAINING PARTICLES 

Chemical Species/ Surface Vd (cm/s) a Method Reference 

SO4 

2! 

Inert plates 0.13 Extraction Lindberg and Lovett (1985) 

Inert plates .0.14 Extraction Lindberg et al. (1990) 

Inert plates 0.14 Extraction Davidson and Wu (1990)a 

Inert bucket 0.51 Extraction Davidson and Wu (1990)a 

Foliage 0.29 Extraction Davidson and Wu (1990)a 

Chaparral 0.15 Extraction Bytnerowicz et al. (1987a) 

Grass canopy 0.10 Gradient Allen et al. (1991) 

Grass canopy 0.07 Gradient Nicholson and Davies (1987) 

Pine foliage 0.07 Extraction Wiman (1981) 

Plant canopies 0.50 Gradient Davidson and Wu (1990)a 

Grass canopy 0.22 Eddy covariance Weseley et al. (1985) 

Cl! 

Inert bucket 3.1 Extraction Dasch and Cadle (1985) 

Inert bucket 5 Extraction Dasch and Cadle (1986) 

Beech canopy 1 Throughfall Höfken et al. (1983) 

Spruce canopy 1.9 Throughfall Höfken et al. (1983) 

NO3 

! 

Inert plates 0.4-2 Extraction Lindberg et al. (1990) 

Ceanothus 0.4 Extraction Bytnerowicz et al. (1987a) 

Glycine max 0.24 Extraction Dolske (1988) 

Ligustrum 0.1-0.5 Extraction John et al. (1985) 

Quercus 0.7-1.1 Extraction Dasch (1987) 

Quercus

 summer 0.55 Throughfall Lovett and Lindberg (1984)

 winter 0.7 Throughfall Lovett and Lindberg (1984) 

Quercus

 summer 0.3 — Lovett and Lindberg (1986)

 winter 0.1 — Lovett and Lindberg (1986) 

Pinus 0.5-1.3 Extraction Dasch (1987) 

Pasture 0.7-0.8 Gradient Huebert et al. (1988) 

Ulmus 1.1 Extraction Dasch (1987) 

NH4 

+ 

Calluna/Molina 0.18 Gradient Duyzer et al. (1987) 

Ceanothus 0.4 Extraction Bytnerowicz et al. (1987a) 

Kalmia 0.03-0.14 Extraction Tjepkema et al. (1981) 

Pinus 0.01-0.06 Extraction Dasch (1987) 

a These data represent the mean of data by measurement technique as reported in the cited reference. The reader is referred to

 the referenced articles for information on the specific cations contributing to the means. 

4-37 
TABLE 4-5. REPRESENTATIVE EMPIRICAL MEASUREMENTS OF DEPOSITION 

VELOCITY (Vd) FOR PARTICULATE DEPOSITION 

Vd Particle Size 

x ± SE (cm/s) (μm) Method Reference 

0.034 ± 0.014 0.15-0.30 Eddy covariance with optical counter, Duan et al. (1988) 

0.021 ± 0.005 0.5-1.0 flat snow surface 

0.1 ± 0.03 37987 Profile, fine SO4 

2!

, short grass Allen et al. (1991) 

0.22 ± 0.06 0.1-2.0 Eddy covariance with flame photometer plus 

denuder, 40-cm grass, fine SO4 

2!

Wesely et al. (1985) 

0.13 ± 0.02 (SO4 

2!

) Inert surface collectors (petri dish) in Lindberg and Lovett 

0.75 ± 0.24 

1.1 ± 0.1 

(K+

) 

(Ca2+) 

oak forest (1985) 

0.9 2.75 Wind tunnel to pine shoots; polystyrene beads; Chamberlain and 

2.5 5.0 within-canopy wind speed, 2.5 m/s Little (1981) 

9.4 8.5 

TABLE 4-6. REPORTED MEAN DEPOSITION VELOCITIES FOR POTASSIUM 

(K+

), SODIUM (Na+), CALCIUM (Ca2+), AND MAGNESIUM (Mg2+) BASE CATION 

CONTAINING PARTICLES 

Chemical Species/Surface Vd (cm/s) Method Reference 

K+ 

Inert plates 0.75 Extraction Lindberg and Lovett (1985) 

Inert bucket 0.51-2.4 Extraction Dasch and Cadle (1985) 

Na+ 

Inert bucket 1.7-2.9 Extraction Dasch and Cadle (1985) 

Inert plate 0.8-8.2 Extraction 

Ca2+ 

Inert plates 1.1 Extraction Lindberg and Lovett (1985) 

Inert plates .2 Extraction Lindberg et al. (1990) 

Inert bucket 1.7-3.2 Extraction McDonald et al. (1982) 

Mg2+ 

Inert bucket 1.1-2.7 Extraction Dasch and Cadle (1985) 

4-38
! Lindberg et al. 1990 found a wide discrepancy between deposition velocities for NO3 

between study sites in Oak Ridge, TN (.2 cm/s) and Göttingen, Germany (.0.4 cm/s). They 

suggest that the increased Vd at Oak Ridge could be explained by the primary occurrence of 

NO3 

!

 in coarse particles that exhibit greater Vd than fine particles (Davidson et al., 1982). Large 

values of Vd for base-cation-containing particles (> 1 cm/s) suggest their occurrence in coarse 

particles (Lindberg and Lovett, 1985). 

The several attempts to estimate Vd for SO4

2!

, NO3 

!

, and NH4

+

 with the throughfall mass 

balance approach (Davidson and Wu, 1990; Gravenhorst et al., 1983; Höfken and Gravenhorst, 

1982) have produced higher Vd values that are considered suspect. They have not been included 

in Tables 4-4 and 4-6. Overestimates of Vd for SO4

2!

 and NO3 

!

 particles derived from 

throughfall mass balance approaches may be the result of gaseous SO2 and HNO3 gaseous 

deposition to foliar surfaces (Lindberg and Lovett, 1985). A similar contribution of NH3 

deposition may lead to erroneously high Vd values for NH4

+

 when the throughfall method is 

! attempted in areas of high NH3 concentrations. Dolske’s (1988) reported Vd values for NO3 

deposition to soybean ranged from 0.4 to 31 with a mean of 0.24 cm/s. However, because 

Dolske's leaf extraction measurements included a component of HNO3 vapor, the Vd values may 

represent more than deposition caused by aerosol nitrate alone. 

The quantitative importance of dry particulate deposition depends upon the chemical 

species, topography, precipitation regime, and surface characteristics, including vegetation 

properties. Across the diverse landscapes of the Integrated Forest Study (Johnson and Lindberg, 

1992a), the relative contribution of dry deposition for Ca2+ ranged from about 0% to nearly 90% 

(Figure 4-8). In contrast, for S the total range was from just over 0% to about 30%. An average 

for these forest systems demonstrates that deposition of (usually coarse) base cations was nearly 

50% by dry particulate deposition (Figure 4-9). Both N and S were around 15%. While the 

relative significance of dry particle deposition varies from site to site, it cannot be excluded from 

the analysis at any site. 

In a recent review, Wesely and Hicks (2000) concluded that a comprehensive 

understanding of particulate deposition remains a distant goal. In general, there is only modest 

confidence in available particulate deposition parameterizations at this time, although recent 

experimental and theoretical efforts to improve this situation have been made (e.g., Erisman 

et al., 1997, and companion articles; Zhang et al., 2001; Kim et al., 2000). 

4-39
Figure 4-8. Range of percent of total deposition delivered in the dry particulate fraction, 

across the sites of the Integrated Forest Study. 

Source: Johnson and Lindberg (1992a). 

Figure 4-9. Contribution of particulate deposition to total deposition of nitrogen, sulfur, 

and base cations. 

Source: Modified from Johnson and Lindberg (1992a); Lovett (1992); Lovett and Lindberg (1993); Lindberg et al. 

(1990); Kelly and Meagher (1986). 

The successful treatment of dry deposition of gaseous pollutants (e.g., SO2 and O3) linking 

turbulent deposition to surface physiographical properties has allowed incorporation of these 

models into landscape and regional scale models. This has allowed gaseous deposition to be 

4-40
scaled up for purposes of atmospheric chemistry and vegetation damage assessment. These 

advances have not been matched by progress in deposition of PM. A serious remaining 

impediment is the lack of suitable techniques for measuring deposition of heterogeneous and 

polydisperse particles, such as the gradient and eddy covariance techniques that are used for 

gaseous pollutant species. As with gaseous pollutants, parameterization of particle deposition in 

hilly terrain, to patchy surfaces (small agricultural fields, forest edges), and under extremely 

windy conditions, remains to be fully developed. These limitations must be addressed before a 

full accounting of regional PM effects on vegetation in natural and managed ecosystems can be 

achieved. 

Occult Deposition 

Gaseous pollutant species may dissolve in the suspended water droplets of fog and clouds. 

The stability of the atmosphere and persistence of the droplets often allow a gas/liquid phase 

equilibrium to develop. This permits the use of air mass history or ambient concentrations of 

specific pollutants to estimate fog or cloud water concentrations. Further estimates of the 

deposition velocity of the polluted droplets allows calculation of depositional fluxes. 

Unfortunately, interception of fog or cloud droplets by plant parts or other receptor surfaces 

remains difficult to predict and to measure. Fog formation influences the total atmospheric 

burden and deposition of particulate matter (Pandis and Seinfeld, 1989) by accreting and 

removing particles from the air, by facilitating particle growth through aqueous oxidation 

reactions, and by enhancing deposition as noted. Aqueous condensation may occur onto 

preexisting fine particles, and such particles may coalesce or dissolve in fog or cloud droplets. 

Material transported in fog and cloud water and intercepted by vegetation escapes detection by 

measurement techniques designed to quantify either dry or wet deposition; hence it is hidden 

(i.e., “occult”) from the traditional measurements. 

Occult deposition of fog and cloud droplets is by impaction and gravitational settling in 

concert with the instantaneous particle (droplet) size. This mode of deposition may be 

significant at high elevation sites, particularly near the base of orographic clouds. While coastal 

fog may be generally more pristine than high-elevation continental fogs, this is not true in areas 

subject to inland radiation fog (e.g., the increasingly polluted San Joaquin Valley of Central 

California) and in areas where the marine layer is advected through a highly polluted urban area 

4-41
(e.g., the Los Angeles basin of Southern California). Fog water in these areas may be at least as 

contaminated as that occurring at higher elevations. 

Low elevation radiation fog has different formation and deposition characteristics than 

high elevation cloud or coastal fog water droplets. A one-dimensional deposition model has 

recently been described for a radiation fog episode (Von Glasow and Bott, 1999). 

A substantially greater concentration of key polluting species (e.g., NO3 

!

, SO4

2!

, organics) may 

be observed in smaller than in larger droplets in fog (Collett et al., 1999). Acidity differences 

exceeding 1 pH unit were also observed in the San Joaquin Valley winter radiation fog, with 

smaller particles being more acidic. This has implications for aqueous phase oxidation of sulfur 

and nitrogen compounds, in particular, while sulfur oxidation by ozone (the dominant reaction in 

this environment even during winter) is well known in typically acidic fog droplets. However, 

the alkaline larger droplets in the San Joaquin Valley could lead to greater nitrate production 

through aqueous ozonation reactions (Collett et al., 1999). The size class distinctions have 

substantial implications for deposition of particulate pollutant species in the fog droplets due to 

the larger Vd for impaction and occult deposition of the larger fog particles. 

Acidic cloud water deposition has been associated with forest decline in industrialized 

areas of the world (Anderson et al., 1999). Clouds can contain high concentrations of acids and 

other ions. Cloud water typically is 5 to 20 times more acid than rain water. This can increase 

pollutant deposition and increases the exposure of vegetation and soils at high-elevation sites by 

more than 50% when compared with rainfall and dry deposition. 

The widespread injury to mountain forests documented in West Germany since the 1970s 

and other parts of Europe, and more recently in the Appalachian Mountains, has been attributed 

to cloud water exposure reducing the cold tolerance of red spruce. Forest injury also has been 

attributed to increased leaching of cations and amino acids and increased deposition of nitrogen 

and aluminum toxicity resulting from acidic deposition and the combined effect of acidic 

precipitation, acid fog, oxidants, and heavy metals (Anderson et al., 1999). The Mountain Acid 

Deposition Program (MADPro) was initiated in 1993 as part of the Clean Air Status and Trends 

Network (CASTnet). MADPro monitoring efforts focused on the design and implementation of 

an automated cloud water collection system in combination with continuous measurement of 

cloud liquid water content (LWC) and meteorological parameters relevant to the cloud 

deposition process. 

4-42
Using the MADPro automated cloud-water collectors at three selected mountain sites 

(Whiteface Mt., NY; Whitetop Mt., Va; and Clingman’s Dome, TN), samples taken hourly from 

nonprecipitating clouds during nonfreezing seasons of the year from 1994 to 1997 and promptly 

analyzed for pH, conductivity, and the concentrations of dissolved ions gave an indication of 

exposures at each of the three sites. Cloud LWC was measured at each site. The mean cloud 

water frequencies and LWC were higher at Whiteface Mountain, NY than in the southern 

Appalachians. The four most prevalent ions found in cloud water samples, in order of 

! decreasing concentrations, were usually SO4

2!

, H+, NH4

+

, and NO3 . The concentrations of these 

ions tended to co-vary within cloud events and typically there was an inverse relationship 

between LWC of the cloud and ionic concentration of the cloudwater. Highest ionic 

concentrations were seen in mid-summer. Ionic concentrations of samples from southern sites 

were significantly higher than samples from Whiteface Mountain, but further analysis indicated 

that this was at least partly due to north to south differences in the LWC of clouds (Anderson 

et al., 1999). 

Several factors make occult deposition particularly effective for the delivery of dissolved 

and suspended materials to vegetation. Concentrations of particulate-derived materials are often 

many-fold higher in cloud or fog water than in precipitation or ambient air in the same area due 

to orographic effects and from gas-liquid partitioning coefficients of specific chemical species. 

Fog and cloud water deliver PM in a hydrated (and, therefore, bioavailable) form to foliar 

surfaces. Previously dry-deposited PM may also become hydrated through delinquence or by 

dissolution in the film of liquid water from fog deposition. The presence of fog itself maintains 

conditions of high relative humidity and low radiation, thus reducing evaporation and 

contributing to the persistence of these hydrated particles on leaf surfaces. Deposition of fog 

water is very efficient (Fowler et al., 1991) with a Vd (fog 10 to 24 μm; Gallagher et al., 1988) 

!1 essentially equal to the aerodynamic conductance for momentum transfer (ra) . This greatly 

enhances deposition by sedimentation and impaction of submicron aerosol particles that exhibit 

very low Vd prior to fog droplet formation (Fowler et al., 1989). The near equivalence of Vd 

and (ra)

!1

 simplifies calculation of fog water deposition and reflects the absence of vegetative 

physiological control over surface resistance. Fog particles outside this size range may exhibit 

!1 Vd below (ra) . For smaller particles, this decline reflects the increasing influence of still air and 

boundary layer effects on impaction as particle size and momentum decline. For larger particles, 

4-43
momentum is sufficient to overcome these near surface limitations, but Vd may decline as 

turbulent eddy transport to the surface becomes inefficient with increasing inertia (Gallagher 

et al., 1988). The deposition to vegetation for PM in fog droplets is directly proportional to wind 

speed, droplet size, concentration, and fog density (liquid water content per volume air) although 

the latter two may be inversely related. In some areas, typically along foggy coastlines or at high 

elevations, occult deposition represents a substantial fraction of total deposition to foliar surfaces 

(Fowler et al., 1991; Figure 4-2). 

4.2.2.3 Magnitude of Deposition 

Dry deposition of PM is most effective for coarse particles including primary geologic 

material and for elements such as iron and manganese. Wet deposition is most effective for fine 

particles of atmospheric (secondary) origin (e.g., nitrogen and sulfur, Table 4-6) and elements 

such as cadmium, chromium, lead, nickel, and vanadium (Reisinger, 1990; Smith, 1990a,b,c; 

Wiman and Lannefors, 1985). The occurrence of occult deposition is more restricted. The 

relative magnitudes of the different deposition modes varies with ecosystem type, location, 

elevation, and chemical burden of the atmosphere. For the Walker Branch Watershed, a 

deciduous forest in rural eastern Tennessee, dry deposition constituted a major fraction of the 

total annual atmospheric input of cadmium and zinc (.20%), lead (.55%), and manganese 

(.90%). Whereas wet deposition fluxes during precipitation events exceeded dry deposition 

fluxes by one to four orders of magnitude (Lindberg and Harriss, 1981), dry deposition was 

nearly continuous. Immersion of high-elevation forests in cloudwater may occur for 10% or 

more of the year, significantly enhancing transfer of PM and dissolved gases to the canopy. 

Occult deposition in the Hawaiian Islands dominated total inputs of inorganic N (Heath and 

Huebert, 1999). Much of this N was volcanically derived during the generation of volcanic fog 

in part through reactions with seawater. In this humid climate, the dominance of occult rather 

than wet deposition is notable. 

High-elevation forests receive larger particulate deposition loadings than equivalent low 

elevation sites. Higher wind speeds enhance the rate of aerosol impaction. Orographic effects 

enhance rainfall intensity and composition and increase the duration of occult deposition. 

Coniferous species in these areas with needle-shaped leaves also enhance impaction and 

retention of PM delivered by all three deposition modes (Lovett, 1984). 

4-44
In more arid regions, such as the western United States, the importance of dry deposition 

may be larger. In the San Gabriel Mountains of southern California, for example, while annual 

deposition of SO4

2!

 (partly of marine origin) was dominated by wet deposition (Fenn and Kiefer, 

1999), deposition of NO3 

!

 was dominated by dry deposition, as was that of NH4

+ at two of three 

sites. Similarly, at a series of low elevation sites in southern California (Padgett et al., 1999), 

dry deposition of NO3 

!

 was dominated by dry deposition. In both cases, however, the 

contribution of gaseous HNO3 was probably substantial. 

Nitrates, Sulfates, and Cations 

Much particulate sulfate and nitrate is found on particles in the 0.1- to 1.0-μm size range 

(U.S. Environmental Protection Agency, 1982). However, most sulfate and nitrate, base cation 

and heavy metal inputs to forested ecosystems result from the deposition of larger particles 

(Chapter 2) (Lindberg and Lovett, 1985; Lindberg et al., 1982). The influence of aerodynamic 

diameter is particularly critical for nitrogen species, because they exist as a wide range of 

particle sizes in the atmosphere (Milford and Davidson, 1987). For example, at many sites in 

North America, NO3 

!

 is characterized by a bimodal size distribution with modes above and 

below 1 μm. The supermicron particles are often the result of reactions between HNO3 and 

coarse alkaline aerosols (Wolff, 1984) as, for example, in the San Joaquin Valley of California 

(Lindberg et al., 1990). Although the annual deposition of NH4

+

 is distributed similarly among 

the fine and coarse particles, particulate NO3 

!

 is found predominantly in the coarse-particle 

2! fraction (Table 4-7). Similar to the pattern for NH4+

, the estimated annual deposition of SO4 

particles occurs in both the fine- and coarse-particulate fractions (Table 4-8), while base cation

deposition is virtually restricted to contributions from coarse particles (Table 4-9). 

Although the annual chemical inputs to ecosystems from particle deposition is significant 

by itself, it is important to compare it with the total chemical inputs from all sources of 

atmospheric deposition (i.e., precipitation, particles, and gaseous dry deposition). Figure 4-10 

shows the mean percentage contribution of NO3 

!

 and NH4

+

, SO4

2!

, and base cation-containing 

particles to the total nitrogen, sulfur, and base cation deposition load to forest ecosystems 

(derived from Tables 4-7 through 4-9). Although the mean contribution of particulate deposition 

to cumulative nitrogen and sulfur deposition is typically less than 20% of annual inputs from all 

4-45
TABLE 4-7. MEAN ANNUAL NITROGEN DEPOSITION (equivalents/ha/year) FROM FINE AND

COARSE PARTICLES COMPARED TO TOTAL NITROGEN DEPOSITION FROM ALL SOURCES

TO A VARIETY OF FOREST ECOSYSTEMS

Dry Particle Deposition

Fine Coarse 

Annual Total

!

 + ! + Region/Forest Type Location NO3 NH4 NO3 NH4 Total Nitrogena Referencesb 

4-46

North AmericaDouglas firLoblolly pineLoblolly pineLoblolly pineLoblolly pine

Slash pineWhite pineRed spruceRed spruceRed spruceAlderMaple/BeechOakOak

EuropeNorway spruceNorway spruce 

Washington

GeorgiaNorth Carolina

Tennessee

Tennessee

Florida

North Carolina

Maine

North Carolina

New York

Washington

New York

Tennessee 

Tennessee 

Norway

Germany 

2

2

3

0.8 

0.2

6

1

2

3

1

1

0.3

1

8

21 

9

27

35

18

14

16

22

14

74

9

5

5

36

21 

62 

58

36

89

27

8

105

23

64

133

5

58

37

83

52 

56 

27

37

66

7

1

4

23

102

43

2

27

13

8

29 

4 

96

102

193

53 

23

131

69

182

253

17

91

55

307

128

110

143 

345

647

997

699

410c 

431

510

545

1,939

1,136

339

567

857

720

775

1,250c 

1,2

1,2

1,2

1,2

3

1,2

1,2

1,2

1,2

1,2

1,2

1,2

4

5

1,2

3 

aIncludes deposition from precipitation, gases, and particles.b1 = Johnson and Lindberg (1992a), 2 = Lovett (1992) and Lovett and Lindberg (1993), 3 = Lindberg et al. (1990), 4 = Kelly and Meagher (1986),

5 = Lindberg et al. (1986).

cIncludes only the growing season from April to October 1987. 
TABLE 4-8. MEAN ANNUAL SULFATE DEPOSITION (equivalents/ha/year) FROM FINE AND COARSE

PARTICLES COMPARED TO TOTAL SULFUR DEPOSITION FROM ALL SOURCES TO A VARIETY OF 

FOREST ECOSYSTEMS

Dry Particle Deposition

Region/Forest Type Location Fine Coarse Total Annual Sulfur Depositiona Referencesb 

4-47

North AmericaDouglas fir Washington Loblolly pine Georgia Loblolly pine North Carolina Loblolly pine Tennessee Loblolly pine Tennessee 

Slash pine Florida White pine North Carolina Red spruce Maine Red spruce North Carolina Red spruce New York Alder Washington Maple/Beech New York Oak forest Tennessee 

EuropeNorway spruce Germany Norway spruce Norway 

12

47

57

27

22

25

33

34 

135

10

6

7

70 

58 

19 

62

74

59

69

44

129

55

115

161

4

62 

32 

190

97 

79 

74

121

116

96

66

154

88

149

296

14 

68 

39 

260

155

98 

320 1,2 

776 1,2 

1,050 1,2 

941 1,2 

570c 3 

514 1,2 

552 1,2 

585 1,2 

2,214 1,2 

1,096 1,2 

325 1,2 

488 1,2 

1,600 4 

1,100c 3 

663 12 

aIncludes deposition from precipitation, gases, and particles.b1 = Johnson and Lindberg (1992a), 2 = Lindberg (1992) and Lindberg and Lovett (1992), 3 = Lindberg et al. (1990); 4 = Lindberg et al. (1986).cIncludes only the growing season from April to October 1987.
TABLE 4-9. MEAN ANNUAL BASE CATION DEPOSITION (equivalents/ha/year) FROM FINE AND COARSE

PARTICLES COMPARED TO TOTAL BASE CATION DEPOSITION FROM ALL SOURCES TO

A VARIETY OF FOREST ECOSYSTEMS

Dry Base Cation Particles Depositiona 

Region/Forest Type Location Fine Coarse Total Total Annual Depositionb Referencesc 

4-48

North AmericaDouglas fir Washington Loblolly pine Georgia Loblolly pine North Carolina Loblolly pine TennesseeMixed Hardwood TennesseeSlash pine Florida White pine North Carolina Red spruce Maine Red spruce North Carolina Red spruce New York Alder Washington Maple/Beech New York Oak forest TennesseeEuropeSpruce Germanyd Spruce Norway 

9

17

30

6.1

9.5d 

17

4

5

25

2

4

1

11

6 

180

130

340

220

121d 

600

150

150

440

36 

180

84 

312

180 

189

147

370

226

131d

617

154

155

465

38 

184

85 

323

186 

670 1,2 

300 1,2 

780 1,2 

410 1,2 

899d 3 

1,200 1,2 

280 1,2 

240 1,2 

1,500 1,2 

230 1,2 

660 1,2 

200 1,2 

452 3

3 

390 1,2 

aIncludes K+, Na+, Ca2+, and Mg+2. bIncludes deposition from precipitation, gases, and particles.c1 = Ragsdale et al. (1992), 2 = Johnson and Lindberg (1992a), 3 = Lindberg et al. (1986).dIncludes H+, K+, Na+, Ca2+, Mg+2 for the growing season from April to October 1987.eIncludes only Ca2+ and K+.
Figure 4-10. Mean (± SE) percent of total nitrogen, sulfur, or base cation deposition 

contributed by fine plus coarse particles. Data are means from Tables 4-7 

through 4-9. 

atmospheric sources, particulate inputs of base cations average half the total base cations 

entering forest ecosystems from the atmosphere. 

An extensive comparison of particle to total chemical deposition is provided by the 

Integrated Forest Study (IFS; Johnson and Lindberg, 1992a; Lovett, 1994; Lovett and Lindberg, 

1993; Lindberg and Lovett, 1992; Ragsdale et al., 1992). Other similar data sets are available 

(Kelly and Meagher, 1986; Miller et al., 1993; Lindberg et al., 1986, 1990). These data in 

(Tables 4-7 through 4-9) clearly indicate that the contribution of coarse and fine aerosols to 

deposition to forest ecosystems is strongly dependent on the chemical species. 

Dry deposition is an important flux of sulfur and nitrogen compounds at all of the IFS sites 

! and ranges from 9 to 59% of total (wet + dry + cloud) deposition for sulfur, 25 to 70% for NO3 

+ and 2 to 33% for NH4 . Only for NH4+

 is wet deposition consistently greater than dry deposition 

(Lovett, 1994). 

After emission from their sources, air pollutants are transformed and transported by 

atmospheric processes until deposited from the atmosphere to an aquatic or terrestrial ecosystem. 

4-49
As a result, ground-level concentrations of an air pollutant depend on the proximity to the 

sources, prevailing meteorology, and nature and extent of atmospheric reactions between the 

source and the receptor (Holland et al., 1999). A more direct relationship exists between source 

strength and downwind ambient concentrations for primary air pollutants (e.g., SO2) than for 

secondary pollutants (e.g., SO4

2!

). Interaction of the chemical and physical atmospheric 

processes and source locations for all of the pollutants have a tendency to produce data patterns 

that show large spatial and temporal variability. 

Holland et al. (1999) analyzed CASTNet monitoring data and using generative additive 

models (GAM) estimated the form and magnitude of trends of airborne concentrations of SO2, 

SO4 

!2

, and nitrogen from 1989 to 1995 at 34 rural long-term CASTNet monitoring sites in the 

eastern United States. These models provide a highly flexible method for describing potential 

nonlinear relationships between concentrations, meteorology, seasonality, and time (e.g., how 

weekly SO2 varies as a function of temperature). For most of the 34 sites in the eastern United 

State, estimates of change in SO2 concentrations showed a decreasing functional form in 1989 to 

1990, followed by a relatively stable period during 1991 to 1992), then a sharper decline 

beginning in 1994 (Holland et al., 1999). 

Regional trends of seasonal and annual wet deposition and precipitation-weighted 

concentrations (PWCs) of sulfate in the United States over the period 1980 to 1995 were 

developed by Shannon (1999) from monitoring data and scaled to a mean of unity. In order to 

reduce some effects of year to year climatological variability, the unitless regional deposition 

and PWC trends were averaged (hereafter referred to a CONCDEP). During the 16-year period 

examined in the study, estimated aggregate emissions of SO2 in the United States and Canada 

fell ~12% from about 1980 to 1982, it remained roughly level for a decade and then fell another 

~15% from 1992 to 1995 — for an overall decrease of about 18%. Eastern regional trends of 

sulfate concentrations and deposition and their average CONCDEP, also exhibited patterns of 

initial decrease, near steady state, and final decrease with year to-to-year variability. The overall 

relative changed in CONCDEPs are greater than the changes in SO2 emissions. 

2! Concentrations and calculated deposition (concentration times amount of water) of SO4 

at the Hubbard Brook Experimental Forest (HBEF) in the White Mountains of central New 

Hampshire have been measured since June of 1964 (Likens et al., 2001). These measurements 

represent the longest continuous record of precipitation chemistry in North America. The 

4-50
long-term measurements generally concur with those of Shannon (1999) discussed above. Major 

declines in emissions of SO2 have been observed during recent decades in the eastern United 

States and have been correlated with significant decreases in SO4

2!

 concentrations in 

precipitation (Shannon, 1999). 

Deposition of sulfates and nitrates are clearly linked to emissions. Reduction in emissions 

must occur before concentrations can be reduced below current levels (Likens et al., 2001). 

Deposition is the key variable as sensitive ecosystems in the eastern North America have not yet 

shown improvement in response to decreased emissions of SO2 (Driscoll et al., 1989; Likens 

et al., 1996). Clearly, additions of other chemicals, such as nitric acid and base cations, must be 

considered in addition to sulfur when attempting to resolve the acid rain problem (Likens et al., 

1996, 1998). The effects of sulfur and nitrogen deposition on ecosystems are discussed in 

Section 4.2.2.2. 

The long-term record indicates that a reduction in the deposition of basic cations (Ca2+, 

Mg+2, K+

, Na+) in bulk precipitation was associated with significant declines in SO4

2!

deposition 

cited above for the HBEF region (Driscoll et al., 1989). Decreases in streamwater 

concentrations of basic cations have decreased simultaneously, suggesting that streamwater 

concentrations of basic cations are relatively responsive to changes in atmospheric inputs. 

Regardless of the cause, the decline in atmospheric influx of basic cations could have important 

effects on nutrient availability as well as on the acid/base status of soil and drainage water 

(Driscoll et al., 1989). 

Trace Elements 

Deposition velocities for fine particles to forest surfaces have been reported in the range of 

1 to 15 cm/s (Smith, 1990a). For example, total, annual heavy metal deposition amounts are 

highly variable depending on specific forest location and upwind source strength (Table 4-10). 

Lindberg et al. (1982) quantified the dry deposition of heavy metals to inert surfaces and to 

leaves of an upland oak forest. As noted for other chemical species, Vd was highly dependent on 

particle size and chemical species (Table 4-11) with the larger particles depositing more 

efficiently. 

The preferential association of heavy metals with fine particles results in reduced control in 

emission control systems. Metal removal efficiencies for baghouse filters are typically 95 to 

4-51
TABLE 4-10. MEAN (± SE) PARTICLE SIZE, DEPOSITION RATES, 

AND DERIVED DEPOSITION VELOCITIES (Vd) FOR HEAVY METAL 

DEPOSITION TO THE UPPER CANOPY (INERT PLATES OR LEAVES) 

OF AN UPLAND OAK FOREST 

Deposition Rate Vd 

Metal Particle Size (μm) (pg/cm/h) (cm/s) 

Manganese 3.4 ± 0.7 91 ± 23 6.4 ± 3.6 

Cadmium 1.5 ± 0.7 0.3 ± 0.1 0.37 ± 0.18 

Zinc 0.9 ± 0.2 6 ± 1 0.38 ± 0.1 

Lead 0.5 23 ± 8 0.06 ± 0.01 

Source: Lindberg et al. (1982). 

TABLE 4-11. TOTAL HEAVY METAL DEPOSITION TO TEMPERATE 

LATITUDE FORESTS 

Forest Deposition kg ha/yr 

Heavy Metal (Range) 

Cadmium 0.002-0.02 

Copper 0.016-0.24 

Lead 0.099-1000 

Nickel 0.014-0.15 

Zinc 0.012-0.178 

Source: Smith (1990c). 

99% for all but mercury, but fine particle capture is much less efficient. Wet scrubber efficiency 

varies with design and pressure drop, typically 50 to 90% (McGowan et al., 1993). Fine 

particles also have the longest atmospheric residence times and, therefore, can be carried long 

distances. Depending on climate conditions and topography, fine particles may remain airborne 

for days to months and may be transported 1,000 to 10,000 km or more from their source. This 

long-distance transport and subsequent deposition qualify heavy metals as regional- and global4-52
scale air pollutants. Ecosystems immediately downwind of major emissions sources (such as 

power generating, industrial, or urban complexes) may receive locally heavy inputs. Mass 

balance budgets (inputs and outputs) of seven heavy metals (cadmium, copper, iron, lead, 

manganese, nickel, and zinc) have been determined at the Hubbard Brook Experimental Forest 

(White Mountain National Forest) in New Hampshire. This forest is about 120 km northwest of 

Boston and relatively distant from major sources of heavy metal emissions. However, 

continental air masses that have passed over centers of industrial and urban activity also 

frequently follow storm tracks over northern New England. Resulting annual inputs for seven 

heavy metals at Hubbard Brook for 1975 to 1991 are presented in Figure 4-11. Note that the 

44-fold decrease in Pb deposition is correlated with removal of Pb from motor vehicle fuels. 

Figure 4-11. Annual total deposition of heavy metals to Hubbard Brook Experimental 

Forest, NH. 

Source: Smith (1990a). 

4-53 
Trace element investigations conducted in roadside, industrial, and urban environments 

have shown that impressive burdens of particulate heavy metals accumulate on vegetative 

surfaces. Lead deposition to roadside vegetation (prior to its removal from fuel) was 5 to 20, 

50 to 200, and 100 to 200 times the Pb deposition to agricultural crops, grasses, and trees, 

respectively, in non-roadside environments. In an urban setting, it has been estimated that the 

leaves and twigs of a 30 cm (12 in.) diameter sugar maple remove 60, 140, 5800, and 820 mg of 

Cd, Cr, Pb, and Ni, respectively, during a single growing season (Smith, 1973). 

Semivolatile Organics 

Organic compounds partition between gas and particle phases, and particulate deposition 

depends largely on the particle sizes available for adsorption (Pankow, 1987; Smith and Jones, 

2000). Dry deposition of organic materials (e.g., dioxins, dibenzofurans, polycyclic aromatics) 

is often dominated by the coarse fraction, even though mass loading in this size fraction may be 

small (Lin et al., 1993) relative to the fine PM fraction. For example, measurements in Bavaria 

in both summer and winter revealed that > 80% of organics were in the fine (< 1.35 μm) fraction 

(Kaupp and McLachlan, 1999). Nevertheless, in most cases, calculated values of dry deposition 

were dominated by the material adsorbed to coarse particles. Wet deposition, in contrast, was 

dominated by the much larger amount of material associated with fine particles. In this Bavarian 

environment (where monthly precipitation is about 50 mm in winter and summer), wet 

deposition dominated, with dry deposition accounting for only 14 to 25% of total deposition 

(Kaupp and McLachlan, 1999). Lower relative contents of more volatile species in summer than 

winter (Kaupp and McLachlan, 1999) indicate the critical importance of gas-particle phase 

interconversions in determining deposition. 

Effective deposition of PM is required before biological effects on plants or ecosystems 

can occur. It is clear that substantially improved techniques for monitoring and predicting 

deposition will be required to characterize with certainty quantitative relationships between rates 

and quantities of PM deposition associated with such effects. 

4-54
4.2.3 Assessment of Atmospheric PM Deposition Effects 

Introduction 

The discussion in the pages that follow assesses and characterizes the overall ecological 

condition or integrity of the ecosystems within the United States affected by the deposition of 

anthropogenic stressors associated with PM and indicates their status. The six Essential 

Ecological Attributes (EEAs) – the landscape condition, biotic condition, and chemical/ physical 

characteristics, ecological processes, hydrology/geomorphology, and natural disturbance regimes 

(Table 4-1) – provide a hierarchical framework for assessing ecosystem status. Measurable 

characteristics related to structure, composition, or functioning of ecological systems may be 

determined by the use of endpoints or ecological indicators of condition that are significant 

either ecologically or to society (Harwell et al., 1999). 

The relationships among the EEAs are complex because all are interrelated (i.e., changes in 

one EEA may affect, directly or indirectly, every other EEA). The ecological processes create 

and maintain patterns composed of the elements in the system and their arrangement; in turn, the 

patterns affect how the processes are expressed (Science Advisory Board, 2002). Changes in 

patterns or processes result in changes in the status and functioning of an ecosystem. The 

information in the sections that follow discusses changes in landscape and biotic patterns and 

in ecological and chemical/physical processes resulting from the stressors in PM deposition 

(Figure 4-12). 

The elements of biotic condition are organized in a nested hierarchy with several levels. 

These include the structural and composition aspects (patterns) of the biota within landscape, 

ecosystem or ecological community, species/population, organism, and genetic/molecular levels 

(Science Advisory Board, 2002). Within these biological levels of organization, changes in the 

biodiversity, composition, and structural elements relate directly to functional integrity (such as 

trophic status or structural integrity within habitats). Changes in biodiversity are of particular 

significance in altering the functioning of ecosystems. 

As previously stated, ecosystems are dynamic, self-adjusting, self-maintaining, complex, 

adaptive systems, in which the patterns of the higher levels of biotic organization emerge from 

the interactions and selection processes at localized levels (Levin, 1998). Ecosystem 

components must have an adequate supply of energy, mineral nutrients, and water to maintain 

themselves and function properly. During the ecological processes of energy and material flow, 

4-55
Figure 4-12. Sample stressors and the essential ecological attributes they affect 

(after Science Advisory Board, 2002). 

the energy obtained by plants (the producers) from sunlight during photosynthesis (primary 

production) and chemical nutrients (e.g., nitrogen, phosphorus, sulfur) taken up from the soil are 

transferred to other species (the consumers) within the ecosystem through food webs. The 

movement of chemical nutrients (materials) through an ecosystem is cyclic, as the nutrients are 

used or stored and eventually returned to the soil by decomposer organisms. Energy, on the 

4-56
other hand, is transferred from organism to organism through an ecosystem in food webs and, 

finally, is dissipated into the environment as heat (Odum, 1993). 

Ecosystem and community patterns are characterized by the interaction of their component 

species, the ecosystems processes of energy flow, nutrient flux, water and material flow, and by 

the effects their activities have on the physical and chemical environment. The flows of energy 

and nutrient cycling provide the interconnectedness among the elements of the biotic hierarchy 

and transform the community from a random collection of numerous species into an integrated 

whole, an ecosystem. Elucidating these interactions across scales is fundamental to 

understanding the relationships between biodiversity and ecosystem functioning (Levin, 1998). 

Human existence on this planet depends on the life-support services provided by the 

interaction of the different EEAs. Both ecosystem structure (biotic conditions) and function 

(ecological processes) play essential roles in providing goods (products) and services 

(Table 4-12; Daily, 1997). Ecosystem processes maintain clean water, clean air, a vegetated 

Earth, and a balance of organisms — the functions that enable humans to survive. The benefits 

they impart include the absorption and breakdown of pollutants, cycling of nutrients, binding of 

soil, degradation of organic waste, maintenance of a balance of gases in the air, regulation of 

radiation balance and climate, and the fixation of solar energy (World Resources Institute, 2000; 

Westman, 1977; Daily, 1997). The economic benefits and values associated with ecosystem 

functions, goods, and services as well as the need to preserve them because of their value to 

human life are discussed by Costanza et al. (1997) and Pimentel et al. (1997). 

Goods such as food crops, timber, livestock, fish, and drinking water usually have market 

value, while ecosystem services such as flood-control benefits, wildlife habitat, cycling of 

nutrients, and removal of air pollutants, usually are very difficult to measure (Goulder and 

Kennedy, 1997). 

Attempts have been made to calculate the value of biodiversity and the world’s ecosystem 

services and natural capital (Goulder and Kennedy 1997; Pimentel et al., 1997; Constanza et al., 

1997). These have been controversial because of a lack of agreement on the measurement and 

philosophical basis for placing value on ecosystem services (Goulder and Kennedy, 1997; Heal, 

2000; Moomaw, 2002). Constanza et al. (1997) state that it may never be possible to make a 

precise estimate of the services provided by ecosystems; however, their estimates indicate the 

relative importance of services, not their true values, considering that the loss of ecosystems can 

4-57
TABLE 4-12. PRIMARY GOODS AND SERVICES PROVIDED BY ECOSYSTEMS

Ecosystem Goods Services 

Agroecosystems Food crops 

Fiber crops 

Crop genetic resources 

Maintain limited watershed functions (infiltration,

 flow control, and partial soil protection) 

Provide habitat for birds, pollinators, and soil 

 organisms important to agriculture 

Sequester atmospheric carbon 

Provide employment 

Coastal 

Ecosystems 

Fish and shellfish 

Fishmeal (animal feed) 

Seaweeds (for food and 

industrial use) 

Salt 

Moderate storm impacts (mangroves, barrier islands) 

Provide wildlife (marine and terrestrial) habitat and

 breeding areas/hatcheries/nurseries 

Maintain biodiversity 

Dilute and treat wastes 

Genetic resources Provide harbors and transportation routes 

Provide human and wildlife habitat 

Provide employment 

Contribute aesthetic beauty and provide recreation 

Forest Ecosystems Timber 

Fuelwood 

Drinking and irrigation water 

Fodder 

Nontimber products (vines,

 bamboos, leaves, etc.) 

Food (honey, mushrooms fruit,

 and other edible plants; game) 

Genetic resources 

Remove air pollutants, emit oxygen 

Cycle nutrients 

Maintain array of watershed functions (infiltration,

 purification, flow control, soil stabilization) 

Maintain biodiversity 

Sequester atmospheric carbon 

Moderate weather extremes and impacts 

Generate soil 

Provide employment 

Provide human and wildlife habitat 

Contribute aesthetic beauty and provide recreation 

Freshwater Drinking and irrigation water 

Fish 

Hydroelectricity 

Genetic resources 

Buffer water flow (control timing and volume) 

Dilute and carry away wastes 

Cycle nutrients 

Maintain biodiversity 

Provide aquatic habitat 

Provide transportation corridor 

Provide employment 

Contribute aesthetic beauty and provide recreation 

Grassland 

Ecosystems 

Livestock (food, game, hides, 

and fiber) 

Drinking and irrigation water 

Genetic resources 

Maintain array of watershed functions (infiltration,

 purification, flow control, and soil stabilization) 

Cycle nutrients 

Remove air pollutants and emit oxygen 

Maintain biodiversity 

Generate soil 

Sequester atmospheric carbon 

Provide human and wildlife habitat 

Provide employment 

Contribute aesthetic beauty and provide recreation 

Source: World Resources Institute (2000). 

4-58 
affect human existence. Constanza et al. (1997) refer to the loss of “total values” for all portions 

of all ecosystem services, whereas the relevant economic analysis for the PM standard setting is 

for marginal changes in ecosystem goods and services due to varying levels of air pollution 

effects, generally not total loss of services. 

The prevailing economic approach to ascertaining value discussed above is benefit-cost 

analysis. Benefit-cost analysis implicitly adopts the utilitarian basis for value. That is, the value 

of a given living thing is the amount of human benefit or satisfaction the thing provides (Goulder 

and Kennedy, 1997). Benefit-cost analysis determines the environmental and public health 

damage and then calculates an appropriate level of emissions reduction required to lower 

concentrations to a standard-setting level. As the costs and benefits extend into the future at 

differing rates, the question arises of what discount rate to use in determining the net present 

value of the costs and benefits. A degraded ecosystem continues to provide fewer services for 

the indefinite future (Moomaw, 2002). 

The approach of Harwell et al. (1999) and the report of the Ecological Processes and 

Effects Committee of the SAB (Science Advisory Board, 2002) also point out the need to 

understand human effects and their costs on ecosystems so that management can define what 

ecological conditions are to be desired. Further, they state that the establishment of ecological 

goals involves a close linkage between scientists and decision makers in which science informs 

decision makers and the public by characterizing the ecological conditions that are achievable 

under particular management regimes. Decision makers then can make choices that reflect 

societal values including issues of economics, politics, and culture. For management to achieve 

their goals, the general public, scientific community, resource managers, and decision makers 

need to be routinely apprised of the condition or integrity of ecosystems so that ecological goals 

may be established (Harwell et al., 1999). 

Biodiversity, the variety of life, encompasses all levels of biological organization, 

including species, individuals, populations, and ecosystems (Wilson, 1997). Human-induced 

changes in biotic diversity and alterations in the structure and functioning of ecosystems are the 

two most dramatic ecological trends of the past century (Vitousek et al., 1997). There are few 

ecosystems on Earth today that are not influenced by humans (Freudenburg and Alario, 1999; 

Vitousek et al., 1997; Matson et al., 1997; Noble and Dirzo, 1997). The scientific literature is 

filled with discussions of the importance of ecosystem structure (patterns) and function 

4-59
(processes). The deposition of PM from the atmosphere has the potential to alter ecosystem 

structure and function by altering nutrient cycling and changing biodiversity. We must, 

therefore, determine how ecosystems respond to both natural and anthropogenic stresses so as 

to know whether anthropogenic stresses are affecting ecosystem services and products 

(Table 4-12). 

Concern has risen in recent years regarding the consequences of changing the biological 

diversity of ecosystems (Tilman, 2000; Ayensu et al., 1999; Wall, 1999; Hooper and Vitousek, 

1997; Chapin et al., 1998). The concerns arise because human activities are creating 

disturbances that decrease biodiversity, alter the complexity and stability of ecosystems, and 

produce changes in ecological processes and the structure, composition and function of 

ecosystems (Figure 4-13; Pimm, 1984; Levin, 1998; Chapin et al., 1998; Peterson et al., 1998; 

Tilman, 1996; Tilman and Downing, 1994; Wall, 1999; Daily and Ehrlich, 1999). The above 

changes can affect ecosystem services vital to human life. 

Figure 4-13. Linkages among various ecosystem goods and services (food, water, 

biodiversity, forest products) and other driving forces (climate change). 

Source: Modified from Ayensu et al. (1999). 

4-60 
4.2.3.1 Effects on Vegetation and Ecosystems 

Exposure to a given mass concentration of airborne PM may lead to widely differing 

phytotoxic responses, depending on the particular mix of deposited particles. Effects of 

particulate deposition on individual plants or ecosystems are difficult to characterize because of 

the complex interactions among biological, physicochemical, and climatic factors. Most direct 

effects, other than regional effects associated with global changes, occur in the severely polluted 

areas surrounding industrial point sources, such as limestone quarries, cement kilns, and metal 

smelting facilities. Fine particles are more widely distributed from their sources than are coarse 

particles. Experimental applications of PM constituents to foliage typically elicit little response 

at the more common ambient concentrations. The diverse chemistry and size characteristics of 

ambient PM and the lack of clear distinction between effects attributed to phytotoxic particles 

and to other air pollutants further confound understanding of the direct effects on foliar surfaces. 

The majority of the documented toxic effects of particles on vegetation reflect their chemical 

content (e.g., acid/base, trace metal, nutrient), surface properties, or salinity. Studies of the 

direct effects of particles on vegetation have not yet advanced to the stage of reproducible 

exposure experiments. The difficulties of experimental application of ambient particles to 

vegetation have been discussed by Olszyk et al. (1989). Studies indicate many phytotoxic gases 

are deposited more readily, assimilated more rapidly, and lead to greater direct injury of 

vegetation than do most common particulate materials (Guderian, 1986). The dose-specific 

responses (dose-response curves) obtained in early experiments following the exposure of plants 

to phytotoxic gases generally have not been observed following the application of particles. 

Unlike gaseous dry deposition, neither the solubility of the particles nor the 

physiographical activity of the surface is likely to be of first order importance in determining 

deposition velocity (Vd). Factors that contribute to surface wetness and stickiness may be 

critical determinants of deposition efficiency. Available tabulation of deposition velocities are 

highly variable and suspect. However, high-elevation forests receive larger particle deposition 

loadings than equivalent lower elevations sites because of higher wind speeds and enhanced 

rates of aerosol impaction, orographic effects on rainfall intensity and composition, increased 

duration of occult deposition, and, in many areas, the dominance of coniferous species with 

needle-shaped leaves (Lovett, 1984), (see Section 4.2.2.3 for a discussion of deposition 

efficacy). Recent evidence indicates that all three modes of deposition (wet, occult, and dry) 

4-61
must be considered in determining inputs to ecosystems or watersheds, because each may 

dominate over specific intervals of space. 

Atmospheric PM may affect vegetation directly following deposition on foliar surfaces or 

indirectly by changing the soil chemistry or by changing the amount of radiation reaching the 

Earth’s surface through PM-induced climate change processes. Indirect effects, however, are 

usually the most significant because they can alter nutrient cycling and inhibit plant nutrient 

uptake. 

Direct Effects of Particulate Matter Deposition 

Coarse and fine PM deposition affects both patterns in the elements of EEA categories 

subsumed under biotic condition and processes subsumed under chemical/physical and 

ecological processes in the EPEC framework described earlier. Measurable responses have been 

observed as reductions in photosynthesis, changes in soil salinity, and foliar effects resulting 

from nitrate, sulfate, and acidic and heavy metal deposition. 

Particles transferred from the atmosphere to foliar surfaces may reside on the leaf, twig, or 

bark surface for extended periods; be taken up through the leaf surface; or be removed from the 

plant via resuspension to the atmosphere, washing by rainfall, or litter-fall with subsequent 

transfer to the soil. Any PM deposited on above-ground plant parts may exert physical or 

chemical effects. The effects of “inert” PM are mainly physical; whereas those of toxic particles 

are both chemical and physical. The effects of dust deposited on plant surfaces or soil are more 

likely to be associated with their chemistry, rather than their mass and chemical effects may be 

more important than any physical effects (Farmer, 1993). Nevertheless, vegetative surfaces 

represent filtration and reaction/exchange sites (Tong, 1991; Youngs et al., 1993). 

Direct Effects of Coarse Particles 

Coarse particles, ranging in size from 2.5 to 100 μm, are chemically diverse, arise 

predominantly from local sources, and are typically deposited near their sources because of their 

sedimentation velocities. Airborne coarse particles are derived from the following sources: 

road, cement kiln, and foundry dust; fly ash; tire particles and brake linings; soot and cooking oil 

droplets; biogenic materials (e.g, plant pollen, fungal spores, bacteria and viruses); abraded plant 

fragments; sea salt; and hydrated deliquescent particles of otherwise fine aerosol. In many rural 

4-62
areas and some urban areas, the majority of the mass in the coarse particle mode derives from 

the elements silicon, aluminum, calcium, and iron, suggesting a crustal origin as fugitive dust 

from disturbed land, roadways, agriculture tillage, or construction activities. Rapid 

sedimentation of coarse particles tends to restrict their direct effects on vegetation largely to 

roadsides and forest edges. 

Physical Effects — Radiation. Dust can cause physical and chemical effects. Deposition 

of inert PM on above-ground plant organs sufficient to coat them with a layer of dust may result 

in changes in radiation received, a rise in leaf temperature, and the blockage of stomata. 

Increased leaf temperature and heat stress, reduced net photosynthesis, and leaf chlorosis, 

necrosis, and abscission were reported by Guderian (1986). Road dust decreased the leaf 

temperature on Rhododendron catawbiense by ~4 °C (Eller, 1977); whereas foundry dust caused 

an 8.7 °C increase in leaf temperature of black poplar (Populus nigra) under the conditions of 

the experiment (Guderian, 1986). Deciduous (broad) leaves exhibited larger temperature 

increases because of particle loading than did conifer (needle) leaves, a function of poorer 

coupling to the atmosphere. Inert road dust caused a three- to four-fold increase in the 

absorption coefficient of leaves of English ivy (Eller, 1977; Guderian, 1986) for near infrared 

radiation (NIR; 750 to 1350 nm). Little change in absorption occurred for photosynthetically 

active radiation (PAR; 400 to 700 nm). The increase in NIR absorption may be accounted for by 

a decease in reflectance and transmission in these wavelengths. The amount of energy entering 

the leaf increased by ~30% in the dust-affected leaves. Deposition of coarse particles increased 

leaf temperature and contributed to heat stress, reduced net photosynthesis, and caused leaf 

chlorosis, necrosis, and abscission (Dässler et al., 1972; Parish, 1910; Guderian, 1986; Spinka, 

1971). 

Starch storage in dust-affected leaves increased with dust loading when under high 

(possibly excessive) radiation, but decreased following dust loading when radiation was limiting. 

These modifications of the radiation environment had a large effect on single-leaf utilization of 

light. The boundary layer properties, determined by leaf morphology and environmental 

conditions, strongly influenced the direct effects of particle deposition on radiation heating 

(Eller, 1977; Guderian, 1986) and on gas exchange. Brandt and Rhoades (1973) attributed the 

reduction in the growth of trees to crust formation from limestone dust on the leaves. Crust 

4-63
formation reduced photosynthesis and the formation of carbohydrates needed for normal growth, 

induced premature leaf-fall, damaged leaf tissues, inhibited growth of new tissue, and reduced 

starch storage. Dust may decrease photosynthesis, respiration, and transpiration; and it may 

allow penetration of phytotoxic gaseous pollutants, thereby causing visible injury symptoms and 

decreased productivity. Permeability of leaves to ammonia increased with increasing dust 

concentrations and decreasing particle size (Farmer, 1993). 

Dust also has been reported to physically block stomata (Krají...ková and MejstÍík, 1984). 

Stomatal clogging by PM from automobiles, stone quarries, and cement plants was also studied 

by Abdullah and Iqbal (1991). The percentage of clogging was low in young leaves when 

compared with old, mature leaves and the amount of clogging varied with species and locality. 

The maximum clogging of stomata observed was about 25%. The authors cited no evidence that 

stomatal clogging inhibited plant function. The heaviest deposit of dust usually occurs on the 

upper surface of broad-leaved plants; whereas the majority of the stomata are on the lower 

surface where stomatal clogging would be less likely. 

Chemical Effects. The chemical composition of PM is usually the key phytotoxic factor 

leading to plant injury. On hydration, cement-kiln dust liberates calcium hydroxide, which can 

penetrate the epidermis and enter the mesophyll; in some cases, this has caused the leaf surface 

alkalinity to reach a pH of 12. Lipid hydrolysis, coagulation of the protein compounds, and 

ultimately plasmolysis of the leaf tissue reduce the growth and quality of plants (Guderian, 

1986). In experimental studies, applications of cement-kiln dust of known composition for 2 to 

3 days yielded dose-response curves between net photosynthetic inhibition or foliar injury and 

dust application rate (Darley, 1966). Lerman and Darley (1975) determined that leaves must be 

misted regularly to produce large effects. Alkalinity was probably the essential phytotoxic 

property of the applied dusts. 

Salinity. Particulate matter enters the atmosphere from oceans following the mixing of air 

into the water column and the subsequent bursting of bubbles at the surface. The effervescence 

of bubbles on the surface of the ocean forcefully ejects droplets of sea water into the air. These 

droplets, concentrated by evaporation, are carried inland by wind and deposited on the seaward 

side of coastal plants (Boyce, 1954). This occurs largely at the surf line (i.e., near land and 

4-64
potentially sensitive terrestrial receptors). This process can be a significant source of sulfate, 

sodium, chloride, and trace elements (as well as living material) in the atmospheric aerosol that 

impacts coastal vegetation. Sea-spray particles (Taback et al., 1979) are approximately 24% 

greater in size than 10 μm, and 54% are between 3 and 10 μm. Thus, only about 20% are fine 

(0 to 2.5 μm) particles; and deposition by sedimentation and impaction is concentrated near the 

coast, whereas the particle size distribution shifts toward the fine fraction over longer inland 

transport distances. Airborne concentrations of this marine PM decrease quickly with distance 

inland from the surf line both by deposition and dilution within the atmospheric mixed layer 

(McKay et al., 1994; Nelis et al., 1994). Near-shore sediments, with associated pollutants 

present in coastal runoff, may be suspended in the surf and reentrained into the air. This can be a 

substantial source of microorganisms and of radionuclides to coastal vegetation (Nelis et al, 

1994; McKay et al., 1994). 

Sea-salt particles can serve as nuclei for the absorption and subsequent reaction of other 

gaseous and particulate air pollutants. Both nitrate and sulfate from the atmosphere have been 

found to associate with coarse and fine sea-salt particles (Wu and Okada, 1994). Direct effects 

on vegetation reflect these inputs, as well as the classical salt injury caused by the sodium and 

chloride that constitute the bulk of these particles. Foliar accumulation of airborne salt particles 

may lead to foliar injury, thusly affecting the species composition in coastal environments 

(Smith, 1984). 

The effects of winds and sea spray on coastal vegetation has been reported in the literature 

since the early 1800s (Boyce, 1954). However, there has been some controversy about whether 

the injury to coastal vegetation resulted from windblown aerial salts or from mechanical injury 

(i.e., sand blasting) due to wind alone. Though the significance of sea water dashed on fore 

dunes and rocky coasts had been recognized by several authors, Wells and Shunk (1937, 1938) 

and Wells (1939) were the first to recognize the importance of salt spray in coastal ecology. 

Wells and Shunk (1937) reported that salt spray carried over dunes was the most important 

factor influencing growth form, zonation, and succession in coastal dunes. Salt spay injury was 

recorded 1.25 miles inland on the North Carolina coast. On the basis of observations in the 

Cape Fear area, they determined that the shape of coastal “wind form” shrubs were the result of 

sea spray carried by high winds. They found injury on shrubs only near the coast, while those at 

4-65
greater distances inland showed no injury whatsoever after a strong southeast wind that persisted 

for a period of 19 hours during cloudy weather and abundant soil moisture. 

To determine the cause of injury, injured and uninjured shoots were titrated for chlorides. 

A marked difference was observed between the injured and uninjured shoots (Wells and Shunk, 

1937, 1938). Experimental spraying of shoots of woody plants with seawater resulted in a 

pattern of injury similar to the injury observed on seaside shrubs. The absence of the more 

inland species, such as persimmon (Diosporos virginiana), turkey oak (Quercus laevis), longleaf 

pine (Pinus palustris Mill., P. australis), and wire grass (Aristida stricta), was explained on the 

basis of intolerance of these species to salt spray. The dominance of live oak (Quercus 

virginiana), as a practically pure stand on Smith Island (also known as Bald Head Island), NC 

and along the eastern and southern NC coast, was determined by Wells (1939) to be due to the 

tree’s tolerance to salt spray. He termed the long term stabilization of the live oak stand as a 

new type of climax, the “salt spray climax.” The later work of Oosting and Billings (1942) near 

Beaufort, NC corroborated the findings of Wells and Shunk (1937, 1938). 

The report by Boyce (1954) is probably the most extensive on salt-spray communities. 

Dune sands in many coastal areas have been shown to have extremely low concentrations of 

dissolved salts. Studies have indicated that the salt content of the coastal dunes of Virginia, 

Massachusetts, and California did not exceed the maximum occurring in ordinary cultivated 

soils. Oosting and Billings (1942) found no correlation between soil salinity and plant 

distribution on the North Carolina coast. Although surface crusts of sand dunes have been 

shown to have high concentrations of chlorides which could be attributed to sea spray, 

concentrations of chlorides in underlying layers was low. The salt content of the surface layer 

varied with exposure of the dunes to oceanic winds (Boyce, 1954). 

Boyce (1954), Wells (1939), and Wells and Shunk (1938) concluded on the basis of their 

studies that necrosis and death of plant tissues results from the high deposition of salt spray and 

high accumulation of the chloride ion (Cl!

) in the plant tissues. Very little salt is taken up by 

plant roots; most enters through the aerial organs. Leaves of plants exposed to salt spray show 

a distinct pattern of injury (Wells and Shunk, 1938). Necrotic areas first appear at the leaf tips 

and upper margins and then progress slowly in an inverted “V” toward the petiole. This leaf 

injury pattern was verified experimentally. Mechanical injury resulting from leaves and twigs 

beating against each another in the wind causes the formation of small lesions through which salt 

4-66
can enter. After entry into the plant, the chloride ion is rapidly translocated to the apices of the 

leaves and twigs where it accumulates to injurious concentrations and results in the death of only 

a portion of the plant. The differential deposition and translocation of the chloride ion results in 

the death of the seaward leaves and twigs. The result is the continued growth of the uninjured 

branches in an inland direction. As a result, the canopy angle varies with the intensity of the 

spray (Boyce, 1954). 

Little or no mineral ions are available in the silicate sands of the coastal dunes. 

Consequently, plants obtain mineral ions needed for growth from the salt spray. Seawater 

contains all of the mineral ions required for plant growth except nitrogen and phosphorus. 

The amount of nitrogen and phosphorus in seawater varies over a wide range (Boyce, 1954). 

Experiments indicated that available nitrogen in sea spray was a conditioning factor. 

Low nitrogen availability increased the tolerance of dune species to salt spray. Increasing 

the availability of nitrogen resulted in a different pattern of plant zonation and distribution. 

Dicotyledonous species were restricted to areas of lower spray intensity. The severity of 

chloride injury was associated more with the amount of available potassium than with the 

concentration of chlorides within the limits of 280 to 360 mg Cl/liter (Boyce, 1954). 

Other sources of phytotoxic saline PM include aerosols from cooling towers and roadway 

deicing salt. Cooling towers used to dissipate waste heat from steam-electric power generating 

facilities may emit salt if brackish salt water is used as a coolant (McCune et al., 1977; Talbot, 

1979). Foliar injury is related to salt droplets deposited by sedimentation or impaction from 

cooling tower drift. The distance of the salt drift determines the amount of deposition and 

location of injury. The environmental conditions most conducive to injury were absence of 

precipitation, which can wash salt off leaves, and high relative humidity (Talbot, 1979). 

Increased injury is associated with wind speed and salt concentrations. Typical toxicity 

symptoms from acute exposures include marginal foliar necrosis and lesions, shoot-tip dieback, 

leaf curl, and interveinal necrosis (McCune et al., 1977). Based on experimental data, Grattan 

et al. (1981) observed that, to cause injury, salt deposited on leaf surfaces must dissolve and be 

absorbed into leaf tissue. Their work also indicated the importance of RH in foliar uptake. 

If RH remained below 70%, even heavy deposition of salt did not induce injury in peppers, 

soybeans, or tomatoes. 

4-67
Injury to vegetation from the application of deicing salt was related to salt spray blown or 

drifting from the highways (Hofstra and Hall, 1971; Viskari and Kärenlampi, 2000). The most 

severe injury was observed nearest the highways. The results presented in these studies agree 

with that of Wyttenbach et al. (1989), who observed that conifers planted near roadway margins 

in the eastern United States often exhibit foliar injury due to toxic levels of saline aerosols 

deposited from deicing solutions. Piatt and Krause (1974) demonstrated that road and site 

factors influence the spread of deicing salt into forested areas. The slope away from the road 

influenced the distance from the road where injury was observed and the percent slope was 

correlated with distance. 

Effects of Fine Particles 

Fine PM in rural areas is generally secondary in nature, having condensed from the vapor 

phase or been formed by chemical reaction from gaseous precursors in the atmosphere, and is 

generally smaller than 1 to 2.5 μm. Nitrogen and sulfur oxides, volatile organic gases, 

condensates of volatilized metals, and products of incomplete combustion are common 

precursors for fine PM. Many of these materials react with an oxidizing atmosphere to 

contribute to high secondary PM concentrations during summer months in many U.S. areas or 

during late fall and winter in areas with high nitrate concentrations. The conclusion that 

sufficient data were not available for adequate quantification of dose-response functions for 

direct effects of fine aerosols on vegetation, reached in the 1982 PM/SOX AQCD (U.S. 

Environmental Protection Agency, 1982), continues to be true today. Only a few studies on the 

direct effects of acid aerosols have been completed (U.S. Environmental Protection Agency, 

1982). The major effects are indirect and occur through the soil (see Section 4.2.3.2). 

Nitrogen. Nitrate is observed in both fine and coarse particles. Nitrates from atmospheric 

deposition represent a substantial fraction of total nitrogen inputs to southeastern forests (Lovett 

and Lindberg, 1986). However, much of this is contributed by gaseous nitric acid vapor; and a 

considerable amount of the particulate nitrate is taken up indirectly through the soil. Garner 

et al. (1989) estimated deposition of nitrogen to forested landscapes in eastern North America at 

10 to 55 kg/ha/year for nitrate and 2 to 10 kg/ha/year for ammonium. About half of these values 

were ascribed to dry deposition. 

4-68
Driscoll et al. (2003) estimated anthropogenic nitrogen inputs to eight large watersheds in 

the northeastern United States for the year 1997. Inputs of total nitrogen deposition ranged from 

14 kg/ha/year in the Casco Bay watershed, ME to 68 kg/ha/year in the Massachusetts Bay 

watershed. Atmospheric deposition of nitrogen was the second largest source ranging in 

amounts from 5 to 10 kg/ha/year (11 to 36% of the total) (Driscoll et al., 2003). Nitrogen 

deposition in the western United States ranges from 1 to 4 kg/ha/year over much of the region to 

as high as 30 kg/year downwind of major urban and agricultural areas. An unknown amount of 

nitrogen deposited to the West Coast originates in Asia (Fenn et al., 2003a). 

Atmospheric additions of particulate nitrogen in excess of vegetation needs are lost from 

the system, mostly in leachate from the soil as nitrate. Managed agricultural ecosystems may be 

able to utilize deposited particulate nitrogen more efficiently than native ecosystems, although 

many cultivated systems also lose considerable nitrogen as nitrate in runoff, deep drainage, or 

soil water. It has proven difficult to quantify direct foliar fertilization by uptake of nitrogen from 

ambient particles. 

There is no doubt that foliar uptake of nitrate can occur, as clearly shown by the efficacy of 

foliar fertilization in horticultural systems. Potassium nitrate was taken up by the leaves of 

deciduous fruit trees (Weinbaum and Neumann, 1977) and resulted in increased foliar nitrogen 

concentrations. Not all forms of nitrogen are absorbed equally, nor are all equally benign. 

Following foliar application of 2600 ppm of nitrogen as calcium nitrate Ca(NO3)2, (NH4)2SO4, 

or urea [(NH2)2CO] to apple canopies (Rodney, 1952; Norton and Childers, 1954), leaf nitrogen 

levels were observed to increase to similar levels; however, calcium nitrate and ammonium 

sulfate caused visible foliar injury, whereas urea did not. Urea is generally the recommended 

horticultural foliar fertilizer. 

The mechanism of uptake of foliarly deposited nitrate is not well established. Nitrate 

reductase is generally a root-localized enzyme: it is generally not present in leaves, but is 

! inducible there. Induction typically occurs when the soil is heavily enriched in NO3 . As the 

root complement of nitrate reductase becomes overloaded, unreduced nitrate reaches the leaves 

through the transpiration stream. Nitrate metabolism has been demonstrated in leaf tissue 

(Weinbaum and Neumann, 1977) following foliar fertilization. Residual nitrate reductase 

activity in leaves may be adequate to assimilate typical rates of particulate nitrate deposition. 

4-69
Uptake of nitrate may be facilitated by codeposited sulfur (Karmoker et al., 1991; Turner and 

Lambert, 1980). 

+ Nitrate reductase is feedback-inhibited by its reaction product NH4 . The common 

atmospheric aerosol, ammonium nitrate [NH4NO3], therefore may be metabolized in two distinct 

biochemical steps: first the ammonium (probably leaving nitric acid) and then the nitrate. 

Losses of nitric acid by volatization during this process, if they occur, have not been quantified. 

Direct foliar effects of particulate nitrogen have not been documented. Application of a 

variety of fine nitrogenous aerosol particles (0.25 μm) ranging from 109 to 244 μg/m3

 nitrogen 

with or without 637 μg/m3

 sulfur caused no consistent short-term (2- to 5-h) effect on gas 

exchange in oak, maize, or soybean leaves (Martin et al., 1992). 

Although no evidence exists for the direct transfer of nutrient particulate aerosols into 

foliage, a few studies give insights into the potential for ammonium and nitrate transfer into 

leaves. Fluxes of both NO3 

!

 and NH4

+

, measured in wet deposition and in throughfall plus 

stemflow in forests, commonly indicate higher fluxes of nitrogen above the canopy (Parker, 

1983; Lindberg et al., 1987; Sievering et al., 1996), and imply net foliar uptake. Lovett and 

Lindberg (1993) reported a linear relationship between inorganic nitrogen fluxes in deposition 

and throughfall, suggesting that uptake may be considered passive to some extent. 

Garten and Hanson (1990) studied the movement of 15N-labeled nitrate and ammonium 

across the cuticles of red maple (Acer rubrum) and white oak (Quercus alba) leaves when 

applied as an artificial rain mixture. Brumme et al. (1992), Bowden et al. (1989), and Vose and 

Swank (1990) have published similar data for conifers. These studies show the potential for 

nitrate and ammonium to move into leaves, where it may contribute to normal physiological 

processes (e.g., amino acid production) (Wellburn, 1990). Garten (1988) showed that internally 

translocated 35S was not leached readily from tree leaves of yellow poplar (Liriodendron 

tulipifera) and red maple (Acer rubrum), suggesting that SO4

2!

 would not be as mobile as the 

nitrogen-containing ions discussed by Garten and Hanson (1990). Further, when the foliar 

extraction method is used, it is not possible to distinguish sources of chemicals deposited as 

+ gases or particles (e.g., HNO3, NO2, NO3 

!

), or sources of ammonium (deposited as NH3 or NH4 ) 

(Garten and Hanson, 1990). 

Particle deposition contributes only a portion of the total atmospheric nitrogen deposition 

reaching vegetation; but, when combined with gaseous and precipitation-derived sources, total 

4-70
nitrogen deposition to ecosystems has been identified as a possible causal factor leading to 

changes in natural ecosystems (see Section 4.2.2). 

Sulfur. Anthropogenic sulfur emissions are > 90% SO2. Most of the remaining emissions 

of sulfur is directly as sulfate (U.S. Environmental Protection Agency, 1996a). Sulfur dioxide is 

hydrophilic and is rapidly hydrated and oxidized to sulfite and bisulfite and then to sulfate, 

which is ~30-fold less phytotoxic. The ratio of SO4

2!

:SO2 increases with the aging of the air 

mass and, therefore, with distance from the source. Sulfate is sufficiently hygroscopic in humid 

air that it may exist significantly in the coarse particulate fraction. Because dilution of both SO2 

! and particulate SO42!

 occurs with distance from the source, damaging levels of particulate SO4 

are rarely deposited. In this case, gas to particle conversion is of benefit to local vegetation. 

Sulfur is an essential plant nutrient. Low dosages of sulfur serve as a fertilizer, particularly 

for plants growing in sulfur-deficient soil (Hogan et al., 1998). However, current levels of 

sulfate deposition reportedly exceed the capacity of most vegetative canopies to immobilize the 

sulfur (Lindberg, 1992; Johnson, 1984). Nitrogen uptake in forests may be regulated loosely by 

sulfur availability, but sulfate additions in excess of needs do not typically lead to injury unless 

deposited in acidic precipitation (Turner and Lambert, 1980). 

There are few field demonstrations of foliar sulfate uptake (Krupa and Legge, 1986, 1998). 

Sulfate in throughfall is often enriched above levels in precipitation. The relative importance of 

foliar leachate and prior dry-deposited sulfate particles remains difficult to quantify (Cape et al., 

1992). Leaching rates are not constant and may respond to levels of other pollutants, including 

acids. Uptake and foliar retention of gaseous and particulate sulfur are confounded by variable 

rates of translocation and accessibility of deposited materials to removal and quantification by 

leaf washing. Following soil enrichment with 35SO4

2!

 in a Scots pine forest, the apparent 

contribution of leachate to throughfall was only a few percent following an initial burst of over 

90%, because of extreme disequilibrium in the labeling of tissue sulfate pools (Cape et al., 

1992). 

Olszyk et al. (1989) provide information on the effects of multiple pollutant exposures 

including particles (NO3 

!

, 142 μg/m3; NH4

+

, 101 μg/m3; SO4

2!

, 107 μg/m3). They found that 

only gaseous pollutants produced direct harmful effects on vegetation for the concentrations 

documented, but the authors hypothesized that the long-term accumulation of the nitrogen and 

4-71
sulfur compounds contributed from particle deposition might have adverse effects on plant 

nutrition over long periods of time. Martin et al. (1992) exposed oak (Quercus macrocarpa), 

soybean (Glycine max), and maize (Zea mays) plants to acute exposures (2 to 5 h) of aerosols 

(0.25 μm) containing only nitrate (109 μg/m3

), ammonium and nitrate (244 and 199 μg/m3), or 

ammonium and sulfate (179 and 637 μg/m3

). They found that these exposures, which exceeded 

the range of naturally occurring aerosol concentrations, had little effect on foliar photosynthesis 

and conductance. Martin et al. (1992) concluded that future investigations should focus on the 

effects of particles on physiological characteristics of plants following chronic exposures. 

Acidic Deposition. The effects of acidic deposition have been accorded wide attention in 

the media and elsewhere (Altshuller and Linthurst, 1984; Hogan et al., 1998). Probably the most 

extensive assessment of acidic deposition processes and effects is the NAPAP Biennial Report to 

Congress: An Integrated Assessment (National Science and Technology Council, 1998). 

Concern regarding the effects of acidic deposition on crops and forest trees has resulted in 

extensive monitoring and research. Exposures to acidic rain or clouds can be divided into 

“acute” exposures to higher ionic concentrations (several μmol/L) and “chronic” long-term 

repeated exposures to lower concentrations (Cape, 1993). Pollutant concentrations in rainfall 

have been shown to have little capacity for producing direct effects on vegetation (Altshuller and 

Linthurst, 1984; Hogan et al., 1998); however, fog and clouds, which may contain solute 

concentrations up to 10 times those found in rain, have the potential to cause direct effects. 

More than 80% of the ionic composition of most cloudwater is made up of four major pollutant 

2! ions: H+

, NH4

+

, NO3 

!

, and SO4 . Ratios of hydrogen to ammonium and sulfate to nitrate vary 

from site to site with all four ions usually present in approximately equal concentrations. 

Available data from plant effect studies suggest that hydrogen and sulfate ions are more likely to 

cause injury than ions containing nitrogen (Cape, 1993). 

The possible direct effects of acidic precipitation on forest trees have been evaluated in 

experiments on seedlings and young trees. The size of mature trees makes experimental 

exposure difficult, therefore necessitating extrapolations from experiments on seedlings and 

saplings; however, such extrapolations must be used with caution (Cape, 1993). Both conifers 

and deciduous species have shown significant effects on leaf surface structures after exposure to 

simulated acid rain or acid mist at pH 3.5. Some species have shown subtle effects at pH 4 and 

4-72
above. Visible lesions have been observed on many species at pH 3 and on sensitive species at 

pH 3.5 (Cape, 1993). The relative sensitivities of forest vegetation to acidic precipitation based 

on macroscopic injury have been ranked as follows: herbaceous dicots > woody dicots > 

monocots > conifers (Percy, 1991). 

Huttunen (1994) described the direct effects of acid rain or acidic mist on epicuticular 

waxes whose ultrastructure is affected by plant genotype and phenotype. The effects of air 

pollutants on epicuticular waxes of conifers have received greater study than the waxes of other 

species. Leaf age and the shorter life span of broad-leaved trees make them less indicative of the 

effects of acid precipitation. Many experimental studies indicate that epicuticular waxes that 

function to prevent water loss from plant leaves can be destroyed by acid rain over a few weeks 

(Huttunen, 1994). Epicuticular waxes are crucial to conifers because of their longevity and 

evergreen foliage. Microscopic observations of epicuticular wax structures have, for a long 

time, suggested links between acidic deposition and aging. In Norway spruce (Picea abies), acid 

rain causes not only the aging of needles (which in northern conditions normally last from 

11 to 14 years) to be shortened, but also accelerates the erosion rate of the waxes as the needles 

age. 

The effects of acidic precipitation and fog on red spruce (Picea rubens) have been studied 

extensively (Schier and Jensen, 1992). Visible foliar injury of the needles in the form of a 

reddish-brown discoloration has been observed on red spruce seedlings experimentally exposed 

to acidic mist, but this visible symptom has not been observed in the field. Ultrastructural 

changes in the epicuticular wax were observed both experimentally and on spruce growing at 

high elevations. Laboratory studies indicate that visible injury usually does not occur unless the 

pH is 3 or less (Schier and Jensen, 1992). Cape (1993) reported that, when compared with other 

species, red spruce seedlings appeared to be more sensitive to acid mist. From studies of 

conifers and a review of the literature, Huttunen (1994) concluded that acidic precipitation 

causes direct injury to tree foliage and indirect effects through the soil. The indirect effects of 

acidic precipitation are discussed in Section 4.2.3.2. 

Based on a review of the many studies in the literature involving field and controlled 

laboratory experiments on crops, Cape (1993) drew a number of conclusions concerning the 

direct effects of acidic precipitation on crops: 

4-73
• foliar injury and growth reduction occur below pH 3; 

• allocation of photosynthate is altered, with increased shoot to root ratios; 

• expanded and recently expanded leaves are most susceptible, and injury occurs first to

epidermal cells;

• leaf surface characteristics such as wettability, buffering capacity, and transport of material 

across the leaf surface contribute to susceptibility and differ among species; 

• data obtained from experiments in greenhouses or controlled environmental chambers

cannot be used to predict effects on plants grown in the field;

• quantitative data from experimental exposures cannot be extrapolated to field exposures

because of differences and fluctuations in concentrations, durations, and frequency of

exposure; 

• there are large differences in response within species; 

• timing of exposure in relation to phenology is of utmost importance; 

• plants may be able to recover from or adapt to injurious exposures; and 

• sequential exposure to acidic precipitation and gaseous pollutants is unlikely to be more

injurious than exposure to individual pollutants.

Studies by Chevone et al. (1986), Krupa and Legge (1986), and Blaschke (1990) differ 

with the last conclusion of Cape listed above. Their studies indicate that interactions between 

acidic deposition and gaseous pollutants do occur. Acidity affects plant responses to both O3 and 

SO2. Chevone et al. (1986) observed increased visible injury on soybean and pinto bean when 

acid aerosol exposure preceded O3 exposure; whereas linear decreases in dry root weight of 

yellow poplar occurred as acidity increased with simultaneous exposures to O3 and simulated 

acid rain. Krupa and Legge (1986) also noted increased visible injury to pinto bean plants when 

aerosol exposure preceded O3 exposure. In none of the studies cited above did acid rain per se 

produce significant growth changes. In contrast, Blaschke (1990) observed a decrease in 

ectomycorrhizal frequency and short root distribution caused by acid rain exposure in 

combination with either SO2 or O3. 

Trace Elements. All but 10 of the 90 elements that comprise the inorganic fraction of the 

soil occur at concentrations of < 0.1% (1000 μg/g) and are termed “trace” elements or trace 

metals. Trace metals with a density greater than 6 g/cm3

, referred to as “heavy metals,” are of 

4-74
particular interest because of their potential toxicity for plant and animals. Although some trace 

metals are essential for vegetative growth or animal health, they are all toxic in large quantities. 

Combustion processes produce metal chlorides that tend to be volatile and metal oxides that tend 

to be nonvolatile in the vapor phase (McGowan et al., 1993). Most trace elements exist in the 

atmosphere in particulate form as metal oxides (Ormrod, 1984). Aerosols containing trace 

elements derive predominantly from industrial activities (Ormrod, 1984). Generally, only the 

heavy metals cadmium, chromium, nickel, and mercury are released from stacks in the vapor 

phase (McGowan et al., 1993). Concentrations of heavy metals in incinerator fly ash increase 

with decreasing particle size. 

Vegetational surfaces, especially the foliage, present a major reaction and filtration surface 

to the atmosphere and act to accumulate particles deposited via wet and dry processes as 

described in Section 4.2.2 (Tong, 1991; Youngs et al., 1993). The chemical constituents of 

particles deposited on foliar surfaces may be taken up through the leaf surface. The greatest 

particle loading is usually on the upper leaf surface where particles accumulate in the mid-vein, 

center portion of the leaves. Additionally, fungal mycelia become particularly abundant on leaf 

surfaces as the growing season progresses and can be found in intimate association with 

deposited particles (Smith, 1990c). 

Investigations of trace elements present along roadsides and in industrial and urban 

environments indicate that impressive burdens of particulate heavy metals can accumulate on 

vegetative surfaces. Foliar uptake of available metals could result in metabolic effects in aboveground tissues. Only a few metals, however, have been documented to cause direct 

phytotoxicity in field conditions. Copper, Zn, and Ni toxicities have been observed most 

frequently. Low solubility, however, limits foliar uptake and direct heavy metal toxicity 

because trace metals must be brought into solution before they can enter into the leaves or bark 

of vascular plants. In those instances when trace metals are absorbed, they are frequently bound 

in leaf tissue and are lost when the leaf drops off (Hughes, 1981). Trace metals in mixtures may 

interact to cause a different plant response when compared with a single element; however, there 

has been little research on this aspect (Ormrod, 1984). In experiments using chambers, 

Marchwi1⁄2ska and Kucharski (1987) studied the effects of SO2 alone and in combination with the 

PM components Pb, Cd, Zn, Fe, Cu, and Mn obtained from a zinc smelter bag filter. The 

combined effects of SO2 and PM further increased the reduction in yield of beans caused by SO2; 

4-75
whereas the combination, though severely injuring the foliage, produced little effect on carrots 

and parsley roots except after long-term exposures (when there was a decrease in root weight). 

Trace metal toxicity of lichens has been demonstrated in relatively few cases. Nash (1975) 

documented zinc toxicity in the vicinity of a zinc smelter near Palmerton, PA. The richness and 

abundance of lichen species were reduced by ~90% in lichen communities at Lehigh Water Gap 

near the zinc smelter when compared with those at Delaware Water Gap. Zinc, Cd, and SO2 

were present in concentrations toxic to some species near the smelter; however, toxic zinc 

concentrations were detected farther away than the detectable limits of SO2 (Nash, 1975). 

Experimental data suggest that lichen tolerance to Zn and Cd falls between 200 and 600 ppm 

(Nash, 1975). 

Though there has been no direct evidence of a physiological association between tree 

injury and exposure to metals, heavy metals have been implicated because their deposition 

pattern is correlated with forest decline. The role of heavy metals has been assessed using 

phytochelatin measurements. Phytochelatins are intracellular metal-binding peptides that act as 

specific indicators of metal stress. Because they are produced by plants as a response to 

sublethal concentrations of heavy metals, they can indicate that heavy metals play a role in forest 

decline (Gawel et al., 1996). Concentrations of heavy metals increased with altitude, as did 

forest decline; and increased concentrations of heavy metals were found across the study region 

that showed increased levels of forest injury. 

Phytochelatin concentrations were measured in red spruce and balsam fir (Abies balsamea) 

needles throughout the 1993 growing season at 1000 m on Whiteface Mountain in New York. 

Mean foliar concentrations in red spruce were consistently higher than in balsam fir from June 

until August, with the greatest and most significant difference occurring at the peak of the 

growing season in mid-July. In July, the phytochelatin concentrations were significantly higher 

than at any other time measured. Balsam fir also exhibited this peak, but maintained a 

consistently low level throughout the season. Both the number of dead red spruce trees and 

phytochelatin concentrations increased sharply with elevation (Gawel et al., 1996). The 

relationship between heavy metals and the decline of forests in northeastern United States was 

further tested by sampling red spruce stands showing varying degrees of decline at 1000 m on 

nine mountains spanning New Hampshire, Vermont, and New York. The collected samples 

indicated a systematic and significant increase in phytochelatin concentrations associated with 

4-76
the extent of tree injury. The highest phytochelatin concentrations were measured during 1994 

from sites most severely affected by forest decline in the Green Mountains in Vermont and the 

Adirondack Mountains in New York. These data strongly imply that metal stress causes tree 

injury and contributes to forest decline in the northeastern United States (Gawel et al., 1996). 

One potential direct effect of heavy metals is on the activity of microorganisms and 

arthropods resident on and in the leaf-surface ecosystem. The fungi and bacteria living on and in 

the surfaces of leaves play an important role in the microbial succession that prepares leaves for 

decay and litter decomposition after their fall (U.S. Environmental Protection Agency, 1996b). 

Numerous fungi were consistently isolated from foliar surfaces at various crown positions from 

London plane trees (Platanas hispanica) growing in roadside environments in New Haven, CT. 

Those existing primarily as saprophytes included Aureobasidium pullulans, Chaetomium sp., 

Cladosporium sp., Epicoccum sp., and Philaphora verrucosa. Those existing primarily as 

parasites included Gnomonia platani, Pestalotioposis sp., and Pleurophomella sp. The 

following cations were tested in vitro for their ability to influence the growth of these fungi: 

Cd, Cu, Mg, Al, Cr, Ni, Fe, Fb, Na, and Zn. Results indicated variable fungal response with no 

correlation between saprophytic or parasitic activity and sensitivity to heavy metals. Both linear 

extension and dry weight data indicated that the saprophytic Chaetomum sp. was very sensitive 

to numerous metals. Aureobasidium pullulans, Epicoccum sp., and especially P. verrucosa, on 

the other hand, appeared to be much more tolerant. Of the parasites, G. platani appeared to be 

more tolerant than Pestalotiopsis sp. and Pleurophomella sp. Metals producing the broadest 

spectrum growth suppression were Fe, Al, Ni, Zn, Mg, and Pb (Smith and Staskawicz, 1977; 

Smith, 1990c). These in vitro studies employed soluble compounds containing heavy metals. 

Trace metals probably occur naturally on leaf surfaces as low-solubility oxides, halides, sulfates, 

sulfides, or phosphates (Clevenger et al., 1991; Koslow et al., 1977). In the event of sufficient 

solubility and dose, however, changes in microbial community structure on leaf surfaces because 

of heavy metal accumulation are possible. 

Organic Compounds. Volatile organic compounds in the atmosphere are partitioned 

between the gas and particle phases, depending on the liquid-phase vapor pressure at the ambient 

atmospheric temperature, the surface area of the particles per unit volume of air, the nature of the 

particles and of the chemical being adsorbed; and they can be removed from the atmosphere by 

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wet and dry deposition (McLachlan, 1996a). Materials as diverse as DDT, polychlorinated 

biphenyls (PCBs), and polynuclear aromatic hydrocarbons (PAHs) are being deposited from the 

atmosphere on rural, as well as urban, landscapes (Kylin et al., 1994). Motor vehicles emit 

particles to the atmosphere from several sources in addition to the tailpipe. Rogge et al. (1993b) 

inventoried the organic contaminants associated with fine particles (diameter # 2.0 μm) in road 

dust, brake-lining-wear particles, and tire-tread debris. More than 100 organic compounds were 

identified in these samples, including N-alkanols, benzoic acids, benzaldehydes, polyalkylene 

glycol ethers, PAHs, oxy-PAH, steranes, hopanes, natural resins, and other compound classes. 

A large number of PAHs, ranging from naphthalene (C10H8) to 5- and 6-ring and higher PAHs, 

their alkyl-substituted analogues, and their oxygen- and nitrogen-containing derivatives are 

emitted from motor vehicle sources (Seinfeld, 1989). 

Plants may be used as environmental monitors to compare the deposition of PAH, 

persistent organic pollutants (POPs), or semivolatile organic components (SOCs) between sites 

(e.g., urban versus rural) (Wagrowski and Hites, 1997; Ockenden et al., 1998; McLachlan, 

1999). Vegetation can be used qualitatively to indicate organic pollutant levels as long as the 

mechanism of accumulation is considered. The substance may enter the plant via the roots or, as 

noted above, be deposited as a particle onto the waxy cuticle of leaves or be taken up through the 

stomata. The pathways are a function of the chemical and physical properties of the pollutant, 

e.g., its lipophilicity, water solubility, vapor pressure (which controls the vapor-particle 

partitioning) and Henry’s law constant; environmental conditions such as ambient temperature 

and the organic content of the soil; and the plant species, which controls the surface area and 

lipids available for accumulation (Simonich and Hites, 1995). Ockenden et al. (1998) have 

observed that, for lipophilic POPs, atmospheric transfer to plant has been the main avenue of 

accumulation. Plants can differentially accumulate POPs. Results have shown differences 

between species with higher concentrations in the lichen (Hypogymnia physiodes) than in Scots 

pine (Pinus sylvestris) needles. Even plants of the same species, because they have different 

growth rates and different lipid contents (depending on the habitat in which they are growing), 

have different rates of sequestering pollutants. These facts confound data interpretations and 

must be taken into account when considering their use as passive samplers. 

Vegetation itself is an important source of hydrocarbon aerosols. Terpenes, particularly 

"-pinene, $-pinene, and limonene, released from tree foliage may react in the atmosphere to 

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form submicron particles. These naturally generated organic particles contribute significantly to 

the blue haze aerosols formed naturally over forested areas (Geron et al., 2000). 

The low water solubility with high lipoaffinity of many of these organic xenobiotics 

strongly control their interaction with the vegetative components of natural ecosystems. The 

cuticles of foliar surfaces are covered with a wax layer that helps protect plants from moisture 

and short-wave radiation stress. This epicuticular wax, consisting mainly of long-chain esters, 

polyesters, and paraffins, has been demonstrated to accumulate lipophilic compounds. Organic 

air contaminants in the particulate or vapor phase are absorbed to, and accumulate in, the 

epicuticular wax of vegetative surfaces (Gaggi et al., 1985; Kylin et al., 1994). Direct uptake of 

organic contaminants through the cuticle or the vapor-phase uptake through the stomata are 

characterized poorly for most trace organics. The phytotoxicity and toxicity of organic 

contaminants to soil microorganisms is not well studied (Foster, 1991). 

4.2.3.2 Ecosystem Response to Stresses 

The ecosystem response to stress begins within the biotic condition attribute at the 

population level with changes in patterns resulting from the response of sensitive individual 

plants or animals. The ecosystem response to pollutant deposition is a direct function of the 

ecosystem’s ability to ameliorate resulting change (Strickland et al., 1993). Plant responses, 

changes in both structural and compositional patterns, and functional ecological processes must 

be scaled in both time and space and be propagated from the individual to the more complex 

levels of community interaction to produce observable changes in an ecosystem (see 

Figure 4-14). Among ecosystem biota, at least three levels of biological interaction are 

involved: (1) the individual plant and its environment, (2) the population and its environment, 

and (3) the biological community composed of many species and its environment (Billings, 

1978). Individual organisms within a population vary in their ability to withstand the stress of 

environmental change. The response of individual organisms within a population is based on 

their genetic constitution (genotype), stage of growth at time of exposure to stress, and the 

microhabitat in which they are growing (Levin, 1998). The range within which these organisms 

can exist and function determines the ability of the population to survive. Those able to cope 

with the stresses survive and reproduce. Competition among the different species results in 

succession (community change over time) and, ultimately, produces ecosystems composed of 

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Figure 4-14. Effects of environmental stress on forest trees are presented on a hierarchial 

scale for the leaf, branch, tree, and stand levels of organization. The 

evaluation of effects within a level of organization are indicated by horizontal 

arrows. The evaluation of interactions between different levels of 

organization are indicated by diagonal arrows. 

Source: Hinckley et al. (1992). 

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populations of plant species that have the capability to tolerate the stresses (Rapport and 

Whitford, 1999; Guderian, 1985). 

The number of species in a community usually increases during succession in unpolluted 

atmospheres. Productivity, biomass, community height, and structural complexity increase. 

Severe stresses, on the other hand, divert energy from growth and reproduction to maintenance 

and return succession to an earlier stage (Waring and Schlesinger, 1985). Ecosystems are 

subject to natural periodic stresses, such as drought, flooding, fire, and attacks by biotic 

pathogens (e.g., fungi, insects). Extremely severe natural perturbations return succession to an 

earlier stage; reduce ecosystem structure and functions (i.e., produce a scarcity of life forms and 

extinguish symbiotic interactions); disrupt the plant processes of photosynthesis and nutrient 

uptake, carbon allocation, and transformation that are directly related to energy flow and nutrient 

cycling; shorten food chains; and reduce the total nutrient inventory (Odum, 1993). This 

transformation, however, sets the stage for recovery that permits the perturbed ecosystem to 

adapt to changing environments (Holling, 1986). Therefore, these perturbations are seldom 

more than a temporary setback, and recovery can be rapid (Odum, 1969). 

In contrast, anthropogenic stresses usually produce severe, debilitating effects. Severely 

stressed ecosystems do not recover readily, but may be further degraded (Odum, 1969; Rapport 

and Whitford, 1999). Anthropogenic stresses can be classified into four main groups: 

(1) physical restructuring (e.g., changes resulting from land use), (2) introduction of exotic 

species, (3) over harvesting, and (4) discharge of toxic substances into the atmosphere, onto 

land, and into water. Ecosystems usually lack the capacity to adapt to the above stresses and 

maintain their normal structure and functions unless the stressor is removed (Rapport and 

Whitford, 1999). These stresses result in a process of ecosystem degradation marked by a 

decrease in biodiversity, reduced primary and secondary production, and a lower capacity to 

recover and return to its original state. In addition, there is an increased prevalence of disease, 

reduced nutrient cycling, increased dominance of exotic species, and increased dominance by 

smaller, short-lived opportunistic species (Odum, 1985; Rapport and Whitford, 1999). 

Discharge of toxic substances into the atmosphere, onto land, and into water can cause acute and 

chronic stresses; and, once the stress is removed, a process of succession begins that can 

ultimately return the ecosystem to a semblance of its former structure. Air pollution stress, if 

acute, is usually short-term and the effects soon visible. Chronic stresses, on the other hand, are 

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long-term stresses whose effects occur at different levels of ecosystem organization and appear 

only after long-term exposures, as in the case of acidic deposition in the northeast or ozone in 

California (Shortle and Bondietti, 1992; U.S. Environmental Protection Agency, 1996b). 

The possible effects of air pollutants on ecosystems have been categorized by Guderian 

(1977) as follows: 

(1) accumulation of pollutants in the plant and other ecosystem components

(such as soil and surface- and groundwater),

 (2) damage to consumers as a result of pollutant accumulation,

 (3) changes in species diversity because of shifts in competition,

 (4) disruption of biogeochemical cycles,

 (5) disruption of stability and reduction in the ability of self-regulation,

 (6) breakdown of stands and associations, and

 (7) expansion of denuded zones. 

How changes in these functions can result from PM deposition and influence ecosystems is 

discussed in the following text. It should be remembered that, although the effects of PM are 

being emphasized, the vegetational components of ecosystems also are responding to multiple 

stressors from multiple sources. 

Response to Direct Effects of Particulate Matter 

In the previous section, it was noted that PM affects patterns in the EEA biotic condition 

and processes in the chemical/physical and ecological processes categories. The presence of 

PM in the atmosphere may affect vegetation directly, following physical contact with the foliar 

surface (Section 4.2), but in most cases, the more significant effects are indirect. These effects 

may be mediated by suspended PM (i.e., through effects on radiation and climate) and by 

particles that pass through the vegetative canopies to the soil. 

The majority of studies dealing with the direct effects of particulate dust and trace metals 

on vegetation were focused on responses of individual plant species and were conducted in the 

laboratory or in controlled environments (Saunders and Godzik, 1986). A few have considered 

the effects of particles on populations, communities, and ecosystems. Most of these focused on 

ecosystems in industrialized areas that are heavily polluted by deposits of both chemically inert 

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and active dusts. Effects can result from direct deposition or indirectly from deposition onto the 

soil. Reductions in growth, yield, flowering, and reproductive processes of plants from PM 

deposition have been reported (Saunders and Godzik, 1986). Sensitivities of individual species 

have been associated with changes in composition and structural patterns of natural ecosystems. 

Evidence from studies of effects of PM deposition, specifically chemically inert and active 

dusts indicates that, within a population, plants exhibit a wide range of sensitivity which is the 

basis for the natural selection of tolerant individuals. Rapid evolution of certain populations of 

tolerant species at sites with heavy trace element and nitrate deposition has been observed 

(Saunders and Godzik, 1986). Tolerant individuals present in low frequencies in populations 

when growing in unpolluted areas have been selected for tolerance at both the seedling and adult 

stages when exposed to trace metal or nitrate deposition (Ormrod, 1984; U.S. Environmental 

Protection Agency, 1993). Chronic pollutant injury to a forest community may result in the loss 

of sensitive species, loss of tree canopy, and maintenance of a residual cover of pollutanttolerant herbs or shrubs that are recognized as successional species (Smith, 1974) (see 

Table 4-13). These changes in forest patterns result from altered ecological processes. 

Responses of ecosystems to stresses (unless severe or catastrophic) are difficult to 

determine because the changes are subtle (Garner, 1991). This is particularly true of responses 

to particles. Changes in the soil may not be observed until accumulation of the pollutant has 

occurred for 10 or more years, except in the severely polluted areas around heavily industrialized 

point sources (Saunders and Godzik, 1986). In addition, the presence of other cooccurring 

pollutants makes it difficult to attribute the effects to PM alone. In other words, the potential for 

alteration of ecosystem function and structure exists but is difficult to quantify, especially when 

there are other pollutants present in the ambient air that may produce additive or synergistic 

responses even though PM concentrations may not be elevated. 

Physical Effects 

The direct effects of limestone dust on plants and ecosystems has been known for many 

years. Changes have been observed in both ecosystem patterns and processes. Long-term 

changes in the structure and composition of the seedling-shrub and sapling strata of an 

experimental site near limestone quarries and processing plants in Giles County in southwestern 

Virginia were reported by Brandt and Rhoades (1972, 1973). Dominant trees in the control area, 

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TABLE 4-13. ECOSYSTEM FUNCTIONS IMPACTED BY AIR POLLUTION

EFFECTS ON TEMPERATE FOREST ECOSYSTEMS

Forest Soil and Vegetation: Activity and Response Ecosystem Consequence and Impact 

(1) Forest tree reproduction, alteration, or inhibition (1) Altered species composition 

(2) Forest nutrient cycling, alteration (2) Reduced growth, less biomass 

a. Reduced litter decomposition 

b. Increased plant and soil leaching and 

soil weathering 

c. Disturbance of microbial symbioses 

(3) Forest metabolism (3) Reduced growth, less biomass 

a. Decreased photosynthesis 

b. Increased respiration 

c. Altered carbon allocation 

(4) Forest stress, alteration (4) Altered ecosystem stress: 

a. Phytophagous insects, increased or increased or decreased insect 

decreased activity infestations; increased or decreased 

b. Microbial pathogens, increased or disease epidemics; and reduced 

decreased activity growth, less biomass, and altered 

c. Foliar damage increased by direct species composition 

air pollution influence 

Source: Smith (1974). 

a part of the oak-chestnut association of the eastern deciduous forests of eastern North America, 

were chestnut oak (Quercus prinus), red oak (Q. rubra), and red maple (Acer rubrum). 

An abundance of uniformly distributed saplings and seedlings were visible under the tree 

canopy, and herbs appeared in localized areas in canopy openings. Chestnut oak dominated the 

area, and the larger trees were 60 to 80 years old. The dusty site was dominated by white oak 

(Q. alba); whereas red oak and tulip poplars (Liriodendron tulipifera) were subcodominants. 

The largest trees were 100 years old and had necrotic leaves, peeling bark, and appeared to be in 

generally poor condition except for tulip poplars (which thrived in localized areas). The site 

contained a tangled growth of seedlings and shrubs, a few saplings, and a prevalence of green 

briar (Smilax spp.) and grape (Vitis spp.). The sapling strata in the area was represented by red 

maple, hickory (Carya spp.), dogwood (Cornus florida), and hop-hornbeam (Ostrya virginiana). 

Saplings of none of the leading dominant trees were of importance in this stratum. The most 

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obvious form of vegetation in the seedling-shrub stratum, because of their tangled appearance, 

were dogwood, hop-hornbeam, redbud (Cercis canadensis), and sugar maple (Acer saccarum). 

Crust formation reduced photosynthesis, induced premature leaf fall and destruction of leaf 

tissues, inhibited growth of new tissue, and reduced the formation of carbohydrate needed for 

normal growth and storage (Brandt and Rhoades, 1973). The authors (Brandt and Rhoades, 

1972), citing Odum (1969), also stated that one result of the accumulation of toxic pollutants in 

the biosphere as the result of human activities is the simplification of both plant and animal 

communities. In plant communities, structure is determined by sampling various strata within 

the community. Each stratum comprises a particular life form (e.g., herbs, seedlings, saplings, 

trees). Dust accumulation favored growth of some species and limited others. For example, 

sugar maple was more abundant in all strata of the dusty site when compared with the control 

site where it was present only as a seedling. The growth of tulip poplar, dogwood, hophornbeam, black haw (Viburnum prunifolium), and redbud (C. canadensis) appeared to be 

favored by the dust. Growth of conifers and acidophiles such as rhododendron (Rhododendron 

maximum), however, was limited. Although dust accumulation began in 1945, the heaviest 

accumulation occurred between 1967 and 1972 during the time of the study. 

Changes in community composition were associated closely with changes in the growth of 

the dominant trees. A decrease in density of seedlings and saplings and in mean basal area, as 

well as lateral growth of red maple, chestnut oak, and red oak, occurred in all strata. On the 

other hand, all of these characteristics increased in the tulip poplar, which was a subordinate 

species before dust accumulation began but had assumed dominance by the time of the study. 

Reduction in growth of the dominant trees had apparently given the tulip poplar a competitive 

advantage because of its ability to tolerate dust. Changes in soil alkalinity occurred because of 

the heavy deposition of limestone dust; however, the facilities necessary for critical analysis of 

the soils were not available. From the foregoing, it is obvious that PM physical effects in the 

vicinity of limestone quarries and processing plants can affect ecosystems. 

Changes in ecosystem structure resulting from exposures to sea salt were cited previously 

(Section 4.3.1.1). The dominance of live oak (Quercus virginiana) as a practically pure stand on 

Smith Island (Bald Head), NC and along the eastern and southern coast of North Carolina has 

been explained as due to its tolerance to salt spray. The absence of more inland species is 

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attributed to their intolerance to salt spray. Wells (1939) termed the long-term stabilization of 

live oak as “salt spray climax,” a new type of climax. 

Acidic Deposition 

The major effects of acidic deposition occur through the soil (National Science Technology 

Council, 1998) and are discussed below under indirect effects. However, included among the 

direct responses of forest trees to acidic deposition are increased leaching of nutrients from 

foliage; accelerated weathering of leaf cuticular surfaces; increased permeability of leaf surfaces 

to toxic materials, water, and disease agents; and altered reproductive processes (Cape, 1993; 

Altshuller and Linthurst, 1984). 

Trace Elements 

Possible direct responses of trace elements on vegetation result from their deposition and 

residence on the phyllosphere (i.e., foliar surfaces). Fungi and other microorganisms living on 

the leaves of trees and other vegetation play an important role in leaf decomposition after 

litterfall (Miller and McBride, 1999; Jensen, 1974; Millar, 1974). Possible effects of heavy 

metals on nutrient cycling and their effects on leaf microflora appear not to have been studied. 

A trace metal must be brought into solution before it can enter into the leaves or bark of 

vascular plants. Low solubility limits entry. In those instances when trace metals are absorbed, 

they frequently are bound in the leaf tissue and then are lost from the plant when the leaf drops 

off (Hughes, 1981) and transferred to the litter layer where they can affect litter decomposition, 

an important source of soil nutrients. Changes in litter decomposition processes influence 

nutrient cycling in the soil and limit the supply of essential nutrients. Both Cotrufo et al. (1995) 

and NikliÁska et al. (1998) point out that heavy metals affect forest litter decomposition. 

Cotrufo et al. (1995) observed that decomposition of oak leaves containing Fe, Zn, Cu, Cr, Ni, 

and Pb was influenced strongly during the early stages by metal contamination. Fungal mycelia 

were significantly less abundant in litter and soil in contaminated sites when compared with 

control sites. NikliÁska et al. (1998) stated that toxic effects of heavy metals on soil respiration 

rate have been reported by many scientists, and that, in polluted environments, this results in 

accumulation of undecomposed organic matter. However, they state that results of experiments 

should identify the most important “natural” factors affecting soil/litter sensitivity because the 

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effects of heavy metals on respiration rates are dose-dependent on the dose of heavy metals, the 

type of litter, types of metals deposited, and the storage time before respiration tests are made. 

Trace metals, particularly heavy metals (e.g., Cd, Cu, Pb, Cr, Hg, Ni, Zn) have the greatest 

potential for influencing forest growth (Smith, 1991). Experimental data indicate that the 

broadest spectrum of growth suppression of foliar microflora resulted from Fe, Al, and Zn. 

These three metals also inhibited fungal spore formation, as did Cd, Cr, Mg, and Ni (see Smith, 

1990c). In the field, the greatest injury occurs from pollution near mining, smelting, and other 

industrial sources (Ormrod, 1984). Direct metal phytotoxicity can occur only if the metal can 

move from the surface into the leaf or directly from the soil into the root. 

Organic Compounds 

Primary and secondary organic compounds formed in the atmosphere, the effects of some 

of which are discussed below, have been variably referred to under the following terms: toxic 

substances, hazardous air pollutants (HAPs), air toxics, semivolatile organic compounds 

(SVOCs), pesticides, and persistent organic pollutants (POPs). Again, it should be remembered 

that chemical substances denoted by such headings are not criteria air pollutants controlled by 

the National Ambient Air Quality Standards under Section 109 of the Clean Air Act (CAA), 

but rather are controlled under Sect.112, Hazardous Air Pollutants (U.S. Code, 1991). Their 

possible effects on humans and ecosystems are discussed in a number of government documents 

and in many other publications. They are mentioned here because many of the chemical 

compounds are partitioned between gas and particle phases in the atmosphere. As particles, they 

can become airborne, be distributed over a wide area, and affect remote ecosystems. Some of 

the chemical compounds are of concern because they may reach toxic levels in food chains of 

both animals and humans; whereas others tend to decrease or maintain the same toxicity as they 

move through the food chain. Some examples of movement through food chains are provided 

below. 

Many chemical compounds from a variety of anthropogenic sources are released into the 

ambient air (see Section 4.2.1). In the atmosphere, the emitted compounds initially go through a 

mixing process, and the airborne particles then are distributed over a wide area and ultimately 

deposited on ecosystem components. Atmospheric deposition of polychlorinated dibenzo-pdioxins and dibenzofurans (PCDD/DFs), as an example, can be divided into three different 

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forms: (1) dry gaseous, (2) dry particle-bound, and (3) wet deposition. Dry particle-bound 

deposition occurs when the PM containing the pollutant is deposited on the plant surface; 

whereas wet deposition ranges from hail to rain to fog and dewfall (McLachlan, 1996b). 

Human exposure to PCDD/DFs has been demonstrated to be caused almost exclusively 

by the ingestion of animal fat from fish, meat, and dairy products. Almost half of all human 

exposure to PCDD/DFs is caused by consumption of beef and dairy products (McLachlan, 

1996b). Cattle obtain most of their PCDD/DFs though grass. Therefore, the grass 6 cattle 

6 milk/beef pathway is critical for human exposure. It has been shown that root uptake/ 

translocation is an insignificant pathway of PCDD/DFs compared to absorption via aerial plant 

parts. Wet and dry particle deposition are the most important for the accumulation of the higher 

chlorinated congeners in vegetation. The persistence of PCDD/DFs in plants has not been 

investigated extensively; however, biodegradation probably does not occur in that these 

compounds are found primarily in the lipophilic cuticle and are very resistant to microbial 

degradation (McLachlan, 1996b). Feed contaminated with soil containing the pollutant can be 

another source of exposure of beef and dairy cattle, as well as chickens. The PCDD/DFs levels 

are near a steady state in milk cows and laying hens; however, animals raised for meat 

production (such as beef cattle and pigs) may accumulate large amounts. Beef cattle and pigs 

cannot excrete the contaminants in a lipid-rich matrix such as milk or eggs. Thus, all of the 

PCDD/DFs ingested are stored in the body. In agricultural food chains, there is a biodilution of 

PCDD/DFs, with the fugacity decreasing by up to three orders of magnitude between the air and 

cows milk (McLachlan, 1996b). Fürst et al. (1993), based on surveys to determine the factors 

that influence the presence of PCDD/DF in cows milk, earlier concluded that regardless of which 

pathway, soil 6 grass 6 cow or air 6 grass 6 cow, it was the congener of the chemical that was 

most important. 

Chlorinated persistent organic pollutants (POPs), such as PCBs, PCDFs, and PCDDs, can 

be transported as particles through the atmosphere from industrial and agricultural sources; be 

brought down via wet and dry deposition in remote regions, such as the Arctic; and have been 

detected in all levels of the Arctic food chain (Oehme et al., 1995). High concentrations of PCB 

(1 to 10 ppm) were found in seals, but the concentrations increased to 10 to 100 ppm in polar 

bears. The polar bear is the top predator in the Arctic and feeds preferentially on ringed seals 

and, to a lesser extent, on other seal species. Bioconcentration factors of organochlorines in the 

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Arctic food web, reaching 107

 for fish and seals, are biomagnified in polar bears (Oehme et al., 

1995). Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans have also been 

found in seals (Oehme et al., 1995). Milk taken from anesthetized polar bears was also found to 

contain PCDD/DF. Very little is known regarding the intake of milk by polar bear cubs. 

However, estimates of the intake of milk containing detectable levels of PCDD/DF and PCB and 

the additional consumption of seal blubber confirm that these pollutants are passed on to the next 

generation (Oehme et al., 1995). 

Section 112 of the CAA provides the legislative basis for U.S. hazardous air pollutant 

(HAP) programs. In response to mounting evidence that air pollution contributes to water 

pollution, Congress included Section 112m (Atmospheric Deposition to Great Lakes and Coastal 

Waters) in the 1990 CAA Amendments that direct the EPA to establish a research program on 

atmospheric deposition of HAPS to the “Great Waters.” 

Actions taken by EPA and others to evaluate and control sources of Great Waters 

pollutants of concern appear to have positively affected trends in pollutant concentrations 

measured in air, sediment, and biota. Details concerning these effects may be found in 

Deposition of Air Pollutants to the Great Waters, Third Report to Congress (U.S. Environmental 

Protection Agency, 2000a). The Third Report (EPA-453/R-00-005, June 2000), like the First 

and Second Reports to Congress, focuses on 15 pollutants of concern, including pesticides, metal 

compounds, chlorinated organic compounds, and nitrogen compounds. The new scientific 

information in the Third Report supports and builds on three broad conclusions presented in the 

previous two EPA Reports to Congress:

 (1) Atmospheric deposition from human activities can be a significant contributor of toxic 

chemicals and nitrogen compounds to the Great Waters. The relative importance of 

atmospheric loading for a particular chemical in a water body depends on many factors 

(e.g., characteristics of the water body, properties of the chemical, and the kind and 

amount of atmospheric deposition versus water discharges).

 (2) A plausible link exists between emissions of toxic pollutants of concern into the air 

above the Great Waters; the deposition of these pollutants (and their transformation 

products); and the concentrations of these pollutants found in the water, sediments, 

and biota, especially fish and shellfish. For mercury, fate and transport modeling and 

exposure assessments predict that the anthropogenic contribution to the total amount 

of methylmercury in fish is, in part, the result of anthropogenic mercury releases 

from industrial and combustion sources increasing mercury body burdens (i.e., 

concentrations) in fish. Also, the consumption of fish is the dominant pathway of 

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exposure to methylmercury for fish-consuming humans and wildlife. However, what is 

known about each stage of this process varies with each pollutant (for instance, the 

chemical species of the emissions and its transformation in the atmosphere). 

(3) Airborne emissions from local as well as distant sources, from both within and outside 

the United States, contribute pollutant loadings to waters through atmospheric 

deposition. Determining the relative roles of particular sources — local, regional, 

national, and possibly global, as well as anthropogenic, natural, and reemission of 

pollutants — contributing to specific water bodies is complex, requiring careful 

monitoring, atmospheric modeling, and other analytical techniques. 

Response to Indirect Effects of Particulate Matter 

The presence of PM in the atmosphere directly affects vegetation following physical 

contact with foliar surfaces (as discussed above), but in many cases the more significant effects 

are indirect. These effects may be mediated by suspended PM (i.e., through effects on radiation 

and climate) and by particles that pass through vegetative canopies to reach the soil. Effects 

mediated through the atmosphere are considered briefly below and in greater detail later, under 

Section 4.5. 

The major indirect plant responses are chiefly soil-mediated and depend primarily on the 

chemical composition of the individual stressors deposited in PM. The chemical stressors must 

be bioavailable in order to produce an effect. The effects of exposures may result in changes in 

biota patterns and in chemical/physical soil conditions that affect ecological processes, such as 

nutrient cycling and uptake by plants. 

The soil environment (composed of mineral and organic matter, water, air, and a vast array 

of bacteria, fungi, algae, actinomycetes, protozoa, nematodes, and arthropods) is one of the most 

dynamic sites of biological interactions in nature (Wall and Moore, 1999; Alexander, 1977). 

The quantity of organisms in soils varies by locality. Bacteria and fungi are usually most 

abundant in the rhizosphere, the soil around plant roots that all mineral nutrients must pass 

through. Bacteria and fungi benefit from the nutrients in the root exudates (chiefly sugars) in the 

soil and, in turn, they play an essential role by making mineral nutrients available for plant 

uptake (Wall and Moore, 1999; Rovira and Davey, 1974). Their activities create chemical and 

biological changes in the rhizosphere by decomposing organic matter and making inorganic 

minerals available for plant uptake. Bacteria are essential in the nitrogen and sulfur cycles and 

make these elements available for plant uptake and growth (see Section 4.3.3). Fungi are 

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directly essential to plant growth. Attracted to their roots by the exudates, they develop 

mycorrhizae in a symbiotic fungus-plant relationship, that is integral in the uptake of the mineral 

nutrients (Allen, 1991). The impact in ecosystems of PM, particularly nitrates, sulfates, and 

metals, is determined by their effects on the growth of the bacteria involved in nutrient cycling 

and the mycorrhizal fungi involved in plant nutrient uptake. 

Atmospheric Turbidity: Effects on Direct Beam and Photosynthetically Active 

Radiation. Photosynthetic processes underlie the contribution of vegetative surfaces to nutrient 

and energy cycling. The characteristics and net receipts of environmental radiation determine 

the rates of both photosynthesis and the heat-driven process of water cycling. Atmospheric 

turbidity due to particulate loading can substantially alter the characteristics and net receipts of 

solar radiation. One measure of atmospheric turbidity, Linke’s turbidity factor, T, can be 

derived as a direct function of light extinction by solid particles. It is defined as the ratio of the 

total extinction coefficient and the extinction due exclusively to gases: 

(4-6) 

where s and w are the relative concentrations of dust and water vapor in the atmosphere, and Fs 

and Fw are the wavelength-dependent scattering coefficients for solid, dry particles and water 

vapor, respectively. The scattering coefficients are in units of inverse distance, such as km!1 

(Rosenberg et al., 1983). According to this expression, a clean atmosphere would have a 

turbidity value of 1. Given that turbidity and visibility are both functions of light scattering, the 

trends in, and physical processes underlying, reduced visibility discussed in Section 4.3 are 

directly relevant to the discussion of radiative effects on vegetation due to PM. 

Turbidity, as defined above, describes the degree of scattering occurring in the atmosphere 

due to particles and gases. Total, particle-based extinction, however, is the sum of both 

scattering and absorption. Absorption of short-wavelength solar radiation reduces the amount of 

radiation reaching the Earth’s surface and leads to atmospheric heating. If the absorbing 

particles re-radiate in the infrared range, some of this energy is lost as long-wave re-radiation to 

space. The balance of this energy is captured at the surface as down-welling infrared radiation. 

Canopy temperature and transpirational water use by vegetation are particularly sensitive to 

4-91
long-wave, infrared radiation. Atmospheric heating by particles reduces vertical temperature 

gradients, potentially reducing the intensity of atmospheric turbulent mixing. The magnitude of 

such potential effects on turbulent transport within canopies remains unknown although the 

damping of eddy transport may inhibit canopy gas exchange. Suppressed tropospheric mixing 

could also intensify local temperature inversions and increase the severity of pollution episodes 

(Pueschel, 1993) with direct inhibitory effects on photosynthetic processes. 

Atmospheric turbidity increases the intensity of diffuse (sky) radiation (Hoyt, 1978). 

In a clear atmosphere, diffuse radiation may be on the order of 10% of total solar radiation 

(Choudhury, 1987). However, in highly turbid, humid conditions, this fraction may increase, 

to as much as 100% of the incident solar intensity in extreme cases. The direct-radiation diffuse 

ratio is highest at solar noon and lowest near dawn or dusk when the path length through the 

atmosphere is longest. The wavelength dependence of particle scattering induces an enrichment 

of PAR with respect to total or direct beam radiation. The influence of this enrichment on plant 

life is discussed in the next section. 

Aerosols produced by incomplete combustion, from forest fires to specifically 

anthropogenic processes such as diesel fuel combustion, contain significant fractions of black 

carbon which absorbs across the solar and terrestrial radiation spectra. The presence of 

absorbing aerosols reduces the ratio of photosynthetically active radiation to total radiation 

received at the surface, potentially reducing photosynthetic water uptake efficiency. The net 

effect of aerosol absorption on the surface depends on the relative magnitudes of the particulate 

absorption coefficients in the visible and infrared area and on the albedo of the Earth’s surface. 

The greater effect of particulate loading on visibility and turbidity and, therefore, radiation 

receipts by the biosphere, is due to scattering. Non-absorbing, scattering aerosols raise the 

overall albedo of the atmosphere and reduce the amount of radiation reaching the surface by the 

amount reflected or scattered back into space. Analysis of data collected by a global network of 

thermopile pyranometers operated by the World Meteorological Organization (WHO) show a 

50-year global trend of a 2.7% per decade reduction in the amount of solar radiation reaching the 

Earth’s surface. This has been associated with an increasing global albedo caused by an 

increasing abundance of atmospheric particles. By evaluating the WMO data set with four 

different approaches to the statistical analyses, Stanhill and Cohen (2001) have estimated that 

average global solar radiation receipts have declined by 20 W m!2

 since 1958. Examples of 

4-92
individual measurement sites include Barrow, Alaska (71 °N), where the average solar irradiance 

from 1963 to 1993 was 100.9 W m!2 and the estimated linear trend was !0.23% per year; and 

Jerusalem, Israel (32 °N), where the average solar irradiance from 1954 to 1994 was 

244.2 W m!2

 and the estimated linear trend was !0.37% per year. This phenomenon has been 

labeled “global dimming.” 

Increased Turbidity and Altered Radiative Flux: Effects on Vegetative Processes. 

In a detailed canopy-response model (e.g., Choudhury, 1987), radiation is considered in its direct 

and diffuse components. Foliar interception by canopy elements is considered for both up- and 

down-welling radiation (a two-stream approximation). In this case, the effect of atmospheric 

PM on turbidity influences canopy processes both by radiation attenuation and by influencing 

the efficiency of radiation interception throughout the canopy through conversion of direct to 

diffuse radiation (Hoyt, 1978). Diffuse radiation is more uniformly distributed throughout the 

canopy and increases canopy photosynthetic productivity by distributing radiation to lower 

leaves. The treatment of down-welling direct-beam radiation in the two-stream approach 

remains an elaboration of the simplified Beer’s Law analogy with solar angle, leaf area 

distribution, and orientation individually parameterized (Choudhury, 1987). Diffuse downwelling radiation is a function of diffuse and direct radiation at the top of the canopy and 

penetration within the canopy according to cumulative leaf area density and foliage orientation. 

Diffuse up-welling radiation results from scattering and reflectance of both direct and diffuse 

down-welling radiation within the canopy and by the soil. 

Rochette et al. (1996) conducted simultaneous measurements of radiation and water use 

efficiencies by maize and found that, in the absence of water stress and with adequate 

fertilization, 90% of all variation in crop net photosynthesis (P-n) could be explained by 

variations in PAR. Alternatively, an evaluation of the available experimental literature and 

statistics on crop yields by Stanhill and Cohen (2001) indicate that plant productivity is more 

affected by changes in evapotranspiration induced by changes in the amount of solar radiation 

plants receive than by changes in the amount of PAR plants receive. 

The enrichment in PAR present in diffuse radiation appears, however, to offset a portion of 

the effect of an increased atmospheric albedo due to atmospheric particles. An observational and 

theoretical study by Bange et al. (1997) of the level of radiation use efficiency (RUE) of 

4-93
sunflowers indicated a degree of compensation for reduced incident radiation by a proportional 

increase in diffuse radiation. Variables measured by Bange et al. (1997) included biomass, 

phenology, leaf area, canopy light extinction, grain size, and harvest index. Crops subject to 

reduced direct beam/increased diffuse radiation produced biomass, phenology, leaf area and 

canopy light extinction at leaves similar to unshaded crops but yielded smaller grains and a 

lower harvest index. RUE was also seen to improve for soybeans and maize with a proportional 

increase in diffuse radiation with respect to direct beam (Sinclair et al., 1992; Healey et al., 

1998) although the effect on harvest index was not indicated. Gu et al. (2002) compared the 

relative efficiencies of canopy photosynthesis to diffuse and direct PAR for a Scots pine forest, 

an aspen forest, a mixed deciduous forest, a tall grass prairie, and a winter wheat crop. They 

concluded (1) diffuse radiation over direct radiation results in higher light use efficiencies by 

plant canopies, (2) diffuse radiation has much less tendency to cause canopy photosynthetic 

saturation, (3) the advantages of diffuse radiation over direct radiation increase with radiation 

level, (4) temperature as well as vapor pressure deficit can cause different responses in diffuse 

and direct canopy photosynthesis, indicating that their effects on terrestrial ecosystem carbon 

assimilation may depend upon radiation regimes, i.e., sky conditions. 

The potentially significant effect of regional haze on the yield of crops because of 

reduction in solar radiation has been examined by Chameides et al. (1999). Using a case study 

approach, Chameides et al. (1999) studied the effects of regional haze on crop production in 

China where regional haze is especially severe. A simplified assessment of the direct effect of 

atmospheric aerosols on agriculture suggests that yields of approximately 70% of crops are being 

depressed by at least 3 to 5% by regional scale air pollution and its associated haze (Chameides 

et al., 1999). 

Effects of Nitrogen Deposition. Nitrogen is required by all organisms. It is a major 

constituent of the nucleic acids that determine the genetic character of all living things and the 

enzyme proteins that drive the metabolic machinery of every living cell (Galloway, 1998; 

Galloway and Cowling, 2002; U.S. Environmental Protection Agency, 1993). Though nitrogen 

composes 80% of the total mass of the Earth’s atmosphere, it is not biologically available. 

Nitrogen fixation is accomplished in nature by certain unique organisms that have developed the 

capability of converting N2 to biologically active reduced forms of nitrogen such as ammonia, 

4-94
amines, and amino acids, which are the structural constituents of proteins and nucleic acids 

(Galloway and Cowling, 2002; Hornung and Langan, 1999; U.S. Environmental Protection 

Agency, 1993). 

Nitrogen has long been recognized as the nutrient most important for plant growth. It is of 

overriding importance in plant metabolism and, to a large extent, governs the utilization of 

phosphorus, potassium, and other nutrients. Most of the nitrogen in soils is associated with 

organic matter. Typically, the availability of nitrogen via the nitrogen cycle controls net primary 

productivity, and possibly, the decomposition rate of plant litter. Photosynthesis is influenced by 

nitrogen uptake in that ~75% of the nitrogen in a plant leaf is used during the process of 

photosynthesis. The nitrogen-photosynthesis relationship is, therefore, critical to the growth of 

trees and other plants (Hornung and Langan, 1999; Chapin et al., 1987). Plants usually obtain 

nitrogen directly from the soil through their roots by absorbing NH4

+

 or NO3 

!

, or it is formed by 

symbiotic organisms (e.g., bacteria, blue-green algae) in the roots. 

Because nitrogen is not readily available and is usually in short supply, it is the chief 

element in agricultural fertilizers (Hornung and Langan, 1999). The realization of the 

importance of nitrogen in crop production resulted in a search for natural nitrogen sources such 

as guano and nitrate deposits. The invention of the Haber-Bosch process in 1913 made reactive 

nitrogen (Nr) available for use in food production, and more than half of the food eaten by the 

peoples of the world today is produced using fertilizer produced by this process (Galloway, 

1998; Galloway and Cowling, 2002). 

In nature, nitrogen may be divided into two groups: nonreactive (N2) and reactive (Nr) 

nitrogen. Reactive N includes all biologically, photochemically, and radioactively active 

nitrogen compounds in the Earth’s atmosphere and biosphere (Galloway et al., 2003). Among 

those included are the inorganic reduced forms of nitrogen (e.g., NH3 and NH4

+

), inorganic 

oxidized forms (e.g., NOx, HNO3, N2O, and NO3 

!

), and organic compounds (e.g., urea, amine, 

proteins, and nucleic acids) (Galloway et al., 2003). 

Food production continues to account for most of the newly created Nr . However, since 

around 1965, the magnitude of Nr created by humans began to exceed natural terrestrial creation 

of Nr and its conversion back to N2 by denitrification. The overall increase in global Nr is the 

result of three main causes: (1) widespread cultivation of legumes, rice, and other crops that 

promote conversion of N2 to organic nitrogen through biological nitrogen fixation; 

4-95
(2) combustion of fossil fuels, which converts both atmospheric N2 and fossil nitrogen to 

reactive NOx; and (3) the Haber-Bosch process, which converts nonreactive NH3 to sustain food 

production and some industrial activities (Galloway and Cowling, 2002; Galloway et al., 2003). 

1 Reactive nitrogen is now accumulating in the environment on all spatial scales m local, 

regional and global (Galloway, 1998; Galloway and Cowling, 2002; Galloway et al. 2003). 

As a result, Nr is accumulating in various environmental reservoirs, e.g., the atmosphere, soils 

and waters (Galloway and Cowling, 2002). The accumulation of Nr in the environment has 

effects on humans and ecosystems (Rabalais, 2002; van Egmond et al., 2002; Galloway, 1998). 

Large uncertainties regarding the rates of Nr accumulation in the various reservoirs limits 

our ability to determine the temporal and spatial distribution of environmental effects. These 

uncertainties are of great significance because of the sequential nature of Nr on environmental 

processes (Galloway and Cowling, 2002). The sequence of transfers, transformations, and 

environmental effects is referred to as the nitrogen cascade (Figure 4-15) (Galloway and

Cowling, 2002; Galloway et al, 2003). A single atom of new NHx or NOx can alter a wide array 

of biogeochemical processes and exchanges among environmental reservoirs. 

The results of the Nr cascade in the global system and the wide variety of changes in the 

nitrogen cycle are both beneficial and detrimental to humans and to ecosystems (Galloway and 

Cowling, 2002; Galloway et al., 2003). Though the synthetic fertilizers used in cultivation and 

cultivation-induced bacterial nitrogen fertilization (BNF) sustain a large portion of the world’s 

population, there are consequences: (1) the wide dispersal of Nr by hydrological and 

atmospheric transport; (2) the accumulation of Nr in the environment because the rates of its 

creation are greater than the rates of its removal through denitrification to nonreactive N2; 

(3) Nr creation and accumulation is projected to continue to increase in the future as per capita 

use of resources by human populations increases; and (4) Nr accumulation contributes to many 

contemporary environmental problems (Galloway et al., 2003). 

Among the contemporary environmental problems as listed in Galloway et al. (2003) are 

the following: 

• increases in Nr lead to production of tropospheric ozone and aerosols and the associated 

human health problems (Wolfe and Patz, 2002). 

• productivity increases in forests and grasslands and then decreases wherever atmospheric 

Nr deposition increases significantly and critical thresholds are exceeded; Nr additions 

probably also decrease biodiversity in many natural habitats (Aber et al., 1995); 

4-96
Figure 4-15. Illustration of the nitrogen cascade showing the movement of the humanproduced reactive nitrogen (Nr) as it cycles through the various 

environmental reservoirs in the atmosphere, terrestrial ecosystems, and 

aquatic ecosystems. 

Source: Galloway et al. (2003). 

• reactive nitrogen in association with sulfur is responsible for acidification and loss of 

biodiversity in lakes and streams in many regions of the world (Vitousek et al., 1997); 

• reactive nitrogen is responsible for eutrophication, hypoxia, loss of biodiversity, and 

habitat degradation in coastal ecosystems. It is now considered the biggest pollution 

problem in coastal waters (Rabalais, 2002); 

• reactive nitrogen contributes to global climate change and stratospheric ozone

depletion, both of which have an effect on the health of humans and ecosystems

(Cowling et al., 1998).

4-97
The effect of increasing nitrogen inputs (e.g., NOx, NO3 

!

, HNO3) on the nitrogen cycle in 

forests, wetlands, and aquatic ecosystems is discussed in detail elsewhere (U.S. Environmental 

Protection Agency, 1993, 1997a; Garner, 1994; World Health Organization, 1997). 

The deposition of nitrogen in the United States from human activity has doubled between 

1961 and 1997 due mainly to the use of inorganic nitrogen fertilizers and NOx emissions from 

fossil fuel combustion, with the largest increase occurring in the 1960s and 1970s (Howarth 

et al., 2002). Among the most important effects of chronic nitrogen deposition are changes in 

the composition of plant communities, disruptions in nutrient cycling, increased soil emissions 

of nitrogenous greenhouse gases, accumulation of nitrogen compounds in the soil with enhanced 

availability of nitrate or ammonium, soil-mediated effects of acidification, and increased 

susceptibility to stress factors in plants (Fenn et al., 1998; Bobbink et al., 1998). A major 

concern is “nitrogen saturation,” the result of the atmospheric deposition of large amounts of 

particulate nitrates. Nitrogen saturation results when additions to soil background nitrogen 

(nitrogen loading) exceed the capacity of plants and soil microorganisms to utilize and retain 

nitrogen (Aber et al., 1989, 1998; Garner, 1994; U.S. Environmental Protection Agency, 1993). 

Under these circumstances, disruptions of ecosystem functions may result (Hornung and Langan, 

1999). 

Possible ecosystem responses to nitrate saturation, as postulated by Aber and coworkers 

(Aber et al., 1989), include (1) a permanent increase in foliar nitrogen and reduced foliar 

phosphorus and lignin caused by the lower availability of carbon, phosphorus, and water; 

(2) reduced productivity in conifer stands because of disruptions of physiological function;

(3) decreased root biomass and increased nitrification and nitrate leaching; and (4) reduced soil 

fertility, resulting from increased cation leaching, increased nitrate and aluminum concentrations 

in streams, and decreased water quality. Nitrate saturation implies that some resource other than 

nitrogen is limiting biotic function. Water and phosphorus and carbon are the resources most 

likely to be the secondary limiting factors for plants and microorganisms, respectively (Aber 

et al., 1989). The appearance of nitrogen in soil solutions is an early symptom of excess 

nitrogen. In the final stage, disruption of forest structure becomes visible (Garner, 1994). 

Changes in nitrogen supply can have a considerable effect on an ecosystem’s nutrient 

balance (Waring, 1987). Large chronic additions of nitrogen disrupt normal nutrient cycling and 

alter many plant and soil processes involved in nitrogen cycling (Aber et al., 1989). Among the 

4-98
processes affected are (1) nutrient uptake and allocation in plants, (2) litter production, 

(3) immobilization (includes ammonification [the release of ammonia] and nitrification [the 

conversion of ammonia to nitrate during decay of litter and soil organic matter]), and (4) nitrate 

leaching and trace gas emissions (Figure 4-16) (Aber et al., 1989; Garner 1994). 

Figure 4-16. Nitrogen cycle (dotted lines indicate processes altered by nitrogen saturation). 

Source: Garner (1994). 

4-99
Subsequent studies have shown that, although an increase in nitrogen mineralization occurs 

initially (i.e., the conversion of soil organic matter to nitrogen in available form [see item 3 

above]), not nitrogen mineralization rates are reduced under nitrogen-enriched conditions. 

Aber et al. (1998) hypothesized that mycorrhizal assimilation and exudation, using 

photosynthate from the host plant as the carbon source, is the dominant process involved in 

immobilization of nitrogen. In addition, studies suggested that soil microbial communities 

change from predominantly fungal (mycorrhizal) communities predominately bacterial during 

nitrate saturation (Aber et al., 1998). 

The growth of most forests in North America is limited by the nitrogen supply. Severe 

symptoms of nitrogen saturation, however, have been observed in high-elevation, nonaggrading 

spruce-fir ecosystems in the Appalachian Mountains, as well as in the eastern hardwood 

watersheds at Fernow Experimental Forest near Parsons, WV. Mixed conifer forests and 

chaparral watersheds with high smog exposure in the Los Angeles Air Basin also are nitrogen 

saturated and exhibit the highest stream water NO3 

!

 concentrations for wildlands in North 

America (Bytnerowicz and Fenn, 1996; Fenn et al., 1998). Forests in southern California, the 

southwestern Sierra Nevada in central California, and the Front Range in northern Colorado have 

all been exposed to highly elevated nitrogen deposition, and nitrogen-saturated watersheds have 

been reported in the above mentioned areas. Annual nitrogen additions through deposition (6 to 

11 kg ha!1

 year!1as through fall) in the southwestern Sierra Nevada are similar to the nitrogen 

storage (4 kg ha!1

 year!1) in vegetation growth increment of western forests suggesting that 

current nitrogen deposition rates may be near the assimilation capacity of the overstory 

vegetation. Ongoing urban expansion will increase the potential for nitrogen saturation of 

forests from urban sources (e.g., Salt Lake City, Seattle, Tucson, Denver, central and southern 

California) unless there are improved emission controls (Fenn et al., 1998). 

Atmospherically deposited nitrogen also can act as a fertilizer in soil low in nitrogen. 

Not all plants, however, are capable of utilizing extra nitrogen, as plants vary in their ability to 

absorb NH4

+

 and NO3 

!

 (Chapin, et al., 1987). Inputs of nitrogen to natural ecosystems that 

alleviate deficiencies and increase growth of some plants can alter competitive relationships and 

alter species composition and diversity (Ellenberg, 1987; Kenk and Fischer, 1988; U.S. 

Environmental Protection Agency, 1993). 

4-100
Not all forest ecosystems react in the same manner to nitrogen deposition. High-elevation 

alpine watersheds in the Colorado Front Range (Bowman, 2000) and a deciduous forest in 

Ontario, Canada also are naturally saturated even though nitrogen deposition has been moderate 

(.8 kg ha!1

 year!1). The nitrogen-saturated forests in North America, including estimated inputs 

and outputs, are shown in Table 4-14 (Fenn et al., 1998). The Harvard Forest hardwood stand in 

Massachusetts, however, has absorbed > 900 kg N/ha without significant NO3 

!

 leaching during 

an 8-year nitrogen amendment study (Table 4-14) (Fenn et al.,1998). Nitrate leaching losses 

were high, on the other hand, in the Harvard Forest pine sites suggesting that deciduous forests 

may have a greater capacity for nitrogen retention. In the 8-year experimental study (1988 to 

1996), nitrate leaching was observed in the pine stand after the first year (1989) in the highnitrogen plots. Further increases were observed in 1995 and 1996, while the hardwood stand did 

not show significant increases in NO3 

!

 leaching until 1996. The sharp contrast in the response of 

the pine and hardwood stands indicates that the mosaic of community types across the landscape 

must be considered when determining the regional scale response to nitrogen deposition (Magill 

! et al., 2000). Johnson et al. (1991a) reported that measurements showing the leaching of NO3 

and Al+3 from high elevation forests in the Great Smoky Mountains indicate that these forests 

have reached saturation. 

Because the competitive equilibrium of plants in any community is finely balanced, the 

alteration of one of any number of environmental parameters, (e.g., continued nitrogen 

additions) can change the vegetation structure of an ecosystem (Bobbink, 1998; Skeffington and 

Wilson, 1988). Increases in soil nitrogen play a selective role. When nitrogen becomes more 

readily available, plants adapted to living in an environment of low nitrogen availability will be 

replaced by plants capable of using increased nitrogen because they have a competitive 

advantage. 

Plant succession patterns and biodiversity are affected significantly by chronic nitrogen 

additions in some North American ecosystems (Figure 4-17). The location of nitrogen saturated 

ecosystems in North America, and the steps leading to nitrogen saturation, are indicated on the 

map in Figure 4-16. Conceptual models of regional nitrogen saturation indicate saturation in 

New England, in the Colorado alpine ecosystems and in California forests. Fenn et al. (1998) 

reported that long-term nitrogen fertilization studies in both New England and Europe, as well, 

suggest that some forests receiving chronic inputs of nitrogen may decline in productivity and 

4-101
TABLE 4-14. NITROGEN-SATURATED FORESTS IN NORTH AMERICA,

INCLUDING ESTIMATED N INPUTS AND OUTPUTS

4-102

Elevation N Input N Output 

Location Forest Type (m) (kg/ha/year) (kg/ha/year) Adirondack Mts. northeastern New York Northern hardwoods or 396-661 9.3 a Stage 1 N loss b Driscoll and 

hardwood/ conifer mix 

Catskill Mts., southeastern New York Mainly hardwood; some eastern 

hemlock 

335-675 10.2 a Stage 1 and 

2 N loss b Stoddard (19

Turkey Lakes Watershed, Ontario, Canada Sugar maple and yellow birch 350-400 7.0-7.7 17.9-23.6 Foster et al. 

(as throughfall) Johnson and 

Whitetop Mt., southwestern Virginia Red spruce 1650 32 c 47 c Joslin and W

Fernow, West Virginia Mixed hardwood 735-870 15-20 6.1 Gilliam et al

Great Smoky Mts. National Park, American beech 1600 3.1 d 2.9 Johnson and Tennessee 

Great Smoky Mts. National Park, Red spruce 1800 10.3 d 19.2 Johnson et a

Becking Site, North Carolina 

Great Smokey Mts. National Park, Red spruce 1740 26.6 20.3 Johnson et a Tower Site, North Carolina Front Range, Colorado Alpine tundra, subalpine conifer 3000-4000 7.5-8.0 7.5 

Williams et 

San Dimas, San Gabriel Mts., Chapparral and grasslands 580-1080 23.3 0.04-19.4 Riggan et al southern California 

Camp Paivika, San Bernadino Mts., Mixed conifer 1600 30 7-26 f Fenn et al. ( southern California 

Klamath Mts, northern California Western coniferous NA Mainly geologic g NA g Dahlgren (19Thompson Forest, Cascade Mts., Red alder 220 4.7 plus > 100 as 38.9 Johnson and Washington 

N2 fixation 

a Estimated total N deposition from wet deposition data is from Driscoll et al. (1991) for the Adirondacks, and from Stoddard and Murdoch (1991) for the Catskills. To

 based on the wet deposition: total N deposition ratio (0.56) at Huntington Forest in the Adirondacks (Johnson and Lindberg, 1992b). Nitrogen deposition can be high

b at high-elevation sites such as Whiteface Mountain (15.9 kg/ha/year; Johnson and Lindberg, 1992b). Stage 1 and 2 of N loss according to the watershed conceptual model of Stoddard (1994). Nitrogen discharge (kg/ha/year) data are not available; only stream water N

data were collected. 

c Values appear high compared to other sites, especially N leaching losses. Joslin and Wolfe (1992) concede that “there is considerable uncertainty associated with the 

 deposition and leaching fluxes.” However, elevated NO3 

! concentrations in soil solution, and lack of a growth response to N fertilization (Joslin and Wolfe, 1994) sup

d forest at Whitetop Mountain is N saturated. Estimated total N deposition from throughfall data. Total deposition was estimated based on the throughfall/total N deposition ratio from the nearby Smokies Tower 

 Lindberg, 1992b). 

e Annual throughfall deposition to the chaparral ecosystem. f Nitrogen output is from unpublished streamwater data (Fenn and Poth, 1999). The low value represents a year of average precipitation, and the high value is for 1995

 nearly double the long-term average. Nitrogen output includes N export in stream water and to groundwater. 

g Annual input and output data are not known, although N deposition in this forest is probably typical for much of the rural western United States (2-3 kg N/ha/year (Yo

 Excess N is from weathering of ammonium in mica schist bedrock. The ammonium was rapidly nitrified, leading to high NO3 

! concentrations in soil solution (Dahlgr
Figure 4-17. Diagrammatic overview of excess nitrogen (N) in North America. 

Adapted from: Fenn et al. (1998). 

experience greater mortality. Long-term fertilization experiments at Mount Ascutney, VT 

suggest that declining coniferous forest stands with slow nitrogen cycling may be replaced by 

fast-growing deciduous forests that cycle nitrogen rapidly (Fenn et al., 1998). 

4-103
Atmospheric nitrogen deposition in the northeastern United States is largely a regional 

problem (Driscoll et al., 2001). In contrast, in the western United States, vast acres of land 

receive low levels of atmospheric nitrogen deposition but are interspersed with hot spots of 

elevated nitrogen deposition downwind of large expanding metropolitan centers or large 

agricultural operations (Fenn et al., 2003b). 

Fenn et al. (1998) have documented the major effects of Nr deposition in terrestrial and 

aquatic ecosystems in the western United States. Primarily these effects are in response to 

nitrogen enrichment of systems that are naturally nitrogen limited. Included in these effects are 

increased greenhouse gas emissions, higher nitrogen concentrations in plant tissues, and 

increased nitrification rates and nitrate (NO3 

!

) levels in soils, streams, and lakes (Fenn et al., 

2003b). A result of chronic Nr enrichment has resulted in important community-level changes 

in vegetation, lichens, mycorrhizae, and phytoplankton, occasionally occurring at relatively low 

levels of nitrogen deposition (3 to 8 kgN/ha/year; Baron et al., 2000). 

Developments in recent decades in the Colorado Front range have resulted in increased 

nitrogen deposition at high-elevation sites since the 1980s . Total deposition values currently 

range from 4 to 8 kgN/ha/year (Baron et al., 2000). Competition among species resulting in 

changes in community composition is one of the most notable responses to environmental 

change (Bowman, 2000). Nitrogen saturation, the result of increased deposition in the alpine 

tundra of Niwot Ridge in the Front Range of the southern Rockies in Colorado, has changed 

nitrogen cycling and provided the potential for replacement in plant species by more 

competitive, faster growing species (Bowman and Steltzer, 1998; Bowman, 2000; Baron et al., 

2000). Plants growing in an alpine tundra, as is true of other plants growing in low resource 

environments (e.g., infertile soil, shaded understory, deserts), have been observed to have certain 

similar characteristics: a slow grow rate, low photosynthetic rate, low capacity for nutrient 

uptake, and low soil microbial activity (Bowman and Steltzer, 1998; Bowman, 2000). 

An important feature of such plants is that they continue to grow slowly and tend to respond 

even less when provided with an optimal supply and balance of resources (Pearcy et al., 1987; 

Chapin, 1991). Plants adapted to cold, moist environments grow more leaves than roots as the 

relative availability of nitrogen increases; however, other nutrients may soon become limiting. 

These patterns of vegetative development affect the plants capacities to respond to variation in 

available resources and to environmental stresses such as frost, high winds, and drought. 

4-104
Preformation of buds 3 to 4 years in advance of emergence, reduced cell numbers, and high 

biomass allocation to belowground organs also limits the ability of many alpine plants to 

respond to variations in their environment (Bowman, 2000). However, significant interspecific 

genetic variation influences the capacity of the alpine species to respond to changes in resource 

availability. The capacity of subalpine and boreal species in particular, and gymnosperms in 

general, to reduce nitrates in either roots or leaves appears to be limited. In addition, the ability 

of trees to use nitrogen varies with the age of the tree and the density of the stand (Waring, 

1987). 

In experimental studies of nitrogen deposition conducted by Wedin and Tilman (1996) 

over a 12-year period on Minnesota grasslands, plots dominated by native warm-season grasses 

shifted to low-diversity mixtures dominated by cool-season grasses at all but the lowest rates of 

nitrogen addition. Grasslands with high nitrogen retention and carbon storage rates were the 

most vulnerable to loss of species and major shifts in nitrogen cycling. The shift to low-diversity 

mixtures was associated with the decrease in biomass carbon to nitrogen (C:N) ratios, increased 

nitrogen mineralization, increased soil nitrate, high nitrogen losses, and low carbon storage 

(Wedin and Tilman, 1996). Naeem et al. (1994) experimentally demonstrated under controlled 

environmental conditions that the loss of biodiversity, genetic resources, productivity, ecosystem 

buffering against ecological perturbation, and loss of aesthetic and commercially valuable 

resources also may alter or impair ecosystems services. 

The long-term effects of increased nitrogen deposition have been studied in several 

western and central European plant communities including lowland heaths, species-rich 

grasslands, mesotrophic fens, ombrotrophic bogs, upland moors, forest-floor vegetation, and 

freshwater lakes (Bobbink, 1998). Large changes in species composition have been observed in 

regions with high nitrogen loadings or in field experiments after years of nitrogen addition 

(Bobbink et al., 1998). The increased input of nitrogen gradually increased the availability of 

nitrogen in the soil, and its retention because of low rates of leaching and denitrification, 

resulting in faster litter decomposition and mineralization rates. Faster growth and greater height 

of nitrophilic species enables these plants to shade out the slower growing species, particularly 

those in oligotrophic or mesotrophic conditions (Bobbink, 1998; Bobbink et al., 1998). Excess 

nitrogen inputs to unmanaged heathlands in the Netherlands has resulted in nitrophilous grass 

species replacing the slower growing heath species (Roelofs et al., 1987; Garner, 1994). 

4-105
Van Breemen and Van Dijk (1988) noted that over the past several decades the composition of 

plants in the forest herb layers has been shifting toward species commonly found in nitrogen-rich 

areas. They also observed a decrease in number of the fruiting bodies of mycorrhizal fungi. 

Other studies in Europe point out the effects of excessive nitrogen deposition on mixed-oak 

forest vegetation along a deposition gradient largely controlled by soil acidity, nitrogen supply, 

canopy composition, and location of sample plots (Brunet et al., 1998; Falkengren-Grerup, 

1998). Results of the study, using multivariate methods, suggest that nitrogen deposition has 

affected the field-layer vegetation directly by increased nitrogen availability and, indirectly, by 

accelerating soil acidity. Time series studies indicate that 20 of the 30 field-layer species 

(nonwoody plants) that were associated most closely with high nitrogen deposition increased in 

frequency in areas with high nitrogen deposition during the past decades. Included in the fieldlayer species were many species generally considered nitrophilous; however, there were also 

several acid-tolerant species (Brunet et al, 1998). In an experimental study involving 15 herbs 

and 13 grasses, Falkengren-Grerup (1998), observed that species with a high nitrogen demand 

and a lesser demand for nutrients other than nitrogen were particularly competitive in areas with 

acidic soils and high nitrogen deposition. The grasses grew better than herbs with the addition of 

nitrogen. It was concluded that, at the highest nitrogen deposition, growth was limited for most 

species by the supply of nutrients other than nitrogen; and, at the intermediate nitrogen 

concentration, the grasses were more efficient than the herbs in utilizing nitrogen. Nihlgård 

(1985) suggested that excessive nitrogen deposition may contribute to forest decline in other 

specific regions of Europe. Additionally, Schulze (1989), Heinsdorf (1993), and Lamersdorf and 

Meyer (1993) attributed magnesium deficiencies in German forests, in part, to excessive 

nitrogen deposition. 

The carbon to nitrogen (C:N) ratio of the forest floor can also be changed by nitrogen 

deposition over time. This change appears to occur when the ecosystem becomes nitrogen 

saturated (Gundersen et al., 1998a). Long-term changes in C:N status have been documented in 

Central Europe and indicate that nitrogen deposition has changed the forest floor. In Europe, 

low C:N ratios coincide with high deposition regions (Gundersen et al., 1998a). A strong 

decrease in forest floor root biomass has been observed with increased nitrogen availability. 

Roots and the associated mycorrhizae appear to be an important factor in the accumulation of 

organic matter in the forest floor at nitrogen-limited sites. If root growth and mycorrhizal 

4-106
formation are impaired by excessive nitrogen deposition, the stability of the forest floor 

vegetation may be affected by stimulating turnover and decreasing the root litter input to the 

forest floor, thus decreasing the nitrogen that can be stored in the forest floor pool (Gundersen 

et al., 1998b). Nitrogen-limited forests have a high capacity for deposited nitrogen to be retained 

by plants and microorganisms competing for available nitrogen (Gundersen et al., 1998b). 

Nitrate leaching has been correlated significantly with nitrate status but not with nitrate 

depositions. The forest floor C:N ratio has been used as a rough indicator of ecosystem nitrogen 

status in mature coniferous forests and the risk of nitrate leaching; analyses of European 

databases indicate an empirical relationship between forest floor C:N ratio and nitrate leaching 

(Gundersen et al., 1998a). Nitrate leaching was observed when the deposition received was 

more than 10 kg N/ha. All of the data sets supported the threshold at which nitrate leaching 

seems to increase at a C:N ratio of 25. Therefore, to predict the rate of changes in nitrate 

leaching, it is necessary to be able to predict the rate of changes in the forest floor C:N ratio. 

Decreased foliar and soil nitrogen and soil C:N ratios, as well as changes in nitrogen 

mineralization rates, have been observed when comparing responses to nitrogen deposition in 

forest stands east and west of the Continental Divide in the Colorado Front Range (Baron et al., 

2000; Rueth and Baron, 2002). Understanding the variability in forest ecosystem response to 

nitrogen input is essential to assessing pollution risks (Gundersen et al., 1998a). 

The plant root is an important region of nutrient dynamics. The rhizosphere includes the 

soil that surrounds and is influenced by plant roots (Wall and Moore, 1999). The mutualistic 

relationship between plant roots, fungi, and microbes is critical for the growth of the organisms 

involved. The plant provides shelter and carbon; whereas the symbiont provides access to 

limiting nutrients such as nitrogen and phosphorus. As indicated above, changes in soil nitrogen 

influence the mycorrhizal-plant relationship. Mycorrhizal fungal diversity is associated with 

above-ground plant biodiversity, ecosystem variability, and productivity (Wall and Moore, 

1999). Aber et al. (1998) showed a close relationship between mycorrhizal fungi and the 

conversion of dissolved inorganic nitrogen to soil nitrogen. During nitrogen saturation, soil 

microbial communities change from being fungal, and probably being dominated by 

mycorrhizae, to being dominated by bacteria. The loss of mycorrhizal function has been 

hypothesized as the key process leading to increased nitrification and nitrate mobility. Increased 

nitrate mobility leads to increased cation leaching and soil acidification (Aber et al., 1998). 

4-107
The interrelationship of above- and below-ground flora is illustrated by the natural 

invasion of heathlands by oaks (Quercus robur). The soil-forming factors under the heath have 

been vegetation typed during the last 2000 years; whereas the invasion by oaks has been taking 

place for only a few decades. Clearly, changes in the ground floor and soil morphology takes 

place when trees colonize heath (Nielsen et al., 1999). The distribution of roots also changed 

under the three different vegetation types. Under both heather and the Sitka spruce plantation, 

the majority of roots are confined to the uppermost horizons; whereas under oak, the roots are 

distributed more homogeneously. There was also a change in the C:N ratio when heather was 

replaced by oaks. Also, the spontaneous succession of the heath by oaks changed the biological 

nutrient cycle into a deeper vertical cycle when compared to the heath where the cycle is 

confined to the upper soil horizons. Soils similar to those described in this Jutland, Denmark 

study with a mainly organic buffer system seem to respond quickly to changes in vegetation 

(Nielsen et al., 1999). 

The effects of changes in root to shoot relationships in plants were observed in studies of 

the coastal sage scrub (CSS) community in southern California, which is composed of the 

drought-deciduous shrubs Artemisia californica, Encelia farinosa, and Eriogonum fasciculatum. 

The CSS in California has been declining in land area and in shrub density over the past 60 years 

and is being replaced in many areas by Mediterranean annual grasses (Allen et al., 1998; Padgett 

et al., 1999; Padgett and Allen, 1999). Nitrogen deposition was considered as a possible cause. 

Up to 45 kg/ha/year are deposited in the Los Angeles Air Basin (Bytnerowicz and Fenn, 1996). 

Tracts of land set aside as reserves, which in many cases in southern California are surrounded 

by urbanization, receive large amounts of nitrogenous compounds from polluted air. The CSS is 

of particular interest, because some 200 sensitive plant species and several federally listed 

animal species are found in the area (Allen et al., 1998). Because changes in plant community 

structure often can be related to increases in the availability of a limiting soil nutrient or other 

resource, experiments were conducted to determine whether increased nitrogen availability was 

associated with the significant loss in native shrub cover. Studies indicated that the three native 

perennial shrubs (Artemisia californica, Eriogonum fasciculatum, and Encelia farinosa) tended 

to be more nitrophilous than the two exotic annual grasses (Bromus rubens, Avena fatua) and the 

weedy pod mustard (Brassica geniculata). These results contrast with most models dealing with 

the adaptation of perennial species to stressful environments (Padgett and Allen, 1999). 

4-108
If nitrogen were the only variable between the invasive annuals and native shrubs, neither shrubs 

nor grasses would have a particular advantage. Although CSS shrubs are able to take up 

nitrogen at high rates, native grasses have a denser seedbank and earlier germination than native 

species. The native seedlings are unable to compete with dense stands of exotic grasses, and 

thus are gradually replaced by the grasses following disturbances such as frequent fire (Eliason 

and Allen, 1997; Cione et al., 2002; Yoshida and Allen, 2001). In addition, nitrogen-induced 

changes in arbuscular mycorrhizal fungi may also affect the growth of native seedlings. 

Nitrogen enrichment of the soils has induced a shift in the arbuscular mycorrhizal community 

composition. Larger-spored fungal species (Scutellospora and Gigaspora), due to a failure to 

sporulate, decreased in number with a concomitant proliferation of small-spored species of 

Glomus aggregatum, G. leptotichum, and G. geosporum, indicating a strong selective pressure 

for the smaller spored species of fungi (Edgerton-Warburton and Allen, 2000). These results 

demonstrate that nitrogen enrichment of the soil significantly alters the arbuscular mycorrhizal 

species composition and richness and markedly decreases the overall diversity of the arbuscular 

mycorrhizal community. The decline in coastal sage scrub species can, therefore, be directly 

linked to the decline of the arbuscular mycorrhizal community (Edgerton-Warburton and Allen, 

2000). 

In addition to excess nitrogen deposition effects on terrestrial ecosystems of the types 

noted above (e.g., dominant species shifts and other biodiversity impacts), direct atmospheric 

nitrogen deposition and increased nitrogen inputs via runoff into streams, rivers, lakes, and 

oceans can noticeably affect aquatic ecosystems as well (Figure 4-15). Estuaries are among the 

most intensely fertilized ecosystems on Earth, receiving far greater nutrient inputs than other 

systems. Chesapeake Bay is a prime example (Fenn et al., 1998). Another illustrative example 

is found in recently reported research (Paerl et al., 2001) characterizing the effects of nitrogen 

deposition on the Pamlico Sound, NC estuarine complex, which serves as a key fisheries nursery 

supporting an estimated 80% of commercial and recreational finfish and shellfish catches in the 

southeastern U.S. Atlantic coastal region. Such direct atmospheric nitrogen deposition onto 

waterways feeding into the Pamlico Sound or onto the sound itself, combined with indirect 

nitrogen inputs via runoff from upstream watersheds, contribute to conditions of severe water 

oxygen depletion; formation of algae blooms in portions of the Pamlico Sound estuarine 

complex; altered fish distributions, catches, and physiological states; and increases in the 

4-109
incidence of disease in fish. Under extreme conditions of especially high rainfall rate events 

(e.g., hurricanes) affecting watershed areas feeding into the sound, the effects of nitrogen runoff 

(in combination with excess loadings of metals or other nutrients) can be massive — e.g., 

creation of the widespread “dead-zone” affecting large areas of the Pamlico Sound for many 

months after hurricane Fran in 1996 and hurricanes Dennis, Floyd, and Irene in 1999 impacted 

eastern North Carolina. 

The primary pathways of nitrogen loss from forest ecosystems are hydrological transport 

beyond the rooting zone into groundwater or stream water, or surface flows of organic nitrogen 

as nitrate and nitrogen loss associated with soil erosion (Fenn et al., 1998). Stream water nitrate 

concentrations have been related to forest successional stage in the eastern United States. 

Logging and fire histories of an area are major variables determining the capability of a forest 

stand to retain nitrogen. Nitrogen concentrations were high in mature ecosystems after 

disturbances such as clearcutting, but lower in mid-successional forests. 

Nitrogen saturation of a high elevation watershed in the southern Appalachian Mountains 

was observed to affect stream water chemistry. High nitrate concentrations have been observed 

in streams draining undisturbed watersheds in the Great Smoky Mountains National Park in 

Tennessee and North Carolina. Nitrate concentrations were highest at higher elevations and in 

areas around old-growth forests that had never been logged (Fenn et al., 1998). 

In the Northeast, nitrogen is the element most responsible for eutrophication in coastal 

waters of the region (Jaworski et al., 1997). There has been a 3- to 8-fold increase in nitrogen 

flux from 10 watersheds in the northeastern United States since the early 1900s. These increases 

are associated with the deposition of nitrogen oxide emissions from combustion, which have 

increased 5-fold. Riverine nitrogen fluxes have been correlated with atmospheric deposition 

onto their landscapes and with nitrogen oxides emissions into their airsheds. Data from 

10 benchmark watersheds with good historical records, indicate that ~36 to 80% of the riverine 

total nitrogen export, with an average of 64%, was derived directly or indirectly from nitrogen 

oxide emissions (Jaworski et al., 1997). 

Nitrogen saturation of a high-elevation watershed in the southern Appalachian Mountains 

was observed to affect stream water chemistry. The Great Smoky Mountains in the southeastern 

United States receive high total atmospheric deposition of sulfur and nitrogen (2,200 Eq/ha/year 

of total sulfur and approximately 1,990 Eq/ha/year of total nitrogen). A major portion of the 

4-110
atmospheric loading is from dry and cloud deposition. Extensive surveys conducted in October 

1993 and March 1994 indicated that stream pH values were near or below pH 5.5 and that the 

acid neutralizing capacity (ANC) was below 50 μeq/L at high elevations. Analysis of stream 

water indicated that nitrate was the dominant anion (Flum and Nodvin, 1995; Nodvin et al., 

1995). The study was expanded to the watershed scale with the monitoring of precipitation, 

throughfall, stream hydrology, and stream chemistry. Nitrogen saturation of the watershed 

resulted in extremely high exports of nitrate and promoted both chronic and episodic stream 

acidification in which the nitrate was the dominant ion. Significant exports of base cation was 

also observed. Nitrification of the watershed soils resulted in elevations of soil solution 

aluminum concentrations to levels known to inhibit calcium uptake in red spruce (Nodvin 

et al., 1995). 

Excessive nitrogen loss is a symptom of terrestrial ecosystem dysfunction and results in the 

degradation of water quality with potentially deleterious effects on terrestrial and aquatic 

ecosystems (Fenn and Poth, 1999). Data from a number of hydrologic, edaphic, and plant 

indicators indicate that the mixed conifer forests and chaparral systems directly exposed to air 

pollution from greater Los Angeles are nitrogen saturated. Preliminary data suggests that 

symptoms of nitrogen saturation are evident in mixed conifer or chaparral sites receiving 

atmospheric deposition of 20 to 25 kg/N/ha/year (Fenn et al, 1996). Available data clearly 

indicate that ecosystems with a Mediterranean climate have a limited capacity to retain nitrogen 

! within the terrestrial system (Fenn and Poth, 1999). A 3-year study of stream water NO3 

concentrations along nitrogen deposition gradients in the San Bernardino Mountains in southern 

California evaluated stream water quality and whether the stream water concentrations covaried 

with nitrogen deposition across pollution gradients in the San Bernardino Mountains. Stream 

water NO3 

!

 concentrations at Devil Canyon in the San Gabriel Mountains northeast of 

Los Angeles are the highest reported in North America for forested watersheds (Fenn and Poth, 

1999). Five of the six streams monitored maintained elevated NO3 

!

 throughout the year. Peak 

nitrate concentrations ranged from 40 to 350 μmol/L. In the San Gorgonio Wilderness, an area 

of low to moderate deposition where 12 streams were sampled, only the five that had the greatest 

air pollution exposure had high NO3 

!

 concentrations. These results suggest a strong association 

between NO3 

!

 levels exported in stream water and the severity of chronic nitrogen deposition to 

the terrestrial watersheds. However, nitrogen processing within terrestrial and aquatic systems, 

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even in areas with high nitrogen deposition, determine stream water NO3 

!

 concentrations 

(Fenn and Poth, 1999). The Fernow Experimental Forest in West Virginia, the Great Smoky 

Mountains National park in Tennessee, and watersheds in southwestern Pennsylvania are the 

! only undisturbed forested sites in North America known to have stream water NO3 

concentrations within the range of values found at Devil Canyon (Fenn and Poth, 1999). 

Effects of Sulfur Deposition. Sulfur is a major component of plant proteins and, as such, 

is an essential plant nutrient. The most important source of sulfur is sulfate taken up from the 

soil by plant roots, even though plants can use atmospheric SO2 (Marschner, 1995). The 

availability of organically bound sulfur in soils depends largely on microbial decomposition, a 

relatively slow process. The major factor controlling the movement of sulfur from the soil into 

vegetation is the rate of release from the organic to the inorganic compartment (May et al., 1972; 

U.S. Environmental Protection Agency, 1982; Marschner, 1995). Sulfur plays a critical role in 

agriculture as an essential component of the balanced fertilizers needed to grow and increase 

worldwide food production (Ceccotti and Messick, 1997). Atmospheric deposition is an 

important component of the sulfur cycle. This is true not only in polluted areas where 

atmospheric deposition is very high, but also in areas of low sulfur input. Additions of sulfur 

into the soil in the form of SO4

2!

 could alter the important organic-sulfur/organic-nitrogen 

relationship involved in protein formation in plants. The biochemical relationship between 

sulfur and nitrogen in plant proteins and the regulatory coupling of sulfur and nitrogen 

metabolism indicate that neither element can be assessed adequately without reference to the 

other. A sulfur deficiency reduces nitrate reductase and, to a similar extent, glutamine 

synthetase activity. Nitrogen uptake in forests, therefore, may be loosely regulated by sulfur 

availability, but sulfate additions in excess of needs do not necessarily lead to injury (Turner and 

Lambert, 1980; Hogan et al., 1998). 

Only two decades ago, there was little information comparing sulfur cycling in forests with 

other nutrients, especially nitrogen. With the discovery of deficiencies in some unpolluted 

regions (Kelly and Lambert, 1972; Humphreys et al., 1975; Turner et al., 1977; Schnug, 1997) 

and excesses associated with acidic deposition in other regions of the world (Meiwes and 

Khanna, 1981; Shriner and Henderson, 1978; Johnson et al., 1982a,b), interest in sulfur nutrition 

and cycling in forests has heightened. General reviews of sulfur cycling in forests have been 

4-112
written by Turner and Lambert (1980), Johnson (1984), Mitchell et al. (1992a,b), and Hogan 

et al. (1998). The salient elements of the sulfur cycle as it may be affected by changing 

atmospheric deposition are summarized by Johnson and Mitchell (1998). Sulfur has become the 

most important limiting factor in European agriculture because of the desulfurization of 

industrial emissions (Schnug, 1997). 

Most studies dealing with the effects of sulfur deposition on plant communities have been 

conducted in the vicinity of point sources and have investigated the above-ground effects of SO2 

or the acidifying effects of sulfate on soils (Krupa and Legge, 1998; Dreisinger and McGovern, 

1970; Legge, 1980; Winner and Bewley, 1978a,b; Laurenroth and Michunas, 1985; U.S. 

Environmental Protection Agency, 1982). Krupa and Legge (1986), however, observed a 

pronounced increase in foliar sulfur concentrations in all age classes of needles of the hybrid 

pine lodgepole × jack pine (Pinus contorta × P. banksiana). This vegetation had been exposed 

to chronic low concentrations of sulfur dioxide (SO2) and hydrogen sulfide (H2S) for more than 

20 years and, then, to fugitive sulfur aerosol. Observations under the microscope showed no 

sulfur deposits on the needle surfaces and led to the conclusion that the sulfur in the needles was 

derived from the soil. The oxidation of elemental sulfur and the generation of protons is well 

known for the soils of Alberta, Canada. This process is mediated by bacteria of the Thiobacillus 

sp. As elemental sulfur is gradually converted to protonated SO4, it can be leached downward 

and readily taken up by plant roots. The activity of Thiobacillus sp. is stimulated by elemental 

sulfur additions (Krupa and Legge, 1986). 

Effects of Acidic Deposition on Forest Soils. Acidic deposition over the past quarter of a 

century has emerged as a critical environmental stress that affects forested landscapes and 

aquatic ecosystems in North America, Europe, and Asia (Driscoll et al., 2001). Acidic 

deposition can originate from transboundary air pollution and affect large geographic areas. It is 

composed of ions, gases, particles derived from gaseous emissions of SO2, NOx, NH3, and 

particulate emissions of acidifying and neutralizing compounds and is highly variable across 

space and time. It links air pollution to diverse terrestrial and aquatic ecosystems and alters the 

interactions of the H+

 with many elements (e.g., S, N, Ca, Mg, Al, and Hg). Acidic deposition 

contributes directly and indirectly to biological stress and the degradation of ecosystems and has 

4-113
played a major role in the recent acidification of soil in some areas of Europe and, to a more 

limited extent, eastern North America (Driscoll et al., 2001). 

Substantial and previously unsuspected changes in soils have been observed in polluted 

areas of eastern North America, the United Kingdom, Sweden, and central Europe and in less 

polluted regions of Australia and western North American (reviewed by Johnson et al., 1999 and 

by Huntington, 2000). In some cases, trends were toward more acidic soils (e.g., Markewitz 

et al., 1998); in others cases, there were no consistent trends, with some soils showing increases 

and some showing decreases at different sampling times and some showing no change (e.g., 

Johnson and Todd, 1998; Trettin et al., 1999; Yanai et al., 1999). 

Significant changes in soil chemistry have occurred at many sites in the eastern United 

States during recent decades. Patterns of change in tree ring chemistry, principally at high 

elevations sites in the eastern United States, reflect the changing inputs of regional pollutants to 

forests. A temporal sequence of changes in uptake patterns, and possibly in tree growth, would 

be expected if significant base cation mobilization and depletion of base cations from eastern 

forest soils has occurred. Temporal changes in the chemistry of the tree rings of red spruce were 

examined as indicators of historical changes in the red spruce’s chemical environment. 

Analysis of changes in wood chemistry from samples across several sites indicate that 

there have been substantial departures from the expected linear decreases in calcium 

accumulation patterns in wood. A region-wide calcium increase above expected levels followed 

by decreasing changes in wood calcium suggest that calcium mobilization began possibly 30 to 

40 years ago and has been followed by reduced accumulation rates in wood, presumably 

associated with decreasing calcium availability in soil (Bondietti and McLaughlin, 1992). 

The period of calcium mobilization coincides with a region-wide increase in the growth rate of 

red spruce; whereas the period of decreasing levels of calcium in wood corresponds temporally 

with patterns of decreasing radial growth at high elevation sites throughout the region during the 

past 20 to 30 years. The decline in wood calcium suggests that calcium loss may have increased 

to the point at which base saturation of soils has been reduced. Increases in aluminum and iron 

typically occur as base cations are removed from the soils by tree uptake (Bondietti and 

McLaughlin, 1992). The changes are spatially and temporally consistent with changes in the 

emissions of SO2 and NO2 across the region and suggest that increased acidification of soils has 

occurred. 

4-114
Studies by Shortle and Bondietti (1992) support the view that changes in soil chemistry in 

eastern North America forest sites occurred many decades ago, “before anybody was looking.” 

Sulfur and nitrogen emissions began increasing in eastern North America in the 1920s and 

continued to increase into the 1980s when sulfur began to decrease but nitrogen emissions did 

not (Garner et al., 1989). Shortle and Bondietti (1992) present evidence that, from the late 1940s 

into the 1960s, the mor humus (organic) layer of acid-sensitive forest sites in eastern North 

America underwent a significant change that resulted in the loss of exchangeable essential base 

cations and interrupted the critical base nutrient cycles between mature trees and the root-humus 

complex. The timing of the effect appears to have coincided with the period when the SOx and 

NOx emissions in eastern North America subject to long-range transport were increasing the 

most rapidly (see above; Shortle and Bondietti, 1992). Although forest ecosystems other than 

the high-elevation spruce-fir forests are not currently manifesting symptoms of injury directly 

attributable to acid deposition, less sensitive forests throughout the United States are 

experiencing gradual losses of base cation nutrients, which, in many cases, will reduce the 

quality of forest nutrition over the long term (National Science and Technology Council, 1998). 

In some cases it may not even take decades, because these forests already have been receiving 

sulfur and nitrogen deposition for many years. The current status of forest ecosystems in 

different U.S. geographic regions varies, as does their sensitivity to nitrogen and sulfur 

deposition. Variation in potential future forest responses or sensitivity are caused, in part, by 

differences in deposition of sulfur and nitrogen, ecosystem sensitivities to sulfur and nitrogen 

additions, and the responses of soils to sulfur and nitrogen inputs (National Science and 

Technology Council, 1998). 

SO

Acidic deposition has played a major role in recent soil acidification in some areas of 

Europe and, to a more limited extent, eastern North America. Examples include the study by 

Hauhs (1989) at Lange Bramke, Germany, which indicated that leaching was of major 

importance in causing substantial reduction in soil-exchangeable base cations over a 10-year 

period (1974 to 1984). Soil acidification and its effects result from the deposition of NO3!

 and 

4

2!

 and associated H+. The effects of excessive nitrogen deposition on soil acidification and 

nutrient imbalances have been well established in Dutch forests (Van Breemen et al., 1982; 

Roelofs et al., 1985; Van Dijk and Roelofs, 1988). For example, Roelofs et al. (1987) proposed 

that NH3/NH4

+

 deposition leads to heathland changes via two modes: acidification of the soil 

4-115
with the loss of cations K+

, Ca2+, and Mg2+; and nitrogen enrichment that results in “abnormal” 

plant growth rates and altered competitive relationships. Nihlgård (1985) suggested that 

excessive nitrogen deposition may contribute to forest decline in other regions of Europe. 

Falkengren-Grerup (1987) noted that, over about 50 years, unexpectedly large increases in the 

growth of beech trees (Fagus sylvatica L.) were associated with decreases in pH and 

exchangeable cations in some sites in southernmost Sweden. 

Likens et al. (1996, 1998) suggested that soils are changing at the Hubbard Brook 

Watershed, NH, because of a combination of acidic deposition and reduced base cation 

deposition. They surmised, based on long-term trends in stream water data, that large amounts 

of Ca and Mg have been lost from the soil-exchange complex over a 30-year period, from 

approximately 1960 to 1990. The authors speculate that the declines in base cations in soils may 

be the cause of recent slowdowns in forest growth at Hubbard Brook. In a follow-up study, 

however, Yanai et al. (1999) found no significant decline in Ca and Mg in the forest floors at 

Hubbard Brook over the period 1976 to 1997. They also found both gains and losses in forest 

floor Ca and Mg between 1980 and 1990 in a regional survey. Thus, they concluded that “forest 

floors in the region are not currently experiencing rapid losses of base cations, although losses 

may have preceded the onset of these three studies.” The biogeochemistry of calcium at 

Hubbard Brook is discussed in detail by Likens et al. (1998). 

Hydrogen ions entering a forest ecosystem first encounter the forest canopy, where they 

are often exchanged for base cations that then appear in throughfall (Figure 4-18 depicts a model 

of H+

 sources and sinks). Base cations leached from the foliage must be replaced through uptake 

from the soil, or foliage cations will be reduced by the amounts leached. In the former case, the 

acidification effect is transferred to the soil where H+

 is exchanged for a base cation at the 

root-soil interface. The uptake of base cations or NH4

+

 by vegetation or soil microorganisms 

causes the release of H+

 in order to maintain charge balance; conversely, the uptake of nutrients 

in anionic form (NO3 

!

, SO4

2!

, PO4

3!

) causes the release of OH! in order to maintain charge 

balance. Thus, the net acidifying effect of uptake is the difference between cation and anion 

+ uptake. The form of ions taken up is known for all nutrients but nitrogen, because either NH4 or 

NO3 

!

 can be used by the vegetation. In that nitrogen is the nutrient taken up in the greatest 

quantities, the uncertainty in the ionic form of nitrogen utilized creates great uncertainty in the 

overall H+

 budget for soils (Johnson, 1992). 

4-116
Figure 4-18. Schematic of sources and sinks of hydrogen ions in a forest. 

Source: Taylor et al. (1994). 

The cycles of base cations differ from those of N, P, and S in several respects. The fact 

that calcium, potassium, and magnesium exist primarily as cations in solution, whereas N, P, and 

S exist primarily as anions, has major implications for the cycling of the nutrients and the effects 

of acid deposition on these cycles. The most commonly accepted model of base cation cycling 

in soils is one in which base cations are released by the weathering of primary minerals to cation 

exchange sites, making the cation available for either plant uptake or leaching (Figure 4-18). 

The introduction of H+

 by atmospheric deposition or by internal processes will affect the fluxes 

of Ca, K, and Mg via cation exchange or weathering processes. Therefore, soil leaching is often 

4-117
of major importance in cation cycles, and many forest ecosystems show a net loss of base cations 

(Johnson, 1992). 

Two basic types of soil change are involved: (1) a short-term intensity type change 

resulting from the concentrations of chemicals in soil water and (2) a long-term capacity change 

based on the total content of bases, aluminum, and iron stored in the soil (Reuss and Johnson, 

1986; Van Breemen et al., 1983). Changes in intensity factors can have a rapid effect on the 

chemistry of soil solutions. Rapid changes in intensity resulting from the addition of sulfur and 

nitrogen in acidic deposition can cause immediate increases in acidity and the mobilization of 

aluminum in soil solutions. Increased aluminum concentrations and an increase in the Ca:Al 

ratio in soil solution have been linked to a significant reduction in the availability of essential 

base cations to plants, an increase in plant respiration, and increased biochemical stress (National 

Science and Technology Council, 1998). 

Capacity changes are the result of many factors acting over long time periods. The content 

of base cations (calcium, magnesium, sodium, and potassium) in soils results from additions via 

atmospheric deposition, decomposition of vegetation, and geologic weathering; losses may occur 

through plant uptake and leaching. Increased leaching of base cations may result in nutrient 

deficiencies in soils, as has been happening in some sensitive forest ecosystems (National 

Science and Technology Council, 1998). 

Aluminum toxicity is a possibility in acidified soils. Atmospheric deposition (or any other 

source of mineral anions) can increase the concentration of Al, especially Al3+, in soil solution 

without causing significant soil acidification (Johnson and Taylor, 1989). Aluminum can be 

brought into soil solution in two ways: (1) by acidification of the soil and (2) by increasing the 

total anion and cation concentration of the soil solution. The introduction of mobile, mineral 

acid anions to an acid soil will cause increases in the concentration of aluminum in the soil 

solution, but extremely acid soils in the absence of mineral acid anions will not produce a 

solution high in aluminum. An excellent review of the relationships among the most widely 

used cation-exchange equations and their implications for the mobilization of aluminum into soil 

solution is provided by Reuss (1983). 

A major concern has been that soil acidity would lead to nutrient deficiency. Calcium is 

essential for root development and the formation of wood, and it plays a major role in cell 

membrane integrity and cell wall structure. Aluminum concentrations in the soil can influence 

4-118
forest tree growth in regions where acidic deposition and natural acidifying processes increase 

soil acidity. Acidic deposition mobilizes calcium and magnesium, which are essential for root 

development and stem growth. Mobilized aluminum can also bind to fine root tips of red spruce, 

further limiting calcium and magnesium uptake (Shortle and Smith, 1988; Shortle et al., 1997). 

There is abundant evidence that aluminum is toxic to plants. Upon entering tree roots, 

aluminum accumulates in root tissues (Thornton et al., 1987; Vogt et al., 1987a, b). Reductions 

in calcium uptake have been associated with increased aluminum uptake (Clarkson and 

Sanderson, 1971), and a number of studies suggest that the toxic effect of aluminum on forest 

trees could be caused by a Ca2+ deficiency (Shortle and Smith, 1988; Smith, 1990a). Mature 

trees have a high calcium requirement relative to agriculture crops (Rennie, 1955). Shortle and 

Smith (1988) attributed the decline of red spruce in eight stands across northern New England 

from Vermont to Maine to an imbalance of Al3+ and Ca2+ in fine root development. 

To be taken up from the soil by roots, Ca must be dissolved in soil water (Lawrence and 

Huntington, 1999). Aluminum in soil solution reduces Ca uptake by competing for binding sites 

in the cortex of fine roots. Tree species may be adversely affected if high aluminum to nutrient 

ratios create a nutrient deficiency by limiting the uptake of calcium and magnesium (Shortle and 

Smith, 1988; Garner, 1994). Acid deposition, by lowering the pH of Al-rich soil, can increase 

Al concentrations in soil water through dissolution and ion exchange processes. Aluminum is 

more readily taken up than is Ca because of its greater affinity for negatively charged surfaces 

and, when present in the forest floor, Al tends to displace adsorbed Ca, causing it to be more 

readily leached. The continued buildup of Al in the forest floor layer, where nutrient uptake is 

greatest, can lower the efficiency of Ca uptake when the Ca:Al ratio in soil water is less than one 

(Lawrence and Huntington, 1999). A reduction in Ca uptake suppresses cambial growth and 

reduces the rate of wood (annual ring) formation, decreases the amount of functional sapwood 

and live crown, and predisposes trees to disease and injury from stress agents when the 

functional sapwood becomes less that 25% of cross-sectional stem area (Smith, 1990a). A 1968 

Swedish report to the United Nations postulated a decrease in forest growth of ~1.5%/year when 

the Ca:Al ratio in soil water is less than one (Lawrence and Huntington, 1999). The concern that 

acidification and nutrient deficiency may result in forest decline remains today. 

Acidic deposition has been firmly implicated as a causal factor in the northeastern highelevation decline of red spruce (DeHayes et al., 1999). The frequency of freezing injury of red 

4-119
spruce has increased over the past 40 years, a period that coincides with increase emissions of 

sulfur and nitrogen oxides and acidic deposition (DeHayes et al., 1999). Studies indicate that 

there is a significant positive association between cold tolerance and foliar Ca in trees exhibiting 

deficiency in foliar Ca. Most of the calcium in conifer needles is insoluble calcium oxalate and 

pectate crystals, which are of little physiological importance. It is the labile calcium ions in 

equilibrium within the plasma membrane that are of major physiological importance (DeHayes 

et al., 1999). The membrane-associated pool of calcium (mCa), although a relatively small 

fraction of total foliar ion pools, strongly influences the response of cells to changing 

environmental conditions. The plant plasma membrane plays a critical role in mediating cold 

acclimation and low-temperature injury. The leaching of Ca associated with acidic deposition is 

considered to be the result of cation exchange due to exposure to the H+ ion. The studies of 

DeHayes et al. (1999) demonstrated that the direct deposition of acidic deposition on needles 

represents a unique environmental stress, in that it preferentially removes mCa, which is not 

readily replaced in the autumn. They propose that direct acidic deposition on red spruce foliage 

preferentially displaces those Ca ions specifically associated with the plasma membranes of 

mesophyll cells resulting in the reduction of mCa and the destabilizing of plasma membranes 

and depletion of messenger Ca ions. Further, DeHayes et al. (1999) state that their studies raise 

the strong possibility that acid rain alteration of the mCa and membrane integrity is not unique to 

red spruce but has been demonstrated in many other northern temperate forest tree species, 

including: yellow birch (Betula alleghaniensis), white spruce (Picea glaucus), red maple (Acer 

rubrum), eastern white pine (Pinus strobus), and sugar maple (Acer saccharum). Assessments of 

mCa, membrane integrity, and the effects of other secondary stressors have not yet been made 

for these species. 

Seasonal and episodic acidification of surface waters have been observed in the eastern 

United States, Canada, and Europe (Hyer et al., 1995). In the Northeast, the Shenandoah 

National Park in Virginia, and the Great Smoky Mountains, episodic acidification has been 

associated with the nitrate ion (Driscoll et al., 2001; Hyer et al., 1995; Eshleman et al., 1995). 

The short-term acid episodes occur during spring snowmelts and large precipitation events 

(Driscoll et al., 2001). Episodic acidification of surface waters has usually been considered to be 

a transient loss of acid neutralizing capacity associated with snowmelt/rainfall runoff and, as 

such, represents a short-term (hours to weeks) effect considered to be distinguishable from the 

4-120
chronic long-term (years to centuries) changes in acidity. Studies of both episodic and chronic 

acidification of surface waters indicate that acidification can have long-term adverse effects on 

fish populations and result in the decline of species richness and the abundance of zooplankton 

and macroinvertebrates (Driscoll et al., 2001; Eshleman et al., 1995). Nitrogen saturation of 

soils and the slow release of nitrates has been shown to inhibit the recovery of acid sensitive 

systems (Driscoll et al., 2001). The acidification of aquatic ecosystems and effects on aquatic 

biota are discussed in more detail in the EPA document Air Quality Criteria for Nitrogen Oxides 

(U.S. Environmental Protection Agency, 1993). 

NH

Air pollution is not the sole cause of soil change. High rates of acidification are occurring 

in less polluted regions of the western United States and Australia because of internal soil 

processes, such as tree uptake of nitrate and nitrification associated with excessive nitrogen 

fixation (Johnson et al., 1991b). Many studies have shown that acidic deposition is not a 

necessary condition for the presence of extremely acidic soils, as evidenced by their presence in 

unpolluted, even pristine, forests of the northwestern United States and Alaska (Johnson et al., 

1991b). Soil can become acidic when H+

 ions attached to NH4

+

 or HNO3 remain in the soil after 

nitrogen is taken up by plants. For example, Johnson et al. (1982b) found significant reductions 

in exchangeable K+

 over a period of only 14 years in a relatively unpolluted Douglas fir 

Integrated Forest Study (IFS) site in the Washington Cascades. The effects of acid deposition at 

this site were negligible relative to the effects of natural leaching (primarily of carbonic acid) 

and nitrogen tree uptake (Cole and Johnson, 1977). Even in polluted regions, numerous studies 

have shown the importance of tree uptake of NH4

+

 and NO3 

!

 in soil acidification. Binkley et al. 

(1989) attributed the marked acidification (pH decline of 0.3 to 0.8 units and base saturation 

declines of 30 to 80%) of abandoned agricultural soil in South Carolina over a 20-year period to 

4

+

 and NO3 

!

 uptake by a loblolly pine plantation. 

An interesting example of uptake effects on soil acidification is that of Al uptake and 

cycling (Johnson et al., 1991b). Aluminum accumulation in the leaves of coachwood 

(Ceratopetalum apetalum) in Australia has been found to have a major effect on the distribution 

and cycling of base cations (Turner and Kelly, 1981). The presence of C. apetalum as a 

secondary tree layer beneath brush box (Lophostemon confertus) was found to lead to increased 

soil exchangeable Al3+ and decreased soil exchangeable Ca2+ (Turner and Kelly, 1981). The 

4-121
constant addition of aluminum-rich litter fall obviously has had a substantial effect on soil 

acidification, even if base cation uptake is not involved directly. 

Given the potential importance of particulate deposition to the base cation status of forest 

ecosystems, the findings of Driscoll et al. (1989, 2001) and Hedin et al. (1994) are especially 

relevant. Driscoll et al. (1989, 2001) noted a decline in both SO4

2!

 and base cations in both 

atmospheric deposition and stream water over the past two decades at Hubbard Brook 

Watershed, NH. The decline in SO4

2!

 deposition was attributed to a decline in emissions, and 

the decline in stream water SO4

2!

 was attributed to the decline in sulfur deposition. Hedin et al. 

(1994) reported a steep decline in atmospheric base cation concentrations in both Europe and 

North America over the past 10 to 20 years. The reductions in SO2 emissions in Europe and 

North America in recent years have not been accompanied by equivalent declines in net acidity 

related to sulfate in precipitation. These current declines in sulfur deposition have, to varying 

degrees, been offset by declines in base cations and may be contributing “to the increased 

sensitivity of poorly buffered systems.” Analysis of the data from the IFS supports the authors’ 

contention that atmospheric base cation inputs may seriously affect ecosystem processes. 

Johnson et al. (1994) analyzed base cation cycles at the Whiteface Mountain IFS site in detail 

and concluded that Ca losses from the forest floor were much greater than occurred historically, 

based on historical changes in forest floor Ca observed in an earlier study. Further, the authors 

suggested that the difference between historical and current net loss rates of forest floor Ca may 

be caused by sharply reduced atmospheric inputs of Ca after about 1970 and may be exacerbated 

by sulfate leaching (Johnson et al., 1994). 

The Ca:Al molar ratio has been suggested to be a valuable ecological indicator of an 

approximate threshold beyond which the risk of forest injury from Al stress and nutrient 

imbalances increases (Cronan and Grigal, 1995). The Ca:Al ratio also can be used as an 

indicator to assess forest ecosystem changes over time in response to acidic deposition, forest 

harvesting, or other process that contribute to acid soil infertility. This ratio, however, may not 

be a reliable indicator of stress in areas with both high atmospheric deposition of ammonium and 

magnesium deficiency via antagonism involving ammonium rather than aluminum and in areas 

with soil solutions with calcium concentrations greater than 500 μmol/L (National Science and 

Technology Council, 1998). Cronan and Grigal (1995), based on a review of the literature, have 

made the following estimates for determining the effects of acidic deposition on tree growth or 

4-122
nutrition: (a) forests have a 50% risk of adverse effects if the Ca:Al ration is 1.0; (b) the risk is 

75% if the ratio is 0.5:1.0; and (c) the risk approaches 100% if the ratio is 0.2:1.0. The Ca:Al 

ratio of soil solution provides only an index of the potential for Al stress. Cronan and Grigal 

(1995) stated that the overall uncertainty of the Ca:Al ratio associated with a given probability 

ratio is considered to be approximately ±50%. Determination of thresholds for potential forest 

effects requires the use of four successive measurement endpoints in the soil, soil solution, and 

plant tissue: (a) soil base saturation less than 15% of effective cation exchange capacity; (b) soil 

solution Ca:Al molar ratio less than 1.0:1.0 for 50% risk; (c) fine roots tissue Ca:Al molar ratio 

less than 0.2:1.0 for 50% risk; and (d) foliar tissue Ca:Al molar ratio less than 12.5:1.0 for 50% 

risk. The application of the Ca:Al ratio indicator for assessment and monitoring of forest health 

risks has been recommended for sites or in geographic regions where the soil base saturation is 

< 15%. 

Critical Loads. The critical loads framework for assessing the effects of atmospheric 

deposition originated in Europe where the concept has generally been accepted as the basis for 

abatement strategies to reduce or prevent injury to the functioning and vitality of forest 

ecosystems caused by long-range transboundary chronic acidic deposition (Løkke et al., 1996). 

The critical load has been defined as a “quantitative estimate of an exposure to one or more 

pollutants below which significant harmful effects on specified sensitive elements of the 

environment do not occur according to present knowledge” (Løkke et al., 1996). This concept is 

useful for estimating the amounts of pollutants that sensitive ecosystems can absorb on a 

sustained basis without experiencing measurable degradation. 

The response to pollutant deposition of an ecosystem is a direct function of the level of 

sensitivity of the ecosystem to the pollutant and its capability to ameliorate change. The 

estimation of ecosystem critical loads requires an understanding of how an ecosystem will 

respond to different loading rates in the long term. The approach can be of special value for 

ecosystems receiving chronic deposition of pollutants such as nitrogen and sulfur. A program 

was designed to develop and evaluate a framework for setting critical loads of nitrogen and 

sulfur in the United States in 1989 (Strickland et al., 1993). A flexible six-step approach has 

been outlined for use with the critical load framework (Figure 4-19). These steps are 

(1) selection of ecosystem components, indicators, and characterization of the resource; 

4-123 
Figure 4-19. Key elements of proposed framework for determining critical loads for 

nitrogen and sulfur in the United States. 

Source: Strickland et al. (1993). 

(2) definition of the functional subregions; (3) characterization of deposition within each of the 

subregions; (4) definition of an assessment endpoint; (5) selection and application of models; 

and (6) mapping of projected ecosystem responses. The approach permits comparability in 

ecosystem characteristic and data availability (Strickland et al., 1993). 

Ecological endpoints or indicators are measurable characteristics related to the structure, 

composition, or functioning of ecological systems (i.e., indicators of condition). One or more 

measurable endpoints are associated with each of the EEA elements listed in Table 4-1. These 

assessment endpoints represent a formal expression of the environmental value that is to be 

protected. If the assessment endpoint is to be used as a regulatory limit, it should be socially 

relevant. Selection of a specific ecosystem for study will depend on the severity of the problem 

of concern for a region. Time scales of response must be considered in selecting and evaluating 

ecosystem response(s) to changes in atmospheric deposition. Responses of aquatic ecosystems 

4-124
to depositions can occur quickly. Surface water acidification associated with nitrate leaching 

should respond to decreases in nitrogen loading in a short period of time. However, changes in 

growth responses of vegetation resulting from soil nutrient imbalances may require years or 

decades to detect. The focus of concern should be on the populations within an ecosystem that 

are sensitive to nitrogen and sulfur deposition (Hunsaker et al., 1993). 

Biogeochemical indicators for monitoring forest nitrogen status have been proposed by 

Fenn and Poth (1998). Because nitrogen is a major constituent of all forms of life and is cycled 

through a complex web of processes involving many biotic and abiotic mechanisms, evaluating 

forest nitrogen status is a challenge. Indicators of ecosystems at risk of nitrogen saturation 

should include those that can be identified when nitrogen availability exceeds biotic demand. 

Such indicators typically should monitor parameters that are normally at background or low 

levels in nitrogen-limited systems and should be those that commonly respond to excess nitrogen 

in a wide range of ecosystems (Fenn and Poth, 1998). Such indicators include foliar nitrogen, 

nutrient ratios (N:P, N:cation), foliar nitrate, foliar *15 N, arginine concentration, soil C:N ratio, 

NO3 

!

 in soil extracts or in soil solution, and flux rates of nitrogenous trace gasses from soil 

(Fenn et al., 1998). The cardinal indicator or manifestation of nitrogen saturation in all 

! ecosystem types, including California forests and chaparral, is increased and prolonged NO3 

loss below the main rooting zone and in stream water. Seasonal patterns of stream water nitrate 

concentrations are especially good indicators of watershed nitrogen status (Fenn and Poth, 

1998). 

In Europe, the elements used in the critical load concept include: a biological indicator, 

a chemical criterion, and a critical value. The biological indicator is the organism used to 

indicate the status of the receptor ecosystem; the chemical criterion is the parameter that results 

in harm to the biological indicator; and the critical value is the value of the chemical criterion 

below which no significant harmful response occurs to the biological indicator (Løkke et al., 

1996). Trees, and sometimes other plants, are used as biological indicators in the case of critical 

loads for forests. The critical load calculation using the current methodology is essentially an 

acidity/alkalinity mass balance calculation. The chemical criterion must be expressible in terms 

of alkalinity. Initially, the Ca:Al ratio was used; but, recently, the (Ca+Mg+K):Al ratio has been 

used (Løkke et al., 1996). 

4-125
Ideally, changes in acidic deposition should result in changes in the status of the biological 

indicator used in the critical load calculation. However, the biological indicator is the integrated 

response to a number of different stresses. Furthermore, some other organisms are more 

sensitive to acid deposition than trees. At high concentrations, Al3+ is known to be toxic to 

plants, inhibiting root growth and, ultimately, plant growth and performance (Løkke et al., 1996; 

National Science and Technology Council, 1998). Sensitivity to Al varies considerably between 

species and within species because of changes in nutritional demands and physiological status 

that are related to age and climate. Experiments have shown that there are large variations in Al 

sensitivity, even among ecotypes. 

Mycorrhizal fungi have been suggested as possible biological indicators by Løkke et al. 

(1996) because they are intimately associated with tree roots, depend on plant assimilates, and 

play an essential role in plant nutrient uptake, thereby influencing the ability of their host plants 

to tolerate different anthropogenically generated stresses. Mycorrhizas and fine roots are an 

extremely dynamic component of below-ground ecosystems and can respond rapidly to stress. 

They have a relatively short lifespan, and their turnover appears to be strongly controlled by 

environmental factors. Changes in mycorrhizal species composition, or the loss of dominant 

mycorrhizal species in areas where diversity is already low, may lead to increased susceptibility 

of plants to stress (Løkke et al., 1996). Stress affects the total amount of carbon fixed by plants 

and modifies carbon allocation to biomass, symbionts, and secondary metabolites. The 

physiology of carbon allocation has also been suggested as an indicator of anthropogenic stress 

(Andersen and Rygiewicz, 1991). Because mycorrhizal fungi are dependent for their growth on 

the supply of assimilates from the host plants, stresses that shift the allocation of carbon reserves 

to the production of new leaves at the expense of supporting tissues will be reflected rapidly in 

decreased fine root and mycorrhizal biomass (Winner and Atkinson, 1986). Decreased carbon 

allocation to roots also affects soil carbon and rhizosphere organisms. Soil dwelling animals are 

important for decomposition, soil aeration, and nutrient redistribution in the soil. They 

contribute to decomposition and nutrient availability mainly by increasing the accessibility of 

dead plant material to microorganisms. Earthworms decrease in abundance, and in species 

number, in acidified soils (Løkke et al., 1996). 

4-126
Effects of Wet and Dry Deposition on Biogeochemical Cycling — the Integrated Forest 

Study. The Integrated Forest Study (IFS; Johnson and Lindberg, 1992a) has provided the most 

extensive data set available on wet and dry deposition, as well as on deposition effects on the 

cycling of elements in forest ecosystems. The overall patterns of deposition and cycling have 

been summarized by Johnson and Lindberg (1992a), and the reader is referred to that reference 

for details. The following is a summary of particulate deposition, total deposition, and leaching 

in the IFS sites. 

Particulate deposition in the IFS was separated at the 2-μm level; however, the decision 

was made to include total particulate deposition in this analysis and may include the deposition 

of particles larger than 10 μm. Particulate deposition contributes considerably to the total impact 

of base cations to most of the IFS sites. On average, particulate deposition contributes 47% to 

total Ca deposition (range: 4 to 88%), 49% of total K deposition (range: 7 to 77%), 41% to total 

Mg deposition (range: 20 to 88%), 36% to total Na deposition (range: 11 to 63%), and 43% to 

total base cation deposition (range: 16 to 62%). Of total particulate deposition, the vast majority 

(> 90%) is > 2 μm in size. 

Figures 4-20 through 4-23 summarize the deposition and leaching of Ca, Mg, K, and total 

base cations for the IFS sites. As noted in the original synthesis (Johnson and Lindberg, 1992a), 

measurements indicated annual gains of base cations for some sites (i.e., total deposition > 

leaching), some losses (total deposition < leaching) at others, and some approximately in 

balance. Not all cations follow the same pattern at each site. For example, a net accumulation of 

Ca occurs at the Coweeta, TN, Durham (Duke), NC and Bradford Forest, Fl sites (Figure 4-20), 

whereas accumulation of K was noted at the Duke, Florida, Thompson, WA, Huntington Forest, 

NY, and White Face Mountain, NY sites (Figure 4-22). Magnesium accumulated only at the 

Florida site (Figure 4-21), and only at the Florida site is there a clear net accumulation of total 

base cations (Figure 4-23). 

As noted previously, the factors affecting net Ca accumulation or loss include the 

soil-exchangeable cation composition; base cation deposition rate; the total leaching pressure 

because of atmospheric sulfur and nitrogen inputs, as well as natural (carbonic and organic) 

acids; and biological demand (especially for potassium). At the Florida site, which has a very 

cation-poor, sandy soil (derived from marine sand), the combination of all these factors leads to 

net base cation accumulation from atmospheric deposition (Johnson and Lindberg, 1992a). 

4-127
Figure 4-20. Calcium deposition in > 2-μm particles, < 2-μm particles, and wet forms 

(upper bars) and leaching (lower bars) in the Integrated Forest Study sites. 

CP = Pinus strobus, Coweeta, TN; DL = Pinus taeda, Durham (Duke), NC; 

GS = Pinus taeda, B. F. Grant Forest, GA; LP = Pinus taeda, Oak Ridge, TN; 

FS = Pinus eliottii, Bradford Forest, FL; DF = Psuedotsuga menziesii, 

Thompson, WA; RA = Alnus rubra, Thompson WA; NS = Picea abies, 

Nordmoen, Norway; HF = northern hardwood, Huntington Forest, NY; 

MS = Picea rubens, Howland, ME; WF = Picea rubens, Whiteface Mountain, 

NY; and ST = Picea rubens, Clingman’s Dome, NC. 

SO

The site showing the greatest net base cation losses, the red alder stand in Washington state, 

is under extreme leaching pressure by nitrate produced by excessive fixation by that species 

(Van Miegroet and Cole, 1984). In the red spruce site in the Smokies, the combined effects of 

4

2!

 and NO3 

!

 leaching are even greater than in the red alder site (Figure 4-24), but a 

considerable proportion of the cations leached from this extremely acid soil consist of H+

 and 

Al3+ rather than of base cations (Johnson and Lindberg, 1992a). Thus, the red spruce site in the 

Smokies is approximately in balance with respect to calcium and total base cations, despite the 

very high leaching pressure at this site (Figures 4-20 and 4-23). 

4-128
Figure 4-21. Magnesium deposition in > 2-μm particles, < 2-μm particles, and wet forms 

(upper bars) and leaching (lower bars) in the Integrated Forest Study sites. 

See Figure 4-20 for site abbreviations. 

Figure 4-22. Potassium deposition in > 2-μm particles, < 2-μm particles, and wet forms 

(upper bars) and leaching (lower bars) in the Integrated Forest Study sites. 

See Figure 4-20 for site abbreviations. 

4-129
Figure 4-23. Base cation deposition in > 2-μm particles, < 2-μm particles, and wet forms 

(upper bars) and leaching (lower bars) in the Integrated Forest Study sites. 

See Figure 4-20 for site abbreviation. 

The relative importance of particulate-base-cation deposition varies widely with site and 

cation and is not always related to the total deposition rate. The proportion of Ca deposition in 

particulate form ranges from a low of 4% at the Whiteface Mountain site to a high of 88% at the 

Maine site (Figure 4-20). The proportion of K deposition as particles ranges from 7% at the 

Smokies site to 77% at the Coweeta site (Figure 4-22). The proportion of total base cation 

deposition ranges from 16% at the Whiteface site to 62% at the Maine site (Figure 4-23). 

Overall, particulate deposition at the site in Maine accounted for the greatest proportion of Ca, 

Mg, K, and base cation deposition (88, 88, 57, and 62%, respectively) even though total 

deposition was relatively low. At some sites, the relative importance of particulate deposition 

varies considerably by cation. At the Whiteface Mountain site, particulate deposition accounts 

for 4, 20, and 40% of Ca, Mg, and K deposition, respectively. At the red spruce site in the 

Smokies, particulate deposition accounts for 46, 26%, 7% of Ca, Mg, and K deposition, 

respectively. 

4-130
! Figure 4-24. Total cation leaching (total height of bar) balanced by SO4

2!

 and NO3 

estimated from particulate deposition (assuming no ecosystem retention, 

particulate sulfur and nitrogen) and by other sources (both deposition and 

internal) of SO4

2!

 and NO3 

!

 (other sulfur and nitrogen sources) and by 

other anions in the Integrated Forest Study sites. See Figure 4-20 for 

site abbreviations. 

! As indicated in the IFS synthesis, SO4

2!

 and NO3 leaching often are dominated by 

atmospheric sulfur and nitrogen (Johnson and Lindberg, 1992a). The exceptions to this are in 

cases where natural nitrogen inputs are high (i.e.,the nitrogen-fixing red alder stand), as are 

NO3 

!

-leaching rates even though nitrogen deposition is low, and where soils adsorb much of 

the atmospherically deposited SO4

2!

 thus reducing SO4

2!

 leaching compared to atmospheric 

sulfur input. 

Sulfate and NO3 

!

 leaching have a major effect on cation leaching in many of the IFS sites 

(Johnson and Lindberg, 1992a). Figure 4-24 shows the total cation leaching rates of the IFS sites 

and the degree to which cation leaching is balanced by SO4

2!

 + NO3 

!

 deposition. The SO4

2!

 and 

NO3 

!

 fluxes are subdivided further into that proportion potentially derived from particulate 

sulfur and nitrogen deposition (assuming no ecosystem retention, a maximum effect) and other 

sulfur and nitrogen sources (i.e., wet and gaseous deposition, internal production). 

4-131
As noted in the IFS synthesis, total SO4

2!

 and NO3 

!

 inputs account for a large proportion 

(28 to 88%) of total cation leaching in most sites. The exception is the Georgia loblolly pine site 

where there were high rates of HCO3 

!

 and Cl! leaching (Johnson and Lindberg, 1992a). The role 

of particulate sulfur and nitrogen deposition in this leaching is generally very small (< 10%), 

however, even if it is assumed that there is no ecosystem sulfur or nitrogen retention. 

It was previously noted in this chapter that the contribution of particles to total deposition 

of nitrogen and sulfur at the IFS sites is lower than that for base cations. On average, particulate 

deposition contributes 18% to total nitrogen deposition (range: 1 to 33%) and 17% to total 

sulfur deposition (range: 1 to 30%). Particulate deposition contributes only a small amount to 

total H+

 deposition (average = 1%; range: 0 to 2%). (It should be noted, however, 

that particulate H+

 deposition in the > 2 μm fraction was not measured.) 

H

Based on the IFS data, it appears that particulate deposition has a greater effect on base 

cation inputs to soils than on base cation losses associated with the inputs of sulfur, nitrogen, and 

+

. It cannot be determined what fraction of the mass of these particles are < 10 μm, but only a 

very small fraction is < 2 μm. These inputs of base cations have considerable significance, not 

only to the base cation status of these ecosystems, but also to the potential of incoming 

precipitation to acidify or alkalize the soils in these ecosystems. As noted above, the potential of 

precipitation to acidify or alkalize soils depends on the ratio of base cations to H+

 in deposition, 

rather than simply on the input of H+

 alone. In the case of calcium, the term “lime potential” has 

been applied to describe this ratio; the principle is the same with respect to Mg and K. Sodium 

is a rather special case, in that it is a poorly absorbed cation and leaching tends to balance input 

over a relatively short term. 

Net balances of base cations tell only part of the story as to potential effects on soils; these 

net losses or gains must be placed in the perspective of the soil pool size. One way to express 

this perspective is to compare soil pool sizes with the net balances. This comparison is made for 

exchangeable pools and net balances for a 25-year period as shown in Figures 4-25 to 4-27. 

It is readily seen that the net leaching losses of cations pose no threat in terms of depleting 

soil-exchangeable Ca 2+, K+

, or Mg+2 within 25 years at the Coweeta, Duke, Georgia, Oak Ridge, 

or Douglas-fir sites. However, there is a potential for significant depletion at the red alder, 

Whiteface Mountain, and Smokies red spruce sites. 

4-132
Figure 4-25. Soil exchangeable Ca2+ pools and net annual export of Ca2+ (deposition minus 

leaching times 25 years) in the Integrated Forest Study sites. See Figure 4-20 

for site abbreviations. 

Figure 4-26. Soil exchangeable Mg2+ pools and net annual export of Mg2+ (deposition 

minus leaching times 25 years) in the Integrated Forest Study sites. 

See Figure 4-20 for site abbreviations. 

4-133
Figure 4-27. Soil exchangeable K+

 pools and net annual export of K+ (deposition minus 

leaching times 25 years) in the Integrated Forest Study sites. See Figure 4-20 

for site abbreviations. 

The range of values for soil-exchangeable turnover is very large, reflecting variations in 

both the size of the exchangeable pool and the net balance of the system. Soils with the highest 

turnover rates are those most likely to experience changes in the shortest time interval, other 

things being equal. Thus, the Whiteface Mountains, Smokies, and Maine red spruce sites, the 

Thompson red alder site, and the Huntington Forest northern hardwood site appear to be most 

sensitive to change. The actual rates, directions, and magnitudes of change that may occur in 

these soils (if any) will depend on weathering inputs and vegetation outputs, in addition to 

deposition and leaching. It is noteworthy that each of the sites listed above as sensitive has a 

large store of weatherable minerals, whereas many of the other soils (with larger exchangeable 

cation reserves) have a small store of weatherable minerals (e.g., Coweeta white pine, 

Duke loblolly pine, Georgia loblolly pine, and Oak Ridge loblolly pine) (Johnson and Lindberg, 

1992a; April and Newton, 1992). 

Base cation inputs are especially important to the Smokies red spruce site because of 

potential aluminum toxicity and calcium and magnesium deficiencies. Johnson et al. (1991a) 

4-134
found that soil-solution Al concentrations occasionally reached levels found to inhibit Ca uptake 

and cause changes in root morphology in solution culture studies of red spruce (Raynal et al., 

1990). In a follow-up study, Van Miegroet et al. (1993) found a slight, but significant, growth 

response to Ca and Mg fertilizer in red spruce saplings near the Smokies red spruce site. Joslin 

et al. (1992) reviewed soil and solution characteristics of red spruce in the southern 

Appalachians, and it appears that the IFS site is rather typical of observations for that area. 

Wesselink et al. (1995) reported on the complicated interactions among changing 

deposition and soils at a beech and spruce forest in Solbing, Germany (including repeated 

sampling of soil exchangeable base cation pools) from 1969 to 1991 and compared these results 

with those of a simulation model. They identified three basic stages of change in this ecosystem. 

During Stage I, there was increased deposition of sulfur and constant deposition of base cations, 

causing increased base cation leaching and reduced base saturation in the soils. During Stage II, 

sulfur deposition is reduced; soil solution SO4

2!

 and base cation leaching decline accordingly, 

but base saturation continues to decrease. During Stage III, two alternative scenarios are 

introduced: (a) sulfur deposition continues to decline, whereas base cation deposition says 

constant; or (b) both sulfur and base cation deposition decline. Under Stage III-a, sulfate and 

base cation leaching continue to decline, and base saturation begins to increase as base cations 

displace exchangeable Al and cause it to transfer to the gibbsite pool. Under Stage III-b, this 

recovery in base saturation is overridden by the reduction in base cation deposition. 

The IFS project, for the first time, accurately quantified atmospheric deposition inputs to 

nutrient cycles using state-of-the-art techniques to measure wet and dry deposition. The main 

aim of the project was to determine effects of atmospheric deposition on nutrient status of a 

variety of forest ecosystems and to determine if these effects are in any way related to current or 

potential forest decline. Acidic deposition appears to be having a significant effect on nutrient 

cycling in most of the forest ecosystems studied in the IFS project. The exceptions were the 

relatively unpolluted Douglas fir, red alder, and Findley Lakes sites in Washington state. The 

nature of the effects, however, varies from one location to another (Johnson,1992). In all but the 

relatively unpolluted Washington sites, atmospheric deposition was having a significant (often 

overwhelming) effect on cation leaching from the soils. In general, nutrient budget data from 

IFS and the literature suggest that the susceptibility of southeastern sites to base cation depletion 

4-135
from soils and the development of cation deficiencies by that mechanism appears to be greater 

than in northern sites (Johnson, 1992). 

Atmospheric deposition may have significantly affected the nutrient status of some IFS 

sites through the mobilization of Al. Soil-solution Al levels in the Smokies sites approach and 

sometimes exceed levels noted to impede cation uptake in solution culture studies. It is therefore 

possible that the rates of base cation uptake and cycling in these sites have been reduced by soil 

solution Al levels. To the extent that atmospheric deposition has contributed to these elevated 

soil-solution Al levels, it has likely caused a reduction in base cation uptake and cycling rates at 

these sites. Nitrate and sulfate are the dominant anions in the Smokies sites, and nitrate pulses 

are the major cause of Al pulses in soil solution (Johnson, 1992). The connection between Al 

mobilization and forest decline is not clear. The decline in red spruce has been more severe in 

the Northeast than in the Southeast; yet all evidence indicates that Al mobilization is most 

pronounced in the southern Appalachians. However, the Whiteface Mountain site which was 

selected for study because it was in a state of decline had soil solution levels lower than in the 

Smokies, which are not in a visibly obvious state of decline (there was no dieback other than the 

fir killed by the balsam wooly adelgid and no needle yellowing). Thus, Al mobilization 

continues to be worthy of further study (Johnson, 1992). 

The simple calculations shown above give some idea of the importance of particulate 

deposition in these forest ecosystems, but they cannot account for the numerous potential 

feedbacks between vegetation and soils nor for the dynamics through time that can influence the 

ultimate response. One way to examine some of these interactions and dynamics is to use 

simulation modeling. The nutrient cycling model (NuCM) has been developed specifically for 

this purpose and has been used to explore the effects of atmospheric deposition, fertilization, and 

harvesting on some of the IFS sites (Johnson et al., 1993). The NuCM model is a stand-level 

model that incorporates all major nutrient cycling processes (uptake, translocation, leaching, 

weathering, organic matter decay, and accumulation). 

Johnson et al. (1999) used the NuCM model to simulate the effects of reduced S, N, and 

base cation deposition on nutrient pools, fluxes, soil, and soil solution chemistry in two 

contrasting southern Appalachian forest ecosystems: the red spruce and Coweeta hardwood 

sites from the IFS project. The scenarios chosen for these simulations included no change; 50% 

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N and S deposition; 50% base cation deposition; and 50% N, S, and base cation deposition (50% 

N, S, base cation). The NuCM simulations suggested that, for the extremely acid red spruce site, 

S and N deposition is the major factor affecting soil-solution Al concentrations and base cation 

deposition is the major factor affecting soil solution base cation concentrations. The effects of 

S and N deposition were largely through changes in soil solution SO4

2!

 and NO3 

!

 and, 

consequently, mineral acid anion (MAA) concentrations rather than through changes in soils. 

This is illustrated in Figures 4-28a,b and 4-29a,b, which show simulated soil-solution mineralacid anions, base cations, Al, and soil base saturation in the B horizon in the red spruce site. 

The 50% S and N scenario caused reductions in soil solution SO4

2!

, NO3 

!

 and, therefore, 

MAA concentrations, as expected. This, in turn, caused short-term reductions in base cation 

concentrations. However, by the end of the 24-year simulation, base cations in the 50% S, 

N scenario were nearly as high as in the no-change scenario because base saturation had 

increased and the proportion of cations as Al decreased. The 50% base cation scenario had 

virtually no effect on soil solution SO4

2!

, NO3 

!

 and, therefore, MAA concentrations, as expected, 

but did cause a long-term reduction in base cation concentrations. This was caused by a longterm reduction in base saturation (Figure 4-29a,b). Thus, the effects of base cation deposition 

were solely through changes in soils rather than through changes in soil solution MAA, as 

postulated by Driscoll et al. (1989). In the less acidic Coweeta soil, base saturation was high and 

little affected by the scenario cited above; Al was unimportant; and S and N deposition had a 

much greater effect than base cation deposition in all respects (Figure 4-30a,b). 

In summary, Johnson et al. (1999) found that the results of the red spruce simulations, in 

part, supported the hypothesis of Driscoll et al. (1989) in that: base cation deposition can have a 

major effect on base cation leaching through time in an extremely acid system. This effect 

occurred through changes in the soil exchanger and not through changes in soil solution MAA 

concentration. On the other hand, S and N deposition had a major effect on Al leaching at the 

Noland Divide site. This occurred primarily because of changes in soil solution MAA 

concentration. At the less acidic Coweeta site, base cation deposition had a minor effect on soils 

and soil solutions; whereas S and N deposition had delayed but major effects on base cation 

leaching because of changes in SO4

2!

 and MAA concentrations. 

4-137
 4-138 

Figure 4-28a. Simulated soil solution mineral acid anions in the red spruce site with no change, 50% 

S deposition, and 50% base cation deposition. 

Source: Redrawn from Johnson et al. (1999).
 4-139 

Figure 4-28b. Simulated soil solution base cations in the red spruce site with no change, 50% N and 

and 50% base cation deposition. 

Source: Redrawn from Johnson et al. (1999).
 4-140 

 Figure 4-29a. Simulated soil solution Al in the red spruce site with no change, 50% N and S deposi

50% base cation deposition. 

Source: Redrawn from Johnson et al. (1999).
 4-141 

Figure 4-29b. Simulated soil solution soil base saturation in the red spruce site with no change, 50% 

and S deposition, and 50% base cation deposition. 

Source: Redrawn from Johnson et al. (1999).
 4-142 

Figure 4-30a. Simulated soil solution mineral acid anions in the Coweeta site with no change, 50% 

S deposition, and 50% base cation deposition. 

Source: Redrawn from Johnson et al. (1999).
4-143 

Figure 4-30b. Simulated soil solution base cations in the Coweeta site with no change, 50% N and 

and 50% base cation deposition. 

Source: Redrawn from Johnson et al. (1999). 
Effects of Trace Elements 

Trace metals are natural elements that are ubiquitous in small (trace) amounts in soils, 

ground water, and vegetation. Many are essential elements required for growth by plants and 

animals as micronutrients. Naturally occurring surface mineralizations can produce metal 

concentrations in soils and vegetation that are as high, or higher, than those in the air and 

deposited near man-made sources (Freedman and Hutchinson, 1981). The occurrence and 

concentration of trace metals in any ecosystem component depend on the sources of the metal 

(i.e., via the soil or as a particulate). Even when air pollution is the primary source, continued 

deposition can result in the accumulation of trace metals in the soil (Martin and Coughtrey, 

1981). Many metals are deposited and bound into soils by chemical processes and are not 

available to plants (Saunders and Godzik, 1986). 

When aerial deposition is the primary source of metal particles, both the chemical form and 

particle size deposited determine the heavy metal concentration in the various ecosystem 

components (Martin and Coughtrey, 1981). Human activities introduce heavy metals into the 

atmosphere and have resulted in the deposition of antimony, cadmium, chromium, copper, lead, 

molybdenum, nickel, silver, tin, vanadium, and zinc (Smith, 1990c). Extensive evidence 

indicates that heavy metals deposited from the atmosphere to forests accumulate either in the 

richly organic forest floor, or in the soil layers immediately below the floor, areas where the 

interaction of roots and soil is greatest. The greater the depth of soil, the lower the metal 

concentration. The accumulation of metal in the soil layers where the biological activity is 

greatest, therefore, has the potential to be toxic to roots and soil organisms and to interfere with 

nutrient cycling (Smith, 1990c). The shallow-rooted plant species are most likely to take up 

metals from the soil (Martin and Coughtrey, 1981). Though all metals can be directly toxic at 

high levels, only copper, nickel, and zinc have been documented as frequently being toxic. 

Toxicity due to cadmium, cobalt, and lead has been seen only under unusual conditions (Smith, 

1990c). Exposures to the above metals at lower concentrations have the potential, over the long 

term, to interfere with the nutrient-cycling processes when they affect mycorrhizal function. 

Biological accumulation of metals through the plant-herbivore and litter-detrivore chains 

can occur. A study of Cd, Pb, and Zn concentrations in earthworms suggested that Cd and Zn 

were bioaccumulated in earthworms, but Pb was not. Studies indicate that heavy metal 

deposition onto the soil, via food chain accumulation, can lead to excessive levels and toxic 

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effects in certain animals. Cadmium appears to be relatively mobile within terrestrial food 

chains; however, the subsequent mobility of any metal after it is ingested by a herbivorous 

animal depends on the site of accumulation within body tissues. Although food chain 

accumulation of a metal may not in itself cause death, it can reduce the breeding potential in a 

population (Martin and Coughtrey, 1981). 

Case studies have revealed that the deposition of Cu and Zn particles around a brassworks 

can result in an accumulation of incompletely decomposed litter. In one study, litter 

accumulation was reported up to 7.4 km from the stack of a primary smelter in southeastern 

Missouri. Similar results were reported around a metal smelter at Avonmouth, England. In the 

latter case, litter accumulation was associated closely with concentrations specifically of Cd, as 

well as with those of Pb, Cu, and Zn (Martin and Coughtrey, 1981). Accumulations of metals 

emitted in PM were also reported in soil litter close to a metal smelter at Palmerton, PA in 1975 

and 1978. The continued presence of Cd, Pb, Zn, and Cu in the upper soil horizons (layers) were 

observed 6 years after the smelter terminated operation in 1980. Metal levels were highest near 

the smelter. Experimental data (using mesh bags containing litter) supports the hypothesis that 

reduced decomposition occurs close to heavy metal sources. 

Accumulation of heavy metals in litter presents the greatest potential for interference with 

nutrient cycling. Microorganisms are essential for the decomposition of organic matter and soil 

fertility. Toxic effects on the microflora can be caused by Zn, Cd, and Cu. Addition of a few 

mg/kg of soil of Zn can inhibit the more sensitive microbial processes (van Beelen and Doelman, 

1997). Experiments by Kandeler et al. (1996) indicated that microbial biomass and enzyme 

activities decreased with increasing heavy metal concentrations. The amount of decrease varied 

among the enzymes, with those involved in carbon cycling being least affected, the activities of 

the enzymes involved in the cycling of N, P, and S (especially arylsulfatase and phosphatase) 

were dramatically affected. 

Metal accumulation in litter can be found mainly around brass works and Pb and Zn 

smelters. Invertebrates inhabiting soil litter may also accumulate metals. Earthworms from 

roadsides were shown to contain elevated concentrations of Cd, Ni, Pb, and Zn; but, interference 

with earthworm activity was not cited (Martin and Coughtrey, 1981). It has been shown, 

however, that when soils are acidic, earthworm abundance decreases, and bioaccumulation of 

metals from soil may increase exponentially with decreasing pH (Løkke et al, 1996). Organisms 

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that feed on earthworms living in soils with elevated levels of Cd, Ni, Pb, and Zn for extended 

periods may accumulate Pb and Zn to toxic levels (Martin and Coughtrey, 1981). Increased 

concentrations of heavy metals have been found in a variety of small mammals living in areas 

with elevated heavy metal concentrations in the soils. Furthermore, increased amounts of metal 

in body tissues were seen in both amphibians and mammals. Levels of Cd in the kidneys and 

liver of white-tailed deer (Odocoileus virginaus) were five times higher at Palmerton, PA than in 

those from 180 km southwest (downwind). The abnormal amounts of metal in the tissues of 

terrestrial vertebrates and the absence or low abundance of wildlife at Palmerton indicated that 

ecological processes within 5 km of the Zn smelter continued to be markedly influenced even 

6 years after its closing (Storm et al., 1994). 

Studies by Babich and Stotsky (1978) support the concept that increased accumulation of 

litter in metal-contaminated areas is due to the effects on the microorganismal populations. 

Cadmium toxicity to microbial populations was observed to decrease and prolong logarithmic 

rates of microbial population increase, to reduce microbial respiration and fungal spore 

formation and germination, to inhibit bacterial transformation, and to induce abnormal 

morphologies. Smith (1991) reported the effects of Cd, Cu, Ni, and Zn on the symbiotic activity 

of fungi, bacteria, and actinomycetes. The formation of mycorrhizae by Glomus mosseae with 

onions was reduced when Zn, Cu, Ni, and Cd was added to the soil. The relationship of the 

fungus with white clover, however, was not changed. It was suggested that the effect of heavy 

metals on vesicular-arbuscular mycorrhizal fungi will vary from host to host (Gildon and Tinker, 

1983). Studies with ericoid plants indicated that, in addition to Calluna vulgaris, mycorrhizae 

also protect Vaccinium macrocarpa and Rhodendron ponticum from heavy metals (Bradley 

et al., 1981). Heavy metals tend to accumulate in the roots, thereby lessening shoot toxicity. 

The effects of sulfur deposition on litter decomposition in the vicinity of smelters must also 

be considered. Metal smelters emit SO2 as well as heavy metals. Altered litter decomposition 

rates have been well documented near SO2 sources (Prescott and Parkinson, 1985). The 

presence of sulfur in litter has been associated with reduced microbial activity (Bewley and 

Parkinson, 1984). In addition, the effects of sulfur on the symbiotic activity of fungi, bacteria, 

and actinomycetes were reported by Smith (1990d). 

The potential pathways of accumulation of trace metals in terrestrial ecosystems, as well as 

the possible consequences of trace metal deposition on ecosystem functions, is summarized in 

4-146
Figure 4-31. The generalized trophic levels found in an ecosystem and the various physiological 

and biological processes that could be affected by trace metals are shown in the figure. 

Reduction in physiological processes can affect productivity, fecundity, and mortality (Martin 

and Coughtrey, 1981). Therefore, any effects on the structure and function of an ecosystem are 

likely to occur through the soil and litter (Tyler, 1972). 

Figure 4-31. Relationship of plant nutrients and trace metals with vegetation. 

Compartments (roman numerals) represent potential storage sites; 

whereas arrows (arabic numerals) represent potential transfer routes. 

Certain species of plants are tolerant of metal-contaminated soils (e.g., soils from mining 

activities) (Antonovics et al., 1971). Certain species of plants also have been used as 

bioindicators of metals (e.g., Astragalus is an accumulator of selenium). The sources of both 

4-147
macroelements and trace metals in the soil of the Botanical Garden of the town of Wroclow, 

Poland, were determined by measuring the concentrations of the metals in Rhododendron 

catawbiense, Ilex aquifolium, and Mahonia aquifolium growing in a garden and comparing the 

results with the same plant species growing in two botanical gardens in nonpolluted areas. 

Air pollution deposition was determined to be the source of metals in plants rather than soils 

contamination (Samecka-Cymerman and Kempers, 1999). 

The effects of Pb in ecosystems are discussed in the EPA document Air Quality Criteria 

for Lead (U.S. Environmental Protection Agency, 1986). Studies have shown that there is cause 

for concern in three areas in which ecosystems may be extremely sensitive to Pb: (1) delay of 

decomposition because of inhibition by Pb of the activity of some decomposer microorganisms 

and invertebrates, (2) subtle shifts toward Pb-tolerant plant populations, and (3) Pb in the soil 

and on the surfaces of vegetation where it may circumvent the processes of biopurification. The 

problems cited above arise because Pb is deposited on the surface of vegetation, accumulates in 

the soil, and is not removed by the surface and ground water of the ecosystem (U.S. 

Environmental Protection Agency, 1986). 

4.2.4 Urban Ecosystems 

Humans dominate Earth’s ecosystems. Evidence accumulating from anthropological and 

archeological research indicates that human influence has been pervasive for thousands of years 

(Grimm et al., 2000). Major human effects on the environment probably began as early as 

12,000 to 15,000 years ago and continue to be a major influence on all natural ecosystems. 

Human activities have been more intense in cities, suburbs, exurbs and in supporting hinterlands 

(Grimm et al., 2000). Interest has increased recently in the study of urban ecological systems. 

Vitousek et al. (1997) pointed out that understanding a human-dominated planet requires 

that the human dimensions of global change; that is, the social, cultural, and other drivers of 

human actions need to be included in ecological analyses. Therefore, humans must be integrated 

into models for more complete understanding of extant ecological systems. 

In the past, ecological plant or animal studies conducted in urban settings used traditional 

ecological approaches and considered humans as agents of disturbance. Although the term 

“urban ecosystem” has been used to describe human-dominated ecosystems, it does not 

adequately take into account the developmental history, sphere of influence, and potential 

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impacts required to understand the true nature of an urban ecosystem (McIntyre, et al., 2000). 

Because urbanization is both an ecological and a social phenomenon, urban ecology implicitly 

recognizes the role humans play in developing unique systems. Therefore, if urban ecology is to 

be a truly interdisciplinary field, both social and natural sciences must be integrated into the 

study of urban ecosystems (McIntyre, et al., 2000). 

Although the study of ecological phenomena in urban environments is not a new area of 

science, the concept of the city as an ecosystem is relatively new for the field of ecology (Grimm 

et al., 2000). There is a wealth of information on the terrestrial components of urban ecological 

systems. However, much of it is organized from the perspective of ecology in cities while the 

more comprehensive perspective identified as ecology of cities is needed (Pickett et al., 2001). 

The basic questions addressed by the literature of ecology in cities relate to how ecological 

patterns and processes differ in cities as compared with other environments. What is the effect 

of the city (i.e., a concentration of human population and activities) on the ecology of organisms 

inside and outside its boundary and influence? The concept of ecology of cities has to do with 

how aggregated parts make up the whole, i.e., how cities process energy or matter relative to 

their surroundings (Grimm et al., 2000). The latter concept includes primary production, species 

richness, biogeophysical budgets, ecosystem patterns and processes, and an open definition of 

urban ecosystems that incorporates the exchanges of materials and influence between cities and 

surrounding landscapes (Pickett et al., 2001). If ecosystems are to be understood, there is a need 

for a new integrative ecology that explicitly incorporates human decisions, culture, institutions, 

and economic systems (Grimm et al., 2000). This makes an ecological approach to land use 

planning essential to maintain long-term sustainability of ecosystem benefits, services, and 

resources (Zipperer et al., 2000). The ecological and social effects of “edge city” need to be 

studied as they may be greater than the previous patterns of suburbanization. The classical 

ecosystem approach and a patch dynamic approach are needed to understand and manage the 

dynamics of urban and urbanizing ecosystems (Zipperer et al., 2000). 

There has been little work on the rates of atmospheric deposition to urban ecosystems 

despite extensive data on concentrations and chemical reactions of air pollutants in cities. 

A search of the literature produced no references that dealt with the effects of PM deposition. 

Lovett et al. (2000), however, reported that urban ecosystems are likely to be subjected to large 

rates of deposition of anthropogenic pollutants. Decades of research on urban air quality 

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indicate that cities are often sources of NOx, SOx, and dust, among many other pollutants. Some 

of these air pollutants are major plant nutrients (e.g., nitrogen) and may be affecting nutrient 

cycles in plant-dominated areas in and around cities, but have not been studied. The gases and 

particles in urban air can increase atmospheric deposition within and downwind of the city. 

Studying deposition rates of atmospheric pollutants in urban areas can provide quantitative 

estimates of amounts of gaseous and particulate air pollutants removed by urban vegetation. 

To determine the patterns of atmospheric deposition and throughfall in the vicinity of a 

large city, Lovett et al. (2000) measured bulk deposition, oak forest throughfall, and particulate 

dust at sites along a transect within and to the north of New York City. They observed that 

concentrations and fluxes of NO3 

!

, NH4

+

, Ca2+, Mg2+, SO4

2!

, and Cl! in throughfall all declined 

significantly with increasing distance from the city, while H+

 concentration and flux increased 

significantly with increasing distance from the city. Most of the change in concentrations and 

fluxes occurred within 45 km of the city. Additionally, it was observed that throughfall nitrogen 

was twice as high in the urban areas compared with suburban and rural areas. Most of the dry 

deposition of nitrate was from gaseous nitrogen oxides. As mentioned earlier, the effects of the 

atmospheric deposition of the particulate pollutants was not mentioned. 

McDonnell et al. (1997) in a 10-year study of ecosystem processes along an urban-rural 

gradient included plant litter dynamics and nitrogen cycling of two key components of a forest 

ecosystem: litter decomposition and heavy metal levels in soil and foliar litter. Foliar litter 

decomposition integrates many features of the abiotic and biotic environment. It is an important 

site of heavy metal incorporation into ecosystems and provides both a habitat and a resource for 

fungi, bacteria, and invertebrates. Litter decomposition integrates the effects of resource quality, 

environmental factors, and activities of decomposer organisms on nutrient cycling and serves as 

an easily measured indicator of the effect of urbanization on an important ecosystem function. 

McDonnell et al. (1997) noted that levels of heavy metals were higher in the foliar litter of urban 

forest soils than in rural forest soils. The levels in urban forest stands approached or exceeded 

the levels reported to affect soil invertebrates, macrofungi, and soil microbial processes. The 

urban forests exhibited reduced fungal biomass and microarthropod densities when compared to 

rural stands. These results supported the concept that urban forests have depauperate 

communities because of anthropogenic stress resulting from poor air quality due to high levels of 

SO2, SO4

2!

, O3 and NO3 

!

; elevated levels of soil- and forest-floor-heavy metals; and low water 

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availability such as those caused by hydrophobic soils (McDonnell et al., 1997). Thus, forests at 

the urban end of the gradient exhibited reduced fungal and microarthropod populations and 

poorer leaf quality than the more rural forests. The potential effect of these conditions on the 

ecosystem processes of decomposition and nitrogen cycling in urban forests appeared to be 

ameliorated by two other anthropogenic factors: increased average temperatures caused by the 

heat island effect and the introduction and successful colonization of earthworms in the urban 

forests (McDonnell et al., 1997). 

McDonnell et al. (1997) observed that the changes in forest nitrogen dynamics were related 

to increased anthropogenic nitrogen deposition in an urban environment. The studies of Aber 

et al. (1989) in the northeastern United States on forest nitrogen dynamics demonstrated that 

elevated nitrogen deposition over many years results in increased nitrification and the 

mineralization of more nitrogen than can be taken up by plants and microorganisms. 

Nitrification can lead to decreases in fine root biomass and increases in nitrate leaching below 

the root zone. These effects of nitrogen deposition were not related to inputs from a specific 

source such as PM. 

While studies of heavy metal deposition in or near cities have been performed, they do not 

cite the effects of metals in the soil. Pouyat and McDonnell (1991) discussed heavy metal 

accumulations in forest soils along an urban-rural gradient in southeastern New York. 

Variations in the amounts of Zn, Cu, Ni, and Cd appeared to be indicative of a pattern of 

atmospheric deposition near point sources (Section 4.3.2.6). The concentrations of heavy metals 

in forest floor and soils corresponded closely with the urban-rural land use gradient. Again, as in 

the study by Lovett et al. (2000), the pollutants were highest near the urban end of the gradient 

and declined toward rural sites, with Pb, Ni and Cu highest near the urban end. 

The air quality of the region around East St. Louis has been of concern due to industries in 

the area (Kaminski and Landsberger, 2000a), which include ferrous and nonferrous metal 

smelters (Pb, Zn, Cu, and Al), coal-fired power plants, producers of organic and inorganic 

chemicals, municipal waste incinerators, and petroleum refineries. The city is also in the path of 

diverse plumes from refineries to the north, coal-fired power plants to the west, and nonferrous 

smelters to the south. Concentrations of heavy metals and metalloids (As, Cd, Cu, Hg, Pb, Sb, 

Zn) in the soil provided a basis for analysis (Kaminski and Landsberger, 2000b). These studies 

of the extent of long-term metal deposition on the soil surface and depth of soil contamination, 

4-151
as well as the leaching dynamics of heavy metals were made to determine possible effects on 

biota uptake or groundwater contamination. The effects on biota are not mentioned; however, 

the soils in the area acted as a sink and there was little groundwater mobility (Kaminski and 

Landsberger, 2000b). 

4.3 AIRBORNE PARTICLE EFFECTS ON VISIBILITY 

4.3.1 Introduction 

Visibility may be thought of as the degree to which the atmosphere is transparent to visible 

light (National Research Council, 1993). The beauty of scenic vistas in many parts of the United 

States is often diminished by haze that reduces contrast, washes out colors, and renders distant 

landscape features indistinct or invisible. This degradation of visibility is due primarily to the 

scattering and absorption of light by fine particles suspended in the atmosphere. One 

quantitative measure of visibility, used traditionally by meteorologists, is the visual range, 

defined as the farthest distance at which a large black object can be distinguished against the 

horizon sky (U.S. Environmental Protection Agency, 1979). 

In August 1977, Congress amended the Clean Air Act (CAA) to establish as a national goal 

“the prevention of any future and remedying of any existing impairment of visibility in 

mandatory Class I Federal areas (many national parks and wilderness areas), which impairment 

results from manmade air pollution” (Title I Part C Section 169A, U.S. Code [1990]). The 1977 

Amendments also included provisions requiring applicants for new major source permits to 

assess the potential for their projects to cause adverse effects on air quality-related values (e.g., 

visibility) in nearby Class I areas. In 1980, the EPA established regulatory requirements under 

Section 169A to address Class I protection from “reasonably attributable” visibility impairment, 

i.e., visibility impairment attributable to a single source or small group of sources. 

The CAA, as amended in 1990 (Section 169B), required EPA both to conduct research on 

regional visibility impairment and to establish the Grand Canyon Visibility Transport 

Commission (GCVTC). The GCVTC was charged with assessing and providing advice to help 

preserve clear days and to improve visibility in the 16 national parks and wilderness areas 

located on the Colorado Plateau. The GCVTC also was mandated to provide recommendations 

to the U.S. EPA for reduction of visibility impairment due to regional haze, described as any 

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perceivable change in visibility (light extinction, visual range, contrast, or coloration) from that 

which would have existed under natural conditions and is caused predominantly by a 

combination of many anthropogenic sources over a wide geographical area (U.S. EPA, 1999a). 

In July 1999, EPA published the Regional Haze Rule (Federal Register, 1999). The regulation 

established a program for the improvement and protection of visibility in the 156 protected Class 

I parks and wilderness areas and included the establishment of baseline and current visibility 

conditions and the tracking of changes in visibility conditions over time. Implementation of the 

regional haze regulations is supported by EPA’s PM2.5 monitoring network and an expanded 

Interagency Monitoring of Protected Visual Environments (IMPROVE) network. The PM2.5 

monitoring network and the IMPROVE network are described briefly later in this section and in 

more detail elsewhere (National Park Service, 1998; Evans and Pitchford, 1991; U.S. 

Environmental Protection Agency, 2000b; U.S. Environmental Protection Agency, 2001a). 

The objective of the visibility discussion in this section is to provide a brief description of 

the fundamentals of atmospheric visibility and to summarize the linkage between PM and 

visibility. Visibility is affected by air quality and, unlike the PM concentration, is not a property 

of an element of volume in the atmosphere. Visibility can be quantified only for a sight path and 

depends on the illumination of the atmosphere and the direction of view. However, the 

concentration of particles in the atmosphere plays a key role in determining visibility. 

Therefore, visibility impairment may be controlled by control of particle concentrations. The 

relationships between particles, other factors, and visibility impairment are described in this 

section. For a more detailed discussion on visibility, the reader is referred to the 1996 Air 

Quality Criteria for Particulate Matter (U.S. Environmental Protection Agency, 1996a); the 

Recommendations of the Grand Canyon Visibility Transport Commission (Grand Canyon 

Visibility Transport Commission, 1996); the National Research Council (National Research 

Council, 1993); the National Acid Precipitation Assessment Program (Trijonis et al., 1991); 

Interim Findings on the Status of Visibility Research (U.S. Environmental Protection Agency, 

1995a); Visibility: Science and Regulation (Watson, 2002), and reports summarizing visibility 

science and data from the IMPROVE visibility monitoring network (Malm, 2000; Sisler, 1996; 

Sisler et al., 1993). 

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4.3.2 Factors Affecting Atmospheric Visibility 

The visual perception of a distant object is influenced by a large number of factors 

including human vision (the eye), the brain’s response to signals received from the eye, the 

interaction of light with the atmosphere (e.g., atmospheric illumination, path and transmitted 

radiance, contrast, and optical properties), and atmospheric pollution from natural and 

anthropogenic sources. Detailed discussion of this full range of topics can be found in the 1996 

PM AQCD (U.S. Environmental Protection Agency, 1996a) and other general references 

(Malm, 1999; Watson, 2002). This section focuses only on those topics that have been 

addressed by more recent research, including atmospheric illumination, the optical properties of 

gases and particles in the atmosphere, and the effects of relative humidity on the optical 

properties of particles. 

4.3.2.1 Optical Properties of the Atmosphere and Atmospheric Particles 

Atmospheric particles and gases attenuate image-forming light as it travels from a viewed 

object to an observer. The fractional attenuation of light per unit distance is known as the light 

extinction coefficient. The light extinction coefficient, bext, is expressed in units of one over 

length, for example inverse kilometers (km-1) or inverse megameters (Mm-1). The light 

extinction coefficient can be expressed as the sum of the light scattering and light absorption 

coefficients of particles and gases: 

(4-7) 

where the subscripts p and g signify particles and gases, and s and a signify scattering and 

absorption. 

The light extinction coefficient can be measured with a reasonable degree of accuracy or 

can be calculated with the size, composition, shape, and the orientation of the particles. The 

light extinction coefficient is influenced by meteorological conditions and optical properties 

along the sight path. 

4-154
Relationship Between Light, Targets, and Objects in a Sight Path 

The appearance of a distant object is determined by light from two sources: the light 

reflected from the object itself (initial radiance) and the light reflected by the intervening 

atmosphere (path radiance). Human vision and the brain’s response to signals received from the 

eye can distinguish between objects by contrast or differences in the radiance of adjacent objects. 

Light reflected by objects is attenuated by scattering and absorption as it travels through the 

atmosphere toward the observer. The portion that reaches the observer is the transmitted 

radiance. 

During the daytime, the sight path is illuminated by the direct rays of the sun, diffuse 

skylight, light that has been reflected from the surface of the Earth, etc. Some of this 

illumination is scattered toward the observer by the air molecules and PM in the sight path. 

The accumulation of the light scattered into the sight path is the path radiance or air light. The 

path radiance significantly influences the light transmitted by the object being viewed. As the 

path radiance increases, the light transmitted by an object decreases. 

The transmitted radiance carries the information about the object; the path radiance only 

carries information about the intervening atmosphere and is often quite featureless. When the 

transmitted radiance is dominant, visibility is good. Conversely, when the path radiance is 

dominant, visibility is poor. In a dense fog, the transmitted radiance from nearby objects can be 

seen, but the transmitted radiance from more distant objects is completely overwhelmed by the 

path radiance (i.e., the light scattered by the fog). Distant objects are lost in the white (or gray) 

of the fog (Gazzi et al., 2001). 

Figure 4-32 illustrates the radiance seen by an observer looking at a hillside or through the

aperture of a measurement instrument. The radiance that enters the eye of the observer (or the 

aperture of a measurement instrument) is known as the apparent radiance (i.e., the sum of the 

transmitted and path radiance). The competition between the transmitted radiance and the path 

radiance determines visibility. 

Light Absorption and Scattering by Gases 

In the ambient atmosphere, the only visible-light-absorbing gas of any consequence is 

nitrogen dioxide (NO2), which primarily absorbs blue light and, if present in sufficient 

concentration across a sight path, contributes to the yellow or brown color seen in urban hazes. 

4-155
Figure 4-32. Light reflected from a target toward an observer. The intervening 

atmosphere scatters a portion of this light out of the sight path and scatters 

light from the sun into the sight path. Some particles and gases also absorb 

a portion of the light from the target. The light scattered into the sight 

path increases with distance from the target, whereas the light transmitted 

from the target decreases with distance from the target. The visual range 

is the closest distance between the target and the observer at which the 

transmitted light can no longer be distinguished from the light scattered 

into the sight path. 

Source: Watson and Chow (1994). 

Usually the absorption by NO2 is much smaller than the scattering by particles that are typically 

present in polluted environments, such as urban areas. The most common exception to this 

situation of relatively small NO2 absorption is in effluent plumes from combustion facilities 

where the particles are effectively removed but the nitrogen oxide (NO), which can convert 

rapidly to NO2, is not removed. Except for such particle-depleted NO plumes, the light 

absorption coefficient for gases is usually ignored in determinations of the light extinction. 

Light scattering by gases in the atmosphere is described by the Rayleigh scattering theory 

(van de Hulst, 1981) and is referred to as Rayleigh scattering. The magnitude of Rayleigh 

scattering depends on the gas density of the atmosphere and varies from about 9 Mm-1 

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to 11 Mm-1 for most locations of interest, depending primarily on site elevation. To simplify 

comparisons of light extinction coefficient values among sites at a variety of elevations, a 

standard value of 10 Mm-1 is often used for the Rayleigh scattering component (Malm, 2000). 

Light Absorption and Scattering by Particles 

Light scattering by particles tends to dominate light extinction except under pristine 

atmospheric conditions when Rayleigh scattering by gas molecules is the largest contributor. 

If the particle size, refractive index, and shape are known, the extinction coefficient can be 

calculated. For particles of sizes similar to the wavelength of visible light, Mie equations for 

homogeneous spheres can be used to calculate the scattering and absorption of individual 

particles. 

Absorption by particles is primarily caused by elemental carbon (also referred to as soot or 

light-absorbing carbon) generated by the incomplete combustion of fossil fuels. Some minerals 

in crustal particles also absorb light and can be a significant factor during fugitive dust episodes. 

Most particle absorption data are determined by measuring light transmission or reflection 

of particles captured on filter media. Absorption estimates made in this way are sensitive to the 

filter substrate used, the optical configuration of the transmission measurement, particle loading 

on the filter, and particle scattering albedo with the result that there are significant uncertainties 

for measurements of filtered particles (Horvath, 1993). Another approach to estimating aerosol 

light absorption is by subtracting concurrent light scattering measurements made with a 

nephelometer from light extinction measurements made with a transmissometer. Substantial 

uncertainty in this difference approach results from the assumption that the point measurement 

of scattering is representative of the scattering over a long path (1 to 10 km) that is typically 

required for transmissometer measurements. A recently field-tested prototype photoacoustic 

spectrometer designed to determine absorption of suspended aerosol and an enclosed-folded path 

transmissometer offer hope for resolving the problems of the filter-based and difference 

approaches to the measurement of light absorption by particles (Arnott et al., 1999). 

The relationship between elemental carbon concentration and particle absorption can be 

calculated using Mie equations for particles with known size distribution, particle density, index 

of refraction, shapes, and for various internal mixtures with non-absorbing aerosol materials 

(Fuller et al., 1999). Mie equations are used to determine the efficiency factors for extinction 

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Qext, Qscat, and Qabs. The efficiency factors represent the fraction of light falling on a circle with 

the same diameter and index of refraction as the particle. While such application of this theory 

can provide a range of absorption efficiencies for various model aerosol distributions, it is rare 

that sufficiently detailed particle characterization data for ambient aerosols are available. Also, 

although elemental carbon is the strongest and most common of the absorbing particles, light 

absorption by elemental carbon particles can be reduced when the particle is covered by other 

chemical species (Dobbins et al., 1994) or may be enhanced when coated with a non-absorbing 

refractive material such as ammonium sulfate (Fuller et al., 1999). 

More commonly, estimates of elemental carbon absorption efficiency are empirically 

determined from the ratios of, or the slopes of regression analysis fits to, absorption coefficient 

and corresponding elemental carbon concentration measurements. Use of the regression 

approach permits the inclusion of crustal component concentrations as a second dependent 

parameter, so that crustal absorption can also be estimated. Uncertainties in the absorption 

efficiency determined empirically are a combination of the measurement uncertainties for the 

absorption coefficients, elemental carbon concentrations, and, where used, the crustal 

concentrations. In reviews of estimates of elemental carbon light absorption mass efficiency 

(i.e., the absorption coefficient per carbon mass concentration), Horvath (1993) and Liousse 

et al. (1993) found values ranging from 2 to 17 m2

/g. Moosmüller et al. (1998) showed that by 

limiting the absorption coefficient estimates to those using photoacoustic methods, the 

absorption efficiency shows a wavelength dependence with highest values (17 m2

/g) at the 

shortest wavelength used (8 = 0.42 μm) and lowest values (3 m2

/g) at the longest wavelengths 

used (8 = 0.8 μm). The center of the visible light wavelength (8 = 0.53 μm) yielded elemental 

carbon absorption efficiencies values of ~10 m2

/g, a commonly used value for elemental carbon 

absorption efficiency. Fuller et al. (1999) suggested that isolated spheres of light absorbing 

carbon have a specific absorption of less than 10 m2

/g. Light absorption by carbon particles will 

be greater than 10 m2

/g only if the particles are internally mixed and the occluding particles are 

sufficiently large. Absorption values for graphitic and amorphous carbon for primary sizes 

typical of diesel soot are ~5 m2

/g. 

Particle scattering tends to dominate light extinction except under pristine atmospheric 

conditions when Rayleigh scattering by gas molecules is the largest contributor. Light-scattering 

by particles has been reported to account for 68 to 86% of the total extinction coefficient in 

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several cities in California (Eldering et al., 1994). When light-scattering increases, visibility is 

impaired because of a decrease in the transmitted radiance and an increase in the path radiance. 

The single most important factor that determines the amount of light scattered by a particle is its 

size, as shown in Figure 4-33 (based on Mie calculations). The maximum single-particle 

scattering efficiency (i.e., scattering per cross-sectional area of a particle) is associated with 

particles with diameters of about the wavelength of visible light (centered at 0.53 μm). 

For particles that are small compared to the wavelength of light, the single-particle scattering 

efficiency is low. For particles larger than the wavelength, the single-particle scattering 

efficiency initially decreases with diameter and then fluctuates around a value of two as size 

increases. However, a larger particle always scatters more light than a smaller particle because 

particle cross-sectional area increases faster with diameter than does the decrease in singleparticle scattering efficiency at any point on the scattering efficiency curve. The mass scattering 

efficiency (i.e., the scattering per mass concentration) peaks for particles that are about 0.5 μm to 

0.8 μm in diameter. Smaller particles are much less efficient at scattering light, but the mass of 

particles increases with particle size faster than the increase in the amount of light they scatter. 

Figure 4-33. Light-scattering efficiency factor (per cross-sectional area), Q, for a 

homogeneous sphere with an index of refraction of 1.50 as a function of the 

size parameter, " = BD/8. 

Source: Penndorf (1958). 

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Use of the Mie equation to calculate light scattering or the light scattering efficiency of 

particles in the atmosphere is severely limited by the general lack of sufficiently detailed particle 

characterization data. At a minimum, size-resolved particle composition data (e.g., aerosol 

collected on an 8-stage impactor) are needed to permit meaningful Mie scattering calculations. 

The chemical composition provides clues to the appropriate particle density and index of 

refraction, while the size distribution is inferred by fitting a distribution function to the 

concentration for each stage. Assumptions are still necessary to address the particle component 

mixture characteristics of the aerosol. Resulting scattering calculations can be compared to 

directly measured particle extinction to assess the reasonableness of the Mie calculations. 

Reported calculated dry scattering efficiencies for sulfates range from 1.2 to 5.6 m2

/g. 

Sulfate scattering efficiencies have been reported to increase by a factor of two when the size 

distribution increased from 0.15 to 0.5 μm (McMurry et al., 1996). Calculated scattering 

efficiencies for carbon particles ranged from 0.9 to 8.1 m2

/g. A scattering efficiency of 1.0 and 

0.6 m2

/g was reported for soil and coarse mass, respectively (U.S. Environmental Protection 

Agency, 1996a; Sisler and Malm, 2000). 

Figure 4-34 gives the volume-specific light scattering efficiency in units of μm-1 as a 

function of particle diameter. The light scattering coefficient is derived by multiplying the 

volume-specific light scattering efficiency factor by the volume concentration. The massspecific light scattering efficiency can be obtained by dividing the values for the curves by the 

density of the PM. 

Similar results have been produced in field nephelometer measurements of ambient particle 

scattering. A variety of nephelometer configurations, unrestricted or size-selective inlets, and 

the control of sample air temperature and relative humidity, permit the composite scattering 

properties of ambient aerosol to be directly observed (Day et al., 1997). When samplecontrolled nephelometer data are combined with collocated particle speciation data, composite 

particle scattering efficiency values for ambient aerosol can be empirically derived (Malm et al., 

2000). 

The scattering efficiency for particles has been reported by White et al. (1994) for dry 

particles < 2.5 μm (2.4 and 2.5 m2

/g) and coarse particles (0.34 to 0.45 m2/g). Other reported 

values for coarse particles include 0.4 and 0.6 m2

/g (White and Macias, 1990; Trijonis et al., 

1987). Nephelometer measurements for light scattering by coarse particles is underestimated 

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Figure 4-34. Volume-specific light-scattering efficiency as a function of particle diameter 

Dp. The calculations were performed for the indicated indices of refraction 

and a wavelength of 550 nm. For large particle diameters, the scattering 

efficiencies tend toward a value of 3/Dp. Mass-specific light-scattering 

efficiencies (in units of m2

/g) can be obtained by dividing the values of the 

curves by the particle density (in units of g/cm3

). 

Source: U.S. Environmental Protection Agency (1996a). 

(White et al., 1994). Chow et al. (2002a) reported scattering efficiencies of 2 to 3 m2/g but in 

some cases > 5 m2

/g for dry particles < 2.5 μm. 

4.3.2.2 Relative Humidity Effects on Particle Size and Light-Scattering Properties 

The ability of some commonly occurring chemical components of atmospheric aerosol to 

absorb water from the vapor phase has a significant effect on particle light scattering. 

Hygroscopic particles, which typically include sulfuric acid, the various ammonium sulfate salts, 

ammonium nitrate, and sodium chloride, change size by the accumulation and loss of water as 

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they maintain equilibrium with the vapor phase as a function of changes in relative humidity. 

For some materials (e.g., sulfuric acid), the growth is continuous and reversible over the entire 

range of relative humidity. For other materials, water absorption begins abruptly for a dry 

particle at a specific relative humidity known as the deliquescent point (e.g., ~80% for 

ammonium sulfate) and continues as relative humidity increases. There is a hysteresis effect 

with these materials in that, once wet, the relative humidity can be reduced below the 

deliquescent point until crystallization occurs at a substantially lower relative humidity (e.g., 

~30% for ammonium sulfate). Figure 4-35 shows the water vapor growth curve for ammonium 

sulfate. 

Figure 4-35. Particle growth curve as a function of relative humidity (RH) showing 

deliquescent growth of ammonium sulfate [(NH4)2 SO4] particles at the 

deliquescent point (A, about 80% RH), reversible hygroscopic growth of 

ammonium sulfate solution droplets at RH > 80%, and hysteresis (the 

droplet remains supersaturated as the RH decreases below 80%) until the 

crystallization point (B, about 38% RH) is reached. 

Source: Adapted from National Research Council (1993) and Tang (1980). 

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The water growth behavior for hygroscopic materials commonly found in atmospheric 

aerosol in pure form or in some mixtures is generally well known as a result of laboratory 

measurements (Tang and Munkelwitz, 1994; Tang, 1997). Models that calculate water growth 

of mixtures from known solubility properties of many common water-soluble chemicals have 

long been available (Zdanovskii, 1948) and have been successfully applied to determine growth 

for particles with known composition (Saxena and Peterson, 1981; Pilinis et al., 1995; Saxena 

et al., 1993). 

The water growth of individual ambient particles can be directly measured using a 

humidity-controlled tandem differential mobility analyzer (TDMA) (McMurry and Stolzenburg, 

1989; Zhang et al., 1993). Inferences can be made about the mixtures of soluble and insoluble 

particle components by comparing TDMA-measured growth and size-resolved aerosol 

composition data with water growth model predictions (Pitchford and McMurry, 1994; Zhang 

et al., 1993; Saxena et al., 1995). A practical limitation of TDMA measurements in studying 

aerosol optical properties is that particles > 0.5 μm are not well measured by this approach. 

Accounting for water growth of atmospheric aerosols is important in determining visibility, 

because particles containing hygroscopic or deliquescent materials change size, index of 

refraction, and, hence, scattering efficiency, with changing relative humidity. The nonlinear 

nature of particle growth curves for hygroscopic aerosols means that substantial light scattering 

changes result from modest relative humidity changes under humid conditions (relative humidity 

> 90%). The magnitude of the water growth effect on light scattering for ambient aerosols can 

be directly measured with humidity-controlled nephelometer measurements (Day et al., 1997). 

Measurements of water growth effects on light scattering are compared to the results of water 

growth and Mie scattering models applied to size-resolved composition data using various 

mixture assumptions to infer average mixture and other aerosol characteristics (Malm et al., 

2000a,b). 

While the importance of inorganic hygroscopic particles is well understood, the role of 

organic compounds in particle water growth has been the subject of recent investigations. 

In their interpretation of TDMA and particle composition data from two locations, Saxena et al. 

(1995) made the case that organic components of the aerosol enhanced water absorption by 

particles at a remote desert location and retarded water absorption at an urban location. They 

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speculated that the latter might be due to hydrophobic organic material coatings on inorganic 

hygroscopic particles. 

Whereas some of the thousands of organic compounds that are in atmospheric aerosols are 

known to be hygroscopic and a significant fraction of the organic aerosol material is known to be 

water soluble, there is a lack of water absorption data for most organic compounds. The 

incomplete water solubility data, combined with incomplete data on the abundance of the 

numerous organic compounds in ambient aerosols, means that organic water growth model 

calculations are not a reasonable approach for assessing the importance of water growth by 

organic aerosol components in the atmosphere. To overcome this constraint, Saxena et al. 

(1995) compared organic concentration to the difference between total aerosol water measured 

by TDMA and model-estimated water for the inorganic hygroscopic aerosol components. In 

contrast, Pitchford and McMurry (1994) used the same remote location data set and showed that, 

on 6 of the 8 sampling days, water uptake by the sulfates and nitrates could account for all of the 

measured water absorption. 

Swietlicki et al. (1999) made TDMA measurements in northern England and found that 

growth takes place in two modes, one mode being less hygroscopic that the other. They 

concluded that growth could be attributed to the inorganic content of the aerosol. Cocker et al. 

(2001) measured hygroscopic properties of Pasadena, CA aerosol and concluded that growth 

factors increased when forest fires were present. McDow et al. (1995) measured water uptake by 

diesel soot, automobile exhaust, and wood smoke particles. They found all three emission types 

absorbed water: the wood smoke sample weight increased by about 10% as sample relative 

humidity was increased; whereas diesel soot sample weight increased by only 2 to 3%. Chughtai 

et al. (1999) examined the hydration characteristics of a number of anthropogenic and natural 

organic materials. They found surface water adsorption increased with age and surface 

oxidation. Hemming and Seinfeld (2001) evaluated the relative hygroscopicity of different 

organics, the differences in the amount of water taken up by mixtures, and the individual 

components of the mixtures in their pure state using the UNIFAC. They found that mixtures 

take up less water than the individual components in the pure state. The relative hygroscopicity 

of atmospheric organics was diacids > monoacids > alcohols > carbonyls. Analysis of humiditycontrolled and size-resolved chemistry data from the Great Smoky Mountains and Grand Canyon 

National Parks (Malm et al., 1997; Malm and Kreidenweis, 1997; Malm et al., 2000a) showed 

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that ambient organic aerosol are, at most, weakly hygroscopic to within the measurement 

uncertainty and modeling assumptions. 

A more detailed discussion of the effects of relative humidity on the size distribution of 

ambient particles appears in Chapter 2 of this document. 

4.3.3 Relationships Between Particles and Visibility 

Visibility, referring to the appearance of scenic elements in an observer’s line of sight, 

depends on more than the optical characteristics of the atmosphere. Numerous scene and 

lighting characteristics are important to this broad definition of visibility. However, under a 

variety of viewing conditions, visibility reduction or haziness is directly related to the extinction 

coefficient. 

Light extinction, the sum of the light scattered and absorbed by particles and gases, is 

frequently used to estimate the effect of air pollution on visibility. Light extinction is usually 

quantified using the light extinction coefficient, i.e., the sum of the light scattering and 

absorption coefficients for gases and particles (see Section 4.3.2.1). 

The influence of particles on visibility degradation is dependent on the particle size, 

composition, and solubility (Pryor and Steyn, 1994). Fine particles (particles with mass mean 

diameters # 2.5 μm) scatter more light than coarse particles. Fine particle species include 

sulfates (assumed to be ammonium sulfate), nitrates (assumed to be ammonium nitrate), 

organics, light-absorbing carbon, and soil (Malm et al., 1994). Of the fine particle species, 

sulfates and nitrates are the most hygroscopic and require the use of a relative humidity 

adjustment factor. The effect of particle light extinction can be determined by totaling the 

scattering and absorption of light by multiplying the mass-specific efficiency values and the 

mass concentration for each of the particle species. The effect of relative humidity and the 

relative humidity adjustment factors are discussed in Section 4.3.2.2. 

Visibility is measured by human observation, the light extinction coefficient (light 

scattering and absorption by particles and gases), and parameters related to the light extinction 

coefficient (visual range, deciview) and fine particle mass concentrations. The equation for the 

light extinction coefficient, light extinction by particles is stated in the discussion of the 

IMPROVE Program. 

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The visual range method of visibility measurement, commonly taken to be the greatest 

distance that a large dark object (e.g., a mountain in shadow) can be seen against the background 

sky (Middleton, 1952), was developed for and continues to function well as an aid in military 

operations and transportation safety. Visual range is inversely proportional to the light 

extinction. Visual range can be calculated from a point measurement of light, assuming that the 

atmosphere and the illumination over the sight path is uniform and the threshold contrast is 2% 

of the extinction coefficient 

(4-8) 

where visual range is in kilometers, bext is in km-1, and a threshold contrast of 2% is assumed. 

If bext is in Mm-1, the Koschmieder constant becomes 3,912. 

An index of haziness, expressed in deciview (dv) units, is also very simply related to the 

light extinction coefficient (Pitchford and Malm, 1994). 

(4-9) 

An important characteristic of this visibility index is that it is more nearly linearly related to 

perceived changes in haze level than either visual range or light extinction. A change of 1 or 

2 dv in uniform haze under many viewing conditions will be seen as a small but noticeable 

change in the appearance of a scene, regardless of the initial haze condition. 

Figure 4-36 illustrates the relationship of light extinction in Mm-1, deciview index, and 

visual range in kilometers. Although the deciview is related to extinction, it is scaled in such a 

way that is perceptually correct (Fox et al., 1999). 

The amount of light that an aerosol of a given mass concentration scatters depends on the 

particle size distribution. Several studies and reports (Chow et al., 2002a; Samuels et al., 1973; 

Waggoner and Weiss, 1980; Waggoner et al., 1981) indicate that the mass concentration of 

particles of < 3 μm in diameter correlate well with light scattering and visibility when the 

measurements are made under dry conditions. The question of whether the relationship between 

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Figure 4-36. Comparison of extinction (Mm-1) and visual range (km). 

Source: Fox et al. (1999). 

light scattering and fine particle mass varies too greatly between geographical locations to allow 

the use of fine particle mass as a metric for visibility effects has been considered. McMurry 

(2000), citing a study by Charlson et al. (1968), stated that the ratio of dry scattering coefficient 

to the dry fine particle mass concentration from different geographical locations did not vary a 

great deal. However, the use of either fine particle mass or the individual major components to 

determine particle-related light scattering requires measurements, estimates, or assumptions 

about concurrent ambient relative humidity in order to accurately estimate the relationship 

between ambient fine particles and light extinction. 

Figure 4-37 shows the relationship between fine particle mass and calculated light 

extinction. The figure, as reported in Chow et al (2002b), was generated using data reported by 

Samuels et al. (1973). According to Samuels et al. (1973), there was a direct correlation 

between particle mass concentration, light scattering, and visibility. However, there were large 

standard errors in the scattering coefficient. 

Most routine aerosol monitoring programs and many special study visibility 

characterization programs were designed to measure the major aerosol components (Malm et al., 

1994; Tombach and Thurston, 1994; Watson et al., 1990); they were not designed to determine 

the microphysical and chemical characteristics of these species. However, the inherent 

limitations of estimating aerosol optical properties from bulk aerosol measurements have been 

addressed, at least in part, by a number of authors. For instance, Ouimette and Flagan (1982) 

have shown using basic theoretical considerations that if an aerosol is mixed externally (i.e., 

separate particles contain the major aerosol components) or, if internally mixed, the index of 

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Figure 4-37. Proportionality of observed daytime haziness to fine particle mass 

concentration in Los Angeles. Visual ranges are 8-h averages of hourly 

human observations, plotted as extinction according to Koschmieder 

formula. Mass concentrations are from 8-h samples collected behind 

a cyclone with 3-μm cut point. Relative humidities were # 70%. 

Source: Chow et al. (2002b). 

refraction is not a function of composition or size and the aerosol density is independent of 

volume, then 

(4-10) 

where "i is the specific mass scattering efficiency and mi is the mass of the individual aerosol 

species. 

Sloane (1983, 1984, 1986), Sloane and Wolff (1985), and more recently, Lowenthal et al. 

(1995) and Malm and Kreidenweiss (1997) have shown that differences in estimated specific 

scattering between external and internal model assumptions are usually less than about 10%. 

In the absence of detailed microphysical and chemical information of ambient particles, the 

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above studies demonstrate that a reasonable estimate of aerosol extinction can be achieved by 

assuming each species is externally mixed. 

The latest IMPROVE network report (Malm, 2000) included calculated aerosol light 

extinction for each of the five major fine fraction particle (PM2.5) components, coarse fraction 

mass (PM10-2.5), and Rayleigh scattering by gases and summed them for an estimate of total light 

extinction in Mm-1 using the following algorithm: 

(4-11)

where each PM term is the product of a constant dry extinction efficiency for that species, the 

mass concentration of the species, and, for sulfate and nitrate, an adjustment factor that is a 

function of relative humidity to account for their hygroscopic behavior. The relative humidity 

adjustment term for sulfate and nitrate, shown in Figure 4-38, is based upon the ammonium 

sulfate growth curve, shown in Figure 4-28, smoothed between the upper and lower curves of the 

hysteresis loop for the relative humidity range of 30 to 80%. The extinction efficiencies for soil 

and coarse mass used in this algorithm are taken from a literature review by Trijonis et al. 

(1987). The extinction efficiency for light absorbing (elemental) carbon of 10 m2

/g is consistent 

with the value reported by Moosmüller et al. (1998) corresponding to 8 = 0.53 in the middle of 

the visible light spectrum. The dry extinction efficiencies of 3 m2

/g for sulfate and nitrate 

species and 4 m2

/g for organic species are based on literature reviews by Trijonis et al. (1991) 

and by White (1991). Trijonis’ best estimate for sulfates is 2.5 m2

/g with an uncertainty of a 

factor of 2, while White’s average low and high estimates for the rural West are 3.0 and 

3.7 m2

/g. For organics, Trijonis estimated a dry extinction efficiency of 3.75 m2/g with an 

uncertainty of a factor of 2, and White’s range for the rural West is 1.8 to 4.1 m2

/g. Malm et al. 

4-169
Figure 4-38. Relative humidity adjustment factor, f(RH), for ammonium sulfate as a 

function of relative humidity. 

Source: Malm (2000). 

(1996) and Malm (2000) used this algorithm to successfully reconstruct light scattering at a total 

of eleven IMPROVE monitoring sites. 

Malm (2000) used additional sophisticated aerosol size, composition, and microphysical 

data from a special study at the Great Smoky Mountains National Park to compare the 

performance of a number of models for calculating light extinction. He found that the simplist 

approach adequately predicted for periods of low light scattering but under-predicted by about 

30% during periods of high sulfate concentration. The greatest improvement over the simple 

model was obtained by including the degree of sulfate ammoniation in the model. This 

produced better estimates of the extinction coefficient over the entire range. Table 4-15 lists

various visibility metrics and methods for visibility measurement. 

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TABLE 4-15. VISIBILITY MEASUREMENT TECHNIQUES

Visibility Metric and Method Measurement Principle 

4-171

Visual Range 

Light extinction (bext) 

Long-path transmissometer 

Short-path transmissometer Contrast transmittance: 

Teleradiometer 

Photographs and time-lapse film Particle scattering (bscat,p): 

 Integrating nephelometer 

Human observation of prevailing visibility. Targets are selected at known distances from an observ

require lights and may differ from daytime targets. Each hour, the observer records the distance (i.ecorresponding to the furthest target that is visible. This method provides the longest history of visibin the United States, as it was used at most U.S. airports from 1948 to 1995. 

Directly measures the radiance of a constant light source (transmitter) after the light travels over a f

The transmittance of the path is calculated by dividing the measured radiance by the calibrated initi

source. The average extinction of the path is calculated from the transmittance and length of the pa

Starting in early 1990s, many airports replaced human observations with automated sensors (i.e., th

Observing System [ASOS], Automated Weather Observing System [AWOS]) to measure 1-min-avThe visibility sensor measures forward scattering using a xenon flash lamp source. Instruments can up to 48 km, but they are not recorded beyond 16 km. 

A telescope is focused on a distant target and the background. Changes in radiance are measured b

Measurements can be made at different wavelengths (e.g., 450, 550, and 630 nm) using narrow baneffects of particles of all sizes. Sensitive to variations in inherent contrast (e.g., bright or dark cloud and nonuniform illumination conditions. Because path radiance depends on how the atmosphere is transmittance represents human perception better than air quality. 

A densitometer measures light transmitted through different portions of a color slide. The film’s lig(the gamma curve) determines target-sky contrast. Computerized photographic simulations can chatransmittance for different meteorological and atmospheric conditions, and can be used to judge how these changes. 

Air is drawn into a nephelometer chamber that is illuminated with white or filtered (typically 500-5detected at 90N to the direction of illumination to measure the amount scattered out of the light path

limit the integrated arc to ~10-170N instead of a full 0-180N, which results in the underestimation of 

from coarse particles. Nephelometers are calibrated with gases of known indices of refraction. Parhygroscopic and volatile species) may be modified as they pass through the chamber, which is inadthe illumination source. 
TABLE 4-15 (cont’d). VISIBILITY MEASUREMENT TECHNIQUES 

Visibility Metric and Method Measurement Principle Particle absorption (babs,p): 

Aethalometer or particle soot absorption photometer 

Photoacoustic spectroscopy Filter transmittance,reflectanceSuspension of insolubleelemental carbon

b

(bComponent extinction

ext sum of bscat,p, bscat,g,

abs,p, and babs,g)

SO

Chemical extinction (bext): 

Filter measurements for 

4

2!, NO3 

!, organics, EC, 

fine soil, and coarse mass, plus clear air scattering 

Particles are collected on a quartz-fiber filter tape. The change in transmittance at selected wavelen

across the filter before and after sampling is measured or compared with the reference area. When a new portion of the tape is moved into the sampling position. Assumes a constant relationship betwmass and quartz filter transmittance of ~19m2/g. Assumes a relationship of 10 m2/g between BC ab

concentration. 

Particles absorb energy from a modulated laser (~514.5 nm) and transfer heat to the surrounding air

heated gas produces sound waves (acoustic signals) that are proportional to the amount of absorbed detected by a high-sensitivity microphone. Absorption appears to vary with illumination wavelengt

Uses densitometer, integrating plate, or integrating sphere spectrophotometer to measure how much reflected. The difference in the logarithms of light transmission through the filter before and after sto the particle deposit. Light transmission of particles collected on a filter may overestimate light abof the incident light is scattered within the filter and by other particles in the deposit. There are no standards for densitometry, integrating plate, or sphere methods. Particles collected on a Nuclepore polycarbonate-membrane filter are extracted in 30% isopropanolwater to form a suspension of insoluble EC particles. Using a spectrophotometer, light transmission 

peaking at 575 nm) is measured through the liquid extract. 

The sum of clean air scattering estimated from temperature and pressure, NO2 absorption estimated 

concentrations, particle scattering measured by nephelometer, and particle absorption (babs,p) measurmethods. Measurements are at a single location rather than along a sight path. 

Six aerosol chemical components are used to calculate chemical extinction. 

4-172

Adapted from: Watson (2002). 
4.3.4 Photographic Modeling of Visibility Impairment 

None of the visibility indices communicate visibility associated with various aerosol 

conditions as well as directly seeing their effects on a scene. Photographic modeling for the 

representation of haze can be useful in portraying changes in visibility specifically due to 

changes in air pollutant concentrations. Photographic modeling holds constant the effects of sun 

angle, cloud cover, and relative humidity and is a cost-effective method of evaluating various air 

quality scenarios. Photographic modeling is difficult to do with actual photographs because of 

the range of possible conditions in the same scene over multiple days; and, over time, 

photographs can be expensive to produce. Another limitation in using photographic models for 

representation of haze is that haze is assumed to be uniformly distributed throughout the scene 

and selected conditions are idealized, so the full range of conditions that occur in a scene are not 

represented. 

Eldering et al. (1996) proposed the use of a model that uses simulated photographs from 

satellite and topographic images to evaluate the effect of atmospheric aerosols and gases on 

visibility. Use of this model requires ground-based photography and data concerning the size 

distribution and chemical composition of atmospheric aerosols, NO2 concentration, temperature, 

and relative humidity for a clear day. Light extinction and sky color are then calculated based on 

differences in aerosol size distribution, NO2 concentration, temperature, and relative humidity. 

The images created represent natural landscape elements. 

Molenar et al. (1994) provides a discussion of existing visual air quality simulation 

methods based on techniques under development for the past 20 years. A photograph taken on a 

very clean, cloud-free day serves as the base image. The photograph is taken during the season 

and at the same time of day as the scene to be modeled. The light extinction represented by the 

scene is derived from aerosol and optical data associated with the day the image was taken, or it 

is estimated from contrast measurements of features in the image. The image is then digitized to 

assign an optical density to each picture element (pixel) for the wavelength bands of interest. 

A detailed topographic map and an interactive image-processing display system is used to 

determine the specific distance, elevation angle, and azimuth angle for each element in the 

picture with respect to the observer’s position. 

Various models are employed to allow the presentation of different air quality scenarios. 

The output from atmospheric aerosol models (e.g., extinction, scattering coefficients, single4-173
scattering albedo, and scattering phase matrix) is incorporated into radiative transfer models to 

calculate the changes in radiant energy (path radiance, image radiance, sky radiance, terrain 

radiance) caused by scattering and absorption by gases and particles as it passes through the 

atmosphere. Atmospheric aerosol models are also used to model the effect of relative humidity 

on the visual air quality (Molenar et al., 1994). 

Molenar et al. (1994) has developed a system call WinHaze that permits the viewing of 

computer-generated uniform hazes superimposed on digitized scenic photographs of both remote 

and urban scenes. The program simulates changes in visual air quality imagery from userspecified changes in optical parameters (e.g., Fext, visual range, or deciview values) or aerosol 

species concentrations. WinHaze includes imaging for various Class I national parks and 

wilderness areas and for Boston, MA; Dallas, TX; Denver, CO; Fort Collins, CO; Phoenix, AZ; 

and Tucson, AZ. The computer software is available through the IMPROVE website 

(http://vista.cira.colostate.edu/ improve/). 

4.3.5 Visibility Monitoring Methods and Networks 

Visibility monitoring studies measure the properties of the atmosphere either at the sampler 

inlets (point measurements), as is the case with air quality measurements, or by determining the 

optical properties of a sight path through the atmosphere (path measurements). Instrumental 

methods for measuring visibility are generally of three types: (1) direct measurement of light 

extinction of a sight path using a transmissometer, (2) measurement of light scattering at one 

location using an integrating nephelometer, and (3) measurement of ambient aerosol mass 

concentration and composition (Mathai, 1995). 

The largest instrumental visibility monitoring network in the United States is the 

Automated Surface Observing System (ASOS), commissioned by the National Weather Service, 

Federal Aviation Administration, and Department of Defense for use at more than 900 airports. 

The system is designed to objectively measure the clarity of the air versus the more subjective 

evaluations of human observations. The system provides real-time data for airport visibility. 

The visibility sensor, instead of measuring how far one can see, measures the clarity of the 

air using a forward-scatter visibility meter. The clarity is then converted to what would be 

perceived by the human eye using a value called Sensor Equivalent Visibility (SEV). Values 

derived from the sensor are not affected by terrain, location, buildings, trees, lights, or cloud 

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layers near the surface. The amount of moisture, dust, snow, rain, and particles in the light beam 

will affect the amount of light scattered. The sensor transmits 1-minute values based on rolling 

10-minute periods. Hourly visibility range data are available only at a quantized resolution of 

18 binned ranges with a visual range of up to 10 miles. The value provides a generally accurate 

and representative visibility measurement within a 2 to 3 mile radius of the site. The forward 

scatter meter was found to correlate fairly well with extinction coefficient measurements from 

the Optec Transmissometer (National Weather Service, 1998). 

Visibility data from the ASOS network is reported in terms of visual range in increments of 

1/4 to 1 statute mile. Visual range conditions exceeding 10 miles are truncated to 10 miles for 

real-time reporting purposes. Data is not extensively archived at ASOS locations; however, 

researchers are able to download the raw data directly from certain sites. In addition, since 1998, 

the raw visibility data (including light extinction measurements corresponding to visual ranges 

exceeding 10 miles) have been archived for a number of sites. Visual range measurements 

beyond 10 miles may be used to derived PM concentrations except in clean environments. 

These data are available from the National Climatic Data Center. 

The largest monitoring network that includes both visibility and aerosol conditions is the 

IMPROVE network. This network was formed in 1987 as a collaborative effort between 

Federal, regional, and state organizations responsible for protection of visibility in the 156 

mandatory Class I Federal areas (national parks and wilderness areas) and other areas of interest 

to land management agencies, states, tribes, and other organizations (National Park Service, 

1998; U.S. Environmental Protection Agency, 1995b, 1996a, 1999b; Eldred et al., 1997; Perry 

et al., 1997; Sisler and Malm, 2000). It is predominantly a rural-based network with more than 

140 sites across the country. The primary monitoring objectives of the IMPROVE program are 

to document current visibility conditions in the mandatory Class I areas, to identify 

anthropogenic chemical species and emission sources of visibility impairment through the 

collection of speciated PM2.5 data, and to document long-term trends for assessing progress 

towards elimination of anthropogenic visibility impairment. The IMPROVE network has also 

been involved in visibility related research, including the advancement of visibility monitoring 

instrumentation and analysis techniques and visibility monitoring and source attribution field 

studies (National Park Service, 1998; Evans and Pitchford, 1991). 

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Visibility monitoring under the IMPROVE network can be divided into three categories: 

aerosol, optical, and scene. Twenty-four hour PM2.5 and PM10 aerosol samples are collected by 

filters at least every third day. The PM2.5 samples are analyzed to determined the mass 

concentration of the major particulate constituents (sulfates, nitrates, organic carbon compounds, 

elemental carbon, chlorides, and crustal elements) and for elements that indicate sources of 

visibility-impairing particles (trace elements and ions). Optical monitoring provides a direct 

measurement of light scattering and absorption. Color photographic imaging documents the 

appearance of the scene under a variety of air quality and illumination conditions (U.S. 

Environmental Protection Agency, 1999b). It is anticipated that all data generated by the 

IMPROVE network will be added to the AIRS database. 

The EPA has deployed a new national monitoring network (Federal Reference Method 

Monitoring network) designed to assess PM2.5 concentrations and composition. There are over 

1,000 monitoring sites in operation and many sites report data to the AIRS. Analyses of these 

data are expected to provide a more complete understanding of visibility conditions, in particular 

urban visibility, across the country. The PM2.5 monitoring effort has been coordinated with 

visibility monitoring efforts currently in place to maximize the benefits of all of the monitoring 

programs (U.S. Environmental Protection Agency, 1997b, 2000b, 2001a). 

The Northeast States for Coordinated Air Use Management (NESCAUM) has established a 

real-time visibility monitoring network (CAMNET) using digital photographic imaging. There 

is currently digital photographic imaging for five urban locations (Boston, MA; Burlington, VT; 

Hartford, CT; Newark, NJ; and New York City, NY), and two rural locations (Acadia National 

Park, ME and Mt. Washington, NH). The visibility images are updated every 15 minutes. Near 

real-time air pollution and meteorological data are updated every hour. Archived images will be 

available for studies of the visual effects of PM air pollution in the Northeast. CAMNET may be 

accessed at www.hazecam.net (Northeast States for Coordinated Air Use Management, 2002; 

Leslie, 2001). 

The Midwest Regional Planning Organization, in cooperation with a number of other 

groups, has also developed a real-time visibility camera network (hazecam). The camera 

network includes several urban (Chicago, IL; Indianapolis, IN; and Cincinnati, OH) and rural 

locations (Seney NWR, MI; Mayville, WI; and Isle Royale National Park, MI/Grand Portage, 

MN). The Midwest hazecam can be found at http://www.mwhazecam.net. 

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4.3.6 Visibility Impairment: Trends and Current Conditions 

In the United States, visibility impairment is caused by particles primarily composed of 

sulfates, nitrates, organic compounds, carbon soot, and crustal dust. Visibility is best in Alaska 

and the western Great Basin. Moderate levels of light extinction are common on the Pacific 

Coast, including the western slopes of the Sierra Nevadas in California and the Cascade Range in 

Oregon and Washington. Visibility is most impaired in the areas encompassing and adjacent to 

the Ohio and Tennessee River Valleys. Visibility gradually improves along the Atlantic 

seaboard northeast of New York City (Watson, 2002). Natural visibility in the East and West is 

about 75 to 150 km (45 to 90 miles) and 200 to 300 km (120 to 180 miles) (U.S. Environmental 

Protection Agency, 2001b). 

4.3.6.1 Trends in Visibility Impairment 

Trends in visibility impairment or haziness often are used as indicators of trends in fine 

particles mass. Observations of visual range, obtained by the National Weather Service and 

available through the National Climatic Data Center of the National Oceanic and Atmospheric 

Administration provide one of the few truly long-term, daily records of impairment related to air 

pollution. After some manipulation including correction for relative humidity effects, the visual 

range data can be used as an indicator of fine mode particle pollution. The data reduction 

process and analyses of resulting trends have been reported by Schichtel et al. (2001), Husar 

et al. (1994), Husar and Wilson (1993), and Husar et al. (1981). 

There are many statistical approaches to estimating trends. These approaches include 

simple correlation and regression analyses, time-series analyses, and methods based on 

nonparametric statistics. A discussion and comparison of the methods for the detection of linear 

trends is provided in Hess et al. (2001). Schimek (1981) provides a discussion of nonlinear 

trends. In its annual air quality trends report, the EPA characterized trends using a 

nonparametric regression analysis approach commonly referred to as the Theil test (U.S. 

Environmental Protection Agency, 1998; Hollander and Wolfe, 1973). 

Regional Trends and Class I Areas 

The two largest contributors to visibility impairment are sulfates and carbon-based 

particles. In the East, sulfates are responsible for 60 to 86% of the visibility impairment. The 

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sulfate contribution decreases further west but is still responsible for between 25 to 50% of the 

visibility impairment. Carbon-based particles are responsible for 10 to 18% of the visibility 

impairment in the East and 25 to 40% in the West. Nitrates account for only 7 to 16% of the 

light extinction in the East but are responsible for between 5 to 45% of the light extinction in the 

West. Crustal material can be a major contributor in the West, accounting for 5 to 25% of the 

light extinction. Elemental carbon also is contributor to light extinction, but to a lesser degree 

(U.S. Environmental Protection Agency, 2001a,b). 

The EPA designated five regional groupings as part of the regional haze program. The 

regions are Northeast (Mid-Atlantic/Northeast Visibility Union), Southeast (Visibility 

Improvement State and Tribal Association of the Southeast), Central (Central States Regional 

and Air Partnership), Midwest (Midwest Regional Planning Organization), and West (Western 

Regional Air Partnership). The regional groupings serve as consensus organizations comprised 

of states, tribes, and federal agencies coordinating the implementation of the regional haze rule. 

Using hourly prevailing daytime visibility data from human observations at weather 

stations, Schichtel et al. (2001) observed that haziness declined approximately 10% across the 

United States between 1980 and 1995. The decrease in haziness was highest in the southeastern 

United States with a 20% decrease in the 90th percentile light extinction and a 12% decrease in 

the 75th percentile over the 15-year period. There was a 17% decrease in the 90th percentile and 

a 9% decrease in the 75th percentile over the eastern United States. Over the eastern United 

States, haziness was greatest during the summer months. The greatest visibility impairment was 

adjacent to the Appalachian Mountains in Tennessee and the Carolinas (extinction coefficient of 

> 0.2 km-1 equivalent to 6 miles). During the cold season, elevated haze (extinction coefficient 

of > 0.2 km-1) was seen between the Great Lakes and the Ohio River Valley, over the gulf states 

between Texas and Florida, along the coast from North Carolina to New Jersey, and along the 

Pacific coast, particularly central and south California. 

Visibility impairment or haziness in the southeastern United States from sulfate emissions 

is greatest in the humid summer months because of the ability of sulfate to absorb atmospheric 

water vapor. Summer haziness increased in the southeastern United States from the 1950s to 

1980 along with increasing SO2 emissions. A statistically significant increase in summer sulfate 

concentrations was noted in two Class I areas in the eastern United States (Shenandoah and the 

Great Smoky Mountains) from 1982 to 1992 (Eldred et al., 1993; Cahill et al., 1996). During 

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that time, the majority of the Southwest showed decreasing sulfur concentrations (Eldred et al., 

1993; Eldred and Cahill, 1994). Increasing summer sulfate concentrations were later shown at 

those two locations by Iyer et al. (2000). 

Limited visibility measurements are available for the upper Midwest region (Illinois, 

Indiana, Michigan, Ohio, and Wisconsin and the tribal lands located in those states). The 

Midwest Regional Planning Organization conducted an initial assessment of the regional haze 

problem in this region using existing reports and available air quality data for four major urban 

areas (St. Louis, Chicago, Detroit, Cincinnati) and Class I areas. The “worst” and “best” 

visibility days occur throughout the year. Particulate sulfates were the major contributors to 

light extinction during the summer months, and nitrates dominated on the worst visibility days 

during the winter and fall in both urban and Class I areas. Organics also were significant 

contributors to light extinction in urban areas. Higher PM2.5 concentrations were correlated with 

poorer visibility in the southern portion of the region (Midwest Regional Planning Organization, 

2001). 

The EPA’s National Air Quality and Emission Trends Report summarized an estimate of 

the regional trends and current conditions in 35 Class I areas and one urban area (Washington, 

DC), using chemical concentrations data from the IMPROVE network (U.S. Environmental 

Protection Agency, 2001a). The visibility trends analysis is an aggregate of 10 eastern Class I 

areas and 26 western Class I areas. Trends were presented for annual average values for the 

clearest (“best”) 20% , middle (“typical”) 20%, and haziest (“worst”) 20% of the days monitored 

each year. The visibility trends, given in changes in deciview values, for the eastern and western 

sites are illustrated in Figures 4-39a and 4-39b. From the figures, it can be seen that the haziest 

days in the West are equivalent to the best days in the East. In the East, there was a 16% 

(1.5 deciview) improvement in haziness on the clearest days since 1992. Improvements in 

visibility were noted in the East for the haziest days. However, based on monitoring data for 

1999, visibility remains significantly impaired, with a visual range of 23 km for the haziest days 

compared to a mean visual range of 84 km for the clearest days. A 25% and a 14% improvement 

in visibility impairment were seen for the clearest and middle days in the West, respectively; 

whereas conditions for the haziest days degraded by 18.5% (1.7 deciviews) between 1997 and 

1999, but were relatively unchanged compared to 1990 conditions (U.S. Environmental 

Protection Agency, 2001a). 

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Figure 4-39a. Aggregate visibility trends (in deciviews) for 10 eastern Class 1 areas. 

Figure 4-39b. Aggregate visibility trends (in deciviews) for 26 western Class 1 areas. 

Source: U.S. Environmental Protection Agency (2001a). 

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Figures 4-40a and 4-40b illustrate aggregate trends in aerosol light extinction, including 

trends by major aerosol component for the haziest 20% of days monitored for the 10 eastern 

Class I areas from 1992 to 1999 and the haziest 20% of days monitored for the 26 western 

Class I areas from 1990 to 1999. The National Ambient Air Trends Report also includes a 

number of maps characterizing aerosol light extinction and key components at 36 IMPROVE 

sites (all rural except Washington, DC) for 1997 through 1999 (U.S. Environmental Protection 

Agency, 2001a). 

Urban Trends. 

Most of the available visibility measurements, with the exception of the airport visual 

range measurements, focus on the impact of pollution on visibility in scenic vistas and regional 

haze (Class I areas). Many urban metropolitan areas monitor daily visibility conditions. These 

findings are generally not available in a published form and may not distinguish between 

pollution- and weather-related effects. Although the EPA Regional Haze Rule addresses 

visibility impairment in Class I areas and calls for states to establish goals for improving 

visibility in these areas and to develop long-term strategies for reducing emissions of air 

pollutants that cause visibility impairment, the steps states take to implement the regulation will 

also improve visibility and health in broad areas across the country. 

Kleeman et al. (2001), citing previously published studies, provided an historical 

description of visibility conditions in Southern California from the early 1930s. Based on airport 

observation data for 1932 to 1949, visibility conditions began to decrease in Los Angeles with 

the advent of industrialization and population growth. Visibility conditions were worse during 

the 1940s than the 1930s, with the lowest visibility conditions occurring between 1944 and 1947. 

During this period, there was nearly a complete loss of extremely good visibility days. Between 

1943 and 1947, the number of extremely good visibility days during the summer season dropped 

from 21% to 0.2%. Between 1950 and 1961, deteriorating visibility conditions extended 

eastward from Los Angeles along the corridor adjacent to the foothills of the San Gabriel and 

San Bernardino Mountains. The visual range in the areas nearest Los Angeles was # 3 miles for 

more than 140 days per year when the relative humidity was < 70%. Further east of Los 

Angeles, past Ontario and San Bernardino, the visibility was # 3 miles for 110 days per year 

during the same time period. Improvements in visibility conditions have been made since the 

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Figure 4-40. (a) Eastern class I area aggregate trends in aerosol light extinction on 

the 20% haziest days, including trends by major aerosol component; 

(b) Western class I area aggregate trends in aerosol light extinction on

the 20% haziest days, including trends by major aerosol component. 

Source: U.S. Environmental Protection Agency (2001a). 

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1970s. The greatest improvements occurred in the western Los Angeles Basin. In Ontario, the 

average number of days per year when the visual range was > 10 miles was 99 between 1976 and 

1978 and increased to 113 between 1988 and 1990 (Kleeman et al., 2001). 

In contrast to the Los Angeles Basin and Ontario area, Denver and surrounding areas have 

experienced high pollution episodes since the 1970s. Climate changes contributed to the high 

pollution episodes by trapping cooler air under a cover of warm air, causing the pollution to 

remain stagnant over the area and producing a brown cloud comprising a variety pollutants 

including nitrogen and sulfur oxides, as well as grit and dust. Debate over the cause of the 

increasing pollution in the Denver area and the controversy over converting coal-fired power 

plants to a cleaner natural gas system led to the initiation of the 1973 Denver Air Pollution 

Study, 1978 Denver Winter Haze Study, 1987-1988 Metro Denver Brown Cloud Study, and the 

1993 Denver Brown Cloud Modeling Study. In 1990, in an effort to improve air quality, Denver 

adopted a visibility standard of 0.076 km!1

 (units of atmospheric extinction per kilometer; 

20.1 deciviews) averaged over 4 hours. While this is a step towards reducing air pollution, the 

Denver region still exceeds the visibility standard 50 to 80 times per year (Lloyd, 2002). 

During the fall and winter of 1988 to 1990, a major air quality study was conducted in 

Phoenix, AZ to address degrading visibility conditions in Phoenix and other urban areas. The 

objectives were to (1) develop a data base of visibility, air quality, and meteorological 

measurements; (2) establish quantitative relationships between light extinction and emission 

sources; and (3) evaluate measurement systems for short-term and long-term monitoring in 

Phoenix. The major contributors to light extinction in Phoenix were residual wood burning, 

primary mobile source emissions, and secondary ammonium nitrate (Chow et al., 1990). 

The Arizona Department of Environmental Quality has conducted optical measurements of 

visibility in Tucson since 1993 and in Phoenix since 1994. The measurements are divided into 

the mean of the “dirtiest” 20% of all hours, the mean of all hours, and the mean of the “cleanest” 

20% of all hours for the entire day and for the 5:00 to 11:00 a.m. period. Figures 4-41a,b

represent the trends in visibility conditions from 1993 to 2001 for Tucson and from 1994 to 2001 

for Phoenix. Visibility on the dirtiest days in the Phoenix metropolitan area has not changed 

since visibility monitoring started; but, visibility on the best days has significantly degraded. 

There is a seasonally related effect on visibility in Phoenix: the dirtiest 20% of all hourly light 

extinction and the mean of all hours are more pronounced during the winter and fall months 

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Figure 4-41a. Light extinction trends in Tucson, Arizona from 1993 to 2002. 

Source: Arizona Department of Environmental Quality (2002). 

Figure 4-41b. Light extinction trends in Phoenix, Arizona from 1994 to 2001. 

Source: Arizona Department of Environmental Quality (2002). 

4-184 
(Arizona Department of Environmental Quality, 2002). Daytime visibility is worst during the 

morning. Samples taken from 1994 to 1996 showed that organic and elemental carbon 

dominated visibility impairment during the dirtiest and cleanest days, whereas ammonium nitrate 

was an important contributor during the 20% dirtiest days (Neuroth and Heisler, 2000). 

The State of Virginia compared visibility trends at five locations both with and without 

pre-1948 airport visual range data. Average annual airport visual-range-data from Roanoke 

(1936 to 1998), DC Reagan National (1930 to 1998), Richmond (1942 to 1998), Lynchburg 

(1935 to 1998), and Elkins, WV (1936 to 1994) were used in the analysis. Trends in visibility 

conditions were dependent on the baseline year used for visibility measurements. When airport 

visual range data from 1948 are used as the baseline data for visibility conditions, the trend in 

visual range shows declining conditions for all sites. When pre-1948 data are included in the 

analysis, visibility conditions basically are unchanged or improved (except for Elkins). When 

1948 data are used as the baseline for visibility trends measurements, visual range decreases at 

all locations. Average annual visual ranged varied between 7+ miles and 8+ miles for all sites 

(Virginia Climate Advisory, 2000). 

4.3.6.2 Current Conditions 

Current visibility conditions have been well-characterized for Class I areas using updated 

data from the IMPROVE network (U.S. Environmental Protection Agency, 2001a; Malm, 2000; 

IMPROVE, 1998). During recent decades, daytime visibility conditions at all major airports 

throughout the United States were recorded hourly by human observation. These data were used 

to determine current visibility conditions and visibility trends in the United States, as well as the 

spatial distribution of visibility conditions (Trijonis et al., 1991). The use of human observation 

is being replaced by an automated observation system, the Automated Surface Observing System 

(ASOS). More than 900 airports are currently commissioned. In addition, the EPA has 

deployed a new national monitoring network to assess PM2.5 concentrations and composition. 

More detailed information on visibility conditions for urban and suburban areas will 

become more widely available as data from the national PM2.5 speciation monitoring network 

and the ASOS airport visibility network are further analyzed. Efforts are currently underway to 

develop a web-based system to allow the use of the high resolution ASOS data in air quality 

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monitoring and assessment programs. The objectives are to collect and quality control an 

archive of the ASOS visibility data, to deliver processed hourly visibility data to public and air 

quality communities, and to use the web-based system to support the acquisition and 

dissemination of visibility data (Falke, 2001). 

4.3.7 Societal Impacts of Particulate Matter Visibility Effects 

Society recognizes the need to impose remedies for repairing and preventing further 

anthropogenic pollutant-related effects on visibility conditions (Ely et al., 1991; Davidson 

et al., 2000a). Information about how the public perceives and values improvements in visibility 

comes from both economic studies and from local or state initiatives to adopt local visibility 

goals and strategies. 

4.3.7.1 Economic Studies 

Various methods have been utilized to help determine the economic valuation of changes 

in visibility. Where possible, direct economic valuation can be determined using marketplace 

cost estimates. Avoided-cost methods estimate the costs of pollution by using the expenditures 

that are made necessary by pollution damage. As an example, if ambient levels of PM result in 

increased frequency of building cleaning or repainting, the appropriately calculated increase in 

these costs is a reasonable estimate of true economic damage. Benefits associated with 

reductions in the pollution levels are then represented by the avoided costs of these damages. 

Estimating the benefits of clear skies is a more difficult and less precise exercise, because, 

although the public values aesthetic views, they are not directly bought and sold in the 

marketplace. However, there are several methods available to economists to estimate the 

economic impact of these kinds of changes in environmental conditions (Freeman, 1993). These 

methods include hedonic valuation or pricing, contingent valuation and contingent choice, and 

travel cost (Johnson and Desvousges, 1997; Hanley and Spash, 1993). The primary methods 

used to date for the valuation of visibility have been the hedonic price and contingent valuation 

methods (Hanley and Spash, 1993). However, this is not an exact science, and there are still 

issues and limitations associated with each of these methods. 

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Hedonic pricing can be used to estimate economic valuations for environmental effects that 

have a direct effect on market values. It relies on the measurement of differentials in property 

values under various environmental quality conditions including air pollution and environmental 

amenities, such as aesthetic views. The hedonic method works by analyzing the way that market 

prices change with changes in environmental quality. Part of the economic costs imposed by 

PM-related reductions in visibility can be estimated by looking at the differences in sales price 

between otherwise identical houses that have different degrees of visibility impairment. 

The contingent valuation method (CVM) is the most widely used method for estimating 

value changes in both visibility and ecosystem functions (Hanley and Spash, 1993; Chestnut, 

1997; Watson and Chow, 1993). The CVM creates hypothetical markets for goods and services 

that have no market-determined price. As part of the evaluation, individuals are shown 

photographs with perceivable differences in visibility levels. Carefully structured surveys are 

administered to estimate the amount of compensation equivalent to a given change in 

environmental quality or, equivalently, how much an individual would be willing to pay (WTP) 

for improvements in environmental quality or willing to accept (WTA) existing conditions 

without further deterioration. There is an extensive scientific literature and body of practice on 

both this theory and technique; however, there are still concerns about the use of this technique 

for quantitative purposes. 

The travel-cost method estimates can be used to estimate the value of recreational benefits 

of an ecosystem based on the environmental quality at the site. The travel-cost method uses 

information on actual behavior rather than responses to hypothetical scenarios. The time and 

travel expenses incurred to visit a site represents the price of access to the site. The willingness 

to pay to maintain the site is determined by the number of times the individual visits the site at 

different travel costs. 

The effects of PM on visibility may differ widely between urban residential and 

recreational areas. Therefore, separate estimates are needed to account for impacts associated 

with changes in visibility in residential and recreational (Class I) areas. Chestnut and Dennis 

(1997) compared the findings of the more recent studies on the economic impact of changes in 

regional haze using the contingent valuation method in residential areas in several eastern cities 

and in Los Angeles and San Francisco and using the hedonic value method in Los Angeles and 

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San Francisco. The findings of the contingent valuation studies are shown in Table 4-16. 

Findings using the contingent and hedonic methods of valuation for Los Angeles and San 

Francisco are compared in Table 4-17. 

Using the contingent valuation method, Chestnut and Rowe (1990) found that 83% of those 

individuals responding to a survey on visibility were willing to pay to improve visibility in the 

national parks. Survey participants were selected from California, Arizona, Missouri, 

New York, and Virginia. The national parks from three regions (California, southwestern United 

States, and southeastern United States) were considered in different versions of the survey. 

The survey included questions on past and future visitations to national parks, potential pollution 

effects from human activities outside of the park, three hypothetical visibility scenarios, 

socioeconomic characteristics, and various photographic presentations of visibility conditions 

within the parks. Higher responses were noted for residents residing in the state or region where 

the national park was located; responses for males and the elderly were generally lower and there 

was a direct correlation between household income and the response. 

Using the results from the Chestnut and Rowe (1990) study, Chestnut and Dennis (1997) 

calculated an extinction coefficient of 85 for in-state residents and 50 for out-of-state residents. 

These extinction coefficients were suggested to represent an annual willingness to pay per 

household of $15 and $9, in 1994 dollars, for a 20% improvement in visual range. 

4.3.7.2 Public Perception and Attitude Studies 

An initiative in Denver to address public concerns about visibility impairment began with a 

series of visibility-related studies in the 1970s through the 1980s, leading to the adoption of a 

visibility standard for the city of Denver in 1990. This standard is based on a light extinction 

level of 0.076 km–1, averaged over four daylight hours, reflecting the short-term nature of the 

perception of changes in visibility conditions. This standard is equivalent to a visual range of 

approximately 50 km. The approach used to develop this standard relied upon citizen judgments 

about acceptable and unacceptable levels of visual air quality (Ely et al., 1991). In Phoenix, 

a study was conducted between 1988 and 1990 to address degrading visibility conditions (Chow 

et al., 1990). This work led the Arizona Department of Environmental Quality to establish a 

Blue Sky Index, which focuses on days in which the visual range, averaged over six daylight 

hours, is 40 km or more. This target is based on a method very similar to that used in Denver for 

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TABLE 4-16. RESIDENTIAL VISIBILITY CONTINGENT VALUATION

STUDY RESULTS

City 

Mean WTP 

($1994) 

Starting-Ending 

Visual Range (VR) 

(miles) 

Extinction 

Coefficient 

WTP for 20% 

Change in VR 

($) Reference 

Atlanta

Chicago

 Unadj. $44 

Partial $28 

17.6-20 346 

222

$63 McClelland 

et al. (1993) 

Full $20 159 

Chicago !$361 

$346 

38233 

38247 

416 $76 Tolley et al. 

(1986) 

$430 38259 

Atlanta !$301 38327 469 $86 

$289 38342 

$432 12-32 

Boston !$222 18-13 422 $77 

$212 18-28 

$262 18-38 

Mobile !$240 38264 312 $57 

$257 38279 

$302 38289 

Washington, DC !$356 15-10 635 $116 

$366 15-25 

$465 15-35 

Cincinnati !$88 38233 120 $22 

$87 38248 

$98 38258 

Miami !$152 38359 256 $47 

$136 38370 

$160 38380 

Cincinnati $198 11.4-16.4 602 $110 Rae (1983) 

Source: Chestnut and Dennis (1997). 

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TABLE 4-17. RESIDENTIAL VISIBILITY VALUATION STUDY RESULTS FOR

LOS ANGELES AND SAN FRANCISCO

Starting-Ending WTP for 

Mean Visual Range Extinction 20% Change 

City Method WTP (VR) (miles) Coefficient in VR Reference 

Los Angeles CVM $130 2-12 1191328 $22 Brookshire 

$333 2-28 et al. (1979) 

$183 12-28 $242 

18.6-16.3 

Property 16.3-18.3 $245-647 Trijonis et al. 

Value (1985) 

San Francisco CVM $211 Loehman et al. 

$124 (1985) 

Property Trijonis et al. 

Value $496-552 (1985) 

Adapted from: Chestnut and Dennis (1997). 

obtaining citizens' judgments as to acceptable levels of visual air quality (BBC Research and 

Consulting, 2003). While in practice these standard target values are exceeded many times per 

year in these areas, they reflect a reasonable degree of consistency in the outcome of the 

approach used to characterize the value that citizens in these two urban areas place on visual 

air quality. 

Studies conducted in other locations have resulted in similar visibility threshold 

determinations, convergent on a minimal visual range of 40 to 60 km. For example, using a 

methodology similar to that used by Ely et al. (1991) in Denver, Pryor (1996) conducted a study 

of the perception of acceptable visibility conditions in two suburban locations in the lower Fraser 

Valley in British Columbia, CN. This study reflected public judgments about the acceptability 

of visual ranges of approximately 40 km to 60 km. Other notable visibility protection initiatives 

have resulted in state visibility standards that are within this range, including standards for the 

Lake Tahoe area (Molenar, 2000) and the state of Vermont. 

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4.4 PARTICULATE MATTER EFFECTS ON MATERIALS 

Effects of air pollution on materials are related to both aesthetic appeal and physical 

damage. Studies have demonstrated that particles, primarily carbonaceous compounds, cause 

soiling of commonly used building materials and culturally important items, such as statutes and 

works of art. Physical damage from the dry deposition of air pollutants, such as PM (especially 

sulfates and nitrates) and SO2, and the absorption or adsorption of corrosive agents on deposited 

particles also can result in the acceleration of naturally occurring weathering processes of 

man-made building and cultural materials. 

In the atmosphere, PM may be “primary,” existing in the same form in which it was 

emitted, or “secondary,” formed by the chemical reactions of free, absorbed, or dissolved gases. 

The major constituents of atmospheric PM are sulfate, nitrate, ammonium, and hydrogen ions; 

particle-bound water; elemental carbon; a great variety of organic compounds; and crustal 

material. A substantial fraction of the fine particle mass, particularly during the warmer months, 

is secondary sulfate and nitrate. Sulfates may be formed by the gas-phase conversion of SO2 to 

H2SO4 by OH radicals and aqueous-phase reactions of SO2 with H2O2, O3, or O2. During the day, 

NO2 may be converted to nitric acid (HNO3) by reacting with OH radicals. Nitrogen dioxide 

also can be oxidized to HNO3 by a sequence of reactions initiated by O3. A more detailed 

discussion of the atmospheric chemistry of PM appears in Chapter 2 of this document. 

4.4.1 Corrosive Effects of Particles and Sulfur Dioxide on 

Man-Made Surfaces 

This section (a) summarizes information on exposure-related effects on materials 

associated with sulfur-containing pollutants (formed by chemical reactions of SO2 with other 

atmospheric pollutants) as addressed in the 1996 PM AQCD (U.S. Environmental Protection 

Agency, 1996a) and (b) presents relevant information derived from very limited research 

conducted and published since completion of that document. The effects of nitrates on 

man-made building materials and naturally occurring cultural materials were discussed in an 

earlier EPA Nitrogen Oxides Criteria Document (U.S. Environmental Protection Agency, 1993). 

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4.4.1.1 Metals 

Metals undergo natural weathering processes in the absence of environmental pollutants. 

The additive effects of pollutants on the natural weathering processes depend on the nature of the 

pollutant, the deposition rate (the uptake of a pollutant by the material’s surface), and the 

presence of moisture. The influence of the metal-protective corrosion film, the presence of other 

surface electrolytes, the orientation of the metal surface, the presence of surface moisture, and 

the variability in the electrochemical reactions also contributes to pollutant exposure effects on 

metal surfaces. 

Several studies demonstrate the importance of the duration of surface wetness (caused by 

dew and fog condensation and rain) on metals. Surface moisture facilitates the deposition of 

pollutants, especially SO2, and promotes corrosive electrochemical reactions on metals (Haynie 

and Upham, 1974; Sydberger and Ericsson, 1977). Of critical importance is the formation of 

hygroscopic salts on the metal, increasing the duration of surface wetness and, thereby, 

enhancing the corrosion process. 

The effect of temperature on the rate of corrosion is complex. Under normal temperature 

conditions, temperature does not have an effect on the rate of corrosion; but when the 

temperature decreases, the relative humidity increases and the diffusivity decreases. Pitchford 

and McMurry (1994) and Zhang et al. (1993) demonstrated particle-size-related effects of 

relative humidity. The corrosion rate decreases as the temperature approaches freezing, because 

ice inhibits the diffusion of SO2 to the metal surface and minimizes electrochemical processes 

(Haynie, 1980; Biefer, 1981; Sereda, 1974). 

The metal-protective corrosion film (i.e., the rust layer on metal surfaces) provides some 

protection against further corrosion. The effectiveness of the corrosion film in slowing down the 

corrosion process is affected by the solubility of the corrosion layer and the concentration and 

deposition rate of pollutants. If the metal-protective corrosion film is insoluble, it may add some 

protection against acidic pollutants. An atmospheric corrosion model that considers the 

formation and dissolution of the corrosion film on galvanized steel was proposed by Spence 

et al. (1992). The model considers the effects of SO2, rain acidity, and duration of wetness on 

the rate of corrosion. Although the model does not specifically characterize particle effects, the 

contribution of particulate sulfate was considered in model development. 

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Whether suspended particles actually increase metal corrosion is not clear. Several studies 

have suggested that suspended particles promote the corrosion of metals (Goodwin et al., 1969; 

Barton, 1958; Sanyal and Singhania, 1956; Baedecker et al., 1991); however, other studies have 

not demonstrated a correlation between particle exposure and metal corrosion (Mansfeld, 1980; 

Edney et al., 1989). Walton et al. (1982) suggested that catalytic species within several species 

in fly ash promote the oxidation of SOx to a corrosive state. Still other researchers indicate that 

the catalytic effect of particles is not significant and that the corrosion rate is dependent on the 

conductance of the thin-film surface electrolytes during periods of wetness. Soluble particles 

likely increase the solution conductance (Skerry et al., 1988; Askey et al., 1993). 

The corrosion of most ferrous metals (iron, steel, and steel alloys) increases with increasing 

SO2 exposure. Steels are susceptible to corrosion when exposed to SO2 in the absence of 

protective organic or metallic coatings. Studies on the corrosive effects of SO2 on steel indicate 

that the rate of corrosion increases with increasing SO2 and is dependent on the deposition rate of 

the SO2 (Baedecker et al., 1991; Butlin et al., 1992a). The corrosive effects of SO2 on aluminum 

is exposure-dependent, but appears to be insignificant (Haynie, 1976; Fink et al., 1971; Butlin 

et al., 1992a). The rate of formation of the patina (protective covering) on copper can take as 

long as five years and is dependent on the SO2 concentration, deposition rate, temperature, and 

relative humidity (Simpson and Horrobin, 1970). Further corrosion is controlled by the 

availability of copper to react with deposited pollutants (Graedel et al., 1987). Butlin et al. 

(1992a), Baedecker et al. (1991), and Cramer et al. (1989) reported an average corrosion rate of 

1 μm/year for copper; however, less than a third of the corrosion was attributed to SO2 exposure, 

suggesting that the rate of patina formation was more dependent on factors other than SO2. 

A report by Strandberg and Johansson (1997) showed relative humidity to be the primary factor 

in copper corrosion and patina formation. The results of the studies on particles and SO2 

corrosion of metals are summarized in Table 4-18. 

4.4.1.2 Painted Finishes 

Exposure to air pollutants affects the durability of paint finishes by promoting 

discoloration, chalking, loss of gloss, erosion, blistering, and peeling. Evidence exists that 

particles can damage painted finishes by serving as carriers for corrosive pollutants (Cowling 

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TABLE 4-18. CORROSIVE EFFECTS OF PARTICULATE MATTER AND SULFUR DIOXIDE 

Metal Exposure Conditions Comments Mild Steel Galvanized Steel 

Specimens exposed to SO2 and O3 under natural and 

artificial conditions, and to NO2 under natural 

conditions. SO2 concentrations ranged from 2.1-60 

μg/m3 . Annual average concentrations were about 

20 μg/m3 . Meteorological conditions were 

unaltered. Specimens exposed at 29 sites for 2 years for mild steel and 1 year for galvanized steel. 

Steel corrosion was dependent on long-term SO2 

exposure. The corrosion rate was about 50 μm/year for mild steel specimens for most industrial sites, but ranged from 21 to 71 μm/year. The corrosion rate ranged from 1.45 to 4.25 μm/year for galvanized steel. The authors concluded that rainfall also may have a significant effect on galvanized steel based on a corrosion rate of 3.4 μm/year seen at a very wet site. 

Zinc Rolled zinc specimens exposed at various sites 

around the country (rural, industrialized, marine) for up to 20 years. Actual pollutant exposures not reported. 

The highest corrosion rates were associated with industrialized environments and marine environments in direct contact with salt spray. 

Zinc Specimens exposed at 5 sites for 1-5 years. 

Average SO2 concentrations ranged from 2 ± 4 to 

15 ± 17 ppb (5.2 ± 10.4 to 39.3 ± 44.5 μg/m3). 

PM concentrations ranged from 14 to 60 μg/m3 . 

Highest pollutant concentrations recorded at 1-year exposure site. 

Average corrosion rate ranged from 0.63-1.33 μm per year. The highest corrosion was noted in the most industrialized area. However, the corrosion rates did not differ significant regardless of the SO2 

concentration, suggesting that SO2 exposure may 

not be the dominant factor in zinc corrosion. 

Carbon Steel Weathering Steel 

See Baedecker et al. (1991) above for exposure conditions. 

Average corrosion rate for samples exposed for 5 years ranged from 6.6-12.8 μm/year for carbon steel and 3.7-5.0 μm/year for weathering steel. Highest corrosion rate noted for samples exposed for 1 year. 

Aluminum See Baedecker et al. (1991) above for exposure 

conditions. 

Corrosion rate was very low at all sites and ranged from 0.036-0.106 μm/year. 

Aluminum See Butlin et al. (1992a) above for exposure 

conditions. 

Corrosion greater on the underside of specimens, possibly because of lack of washoff and increased PM in area. Maximum corrosion rate was 0.85 μm/year. Pit depths of up to 72 μm were noted after 2 years of exposure. 

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TABLE 4-18 (cont’d). CORROSIVE EFFECTS OF PARTICULATE MATTER AND SULFUR DIOXI

Metal Exposure Conditions Comments Copper See Baedecker et al. (1991) above for exposure Average corrosion rate for 3- and 5-year exposures 

conditions. was about 1 μm/year but the soluble portion was less 

than a third of that which could be contributed to SO2 

exposure. Dry deposition of SO2 was not as important 

in patina formation as wet deposition of H+ . 

Copper See Butlin et al. (1992a) above for exposure Majority of test sites showed a corrosion rate of 

conditions. 1 ± 0.2 μm/year. The corrosion rate was 1.48 μm/year 

at the site receiving the most rainfall. The lowest corrosion rate, 0.66 μm/year, was associated with low rainfall, low SO2. 

Copper Specimens exposed to 4-69 ppb (10.4-180.7 μg/m3) 

and 1.0 ppm (2,618.7 μg/m3) SO2 for 20 h at 

SO2 had no effect on copper when relative humidity 

was < 75%. Increasing relative humidity increases 

various relative humidities. patina formation in presence of trace SO2. 

No SO2-related effects were noted on copper 

specimens exposed to high SO2 regardless of the 

percent relative humidity. 

Copper Specimens exposed artificially to 0.49 ± 0.01 ppm 

(187 ± 3.8 μg/m3) SO2 for 4 weeks at 70 and 90% 

Corrosive effect of SO2 on copper increased with 

increasing relative humidity. 

relative humidity. 

Iron Specimens from restorations of Acropolis Specimens used after 1950s had an oxidation rate 

monuments over many years. The oldest specimens 25% greater than those specimens before 1950. were 142 years old. Other specimens used for The accelerated oxidation had a negative impact monument restoration after 1950. on the structure, producing a quicker formation of 

cracks inside the marble. 

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and Roberts, 1954) or by staining and pitting of the painted surfaces (Fochtman and Langer, 

1957; Wolff et al., 1990). 

The erosion rate of oil-based house paint has been reported to be enhanced by exposure to 

SO2 and high humidity. An erosion rate of 36.71 ± 8.03 μm/year was noted for oil-based house 

paint samples exposed to SO2 (78.6 μg/m3

), O3 (156.8 μg/m3), and NO2 (94 μg/m3), and low 

humidity (50%) (Spence et al., 1975). The erosion rate increased with increased SO2 and 

humidity. The authors concluded that SO2 and humidity accounted for 61% of the erosion. 

Acrylic and vinyl coil coatings show less pollutant-related erosion. Erosion rates range from 

0.7 to 1.3 μm/year and 1.4 to 5.3 μm/year, respectively. Similar findings on SO2-related erosion 

of oil-based house paints and coil coatings have been reported by other researchers (Davis et al., 

1990; Yocom and Grappone, 1976; Yocom and Upham, 1977; Campbell et al., 1974). Several 

studies have suggested that the effect of SO2 is caused by its reaction with extender pigments 

such as calcium carbonate and zinc oxide (Campbell et al., 1974; Xu and Balik, 1989; Edney, 

1989; Edney et al., 1988, 1989). However, Miller et al. (1992) suggested that calcium carbonate 

acts to protect paint substrates. Another study indicated that exposure to SO2 can increase the 

drying time of some paints by reacting with certain drying oils and will compete with the 

auto-oxidative curing mechanism responsible for crosslinking the binder (Holbrow, 1962). 

4.4.1.3 Stone and Concrete 

Numerous studies suggest that air pollutants can enhance the natural weathering processes 

on building stone. The development of crusts on stone monuments has been attributed to the 

interaction of the stone’s surface with sulfur-containing pollutants, wet or dry deposition of 

atmospheric particles, and dry deposition of gypsum particles from the atmosphere. Because of 

a greater porosity and specific surface, mortars have a greater potential for reacting with 

environmental pollutants (Zappia et al., 1998). Details on these studies are discussed in 

Table 4-19. The stones most susceptible to the deteriorating effects of sulfur-containing 

pollutants are the calcareous stones (limestone, marble, and carbonated cement). Exposurerelated damage to building stones result from the formation of salts in the stone that are 

subsequently washed away during rain events, leaving the stone surface more susceptible to the 

effects of pollutants. Dry deposition of sulfur-containing pollutants promotes the formation of 

gypsum on the stone’s surface. Gypsum is a gray to black crusty material comprised mainly 

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TABLE 4-19. CORROSIVE EFFECTS OF PARTICULATE MATTER AND SULFUR DIOXIDE 

Stone Exposure Conditions Comments Vermont marble Runoff water was analyzed from seven summer Between 10 and 50% of calcium in runoff water estimated from 

storms. SO2 concentration stated to be low. gypsum formation from dry deposition of SO2. 

Marble sandstone Analysis of runoff water for five slabs test-exposed Pollutant exposure related erosion was primarily caused by dry 

to ambient conditions at a angle of 30° to horizontal. deposition of SO2 and nitric acid between rain events and wet de

of hydrogen ion. Recession estimates ranged from 15-30 μm/yemarble and 25-45 μm/year for limestone. A large portion of the results from the reaction of CO2 with the calcium in the stone. 

Limestone Ambient air conditions. Exposure ranged from Increased stone weight loss with increased SO2. Rainfall did not 

70-1,065 days. Averaged pollutant exposure significantly affect stone degradation. Stone loss associated with ranged from 1.4-20.4 ppb (3.7-53.4 μg/m3) SO2; SO2 exposure estimated to be 24 μm/year. Slight trend in decrea

4.1-41.1 ppb NOx; 2.4-17.4 ppb (4.5-32.7 μg/m3) stone loss with increasing length of exposure. NO2; 10.1-25.6 ppb (19.8-50.2 μg/m3) O3. 

Portland limestone Experimental tablets exposed under sheltered and Significant correlations existed between the mean annual SO2 White Mansfield unsheltered ambient air conditions. Exposure for concentration, rainfall volume, and hydrogen ion loading and  dolomitic sandstone l and 2 years. the weight changes. Monk’s Park limestone 

Sandstones (calcite and Ambient air; low concentrations of sulfates, SO2, Insignificant differences in erosion rate found between calcite 

noncalcite stones) and nitrates; RH sufficient to produce condensation and noncalcite sandstone. Moisture affected the rate of pollutan

on stones rarely occurred. deposition and enhanced susceptibility to pollutant related erosio

Rain events given as primary factor affecting stone erosion. Pollutant related erosion judged to be insignificant. 

Limestones Ambient air; urban and rural locations in Crusts on stones were found to contain two layers. Top layer, usSandstones Mediterranean. black in color, composed of gypsum between 40 and 400 μm thiMarble Innermost layer, ranging from brown to orange in color, primariGranite consisted of calcite, between 10 and 600 μm thick. Gypsum-rich Basalt layer thought to be the result of sulfation of the calcitic layer by 

atmospheric pollutants or dry or wet deposition of atmospheric d

Portland limestone Samples exposed to SO2, NO2, and NO at 10 ppmv, In the absence of moisture, little reaction is seen. Sulfur dioxide Massangis Jaune Roche both with and without O3 and under dry (coming to oxidized to sulfates in the presence of moisture. The effect is en limestone equilibrium with the 84% RH) or wetted with in the presence of O3. Massangis Jaune Roche limestone was the White Mansfield CO2-equilibrated deionized water conditions. affected by the pollutant exposure. Crust lined pores of specime dolomitic Exposure was for 30 days. exposed to SO2. 

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TABLE 4-19 (cont’d). CORROSIVE EFFECTS OF PARTICULATE MATTER AND SULFUR DIOX

Stone Exposure Conditions Comments Monk’s Park limestone Samples exposed for 2 months under both sheltered and Significant amounts of gypsum were noted on the Portland Portland limestone unsheltered conditions. Mean daily atmospheric SO2 Sheltered stones also showed soiling by carbonaceous part

concentration was 68.7 μg/m3 and several heavy rainfalls. and other combustion products. Etch holes and deep etchin

noted in some of the exposed unsheltered samples. 

Carrara marble Sample exposed in laboratory to 3 ppm SO2 and 95% RH Exposure to particles from combustion processes enhanced Travertine at 25 °C for 150 days. Samples were coated with three sulfation of calcareous materials by SO2 because of metal cTranistone carbonaceous particle samples from combustion sources, of particles. 

and with activated carbon and graphite. 

Carrara marble Samples exposed in sheltered ambient environment for 6, Carrara marble found to be more reactive with SO2 than GGeorgia marble 12, or 20 months. marble, possibly because of the compactness of the Georgi

marble. Greater effects noted when samples were also expto NO2. 

Carrara marble Samples exposed for 6 months (cold and hot conditions) Pollutant exposed samples showed increased weight gain o

in ambient environment. PM concentrations ranged from that expected from natural weathering processes. There w57.3 to 116.7 μg/m3 (site 1) and 88 to 189.8 μg/m3 (site 2). blackening of stone samples exposed to carbonaceous rich 

Some exposures also were associated with high SO2, NO, 

and NO2. 

Monk’s Park limestone Samples artificially exposed to fly-ash containing Exposure to fly-ash did not enhance oxidation of SO2 to suPortland limestone 1,309.3 μg/m3 SO2 (0.5 ppm) at 95% RH and 25 °C for Mineral oxides in fly ash contributed to sulphation of CaC

81 or 140 days. Fly-ash samples from five different sources were used in the study. 

Lime mortar Samples exposed to 7,856 μg/m3 (3 ppm) SO2 at 100% Exposure to SO2 produced significant quantities of calcium Pozzolan mortar RH and 25 °C for 30, 60, or 90 days; samples sprayed and calcium sulfate on specimens; however, the amount prCement mortar with bidistilled water every 7 days to simulate rainfall. was dependent of the porosity, specific surface, and alkalin

the sample. 

Limestone Samples exposed under actual ambient air conditions at TSP exposure increased the cleaning frequency for stone Travertine marble two locations in Rome. Monitoring data obtained for SO2, monuments. Monuments are soiled proportionately overtim

NO, NO2, and total suspended particulates (TSP) but not based on brightness values. Horizontal surfaces showed hi

reported. Exposure was for four seasons. graying values because of particle sediment. 

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TABLE 4-19 (cont’d). CORROSIVE EFFECTS OF PARTICULATE MATTER AND SULFUR DIOX

Stone Exposure Conditions Comments Limestone Samples from structures exposed for varying Black layers were found to be primarily comprised of iron compounQuartz-cemented periods of time under ambient air conditions. silicate, soot, and dirt. 

sandstone Samples selected because of black layer on surface. 

Calcite-cemented

 sandstone 

Granite Brick Carrara marble Samples from structures taken from monuments in Sulfate was higher in fog compared to other ions. The average conc

Venice, Italy. Samples included those shielded of chlorides and sulfates were 7-16 times higher in fog than rain. Thfrom running water (unwashed), areas at the of sulphonation on stone samples from areas shielded from running winterface of the washed and unwashed areas, and < 40%. At the interface between washed and unwashed surfaces and washed areas. Samples of fog and rain near the areas, sulphonation was > 40%. Dendrite shaped crust from the tranmonument were also taken for analysis. of calcium carbonate into gypsum was found on samples shielded fro

Samples from the washed areas displayed superficial granular disagga natural process of deterioration ascribed to natural agents; howeveprocess was accelerated due to the ambient air sulfates. 

Limestone Samples of ancient grey crust formed between Crust samples contained calcite, soil dust, carbonaceous particles, anSandstone 1180 and 1636 on the Church of Saint Trophime in crystals. 

Arks and formed between 1530 and 1187 on the Palazzo d’Accursio in Bolonga. 

Carrara marble Samples of the stones and mortars were Mortars were more reactive than the stones. Of the mortars, cement Travertine marble representative of those used in the past and currently pozzolan mortar were more reactive than the lime mortar. Carrara mTrani limestone for new construction and restorations. Samples was the least reactive of the stones. The maximum amount of degraPortland limestone were exposed for 6, 12, and 24 months under was found in areas sheltered from rain. Lime mortar ambient conditions in Milan. Pozzolan mortar Cement mortar Lime mortar Sample of black crust taken from Zamboni Exposure to environmental pollutants caused the formation of two se

Tower Gate. layers on the mortar: an outer thin surface black crust composed of 

and carbonaceous particles and the inner composed of products from dissolution and sulphation of the carbonate matrix in the mortar. 

Carrara marble Samples of crust removed from Milan General Gypsum main component of crust followed by carbonaceous particle

Hospital, built around 1937. oxides. Estimated rate of crust formation was 2-5 μm/year. Total am

gypsum formed over the lifetime of exposure was 5-13 mg/cm2, an e

0.2 mg/cm2/year. 

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of calcium sulfate dihydrate from the reaction of calcium carbonate (calcite) in the stone with 

atmospheric SO2 and moisture (relative humidities exceeding 65%) according to the following 

reaction. 

(4-12) 

The sulfate anions formed in the moist air reacts with the Ca2+ through diffusion processes 

forming the gypsum (Zuburtikudis and Triantafyllou, 2001). Approximately 99% of the sulfur 

in gypsum is sulfate because of the sulfonation process caused by the deposition of SO2 aerosol. 

Sulfites also are present in the gypsum layer as an intermediate product (Sabbioni et al., 1996; 

Ghedini et al., 2000; Gobbi et al., 1998; Zappia et al., 1998). Gypsum is more soluble than 

calcite and is known to form on limestone, sandstones, and marble when exposed to SO2. 

Gypsum also has been reported to form on granite stone by replacing silicate minerals with 

calcite (Schiavon et al., 1995). Gypsum occupies a larger volume than the original stone, 

causing the stone’s surface to become cracked and pitted. The rough surface serves as a site for 

deposition of airborne particles. As the gypsum grows, it loosens and falls apart (Zuburtikudis 

and Triantafyllou, 2001). 

The dark colored gypsum is created by surface deposition of carbonaceous particles 

(noncarbonate carbon) from combustion processes occurring in the area (Sabbioni, 1995; 

Saiz-Jimenez, 1993; Ausset et al., 1998; Hermosin and Saiz-Jimenez, 2000), trace metals 

contained in the stone, dust, and numerous other anthropogenic pollutants. After analyzing 

damaged layers of several stone monuments, Zappia et al. (1993) found that the dark-colored 

damaged surfaces contained 70% gypsum and 20% noncarbonate carbon. The lighter colored, 

damaged layers were exposed to rain and contained 1% gypsum and 4% noncarbonate carbon. 

It is assumed that rain removes reaction products, permitting further pollutant attack on the stone 

monument and likely redeposits some of the reaction products at rain runoff sites on the stone. 

After sulfur compounds, carbon was reported to be the next highest element in the dark crust on 

historical monuments in Rome. Elemental carbon and organic carbon accounted for 8 and 39% 

of the total carbon in the black crust samples. The highest percentage of carbon, carbonate 

carbon, was derived from the carbonate matrix in the stones. The high ratio of organic carbon to 

elemental carbon indicates the presence of a carbon source other than combustion processes 

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(Ghedini et al., 2000). Cooke and Gibbs (1994) suggested that stones damaged during times of 

higher ambient pollution exposure likely would continue to exhibit a higher rate of decay, 

termed the “memory effect,” than newer stones exposed under lower pollution conditions. 

Increased stone damage also has been associated with the presence of sulfur-oxidizing bacteria 

and fungi on stone surfaces (Garcia-Vallès et al., 1998; Young, 1996; Saiz-Jimenez, 1993; 

Diakumaku et al., 1995). 

Dissolution of gypsum on the stone’s surface initiates structural changes in the crust layer. 

Garica-Vallès et al. (1998) proposed a double mechanism: the dissolution of the gypsum, in the 

presence of sufficient moisture, followed by recrystallization inside fissures or pores. In the 

event of limited moisture, the gypsum is dissolved and recrystallizes at its original location. 

According to the authors, this would explain the gypsum-rich crustal materials on stone surfaces 

sheltered from precipitation. 

Moisture was found to be the dominant factor in stone deterioration for several sandstones 

(Petuskey et al., 1995). Dolske (1995) reported that the deteriorative effects of sulfur-containing 

rain events, sulfates, and SO2 on marble were largely dependent on the shape of the monument 

or structure rather than the type of marble. The author attributed the increased fluid turbulence 

over a non-flat vertical surface versus a flat surface to the increased erosion. Sulfur-containing 

particles also have been reported to enhance the reactivity of Carrara marble and Travertine and 

Trani stone to SO2 (Sabbioni et al., 1992). Particles with the highest carbon content had the 

lowest reactivity. 

The rate of stone deterioration is determined by the pollutant and the pollutant 

concentration, the stone’s permeability and moisture content, and the pollutant deposition 

velocity. Dry deposition of SO2 between rain events has also been reported to be a major 

causative factor in pollutant-related erosion of calcareous stones (Baedecker et al., 1991; Dolske, 

1995; Cooke and Gibbs, 1994; Schuster et al., 1994; Hamilton et al., 1995; Webb et al., 1992). 

Sulfur dioxide deposition increases with increasing relative humidity (Spiker et al., 1992), but 

the pollutant deposition velocity is dependent on the stone type (Wittenburg and Dannecker, 

1992), the porosity of the stone, and the presence of hygroscopic contaminants. 

Although it is clear from the available information that gaseous pollutants (dry deposition 

of SO2 in particular) will promote the decay of some types of stones under specific conditions, 

carbonaceous particles (noncarbonate carbon) may help to promote the decay process by aiding 

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in SO2 transformation to a more acidic species (Del Monte and Vittori, 1985). Several authors 

have reported enhanced sulfation of calcareous material by SO2 in the presence of particles 

containing metal oxides (Sabbioni et al., 1996; Hutchinson et al., 1992). 

4.4.2 Soiling and Discoloration of Man-Made Surfaces 

Ambient particles can cause soiling of man-made surfaces. Soiling has been defined as the 

deposition of particles of less than 10 μm on surfaces by impingement. Soiling generally is 

considered an optical effect, that is, soiling changes the reflectance from opaque materials and 

reduces the transmission of light through transparent materials. Soiling can represent a 

significant detrimental effect, requiring increased frequency of cleaning of glass windows and 

concrete structures, washing and repainting of structures, and, in some cases, reducing the useful 

life of the object. Particles, especially carbon, may also help catalyze chemical reactions that 

result in the deterioration of materials during exposure. 

It is difficult to determine the accumulated particle levels that cause an increase in soiling. 

Soiling is dependent on the particle concentration in the ambient environment, particle size 

distribution, the deposition rate, and the horizontal or vertical orientation and texture of the 

surface being exposed (Haynie, 1986). The chemical composition and morphology of the 

particles and the optical properties of the surface being soiled will determine the time at which 

soiling is perceived (Nazaroff and Cass, 1991). Carey (1959) reported that the average observer 

could observe a 0.2% surface coverage of black particles on a white background. Work by 

Bellan et al. (2000) indicated that it would take a 12% surface coverage by black particles before 

there is 100% accuracy in identifying soiling. Sharples et al. (2001) studied the effect of air 

pollution, moisture, and the function of the room/building on glazing daylight transmittance for a 

number of building windows. They found that the direct soiling load to a window was 

dependent on the immediate external and internal environment. For instance, there was only a 

10% reduction in daylight transmittance for windows from an office building that had not been 

cleaned for 5 years compared to clean windows. The reduction in transmittance for windows in 

a swimming pool complex was in excess of 20% due to soiling of the interior surface. For most 

office buildings, there was a reduction of glazing transmittance ranging from 3 to 10%, with 

most windows showing about a 3% reduction. The rate at which an object is soiled increases 

linearly with time; however, as the soiling level increases, the rate of soiling decreases. The 

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buildup of particles on a horizontal surface is counterbalanced by an equal and opposite 

depletion process, which is based on the scouring and washing effects of wind and rain (Schwar, 

1998). 

4.4.2.1 Stones and Concrete 

Most of the research evaluating the effects of air pollutants on stone structures has 

concentrated on gaseous pollutants. The deposition of the sulfur-containing pollutants is 

associated with the formation of gypsum on the stone (see Section 4.5.1.3). The dark color of 

gypsum is attributed to soiling by carbonaceous particles from nearby combustion processes. 

A lighter gray colored crust is attributed to soil dust and metal deposits (Ausset et al., 1998; 

Camuffo, 1995; Moropoulou et al., 1998). Realini et al. (1995) recorded the formation of a dark 

gypsum layer and a loss of luminous reflection in Carrara marble structures exposed for 1 year 

under ambient air conditions. Dark areas of gypsum were found by McGee and Mossitti (1992) 

on limestone and marble specimens exposed under ambient air conditions for several years. The 

black layers of gypsum were located in areas shielded from rainfall; whereas particles of dirt 

were concentrated around the edges of the gypsum formations. Lorusso et al. (1997) attributed 

the need for frequent cleaning and restoration of historic monuments in Rome to exposure to 

total suspended particulates. They also concluded that, based on a decrease in brightness 

(graying), surfaces are soiled proportionately over time; however, graying is higher on horizontal 

surfaces because of sedimented particles. Davidson et al. (2000b) evaluated the effects of air 

pollution exposure on a limestone structure on the University of Pittsburgh campus, using 

estimated average TSP levels in the 1930s and 1940s and actual values for the years 1957 to 

1997. Monitored levels of SO2 were available for the years 1980 to 1998. Based on the 

available data concerning pollutant levels and photographs, it was thought that soiling began 

while the structure was under construction. With decreasing levels of pollution, the soiled areas 

have been slowly washed away, the process taking several decades, leaving a white, eroded 

surface. 

4.4.2.2 Household and Industrial Paints 

Few studies are available that evaluate the soiling effects of particles on painted surfaces. 

Particles composed of elemental carbon, tarry acids, and various other constituents are 

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responsible for the soiling of structural painted surfaces. Coarse-mode particles (> 2.5 μm) 

initially contribute more soiling of horizontal and vertical painted surfaces than do fine-mode 

particles (< 2.5 μm), but are more easily removed by rain (Haynie and Lemmons, 1990). The 

accumulation of fine particles likely promotes remedial action (i.e., cleaning of the painted 

surfaces); whereas coarse-mode particles are primarily responsible for soiling of horizontal 

surfaces. Rain interacts with coarse particles, dissolving the particle and leaving stains on the 

painted surface (Creighton et al., 1990; Haynie and Lemmons, 1990). Haynie and Lemmons 

(1990) proposed empirical predictive equations for changes in surface reflectance of 

gloss-painted surfaces that were exposed protected and unprotected from rain while oriented 

horizontally or vertically. 

Early studies by Parker (1955) and Spence and Haynie (1972) demonstrated an association 

between particle exposure and the increased frequency of cleaning of painted surfaces. Particle 

exposures also caused physical damage to the painted surface (Parker, 1955). Unsheltered 

painted surfaces are initially more soiled by particles than sheltered surfaces, but the effect is 

reduced by rain washing. Reflectivity is decreased more rapidly on glossy paint than on flat 

paint (Haynie and Lemmons, 1990). However, surface chalking of the flat paint was reported 

during the exposure. The chalking interfered with the reflectance measurements for particle 

soiling. Particle composition measurements that were taken during exposure of the painted 

surfaces indicated sulfates to be a large fraction of the fine mode and only a small fraction of the 

coarse mode. Although no direct measurements were taken, fine-mode particles likely also 

contained large amounts of carbon and possibly nitrogen or hydrogen (Haynie and Lemmons, 

1990). 

4.5 ATMOSPHERIC PARTICULATE MATTER, CLIMATE CHANGE, 

AND EFFECTS ON SOLAR UV-B RADIATION TRANSMISSION 

Atmospheric particles alter the amount of solar radiation transmitted through the Earth’s 

atmosphere. The absorption of solar radiation by atmospheric particles, together with the 

trapping of infrared radiation emitted by the Earth’s surface by certain gases, enhances the 

heating of the Earth’s surface and lower atmosphere i.e., the widely-known “greenhouse effect.” 

Increases in the atmospheric concentration of these gases due to human activities may lead to 

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impacts, due to climate change, on human health and the environment. Lesser consequences of 

airborne particles include alterations in the amount of ultraviolet solar radiation (especially 

UV-B, 290 to 315 nm) penetrating through the Earth’s atmosphere and reaching its surface 

where UV radiation can exert various effects on human health, plant and animal biota, and other 

environmental components. 

The effects of atmospheric PM on the transmission of electromagnetic radiation emitted by 

the sun at ultraviolet and visible wavelengths, and by the Earth at infrared wavelengths, depend 

on the radiative properties (extinction efficiency, single-scattering albedo, and asymmetry 

parameter) of the particles, which depend, in turn, on the size and shape of the particles, the 

composition of the particles, and the distribution of components within individual particles. 

In general, the radiative properties of particles are size- and wavelength-dependent, with the 

extinction cross section tending toward its maximum when the particle radius is similar to the 

wavelength of the incident radiation. This means that fine particles present mainly in the 

accumulation mode would be expected to exert a greater influence on the transmission of 

electromagnetic radiation than would coarse particles. The chemical components of particles 

can be crudely summarized in terms of the broad classes identified in Chapter 2 of this 

document. These classes include nitrate, sulfate, mineral dust, elemental carbon, organic carbon 

compounds (e.g., PAHs), and metals derived from high-temperature combustion or smelting 

processes. The major sources of these components are shown in Table 3-9 of Chapter 3 in this 

document. 

Knowledge of the effects of PM on the transfer of radiation in the visible and infrared 

spectral regions is needed for assessing relationships between particles and climate change 

processes, as well as environmental and biological effects. Knowledge of the factors controlling 

the transfer of solar radiation in the ultraviolet spectral range is needed to assess potential the 

biological and environmental effects associated with exposure to UV-B radiation. Climate 

change processes, their potential to affect human and environmental health, and their potential 

relationships to atmospheric PM are discussed below. Solar ultraviolet radiation processes and 

the related effects of atmospheric PM are then summarized and discussed in the next section. 

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4.5.1 Atmospheric Particle Interactions with Solar and Terrestrial Radiation 

Related to Climate Change 

4.5.1.1 The Projected Impacts of Global Climate Change 

The study of atmospheric processes involved in mediating global climate change and its 

potential consequences for human health and global ecosystems is an area of active research. 

The most thorough evaluation of currently available science regarding climate change is the 

Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC, 

2001a). Earlier assessments include those conducted by the United Nations Environment 

Program (UNEP, 1986), the World Meteorological Organization (WMO, 1988), the EPA (U.S. 

Environmental Protection Agency, 1987), and others (e.g., Patz et al., 2000 a,b). The reader 

is referred to these documents for more complete discussion of climate change science. 

An abbreviated list of the IPCC conclusions, to date, and a short discussion of the potential 

impacts of climate change on human health and welfare is provided here to serve as the context 

for the discussion of the role of PM in climate. 

The IPCC TAR (2001a) notes that the increasing body of observations indicates that the 

Earth is warming and that other climate changes are underway. These observations include the 

global surface temperature record for the time period beginning in the year 1860, the satellite 

temperature record begun in 1979, changes in snow and ice cover recorded since the 1950s, sea 

level measurements taken throughout the 20th century, and sea surface temperature observations 

recorded since the 1950s. Other evidence includes a marked increase over the past 100 years in 

the frequency, intensity and persistence of the zonal atmospheric circulation shifts known as the 

El Nino-Southern Oscillation (ENSO). ENSO events occur when the tropical ocean has 

accumulated a large, localized mass of warm water that interrupts cold surface currents along 

South America, altering precipitation and temperature patterns in the tropics, subtropics and the 

midlatitudes. 

Atmospheric concentrations of greenhouse gases (GHGs), which trap solar energy within 

the climate system, are continuing to increase due to human activities. These activities will 

continue to change the composition of the atmosphere throughout the 21st century. The IPCC 

TAR describes the scientific evidence that ties the observed increase in GHGs over the past 

50 years to human activities (IPCC, 2001b). 

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The IPCC (1998, 2001b) reports also describe the results of general circulation model 

(GCM) studies that predict that human activities will alter the climate system in a manner that is 

likely to lead to marked global and regional changes in temperature, precipitation, and other 

climate properties. Global mean sea level is expected to increase, as will the number of extreme 

weather events including floods and droughts, thus inducing changes in soil moisture. These 

changes will directly impact human health, ecosystems, and global economic sectors, e.g., 

hydrology and water resources, food and fiber production, etc., (IPCC 1998, 2001b). Table 4-20 

summarizes these projected impacts. Wide variations in the course and net impacts of climate 

change in different geographic areas can be expected. In general, projected climate change 

impacts can be expected to represent additional stressors on those natural ecosystems and human 

societal systems already impacted by increasing resource demands, unsustainable resource 

management practices, and pollution — with wide variation likely across regions and nations in 

their ability to cope with the consequent alterations in ecological balances, in the availability of 

adequate food, water, and clean air, and in human health and safety. However, although many 

regions are likely to experience severe adverse impacts of climate change (some possibly 

irreversible), certain climate change impacts may be beneficial in some regions. For example, 

ecosystems that may benefit from warmer temperatures or increased CO2 fertilization include 

west coast coniferous forests and some western rangelands. In addition, reduced energy 

requirements for heating, reduced road salting and snow-clearance costs, and longer open-water 

seasons in northern channels and ports may benefit communities in the northern latitudes. The 

IPCC report, “The Regional Impacts of Climate Change” (IPCC, 1998), describes the projected 

effects of human-induced climate change on the different regions of the globe, including Africa, 

the Arctic and Antarctic, the Middle East and arid Asia, Australasia, Europe, Latin America, 

North America, the small island nations, temperate Asia, and tropical Asia. 

It must be borne in mind that while current climate models are successful in simulating 

present annual mean climate and the seasonal cycle on continental scales, they are less 

successful at regional scales. Clouds and humidity, essential factors in defining local and 

regional (“sub-grid”) climate, are significantly uncertain (IPCC, 2001a). Due to modeling 

uncertainties, both in reproducing regional climate and in predicting future economic activity, 

the projected impacts discussed above are also uncertain. 

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TABLE 4-20. EXAMPLES OF IMPACTS RESULTING FROM PROJECTED

CHANGES IN EXTREME CLIMATE EVENTS

Projected changes during the 21st Century in Representative Examples of Projected Impactsb 

Extreme Climate Phenomena and their Likelihooda (all high confidence of occurrence in some areasc

) 

Simple Extremes 

Higher maximum temperatures; more hot days and heat C Increased incidence of death and serious illness in older age groups 

wavesd

 over nearly all land areas (very likelya) and urban poor 

C Increased heat stress in livestock and wildlife 

C Shift in tourist destinations 

C Increased risk of damage to a number of crops 

C Increased electric cooling demand and reduced energy supply 

reliability 

Higher (increasing) minimum temperatures; fewer cold C Decreased cold-related human morbidity and mortality 

days, frost days, and cold wavesd

 over nearly all land C Decreased risk of damage to a number of crops, and increased risk to 

areas (very likelya

) others 

C Extended range and activity of some pest and disease vectors 

C Reduced heating energy demand 

More intense precipitation events (very likelya

 over many 

years) 

C Increased flood, landslide, avalanche, and mudslide damage 

C Increased soil erosion 

C Increased flood runoff could increase recharge of some floodplain 

aquifers 

C Increased pressure on government and private flood insurance systems 

and disaster relief 

Complex Extremes 

Increased summer drying over most mid-latitude 

continental interiors and associated risk of drought 

(likelya

) 

C Decreased crop yields 

C Increased damage to building foundations caused by ground shrinkage 

C Decreased water resource quantity and quality 

C Increased risk of forest fire 

Increased tropical cyclone peak wind intensities, mean C Increased risk to human life, risk of infections, disease epidemics, 

and peak precipitation intensities (likelya

 over some and many other risks 

areas)e C Increased coastal erosion and damage to coastal buildings and 

infrastructure 

C Increased damage to coastal ecosystems such as coral reefs and 

mangroves 

Intensified droughts and floods associated with El Niño C Decreased agricultural and rangeland productivity in drought- and 

events in many different regions (likelya) (see also under flood-prone regions 

droughts and intense precipitation events) C Decreased hydropower potential in drought-prone regions 

Increased Asian summer monsoon precipitation C Increased flood and drought magnitude and damages in temperate 

variability (likelya

) and tropical Asia 

Increased intensity of mid-latitude storms C Increased risks to human life and health 

(little agreement between current models)d C Increased property and infrastructure losses 

C Increased damage to coastal ecosystems 

a

Likelihood refers to judgmental estimates of confidence used by TAR WGI: very likely (90-99% chance); likely (66-90% chance). 

 Unless otherwise stated, information on climate phenomena is taken from the Summary for Policymakers, TAR WGI. TAR

 WGI = Third Assessment Report of Working Group 1 (IPCC, 2001a).

b

These impacts can be lessened by appropriate response measures. 

c

High confidence refers to probabilities between 67 and 95%. 

d

Information from TAR WGI, Technical Summary. 

e

Changes in regional distribution of tropical cyclones are possible but have not been established. 

Source: IPCC (2001b). 

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Findings from the IPCC TAR (2001a), Regional Impacts Assessment (1998), and other 

regional assessments illustrate well the considerable uncertainties and difficulties in projecting 

likely climate change impacts on regional or local scales. The findings predict a mixture of 

positive and negative projected potential climate change impacts for U.S. regions differing 

widely among regions. Projections of region-specific climate change impacts are complicated 

by the need to evaluate potential effects of local- or regional-scale changes in key air pollutants. 

4.5.1.2 Airborne Particle Relationships to Global Warming and Climate Change 

Atmospheric particles both scatter and absorb incoming solar radiation. Visibility 

reduction is caused by particle scattering in all directions, whereas climate effects are mainly 

caused by particle scattering in the upward direction. Upward scattering of solar radiation 

reduces the total amount of energy received by the Earth system, leading to surface cooling. 

The effect on climate due to upward scattering and to absorption of radiation by aerosol can be 

roughly quantified as a “radiative forcing” (Houghton et al., 1990). Global and regional climate 

(at equilibrium) is defined by the balance between a large number of “positive” and “negative” 

forcings induced by different components of the Earth system. The Earth system responds to 

these forcings in a potentially complex way due to feedback mechanisms that are theorized but 

difficult to model. In the absence of information about climate feedbacks, radiative forcing 

values for many components of the climate system are estimated as a tool for approximating 

their relative importance in climate change. Forcing estimates for various classes of atmospheric 

particles are derived on the basis of climate modeling studies and reported by the IPCC (IPCC, 

2001b). 

Particulate matter appears to play a significant role in defining climate on both global and 

regional scales. Significant reductions over the past 50 years in solar radiation received at the 

Earth’s surface on a globally averaged basis correlate with increases in atmospheric aerosol 

(Stanhill and Cohen, 2001). While this correlation seems clear, quantifying the cooling and 

warming effects of aerosol in relation to GHG-related warming is difficult. Aerosols complicate 

the prediction of climate change due to their spatial and temporal inhomogeneities and uncertain 

radiative properties. However, inclusion of modeled atmospheric sulfate concentrations 

substantially improved the agreement between modeled and observed surface temperatures 

(Kiehl and Briegleb, 1993). Haywood et al. (1999) also found that the inclusion of 

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anthropogenic aerosols results in a significant improvement between calculations of reflected 

sunlight at the top of the atmosphere and satellite observations in oceanic regions close to 

sources of anthropogenic PM. On a regional scale, the suspected influence of aerosols upon 

climate relates to regional hydrological cycles. Evidence is accumulating that pollution aerosols 

reduce precipitation frequency by clouds, potentially leading to drought in some parts of the 

world (Ramanathan et al., 2001). 

Greenhouse Gases, Particulate Matter, and the Earth’s Radiative Equilibrium 

According to simple radiative transfer theory, at thermal equilibrium, the Earth’s surface 

should be near !15 °C. This is the temperature of a theoretical “black body” that is receiving 

and then re-emitting 342.5 W m!2

, the globally averaged amount of solar radiation absorbed and 

then re-emitted by the Earth as infrared terrestrial radiation. In fact, satellite observations 

indicate that the Earth’s average planetary temperature is remarkably close to the theoretical 

black body value at !18 °C, a temperature at which liquid water ordinarily does not exist. 

At its surface, however, the Earth’s average temperature is +15 °C. The 33 °C temperature 

differential between Earth’s planetary temperature and its surface temperature is due to the 

existence of infrared radiation-absorbing components in the atmosphere, i.e., GHGs including 

carbon dioxide, methane, several other trace gases, and some types of particles and clouds. The 

phenomenon of planetary surface warming due to the atmospheric absorption and re-emission of 

infrared radiation is popularly known as the “greenhouse effect” (Arrhenius, 1896; Schneider, 

1992). Radiation trapped by the Earth’s atmosphere is reflected back to its surface, with some 

small fraction absorbed by dark atmospheric particles. The fraction of this radiation that is not 

directly re-emitted as long-wave terrestrial radiation transforms into heat energy that drives the 

atmospheric processes that form the basis of weather and climate. Eventually this energy is 

transformed a second time, to terrestrial radiation, and is re-emitted as part of the process that 

maintains Earth’s radiative equilibrium. 

The evidence of a physical relationship between radiatively active air pollutants (GHGs 

and particles) and solar energy retained by the Earth system suggests that continuously changing 

atmospheric concentrations of air pollutants, along with other alterations to the climate system 

due to human activities, may be shifting the Earth’s radiative equilibrium. 

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Radiatively active gases in the atmosphere are largely responsible for the greenhouse 

effect, although some light-absorbing particles and clouds contribute to atmospheric and surface 

warming (IPCC, 2001a). The majority of clouds and particles play a role in counteracting the 

greenhouse effect by increasing the degree to which the Earth is able to reflect solar radiation, 

i.e. its albedo. Successful modeling of the Earth’s climate and, therefore, assessment of the 

degree of human-induced climate change and development of appropriate policy depends on the 

high quality information on the relative efficiencies, amounts, and spatial and temporal 

distributions of the various radiatively active components of the atmosphere involved in 

absorbing and/or reflecting solar and terrestrial radiation. 

“Forcing” and the Earth’s Radiative Balance. 

A measure of the relative influence of a given component of the climate system on the 

Earth’s radiative balance is its “radiative forcing”. Radiative forcing, in W m!2

, is a quantity that 

was developed by the climate modeling community as a first order-only means of estimating 

relative effects of anthropogenic and natural processes on the surface-troposphere system. 

No more precise metric has yet been found to replace radiative forcing as a measure of impact of 

upon global climate (IPCC, 2001a). The convention for this quantity assigns a negative forcing 

to climate system components that reflect solar radiation back into space and assigns a positive 

forcing to those that enhance the greenhouse effect or otherwise act to enhance the heatabsorbing capacity of the Earth system. Purely reflective atmospheric aerosol, snow-covered 

land surfaces, and dense sea ice provide a negative forcing, while black carbon-containing 

atmospheric aerosol, GHGs, and dark ocean surfaces provide a positive forcing of the climate 

system. 

The IPCC reports included estimated values for forcing by the individual, radiatively active 

gas and particle-phase components of the atmosphere. These estimates are derived primarily 

through expert judgment, incorporating the results of peer-reviewed modeling studies. 

Uncertainty ranges are assigned that reflect the range of modeled values reported in these 

studies. According to the available research on climate forcing by aerosol, the panel has 

provided estimates for sulfate, organic, black carbon, biomass burning, and mineral dust aerosol. 

The current estimate of forcing due to long-lived, well-mixed GHGs accumulated in the 

atmosphere from the preindustrial era (~1750) through the year 2000 is +2.4 W m!2

 (IPCC, 

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2001a). In contrast, forcing due to a sulfate aerosol-related increase in planetary albedo has been 

assigned a value of !0.4 W m!2. Biomass burning and fossil-fuel-related organic aerosol also 

increase the Earth’s reflectivity and are estimated to contribute a !0.2 W m!2

 and !0.1 W m!2 

forcing, respectively. Fossil-fuel black carbon is expected to warm the atmosphere, resulting in 

an estimated +0.2 W m!2

 forcing. No estimate for forcing by nitrate aerosol has been proposed 

due to the wide discrepancies in current global modeling results and the difficulties associated 

with obtaining accurate ambient samples of nitrate concentrations and size distributions. 

Likewise, no specific estimate has been offered for forcing by mineral dust aerosol introduced 

into the atmosphere due to human activities beyond the assignment of a tentative range of 

!0.6 to +0.4 W m!2. The estimated forcing and associated uncertainty for each aerosol type is 

shown in relation to forcing estimates for the known GHGs along with an indication of the level 

of confidence in each of these estimates in Figure 4-42. 

The relationship between perturbations to the Earth’s radiative balance and climate is 

complicated by various feedbacks within the climate system. An example would be the positive 

feedback associated with melting sea ice. As sea ice melts with increasing surface temperatures, 

the dark ocean surface is revealed which absorbs, rather than reflects, solar radiation. Such a 

feedback increases the rate of surface warming. The role of feedbacks in determining the 

sensitivity of climate to changes in radiative forcing is described in detail by the IPCC in its third 

assessment report (IPCC, 2001a). 

One possible feedback worth consideration in the discussion of the role of aerosols in 

climate may result from the possible sensitivity of aerosol number and mass to atmospheric 

temperature (Hemming and Seinfeld, 2001). Increasing atmospheric temperatures may result in 

a reduction of aerosol as semivolatile organic and inorganic aerosol constituents evaporate, 

leading to a change in aerosol forcing. As described in Chapter 2 of this document, and further 

discussed below, ambient aerosols are known to contain complex chemical mixtures of both 

scattering and absorbing materials. The feedback that may result from this phenomenon will 

depend upon whether the aerosols become more absorbing or more reflective with the loss of 

semivolatile material. Research is currently underway to evaluate both the role of temperature in 

determining aerosol mass and in defining the link between air quality and climate, but no 

literature yet exists to support an assessment of these effects. The following discussion, 

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Figure 4-42. Estimated global mean radiative forcing exerted by gas and various particle 

phase species for the year 2000, relative to 1750. 

Source: IPCC (2001a). 

therefore, will focus solely on the relative forcing properties of aerosols, for which a body of 

scientific research is available for consideration. 

The physical and chemical properties of atmospheric aerosols, and their regional 

distribution and temporal nature, all play a role in determining the degree to which they force 

climate. These details are described, below, followed by a description of the “indirect” effect of 

aerosols due to changes in cloud properties, and then by brief discussions of the sources of 

uncertainty in determining aerosol-related climate forcing and of the link to human health and 

the environment. 

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The Physics of Particulate Matter and Its Climate Effects 

Four wavelength-dependent physical properties of ambient aerosols are needed to calculate 

their optical depth and, thus, their radiative properties: the mass light-scattering efficiency (asp), 

the functional dependence of light-scattering on relative humidity f(RH), the single-scattering 

albedo (T0), and the scattering asymmetry parameter (g); (Charlson et al., 1992; Penner et al., 

1994). Direct forcing by aerosols is especially sensitive to single-scattering albedo. Depending 

on the color of the underlying surface (i.e. the surface albedo) small changes in T0 can change 

the sign of the calculated aerosol forcing (Hansen et al., 1997). 

The wavelength-dependent phase function and scattering and absorption coefficients are 

calculated using Mie scattering theory. These values are then used to calculate mass lightscattering efficiency, single-scattering albedo, and the asymmetry factor. Mie calculations 

require the ratio of particle size versus wavelength and the complex refractive index of the 

particle — a composition-dependent property (Salby, 1996). The relationship between lightscattering and relative humidity likewise depends upon composition, as water absorption 

depends upon the presence of hygroscopic material within the particle. Therefore, according to 

the current understanding of aerosol optics, good information about composition and size 

distribution is needed to successfully predict aerosol-related forcing. 

The Chemistry of Particulate Matter and Its Climate Effects 

Although forcing estimates are reported for specific aerosol classes (i.e., sulfate, black 

carbon, dust, etc.) it is understood that shortly after emission, primary aerosols undergo chemical 

transformation in the atmosphere. These transformations occur through the partitioning of gas 

phase compounds, by coagulation with another aerosol, or through a combination of both 

processes (Seinfeld and Pandis, 1998). While the radiative properties of nonabsorbing aerosol, 

such as sulfates or nitrates, are primarily sensitive to particle size, the radiative properties of 

aerosols containing absorbing constituents (i.e., black carbon and mineral aerosols) are also 

sensitive to chemical composition and mixing state. Studies of refractive index changes as a 

function of composition have shown that the type of mixing present both in the population of 

aerosols (i.e., “internal” versus “external” mixtures) and the extent of mixing within individual 

aerosols influence aerosol optical properties when absorbing material is present (Fuller et al., 

1999). Modeled estimates for radiative forcing by black carbon-containing aerosols do, in fact, 

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range widely depending on whether the aerosol population is assumed to be internally or 

externally mixed and whether the absorbing carbon is uniformly distributed in the particle or 

whether it exists as a core surrounded by nonabsorbing material. The IPCC Third Assessment 

Report (2001a) provides a tabulation of studies and their forcing estimates for black carbon as it 

exists in different mixing states. For example, Haywood and Shine (1995) calculated that 

externally mixed fossil-fuel black carbon forces climate by +0.2 W m!2

. Jacobson (2000) 

assumed that if black carbon exists as a solid core contained within an otherwise nonabsorbing 

droplet, the global forcing will be +0.54 W m!2

. 

Chemically and physically detailed models at high spatial resolution are required to 

accurately represent the chemical transformation and size evolution of aerosol within the 

ambient atmosphere. Such models, however, are impractical for global scale climate modeling. 

Parameterizations are formulated to represent processes occurring at spatial and temporal scales 

that are too fine for climate models (i.e., “sub-grid” processes). Several important fundamental 

chemical and physical processes, however, are not yet well-enough defined to assure reasonable 

parameterization in large scale climate models. Prediction of the organic and black carbon 

content of ambient aerosols and their associated radiative properties, as described above, and the 

aerosol-induced changes in cloud properties, remain especially weak. 

Modeling of the effect of black carbon aerosols on climate, to date, has been done on the 

basis of limited and poor quality data regarding total global emissions, aerosol composition, and 

the mixing state of ambient aerosols. Appendix 2B.2 of Chapter 2 in this document describes 

the many problems associated with the most commonly used organic carbon/elemental carbon 

measurement method (thermal-optical reflectance and transmittance). For example, elemental 

carbon comprises a relatively small fraction of total carbon in biomass burning-related aerosol 

which is, nevertheless, an important source of black carbon in the atmosphere due to the 

extensive use of biofuels in developing countries and to the prevalence of natural and 

agriculture-related open biomass burning (Ludwig et al., 2003). The concentrations of elemental 

carbon in these aerosols are close to the detection limits of thermal-optical measurement 

systems, adding a high degree of uncertainty to the reported values. Thermal-optical 

measurements also do not provide information about the specific chemical composition of 

carbonaceous aerosol that is needed to calculate refractive indices. Furthermore, the technique is 

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based on filter collection, which only provides an average concentration of organic and 

elemental carbon and no information about mixing states. 

The magnitude of the errors introduced into estimates of black carbon-related radiative 

forcing due to the lack of detailed information about optical properties of ambient black carbon 

aerosol cannot yet be estimated. The forcing estimate of +0.2 W m!2

 provided by the IPCC is 

based upon a summary of the available modeling estimates, some of which are founded on 

simplistic assumptions about the composition and mixing state of ambient BC aerosol. 

New methods, such as photoacoustic spectroscopy, hold promise for detecting quantities of 

light absorbing materials in ambient aerosol streams (Arnott et al., 1999; Moosmuller et al., 

2001). The photoacoustic method is capable of identifying the absolute fraction of a given 

ambient aerosol sample that absorbs radiation at a given wavelength at high temporal resolution. 

The method does not alter the composition of the aerosol sample through heating and oxidation, 

thus introduces no chemical artifacts. Present photoacoustic instruments detect absorption at 

wavelengths of interest for visibility studies, but the measurement principle can be adapted to 

climate studies through the selection of climate-forcing relevant wavelengths. Specific chemical 

speciation at the organic and inorganic compound level coupled with photoacoustic 

measurements of the light absorbing properties of ambient aerosol would provide the best 

available test of atmospheric chemistry models attempting to simulate the evolution in aerosol 

radiative properties between emission and atmospheric removal. 

The Regional and Temporal Dependence of Aerosol Forcing 

In addition to information about the composition and size distributions of ambient aerosol, 

details regarding atmospheric concentrations on a spatial and temporal basis are needed to 

estimate climate forcing. Unlike the long-lived GHGs, aerosols have lifetimes averaging only 

days to weeks or less, leading to inhomogeneity in regional and global-scale concentrations. 

Industrial processes and other human activities that produce air pollution aerosols also vary on a 

seasonal, monthly, weekly, or diurnal basis, introducing other complications into forcing 

estimates (IPCC, 2001a). Peak aerosol concentrations, along with the greatest temporal 

variation, exist near emissions sources. 

Deviations from global mean forcing estimates can be very large on the regional scale. 

For instance, Tegen et al. (1996) found that local radiative forcing exerted by dust raised from 

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disturbed lands ranges from !2.1 W m!2

 to +5.5 W m!2 over desert areas and their adjacent seas. 

The largest regional values of radiative forcing caused by anthropogenic sulfate are about 

!3 W m!2 in the eastern United States, south central Europe, and eastern China (Kiehl and 

Briegleb, 1993). These regional maxima in aerosol forcing are at least a factor of 10 greater than 

their global mean values shown in Figure 4-41. By comparison, regional maxima in forcing by 

the well-mixed GHGs are only about 50% greater than their global mean value (Kiehl and 

Briegleb, 1993). Thus, the estimates of local radiative forcing by particles also are large enough 

to completely cancel the effects of GHGs in many regions and to cause a number of changes in 

the dynamic structure of the atmosphere that still need to be evaluated. A number of 

anthropogenic pollutants whose distributions are highly variable are also effective greenhouse 

absorbers. These gases include O3 and, possibly HNO3, C2H4 , NH3, and SO2, all of which are 

not commonly considered in radiative forcing calculations (Wang et al., 1976). High-ozone 

values are found downwind of urban areas and areas where there is biomass burning. However, 

Van Dorland et al. (1997) found that there may not be much cancellation between the radiative 

effects for ozone and for sulfate because both species have different seasonal cycles and show 

significant differences in their spatial distribution. 

“Indirect” Effects of Particulate Matter on Climate. 

Aerosols directly affect climate by scattering and absorbing solar and terrestrial radiation. 

Depending on chemical composition, they can also nucleate new cloud droplets. For a given 

total liquid water content (LWC), increasing cloud droplet number means smaller droplets that 

scatter solar radiation more effectively, reduce the amount of precipitation from the cloud, and, 

consequently, increase cloud lifetime. Observational evidence exists for this impact of 

hygroscopic aerosol on both warm and ice cloud properties. Increased albedo and increased 

cloud lifetime effects are treated separately by the climate modeling community. Particleinduced increases in cloud albedo are referred to as the “first” indirect effect, and changes to the 

cloud lifetime due to reduced precipitation are referred to as the “second” indirect effect. The 

highly uncertain estimated forcing due to the effects of particles on cloud albedo is given as a 

range from 0 to +2.0 W m!2

 (IPCC, 2001a). A further effect, referred to as the “semi-indirect 

effect” is the reduction in cloud reflectivity due to the inclusion of black carbon-containing 

aerosol within the cloud drops themselves or as interstitial aerosol (Hansen et al., 2000). 

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Sulfate aerosols, especially those > 50 nm in diameter, are believed to alter clouds to the 

largest extent due to their efficient nucleation of cloud drops and ice crystals (Twomey, 1974). 

Organic aerosols that contain highly oxidized carbon compounds may be similarly efficient in 

nucleating cloud droplets (Novakov and Penner, 1993). Both satellite and in-situ aircraft 

observations reinforce the hypothesis that pollutant aerosols increase cloud reflectivity and 

lifetime (Ramanathan et al., 2001). 

An important consequence of this property of aerosols on regional climate includes 

suppression of rain over polluted regions. Satellite observations show that precipitation occurs 

only outside of pollution tracks, while clouds within pollution tracks show a reduction in 

effective cloud drop radius to below the precipitation threshold (Rosenfeld, 1999). Desert dust 

also appears to alter the microphysical properties of clouds, suppressing precipitation from warm 

clouds while nucleating ice crystals in cold clouds. 

While climate models are not yet equipped for modeling the effect of aerosols on regional 

and global hydrological cycles, it has been proposed that aerosols will reduce precipitation 

efficiency over land where anthropogenic activities provide a major source of cloud 

condensation nuclei. Several studies using coupled ocean-atmosphere general circulation 

models support the possibility of a “spin down” effect upon hydrological cycles due only to a 

reduction in surface radiation receipts from sulfate aerosol scattering. When indirect effects are 

included, the reduction in precipitation rates from clouds is large enough to reverse the effect of 

GHG-related forcing (Ramanathan et al., 2001). 

Sources of Uncertainty in Aerosol Forcing Estimates 

Uncertainties in calculating the direct effect of airborne particles arise from a lack of 

knowledge concerning their vertical and horizontal variability, their size distribution, chemical 

composition, and the distribution of components within individual particles. For instance, 

gas-phase sulfur species may be oxidized to form a layer of sulfate around existing particles in 

continental environments or they may be incorporated in sea-salt particles (e.g., Li-Jones and 

Prospero, 1998). In either case, the radiative effects of a given mass of the sulfate will be much 

lower than if pure sulfate particles were formed. It must also be stressed that the overall 

radiative effect of particles at a given location is not simply determined by the sum of effects 

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caused by individual classes of particles because of radiative interactions between particles with 

different radiative characteristics and with gases. 

That calculations of the indirect effects of particles on climate are subject to much larger 

uncertainties than are calculations of their direct effects reflects uncertainties in a large number 

of chemical and microphysical processes that determine aerosol chemistry, size distribution, and 

the number of droplets within a cloud. A complete assessment of the radiative effects of PM 

will require computationally intensive calculations that incorporate the spatial and temporal 

behavior of particles of varying composition that have been emitted from, or formed from 

precursors emitted from, different sources. Refining the values of model-input parameters (such 

as improving emissions estimates) may be as important as improving the models per se in 

calculations of direct radiative forcing (Pan et al., 1997) and indirect radiative forcing (Pan et al., 

1998). However, the uncertainties associated with the calculation of radiative effects of particles 

will likely remain much larger than those associated with well-mixed GHGs. 

Aerosol-Related Climate Effects, Human Health, and the Environment. 

The specific impacts on human health and the environment due to aerosol effects on the 

climate system can not be calculated with confidence given the present difficulty in accurately 

modeling an aerosol’s physical, chemical, and temporal properties and its regionally dependent 

atmospheric concentration levels, combined with the difficulties of projecting location-specific 

increases or decreases in anthropogenic emissions of atmospheric particles (or their precursors). 

However, substantial qualitative information, from observation and modeling, indicates that 

aerosol forces climate both positively and negatively, globally and regionally, and may be 

negatively impacting hydrological cycles on a regional scale. Global and other regional scale 

impacts are suspected on the basis of current, though uncertain, modeling studies suggesting that 

climate change in general can have positive and negative effects on human health, human 

welfare, and the environment. 

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4.5.2 Atmospheric Particulate Matter Effects on the Transmission of Solar 

Ultraviolet Radiation Transmission: Impacts on Human Health and 

the Environment 

4.5.2.1 Potential Effects of Increased Ultraviolet Radiation Transmission 

The transmission of solar UV-B radiation through the Earth’s atmosphere is controlled by 

ozone, clouds, and particles. The depletion of stratospheric ozone caused by the release of 

anthropogenically produced chlorine (Cl)- and bromine (Br)-containing compounds has resulted 

in heightened concern about potentially serious increases in the amount of solar UV-B radiation 

(SUVB) reaching the Earth’s surface. SUVB is also responsible for initiating the production of 

hydroxyl radicals (C

OH) that oxidize a wide variety of volatile organic compounds, some of 

which can deplete stratospheric ozone (e.g., CH3Cl, CH3Br), absorb terrestrial infrared radiation 

(e.g., CH4), and contribute to photochemical smog formation (e.g., C2H4, C5H8). 

Increased penetration of SUVB to the Earth’s surface as the result of stratospheric ozone 

depletion continues to be of much concern because of projections of consequent increased 

surface-level SUVB exposure and associated potential negative impacts on human health, plant 

and animal biota, and man-made materials. Several summary overviews (Kripke, 1989; Grant, 

1989; Kodama and Lee, 1994; Van der Leun et al., 1995, 1998) of salient points related to 

stratospheric ozone depletion and bases for concern provide a concise introduction to the subject. 

Only a brief summary will be given here. Stratophospheric ozone depletion results from 

(a) anthropogenic emissions of certain trace gases having long atmospheric residence times, e.g., 

chlorofluorocarbons (CFCs), carbon tetrachloride (CCl4), and Halon 1211 (CF2Cl Br) and 1301 

(CF3Br) — which have atmospheric residence times of 75 to 100 years, 50 years, 25 years, and 

110 years, respectively; (b) their tropospheric accumulation and gradual transport, over decades, 

up to the stratosphere, where (c) they photolyze to release Cl and Br that catalyze ozone 

destruction; leading to (d) stratospheric ozone depletion. Such ozone depletion is most marked 

over Antarctica during spring in the southern hemisphere, to a less marked but still significant 

extent over the Arctic polar region during late winter and spring in the northern hemisphere, and 

to a lesser extent, over midlatitude regions during any season. Given the long time frame 

involved in the transport of such gases to the stratosphere and their long residence times there, 

any effects already seen on stratospheric ozone are likely to have been caused by the 

atmospheric loadings of trace gases from anthropogenic emissions over the past few decades. 

Ozone-depleting gases already in the atmosphere will continue to affect stratospheric ozone 

4-220
concentrations well into the 21st century. Shorter-lived gases, such as CH3Br, also exert 

significant ozone depletion effects. 

The main types of deleterious effects hypothesized as likely to result from stratospheric 

ozone depletion and consequent increased SUVB penetration through the Earth’s atmosphere 

include the following: 

(1) Direct Human Health Effects, such as skin damage (sunburn), leading to more rapid 

aging and increased incidence of skin cancer; ocular effects (retinal damage and 

increased cataract formation, possibly leading to blindness); and suppression of 

some immune system components (contributing to skin cancer induction and spread 

to nonirradiated skin areas, as well as possibly increasing susceptibility to certain 

infectious diseases). 

(2) Agricultural/Ecological Effects, mediated largely through altered biogeochemical 

cycling resulting in consequent damaging impacts on terrestrial plants (leading to 

possible reduced yields of rice, other food crops, and commercially important trees, as 

well as to biodiversity shifts in natural terrestrial ecosystems); and deleterious effects 

on aquatic life (including reduced ocean zooplankton and phytoplankton as important 

base components of marine food-chains supporting the existence of commercially 

important, edible fish and other seafood, as well as to other aquatic ecosystem shifts). 

(3) Indirect Human Health and Ecological Effects, mediated through increased 

tropospheric ozone formation (and consequent exacerbation of surface-level, ozonerelated health and ecological impacts) and alterations in the concentrations of other 

important trace species, most notably the hydroxyl radical and acidic aerosols. 

(4) Other Types of Effects, such as faster rates of polymer weathering because of 

increased UV-B radiation and other effects on man-made commercial materials 

and cultural artifacts, secondary to climate change or exacerbation of air pollution 

problems. 

Extensive qualitative and quantitative characterizations of stratospheric ozone depletion 

processes and projections of their likely potential impacts on human health and the environment 

have been the subjects of periodic (1988, 1989, 1991, 1994, 1998) international assessments 

carried out under WMO and UNEP auspices since the 1987 signing of the Montreal Protocol on 

Substances that Deplete the Ozone Layer. For more detailed up-to-date information, the reader 

is referred to recent international assessments of (a) processes contributing to stratospheric ozone 

depletion and the status of progress towards ameliorating the problem (WMO, 1999) and 

(b) revised qualitative and quantitative projections of potential consequent human health and 

environmental effects (UNEP, 1998, 2000). 

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Of considerable importance is the growing recognition, as reflected in these newer 

assessments, of the impacts of enhanced solar radiation on biogeochemical cycles (see, for 

example, Zepp et al. [1998]). As noted in that paper, the effects of UV-B radiation (both in 

magnitude and direction) on trace gas (e.g., CO) emissions and mineral nutrient cycling are 

species specific and can affect a variety of processes. These include, for example, changes in the 

chemical composition of living plant tissue, photodegradation of dead plant matter (e.g., ground 

litter), release of CO from vegetation previously charred by fire, changes in microbial 

decomposer communities, and effects on nitrogen-fixing microorganisms and plants. In 

addition, changes in the amount and composition of organic matter, caused by enhanced UV-B 

penetration, affect the transmission of solar ultraviolet and visible radiation through the water 

column. These changes in light quality broadly impact the effects of UV-B on aquatic 

biogeochemical cycles. Enhanced UV-B levels cause both positive and negative effects on 

microbial activities in aquatic ecosystems that can affect nutrient cycling and the uptake or 

release of GHGs. Thus, there are emerging complex issues regarding interactions and feedbacks 

between climate change and changes in terrestrial and marine biogeochemical cycles because of 

increased UV-B penetration to the Earth’s surface. 

In contrast to the above types of negative impacts projected as likely to be associated with 

increased UV-B penetration to Earth’s surface, some research results suggest possible beneficial 

effects of increased UV-B radiation. For example, a number of U.S. and international studies 

have focused on the protective effects of UV-B radiation with regard to the incidence of cancers 

other than skin cancer. One of the first of these studies investigated potential relationships 

between sunlight, vitamin D, and colon cancer (Garland and Garland, 1980). More recent 

studies continue to provide suggestive evidence that UV-B radiation may be protective against 

several types of cancer and some other diseases. For example, Grant (2002) conducted a number 

of ecologic-type epidemiologic studies, which suggest that UV-B radiation, acting through the 

production of vitamin D, is a risk-reduction factor for mortality due to several types of cancer, 

including cancer of the breast, colon, ovary, and prostate, as well as non-Hodgkin lymphoma. 

Other related studies that provide evidence for protective effects of UV-B radiation include 

Gorham et al. (1989, 1990); Garland et al. (1990); Hanchette and Schwartz (1992); Ainsleigh 

(1993); Lefkowitz and Garland (1994); Hartge et al. (1996); and Freedman et al. (1997). 

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As noted in the above detailed international assessments, since the signing of the Montreal 

Protocol, much progress has been made in reducing emissions of ozone depleting gases, leading 

to estimates that the maximum extent of stratospheric ozone depletion may have leveled off 

during recent years. This is expected to be followed by a gradual lessening of the problem and 

its impacts during the next half-century. However, the assessments also note that the modeled 

projections are subject to considerable uncertainty (see, for example, UNEP [2000]). Varying 

potential roles of atmospheric particles, discussed below, are among salient factors complicating 

predictive modeling efforts. 

4.5.2.2 Airborne Particle Effects on Atmospheric Transmission of Solar 

Ultraviolet Radiation 

A given amount of ozone in the lower troposphere has been shown to absorb more solar 

radiation than an equal amount of ozone in the stratosphere, because of the increase in its 

effective optical path produced by Rayleigh scattering in the lower atmosphere (Brühl and 

Crutzen, 1988). The effects of particles are more complex. The impact of particles on the 

SUVB flux throughout the boundary layer are highly sensitive to the altitude of the particles and 

to their single-scattering albedo. Even the sign of the effect can reverse as the composition of 

the particle mix changes from scattering to absorbing types (e.g., from sulfate to elemental 

carbon or PAHs) (Dickerson et al., 1997). In addition, scattering by particles also may increase 

the effective optical path of absorbing molecules, such as ozone, in the lower atmosphere. 

The effects of particles present in the lower troposphere on the transmission of SUVB have 

been examined both by field measurements and by radiative transfer model calculations. The 

presence of particles in urban areas modifies the spectral distribution of solar irradiance at the 

surface. Shorter wavelength (i.e., ultraviolet) radiation is attenuated more than visible radiation 

(e.g., Peterson et al., 1978; Jacobson, 1999). Wenny et al. (1998) also found greater attenuation 

of SUVB than SUVA (315 to 400 nm). However, this effect depends on the nature of the 

specific particles involved and, therefore, is expected to depend strongly on location. Lorente 

et al. (1994) observed an attenuation of SUVB ranging from 14 to 37%, for solar zenith angles 

ranging from about 30o

 to about 60o, in the total (direct and diffuse) SUVB reaching the surface 

in Barcelona during cloudless conditions on very polluted days (aerosol scattering optical depth 

at 500 nm, 0.46 . J500 nm . 1.15) compared to days on which the turbidity of urban air was 

similar to that for rural air (J500 nm . 0.23). 

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Particle concentrations that can account for these observations can be estimated roughly by 

combining Koschmieder’s relation for expressing visual range in terms of extinction coefficient 

with one for expressing the mass of PM2.5 particles in terms of visual range (Stevens et al., 

1984). By assuming a scale height (i.e., the height at which the concentration of a substance 

falls off to 1/e of its value at the surface) of 1 km for PM2.5 , an upper limit of 30 μg/m3

 can be 

derived for the clear case and between 60 and 150 μg/m3

 for the polluted case. Estupiñán et al. 

(1996) found that summertime haze under clear sky conditions attenuates SUVB between 5 and 

23% for a solar zenith angle of 34° compared to a clear-sky day in autumn. Mims (1996) 

measured a decrease in SUVB by about 80% downwind of major biomass burning areas in 

Amazonia in 1995. This decrease in transmission corresponded to optical depths at 340 nm 

ranging from 3 to 4. Justus and Murphey (1994) found that SUVB reaching the surface 

decreased by about 10% because of changes in aerosol loading in Atlanta, GA from 1980 to 

1984. In addition, higher particle levels in Germany (48 °N) may be responsible for the greater 

attenuation of SUVB than seen in New Zealand (Seckmeyer and McKenzie, 1992). 

In a study of the effects of nonurban haze on SUVB transmission, Wenny et al. (1998) 

derived a very simple regression relation between the measured aerosol optical depth at 312 nm, 

(4-13) 

and the transmission of SUVB to the surface at solar noon. In principle, values of J312 nm could 

be found from knowledge of the aerosol optical properties and visual range values. Wenny et al. 

(1998) also found that absorption by particles accounted for 7 to 25% of the total (scattering + 

absorption) extinction. Relations such as the one above are strongly dependent on local 

conditions and should not be used in other areas without knowledge of the differences in aerosol 

properties. Although all of the above studies reinforce the idea that particles play a major role in 

modulating the attenuation of SUVB, none included measurements of ambient PM 

concentrations, so direct relations between PM levels and SUVB transmission could not be 

determined. 

Vuilleumier et al. (2001) concluded that variations in aerosol scattering and absorption 

were responsible for 97% of the variability in the optical depth measured at seven wavelengths 

from 300 to 360 nm at Riverside, CA from 1 July to 1 November 1997. Similar measurements 

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made at nearby Mt. Wilson, located above the main surface haze layer, showed that 80% of the 

variations in optical depth were still driven by variations in aerosol scattering and absorption. 

The remainder of the variability in optical depth was attributed mainly to variability in ozone 

under clear-sky conditions. However, these results cannot be extrapolated to other locations, 

because these effects are coupled and nonlinear and, thus, are not straightforward. They depend 

on the concentrations of these species and on the physical and chemical characteristics of the 

particles. Hence, any quantitative statements regarding the relative importance of particles and 

ozone will be location-specific. 

Liu et al. (1991) roughly estimated the overall effects on atmospheric transmission of 

SUVB of increases of anthropogenic airborne particles that have occurred since the beginning of 

the industrial revolution. Based on (a) estimates of the reduction in visibility from about 95 km 

to about 20 km over nonurban areas in the eastern United States and in Europe, (b) calculations 

of optical properties of airborne particles found in rural areas to extrapolate the increase in 

extinction at 550 to 310 nm, and (c) radiative transfer model calculations, Liu et al. (1991) 

concluded that the amount of SUVB reaching Earth’s surface likely has decreased from 5 to 18% 

since the beginning of the industrial revolution. This was attributed mainly to scattering of 

SUVB back to space by sulfate-containing particles. Radiative transfer model calculations have 

not been done for urban particles. 

Although aerosols are expected to decrease the flux of SUVB reaching the surface, 

scattering by particles is expected to result in an increase in the actinic flux within and above the 

aerosol layer. However, when the particles significantly absorb SUVB, a decrease in the actinic 

flux is expected. Actinic flux is the radiant energy integrated over all directions at a given 

wavelength incident on a point in the atmosphere and is the quantity needed to calculate rates of 

photolytic reactions in the atmosphere. Blackburn et al. (1992) measured attenuation of the 

photolysis rate of ozone and found that aerosol optical depths near unity at 500 nm reduced 

ozone photolysis rate by as much as a factor of two. Dickerson et al. (1997) showed that the 

photolysis rate for NO2, a key parameter for calculating the overall intensity of photochemical 

activity, could be increased within and above a scattering aerosol layer extending from the 

surface, although it would be decreased at the surface. This effect is qualitatively similar to what 

is seen in clouds, where photolysis rates are increased in the upper layers of a cloud and above 

the cloud (Madronich, 1987). For a simulation of an ozone episode that occurred during July 

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1995 in the Mid-Atlantic region, Dickerson et al. (1997) calculated ozone increases of up to 

20 ppb compared to cases that did not include the radiative effects of particles in urban airshed 

model (UAM-IV) simulations. In contrast, Jacobson (1998) found that particles may have 

caused a 5 to 8% decrease in O3 levels during the Southern California Air Quality Study in 1987. 

Absorption by organic compounds and nitrated inorganic compounds was hypothesized to 

account for the reductions in UV radiation intensity. 

The photolysis of ozone in the Hartley bands also leads to production of electronically 

excited oxygen atoms, O(1

D), that then react with water vapor to form hydroxyl radicals. Thus, 

enhanced photochemical production of ozone is accompanied by the scavenging of species 

involved in greenhouse warming and stratospheric depletion. However, these effects may be 

neutralized, or even reversed, by the presence of absorbing material in the particles. Any 

evaluation of the effects of particles on photochemical activity, therefore, will depend on the 

composition of the particles and will also be location-specific. 

Further complicating any straightforward evaluation of UV-B penetration to specific areas 

of the Earth’s surface are the influences of clouds, as discussed by Erlick et al. (1998), Frederick 

et al. (1998), and Soulen and Fredrick (1999). The transmission of solar UV and visible 

radiation is highly sensitive to cloud type, cloud amount, and the extent of their external or 

internal mixing with cloud droplets. Even in situations of very low atmospheric PM (e.g., over 

Antarctica), interannual variations in cloudiness over specific areas can be as important as ozone 

levels in determining ultraviolet surface irradiation, with net impacts varying monthly to 

seasonally (Soulen and Fredrick, 1999). Evaluations of the effects of changes in the 

transmission of solar UV-B radiation to the surface have been performed usually for cloud-free 

or constant cloudiness conditions. 

Given the above considerations, the quantification of projected effects of variations in 

atmospheric PM on human health or on the environment related to the effects of particles on the 

transmission of solar UV-B radiation would require location-specific evaluations that take into 

account (a) composition, concentration, and internal structure of the particles; (b) temporal 

variations in atmospheric mixing height and depths of layers containing the particles; and 

(c) the abundance of ozone and other absorbers within the planetary boundary layer and the free

troposphere. The outcome of such modeling effects would likely vary from location to location 

in terms of increased or decreased surface level UV-B exposures because of location-specific 

4-226
changes in atmospheric PM concentrations or composition. For example, to the extent that any 

location-specific scattering by airborne PM were to affect the directional characteristics of 

ultraviolet radiation at ground level, and thereby enhance radiation incident from low angles 

(Dickerson et al., 1997), the biological effectiveness (whether deleterious or beneficial) of 

resulting ground-level UV-B exposures could be enhanced. Airborne PM also can reduce the 

ground-level ratio of photorepairing radiation (UV-A and short-wavelength visible) to damaging 

UV-B radiation. Lastly, PM deposition is a major source of PAHs in certain freshwater lakes 

and coastal areas; and the adverse effects of solar UV are enhanced by the uptake of PAHs by 

aquatic organisms. Thus, although airborne PM may, in general, tend to reduce ground-level 

UV-B, its net effect in some locations may be to increase UV damage to certain aquatic and 

terrestrial organisms, as discussed by Cullen and Neale (1997). 

4.6 SUMMARY AND KEY CONCLUSIONS 

4.6.1 Particulate Matter Effects on Vegetation and Ecosystems 

The first section of this chapter assessed and characterized the overall ecological integrity 

and indicated the status of ecosystems within the United States affected by the deposition of the 

anthropogenic stressors associated with PM. There are six essential environmental attributes 

(EEAs) — landscape condition, biotic condition, and chemical/physical characteristics, and 

ecological processes, hydrology/geomorphology, and natural disturbance regimes that can be 

used to provide a hierarchical framework for characterizing ecosystem status. The first three can 

be separated into “patterns” and the last three into “processes.” The ecological processes create 

and maintain the ecosystem elements in patterns. The patterns, in turn, affect how the ecosystem 

processes are expressed. Patterns at the higher level of biological organization emerge from the 

interactions and selection processes at localized levels. Changes in patterns or processes result 

in changes in the status and functioning of an ecosystem. The relationships among the EEAs are 

complex because all are interrelated (i.e., changes in one EEA may affect, directly or indirectly, 

every other EEA). 

Human existence on Earth depends on the life-support services provided by the interaction 

of the different EEAs. Ecosystem processes and patterns provide the functions that maintain 

clean water, clean air, a vegetated earth, and a balance of organisms that enable humans to 

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survive. The benefits they impart include the absorption and breakdown of pollutants, cycling of 

nutrients, binding of soil, degradation of organic waste, maintenance of a balance of gases in the 

air, regulation of radiation balance, climate, and fixation of solar energy. Concern has arisen in 

recent years regarding biodiversity and the integrity of ecosystems. Because the activities of few 

ecosystems on Earth today are not influenced by humans, understanding the changes in 

biodiversity and nutrient cycling resulting from anthropogenic PM deposition are of great 

importance. 

The PM whose effects on vegetation and ecosystems is discussed in this section is not a 

single pollutant but represents a heterogeneous mixture of particles differing in origin, size, and 

chemical constituents. The effects of exposure to a given mass concentration of PM of particular 

size may, depending on the particular mix of deposited particles, lead to widely differing 

phytotoxic responses. 

The deposition of PM onto vegetation and soil, depending on its chemical composition 

(acid/base, trace metal, or nutrients, e.g., nitrates or sulfates), can produce direct or indirect 

responses within an ecosystem. The ecosystem response to pollutant deposition is a direct 

function of the level of sensitivity of the ecosystem and its ability to ameliorate resulting change. 

Changes in ecosystem structural patterns and the functioning of ecological processes, must be 

scaled in both time and space and propagated to the more complex levels of community 

interaction to produce observable ecosystem changes. 

The stressors of greatest environmental significance are particulate nitrates and sulfates 

whose indirect effects occur primarily via their deposition onto the soil. Upon entering the soil 

environment, they can alter the ecological processes of energy flow and nutrient cycling, inhibit 

nutrient uptake, change ecosystem structure, and affect ecosystem biodiversity. The soil 

environment is one of the most dynamic sites of biological interaction in nature. It is inhabited 

by microbial communities of bacteria, fungi, and actinomycetes. Bacteria are essential 

participants in the nitrogen and sulfur cycles that make these elements available for plant uptake. 

Fungi in association with plant roots form mycorrhizae, a symbiotic relationship that is integral 

in mediating plant uptake of mineral nutrients. Changes in the soil environment that influence 

the role of the bacteria in nutrient cycling and fungi in making minerals available for plant 

utilization, determine plant and ultimately ecosystem response. 

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The effects on the growth of plants resulting from the deposition of nitrates and sulfates 

and the acidifying effect of the associated H+ ion in wet and dry deposition are the most 

important environmentally. Nitrogen is of overriding importance in plant metabolism and, to a 

large extent, governs the utilization of phosphorus, potassium, and other nutrients. Typically, 

the availability of nitrogen via the nitrogen cycle controls net primary productivity, and possibly, 

the decomposition rate of plant litter. Plants usually obtain nitrogen directly from the soil by 

absorbing NH4

+

 or NO3 

!

 through their roots, or it is formed by symbiotic organisms in their 

roots. Plants vary in their ability to absorb ammonium and nitrate from the soil. 

Although nitrogen as molecular nitrogen (N2) is the most abundant element in the 

atmosphere, it only becomes available to support the growth of plants after it is converted into 

reactive forms. In nature, nitrogen may be divided into two groups: reactive (Nr) and 

nonreactive (N2). Reactive Nr includes the inorganic reduced forms of nitrogen (e.g., ammonia 

[NH3] and ammonium [NH4

+

]), inorganic oxidized forms (e.g., nitrogen oxides [NOx], nitric acid 

[HNO3], nitrous oxide [N2O], and nitrate [NO3 

!

]), and organic compounds (e.g., urea, amine, 

proteins, and nucleic acids). 

Reactive nitrogen is now accumulating in the environment on all spatial scales — local, 

regional, and global. The three main causes of the increase in global Nr is the result of the 

(1) widespread cultivation of legumes, rice, and other crops that promote conversion of N2 to 

organic nitrogen through biological nitrogen fixation; (2) combustion of fossil fuels, which 

converts both atmospheric N2 and fossil nitrogen to reactive NOx; and (3) the Haber-Bosch 

process, which converts nonreactive NH3 to sustain food production and some industrial 

activities. The major changes in the nitrogen cycle due to the cited causes can be both beneficial 

and detrimental to the health and welfare of humans and ecosystems. 

Reactive nitrogen can be widely dispersed and accumulate in the environment when the 

rates of its formation exceed the rates of removal via denitrification. Reactive nitrogen creation 

and accumulation is projected to increase as per capita use of resources by human populations 

increases. The cascade of environmental effects of increases in Nr include the following: 

(1) production of tropospheric ozone and aerosols that induce human health problems; 

(2) increases in the productivity in forests and grasslands followed by decreases wherever

deposition increases significantly and exceeds critical loads; Nr additions probably also decrease 

biodiversity in many natural habitats; (3) acidification and loss of biodiversity in lakes and 

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streams in many regions of the world in association with sulfur; (4) eutrophication (now 

considered the biggest pollution problem in coastal waters), hypoxia, loss of biodiversity, and 

habitat degradation in coastal ecosystems; and (5) contributes to global climate change and 

stratospheric ozone depletion, which can in turn affect ecosystems and human health (see 

Table 4-21). 

TABLE 4-21. EFFECTS OF REACTIVE NITROGEN 

Direct effects of Nr on ecosystems include:

 – Increased productivity of Nr-limited natural ecosystems.

– Ozone-induced injury to crop, forest, and natural ecosystems and predisposition to attack by pathogens

and insects. 

– Acidification and eutrophication effects on forests, soils, and freshwater aquatic ecosystems. 

– Eutrophication and hypoxia in coastal ecosystems. 

– N-saturation of soils in forests and other natural ecosystems. 

– Biodiversity losses in terrestrial and aquatic ecosystems and invasions by N-loving weeds. 

– Changes in abundance of beneficial soil organisms that alter ecosystem functions. 

Indirect effects of Nr on other societal values include:

 – Increased wealth and well being of human populations in many parts of the world.

– Significant changes in patterns of land use.

– Regional hazes that decrease visibility at scenic vistas and airports.

– Depletion of stratospheric ozone by N2O emissions.

– Global climate change induced by emissions of N2O and formation of tropospheric ozone.

– Damage to useful materials and cultural artifacts by ozone, other oxidants, and acid deposition.

– Long-distance transport of Nr, which causes harmful effects in countries distant from emission sources 

and/or increased background concentrations of zone and fine PM. 

In addition to these effects, it is important to recognize that:

 – The magnitude of Nr flux often determines whether effects are beneficial or detrimental. 

– All of these effects are linked by biogeochemical circulation pathways of Nr. 

– Nr is easily transformed among reduced and oxidized forms in many systems. Nr is easily distributed by 

hydrologic and atmospheric transport processes. 

Changes in nitrogen supply can have a considerable effect on an ecosystem’s nutrient 

balance. Large chronic additions of nitrogen influence normal nutrient cycling and alter many 

plant and soil processes involved in nitrogen cycling. “Nitrogen saturation” results when Nr 

concentrations exceed the capacity of a system to use it. Saturation implies that some resource 

other than nitrogen is limiting biotic function. Water and phosphorus for plants and carbon for 

microorganisms are most likely to be secondary limiting factors. The appearance of nitrogen in 

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soil solution is an early symptom of excess nitrogen. In the final stage, disruption of ecosystem 

structure becomes apparent. 

Possible ecosystem responses to nitrogen saturation include (1) permanently increased 

foliar nitrogen and reduced foliar phosphorus and lignin caused by the lower availability of 

carbon, phosphorus, and water; (2) reduced productivity in conifer stands because of disruptions 

of physiological function; (3) decreased root biomass and increased nitrification and nitrate 

leaching; (4) reduced soil fertility, resulting from increased cation leaching, increased nitrate 

and aluminum concentrations in streams; (5) decreased water quality; and (6) changes in soil 

microbial communities from predominantly fungal (mycorrhizal) communities to those 

dominated by bacteria during saturation. 

Although soils of most North American forest ecosystems are nitrogen-limited, 

some exhibit severe symptoms of nitrogen saturation (See Table 4-14). They include the 

high-elevation, nonaggrading spruce-fir ecosystems in the Appalachian Mountains; the eastern 

hardwood watersheds at the Fernow Experimental Forest near Parsons, WV; forests in southern 

California, the southwestern Sierra Nevada in Central California; and the Front Range in 

northern Colorado. The mixed conifer forest and chaparral watershed with high smog exposure 

in the Los Angeles Air Basin exhibit the highest stream water NO3 

!

 concentrations for wildlands 

in North America. 

Increases in soil nitrogen play a selective role in ecosystems. Plants adapted to living in an 

environment of low nitrogen availability will be replaced by nitrophilic plants capable of using 

increased nitrogen because they have a competitive advantage when nitrogen becomes more 

readily available. Plant succession patterns and biodiversity are affected significantly by chronic 

nitrogen additions in some North American ecosystems. Long-term nitrogen fertilization studies 

in both New England and Europe suggest that some forests receiving chronic inputs of nitrogen 

may decline in productivity and experience greater mortality. Declining coniferous forest 

stands with slow nitrogen cycling may be replaced by deciduous fast-growing forests that 

cycle nitrogen. 

Linked to the nitrogen cascade is the deposition of Nr and sulfates and the associated 

hydrogen ion in acidic precipitation, a critical environmental stress that affects forest landscapes 

and aquatic ecosystems in North America, Europe, and Asia. Composed of ions, gases, and 

particles derived from gaseous emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), 

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ammonia (NH3) and particulate emissions of acidifying and neutralizing compounds, acidic 

precipitation varies highly across time and space. Its deposition and the resulting soil acidity can 

lead to nutrient deficiencies and to high aluminum-to-nutrient ratios that limit uptake of calcium 

and magnesium and create a nutrient deficiency. Aluminum accumulation in root tissue can 

reduce calcium uptake and causes Ca2+deficiencies. Tree species can be adversely affected if 

altered Ca:Al ratios impair the uptake of calcium or magnesium. Calcium is essential in the 

formation of wood and the maintenance of the primary plant tissues necessary for tree growth. 

Studies suggest that the decline of red spruce stands in Vermont may be related to the Ca:Al 

ratio (Johnson and Lindberg, 1992b). 

The evidence of the effects of wet and dry particulate deposition SO4 

!

 and Nr species on 

nutrient cycling in forest ecosystems is provided by the IFS. The deposition data from the study 

illustrates several important aspects of the atmospheric exposure characteristics across a wide 

elevational gradient and over a wide spatial scale. Atmospheric deposition plays a significant 

role in the biogeochemical cycles at all IFS sites, but is most important in the east at highelevation sites. The flux of the sulfate ion, Nr compounds, and H+

 ions in throughfall at all sites 

is dominated by atmospheric deposition. Atmospheric deposition may have significantly 

affected the nutrient status of some IFS sites through the mobilization of aluminum by impeding 

cation uptake. Nitrates and sulfate are the dominant anions in the Smokies. Pulses of nitrates 

are the major causes of aluminum pulses in soil solutions. However, the connection between 

aluminum mobilization and forest decline is not clear, hence, aluminum mobilization presents a 

situation worthy of further study. 

Notable impacts of excess nitrogen deposition also have been observed with regard to 

aquatic systems. For example, atmospheric nitrogen deposition into soils in watershed areas 

feeding into estuarine sound complexes (e.g., the Pamlico Sound of North Carolina) appear to 

contribute to excess nitrogen flows in runoff (especially during and after heavy rainfall events 

such as hurricanes) from agricultural practices or other uses (e.g., fertilization of lawns or 

gardens). Massive influxes of such nitrogen into watersheds and sounds can lead to dramatic 

decreases in water oxygen and increases in algae blooms that can cause extensive fish kills and 

damage to commercial fish and sea food harvesting. 

The critical loads concept is useful for estimating the amounts of pollutants (e.g., Nr and 

acidic precipitation) that sensitive ecosystems can absorb on a sustained basis without 

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experiencing measurable degradation. The estimation of ecosystem critical loads requires an 

understanding of how an ecosystem will respond to different loading rates in the long term and 

can be of special value for ecosystems receiving chronic deposition of Nr and sulfur 

independently and as acidic deposition when in combination. 

Changes in the soil can result from the deposition of heavy metals. Exposures to heavy 

metals are highly variable, depending whether deposition is by wet or dry processes. Few (e.g., 

Cu, Ni, Zn) have been documented to have direct phytoxicity under field conditions. Exposure 

to coarse particles of natural origin and elements such as Fe and Mg are more likely to occur via 

dry deposition, while fine particles of atmospheric origin are more likely to contain elements 

such as Ca, Cr, Pb, N, and V. Ecosystems immediately downwind of major emissions sources 

such as power generating, industrial, or urban complexes can receive locally heavy inputs. 

By negatively affecting litter decomposition, heavy-metal accumulation presents the greatest 

potential for influencing nutrient cycling. Microbial populations decreased and logarithmic rates 

of microbial increase were prolonged as a result of cadmium toxicity. Additionally, the presence 

of Ca, Cu, and Ni were observed to impair the symbiotic activity of fungi, bacteria, and 

actinomycetes. 

Phytochelatins produced by plants as a response to sublethal concentrations of heavy 

metals are indicators of metal stress and can be used to indicate that heavy metals are involved in 

forest decline. The increasing concentrations of phytochelatins across regions and at greater 

altitudes associated with greater levels of forest injury implicate them in forest decline. 

The ambient concentration of particles, the parameter for which there is most data 

(Chapter 3), is, at best, an indicator of exposure. The amount of PM entering the immediate 

plant environment and deposited onto the plant surfaces or soil in the vicinity of the roots, 

determines the biological effect. Three major routes are involved during the wet and dry 

deposition processes: (1) precipitation scavenging in which particles are deposited in rain and 

snow; (2) occult (fog, cloud water, and mist interception); and (3) dry deposition, a much slower, 

yet more continuous removal to plant surfaces. 

Deposition of PM on the surfaces of above-ground plant parts can have either a physical 

and/or a chemical effect. Particles transferred from the atmosphere to plant surfaces may cause 

direct effects if they (1) reside on the leaf, twig, or bark surface for an extended period; (2) are 

taken up through the leaf surface; or (3) are removed from the plant via resuspension to the 

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atmosphere, washing by rainfall, or litter-fall with subsequent transfer to the soil. Ecosystem 

effects have been observed in the neighborhoods of limestone quarries. 

Secondary organics formed in the atmosphere have been variously subsumed under the 

following terms: toxic substances, pesticides, HAPS, air toxics, SVOCs, and POPs. The 

substances alluded to under the above headings are controlled under CAA Sect.112, Hazardous 

Air Pollutants, not as criteria pollutants controlled by NAAQS under CAA Sections 108 and 109 

(U.S. Code, 1991). Their possible effects on humans and ecosystems are discussed in many 

other government documents and publications. They are noted in this chapter because, in the 

atmosphere, many of the chemical compounds are partitioned between gas and particle phases 

and are deposited as PM. As particles, they become airborne and can be distributed over a wide 

area and affect remote ecosystems. Some of the chemical compounds are of concern to humans 

because they may reach toxic levels in the food chains of both animals and humans, whereas 

others tend to decrease or maintain the same toxicity as they move through food chains. 

An important characteristic of fine particles is their ability to affect the flux of solar 

radiation passing through the atmosphere directly, by scattering and absorbing solar radiation, 

and, indirectly, by acting as cloud condensation nuclei that, in turn, influence the optical 

properties of clouds. Regional haze has been estimated to diminish surface solar visible 

radiation by approximately 8%. Crop yields have been reported as being sensitive to the amount 

of sunlight received, and crop losses have been attributed to increased airborne particle levels in 

some areas of the world. 

Key conclusions and findings about PM-related effects on vegetation and ecosystems 

include the following: 

C A number of ecosystem-level conditions (e.g., nitrogen saturation, terrestrial and aquatic

acidification, coastal eutrophication) that can lead to negative impacts on human health

and welfare have been associated with chronic, long-term exposure of ecosystems to

elevated inputs of compounds containing Nr, sulfur and/or associated hydrogen ions.

C Some percentage of total ecosystem inputs of these chemicals is contributed by deposition 

of atmospheric particles, although the percentage greatly varies temporally and 

geographically and has not generally been well quantified. 

C Unfortunately, our ability to relate ambient concentrations of PM to ecosystem response is 

hampered by the following significant data gaps and uncertainties: 

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! The lack of a long-term, historic database of annual speciated PM deposition rates 

in sensitive ecosystems precludes establishing relationships between PM deposition 

(exposure) and ecosystem response at this time. Except for data from the IMPROVE 

network and from some CASTNet sites, much of the PM monitoring effort has focused 

on urban or near urban areas, rather than on sensitive ecosystems. 

! Modeled deposition rates, used in the absence of monitored data, can be highly 

uncertain, since there are a multitude of factors that influence the amounts of PM 

that get deposited from the air onto sensitive receptors. These factors include the 

mode of deposition (wet, dry, occult), windspeed, surface roughness/stickiness, 

elevation, particle characteristics (e.g., size, shape, chemical composition, etc.), 

and relative humidity. 

! Each ecosystem is unique from all others, since each has developed within a context 

framed by the topography, underlying bedrock, soils, climate, meteorology, hydrologic 

regime, natural and land use history, species associations that co-occur at that location 

(i.e., soil organisms, plants, etc.), and successional stage. Because of this variety, and 

insufficient baseline data on each of these features for most ecosystems, it is currently 

impossible to extrapolate with much confidence any effect from one ecosystem to 

another, or to predict an appropriate “critical load.” Thus, a given PM deposition rate 

or load of nitrates in one ecosystem may produce entirely different responses than the 

same deposition rate at another location. 

! There remain large uncertainties associated with the length of residence time of Nr in 

a particular ecosystem component or reservoir, and thus, its impact on the ecosystem 

as it moves through the various levels of the N cascade. 

C As additional PM speciated air quality and deposition monitoring data become available,

there is much room for fruitful research into the areas of uncertainty identified above. 

4.6.2 Particulate Matter-Related Effects on Visibility 

Visibility is defined as the degree to which the atmosphere is transparent to visible light 

and the clarity and color fidelity of the atmosphere. Visual range is the farthest distance a black 

object can be distinguished against the horizontal sky. Visibility impairment is any humanly 

perceptible change in visibility. For regulatory purposes, visibility impairment, characterized by 

light extinction, visual range, contrast, and coloration, is classified into two principal forms: 

(1) “reasonably attributable” impairment, attributable to a single source or small group of 

sources, and (2) regional haze, any perceivable change in visibility caused by a combination of 

many sources over a wide geographical area. 

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Visibility is measured by human observation, light scattering by particles, the light 

extinction-coefficient, and parameters related to the light-extinction coefficient (visual range and 

deciview scale), and fine PM mass concentrations. 

The air quality within a sight path will affect the illumination of the sight path by scattering 

or absorbing solar radiation before it reaches the Earth’s surface. The rate of energy loss with 

distance from a beam of light is the light extinction coefficient. The light extinction coefficient 

is the sum of the coefficients for light absorption by gases (Fag), light scattering by gases (Fsg), 

light absorption by particles (Fap), and light scattering by particles (Fsp). Corresponding 

coefficients for light scattering and absorption by fine and coarse particles are Fsfp and Fafp, and 

F and Facp, respectively. Visibility within a sight path longer than approximately 100 km (60 scp 

mi) is affected by the change in the optical properties of the atmosphere over the length of the 

sight path. 

Visual range was developed for, and continues to be used as, an aid in military operations 

and to a lesser degree in transportation safety. Visual range is commonly taken to be the greatest 

distance a dark object can be seen against the background sky. The deciview is an index of 

haziness. A change of 1 or 2 deciviews is seen as a noticeable change in the appearance of a 

scene. 

Visibility impairment is associated with airborne particle properties, including size 

distributions (i.e., fine particles in the 0.1- to 1.0-μm size range) and aerosol chemical 

composition, and with relative humidity. With increasing relative humidity, the amount of 

moisture available for absorption by particles increases, thus causing the particles to increase in 

both size and volume. As the particles increase in size and volume, the light scattering potential 

of the particles also generally increases. Visibility impairment is greatest in the eastern United 

States and Southern California. In the eastern United States, visibility impairment is caused 

primarily by light scattering by sulfate aerosols and, to a lesser extent, by nitrate particles and 

organic aerosols, carbon soot, and crustal dust. Up to 86% of the haziness in the eastern United 

States is caused by atmospheric sulfate. Farther West, scattering contributions to visibility 

impairment decrease from 25 to 50%. Light scattering by nitrate aerosols is the major cause of 

visibility impairment in southern California. Nitrates contribute about 45% to the total light 

extinction in the West and up to 17% of the total extinction in the East. Organic particles are the 

second largest contributors to light extinction in most U.S. areas. Organic carbon is the greatest 

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cause of light extinction in the West, accounting for up to 40% of the total extinction and up to 

18% of the visibility impairment in the East. Coarse mass and soil, primarily considered 

“natural extinction,” is responsible for some of the visibility impairment in the West, accounting 

for up to 25% of the light extinction. 

Key conclusions and findings about PM-related effects on visibility include the following: 

C Airborne particles degrade visibility by scattering and absorbing light. These optical 

properties can be well characterized in terms of a light extinction coefficient, which is the 

fractional attentuation of light per unit distance. The extinction coefficient produced by a 

given distribution of particle sizes and compositions is strictly proportional to the particle 

mass concentration. 

C The efficiency with which different particles attenuate light depends upon particle size, 

with fine particles in the accumulation mode being much more important in causing 

visibility impairment that coarse-mode particles. Thus, it is fine-particle mass 

concentrations that tend to drive extinction coefficients in polluted air. 

C The spatial and temporal variability in the observed extinction coefficient per mass of fine 

particles is mainly due to the effects of particle-bound water, which varies with relative 

humidity and is removed by drying when ambient PM2.5 mass concentrations are measured 

using the Federal Reference Method. 

C Improved relationships between ambient fine particle levels and visibility impairment 

have been developed as a result of refinements in algorithms that relate particle size and 

composition and relative humidity to light extinction, and thus, to visual range, as well as 

the availability of much expanded databases of PM2.5 concentrations and related 

compositional information and higher resolution visibility data. 

C Various local initiatives to address visibility impairment have demonstrated the usefulness 

of approaches now being used to evaluate public perceptions and attitudes about visibility 

impairment and public judgments as to the importance of standards to improve visibility 

relative to current conditions. 

C Various such initiatives, conducted in areas with notable scenic vistas (e.g., Denver, CO), 

have resulted in local standards that reflect what might be referred to as “adverse 

thresholds” associated with a minimum visual range of approximately 40 to 60 km. 

C These various local standards take into account that visibility impairment is an 

instantaneous effect of ambient PM2.5 levels and that the public primarily values enhanced 

visibility during daylight hours. These considerations are reflected in local standards that 

are based on sub-daily averaging times (e.g., 4 to 6 hours), typically averaged across midday hours. 

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C This general convergence of visual range values and averaging times that have been 

determined to be acceptable to the public in a number of such locations suggests that these 

values and averaging times are relevant for consideration in assessing the need for a 

national secondary standard to protect visibility in such areas. 

4.6.3 Particulate Matter-Related Effects on Materials 

Building materials (metals, stones, cements, and paints) undergo natural weathering 

processes from exposure to environmental elements (wind, moisture, temperature fluctuations, 

sunlight, etc.). Metals form a protective film of oxidized metal (e.g., rust) that slows 

environmentally induced corrosion. On the other hand, the natural process of metal corrosion 

from exposure to natural environmental elements is enhanced by exposure to anthropogenic 

pollutants, particularly SO2, that render the protective film less effective. 

Dry deposition of SO2 enhances the effects of environmental elements on calcareous stones 

(limestone, marble, and cement) by converting calcium carbonate (calcite) to calcium sulfate 

dihydrate (gypsum). The rate of deterioration is determined by the SO2 concentration, the 

stone’s permeability and moisture content, and the deposition rate; however, the extent of the 

damage to stones produced by the pollutant species apart from the natural weathering processes 

is uncertain. Sulfur dioxide also has been found to limit the life expectancy of paints by causing 

discoloration, loss of gloss, and thinning of the paint film layer. 

A significant detrimental effect of particle pollution is the soiling of painted surfaces and 

other building materials. Soiling changes the reflectance of opaque materials and reduces the 

transmission of light through transparent materials. Soiling is a degradation process that requires 

remediation by cleaning or washing, and, depending on the soiled surface, repainting. Available 

data on pollution exposure indicates that particles can result in increased cleaning frequency of 

the exposed surface and may reduce the usefulness of the soiled material. Attempts have been 

made to quantify the pollutants exposure levels at which materials damage and soiling have been 

perceived. However, to date, insufficient data are available to advance our knowledge regarding 

perception thresholds with respect to pollutant concentration, particle size, and chemical 

composition. 

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4.6.4 Effects of Atmospheric Particulate Matter on Global Warming 

Processes and Transmission of Solar Ultraviolet Radiation 

The physical processes (i.e., scattering and absorption) responsible for airborne particle 

effects on transmission of solar visible and ultraviolet radiation are the same as those responsible 

for visibility degradation. Scattering of solar radiation back to space and absorption of solar 

radiation determine the effects of an aerosol layer on solar radiation. 

Atmospheric particles greatly complicate projections of future trends in global warming 

processes because of emissions of GHGs; consequent increases in global mean temperature; 

resulting changes in regional and local weather patterns; and mainly deleterious (but sometimes 

beneficial) location-specific human health and environmental effects. The body of available 

evidence, ranging from satellite to in situ measurements of aerosol effects on radiation receipts 

and cloud properties, is strongly indicative of an important role in climate for aerosols. This 

role, however, is poorly quantified. No significant advances have been made in reducing the 

uncertainties assigned to forcing estimates provided by the IPCC for aerosol-related forcing, 

especially for black carbon-containing aerosol. The IPCC characterizes the scientific 

understanding of GHG-related forcing as “high” in contrast to that for aerosol, which it describes 

as “low” to “very low.” 

Quantification of the effect of anthropogenic aerosols on hydrological cycles requires more 

information than is presently available regarding ecosystems responses to reduced solar radiation 

and other changes occurring in the climate system. However, several global-scale studies 

indicate that aerosol cooling alone can slow down the hydrological cycle, while cooling plus the 

nucleation of additional cloud droplets can dramatically reduce precipitation rates. 

In addition to direct climate effects through the scattering and absorption of solar radiation, 

particles also exert indirect effects on climate by serving as cloud condensation nuclei, thus 

affecting the abundance and vertical distribution of clouds. The direct and indirect effects of 

particles appear to have significantly offset global warming effects caused by the buildup of 

GHGs on a globally-averaged basis. However, because the lifetime of particles is much shorter 

than the time required for complete mixing within the northern hemisphere, the climate effects of 

particles generally are felt much less homogeneously than are the effects of long-lived GHGs. 

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Any effort to model the impacts of local alterations in particle concentrations on projected 

global climate change or consequent local and regional weather patterns would be subject to 

considerable uncertainty. 

Atmospheric particles also complicate estimation of potential future impacts on human 

health and the environment projected as possible to occur because of increased transmission of 

SUVB through the Earth’s atmosphere, secondary to stratospheric ozone depletion due to 

anthropogenic emissions of chlorofluorcarbons (CFCs), halons, and certain other gases. The 

transmission of SUVB radiation is affected strongly by atmospheric particles. Measured 

attenuations of UV-B under hazy conditions range up to 37% of the incoming solar radiation. 

Measurements relating variations in PM mass directly to SUVB transmission are lacking. 

Particles also can affect the rates of photochemical reactions occurring in the atmosphere, e.g., 

those involved in catalyzing tropospheric ozone formation. Depending on the amount of 

absorbing substances in the particles, photolysis rates either can be increased or decreased. 

Thus, the atmospheric particle effects on SUVB radiation vary depending on the size and 

composition of particles and can differ substantially over different geographic areas, and from 

season to season over the same area. Any projection of the effects of location-specific airborne 

PM alterations on increased atmospheric transmission of solar UV radiation (and associated 

potential human health or environmental effects) due to stratospheric ozone-depletion would, 

therefore, also be subject to considerable uncertainty. 

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Appendix 4A 

Common and Latin Names 

Alder, hazel Alnus serrulata (Aiton) Willdenow 

Alder, red Alnus rubra Bong. 

Bean, common Phaseolus vulgaris L. 

Beech Fagus sylvatica L. 

Birch, yellow Betula alleghaniensis Britt. 

Blackhaw Viburnum prunifolium L. 

Brush box Lophostemon confertus (R. BR.) P.G. Wilson & Waterhouse 

Ceanothus, hoaryleaf Ceanothus crassifolius Torry 

Chaparral Ceanothus crassifolius 

Coachwood Ceratopetalum apetalum, D.Don 

Corn Zea mays L. 

Dogwood, flowering Cornus florida L. 

Elm Ulmus spp. 

Fir, balsam Abies balsamea (L) Mill. 

Fir, Douglas Pseudotsuga menziesii (Mirb.) Franco. 

Fir, fraser Abies fraseri (Pursh.) Poir 

Grape Vitis spp. 

Grass, red brome Bromus rubens L. 

Grass, purple moor Molina caerulea (L.) Moench. 

Greenbriar Smilax spp. 

Gum, sweet Liquidambar styraciflua L. 

Haw, black Viburnum prunifolium l. 

Heather, Scottish Calluna vulgaris Salisb. 

Hickory Carya spp. 

4A-1
Hophornbeam Ostrya virginiana (Mill.) Koch 

Ivy, English Hedera helix L. 

Laurel, mountain Kalmia latifolia L. 

Lichen, monks hood Hypogymnia physiodes 

Maize Zea mays L. 

Maple, red Acer rubrum L. 

Maple Acer spp. 

Maple, sugar Acer saccharum Marsh. 

Mustard, small podded Brassica geniculata L. 

Nettle, stinging Urtica dioica L. 

Oak, bur Quercus macrocarpa Michx. 

Oak, English Quercus rober L. 

Oak, chestnut Quercus prinus = Q. montana Willd. 

Oak, live Quercus virginiana Mill. 

Oak, northern red Quercus rubra L. 

Oak, turkey Quercus laevis Walt. 

Oak, white Quercus alba L. 

Oak Quercus spp. 

Oats, domestic Avena sativa L. 

Oats, wild Avena fatua L 

Persimmon, common Diosporos virginiana L. 

Pine, eastern white Pinus strobus L. 

Pine, jack pine Pinus banksiana Lamb. 

Pine, loblolly Pinus taeda L. 

Pine, lodgepole Pinus contorta Loud. 

Pine, Scots (Scotch) Pinus sylvestris L. 

Pine, slash Pinus elliotti Englem. 

Pine, lodgepole x Pinus contorta (Douglas ex Loud) x P. banksiana Lamb. 

jack pine 

4A-2
Poplar, black Populus nigra L. 

Poplar, white Populus alba L. 

Poplar, yellow or tulip Liriodendron tulipifera L. 

Privet Ligustrum spp. 

Purple Moor Grass Molina caerulea (L.) Moench. 

Ragweed Ambrosia spp. 

Rhododendron, Catawba Rhododendron catawbiense Michx. 

Rhododendron, rosebay Rhododendron maximum L. 

Sage, coastal Artemisia californica Less. 

Scottish Heather Calluna vulgaris Salisb. 

Soybean Glycine max (L.) Merr. 

Spruce, Norway Picea abies (L.) Karst. 

Spruce, red Picea rubens Sarg. 

Spruce, sitka Picea sitchensis (Bong.) Carr. 

Spruce, white Picea glauca (Moench.) Voss. 

Sunflower Helianthus annuus L. 

Sweetgum Liquidambar styraciflua L. 

Tulip poplar Liriodendron tulipifera L. 

EUMYCOTA-FUNGI 

Zygomycota 

Vesicular Arbuscular Mycorrhizae 

Scutellospora 

Gigaspora 

Glomus agrigatum 

Glomus leptototicum 

4A-3
Ascomycotina 

Chaetomium sp. 

Fungi Imperfecti 

Aureobasidium pullulans 

Cladosporium sp. 

Epicoccum sp. 

Pestalotiopsis 

Phialophora verrucosa 

Pleurophomella = Sirodothis 

4A-4
5. HUMAN EXPOSURE TO PARTICULATE MATTER

AND ITS CONSTITUENTS

5.1 INTRODUCTION 

5.1.1 Purpose 

The U.S. Environmental Protection Agency’s (EPA’s) regulatory authority for particulate 

matter (PM) applies primarily to ambient air and those sources that contribute to ambient air PM 

concentrations. Most of the epidemiological studies discussed in Chapter 8 relate measured 

community levels of airborne pollutants to population-based health statistics. Of necessity, these 

studies rely on some simplifying assumptions regarding exposures. One such assumption is that 

air pollutant concentrations measured at a community (or population-oriented) monitoring site 

(or the average concentration of several such sites) can serve as a surrogate for the average 

personal exposure to ambient PM for the population. However, total personal exposure to PM 

includes both ambient and nonambient components; and both components may contribute to 

adverse health effects. Thus, a major emphasis must be to develop an understanding of exposure 

to PM from sources that contribute to ambient air pollution. Ultimately, it will be necessary to 

account for both ambient and nonambient components of personal exposure in order to fully 

understand the relationship between PM and health effects. In addition, knowledge of an 

individual’s personal exposure to ambient, nonambient, and total PM would provide useful 

information for studies where health outcomes are tracked individually. 

Exposure to environmental contaminants can be interpreted in a number of ways. For 

airborne PM, an individual’s exposure is ideally based on measurements of PM concentrations 

in the air in the individual’s breathing zone as the individual moves through space and time. 

However, epidemiological studies frequently use the ambient concentration as a surrogate for 

exposure. Therefore, understanding exposure is important because adverse health effects 

associated with elevated PM concentrations occur at the individual level. Human exposure data 

and models provide the link between ambient concentrations (from monitoring data or estimated 

with atmospheric transport models) and lung deposition and clearance models that enable 

estimates of the source-air concentration-exposure-dose relationship for input into dose-response 

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assessments for PM from ambient sources. Personal exposure includes contributions from many 

different types of particles, from many sources, and in many different environments. 

The goal of this chapter is to provide current information on the development of human 

exposure data and models. This includes information on (a) relationships between PM measured 

at ambient sites and personal exposures to PM from both ambient and nonambient sources, and 

(b) factors that affect these relationships. The human exposure data and models presented in this 

chapter provide critical links between ambient monitoring data and PM dosimetry as well as 

between the toxicological studies and epidemiologic studies presented in other chapters. The 

specific objectives of this chapter are: 

(1) To provide an overall conceptual framework of exposure science as applied to PM,

including the identification and evaluation of factors that determine personal PM

exposure;

(2) To provide a concise summary and review of recent data (since 1996) and findings from 

pertinent PM exposure studies; 

(3) To characterize quantitative relationships between ambient air quality measurements 

(mass, chemical components, number, etc. as determined at a community monitoring site) 

and total personal PM exposure and its ambient and nonambient components; and 

(4) To evaluate the implications of using ambient PM concentrations as a surrogate for

personal exposure in epidemiologic studies of PM health effects. 

5.1.2 Particulate Matter Mass and Constituents 

Current EPA PM regulations are based on mass as a function of aerodynamic size. 

However, the EPA also measures the chemical composition of PM in both mo`nitoring and 

research studies. The composition of PM is variable and, as discussed in Chapters 7 and 8, 

health effects may be related to PM characteristics other than mass. Because PM from ambient 

air and other microenvironments may have different physical and chemical characteristics, PM 

from such different sources may also have different health effects. Ultimately, to understand and 

control health effects caused by PM exposures from all sources, it is important to quantify and 

understand exposure to those chemical constituents from various sources that are responsible for 

adverse health effects. 

The National Research Council (NRC) recognized the distinction between measuring 

exposure to PM mass and to chemical constituents in the document Research Priorities for 

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Airborne Particulate Matter I: Immediate Priorities and a Long-range Research Portfolio 

(National Research Council, 1998). Specifically, under Research Topic 1, Outdoor Measures 

versus Actual Human Exposures, the NRC recommended evaluation of “the relationships 

between concentrations of particulate matter and gaseous co-pollutants measured at stationary 

outdoor air monitoring sites and the contributions of these concentrations to actual personal 

exposures . . .” for PM mass. The NRC Research Topic 2 recommends evaluating exposures to 

biologically important constituents and specific characteristics of PM that cause responses in 

potentially susceptible subpopulations and the general population. It also was recognized by the 

NRC that “a more targeted set of studies under this research topic (#2) should await a better 

understanding of the physical, chemical, and biological properties of airborne particles 

associated with the reported mortality and morbidity outcomes” (National Research Council, 

1999). The NRC also stated that the later studies “should be designed to determine the extent to 

which members of the population contact these biologically important constituents and size 

fractions of concern in outdoor air, outdoor air that has penetrated indoors, and air pollutants 

generated indoors” (National Research Council, 1999). Thus, exposure studies should include 

contributions from all sources. The emphasis in this chapter on PM mass reflects the current 

state of the science. Where available, data also have been provided on chemical constituents 

although in most cases the data are limited. As recognized by the NRC, a better understanding 

of exposures to PM chemical constituents from multiple sources will be required to more fully 

identify, understand, and control those sources of PM contributing to adverse health effects and 

to accurately define the relationship between PM exposure and health outcomes due to either 

short- or long-term exposures. 

5.1.3 Relationship to Past Documents 

Early versions of PM criteria documents did not emphasize total human exposure, but 

rather focused almost exclusively on outdoor air concentrations. For instance, the 1969 Air 

Quality Criteria for Particulate Matter (National Air Pollution Control Administration, 1969) 

did not discuss either exposure or indoor concentrations. The 1982 EPA PM Air Quality 

Criteria Document (1982 PM AQCD), however, provided some discussion of indoor PM 

concentrations reflecting an increase in microenvironmental and personal exposure studies (U.S. 

Environmental Protection Agency, 1982). The new data indicated that personal activities, along 

5-3
with PM generated by personal and indoor sources (e.g., cigarette smoking), could lead to high 

indoor levels and high personal exposures to total PM. Some studies reported indoor 

concentrations that exceeded PM concentrations found in the air outside the monitored 

microenvironments or at nearby monitoring sites. Between 1982 and 1996, many more studies 

of personal and indoor PM exposure demonstrated that, in most inhabited domestic 

environments, indoor PM mass concentrations and personal PM exposures of the residents were 

greater than ambient PM mass concentrations measured simultaneously (e.g., Sexton et al., 1984; 

Spengler et al., 1985; Clayton et al., 1993). As a result, the National Research Council (1991) 

recognized the potential importance of indoor sources of contaminants (including PM) in 

causing health outcomes. 

The 1996 PM AQCD (U.S. Environmental Protection Agency, 1996) reviewed the human 

PM exposure literature through early 1996, mainly to evaluate the use of ambient air monitors as 

surrogates for PM exposure in epidemiological studies. Many of the studies cited showed poor 

correlations between personal exposure or indoor measurements of PM mass and outdoor or 

ambient site measurements. Conversely, Janssen et al. (1995) and Tamura et al. (1996a) showed 

that in the absence of major nonambient sources, total PM exposures to individuals tracked 

through time were highly correlated with ambient PM concentrations. Analyses of these latter 

two studies led to the consideration of ambient and nonambient exposures as separate 

components of total personal exposure. As a result, the 1996 PM AQCD (U.S. Environmental 

Protection Agency, 1996) distinguished between ambient and nonambient PM personal exposure 

for the first time. This chapter builds on the work of the 1996 PM AQCD by further evaluating 

the ambient and nonambient components of PM and by reporting research that evaluates the 

relationship between ambient concentrations and total, ambient, and nonambient personal 

exposure. 

5.1.4 Chapter Structure 

The chapter is organized to provide information on the principles of exposure, to review 

the existing literature, and to summarize key findings and limitations in the information. 

Specific sections are described below: 

• Section 5.2 discusses the basic concepts of exposure including definitions, methods for 

estimating exposure, and methods for estimating ambient air components of exposure. 

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• Section 5.3 presents PM mass data, including a description of the key available studies, 

the relationship of PM exposures with ambient concentrations, and factors that affect 

the relationship. 

• Section 5.4 presents data on PM constituents, including a description of the key available 

studies, the relationship with ambient concentrations, and factors that affect the 

relationship. 

• Section 5.5 then discusses the implications of using ambient PM concentrations in 

epidemiologic studies of PM health effects. 

• Section 5.6 summarizes key findings and limitations of the information. 

5.2 BASIC CONCEPTS OF EXPOSURE 

5.2.1 The Concept of Exposure 

“There is reasonable agreement that human exposure [to a substance] means contact with 

the chemical or agent.” However, contact can be either with “(a) the visible exterior of the 

person (skin and openings into the body such as mouth and nostrils), or (b) the so-called 

exchange boundaries where absorption takes place (skin, lung, gastrointestinal tract)” (Federal 

Register, 1986). In its 1992 Guidelines for Exposure Assessment (U.S. Environmental 

Protection Agency, 1992), EPA defined exposure as “taking place at the visible external 

boundary, as in (a) above, [concluding that this definition] is less ambiguous and more consistent 

with nomenclature in other scientific fields.” The 1992 Guidelines stated: 

“Under this definition, it is helpful to think of the human body as having a hypothetical 

outer boundary separating inside the body from outside the body. This outer boundary 

of the body is the skin and the openings into the body such as the mouth, the nostrils, 

and punctures and lesions in the skin. As used in these Guidelines, exposure to a 

chemical is the contact of that chemical with the outer boundary. An exposure 

assessment is the quantitative or qualitative evaluation of that contact; it describes the 

intensity, frequency, and duration of contact, and often evaluates the rates at which the 

chemical crosses the boundary (chemical intake or uptake rates), the route by which it 

crosses the boundary (exposure route; e.g., dermal, oral, or respiratory), and the resulting 

amount of the chemical that actually crosses the boundary (a dose) and the amount 

absorbed (internal dose)” (U.S. Environmental Protection Agency, 1992). 

When applied to PM exposure by the inhalation route, the concentration of interest is that 

of PM in the air that will enter the respiratory system, not the average concentration of inspired 

5-5
and exhaled air as measured at the mouth or nostrils. Therefore, a measurement of inhalation 

exposure to PM is based on measurements of the PM concentration near the breathing zone, 

but not affected by exhaled air. 

5.2.2 Components of Exposure 

The total exposure of an individual over a discrete period of time includes exposures to 

many different types of particles from various sources while in different microenvironments. 

Duan (1982) defined a microenvironment as “a [portion] of air space with homogeneous 

pollutant concentration.” It also has been defined as a volume in space for a specific time 

interval during which the variance of concentration within the volume is significantly less than 

the variance between that microenvironment and surrounding ones (Mage, 1985). In general, 

people pass through a series of microenvironments including outdoor, in-vehicle, and indoor 

microenvironments as they go through space and time. Thus, total daily exposure for a single 

individual to PM must be expressed as the sum of various exposures for the microenvironments 

that the person occupies in the day (modified from National Research Council, 1991). 

In a given microenvironment, particles may originate from a wide variety of sources. 

For example, in an indoor microenvironment, PM may be generated by (1) indoor activities, 

(2) outdoor PM entering indoors, (3) the chemical interaction of outdoor air pollutants and 

indoor air or indoor sources, (4) transport from another indoor microenvironment, or (5) personal 

activities. All of these disparate sources should be accounted for when estimating total human 

exposure to PM. 

An analysis of personal exposure to PM mass (or to its constituent compounds) requires 

definition and discussion of several classes of particles and exposure. In this chapter, PM 

metrics will be described in terms of exposure or as an air concentration. Particulate matter also 

may be described according to both its source (i.e., ambient, nonambient) and the 

microenvironment in which exposure occurs. Table 5-1 provides a summary of the terms used 

in this chapter, the notations used for these terms, and their definitions. These terms are used 

throughout this chapter and provide the terminology for evaluating personal exposure to total 

PM and to PM from ambient and nonambient sources. 

Exposures are significant only if they are associated with a biologically relevant duration 

of contact with a substance of concern (Lioy, 1990; National Research Council, 1991). 

5-6
TABLE 5-1. TYPES OF PARTICULATE MATTER USED IN EXPOSURE

AND CONCENTRATION VARIABLES

General Definitions 

Term Notation Definition 

Concentration C General term for the average air concentration over some specified time 

period, used with subscript to indicate concentration of a specific type of 

PM, usually expressed in μg/m3

 units. 

Personal Exposure E General term for the average personal exposure over some specified time 

period, used with subscript to indicate exposure to a specific type of PM, 

quantified as the concentration at the oral/nasal contact boundary. 

Types of Particulate Matter 

Term Subscript Definition 

Ambient PM a PM in the atmosphere measured at a community ambient monitoring 

site either emitted into the atmosphere directly (primary PM) or formed 

in it (secondary PM). Major sources of PM species are industry, motor 

vehicles, commerce, domestic emissions such as wood smoke, and natural 

wind-blown dust or soil. (C without subscript is used for Ca. Ea is used for 

exposure to ambient PM while outdoors.) 

Ambient-Outdoor PM o Ambient PM in an outdoor microenvironment. (Co is frequently 

considered to be equal to, or at least represented by, C.) 

Indoor PM i All PM found indoors. 

Ambient-Indoor PM ai Ambient PM that has infiltrated indoors (i.e., has penetrated indoors and 

remains suspended). Eai is used for ambient exposure while indoors. 

Indoor-generated PM ig PM generated indoors. 

Indoor-reaction PM ir PM formed indoors by pollutants from outdoors reacting with indoorgenerated pollutants. 

Personal Cloud PM pc PM contributing to personal exposure but not contained in indoor or 

outdoor measurements of PM, usually related to personal activities. 

Personal PM s PM at the oral/nasal contact zone as the subject moves through space 

(of a subject) and time. 

Concentration and Exposure Variable Used without Subscripts 

Term Notation Definition 

Ambient Concentration C Concentration measured at a community ambient air monitoring site 

(or the average of several such sites). 

Total Personal

 Exposure 

T Total personal exposure as measured by a personal exposure monitor 

(PEM). 

Ambient Exposure A Personal exposure to the ambient component of total personal exposure, 

i.e., personal exposure to that PM measured at an ambient air community 

monitoring site. Includes exposure to C and Cai but not to resuspended 

ambient PM previously deposited indoors. 

Nonambient Exposure N Personal exposure to nonambient PM. 

5-7
TABLE 5-1 (cont’d). TYPES OF PARTICULATE MATTER USED IN EXPOSURE 

AND CONCENTRATION VARIABLES 

Relationships among Concentration and Exposure Variables for a Two-Compartment Model 

(Indoors and Outdoors) 

T = A + N 

A = Ea + Eai 

A = yC + (y-1) Cai, where y = fraction of time outside 

N = Eig + Eir + Epc 

Application of this concept to PM exposure is complicated by a lack of understanding of the 

biological mechanisms of PM toxicity. It is not certain whether the relevant duration is the 

instantaneous exposure to a peak concentration, or hourly, daily, or long-term exposure for 

months or years (or possibly all of the above). Similarly, it is not certain as to how PM toxicity 

depends on particle size or particle composition; whether number, surface area, or mass is the 

appropriate metric; or how PM toxicity may be influenced by conditions that might increase 

susceptibility such as age, preexisting disease conditions (chronic obstructive pulmonary disease 

[COPD], asthma, diabetes, etc.), exposure to infectious agents, exposure to heat or cold, stress, 

etc. A person’s exposure is clearly influenced by the distribution of many variables and 

parameters. A measurement at a single point in space and time along each distribution cannot 

adequately describe a person’s exposure. Thus, it is important to think of exposure as a path 

function with the instantaneous exposure varying as the PM concentration and composition 

varies as the person moves through space and time. 

The 1997 NAAQS were developed largely on the basis of evidence from epidemiologic 

studies that found relatively consistent associations between outdoor PM mass concentrations 

and observed health effects. Thus, an emphasis in this chapter is on the relationship between PM 

concentrations measured at ambient sites and personal exposures to the PM measured at those 

ambient sites (National Research Council, 1998), i.e., ambient PM exposure. Although this is an 

emphasis, it should be kept in mind that every particle that deposits in the lung becomes part of 

the dose delivered to an individual. It is likely that the nonambient component of total exposure 

also exerts health effects not necessarily detected by community time-series epidemiologic 

studies. 

5-8
5.2.3 Quantification of Exposure 

Quantification of inhalation exposure to PM or any other air pollutant starts with the 

concept of the variation in the concentration of the air pollutant in the breathing zone (but 

unperturbed by exhaled breath) as measured by a personal exposure monitor as a person moves 

through space and time. The relationships among the various types of exposure quantities can be 

easily seen in the hypothetical exposure time profile shown in Figure 5-1. The peak exposure, 

instantaneous exposure, and average exposure have units of concentration, for PM, usually 

μg/m3

. The integrated exposure has units of concentration × time. 

Figure 5-1. Hypothetical exposure time profile: pollutant exposure as a function of time 

showing how the averaged exposure, integrated exposure, and peak exposure 

relate to the instantaneous exposure. (t2 ! t1 = T.) 

Source: Adapted from Duan (1989); Zartarian et al. (1997). 

5-9 
The integrated exposure may be written as: 

(5-1)

where g is the integrated personal exposure during the time period t1 to t2 and Cs is the 

instantaneous exposure of the subject as the subject moves through space and time (Lioy, 1990; 

National Research Council, 1991; Georgopoulos and Lioy, 1994). Because Cs is a function of 

both space and time, it cannot generally be correctly approximated by a measurement at a single 

point in space or at a single time. 

For most of the discussion in this chapter, we will be interested in the average exposure 

given as: 

(5-2) 

where E is the average exposure during the time period t1 to t2. Most studies report 24-h 

averages, although some studies measure 12-h or 2- or 3-day averages. 

Equations 5-1 and 5-2 apply to a specific individual moving through space and time on a 

specific path. When treating populations, the distribution of the values of the variables and 

parameters must be considered. Georgopoulos and Lioy (1994) showed how to modify these 

equations to consider the probability distributions. 

5.2.4 Methods To Estimate Personal Exposure 

Personal exposure may be estimated using either direct or indirect approaches. Direct 

approaches measure the contact of the person with the chemical concentration in the exposure 

media over an identified period of time. Direct measurement methods include personal exposure 

monitors (PEMs) for PM that are worn continuously by individuals as they encounter various 

microenvironments while performing their daily activities. Indirect approaches use models and 

available information on concentrations of chemicals in microenvironments, the time individuals 

spend in those microenvironments, and personal PM-generating activities to estimate personal 

5-10
exposure. This section describes methods used to directly measure personal exposures and 

microenvironmental concentrations as well as the models used to estimate exposure. Several 

approaches to estimating personal exposure to ambient PM are also described. 

5.2.4.1 Direct Measurement Methods 

5.2.4.1.1 Personal Exposure Monitoring Methods 

In theory, personal exposure to total PM is defined as the concentration of PM in inhaled 

air entering the nose or mouth. Practically, it is measured by sampling PM with a PEM worn by 

a person and sampling from a point near the breathing zone (but not affected by exhaled breath). 

Personal exposure monitors (PEMs) for PM use measurement techniques similar to those 

employed for measuring ambient PM. Most PEMs rely on filter-based mass measurement of a 

particle size fraction (PM10 or PM2.5) usually integrated over either a 24- or 12-h period at flow 

rates of 2 to 4 L/min using battery-operated pumps. Because PEMs must be worn by study 

participants, they must be quiet, compact, and battery-operated. These requirements limit the 

type of pumps and the total sample volume that can be collected. Generally, small sample 

volumes limit personal exposure measurements to PM mass and a few elements detected by 

X-ray fluorescence (XRF). In most studies, PM2.5 and PM10 have not been collected 

concurrently; thus, very few data are available by which to estimate personal exposure to coarse 

thoracic PM (i.e., PM10-2.5) exposures. 

One of the earliest versions of a PEM was developed by the Harvard School of Public 

Health for use in the Particle Total Exposure Assessment Methodology (PTEAM) study (Clayton 

et al., 1993; Thomas et al., 1993; Özkaynak et al., 1996a). This 4 L/min sampler used a sharp 

cut impactor with 2.5 or 10 μm 50% cut points (Marple et al., 1987). A version of this device is 

manufactured by MSP, Inc. (Minneapolis, MN). Several exposure studies have used the MSP 

PEM (usually the 2.5 μm PEM version) with a flow rate of 2 L/min (Kamens et al., 1991; 

Williams et al., 2000a,b; Landis et al., 2001). Modifications to the MSP PEM, including a 

“scalper” nozzle to exclude particles > 4 μm, are described in Pellizzari et al. (1999). The 

penetration curve for the MSP scalper and the MSP 2.5 μm impactor (for 2 L/min flow rate) are 

given in Rodes et al. (2001). 

More recently the Harvard School of Public Health has developed a personal particle 

sampler that can operate as a PM2.5 or a PM1 sampler and that can be used in a personal 

5-11
multipollutant sampler (Sioutas et al., 1996; Demokritou et al., 2001). The PM2.5 version 

(4 L/min) has been used in an exposure study (Liu et al., 2002). Evaluations of other PEMs have 

been reported (Williams et al., 1999; Lanki et al., 2002). 

Other methods used for ambient PM have also been adapted for use as PEMs. For 

example, a personal nephelometer that measures light scattering has been worn by subjects and 

used in personal exposure studies to obtain real-time PM measurements (Quintana et al., 2000; 

Rea et al., 2001; Magari et al., 2002; Lanki et al., 2002). Light scattering instruments are most 

sensitive to particles in the accumulation-mode size range. Recent developments in light 

scattering devices have made possible the short-term measurement of personal and indoor 

exposures (Liu et al., 2002). These devices include the Radiance nephelometer (Radiance 

Research, Seattle, WA), the personal DataRAM or pDR (Thermo MIE Inc., Smyrna, GA), and 

the DustTrak aerosol monitor (TSI Inc., St. Paul, MN; Brauer, 1995; Howard-Reed et al., 2000; 

Quintana et al., 2000, 2001; Chang et al., 2000; Magari et al., 2002). The Radiance 

nephelometer is an active monitor with a 530-nm wavelength defined by an optical filter. The 

pDR uses a wavelength of 880 nm, making it more sensitive than the nephelometer to particles 

> 1 μm in diameter but less sensitive to particles < 0.3 μm diameter. The pDR (usually used in 

the passive mode, without a size fractionation inlet) has been widely used in exposure studies 

because of its light weight (no pump required), data logging capability, and easy handling 

(Quintana et al., 2000, 2001; Williams et al., 2000a; Muraleedharan and Radojevic, 2000; Rea 

et al., 2001). A portable condensation nuclei counter (with a lower size limit of 20 nm diameter) 

has also been used in exposure studies (Abraham et al., 2002). 

Simultaneous measurements of both gravimetric mass and light scattering coefficients have 

provided assessment of both the nephelometer and pDR in outdoor and indoor air (Brauer, 1995; 

Quintana et al., 2000; Scheff and Wadden, 1979; Waggoner and Weiss, 1980; Sioutas et al., 

2000). Less information is available to assess their use in personal environments. Liu et al. 

(2002) compared the pDR and the Radiance Nephelometer against gravimetric measurements 

from both the Harvard impactor for PM2.5 (HI2.5) and the Harvard personal environmental 

monitor (HPEM2.5) in indoor, outdoor, and personal settings at residences and on the person of 

elderly subjects living across the metropolitan Seattle area. 

Ten-minute averages for collocated indoor nephelometers and pDRs at diverse residential 

sites have produced varied coefficients of determination across homes (R2

 = 0.75 to 0.96). The 

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differences between the nephelometer and the pDR increased during cooking hours. When 

regressed against 24-h gravimetric measurements made with the HI2.5, the Radiance 

nephelometer showed higher coefficients of determination indoors (R2

 = 0.81 [non-cooking 

days] and 0.93 [days with cooking]) and outdoors (R2

 = 0.88 [outdoors]) than the pDR indoors 

(R2

 = 0.84 [non-cooking days] and 0.77 [days with cooking]). A comparison of the pDR versus 

the HPEM2.5, when both were carried by subjects, gave lower coefficients of determination 

(R2

 = 0.44 [non-cooking days] and 0.60 [days with cooking]. 

Liu et al. (2002) also report: 

“The pDR measurements generally overestimated gravimetric PM2.5 measurements, 

with a mean indoor pDR/HI2.5 radio of 1.56 + 0.07. As the pDR uses a calibration 

dust density of 2.6 g/cm3

, overestimating gravimetric measurements by a factor of 

1.56 gives an estimated indoor particle density of 1.67g/cm3

. This estimated indoor 

particle density is within the range for sulfates (1.76), nitrate (2.1), organic carbon 

(1.4), and elemental carbon (2.0) (Ames et al., 2000). Somewhat different results were 

obtained for the comparison with the personal gravimetric measurements. When both 

the pDR and HPEM2.5 were carried by subjects, the mean personal pDR/HPEM2.5 ratio 

was 1.27 + 0.08. This overestimation factor leads to an estimated density of the 

personal aerosol of 2.05 g/cm3

. This larger density seem reasonable if the personal 

aerosol is largely suspended crustal material, as has been found by Ozkaynak et al. 

(1996a). These overestimation factors were similar but slightly lower than the mean 

indoor pDR/HI2.5 ratio 1.41 reported by Quintana et al. (2000) and the mean personal 

pDR/PEM ratio 1.49 by Howard-Reed et al. (2000).” 

Howard-Reed et al. (2000) compared pDR readings, averaged over 24 h with 

corresponding gravimetric measurements taken with MSP and PM2.5 PEMs (2 L/min). Both 

personal samplers were worn by participants in a Baltimore exposure study. Howard-Reed et al. 

(2000) reported a coefficient of determination of R2

 = 0.66 and a regression equation of pDR 

(μg/m3

) = 1.10 × PEM (μg/m3) + 5.84 for 34 24-h measurements. Lanki et al. (2002), using 

the pDR in a flow-through mode and a PM2.5 PEM of their own design, found R2

 = 0.66 and 

pDR = 1.85 × PEM + 4.52. Sioutas et al. (2000) have also evaluated the pDR in a flow-through 

mode of operation (2 L/min, with a diffusion dryer in the inlet). They found a significant 

increase in pDR to gravimetric mass for relative humidity (RH) above 60%. The ratio was 2:1 at 

RH = 80%. They subsequently used a diffusion drying tube in the inlet. With this system, they 

were able to study the sensitivity of the pDR to particle size by comparing the pDR reading to 

the mass measured by the multi-orifice, uniform deposit detector (MOUDI) as a function of the 

5-13
particle mass mean diameter (MMD) as determined by the MOUDI. As the MMD increases 

from 0.3 to 1.1 μm, the pDR-to-MOUDI ratio increases from approximately 0.7 to about 1.6. 

As the MMD increases to 1.5 μm, the ratio decreases to about 1.0. 

Chang et al. (2001) evaluated the DustTrak against a MSP PEM in a roll-around system. 

Particles were passed through at 2.5 μm PEM size-selective inlet and a Nafion diffusion dryer 

before entering the DustTrak. Hourly PM2.5 concentrations measured by the DustTrak were 

averaged and compared to 12-h integrated MSP PEM PM2.5 measurements. The DustTrak 

concentrations were higher than the PEM by a factor of about 2. The use of a DustTrak 

correction factor (2.07 in summer and 2.02 in winter) resulted in good agreement between the 

two methods, with R2

 = 0.87 in the summer (35 pairs) and R2 = 0.81 in the winter (42 pairs). 

5.2.4.1.2 Microenvironmental Monitoring Methods 

Direct measurements of microenvironmental PM concentrations, which are used with 

models to estimate personal exposure to PM, also employ methods similar to those used for 

ambient PM. These methods differ from PEMs in that they are stationary with respect to the 

microenvironment (i.e., a stationary PEM). Microenvironmental monitoring methods include 

filter-based mass measurements of particle size fractions (PM10, PM2.5) usually integrated over 

either a 24- or 12-h period such as the Federal Reference Methods (FRMs) for PM2.5 and PM10. 

Flow rates vary between various devices from 4 to 20 L/min. Larger sample volumes allow 

more extensive chemical characterization to be conducted on microenvironmental samples. 

Because more than one pumping system can be used in a microenvironment, PM2.5 and PM10 can 

be collected simultaneously. 

The Harvard School of Public Health has developed a 10 L/min sampler, known as the 

Harvard Impactor (HI2.5, Air Diagnostics and Engineering, Inc., Naples, ME), which has been 

used as an indoor or outdoor monitor in exposure studies (Liu et al., 2002). Babich et al. (2000) 

reported that the HI2.5 showed excellent agreement (R2

 = 0.99) with the PM2.5 Federal Reference 

Method (FRM) for 81 24-h samples in Riverside and Bakersfield, CA. The HI also showed good 

precision (4.8%) for 243 24-h collocated samples over eight studies. Other continuous ambient 

PM measurement methods that have been utilized for microenvironmental monitoring include 

the Tapered Element Oscillating Microbalance (TEOM) and nephelometers. Various continuous 

5-14
techniques for counting particles by size (Climet, LASX, SMPS, APS) also have also been used. 

Measurement techniques are discussed in Chapter 2. 

5.2.4.2 Indirect or Modeling Methods 

5.2.4.2.1 Personal Exposure Models 

As a relatively new field, exposure modeling for PM mass (PM2.5 and PM10-2.5) and PM 

chemical constituents faces significant methodological challenges and input data limitations. 

Exposure models typically use one of two general approaches: (1) a time-series approach that 

estimates individuals’ microenvironmental exposures sequentially over time or (2) a timeaveraged approach that estimates individuals’ microenvironmental exposures using average 

microenvironmental concentrations and the total time spent in each microenvironment. 

Although the time-series approach to modeling personal exposures provides the appropriate 

structure for accurately estimating personal exposures (Esmen and Hall, 2000; Mihlan et al., 

2000), a time-averaged approach typically is used when the input data needed to support a timeseries model are not available. However, the time-varying dose profile of an exposed individual 

can only be modeled by using the time-series approach (McCurdy, 1997, 2000). 

Even though the processes that lead to exposure are nonlinear in nature, personal exposure 

models are often used to combine microenvironmental concentration data with human activity 

pattern data to estimate personal exposures. Time-averaged models can be used to estimate 

personal exposure for an individual or for a defined population. Total personal exposure models 

estimate exposures for all of the different microenvironments in which a person spends time, and 

total average personal exposure is calculated from the sum of these microenvironmental 

exposures: 

(5-3) 

where Ej

 is the personal exposure in each microenvironment, j (Duan, 1982). Example 

microenvironments include outdoors, indoors at home, indoors at work, and in transit. Each 

microenvironmental exposure, Ej

, is calculated from the average concentration in 

microenvironment j, Cj

, weighted by the time spent in microenvironment j, tj. T is the sum 

5-15
of tj

 over all j. This model has been applied to concentration data in a number of studies 

(Ott, 1984; Ott et al., 1988, 1992; Miller et al., 1998; Klepeis et al., 1994; Lachenmyer and 

Hidy, 2000). 

Many exposure studies employ 24-h average measurements of concentration indoors and 

outdoors and use these concentrations with time spent indoors and outdoors, as shown in 

Equation 5-3. It is important to note that although measurement data may be an average 

concentration over some time period (i.e., 24 h), significant variations in PM concentrations can 

occur during that time period. Thus, an error may be introduced if real-time concentrations are 

highly variable and an average concentration for a microenvironment is used to estimate 

exposure when the individual is in that microenvironment for only a fraction of the total time. 

This may create large errors if the indoor (e.g., in a house) 24-h average includes significant time 

periods when there are no people in the house, because the indoor concentrations are increased 

by the activities of people. In an effort to overcome these errors, the Air Pollution Exposure 

Distribution within Urban Populations in Europe (EXPOLIS) study (Kousa et al., 2002) turned 

outdoor samplers off when the subject was indoors and the indoor sampler off with the subject 

was outdoors. This provides a better estimate of Ea and Ei

 to compare with the PEM 

measurement and allows a better calculation of Epc. However, it does not provide data that can 

be used to regress C or Co with Ci

. This problem could be overcome with continuous 

monitoring, and the extent of the error could be calculated. Also, shorter time interval 

information might be useful in examining relationships between acute effects and short-term 

exposures. 

Microenvironmental concentrations used in the exposure models can be measured directly 

or estimated by one or more microenvironmental models. Microenvironmental models vary in 

complexity from a simple indoor/outdoor (I/O) ratio to a multi-compartmental mass-balance 

model. A discussion of microenvironmental models is presented in Section 5.3.4.2.2. 

On the individual level, the time spent in the various microenvironments is obtained from 

time-activity diaries that are completed by the individual. For population-based estimates, the 

time spent in various microenvironments is obtained from human activity databases. Many of 

the largest human activity databases have been consolidated by the EPA’s National Exposure 

Research Laboratory (NERL) into one comprehensive database called the Consolidated Human 

Activity Database (CHAD). CHAD contains over 22,000 person-days of 24-h activity data from 

5-16
11 different human activity-pattern studies (McCurdy et al., 2000). Population cohorts with 

diverse characteristics can be constructed from the activity data in CHAD and used for exposure 

analysis and modeling (McCurdy, 2000). These databases can also be used to estimate 

inhalation rates based on activity levels, age, gender, and weight for dosimetry calculations. 

A human activity database may contain information on human location, activity, and exertion 

rate as well as information such as the presence of combustion sources (e.g., wood fireplaces, 

smokers). However, in exposure studies, “activity” usually refers to a person’s location in space, 

i.e., in what microenvironment at what times. In dosimetry, “activity” is used as an indication of 

the level of physical exertion and is used to estimate breathing rate and the extent of mouth, 

nose, or combined breathing. Table 5-2 is a summary of the human activity studies in CHAD. 

Methodologically, personal exposure models can be divided into three general types: 

(1) statistical models based on empirical data obtained from one or more personal monitoring 

studies, (2) simulation models based upon known or assumed physical relationships, and 

(3) physical-stochastic models that include Monte Carlo or other techniques to explicitly address 

variability and uncertainty in model structure and input data (Ryan, 1991; MacIntosh et al., 

1995). The attributes, strengths, and weaknesses of these model types are discussed by Ryan 

(1991), National Research Council (1991), Frey and Rhodes (1996), and Ramachandran and 

Vincent (1999). A review of the logic of exposure modeling is found in Klepeis (1999). 

Personal exposure models that have been developed for PM are summarized in Table 5-3. 

The regression-based models (Johnson et al., 2000; Janssen et al., 1997; Janssen et al., 1998a) 

were developed specifically to account for the observed difference between personal exposure 

and microenvironmental measurements and are based on data from a single study, limiting their 

utility for broader purposes. Other types of models in Table 5-3 were limited by a lack of data 

for the various model inputs. For example, ambient PM monitoring data are not generally of 

adequate spatial and temporal resolution for these models. Lurmann and Korc (1994) assumed a 

constant relationship between coefficient of haze (COH) and PM10 and used site-specific COH 

information to stochastically develop a time series of 1-h PM10 data from every sixth day 

24-h PM10 measurements. A mass-balance model typically was used for indoor 

microenvironments when sufficient data were available, such as for a residence. For most other 

microenvironments, I/O ratios were used because of the lack of data required for a mass-balance 

model. In addition, only the deterministic model, PMEX, included estimation of inhaled dose 

5-17
TABLE 5-2. ACTIVITY PATTERN STUDIES INCLUDED IN THE CONSOLIDATED HU

ACTIVITY DATABASE (CHAD)

5-18

Calendar Time Period 

Diary 

Documentation or 

Study Name of the Study Age1 Days2 Type3 Time4 Rate5 Reference Baltimore, MD Jan-Feb 1997 65+ 391 Diary; 15-min 24-h Standard No Williams et al. (2000a,b) Mul

Jul-Aug 1998 blocks part 

CARB: Adolescents Oct 1987-Sept 1988 12-94 1762 Retrospective 24-h Standard No Robinson et al. (1991) 

and Adults Wiley et al. (1991a) 

CARB: Children Apr 1989-Feb 1990 0-11 1200 Retrospective 24-h Standard No Wiley et al. (1991b) Cincinnati (EPRI) Mar-Apr and Aug 1985 0-86 2614 Diary 24 h; nominal

 7 pm-7 am 

Yes Johnson (1989) 3 co

rem

Denver (EPA) Nov 1982-Feb 1983 18-70 805 Diary 24 h; nominal

 7 pm-7 am 

No Akland et al. (1985) 

Johnson (1984) 

Part 

days

Los Angeles: Elem. Oct 1989 10-12 51 Diary 24-h Standard Yes Spier et al. (1992) 7 P- School Children Los Angeles: Sept-Oct 1990 13-17 43 Diary 24-h Standard Yes Spier et al. (1992) 23 P

High School Adoles. 

National: NHAPS-A8 Sept 1992-Oct 1994 0-93 4723 Retrospective 24-h Standard No9 Klepeis et al. (1995) A n

Tsang and Klepeis (1996) surv

National: NHAPS-B8 As above 0-93 4663 Retrospective 24-h Standard No9 As above As aU. Michigan: Feb-Dec1997 0-13 5616 Retrospective 24-h Standard No Institute for Social Research 2 da Children (1997) day Valdez, AK Nov 1990-Oct 1991 11-71 401 Retrospective Varying 24-h No Goldstein et al. (1992) 4 P-

period 

Washington, DC

 (EPA) 

Nov 1982-Feb 1983 18-98 699 Diary 24 h; nominal

 7 pm-7 am 

No Akland et al. (1985) 

Hartwell et al. (1984) 

Part 

rem

1 All studies included both genders. The age range depicted is for the subjects actually included; in most cases, there was not an upper limit for the adult studies. Ages 

Age 0 = babies < 1 year old. 

2 The actual number of person-days of data in CHAD after the “flagging” and removal of questionable data. See the text for a discussion of these procedures. 3 Retrospective: a “what did you do yesterday” type of survey; also known as an ex-post survey. Diary: a “real-time” paper diary that a subject carried as he or she we4 Standard = midnight to midnight. 5 Was activity-specific breathing rate data collected? 6 PEM = a personal monitoring study. In addition to the diary, a subject carried a small CO or PM2.5 monitor throughout the sampling period. 7 P-D removed = The number of person-days of activity pattern data removed from consolidated CHAD because of missing activity and location information; complete

 the text. 

8 National Human Activity Pattern Study; A = the air version; B = the water version. The activity data obtained on the two versions are identical. 9 A question was asked regarding which activities (within each 6-h time block in the day) involved “heavy breathing,” lifting heavy objects, and running hard. 
TABLE 5-3. PERSONAL EXPOSURE MODELS FOR PARTICULATE MATTER

Study Model Citation Name Model Type Microenvironments or Predictors Output Notes Time-series Models Hayes and PMEX Deterministic Indoors: residential, work, school Inhaled dose of PM10 Used IAQM Marshall (1999) Outdoors: near roadway, other 

Motor vehicle 

Hourly for 24 h By age/gender groups 

Used human activity breathing rate info 

Source contributions 

Johnson et al. None Regression- Auto travel, roadside, ETS, food PM2.5 exposure Developed from scr(2000) based prep., grilling, high ambient PM 24-h average (Chang et al., 2000) 

Klepeis et al. None Stochastic ETS, cooking, cleaning, attached Respirable particle (PM3.5) (1994) garage, wood burning exposure 

Lurmann and REHEX-II Stochastic 12 residential with different Distribution of PM10 exposure Fixed I/O ratio of 0Korc (1994) sources, restaurant/bar, for population and 1.2 for in transi

nonresidential indoors, in transit, outdoors Three averaging times (1 h, 24 h, season) Reduced form mass 

with PM sources 

Koontz and CPIEM Stochastic Indoors: residence, office, Distribution of PM10 exposure Used California actNiang (1998) industrial plant, school, public for population rate data 

building, restaurant/lounge, other. Used either a mass bOutdoors, in vehicle distribution for indo

Indoor sources inclu

Time-averaged Models Clayton et al. SIM Stochastic Distribution of annual Based on 3-day amb(1999a) PM2.5 exposures 

Janssen et al. None Regression- Smoking parent, ETS exposure, Accounts for difference between Children only (1997) based outdoor physical activity personal and microenvironmental 

PM10 

Janssen et al. None Regression- Number of cigarettes smoked, hours Accounts for difference between Adults only (1998a) based of ETS exposure, residence on busy personal and microenvironmental 

road, time in vehicle PM10 

5-19
TABLE 5-3 (cont’d). PERSONAL EXPOSURE MODELS FOR PARTICULATE MATT

Study Citation 

Model Name Model Type Microenvironments or Predictors Output Notes 

Time-averaged Models (cont’d) Ott et al. (2000) 

RCS Statistical Not separated Distribution of PM10 exposure 

for population 

A random-componemodel that uses distand estimated nonam

Results for Ontario, 72-h compared to 2Riverside, CA and P

Burke et al. (2001) 

SHEDSPM Stochastic Outdoors, indoors: residence, office, stores, school, in vehicle, 

restaurant/lounge 

PM2.5 exposure distributions 

for population by age, gender, smoking and employment status; PM2.5 exposure uncertainty 

predictions. Percent contribution from PM of ambient origin to total personal exposures 

A 2-stage Monte-Capredicting populatioaverage personal exbeen applied to Philand temporally inter

measurements from census data. Does n

from active smoking 

Chao and Tung (2001) 

None Mass balance 

with empirical corrections 

Indoors in unoccupied residences in Hong Kong 

Predictions of ambient PM in indoor microenvironments 

Model makes correc(residence with mullimited intermixing) 

5-20
from activity-specific breathing rate information. Data from recent PM personal exposure and 

microenvironmental measurement studies should help in the future to facilitate the development 

of improved personal exposure models for PM. 

An integrated human exposure source-to-dose modeling system that will include exposure 

models to predict population exposures to environmental pollutants, such as PM, currently is 

being developed by the EPA/NERL. A first-generation population exposure model for PM, 

called the Stochastic Human Exposure and Dose Simulation (SHEDS-PM) model, recently has 

been developed. The SHEDS-PM model uses a 2-stage Monte Carlo sampling technique 

previously applied by MacIntosh et al. (1995) for benzene exposures. This technique allows for 

separate characterization of variability and uncertainty in the model predictions to predict the 

distribution of total exposure to PM for the population of an urban/metropolitan area and to 

estimate the contribution of ambient PM to total PM exposure. Results from a case study using 

data from Philadelphia, PA have been reported (Burke et al., 2001). Work is underway to link 

exposure modeling with dosimetry so as to provide estimates of integrated PM doses for 

different regions of the lung. In the future, both exposure and dose metrics generated for various 

subgroups of concern should aid in evaluation of PM health effects. 

5.2.4.2.2 Microenvironmental Models 

The mass-balance model has been used extensively in exposure analysis to estimate PM 

concentrations in indoor microenvironments (Calder, 1957; Sexton and Ryan, 1988; Duan, 1982, 

1991; McCurdy, 1995; Johnson, 1995; Klepeis et al., 1995; Dockery and Spengler, 1981; Ott, 

1984; Ott et al., 1988, 1992, 2000; Miller et al., 1998; Mage et al., 1999; Wilson et al., 2000). 

The mass balance model describes the infiltration of particles from outdoors into the indoor 

microenvironment, the removal of particles in indoor microenvironments, and the generation of 

particles from indoor sources: 

(5-4) 

where V = volume of the well-mixed indoor air (m3

); 

Ci = concentration of indoor PM (μg/m3

); 

v = volumetric air exchange rate between indoors and outdoors (m3

/h); 

5-21 
P = penetration ratio, the fraction of ambient (outdoor) PM that is not removed 

from ambient air during its entry into the indoor volume; 

C = concentration of PM in the ambient air (μg/m3

); 

k = removal rate (h-1); and 

Qi = rate of generation of particles from indoor sources (μg/h). 

Qi contains a variety of indoor, particle-generating sources, including combustion or 

mechanical processes; condensation of vapors formed by combustion or chemical reaction; 

suspension from bulk material; and resuspension of previously deposited PM. The removal rate, 

k, includes dry deposition to interior surfaces by diffusion, impaction, electrostatic forces, and 

gravitational fallout. It may include other removal processes, such as filtration by forced air 

heating, ventilation, or air conditioning (HVAC) or by independent air cleaners. All parameters 

except V are functions of time. P and k also are functions of particle aerodynamic diameter, Da, 

air exchange rate, <, and house characteristics such as the surface to volume ratio, type of 

surface, etc. All variables in Equation 5-3 will have distributions within the population and, in 

some cases, may vary by a factor of 5 to 10. It is important to determine the distribution of these 

variables. Sensitivity and uncertainty analyses are necessary when attempting to explain model 

results. 

In addition to the mass-balance model, some single-source or single-microenvironment 

models exist. However, most are used to estimate personal exposures to environmental tobacco 

smoke (ETS). These models include both empirically based statistical models and physical 

models based on first principles: some are time-averaged, whereas others are time-series. These 

models evaluate the contribution of ETS to total PM exposure in an enclosed microenvironment 

and can be applied as activity-specific components of total personal exposure models. Examples 

of ETS-oriented personal exposure models include Klepeis (1999), Klepeis et al. (1996, 2000), 

Mage and Ott (1996), Ott (1999), Ott et al. (1992, 1995), and Robinson et al. (1994). 

5.2.4.3 Methods for Estimating Personal Exposure to Ambient Particulate Matter 

In keeping with the various components of PM exposure described in Section 5.3.2, 

personal exposure to PM can be expressed as the sum of exposure to particles from different 

sources summed over all microenvironments in which exposure occurs. Total personal exposure 

may be expressed as: 

5-22
(5-5) 

(5-6) 

where T is the total personal exposure to ambient and nonambient PM, Ea is personal exposure to 

ambient PM while outdoors, Eai is personal exposure to ambient PM that has infiltrated indoors 

while indoors, Eig is personal exposure to indoor-generated PM, Eir is exposure to indoorreaction PM, and Epc is personal exposure to PM from personal activity (personal cloud). T can 

also be expressed as A + N where A is ambient PM exposure (Ea + Eai) and N is nonambient PM 

exposure (Eig + Eir + Epc). Although personal exposure to ambient and nonambient PM cannot be 

measured directly, they can be calculated or estimated from other measurement data. 

Approaches for estimating these components of PM exposure are described in the next section. 

5.2.4.3.1 Mass Balance Approach 

Ambient-Indoor Concentrations of Particulate Matter 

The mass balance model described above (Equation 5-4) has been used to estimate PM 

concentrations in indoor microenvironments. This model also may be used to estimate ambientindoor (Cai) and indoor-generated (Cig) PM concentrations. The mass balance model can be 

solved for Cai and Cig assuming equilibrium conditions, i.e., all variables remain constant 

(Ott et al., 2000; Dockery and Spengler, 1981; Koutrakis et al., 1992) and no indoor reaction 

PM (Cir). By substituting a = </V, where a = the number of air exchanges per hour substituting, 

dCai + dCig for dCi

 in Equation 5-4, and assuming that dCai and dCig = 0, i.e., ambient-indoor 

PM (Cai) and indoor-generated PM (Cig) are at equilibrium, Cai and Cig are given by 

Equations 5-7 and 5-8. 

(5-7) 

(5-8) 

5-23 
Equations 5-7 and 5-8 assume equilibrium conditions and, therefore, are valid only when the 

parameters P, k, a, C, and Qi

 are not changing rapidly and when the Cs are averaged over several 

hours. It should be understood that equilibrium is a simplification of indoor microenvironments 

that are occupied by residents. This assumption of equilibrium may only represent a virtual set 

of individuals or populations at risk. Under certain conditions (e.g., air-conditioned homes, 

homes with HVAC or air cleaners that cycle on and off, or ambient pollutants with rapidly 

varying concentrations), nonequilibrium versions of the mass-balance model (Ott et al., 2000; 

Freijer and Bloemen, 2000; Isukapalli and Georgopoulos, 2000) are likely to provide a more 

accurate estimate of Cai and Cig. However, the equilibrium model provides a useful, although 

simplified, example of the basic relationships (Ott et al., 2000). 

Equation 5-7 may be rearranged further to give Cai/C, the equilibrium fraction of ambient 

PM that is found indoors, defined as the infiltration factor, FINF, (Dockery and Spengler, 1981). 

(5-9) 

The penetration ratio, P, and the decay rate, k, can be estimated using a variety of techniques. 

A discussion of these variables and estimation techniques is given in Section 5.4.3.2.2. Both 

P and k are a function of particle aerodynamic diameter, air exchange rate, and housing 

characteristics. FINF will also be a function of these parameters; and, as a result, FINF may vary 

substantially within a population. Distributions of this parameter should be estimated to 

understand the uncertainty and variability associated with estimating exposure to PM of ambient 

origin. The distribution of daytime FINF, as estimated from PTEAM data, is shown in Figure 

5-2a (Wilson et al., 2000). 

Personal Exposure to Ambient Particulate Matter 

Personal exposure to ambient PM, A, may be estimated using ambient-indoor PM 

concentration, Cai, from the mass balance model, ambient outdoor PM concentrations, C, and 

information on the time an individual spent in the various microenvironments. For a twocompartment model, A may be expressed as 

5-24
Figure 5-2. Distribution of FINF (a) and " (b) for daytime as estimated from PTEAM study 

data. 

Source: Wilson et al. (2000). 

5-25 
(5-10) 

where y is the fraction of time that an individual spent outdoors, and (1 – y) is the fraction of 

time spent indoors. 

It is convenient to express personal exposure to ambient PM, A, as the product of the 

ambient PM concentration, C, and a personal exposure or attenuation factor. Following the 

usage in several recent papers (Zeger et al., 2000; Dominici et al., 2000; Ott et al., 2000), the 

symbol " will be used for this attenuation factor. Equation 5-10 can be rearranged to obtain an 

expression for ": 

(5-11) 

Substituting equation 5-9 in equation 5-11 gives a relationship for " in terms of the infiltration 

factor FINF and the fraction of time spent in the various microenvironments: 

(5-12) 

Thus, personal exposures to ambient PM, A, may be calculated from measurable quantities: 

(5-13) 

The factor " can be measured directly or calculated from measured or estimated values of the 

parameters a, k, and P and the time spent in various microenvironments from activity pattern 

diaries (Wilson et al., 2000). Because " depends on housing and lifestyle factors, air exchange 

rate, and PM deposition rate, it could vary to a certain extent from region to region and from 

season to season. Consequently, predicted exposures based on these physical modeling concepts 

provide exposure distributions derived conceptually as resulting from housing, lifestyles, and 

meteorological considerations. For any given population the coefficient " may represent 

5-26
substantial intra- and inter-personal variability based on personal activities, housing 

characteristics, particle size, and composition. The distribution of daytime " as estimated from 

PTEAM data is shown in Figure 5-2b. Note that the distribution of " is shifted to higher values 

compared to FINF because of the inclusion of time outdoors in ". Distributions of " should be 

determined using population studies in order to evaluate the uncertainty and variability 

associated with model exposures. 

The mass-balance model has been used to separate indoor concentrations into ambient and 

nonambient components. This approach, based on Equation 5-5 as given in Duan (1982) and 

called superposition of component concentrations, has been applied using multiple 

microenvironments to estimate exposures to carbon monoxide (Ott, 1984; Ott et al., 1988, 1992), 

volatile organic compounds (Miller et al., 1998), and particles (Koutrakis et al., 1992; Klepeis 

et al., 1994). However, in these studies and in most of the exposure literature, the ambient and 

nonambient components are added to yield a personal exposure from all sources of the pollutant. 

The use of the mass-balance model, ambient concentrations, and exposure parameters to 

estimate exposure to ambient PM and exposure to indoor-generated PM separately as different 

classes of exposure has been discussed in Wilson and Suh (1997) and in Wilson et al. (2000). 

5.2.4.3.2 The Sulfate Ratio Technique for Estimating Ambient PM Exposure 

The ratio of personal exposure to ambient concentration for sulfate has been recommended 

as a technique to estimate " (Wilson et al., 2000). If sulfate has no indoor sources, then AS

 = TS. 

(Superscript S refers to sulfate; superscript 2.5 to PM2.5.) As can be seen in Equation 5-11, P and 

k depend on particle size, but a and y do not. It has been known since the mid-1970s that sulfate 

and accumulation-mode mass have similar size distributions (Whitby, 1978). Therefore, if the 

coarse mode contribution to PM2.5 is small, so that sulfate and PM2.5 would have similar size 

distributions, then TS

/CS = A2.5/C2.5 = "S = "2.5. Sulfate is formed in the ambient air via 

photochemical oxidation of gaseous sulfur dioxide arising from the primary emissions from the 

combustion of fossil fuels containing sulfur. It also arises from the direct emissions of sulfurcontaining particles from nonanthropogenic sources (e.g., volcanic activity, windblown soil). 

In the indoor environment, the only common sources of sulfate may be resuspension by human 

activity of deposited PM containing ammonium sulfates or soil sulfates that were tracked into 

the home. However, resuspended PM will be mostly larger than PM2.5. In some homes, an 

5-27
unvented kerosene heater using a high-sulfur fuel may be a major contributor during winter 

(Leaderer et al., 1999). The use of matches to light cigarettes or gas stoves can also be a source 

of sulfates. 

Studies that have used the sulfate ratio technique to estimate " and ambient PM exposures 

are discussed in Section 5.4.3.1. When there are no indoor sources of accumulation-mode 

sulfates, one may deduce that the ambient-to-personal relationship found for sulfates probably 

would be the same as that for other PM with the same size range and physical/chemical 

properties. This assumption has been validated for several homes in Boston (Sarnat et al., 2002). 

For particle sizes within the accumulation-mode size range, the ratio Cai/C was similar for sulfate 

and PM2.5 as estimated from SMPS measurements. However, ambient PM with different 

physical or chemical characteristics than sulfate will not behave similarly to sulfate. Sulfate has 

been used as a marker of outdoor air in the indoor microenvironments (Jones et al., 2000; Ebelt 

et al., 2000). However, the personal exposure of sulfate (Ts

 = As) should not be taken as an 

indicator or surrogate for ambient PM2.5 exposure (A2.5) unless it has been previously determined 

that PM2.5 and sulfate concentrations are highly correlated. This may be the case in some air 

sheds with high sulfate concentrations, but will not be true in general. 

5.2.4.3.3 Source-Apportionment Techniques 

Source apportionment techniques provide a method for determining personal exposure to 

PM from specific sources. If a sufficient number of samples are analyzed with sufficient 

compositional detail, it is possible to use statistical techniques to derive source category 

signatures, identify indoor and outdoor source categories, and estimate their contribution to 

indoor and personal PM. Daily contributions from sources that have no indoor component can 

be used as tracers to generate exposure to ambient PM of similar aerodynamic size or directly as 

exposure surrogates in epidemiological analyses. Studies that have used source-apportionment 

are discussed in Section 5.4.3.3 (i.e., Özkaynak and Thurston, 1987; Yakovleva et al., 1999; Mar 

et al. 2000; Laden et al., 2000). 

5.2.4.3.4 Comparison and Validation of Methods 

There are as yet no published comparisons or validations of the various methods for 

estimating personal exposure to ambient PM on an average or individual daily basis. 

5-28
5.3 SUMMARY OF PARTICULATE MATTER MASS DATA 

5.3.1 Types of Particulate Matter Exposure Measurement Studies 

A variety of field measurement studies have been conducted to quantify personal exposure 

to PM mass, to measure microenvironmental concentrations of PM, to evaluate relationships 

between personal exposure to PM and PM air concentrations measured at ambient sites, and to 

evaluate factors that affect exposure. In general, exposure measurement studies are of two types 

depending on how the participants are selected for the study. In a probability study, participants 

are selected using a probability sampling design where every member of the defined population 

has a known, positive probability of being included in the sample. Probability study results can 

be used to make statistical inferences about the target population. In a purposeful or 

nonprobability design, any convenient method may be used to enlist participants; and the 

probability of any individual in the population being included in the sample is unknown 

(National Research Council, 1991). Participants in purposeful samples may not have the same 

characteristics that would lead to exposure as the rest of the unsampled population. Thus, results 

of purposeful studies apply only to the subjects sampled on the days that they were sampled and 

not to other subjects or other periods of time. Although such studies may report significant 

differences, confidence intervals, and p values, they do not have inferential validity (Lessler and 

Kalsbeek,1992). Purposeful studies, however, may have generalizability (external validity). 

The extent of generalizability is a matter of judgement based on study participant characteristics. 

Purposeful studies of PM personal exposure can provide data with which to develop 

relationships based on important exposure factors and can provide useful information for 

developing and evaluating either statistical or physical/chemical human exposure models. 

Regardless of the sampling design (probability or purposeful), there are three general 

categories of study design that can be used to measure personal exposure to PM and evaluate the 

relationship between personal PM exposure levels and ambient PM concentrations measured 

simultaneously: (1) longitudinal, in which each subject is measured for many days; (2) pooled, 

in which each subject is measured for only one or two days, different days for different subjects; 

and (3) daily-average, in which many subjects are measured on the same day. Only one study, 

in which 14 subjects were measured for 14 days, provided sufficient data for a comparison of 

longitudinal and daily-average data (Lioy et al., 1990). Longitudinal and pooled studies are 

discussed in Section 5.4.3.1.1. 

5-29
5.3.2 Available Data 

5.3.2.1 Personal Exposure Data 

Table 5-4 gives an overview of the personal exposure studies that have been reported since

the 1996 PM AQCD. In addition, major studies that were reported before that time also have 

been included to provide a comprehensive evaluation of data in this area. Table 5-4 gives 

information on the sampling and study designs, the study population, the season, number of 

participants, PM exposure metric, and the PM size fraction measured. 

Although there are a number of studies listed in the table, the data available with which to 

evaluate longitudinal relationships and the factors that influence these are limited. Few studies 

are based on probability sampling designs that allow study results to be inferred to the general 

population and to develop distributional data or exposures and the factors that affect exposure. 

Unfortunately, none of these probability studies used a longitudinal study design. This limits our 

ability to provide population estimates and distributional data on the relationship between 

personal PM exposures and ambient site measurements. In addition, most of the probability 

studies of PM exposure were conducted during a single season; thus, variations in ambient 

concentrations, air exchange rates, and personal activities are not accounted for across seasons. 

In these cases, study results are only applicable to a specific time period. Longitudinal studies, 

on the other hand, generally have small sample sizes and use a purposeful sampling design. 

Some studies did not include ambient site measurements to allow comparisons with the exposure 

data. Approximately half of these studies monitored PM2.5. Only one or two studies measured 

both PM10 and PM2.5 to provide information on PM10-2.5. 

Four large-scale probability studies that quantify personal exposure to PM under normal 

ambient source conditions have been reported in the literature. These include the EPA’s 

PTEAM study (Clayton et al., 1993; Özkaynak et al., 1996a,b); the Toronto, Ontario study 

(Clayton et al., 1999a; Pellizzari et al., 1999); the EXPOLIS study (Jantunen et al., 1998, 2000; 

Oglesby, et al., 2000; Gotshi et al., 2002; Kousa et al., 2002); and a study of a small, highly 

polluted area in Mexico City (Santos-Burgoa et al., 1998). A fifth study conducted in Kuwait 

during the last days of the oil-well fires (Al-Raheem et al., 2000) is not reported here because the 

ambient PM levels were not representative of normal ambient source conditions. 

5-30
TABLE 5-4. SUMMARY OF RECENT PM PERSONAL EXPOSURE STUDIES

Study Location and No. of Age Days per PM Exposurea PM Size C

Study Design Population Subjects Study Period (Years) Subject Metrics Measuredb 

Probability Studies 

5-31

Pooledc Indianapolis, IN 240 1996 16-? One 72-h 

sample/ 

subject 

P, I, A, O PM2.5, PM10 

Pooled Riverside, CA, 

PTEAM 

178 Fall 1990 10-70 1 (12 h) P, I, O, A PM10 

Pooled Basel, Switzerland, 

EXPOLIS 

50 1997 1 (48 h) P, I, P PM2.5 

N

Pooled Toronto, Canada 732 Sept 1995­

Aug 1996 

16+ 3 P, I, O, A PM2.5 (12 mo) 

PM10 (3 mo) 

Pooled Mexico City 66 1992 < 65 1 P, I, O PM10 

Pooled Baltimore, MD 

nonsmoke exposed adults, children, COPD patients 

20 Adults 

21 Children 

15 COPD 

Summer 1998, 

winter 1999 

A 75 

Ch 9-13 

COPD 65 

12-days 

8-days for 

children in 

summer 

P, A PM2.5 

PM10 

O

VO

Purposeful Studies Longitudinald Wageningen, 13 1995 10-12 6 P, A, School PM2.5, PM10 

Netherlands, school children 

Longitudinal Amsterdam (Am), 41 (Am) Winter 1998, 50-84 22 (Am) P, I, O PM2.5 

Helsinki (Hls), elderly 49 (Hls) spring 1999 27 (Hls) angina or coronary heart disease 

Longitudinal Baltimore, elderly 21 July-Aug 1998 72-93 5-22 P, I, O, A PM2.5, PM10 C

healthy and COPD 

Longitudinal Fresno I 516 Feb 1999, 60+ 24 P, I, O, A PM2.5, PM10 

Fresno II (elderly) Apr-May 1999 24 P, I, O, A PM2.5, PM10 
TABLE 5-4 (cont’d). SUMMARY OF RECENT PM PERSONAL EXPOSURE STUD

Study Study Location and No. of Age Days per PM Exposurea PM Size 

Design Population Subjects Study Period (Years) Subject Metrics Measuredb 

Purposeful Studies (cont’d) Longitudinal Los Angeles, elderly 30 Summer/fall 56-83 4 P, I, O PM2.5 

COPD subjects 1996 

Longitudinal Boston, COPD subjects 18 Winter 1996­ 12 P, I, O, A PM2.5, PM10 

1997, 

summer 1996 

Longitudinal Vancouver, British 16 April-Sept 1998 54-86 7 P, A PM2.5, PM10 

Columbia, COPD 

Longitudinal Amsterdam and 45 1994-1995 10-12 4-8 P, A, School PM10 

Wageningen, Neth., school children 

Longitudinal Amsterdam, adults 37 1994 51-70 5-8 P, I, A PM10 

Longitudinal Baltimore, elderly 15 Summer 1998, 75 ± 6.8 12 P PM2.5, PM10 

spring 1999 

Longitudinal Baltimore, elderly, 56 Summer 1998, Adults: 75 ± 6.8 12 P, I, O, A PM2.5 

COPD, children winter 1999 Children: 9-13 

COPD: 65 ± 6.6 

Longitudinal Tokyo, Japan, 18 1992 3 P, I, O, A SPM 

elderly housewives 

Longitudinal Osaka, Japan 26 Fall 1990-1995 Multiple days P, I, O PM2, PM2-10, 

PM>10 

Pooled Milan, Italy, office 100 Spring/summer 1 P, Home, PM10 

workers and winter, year Office, 

not stated Commuting 

Pooled Banská Bystrica, 49 1997-1998 15-59 1 P, I, O, A PM10, PM2.5 

Slovakia 

Longitudinal Wageningen, NL 13 Mar-June 1995 12-14 5-8 P, A, I at school PM2.5, PM10 

Longitudinal Mpala, Kenya 252 1996-1998 5-75 2 years I Undefined 

:E diary Optical MIE 

5-32
TABLE 5-4 (cont’d). SUMMARY OF RECENT PM PERSONAL EXPOSURE STUD

5-33

Study Design 

Study Location and Population 

No. of 

Subjects Study Period 

Age 

(Years) 

Days per 

Subject 

PM Exposurea 

Metrics 

PM Size 

Measuredb 

Longitudinal London, UK 10 1997 9-11 5 day/season P, I, O PM2.5, PM10 

3 seasons 

Pooled Zurich, CH 10 1998 Adults 12 h/day for P, I, O Pollen 

3 days 

Pooled Minneapolis/St. Paul, 32 Spring, summer, 24-64 2-15 P, I, O PM2.5 

MN Volunteers fall 1999 

Pooled Birmingham, 11 healthy Season and year Adults, Adults and P, I, O PM10 

UK - healthy adults, adults, not given Adults > 65 children 10, children and 18 susceptible Child < 10 susceptibles susceptibles 5, daytime 

only 

Pooled Santiago, Chile 8 in 1998 Winters 10-12 5 P, I, O PM2.5, PM10 

children 20 in 1999 1998 & 1999 

Pooled Copenhagen, DK 68 subjects Winter 1999, 20-33 2 P, A PM2.5, BS 

non-smoking students spring, summer, (from PM2.5 

fall 2000 filter) 

a b All based on gravimetric measurements. P = personal, I = indoors, O = outdoors, A = ambient. c d Pooled; data from many subjects with only a few days per subject. Longitudinal; one subject measured for many days. 
Recent longitudinal exposure studies have focused on potentially susceptible 

subpopulations such as the young and elderly with preexisting respiratory and heart diseases 

(hypertension, COPD, and congestive heart disease). This is in keeping with epidemiological 

studies that indicate mortality associated with high levels of ambient PM2.5 is greatest for elderly 

people with cardiopulmonary disease (U.S. Environmental Protection Agency, 1996). 

Longitudinal studies were conducted in the Netherlands by Janssen (1998) and Janssen et al. 

(1997, 1998a,b, 1999b,c) on purposefully selected samples of adults (50 to 70 years old) and 

children (10 to 12 years old). School children have also been studied in Chile (Rojas-Bracho 

et al., 2002). Several additional studies have focused on nonsmoking elderly populations in 

Amsterdam and Helsinki (Janssen et al., 2000), Tokyo (Tamura et al., 1996a), Baltimore, MD 

(Liao et al., 1999; Williams et al., 2000a,b,c), and Fresno, CA (Evans et al., 2000). These 

cohorts were selected because of the low incidence of indoor sources of PM (such as combustion 

or cooking). This should allow an examination of the relationship between personal and ambient 

PM concentrations without the large influences caused by smoking, cooking, and other indoor 

particle-generating activities. The EPA has a research program focused on understanding PM 

exposure characteristics and relationships. Within the program, longitudinal studies are being 

conducted on elderly participants with underlying heart and lung disease (COPD, patients with 

cardiac defibrillator, and myocardial infarction), an elderly environmental justice cohort, and 

asthmatics. These studies are being conducted in several cities throughout the United States and 

over several seasons (Rodes et al., 2001; Conner et al., 2001; Landis et al., 2001; Rea et al., 

2001). 

A series of studies by Phillips et al. (1994, 1996, 1997a,b, 1998a,b, 1999) examined 

personal ETS exposure in several European cities. Participants varied by age and occupation. 

Respirable suspended particulate (RSP) concentrations were reported. These studies are not 

included in Table 5-4 because of their focus on ETS exposure (which is not the focus of this 

chapter). A small personal exposure study in Zurich, Switzerland was reported by Monn et al. 

(1997) for PM10. This study also is not listed in Table 5-4 because indoor and outdoor 

measurements were not taken simultaneously with the personal measurements and other details 

of the study were not published. 

5-34
5.3.2.2 Microenvironmental Data 

Usually, personal PM monitoring is conducted using integrated measurements over a 

12- or 24-h period. As such, total PM exposure estimates based on PEM measurements do not 

capture data from individual microenvironments. Recent studies have examined PM 

concentrations in various microenvironments using a number of different types of instruments 

ranging from filter-based to continuous particle monitors. Details on the instruments used, 

measurements collected, and findings of these studies according to microenvironment 

(residential indoor, nonresidential indoor, and traffic-related) are summarized in Table 5-5. 

Those studies which collected microenvironmental data as part of a personal exposure 

monitoring study are summarized in Table 5-4 above. In general, the studies listed in Table 5-5 

are relatively small, purposeful studies designed to provide specific data on the factors that affect 

microenvironmental concentration of PM from both ambient and nonambient sources. 

Recently published studies have used various types of continuous monitors to examine 

particle concentrations in specific microenvironments and resulting from specific activities. 

Continuous particle monitors such as the scanning mobility particle sizer (SMPS), aerodynamic 

particle sizer (APS), and Climet have been used to measure particle size distributions in 

residential microenvironments (Abt et al., 2000a; Long et al., 2000, 2001a; Wallace et al., 1997; 

Wallace, 2000a; McBride et al., 1999; Vette et al., 2001; Wallace and Howard-Reed, 2002). 

These studies have been able to assess the infiltration factor for ambient particles to indoor 

microenvironments, as well as penetration factors and deposition rates. Continuous instruments 

are also a valuable tool for assessing the impact of particle resuspension caused by human 

activity. A semi-quantitative estimate of PM exposure can be obtained using personal 

nephelometers that measure PM using light-scattering techniques. Recent PM exposure studies 

have used condensation nuclei counters (1-s averaging time) and personal nephelometers (1-min 

averaging time) to measure PM continuously (Howard-Reed et al., 2000; Quintana et al., 2000; 

Magari et al., 2002; Lanki et al., 2002) in various microenvironments. These data have been 

used to identify the most important ambient and nonambient sources of PM, to provide an 

estimate of source strength, and to compare modeled time activity data and PEM 24-h mass data 

to nephelometer measurements (Rea et al., 2001). 

5-35
TABLE 5-5. SUMMARY OF RECENT MICROENVIRONMENTAL PM MEASUREMEN

Reference Study Description Instrument(s) Size Fraction (μm) Summary of Measurements NResidential Indoor: Nonsmoking Homes Abt et al. (2000a) 

2 homes, 2 seasons, 

SMPS Detailed traces of PM in various size classes for 

different air exchange rates (< 1h-1 to > 2 h-1). 

Mc

Boston, MA 6 days APS 

Long et al. 9 homes, SMPS 0.02-10 μm Continuous PM distributions and size S(2000a) 2 seasons distributions obtained for indoor and outdoor oBoston, MA APS air using SMPS and APS monitors. p

ainv

Anuszewski 9 homes, Nephelometer (Radiance) PM1 Simultaneous indoor and outdoor PM measured Het al. (1998) Seattle, WA 

18 days continuously; 1-h avg time, I/O = 0.98; air 

exchange rate: 0.7-1.7 h-1 . 

hfi

Leaderer et al. (1999) Southwest VA 

58 homes, summer PM10 24 h mean: 

Regional air 26.0 ± 11.5 μg/m3 (n = 47); 

Outdoor homes 28.0 ± 17.7 μg/m3 (n = 43); 

Indoor w/ AC 28.9 ± 18.7 μg/m3 (n = 49); Indoor w/o AC 33.3 ± 14.2 μg/m3 (n = 8). 

Einin

PM2.5 24 h mean: In

Regional air 20.2 ± 9.9 μg/m3 (n = 50); 

Outdoor homes 21.8 ± 14.8 μg/m3 (n = 43); 

Indoor w/ AC 18.7 ± 13.2 μg/m3 (n = 49); Indoor w/o AC 21.1 ±7.5 μg/m3 (n = 9). 

hn

Wallace et al. 1 home, SMPS 6 size bins; Time activity data, whole-house air exchange 0(1997); Wallace 4 years Climet 100 size channels rates. c(2000b) PAHs 0.01-0.4 μm Continuous carbon monoxide; descriptive data 2Reston, VA Black carbon for monitored pollutants; size profiles for six e

indoor particle sources. p

Howard-Reed 15 participants Nephelometer (personal 0.1-10 μm Continuous (15-min avg) PM and time activity Tet al. (2000) MIE) data; 24-h PM mass; participants from nFresno, CA PEM PM2.5 Baltimore and Fresno PM panel studies. pBaltimore, MD Descriptive statistics from each study for five s

microenvironments. b

c

5-36
TABLE 5-5 (cont’d). SUMMARY OF RECENT MICROENVIRONMENTAL PM MEASUREM

Reference Study Description Instrument(s) Size Fraction (μm) Summary of Measurements NResidential Indoor: Nonsmoking Homes (cont’d) Rea et al. (2001) 15 participants Nephelometer (personal 0.1-10 μm 54 ± 31% of average daily PM2.5 exposure CBaltimore, MD MIE) occurred indoor residences, where participants aFresno, CA PEM PM2.5 and PM10 spent 83 ± 10% of their time. A significant m

portion of PM2.5 exposure occurred where ti

participants spent 4-13% of their time. c

(6

Quintana et al. Asthmatic children indoor Nephelometer (personal 0.1-10 μm Nephelometer correlates best with PM2.5: In(2000) and outdoor MIE) vs. Indoor PM2.5 r = 0.66, vs. indoor PM10 cSan Diego, CA 9 homes Harvard impactors PM2.5 and PM10 r = 0.13, vs. outdoor PM2.5 r = 0.42, vs. h

TEOM outdoor PM10 r = 0.20. P

Chang et al. 1 person performing “Roll-around” monitor PM2.5 One-hour personal O3 exposures were O(2000) predetermined activities (RAS) significantly lower in indoor than outdoor sBaltimore, MD (PM2.5, CO, VOC, O3, NO2, microenvironments. One-hour personal CO c

SO2) exposures were highest in vehicles. Personal 

and ambient PM2.5 correlations were strongest 

for outdoor microenvironments and those with high air exchange rates (i.e., vehicles). 

Lioy et al. 10 vacuum cleaners 0.3-0.5 μm Vacuum cleaners ranged in collection efficiency (1999) from 29-99%. Substantial fine particle 

emissions from motors with emission rates from 0.028-128.8 μg/min. 

Ezzati and 55 Native huts MiniRam (MIE) Not specified. Measured PM surrounding wood fires in EKammen (2001) Mpala, Kenya 

2-years Optical device detects 

particles 1-10 μm, but it 

unvented huts. PM measures were up to 8000 μg/m3, but uncalibrated against wood 

is not PM10 smoke. 

Chao and Tung 5 unoccupied homes DustTrak (TSI) PM2.5 real time, In the limit as air exchange goes to zero, there D(2001) measured indoors and calibrated against an appears to be a residual source, perhaps from PHong Kong outdoors, along with air Andersen Mark II drafts or thermal effects. o

exchange rates Above air exchange rates of 4.5/hr penetration 

goes to 1, but indoor turbulence resuspends previously settled PM2.5. 

5-37
TABLE 5-5 (cont’d). SUMMARY OF RECENT MICROENVIRONMENTAL PM MEASUREM

Reference Study Description Instrument(s) Size Fraction (μm) Summary of Measurements NResidential Indoor: Nonsmoking Homes (cont’d) Fischer et al. Measured traffic related Harvard Impactors PM2.5 and PM10. EC was Outdoor PM10 and PM2.5 were approximately “(2000) differences of PM and measured by reflectance 15-20% higher at higher traffic streets than at reAmsterdam, NL VOCs, indoor/outdoor in 18 of the PM2.5 filters. PAH the quiet streets on the same days. However, p

paired homes at varying also measured as much larger differences were found for PAH indistances from traffic indicator of diesel and EC which are traffic specific. e

traffic. 

Kingham et al. Measured PM at ten homes Harvard Impactors PM2.5 and PM10 and Median ratio = 1 (no indoor combustion F(2000) of non-smokers, < 50 m and PAH. EC measured by sources). pHuddersfield, > 300 m from traffic filter reflectance. sUK d

Morawska et al. Measured PM indoors and Scanning mobility particle Submicron PM, For supra- and submicron particles, indoor = A(2001) outdoors at 16 homes while sizer, aerodynamic particle supramicron PM, outdoor for normal ventilation conditions of > 2 gBrisbane, residents were absent. sizer, and a TSI Dust Trak PM2.5 air changes/hour. cAustralia Air exchange rate estimated, fo

not measured 

Abraham et al. Measured ultrafine PM in TSI 8525 < 1 :m optical diameter 1-second readings collected while noting T(2002) various home and traffic presence of sources. s

microenvironments 

Geller et al. Comparison of indoor and USC Personal PM 5 Lpm PM2.5 and PM10-2.5 I2.5 = 4.3 + 0.74 O T(2002) outdoor PM in homes in I10-2.5 = 3 + 0.3 O 2

Coachella Valley, CA I EC = 0.84 O EC e

I OC > O OC. (O

m

Gotschi et al. Measured Black Smoke XPOLIS PM monitor and PM2.5 PM measured I and O for two nights while B(2002) (BS) by reflection from EEL 43 reflectometer. subject was home (5 pm - 8 am). 183 sampled c

PM2.5 filters for 4 European PM2.5 and BS were compared for these samples. in

cities, indoor and outdoor. 

Sarnat et al. Measured PM2.5 for sulfate Harvard Impactors with PM2.5 The ratio of I/O PM2.5, sulfate and size S(2002) analysis in and out of SMPS and APS particle sub-fractions are developed as a function of c

6 Boston homes counters season and air exchange rate. s

rec

Wallace and Measured ultrafine, fine and TSI 3071 SMPS 10 nm to > 10 :m Analysis of particle counts as a function of air WHoward-Reed coarse PM in one Reston, TSI 3320 APS exchange rate and meteorological variables. T(2002) VA tri-level home for Climet 500-I 1 

18 months with air MIE pDR-1000 cexchange and meteorologic data 

5-38
TABLE 5-5 (cont’d). SUMMARY OF RECENT MICROENVIRONMENTAL PM MEASUREM

Reference Study Description Instrument(s) Size Fraction (μm) Summary of Measurements Residential Indoor: Other Home Types Brauer et al. (1996) 

22 rural Mexican homes (smoking and nonsmoking) 

Inertial impactor Radiance 

PM10 Indoor PM2.5: 132-555 μg/m3 , 

PM10: 282-768 μg/m3 . 

VN

Mexico nephelometer PM2.5 Outdoor PM2.5: 37 μg/m3 . 

PM10: 68 μg/m3; I/O PM2.5: 1.8-12.4; 

w0

PM10: 4.7-10.0. 

Jenkins et al. Smoking and nonsmoking Fluoropore membrane Particle phase ETS Mean PM3.5 concentrations were 17-20 μg/m3 (1996 a,b) homes filters markers in smoking homes over nonsmoking homes. 16 U.S. Cities 

McBride et al. Combustion source Met-One laser particle Ratios of particle counts at 1.0 and 5.7 m from P(1999) (incense) and walking counter the combustion source/activity were obtained. eNA (1 room, carpeted) 

Vette et al. Detached SMPS 0.01-2.5 μm Temporal relationships between indoor and D(2001) semioccupied residence LASX outdoor aerosol concentrations evaluated; cFresno, CA penetration factors and deposition rates re

estimated. Fresno panel study empty residence. P

Douce et al. ETS measured by 37 mm filter and XAD-4 PM5 Samples extracted and analyzed for UV S(2001) 4 methods in smokers resin backup absorption, fluorescence, solanesol, and s

homes and offices scopoletin. 

Nonresidential Microenvironments Bohadana et al. Manufacturing plant, Not given 443 personal time-weighted average (2000) woodworkers occupations samples of airborne dust. 

Donham et al. (2000) San Francisco, 

34 poultry workers NIOSH Method 0600 

monitors probed respirators 

PM5 Total dust sampled indoor respiratory masks. 

Personal monitoring: 630 ± 980 μg/m3 (n = 210) ranging from 10-7730 μg/m3 . 

Rto

CA 

Klepeis et al. Airport lounge, ETS TSI 8510 piezobalance PM3.5 Estimated cigarette emission rate of P(1996) 1.43 mg/min/cigarette. mSan Francisco, mCA 

Nieuwenhuijsen Agricultural activities PM4 Average respirable fraction: 4.5 mg/m3 . et al. (1999) 

Teschke et al. (1999) 

Wood production, wood finishing, wood construction workers 

PM .50 1,632 observations from 1979-1997. 

Arithmetic mean exposure: 7.93 mg/m3 . 

Geometric mean exposure: 1.86 mg/m3 . 

5-39
TABLE 5-5 (cont’d). SUMMARY OF RECENT MICROENVIRONMENTAL PM MEASUREM

Reference Study Description Instrument(s) Size Fraction (μm) Summary of Measurements Nonresidential Microenvironments (cont’d) Baek et al. (1997) 

Indoor and outdoor smoking restaurants 

PM3.5 Indoor concentrations: 33-475 μg/m3 

Outdoor concentrations: 12-172 μg/m3 

Na

Korea I/O: 2.4. 

Ott et al. (1996) California 

Bar before and after smoking prohibited 

Piezobalance PM3.5 Smoking permitted: 

indoor 26.3-182 μg/m3; outdoor < 5-67 μg/m3 

I/I/

Smoking prohibited: indoor 4-82 μg/m3; outdoor 2-67 μg/m3 . 

Houseman et al. (2002) Boston, MA 

Indoor and outdoor restaurants, stores 

TSI DustTrak Indoor restaurants: 14-278 μg/m3 

Outdoor restaurants: 7-281 μg/m3 

Indoor stores: 12-206 μg/m3 

Outdoor stores: 7-281 μg/m3 . 

ANsm0

Brauer and Mannetje (1998) 

Indoor restaurants, various smoking policies 

PM2.5 

PM10 

Nonsmoking: PM2.5 7-65 μg/m3; 

PM10 < 10-74 μg/m3 

Vancouver, BC Restricted smoking (> 40% nonsmoking) PM2.5 

11-163 μg/m3; PM10 24-89 μg/m3 Unrestricted smoking: PM2.5 47-253 μg/m3; 

PM10 51-268 μg/m3 . 

Lee and Chang (1999) 

Indoor and outdoor 5 classrooms 

PM10 Indoor PM10: 30-470 μg/m3 

Outdoor PM10: 20-617 μg/m3 

Hong Kong 

Chan (2002) A Hong Kong Office TEOM 1400a PM2.5 Daily 9 a.m - 6 p.m./weekday for 9 months. THong Kong monitored indoor and In

outdoor 

Levy et al. Arbitrary choice of one TSI DustTrak 8520 PM counter calibrated Measured inside and outside the various 1(2002) library, coffee shop, urban to PM2.5 microenvironments tested, weekdays morning pBoston, MA shopping mall, food court, TSI P-Trak 8525 and afternoon. At least 3 visits, several 10-min 

apartment, hospital, < 1 :m OD averages. subway, diesel bus in Boston, MA 

5-40
TABLE 5-5 (cont’d). SUMMARY OF RECENT MICROENVIRONMENTAL PM MEASUREM

Reference Study Description Instrument(s) Size Fraction (μm) Summary of Measurements Traffic-Related Microenvironments (TRMs) Praml and Schierl (2000) 

Trams and buses, rural and urban 

Continuous millipore polycarbonate filter 

PM10 n = 201 4-h trips, mean concentration 

155 μg/m3 range: 13-686 μg/m3 

Tro

Munich, I/O: 2.8. DGermany 

Monn et al. Spatial scale from a city Harvard PM10 48- or 72-h avg times; horizontal distance from N(1997) street microenvironment monitor street: 0, 15, 50, and 80 m; vertical distance hSwitzerland from street: 20 m. fr

Mean PM10 27.3 ± 3.0 μg/m3 . N

a

Rodes et al. In-vehicle, various road PM2.5 Vehicles in front of the monitored vehicle A(1998) Saevanenso, 

types, 2-h trips 

PM10 

accounted for most of the in-vehicle commuting exposure; average I/O: 0.6-0.8 h-1 for PM2.5; 

vM

Los Angeles, CA carpool lane concentrations were 30-60% lower o

than noncarpool lane concentrations. 

Roorda-Knape Gradient in distance from Harvard impactor PM2.5 PM monitoring at 50, 100, 150, and 300 m Net al. (1998) roadway PM10 from roadway; 1-week avg time. dvan Vliet et al. Black smoke P(1997) bNetherlands w

Houseman et al. (2002) Boston, MA 

Indoor and outdoor vehicles buses, subways 

TSI DustTrak PM10 Vehicle concentrations ranged from 

33-170 μg/m3 . Outdoor vehicle concentrations 

ranged from 40-144 μg/m3 . Bus concentrations: 17-268 μg/m3; 

outdoor 10-203 μg/m3 . 

Subway: 28-174 μg/m3; outdoor 8-203 μg/m3 . 

Twwc

Brauer et al. Commuting environments APC-1000 PM < 5: greatest concentrations by (1999) combustion powered vehicles. Vancover, BC PM > 5: greatest concentrations by 

bicycling and buses. 

Janssen et al. (1997) Netherlands 

Background and roadway PM2.5 

PM10 

PM2.5 background: 21-35 μg/m3; roadway 

23-43 μg/m3 . 

PM10 background: 13-32 and 29-62 μg/m3; 

roadway 16-56 and 30-75 μg/m3 . 

A3 

in 7

5-41
TABLE 5-5 (cont’d). SUMMARY OF RECENT MICROENVIRONMENTAL PM MEASUREM

Reference Study Description Instrument(s) Size Fraction (μm) Summary of Measurements Traffic-Related Microenvironments (TRMs) (cont’d) Adams et al. PM by volunteers in TRM 16 L/min personal PM2.5 Volunteers rode/cycled along fixed routes P(2001) in London, UK monitor. Porous foam repetitively. thLondon, UK size selector S

Alm et al. (1999) Kuopio, Finland 

9-km commuter route, rush hours 

Climet 6 channels Windows closed, vents open 

air exchange rate 36-47 h-1 . 

Mth

1/mo d

a

Chan et al. PM measured in TRM in TSI 8520 DustTrac PM2.5 and PM10 Repetitive sampling over 8 fixed routes. H(2002) Hong Kong Calibrated to Partisol THong Kong PM2.5 

HiVol PM10 

Hoek et al. BS in Netherlands Black Smoke measured Not Recorded GIS used to interpolate background and regional M(2001) interpolated to outdoor from filters ambient BS with added increment for distance vHoek et al. locations relative to traffic to traffic < 50 m and < 100 m. in(2002) at subject’s homes 

Jinsart et al. PM Exposure of traffic Sibata personal single PM2.5 and PM10 Sampled 12-h while on duty at post at E(2002) police at intersection post in nozzle intersection. thBangkok Bangkok, Thailand 2.5 L/min $ 

c

Lena et al. PM2.5 and EC in the Bronx, PM2.5 @ 4 L/min PM2.5 Sampled 10-h at sidewalk locations while E(2002) NY in area of high Diesel 3 Lpm quartz filter for EC counting cars and trucks for 3-weeks in aNew York, NY truck traffic EEL for BS summer of 1999. 

Zhu et al. (2002) Ultrafine near Interstate CPC TSI 3022A 6 nm-220 nm 200 m upwind and 17, 20, 30, 90, 150, and ULos Angeles, CA Highway in Los Angeles, SMPS TSI 3936 300 m downwind. fr

CA with heavy Diesel BC aethalometer atraffic 

5-42
5.3.2.3 Traffic-Related Microenvironments 

There has been increasing interest in the possible role of traffic-related pollutants. 

Distance to roadways has been used as a surrogate for exposure to traffic-related pollutants 

(Hoek et al., 2001), and this exposure indicator was subsequently used in an epidemiological 

study (Hoek et al., 2002). A traffic model, using traffic volume, direct exhaust emissions rate, 

and a reentrainment rate has been used to estimate concentrations of traffic-related emissions at 

several schools in east Los Angeles (Korenstein and Piazza, 2002). Personal exposure studies 

have been made in a variety of commuting situations including vehicle traffic (Adams et al., 

2001; Chan et al., 2002). Other studies have measured various indicators of traffic near 

roadways (Lena et al., 2002), inside vehicles in traffic (Abraham et al., 2002), and in several 

types of traffic-related microenvironments (Levy et al., 2002). Table 5-5 above provides a brief 

description of these studies, instruments used, measurements made, and key findings. 

5.3.2.4 Reanalyses of Previously-Reported Particulate Matter Exposure Data 

Papers that have reanalyzed and interpreted the data collected in previous PM exposure 

studies are summarized in Table 5-6. These reanalyses are directed toward understanding the 

personal cloud, the variability in total PM exposure, and the personal exposure-to-ambient 

concentration relationships for PM. Brown and Paxton (1998) determined that the high 

variability in personal exposure to PM makes the personal-to-ambient PM relationship difficult 

to predict. Wallace (2000b) used data from a number of studies to test two hypotheses: elderly 

COPD patients have (1) smaller personal clouds and (2) higher correlations between personal 

exposure and ambient concentrations compared to healthy elderly, children, and the general 

population. The analysis by Wallace (2000a) and three subsequent longitudinal studies 

(Williams 2000a,b,c; Ebelt et al., 2000; Sarnat et al., 2000) supported hypothesis 1 but not 

hypothesis 2. Özkaynak and Spengler (1996) showed that at least 50% of personal PM10 

exposure for the general population comes from ambient particles. Wilson and Suh (1997) 

concluded that fine and coarse particles should be treated as separate classes of pollutants 

because of differences in characteristics and potential health effects. Wilson et al. (2000) gave a 

review of what they call the “exposure paradox” and determined that personal PM needs to be 

divided into different classes according to source type and that correlations between personal and 

ambient PM will be higher when nonambient sources of PM are removed from the personal PM 

5-43
TABLE 5-6. PAPERS REPORTING REANALYSES OF PARTICULATE MATTER EXPOSUR

Reference Study Cited Objectives/Hypotheses Findings Wallace (2000a) 

PTEAM (Özkaynak et al., 1990; Spengler et al., 1989; Wiener 1988, 1989; Wiener et al., 1990) THEES (Lioy et al., 1990) Amsterdam COPD (Janssen et al., 1997, 1998a) Boston COPD (Rojas-Bracho et al., 2000) 

Examines the differences between pooled and longitudinal correlations in personal and ambient (or outdoor) data for PM2.5 and PM10. 

Discusses the personal cloud for PM2.5 and PM10. 

Hypothesizes that COPD patients have (1) smaller personal clouds (supported) and (2) higher correlations of personal exposure with outdoor concentrations because of reduced mobility (not supported). 

Median longitudinal correlation the pooled correlation coefficienPersonal cloud for PM10: 3-67 μ

Personal cloud for elderly COPD 

6-11 μg/m3; PM2.5 . 6 g/m3) than 

(PM10: 27-56 μg/m3; PM2.5: 11-2

and the general population. Howexposure with ambient concentrelderly COPD than for other gro

Özkaynak and Spengler (1996) 

Dockery and Spengler (1981) PTEAM (Özkaynak et al., 1996a,b) Netherlands (Janssen et al., 1995) 

Uses statistical modeling techniques to examine the relationship between ambient PM concentrations and personal exposures. Data analysis involves use of air exchange rates, penetration factors, and June 25, 2004 ratios, as well as examining exposure in various microenvironments (traveling, working, outdoors, indoors) activities (exposure to smoke, cooking), and source strengths. 

The important components of peduring contact with indoor sourcplaces. Ambient aerosols contribute aboPM10 exposures of the general p

The contribution of ambient aeroinhaled particles is significant. 

Brown and Paxton (1998) 

THEES (Lioy et al., 1990) PTEAM pilot (Wallace, 1996) Boston and Nashville COPD (Rojas-Bracho et al., 2000); Bahadori et al., 1998) 

Cross-sectional and longitudinal regression analysis on data sets. 

Individual personal PM exposurwhich makes the personal-to-amto predict. 

Wilson and Suh (1997) 

Philadelphia (Burton et al., 1996; Suggs and Burton, 1983) EPA AIRS database 

Determines the utility of fine and coarse PM concentrations as indicators of time-series epidemiology with regard to day-today variability, area uniformity, and June 25, 2004 PM ratios. Necessary to treat personal exposure to ambient PM and personal exposure to nonambient PM as separate components of total personal PM exposure. 

Fine and coarse particles should of pollutants. Fixed-site ambient fine-particle reasonable indication of the variambient fine particles across the measurements most likely will n

Wilson et al. (2000) 

New Jersey (Lioy et al., 1990) Japan (Tamura et al., 1996a) PTEAM (Clayton et al., 1993; Özkaynak et al., 1996a,b) Netherlands (Janssen, 1998a; Suh et al., 1992) 

Synoptic review of the “exposure paradox”: low correlations between personal exposure and ambient PM concentrations in spite of the existence of statistical association between ambient PM and epidemiologic health effects. Uses personal exposure equation, mass balance, regression analysis, and deductive logic. 

Personal PM exposure needs to classes according to source type(outdoor and indoors) and expossource and personal activity). Correlations are higher between PM concentrations when PM exsources are removed. 

5-44
TABLE 5-6 (cont’d). PAPERS REPORTING REANALYSES OF PARTICULATE MATTER EXPO

Reference Study Cited Objectives/Hypotheses Findings Mage et al. (1999) 

Japan (Tamura et al., 1996a) State College (Suh et al., 1995) Netherlands (Janssen et al., 1997, 1998a, 1999a) New Jersey (Lioy et al., 1990) PTEAM (Clayton et al., 1993; Özkaynak et al., 1996a,b) 

Examines the influence of nonambient PM on total PM concentrations and how it may confound the outdoor/personal PM relationship. Missing data and outlier values created using an algorithm. Linear regression analysis of subsequent data sets. 

Variation in daily personal expolifestyles and no ETS exposure aambient PM concentrations. Exposure to ambient PM is highambient PM concentrations meaIndoor PM does not confound thmortality and ambient PM. 

Mage (1998) PTEAM (Clayton et al., 1993; 

Özkaynak et al., 1993, 1996a,b) 

Uses a reduced-form mass-balance model to predict the average fraction of ambient PM to which the average person is exposed. 

On average, a person is exposed 

> 64% of ambient PM10 measure

Monn (2001) Multiple Literature Review Objective review of literature published since 1996 as an 

implicit update to the 1996 U.S. EPA PM AQCD. Emphasis on European studies. 

“It is important to note that a pera priori provide more valid data measurement, i.e. a personal sameffects from outdoor combustion by sources other than outdoor sothe exposure-effect outcome.” “Despite some lack of correlatio

and outdoor values, outdoor fine strongly associated with mortalithat outdoor sources (e.g. vehicuentity” (Dockery et al., 1993; Sc

Rotko et al. (2000a) 

Jantunen et al. (1998) Carrer et al. (1997) Koistinen et al. (1999) 

Compares exposure relationships between the six EXPOLIS European cities (Athens, Basel, Grenoble, Helsinki, Milan, Prague). 

Demographic bias exists becausindividuals are more likely to reSocioeconomic bias exists in low to participate in diary keeping anWeighting is required for inter-cSelection bias is not a problem ffactors influencing personal exp

Rotko et al. (2000b) 

Rotko et al. (2000a), Jantunen et al. (1998) 

Determines sociodemographic influences of exposure in Helsinki. 

Distinct male vs. female differenexposures to PM2.5, related to ET

between sociodemographic grou

No sociodemographic difference

concentrations. Lower occupational status contrexposures than higher (professio

Rodes et al. (2001) 

EPA Baltimore and Fresno 1 and 2 Investigates relationships between the different retirement 

centers and identify most likely factors influencing personal and indoor concentrations. 

Mean personal exposure PM2.5 w

concentrations. Personal cloud 

negligible but cloud for PM10 wa

data were less than ambient conc

5-45
concentration. Mage (1998) conducted analysis using the PTEAM data and showed that the 

average person in PTEAM (Riverside, CA in the fall) was exposed to > 75% of ambient PM2.5 

and > 64% of ambient PM10. Mage et al. (1999) used an algorithm to fill in missing data and 

outliers to analyze data sets and showed that variation in daily personal exposures for subjects 

with similar activity patterns and no ETS exposure are driven by variation in ambient PM 

concentrations. 

5.3.3 Factors Influencing and Key Findings on Particulate Matter 

Exposures 

5.3.3.1 Relationship of Personal/Microenvironmental Particulate Matter with Ambient 

Particulate Matter 

Understanding the relationship between ambient site measurements and personal exposure 

to PM is important for several reasons. First, it allows us to examine the extent to which 

ambient measurements for PM and various PM constituents can serve as valid surrogates for 

exposure to ambient PM or ambient constituents of PM in epidemiological studies. Second, it 

provides information that may improve surrogate exposure measurements and, hence, increase 

the power of epidemiologic studies. Finally, because compliance with the NAAQS is based on 

ambient monitoring, it can be used to understand the effect of regulation on exposures to PM and 

its constituents and can, therefore, help link the effect of regulations to health outcomes. Many 

of the studies summarized in Table 5-4 have analyzed this relationship using measurements of 

personal PM exposures and ambient PM concentrations. Of main interest are the PM 

concentrations measured in ambient, indoor, and outdoor air; personal exposure measurements; 

the statistical correlations between measurements; and the attenuation and/or infiltration factors 

developed for personal exposure and indoor microenvironments. Attenuation and infiltration 

factors are discussed in Section 5.3.4.3.1. Information on correlation analysis is provided below. 

5.3.3.1.1 Types of Correlations 

The three types of correlation data that will be discussed in this section are longitudinal, 

“pooled,” and daily-average correlations. Longitudinal correlations are calculated when data 

from a study includes measurements over multiple days for each subject (longitudinal study 

design). Longitudinal correlations describe the temporal relationship between daily personal PM 

exposure or microenvironment concentration and daily ambient PM concentration for each 

5-46
individual subject. The longitudinal correlation coefficient, r, may differ for each subject. 

An analysis of the variability in r across subjects can be performed with this type of data. 

Typically, the median r is reported along with the range across subjects in the study. Pooled 

correlations are calculated when a study involves one or only a few measurements per subject 

and when different subjects are studied on subsequent days. Pooled correlations combine 

individual subject/individual day data for the correlation calculation. Pooled correlations 

describe the relationship between daily personal PM exposure and daily ambient PM 

concentration across all subjects in the study. For some studies, the multiple days of 

measurements for each subject were assumed to be independent (after autocorrelation and 

sensitivity analysis) and combined together in the correlation calculation (Ebelt et al., 2000). 

Daily-average correlations are calculated by averaging exposure across subjects for each day. 

Daily-average correlations then describe the relationship between the daily average exposure and 

daily ambient PM concentration. The term cross-sectional is used to refer to both pooled and 

daily average correlations, so the meaning of this term must be determined from context. 

Pooled correlations have been simulated from longitudinal data by using a randomsampling procedure to select a random day from each subject’s measurements for use in the 

correlation. This procedure was repeated many times, and statistics (such as the mean and 

standard deviation of the pooled correlation coefficient) were reported (Janssen et al., 1997, 

1998a, 1999c). 

The type of correlation analysis can have a substantial effect on the resultant r value. Mage 

et al. (1999) mathematically demonstrated that very low correlations between personal exposure 

and ambient concentrations could be obtained when people with very different nonambient 

exposures are pooled, even though their individual longitudinal correlations are high. The 

longitudinal studies conducted by Tamura et al. (1996a) and Janssen et al. (1997, 1998a, 1999c) 

determined that the longitudinal correlations between personal exposure and ambient PM 

concentrations were higher than the correlations obtained from a pooled data set. Wallace 

(2000a) reviewed a number of longitudinal studies and found that the median longitudinal 

correlation coefficient was higher than the pooled correlation coefficient for the same data (see 

Tables 1 and 2, Wallace, 2000a). 

Mage et al. (1999) examined three longitudinal exposure data sets where several subjects 

were measured each day. They showed that by averaging daily exposures across subjects, daily5-47
average correlations could be obtained. These were all higher than the median longitudinal 

correlations. Williams et al. (2000a,b) and Evans et al. (2000) have also reported higher 

correlation coefficients for daily-average correlations compared to longitudinal correlations. The 

higher correlations found between daily-average personal exposures and ambient PM 

concentrations, as opposed to lower correlations found between individual exposures and 

ambient PM levels, have been attributed to the statistical process of averaging (Ott et al., 2000). 

Personal exposures include contributions from nonambient as well as ambient PM 

concentrations. When several subjects are measured on the same day, the mean variability due 

to variations in nonambient exposures is reduced due to averaging. Therefore, the correlation 

between personal exposure and ambient concentrations increases as the number of subjects 

measured daily increases. Ott et al. (2000), using the theory on which their Random Component 

Superposition (RCS) model is based, predict expected correlations above 0.9 for the PTEAM 

study and above 0.70 for the New Jersey study (Lioy et al., 1990) if 25 subjects had been 

measured daily in each study. 

5.3.3.1.2 Correlation Data from Personal Exposure Studies 

Measurement data and correlation coefficients for the personal exposure studies described 

in Section 5.4.2.1 are summarized in Table 5-7. All data are based on mass measurements. The 

studies are grouped by the type of study design, longitudinal or pooled. For each study in 

Table 5-7, summary statistics for the total personal PM exposure measurements are presented 

as well as statistics for residential indoor, residential outdoor, and ambient PM concentrations 

when available. The correlation coefficients, r, between total personal PM exposures and 

ambient PM concentrations also are presented and classified as longitudinal or pooled 

correlations. When reported, p-values for the correlation coefficients are included. Correlation 

coefficients between personal, indoor, outdoor, and ambient also are reported when available. 

5.3.3.1.3 Correlations Between Personal Exposures, Indoor, Outdoor, and 

Ambient Measurements 

Longitudinal and pooled correlations between personal exposure and ambient or outdoor 

PM concentrations varied considerably between study and study subjects. Most studies report 

longitudinal correlation coefficients that range from < 0 to .1, indicating that an individual’s 

5-48
TABLE 5-7. PERSONAL MONITORING STUDIES FOR PARTICULATE MATTER: MEASURED C

AND CORRELATION COEFFICIENTS

5-49

Personal-Ambient 2 

Measured Concentration Levels (μg/m3) Correlation Coefficients (r) 

Size Avg. Sample Residential Residential 

Fraction Time Statistic Size1 Personal Indoor Outdoor Ambient Type3 Value (Range) Notes 

Longitudinal Studies Ebelt et al. (2000) – Vancouver, BC 

PM2.5 24 h x) ± SD 106 18.2 ± 14.6 11.4 ± 4.1 Median L 0.48 (!0.68-0.83) n = 16 COPD subj

Range 2-91 4-29 P 0.15 

Evans et al. (2000) – Fresno, CA 

PM2.5 24 h x)

Range 

24 13.3 

1-24 

9.7 

4-17 

20.5 

4-52 

21.7 

6-37 

P 0.414 Fresno-1 study 

PM2.5 24 h x)

Range 

12 11.1 

7-16 

8.0 

4-12 

10.1 

5-20 

8.6 

4-16 

P 0.844 Fresno-2 study 

Janssen et al. (1997) – Netherlands 

PM10 24 h x) ± SD 301 105.2 ± 28.7 38.5 ± 5.6 Median L 0.63 (0.1-0.9) n = 45 school chil

Range 57-195 25-56 Median L 0.63 With nonsmoking 

Median L 

Mean P Mean P Mean P 

0.59 

0.28 (0.12)5 

0.45 (0.16)5 0.20 (0.19)5 

With smoking parAll With nonsmoking With smoking par

Janssen et al. (1998a) – Netherlands 

PM10 24 h x) ± SD 

Range 

262 61.7 ± 18.3 

38-113 

35.0 ± 9.4 

19-65 

41.5 ± 4.3 

32-50 

Median L 

Median P Median P 

0.50 (!0.41-0.92) 

0.50 (0.07-0.83)5 

0.34 (!0.09-0.67)5 

n = 37 adults No ETS exposure All 

Janssen et al. (1999c) – Netherlands 

PM2.5 24 h x) ± SD 

Range 

77 28.3 ± 11.3 

19-60 

17.1 ± 2.8 

14-22 

Median L 

Median P 

0.86 (!0.11-0.99) 

0.41 (!0.28-0.93)5 

n = 13 school chil

PM2.5 24 h x) ± SD 

Range 

55 24.4 ± 4.9 

19-33 

17.1 ± 2.6 

15-22 

Median L 

Median P 

0.92 

0.825 

With nonsmoking 

PM2.5 24 h x) ± SD 22 37.0 ± 17.4 17.1 ± 3.7 With smoking par

Range 21-60 14-21 
TABLE 5-7 (cont'd). PERSONAL MONITORING STUDIES FOR PM: MEASURED CONCENT

CORRELATION COEFFICIENTS 

5-50

Personal-Ambient 2 

Measured Concentration Levels (μg/m3) Correlation Coefficients (r) 

Size 

Fraction 

Avg. 

Time Statistic 

Sample 

Size1 Personal 

Residential 

Indoor 

Residential 

Outdoor Ambient Type3 Value (Range) Notes 

Janssen et al. (2000) – Netherlands 

PM2.5 24 h x) ± SD 338 24.3 ± 25.7 28.6 ± 41.8 20.6 ± 4.0 Median L 0.79 (!0.41-0.98) n = 36 elderly w/C

Range 9-134 9-239 

13-31 Median L 0.85 

disease No ETS exposures 

Janssen et al. (2000) – Finland 

PM2.5 24 h x) ± SD 336 10.8 ± 4.4 11.0 ± 4.0 12.6 ± 2.0 Median L 0.76 (!0.12-0.97) n = 46 elderly w/C

Range 4-33 3-27 10-18 disease 

Linn et al. (1999) – Los Angeles 

PM2.5 24 h x) ± SD 60 23.8 ± 15.1 23.5 ± 15.3 24.8 ± 14.5 P 0.266 

Range 4-65 4-92 4-63 

PM10 24 h x) ± SD 59 34.8 ± 14.8 32.6 ± 15.6 39.8 ± 18.3 33 ± 15 P 0.226 

Range 5-85 9-105 7-97 9-?? 

Rojas-Bracho et al. (2000) – Boston 

PM2.5 12 h x) ± SD 224 21.6 ± 13.6 17.5±14.1 14.2 ± 11.2 Median L 0.61 (0.10-0.93)6 n = 17 adults 

Range 1-128 2-73 1-57 

PM10 12 h x) ± SD 225 37.2 ± 22.8 31.9 ± 25.2 22.2 ± 18.7 Median L 0.35 (0.0-0.72)6 

Range 9-211 2-329 3-76 

PM10-2.5 12 h x) ± SD 222 15.6 ± 14.6 14.5 ± 9.2 8.1 ± 6.8 Median L 0.30 (0.0-0.97)6 

Range !11-103 !3-255 !2-64 
TABLE 5-7 (cont'd). PERSONAL MONITORING STUDIES FOR PM: MEASURED CONCENT

CORRELATION COEFFICIENTS 

5-51

Personal-Ambient 2 

Measured Concentration Levels (μg/m3) Correlation Coefficients (r) 

Size 

Fraction 

Avg. 

Time Statistic 

Sample 

Size1 Personal 

Residential 

Indoor 

Residential 

Outdoor Ambient Type3 Value (Range) Notes 

Sarnat et al. (2000) – Baltimore 

PM2.5 24 h x) ± SD 3736 26.7 ± 13.7 25.2 ± 11.5 Median L 0.76 (!0.21-0.95)7 n = 15 adults, 

x

) ± SD 18.5 ± 11.2 summer 

5.6 ± 49.0 Median L 0.25 (!0.38-0.81)7 n = 15 adults, 

winter Ventilation:

P P 

0.898 0.758 

 High summer Med. summer 

P 0.508 Low summer P 0.448 Winter 

PM10 24 h x) ± SD 

x

) ± SD 

3736 33.9 ± 11.7 

28.0 ± 16.5 

34.0 ± 12.8 

7.5 ± 73.2 

Median L Median L 

0.64 (0.08-0.86)7 

0.53 (!0.79-0.89)7 

Summer Winter 

PM10-2.5 24 h x) ± SD 

x

) ± SD 

3736 7.2 ± 4.0 

9.6 ± 7.9 

8.4 ± 2.3 

!1.3 ± 24.2 

Median L Median L 

0.11 (!0.60-0.64)7 

0.32 (!0.48-0.68)7 

Summer Winter 

Tamura et al. (1996a) – Tokyo 

PM10 48 h P 0.83 n = 7 elderly adult

Williams et al. (2000a,b) – Baltimore 

PM2.5 24 h x)

Range 

23 13.0 

7-25 

9.4 

4-19 

22.0 

7-52 

22.0 

8-59 

Median L 

P 

0.80 (0.38-0.98)6 

0.894 

n = 21 elderly adu

PM10 24 h x) 28 11.0 30.0 29.9 

Range 4-23 13-66 13-74 

PM10-2.5 24 h x) 26 1.0 8.0 8.0 

Range !3-5 !2-16 1-15 
TABLE 5-7 (cont'd). PERSONAL MONITORING STUDIES FOR PM: MEASURED CONCENT

CORRELATION COEFFICIENTS 

5-52

Personal-Ambient 2 

Measured Concentration Levels (μg/m3) Correlation Coefficients (r) 

Size 

Fraction 

Avg. 

Time Statistic 

Sample 

Size1 Personal 

Residential 

Indoor 

Residential 

Outdoor Ambient Type3 Value (Range) Notes 

Williams et al. (2000a,b) – Baltimore (cont’d) 

Keeler et al. Mean 20 68.4 (39.2) 52.2 25.8 

(2002) (Std) asthmati (30.6) (11.8) 

Detroit, MI 

PM1024-h 

PM2.524-h 

c children 34.4 

(21.7) 

15.6 

(8.2) 

Landis et al. Mean 10 12.8 10.2 21.0 Median L r = 0.82 

(2001) elderly (0.51-0.92) 

Baltimore, MD retirees 4.5 4.0 10.2 

PM2.524-h r = 0.95 

Sulfate24-h (0.74-0.97) 

Pooled Studies Bahadori (1998) – Nashville 

PM2.5 12 h x) ± SD 

Range 

30 21.7 ± 10.5 

10-67 

15.5 ± 6.6 

5-40 

23.4 ± 6.8 

3-61 

P 0.09 n = 10 COPD subj

daytime 

PM10 12 h x) ± SD 

Range 

30 33.0 ± 16.9 

5-88 

21.6 ± 10.7 

9-77 

32.5 ± 8.1 

7-76 

P !0.08 n = 10 COPD subj

daytime 

Pellizzari et al. (1999) – Toronto 

PM2.5 3 d x) 922 28.4 21.1 15.1 P 0.23 n = 178; n for indo

outdoor lower than 

personal 

PM10 3 d x) 141 67.9 29.8 24.3 No correlations re

Oglesby et al. (2000) – EXPOLIS Basel 

PM2.5 48 h x) ± SD 4420 23.7 ± 17.1 19.0 ± 11.7 P 0.07 All 

17.5 ± 13.0 17.7 ± 7.1 P 0.21 No ETS exposure 

Santos-Burgoa et al. (1998) – Mexico City 

PM10 24 h x) ± SD 66 97 ± 44 99 ± 50 P 0.26 

Tamura et al. (1996b) – Osaka 

PM2 48 h P 0.74 

PM10 48 h P 0.67 
TABLE 5-7 (cont'd). PERSONAL MONITORING STUDIES FOR PM: MEASURED CONCENT

CORRELATION COEFFICIENTS 

5-53

Personal-Ambient 2 

Measured Concentration Levels (μg/m3) Correlation Coefficients (r) 

Size 

Fraction 

Avg. 

Time Statistic 

Sample 

Size1 Personal 

Residential 

Indoor 

Residential 

Outdoor Ambient Type3 Value (Range) Notes 

Pellizzari et al. (2001) – Indianapolis 

PM2.5 72 h Median 250 23 18 18 18 P 0.102 Between the Loga

of concentrations 

Brauer et al. (2000) Banska Bystrica 

PM2.5 24 h Mean PM10 1.2212e + 10 796655535 354522326 P PM10 Multivariate mode

PM10 summer R2 = 0.17 PM10 and nicotine 

PM10 winter PM2.5 summer PM2.5 winter SO4 winter 

Kousa et al. (2002) 

Helsinki P 0.69 Leisure time, non-

+ 3 cities exposed, Helsinki

PM2.5 48 h log-transformed 

Abbreviations used: h = Hour P = Pooled correlation

Avg. = Averaging (time) i-a = Indoor-ambient correlation p-i = Personal-indoor cor

Conc. = Concentration i-o = Indoor-outdoor correlation p-o = Personal-outdoor co

CV = Cardiovascular L = Longitudinal correlation SD = Standard deviation

d = Day Med. = Median Stat. = Statistic

ETS = Environmental tobacco smoke o-a = Outdoor-ambient correlation x) = Mean

Notes:

1 Sample size is for personal concentrations; indoor, outdoor and ambient sample sizes may differ.2 Correlation coefficient is for personal-residential outdoor if no ambient concentration data reported.3 See text for description of types of correlations.4 Daily-averaged correlation (values for individual subjects averaged for each day).5 Pooled correlations estimated using a Monte Carlo sampling procedure, n = 1000. If mean P is shown, then SD given; if median P is shown, then range is given.

6 Obtained from a regression equation; r = ( ) R . 2 

7 Spearman rank correlations. 

R2 8 Calculated, r = ( ) , from R2 from a mixed model regression. 
activities and residence type may have a significant effect on total personal exposure to PM. 

General population studies tend to show lower correlations because of the higher variation in the 

levels of PM-generating activities. In contrast, the absence of indoor sources for the populations 

in several of the longitudinal studies resulted in high correlations between personal exposure and 

ambient PM within subjects over time for these populations. But even for these studies, 

correlations varied by individual depending on their activities and the microenvironments that 

they occupied. 

Probability Studies 

In the Toronto study (Pellizzari et al., 1999), pooled correlations were derived for personal, 

indoor, outdoor, and fixed-site ambient measurements. This study involved a probability sample 

of 732 participants who represented the general population of people 16 years and older. The 

study included between 185 and 203 monitoring periods with usable PM data for personal, 

residential indoor, and outdoor measurements. For PM10 measurements, the mean concentrations 

were 67.9 μg/m3

 for personal, 29.8 μg/m3 for indoor, and 24.3 μg/m3 for outdoor air samples. 

For PM2.5, the mean concentrations were 28.4 μg/m3

 for personal, 21.1 μg/m3 for indoor air, and 

15.1 μg/m3

 for outdoor air samples. A low, but significant, correlation (r = 0.23, p < 0.01) was 

reported between personal exposure and ambient measurements. The correlations between 

indoor concentrations and the various outdoor measurements of PM2.5 ranged from 0.21 to 0.33. 

The highest correlations were for outdoor measurements at the residences with the ambient 

measurements made at the roof site (0.88) and the other fixed site (0.82). Pellizzari et al. (1999) 

state that much of the difference among the data for personal/indoor/outdoor PM: 

“. . . can be attributed to tobacco smoking, since all variables reflecting 

smoking . . . were found to be highly correlated with the personal (and indoor) 

particulate matter levels, relative to other variables that were measured . . . none 

of the outdoor concentration data types (residential or otherwise) can adequately 

predict personal exposures to particulate matter.” (p. 729) 

Using a Random Component Superposition (RCS) statistical model, Ott et al. (2000) 

calculated an attenuation factor of 0.61 for personal exposure for PM10 for the Toronto study. 

The mean nonambient exposure component for PM10 was estimated as 53 μg/m3

 with a standard 

5-54
deviation of 85 μg/m3

. Although the data were available for PM2.5, similar calculations were 

not made. 

PM10 data from the PTEAM study were analyzed using the same approach (Ott et al., 

2000). For PTEAM, an attenuation factor of 0.55 was calculated for personal exposure. 

Infiltration factors were calculated for each residence with an average of 0.56 and a standard 

deviation of 0.15. Values ranged from a minimum of 0.19 to a maximum of 0.87 showing the 

substantial variability that can be seen between homes depending upon the housing 

characteristics and operation of the HVAC system. The mean ± SD nonambient exposure 

component for PM10 was estimated as 59 ± 46 μg/m3

. 

r

Santos-Burgoa et al. (1998) described a 1992 study of personal exposures and indoor 

concentrations to a randomly sampled population near Mexico City. The sample of 66 

monitored subjects included children, students, office and industrial workers, and housewives. 

None of the people monitored were more than 65 years old. The mean 24-h personal exposure 

and indoor concentrations were 97 ± 44 and 99 ± 50 μg/m3

, respectively, with an rPersonal/Ambient 

= 0.26 (p = 0.099). Other correlations of interest were rPersonal/Indoor = 0.47 (p = 0.002) and 

Indoor/Ambient = 0.23 (p = 0.158). A strong statistical association was found between personal 

exposure and socioeconomic class (p = 0.047) and a composite index of indoor sources at the 

home (p = 0.039). 

Correlation analysis for personal exposure has not yet been reported for EXPOLIS. Some 

preliminary results (Jantunen et al., 2000) show that in Basel and Helsinki a single ambient 

monitoring station was sufficient to characterize the ambient PM2.5 concentration in each city. 

Using microenvironmental concentration data collected while the subjects were at home, at 

work, and outdoors, they calculated the sum of the time-weighted-averages of these data and 

found the results closely matched the personal PM2.5 exposure data collected by the monitors 

carried by most of the subjects, although a few subjects (mostly smokers) were noticeable 

exceptions. 

Longitudinal Studies 

A number of longitudinal studies using a purposeful sampling design have been conducted 

and reported in the literature since 1996. Several of these studies (Janssen et al., 1998a, 1999b, 

2000; Williams et al., 2000b; Evans et al., 2000) support the previous work by Janssen et al. 

5-55
(1995) and Tamura et al. (1996a) and demonstrate that, for individuals with little exposure to 

nonambient sources of PM, correlations between total PM exposure and ambient PM 

measurements are high. Other studies (Ebelt et al., 2000; Sarnat et al., 2000) show strong 

correlations for the SO4

2!

 component of PM2.5 but poorer correlations for PM2.5 mass. Still other 

studies show only weak correlations (Rojas-Bracho et al., 2000; Linn et al., 1999). Even when 

strong longitudinal correlations are demonstrated for individuals in a study, the variety of living 

conditions may lead to variations in attenuating factors or the fraction of ambient PM 

contributing to personal exposure. Groups with similar living conditions, especially if 

measurements are conducted during one season, may have similar " and, therefore, very high 

correlations between personal exposure and ambient concentrations, even for pooled 

correlations. However, when studies contain subjects with homes of very different ventilation 

characteristics or cover more than one season, variations in " can be high across subjects, thus, 

showing poor pooled correlations even in the absence of indoor sources. 

Elderly Subjects. Janssen et al. (2000) continued their longitudinal studies with 

measurements of personal, indoor, and outdoor concentrations of PM2.5 for elderly subjects with 

doctor-diagnosed angina pectoris or coronary heart disease. Studies were conducted in 

Amsterdam, the Netherlands and Helsinki, Finland in the winter and spring of 1998 and 1999. 

In the Amsterdam study with 338 to 417 observations, mean PM2.5 concentrations were 24.3, 

28.6, and 20.6 μg/m3

 for personal, indoor, and outdoor samples, respectively. If the 

measurements with ETS in the home were excluded, the mean indoor concentration dropped to 

16 μg/m3

, which was lower than outdoor concentrations. In the Helsinki study, the mean PM2.5 

concentrations were 10.8 μg/m3 for personal, 11.0 μg/m3 for indoor air, and 12.6 μg/m3 outdoor 

air samples. The authors noted that for this group of subjects, personal exposure, indoor 

concentrations, and ambient concentrations of PM2.5 were highly correlated within subjects over 

time. Median Pearson’s correlation coefficients between personal exposure and outdoor 

concentrations were 0.79 in Amsterdam and 0.76 in Helsinki. The median Pearson’s r for the 

indoor/outdoor relationship was 0.85 for the Amsterdam study when homes with ETS were 

excluded. The correlation for indoors versus outdoors was 0.70 for all homes. 

Results from the correlation analysis can be used to estimate infiltration factors and 

penetration factors for these two groups of subjects. In Amsterdam, the attenuation factor was 

5-56
0.43 and the infiltration factor was 0.47. Very similar results were seen in Helsinki for the 

attenuation factor (0.45) and the infiltration factor (0.51). 

A series of three PM personal monitoring studies involving elderly subjects was conducted 

in Baltimore County, MD, and Fresno, CA. The first study was a 17-day pilot (January to 

February 1997) to investigate daily personal and indoor PM1.5 concentrations, as well as 

outdoor PM2.5 and PM10-2.5 concentrations experienced by nonsmoking elderly residents of a 

retirement community located near Baltimore (Liao et al., 1999; Williams et al., 2000c). The 

26 residents were aged 65 to 89 (mean = 81) years, and 69% of them reported a medical 

condition such as hypertension or coronary heart disease. In addition, they were quite sedentary: 

on average, less than 5 h/day were spent on ambulatory activities. Because most of the residents 

ate meals in a communal dining area, the average daily cooking time in the individual 

apartments was only 0.5 h (range = 0 to 4.5 h). About 96% of the residents’ time was spent 

indoors (Williams et al., 2000c). Personal monitoring, conducted for five subjects, yielded 

longitudinal correlation coefficients between ambient concentrations and personal exposure 

ranging from 0.00 to 0.90. 

The main Baltimore study and the Fresno study were conducted using similar monitoring 

techniques and study design. Concentrations measured in these studies are summarized in 

Table 5-8. For PM2.5, personal exposure and indoor air concentrations are similar for all three 

studies, even though outdoor air concentrations for Fresno in the spring are only half of those 

measured for Fresno in the winter and for Baltimore. This difference is presumably due to high 

penetration efficiencies in the spring in Fresno when the weather was warm and participants 

tended to keep the windows and doors of their homes open. These data also show that, even 

when correlations are high, the use of an ambient monitor as a surrogate for exposure in 

epidemiologic studies can bias the strength of the health effect found due to differing exposure 

levels. 

Calculated correlation coefficients are summarized in Tables 5-9 and 5-10. In Table 5-9, 

the Baltimore results show high daily average correlations for both PM2.5 and PM10. These 

results primarily represent the behavior of fine-particle regional sulfate for a group of 

participants who have few indoor or personal sulfate sources. However, even for this group, 

there was a wide range of individual correlation coefficients. The Fresno data, on the other 

hand, shows much lower daily average correlations. The residential site may have been 

5-57
TABLE 5-8. MEAN CONCENTRATION FOR PM MASS REPORTED FOR THE 

BALTIMORE (Williams et al., 2000a,b,c) AND THE FRESNO 

(Evans et al., 2000) STUDIES 

PM2.5 Concentration (μg/m3

) PM10 Concentration (μg/m3) 

Study Personal Indoors Outdoors Personal Indoors Outdoors 

Baltimore 13.0 ± 4.2 10.5 ± 4.9 22.0 ± 12.0 

Fresno-Winter 13.3 ± 5.9 9.7 ± 5.0 20.5 ± 13.4 

Fresno-Spring 11.1 ± 2.8 8.0 ± 1.8 10.1 ± 3.2 

— 13.5 ± 6.3 30.0 ± 13.7 

— 15.1 ± 4.1 28.2 ± 15.9 

37.3 16.7 ± 3.1 28.7 ± 6.6 

TABLE 5-9. DAILY-AVERAGE CORRELATION COEFFICIENTS REPORTED FOR 

THE BALTIMORE (Williams et al., 2000a,b,c) AND THE FRESNO 

(Evans et al., 2000) STUDIES 

PM2.5 R2 PM10 R2 

Study Ambient/Outdoor Personal/Ambient Personal/Indoors Ambient/Outdoor 

Baltimore 0.92 0.80 0.98 0.89 

(0.14-0.80)a (0.20-0.99)a 

Fresno-Winter 0.48 — — 0.48 

Fresno-Spring 0.53 0.7 0.77 0.61 

a

Range for individual participants. 

TABLE 5-10. REGRESSION ANALYSIS REPORTED FOR INDOOR/OUTDOOR 

RELATIONSHIPS FOR PM2.5 IN THE BALTIMORE (Williams et al., 2000a,b,c) 

AND FRESNO (Evans et al., 2000) STUDIES 

Daily Average Individual 

Study R2 Slope 

Intercept 

(μg/m3

) R2 Slope 

Intercept 

(μg/m3

) 

Baltimore 0.92 0.39 1.5 0.73 ± 0.16 0.43 ± 0.15 0.9 ± 2.6 

Fresno-Winter 0.86 nr nr 0.55 ± 0.25 0.25 ± 0.17 4.4 ± 3.2 

Fresno-Spring 0.56 nr nr 0.39 ± 0.21 0.49 ± 0.38 3.0 ± 3.7 

nr = not reported. 

5-58 
influenced by highway traffic which could account for the low correlations between ambient and 

outdoor residential monitors. 

In addition, the composition of particles is not the same for these two locations. In Fresno, 

ammonium nitrate represents a much larger fraction of fine-particle mass. Because this 

compound is in equilibrium with its precursor gases, ammonia and nitric acid, its partitioning 

between the gaseous and particulate phase can be easily disturbed while outdoor particles 

infiltrate indoors. Thus, differences in June 25, 2004 ratios and correlations between Fresno 

and Baltimore could be a function of differences in ventilation, indoor sources, and sampling 

artifacts. 

The correlation analysis in Table 5-10 shows correlation coefficients as well as the slope 

(infiltration factor) and the intercept (indoor concentration due to nonambient sources) for the 

Baltimore and Fresno studies. These data show strongest correlations for Baltimore, where there 

are very low indoor concentrations from nonambient sources. Correlations are not as strong for 

Fresno, where there are higher concentrations from nonambient sources. The infiltration factors 

for Baltimore and Fresno-spring are very similar at approximately 0.5. The infiltration factors 

for Fresno-winter are considerably lower. 

Subjects with COPD. Linn et al. (1999) describe a 4-day longitudinal assessment of 

personal PM2.5 and PM10 exposures (on alternate days) in 30 COPD subjects aged 56 to 83 years. 

Concurrent indoor and outdoor monitoring was conducted at their residences in the Los Angeles 

area during summer/autumn of 1996. PM10 data from the nearest fixed-site monitoring station to 

each residence was also obtained. Pooled correlations for personal exposure to outdoor 

measurements gave R2

 values of 0.26 and 0.22 for PM2.5 and PM10, respectively. Correlations of 

day-to-day changes in PM2.5 and PM10 measured outside the homes and correlated with 

concurrent PM10 measurements at the nearest ambient monitoring location gave R2

 values of 

0.22 and 0.44, respectively. Correlations of day-to-day changes in PM mass measured indoors 

correlated with outdoor measurements at the homes gave R2

 values of 0.27 and 0.19 for PM10 

and PM2.5, respectively. 

Personal, indoor, and outdoor PM2.5, PM10, and PM2.5-10 correlations were reported by 

Rojas-Bracho et al. (2000) for a study conducted in Boston, MA on 18 individuals with COPD. 

Both the mean and median personal exposure concentrations were higher than the indoor 

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concentrations, which were higher than outdoor concentrations for all three PM measurement 

parameters. June 25, 2004 geometric mean ratios were 1.4 ± 1.9 for PM10, 1.3 ± 1.8 for PM2.5, 

and 1.5 ± 2.7 for PM10-2.5. Median longitudinal R2

s between personal exposure and ambient PM 

measurements were 0.12 for PM10, 0.37 for PM2.5 and 0.07 for PM10-2.5. The relationship between 

the indoor and outdoor concentrations was strongest for PM2.5, with a median R2

 of 0.55 and with 

significant R2

 values for 11 homes. For PM10, the median R2 value was 0.25, with significant 

values for eight homes. Only five homes had significant indoor/outdoor associations for PM10-2.5, 

with an insignificant median R2

 value of 0.04. The poor correlations for PM10-2.5 are a result of 

poorer penetration efficiencies, higher decay rates, and spatial inhomogeneities. 

Bahadori et al. (1998) reported a pilot study of PM exposure of 10 nonrandomly chosen 

COPD patients in Nashville, TN during the summer of 1995. Each subject alternately carried a 

personal PM2.5 or PM10 monitor for a 12-h daytime period (8:00 am to 8:00 pm) for 

6 consecutive days. These same pollutants were monitored simultaneously indoors and outdoors 

at their homes. All of the homes were air-conditioned and had low air exchange rates (mean = 

0.57/h), which may have contributed to the finding that mean indoor PM2.5 was 66% of the mean 

ambient PM2.5. This can be contrasted with the PTEAM study in Riverside, CA, where no air 

conditioners were in use and the mean indoor PM2.5 was 98% of the mean ambient PM2.5 

(Clayton et al., 1993). Data sets were pooled for correlation analysis. Resulting pooled 

correlations between personal and outdoor concentrations were r = 0.09 for PM2.5 and !0.08 

for PM10. 

5.3.3.1.4 Personal Exposure to Sulfate Compared to Personal Exposure to Ambient 

Particulate Matter 

A study conducted in Vancouver involving 16 COPD patients aged 54 to 86 years reported 

low median longitudinal (r = 0.48) and pooled (r = 0.15) correlation coefficients between 

personal exposures and ambient concentrations of PM2.5 (Ebelt et al., 2000). However, the mean 

correlation between personal exposures to sulfate and ambient concentrations of sulfate was 

much higher (r = 0.96). Because there are typically minimal indoor sources of sulfate, the 

relationship between ambient concentrations and personal exposures to sulfate would not be 

weakened by variability in an indoor-generated sulfate component as, for example, in the case 

of PM2.5 for which there are many primary indoor sources as well as some secondary indoor 

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sources. Correlations of ambient concentrations versus personal exposures for PM2.5 and sulfate 

are compared in Figure 5-3. 

Figure 5-3. Comparison of correlation coefficients for longitudinal analyses of personal 

exposure versus ambient concentrations for individual subjects for PM2.5 

and sulfate. 

Another study, conducted in Baltimore, MD, involved 15 nonsmoking adult subjects 

(> 64 years old) who were monitored for 12 days during summer 1998 and winter 1999 (Sarnat 

et al., 2000). All subjects (nonrandom selection) were retired, physically healthy, and lived in 

nonsmoking private residences. Each residence, except one, was equipped with central 

air-conditioning; however, not all residences used air-conditioning throughout the summer. The 

average age of the subjects was 75 years (± 6.8 years). Sarnat et al. (2000) reported higher 

longitudinal and pooled correlations for PM2.5 during summer than winter. Similar to Ebelt et al. 

2! (2000), Sarnat et al. (2000) reported stronger associations between personal exposure to SO4 

5-61
and ambient concentrations of SO4

2!

 than for total personal PM2.5 exposure and ambient PM2.5 

concentrations. The ranges of correlations are shown in Figure 5-3, along with similar data from 

Ebelt et al. (2000). 

The higher correlation coefficients and the narrower range of the correlation coefficient for 

sulfate suggest that removing indoor-generated and personal-activity PM from total personal PM 

would result in a higher correlation with ambient concentrations. If, as discussed in 5.3.4.3.1, 

there are no indoor sources, a personal exposure measurement for sulfate gives the ambient 

exposure of sulfate; the ratio of personal sulfate to ambient sulfate gives the attenuation 

coefficient on an individual, daily basis; and the attenuation coefficient times the ambient PM2.5 

concentration gives the individual, daily values of ambient PM2.5 exposures (Wilson et al., 2000). 

This technique applies only to the nonvolatile components of fine PM, as measured by PM2.5. 

It requires that the sulfate concentration be large enough so that it can be measured with 

reasonable accuracy. It does not require that sulfate be correlated with PM2.5 or the non-sulfate 

components of PM2.5, because the sulfate data is used to estimate the attenuation coefficient, 

not PM2.5. The technique does require that there be minimal indoor sources of sulfate, as 

indicated by a near-zero intercept for the regression, and that the size distribution of PM2.5 

and sulfate be similar. 

Sarnat et al. (2001) subsequently extended the Baltimore study to include 20 older adults, 

21 children, and 15 individuals with COPD for a total of 56 subjects. In both studies 

(Sarnat et al., 2000, 2001), personal and ambient sulfate data were used to estimate the 

ambient PM2.5 exposure. They used this information in mixed-model analysis (mixed models 

account for differences among individual subjects), but did not report correlations between 

ambient PM2.5 exposure and ambient PM2.5 concentrations based on the pooled data set. 

However, Sarnat et al. (2001) did report slopes from the mixed model analyses. The t-statistic 

for the slope of ambient exposure versus ambient concentration, as compared to total personal 

exposure versus ambient concentration, increased from 9.96 to 11.12 (total exposure versus 

ambient concentration) for the summer period and from 4.36 to 19.88 (ambient exposure versus 

ambient concentration) for the winter period. 

The study conducted by Sarnat et al. (2000) also illustrates the importance of ventilation on 

personal exposure to PM. During the summer, subjects recorded the ventilation status of every 

visited indoor location (e.g., windows open, air-conditioning use). As a surrogate for the air 

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exchange rate, personal exposures were classified by the fraction of time the windows were open 

while a subject was in an indoor environment (Fv). Sarnat et al. (2000) reported regression 

analyses of personal exposure on ambient concentration for PM2.5 and for sulfate for each of the 

three ventilation conditions (Figure 5-4). The correlation between personal exposure and 

ambient concentration is higher for sulfate than for PM2.5, presumably because PM2.5 has indoor 

sources as well as ambient sources but sulfate has only ambient sources. As expected, the 

improvement is better for the lower ventilation conditions, because under these conditions the 

ambient concentration is larger. For the lowest ventilation condition, R2

 improves from 

0.25 to 0.72. 

5.3.3.1.5 Personal Exposure to Ambient and Nonambient Particulate Matter 

The utility of treating personal exposure to ambient PM, A, and personal exposure to 

nonambient PM, N, as separate and distinct components of total personal exposure to PM, T, 

was pointed out by Wilson and Suh (1997). The PTEAM study measured, in addition to indoor, 

outdoor, and personal PM10, the air exchange rate for each home and collected information on 

the time spent in various indoor and outdoor microenvironments. This information is available 

for 147 12-h daytime periods. With this information and statistically estimated values of P and 

k, it is possible to estimate the daytime A and N as described in Section 5.2.4.3. Various 

examples of this information have been reported (Mage et al., 1999; Wilson et al., 2000). 

Graphs showing the relationships between ambient concentration and the various components of 

personal exposure (T, A, and N) are shown in Figure 5-5. The correlation coefficient for the 

pooled data set improves from r = 0.377 for T versus C (Figure 5-5a) to r = 0.856 for A versus C 

(Figure 5-5b) because of the removal of the N, which, as shown in Figure 5-5c, is highly variable 

and independent of C. The correlation between A and C is less than 1.0 because of the 

day-to-day variation in the " of each individual. The regression of T on C gives α = 0.711 

and N = 81.6 μg/m3. The regression of A on C gives α = 0.625. The regression of N on C 

gives N = 79.2 μg/m3. The finite intercept in the regression with A must be attributed to bias or 

error in some of the measurements. No reported studies, other than PTEAM, have provided the 

quantity of data on individual, daily values of T, C, Ci

, and a that are required to conduct an 

analysis comparable to that shown in Figure 5-5. It should be noted that the PTEAM study was 

conducted in southern California in the fall, when houses were open and air exchange rates were 

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Figure 5-4. Personal exposure versus ambient concentrations for PM2.5 and sulfate for 

well, moderately, and poorly ventilated indoor environments. (Slope estimated 

from mixed models). 

Source: Sarnat et al. (2000). 

5-64 
Figure 5-5. Regression analyses of aspects of daytime personal exposure to PM10 estimated 

using data from the PTEAM study: (a) total personal exposure to PM, T, 

regressed on ambient concentration, C; (b) personal exposure to ambient PM, 

A, regressed on C; and (c) personal exposure to nonambient PM, N, regressed 

on C. 

Source: Data taken from Clayton et al. (1993). Adapted from Mage et al. (1999) and Wilson et al. (2000). 

5-65 
high and relatively uniform. These are best case conditions for showing high correlations 

between ambient site measurements and personal correlations. Such high correlations are not 

usually found and would not be expected with lower and more variable air exchange rates. 

The RCS model introduced by Ott et al. (2000) presents a modeling framework to 

determine the average contribution of ambient PM10 and indoor-generated PM10 to average 

personal exposures in large urban metropolitan areas. The model has been tested using personal, 

indoor, and outdoor PM10 data from three urban areas (Riverside, CA; Toronto; and Phillipsburg, 

NJ). Results suggest that it is possible to separate the average ambient and nonambient PM 

contributions to personal exposures on a community-wide basis. However, as discussed in the 

paper, the authors make some assumptions that require individual consideration in each 

city-specific application of the model for exposure or health effects investigations. Primarily, 

housing factors, air-conditioning, seasonal differences, and complexities in time-activity profiles 

specific to the cohort being studied have to be taken into account prior to adopting the model to 

a given situation. Finally, the exposure-based analyses presented here do not predict the relative 

contribution of indoor and outdoor PM to particle mass burden to the lung as a function of 

human activities and different microenvironmental sources and concentrations of PM and its 

co-pollutants. 

5.3.3.2 Factors That Affect Relationships Between Personal Exposures and Ambient PM 

A number of factors can affect the relationship between personal PM exposures and PM 

measured at ambient-site community monitors. Spatial variability in outdoor 

microenvironmental concentrations of PM and variations in penetration of PM into indoor 

microenvironments influence the relationship of ambient PM concentration to ambient PM 

exposure. Air exchange rates and decay rates in indoor microenvironments influence the 

relationship of ambient PM concentrations to both ambient and total PM exposure, whereas 

personal activities that generate particles influence the relationship to total, but not ambient PM 

exposure. Information on these effects is presented in detail in the following sections. 

5.3.3.2.1 Spatial Variability and Correlations Over Time 

Chapter 3, Section 3.2.3, presents information on the spatial variability of PM mass and 

chemical components at fixed-site ambient monitors; for purposes of this chapter, this spatial 

5-66
variability is called an “ambient gradient.” The data presented in Section 3.2.3 indicate that 

ambient gradients of PM and its constituents exist to a greater or lesser degree in urban areas. 

These and any other gradients that may exist between a fixed-site monitor and the outdoor 

microenvironments near where people live, work, and play obviously affect their exposure. 

The purpose of this section is to review the available data on ambient monitor-to-outdoor 

microenvironmental concentration gradients or relationships that have been measured by 

researchers since 1996. These analyses, presented below, are generally consistent with earlier 

studies covered in the 1996 PM AQCD. A few outdoor-to-outdoor monitoring studies also are 

included to highlight relationships among important microenvironment categories. To assess 

spatial variability or gradients, the spatial correlations in the data are usually analyzed. 

However, it should be noted that high temporal correlation between two monitoring locations 

does not imply low spatial variability or low ambient gradients. A high temporal correlation 

between two sites indicates that changes in concentrations at one site may be estimated from data 

collected at another site. 

In a paper on the EXPOLIS-EAS study, Oglesby et al. (2000) concluded that in Basel, 

Switzerland little spatial variability exists between PM levels measured at fixed site monitors 

and the participants’ outdoor microenvironments. The authors reported a high correlation 

between home outdoor PM2.5 levels (48-h measurements beginning and ending at 8:00 a.m.) and 

the corresponding 24-h average PM4 (time-weighted values calculated from midnight to 

midnight) measured at a fixed monitoring station (n = 38, rsp = 0.96, p < 0.001). They 

considered each home outdoor monitor as a temporary fixed monitor and concluded that 

“the PM2.5 level measured at home outdoors . . . represents the fine particle level prevailing in 

the city of Basel during the 48-h measuring period.” 

In a study conducted in Helsinki, Finland, Buzorius et al. (1999) concluded that a single 

monitor may be used to adequately describe the temporal variations in concentration across the 

metropolitan area. Particle size distributions were measured using a differential mobility particle 

sizer (DMPS; Wintlmayer) coupled with a condensation particle counter (CPC TSI 3010, 3022) 

at four locations, including the official air monitoring station, which represented a “background” 

site. The monitoring period varied between 2 weeks and 6 mo for the sites, and data were 

reported for 10-min and 1-, 8-, and 24-h averages. As expected, temporal variation decreased as 

the averaging time increased. The authors reported that particle number concentration varied in 

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magnitude with local traffic intensity. Linear correlation coefficients computed for all possible 

site-pairs and averaging times showed that the correlation coefficient improved with increasing 

averaging time. Using wind speed and direction vectors, lagged correlations were calculated and 

were generally higher than the “raw” data correlations. The authors noted that weekday 

correlations were higher than weekend correlations as “traffic provides relatively uniform spatial 

distribution of particulate matter” and concluded that, even for time periods of 10 min and 1 h, 

sampling at one station can describe temporal variations across relatively large areas of the city 

with r > 0.7. 

Dubowsky et al. (1999) pointed out that, although the variation of PM2.5 mass 

concentration across a community may be small, there may be significant spatial variations of 

specific components of the total mass on a local scale. An example is given of a study of 

concentrations of polycyclic aromatic hydrocarbons (PAHs) at three indoor locations in a 

community: (1) an urban site, (2) a semi-urban site 1.6 km away, and (3) a suburban site located 

further away. The authors found the geometric mean PAH concentrations at these three 

locations varied respectively as 31:19:8 ng/m3

 and suggested that the local variations in traffic 

density were responsible for this gradient. Note that these concentrations are 1,000 times lower 

than the total PM mass concentration and that such a small gradient would not be detectable for 

total PM2.5 mass measurements on the order of 25 μg/m3

. 

The Total Human Environmental Exposure Study (THEES) reported by Waldman et al. 

(1991) measured indoor, outdoor, and personal benzo(a)pyrene (BaP) levels and found that the 

outdoor BaP was the same at all outdoor sites across the three sampling periods. This study 

showed seasonal differences for BaP levels as well as for exposures due to indoor and outdoor 

sources and individual activities. 

Leaderer et al. (1999) monitored 24-h PM10, PM2.5, and sulfates during the summers of 

1995 and 1996 at a regional site in Vinton, VA (6 km from Roanoke, VA). One similar 24-h 

measurement was made outdoors at residences in the surrounding area at distances ranging from 

1 to > 175 km from the Vinton site, at an average separation distance of 96 km. The authors 

reported significant correlations for PM2.5 and sulfates between the residential outdoor values 

and those measured at Vinton on the same day. In addition, the mean values of the regional site 

and residential site PM2.5 and sulfates showed no significant differences in spite of the large 

distance separations and mountainous terrain intervening in most directions. However, for the 

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concentrations of coarse particles estimated as PM10 minus PM2.5, no significant correlations 

among these sites were found (n = 30; r = !0.20). 

Lillquist et al. (1998) found no significant gradient in PM10 concentrations in Salt Lake 

City, UT when levels were low, but a gradient existed when levels were high. Outdoor PM10 

levels were measured for a period of about 5 mo at three hospitals using a Minivol 4.01 sampler 

(Airmetrics, Inc.) operating at 5 L/min and at the Utah Department of Air Quality (DAQ) 

ambient monitoring station located 3 to 13 km from the hospitals. 

Pope et al. (1999) monitored ambient PM10 concentrations in Provo, UT (Utah Valley) 

during the same time frame the following year and reported nearly identical concentrations at 

three sites separated by 4 to 12 km. Pearson correlation coefficients for the data were between 

0.92 and 0.96. The greater degree of variability in the Salt Lake City PM10 data relative to the 

Provo data may be related to the higher incidence of windblown crustal material in Salt Lake 

City. 

Väkevä et al. (1999) found significant vertical gradients in submicron particles existed in 

an urban street canyon of Lahti, Finland. Particle number concentrations were measured using a 

TSI screen diffusion battery and a condensation particle counter at 1.5 and 25 m above the street 

at rooftop level. The authors found a five-fold decrease in concentration between the two 

sampling heights and attributed the vertical gradient to dilution and dispersion of pollutants 

emitted at street level. 

White (1998) suggested that the higher random measurement error for the coarse PM 

fraction compared to the error for the fine PM fraction may be responsible for a major portion of 

the apparent greater spatial variability of coarse ambient PM concentration compared to fine 

ambient PM concentration in a community (e.g., Burton et al., 1996; Leaderer et al., 1999). 

When PM2.5 and PM10 are collected independently and the coarse fraction is obtained by 

difference (PM10-2.5 = PM10-PM2.5), the expected variance in the coarse fraction is influenced by 

the variances of the PM10 and PM2.5 measurements. When a dichotomous sampler collects PM2.5 

and PM10-2.5 on two separate filters, the coarse fraction is expected to have a larger error than the 

fine fraction. There is a possible error caused by loss of mass below the cut-point size and a gain 

of mass above the cut-point size that is created by the asymmetry of the product of the 

penetration times PM concentration about the cut-point size. Because a dichotomous PM 

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sampler collects coarse mass using an upper and lower cut point, it is expected to have a larger 

variance than for fine mass collected using only one cut point. 

Wilson and Suh (1997) concluded that PM2.5 and PM10 concentrations are correlated more 

highly across Philadelphia than are PM10-2.5 concentrations. Ambient monitoring data from 1992 

to 1993 was reviewed for PM2.5, PM10-2.5, and PM10, as well as for PM2.5 and PM10-2.5 dichotomous 

data for 212 site-years of information contained in the AIRS database (U.S. Environmental 

Protection Agency, 2000). The authors also observed that PM10 was frequently correlated more 

highly with PM2.5 than with PM10-2.5. The authors noted that PM2.5 constitutes a large fraction 

of PM10 and that this is the likely reason for the strong agreement between PM2.5 and PM10. 

Similar observations were made by Keywood et al. (1999) in six Australian cities. The authors 

reported that PM10 was more highly correlated with PM2.5 than with coarse PM (PM10-2.5) 

suggesting that “variability in PM10 is dominated by variability in PM2.5.” 

Lippmann et al. (2000) examined the site-to-site temporal correlations in Detroit (1981 to 

1994) and found the ranking of median site-to-site correlations was O3 (0.83), PM10 (0.78), TSP 

(0.71), NO2 (0.70), CO (0.50), and SO2 (0.49). The authors explain that O3 and a fraction of TSP 

and PM10 (e.g., sulfate) are secondary pollutants that would tend to be distributed more 

uniformly spatially within the city than primary pollutants such as CO and SO2, which are more 

likely to be influenced by local emission sources. Lippman et al. (2000) concluded “. . . spatial 

uniformity of pollutants may be due to area-wide sources, or to transport (e.g., advection) of 

fairly stable pollutants into the urban area from upwind sources. Relative spatial uniformity of 

pollutants would therefore vary from city to city or region to region.” 

Goswami et al. (2002) used data collected at outdoor monitors of homes in a large 

exposure study in Seattle, WA to analyze the spatial variability of outdoor PM2.5 concentrations. 

The day-to-day variability between sites was 10 times higher than the spatial variability between 

sites. However, differences between sites was sufficient to potentially contribute to 

measurement error. An examination of the spatial characteristics of the monitoring sites showed 

that the most representative monitoring sites were located at elevations of 80 to 120 m above sea 

level and at distances of 100 to 300 m from the nearest arterial road. 

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5.3.3.2.2 Physical Factors Affecting Indoor Microenvironmental Particulate Matter 

Concentrations 

Several physical factors can affect ambient particle concentrations in the indoor 

microenvironment, including air exchange, penetration, and particle deposition. Combined, 

these factors are critical variables that describe ambient particle dynamics in the indoor 

microenvironments and, to a large degree, significantly affect an individual’s personal exposure 

to ambient particles while indoors. The relationship between the concentrations of ambient 

particles outdoors, C, and ambient particles that have infiltrated indoors, Cai, is given by 

(5-14) 

where P is the penetration factor; a is the air exchange rate; and k is the particle deposition rate. 

(As discussed in Section 5.3.2.3.1, use of this model assumes equilibrium conditions and 

assumes that all variables remain constant.) Particle penetration is a dimensionless quantity that 

describes the fraction of ambient particles that effectively penetrates the building shell. “Air 

exchange” is a term used to describe the rate at which the indoor air in a building or residence is 

replaced by outdoor air. The dominant processes governing particle penetration are air exchange 

and deposition of particles as they traverse through cracks and crevices and other routes of entry 

into the building. Although air exchange rates have been measured in numerous studies, very 

few field data existed prior to 1996 to determine size-dependent penetration factors and particle 

deposition rates. All three parameters (P, a, and k) vary substantially depending on building 

type, region of the country, and season. In the past several years, researchers have made 

significant advancements in understanding the relationship between particle size and penetration 

factors and particle deposition rates. This section highlights studies that have been conducted to 

better understand physical factors affecting indoor particle dynamics. 

Air Exchange Rates 

The air exchange rate, a, in a residence varies depending on a variety of factors, including 

geographical location, age of the building, the extent to which window and doors are open, and 

season. Murray and Burmaster (1995) used measured values of a from households throughout 

the United States to describe empirical distributions and to estimate univariate parametric 

5-71
probability distributions of air exchange rates. Figure 5-6 shows the results classified by season 

and region. In general, a is highest in the warmest region and increases from the coldest to the 

warmest region during all seasons. Air exchange rates also are quite variable within and 

between seasons, as well as between regions (Figure 5-6). Data from the warmest region in 

summer should be viewed cautiously, because many of the measurements were made in southern 

California in July when windows were more likely to be open than in other areas of the country 

where air-conditioning is used. Use of air-conditioning generally results in lowering air 

exchange rates. In a separate analysis of these data, Koontz and Rector (1995) suggested that a 

conservative estimate for air exchange in residential settings would be 0.18 h-1 (10th percentile) 

and a typical air exchange would be 0.45 h_1 (50th percentile). 

Figure 5-6. Air exchange rates measured in homes throughout the United States. 

Climatic regions are based on heating-degree days: coldest region $ 7000, 

colder region = 5500 to 6999, warmer region = 2500 to 4999, and warmest 

region # 2500 heating-degree days. 

Source: Adapted from Murray and Burmaster (1995). 

5-72 
These data provide reasonable experimental evidence that a varies by season in locations 

with distinct seasons. As a result, infiltration of ambient particles may be more efficient during 

warmer seasons when windows are likely to be opened more frequently and air exchange rates 

are higher. This suggests that the fraction of ambient particles present in the indoor 

microenvironment would be greater during warmer seasons than colder seasons. For example, 

in a study conducted in Boston, MA, participants living in non-air-conditioned homes kept the 

windows closed except during the summer (Long et al., 2000). This resulted in higher and more 

variable air exchange rates in summer than during any other season (Figure 5-7). During 

nighttime periods when indoor sources are negligible, the indoor/outdoor concentration ratio or 

infiltration factor may be used to determine the relative contribution of ambient particles in the 

indoor microenvironment. Particle data collected during this study were used to determine the 

indoor/outdoor concentration ratios by particle size (Figure 5-8). For these nine homes in 

Boston the fraction of ambient particles penetrating indoors was higher during summer when air 

exchange rates were higher than during fall when air exchange rates were lower (Long et al., 

2001a). 

Figure 5-7. Box plots of hourly air exchange rates stratified by season in Boston, MA 

during 1998. 

Source: Long et al. (2000). 

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Figure 5-8. Geometric mean infiltration factor (indoor/outdoor ratio) for hourly 

nighttime, nonsource data for two seasons. Box plots of air exchange rates 

are shown as inserts for each plot. (Boston, 1998) 

Source: Long et al. (2001a). 

Wallace et al. (2002) conducted a year-long investigation of air rates in an occupied house 

to establish the effects of temperature, wind velocity, use of exhaust fans, and window-opening 

behavior. Air exchange rates were calculated by periodically injecting a tracer gas (SF6) into the 

return air duct and measuring the concentration in 10 indoor locations sequentially every minute 

by a gas chromatograph equipped with an electron capture detector. Temperature and relative 

humidity were measured outdoors and in multiple indoor locations. Wind speed and direction in 

the horizontal plane were also measured. Use of the thermostat-controlled attic fan was recorded 

automatically. Indoor temperatures increased from 21 °C in winter to 27 °C in summer. 

Windows were open only a few percent of the time in winter but more than half the time in 

summer. About 4,600 hour-long average air exchange rates were calculated from the measured 

tracer gas decay rates. The mean (±SD) rate was 0.65 (0.56) h-1. Tracer gas decay rates in 

different rooms were very similar, ranging only from 0.62 to 0.67 h-1, suggesting that the air in 

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the house was well mixed throughout the year. The strongest influence on air exchange rates 

was opening windows, which could increase the rate to as much as 2 h-1 for extended periods, 

and up to 3 h-1 for short periods of a few hours. The use of the attic fan also increased air 

exchange rates by amounts up to 1 h-1. Use of the furnace fan had no effect on air exchange rates 

(ducts were all inside the house). A clear effect of indoor-outdoor temperature difference, aT, 

could be discerned. However, wind speed and direction were found to have very little influence 

on air exchange rates at the house where the measurements were made. 

The air exchange rate, a, is a critical parameter in determining the fraction of ambient PM 

found indoors and the extent of build-up of indoor PM due to indoor sources. Wallace et al. 

(2002) provided a brief review of the existing literature on the theory and measurement of air 

exchange rates. Open windows and frequent opening of doors lead to higher values of a. 

However, many homes are kept closed for heating in winter and air-conditioning in summer. 

Windows may or may not be opened during moderate weather conditions. In some areas, on the 

other hand, heating or air-conditioning may not be required and ventilation by open windows 

may be more frequent. Thus, a may vary geographically with climate. However, wind speed 

and direction were found to have very little influence on air exchange rates at the house where 

measurements were made. The variation of a with aT, as shown in Figure 5-9, is given by 

a (h-1) = 0.176 + 0.0162 aT (°C). Thus, an increase of 10 °C in aT would lead to an increase 

in a of 0.164 h-1, or almost a doubling of the value of a for no indoor/outdoor temperature 

difference and no open windows or forced ventilation. 

The observation of a minimal effect of wind speed on a is an important finding. If a were 

strongly dependent on wind speed, especially at low wind speeds, there might be a correlation 

between the indoor-generated PM found indoors and the ambient concentration outdoors. Such a 

correlation could lead to a confounding of the relationship of ambient PM with health outcomes 

by indoor-generated PM. Wallace et al. (2002) suggested that “the generally tighter construction 

of homes and the use of vapor barriers may have reduced the effect of wind speed and direction 

on residential air change rates compared to earlier studies.” 

Wind speed might be expected to have a larger effect on a in a home with open windows. 

Under conditions of large a, the ambient infiltrated indoors PM concentration will be a large 

fraction of the outdoor PM and the two concentrations will be highly correlated. However, the 

indoor-generated PM concentration will be kept low by the high a, making a significant 

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Figure 5-9. Regression of air exchange rate on absolute indoor-outdoor temperature 

difference. Overnight values (midnight-8 a.m.) in winter (January-March 

2000). a (h-1) = 0.176 (0.011 SE) + 0.0164 (0.0005) ÎT C° (N = 183, R2

 = 0.82). 

correlation between ambient PM concentrations and indoor-generated PM concentrations 

unlikely. The observed lack of a strong wind effect on a in closed houses (Wallace et al., 2002; 

Howard-Reed et al., 2002) and the observed lack of correlation between ambient PM 

concentrations and indoor-generated PM concentrations indicate that the possibility of 

correlation of ambient and indoor-generated PM concentrations can be discounted. 

Particle Deposition Rates and Penetration Factors 

Physical factors affecting indoor particle concentrations including particle deposition rates, 

k, and penetration factors, P, are possibly the most uncertain and variable quantities. Although k 

can be modeled with some success, direct measurements are difficult, and results often vary 

from study to study. Particle deposition rates vary considerably depending on particle size 

because of the viscous drag of air on the particles hindering their movement to varying degrees. 

The nature and composition of particles also affect deposition rates. Surface properties of 

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particles, such as their electrostatic properties, can have a significant influence on deposition 

rates. In addition, thermophoresis can also affect k, but probably to a lesser degree in the indoor 

microenvironment, because temperatures generally vary over a small range. Combined, these 

effects can produce order of magnitude variations in k between particles of different size and, in 

the case of electrophoresis and thermophoresis, particles of the same size. 

Particle penetration efficiency into indoor microenvironments depends on particle size and 

air exchange rates. Penetration varies with particle size because of the size-dependent deposition 

of particles caused by impaction, interception, and diffusion of particles onto surfaces as they 

traverse through cracks and crevices. Penetration also is affected by air exchange rates. When 

air exchange rates are high, P approaches unity because the majority of ambient particles have 

less interaction with the building shell. In contrast, when air exchange rates are low, P is 

governed by particle deposition as particles travel through cracks and crevices. 

Significant advancements have been made in the past few years to better characterize 

particle deposition rates and penetration factors. Several new studies, including two in which 

semi-continuous measurements of size distributions were measured indoors and outdoors, have 

produced new information on these quantities, which are key to understanding the contributions 

of ambient PM to indoor PM concentrations (Equation 5-7). 

Studies involving semi-continuous measurements of indoor and outdoor particle size 

distributions have been used to estimate k and P as a function of particle size (Vette et al., 2001; 

Long et al., 2001a; Abt et al., 2000b). These studies each demonstrated that the indoor/outdoor 

concentration ratios (Cai/C in Equation 5-9) were highest for accumulation mode particles and 

lowest for ultrafine and coarse-mode particles. Various approaches were used to estimate sizespecific values for k and P. Vette et al. (2001) and Abt et al. (2000b) estimated k by measuring 

the decay of particles at times when indoor levels were significantly elevated. Vette et al. (2001) 

estimated P using measured values of k and indoor/outdoor particle measurements during 

nonsource nighttime periods. Long et al. (2001a) used a physical-statistical model, based on 

Equation 5-12, to estimate k and P during nonsource nighttime periods. The results for k 

reported by Long et al. (2001a) and Abt et al. (2000b) are compared with other studies in 

Figure 5-10. Although not shown in Figure 5-10, the results for k obtained by Vette et al. (2001) 

were similar to the values of k reported by Abt et al. (2000b) for particle sizes up to 1 μm. 

Results for P by Long et al. (2001a) showed that penetration was highest for accumulation-mode 

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Figure 5-10. Comparison of deposition rates from Long et al. (2001a) with literature 

values (from Abt et al., 2000b). Error bars represent standard deviations 

for same-study estimates. 

Source: Adapted from Abt et al. (2000b); Long et al. (2001a). 

particles and decreased substantially for coarse-mode particles (Figure 5-11). The results for 

P reported by Vette et al. (2001) show similar trends, but were lower than those reported by 

Long et al. (2001a). This is likely because of the lower air exchange rates in the one Fresno, CA 

test home (a . 0.5 h-1; Vette et al., 2001) than in the nine test homes in Boston, MA study 

(a > 1 h-1; Long et al., 2001a). These data for P and k illustrate the role that the building shell 

may provide in increasing the concentration of particles because of indoor sources and reducing 

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Figure 5-11. Penetration efficiencies and deposition rates from models of nightly average 

data. Error bars represent standard errors. 

Source: Boston (1998). 

the concentration of indoor particles from ambient sources, especially for homes with low air 

exchange rates. 

Several other studies have investigated particle loss as a function of particle size. The 

penetration of particles across building envelopes has been modeled for several sizes of idealized 

rectangular cracks (Liu and Nazaroff, 2001). Particles of 0.1 to 1.0 μm diameter had penetration 

efficiencies near 1.0. Supermicron and ultrafine particles were removed to a greater degree by 

gravitational settling and Brownian diffusion. Thatcher et al. (2002) conducted an experimental 

study of the effects of room furnishings and air speed on particle deposition rates indoors. The 

deposition loss rate (k) increased by as much as a factor of 2.6 in going from a bare room (35 m2 

surface area) to a fully furnished room (12 m2 additional surface area) with the greatest increase 

seen for the smallest particles. Air speed increases from < 5 to 19 cm/s enhanced the deposition 

rates by factors of 1.3 to 2.4, with greater effects on large particles than small particles. The 

5-79
authors stated that, “The significant effect of particle size and room conditions on deposition loss 

rates argues against using a single first-order loss-rate coefficient to represent deposition for 

integrated mass measurements (PM2.5 or PM10).” Riley et al. (2002) have modeled the 

infiltration of particles into two building types: offices and residences. They developed 

representative values of P, k, and ventilation-system filter efficiencies for particle sizes from 

0.001 to 10 μm. They then used a typical rural and urban outdoor size distribution and 

calculated predicted indoor concentrations for number, surface area, and volume distributions. 

Compositional Differences Between Indoor-Generated and Ambient Particulate Matter 

Wilson et al. (2000) discussed the differences in composition between particles from 

indoor and outdoor sources. They noted that, because of the difficulty in separating indoor PM 

into ambient and nonambient PM, there is little direct experimental information on the 

composition differences between the two. Although experimental data are limited, Wilson et al. 

(2000) suggested the following: 

“Photochemistry is significantly reduced indoors; therefore, most secondary sulfate 

[H2SO4, NH4HSO4, and (NH4)2SO4] and nitrate (NH4NO3) found indoors come from 

ambient sources. Primary organic emissions from incomplete combustion may be 

similar, regardless of the source. However, atmospheric reactions of polyaromatic 

hydrocarbons and other organic compounds produce highly oxygenated and nitrated 

products, so these species are also of ambient origin. Gasoline, diesel fuel, and vehicle 

lubricating oil all contain naturally present metals or metal additives. Coal and heavy 

fuel oil also contain more metals and nonmetals, such as selenium and arsenic, than do 

materials such as wood or kerosene burned inside homes. Environmental tobacco 

smoke (ETS), however, with its many toxic components, is primarily an indoorgenerated pollutant.” 

Particles generated indoors may have different chemical and physical properties than those 

generated by anthropogenic ambient sources. Siegmann et al. (1999) have demonstrated that 

elemental carbon in soot particles generated indoors has different properties than in those 

generated outdoors by automotive or diesel engines. In the United States, combustion-product 

PM in the ambient/outdoor air generally is produced by burning fossil fuels (e.g., coal, gasoline, 

fuel oil) and wood, whereas combustion-product PM from indoor sources is mainly produced by 

biomass burning (e.g., tobacco, wood, foods). However, some indoor sources of PM (such as 

cigarette smoking, meat cooking, and coal burning) occur both indoors and outdoors and may 

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constitute an identifiable portion of measured ambient PM (Cha et al., 1996; Kleeman and Cass, 

1998). 

Indoor Air Chemistry 

Gas- and aerosol-phase chemical reactions in the indoor microenvironment are responsible 

for secondary particle formation and modification of existing particles. This process could be 

complex and may influence the interpretation of exposures to indoor-generated particles in 

instances when particles are generated by outdoor gases reacting with gases generated or 

released indoors to produce fresh particles. For example, homogeneous gas phase reactions 

involving ozone and terpenes (specifically d-limonene, "-terpinene, and "-pinene) have been 

identified as an important source of submicron particles (Weschler and Shields, 1999). Terpenes 

are present in several commonly available household cleaning products, and d-limonene has 

been identified in more than 50% of the buildings monitored in the Building Assessment and 

Survey Evaluation (BASE) study (Hadwen et al., 1997). Long et al. (2000) found that when 

PineSol (primary ingredient is "-pinene) was used indoors, indoor PM2.5 mass concentrations 

increased from 11 to 32 μg/m3

 (indoor O3 concentrations unknown, but ambient O3 levels were 

44 to 48 ppb). Similarly, a 10-fold increase in number counts of 0.1 to 0.2 μm particles was 

observed in an experimental office containing supplemental d-limonene and normally 

encountered indoor O3 concentrations (< 5 to 45 ppb), resulting in an average increase in particle 

mass concentration of 2.5 to 5.5 μg/m3

 (Weschler and Shields, 1999). Ozone appears to be the 

limiting reagent, because particle number concentration varied proportionally to O3 

concentrations (Weschler and Shields, 1999). Other studies showed similar results (e.g., Jang 

and Kamens, 1999; Wainman et al., 2000). Such particles, if toxic, would represent an increased 

health risk due to ambient air pollution. However, the concentration would depend on the 

ambient O3 concentration, the O3 infiltration factor, and the indoor generation rate of terpenes. 

It seems unlikely that the concentration of the resulting particles would be correlated with 

ambient PM on an individual or population basis. Therefore, it seems more appropriate to 

consider indoor-reaction particles as part of nonambient exposure. However, since ambient O3 

and ambient PM2.5 are correlated in the summertime, indoor-reaction particles due to O3-terpene 

reactions might be correlated with outdoor PM2.5 mass. 

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Indoor Sources of Particles 

The major sources of indoor PM in nonsmoking residences and buildings include 

suspension of PM from bulk material, cooking, cleaning, and the use of combustion devices such 

as stoves and kerosene heaters. Human and pet activities also lead to PM detritus production 

(from tracked-in soil, fabrics, skin and hair, home furnishings, etc.) that is found ubiquitously in 

house dust deposited on floors and other interior surfaces. House dust and lint particles may be 

resuspended indoors by agitation (cleaning) and turbulence (HVAC systems, human activities, 

etc.). Ambient particles that have infiltrated into the indoor microenvironments also may be 

resuspended after deposition to indoor surfaces. Typically, resuspension of particles from any 

source involves coarse particles (> 1 μm); particles with smaller diameters are not resuspended 

efficiently. On the other hand, cooking produces both fine- and coarse-mode particles, whereas 

combustion sources typically produce ultrafine particles. 

Environmental tobacco smoke (ETS) is also a major indoor source of PM. It is, however, 

beyond the scope of this chapter to review the extensive literature on ETS. A number of articles 

provide source-strength information for cigarette or cigar smoking (e.g., Daisey et al. [1998] and 

Nelson et al. [1998]). 

A study conducted on two homes in the Boston metropolitan area (Abt et al., 2000a) 

showed that indoor PM sources predominate when air exchange rates were < 1 h!1

, and outdoor 

sources predominate when air exchange rates were $ 2 h!1

. The authors attributed this to the fact 

that when air exchange rates were low (< 1 h!1

), particles released from indoor sources tend to 

accumulate, because particle deposition is the mechanism governing particle decay and not air 

exchange. Particle deposition rates are generally < 1 h!1

, especially for accumulation-mode 

particles. When air exchange rates were higher ($ 2 h!1

), infiltration of ambient aerosols and 

exfiltration of indoor-generated aerosols occur more rapidly, reducing the effect of indoor 

sources on indoor particle levels. The study also confirmed previous findings that the major 

indoor sources of PM are cooking, cleaning, and human activity. The authors discussed the size 

characteristics of these ubiquitous sources as follows: 

“The size of the particles generated by these activities reflected their formation 

processes. Combustion processes (oven cooking, toasting, and barbecuing) 

produced fine particles and mechanical processes (sauteing, frying, cleaning, 

and movement of people) generated coarse particles. These activities increased 

particle concentrations by many orders of magnitude higher than outdoor levels 

and altered indoor size distributions.” (Abt et al., 2000a; p. 43) 

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They also noted that variability in indoor PM for all size fractions was greater (2 to 33 times 

higher) than for outdoor PM, especially for short averaging times. 

In a separate study conducted in nine nonsmoking homes in the Boston area, Long et al. 

(2000) concluded that the predominant source of indoor fine particles was infiltration of outdoor 

particles and that cooking activities were the only other significant source of fine particles. 

Coarse particles, however, had several indoor sources such as cooking, cleaning, and various 

indoor activities. This study also concluded that more than 50% of the particles (by volume) 

generated during indoor events were ultrafine particles. Events that elevated indoor particle 

levels were found to be brief, intermittent, and highly variable, thus requiring the use of 

continuous instrumentation for their characterization. Because the concentration of ultrafine 

particles is greater near the source (they grow in size into the accumulation mode as they age), 

the personal cloud for ultrafine particles may be higher than for accumulation mode particles if 

the person is nearer the source than the indoor monitor. Table 5-11 provides information on the 

mean volume mean diameter (VMD) for various types of indoor particle sources. The 

differences in mean VMD confirm the clear separation of source types and suggest that there 

is very little resuspension of accumulation-mode PM. In addition, measurements of OC and EC 

indicated that OC had significant indoor sources whereas EC was primarily of ambient origin. 

Vette et al. (2001) found that resuspension was a significant indoor source of particles 

> 1 μm. Concentrations of fine particles were not affected by resuspension. Figure 5-12 shows

the diurnal variability in the indoor/outdoor aerosol concentration ratio from an unoccupied 

residence in Fresno, CA. The study was conducted in the absence of common indoor particle 

sources such as cooking and cleaning. The data in Figure 5-12 show the mean indoor/outdoor 

concentration ratio for particles > 1 μm increased dramatically during daytime hours. This 

pattern was consistent with indoor human activity levels. In contrast, the mean indoor/outdoor 

concentration ratio for particles < 1 μm (ultrafine and accumulation-mode particles) remain 

fairly constant during both day and night. 

Wallace and Howard-Reed (2002) used three instruments (SMPS, APS, and Climet) to 

measure ultrafine, fine, and coarse particles in an inhabited residence for 18 months. They 

confirmed the observations of Abt et al. (2000a) and Long et al. (2000) that indoor sources 

primarily generate ultrafine and coarse particles. Wallace and Howard-Reed reported that 

“Indoor sources affecting ultrafine particle concentrations were observed 22% of the time, and 

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TABLE 5-11. VOLUME MEAN DIAMETER (VMD) AND MAXIMUM PM2.5 

CONCENTRATIONS OF INDOOR PARTICLE SOURCES a,b 

Size Statistics PM2.5 

Indoor Activity Background a,e Maximum Concentration c,d

Mean VMD Mean VMD Mean SD 

Particle Source n (μm) (μm) (μg/m3) (μg/m3) 

Cooking

 Baking (Electric) 8 0.189f 0.221f 14.8 7.4

 Baking (Gas) 24 0.107f 0.224f 101.2 184.9

 Toasting 23 0.138f 0.222f 54.9 119.7

 Broiling 4 0.114f 0.236f 29.3 43.4

 Sautéing 13 0.184f

, 3.48g 0.223f, 2.93g 65.6 95.4

 Stir-Frying 3 0.135f 0.277f 37.2 31.4

 Frying 20 0.173f 0.223f 40.5 43.2

 Barbecuing 2 0.159f 0.205f 14.8 5.2 

Cleaning

 Dusting 11 5.38g 3.53g 22.6 22.6

 Vacuuming 10 3.86g 2.79g 6.5 3.9

 Cleaning with Pine Sol 5 0.097f 0.238f 11 10.2 

General Activities

 Walking Vigorously (w/Carpet) 15 3.96g 3.18g 12 9.1

 Sampling w/Carpet 52 4.25g 2.63g 8 6.6

 Sampling w/o Carpet 26 4.28g 2.93g 4.8 3

 Burning Candles 7 0.311f 0.224f 28 18 

Notes:

a All concentration data corrected for background particle levels.

b Includes only individual particle events that were unique for a given time period and could be detected above

 background particle levels. 

c PM concentrations in μg/m3

. d Maximum concentrations computed from 5-min data for each activity. 

e Background data are for time periods immediately prior to the indoor event. 

f Size statistics calculated for PV0.02-0.5 using SMPS data. g Size statistics calculated for PV0.7-10 using APS data. 

Source: Long et al. (2000). 

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Figure 5-12. Mean hourly indoor/outdoor particle concentration ratio from an 

unoccupied residence in Fresno, CA during spring 1999. 

Source: Vette et al. (2001). 

sources affecting fine and coarse particle concentrations were observed 12 and 15% of the time, 

respectively . . . Indoor sources, such as cooking with natural gas, and simple physical activities, 

such as walking, accounted for a majority (50-90%) of the ultrafine and coarse particle 

concentrations, whereas outdoor sources were more important for accumulation-mode particles 

between 0.1 and 1 μm in diameter.” 

5.3.3.2.3 Time-Activity Patterns 

Total exposure to PM is the sum of various microenvironmental exposures that an 

individual encounters during the day and will depend on the microenvironments occupied. 

As discussed previously, PM exposure in each microenvironment is the sum of exposures from 

ambient PM (in outdoor and indoor microenvironments), indoor-generated PM, and indoorreaction PM. In addition, there is exposure to PM generated by personal activities that is 

determined by the specific personal activities that the individual conducts while in those 

5-85
microenvironments. As mentioned before, PM exposures and their components are variable 

across the population and, thus, each are distributions rather than point estimates. A thorough 

analysis of these distributions would require a comprehensive sensitivity and uncertainty 

analysis. 

Determining microenvironments and activities that contribute significantly to human 

exposure begins with establishing human activity pattern information for the general population 

as well as for subpopulations. Personal exposure and time-activity pattern studies have shown 

that different populations have varying time-activity patterns and, accordingly, different personal 

PM exposures. Both characteristics will vary greatly as a function of age, health status, ethnic 

group, socioeconomic status, season, and region of the country. Collecting detailed time-activity 

data can be very burdensome on participants but is clearly valuable in assessing human exposure 

and microenvironments. For modeling purposes, human activity data frequently come from the 

general databases that are discussed below. 

The gathering of human activity information, often called “time-budget” data, started in the 

1920s; however, their use for exposure assessment purposes only began to be emphasized in the 

1980s. As noted earlier, many of the largest U.S. human activity databases have been 

consolidated by the EPA’s National Exposure Research Laboratory (NERL) into one 

comprehensive database (CHAD) containing more than 22,000 person-days of 24-h activity 

(Glen et al., 1997; McCurdy et al., 2000). The information in CHAD is accessible for 

constructing population cohorts of people with diverse characteristics that are useful for analysis 

and modeling (McCurdy, 2000). See Table 5-2 for a summary listing of human activity studies 

in CHAD. Most of the databases in CHAD are also available elsewhere, including the National 

Human Activity Pattern Survey (NHAPS), California’s Air Resources Board (CARB), and the 

University of Michigan’s Institute for Survey Research data sets. 

Although CHAD provides a very valuable resource for time and location data, there is little 

information on PM-generating personal activities. In addition, very few of the time-activity 

studies have collected longitudinal data within a season or over multiple seasons. Such 

longitudinal data are important in understanding potential variability in activities and how they 

affect correlations between PM exposure and ambient site measurements for both total PM and 

PM of ambient origin. 

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5.3.3.3 Effect of Ambient Sources on Exposures to Particulate Matter 

Different sources may generate ambient PM with different aerodynamic and chemical 

characteristics, which may, in turn, result in different health responses. Thus, to fully understand 

the relationship between PM exposure and health outcomes, exposure from different sources 

should be identified and quantified. Source apportionment techniques provide a method for 

determining personal exposure to PM from specific sources. Daily contributions from sources 

that have no indoor component can be used as tracers to generate exposure estimates for ambient 

PM of similar aerodynamic size or used directly as exposure surrogates in epidemiological 

analyses. The most recent EPA PM Research Needs Document (U. S. Environmental Protection 

Agency, 1998) recommended using source apportionment techniques to determine daily time 

series of source categories for use in community, time-series epidemiology. 

A number of epidemiologic studies (discussed more fully in Chapter 8) have evaluated 

relationships between health outcomes and sources of PM determined from measurements at a 

community monitor. These studies suggest the importance of examining sources and 

constituents of indoor, outdoor, and personal PM. For example, Özkaynak and Thurston (1987) 

evaluated the relationship between PM sources and mortality in 36 Standard Metropolitan 

Statistical Areas (SMSAs). Particulate matter samples from the EPA’s Inhalable Particle (IP) 

Network were analyzed for SO4

2!

 and NO3 

!

 by automated colorimetry, and elemental 

composition was determined with X-ray fluorescence (XRF). Mass concentrations from five PM 

source categories were determined from multiple regression of absolute factor scores on the 

mass concentration: (1) resuspended soil, (2) auto exhaust, (3) oil combustion, (4) metals, and 

(5) coal combustion. 

In another study, Mar et al. (2000) applied factor analysis to evaluate the relationship 

between PM composition (and gaseous pollutants) in Phoenix, AZ. In addition to daily averages 

of PM2.5 elements from XRF analysis, they included in their analyses OC and EC in PM2.5 and 

gaseous species emitted by combustion sources (CO, NO2, and SO2). They identified five source 

factors classified as (1) motor vehicles, (2) resuspended soil, (3) vegetative burning, (4) local 

SO2, and (5) regional sulfate. Additionally, Laden et al. (2000) applied specific rotation factor 

analysis to XRF PM composition data from six eastern cities (Ferris et al., 1979). Fine PM was 

regressed on the recentered scores to determine the daily source contributions. Three main 

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sources were identified: (1) resuspended soil (Si), (2) motor vehicle (Pb), and (3) coal 

combustion (Se). 

Source apportionment or receptor modeling has been applied to the personal exposure data 

to understand the relationship between personal and ambient sources of PM. Application of 

source apportionment to ambient, indoor, and personal PM composition data is especially useful 

in sorting out the effects of particle size and composition. If a sufficient number of samples are 

analyzed with sufficient compositional detail, it is possible to use statistical techniques to derive 

source category signatures, to identify indoor and outdoor source categories, and to estimate 

their contribution to indoor and personal PM. 

Positive Matrix Factorization (PMF) has been applied to the PTEAM database by 

Yakovleva et al. (1999). The authors utilized mass and XRF elemental composition data from 

indoor and outdoor PM2.5 and personal, indoor, and outdoor PM10 samples. PMF is an advance 

over ordinary factor analysis, because it allows measurements below the quantifiable limit to be 

used by weighting them according to their uncertainty. This effectively increases the number of 

species that can be used in the model. The factors used by the authors correspond to general 

source categories of PM, such as outdoor soil, resuspended indoor soil, indoor soil, personal 

activities, sea salt, motor vehicles, nonferrous metal smelters, and secondary sulfates. PMF, by 

identifying the various source factors and apportioning them among the different monitor 

locations (personal, indoor, and outdoor), was able to estimate the contribution of resuspended 

indoor dust to the personal cloud (15% from indoor soil and 30% from resuspended indoor soil). 

Factor scores for these items then were used in a regression analysis to estimate personal 

exposures (Yakovleva et al., 1999). 

The most important contributors to PM10 personal exposure were indoor soil, resuspended 

indoor soil, and personal activities; these accounted for approximately 60% of the mass 

(Yakovleva et al., 1999). Collectively, they include personal-cloud PM, smoking, cooking, and 

vacuuming. For both PM2.5 and PM10, secondary sulfate and nonferrous metal operations 

accounted for another 25% of PM mass. Motor vehicle exhausts, especially from vehicles 

started inside attached garages, accounted for another 10% of PM mass. The PTEAM study was 

conducted in Riverside, CA in the fall of 1990. Yakovleva et al. (1999) cautioned that their 

results may not apply to other geographic areas, seasons of the year, or weather conditions. 

5-88
Simultaneous measurement of personal (PM10) and outdoor measurements (PM2.5 

and PM10) were evaluated as a three-way problem with PMF, which allowed for differentiation 

of source categories based on their variation in time and type of sample, as well as their variation 

in composition. By using this technique, three sources of coarse-mode, soil-type PM were 

identified. One was associated with ambient soil, one with indoor soil dispersed throughout the 

house, and one with soil resulting from the personal activity of the subject. 

Two other source apportionment models have been applied to ambient measurement data 

and can be used for the personal exposure studies. The effective variance weighted Chemical 

Mass Balance (CMB) receptor model (Watson et al., 1984, 1990, 1991) solves a set of linear 

equations that incorporate the uncertainty in the sample and source composition. CMB requires 

the composition of each potential source of PM and the uncertainty for the sources and ambient 

measurements. Source apportionment with CMB can be conducted on individual samples; 

however, composition of each of the sources of PM must be known. An additional source 

apportionment model, UNMIX (Henry et al., 1994) is a multivariate source apportionment 

model. UNMIX is similar to PMF, but does not explicitly use the measurement uncertainties. 

Because measurement uncertainties are not used, only species above the detection limit are 

evaluated in the model. UNMIX provides the number of sources and source contributions and 

requires a similar number of observations as PMF. 

The Yakovleva et al. (1999) study demonstrated that source apportionment techniques also 

could be very useful in determining parameters needed for exposure models and for determining 

exposure to ambient PM. Exposure information, similar to that obtained in the PTEAM study, 

but including other PM components useful for definition of other source categories (e.g., OC, 

EC; organic tracers for EC from diesel vehicle exhaust, gasoline vehicle exhaust, and wood 

combustion; nitrate; Na, Mg, and other metal tracers; and gas-phase pollutants) would be useful 

as demonstrated in the use of EC/OC and gas-phase pollutants by Mar et al. (2000). 

5.3.3.4 Correlations of Particulate Matter with Other Pollutants 

Correlations between ambient concentrations and between ambient concentrations and 

personal exposures for PM and other pollutants are of importance in understanding possible 

confounding in epidemiological studies and are discussed more fully in Chapter 9. Available 

information from exposure studies is presented in this section. Several epidemiologic studies 

5-89
PM

have included the gaseous pollutants CO, NO2, SO2, and O3 along with PM10 or PM2.5 in the 

analysis of the statistical association of health responses with pollutants. In a recent study, 

the personal exposure to O3 and NO2 was determined, as well as that to PM2.5 and PM10-2.5 for 

a cohort of 15 elderly subjects in Baltimore, MD, although measured personal exposures 

to O3, NO2, and SO2 were below their respective level of detection (LOD) for 70% of the 

samples. Spearman correlations for 14 subjects in summer and 14 subjects in winter are given 

in Table 5-12 for relationships between personal PM2.5 and ambient concentrations of PM2.5, 

10-2.5, O3, and NO2. In contrast to ambient concentrations, neither personal exposure to 

total PM2.5 nor to ambient PM2.5 was correlated significantly with personal exposures to the 

co-pollutants, nonambient PM2.5 and ambient PM10-2.5, O3, NO2, and SO2. Personal-ambient 

associations for PM10-2.5, O3, NO2, and SO2 were similarly weak and insignificant. Based on 

these results, Sarnat et al. (2000) concluded that the potential for confounding of PM2.5 

by O3, NO2, or PM10-2.5 appears to be limited, because, despite significant correlations observed 

among ambient pollutant concentrations, the correlations among personal exposures were low. 

Sarnat et al. (2001) further evaluated the role of gaseous pollutants in PM epidemiology by 

extending the measurements taken on the earlier adult cohort of 20 individuals in Baltimore, MD 

through inclusion of additional PM and gaseous pollutant measurements that were collected 

during the same 1998 to 1999 period from 15 individuals with COPD and from 21 children. 

Twenty-four-hour average personal exposures for PM2.5, O3, SO2 and NO2, and corresponding 

ambient concentrations for PM2.5, O3, SO2, NO2 and CO for all 56 subjects were collected over 

12 consecutive days. Results from correlation and regression analysis of the personal and 

ambient data showed that personal PM2.5 and personal gaseous pollutant exposures were 

generally not correlated. The analysis also showed that ambient PM2.5 concentrations had 

significant associations with personal PM2.5 exposures in both seasons. On the other hand, 

ambient gaseous pollutant concentrations were not correlated with their corresponding personal 

exposure concentrations. However, ambient gaseous concentrations were found to be strongly 

associated with personal PM2.5 exposures, suggesting that ambient gaseous concentrations 

for O3, NO2, and SO2 are acting as surrogates, as opposed to confounders, of PM2.5 in the 

estimation of PM health effects based on multipollutant models. This study did not measure 

personal CO and did not find a significant association between summertime ambient CO and 

personal PM2.5 (a significant wintertime association, however, was found). 

5-90
TABLE 5-12. CORRELATIONS BETWEEN PERSONAL PM2.5 AND AMBIENT 

POLLUTANT CONCENTRATIONS1 

Personal PM2.5 of Ambient 

Personal PM2.5 vs. Ambient Origin vs. Ambient 

SUMMER 

Subject 

SA1 

PM2.5 

0.55 

O3 

0.15 

NO2 

0.38 

PM2.5-10 

!0.12 

O3 

0.27 

NO2 

0.71 

PM10-2.5 

0.15 

SA2 0.85 0.31 0.66 0.57 0.21 0.64 0.68 

SA5 0.89 0.18 0.82 0.64 0.33 0.81 0.79 

SB1 0.65 0.40 !0.15 0.38 0.89 !0.74 !0.03 

SB2 !0.21 !0.62 0.81 0.15 0.26 0.08 0.33 

SB3 0.82 0.55 !0.14 !0.04 0.52 !0.20 0.00 

SB4 0.73 0.62 !0.34 !0.12 0.45 !0.29 !0.14 

SB5 0.73 0.45 !0.42 0.23 0.36 !0.48 0.33 

SB6 0.53 0.15 !0.38 0.12 !0.03 !0.57 0.32 

SC1 0.95 0.78 0.66 0.65 0.83 0.63 0.57 

SC2 0.78 0.68 0.36 0.51 0.66 0.65 0.76 

SC3 0.85 0.78 0.73 0.68 0.69 0.71 0.80 

SC4 0.78 0.66 0.59 0.70 0.50 0.50 0.51 

SC5 0.55 0.51 0.32 0.43 0.34 0.33 0.27 

WINTER WA1 0.22 !0.18 !0.26 !0.05 !0.78 !0.04 !0.24 

WA2 !0.38 !0.07 !0.36 !0.70 !0.15 !0.15 0.02 

WA4 !0.18 0.67 !0.22 !0.29 !0.33 0.20 0.00 

WA5 0.22 !0.43 0.61 0.50 !0.72 !0.09 0.40 

WB1 0.80 !0.84 0.77 0.41 !0.87 0.53 0.66 

WB2 0.62 !0.32 0.59 0.09 !0.76 0.59 0.59 

WB3 0.55 !0.45 0.62 0.04 !0.77 0.56 0.60 

WB4 !0.12 !0.01 0.34 !0.10 !0.80 0.68 0.48 

WC1 0.74 !0.62 !0.15 0.44 !0.64 0.02 0.69 

WC2 0.79 !0.88 0.17 0.77 !0.87 0.25 0.71 

WC3 0.28 !0.42 0.03 0.57 !0.77 0.30 !0.45 

WC4 0.19 !0.84 0.50 0.45 !0.72 0.22 0.67 

WC5 0.81 !0.62 0.08 0.81 !0.76 0.05 0.42 

WC6 0.01 !0.03 0.65 0.37 !0.75 0.19 !0.45 

Median Summer 0.76 0.48 0.37 0.41 0.41 0.42 0.33 

Median Winter 0.25 !0.43 0.26 0.39 !0.76 0.21 0.45 

1

Correlations represent Spearman’s r values; italicized values indicate significance at the p < 0.05 level. 

Source: Sarnat et al. (2000). 

5-91 
Personal EC and SO4

2!

 were also measured during the winter for the cohort of COPD 

2! patients only. The analysis of this subset of the data showed that personal SO4 was 

significantly and negatively associated with ambient O3 and SO2, and that personal EC was 

significantly associated with ambient O3, NO2, and CO. The authors interpret these findings as 

suggesting that O3 is primarily a surrogate for secondary particle exposures, whereas ambient 

CO and NO2 are primarily surrogates for particles from traffic. Sarnat et al. (2001) cautioned 

that these findings were from only one location and various physical and personal factors, such 

as ventilation, time spent outdoors, and household characteristics, could affect the strength of the 

reported associations for certain individuals and cohorts even though the qualitative results 

found were unlikely to change. 

PM

A newly developed Roll-Around System (RAS) was used to evaluate the hourly 

relationship between gaseous pollutants (CO, O3, NO2, SO2, and VOCs) and PM (Chang et al., 

2000). Exposures were characterized over a 15-day period for the summer and winter in 

Baltimore, MD based on scripted activities to simulate activities performed by older adults 

(65+ years of age). Spearman rank correlations were reported for PM2.5, O3, CO, and toluene for 

both the summer and winter. The correlations are given for each microenvironment in 

Table 5-13: indoor residence, indoor other, outdoor near roadway, outdoor away from road, 

and in vehicle. No significant relationships (p < 0.05) were found between hourly PM2.5 and O3. 

Significant relationships were found between hourly PM2.5 and CO in: indoor residence, winter; 

indoor other, summer and winter; and outdoor away from roadway, summer. Significant 

relationships also were found between hourly PM2.5 and toluene in: indoor residence, winter; 

indoor other, winter; and in vehicle, winter. The significant relationships between CO and PM2.5 

in the winter may be caused by reduced air exchange rates that could allow them to accumulate 

(Chang et al., 2000). Note that, although no significant correlation was found between in-vehicle 

2.5 and CO, another significant component of vehicle exhaust, toluene (Conner et al., 1995), 

was significantly correlated to PM2.5 in the winter. 

Carrer et al. (1998) presented data on the correlations among microenvironmental and 

personal PM10 exposures and concentrations and selected environmental chemicals monitored 

simultaneously (using methods not described). These chemicals were nitrogen oxides (NOx), 

carbon monoxide (CO), and total volatile organic compounds (TVOC), benzene, toluene, xylene, 

5-92
TABLE 5-13. CORRELATIONS BETWEEN HOURLY PERSONAL PM2.5 AND 

GASEOUS POLLUTANTS 

Indoor Outdoor Near Outdoor Away 

Residence Indoor Other Roadway from road In Vehicle 

N rs N rs N rs N rs N rs 

PM2.5 vs. O3

 Summer 35 0.29 16 !0.14 10 0.05 12 0.45 37 0.21

 Winter 56 0.05 37 !0.06 11 !0.28 7 0.04 34 !0.10 

PM2.5 vs. 

CO

 Summer 41 0.25 19 0.59a 13 0.14 12 0.62 46 0.23

 Winter 59 0.43a 39 0.62a 13 0.37 8 0.41 37 0.1 

PM2.5 vs. Toluene

 Summer 46 0.23 21 !0.14 14 0.26 14 0.02 48 0.12

 Winter 66 0.38a 47 0.44a 17 0.4 8 0.48 42 0.43a 

a Correlations represent Spearman’s r values; italicized values indicate significance at the p < 0.05 level. 

Source: Chang et al. (2000). 

and formaldehyde. The Kendall J correlation coefficient was used; only results significant at 

p < 0.05 are mentioned here. Significant associations were found only between the following 

pairs of substances (J shown in parentheses): personal PM10 (24 h) and NOx (0.34), CO (0.34), 

TVOC (0.18), toluene (0.19), and xylene (0.26); office PM10 and NOx (0.31); home PM10 

and NOx (0.24), CO (0.24), toulene (0.17), and xylene (0.25). Even though most of these 

chemicals are associated with motor vehicular emissions, there was no significant correlation 

between commuting PM10 and any of the substances. 

5-93
5.4 SUMMARY OF PARTICULATE MATTER CONSTITUENT DATA 

5.4.1 Introduction 

Atmospheric PM contains a number of chemical constituents that may be of significance to 

human exposure and health effects. These constituents may be either components of the ambient 

particles or bound to the surface of particles. They may be elements, inorganic species, or 

organic compounds. A limited number of studies have collected data on concentrations of 

elements, acidic aerosols, and PAHs in ambient, personal, and microenvironmental PM samples. 

However, there have not been extensive analyses of the constituents of PM in personal or 

microenvironmental samples. Data from relevant studies are summarized in this section. The 

summary does not address bacteria, bioaerosols, viruses, or fungi (e.g., Owen et al., 1992; Ren 

et al., 1999). 

5.4.2 Monitoring Studies That Address Particulate Matter Constituents 

Relevant studies published in recent years that have measured the constituents of PM in 

personal or microenvironmental samples are summarized in Tables 5-11 and 5-12 for personal 

exposure measurements of PM and microenvironmental samples, respectively. Studies that 

measured both personal and microenvironmental samples are included in Table 5-11. 

The largest database on personal, microenvironmental, and outdoor measurements of PM 

elemental concentrations is the PTEAM study (Özkaynak et al., 1996b). The results are 

highlighted in the table and discussed below. The table shows that a number of studies have 

measured concentrations of elements (by XRF), organic carbon (OC), various indicators of 

elemental carbon (EC), aerosol acidity, sulfate, ammonia, and nitrate. Additionally, a number of 

studies have measured PAHs, both indoors and outdoors. Other than the PAHs, there are few 

data on the organic constituents of PM. 

5.4.3 Key Findings 

5.4.3.1 Correlations of Personal and Indoor Concentrations with Ambient Concentrations 

of Particulate Matter Constituents 

The elemental composition of PM10 in personal samples was measured in the PTEAM 

study, the first probability-based study of personal exposure to particles. A number of important 

observations made from the PTEAM data collected in Riverside, CA are summarized by 

5-94
Özkaynak et al. (1996b). Population-weighted daytime personal exposures averaged 

150 ± 9 μg/m3 compared to concurrent indoor and outdoor concentrations of 95 ± 6 μg/m3. 

The personal exposure measurements suggested that there was a “personal cloud” of particles 

associated with personal activities. Daytime personal exposures to 14 of the 15 elements 

measured in the samples were considerably greater than concurrent indoor or outdoor 

concentrations; sulfur was the only exception. 

The PTEAM data also showed good agreement between the concentrations of the elements 

measured outdoors in the backyard of the residences with the concentrations measured at the 

central site in the community. The agreement was excellent for sulfur. Although the particle 

and element mass concentrations were higher in personal samples than for indoor or outdoor 

samples, a nonlinear mass-balance method showed that the penetration factor was nearly 1 for all 

particles and elements. 

Measurements of element concentrations in NHEXAS are similar to the PTEAM results in 

that they show elevated concentrations of arsenic (As) and lead (Pb) in personal samples relative 

to indoor and outdoor samples (Clayton et al., 1999b). The elevated concentrations of As and Pb 

were consistent with elevated levels of PM50 in personal samples (median particle exposure of 

101 μg/m3

) as compared to indoor concentrations (34.4 μg/m3). There was a strong association 

between personal and indoor concentrations and between indoor and outdoor concentrations for 

both As and Pb. However, there were no central site ambient measurements for comparison to 

the outdoor or indoor measurements at the residences. 

Manganese (Mn) concentrations were measured in PM2.5 samples collected in Toronto 

(Crump, 2000). The mean PM2.5 Mn concentrations were higher outdoors than indoors. 

However, the outdoor concentrations measured at the participants’ homes were lower than those 

measured at two fixed locations. Crump (2000) suggested that the difference in the 

concentrations may have been because the fixed locations were likely closer to high-traffic areas 

than were the participants’ homes. 

Studies of acidic aerosols and gases typically measure strong acidity (H+

), SO4

2!

, NH 4+, 

and NO!3

. The relationship between the concentrations of these ions and the relationship 

between indoor and outdoor concentrations have been addressed in a number of studies during 

which personal, microenvironmental, and outdoor samples have been collected, as shown in 

Tables 5-14 and 5-15. Key findings from these studies include the following: 

5-95
TABLE 5-14. STUDIES THAT HAVE MEASURED PARTICULATE MATTER CONSTITU

PERSONAL EXPOSURE SAMPLES

PM Constituent Study Name/Reference Study Location Population Size/No. of Samples Summary of Results Elements PTEAM/Özkaynak et al. Riverside, CA 178 adults Outdoor air was the major source for mo

(1996b) providing 70 to 100% of the observed in

12 of the 15 elements. Correlation coeffmonitoring site vs. outdoor at the residen0.5 to 0.9 for other elements (except Cu)

As and Pb NHEXAS/Clayton et al. EPA Region 5 167 samples Personal As and Pb levels higher than in

(1999b) No community ambient site for compari

Mn Pellizzari et al. (1998, Toronto 925 personal samples Mean PM2.5 Mn higher outdoors than ind

1999); Clayton et al. concentrations higher at two fixed locati(1999a); Crump (2000) homes. 

Acid Aerosol Sarnat et al. (2000) Baltimore, MD 20 adults High correlations between personal and 

Constituents measurements in summer and winter. 

Brauer et al. (1989) Boston, MA — Personal exposures to aerosol strong aci

concentrations measured at stationary sit

Suh et al. (1992) Uniontown, PA 24 children for 2 days Personal exposures to H+ and SO4 

2! lowe

higher than indoor microenvironmental l

NO3 

! higher than indoor or outdoor leve

Suh et al. (1993a,b) State College, PA 47 children Results similar to Uniontown, PA study. Suh et al. (1994) Results indicate strong neutralization of Waldman and Liang Georgia and Hospital, day cares Indoor sulfate levels were 70 to 100% of (1993); Waldman et al. New Jersey ammonia levels 5- to 50-times higher tha(1990) aerosols were largely neutralized. 

PAHs Zmirou et al. (2000) Grenoble, France 38 adults Ambient air concentrations close to traff

3.5-times higher than personal exposure 

Individual Conner et al. (2001) Baltimore, MD 3 sets of indoor-outdoor-personal CCSEM was used to identify individual 

Particle Same study filters, > 2000 particles/filter by most likely sources. 

 Analyses by locations for both 

CCSEM 

Trace Elemental Landis et al. (2001) 19 days with P, I, A for P and A highly correlated for PM2.5 and  analyses by 10 elderly retirees significantly correlated for soil and trace  XRF 

Elemental Kinney et al. (2002) New York City, 46 student volunteers, 1 week in 29 PM constituents were measured.  analysis by NY summer and winter, PM2.5 Most were similar for P, I, and O, sugge

HR-ICP-MS dominant. 

5-96
TABLE 5-15. STUDIES THAT HAVE MEASURED PARTICULATE MATTER CONSTITU

MICROENVIRONMENTAL SAMPLES

PM Constituent 

Study Name/Reference Study Location Population Size/No. of Samples Summary of Results 

Acid Aerosol

 Constituents 

Jones et al. (2000) Birmingham, 

England 

12 residences Sulfate I/O ratios ranged from 0.7 to 

fractions. 

Patterson and Eatough (2000) 

Lindon, UT 1 school Ambient sulfate, SO2, nitrate, soot, an

showed strong correlations with indoambient PM2.5 mass was not a good in

exposure. 

Leaderer et al. (1999) Virginia and 

Connecticut 

232 homes The regional ambient air monitoring 

estimate of indoor and outdoor sulfat

I/O sulfate ratio of 0.74 during summconcentrations were an order of magnoutdoors. Nitrous acid levels higher 

Brauer et al. (1990) Boston, MA 11 homes Outdoor levels of H+, SO2, HNO3, an

levels in winter and summer. I/O rati

ratios of SO4 

2! indicated neutralizatio

ammonia. 

PAHs Chuang et al. (1999) Durham, NC 24 homes Measurements with continuous moni

higher indoors than outdoors. 

Dubowsky et al. (1999) 

Boston, MA 3 buildings PAHs indoors attributable to traffic, c

burning. 

Sheldon et al. (1993a,b) 

Placerville and Roseville, CA 

280 homes Mass balance model used to estimate 

sources such as smoking, wood-burni

PAHs and

 phthalates 

PTEAM/Özkaynak et al. (1996b), Sheldon et al. (1993c) 

Riverside, CA 120 homes 12-h I/O ratios for particulate-phase P

1.4 during the day and 0.64 to 0.85 duconcentrations of phthalates and the ndetectable phthalates were higher ind

5-97

Abbreviations used:

 I/O = Indoor/Outdoor.

 A = ambient. I = indoor. O = outdoor. P = personal. 
• Acid aerosol concentrations measured at the residences in the Uniontown, PA study were 

significantly different from those measured at a fixed ambient site located 16 km from the 

community. However, Leaderer et al. (1999) reported that the regional ambient air 

monitoring site in Vinton, VA provided a reasonable estimate of indoor and outdoor 

sulfate measurements during the summer at homes without tobacco combustion. 

• Approximately 75% of the fine aerosol indoors during the summer was associated with 

outdoor sources based on I/O sulfate ratios measured in the Leaderer et al. (1999) study. 

• Personal exposures to strong acidity (H+

) were lower than corresponding outdoor levels 

measured in studies by Brauer et al. (1989, 1990) and Suh et al. (1992). However, the 

personal exposure levels measured by Suh et al. (1992) were higher than the indoor 

microenvironmental levels. 

• Personal exposures to NH4

+

 and NO3 

!

 were reported by Suh et al. (1992) to be higher than 

either indoor or outdoor levels. 

Personal exposures to SO4

2!

 were also lower than corresponding outdoor levels, but higher 

than the indoor microenvironmental levels (Suh et al., 1992; 1993a,b) as shown in Table 5-16. 

The fact that the personal and indoor H+

 concentrations were substantially lower than 

outdoor concentrations suggests that a large fraction of aerosol strong acidity is neutralized by 

ammonia. Ammonia is emitted in relatively high concentrations in exhaled breath and sweat. 

The difference between indoor and outdoor H+

 concentrations in the Suh et al. (1992, 1993a,b) 

studies was also much higher than the difference for indoor and outdoor SO4

2!

, indicative of 

neutralization of the H+

. Results of the Suh et al. (1992, 1993a,b) studies also showed 

substantial interpersonal variability of H+

 concentrations that could not be explained by variation 

in outdoor concentrations. 

Similar results for ammonia were reported by Waldman and Liang (1993). They reported 

that levels of ammonia in monitored institutional settings were 10- to 50-times higher than 

outdoors and that acid aerosols were largely neutralized. Leaderer et al. (1999) reported that 

ammonia concentrations during both winter and summer in residences were an order of 

magnitude higher indoors than outdoors, consistent with results of other studies and the presence 

of sources of ammonia indoors. 

Sulfate aerosols appear to penetrate indoors effectively. Waldman et al. (1990) reported 

I/O ratios of 0.7 to 0.9 in two nursing care facilities and a day-care center. Sulfate I/O ratios 

were measured for three particle size fractions in 12 residences in Birmingham, England by 

Jones et al. (2000). The sulfate I/O ratios were 0.7 to 0.9 for PM < 1.1 μm, 0.6 to 0.8 for PM 

5-98
TABLE 5-16. SUMMARY STATISTICS FOR PERSONAL, INDOOR, AND

OUTDOOR CONCENTRATIONS OF SELECTED AEROSOL COMPONENTS IN

TWO PENNSYLVANIA COMMUNITIES

Concentration (nmol m-3) 

Number of 

Aerosol Home Type 

Samples 

(In/Out)a 

Indoor (12 h) 

GM ± GSDb 

Outdoor (24 h) 

GM ± GSDb 

Personal (12 h) 

GM ± GSDb 

State College 

NO-3 A/C Homesc 53/71 2.1 ± 2.7 1.4 ± 2.1 — 

Non-A/C 254/71 3.2 ± 2.3 1.4 ± 2.1 — 

SO4 

2! A/C Homes 56/75 61.8 ± 2.5 109.4 ± 2.4 — 

Non-A/C 

All Homesd 

259/75 

214/76 

96.7 ± 2.5 

69.1 ± 2.6 

109.4 ± 2.4 

91.0 ± 2.5 

— 

71.5 ± 2.4 

NH4 

+ All Homes 314/155 154.7 ± 2.8 104.4 ± 2.3 — 

H+ A/C Homes 28/74 4.2 ± 4.3 82.5 ± 2.6 

Non-A/C 230/74 11.2 ± 3.1 82.5 ± 2.6 

All Homese 163/75 9.1 ± 3.5 72.4 ± 2.9 18.4 ± 3.0 

Uniontown 

SO4 

2! All Homese 91/46 87.8 ± 2.1 124.9 ± 1.9 110.3 ± 1.8 

NH4 

+ All Homese 91/44 157.2 ± 2.8 139.4 ± 2.1 167.0 ± 2.0 

H+ All Homese 91/46 13.7 ± 2.5 76.6 ± 2.7 42.8 ± 2.2 

a

In/Out = Indoor samples/outdoor samples.

b

GM ± GSD = Geometric mean ± geometric standard deviation.

c

A/C Homes = Homes that had air-conditioning (A/C); this does not imply that it was on during the entire

sampling period. 

Non-A/C = Homes without air conditioning.

d

The sample size, n, for the personal monitoring = 209.

e

n = 174 for personal monitoring.

Source: Suh et al. (1992, 1993a,b). 

1.1 to 2.1 μm, and 0.7 to 0.8 for PM 2.1 to 10 μm. Suh et al. (1993b) reported that personal and 

outdoor sulfate concentrations were highly correlated, as depicted in Figure 5-13. 

Patterson and Eatough (2000) reported indoor/outdoor relationships for a number of PM2.5 

components and related species in Lindon, UT during January and February of 1997. Outdoor 

samples were collected at the Utah State Air Quality monitoring site. Indoor samples were 

collected in the adjacent Lindon Elementary School. The infiltration factors, Cai/C, given by the 

5-99
Figure 5-13. Personal versus outdoor SO4

2!

 in State College, PA. Open circles represent 

children living in air-conditioned homes; the solid line is the 1:1 line. 

Source: Suh et al. (1993b). 

slope of the regression lines (Table 5-17), were low (0.27 for sulfate and 0.12 for PM2.5) possibly 

because of removal of particles in the air heating and ventilation system. The authors concluded 

that indoor PM2.5 mass may not always be a good indicator of exposure to ambient combustion 

material due to the influence of indoor particle sources. Presumably this occurs because the 

concentrations of indoor-generated particles are not well correlated with the concentrations of 

ambient combustion particles. However, ambient sulfate, SO2, nitrate, soot, and total particle 

number displayed strong correlations with indoor exposure, presumably because these species 

have few indoor sources in the absence of indoor combustion. Ambient PM2.5 mass was not a 

good indicator of indoor PM2.5 mass exposure, presumably due to uncorrelated indoor sources 

of PM2.5 mass. 

5-100
TABLE 5-17. REGRESSION ANALYSIS OF INDOOR VERSUS OUTDOOR 

CONCENTRATIONS (units are nmol/m3

, except for soot and metals, which are μg/m3 

and absorption units/m3, respectively.)a 

Species Slope = FINF Intercept = Cig R2 

Average 

Outdoors, Co 

SO2 All Samples 0.0272 ± 0.0023 0.34 ± 0.13 0.73 38 

SO2 Day Samples 0.0233 ± 0.0037 0.75 ± 0.26 0.62 56 

SO2 Night Samples 0.0297 ± 0.0029 0.099 ± 0.075 0.82 20 

Sulfate All Samples 0.267 ± 0.024 !0.14 ± 0.48 0.7 16 

Sulfate Day Samples 0.261 ± 0.034 0.40 ± 0.66 0.71 16 

Sulfate Night Samples 0.282 ± 0.035 !0.84 ± 0.68 0.7 16 

Nitrate All Samples 0.0639 ± 0.0096 0.9 ± 1.5 0.54 134 

Nitrate Day Samples 0.097 ± 0.0096 !0.4 ± 1.4 0.88 126 

Nitrate Night Samples 0.047 ± 0.011 1.5 ± 1.8 0.44 139 

Soot Day Samples 0.43 ± 0.25 3.5 ± 1.7 0.43 6 

Soot Night Samples 0.33 ± 0.13 0.00 ± 0.55 0.69 4 

Total Acidity All Samples 0.04 ± 0.73 0.42 ± 0.23 0 0.2 

Metals All Samples 0.10 ± 0.30 0.0014 ± 0.0042 0.01 0.0042 

a

Lindon Elementary School, Lindon, UT January and February 1997. 

Source: Patterson and Eatough (2000). 

Oglesby et al. (2000) conducted a study to evaluate the validity of fixed-site fine particle 

concentration measurements as exposure surrogates for air pollution epidemiology. Using 48-h 

EXPOLIS data from Basel, Switzerland, they investigated the personal exposure/outdoor 

concentration relationships for four indicator groups: (1) PM2.5 mass, (2) sulfur and potassium 

for regional air pollution, (3) lead and bromine for traffic-related particles, and (4) calcium for 

crustal particles. The authors reported that personal exposures to PM2.5 mass were not correlated 

to corresponding home outdoor levels (n = 44, r = 0.07). In the study group reporting neither 

relevant indoor sources nor relevant activities, personal exposures and home outdoor levels of 

sulfur were highly correlated (n = 40, r = 0.85). These results are consistent with spatially 

homogeneous regional pollution and higher spatial variability of traffic and crustal materials. 

5-101
Indoor, outdoor, and personal concentrations of PM2.5 mass and a variety of PM 

constituents were measured at an 18-story retirement facility in Towson, MD for 28 24-hour 

monitoring periods during July and August 1998 (Landis et al., 2001). Indoor and outdoor 

measurements were made with a Versatile Air Pollutant Sampler (VAPS). Indoor, outdoor, and 

personal samples were made with a Personal Exposure Monitor (PEM). The VAPS 

(a dichotomous sampler) collected PM2.5 (15 L/min) and PM10-2.5 (2 L/min) while the PEM 

collected PM2.5 (2 L/min). A comparison of the VAPS and the PEM indicated that the indoor 

PEM collected much higher mass and more soil components than the indoor VAPS although the 

differences between outdoor results were smaller and not significant. These differences were 

attributed to the presence of a larger coarse particle concentration inside (and perhaps larger 

diameter particles) and either more particle bounce or a higher 50% cut point for the PEM. 

In their analysis, Landis et al. (2001) compared indoor and outdoor VAPS data, as well as 

outdoor and personal PEM data. 

As shown in Table 5-18 (PEM) and Table 5-19 (VAPS), higher correlations were found for

fine-particle components of PM2.5 and lower correlations for coarse-particle components. Like 

Patterson and Eatough (2000), Landis et al. (2001) found low infiltration factors for nitrate along 

with a reasonable correlation suggesting that fine-mode ammonium nitrate may be evaporating 

after it penetrates indoors. Neither sulfate nor nitrate had indoor sources. 

Indoor and outdoor PM2.5 and PM10 mass and chemical composition were measured in 

13 homes (2 to 4 days for each home) in the Coachella Valley, a unique desert area in southern 

California during the winter and spring of 2000 (Geller et al., 2002). Maximum infiltration of 

ambient PM would be expected during this period because the mild climate minimizes the use of 

– – heating or air conditioning. Regression analysis was used to estimate FINF and Cig. Results are 

shown in Table 5-20. The Coachella PM is generally considered to be rich in coarse PM and 

epidemiological studies have associated PM10 (Ostro et al., 1999) and estimated PM10-2.5 (Ostro 

et al., 2000) with mortality. However, the results of Geller et al. (2002) indicate that even during 

periods of high air exchange rates, indoor exposures would be dominated by PM2.5. Geller et al. 

(2002) also reported results for some chemical components of PM2.5 and PM10-2.5 as well as EC 

and OC in PM2.5. For EC, FEC 

INF = 0.74. Indoor concentrations INF = 0.74, in good agreement with F2.5 

of OC were much higher than outdoor concentrations; the average I/O ratio was 1.77. 

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c 

TABLE 5-18. MIXED MODEL ANALYSIS OF PERSONAL VERSUS 

OUTDOOR CONCENTRATIONS a 

— – – – – – – R2 – " C A N T A + N 

PM2.5 (μg/m3

) 0.82 0.46 ± 0.04 21 9.66 3.57 ± 0.80 12.8 13.2 

Sulfateb

 (μg/m3) 0.95 0.40 ± 0.02 10.2 4.08 0.1 ± 0.04 4.5 4.2 

Soilc

 (ng/m3) 0.03 0.05 ± 0.12 458 93 544 ± 95 553 637 

TEOd

 (ng/m3) 0.39 0.43 ± 0.11 165 71 99 ± 23 170 170 

– – – – a –

" (slope) and N (intercept) taken from mixed model results. A calculated as – – – – –" C C; T is measured value. 

Note good agreement between measured T and T estimated from A + N except for soil. b as (NH4)2SO4.

Soil = sum of oxides Si, Ca, Fe, and Ti (due to coarse PM in PM2.5). d TEO = trace element oxides (mostly fine PM components). 

Source: Landis et al. (2001). 

TABLE 5-19. REGRESSION ANALYSIS OF INDOOR VERSUS 

OUTDOOR CONCENTRATIONS a 

– – – – – – – R2 FINF C Cai Cig Ci Cai + Cig 

PM2.5 (μg/m3) 0.74 0.35 18.9 6.6 0.32 6.7 6.9 

Sulfateb

 (μg/m3) 0.98 0.41 10.4 4.4 !0.24 4 4.2 

OC C 1.4 (μg/m3

) 0.44 0.3 5.4 1.6 8 9.7 9.6 

ECc

 (μg/m3) 0.58 0.32 0.5 0.16 0.09 0.4 0.25 

NaCl (ng/m3

) 0.32 0.06 231 14 37 48 53 

Soild

 (ng/m3) 0.3 0.07 363 25 51 74 76 

TEOe

 (ng/m3) 0.38 0.35 94 33 7 39 40 

Nitrate (ng/m3

) 0.83 0.09 372 33 12 68 45 

– – – – – a FINF (slope) and Cig (intercept) taken from regression equation. Cai calculated as C C FINF; Ci

 is measured value. – – – – Note reasonable agreement between measured Ci

 and Ci estimated from Cai + Cig except for EC and nitrate. b as (NH4)2SO4.

revised regression with outliers omitted. d Soil = sum of oxides Si, Ca, Fe, and Ti (due to coarse PM in PM2.5). e TEO = trace element oxides (mostly fine PM components). 

Source: Landis et al. (2001). 

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c 
TABLE 5-20. REGRESSION ANALYSIS OF INDOOR VERSUS 

OUTDOOR CONCENTRATIONS a 

– – – – – R2 FINF C Cai Cig Ci 

Fine 0.37 0.74 15 11.1 4.3 15.4 

Coarse 0.35 0.3 8.6 2.6 3 5.6 

– – – – – – a FINF (slope) and Cig (intercept) taken from regression equation. Cai calculated as C C FINF. Ci

 is measured value. – – – – Note excellent agreement between measured Ci

 and Ci estimated from Cai + Cig. 

Source: Geller et al. (2002). 

PAHs have been measured in studies by EPA and the California Air Resources Board. 

PAH results from a probability sample of 125 homes in Riverside are discussed in reports by 

Sheldon et al. (1992a,b) and Özkaynak et al. (1996b). Data for two sequential 12-h samples 

were reported for PAHs by ring size (3 to 7) and for individual phthalates. The results can be 

summarized as follows. 

• The particulate-phase 5- to 7-ring species had lower relative concentrations than the more 

volatile 3- to 4-ring species. 

• The 12-h I/O ratios for the 5- to 7-ring species ranged from 1.1 to 1.4 during the day and 

from 0.64 to 0.85 during the night (Sheldon et al., 1993a). 

• An indoor air model used to calculate indoor “source strengths” for the PAHs showed that 

smoking had the strongest effect on indoor concentrations. 

Results from a larger PAH probability study in 280 homes in Placerville and Roseville, CA 

(Sheldon et al., 1993a,b) were similar to the 125-home study. The higher-ring, particle-bound 

PAHs had lower indoor and outdoor concentrations than the lower-ring species. For most PAHs, 

the I/O ratio was greater than 1 for smoking and smoking/fireplace homes and less than 1 for 

fireplace-only, wood stove, wood stove/gas heat, gas heat, and “no source” homes. 

A study of PAHs in indoor and outdoor air was conducted in 14 inner-city and 10 rural 

low-income homes near Durham, NC in two seasons (winter and summer) in 1995 (Chuang 

et al., 1999). Fine-particle-bound PAH concentrations measured with a real-time monitor were 

usually higher indoors than outdoors (2.47 ± 1.90 versus 0.53 ± 0.58 μg/m3

). Higher indoor 

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levels were seen in smokers’ homes compared with nonsmokers’ homes, and higher outdoor and 

indoor PAH levels were seen in urban areas compared with rural areas. 

In a study reported by Dubowsky et al. (1999), the weekday indoor PAH concentrations 

attributable to traffic (indoor source contributions were removed) were 39 ± 25 ng/m3

 in a 

dormitory that had a high air exchange rate because of open windows and doors; 26 ± 25 ng/m3 

in an apartment; and 9 ± 6 ng/m3 in a suburban home. The study showed that both indoor and 

outdoor sources (especially motor vehicular traffic) contributed to indoor PAH concentrations. 

BaP concentrations were measured in the THEES study (Waldman et al., 1991). A comprehensive analysis of the data showed considerable seasonal variability of indoor and outdoor 

sources and resultant changes in personal exposures to BaP. 

The indoor and outdoor concentrations of 30 PAHs were measured in 55 nonsmoking 

residences in Los Angeles, CA; Houston, TX; and Elizabeth, NJ (Naumova et al., 2002). 

A comparison of I/O ratios of low molecular weight PAHs (3 to 4 rings) and higher molecular 

weight PAHs (5 to 7 rings) indicated that indoor sources had a significant effect on indoor 

concentrations of 3-ring PAHs and a smaller effect on 4-ring PAHs, while outdoor sources 

dominated the indoor concentrations of 5- to 7-ring PAHs. 

5.4.4 Factors Affecting Correlations Between Ambient Measurements 

and Personal or Microenvironmental Measurements of 

Particulate Matter Constituents 

The primary factors affecting correlations between personal exposure and ambient air PM 

measurements are discussed in Section 4.3.2. These include air exchange rates, particle 

penetration factors, decay rates, removal mechanisms, indoor air chemistry, indoor sources, and 

freshly-generated particles indoors. The importance of these factors varies for different PM 

constituents. For acid aerosols, indoor air chemistry is particularly important as indicated by the 

discussion of the neutralization of the acidity by ammonia which is present at higher 

concentrations indoors because of the presence of indoor sources. For SVOCs, including PAHs 

and phthalates, the presence of indoor sources will substantially affect the correlation between 

indoor and ambient concentrations (Özkaynak et al., 1996b; Sheldon et al., 1993b). Penetration 

factors for PM will affect correlations between indoors and outdoors for most elements, except 

Pb, which may have significant indoor sources in older homes. Indoor air chemistry, decay 

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rates, and removal mechanisms may affect soot and OC. Furthermore, reactions between indoor 

or outdoor gases and particles may produce freshly generated aerosols indoors. These factors 

must be fully evaluated when attempting to correlate concentrations of PM constituents in 

ambient, personal, or indoor samplers. 

5.4.5 Limitations of Available Data 

The previous discussion demonstrates that there is limited data available that can be used 

to compare personal, microenvironmental, and ambient air concentrations of PM constituents. 

Because of resource limitations, PM constituents have not been measured in many studies of PM 

exposure. There are little data on freshly generated indoor aerosols. Although there are some 

data on acid aerosols, the comparisons between the personal and indoor data generally have been 

with outdoor measurements at the participants’ residences, not with community ambient air 

measurement sites. The relationship between personal exposure and indoor levels of acid 

aerosols is not clear because of the limited database, although there is some evidence that acidity 

will be neutralized by indoor ammonia (Suh et al., 1994). The exception is total sulfate, for 

which there appears to be a strong correlation between indoor and ambient concentrations. 

With the exception of PAHs, there are practically no data available with which to relate 

personal or indoor concentrations to outdoor or ambient site concentrations of SVOCs that may 

be generated from a variety of combustion and industrial sources. The relationship between 

exposure and ambient concentrations of particles from specific sources, such as diesel engines, 

has not been determined. 

Although there is an increasing amount of research being performed to measure PM 

constituents in different PM size fractions, the current data are inadequate to adequately assess 

the relationship between indoor and ambient concentrations of most PM constituents. Additional 

information is also needed on PM exposures that result from outdoor gases reacting with indoor 

gases. This is a source that could also vary with outdoor PM, e.g., when the outdoor gas is O3. 

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5.5 IMPLICATIONS OF USING AMBIENT PM CONCENTRATIONS 

IN TOXICOLOGICAL AND EPIDEMIOLOGICAL STUDIES OF 

PM HEALTH EFFECTS 

5.5.1 Toxicology 

Most studies of PM toxicity have used either pure chemicals or ambient PM. Indoorgenerated PM differs somewhat from ambient PM in terms of sources, size, and composition. 

It is possible, therefore, that indoor-generated PM could have different toxicological properties. 

Environmental tobacco smoke (ETS) has been studied extensively; however, little toxicological 

information exists on PM from other indoor sources. Long et al. (2001b) have assessed the in 

vitro toxicity of 14 paired indoor and outdoor PM2.5 samples collected in nine Boston-area 

homes. Bioasseys using rat alveolar macrophages (AMs) and measuring tumor necrosis factor 

(TNF) were used to assess particle-induced proinflammatory responses. TNF production was 

found to be significantly higher in AMs exposed to indoor PM than to those exposed to outdoor 

PM. This result held even after normalization for endotoxin concentrations, which were higher 

in indoor samples. The authors concluded that their results “suggest that indoor-generated 

particles may be more bioactive than ambient particles.” PM, in its various forms, produces 

many types of biological effects. It seems possible that indoor PM could be more active than 

ambient PM for some effects and less for others. 

5.5.2 Potential Sources of Error Resulting from Using Ambient Particulate 

Matter Concentrations in Epidemiological Analyses 

In this section, the exposure issues that relate to the interpretation of the findings from 

epidemiologic studies of PM health effects are examined. This section examines the errors that 

may be associated with using ambient PM concentrations in epidemiologic analyses of PM 

health effects. First, implications of associations found between personal exposure and ambient 

PM concentrations are reviewed. This is discussed separately in the context of either community 

time-series studies or long-term, cross-sectional studies of chronic effects. Next, the role of 

compositional and spatial differences in PM concentrations are discussed and how these may 

influence the interpretation of findings from PM epidemiology. Finally, using statistical 

methods, an evaluation of the influence of exposure measurement errors on PM epidemiologic 

studies is presented. 

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Measurement studies of personal exposures to PM are still few in number and limited in 

spatial, temporal, and demographic coverage. Consequently, with the exception of a few 

longitudinal panel studies, most epidemiologic studies of PM health effects rely on ambient 

community monitoring data giving 24-h average PM concentration measurements. Moreover, 

because of limited sampling for PM2.5, many of these epidemiologic studies used available PM10 

or in some instances relied on historic data for other PM measures or indicators, such as 

TSP, SO4

2!

, IP15 (inhalable particles with an upper cut of 15 μm), RSP (respirable suspended 

particles), COH (coefficient of haze), etc. A critical question often raised in the interpretation of 

results from acute or chronic epidemiological community-based studies of PM is whether the use 

of ambient stationary site PM concentration data influenced or biased the findings from those 

studies. 

If it is assumed that total personal PM exposure is responsible for observed effects, use of 

ambient concentrations could lead to misclassification of individual exposures and to errors in 

the epidemiologic analysis of pollution and health data depending on the pollutant and on the 

mobility and lifestyles of the population studied. Ambient monitoring stations can be some 

distance away from the individuals and may represent only a fraction of all likely outdoor 

microenvironments that individuals come in contact with during the course of their daily lives. 

Furthermore, most individuals are quite mobile and move through multiple microenvironments 

(e.g., home, school, office, commuting, shopping) and engage in diverse personal activities at 

home (e.g., cooking, gardening, cleaning, smoking). Some of these microenvironments and 

activities may have different sources of PM and result in distinctly different concentrations of 

PM than that monitored by the fixed-site ambient monitors. Consequently, exposures of some 

individuals could be classified incorrectly if only ambient monitoring data are used to estimate 

individual-level total personal PM exposures. Thus, improper assessment of exposures using 

data routinely collected by the neighborhood monitoring stations could conceivably lead to a 

bias or increase in the standard error in epidemiologic analyses. Except for extremely unlikely 

situations, however, the bias would be expected to reduce the estimated health risk coefficient. 

Many individuals are typically exposed to particles in a multitude of indoor and outdoor 

microenvironments during the course of a day. Thus, concern about possible error introduced in 

the estimation of PM risk coefficients using ambient, as opposed to personal, PM measurements 

has received considerable attention recently from exposure analysts, epidemiologists, and 

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biostatisticians. Some exposure analysts contend that, for community time-series epidemiology 

to yield information on the statistical association of a pollutant with a health response, there must 

be an association between personal exposure to a pollutant and the ambient concentration of that 

pollutant because people tend to spend around 90% time indoors and are exposed to both indoorgenerated and ambient-infiltrated PM (cf. Wallace, 2000b; Brown and Paxton, 1998; Ebelt et al., 

2000). Consequently, numerous findings reported in the epidemiologic literature on significant 

associations between ambient PM concentrations and various morbidity and mortality health 

indices, in spite of the low correlations between ambient PM and concentrations and measures of 

personal exposure, have been described by some exposure analysts as an exposure paradox 

(Lachenmyer and Hidy, 2000; Wilson et al., 2000). 

To resolve that exposure paradox, two types of analyses must considered. The first type of 

analysis is to examine the correlations between ambient PM concentrations and personal 

exposures that are relevant to most of the existing PM epidemiologic studies using either pooled, 

daily-average, or longitudinal exposure data. The second approach is to study the degree of 

correlations between the two key components of personal PM exposures (i.e., exposures caused 

by ambient PM and exposures caused by nonambient PM) with ambient or outdoor PM 

concentrations, for each of the three types of exposure study designs. Yet, even with these two 

approaches, it may still be difficult to examine complex synergisms which, in some situations, 

may preclude simple decoupling of indoor and outdoor particles either in terms of exposure or 

total dose delivered to the lung. In addition, several factors that influence either the exposure or 

health response characterization of the subjects have to be addressed. Such factors include: 

• spatial variability of PM components, 

• health or sensitivity status of subjects, 

• variations of PM with other co-pollutants, 

• co-generation of fine and ultrafine particles from outdoor air and indoor gaseous pollutants, 

• formal evaluation of exposure errors in the analysis of health data, and 

• how the results may depend on the variations in epidemiological study design. 

To facilitate the discussion of these topics, a brief review of concepts pertinent to exposure 

analysis issues in epidemiology is presented. 

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5.5.2.1 Associations Between Personal Exposures and Ambient Particulate 

Matter Concentrations 

As defined in Sections 5.3 and 5.4, personal exposures to PM result from an individual’s 

exposures to PM in many different types of microenvironments (e.g., outdoors near home, 

outdoors away from home, indoors at home, indoors at office or school, commuting, restaurants, 

malls, other public places). Total personal exposures that occur in these indoor and outdoor 

microenvironments can be classified as those resulting from ambient PM (ambient PM exposure 

includes exposure to ambient PM while outdoors and exposure while indoors to ambient PM that 

has infiltrated indoors) and those primarily generated by indoor sources, indoor reaction, and 

personal activities (nonambient PM exposure). The associations between personal exposures 

and ambient PM concentrations that have been reported from various personal exposure 

monitoring studies under three broad categories of study design, (1) longitudinal, (2) dailyaverage, or (3) pooled exposure studies, and are summarized below. 

In Sections 5.4.3.1.2 and 5.4.3.1.3, recent studies conducted mainly in the United States 

and involving children, the elderly, and subjects with COPD were reviewed, and they indicated 

that both intra- and interindividual variability in the relationships between personal exposures 

and ambient PM concentrations were observed. A variety of different physical, chemical, and 

personal or behavioral factors were identified by the original investigators that seem to influence 

the magnitude and the strength of the associations reported. 

For cohort studies in which individual daily health responses are obtained, individual 

longitudinal PM personal exposure data (including ambient and nonambient components) may 

provide the appropriate indicators. In this case, health responses of each individual can be 

associated with the total personal exposure, the ambient exposure, or the nonambient exposure of 

each individual. Additionally, the relationships of personal exposure indicators with ambient 

concentration can be investigated. In the case of community time-series epidemiology, however, 

it is not feasible to obtain experimental measurements of personal exposure for the millions of 

people over the periods of years that are needed to investigate the relationship between air 

pollution and infrequent health responses such as deaths or even hospital admissions. The 

epidemiologist must work with the aggregate number of health responses occurring each day and 

a measure of the ambient concentration that is presumed to be representative of the entire 

community. The relationship of PM exposures of the potentially susceptible groups to 

monitored ambient PM concentrations depends on their activity pattern and level, residential 

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building and HVAC factors (which influence the infiltration factor), status of exposure to ETS, 

amount of cooking or cleaning indoors, and seasonal factors, among others. For these special 

subgroups, average personal exposures to ambient PM correlate well with ambient PM 

concentrations regardless of individual variation in the absence of major microenvironmental 

sources. 

Even though both ambient and nonambient PM exposure contribute to daily baseline PM 

dose received by the lung, there seem to be clear differences in the relationships of ambient and 

nonambient PM exposure with ambient PM concentration. Various researchers have shown that 

nonambient PM exposure is independent of ambient PM concentration, but that ambient PM 

exposure is a function of ambient PM concentration. Wilson et al. (2000) explained the 

difference based on different temporal patterns that affect PM concentrations, “Concentrations of 

ambient PM are driven by meteorology and by changes in the emission rates and locations of 

emission sources, while concentrations of nonambient PM are driven by the daily activities of 

people.” Still, although nonambient PM exposure may not correlate with ambient PM 

concentration or ambient PM exposure, it will nevertheless add to the daily baseline dose 

received by the lung. An important concern, for which there is little information, is the relative 

biological activity of ambient and indoor-generated PM both in terms of the type of toxic effect 

and the relative potency for a given effect. 

Ott et al. (2000) also discussed the reasons for assuming that nonambient PM exposure is 

independent of ambient PM exposure and ambient PM concentration. They showed that the 

nonambient component of total personal exposure is uncorrelated with the outdoor concentration 

data. Ott et al. (2000) demonstrated that the ambient PM exposure was similar for three 

population-based exposure studies: two large probability-based studies (the PTEAM study 

conducted in Riverside [Clayton et al., 1993; Thomas et al., 1993; Özkaynak et al., 1996a,b] and 

a study in Toronto [Pelizzarri et al., 1999; Clayton et al., 1999a]), and a nonprobability-based 

study conducted in Phillipsburg (Lioy et al., 1990). Based on these three studies, they concluded 

that the average nonambient PM exposure and the distribution of individual, daily values of 

nonambient PM exposure can be treated as constant from city to city. 

Dominici et al. (2000) examined a larger database consisting of five different PM exposure 

studies and also concluded that nonambient PM exposure can be treated as relatively constant 

from city to city, although their data show greater variability than the data reported by Ott 

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(2000). However, the constancy of nonambient PM exposure remains an open question. If daily 

values of nonambient PM exposure were constant, this would imply a zero correlation with 

ambient PM concentration. However, this hypothesis of constant individual, daily nonambient 

PM exposure has not been fully established, since only a few studies have produced the data 

needed to estimate daily, individual values of nonambient PM exposure. Although nonambient 

PM exposure is independent of ambient PM concentration, it may not be independent of the 

attenuation factor (ambient PM exposure/ambient PM concentration). Sarnat et al. (2000) 

showed that nonambient PM exposure increases as the ventilation rate (and attenuation factor) 

decreases. By comparing winter and summer regression equations Lachenmeyer and Hidy 

(2000) also showed that as the slope, which gives the attenuation factor, decreases, the intercept, 

which gives the nonambient PM exposure, increases. If ambient and nonambient PM2.5 are not 

correlated, as seems likely, statistical analyses using ambient concentrations will not be able to 

demonstrate health effects, or the lack of health effects, due to indoor-generated PM. However, 

indoor-generated PM could raise the overall exposure level, contribute to the observed health 

effects, and make it more difficult to observe a threshold level for ambient PM. 

Mage et al. (1999) assumed that the PM10 concentration component from indoor sources 

(e.g., smoking, cooking, cleaning, burning candles) is not correlated with the outdoor 

concentration. They indicated that this lack of correlation is to be expected because people are 

unaware of ambient concentrations and do not necessarily change their smoking or cooking 

activities as outdoor PM10 concentrations vary, an assumption supported by other empirical 

analyses of personal exposure data. For the PTEAM data set, Mage et al. (1999) have shown 

that individual, daily exposures to indoor-generated PM and daily ambient PM concentrations 

have a correlation coefficient near zero (R2

 = 0.005). Wilson et al. (2000) have shown that 

individual, daily values of concentration of ambient PM which has infiltrated indoors and 

indoor-generated PM concentrations also have a near zero correlation (R2

 = 0.03). Figure 5-14 

shows the relationship of estimated values of nonambient PM exposure with ambient PM 

concentrations (calculated by EPA, daily individual values from PTEAM and daily average 

values for the cohort from THEES). 

Based on these results, it is reasonable to assume that nonambient PM exposure will 

ordinarily have little correlation with ambient PM concentration. A possible exception could be 

caused by indoor-reaction PM, PM formed when an ambient gas infiltrates indoors and reacts 

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Figure 5-14. Plots of nonambient exposure to PM10 showing (a) daily individual daytime 

values from PTEAM data and (b) daily-average values from THEES data. 

Source: Data taken from (a) Clayton et al. (1993) and (b) Lioy et al. (1990). 

5-113 
with an indoor-generated gas, e.g., the reaction of O3 with terpenes from air cleaners. However, 

ambient O3 does not appear to be highly correlated with ambient or personal PM2.5 (see 

Table 5-12, Sarnat et al., [2000]). Additionally, not every home will use air fresheners or have 

the same level of terpene emissions. Hence, indoor-reaction PM concentrations would not be 

expected to correlate with ambient PM concentrations on a community basis. Therefore, in 

linear nonthreshold models of PM health effects, nonambient PM exposure is not expected to 

contribute to the relative risk determined in a regression of health responses on ambient PM 

concentration. Furthermore, in time-series analysis of pooled or daily health data, it is expected 

that ambient PM exposure, rather than total personal PM exposure, will have the stronger 

association with ambient PM concentration. 

5.5.2.2 Role of Compositional Differences in Exposure Characterization for Epidemiology 

The majority of the available data on PM exposures and relationships with ambient PM 

have come from a few large-scale studies, such as PTEAM, or longitudinal studies on selected 

populations. Consequently, for most analyses, exposure scientists and statisticians had to rely 

on PM10 or PM2.5 mass data, instead of elemental or chemical compositional information on 

individual or microenvironmental samples. In a few cases, researchers have examined the 

factors influencing I/O ratios or penetration and deposition coefficients using elemental mass 

data on personal, indoor, and outdoor PM data (e.g., Özkaynak et al., 1996a,b; Yakovleva et al., 

1999). These results have been informative in terms of understanding relative infiltration of 

different classes of particle sizes and sources into residences (e.g., fossil fuel combustion, mobile 

source emissions, soil-derived). Clearly, accumulation-mode particles, associated with 

stationary or mobile combustion sources, have greater potential for penetration into homes and 

other microenvironments than crustal material. There will be variability in the chemical 

composition of these broad categories of source classes as well as probable variations in relative 

toxicity. Moreover, when particles and reactive gases are present indoors in the presence of 

other pollutants or household chemicals, they may react to form additional or different 

compounds and particles with yet unknown physical, chemical, and toxic compositions 

(Wainman et al., 2000). Thus, if indoor-generated and ambient PM were responsible for 

different types of health effects or had significantly different toxicities on a per unit mass basis, 

it would then be important that ambient and nonambient exposure should be separated and 

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treated as different components, much like the current separation of PM10 into PM2.5 and PM10-2.5. 

These complexities in personal exposure profiles may introduce nonlinearities and other 

statistical challenges in the selection and fitting of concentration-response models. 

Unfortunately, PM health effects models have not yet been able to meaningfully consider such 

complexities. 

It is important to also note that individuals spend time in places other than their homes and 

outdoors. Many of the interpretations reported in the published literature on factors influencing 

personal PM10 exposures, as well as in this chapter, come from the PTEAM study. The PTEAM 

study was conducted 10 years ago in one geographic location in California, during one season, 

and most residences had very high and relatively uniform air exchange rates. Non-home indoor 

microenvironments were not monitored directly during the PTEAM study. Commuting 

exposures from traffic or exposures in a variety of different public places or office buildings 

could not be assessed directly. Nonresidential buildings may have lower or higher ambient 

infiltration rates depending on the use and type of the mechanical ventilation systems employed. 

Because the source and chemical composition of PM affecting personal exposures in different 

microenvironments vary by season, day-of-the-week, and time of day, it is conceivable that 

some degree of misclassification of exposures to toxic PM agents of concern could be introduced 

when health-effects models use only daily-average mass measures such as PM10 or PM2.5. 

However, because of the paucity of currently available data on many of these factors, it is not 

now possible to ascertain the potential magnitude or severity of any such complex exposure 

missclassification problems or their potential implications for interpretation of results from PM 

epidemiology. 

5.5.2.3 Role of Spatial Variability in Exposure Characterization for Epidemiology 

Chapter 3, Section 3.2.3 and Chapter 5, Section 5.3 present information on the spatial 

variability of PM mass and chemical components at fixed-site ambient monitors; for purposes of 

this chapter, this spatial variability is called an “ambient gradient.” Any gradient that may exist 

between a fixed-site monitor and the outdoor microenvironments near where people live, work, 

and play will obviously affect the concentration profile actually experienced by people as they 

go about their daily lives. 

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However, the evidence so far indicates that PM concentrations, especially fine PM (mass 

and sulfate), generally are uniformly distributed in most metropolitan areas. This reduces the 

potential for exposure misclassification because of outdoor spatial gradients when a limited 

number of ambient PM monitors are used to represent population average ambient exposures in 

time-series or cross-sectional epidemiological studies of PM. This topic is further discussed in 

Section 5.6.5. However, as discussed earlier, the same assumption is not necessarily true for 

different components of PM such as PM10-2.5, because source-specific and other spatially 

nonuniform pollutant emissions could alter the spatial profile of individual PM components in a 

community. For example, particulate and gaseous pollutants emitted from motor vehicles tend 

to be higher near roadways and inside cars. Likewise, acidic and organic PM species may be 

location- and time-dependent. Furthermore, human activities are complex. If outdoor PM 

constituent concentration profiles are either spatially or temporally variable, it is likely that 

exposure misclassification errors could be introduced in the analysis of PM air pollution and 

health data. 

5.5.3 Analysis of Exposure Measurement Error Issues in Particulate Matter 

Epidemiology 

The effects of exposure misclassification on the relative risk estimates of disease using the 

classical 2 × 2 contingency design (i.e., exposed/nonexposed versus diseased/nondiseased) have 

been studied extensively in the epidemiologic literature. It has been shown that the magnitude of 

the exposure-disease association (e.g., relative risk), because of either misclassification of 

exposure or disease alone (i.e., nondifferential misclassification), biases the effect results toward 

the null; and differential misclassification (i.e., different magnitudes of disease misclassification 

in exposed and nonexposed populations) can bias the effect measure toward or away from the 

null value relative to the true measure of association (Shy et al., 1978; Gladen and Rogan, 1979; 

Copeland et al., 1977; Özkaynak et al., 1986). However, the extension of these results from 

contingency analysis design to multivariate models (e.g., log-linear regression, Poissson, logit) 

typically used in recent PM epidemiology has been more complicated. 

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5.5.3.1 Time-Series Analyses 

Researchers have investigated the appropriateness of using ambient PM concentration as 

an exposure metric and have developed a framework for analyzing measurement errors typically 

encountered in the analysis of time-series mortality and morbidity effects from exposures to 

ambient PM (cf. Zeger et al., 2000; Dominici et al., 2000; Samet et al., 2000). Use of this 

framework, discussed more extensively in Chapters 8 and 9, leads to the following conclusions: 

the deviation of an individual’s personal exposure from the risk-weighted average exposure due 

to variations in ambient concentrations, infiltration rates, and indoor-generated PM 

concentrations is a Berkson error and will not bias the estimated regression coefficient ($, the 

increase in risk per unit increase in PM) in a time-series analysis of mortality as a function of 

ambient PM concentrations. However, the difference between the average personal exposure 

and the true ambient concentration will bias $. If the daily, individual values of ambient PM 

exposure and nonambient PM exposure are poorly correlated and the attenuation coefficient, ", 

equal to the ambient PM concentration/ambient PM exposure were constant, the bias would be 

given by $C = "$A where $C is calculated using the ambient PM concentration, C, and $A is 

calculated using the ambient PM exposure, A; i.e., the risk determined from an analysis using the 

daily ambient PM concentrations would be lower than the risk obtained using ambient PM 

exposure by the factor ". However, $C provides the correct information on the change in health 

risks that would be produced by a change in ambient concentrations. However, if the daily, 

individual values of ambient PM exposure and nonambient PM exposure are highly correlated 

and the nonambient PM is toxic for the effect being studied, nonambient PM exposure will act as 

a confounder and could introduce substantial bias into $C. Only one study has reported the 

association between the daily, individual values of ambient PM exposure and nonambient PM 

exposure. Wilson et al. (2000), in a further analysis of the PTEAM data set, found a coefficient 

of determination, R2

 = 0.03, suggesting that the daily, individual values of ambient PM exposure 

and nonambient PM exposure are independent, meaning that nonambient PM exposure will not 

confound $C. 

5.5.3.2 Studies of Chronic Effects 

The Six Cities (Dockery et al., 1993) and American Cancer Society (ACS) (Pope et al., 

1995) Studies have played an important role in assessing the health effects from long-term 

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exposures to particulate pollution. Even though these studies often have been considered as 

chronic epidemiologic studies, it is not easy to differentiate the role of historic exposures from 

those of recent exposures on chronic disease mortality. In the Six Cities study, fine particles and 

sulfates were measured at the community level, and the final analysis of the database used six 

city-wide average ambient concentration measurements. This limitation also applies to the ACS 

study but has less impact because of the larger number of cities considered in that study. In a 

HEI-sponsored reanalysis of the Six Cities and the ACS data sets, Krewski et al. (2000) 

examined some of the exposure misclassification issues either analytically or through sensitivity 

analysis of the aerometric and health data. The HEI reanalysis project also addressed exposure 

measurement error issues related to the Six Cities study. For example, the inability to account 

for exposures prior to the enrollment of the cohort hampered accurate interpretation of the 

relative risk estimates in terms of acute versus chronic causes. Although the results seem to 

suggest that past exposures are more strongly associated with mortality than recent exposures, 

the measurement error for long-term averages could be higher, influencing these interpretations. 

As another example, Krewski et al. (2000), using the individual mobility data available for the 

Six Cities cohort, analyzed the mover and nonmover groups separately. The relative risk of fine 

particle effects on all-cause mortality was shown to be higher for the nonmover group than for 

the mover group, suggesting the possibility of higher exposure misclassification biases for the 

movers. The issue of using selected ambient monitors in the epidemiological analyses was also 

investigated by the ACS and Six Cities studies reanalysis team. Krewski et al. (2000) described 

the sensitivity of results to choices made in selecting stationary or mobile-source-oriented 

monitors. For the ACS study, reanalysis of the sulfate data using only those monitors designated 

as residential or urban and excluding sites designated as industrial, agricultural, or mobile did 

not appreciably change the risk estimates. On the other hand, application of spatial analytic 

methods designed to control confounding at larger geographic scales (i.e., between cities) caused 

changes in the particle and sulfate risk coefficients. Spatial adjustment may account for 

differences in pollution mix or PM composition, but many other cohort-dependent risk factors 

will vary across regions or cities in the United States. Therefore, it is difficult to interpret these 

findings solely in terms of spatial differences in pollution composition or relative PM toxicity 

until further research is concluded. 

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The influence of measurement errors in air pollution exposure and health effects 

assessments has also been examined by Navidi et al. (1999). This study developed techniques to 

incorporate exposure measurement errors encountered in long-term air pollution health-effects 

studies and tested them on the data from the University of Southern California Children’s Health 

Study conducted in 12 communities in California. These investigators developed separate error 

analysis models for direct (i.e., personal sampling) and indirect (i.e., microenvironmental) 

personal exposure assessment methods. These models were generic to most air pollutants, 

but a specific application was performed using a simulated data set for studying ozone health 

effects on lung function decline in children. Because the assumptions made in their 

microenvironmental simulation modeling framework were similar to those made in estimating 

personal PM exposures, it is useful to consider the conclusions from Navidi et al. (1999). 

According to Navidi et al. (1999), neither the microenvironmental nor the personal sampler 

method produces reliable estimates of the exposure-response slope for O3 when measurement 

error is uncorrected. Because of nondifferential measurement error, the bias was toward zero 

under the assumptions made in Navidi et al. (1999) but could be away from zero if the 

measurement error was correlated with the health response. A simulation analysis indicated that 

the standard error of the estimate of a health effect increases as the errors in exposure assessment 

increase (Navidi et al., 1999). According to Navidi et al. (1999), when a fraction of the ambient 

level in a microenvironment is estimated with a standard error of 30%, the standard error of the 

estimate is 50% higher than it would be if the true exposures were known. It appears that errors 

in estimating ambient PM indoor/ambient PM outdoor ratios have much more influence on the 

accuracy of the microenvironmental approach than do errors in estimating time spent in these 

microenvironments. 

Epidemiologic studies of chronic effects that use long-term average ambient PM 

concentrations as the exposure metric generally do not address the nonambient component of 

personal exposure (N). However, if N contributes to the health effect being studied and the 

average N is different in different cities, the correlation between the average ambient PM 

concentration and the health effect could be reduced. In an analysis of the effect of nonambient 

exposure on time-series epidemiology, Dominici et al. (2000) examined nonambient exposure 

data from several cities and concluded that the average nonambient PM exposure varies little 

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among cities in developed countries. Also, Ott et al. (2000) examined estimated average and 

daily, individual values of nonambient exposure from three studies and concluded that both the 

average and the distribution of daily, individual values were similar for the three studies. 

5.5.4 Conclusions from Analysis of Exposure Measurement Errors on 

Particulate Matter Epidemiology 

Personal exposures to PM are influenced by a number of factors and sources of PM located 

in both indoor and outdoor microenvironments. However, PM resulting from ambient sources 

penetrate into indoor environments such as residences, offices, public buildings, etc., in which 

individuals spend a large portion of their daily lives. The correlations between total personal 

exposures and ambient or outdoor PM concentrations can vary depending on the relative 

contributions of indoor PM sources to total personal exposures. Panel studies of both adult and 

young subjects have shown that, in fact, individual correlations of personal exposures with 

ambient PM concentrations could vary from person to person, and even day to day, depending 

on the specific activities of each person. Separation of PM exposures into two components, 

ambient PM and nonambient PM, would reduce uncertainties in the analysis and interpretation 

of PM health effects data. Nevertheless, because ambient PM is an integral component of total 

personal exposures to PM, statistical analyses of cohort-average exposures are strongly 

correlated with ambient PM concentrations when a large underlying population is studied. 

Using the PTEAM study data, analysis of exposure measurement errors, in the context of timeseries epidemiology, has also shown that errors or uncertainties introduced by using surrogate 

exposure variables, such as ambient PM concentrations, could lead to biases in the estimation of 

health risk coefficients. These then would need to be corrected by suitable calibration of the PM 

health risk coefficients. Correlations between the PM exposure variables and other covariates 

(e.g., gaseous co-pollutants, weather variables) also could influence the degree of bias in the 

estimated PM regression coefficients. However, most time-series regression models employ 

seasonal or temporal detrending of the variables, thus reducing the magnitude of this crosscorrelation problem (Özkaynak and Spengler, 1996). 

Ordinarily, exposure measurement errors are not expected to influence the interpretation of 

findings from either the chronic or time-series epidemiologic studies that have used ambient 

concentration data if they include sufficient adjustments for seasonality and key confounders. 

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There is no question that better estimates of exposures to components of PM of health concern 

are beneficial. Composition of PM may vary in different geographic locations and different 

exposure microenvironments. Compositional and spatial variations could lead to further errors 

in using ambient PM measures as surrogates for exposures to PM. Even though the spatial 

variability of PM (PM2.5 in particular) mass concentrations in urban environments seems to be 

small, the same conclusions drawn above regarding the influence of measurement errors may not 

necessarily hold for all of the toxic PM components. Again, the expectation based on statistical 

modeling considerations is that these exposure measurement errors or uncertainties will most 

likely reduce the statistical power of the PM health effects analysis, making it difficult to detect 

a true underlying association between the correct exposure metric and the health outcome 

studied. However, until more data on exposures to specific toxic components of PM become 

available, existing studies on PM exposure measurement errors must be relied on; thus, at this 

time, the use of ambient PM concentrations as a surrogate for exposures is not expected to 

change the principal conclusions from PM epidemiologic studies that use community average 

health and pollution data. 

5.6 SUMMARY OF OBSERVATIONS AND LIMITATIONS 

Exposure Definitions and Components 

• Personal exposure to PM mass or its constituents results when individuals come in contact 

with particulate pollutant concentrations in locations or microenvironments that they 

frequent during a specific period of time. Various PM exposure metrics can be defined 

according to its source (i.e., ambient, nonambient) and the microenvironment where 

exposure occurs. 

• Personal exposure to PM results from an individual’s exposure to PM in many different 

types of microenvironments (e.g., outdoors near home, outdoors away from home, indoors 

at home, indoors at office or school, commuting, restaurants, malls, other public places). 

Thus, total daily exposure to PM for a single individual can be expressed as the sum of 

various microenvironmental exposures that the person encounters during the course of 

a day. 

• In a given microenvironment, particles may originate from a wide variety of sources. In an 

indoor microenvironment, PM may originate from PM-generating activities (e.g., cooking, 

cleaning, smoking, resuspending PM from PM resulting from both indoor and outdoor 

sources that had settled out), from outside (outdoor PM entering through cracks and 

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openings in the structure), and from the chemical interaction of pollutants from outdoor 

air with indoor-generated pollutants. 

• The total daily exposure to PM for a single individual can also be expressed as the sum of 

contributions of ambient and nonambient PM. Nonambient PM exposure is due to PM 

generated by indoor sources, personal activities, and chemical reactions in indoor air. 

Ambient PM exposure includes exposure to ambient PM while outdoors, and ambient PM 

that has infiltrated indoors while indoors. However, within a large population, there will be 

distributions of total personal exposure and its components due to variations in human 

activities and microenvironmental concentrations and sources each individual encounters. 

• Exposure models are useful tools for examining the importance of sources, microenvironments, and physical and behavioral factors that influence personal exposures to PM. 

However, the development and evaluation of population exposure models for PM and its 

components have been limited; and improved modeling methodologies and new model input 

data are needed. 

Factors Affecting Concentrations and Exposures to Particulate Matter 

• Concentrations of PM indoors are affected by several factors and mechanisms: 

ambient concentrations outdoors, air exchange rates, particle penetration factors,

particle production from indoor sources and indoor air chemistry, and indoor particle

decay rates and removal mechanisms caused by physical processes or resulting from

mechanical filtration, ventilation or air-conditioning devices.

• Average personal exposures to PM mass and its constituents are influenced by

microenvironmental PM concentrations and by how much time each individual spends

in these various indoor and outdoor microenvironments. Nationwide, individuals, on

average, spend nearly 90% of their time indoors (at home and in other indoor locations)

and about 6% of their time outdoors.

• Personal exposures are associated with both indoor as well as outdoor sources. 

The personal exposure/outdoor concentration ratios present substantial intra- and

interpersonal variability. This variability is due to both the presence of personal

and microenvironmental sources and the varying effect of the outdoor particles on

indoor environments.

• Home characteristics may be the most important factor that affects the relationship 

between the average population exposures and ambient concentrations. Air exchange rate 

seems to be an important home characteristic surrogate that can explain a large fraction 

of the observe inter- and intrapersonal variability. One reason why longitudinal studies 

(many repeated measurements per person) provide stronger correlations between personal 

exposure and outdoor concentrations than pooled studies (few repeated measurements per 

individual) may be because home characteristics remain the same. 

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• Because home characteristics constitute the most important factor affecting personal 

exposures, one would expect that correlations between average population exposures 

and outdoor concentrations will vary by season and geography. 

• The relative size of personal exposure to ambient PM relative to nonambient PM depends 

on the ambient concentration, the infiltration rate of outdoor PM into indoor 

microenvironments, the amount of PM generated indoors (e.g., ETS, cooking and cleaning 

emissions), and the amount of PM generated by personal activity sources. Infiltration 

rates primarily depend on air exchange rate, size-dependent particle penetration across the 

building membrane, and size-dependent removal rates. All of these factors vary over time 

and across subjects and building types. 

• The relationship between PM exposure, dose, and health outcome could depend on the 

concentration, composition, and toxicity of PM or PM components originating from 

different sources. Application of source apportionment techniques to indoor and 

outdoor PM2.5 and personal, indoor, and outdoor PM10 composition data have identified 

the following general source categories: outside soil, resuspended indoor soil, indoor 

soil, personal activities, sea salt, motor vehicles, nonferrous metal smelters, and 

secondary sulfates. 

• Only a limited number of studies have measured the physical and chemical constituents of 

PM in personal or microenvironmental samples. Available data on PM constituents 

indicate the following: 

– personal and indoor sulfate measurements often are correlated highly with outdoor and 

ambient sulfate concentration measurements; 

– for acid aerosols, indoor air chemistry is particularly important because of the 

neutralization of the acidity by ammonia, which is present at higher concentrations 

indoors because of the presence of indoor sources of ammonia; 

– for SVOCs, including PAHs and phthalates, the presence of indoor sources will 

substantially affect the relation between indoor and ambient concentrations; 

– penetration and decay rates are functions of size and will cause variations in the 

attenuation factors as a function of particle size (infiltration rates will be higher for 

PM1 and PM2.5 than for PM10, PM10-2.5 or ultrafine particles); and 

– indoor air chemistry may increase indoor concentrations of organic PM. 

• Even though there is an increasing amount of research being performed to measure PM 

constituents in different PM size fractions, with few exceptions (i.e., sulfur or sulfates) the 

current data are inadequate to adequately assess the relationship between personal, indoor, 

and ambient concentrations of most PM constituents. 

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Correlations Between Personal Exposures, Indoor, Outdoor, and Ambient Measurements 

• Most of the available personal data on PM measurements and information on the 

relationships between personal and ambient PM come from a few large-scale studies (e.g., 

the PTEAM study) or the longitudinal panel studies that have been conducted on selected 

populations, such as the elderly. 

• Panel and cohort studies that have measured PM exposures and concentrations typically

have reported their results in terms of three types of correlations: (1) longitudinal,

(2) pooled, and (3) daily-average correlations between personal and ambient or outdoor PM. 

• The type of correlation analysis performed can have a substantial effect on the resulting

correlation coefficient. Low correlations with ambient concentrations could result when

people with very different nonambient exposures are pooled even though their individual

personal exposures may temporally be highly correlated with ambient concentrations.

• Longitudinal and pooled correlations between personal exposure and ambient or outdoor

PM concentrations reported by various investigators varied considerably among the

different studies and in each study between the study subjects. Most studies report

longitudinal correlation coefficients that range from close to 0 to near 1.0, indicating that

individual’s activities and residence type may have a significant effect on total personal

exposures to PM.

• Longitudinal studies that measured sulfate found high correlations between personal and

ambient sulfate.

• In general, probability-based population studies tend to show low pooled correlations 

because of the high differences in levels of nonambient PM-generating activities from one 

subject to another. In contrast, the absence of indoor sources for the populations in several 

of the longitudinal panel studies resulted in high correlations between personal exposure 

and ambient PM within subjects over time for these populations. However, even for these 

studies, correlations varied by individual depending on their activities and the 

microenvironments that they occupied. 

Potential Sources of Error Resulting from Using Ambient Particulate Matter 

Concentrations in Epidemiological Analyses 

As yet, there is no clear consensus among exposure analysts as to how well community 

monitor measurements of ambient air PM concentrations represent a surrogate for personal 

exposure to total PM or to ambient PM. 

• Measurement studies of personal exposures to PM are still few in number and limited in

spatial, temporal, and demographic coverage. Consequently, with the exception of a few

longitudinal panel studies, most epidemiological studies on PM health effects have relied

on daily-average PM concentration measurements obtained from ambient community

monitoring data as a surrogate for the exposure variable.

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• Because individuals are exposed to particles in a multitude of indoor and outdoor 

microenvironments during the course of a day, error is introduced in the estimation of 

PM risk coefficients using ambient, as opposed to personal, PM measurements. 

• Total personal exposures to PM could vary from person to person, and even day to day, 

depending on the specific activities of each person. Separation of PM exposures into two 

components, ambient and nonambient PM, would reduce potential uncertainties in the 

analysis and interpretation of PM health effects data. 

• Available data indicate that PM mass concentrations, especially fine PM, typically are 

distributed relatively uniformly in most metropolitan areas, thus reducing the potential for 

exposure misclassification because of spatial variability when a limited number of ambient 

PM monitors are used to represent population average ambient exposures in community 

time-series or long-term, cross-sectional epidemiological studies of PM. 

• Even though the spatial variability of PM (in particular, PM2.5) mass concentrations in urban 

environments seems to be small, the same conclusions drawn above regarding the influence 

of measurement errors may not necessarily hold for all PM components. 

• There are important differences in the relationship of ambient PM concentrations with 

ambient PM exposures and with nonambient PM exposures. Various researchers have 

shown that ambient PM exposure is a function of ambient PM concentration and that 

concentrations of ambient PM are driven by meteorology, by changes in source emission 

rates, and in locations of emission sources relative to the measurement site. However, 

nonambient PM exposure is independent of ambient PM concentration because 

concentrations of nonambient PM are driven by the daily activities of people. 

• Because personal exposures also include a contribution from ambient concentrations, the 

correlation between daily-average personal exposure and the daily-average ambient 

concentration increases as the number of subjects measured daily increases. An application 

of a Random Component Superposition model has shown that the contributions of ambient 

PM10 and indoor-generated PM10 to community mean exposure can be decoupled in 

modeling urban population exposure distributions. 

• If linear nonthreshold models are assumed in time-series analysis of daily-average ambient 

PM concentrations and community health data, nonambient PM exposure is not expected to 

contribute to the relative risk estimates determined by regression of health responses on 

ambient PM concentration. 

• Using the PTEAM study data, analysis of exposure measurement errors in the context of 

time-series epidemiology has shown that the error introduced by using ambient PM 

concentrations as a surrogate for ambient PM exposure biases the estimation of health risk 

coefficients low by the ratio of ambient PM exposure to ambient PM concentration (called 

the attenuation factor). However, the health risk coefficient determined using ambient PM 

concentrations provides the correct information on the change in health risks that would be 

produced by a change in ambient concentrations. 

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• Because sources and chemical composition of PM affecting personal exposures in different 

microenvironments vary by season, day of the week, and time of day, it is likely that some 

degree of misclassification of exposures to PM toxic agents of concern will be introduced 

when health-effects models use only daily-average mass measures such as PM10 or PM2.5. 

Because of the paucity of currently available data on many of these factors, at this point it is 

impossible to ascertain the significance of these more complex exposure misclassification 

problems in the interpretation of results from PM epidemiology. 

• Exposure measurement errors may depend on particle size and composition. PM2.5 better 

reflects personal exposure to PM of outdoor origin than PM10. Various indicators of 

ultrafine particle concentrations or other components of PM may also be useful exposure 

indicators for epidemiological studies. 

• Seasonal or temporal variations in the measurement errors and their correlations between 

different PM concentration measures and co-pollutants (e.g., SO2, CO, NO2, O3) could 

influence the error analysis results but are not likely to change the interpretation of current 

findings. 

• Multipollutant personal exposure studies have suggested that ambient concentrations of 

gaseous co-pollutants serve as surrogates of personal exposures to particles rather than as 

confounders. The implications for epidemiology are discussed in Chapters 8 and 9. 

• Ordinarily, PM exposure measurement errors are not expected to influence the 

interpretation of findings from either the community time-series or long-term epidemiologic 

studies that have used ambient concentration data if they include sufficient adjustments for 

seasonality and key personal and geographic confounders. 

• In the context of long-term epidemiological studies, it appears that the errors introduced in 

estimating ambient PM indoor/ambient PM outdoor ratios have much more influence on the 

accuracy of the microenvironmental exposure estimation approach than do errors in 

estimating time spent in these microenvironments. 

• To reduce exposure misclassification errors in PM epidemiology, conducting new cohort 

studies of sensitive populations with better real-time techniques for exposure monitoring 

and further speciation of indoor-generated, ambient, and personal PM mass are essential. 

• Based on statistical modeling considerations, it is expected that existing PM exposure 

measurement errors or uncertainties most likely will reduce the statistical power of the PM 

health effects analysis, thus making it difficult to detect a true underlying association 

between the correct exposure metric and the health outcome studied. 

• Although exposure measurement errors for fine particles are not expected to influence the 

interpretation of findings from either the community time-series or the long-term, crosssectional epidemiological studies that have used ambient concentration data, they may 

underestimate the strength of the effect. Sufficient data are not available to evaluate the 

effect of exposure measurement error for other PM species or size fractions. 

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Key Findings 

• Most people spend most of their time indoors where they are exposed to indoor-generated

PM and ambient PM that has infiltrated indoors. 

• Indoor-generated and ambient PM differ in sources, sizes, chemical composition, and

toxicity. 

• The ambient PM concentration and the indoor PM concentration can be measured by

outdoor and indoor monitors. The total personal exposure can be measured by a personal

exposure monitor carried by the person. However, the concentrations of indoor-generated

PM and ambient PM that has infiltrated indoors and the related values of ambient and

nonambient PM exposures must be estimated. 

• From a regression of individual, daily values of total personal exposure on daily PM 

concentrations, the intercept gives the average nonambient PM exposure and the slope gives 

the average attenuation factor (the ratio of ambient PM exposure to ambient PM 

concentration). 

• Similarly, from a regression of individual, daily values of indoor PM concentration on daily 

ambient concentrations, the intercept gives the average concentration of indoor-generated 

PM and the slope gives the average infiltration factor (concentrations of ambient PM that 

has infiltrated indoors/ambient PM concentration). 

• The attenuation factor and the infiltration factor depend on the penetration coefficient, the 

fraction of ambient PM that penetrates through the walls, doors, windows, etc.; the 

deposition or removal rate, a measure of how rapidly PM within the indoor 

microenvironment is removed by deposition to surfaces or by filtration in a heating/cooling 

system; and the air exchange rate, a measure of how rapidly indoor air is replaced by 

outdoor air. The attenuation factor also depends on the fraction of time spent outdoors. 

• The air exchange rate is an important variable for determining the concentration of ambient 

PM found indoors. It can be measured by release and measurement of an inert tracer gas 

indoors. The air exchange rate increases with the opening of windows or doors or the 

operation of window or attic fans. It also increases as the indoor/outdoor temperature 

difference increases. For closed homes, i.e., no open windows or doors, the air exchange 

rate does not appear to be a function of wind speed or direction. 

• The penetration coefficient and the deposition rate can be estimated from measurements of 

outdoor and indoor concentrations under conditions when there are no indoor sources 

(nighttime or unoccupied home). These parameters are functions of particle size. The 

penetration coefficient is high and the deposition rate is low for accumulation mode particles 

(0.1 to 1.0 μm). The penetration coefficient is lower and the deposition rate is higher for 

ultrafine particles (< 0.1 μm) and coarse mode particles (> 1.0 μm). The attenuation factor 

and the infiltration factor are higher for particles in the accumulation mode than for ultrafine 

or coarse particles. 

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• The attenuation factor and the infiltration factor will vary with the air exchange rate and, 

therefore, will vary with season and housing characteristics. These factors will increase with 

increased opening of windows and doors. For closed homes, these factors will increase with 

an increase of the indoor/outdoor temperature difference but they do not appear to be 

affected by wind speed or direction. 

• The regression technique is useful for finding average values of the attenuation factor and 

the nonambient exposure and possibly for estimating the distribution of individual, daily 

values of the nonambient PM exposure. 

• Individual, daily values of the ambient PM exposure, the nonambient PM exposure, and the 

attenuation factor may be determined from individual, daily values of the total PM personal 

exposure and daily ambient PM concentrations by several techniques: 

– Mass balance technique. Direct measurement of the air exchange rate, measurement of 

the fraction of time spent outdoors by a diary of the subject’s activity pattern, and use 

of the equilibrium mass balance equation for the attenuation factor with estimated 

values of the penetration coefficient and the deposition rate. 

– Sulfate ratio technique. Individual, daily values of the attenuation factor (for PM2.5) 

will be given by individual, daily values of personal exposure to sulfate / the daily 

ambient sulfate concentration, provided that there are no indoor sources of sulfate and 

sulfate and PM2.5 have similar particle size distributions. 

– Recursive technique. Indoor-generated emissions, which tend to be episodic, can be 

removed from a continuous record of indoor PM concentration, allowing separation of 

indoor-generated PM and ambient PM that has infiltrated indoors. 

• In pooled studies (different subjects measured on different days), individual, daily values 

of the total PM exposure are usually not well correlated with the daily ambient PM 

concentrations. In longitudinal studies (each subject measured for multiple days), 

individual, daily values of the total PM personal exposure and the daily ambient PM 

concentrations are found to be highly correlated for some, but not all, subjects. 

• Only one study has reported estimated individual, daily values of ambient and nonambient 

PM exposure. Individual, daily values of the total PM personal exposure and the daily 

ambient PM concentrations were poorly correlated. However, individual, daily values of 

ambient PM exposure and the daily ambient PM concentrations were highly correlated. 

Individual daily values of ambient and nonambient PM exposure were not well correlated. 

Individual daily values of nonambient PM exposure and daily ambient PM concentrations 

were also not well correlated. 

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