Particulate Matter 

Urban-Focused Visibility Assessment

Second External Review Draft

January 2010
DISCLAIMER

This second draft document has been prepared by staff from the Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency. Any opinions, findings, 

conclusions, or recommendations are those of the authors and do not necessarily reflect the 

views of the EPA. This document is being circulated to obtain review and comment from the 

Clean Air Scientific Advisory Committee (CASAC) and the general public. Comments on this 

draft document should be addressed to Vicki Sandiford, U.S. Environmental Protection Agency, 

Office of Air Quality Planning and Standards, C504-06, Research Triangle Park, North Carolina 

27711 (email: sandiford.vicki@epa.gov). 

Elements of this report (see Acknowledgements below) have been provided to the U.S. 

Environmental Protection Agency (EPA) by Abt Associates Inc. and Stratus Consulting Inc. in 

partial fulfillment of Contract No. EP-D-08-100, Work Assignment 0-11. Any opinions, 

findings, conclusions, or recommendations are those of the authors and do not necessarily reflect 

the views of the EPA or its contractors. 

ACKNOWLEDGEMENTS

In addition to EPA staff, personnel from Abt Associates Inc. and Stratus Consulting Inc. 

contributed to the writing of this document. The specific chapter where Abt Associates Inc. and 

Stratus Consulting Inc. made contributions is chapter 2 (Urban Visibility Preference Studies).
EPA 452/P-10-002

January 2010

Particulate Matter

Urban-Focused Visibility Assessment

Second External Review Draft 

U.S. Environmental Protection Agency

Office of Air and Radiation

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 27711
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TABLE OF CONTENTS

List of Tables............................................................................................................................................ iv

List of Figures............................................................................................................................................ v

List of Acronyms/Abbreviations............................................................................................................ vii

1 Introduction................................................................................................................................ 1-1

1.1 PM NAAQS BACKGROUND.................................................................................................... 1-3

1.2 VISIBILITY EFFECTS SCIENCE OVERVIEW....................................................................... 1-6

1.3 GOALS AND APPROACH ........................................................................................................ 1-9

1.4 SCOPE OF URBAN-FOCUSED VISIBILITY ASSESSMENT.............................................. 1-11

1.4.1 Background.................................................................................................................... 1-11

1.4.2 Selection of Alternative Scenarios for First Draft Assessments.................................... 1-12

1.4.3 Selection of Alternative Scenarios for Second Draft Assessments ............................... 1-13

1.5 ORGANIZATION OF DOCUMENT ....................................................................................... 1-16

2 Urban Visibility Preference Studies......................................................................................... 2-1

2.1 METHODS USED IN PREVIOUS STUDIES ........................................................................... 2-1

2.2 DENVER, COLORADO ............................................................................................................. 2-3

2.3 VANCOUVER, BRITISH COLUMBIA, CANADA ................................................................. 2-7

2.4 PHOENIX, ARIZONA.............................................................................................................. 2-11

2.5 WASHINGTON, DC................................................................................................................. 2-14

2.5.1 Washington, DC 2001.................................................................................................... 2-15

2.5.2 Washington, DC, 2009................................................................................................... 2-17

2.6 SUMMARY OF PREFERENCE STUDIES AND SELECTION OF CANDIDATE 

PROTECTION LEVELS........................................................................................................... 2-25

3 Estimation of Current PM Concentrations and PM Light Extinction ................................. 3-1

3.1 SUMMARY OF PREVIOUS CHARACTERIZATIONS OF PM CONCENTRATIONS AND 

LIGHT EXTINCTION ................................................................................................................ 3-1

3.1.1 PM2.5 and PM10-2.5 ............................................................................................................ 3-1

3.1.2 PM light extinction .......................................................................................................... 3-4

3.2 OVERVIEW OF APPROACH AND DATA SOURCES FOR URBAN STUDY ANALYSIS 3-9

3.2.1 Study Period, Study Areas, Monitoring Sites, and Sources of Ambient PM Data ........ 3-10

3.2.2 Use of CMAQ Model Validation Runs for 2004 to Augment Ambient Data ............... 3-16

3.2.3 Use of Original IMPROVE Algorithm to Estimate PM light extinction....................... 3-17

IMPROVE ALGORITHM..................................................................................................................... 3-19

3.3 DETAILED STEPS ................................................................................................................... 3-19

3.3.1 Hourly PM2.5 Component Concentrations..................................................................... 3-19

3.3.2 Hourly PM10-2.5 Concentrations...................................................................................... 3-26

3.3.3 Hourly Relative Humidity Data ..................................................................................... 3-27

3.3.4 Calculation of Daylight 1-Hour PM Light Extinction ................................................... 3-27
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3.3.5 Exclusion of Hours with Relative Humidity Greater than 90 Percent from PM Light 

Extinction NAAQS Scenarios and Most Results........................................................... 3-28

3.3.6 Calculation of Daily Maximum 1-Hour PM Light Extinction....................................... 3-32

3.4 SUMMARY OF RESULTS FOR CURRENT CONDITIONS................................................. 3-32

3.4.1 Levels of Estimated PM2.5, PM2.5 Components, PM10-2.5, and Relative Humidity........ 3-32

3.4.2 Levels of Estimated PM light extinction ....................................................................... 3-35

3.4.3 Patterns of Relative Humidity and Relationship between Relative Humidity and PM light 

extinction........................................................................................................................ 3-41

3.4.4 Tile Plots of Hourly PM Light Extinction ..................................................................... 3-44

3.4.5 Extinction Budgets for High PM Light Extinction Conditions ..................................... 3-61

3.5 POLICY RELEVANT BACKGROUND.................................................................................. 3-64

4 PM light extinction under “What If” Conditions of Just Meeting Specific Alternative 

Secondary NAAQS..................................................................................................................... 4-1

4.1 ALTERNATIVE SECONDARY NAAQS BASED ON MEASURED PM LIGHT 

EXTINCTION AS THE INDICATOR ....................................................................................... 4-1

4.1.1 Indicator and Monitoring Method.................................................................................... 4-1

4.1.2 Alternative Secondary NAAQS Scenarios based on Measured PM light extinction ...... 4-1

4.1.3 Monitoring Site Considerations for Alternative Secondary NAAQS Based on Measured 

PM light extinction .......................................................................................................... 4-2

4.1.4 Approach to Modeling “What If” Conditions for Alternative Secondary NAAQS Based 

on Measured PM Light Extinction................................................................................... 4-3

4.2 ALTERNATIVE SECONDARY PM2.5 NAAQS BASED ON ANNUAL AND 24-HOUR PM2.5 

MASS......................................................................................................................................... 4-10

4.2.1 Secondary NAAQS Scenarios Based on Annual and 24-hour PM2.5 Mass................... 4-10

4.2.2 Approach to Modeling Conditions If Secondary PM2.5 NAAQS Based on Annual and 24-

hour PM2.5 Mass Were Just Met..................................................................................... 4-11

4.3 RESULTS FOR “JUST MEETING” EACH ALTERNATIVE SECONDARY NAAQS 

SCENARIO................................................................................................................................ 4-12

5 References................................................................................................................................... 5-1

Appendices.............................................................................................................................................. 5-1

Appendix A - PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Analysis of 

PM light extinction in the 15 Study Areas.............................................................................. A-1

Appendix B - Distributions of Estimated PM2.5 And Other Components...................................... B-1

Appendix C - Development of PRB Estimates of PM2.5 components, PM10-2.5, and PM light 

extinction....................................................................................................................................C-1

Appendix D - Relationships between PM Mass Concentration and PM light extinction under 

Current Conditions................................................................................................................... D-1

Appendix E - Differences in Daily Patterns of Relative Humidity and PM light extinction between 

Areas and Seasons..................................................................................................................... E-1

Appendix F - Distributions of Maximum Daily and Hourly Daylight PM light extinction - Under 

“Just Meet” Conditions.............................................................................................................F-1
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Appendix G – ADditional Information on the Exclusion of Daylight Hours with Relative Humidity 

Greater Than 90 Percent..........................................................................................................G-1

APPENDIX H – Inter-Year Variability..............................................................................................H-1

APPENDIX I – Daylight Hours............................................................................................................ I-1
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LIST OF TABLES

Table 2-1. VAQ of Denver photos substantively misclassified by majority of participants...... 2-5

Table 2-2. Summary of photographs used in British Columbia study........................................ 2-9

Table 2-3 Logit Analysis Results.............................................................................................. 2-28

Table 2-4 Logit model estimated VAQ values corresponding to various percent acceptability 

values for the four cities..................................................................................... 2-29

Table 3-1. Annual Mean Reconstructed 24-hour Light Extinction Estimates............................ 3-7

Table 3-2. Urban Visibility Assessment Study Areas .............................................................. 3-11

Table 3-3. PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Tacoma 

Study Area ......................................................................................................... 3-14

Table 3-4. Number of days per quarter in each study area ....................................................... 3-15

Table 3-5 Assumed daylight hours by season (Local Standard Time)..................................... 3-28

Table 3-6 Comparison of Meteorological Parameters for Daylight Hours with Relative Humidity 

Greater than 90 Percent and Other Daylight Hours, During 2005 -2007 .......... 3-31

Table 3-7 Percentage of daily maximum hourly values and individual hourly values of daylight 

PM light extinction exceeding CPLs (excluding hours with relative humidity 

greater than 90 percent). .................................................................................... 3-39

Table 4-1. Alternative Secondary NAAQS Scenarios for PM Light Extinction ........................ 4-2

Table 4-2. Current Conditions PM light extinction design values for the study areas. ............... 4-5

Table 4-3. Percentage reductions in non-PRB light extinction required to “just meet” the 

NAAQS scenarios based on measured light extinction. ...................................... 4-9

Table 4-4. Percentage reductions required in non-PRB PM2.5 mass to “just meet” NAAQS 

scenarios based on annual and 24-hour PM2.5 mass........................................... 4-12

Table 4-5. PM light extinction design values for “just meeting” secondary NAAQS scenarios 

based on measured PM light extinction (excluding hours with relative humidity 

greater than 90 percent) ..................................................................................... 4-17

Table 4-6. PM light extinction design values for “just meeting” secondary NAAQS scenarios 

based on PM2.5 mass (excluding hours with relative humidity greater than 90 

percent) .............................................................................................................. 4-18

Table 4-7. Percentage of days across three years (two in the case of Phoenix and Houston) with 

maximum 1-hour daylight PM light extinction above CPLs when “just meeting” 

the NAAQS scenarios........................................................................................ 4-19
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LIST OF FIGURES

Figure 1-1 Progression from PM characteristics to PM light extinction that shows the modeling 

approach (shaded light green) as well as the use of direct measurements (shaded 

blue) as alternative ways to estimate PM light extinction. .................................. 1-7

Figure 1-2 Progression from PM light extinction to value of visual air quality (VAQ)............. 1-9

Figure 2-1 Percent of Denver participants who considered VAQ in each photograph 

“acceptable.” ........................................................................................................ 2-4

Figure 2-2 Photograph time of day information ......................................................................... 2-6

Figure 2-3 Denver photograph time of day results (9:00 a.m. photographs eliminated), with the 

broader range (17.7 dv and 24.6 dv) of the 50% acceptability criteria shown. ... 2-7

Figure 2-4 Composite Chilliwack, BC photograph shows VAQ of 14.1 dv and 34 dv.............. 2-8

Figure 2-5 Percent of BC participants who consider VAQ in each photograph “acceptable.” 2-11

Figure 2-6 Reproduction of image with the best VAQ (15 dv) used in the Phoenix study...... 2-13

Figure 2-7 Percent of Phoenix participants who consider VAQ in each image “acceptable.”. 2-14

Figure 2-8 Reproduction of the image with the best VAQ (8.8 dv) used in the Washington, DC 

study................................................................................................................... 2-16

Figure 2-9 Percent of 2001 Washington participants who considered VAQ acceptable in each 

image.................................................................................................................. 2-17

Figure 2-10 Percent of 2009 Test 1 study participants who considered VAQ acceptable in each 

image, showing the range of the lower and upper bound of 50% acceptability 

criteria. ............................................................................................................... 2-19

Figure 2-11 Combined results of two Washington preference studies (showing 50% 

acceptability criteria from 2009, Test 1)............................................................ 2-20

Figure 2-12 Comparison of results from Test 1 and Test 2 (Smith and Howell, 2009). .......... 2-21

Figure 2-13 Average quality of visibility ratings for the Washington, DC WinHaze images by 

participants in Tests 1-3 conducted by Smith and Howell (2009)..................... 2-22

Figure 2-14 Comparison of results from the Smith and Howell (2009) Test 1 and Test 3. ..... 2-24

Figure 2-15 Composite results from Smith and Howell (2009) Tests 1 and 3, and Abt (2001) 

Washington, DC pilot study............................................................................... 2-25

Figure 2-16 Summary of results of urban visibility studies in four cities, showing the identified 

range of the 50% acceptance criteria . ............................................................. 2-26

Figure 3-1. Annual average and 24-hour (98th percentile 24-hour concentrations) PM2.5 

concentrations in μg/m3, 2007. ............................................................................ 3-2

Figure 3-2. Reconstructed 24-hour light extinction in U.S. urban areas in 2003 ....................... 3-6

Figure 3-3. Isopleth map of annual total reconstructed particulate extinction based on 2000-2004 

IMPROVE data.................................................................................................... 3-8

Figure 3-4. January and August monthly average diurnal profiles of PM2.5 components derived 

from the 2004 CMAQ modeling platform, for the Detroit study area............... 3-18

Figure 3-5. Sequence of steps used to estimate hourly PM2.5 components and PM light extinction

............................................................................................................................ 3-22

Figure 3-6. Example from Detroit study area. .......................................................................... 3-23

Figure 3-7. Distribution of PM parameters and relative humidity across the 2005-2007 period, 

by study area ...................................................................................................... 3-33
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Figure 3-8. Distributions of estimated daylight 1-hour PM light extinction and maximum daily 

daylight 1-hour PM light extinction across the 2005-2007 period, by study area 

(excluding hours with relative humidity greater than 90 percent)..................... 3-37

Figure 3-9. Distributions of 1-hour PM light extinction levels by daylight hour across the 2005-

2007 period, by study area (excluding hours with relative humidity greater than 

90 percent). ........................................................................................................ 3-40

Figure 3-10. Distributions of 1-hour relative humidity levels by daylight hour across the 2005-

2007 period, by study area (excluding hours with relative humidity greater than 

90 percent). ........................................................................................................ 3-42

Figure 3-11. Scatter plot of daylight 1-hour relative humidity (percent) vs. reconstructed PM 

light extinction (Mm-1) across the 2005-2007 period, by study area (excluding 

hours with relative humidity greater than 90 percent)....................................... 3-43

Figure 3-12 Tile Plots of Hourly PM Light Extinction ............................................................ 3-46

Figure 3-13 Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 1-

hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours

............................................................................................................................ 3-66

Figure 4-1 Comparison of Daily Max and All Daylight Hour Design Values........................... 4-6

Figure 4-2 Comparison of Required Percentage Reductions in Non-PRB PM Light Extinction 

Needed to Meet NAAQS Scenarios................................................................... 4-10

Figure 4-3 Distributions of daily maximum daylight 1-hour PM light extinction under two “just 

meeting” secondary NAAQS scenarios (excluding hours with relative humidity 

greater than 90 percent) ..................................................................................... 4-15
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LIST OF ACRONYMS/ABBREVIATIONS

AQS EPA’s Air Quality System 

BAM Beta Attenuation Mass Monitor 

BC British Columbia

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CASAC Clean Air Scientific Advisory Committee 

CBSA Consolidated Business Statistical Area 

CCN Cloud Condensation Nuclei 

CDPHE Colorado Department of Public Health and Environment

CMAQ Community Multiscale Air Quality 

CONUS CMAQ simulations covering continental US 

CPL Candidate Protection Level

CRA Charles River Associates

CSA Consolidated Statistical Area

CSN Chemical Speciation Network 

CTM Chemical Transport Model 

DRE Direct Radiative Effects

dv deciview 

EPA United States Environmental Protection Agency 

FEM Federal Equivalent Method 

FRM Federal Reference Method 

GEOS Global Scale Air Circulation Model 

IMPROVE Interagency Monitoring of Protected Visual Environment 

ISA Integrated Science Assessment 

Km Kilometer 

LCD Liquid Crystal Display

LOESS Locally weighted Scatter Plot Smoothing 

Mm Megameter

MSA Metropolitan Statistical Area

N Nitrogen 

NAAQS National Ambient Air Quality Standards 

NARSTO North American Research Strategy for Tropospheric Ozone

NCEA National Center for Environmental Assessment 

NOAA National Oceanic and Atmospheric Administration 
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NOx Nitrogen oxides 

NPS National Park Service 

NRC National Research Council 

NWS National Weather Service 

OAQPS Office of Air Quality Planning and Standards 

OAR Office of Air and Radiation 

OMB Office of Management and Budget 

ORD Office of Research and Development

PA Policy Assessment 

PM Particulate Matter 

PM2.5 Particles with a 50% upper cut-point of 2.5 μm aerodynamic 

diameter and a penetration curve as specified in the Code of 

Federal Regulations. 

PM10 Particles with a 50% upper cut-point of 10± 0.5 μm aerodynamic 

diameter and a penetration curve as specified in the Code of 

Federal Regulations. 

PM10-2.5 Particles with a 50% upper cut-point of 10 μm aerodynamic 

diameter and a lower 50% cut-point of 2.5 μm aerodynamic 

diameter. 

PRB Policy Relevant Background 

REA Risk and Exposure Assessment 

RF Radiative Forcing 

RH Relative Humidity

SANDWICH Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbonaceous 

mass approach 

SEARCH Southeastern Aerosol Research and Characterization Study

SMOKE Sparse Matrix Operator Kernal Emissions 

S Sulfur 

SO2 Sulfur Dioxide 

SOx Sulfur Oxides 

STP Standard Temperature and Pressure

TEOM Tapered Element Oscillating Microbalance

UBC University of British Columbia

UFVA Urban-Focused Visibility Impact Assessment 

VAQ Visual Air Quality 
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1 1 INTRODUCTION

2 The U.S. Environmental Protection Agency (EPA) is presently conducting a review of 

3 the national ambient air quality standards (NAAQS) for particulate matter (PM). Sections 108 

4 and 109 of the Clean Air Act (Act) govern the establishment and periodic review of the NAAQS. 

5 The NAAQS are to be based on air quality criteria, which are to accurately reflect the latest 

6 scientific knowledge useful in indicating the kind and extent of identifiable effects on public 

7 health or welfare that may be expected from the presence of the pollutant in ambient air. The 

8 EPA Administrator is to promulgate and periodically review, at no later than five-year intervals, 

9 “primary” (health-based) and “secondary” (welfare-based) NAAQS for such pollutants. Based 

10 on periodic reviews of the air quality criteria and standards, the Administrator is to make 

11 revisions in the air quality criteria and standards, and to promulgate any new standards, as may 

12 be appropriate. The Act also requires that an independent scientific review committee advise the

13 Administrator as part of this NAAQS review process, a function performed by the Clean Air 

14 Scientific Advisory Committee (CASAC).

15 The current NAAQS for PM are a suite of identical primary and secondary standards 

16 established to provide protection from health and welfare effects related to fine and coarse 

17 particles, using PM2.5 and PM10 as indicators, respectively (71 FR 61144, October 17, 2006). 

18 With regard to the primary standards for fine particles, in 2006 EPA revised the level of the 24-

hour PM2.5 standard to 35 μg/m3 (calculated as a 3-year average of the 98th 19 percentile of 24-hour 

20 concentrations at each population-oriented monitor), retained the level of the annual PM2.5 annual 

standard at 15 μg/m3 21 (calculated as the 3-year average of the weighted annual mean PM2.5 

22 concentrations from single or multiple community-oriented monitors), and revised the form of 

the annual PM2.5 standard by narrowing the constraints on the optional use of spatial averaging1

30 The 2006 secondary standards for PM2.5 and PM10 were set to be identical to the primary 

31 standards, on the basis that these standards would, in conjunction with the Regional Haze 

23 . 

24 With regard to the primary standards for PM10, EPA retained the 24-hour PM10 standard at 150 

μg/m3 25 (not to be exceeded more than once per year on average over 3 years) and revoked the 

26 annual standard because available evidence generally did not suggest a link between long-term 

27 exposure to current ambient levels of coarse particles and health effects. The 2006 primary 

28 standards were based primarily on a large body of epidemiological evidence relating ambient PM 

29 concentrations to various adverse health outcomes. 

 1 In the revisions to the PM NAAQS finalized in 2006, EPA tightened the constraints on the spatial averaging option 

limiting the conditions under which some areas may average measurements from multiple community-oriented 

monitors to determine compliance (see 71 FR 61165-61167, October 17, 2006).
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Program2

The next periodic review of the PM NAAQS is now underway.

1 , provide appropriate protection to address PM-related welfare effects, including

2 visibility impairment, effects on vegetation and ecosystems, materials damage and soiling, and 

3 effects on climate change. (As noted below, this judgment was reversed and remanded by the 

4 Court of Appeals for the District of Columbia Circuit.)

3 5 In the Integrated 

6 Review Plan for the National Ambient Air Quality Standards for Particulate Matter, March 2008 

7 (US EPA, 2008a), EPA outlined the science policy questions that will frame this review, outlined 

8 the process and schedule that the review will follow, and provided more complete descriptions of 

9 the purpose, contents, and approach for developing the key documents that will be developed in 

the review.4

17 Building upon the visibility effects evidence presented in the PM ISA, as well as CASAC 

18 advice (Samet, 2009a and b) and public comments on the plan for and first draft of the UFVA

19 (US EPA, 2009b, c), EPA’s Office of Air Quality Planning and Standards (OAQPS) has 

20 developed this second draft Urban-Focused Visibility Assessment (UFVA) describing the 

21 quantitative assessments conducted by the Agency to support the review of the secondary PM 

22 standards. This draft document presents the methods, key results, observations, and related 

23 uncertainties associated with the quantitative analyses performed. Revisions to this second draft

24 UFVA draw upon the final ISA and reflect consideration of CASAC and public comments on the 

25 first draft UFVA, as described in section 1.2 below. 

10 EPA has recently completed the process of assessing the latest available policy11 relevant scientific information to inform the review of the PM standards. The final assessment is 

12 contained in the final Integrated Science Assessment for Particulate Matter (ISA, US EPA, 

13 2009a) which was released in December 2009. The final PM ISA includes a summary of the 

14 scientific evidence for the relationship of PM to visibility effects, remote area and urban haze 

15 conditions, the PM components responsible for visibility impacts, and studies of public 

16 preference with respect to urban visibility conditions.

26 The final ISA and final UFVA will inform the policy assessment and rulemaking steps 

27 that will lead to final decisions on the secondary PM NAAQS. A draft Policy Assessment (PA) 

28 is now being prepared by OAQPS staff to provide a transparent staff analysis of the scientific 

 2 See http://www.epa.gov/air/visibility/program.html for more information on EPA’s Regional Haze Program.

3 See http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_index.html for more information on the current and 

previous PM NAAQS reviews.

4 On November 30, 2007, EPA held a consultation with the Clean Air Scientific Advisory Committee (CASAC) on 

the draft IRP (Henderson, 2008). Public comments were also requested on the draft plan and presented at that 

CASAC teleconference. The final IRP incorporated comments received from CASAC and the general public on the 

draft plan as well as input from senior Agency managers. CASAC is an independent scientific advisory committee 

established to meet the requirements of section 109(d)(2) of the Clean Air Act. See

http://yosemite.epa.gov/sab/sabpeople.nsf/WebCommittees/CASAC for more information, and, in particular, 

information on the CASAC PM Review Panel activities.
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1 basis for alternative policy options for consideration by senior EPA management prior to 

2 rulemaking. The PA is intended to help “bridge the gap” between the Agency’s scientific 

3 assessments, presented in the ISA and UFVA, and the judgments required of the Administrator 

4 in determining whether it is appropriate to retain or revise the secondary PM standards. The PA 

5 will integrate and interpret information from the ISA and the UFVA to frame policy options and 

6 to facilitate CASAC’s advice to the Agency and recommendations on any new standards or 

7 revisions to existing standards as may be appropriate, as provided for in the Clean Air Act. A 

8 very preliminary draft PA was released in September 2009 to facilitate discussion on the overall 

9 structure, areas of focus, and level of detail to be included in a first external review draft PA 

10 document, which EPA plans to release for CASAC review and public comment in February of 

11 2010. This preliminary draft PA was discussed in conjunction with CASAC review of and 

12 public comment on the second draft ISA, first draft UFVA, and first draft health risk assessment 

13 documents produced in support of this PM NAAQS rulemaking.

14 1.1 PM NAAQS BACKGROUND

15 In the review of the secondary PM NAAQS completed in 2006, EPA took into account 

16 that the Regional Haze Program, authorized under sections 169A and 169B of the CAA, was 

17 established to address all human-caused visibility impairment in federal Class I areas. The

18 national goal of this program is to prevent any future, and remedy any existing, impairment of 

19 visibility in mandatory class I Federal areas (Class I areas) which impairment results from 

20 manmade air pollution. This program also mandates that states develop SIPs to ensure that 

21 reasonable progress is made towards meeting those goals. Because Congress explicitly targeted 

22 Class I areas for this pristine level of protection, it can be concluded that Congress did not 

envision such a stringent goal in non-Class I areas. See American Trucking Ass’n v. 

33 Recognizing that efforts were underway to provide increased protection to Class I areas 

34 under the Regional Haze Program, EPA focused the 2006 PM NAAQS review on visibility 

35 impairment in non-Class I areas. Because most of the available non-Class I PM data came from 

23 Browner, 

24 175 F. 3d 1027, 1056-57 (D. C. Cir. 2002) (upholding this position). However, Congress 

25 recognized that visibility impairment can and often does occur in areas outside federal Class I 

26 areas, including urban areas and judged that protection from visibility impairment was important 

27 in those areas as well. In this regard, Congress included visibility effects in the definition of 

28 public welfare effects that should be protected under the national ambient air quality standards 

29 (NAAQS) program authorized in sections 108 and 109 of the CAA. As a result, EPA may 

30 establish secondary standards addressing visibility impairment notwithstanding existence of the 

31 Regional Haze Program. Under the NAAQS program, it is up to the Administrator to judge what 

32 is the requisite level of public welfare visibility protection. 
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1 PM monitoring sites located primarily in urban areas, the assessments took on an urban focus. In 

2 addition, EPA considered available information on people’s preferences for different levels of 

3 visual air quality which came from studies conducted in urban areas and from existing urban 

4 visibility programs and goals. 

5 In an effort to minimize the factors that historically had complicated efforts to address 

6 visibility impairment nationally, given the substantial East/West differences observed in Class I 

7 areas, EPA staff noted that with respect to fine particles, East/West differences are substantially 

8 smaller in urban than in rural areas. Further, relative humidity levels, though generally higher in 

9 eastern than western areas, are appreciably lower in both regions during daylight as compared to 

10 nighttime hours. The PM2.5 data available at that time in urban areas were obtained using a filter 

11 –based Federal Reference Method (FRM) which captures ambient PM2.5 on a filter and then dries 

12 it to get the dry PM2.5 mass concentration. By drying the sample, most water and to some extent 

13 other labile PM compounds evaporate so that the original characteristics (e.g., particle size and 

14 composition) of the ambient PM are altered. Using PM and meteorological data from 161 cities, 

15 EPA staff assessed the correlations between PM2.5 levels and reconstructed light extinction (RE) 

16 during daylight hours for different regions of the country. This assessment showed that the 

17 strongest correlation in the relationship of ambient PM light extinction to dry PM2.5 mass 

18 concentration was during afternoon periods when lower relative humidity conditions generally 

19 prevailed in all regions of the country and ambient PM was drier (US EPA, 2005). While EPA 

20 recognized that the effect of ambient PM on visibility results from the ambient particle 

21 characteristics of size, concentration, and composition (including associated water) present in the 

22 air in the sight path of the observer, given the data availability at the time, EPA viewed the FRM 

23 altered PM2.5 mass concentration as a permissible indicator for addressing ambient PM-related 

24 visibility effects at the national scale during afternoon hours. Thus, the 2005 Staff Paper chose 

25 to address the issue in terms of averaging time rather than indicator, discussing the use of a sub26 daily afternoon dry PM2.5 standard, because the generally lower afternoon relative humidity 

27 tended to produce a more uniform relationship between light extinction and dry PM2.5 mass 

28 concentration throughout the country, therefore providing a more uniform level of visibility 

29 protection nationwide. This more uniform level of visibility protection, however, was limited to 

30 the afternoon hours of the day when relative humidity and visibility impairment tend to be the 

31 lowest.

32 Based on the above, in the 2005 PM Staff Paper, EPA staff recommended a separate sub33 daily secondary standard to address visibility impairment using dried PM2.5 mass concentration 

34 as the indicator, a recommendation endorsed by CASAC. In the 2006 proposal notice, however, 

35 EPA proposed to revise the secondary standards by making them identical to the suite of 

36 proposed primary standards for fine and coarse particles, to provide protection against PM-
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1 related public welfare effects including visibility impairment, effects on vegetation and 

2 ecosystems, materials damage and soiling, and climate, while soliciting comment on adding a 

3 new sub-daily PM2.5 secondary standard to address visibility impairment primarily in urban areas

4 (71 FR 2620). CASAC provided additional advice to EPA in a letter to the Administrator 

5 requesting reconsideration of CASAC’s recommendations for both the primary and secondary 

6 PM2.5 standards as well as standards for thoracic coarse particles (Henderson, 2006). With 

7 regard to the secondary standard, CASAC reaffirmed “... the recommendation of Agency staff 

8 regarding a separate secondary fine particle standard to protect visibility.... the CASAC wishes 

9 to emphasize that continuing to rely on primary standards to protect against all PM-related 

10 adverse environmental and welfare effects assures neglect, and will allow substantial continued 

11 degradation, of visual air quality over large areas of the country” (Henderson, 2006).

12 On September 21, 2006, EPA announced its final decisions to provide increased 

13 protection of public welfare by making the secondary NAAQS identical to the revised primary 

14 standards (71 FR 61144, October 17, 2006). This suite of secondary standards was designed to 

15 address both visibility and other non-visibility welfare related effects. Specifically, with regard 

16 to the secondary welfare effect of visibility impairment, the Administrator believed that revising 

17 both the 24-hour and annual PM2.5 secondary standards to be identical to the revised suite of 

18 PM2.5 primary standards was a reasonable policy approach to address visibility impairment 

19 primarily in urban areas. In particular, EPA revised the level of the 24-hour PM2.5 standard to 35 

μg/m3, retained the level of the annual PM2.5 standard at 15 μg/m3 20 , and revised the form of the 

21 annual PM2.5 standard by narrowing the constraints on the optional use of spatial averaging. 

22 With regard to the other non-visibility PM-related welfare effects such as vegetation and 

23 ecosystems, materials damage and soiling, and climate, the Administrator concluded that it was 

24 appropriate to address these effects by revising the current suite of PM2.5 secondary standards, 

25 making them identical in all respects to the suite of primary PM2.5 standards, while retaining the 

26 current 24-hour PM10 secondary standard and revoking the current annual PM10 secondary 

27 standard. In particular for coarse particles, EPA retained PM10 as the indicator for purposes of 

28 regulating the coarse fraction of PM10 and retained the 24-hour secondary PM10 standard at 150

μg/m3 29 and revoked the annual secondary PM10 standard.

30 Several parties filed petitions for review following promulgation of the revised PM 

31 NAAQS in 2006. These petitions addressed a number of issues, including the decision to set the 

32 secondary PM2.5 standards identical to the primary standards. On judicial review the court 

33 remanded the secondary PM2.5 NAAQS to EPA because the Agency failed to adequately explain 

34 why setting the PM2.5 secondary standards equal to the primary PM2.5 standards provided the 

35 required protection from visibility impairment. In particular, the Agency failed to identify a 

36 target level of visibility impairment that would be requisite to protect the public welfare, and 
January 2010 1-6 DRAFT - Do Not Quote or Cite

1 improperly relied on a misleading comparison of the number of counties which would be in 

2 nonattainment for the revised primary NAAQS compared to one alternative secondary standard 

3 under consideration. Among other things, this equivalence analysis failed to address the issue of 

4 regional differences in humidity-related effects on visibility. American Farm Bureau Federation

5 v. EPA, 559 F. 3d 512, 530-31 (D.C. Cir. 2009).

6 1.2 VISIBILITY EFFECTS SCIENCE OVERVIEW

7 Light extinction is the loss of light per unit of distance and occurs when light is scattered

8 and/or absorbed. Particulate matter and gases can both scatter and absorb light. Light scattering 

9 by gases (e.g., nitrogen, oxygen, etc.) that comprise the pollutant free or clean atmosphere (also 

10 known as Rayleigh or clean-air scattering) is related to the density of the air, which is 

11 sufficiently constant with elevation that it can be taken to be a time invariant constant that 

12 depends principally on elevation above sea level. NO2 is the only atmospheric pollutant gas that 

13 absorbs light appreciably and its effects are generally small (i.e., less than 5%) compared to PM 

14 light extinction. Hereinafter the phrase “PM light extinction” indicates that the Rayleigh 

contribution to light extinction (nominally considered 10 Mm-1 15 ) has been subtracted out and the 

16 NO2 contribution is considered negligible or is simply excluded due to the measurement 

17 approach used. By contrast, the term “light extinction” or “total light extinction” is meant to 

18 include both the Rayleigh and NO2 contributions. 

19 Visual air quality is defined as the visibility effect caused solely by air quality conditions 

20 and excluding those associated with meteorological conditions like fog and precipitation. It is 

commonly measured as either light extinction (in terms of inverse megameters, Mm-1 21 ) or the 

22 haziness index (in terms of deciview, dv) (Pitchford and Malm, 1993). The haziness index 

23 measured in deciview units was developed for use in visibility perception studies because it has a 

24 more linear relationship to perceived changes in haze compared with light extinction. It is 

25 defined as ten times the natural logarithmic of one tenth of the light extinction in inverse 

megameter units (Mm-1 26 ) (Pitchford and Malm, 1993). Light extinction and haziness are physical 

27 measures of the amount of visibility impairment (e.g., the amount of “haze”), with both 

28 increasing as the amount of haze increases.

29 PM is a heterogeneous mixture of particles of different sizes and chemical compositions. 

30 While visibility impairment has been associated most often with PM2.5, larger particles such as 

31 those found in PM10 may be a significant contributor in some areas. Thus, UFVA considers the 

32 visibility impairment caused by all particles 10 microns or smaller. As stated above, the degree 

33 of visibility impairment caused by a given mass of PM depends in large part on the size, density 

34 and chemical composition of the PM. If the ambient PM has a large number of hygroscopic 

35 particles, and also occurs when the relative humidity of the air is higher, those particles will be 
January 2010 1-7 DRAFT - Do Not Quote or Cite

1 larger in size so that the PM will have a larger haze effect than if PM with the same 

2 concentration and composition minus the water was present and the ambient air had lower 

3 relative humidity. 

4 As shown in Figure 1-1, the ambient PM light extinction can be estimated from dry PM 

5 mass and composition data and relative humidity using an algorithm that accounts for water 

6 present in hygroscopic PM components and uses assumed light extinction efficiencies for each of 

7

8 Figure 1-1 Progression from PM characteristics to PM light extinction that shows the 

9 modeling approach (shaded light green) as well as the use of direct measurements (shaded 

10 blue) as alternative ways to estimate PM light extinction.

11

12 

13 the major PM species. Ambient PM light extinction is most accurately determined by direct

14 measurements. However, because there is limited ambient PM light extinction data available in 
January 2010 1-8 DRAFT - Do Not Quote or Cite

1 urban areas, the assessments below will principally use monitored and modeled dry PM mass 

2 and species estimates, along with relative humidity measurements as input to a simple algorithm 

3 for estimating ambient PM light extinction. 

4 The extent to which any amount of light extinction affects a person’s ability to view a 

5 scene depends on both scene and light characteristics. For example the appearance of a nearby 

6 object (i.e., a building) is generally less sensitive to a change in light extinction than the 

7 appearance of a similar object at a greater distance. For a scene with known characteristics, the 

8 degradation in the scene associated with a change in light extinction can be determined and the 

9 resulting appearance can be realistically displayed on a digital photograph of the scene using the

10 WinHaze system. Figure 1-2 below shows the progression from PM light extinction to perceived 

11 visual air quality impacts to the valuation of those perceived impacts.

12 Survey studies have used sets of photographs or computer simulated images developed 

13 from a base photo depicting a range of visibility conditions on urban scenes to assess the 

14 individual’s opinion on the acceptability of conditions. For the specific scenes used in such 

15 studies there is a known or predetermined one-to-one correspondence between the computer 

16 generated haze in the photographs and the associated amount of ambient PM light extinction. 

17 For visibility preference studies, visibility levels are generally characterized using the haze index 

18 in units of deciview (similar to the decibel scale for sound).

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36
January 2010 1-9 DRAFT - Do Not Quote or Cite

1 Figure 1-2 Progression from PM light extinction to value of visual air quality 

2 (VAQ)

3

4 1.3 GOALS AND APPROACH 

5 The principal goal of the UFVA is to characterize recent levels of visibility impairment in 15 

6 urban areas, as well as “just meet” scenarios for both the current secondary PM2.5 standards, as 

7 well as various alternative standards, including those which utilize a different indicator, and a 

8 range of forms that may better reflect the relationship between PM and visibility impairment. In 

9 particular, this UFVA focuses on the use of a PM light extinction-based indicator for a possible 

10 secondary PM NAAQS (see Figure 1-1 and 1-2). This is done by comparing estimates of hourly 

11 PM light extinction in 15 major U.S. urban areas over the three-year period 2005-2007 to the 

12 candidate protection levels (CPLs), which are a range of light extinction values beyond which 

13 half of the participants in assessed urban visibility preference studies indicated the haze 

14 conditions were unacceptable (see discussion in chapter 2 below and Stratus Consulting Inc., 
January 2010 1-10 DRAFT - Do Not Quote or Cite

1 2009). In addition, this second draft UFVA includes additional characterizations of the 

2 effectiveness of a sub-daily PM2.5 mass concentration indicator, which was explored in the 2005 

3 PM staff paper and which was considered a viable option by EPA staff and CASAC in the 2006 

4 review. These latter assessments are summarized in Appendix D. 

5 The previous PM NAAQS review used the results of visibility preference survey studies 

6 conducted in Denver (1990), Phoenix (2003), and British Columbia (1993) as the basis for 

7 suggesting that a standard set to protect visibility conditions to a level within a visual range from 

between about 40 km to about 60 km (corresponding to light extinction from ~100 Mm-1 8 to ~67 

Mm-1) could represent an appropriate degree of welfare protection from PM5

14 The EPA staff, with contractor support, has conducted a more detailed, in-depth 

15 assessment of the results from these studies, including the two Washington, DC studies. This 

16 assessment includes an analysis that combines data from across all studies using graphical and 

17 logit model analysis to examine the consistency of the results between the surveys (Stratus 

18 Consulting Inc., 2009). Based on the results of this analysis, we have been able to refine the 

19 range of visibility conditions that could represent an appropriate degree of public welfare 

20 visibility protection that was put forth in the 2006 review, and to determine a central tendency 

21 value for the CPLs. These analyses and results are described below in chapter 2. 

9 . With the 

10 exception of a small pilot study conducted in Washington, DC in 2001 (9 participants; Abt 

11 Associates Inc., 2001), and a replicate study also conducted for Washington, DC in 2009 (26 

12 participants; Smith and Howell, 2009), there are no additional visibility preference survey studies 

13 upon which to base the selection of CPLs. 

22 In the previous PM NAAQS review, the characterization of urban visibility conditions 

23 were based on IMPROVE algorithm estimates using the 2001 to 2003 PM2.5 mass and speciation 

24 data from 161 urban areas by assuming a constant composition for every hour of the day equal to 

25 the 24-hour measured composition and by using either actual or monthly average (10-year mean) 

26 hour of the day relative humidity. Statistical relationships between hourly light extinction 

27 estimates and concurrent hourly PM2.5 mass concentrations were used to show that daytime and 

28 especially afternoon relationships are relatively strong with a similar linear relationship for both 

eastern and western urban areas (i.e. R2>0.6, slope ~6 m2 29 /g). 

30 The current assessment of urban visibility conditions (as described in chapter 3) uses a 

31 modeling approach to estimate hourly light extinction using PM2.5 mass and speciation data with 

32 measured relative humidity. However, it differs by replacing the unrealistic assumption of 

33 constant composition for PM2.5, with composition that is made to vary during the day using 

34 urban-specific monthly mean diurnal variations of species concentrations determined from 

 5 Light extinction is inversely related to visual range.
January 2010 1-11 DRAFT - Do Not Quote or Cite

1 regional air quality model results, while constraining the means of the hourly species 

2 concentration for each day to closely match the 24-hour duration measured species 

3 concentrations. 

4 1.4 SCOPE OF URBAN-FOCUSED VISIBILITY ASSESSMENT

5 This section provides an overview of the scope and key design elements of the UFVA, 

6 including the process that has been followed to design the analyses. Following initiation of this 

7 PM NAAQS review in 2007, we began the design of the assessments in the UFVA by revisiting 

8 the analyses completed during the previous PM NAAQS review (Abt Associates Inc., 2001; US 

9 EPA, 2005, chapter 6) with an emphasis on considering key limitations and sources of 

10 uncertainty recognized in that review. 

11 1.4.1 Background

12 As an initial step in this review, EPA invited a wide range of external experts as well as 

13 EPA staff, representing a variety of areas of expertise to participate in a workshop titled, 

14 “Workshop to Discuss Policy-Relevant Science to Inform EPA’s Integrated Plan for the Review 

15 of the Secondary PM NAAQS” (72 FR 34005, June 20, 2007). This workshop provided an 

16 opportunity for the participants to broadly discuss the key policy-relevant issues around which 

17 EPA would structure the PM NAAQS review and to discuss the most meaningful new science 

18 that would be available to inform our understanding of these issues. One session of this 

19 workshop centered on issues related to visibility impacts associated with ambient PM. 

20 Specifically, the discussions focused on the extent to which new research and/or improved 

21 methodologies were available to inform how EPA evaluated visibility impairment in this review. 

22 Based in part on these workshop discussions, EPA developed a draft IRP outlining the 

23 schedule, the process, and the key policy-relevant science issues that would guide the evaluation 

24 of the air quality criteria for PM and the review of the primary and secondary PM NAAQS, 

25 including initial thoughts for conducting quantitative assessments (US EPA, 2007, chapter 6). 

26 On November 30, 2007, CASAC held a teleconference with EPA to provide its comments on the 

27 draft IRP (72 FR 63177, November 8, 2007). Public comments were also presented at that 

28 teleconference. A final IRP incorporating comments received from CASAC and the general 

29 public on the draft plan was issued in March 2008 (US EPA, 2008a).

30 In articulating a rationale for the urban focus of this assessment, we reviewed the 

31 available information and found the following information compelling: 1) PM levels in urban 

32 areas are often in excess of those of the surrounding region since urban haze typically includes 

33 both regional and local contributions (US EPA, 2009a; sections 9.2.3.3 and 9.2.3.4), suggesting 

34 the potential for higher levels of PM-induced visibility impairment in urban areas; 2) the 
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1 existence of numerous urban visibility protection programs and goals demonstrating that urban 

2 VAQ is noticed and considered an important value to urban residents (US EPA, 2009a; section 

3 9.2.4); and 3) the existence of large urban populations means that potentially more people are 

4 routinely affected by poor VAQ than in rural areas. These features of urban areas have led EPA 

5 staff to conclude that urban dwellers represent a susceptible population group for adverse PM6 related effects on visibility. However, this conclusion is not meant to imply that there are not 

7 other susceptible populations or individuals living in other non-urban and non-Class I areas that 

8 are currently adversely impacted by ambient PM-related visibility conditions. Unfortunately, 

9 visibility preferences and PM levels in these areas have not been well characterized. Although 

10 this visibility assessment focuses only on selected urban areas, a new secondary PM standard would 

11 apply to all non-Class I areas of the country.

12 On October 6-8, 2008 the EPA sponsored an urban visibility workshop in Denver, 

13 Colorado to identify and discuss methods and materials that could be used in “next step” projects 

14 to develop additional information about people’s preferences for reducing existing impairment of 

15 urban visibility, and about the value of improving urban visibility. Invited individuals came 

16 from a broad array of relevant technical and policy backgrounds, including visual air quality 

17 (VAQ) science, sociology, psychology, survey research methods, economics, and EPA’s process 

18 of setting NAAQS. The 23 people who attended the workshop (including one via teleconference 

19 line) came from EPA, the National Oceanic and Atmospheric Administration (NOAA), National 

Park Service, academia, regional and state air pollution planning agencies, and consulting firms.6

23 1.4.2 Selection of Alternative Scenarios for First Draft Assessments

20

21 The information discussed at this Workshop was useful in informing subsequent steps in the 

22 process.

24 In designing the quantitative assessments to include in the first draft UFVA, EPA staff 

25 developed a planning document outlining the initial design for the PM NAAQS visibility 

26 assessment - Particulate Matter National Ambient Air Quality Standards: Scope and Methods 

27 Plan for Urban Visibility Impact Assessment, henceforth Scope and Methods Plan (US EPA, 

28 2009b). This planning document was released for CASAC consultation and public review in 

29 February 2009. Based on consideration of CASAC and public comments on the Scope and 

30 Methods Plan, along with ongoing review of the latest PM-related literature, several aspects of 

31 the original scope of the urban visibility conditions assessment, as depicted in Figure 1-1 of 

32 section 1.3 of the Scope and Methods document (US EPA, 2009b), were modified in the first 

33 draft UFVA (US EPA, 2009c). Taking into account the nature of urban versus more remote area 

 6 To view the complete report from the October 2008 urban visibility workshop, see: 

http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.h tm
January 2010 1-13 DRAFT - Do Not Quote or Cite

1 PM composition, and input received at the April 2, 2009 CASAC meeting, EPA staff concluded 

2 that it was unnecessary to develop a new urban-optimized algorithm at this time and that it 

3 remained appropriate in the context of this assessment to use the original IMPROVE algorithm 

4 to relate urban PM to local haze (PM light extinction). One of the primary reasons for initially 

5 considering an urban-optimized algorithm was a concern that the organic components of PM in 

6 urban areas, being generally nearer their emission sources, would have a lower ratio to the 

7 measured organic carbon mass than the ratio of organic component mass to measured organic 

8 carbon mass currently used for the more aged PM organic components found in remote areas. 

9 As described below in chapter 3, this concern has been addressed by using the SANDWICH 

10 mass balance approach to estimate the PM organic component mass, which negates the need to 

11 estimate organic component mass from measured organic carbon mass.

12 With regard to the urban visual air quality preference assessment described in the Scope 

13 and Methods document (US EPA, 2009b, section 1.3), more significant modifications occurred. 

14 EPA staff decided to conduct a reanalysis of the urban visibility preference studies available at 

15 the time of the 2006 PM NAAQS review, rather than conduct new public preference studies, as it 

16 has become apparent that the results of these studies would be unlikely to be completed in time 

17 to inform this review. Recognition that the initial plans described in the Scope and Methods 

18 document were possibly overly ambitious was also shared by members of CASAC (see 

19 individual member comments; Samet, 2009a). The analysis, therefore, relied on pre-existing, 

20 rather than new, urban visibility preference studies and was designed to explore the similarities 

21 and differences (comparability) among these studies. Information drawn from these results 

22 informed the selection of VAQ candidate protection levels (CPLs) (described in chapter 2 below) 

23 to be used in subsequent impact assessments. Further, information presented during the public 

24 comment phase of the April 2, 2009 CASAC meeting and later provided to EPA staff, led to the 

25 inclusion of a recent study by Smith and Howell (2009) for Washington, DC in the reanalysis.

26 1.4.3 Selection of Alternative Scenarios for Second Draft Assessments

27 The first draft UFVA was reviewed at an October 2009 CASAC meeting, and a CASAC 

28 letter providing its advice and recommendations was submitted to the Administrator in 

29 November 2009 (Samet, 2009b). In its letter, the CASAC indicated support for EPA staff’s 

30 approach to evaluating the nature and degree of PM-related visibility impairment, including 

31 EPA’s focus on non-Class I areas, including in particular, urban areas as an “effective 

32 complement” to the Regional Haze Rule. In this regard, CASAC expressed support for 

33 consideration of a new PM light extinction indicator, a one hour averaging time, and for the 

34 range of selected candidate light extinction levels. 

35 • Indicator: PM Light Extinction 
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1 There are a number of different ways to measure ambient PM: particle counts, surface

2 area, volume, mass concentrations, and concentration of components. Each of these different 

3 characteristic of ambient PM can be important in the context of different effects. For example, 

4 particle count may be important from the perspective of cloud formation or to characterize the 

5 abundance of ultrafine PM, which is of interest for health effects. In a similar way PM light 

6 extinction measures the characteristic of ambient PM most relevant and directly related to the 

7 effect of PM visibility impairment. Thus, as described in the Scope and Methods document (US 

8 EPA, 2009b) and first draft UFVA, EPA staff is continuing to focus assessments in this second 

9 draft document in terms of ambient PM light extinction as the indicator for PM visibility 

10 impairment, instead of the traditional PM2.5 mass concentration. Unlike PM mass concentration, 

11 which generally changes the composition and size of the particles by driving off most of the 

12 water, ambient PM light extinction captures the PM-induced visibility impairment of the

13 particles as they exist in the atmosphere. PM light extinction, like conventional PM mass 

14 concentration, is a measurable physical characteristic of atmospheric PM. 

15 Section 109 (b) (2) of the CAA states that “Any national secondary ambient air quality 

16 standard prescribed under subsection (a) of this section shall specify a level of air quality the 

17 attainment and maintenance of which ... is requisite to protect the public welfare from any 

18 known or anticipated adverse effects associated with the presence of such air pollutant in the 

19 ambient air....” (emphasis added). In addition, section 108 (a) (2) states that the air quality 

20 criteria “for an air pollutant shall accurately reflect the latest scientific knowledge useful in 

21 indicating the kind and extent of all identifiable effects on public health or welfare which may be 

22 expected from the presence of such pollutant in the ambient air, in varying quantities. The 

23 criteria ... shall include information on (A) those variable factors (including atmospheric 

24 conditions) which of themselves or in combination with other factors may alter the effects on 

25 public health or welfare of such air pollutant;...” (emphasis added). Thus, EPA staff believes 

26 that the visibility effects of PM important to the public welfare are precisely the visibility effects 

27 of PM occurring in the ambient air, which necessarily entails association with ambient 

28 atmospheric conditions that affect the nature or magnitude of the PM visibility effect. These 

29 ambient conditions lead to constant changes in the size and composition of particles as these 

30 particles come in contact with other pollutants or natural components, become oxidized/age as 

31 they are transported great distances, and shrink or grow in the absence or presence of water 

32 vapor, or other atmospheric gases. The combined effect of all these interactions of ambient PM 

33 with real time atmospheric conditions and chemistry on the public welfare effect of visibility 

34 impairment depends on factors other than dry PM mass concentration alone. Use of PM light 

35 extinction as the indictor for a secondary PM NAAQS is thus a more appropriate and direct 
January 2010 1-15 DRAFT - Do Not Quote or Cite

1 measure of the relationship between ambient PM and the public welfare effect of visibility 

2 impairment than any dry PM mass concentration (either PM2.5 or any other dry mass fraction). 

3

4 • Averaging times: Daylight Daily Max. 1 Hour or All Daylight Hours

5 It is necessary to also identify an averaging time to apply along with the CPLs in the 

6 assessments described in chapters 3 and 4. Because the nature of visibility impairment and its 

7 impact on the public welfare is sufficiently different and less well understood at night, this 

8 assessment only considers daylight hour visibility. Though not directly supported by preference 

9 or other studies, EPA staff believes that a short averaging time (e.g. an hour) may be more 

10 appropriate than longer time periods (e.g. multiple hours) since VAQ impacts are instantaneously 

11 perceived. This is also consistent with staff’s belief that most individuals in an urban setting 

12 experience urban VAQ in relatively short-term incidental and intermittent periods when they 

13 have the opportunity to be outdoors (e.g. during commutes to work, school, shopping, etc.). 

14 Since this fraction of the public may experience poor VAQ during a relatively small time period 

15 and not have the opportunity to see it improve later during the same day, it seems appropriate to 

16 EPA staff to consider assessing the current and projected conditions in chapters 3 and 4 by 

17 comparing the 1-hour daily maximum light extinction to each of the three CPLs supported by the 

18 preference studies. There is uncertainty associated with predicting the duration of the effect 

19 associated with such brief periods of exposures, i.e., it is not known how long the person 

20 remembers the poor VAQ conditions once he/she goes indoors and is removed from the sight.

21 Alternately, a complementary fraction of the public may have multiple or continuing 

22 opportunities to experience visibility throughout the day. People in this situation can experience 

23 a variety of conditions ranging from improvement, maintenance, or diminished VAQ throughout 

24 the day. For them, a day with several hours that exceed acceptable VAQ levels may represents a 

25 greater impact on their wellbeing than on a day with only one such hour. To assess impacts 

26 more related to this portion of the population, in which the degree of impact depends upon the

27 conditions present across multiple hours of exposure, EPA staff has also considered all daylight 

28 hours which have light extinction levels beyond the three CPLs, as well as the 1-hour daily 

29 maximum light extinction in the assessments described in chapters 3 and 4.

30

31 • Level: Candidate Protection Levels (CPLs)

32 In order to identify a range of light extinction levels associated with acceptable VAQ to 

33 compare to current and projected conditions in the assessment in chapters 3 and 4 of this 

document, CPLs have been selected in a range from 20 dv to 30 dv (74 Mm-1 to 201 Mm-1 34 ) based 

on the composite results and the effective range of 50th 35 percentile acceptability across the four 

urban preference study areas shown in Figure 2-16. A midpoint of 25 dv (122 Mm-1 36 ) was also 
January 2010 1-16 DRAFT - Do Not Quote or Cite

1 selected for use in the assessment. These three values provide a low, middle, and high set of 

2 light extinction conditions that are used in subsequent sections of the UFVA to provisionally 

3 define daylight hours with urban haze conditions that have been judged unacceptable by the 

4 participants of these preference studies. As discussed in greater detail in section 1.2 above, PM 

5 light extinction is taken to be light extinction minus the Rayleigh scatter (i.e. light scattering by 

atmospheric gases is about 10 Mm-1 6 ), so the PM light extinction levels that correspond to low, 

middle and high CPLs are about 64 Mm-1, 112 Mm-1 and 191 Mm-1 7 , respectively.

8

9 • Forms: Percentiles and Relative Humidity Constraints

10 In considering an appropriate range of forms to consider in the analyses of alternative PM 

11 light extinction visibility standards analyzed in chapter 4 of this second draft UFVA, staff 

12 considered what frequency of conditions at or below the CPLs should be considered acceptable. 

13 Again, none of the preference studies provided insight into this aspect of acceptability. Because 

14 the nature of the public welfare effect is one of aesthetics and/or on feelings of wellbeing and not 

15 directly related to a physical health outcome, EPA staff believes that it is not necessary to 

16 eliminate all such exposures and that some number of hours/days with poor VAQ can reasonably 

be tolerated. In the first draft UFVA, staff selected the 90th and 95th 17 percentiles to assess. In the 

18 CASAC letter following the review of the first draft UFVA, CASAC recommended that other 

19 percentiles be considered, up to and including the 98th percentile used for the current 24-hour primary 

and secondary standards. EPA staff is therefore considering the 90th, 95th and 98th 20 percentiles per 

21 year in this document. Due to inter-annual variability in meterology and other circumstances 

22 that affect air quality, EPA staff is recommending using a three year average form of the 

23 standard for purposes of consistency and stability, as is the current 24-hour primary PM standard. 

24 By considering all of the combinations of the two hourly forms (i.e. each daylight hour and 

25 daylight 1-hour daily maximum), the three CPLs and the three frequencies, a total of 18 separate 

26 alternative secondary PM NAAQS scenarios are generated for use in the assessments described 

27 below in chapters 3 and 4 (See table 4-1). An additional CASAC recommendation, that the 

28 relative humidity (RH) limit be lowered from 95% to 90% and used as a screen (i.e., hours above 

29 it should be discarded) rather than as a cap, to more clearly exclude weather events like fog or 

30 precipitation and to minimize effects of measurement error and spatial variability, has also been 

31 incorporated in this draft. 

32 1.5 ORGANIZATION OF DOCUMENT 

33 The remainder of this document is organized as follows: Chapter 2 includes an analysis 

34 of the urban visibility preference studies with a discussion of similarities and differences 

35 regarding the approaches and methods used and results obtained for each study. This chapter 
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1 also includes a summary discussion of the results of a composite assessment of the combined 

2 results from the four urban areas (Denver, Phoenix, British Columbia, and Washington, DC, an 

3 accompanying logit (statistical) analysis, and use of these results in the selection of the 

4 alternative levels evaluated in the remainder of the assessment. Chapter 3 describes the 

5 analytical approach, methods, and data used in conducting the assessment of recent urban 

6 visibility conditions, both in terms of PM2.5 and PM light extinction indicators for the set of 

7 urban case studies included in this analysis. Selected results are presented in chapter 3, with 

8 additional results found in the Appendices. Chapter 4 presents estimates of PM2.5 and PM light 

9 extinction conditions generated for the urban case studies for six alternative PM2.5 and light 

10 extinction scenarios. Additional information regarding approaches, results, method validation 

11 studies and uncertainty assessments for both chapters 3 and 4 are presented in Appendices A-I).

12
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1 2 URBAN VISIBILITY PREFERENCE STUDIES

2 The purpose of this chapter is to describe the reanalysis of available urban visibility

3 preference studies conducted by EPA staff with contractor support. The goal of this reanalysis 

4 was to provide information useful in selecting a range of light extinction CPLs for use in 

5 subsequent UFVA assessments of current and alternative VAQ conditions. The available urban 

6 visibility preference studies all used a similar group interview type of survey to investigate the 

7 level of visibility impairment that participants described as “acceptable. While each study asked 

8 the basic question, “What level of visibility degradation is acceptable?”, the term “acceptable” 

9 was not defined, so that each person’s response was based on his/her own values and preferences 

10 for VAQ.

11 The reanalysis included three completed urban visibility preference survey studies plus a 

12 pair of smaller focus studies designed to explore and further develop urban visibility survey 

13 instruments. The three western studies included Denver, Colorado (Ely et al., 1991), one in the 

14 lower Fraser River valley near Vancouver, British Columbia (BC), Canada (Pryor, 1996), and 

15 one in Phoenix, Arizona (BBC Research & Consulting, 2003). A pilot focus group study was 

16 also conducted for Washington, DC (Abt Associates Inc., 2001). In response to an EPA request 

17 for public comment on the Scope and Methods Plan (74 FR 11580, March 18, 2009), Dr. Anne 

18 Smith provided comments (Smith, 2009) about the results of a new Washington, DC focus group 

19 study that had been conducted using methods and approaches similar to the method and approach 

20 employed in the EPA pilot study (Smith and Howell, 2009). When taken together, these studies 

21 from the four different urban areas included a total of 852 individuals, with each individual 

22 responding to a series of questions answered while viewing a set of images of various urban 

23 VAQ conditions. 

24 2.1 METHODS USED IN PREVIOUS STUDIES

In all but one7

 7 Smith and Howell (2009) used digital projection technology not used by the other studies to present the series of 

VAQ conditions. Some of the participants in the Smith and Howell study were shown images using a LCD 

projector connected to a laptop computer. In other sessions, participants in the Smith and Howell study were shown 

images on a computer monitor connected to the computer.

25 of the visibility preference studies assessed in this document, participants 

26 were shown a series of different VAQ conditions projected on a large screen using a slide 

27 projector. In the earliest two studies (the Denver and lower Frazer River Valley British 

28 Columbia studies) the range of VAQ conditions were presented by projecting photographs 

29 (slides) of actual VAQ conditions. The photographs were taken on different days from the same 

30 location, and presented the same scene. Photographs were selected to avoid depicting significant 
January 2010 2-2 DRAFT - Do Not Quote or Cite

1 weather events (e.g., rain, snow, or fog), and where measured extinction data were available 

2 from the time the photograph was taken. 

3 The Phoenix study and the Washington, DC projects used computer generated

4 photographic-quality images to present different VAQ conditions. Using an original near5 pristine base photograph, additional images representing a range of VAQ conditions were 

6 generated using the WinHaze software program, which is based on a technique described in 

7 Molenar et al. (1994). The Phoenix study and the 2001 Washington, DC project projected slides 

8 of digital images prepared by WinHaze. The 2009 Washington, DC project presented images 

9 directly from the desktop version of WinHaze using either a liquid crystal display (LCD) 

10 projector or a computer monitor.

11 WinHaze analysis synthetically superimposes a uniform haze on a digitized, actual 

12 photograph. The WinHaze computer algorithm calculates how a given extinction level would 

13 impair the appearance of each individual portion of the photograph. A major advantage of 

14 presenting WinHaze-generated images is that they provide viewers depictions of alternative 

15 VAQ levels, with each image containing exactly the same scene, with identical light angle, time 

16 of day properties, weather conditions, and specific scene content details (e.g., the amount of 

17 traffic in a intersection). Additional details about WinHaze, and a discussion of the applicability 

18 of WinHaze images for regulatory purposes, is in the 2004 PM Criteria Document (U.S. EPA, 

19 2004). The desktop version of WinHaze is available online (Air Resources Specialists, 2008).

20 The first urban visibility preference study (Denver, CO; Ely et al., 1991) developed the 

21 basic survey method used in all the subsequent studies. Although there are variations in specific 

22 details in each study, all the studies use a similar overall approach (key variations are discussed 

23 in the section on each study later in this chapter). This approach consisted of conducting a series 

24 of group interview sessions, where the participants were shown a set of photographs or images of 

25 alternative VAQ conditions and asked a series of questions. The group interview sessions were 

26 conducted multiple times with different participants. Ideally the participants will be a 

27 representative sample of the residents of the metropolitan area. While all studies agree that this 

28 is the preferred approach, due to the high cost of organizing and conducting a series of in-person 

29 group interviews with a large, statistically representative sample, only the Phoenix study was 

30 able to fully meet this objective. During the group interview sessions, the participants were 

31 instructed to consider whether the VAQ in each photograph or image would meet an urban 

32 visibility standard, according to their own preferences and considering three factors: 

33

34 1. The standard would be for their own urban area, not a pristine national park area 

35 where the standards might be stricter.
January 2010 2-3 DRAFT - Do Not Quote or Cite

1 2. The level of an urban visibility standard violation should be set at a VAQ level 

2 considered to be unreasonable, objectionable, and unacceptable visually.

3 3. Judgments of standards violations should be based on visibility only, not on 

4 health effects.

5 The photographs (images) were not shown in order of ascending or descending VAQ 

6 conditions; the VAQ conditions were shown in a randomized order (with the same order used in 

7 each group interview session). In order to check on the consistency of each individual’s 

8 answers, the full set of photographs (images) shown during the group interview included 

9 duplicates with the identical VAQ conditions.

10 The participants were initially given a set of “warm up” exercises to familiarize them 

11 with how the scene in the photograph or image appears under different VAQ conditions. The 

12 participants next were shown 25 randomly ordered photographs (images), and asked to rate each 

13 one based on a scale of 1 (poor) to 7 (excellent). They were then shown the same photographs or 

14 images again (in the same order), and asked to judge whether each of the photographs (images) 

15 would violate what they would consider to be an appropriate urban visibility standard (i.e., 

16 whether the level of impairment was “acceptable” or “unacceptable”). 

17 2.2 DENVER, COLORADO 

18 The Denver urban visibility preference study (Ely et al., 1991) was conducted on behalf 

19 of the Colorado Department of Public Health and Environment (CDPHE). The study consisted 

20 of a series of focus group sessions conducted in 1989 with participants from 16 civic 

associations, community groups, and employees of state and local government organizations.8

24 During the 16 focus group sessions, a total of 214 individuals were asked to rate 

25 photographs of varying visibility conditions in Denver. The photographs were taken November 

26 1987 through January 1988 by a camera in Thornton, Colorado. Thornton is suburb of Denver, 

27 located approximately six miles north of downtown Denver. The photographs were taken as part 

28 of a CDPHE study of Denver’s air quality. The scene in the photographs was toward the south 

29 from Thornton and included a broad view of downtown Denver and the mountains to the south. 

30 Each group was shown one of two sets of 20 randomly ordered unique photographs (13 of the 

31 sessions included 5 duplicate slides, for a total of 25 photographs, to evaluate consistency of 

32 responses). The two sets of different slides were used to investigate whether the responses 

33 between the two sets of photographs were different (no differences were found). Approximately 

21

22 The participants were not selected to be a fully representative sample of the Denver metropolitan 

23 population but were instead selected to take advantage of previously scheduled meetings. 

 8 No preference data were collected at a 17th focus group session due to a slide projector malfunction.
January 2010 2-4 DRAFT - Do Not Quote or Cite

1 100 participants viewed each photograph. Projected color slides were used to present the 

2 photographs to focus group participants, and were projected on a large screen 

3 The VAQ conditions in each Denver photograph were recorded when the photograph was 

taken and measured by a transmissometer yielding hourly average light extinction, bext 4 . The 

5 transmissometer was located in downtown Denver, approximately eight miles from the camera 

6 and in the middle of the camera’s view path. Ely et al. (1991) provide the time of day and 

7 measured extinction level for each photograph. The extinction levels presented in the Denver 

photographs ranged from 30 to 596 Mm-1 8 . This corresponds to 11dv to 41dv, approximating the 

10th to 90th 9 percentile of wintertime visibility conditions in Denver in the late 1980s. 

10 The participants first rated the VAQ in each photograph on a 1 to 7 scale, and 

11 subsequently were asked if each photograph would violate an urban visibility standard. The 

12 individual’s rating on the 1 to 7 scale and whether the photograph violated a visibility standard 

13 were highly correlated (Pearson correlation coefficient greater than 80%). 

14 The percent of participants who found a photograph acceptable to them (i.e., would meet 

15 an appropriate urban visibility standard) was calculated for each photograph. Figure 2-1 shows 

16 the results of the Denver participants’ responses, with VAQ measured in deciviews. 

17

Figure 2-1 Percent of Denver participants who considered VAQ in each 

photograph “acceptable.” 

0%

50%

100%

10 15 20 25 30 35 40 45

Deciview

% participants rating 

"acceptable"

% acceptable Denver standard

18

19 Ely et al. (1991) introduce a “50% acceptability” criteria analysis of the Denver 

20 preference study results. The 50% acceptability criteria is designed to identify the VAQ level 

21 that best divides the photographs into two groups: those with a VAQ rated as acceptable by the 
January 2010 2-5 DRAFT - Do Not Quote or Cite

1 majority of the participants, and those rated not acceptable by the majority of participants. While 

2 no single VAQ level creates a perfect separation between the two groups, the CDPHE identified 

3 a VAQ of 20.3 dv as the point that best separates the Denver study responses into “acceptable” 

4 and “not acceptable” groups. Based in part on the findings of the Denver visibility preference 

study, the CDPHE established a Denver visibility standard at bext = 76 Mm-1 5 (dv = 20.3).

6 Using 20.3 dv as the 50% acceptability criteria led to six photographs being 

7 inconsistently rated by the majority of the viewers. A photograph was inconsistently rated for 

8 two possible reasons; either the photograph’s VAQ was at least 1 dv better than the Denver 

9 standard (i.e., dv < 19.3) but was judged to be “unacceptable” by a majority of the participants 

10 rating that photograph, or the VAQ was at least 1 dv worse than the standard (> 21.3 dv) but 

11 found to be acceptable by the majority of the participants. This definition of inconsistent rating

12 helps evaluate the robustness of the study results to support the selection of the Denver urban 

visibility standard at 76 Mm-1 13 (20.3 dv) by identifying photographs with VAQ a minimum of 1 

14 dv above or below the standard and ignoring “near misses” involving photographs within 1 dv of 

15 the standard. A change of 1 or 2 dv in uniform haze under many viewing conditions will be seen 

16 as a small but noticeable change in the appearance of a scene, regardless of the initial haze 

17 condition (U.S. EPA, 2004).

18 Table 2-1 presents information about the six photographs that were inconsistently rated. 

19 All six of the inconsistently rated photographs were taken at 9:00 a.m. The five inconsistently 

20 rated photographs with a VAQ better than the Denver standard have a VAQ at least 2 dv below 

21 the standard. The VAQ in the only inconsistently rated photograph with air quality worse than 

22 the standard (Photograph #6) is 1.1 dv above the standard. The study used 18 photographs from 

23 9:00 a.m., so a third of the 9:00 a.m. photographs were inconsistently rated. Conversely, none of 

24 the 32 photographs taken at noon or 3:00 p.m. were inconsistently rated. 

25

26 Table 2-1. VAQ of Denver photos substantively misclassified by majority of participants

27

Photograph #

VAQ in photograph 

in extinction 

(Mm-1)

VAQ in 

photograph (dv)

% of participants 

who rated the photo 

“acceptable”

Time of day of 

photograph

14 44 13.8 43% 9:00 a.m.

18 54 16.9 43% 9:00 a.m.

19 54 16.9 31% 9:00 a.m.

20 55 17.0 42% 9:00 a.m.

24 60 17.9 13% 9:00 a.m.

36 85 21.4 72% 9:00 a.m.

28
January 2010 2-6 DRAFT - Do Not Quote or Cite

1 Figure 2-2 shows the same data results about percent of participants who rated each 

2 photograph acceptable as in Figure 2-1, but with the time of day of each photograph indicated by 

3 different colors. The time of day colors clearly indicate how inconsistently participants rated 

4 some of the 9:00 a.m. photographs.

5 Eliminating the 9:00 a.m. photographs creates a “hole” in the range of remaining 

6 photographs; there are no photographs with a VAQ between 17.7 dv and 20.3 dv. As seen in 

7 Figure 2-3, this is a critical range in evaluating the responses. All of the photographs with a 

8 VAQ equal to or better (i.e., a lower dv value) than 17.7 dv are rated acceptable by the majority 

9 of the participants, and all photographs with a VAQ at or above 20.3 dv are rated not acceptable. 

10 After eliminating the 9:00 a.m. photographs, any VAQ level between 17.7dv and 20.3 dv would 

11 completely divide the photographs into two groups with no inconsistent ratings.

12

Figure 2-2 Photograph time of day information 

0%

50%

100%

10 15 20 25 30 35 40 45

Deciview

% participants rating 

"acceptable"

9:00 AM 12:00 PM 3:00 PM Denver standard

13

14 A modestly broader range of VAQ conditions provides an even more unambiguous 

15 interpretation of the Denver study results. Every photograph with a VAQ of 17.7 dv or lower 

16 was rated acceptable by 89% or more of participants, and every photograph with a VAQ of 24.6 

17 or higher was rated not acceptable by 84% or more of the participants. The 17.7 dv to 24.6 dv 

18 range separating the results is shown in Figure 2-3, which also eliminates the 9:00 a.m. results.

19

20

21

22
January 2010 2-7 DRAFT - Do Not Quote or Cite

1

Figure 2-3 Denver photograph time of day results (9:00 a.m. photographs 

eliminated), with the broader range (17.7 dv and 24.6 dv) of the 50% acceptability criteria 

shown. 

 

0%

50%

100%

10 15 20 25 30 35 40 45

Deciview

% Participants Rating 

"Acceptable"

12:00 PM 3:00 PM Denver Standard Lower Bound Upper Bound

2 2.3 VANCOUVER, BRITISH COLUMBIA, CANADA 

3 The BC urban visibility preference study (Pryor, 1996) was conducted on behalf of the 

4 BC Ministry of Environment following the methods used in the Denver study. Participants were 

5 students at the University of British Columbia, who were in one of four focus group sessions 

6 with between 7 and 95 participants. A total of 180 participants completed the surveys (29 did 

7 not complete the survey). 

8 The BC study used photographs (projected as slides) depicting various VAQ conditions 

9 in two cities (Chilliwack and Abbotsford) in the lower Fraser River valley in southwestern BC. 

10 Abbotsford is located approximately 75 miles east of Vancouver, BC, and had a 2006 population 

11 of 159,000 (Statistics Canada, 2009a). Abbotsford has a diverse and successful economy, with 

12 approximately 25% of the labor force working in the Vancouver metropolitan area. Chilliwack 

13 is adjacent to Abbotsford to the east. Both cities have experienced rapid population growth, 

14 growing faster than the Vancouver metropolitan area, and are considered suburbs (or exurbs) of 

15 Vancouver. 

16 The survey was conducted at the University of British Columbia (UBC) in 1994. The 

17 participants were 206 undergraduate and graduate students enrolled in classes in UBC’s 

18 Department of Geography. Information about student demographics and where they lived prior 

19 to enrolling at UBC (which potentially influences their knowledge of, and preferences for, 

20 Vancouver area visibility) is not available. 
January 2010 2-8 DRAFT - Do Not Quote or Cite

1 The BC survey showed 20 unique photographs to the participants in random order. Ten 

2 photographs were from Chilliwack, and 10 were from Abbotsford. The Chilliwack photographs 

3 were taken at the Chilliwack Hospital, and the scene includes a complex foreground with 

4 downtown buildings, with mountains in the background up to 40 miles away. Figure 2-4 is a 

5 composite of two of the Chilliwack photographs used in the preference study, showing the scene 

6 with a good visibility day (14.1 dv) in the middle and a significantly impaired day (34 dv) around 

7 the border (Jacques Whitford AXYS, 2007). The Abbotsford photographs were taken at the 

8 Abbotsford Airport. The Abbotsford scene includes fewer man-made objects in the foreground 

9 and is primarily a more rural scene with the mountains in the background up to 36 miles away.

10

11

Figure 2-4 Composite Chilliwack, BC photograph shows VAQ of 14.1 dv and 34 dv.

12

13 The photographs were taken in July and August 1993 as part of a VAQ and fine 

14 particulate monitoring project sponsored by the BC Ministry of Environment, Lands and Parks 

15 (REVEAL, the Regional Visibility Experimental Assessment in the Lower Fraser Valley). All of 

16 the photographs were taken at either 12:00 p.m. or 3:00 p.m. VAQ data were available for each 

17 photograph from visibility monitors near the location of each camera. The types of VAQ 

18 measurement data available from the two locations were not identical. The Chilliwack location 

19 used both an open-chamber nephelometer and a long path transmissometer and collected hourly 

20 average data on both aerosol light scattering (bsp) and total extinction (bext), respectively. The 
January 2010 2-9 DRAFT - Do Not Quote or Cite

1 visibility monitoring at the Abbotsford location had only a nephelometer and collected only bsp

2 data.

3 As explained in section 1.3, total light extinction is the sum of scattering by gases (bsg) 

4 and particles (bsp) plus light absorption by gases (bag) and particles (bap). In order to present the 

5 preference results from the BC study in comparable terms, bext for the Abbotsford photographs is 

6 estimated by assuming that the average of the ratios of PM light extinction (i.e., bap + bsp) to PM 

7 light scattering (bsp) for all ten of the Chilliwack photographs can be multiplied by the 

8 Abbotsford nephelometer determined bsp values corresponding to each of its photographs to 

9 estimate its PM light extinction value. By assuming that absorption by gases (bag) is zero, total 

10 light extinction is equal to the PM light extinction (i.e., bap + bsp) plus particle scattering by gases 

(i.e., bsg that is approximately equal to 10 Mm-1 11 ). Table 2-2 presents the data from the 

12 photographs used in the BC study, including the estimated bext for the Abbotsford photographs. 

13 There are two caveats to be noted about the extinction data for the photographs reported 

14 in Pryor, 1996. First, in Table 2 of the original article, two of the Abbotsford photographs are 

15 listed with the same date and time (12:00 p.m., 7/26/1993). There is no information provided for 

16 a 3:00 p.m., 7/26/1993 Abbotsford photograph, although there is a Chilliwack photograph from 

17 that time. The preference and VAQ data are presumed to be correct for both photographs and 

18 one of the two identical date/time labels is assumed to be a typographic error. The second caveat 

19 is that bsp levels from the same date and time can differ substantially between Abbotsford and 

20 Chilliwack, and the relative levels can change rapidly, even though the two cities are only 25 

21 miles apart. For example, at 12:00 p.m. on 8/19/1993, the bsp level in Chilliwack was about one22 third of the Abbotsford bsp level. By 3:00 p.m. the situation was reversed, with the Chilliwack 

23 bsp level 50% higher than Abbotsford. In those three hours the Chilliwack bsp level had more 

than doubled (from 46 Mm-1 to 105 Mm-1 24 ), and the Abbotsford level had fallen by over half 

(from 145 Mm-1 to 67 Mm-1 25 ). Such substantial changes in measured bsp levels occurring across a 

26 relatively short period of time and short distance, may reflect an inherent uncertainty introduced 

27 by using a single measure of light extinction from a portion of visual scene (where the 

28 nephelometer or transmissometer was operating) to assess visibility conditions throughout an 

29 actual photographs of a complex scene. Spatial and temporal non-uniformity of visibility 

30 conditions within a scene are an atmospheric condition known to occur on some days, and may 

31 contribute to the variability in participant responses in preference studies utilizing actual 

32 photographs.

33

34

35

36 Table 2-2. Summary of photographs used in British Columbia study
January 2010 2-10 DRAFT - Do Not Quote or Cite

1

Date Time bsp bext

Ratio 

(bext-bsg)/bsp

Estimated 

bext Deciview

Chilliwack

7/26/93 12:00 p.m. 86 128 1.372 NA 25.49

7/26/93 3:00 p.m. 67 112 1.522 NA 24.16

7/27/93 12:00 p.m. 63 105 1.508 NA 23.51

7/27/93 3:00 p.m. 119 185 1.471 NA 29.18

8/2/93 12:00 p.m. 18 37 1.5 NA 13.08

8/2/93 3:00 p.m. 20 36 1.3 NA 12.81

8/5/93 12:00 p.m. 45 70 1.333 NA 19.46

8/5/93 3:00 p.m. 51 96 1.686 NA 22.62

8/19/93 12:00 p.m. 46 81 1.543 NA 20.92

8/19/93 3:00 p.m. 105 170 1.524 NA 28.33

Average 62 102 1.476 21.96

Abbotsford

7/26/93 12:00 p.m. 39 NA NA 68 19.17

7/26/93 12:00 p.m. 82 NA NA 131 25.73

7/27/93 12:00 p.m. 104 NA NA 205 30.20

7/27/93 3:00 p.m. 132 NA NA 164 27.97

8/2/93 12:00 p.m. 24 NA NA 45 15.04

8/2/93 3:00 p.m. 25 NA NA 47 15.48

8/5/93 12:00 p.m. 62 NA NA 121 24.93

8/5/93 3:00 p.m. 75 NA NA 102 23.22

8/19/93 12:00 p.m. 67 NA NA 224 31.09

8/19/93 3:00 p.m. 145 NA NA 109 23.89

Average 76 122 23.67

2

3

4 Figure 2-5 presents the results of the BC study. The division corresponding to the 

5 Denver “50% acceptable” criteria occurs between 22.6 dv and 23.2 dv. All of the photographs 

6 with a VAQ better than 22.6 dv were rated acceptable by the majority of the participants with 

7 one exception (47% of the participants judged the 19.2 dv photograph to be acceptable). All 

8 photographs with a VAQ better than 19.2 dv were rated acceptable by over 90% of the 

9 participants. All photographs with a VAQ worse than 22.6 dv were rated not acceptable by the 
January 2010 2-11 DRAFT - Do Not Quote or Cite

1 majority of the participants, and all photographs with a VAQ worse than 28.3 dv were rated not 

2 acceptable by over 90% of the participants.

3 Figure 2-5 also suggests that there may be some difference between the preferences 

4 expressed for the Chilliwack scene and those for the Abbotsford scene. All photographs were 

5 rated by the same individuals (students at UBC), but the summary of the responses indicate that 

6 the participants may have rated as acceptable a worse level of impaired VAQ impairment (e.g., 

7 higher dv levels) in photographs showing more of a downtown area (Chilliwack) than in less 

8 congested scenes (Abbotsford). The strongest evidence for this hypothesis, however, is the 

9 preference for a single photograph (the 19.0 dv photograph from Abbotsford, rated as acceptable 

10 by 47%), previously identified as an outlier observation. 

11

12 Figure 2-5 Percent of BC participants who consider VAQ in each photograph 

13 “acceptable.”

14

0%

50%

100%

10 15 20 25 30 35 40 45

% Participants Rating "Acceptable"

Deciview

Results of British Columbia Visiblity Study

Chilliwack Abbotsford

15

16

17 The BC Ministry of the Environment is considering the BC urban visibility preference 

18 study as part of establishing urban and wilderness visibility goals in BC.

19 2.4 PHOENIX, ARIZONA 

20 The Phoenix urban visibility preference study (BBC Research & Consulting, 2002),

21 which was conducted on behalf of the Arizona Department of Environmental Quality, used
January 2010 2-12 DRAFT - Do Not Quote or Cite

1 group interviews based on the methods used in the Denver study, with two major exceptions: (1) 

2 the focus group participants were selected as a representative sample of the Phoenix area 

3 population, and (2) the pictures presented in the focus groups were computer-generated images 

4 to depict specific uniform haze conditions. 

5 The Phoenix study included 385 participants in 27 separate focus group sessions. 

6 Participants were recruited using random digit dialing to obtain a sample group designed to be 

7 demographically representative of the larger Phoenix population. During July 2002, group 

8 interview sessions took place at six neighborhood locations throughout the metropolitan area to 

9 improve the participation rate. Participants received $50 as an inducement to participate.

10 Three sessions were held in Spanish in one region of the city with a large Hispanic 

11 population (25%), although the final overall participation of native Spanish speakers (18%) in 

12 the study was below the targeted level. The age distribution of the participants corresponded 

13 reasonably well to the overall age distribution in the 2000 U.S. Census for the Phoenix area 

14 (BBC Research & Consulting, 2002). Participants slightly over-represented the middle-income 

15 range ($50,000 to $74,999), compared with 2000 Census data, and slightly under-represented 

16 very low-income ranges (under $24,999). The distribution of participant education levels was

17 fairly consistent with the education distribution in the 2000 Census.

18 Photographic-quality slides of the images were developed using the WinHaze software 

19 (Molenar et al., 1994). The scene used in the Phoenix study images was taken at a water 

20 treatment plant. The view is toward the southwest, including downtown Phoenix, with the Sierra 

21 Estrella Mountains in the background at a distance of 25 miles. Figure 2-6 shows the image with 

22 the best VAQ (15 dv). 

23
January 2010 2-13 DRAFT - Do Not Quote or Cite

Figure 2-6 Reproduction of image with the best VAQ (15 dv) used in the Phoenix 

study.

1

2 The study used a total of 21 unique WinHaze images. Four of the 21 unique images were 

3 randomly selected and used twice to evaluate consistency; participants viewed a total of 

4 25 images. The 25 images were randomly ordered, with all participants viewing the images in 

5 the same order. The WinHaze images used in the Phoenix study do not include layered haze, a 

6 frequent and widely recognized form of visibility impairment in the Phoenix area.

7 The VAQ levels in the 21 unique images ranged from 15 dv to 35 dv (the extinction 

coefficient bext ranged from 45 Mm-1 to 330 Mm-1 8 ). As in the Denver study, participants first 

9 individually rated the randomly shown slides on the same VAQ scale of 1 to 7. Participants were 

10 instructed to rate the photographs solely on visibility and to not base their decisions on either 

11 health concerns or what it would cost to have better visibility. Next, the participants individually 

12 rated the randomly ordered slides as “acceptable” or “not acceptable,” defined as whether the 

13 visibility in the slide is unreasonable or objectionable. 

14 Figure 2-7 presents the percent acceptability results from the Phoenix study. The 

15 combination of the use of WinHaze images and the larger number of participants than in the 

16 Denver study may account for the “smoother” backwards S-shaped pattern of preferences.

17

18

19

20

21

22
January 2010 2-14 DRAFT - Do Not Quote or Cite

1

Figure 2-7 Percent of Phoenix participants who consider VAQ in each image 

“acceptable.” 

0%

50%

100%

10 15 20 25 30 35 40 45

Deciview

% participants rating "acceptable"

2

3 Ninety percent or more of the participants rated a VAQ of 20 dv or better as acceptable, and 70% 

4 rated a VAQ of 22 dv or better as acceptable. The “50% acceptable criteria” was met at 

5 approximately 24.3 dv (with 51.3% of the participants rating that image as acceptable). The 

6 percent acceptability declines rapidly as VAQ worsens; only 27% of the participants rated a 

7 26 DV image as acceptable, and fewer than 10% rated a 29 dv image as acceptable.

8 The Phoenix urban visibility study formed the basis of the decision of the Phoenix 

9 Visibility Index Oversight Committee for a visibility index for the Phoenix metropolitan area 

10 (Arizona Department of Environmental Quality, 2003). The Phoenix Visibility Index establishes 

11 an indexed system with 5 categories of visibility conditions, ranging from “Excellent” (14 dv or 

12 less, which was a better VAQ than any of the images used in the Phoenix study) to “Very Poor” 

13 (29 dv or greater, which less than 10% of the study participants rated as acceptable). The 

14 “Good” range is 15 dv to 20 dv (more than 90% of the participants rated images in this VAQ 

15 range as acceptable). The environmental goal of the Phoenix urban visibility program is to 

16 achieve continued progress through 2018 by moving the number of days in poorer quality 

17 categories into better quality categories.

18 2.5 WASHINGTON, DC

19 One of the Washington, DC urban visibility pilot studies was conducted on behalf of 

20 EPA (Abt Associates Inc., 2001). It was designed to be a pilot focus group study, an initial 
January 2010 2-15 DRAFT - Do Not Quote or Cite

1 developmental trial run of a larger study. The intent of the pilot study was to refine both focus 

2 group method design and potential survey questions. Due to funding limitations, only a single 

3 focus group session took place, consisting of one extended session with nine participants. No 

4 further urban visibility focus group sessions were held in Washington, DC on behalf of EPA.

5 In March 2009, Dr. Anne Smith conducted a separate study of Washington urban 

6 visibility, using the same photographs and similar approach as the 2001 study (Smith and 

7 Howell, 2009). On behalf of the Utility Air Regulatory Group, Dr. Smith presented comments 

8 (Smith, 2009) to the CASAC at a public meeting held on April 2, 2009 to review EPA’s plan

9 (US EPA, 2009b) for conducting further urban visibility studies in support of PM NAAQS 

10 reviews. Dr. Smith submitted the Smith and Howell (2009) report to the CASAC as part of the 

11 public comment process. The Smith and Howell study conducted three study variations of a 

12 Washington, DC, preference study, including one experiment involving 26 participants designed 

13 to replicate the EPA 2001 preference study. 

14 Both the Abt Associates Inc. (2001) study results and the results of the Smith and Howell 

15 (2009) study are discussed below.

16 2.5.1 Washington, DC 2001

17 The EPA’s Washington, DC study (Abt Associates Inc., 2001) adopted the general study 

18 methods used in the Denver, BC, and Phoenix studies, modifying them appropriately to be 

19 applicable in an eastern urban setting. Washington’s (and the entire East’s) current visibility 

20 conditions are typically substantially worse than western cities and have different characteristics. 

21 Washington’s visibility impairment is primarily a uniform whitish haze dominated by sulfates, 

22 and the relative humidity levels are higher compared with the western study areas. In addition, 

the relatively low-lying terrain9

26 The Washington, DC focus group session included questions on valuation, as well as on 

27 preferences. The focus group content dealing with preferences for an urban visibility standard 

28 was similar to the focus group sessions in the western studies.

23 in Washington, DC provides substantially shorter maximum 

24 sight distances. Many residents are not well informed that anthropogenic emissions impair 

25 visibility on hazy days.

29 A single scene of a panoramic photograph taken from Arlington National Cemetery in 

30 Virginia was used, and included an iconic view of the Potomac River, the National Mall, and 

31 downtown Washington, DC. All of the distinct buildings in the scene are less than four miles 

32 from the camera, and the higher elevations in the background are less than 10 miles from the 

33 camera. Figure 2-8 presents the photograph used in the study. 

34

 9

The maximum elevation in Washington, DC is 409 feet. 
January 2010 2-16 DRAFT - Do Not Quote or Cite

Figure 2-8 Reproduction of the image with the best VAQ (8.8 dv) used in the 

Washington, DC study.

1

2 The Washington, DC study used 20 unique images generated by WinHaze, each prepared 

3 from the same original photograph. Humidity and gaseous light scattering was held constant in 

4 preparing the WinHaze images, as was the relative chemical mix of aerosol particulates in the 

5 photos (i.e., only the aerosol concentrations were increased to create the images with worse 

6 VAQ). Five of the images were repeated as a consistency check, so participants viewed a total 

7 of 25 slides. The range of VAQ in the images ranged from 8.8 to 38.3 dv.

8 Figure 2-9 presents the percent acceptability results from the 2001 Washington study. 

9 Because only nine participants were involved in the study, the possible values of “percent 

10 acceptable” are limited to multiples of 1/9. Figure 2-9 also shows an anomalous result involving 

11 one of the five repeated images. Three of the repeat images had the same ranking each time they 

12 were presented (i.e., all nine participants rated them acceptable or not acceptable both times they 

13 rated that slide). One of the images (the image with 8.8 dv, the best VAQ image used in the 

14 study) was rated acceptable by all nine participants the first time it was used, but the repeat of 

15 that slide was rated not acceptable by one participant. Another image, however, had a 

16 substantially different result. The 30.9 dv image was rated acceptable by five of the nine 

17 participants the first time it was presented, but the repeat of the slide was only rated acceptable

18 by one of the nine participants. The responses for all five pairs of repeated images are shown in 
January 2010 2-17 DRAFT - Do Not Quote or Cite

1 red on Figure 2-9, including the images which were identically rated both times they were 

2 presented.

3

4

Figure 2-9 Percent of 2001 Washington participants who considered VAQ 

acceptable in each image

Results of 2001 Washington DC Preference Study

0%

50%

100%

5 10 15 20 25 30 35 40 45

Deciview

% Participants Rating 

"Acceptable"

Unique Images 5 Repeated Images

5

6 In the 2001 Washington, DC study, all images with a VAQ below 25.9 dv were rated 

7 acceptable by the majority of the participants, and all images with a VAQ below 29.2 dv were 

8 rated acceptable by at least four of the nine (44%) participants. All images with a VAQ above 

9 30.9 dv were rated not acceptable. The “50% acceptability criteria” division occurs in the range 

10 of 25.9 dv to 30.9 dv, with the anomalous result of the inconsistent responses to the repeated 

11 image with 30.9 dv effectively broadening this range and adding uncertainty to identifying a 

12 clear division.

13 2.5.2 Washington, DC, 2009 

14 The Smith and Howell (2009) study conducted additional focus group sessions based on 

15 the methods and materials used in the 2001 Washington, DC study. Smith and Howell recreated 

16 the WinHaze images used in the 2001 Washington, DC urban visibility preference study, using 

17 the description in the report on the 2001 study (Abt Associates Inc., 2001), and created images 

18 using currently available desktop computer version of WinHaze (Version 2.9.0). Smith and 
January 2010 2-18 DRAFT - Do Not Quote or Cite

1 Howell used a shortened version of the same question protocol as the 2001 study. The WinHaze 

2 images were presented to a total of 64 participants who were all employees of Charles River 

3 Associates (CRA International, Inc). (Smith and Howell also are CRA International employees).

4 The CRA employees were based at the firm’s Washington, DC and Houston, Texas offices (44 

5 and 20 participants, respectively). The Houston participants were included to explore whether 

6 familiarity with Washington, DC VAQ conditions developed from currently living in the 

7 Washington region noticeably influenced the responses. As noted by Smith and Howell, the 

8 participants were not a representative sample of either metropolitan area’s population; all 

9 participants were employed, and the participant group included a higher proportion of college 

10 educated individuals and higher household incomes than the general population.

11 Eight of the Washington-based participants and all of the Houston participants viewed the 

12 WinHaze images on a desktop computer monitor. The remaining Washington participants 

13 viewed the images projected on a screen. 

14 The stated purpose of the Smith and Howell study was to explore the robustness of the 

15 2001 results. To investigate this issue, Smith and Howell conducted three different tests 

16 concerning urban visibility preferences. Each participant was involved with only one test. The 

17 three tests were:

18 ♦ Test 1 - replicated the Abt Associates Inc. (2001) study

19

20 ♦ Test 2 - reduced the upper end of the range of VAQ by eliminating the 11 images 

21 used in Test 1 with a VAQ above 27.1 dv

22

23 ♦ Test 3 - increased the upper end of the range of VAQ by including two new images 

24 of worse VAQ; the two new images had a VAQ of 42 dv and 45 dv

25

26 Sixteen employees from the Washington, DC office and 10 participants from the Houston 

27 office took Test 1 (a total of 26 participants). All the participants viewed the same unique 20 

28 Washington, DC WinHaze images as the 2001 study (plus repeated images for a total of 25 

29 images shown to participants). Images were presented in the same random order as in the 2001 

30 study. Figure 2-10 presents the results of Test 1. The results for the 16 Washington participants 

31 are indicated in blue and results for the 10 Houston participants in red. Although all images used 

32 in the study were of Washington, DC, the results suggest that there is not a significant difference 

33 in the preferences of participants based in the two offices. The scene in the images is an 

34 immediately recognizable iconic view of the National Mall and downtown Washington, DC, 

35 which may influence the similarity of responses by residents of the two cities.

36

37
January 2010 2-19 DRAFT - Do Not Quote or Cite

1

Figure 2-10 Percent of 2009 Test 1 study participants who considered VAQ 

acceptable in each image, showing the range of the lower and upper bound of 50% 

acceptability criteria.

0%

50%

100%

5 10 15 20 25 30 35 40

Deciview

% Participants Rating "Acceptable"

Washington Houston Lower Bound Upper Bound

2

3 Using the combined Test 1 results from the two CRA offices (26 total participants), the 

4 majority of participants in the 2009 study rated all VAQ images with 25.9 dv or less as 

5 acceptable and all VAQ images with 29.2 dv or greater as not acceptable. The image of 27.1 dv

6 was rated as acceptable by 50% of the total participants (56% of the Washington-based and 40% 

7 of the Houston-based participants). All images with a VAQ less than 22.9 dv were rated 

8 acceptable by at least 90% of the participants, and all images with a VAQ greater than 32.3 dv

9 were rated not acceptable by 88% of the participants.

10 Figure 2-11 presents the Abt 2001 study and Smith and Howell 2009 (Test 1) study

11 results on a single graph, representing the results of 35 total participants of preferences for urban 

12 visibility in Washington, DC. The results from the 2009 study on Figure 2-11 combine the Test 

13 1 responses from the two CRA offices. Figure 2-11 also shows the 50% acceptability criteria 

14 range (22.9 dv to 32.3 dv) from the 2009 Test 1. In comparison, the 2001 study 50% 

15 acceptability range was 25.9 dv to 30.9 dv. Inspection of the points in Figure 2-11 indicates that 

16 the results from the 2009 study (Test 1) are not appreciably different than the results of the 2001 

17 Washington study.
January 2010 2-20 DRAFT - Do Not Quote or Cite

1 In Test 2, Smith and Howell reduced the range of VAQ images to images with a VAQ of 

2 27.1 dv or less. The 26 participants in the Test 2 study were different people than the Test 1 

3 participants. Test 2 presented only the nine unique clearest WinHaze images from the full Test 1 

4 set of 20 images, along with 3 duplicates for a total of 12 images. This constricted the VAQ 

5 levels presented to the range that the majority of participants in the 2001 study rated as 

6 acceptable and reduced the upper end of the VAQ range by 11.2 dv. 

7

Figure 2-11 Combined results of two Washington preference studies (showing 50% 

acceptability criteria from 2009, Test 1).

0%

50%

100%

5 10 15 20 25 30 35 40

% Participants Rating "Acceptable"

Deciview

2001 Study 2009, Test 1 Lower Bound Upper Bound

8

9

10 Figure 2-12 presents the Test 1 and Test 2 results. Test 2 found a substantial shift in the 

11 responses regarding which VAQ levels are considered acceptable. The smaller number of 

12 images used in Test 2 made identifying the range of the 50% acceptability criteria more difficult 

13 than in Test 1. The lower bound of the range occurs between 15.6 and 18.7 dv, and the upper 

14 bound occurs between 24.5 and 27.1 dv. Smith and Howell conclude that the shift in the 

15 acceptability responses between Test 1 and Test 2 suggests that the VAQ levels identified as 

16 acceptable in an urban visibility preference study conducted using the general approach 

17 previously used in the all the studies may be influenced by the range of VAQ images presented.

18

19

20
January 2010 2-21 DRAFT - Do Not Quote or Cite

Figure 2-12 Comparison of results from Test 1 and Test 2 (Smith and Howell, 2009).

Results of Smith and Howell Test 1 and Test 2

0%

50%

100%

5 10 15 20 25 30 35 40

Deciview

% Participants Rating "Acceptable"

Test 2 Test 1

1 In order for the range of images shown to be able to influence the acceptability ratings, 

2 participants would need to be aware of the upper and lower bounds of the range prior to the 

3 judging acceptability. However, since they were shown images randomly with respect to the 

4 VAQ levels, asked to rate each one before going to the next image, and were not given a chance 

5 to revise their acceptability ratings, this was not possible during the acceptability exercise itself. 

6 The only other opportunity participants could have to learn the VAQ range is during the VAQ 

7 rating exercise done just prior to the acceptability rating. However, in the VAQ rating exercise 

8 where the participants were asked to rate the quality of visibility for the shown images on a scale 

9 from 1 to 7, the images were also shown in a random order, participants were not aware how 

10 many photographs would be shown or the range of conditions, they were asked to rate each one 

11 using a value from 1 to 7 before going on to the next image and they did not have the opportunity 

12 to revise the ratings of earlier viewed images. 

13 Figure 2-13 shows the average visibility rating on the 1 to 7 scale for each image used in 

14 each of the three tests conducted by Smith and Howell (2009). The consistency observed in the

15 relationship between VAQ deciview levels and the average scores assigned across the three tests

16 demonstrates that the participants come to the survey with the capability to consistently rate the 

17 haze levels shown in the images, regardless of the breadth of the range used or the order or 

18 number of slides shown, and that they are aware of a full range of conditions, even when, as was 

19 the case in Test 2, they were not shown the worst haze images. 
January 2010 2-22 DRAFT - Do Not Quote or Cite

1

2 Figure 2-13 Average quality of visibility ratings for the Washington, DC WinHaze 

3 images by participants in Tests 1-3 conducted by Smith and Howell (2009).

4

5

6 Why then did Test 2 participants in the subsequent part of the survey rate images of haze 

7 levels as unacceptable that were rated acceptable by participants in the other tests and the earlier 

Washington, DC pilot study? In a three sentence script10

17 Smith and Howell (2009) concluded that the effects of a changed range on the 

18 acceptability ratings results demonstrates that VAQ preference studies results are not robust and 

19 do not reflect an enduring view on the “unacceptability” of different levels of VAQ degradation. 

8 that constituted the only instructions 

9 read prior to the acceptability rating, the participants were told that they would see the same set 

10 of slides that they had just rated (i.e., on the 1 to 7 scale), and they were asked to rate them 

11 according to whether the VAQ depicted were acceptable or unacceptable to them. Apparently by 

12 directing then to rate the same images for acceptability, the participants understood that their 

13 choices of visibility conditions were restricted to a range of conditions shown in the 1 to 7 

14 ratings that they had just completed. For participants in Test 2 this would mean that by their own 

15 1 to 7 ratings the range was restricted to include no poor visibility conditions (i.e. only scenes 

16 rated from 3 to 7). 

 10 The complete script for the acceptability/unacceptability part of the study is as follows. “Now you will 

be shown the same set of slides that you just rated. Again each image will illustrate the effects of a different level of 

visibility. This time, rate the slides according to whether the visibility is acceptable or unacceptable to you.”
January 2010 2-23 DRAFT - Do Not Quote or Cite

1 However, there is an alternative explanation. It seems more likely that the use of such a severely 

2 truncated range of VAQ conditions in Test 2, which did not include any of the images of VAQ 

3 that previous studies identified as unacceptable, in effect fundamentally changed the implied 

4 instructions for the participants. Instead of conveying that they were to identify VAQ levels that 

5 they found acceptable among a full range of VAQ conditions from very poor to very good, the 

6 implied message was that they should identify the VAQ levels that they found acceptable among 

7 a curtailed range of VAQ conditions that only included average to very good VAQ. By this 

8 reasoning, it would be inappropriate to include Test 2 results with those of the other tests as a 

9 measure of VAQ preference for Washington, DC.

10 In Test 3, Smith and Howell expanded the VAQ range of WinHaze images shown to the 

11 participants, including two new images with a worse VAQ. The new images had a VAQ of 42 

12 dv and 45 dv, raising the upper end of the VAQ range by 6.7 dv. Test 3 also reduced the total 

13 number of images shown to participants to 19 images by eliminating the use of the five repeat

14 images in Test 1, and also eliminated three additional images in order to reduce the participants’ 

15 time burden. The three deleted images had a VAQ of 11.1, 15.6, and 24.5 dv. The best VAQ 

16 image shown to Test 3 participants was 8.8 dv (same as the best VAQ image in Tests 1 and 2). 

17 However, in Test 3 there were no images with VAQ between 8.8 dv and 18.7 dv, creating a 

18 significant “hole” in the distribution of VAQ conditions presented to the Test 3 participants. 

19 Test 3 was conducted with 12 participants from the CRA Washington office (none of whom 

20 participated in Test 1 or Test 2). No Houston participants were involved with Test 3. Figure 2-

21 13 shows that the Test 3 average ratings from 1 to 7 during the VAQ rating exercise increased 

22 the average participant rating by about 1 at the low end of the scale (very poor VAQ). The 

23 results of Test 3 are shown in Figure 2-14, along with the results of Test 1.

24 Test 3 resulted in an overall increase in the percent of respondents rating as acceptable 

25 the VAQ images used in both tests. In Test 3 all images with a VAQ below 22.9 dv were rated 

26 acceptable by 100% of the participants (similar to the Test 1 results), implying there was no 

27 general change in the acceptability of the images with good VAQ. However, for all VAQ 

28 images (that were used in both studies) between 25.9 dv and 33.6 dv, a noticeably larger

29 percentage of the participants in Test 3 rated the image as acceptable than in Test 1. At VAQ 

30 levels worse than 33.6 dv, the majority of the participants found the VAQ level not acceptable in 

31 both tests. While the differences are noticeable, the small number of participants in Test 3 (i.e. 

32 12) makes the significance of the difference unclear.

33

34

35

36
January 2010 2-24 DRAFT - Do Not Quote or Cite

Figure 2-14 Comparison of results from the Smith and Howell (2009) Test 1 and 

Test 3. 

0%

50%

100%

5 10 15 20 25 30 35 40 45

Deciview

% Participants Rating "Acceptable"

Test 3 Test 1

1

2 The range limitations identified with Test 2 (i.e. an overly restrictive range) that resulted 

3 in its results being eliminated from consideration in the selection of appropriate CPLs, do not 

4 apply to Test 3 results due to its somewhat more complete coverage of the 1 to 7 rating range in 

5 the VAQ rating exercise. In that sense, Test 3 results may be considered somewhat more reliable 

6 than those from Test 1 and the original Washington, DC pilot study. However the number of 

7 participants in Test 3 (i.e., 12) is small enough that the statistical uncertainty of the results may 

8 be an issue if used alone. Given that most of the same images of VAQ conditions were used in 

9 all of the tests, composite acceptability ratings (i.e., from the original pilot study and from Tests 

10 1and 3) of each image were developed to increase the number of participant ratings for each

11 image. Figure 2-15 shows the composite results of these three groups involving a total of 47 

12 participants. The 50% acceptability criteria value for this composite dataset lies unambiguously 

13 between the 30.1 dv (at 51.1%) and the 30.9 dv points (at 46.3%). This analysis does not address 

14 the question of whether a significant “hole” in the Test 3 VAQ distribution between 8.8 dv and 

15 18.7 dv potentially had an effect on participant acceptability responses.

16

17

18

19
January 2010 2-25 DRAFT - Do Not Quote or Cite

1 Figure 2-15 Composite results from Smith and Howell (2009) Tests 1 and 3, and 

2 Abt (2001) Washington, DC pilot study.

3

4

5

6 2.6 SUMMARY OF PREFERENCE STUDIES AND SELECTION OF 

7 CANDIDATE PROTECTION LEVELS

8 Each of the studies reviewed in this assessment investigates the common question, “What 

9 level of visibility degradation is acceptable?” The approaches used in the four studies are similar 

10 and are all derived from the method first developed for the Denver urban visibility study. As a 

11 result, EPA staff has concluded that it is reasonable to compare the results to identify overall 

12 trends in the study findings and that this comparison can usefully inform the selection of CPLs 

13 for use in further analyses. However, because variations in the specific materials and methods 

14 used in each study introduce uncertainties, direct comparison of the study results should take 

15 these factors into account. Key differences between the studies include: 

16

17 ♦ Image presentation methods (e.g., projected slides of actual photos, projected images 

18 generated using WinHaze (a significant technical advance in the method of presenting

19 VAQ conditions), use of computer monitor screen

20
January 2010 2-26 DRAFT - Do Not Quote or Cite

1 ♦ Number of participants in each study, 

2

3 ♦ Participant representativeness of the general population of the relevant metropolitan 

4 area, and 

5

6 ♦ Specific wording used to frame the questions used in the group interview process.

7

8 Figure 2-16 presents a graphical summary of the results of the studies in the four cities

9 and draws on results previously presented in Figures 2-3, 2-5, 2-7 and 2-11. 

10

Figure 2-16 Summary of results of urban visibility studies in four cities, showing the 

identified range of the 50% acceptance criteria .

11 

11

12 For clarity in Figure 2-16, the Denver results omit the 9:00 a.m. photograph results, the 

13 Chilliwack and Abbotsford photographs appear as a single set of data for the BC study, and the 

14 results from 2001 and 2009 (Test 1) studies of VAQ preferences in Washington, DC are 

15 presented as a single combined set of data. The results from the 2009 Washington, DC study 

16 Tests 2 and 3 are not included on Figure 2-16; Test 2 is not a comparable study because it 

17 restricted the range of VAQ conditions to only those rated average to best (e.g., 3-7) visibility in 

 11 Top scale shows light extinction in inverse megameter units; bottom scale in deciviews. Logit analysis estimated 

response functions are shown as the color-coded curved lines for each of the four urban areas
January 2010 2-27 DRAFT - Do Not Quote or Cite

1 the VAQ ratings, and Test 3 is not included because of concerns for the effects on having a 

2 significant “hole” in the VAQ distributions shown to participants between 8.8 dv and 18.7 dv. 

3 Figure 2-16 shows the results of a logistical regression analysis using a logit model of the 

4 greater than 19,000 ratings of haze images as acceptable or unacceptable. The logit model is a 

5 generalized linear model used for binomial regression analysis which fits explanatory data about 

6 binary outcomes (in this case, a person rating a VAQ image as acceptable or not) to a logistic 

7 function curve. 

8 In the context of the preference studies, the logit model determines a function that best 

9 estimates the percentage of respondents that rate an image acceptable based on a set of 

10 explanatory variables. The observations on the dependent variable have one of two discrete 

11 values: 1 (the person rated the image acceptable) or 0 (unacceptable). For this application, the 

12 logit model determines an equation estimating the proportion of participants who will find any 

13 particular deviciew level acceptable. There were two basic types of explanatory (independent) 

14 variables used: one continuous numerical variable (the image’s haziness level or VAQ in 

15 deciviews), and a set of discrete variables that identify which city the observation is from. 

16 The fundamental form of a logistic function is;

z e

probability yes f z − + = =

1

1 17 (" ") ( )

18 where the variable z, known as the logit, is the influence of all the explanatory variables;

... 19 z = β o + β1 x1 + β 2 x2 +

20 In this analysis the estimated logistic function f(z) is the estimated probability of the 

21 participants in the study rating an image as acceptable, given the dv value of the image and what 

22 city the observation came from. In this application the logit is

( ) ( ) ( ) 23 z = Intercept + β1dv + β 2BC + β 3 dv × BC + β 4DC + β 5 dv × DC + β 6Phoenix + β 7 dv × Phoenix

24

25 The variables BC (British Columbia), DC (Washington, DC), and Phoenix are “dummy” 

26 variables. For example, the BC variable is set equal to one if the observation is from the BC 

27 study, and set to zero if that observation is from a study for a different city. Denver is used as 

28 the omitted city dummy variable, allowing the estimated coefficients on the other three city 

29 dummy variables to estimate if the response function is different in those cities than in Denver. 

30 For example the estimated total intercept for Washington becomes Intercept + β4, and the 

31 estimated slope of the function is β4 + β5. A statistically significant estimate of the interaction 
January 2010 2-28 DRAFT - Do Not Quote or Cite

1 term coefficient (β3, β5, or β7) for a particular city implies that the response function has a 

2 different slope than the Denver function.

The logit analysis was conducted using STATA® 3 Data Analysis and Statistical Software 

4 (Release ES 10.1), using the LOGIT procedure. Table 2-3 presents the parameter estimates from 

5 the logit analysis, which investigates whether both slope and the intercept of the estimated 

response function differ between cities. The pseudo-R2 6 estimate was 0.4756 and the 

loglikelihood chi2 7 test also strongly rejects the null hypothesis that there is no effect of the 

8 explanatory variables on the probability that a respondent would find a image acceptable 

(Pr(chi2 9 )=0 < 0.000). In other words, the acceptability ratings depend both on the deciview value 

10 and city.

11 Table 2-3 Logit Analysis Results

Variable Coefficient

Standard 

Error z-statistic Pr /z/ = 0

5% confidence 

estimate

95% confidence 

estimate

Dv -0.3862 0.0094 -41.16 ~0 -0.4045 -0.3678

British Columbia 1.0496 0.3589 2.92 0.003 0.3463 1.7530

Washington, DC 2.9450 0.8458 3.48 ~0 1.2873 4.6026

Phoenix 3.5682 0.3015 11.84 ~0 2.9773 4.1591

BC x dv -0.0029 0.0162 -0.18 0.86 -0.0345 0.0288

Wash. X dv 0.0200 0.0293 0.68 0.495 -0.0374 0.0774

Phoenix x dv -0.0797 0.0136 -5.88 ~0 -0.1063 -0.0531

Constant 7.6844 0.1830 41.99 ~0 7.3257 8.0431

12

13 The city intercept coefficients are all positive and statistically significant indicating that 

14 the response functions for different cities shifted right relative to the function for Denver. 

15 However, only the Phoenix interaction term is insignificant, indicating that the Phoenix response 

16 function has a different slope than the other three cities, as can be seen in Figure 2-16. The 

17 negative estimated coefficient on the Phoenix interaction term results in the Phoenix response 

18 function being steeper than the other cities’ functions. Figure 2-16 also shows the Washington, 

19 DC function is modestly less steep than the others, but the decrease in the slope is not 

20 statistically significant.

21 The model results can be used to estimate the VAQ deciview values where the estimated 

22 response functions cross the 50% acceptability level, as well as any alternative criteria levels. 

23 Selected examples of these are shown in Table 2-4.

24

25
January 2010 2-29 DRAFT - Do Not Quote or Cite

1 Table 2-4 Logit model estimated VAQ values corresponding to various percent 

2 acceptability values for the four cities.

3

Denver British 

Columbia

Phoenix Washington, 

DC

90% Acceptability criteria 14.21 16.80 24.15 23.03

75% Acceptability criteria 17.05 19.63 21.80 26.03

50% Acceptability criteria 19.90 22.45 24.15 29.03

25% Acceptability criteria 22.74 25.28 26.51 32.03

10% Acceptability criteria 25.59 28.10 28.87 35.03

4

5 Figure 2-16 also contains lines at 20 dv and 30 dv that effectively and pragmatically 

6 identify a range where the 50% acceptance criteria occur across all four of the urban preference 

7 studies. Out of the 114 data points shown in Figure 2-16, only one photograph (or image) with a 

8 VAQ below 20 dv was rated as acceptable by less than 50% of the participants who rated that 

photograph.12 9 Similarly, only one image with a VAQ above 30 dv was rated acceptable by more 

than 50% of the participants who viewed it.13

13 There are several hypotheses that may explain why the VAQ acceptability response 

14 curves for the four cities are different and why some study results have greater variability than 

others.

10 These upper and lower range values are also 

supported by the logit model data which estimates 50th 11 percentile acceptability values near 20 dv 

12 for Denver and near 30 dv for Washington, DC (see Table 2-4). 

14

 12 Only 47% of the BC participants rated a 19.2 dv photograph as acceptable.

15 First, as mentioned, the use of photographs (Denver and BC surveys) versus WinHaze16 generated images (Phoenix and Washington, DC surveys) may play a significant role in 

17 preference studies, perhaps introducing bias (such as suggested by the responses to the 9:00 a.m. 

18 Denver photographs) as well as variability. Further, the use of photographs from different days 

19 and times of day that rely on associated ambient measurements of light extinction to characterize 

20 their VAQ level can introduce two other types of uncertainty. The intrinsic appearance of the 

21 scene can change due to the changing shadow pattern and cloud conditions, and spatial variations 

22 in air quality can result in ambient light extinction measurements not being representative of the 

23 sight-path-averaged light extinction. WinHaze has neither of these sources of uncertainty 

13 In the 2001 Washington, D.C. study, a 30.9 dv image was used as a repeated slide. The first time it was shown 

56% of the participants rated it as acceptable, and 11% rated it as acceptable the second time it was shown. The 

same VAQ level was rated as acceptable by 42% of the participants in the 2009 study (Test 1). 14 Variability here refers to the degree of scatter of the average acceptability ratings for each image around the logit 

curve for that city.
January 2010 2-30 DRAFT - Do Not Quote or Cite

1 because the same base photograph is used (i.e. no intrinsic change in scene appearance) and the 

2 modeled haze that is displayed in the photograph is determined based on uniform light extinction 

3 throughout the scene.

4 Second, variation in the degree of representativeness of the participants and the sizes of 

5 the participant samples involved may also be important factors. The small sample size and fairly 

6 uniform population of respondents is a plausible explanation for the noisiness of the combined 

7 Washington, DC results (35 participants, including 26 from a single consulting firm and 10 of 

8 those from a different city) compared with the larger and more representative population of 

9 responders from Phoenix (385 participants, carefully selected to be representative of the Phoenix 

10 population).

11 A third hypothesis promoted by Smith and Howell (2009) is that the range of VAQ 

12 images presented in the survey may influence the results. As discussed above, a more plausible 

13 explanation it that the range of haze images shown to participants in the VAQ 1 to 7 rating 

14 exercise was interpreted by participants as a restriction on acceptability rating exercise to confine 

15 their rating to the range VAQ conditions shown, which for Test 2 was curtailed to only average 

16 to good VAQ conditions. When other evidence is taken into account, the Smith and Howell 

17 hypothesis seems an even more unlikely explanation for the differences in results between the 

18 four urban preference studies. For example the Denver study included photographs with the 

haziest conditions among the four studies, but resulted in the lowest haze condition for the 50th 19

20 percentile preference ratings among the four, not the highest as might be expected if the range of

21 haze levels were a significant factor influencing the results of preference studies. Also, 

22 inspection of the average VAQ 1 to 7 ratings for the Phoenix and Denver studies showed that 

23 they spanned the full ratings range of values similar to those for the Smith and Howell Test 1 and 

24 3, so the participants in those studies were not presented with a restricted range within which to 

25 select acceptable VAQ conditions, suggesting that the range itself was not an important factor 

26 influencing their results. Values for the British Columbia 1 to 7 VAQ rating exercise were not 

27 readily available.

28 A fourth major hypothesis is that urban visibility preferences may differ by location, and 

29 the differences may arise from inherent differences in the cityscape scene used in each city. The 

30 key evidence to suggest this hypothesis is that the apparent differences between the Denver 

31 results (which found the 50% acceptance criteria occurred in the best VAQ levels among the four 

32 cities) and the Washington, DC results (which found the 50% acceptance criteria occurred at the 

33 worst VAQ levels among the four cities). This hypothesis suggests that these results may occur 

34 because the most prominent and picturesque feature of the cityscape of Denver is the clearly 

35 visible snow-covered mountains in the distance, while the prominent and picturesque features of 
January 2010 2-31 DRAFT - Do Not Quote or Cite

1 the Washington, DC cityscape are buildings relatively nearby without prominent and/or valued 

2 scenic features that are more distant. 

3 Finally, and perhaps of significant importance is that the sensitivity of individual scenes 

4 to perceived changes in VAQ under changing light extinction levels can be quite different. As in 

5 the fourth hypothesis, this may in part explain why the Denver study scene, with its long distance 

6 to the mountain backdrop, resulted a preference for the best VAQ level, with a 50% criteria value 

7 of about 20 dv, while the Washington, DC study scene, with much shorter sight paths yielded a

8 50% criteria VAQ value at a substantially worse level of about 30 dv. The distinction between 

9 the last two hypotheses are that the earlier one speaks to the desirability of seeing distant 

10 mountains versus this hypothesis which concerns the ability to perceive changes in haze at lower 

11 light extinction levels. Additional studies, including directly comparable studies using similar 

12 methods in diverse cities, would be useful to gain further understanding of preferences for urban 

13 visibility.

Based on the composite results and the effective range of 50th 14 percentile acceptability 

15 across the four urban preference studies shown in Figure 2-16, CPLs have been selected in a 

range from 20 dv to 30 dv (74 Mm-1 to 201 Mm-1 16 ) for the purpose of comparing to current and 

17 projected conditions in the assessment in chapters 3 and 4 of this document. A midpoint of 25

dv (122 Mm-1 18 ) was also selected for use in the assessment. These three values provide a low, 

19 middle, and high set of light extinction conditions that are used in subsequent chapters of the 

20 UFVA to provisionally define daylight hours with urban haze conditions that have been judged 

21 unacceptable by the participants of these preference studies. As discussed earlier (section 1.2) 

22 PM light extinction is taken to be light extinction minus the Rayleigh scatter (i.e. light scattering 

by atmospheric gases is about 10 Mm-1 23 ), so the low, middle and high CPL levels correspond to 

PM light extinction levels of about 64 Mm-1, 112 Mm-1 and 191 Mm-1 24 .
January 2010 3-1 DRAFT - Do Not Quote or Cite

1 3 ESTIMATION OF CURRENT PM CONCENTRATIONS AND PM 

2 LIGHT EXTINCTION

3 The goals of the “current conditions” portion of this urban-focused visibility impact 

4 assessment are to characterize hourly PM light extinction conditions in a set of urban study areas 

5 during 2005-2007, in order (1) to improve understanding of the levels, patterns, and causes of 

6 daylight hours PM light extinction; (2) to provide the starting point for projections of PM light 

7 extinction levels under “what if” scenarios; and (3) to examine the correlation between PM light 

8 extinction and potential alternative indicator(s) based on PM2.5 mass concentration. This chapter 

9 addresses the first goal. Chapter 4 addresses the second goal regarding “what if” scenarios. 

10 Appendix D addresses the third goal. A number of other appendices address related topics of 

11 particular interest in more detail.

12 3.1 SUMMARY OF PREVIOUS CHARACTERIZATIONS OF PM 

13 CONCENTRATIONS AND LIGHT EXTINCTION 

14 3.1.1 PM2.5 and PM10-2.5

15 Chapter 2 of the 2005 Staff Paper from the previous review and chapters 3 (especially 

16 section 3.5) and 9 (especially section 9.2.3) and Annex A of the final ISA (US EPA, 2009a) from 

17 the current review present extensive characterizations of the levels, composition, and temporal 

18 and spatial patterns of PM2.5 in U.S. urban areas. Both documents present data summaries based 

19 on the approximately 1000 PM2.5 monitoring sites in the U.S. The characterizations in the 2005 

20 Staff Paper were based on 2001-2003 data. The characterizations in the ISA are based on 2005-

21 2007 data, which is the same time period used in this visibility assessment. While there 

22 generally have been reductions in the concentrations of PM2.5 in many areas as a result of 

23 emission reductions of PM2.5 and its precursors, the general patterns, and the diversity of patterns 

24 across areas, noted in the 2005 Staff Paper still prevailed in the 2005-2007 period. 

25 Using 2005-2007 air quality data, 38 urban areas violated the annual PM2.5 NAAQS set at 

a level of 15 μg/m3 26 in 1997 and retained in the last review completed in 2006. Seventy-six areas 

violated the 2006 24-hour NAAQS level of 35 μg/m3 27 . There is considerable but not complete 

28 overlap in the areas not meeting the two NAAQS. It should be noted that in many parts of the 

29 U.S., PM2.5 concentrations in 2005 were high relative to the next three years. Figure 3-1 

30 illustrates PM2.5 air quality in 2007 by representing each monitor by a symbol whose color 

reflects the annual mean of the concentration at that site or the 98th 31 percentile 24-hour 

32 concentration, in both cases in that one year. 

33
January 2010 3-2 DRAFT - Do Not Quote or Cite

Figure 3-1. Annual average and 24-hour (98th 1 percentile 24-hour concentrations) PM2.5 

concentrations in μg/m3 2 , 2007.

3

4

5

6

7
January 2010 3-3 DRAFT - Do Not Quote or Cite

1 Each urban area exhibits its own detailed patterns of observed concentration levels, 

2 temporal and spatial variation, and composition. These differences are due to differences in local 

3 and transported emissions and in meteorology. Because of differences in the placement of PM2.5

4 monitoring sites in each urban area, the actual levels and spatial pattern of PM2.5 and PM2.5

5 species concentrations may not be consistently discernable in all areas. This variability and 

6 limited monitoring network make it difficult to offer concise generalizations, although some 

7 broad similarities can be drawn among areas.

8 Midwestern, southeastern, and eastern urban areas have much higher sulfate levels than 

9 do more western areas, attributable to the much higher emissions of SO2 in and upwind of them. 

10 Upper midwestern areas and to a lesser extent upper eastern areas have notable nitrate 

11 concentrations in winter but not in summer, while southeastern areas generally lack notable 

12 nitrate even in winter. Many western urban areas have notable nitrate year round. In all areas, 

13 carbonaceous material is an important component of PM2.5 and is attributable to many emission 

14 sources of organic material in PM form and of organic PM precursor gases. In some areas with 

15 high local use of wood for residential heating carbonaceous material is dominant during the 

16 heating season. PM2.5 derived from crustal sources is generally a small fraction of total mass, 

17 except during local high wind events or due to brief periods of intercontinental transport of dust 

18 from Africa or Asia. 

19 Comparison of PM2.5 species concentrations within and outside urban areas leads to the 

20 conclusion that, in the eastern areas with high sulfate concentrations, the large majority of the 

21 sulfate affecting any given urban area originates outside that area. Inward transport and local 

22 generation of nitrate and carbonaceous material are more evenly balanced in eastern areas, with 

23 some differences among areas. In western areas, local sources dominate for carbonaceous 

24 material and nitrate, with the origins of the small sulfate component being more balanced. See 

25 Figure 9-24 of the final ISA (US EPA, 2009a).

26 Southeastern areas have their highest PM2.5 concentrations in the summer, when 

27 conditions are most conducive to sulfate formation. More northern areas, being affected by a 

28 more balanced mix of contributors, tend not to have such a strongly seasonal pattern. The 

29 seasonal patterns in western areas are individual and varied, related to differences in local 

30 sources and formation and dispersion conditions. In all areas, inversion conditions with low 

31 wind speeds are conducive to high concentrations due to the trapping of emissions from local 

32 sources. Some western areas, especially those with valley or bowl-like topography, are 

33 especially affected.

34 There is at present no systematic monitoring network in place for PM10-2.5, as states have 

35 until January 1, 2011, to implement required monitoring sites for PM10-2.5. Consequently, 

36 estimates of PM10-2.5 must be developed using data from PM2.5 and PM10 monitoring sites and 
January 2010 3-4 DRAFT - Do Not Quote or Cite

1 equipment, which are not always collocated and consistent. The 2005 Staff Paper presented such 

2 estimates in section 2.4.3. The final ISA presents such estimates in Figure 3-10 and Table 3-9 of 

3 section 3.5.1.1. The 2005 Staff Paper used a data-inclusive approach in which the best available 

4 data on PM2.5 and PM10 concentrations – in some cases not very robust data – were used to 

5 estimate 2001-2003 PM10-2.5 concentrations for 351 metropolitan area counties. For these 

6 counties, the annual mean PM10-2.5 concentrations were generally estimated to be below 40 

μg/m3, with one maximum value as high as 64 μg/m3 and a median of about 10-11 μg/m3 7 . The 

8 ISA used a much more data-restrictive approach based only on paired (collocated) low-volume 

9 filter-based samplers for both PM10 and PM2.5. The ISA reports that only 40 counties have such 

10 paired samplers. Using these available co-located PM measurements from 2005-2007, the mean 

24-hr PM10-2.5 concentration in these 40 counties was 13 μg/m3 11 . This urban visibility assessment 

12 has used a data-inclusive approach to estimating PM10-2.5 concentrations, similar to that used for 

13 the 2005 Staff Paper, where needed to obtain hourly PM10-2.5 estimates for 15 study areas, which 

14 are reported below in section 3.3.2.

15 Additional detail on PM2.5, PM10, and PM10-2.5 concentrations, composition, and patterns 

16 appears in section 3.5.1.1 of the ISA. Also, chapter 6 of the 2004 PM Assessment by NARSTO 

17 contains more detailed characterizations of PM in different parts of the U.S. 

18 3.1.2 PM light extinction

19 While total light extinction is directly measurable using a transmissometer and PM light 

20 extinction can be measured with other instruments, there are very few regularly operating 

21 monitoring sites measuring either form of light extinction in urban areas, and generally those that 

do operate do not submit data to AQS.15

 15 EPA is aware of routine, long-term direct measurement of light extinction using transmissometers only in the 

Phoenix, AZ, Denver, CO, and Washington, DC urban areas, none of which submit data to AQS, although the site in 

Washington submits data to the IMPROVE program data system. Also, there is a large network of “visual range” 

monitors in operation at U.S. airports, aimed at providing information to determine landing and takeoff safety. Due 

to their locations and to the lack of data resolution (values of visual range above the level needed for unlimited 

airport operations are not individually reported) the data from these monitors are not suitable for use in this 

assessment. The final PM ISA discusses these monitors in section 9.2.2.3.

22 Consequently, any characterization of PM light 

23 extinction conditions based on actual measurements is necessarily less comprehensive than for 

24 PM2.5 and PM10-2.5. Many monitoring sites that employ nephelometers, which measure light 

25 scattering, operate that equipment in a heated mode for purposes of tracking “dry” PM2.5 mass 

26 concentrations, and actual light scattering due to ambient PM is not reportable. There are many 

27 more filter-based Aethalometers® and similar instruments for measuring light absorption in 

28 operation and reporting to AQS, but light absorption is typically a small fraction of total PM 

29 light extinction, so these data alone are not a good indicator of overall PM light extinction in 
January 2010 3-5 DRAFT - Do Not Quote or Cite

1 urban areas. Also, there are unresolved issues of data corrections and comparability for the light 

2 absorption data from these instruments now residing in AQS.

3 PM light extinction can be “reconstructed” from measurements of PM2.5 mass 

4 components and PM10-2.5 concentrations, in combination with relative humidity values, using 

5 either of two versions of the formula known as the IMPROVE algorithm but excluding its term 

6 for Rayleigh scattering by gases in clean air. (Section 9.2.2.2 of the ISA gives an overview of 

7 the algorithm and its basis. Section 3.2.3 of this document discusses the application of the 

8 original version of the IMPROVE algorithm in this assessment. PM2.5 component measurements 

9 are generally available only on a 24-hour average basis, so it generally is possible to estimate 

10 only 24-hour average PM light extinction, unless additional information on hourly patterns is 

brought to bear.16

19 The CSN network provides 24-hour PM2.5 species measurements at about 200 urban 

20 sites, from which mass components can be derived. These sites have a mix of daily, one day in 

21 three, and one day in six sampling schedules. The 2005 Staff Paper (and its references) may be 

22 the only readily available prior assessment to use these urban PM2.5 speciation monitoring data, 

23 along with estimates of PM10-2.5 concentrations and data on relative humidity, to reconstruct daily 

24-hour average light extinction in urban areas, for the year 2003.

11 Because EPA’s Regional Haze Rule (RHR) currently requires states to 

12 address visibility problems in Class I visibility protection areas, which are nearly all rural and 

13 remote, there is a large body of literature characterizing light extinction in remote rural areas, 

14 based on data from the IMPROVE network’s 24-hour samplers and on special studies. Sections 

15 9.2.3.2 and 9.2.3.4 of the ISA summarize this literature. Section 9.2.3.3 of the ISA contrasts 

16 concentrations of PM and PM components between rural and urban areas using data from the 

17 rural IMPROVE network and the urban Chemical Speciation Network (CSN), but does not 

18 present estimates of light extinction in urban areas.

17

 16 When the IMPROVE algorithm is used to estimate 24-hour light extinction from 24-hour PM2.5 species and 

24 One presentation of the 

25 results was in the form of a scatter plot of daily 24-hour reconstructed light extinction versus 24-

26 hour PM2.5 concentration. This graphic appears here as Figure 3-2. (For the immediate purpose 

27 of this section, it is the distribution of the data points along the y-axis that is of interest, not the 

28 relationship between light extinction and PM2.5 concentrations; the latter subject is addressed in 

PM10-2.5 concentrations, an assumption is made that every hour has the same PM concentrations but its own relative 

humidity value. Hourly estimates of light extinction, including the strongly non-linear effect of relative humidity, 

are then averaged to get the 24-hour light extinction estimate. 17 Estimates of light extinction in the 2005 Staff Paper include Rayleigh scattering of 10 Mm-1 and thus represent 

“total” light extinction (excluding NO2 absorption). Adjustment for consistency must be made before any close 

comparisons to PM light extinction values in this document. 
January 2010 3-6 DRAFT - Do Not Quote or Cite

Appendix D.) Generally, most days have light extinction below 200 inverse megameters (Mm-1 1 ), 

but a small percentage of values were as high as about 750 Mm-1.

18

3

2

4 Figure 3-2. Reconstructed 24-hour light extinction in U.S. urban areas in 2003 

5 Source: Schmidt et al., 2005

6

 18 Unfortunately, the file of paired data used to create this scatter plot is no longer available, so the actual 

distribution of light extinction values cannot be described more specifically.
January 2010 3-7 DRAFT - Do Not Quote or Cite

1 In addition to this scatter plot, a table developed for the previous PM NAAQS review 

2 presented the annual average of estimates of 24-hour reconstructed light extinction values, 

3 averaged across 161 urban areas grouped into seven regions (Schmidt, et al., 2005). Table 3-1 

4 reproduces these estimates. For regions excluding Southern California, annual average 24-hour 

light extinction ranged from 73 to 118 Mm-1 5 . The estimate of the annual average 24-hour light 

extinction for Southern California was 168 Mm-1 6 . These estimates were based on 10-year 

7 average 1-hour relative humidity values and 2003 PM monitoring data. 

8

9 Table 3-1. Annual Mean Reconstructed 24-hour Light Extinction Estimates

by Region (Mm-1 10 )

11

Region Reconstructed 24-hour Light 

Extinction in 2003

Northeast 108

Southeast 98

Industrial Midwest 118

Upper Midwest 80

Southwest 73

Northwest 76

Southern California 168

12 Source: Output D.3, Schmidt et al., 2005. We note these regions were used to summarize PM2.5 patterns for the PM 

13 NAAQS review 1997 (US EPA, 1996).

14
January 2010 3-8 DRAFT - Do Not Quote or Cite

1 Figure 3-3 is a contour map of annual average reconstructed 24-hour total light extinction 

2 based on IMPROVE monitoring sites in 2000-2004, nearly all of which are remote and rural (the 

3 three urban sites in Phoenix, AZ, Washington, DC, and Puget Sound, WA are indicated by 

4 square symbols). A comparison of the mean urban light extinction levels by region listed in 

5 Table 3-1, with this map of rural light extinction indicates that in most parts of the U.S., light 

6 extinction levels in urban areas are notably higher than in the surrounding remote rural area, with 

7 the northeast and the southeast regions having the most similarity between rural and urban light 

8 extinction levels. This is consistent with observations of an “urban excess” of PM2.5 and

9

10 Figure 3-3. Isopleth map of annual total reconstructed particulate extinction based 

11 on 2000-2004 IMPROVE data.

12

13

14 (Source: Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the 

15 United States Report IV, November 2006.)

16

17 PM10-2.5, and with the known high regional concentrations of sulfate in these eastern areas.

18 One-hour light extinction values of course vary above and below the 24-hour average, 

19 due to diurnal variations in PM2.5 component concentrations, PM10-2.5 concentrations, and 

20 relative humidity. Although light extinction was formally reconstructed on an hourly basis in the 

21 2005 Staff Paper analysis for the last review cited above, the actual full strength of the diurnal 
January 2010 3-9 DRAFT - Do Not Quote or Cite

1 pattern could not be discerned in that analysis because component mix was assumed not to vary 

2 from hour to hour. Under the unverified assumption of constant component mix and using actual 

3 hourly relative humidity data, the daily maximum daylight 1-hour light extinction values were 

roughly 50 percent higher than the 24-hour average light extinction values.19

6 3.2 OVERVIEW OF APPROACH AND DATA SOURCES FOR URBAN STUDY 

7 ANALYSIS

4 The new analysis 

5 presented in this document includes a closer look at diurnal patterns, for 15 study areas.

8 As explained above, there are limited data from direct measurements of light extinction in 

9 urban areas. Consequently, this assessment has reconstructed hourly PM light extinction levels 

10 for daylight hours from values of hourly PM2.5 components, PM10-2.5, and relative humidity. 

11 Hourly monitoring data for PM2.5 components and PM10-2.5 are also generally lacking, so the 

12 estimates of these parameters necessarily in turn have been developed from a combination of 

13 other available ambient monitoring data and air quality modeling results from a chemical 

14 transport model (CTM) run. Specifically, the ambient monitoring data starting points are 24-

15 hour PM2.5 mass measured by filter-based Federal Reference Method (FRM) or Federal 

Equivalent Method (FEM) monitors20

27 The CTM run was the “actual emissions” or “validation” run of the 2004 CMAQ 

modeling platform with boundary conditions provided by GEOS-Chem global scale CTM.

16 , 24-hour PM2.5 components measured by the filter-based 

17 monitors of the Chemical Speciation Network, and hourly PM2.5 mass measured by continuous 

18 instruments such as the Tapered Element Oscillating Microbalance (TEOM), beta attenuation 

19 monitors (BAMs), and nephelometers, which were used at different sites. The CTM-based 

20 diurnal profiles for individual components, in conjunction with hourly PM2.5 measurements, are 

21 used to adjust and allocate the 24-hour PM2.5 components measurements to individual hours of 

22 each day, as described in detail below. In addition, levels of hourly PM10-2.5 mass are calculated 

23 from separate measurements of hourly PM10 and hourly PM2.5 if both are available, or by 

24 applying PM10-2.5 to PM2.5 ratios to hourly PM2.5 data if both types of hourly measurements are 

25 not available. The ambient data are from 2005-2007 and were all obtained from AQS in the first 

26 half of 2009. 

21

 19 These observations on diurnal patterns come from examination of “Output D.3 (Relationship RE & PM2.5; Diurnal 

RE; Timeframe) 8 of 30” and “Output D.3 (Relationship RE & PM2.5; Diurnal RE; Timeframe) 17 of 30”, Analyses 

of Particulate Matter (PM) Data for the PM NAAQS Review, Schmidt et al., 2005.

28 The 

29 CTM modeling is used as one element in the development of realistic diurnal variations for each 

20 Filter-based Federal Reference Method samplers and filter-based Federal Equivalent Method samplers will both 

be referred to as FRM samplers in the remainder of this document.

21 GEOS-Chem is the NASA Goddard Earth Observing System-CHEMistry (global 3-D CTM for atmospheric 

composition). This modeling platform, with an appropriately different emissions scenario, is also the basis for the 

estimates of policy relevant background concentrations of PM2.5 presented in section 3.6 of the ISA (US EPA, 

2009a). 
January 2010 3-10 DRAFT - Do Not Quote or Cite

1 of the major PM2.5 components used to estimate PM light extinction, anchored to site-specific, 

2 day-specific measurements of 24-hour concentrations. That is, monthly averaged diurnal 

3 profiles for the five major components were generated using the CTM results, which were then 

4 combined with hour-specific measurements of PM2.5 to generate hourly concentration variations 

5 for each of the 24-hour CSN sample days during the 2005-2007 period.

6 3.2.1 Study Period, Study Areas, Monitoring Sites, and Sources of Ambient PM Data

7 At the time this assessment began, the ambient monitoring data from 2005-2007, but not 

8 from 2008, had been certified as accurate and complete by the state/local monitoring agencies 

9 that collected them, and the data had been extensively summarized and presented in the first draft 

10 ISA. The EPA staff aimed to develop estimates of daylight hours PM light extinction for a 

11 reasonably representative number of days in each year of 2005-2007, to allow the application of 

12 statistical forms based on three years of data. However, as explained in more detail below, in 

13 several study areas the limited availability of starting data for these estimates resulted in estimate 

14 sets that do not cover all three years. Also, even in areas with some data in all three years, the 

15 number of days with valid estimates differs by year and is in some cases not large by typical 

16 standards of monitoring data completeness.

17 For efficiency in the analysis, this visibility assessment uses the same 15 urban study 

18 areas selected for the health risk assessment. These areas are listed in Table 3-2, along with the 

19 area-wide (maximum) FRM-based 2005-2007 annual and 24-hour PM2.5 design values for each 

20 study area based on the highest-reading monitor in each area, and for the specific site used in this 

assessment.22

22

21 (See below for an explanation of the “site-specific” columns in Table 3-2.) 

 22 2005-2007 PM2.5 design values were taken from the information posted at 

http://www.epa.gov/airtrends/values.html, and are consistent with the design values used in the health risk 

assessment to “roll back” current concentrations to represent achievement of alternative annual and 24-hour PM2.5 

NAAQS. Except in Dallas and Fresno, the area-wide design values are the highest design values of any monitoring 

site in the designated (1997 NAAQS) nonattainment area that has sufficiently complete data to allow the calculation 

of a design value according to the provisions of 40 CFR 50 appendix N. For Dallas, the design values come from a 

site with nearly complete data, and are somewhat higher than the highest values from a site with complete data (see 

the draft PM Risk Assessment, US EPA, 2009c, section 3.2.3) For Fresno, the area-wide design value is for the 

Fresno-Madera CSA, which is only a portion of the San Joaquin Valley nonattainment area. Also, note that there 

are three cases in which the nonattainment area does not include certain areas sometimes thought of as being part of 

the area named in Table 2; monitors in these non-included areas were not considered in this assessment. (1) The 

design value shown for Pittsburgh is for the Pittsburgh-Beaver nonattainment area; the Liberty-Clairton 

nonattainment area is within the Pittsburgh CBSA but is distinct for regulatory purposes, and was not considered in 

this assessment. (2) Baltimore was treated separately, although part of a CSA with Washington DC. (3) Berks Co., 

PA is part of the Philadelphia-Camden-Vineland CSA, but not part of the Philadelphia-Wilmington nonattainment 

area.
January 2010 3-11 DRAFT - Do Not Quote or Cite

1 Table 3-2. Urban Visibility Assessment Study Areas

Study Area

Area-wide 2005-

2007 

Annual 

Design Value

(μg/m3)

Area-wide

2005-2007

24-hour

Design Value

(μg/m3)

Site-specific

2005-2007

Annual 

Design Value

(μg/m3)

Site-specific

2005-2007

24-hour

Design Value

(μg/m3)

2005 Staff 

Paper 

Region

(See map in 

Table 3-1)

Tacoma 10.2 43 Same Same Northwest

Fresno 17.4 63 Same Same Southern 

California*

Los Angeles 19.6 55 Same Same Southern 

California

Phoenix 12.6 32 7.9 15 Southwest

Salt Lake City 11.6 55 10.7 48 Northwest

Dallas 12.8 26 11.5 25 Southeast

Houston 15.8 31 13.1 25 Southeast

St. Louis 16.5 39 14.5 34 Midwest

Birmingham 18.7 44 Same Same Southeast

Atlanta 16.2 35 15.7 33 Southeast

Detroit 17.2 43 Same Same Midwest

Pittsburgh 16.5 43 15.0 40 Industrial 

Midwest

Baltimore 15.6 37 14.5 35 Northeast

Philadelphia 15.0 38 14.7 37 Northeast

New York 15.9 42 14.4 42 Northeast

* While not generally considered to be part of Southern California as the term is commonly used, Fresno lies just 

south of the line used in the 2005 Staff Paper (based on earlier work by others) to separate the Southern California 

region from the Northwest region.

2

3 For time reasons and because it was anticipated that some study areas would not contain 

4 more than one suitable study site, EPA staff sought to identify the single best study site in each 

5 area. In identifying the single best study site in each study area first consideration was given to 

6 the availability of collocated 24-hour data on PM2.5 and its components, because the contribution 

7 of PM2.5 components to PM light extinction will typically dominate the contribution from PM10-

8 2.5. Ideally, within each study area the three types of PM2.5 data (FRM PM2.5, CSN PM2.5

9 components, continuous PM2.5) would be available at a common site, and that site would be 

10 located in a manner consistent with reliance on it to characterize visibility as it would be 

11 perceived by a large number of area residents and visitors. As can be seen in Table 3-2, in 10 of 

12 the 15 study areas the site providing FRM data for this assessment is not the area-wide design 

13 value site, because the area-wide design value site did not have collocated CSN and/or 

14 continuous PM2.5 data.

15 Appendix A provides details on the site(s) identified and used in each study area, 

16 including information on the type of monitoring equipment that provided the data and other 

17 information that may help interpret the results of the analysis. A portion of this table for a single 
January 2010 3-12 DRAFT - Do Not Quote or Cite

1 site – Tacoma – is presented here as Table 3-3 as an example. When viewing this document 

2 electronically, the site IDs in these tables are active links and can be used to view the location of 

the site via GoogleMaps.23

4 In 11 of the study areas, the three types of PM2.5 data were available at a common site. In 

5 the remaining four areas, Phoenix, AZ, Pittsburgh, PA, Baltimore, MD, and St. Louis, MO-IL, 

6 two types of data were available at one site, but the remaining type of data had to be taken from 

7 another site and treated as being representative of the former site. 

3 

8 The monitoring agencies described all but one of these sites as neighborhood or urban 

9 scale, indicating those agencies’ opinion that the sites represent concentrations in an area at least 

10 0.5 to 4 km across. An aerial view of the remaining site (in Phoenix) which did not have a scale 

11 characterization recorded in AQS suggests that it may be middle or neighborhood scale. As 

12 already stated, selected sites are not necessarily the locations of the maximum measured annual 

13 or 24-hour PM2.5 levels in their urban area.

14 Site days which were missing 1-hour PM2.5 concentration data points for more than 25 

15 percent of daylight hours were excluded from the analysis, because such data gaps were judged 

16 to result in too much uncertainty in estimates of 1-hour PM2.5 components, 1-hour light 

17 extinction, and daily maximum light extinction. Days with fewer missing 1-hour PM2.5

18 concentration data points were retained, but no estimate of light extinction was made for hours 

19 without 1-hour PM2.5 concentration data (see below for more explanation).

20 Hourly PM10-2.5 presented more varied challenges. In four areas (Birmingham, Detroit, 

21 Baltimore, and Philadelphia) the site that provides the continuous PM2.5 data also hosts a 

22 continuous FEM PM10 monitor, and hourly PM10-2.5 could be calculated by difference for most 

23 hours. In other areas, this was not the case, and either (1) hourly instruments at two different 

24 sites were used in this subtraction (Tacoma, Los Angeles-South Coast Air Basin, Phoenix, St. 

25 Louis, Atlanta, and New York-N. New Jersey) or (2) a single regionally applicable PM10-2.5 to 

26 PM2.5 ratio calculated as part of the last review based on 2001-2003 24-hour FRM/FEM PM10

27 and PM2.5 samples was applied to 2005-2007 hourly PM2.5 data to estimate hourly PM10-2.5

28 (Fresno, Salt Lake City, Dallas, Houston, and Pittsburgh). In the case of Los Angeles-South 

29 Coast Air Basin, the continuous PM10 and PM2.5 sites were quite distant and separated by a range 

30 of hills, so the estimates of PM10-2.5 and its contribution to PM light extinction are more uncertain 

31 than if the monitors were clearly within the same air mass. Obviously, for the five study areas 

32 for which 1-hour PM10-2.5 was estimated by application of ratios, PM10-2.5 estimates can only 

 23 Additional meta data on each monitoring site, and access to daily and annual data listings, can be conveniently 

obtained using GoogleEarth and the PM2.5, PM10, and CSN monitoring network KML files that can be downloaded 

from http://www.epa.gov/airexplorer/monitor_kml.htm.
January 2010 3-13 DRAFT - Do Not Quote or Cite

1 represent broad trends, not hour-specific conditions at the particular site. More description of the 

2 methods used for estimating hourly PM10-2.5 appears in section 3.3.2.

3 The sampling schedule for CSN PM2.5 speciation monitoring was one-in-six days for 

4 Tacoma, Phoenix, Houston, Detroit, and Philadelphia, and one-in-three days for the other study 

5 areas. Not every scheduled CSN site day in 2005-2007 had data for all three types of PM2.5 data, 

6 due to missed or invalid samples. Also, for continuous PM2.5, values for a small number of hours 

7 of an otherwise data-sufficient day were sometimes missing, due to equipment failure or 

8 servicing. EPA staff retained only those days in which 75 percent or more of daylight hours had 

9 measurements of PM2.5 (see section 3.3. for more details). If for isolated hours at a site (or site 

10 pair) with collocated measurements, PM10-2.5 concentrations could not be estimated because of 

11 gaps in the same-hour continuous PM10 and/or PM2.5 data, EPA staff used the regional ratio 

12 approach described above to estimate PM10-2.5 for those specific hours. Table 3-4 provides more 

13 detailed information on the quarterly distribution of the successfully matched and sufficiently 

14 complete data available for use. As described later, for some parts of this assessment EPA staff 

15 substituted data for the single missing quarters of data in Phoenix and Houston, to achieve 

16 seasonal balance.

17 In this assessment, we have not excluded PM concentration data that may have been 

18 affected by exceptional events such as wildfires and wind storms. Under EPA’s Exceptional 

19 Events rule, for existing NAAQS states may request exclusion of such data from regulatory 

20 determinations, and accordingly such data are not reflected in design values for existing NAAQS 

21 once exclusion is approved by EPA. A similar arrangement presumably would apply to a new or 

22 revised secondary PM NAAQS. Design values for PM light extinction under current conditions 

23 (Table 4-2) and percentage reductions to “just meet” alternative secondary NAAQS based on PM 

24 light extinction (Table 4-3), presented below, may thus be overestimates. Overestimation is 

25 more likely for the western study sites than for the eastern study sites. However, PM2.5 design 

26 values shown in Table 3-2, and associated estimates of the reductions needed from 2005-2007 

27 PM2.5 level to just meet alternative secondary NAAQS based on PM2.5 mass (Table 4-4) do 

28 reflect the exclusion of at least some data affected by exceptional events.
January 2010 3-14 DRAFT - Do Not Quote or Cite

1 Table 3-3. PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Tacoma 

2 Study Area

3

Study 

Area

First PM2.5

Monitoring Site

Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Tacoma

AQS ID 530530029

State: Washington 

City: Tacoma 

MSA: Tacoma, WA 

Local Site Name: TACOMA -

L STREET 

Address: 7802 SOUTH L 

STREET, TACOMA

0.5 miles east of I-5

2005-2007 annual DV = 10.2

2005-2007 24-hr DV = 43

This is the highest 24-hour 

PM2.5 DV site in the SeattleTacoma-Olympia, WA annual 

PM2.5 nonattainment area

Neighborhood Scale

Parameters taken from this 

site:

♦ 24-hour FRM PM2.5 mass 

(AQS parameter 88101; 

one-in-three sampling 

schedule)

♦ PM2.5 speciation (one-insix sampling schedule)

♦ 1-hour PM2.5 mass (AQS 

parameter 88502, 

Acceptable PM2.5 AQI & 

Speciation Mass)

Correlated Radiance 

Research M903 

Nephelometry

No continuous PM10

monitoring at this site, see 

right hand column.

N/A

AQS ID 530530031

State: Washington 

City: Tacoma 

MSA: Tacoma, WA 

Local Site Name: TACOMA -

ALEXANDER AVE 

Address: 2301 ALEXANDER AVE, 

TACOMA, WA 

6.4 miles NNE of PM2.5 site

Neighborhood Scale

Parameters taken from this site: 

♦ 1-hour PM10 STP mass (AQS parameter 

81102)

♦ Sample Collection Method: 

INSTRUMENTAL-R&P SA246BINLET 

♦ Sample Analysis Method: TEOMGRAVIMETRIC 

7% of PM10-2.5 values were determined using 

regional average PM10-2.5:PM2.5 ratios from 

2005 Staff Paper

Additional Explanation

• In this Table, the 1-hour concentration parameter “88502, Acceptable PM2.5 AQI & Speciation Mass” is the same as the ISA refers to 

as “FRM-like” PM2.5 mass. An entry of “88501, PM2.5 Raw Data” indicates that the monitoring agency makes no representation as to 

the degree of correlation with FRM PM2.5 mass. The latter type of continuous PM2.5 data were used only when the former were 

unavailable.

• Where PM10 was reported in STP, it was converted to LC before PM10-2.5 was calculated.

• For convenience, continuous PM2.5 data was obtained through the AirNow website rather than from AQS, as an initial exploration 

indicated that not all the desired 1-hour data had been submitted to AQS.
January 2010 3-15 DRAFT - Do Not Quote or Cite

1 Table 3-4. Number of days per quarter in each study area 

2

Study Area Total Number

of Days

2005 2006 2007

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Tacoma 110 0 0 0 0 13 15 15 14 13 13 14 13

Fresno 324 19 24 27 27 30 29 29 27 26 28 30 28

Los Angeles 302 28 28 22 28 26 26 27 22 21 26 24 24

Phoenix 86 0 13 11 14 12 13 11 12 0 0 0 0

Salt Lake City 306 27 28 30 26 20 28 31 20 23 25 19 29

Dallas 274 22 24 26 22 23 23 24 24 18 23 24 21

Houston 149 21 20 10 14 14 12 8 12 15 14 9 0

St. Louis 294 27 27 24 27 28 19 27 29 29 25 22 10

Birmingham 350 30 30 29 30 29 29 30 30 30 30 27 26

Atlanta 295 22 25 25 24 28 27 26 27 25 19 26 21

Detroit 141 12 12 10 11 12 13 11 15 11 11 12 11

Pittsburgh 284 26 23 25 23 22 25 24 26 22 22 23 23

Baltimore 187 19 17 15 11 15 16 19 18 12 12 17 16

Philadelphia 145 15 11 13 10 9 13 10 13 13 14 12 12

New York 228 22 23 13 15 23 19 18 21 19 15 19 21

Note: Only days with matched and sufficiently complete data were retained in the assessment.
January 2010 3-16 DRAFT - Do Not Quote or Cite

1 3.2.2 Use of CMAQ Model Validation Runs for 2004 to Augment Ambient Data 

2 Because systematic monitoring data on hourly PM2.5 component concentrations are not 

3 available for most of the 15 study areas, EPA staff extracted and applied certain information 

4 from the modeling platform for calendar year 2004 described in section 3.7.1.2 of the ISA, in 

5 which the global-scale circulation model GEOS-Chem was paired with the regional scale air 

quality model CMAQ.

24

11 The EPA staff identified the one or more 36 km-by-36 km CMAQ grid cells generally 

12 corresponding to the urbanized area surrounding each study site, thus omitting grid cells 

dominated by rural land uses.

6 The main use of this platform in the ISA is to estimate policy-relevant 

7 background concentrations of PM2.5. For the urban-focused visibility assessment described here, 

8 however, we used results from the validation run of the platform, in which emissions for all 

9 emission source types and countries are included, to develop realistic diurnal variations of the 

10 major PM2.5 components.

25 13 We then extracted from the detailed model output for these grid 

14 cells the day/hour-specific concentrations of sulfate, nitrate, elemental carbon, organic carbon, 

15 and “crustal/unspeciated” PM2.5 during 2004, and then we averaged across grid cells and then 

across days within the month for each individual hour of the day.26

 24 Similar modeling was not available for 2005, 2006, or 2007.

16 Thus, for each species, EPA 

17 staff obtained 24 hour-of-day values for a month, for each of the 12 calendar months. We then 

18 averaged the 24 hour-of-day values in each monthly set for each component to obtain the 

19 corresponding 24-hour average concentration for the month. We then divided each hour-of-day 

20 value by the 24-hour value, to obtain a normalized diurnal profile for the pollutant, which was 

21 taken as the initial representation of all days in that month for 2005, 2006, and 2007 (but further 

22 adjusted day-by-day in a later step). In total, this resulted in 5 (components) x 12 (months) x 15 

23 (study areas) = 900 profiles. Visual examination of a number of these showed them to be 

24 reasonably smooth and generally to show morning (and sometimes also late afternoon/evening) 

25 peaks which are the anticipated effect of higher vehicle traffic and lower mixing heights. The 

26 peaks were generally moderate, as would be expected in light of the averaging of predictions for 

27 multiple large grid cells, the averaging across days, and the generally moderate diurnal profiles 

28 for SMOKE pre-processing of emissions in the CMAQ modeling platform. (Note, however, that 

25 Urbanized area here refers to a specific land area identified by the U.S. Census Bureau based on population 

density and other factors. Shape files for these areas were compared to the CMAQ grid to identify the grid cells to 

be used.

26 For several of the listed components that are not direct CMAQ outputs, concentrations were estimated by postprocessing to aggregate the appropriate CMAQ outputs. The “crustal/unspeciated” CMAQ output results from nonreactive dispersion of that portion of the PM2.5 emission inputs not assigned during SMOKE processing to a more 

specific CMAQ species, and is considered in most EPA analyses to represent the same material as the “soil” 

component reported for IMPROVE sampling.
January 2010 3-17 DRAFT - Do Not Quote or Cite

1 as described below a later step in the estimation process reduces the smoothness in the diurnal 

2 pattern of PM components.) Sulfate, as would be expected for a regionally transported pollutant, 

3 generally had a flatter diurnal profile than for other components. Hourly nitrate concentrations 

4 were low when expected: during warmer months and in warmer areas. Figure 3-4 shows 

5 example diurnal profiles for the five PM2.5 components, for the Detroit study area for the months 

6 of January and August. Diurnal profiles like these were applied to 24-hour CSN measurements 

7 of component concentrations, as explained in detail below.

8 3.2.3 Use of Original IMPROVE Algorithm to Estimate PM light extinction

9 The EPA staff used the original IMPROVE light extinction algorithm, rather than the 

10 more recent revised version, because the original version is considered more representative of 

urban situations, when emissions are still fresh rather than aged as at remote IMPROVE sites.27

17 bextPM = 3 x f(RH) x [Sulfate]

11 

12 To maintain consistency with the form of the candidate protection levels (CPLs) for PM light 

13 extinction identified in chapter 2, EPA staff excluded from the IMPROVE algorithm for total 

14 light extinction the term for Rayleigh scattering by gases in clean air. The formula for PM light

15 extinction using the traditional IMPROVE algorithm but without the Rayleigh scattering term is 

16 shown below.

18 + 3 x f (RH) x [Nitrate]

19 + 4 x [Organic Mass]

20 +10 x [Elemental Carbon]

21 + 1 x {Fine Soil]

22 + 0.6 x [Coarse Mass]

PM light extinction (bextPM) is in units of Mm-1 23 , the mass concentrations of the 

components indicated in brackets are in μg/m3 24 , and f(RH) is the unitless water growth term that 

25 depends on relative humidity. We refer to the first five terms in this algorithm as the five PM2.5

26 components. In this algorithm, the sulfate and nitrate components are to be expressed as fully 

27 neutralized and as retained and measured in the IMPROVE sampling and laboratory methods. 

28 Associated water is to be omitted from all bracketed terms since the water absorption effect is 

29 reflected in the f(RH) term. The organic mass component is to include the mass of associated 

30 elements in addition to carbon. As described below, we included steps in our development of 

31 estimates of hourly component concentration to ensure consistency with these aspects of the 

32

 27 Other differences between the original and revised algorithms include estimates of sea salt contributions which 

can be important for near-coastal locations, inclusion of site-elevation specific Rayleigh light scattering and 

provision for calculating NO2 light absorption when NO2 data are available. Their exclusion in this assessment is not 

expected to make any appreciable difference to the results or conclusions.
January 2010 3-18 DRAFT - Do Not Quote or Cite

1 Figure 3-4. January and August monthly average diurnal profiles of PM2.5 components 

2 derived from the 2004 CMAQ modeling platform, for the Detroit study area.

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

CMAQ Relative Patterns (JANUARY)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

soil

ec

no3

so4

ocm

CMAQ Relative Patterns (AUGUST)

0

0.5

1

1.5

2

2.5

3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

soil

ec

no3

so4

ocm
January 2010 3-19 DRAFT - Do Not Quote or Cite

1 IMPROVE algorithm.

2 3.3 DETAILED STEPS

3 3.3.1 Hourly PM2.5 Component Concentrations

4 The task of estimating hourly PM2.5 component concentrations is in a sense over5 determined, given the four types of available information: 24-hour PM2.5 mass by filter-based

6 FRM, 24-hour component concentrations by CSN, hourly PM2.5 mass by continuous instrument, 

7 and diurnal profiles of components from the 2004 CMAQ run. There are multiple ways in which 

8 two or three of these four data sources could be used to estimate hourly PM2.5 component 

9 concentrations, and the result generally can be expected to be at least somewhat inconsistent with 

10 the information in the remaining data source(s). For example, each 24-hour PM2.5 component 

11 mass from CSN sampling can be apportioned to hours based on the monthly average diurnal 

12 profile developed from the 2004 CMAQ run, but then in general the hourly values of PM2.5 mass

13 determined by summing the components in an hour would not exactly match the data from the 

14 continuous PM2.5 instrument. EPA staff therefore used a sequence of steps which achieves a 

15 prioritized compromise among the data sources. In this sequence, we have given greater weight 

16 to the 24-hour FRM, CSN, and continuous PM2.5 mass data because these are instrument-based 

17 and location- and day-specific, than to the CMAQ-based profiles which are CTM-based, 

18 averaged to the month, and extrapolated from 2004 to each of 2005, 2006, and 2007. 

19 Because of differences in filter materials, sample collection, laboratory analysis, and data 

20 reporting, there are differences between the contribution of some PM components to PM2.5 mass 

21 as reported by a filter-based 24-hour FRM sampler, and the mass of the same components as 

22 reported by CSN (or IMPROVE) sampling. The following summary of these differences may be 

23 helpful in understanding the steps used to develop estimates of hourly PM2.5 components in this 

24 analysis. In the IMPROVE algorithm for reconstructing light extinction, the light extinction 

25 contribution multipliers per unit of mass concentration of components are not all the same for the 

26 five principal components. Consequently, care is required to estimate these components as 

27 consistently as possible with the IMPROVE sampling and analytical methods so that particle 

28 mass is correctly assigned to the right component.

29 • Nitrate: CSN (and IMPROVE) sampling uses a Nylon filter for purposes of nitrate ion 

30 quantification, while FRM sampling uses a Teflon filter for PM2.5 mass as a whole. The 

31 Nylon filter limits the loss of nitrate in the form of nitric acid vapor which could 

32 otherwise occur if the filter temperature rises above the temperature at the time of 

33 collection, compared to the Teflon filter. The fine particle nitrate ion collected on nylon 

34 and Teflon filters are assumed to be associated with ammonium ions, and for this analysis 
January 2010 3-20 DRAFT - Do Not Quote or Cite

ammonium is assumed to evaporate at the same rate as nitrate on the FRM filters28

•

1 . 

2 Hence, the nitrate ion and calculated ammonium nitrate concentrations reported by CSN 

3 (and IMPROVE) sampling typically will be higher than the nitrate contribution to FRM 

4 PM2.5 mass, particularly under warm ambient conditions. The latter steps make nitrate 

5 mass as reported for a CSN (or IMPROVE) site higher than the nitrate contribution to 

6 PM2.5 mass reported by a FRM sampler at the same site. On the other hand, FRM 

7 sampling may result in some water that is associated with nitrate being included in the 

8 reported PM2.5 mass, while the nitrate mass reported by CSN (or IMPROVE) sampling 

9 excludes all water. Continuous PM2.5 samplers employ a variety of methods for 

10 measuring PM2.5 mass, with correspondingly different behaviors regarding retention/loss 

11 of nitrate. In this assessment’s approach to estimating actual ambient concentrations and 

12 PM light extinction, the FRM measurement of nitrate is used in the calculation of the 

13 concentration of organic carbonaceous material, but not in estimating ambient 

14 concentrations of nitrate or PM light extinction. The CSN-reported nitrate ion 

15 concentration and corresponding ammonium nitrate mass is used for the latter purposes.

Sulfate:

•

16 Unlike nitrate, sulfate is not subject to loss once collected by a filter, so the 

17 sulfate ion mass reported by a CSN (or IMPROVE) sampler will be about the same as the 

18 contribution of sulfate ion to the mass reported by FRM sampling. In FRM sampling, 

19 sulfate ion may not be fully neutralized. When IMPROVE data are used to estimate light 

20 extinction, it is assumed that sulfate ion is fully neutralized. Even more important than 

21 nitrate, FRM sampling results in water that is associated with sulfate being included in 

22 the reported PM2.5 mass. While the water associated with the measured sulfate ion is 

23 used in the calculation of the concentration of organic carbonaceous material, it is not 

24 used in estimating ambient concentrations of sulfate or PM light extinction. 

25 Elemental and Organic Carbon: Only the mass of carbon atoms is included in the 

26 reported elemental carbon and organic carbon for a CSN (or IMPROVE) sampler. In 

27 addition, the assignment of carbon atoms between the reported elemental and organic 

28 amounts is dependent on the specifics of the two different thermo-optical analytical 

methods used in the CSN vs. the IMPROVE network.29

 28 EPA staff recognizes that fine particle nitrate may be in the form of calcium or sodium nitrate, but like the 

IMPROVE program treats nitrate as ammonium nitrate.

29 Also, the quartz filter used to 

30 quantify carbonaceous material in CSN and IMPROVE sampling both absorbs and loses 

31 organic vapors during sampling, while the Teflon filter in a FRM sampler does not 

32 absorb organic vapors (although PM on the filter may do so). Therefore, some method 

33 other than direct measurement must be used to estimate the total mass concentration of 

34 organic carbonaceous material in ambient air. The IMPROVE program adjusts for 

35 absorption of vapors by subtracting a monthly average backup filter value, and then 

36 applies a standard adjustment factor (1.4 in the original IMPROVE method) to the 

37 remaining organic carbon measurement to estimate organic carbonaceous material. In 

38 contrast, the standard reports from CSN sampling submitted to AQS do not include these 

39 two adjustments, but it is routine for EPA staff to apply adjustments for the same 

29 While CSN carbon sampling and analysis methods have recently been harmonized with IMPROVE methods at 

many CSN sites, it was not until mid-2007 that the first 57 sites were using the harmonized methods. Consequently, 

most of the elemental and organic carbon data used in this assessment were obtained with the original CSN methods.
January 2010 3-21 DRAFT - Do Not Quote or Cite

1 purpose, after reporting of CSN data to AQS. The latter are based on network-wide filter 

2 field blanks and are judged as very approximate. For this assessment, the SANDWICH 

3 approach to such adjustments (Frank, 2006) is used to estimate the organic mass through 

4 a material balance of components measured on the CSN and FRM samplers.

5 • Hourly PM2.5: The continuous instruments used for measuring hourly PM2.5 mass were 

6 different among sites (as listed in Appendix A). None of the instrument types that 

7 provided hourly data for this assessment, when averaged over 24 hours, exactly matches 

8 either the measurement of PM2.5 mass from a FRM sampler or the sum-of-components 

9 reportable from CSN sampling. Differences can arise because of differences in water 

10 capture and retention, inconsistent absorption and loss of organic vapors and nitric acid 

11 vapor, etc. Furthermore, comparability between hourly and 24-hour integrated 

12 measurements can only be made on a daily average basis. Consequently, the continuous 

13 instruments providing data to this assessment can be assumed to have a range of 

14 correlation performance versus the FRM. In light of these consistency issues, the hourly 

15 data from the continuous instruments were taken to be most indicative of the relative 

16 concentrations of PM2.5 from hour-to-hour, with less reliance on the absolute accuracy of 

the continuous instruments.30

18 Taking into consideration the above information, EPA staff combined the four types of 

19 available PM2.5 data in each study area using the following steps. Figure 3-5 provides a flow 

20 chart to assist in understanding these steps.

17

21 1. The SANDWICH method (Frank, 2006) was used to subdivide the 24-hour PM2.5 mass 

22 reported by the FRM for each day and site into sulfate (including associated ammonium 

23 and residual water during filter equilibration and weighing), nitrate (including associated 

24 ammonium, but not necessarily enough to fully neutralize the sulfate ion, and residual 

25 water during filter weighing), elemental carbon, organic carbonaceous mass, and fine 

26 soil/crustal mass. This is done using information from the CSN measurements, physical 

27 models, and day-specific temperatures. The primary purpose of this SANDWICH step is 

28 to estimate organic carbonaceous mass. Significantly, in the SANDWICH method, the 

29 component referred to as organic carbonaceous mass is actually a residual whose value is 

30 determined as the difference between the PM2.5 mass determined from weighing the FRM 

31 filter and the sum of the estimated masses of the other four mass components as listed 

32 above. Therefore, it is not necessary to adjust for organic carbon sampling artifacts or to 

33 apply the 1.4 factor commonly used to estimate organic carbonaceous material from 

34 IMPROVE measurements of organic carbon. The SANDWICH procedure did not 

35 consider sea salt in the material balance, since this is generally a very small mass 

36 constituent for the urban areas considered in this analysis. For the same reason, sea salt 

37 was also not considered in the aerosol based light extinction algorithm. 

38

 30 In 2006, EPA developed and promulgated criteria for approval of continuous PM2.5 samplers as “federal 

equivalent methods”. These criteria assure a minimum level of correlation between approved continuous 

instruments and the FRM method, when data from both are expressed as 24-hour average concentrations. However, 

in 2005-2007 no commercially available instruments were yet approved under those criteria. 
January 2010 3-22 DRAFT - Do Not Quote or Cite

1 Figure 3-5. Sequence of steps used to estimate hourly PM2.5 components and PM light 

2 extinction

3

4 2. The CMAQ-derived monthly diurnal profiles for the sulfate, nitrate, elemental carbon, 

5 organic carbon and fine soil/crustal components, like the examples for Detroit in Figure 

6 3-4, were multiplied by the day-specific SANDWICH-based estimates of the 24-hour 

7 average concentrations of these five PM2.5 components, to get day-specific hourly 

8 estimates of these five components (including ammonium and water associated with 

9 sulfate and nitrate ion).

10 3. The hourly concentrations of these five components (including ammonium and water 

11 associated with sulfate and nitrate ion when the filter is weighed) were added together, to 

12 get a sum-of-components estimate of hourly PM2.5 mass for the day of the FRM 

13 sampling.

14 4. The hourly data from the continuous PM2.5 instrument on the day of the FRM sampling 

15 were normalized by their 24-hour average, to get a diurnal profile. (Recall that days were 

16 not used in this assessment if hourly PM2.5 mass data were missing for more than 25 

17 percent of daylight hours.) This profile was applied to the 24-hour PM2.5 mass reported 

18 by the FRM sampler, to get a preliminary, FRM-consistent estimate of hourly PM2.5 mass 

19 for the day of the FRM sampling. This is straightforward when all 24 values of 1-hour 
January 2010 3-23 DRAFT - Do Not Quote or Cite

1 PM2.5 mass were available for the day. However, for some (but not many) days, some 

2 values for continuously measured hourly PM2.5 mass were missing. In such cases, EPA 

3 staff used only the hours with valid 1-hour PM2.5 mass values to develop the diurnal 

4 profile and then applied the profile to the FRM value as just described. This keeps the 

5 average of the valid 1-hour PM2.5 values equal to the 24-hour value from the FRM 

6 sampler.

7 5. The two estimates of hourly PM2.5 mass from steps 3 and 4 were compared, hour-by8 hour. By virtue of the way they were derived, the averages of these estimates across all 

9 24 hours of the day will necessarily be the same (and will be equal to the 24-hour FRM 

10 measurement). However, while the diurnal pattern of these two estimates of the same 

11 physical parameter should also be generally similar, it can be expected (and it is 

12 observed) that the hourly measurements from the continuous PM2.5 instruments (after 

13 adjustment to be consistent with the FRM data) have more hour-to-hour variability. 

14 Figure 3-6 gives an example of this comparison, for one day for the Detroit study area.

15 Figure 3-6. Example from Detroit study area.

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Concentration, ug/m3

FRM adjusted Hourly PM2.5

Sum of diurnalized SANDWICHED species data using CMAQ profiles

Example comparison from the Detroit study area of hourly PM2.5 mass on March 24, 2006 as 

estimated by applying CMAQ-based diurnal profiles to SANDWICH estimates of 24-hour 

component concentrations versus applying a diurnal profile derived from continuous PM2.5

measurements to FRM PM2.5 mass.

16

17 6. Given that the continuous instrument is reacting to hour-specific local conditions that can 

18 vary from hour-to-hour due to real variations in local emissions and dispersion/transport 

19 conditions, while the CMAQ-based estimates contain much less specific information, the 

20 diurnal pattern of PM2.5 mass observed by the continuous instrument (adjusted to be 
January 2010 3-24 DRAFT - Do Not Quote or Cite

1 consistent with the FRM value for 24-hour average PM2.5) was taken as more reliable. 

2 Within each hour, the estimates of all five components from step 2 were increased or 

3 decreased by a common percentage (referred to below as Ai where the subscript i 

4 indicates the hour) so that the sum of the five components after this adjustment was equal 

5 to the estimate of the hourly PM2.5 mass from step 4. The adjustment percentage varied 

6 from hour-to-hour. Necessarily, in some hours the adjustment is an increase in the 

7 concentrations of all components, and in other hours it is a decrease. While this 

8 adjustment preserves the consistency between the 24 values of hourly PM2.5 mass and the 

9 24-hour FRM mass, it can disturb the consistency between the daily average of hourly 

10 estimates of PM2.5 components and the SANDWICH-based estimates of 24-hour average 

11 component concentrations. This disturbance was generally small, because the 

12 adjustments necessarily go in one direction for some hours and the other direction for 

13 other hours. For example, for the particular day in Detroit used for illustration purposes 

14 in Figure 6, the effect of this step was to cause a discrepancy of 3 percent between the 

15 SANDWICH-based values of 24-hour sulfate concentration and the average of the 24 

16 estimates of 1-hour sulfate concentrations (the positive percent indicates a higher 

17 concentration in the result of this step than the SANDWICH-based value). The 

18 discrepancies were 1, 1, 2, and 2 percent for nitrate, elemental carbon, organic carbon, 

19 and fine soil/crustal, respectively.

20 7. Each hourly estimate of sulfate concentration from step 6 (which includes estimates of 

21 associated ammonium and particle bound water) was adjusted so that it excludes water 

22 and reflects full neutralization and therefore is consistent with the reporting practices of 

23 the IMPROVE program and the IMPROVE algorithm. This was done via these sub24 steps:

25 a. The 24-hour CSN value for the dry mass of sulfate ion (not SANDWICHed, no 

26 ammonium or water) was multiplied by 1.375 to reflect an assumption of full 

neutralization of dry sulfate mass.31

28 b. The ratio of this fully neutralized 24-hour sulfate mass to the SANDWICH-based 

29 24-hour sulfate value was calculated.

27

30 c. This ratio was applied to each individual hour’s sulfate concentration from step 6.

31 As in Step 6, it is possible for the 24 final hourly sulfate estimates to no longer be 

32 exactly consistent with the 24-hour CSN sulfate measurement, both reported as fully 

33 neutralized sulfate ion.

34

35 8. A similar adjustment as in step 7 (for sulfate) was made to each hour’s nitrate 

36 concentration from step 6, so that the estimate of hourly nitrate would reflect actual 

37 atmospheric conditions and be consistent with the IMPROVE algorithm. However, the 

38 ratio approach used in step 7(b) for sulfate could not be applied for nitrate, so this 

39 adjustment had to be more complicated. Because in warm weather the FRM Teflon filter 

 31 While it would have been possible to develop a more realistic estimate of partially neutralized sulfate, the 

assumption of full neutralization was used to maintain consistency with the basis for the f(RH) term in the 

IMPROVE algorithm.
January 2010 3-25 DRAFT - Do Not Quote or Cite

1 does not retain nitrate, the initial FRM-consistent nitrate estimate derived by applying the 

2 SANDWICH method to the FRM and CSN data can be zero. Such a zero value makes it 

3 impossible to use the ratio approach in 7(b). Instead, the adjustment was made as 

4 follows:

5 a. The 24-hour CSN value for nitrate ion (not SANDWICHed, no ammonium or 

6 water) was multiplied by 1.29 to reflect an assumption of full neutralization by 

7 ammonia.

8 b. This 24-hour value was then diurnalized using the CMAQ-based profile, similar 

9 to step 2. 

10 c. Each resulting hourly value of nitrate was further multiplied by the Ai factor from 

11 step 6.

12 d. This new estimate of hourly nitrate was used to replace the initial nitrate value 

13 that had resulted from step 6.

14 For cooler areas and days in which the 24-hour SANDWICH results include some nitrate, 

15 the effect of these steps for nitrate are exactly the same as the effects of step 7 for sulfate 

16 (except for the 1.29 vs. 1.375 neutralization factor). For warmer areas and days in which 

17 the 24-hour SANDWICH results did not include any nitrate even though nitrate was 

18 measured on the CSN Nylon filter, the effect of these steps is to assign the CSN nitrate to 

19 each hour using a combination of the information in the CMAQ-based profiles and the 

20 information provided by the continuous PM2.5 sampler. As in Step 6, it is possible for the 

21 24 final hourly nitrate estimates to no longer be exactly consistent with the 24-hour CSN 

22 nitrate measurement.

23 The net effect of these steps is believed by EPA staff to result in hourly PM light 

24 extinction estimates with the following features with respect to some of the complicating aspects 

25 of PM sampling:

26 • The 24-hour average of the hourly nitrate concentrations used to estimate hourly PM light 

27 extinction agrees closely but not exactly with the 24-hour value provided by the CSN 

28 sampling, and generally is higher than the contribution of nitrate to the FRM measure of 

29 PM2.5 mass. In some mid-day hours in some areas, estimated hourly nitrate is zero which 

30 is a more realistic approach than applying a 24-hour species mix to each hour.

31 • The 24-hour average of the hourly organic carbonaceous material concentrations used to 

32 estimate hourly PM light extinction achieves FRM mass balance closure, taking into 

33 account also the difference in nitrate and the possibly partial neutralization of sulfate ion 

34 on the FRM filter. Because the Teflon filter used in FRM sampling is less subject to 

35 positive artifacts for organic material, this approach sidesteps an area of uncertainty in the 

36 IMPROVE sampling method. By relying on mass closure as the driving principle for 
January 2010 3-26 DRAFT - Do Not Quote or Cite

1 estimating organic material, it is not necessary to choose a multiplier to relate organic 

carbon to organic carbonaceous material.32

3 • The 24-hour average of the hourly elemental carbon concentrations used to estimate 

4 hourly PM light extinction agrees closely but not exactly with the 24-hour value provided 

5 by the CSN sampling, and with the contribution of elemental carbon to the FRM measure 

6 of PM2.5 mass. Elemental carbon is generally defined by the thermal optical transmission 

7 method used in CSN, rather than the thermal optical reflectance method used in 

8 IMPROVE.

2

9 3.3.2 Hourly PM10-2.5 Concentrations

10 Three different paths were used to estimate hourly PM10-2.5 concentrations depending on 

11 data availability, in the following order of preference:

12

13 1. When hourly data from a collocated PM10 instruments were available at the continuous 

14 PM2.5 site in a study area, PM2.5 was subtracted hour-by-hour from PM10. Negative 

15 values were reset to zero. This was the approach most often used in Birmingham, 

16 Detroit, Baltimore, and Philadelphia. This method should result in reliable estimates of 

17 actual PM10-2.5 at the study site. (How well the study site represents the study area 

18 generally, or the most visibility-impacted portions, of the study area is a separate issue.)

19

20 2. When collocated continuous PM10 data were not available at the continuous PM2.5 site in 

21 a study area, but continuous PM10 data were available at another site in or near the same 

22 study area, PM10-2.5 was estimated by subtraction, implicitly assuming that the latter site 

23 was also representative of PM10 at the former site. This was the approach most often 

24 used in Los Angeles, Phoenix, St. Louis, Atlanta, and New York. As a result, estimates 

25 of PM10-2.5 for these areas could be affected by site-to-site differences. In particular, the 

26 two sites in Los Angeles were a good distance apart, and the PM10 site in Victorville may 

27 represent influences from agricultural operations rather than typical urban influences. In 

28 St. Louis, the PM10 site may also have been influenced by particular local sources. In 

29 both cases, very high estimates of hourly PM10-2.5 may not represent reality at the PM2.5

30 site, although they may be reasonable estimates for the PM10 site. 

31

32 3. If neither of the first two methods was possible, a regional average ratio of PM10-2.5 to 

33 PM2.5 determined from an analysis of 24-hour data for the 2005 Staff Paper was applied 

34 to hourly PM2.5 from the continuous instrument associated with the study area. This was 

35 the approach used for all hours in Tacoma, Fresno, Salt Lake City, Dallas, Houston, and 

36 Pittsburgh. With this approach, it is not possible for there to be any particularly high 

37 estimates of hourly PM10-2.5. 

38

 32 In other work, EPA staff has observed that when applied to urban sampling data together with CSN network-wide 

field blanks applied to reported OC measured concentrations, the multipliers that can be back-calculated from the 

results of the SANDWICH method tend to be nearer to 1.4 than to the higher value used in the new IMPROVE 

algorithm.
January 2010 3-27 DRAFT - Do Not Quote or Cite

1 The estimation of PM10-2.5 was further complicated because some types of data were 

2 missing for isolated hours in the 2005-2007 period. As result, even for a single study area more 

3 than one method sometimes had to be used to estimate hourly PM10-2.5. Appendix A gives more 

4 specifics about the estimation of hourly PM10-2.5 in each study area.

5 The three-path approach described here is similar to that used for the visibility analysis 

6 reported in the 2005 Staff Paper. While the second and third paths involve the use of data and 

7 assumptions that are not robust compared to the use of paired, collocated, same-method 

8 continuous instruments or compared to the use of paired low-volume filter-based samplers, in 

9 most areas and periods the contribution to PM light extinction from the resulting PM10-2.5

10 concentrations was not large compared to the PM light extinction due to PM2.5 components.

11 3.3.3 Hourly Relative Humidity Data

12 Hourly relative humidity (RH) data for each study area’s primary monitoring site were 

13 obtained hour-by-hour from the closest available non-missing relative humidity measurement, as 

14 reported by either an air monitoring station reporting such data to AQS or a National Weather 

15 Service (NWS) station. For the AQS RH data, parameter 62201 values were utilized. RH data 

from both sources are expressed as percentages.33

17 3.3.4 Calculation of Daylight 1-Hour PM Light Extinction

16

18 Because the interest in this analysis is on visibility during daylight hours, EPA staff 

19 applied a scheme to denote those hours that would be considered daylight hours. For simplicity, 

20 all the days within each “season” in all study areas were considered to have the same daylight 

hours.34

24 The original IMPROVE algorithm was applied hour-by-hour to estimate PM light 

25 extinction in each study area for each daylight hour. When doing so, we capped the value of the 

26 humidity adjustment factor in the IMPROVE algorithm (“f(RH)”) at the value of 7.4 that it has 

27 for a relative humidity of 95 percent. The effect of measurement errors in relative humidity at 

28 values above 95 percent on the value of f(RH) and thus on reconstructed PM light extinction is 

21 Table 3-5 shows the dividing times used to denote daylight hours for the study areas. 

22 Unless otherwise stated, all subsequent discussion of the results refers only to the values of 

23 parameters during these daylight hours.

 33 After release of the first public review draft of this assessment, and error was discovered in the data processing 

that assembled the relative humidity data base, such that the nearest site was not used as described here. That error 

has been corrected in this second public review draft. See also “Corrections to Relative Humidity Values Used in

the Draft UFVA, Corrected Graphics, Tables, and Availability of Detailed Data File for Current Conditions”., P. 

Lorang, November 10, 2009. 34 This simple approach does not account for the effects of the actual date within a three-month season, latitude, or

east-west position within a time zone on the actual local hours that are entirely daylight. Appendix I examines the 

possible impact of this simplification, concluding that it is unlikely to affect later answers to policy relevant 

questions.
January 2010 3-28 DRAFT - Do Not Quote or Cite

1 considerable because of the highly nonlinear form of the function in that range. This creates 

2 uncertainty as to the representativeness of the extinction values calculated with high values of 

relative humidity.35

4

3 .

5 Table 3-5 Assumed daylight hours by season (Local Standard Time)

November-January February-April May-July August-October

First hour that is 

entirely daylight 8:00-9:00 AM 7:00-8:00 AM 5:00-6:00 AM 6:00-7:00 AM

Last hour that is 

entirely daylight 3:00-4:00 PM 5:00-6:00 PM 6:00-7:00 PM 5:00-6:00 PM

Number of daylight 

hours 8 11 14 12

6

7 3.3.5 Exclusion of Hours with Relative Humidity Greater than 90 Percent from PM 

8 Light Extinction NAAQS Scenarios and Most Results

9

10 As advised by CASAC as part of its comments on the first public review draft of this 

11 assessment, EPA staff considered whether to structure the PM light extinction NAAQS scenarios 

12 so that ambient data obtained during daylight hours in which relative humidity was greater than 

13 90 percent would play no role in the indicator/form of the NAAQS, i.e., so that those data would 

14 not enter into the calculation of the design value. EPA staff obtained hourly meteorological 

15 parameters from National Weather Service monitoring sites near 11 of the 15 study sites (usually 

16 a major airport), for 2005 through 2007, for all days in this period including days for which PM 

observations to support estimate of PM light extinction are not available36

 35 The IMPROVE program also caps the value of f(RH) at its value for a relative humidity of 95% when reporting 

visibility in deciviews. 

17 . For these sites, we 

18 compared the occurrence of liquid precipitation, hail, other frozen precipitation, fog, and 

19 haze/mist during daylight hours with humidity greater than 90 percent and during all other 

20 daylight hours. These five conditions are generally considered natural causes of reduced 

21 visibility. Table 3-6 presents this comparison. The percentages of hours with each of these five 

22 conditions individually and for any one or more of the five conditions together are shown for the 

23 two sets of daylight hours. NWS observations of these conditions are instantaneous, and are 

24 generally made about 50 minutes after the hour. The relative humidity observations are made at 

25 the same time. It should be noted that this analysis of the co-occurrence of high relative 

26 humidity and these five conditions uses data from NWS sites other than the AQS sites that 

36 Through an oversight, EPA staff did not obtain NWS data for Los Angeles, St. Louis, Houston, and Detroit in 

time for processing and incorporation of results into Table 3-6. These data will be added in the final version of this 

assessment.
January 2010 3-29 DRAFT - Do Not Quote or Cite

1 provided the relative humidity value for the light extinction estimate. AQS sites could not be 

2 used for this analysis because they generally do not report similar weather condition data.

3 The comparison for the 11 sites shows that in the set of hours with relative humidity 

4 above 90 percent, the frequencies of liquid precipitation (rain), fog and haze/mist individually 

5 and the frequency of any one or more of them together were considerably higher than in the set 

of hours with lower relative humidity. 37

15 Rain, fog, and mist cause a natural reduction in visibility, independent of PM 

16 concentrations. To reduce the likelihood of a secondary PM NAAQS based on an indicator/form 

17 that could be affected by measurements made under natural weather conditions that reduce 

18 visibility, for this assessment EPA staff eliminated the estimates of PM light extinction from any 

daylight hours with relative humidity above 90 percent from design value calculations.

6 The frequencies of hail and other frozen precipitation 

7 were too low for meaningful comparisons. Moreover, except in Tacoma, the frequency of rain or 

8 fog at the observation moments during the hours with relative humidity less than or equal to 90 

9 percent was less than 6 percent. Also, a separate analysis (not shown) indicated that rainy hours 

10 with lower relative humidity experience considerably less accumulation than rainy hours with 

11 higher relative humidity. Based on this assessment, the 90% relative humidity cutoff criteria is 

12 effective in that on average less than 6% of the hours are removed from consideration, yet those 

13 hours have on average over twelve times the likelihood of weather conditions that directly reduce 

14 visibility compared to hours with 90% or less relative humidity. 

38

26 More information on this topic can be found in Appendix G, which reports by study area 

27 the percentages of daylight hours that were excluded from design values, the distribution of the 

28 excluded hours by time of day, and the percentage of days that had one or more daylight hours 

19 Also, 

20 because PM light extinction during such hours is not as likely to be the primary cause of adverse 

21 effects on the public, all figures and tables in the body of this document and in Appendices that 

22 present PM light extinction values or statistics exclude values for such hours (unless explicitly 

23 stated to include them), so that the patterns of PM light extinction during the remaining daylight 

24 hours can be seen clearly. Figures and tables that present PM component concentrations and 

25 relative humidity values are based on all daylight hours, however.

 37 The “haze/mist” category is not an original NWS reporting category. It is a combination of three original NWS 

weather categories: mist, smoke, and haze that were prepared earlier by EPA staff for another purpose. EPA staff 

was unable to separate the occurrence of these three conditions in time for this version of this assessment. Of these, 

only mist, defined as fog-like conditions that do not impair visibility below 0.5 nautical miles, is clearly a natural 

condition during which people would not consider limited visibility to be aesthetically undesirable. Consequently, 

the columns of Table 3-6 for “haze/mist” and “any” must be interpreted accordingly. 38 Another consideration is that instruments used to measure light extinction could be adversely affected if allowed 

to operate without heating or other protective method (such as diffusion drying of incoming air) when relative 

humidity is very high. If protected, however, the measured light scattering would not reflect actual ambient 

conditions.
January 2010 3-30 DRAFT - Do Not Quote or Cite

1 eliminated. Appendix G also contains box plots which contrast the distributions of daylight 1-

2 hour PM light extinction values (and maximum daily daylight 1-hour PM light extinction, see 

3 section 3.3.6) before and after this elimination step. The tile plots in Figure 3-12 also present 

4 additional detailed information on the specific hours that had relative humidity values above 90 

5 percent, and on the PM light extinction values during those and other daylight hours.
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1 Table 3-6 Comparison of Meteorological Parameters for Daylight Hours with Relative Humidity Greater than 90 

2 Percent and Other Daylight Hours, During 2005 -2007

Study Area

Daylight Hours with Relative Humidity <= 90% Daylight Hours with Relative Humidity > 90%

Number 

of 

Hours

Percentage of Hours with Weather Condition Number 

of 

Hours

Percentage of Hours with Weather Condition

Liquid 

Precip.

Hail

Other 

Frozen 

Precip.

Fog Haze/Mist Any Liquid 

Precip.

Hail

Other 

Frozen 

Precip.

Fog Haze/Mist Any

Tacoma 18293 12 0 0 0 3 13 7987 24 0 1 7 26 45

Fresno 24245 3 0 0 2 16 18 1615 12 0 0 44 60 79

Los 

Angeles

Phoenix 26045 1 0 0 0 0 1 235 50 0 0 11 27 56

Salt Lake 

City

24989 4 0 2 1 4 8 1291 21 0 30 33 57 69

Dallas 25519 3 0 0 1 4 6 761 47 0 1 18 71 80

Houston

St. Louis

Birmingham 23826 4 0 0 1 8 11 2454 30 0 0 32 55 61

Atlanta 23696 5 0 0 1 7 10 2584 39 0 0 34 61 71

Detroit

Pittsburgh 22254 5 0 7 1 8 17 4026 36 0 9 26 54 69

Baltimore 22867 4 0 1 2 9 12 3413 36 0 3 30 64 74

Philadelphia 24302 6 0 0 1 6 11 1978 44 0 4 26 64 80

New York 24963 6 0 1 1 9 13 1317 52 0 8 41 79 89
January 2010 3-32 DRAFT - Do Not Quote or Cite

1 3.3.6 Calculation of Daily Maximum 1-Hour PM Light Extinction

2 Daily maximum 1-hour PM light extinction is a statistic of interest in this assessment, as 

3 briefly discussed in section 1.4.3. The daylight hour with the maximum value of PM light 

4 extinction and the corresponding PM light extinction value were identified for each day for each 

5 study area. As mentioned in section 3.2.1, days which were missing 1-hour PM2.5 values for 

6 more than 25 percent of daylight hours were not used in this analysis. No further completeness 

7 requirement for 1-hour data during a day was applied when selecting the daylight hour with the 

8 maximum value of PM light extinction. 

9 3.4 SUMMARY OF RESULTS FOR CURRENT CONDITIONS

10 3.4.1 Levels of Estimated PM2.5, PM2.5 Components, PM10-2.5, and Relative Humidity

11 Figure 3-7 presents box-and-whisker plots to illustrate the distributions in each study area 

12 of the estimates of 1-hour PM2.5 (the diurnalized FRM value, resulting from step 4 in section 

13 3.4.1), PM10-2.5, and relative humidity over the entire 2005-2007 study period. In the plot for 

14 each parameter, areas are ordered by longitude, to make it easier to see east-versus-west regional 

15 differences. For these three parameters, the distributions are given for all the daylight 1-hour 

16 estimates, including hours with relative humidity greater than 90 percent. Similar plots of the 

17 daily maximum daylight 1-hour values of PM2.5 and PM10-2.5 concentrations and relative 

18 humidity are available in Appendix B, as are plots of all daylight 1-hour values for each of the 

PM2.5 component species.

39

20 From these plots we see that the distributions of PM2.5 generally trend toward higher 

21 concentrations from west to east except for the two California urban locations which have PM2.5

22 concentrations more typical of eastern areas. The lowest median PM2.5 concentrations are in 

23 Tacoma, WA, and Phoenix, AZ. Median PM10-2.5 concentrations are highest in St. Louis, MO, 

24 and Phoenix, AZ, and lower elsewhere. The highest outlier PM10-2.5 concentrations are in St. 

25 Louis, MO, and Los Angeles, CA. Relative humidity is lowest for the western urban areas 

26 except for Tacoma, WA, which is similar to the northeastern urban locations with respect to 

27 humidity. These hourly daylight PM concentration and relative humidity box and whisker plots 

28 are consistent with our expectations based on regional 24-hour PM concentration values and 

29 humidity climatology.

19

30

 39 In all box-and-whisker plots in this document, the box represents the 25th to 75th percentile range and the whiskers 

represent the 10th and 90th percentile points of the data; individual data points below the 10th percentile and above 

the 90th percentile are graphed as small circles (which may not all be visible because they may lie on top of one 

another as is the case for relative humidity in Figure 3-7(c) because relative humidity is reported on as an interger. 
January 2010 3-33 DRAFT - Do Not Quote or Cite

1 Figure 3-7. Distribution of PM parameters and relative humidity across the 2005-2007 

2 period, by study area

3

4 (a) Estimates of 1-hour PM2.5 mass, based on applying continuous instrument-based 

5 diurnal profiles to 24-hour FRM PM2.5 mass

6

7

8 (b) Estimates of 1-hour PM10-2.5

9

10
January 2010 3-34 DRAFT - Do Not Quote or Cite

1 Figure 3-7. (cont.). Distribution of PM parameters and relative humidity across the 2005-

2 2007 period, by study area

3

4 (c) 1-hour relative humidity

5

6
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1 3.4.2 Levels of Estimated PM light extinction

2 Figure 3-8 presents box-and-whisker plots to illustrate the distributions of the estimates 

3 of daylight 1-hour reconstructed PM light extinction levels in each area in each year (excluding 

4 hours with relative humidity greater than 90 percent). The distribution of (a) the daily maximum 

5 1-hour values and (b) the individual 1-hour values are both shown. The horizontal dashed lines 

6 in the plots represent the low, middle, and high candidate protection levels (CPLs) for PM light 

7 extinction as discussed in section 2.6. These benchmarks for PM light extinction are 64, 112, 

and 191 Mm-1 8 , corresponding to the benchmark VAQ values of 20 dv, 25 dv and 30 dv. Table 3-

9 7 provides (a) the percentages of days (across all of 2005-2007, unweighted) in which the daily 

10 maximum daylight 1-hour PM light extinction level was greater than each of the three candidate 

11 protection levels (excluding hours with relative humidity greater than 90 percent), and (b) the 

12 similar percentage based on all daylight hours (with the same exclusion).

13 As was also seen in the comparable PM2.5 concentration box and whisker plots in Figure 

14 3-7, the high percentile hourly PM light extinction values in Figure 3-8 tend to be higher in the 

15 eastern urban areas and lower in the non-California western urban areas. The distributions of 

16 maximum daily PM light extinction values are higher (Figure 3-8b), as expected, than for all 

17 hours (Figure 3-8a). Both Figure 3-8 and Table 3-7 indicate that all 15 urban areas have daily 

18 maximum hourly PM light extinctions that exceed even the highest of the CPLs some of the 

19 time. Again, the non-California western urban locations have the lowest frequency of maximum 

20 hourly PM light extinction with values in excess of the high CPL for 8 percent or fewer of the 

21 days. Except for the two Texas and the non-California western urban areas, all of the other 

22 urban areas exceed that high CPL from about 20 percent to over 60 percent of the days. Based 

23 on these estimated maximum hourly PM light extinction estimates, all 15 of the urban areas 

24 exceed the low CPL for about 40 percent to over 90 percent of the days. As noted in section 

25 3.2.1, in 10 of the 15 study areas the study site used in this assessment is not the site in the study 

26 area with the highest concentrations of PM2.5. Thus, these estimates may not characterize 

27 visibility in the worst-visibility portion of each study area.

28 In the last review of the secondary PM NAAQS, the pattern of light extinction during the 

29 day was of particular interest. To illustrate the distributions of 1-hour PM light extinction levels 

30 in specific daylight hours, Figure 3-9 shows the distributions of 1-hour PM light extinction

31 across the entire three-year study period, individually for the study areas (excluding hours with 

32 relative humidity greater than 90 percent). (Appendix E provides additional graphics related to 

33 temporal/spatial patterns of light extinction.) These plots show that high PM light extinction can 

34 occur during any of the daylight hours, though for most of these urban areas the morning hours 
January 2010 3-36 DRAFT - Do Not Quote or Cite

have somewhat higher PM light extinction than in the afternoon.

40

4

1 Urban areas without a 

2 preference for morning high PM light extinction include Phoenix, AZ; Salt Lake City, UT; 

3 Tacoma, WA; Fresno, CA; and Philadelphia, PA.

 40 If hours with relative humidity greater than 90 percent were not eliminated, the tendency for higher PM light 

extinction in the morning hours would be stronger.
January 2010 3-37 DRAFT - Do Not Quote or Cite

1 Figure 3-8. Distributions of estimated daylight 1-hour PM light extinction and maximum 

2 daily daylight 1-hour PM light extinction across the 2005-2007 period, by study area

3 (excluding hours with relative humidity greater than 90 percent).

4

5 (a) Maximum daily values

6

7

8

9

10

11

12

13

14
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1 (b) Individual 1-hour values

2

3

4

5

6
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1

2 Table 3-7 Percentage of daily maximum hourly values and individual hourly values 

3 of daylight PM light extinction exceeding CPLs (excluding hours with relative humidity 

4 greater than 90 percent).

Study Area

Number of Days with 

Estimates

Candidate Protection Level

64Mm-1 112 Mm-1 191 Mm-1

(a) Percentage of Daily Maximum Hourly Values 

Exceeding CPL

Tacoma 109 52 22 4

Fresno 324 75 52 30

Los Angeles 300 90 83 62

Phoenix 86 42 7 1

Salt Lake City 306 44 17 8

Dallas 273 80 41 10

Houston 148 79 45 11

St. Louis 289 98 78 40

Birmingham 349 89 65 34

Atlanta 279 91 75 31

Detroit 141 87 68 43

Pittsburgh 277 85 57 26

Baltimore 181 80 50 23

Philadelphia 143 86 64 31

New York 225 83 59 28

Average 229 77 52 26

Number of Daylight Hours 

with Estimates

(b) Percentage of Individual Daylight Hours 

Exceeding CPL

Tacoma 1087 14 4 1

Fresno 3533 41 21 10

Los Angeles 3048 68 43 20

Phoenix 988 11 1 0

Salt Lake City 3366 17 7 3

Dallas 3043 33 10 2

Houston 1504 35 8 1

St. Louis 3096 66 36 11

Birmingham 3763 57 25 8

Atlanta 2507 60 28 5

Detroit 1547 62 36 14

Pittsburgh 2842 53 25 7

Baltimore 1873 55 24 7

Philadelphia 1468 55 28 9

New York 2296 53 28 9

Average 2397 45 22 7
January 2010 3-40 DRAFT - Do Not Quote or Cite

1 Figure 3-9. Distributions of 1-hour PM light extinction levels by daylight hour across the 2005-2007 period, by study area

2 (excluding hours with relative humidity greater than 90 percent).

3

4

5
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1 3.4.3 Patterns of Relative Humidity and Relationship between Relative Humidity and 

2 PM light extinction

3 Figure 3-10 shows the distribution of relative humidity values at each daylight hour, for 

4 each study area across 2005-2007 (excluding hours with relative humidity greater than 90 

percent).

41

11 To allow closer inspection of the relationship between PM light extinction values and 

12 relative humidity values, Figure 3-11 is a scatter plot of actual 1-hour relative humidity and 1-

13 hour reconstructed PM light extinction (excluding hours with relative humidity greater than 90 

14 percent). Horizontal lines are included in each of the individual plots corresponding to the three 

15 benchmarks for PM light extinction and a vertical line in each for the 90 percent relative 

16 humidity cutoff. There are many instances with PM light extinction greater than the candidate 

17 protection levels when relative humidity is 90 percent or lower. Notice that in Figure 3-11 there 

18 also are plenty of high humidity conditions for each urban area that correspond to low PM light 

19 extinction values. This is because humid air does not by itself contribute to light extinction. 

20 Particles composed of material that absorbs water in high relative humidity conditions (e.g., 

21 sulfate and nitrate PM) swell to larger solution droplets that scatter more light than their smaller 

22 dry particle counterparts in a less humid environment. The magnitude of the relative humidity 

23 effect on light extinction depends directly on the concentration of these hygroscopic PM 

24 components. (Figure 3-11 reveals skips in reported relative humidity values for some but not all 

25 the study areas. This is a result of calculations of relative humidity from dry and wet bulb 

26 temperatures reported to the nearest whole Celsius degree.)

5 As expected, in every area relative humidity is lowest in the early afternoon, 

6 typically the warmest part of the day. Relative humidity is most similar across areas in the early 

7 afternoon, as observed in the 2005 Staff Paper. However, even in this period there are notable 

8 differences among areas. This variation was not as evident in the information presented in the 

9 2005 Staff Paper because only regionally averaged information was presented. In all areas, there 

10 is considerable variation in hour-specific relative humidity during the three-year period.

 41 Similar information on diurnal patterns but broken out by season is given in Appendix E.
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1 Figure 3-10. Distributions of 1-hour relative humidity levels by daylight hour across the 2005-2007 period, by study area

2 (excluding hours with relative humidity greater than 90 percent).

3

4

5
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Figure 3-11. Scatter plot of daylight 1-hour relative humidity (percent) vs. reconstructed PM light extinction (Mm-1 1 ) across 

2 the 2005-2007 period, by study area (excluding hours with relative humidity greater than 90 percent).

3

4

5
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1 3.4.4 Tile Plots of Hourly PM Light Extinction

2 Figure 3-12 consists of “tile plots” that show the estimated levels of 1-hour PM light 

3 extinction for each daylight hour for each study area. These plots assist in understanding the 

4 times of the year and hours of the day in which high relative humidity and high PM light 

5 extinction occur, both separately and together.

6 Time runs horizontally with each row of tiles representing a single day from midnight 

7 (left site) to midnight (right side), and vertically from January (top) to December (bottom). Each 

8 tile represents one hour of the year for which data to estimate PM light extinction were 

9 sufficient. Sites with 1:3 speciation sampling have more (and smaller) tiles than sites with 1:6 

10 speciation sampling. The tick marks on the vertical axis identify the first available sample day of 

11 each month identified by its month number. 

12 PM light extinction is presented in terms of four ranges or bins defined by the two 

13 intervals between the three CPLs, a bin above the high CPL, and a bin below the low CPL. For 

14 the hours with relative humidity of 90 percent and below (referred to as “Low RH bext” in the 

15 figure legend), shades of green are used to indicate the CPL range. Contrasting blue color scales 

16 are used for the tiles representing hours with relative humidity greater than 90 percent (referred 

17 to as “High RH bext” in the shading legend), so that the hours excluded from the PM NAAQS 

18 scenarios (see section 3.3.5 and Chapter 4) can be distinguished. Hours with missing PM2.5 data 

19 from the continuous instrument have no estimates of PM light extinction and are white. Such 

20 cases are rare, following the prior complete exclusion of days in which more than 25 percent of 

21 daylight hours were missing such data. 

22 Note that for Tacoma and Phoenix there are plots for only two years because the third 

23 year did not have suitable data, and for Phoenix and Houston only 9 months are shown for one of 

24 the available years because suitable data were not available for the remaining quarter (the 

25 available 9 months of results are stretched over the same vertical distance as the 12 months in the 

26 other cases). 

27 One observation that can be made in looking at these tile plots is that in very many cases, 

28 days which have one or more hours with high PM light extinction excluded because of high 

29 relative humidity have other hours with high PM light extinction which are not excluded.

30 Although none of the PM light extinction NAAQS scenarios considered in Chapter 4 are 

31 based on a averaging period longer than one hour, these tile plots can be used to get a rough 

32 sense of whether hours with high PM light extinction tend to be isolated, such than average 

33 values over several hours would be considerably lower, or tend to occur together, such that a 

34 longer averaging period would produce roughly the same design value. A number of the eastern 

35 urban areas have numerous day-long haze episodes throughout the year (e.g. St. Louis, Detroit, 

36 Pittsburgh, Philadelphia and New York) or seasonally (e.g. Fresno and Salt Lake City, in the 
January 2010 3-45 DRAFT - Do Not Quote or Cite

1 winter, and Los Angeles and Atlanta in the summer). Some of the urban areas have morning 

2 haze levels that diminish later in the day on a year-around basis (e.g. Dallas) or seasonally (e.g. 

3 Los Angeles, Birmingham and Atlanta in winter and Tacoma, Fresno, and St. Louis in the 

4 summer). This type of information may be useful in this regard during the subsequent 

5 preparation of the Policy Assessment Document.

6
January 2010 3-46 DRAFT - Do Not Quote or Cite

1 Figure 3-12 Tile Plots of Hourly PM Light Extinction

2

3
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
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1 Figure 3-12. Tile Plots of Hourly PM Light Extinction, continued

2
January 2010 3-61 DRAFT - Do Not Quote or Cite

1 3.4.5 Extinction Budgets for High PM Light Extinction Conditions

2 An extinction budget for a single period shows the contribution that each PM component

3 makes to PM light extinction via the additive terms of the IMPROVE algorithm. It can be 

4 expected that the pattern in the extinction budgets will vary by time of year and by study area. 

5 Examination of extinction budgets allows initial insights into what pollutants cause poor urban 

6 visibility and what emission reduction approach may be most effective in reducing PM light 

7 extinction.

8 Figure 3-13 presents (a) day-specific maximum daylight 1-hour light extinction budgets 

9 for the 10 percent of the days in each study area that have the highest daily maximum 1-hour PM 

10 light extinction levels (excluding hours with relative humidity greater than 90 percent), and (b) 

11 similar but more aggregated information based on all individual daylight hours. For the 

12 maximum daily budgets, the day and hour of each hourly budget are indicated on the horizontal 

13 axis, and the hours are arranged chronologically. There are too many individual daylight hours 

14 within the top 10 percent group to display separately, so component concentrations for all days 

within 10 one-percentile-point-range “bins” have been averaged together for display.42

28 Looking at individual urban areas, the following are some highlights:

15 Note that 

16 the vertical scale differs from figure to figure, to accommodate the wide variation in PM light 

17 extinction values. The pattern of results shown in Figures 3-13 is generally as expected in light 

18 of emissions and climate differences among study areas. Except for the PM2.5 soil component, 

19 each of the components of PM light extinction is a major contributor to extreme light extinction 

20 events at some time and location. In the West, carbonaceous PM2.5 (i.e., organic mass and 

21 elemental carbon), nitrate, and/or coarse mass (especially in Phoenix) tend to be most 

22 responsible for these high haze hours. In the East it tends to be sulfate, nitrate, and the 

23 carbonaceous PM2.5 components that are the large contributors to PM light extinction. From the 

24 sample period dates we can determine the seasonal variations in major components. Nitrate and 

25 carbonaceous PM2.5 contribute more to the extreme light extinction periods during winter, while 

26 sulfate contributes more in the summer. In many of the more northerly eastern urban areas, a 

27 combination of sulfate and nitrate contributes to high light extinction year-round. 

29 • Tacoma has its highest light extinction hours in the colder months and primarily 

30 due to carbonaceous PM2.5 components. Because coarse PM was estimated by 

31 applying a regional factor to the local PM2.5 mass value, it would not have been 

32 possible for the results to indicate a significant coarse PM contribution to PM 

33 light extinction even if one existed at this site. However, from what EPA staff 

 42 Note that this binning approach may combine days with dissimilar extinction budgets into one bin because their 

PM light extinction values are similar, obscuring some of the heterogeniety among hours.
January 2010 3-62 DRAFT - Do Not Quote or Cite

1 know of the area, it is unlikely that there is a significant contribution from coarse 

2 PM.

3 • Extreme haze hours in the two California urban areas are primarily caused by 

4 high nitrate PM2.5, though Los Angeles has two extreme hours associated with 

5 coarse PM and several other hours with moderate contribution from coarse PM. 

6 Recall that estimates of coarse PM in Los Angeles are based in part on hourly 

7 PM10 measurements in Victorville, and may not represent coarse PM at the PM2.5

8 mass and speciation site in Rubidoux or in the larger South Coast Basin. Also, 

9 such high coarse PM values may indicate influence from exceptional winds in 

10 Victorville. Figure B-1(b) in Appendix B shows that next several other days with 

11 high daily maximum PM coarse concentrations had concentrations only about 60 

12 percent or less than on the two days appearing in Figure 3-13; the fact that these 

13 other days do not appear among the top 10 percent indicates that other 

14 contributors to PM light extinction were low on those days. Whether or not the 

15 PM10 measurements in Victorville represent the PM2.5 mass and speciation site in 

16 Rubidoux, it can be concluded that nitrate and to a lesser extent sulfate dominate 

17 PM light extinction on the days likely to be above the CPLs. Because coarse PM 

18 for Fresno was estimated by applying a regional factor to the local PM2.5 mass 

19 value, it would not have been possible for the results to indicate a significant 

20 coarse PM contribution to PM light extinction even if one existed at the Fresno 

21 site. However, given the presence of agricultural operations and occasional high 

22 winds in the San Joaquin Valley, the possibility of a significant contribution from 

23 coarse PM in some hours cannot not be ruled out.

24 • Phoenix is unique among the 15 urban areas in having most of its extreme light 

25 extinction caused by coarse PM, though there are a few top-10-percent days 

26 where the maximum hourly haze is dominated by carbonaceous, sulfate, and 

27 nitrate PM2.5. Unlike for Los Angeles, this domination by coarse PM is no doubt 

28 correct. PM10 measurements for Phoenix come from a site near the center of the 

29 metro area, while the PM2.5 measurements are from a more peripheral site (see 

30 Appendix A) and are probably underestimates of PM2.5 at the PM10 measurement 

31 site, this would have only a small effect on estimates of coarse PM. While it is 

32 quite possible that the very highest coarse PM concentration (indicated in Figure 

B-1(b) to be about 500 μg/m3 33 ) reflects the effect of exceptional winds, and might 

34 be excluded under the Exceptional Event rule, the next-highest non-excludable 

35 values of PM light extinction almost certainly would also be dominated by coarse 
January 2010 3-63 DRAFT - Do Not Quote or Cite

PM concentrations in the range of 150 to 200 μg/m3 1 and many might not be 

2 excludable.

3 • Salt Lake City has extreme haze hours caused mostly by nitrate in the winter with 

4 some periods with carbonaceous PM2.5 being the major contributor. Because 

5 coarse PM in Salt Lake City was estimated by applying a regional factor to the 

6 local PM2.5 mass value, it would not have been possible for the results to indicate 

7 a significant coarse PM contribution to PM light extinction even if one existed at 

8 this site. However, from what EPA staff know of the area, it is unlikely that there 

9 is a frequent large contribution from coarse PM. The area typically has at most a 

10 few days per year with measured 24-hour average PM10 as high as of 150-200 

μg/m3 11 . If this were all coarse PM, the contribution to 24-hour average light 

extinction would be 90-120 Mm-1 12 , with the possibility of much higher hourly 

13 contributions by coarse mass during these few days.

14 • Dallas and Houston have high contributions to PM light extinction by sulfate 

15 PM2.5, but Dallas has some winter hours with extreme PM light extinction with 

16 substantial contributions from nitrate and organic carbonaceous material, while 

17 Houston seems to have less contribution by nitrate. Because coarse PM in both 

18 Dallas and Houston was estimated by applying a regional factor to the local PM2.5

19 mass value, it would not have been possible for the results to indicate a significant 

20 coarse PM contribution to PM light extinction even if one existed at this site. 

21 However, from what EPA staff know of the areas, it is unlikely that there is a 

22 frequent large contribution from coarse PM. Houston typically has at most a few 

days per year with measured 24-hour average PM10 as high as of 150-200 μg/m3 23 . 

24 If this were all coarse PM, the contribution to 24-hour average light extinction 

would be 90-120 Mm-1. Dallas typically does not have PM10 as high as 150 μg/m3 25

26 • Sulfate in the summer and nitrate in the fall and winter are responsible for most of 

27 the extreme light extinction at St. Louis, though there are several maximum 

28 hourly periods where coarse PM is a major component. Recall that estimates of 

29 coarse PM in St. Louis may be affected by a very local source (see Appendix A), 

30 and thus the instances of high PM light extinction due to coarse PM may be 

31 limited in geographic scope.

32 • Birmingham and Atlanta are similar in having sulfate year-round and winter 

33 carbonaceous PM2.5 as major contributors to their extreme light extinction periods. 

34 Coarse PM for Birmingham was estimated using data from a single site, and the 

35 estimates should be reasonably representative. Coarse PM for Atlanta was 
January 2010 3-64 DRAFT - Do Not Quote or Cite

1 estimated using data from two fairly close sites and the estimates should be 

2 reasonably representative.

3 • Detroit has frequent large light extinction contributions from nitrate PM2.5, mostly 

4 in the winter, as well as some contributions from sulfate PM2.5 year-round and 

5 several fall and winter days with high contributions from carbonaceous PM2.5. 

6 Coarse PM makes a notable contribution on a few days. Coarse PM for Detroit 

7 was estimated using data from a single site near an automobile plant, and the 

8 estimates should be reasonably representative for that site. 

9 • The remaining four urban locations (Pittsburgh, Baltimore, Philadelphia, and New 

10 York) are similar in that most of their extreme light extinction is from year-round 

11 combinations of sulfate and nitrate. New York also has some winter elemental 

12 and organic carbonaceous contributions to its extreme light extinction. Recall that 

13 the PM2.5 site representing the New York area is actually in Elizabeth, NJ; 

14 emissions from diesel trucks on nearby interstate highways and/or diesel engines 

15 associated with port activities might explain the carbonaceous contributions. 

16 Coarse PM for Baltimore and Philadelphia was estimated using data from a single 

17 site in each area, and the estimates should be reasonably representative. Coarse 

18 PM for New York was estimated using data from two fairly distant sites and the 

19 estimates may not be representative of both sites. Because coarse PM was 

20 estimated for Pittsburgh by applying a regional factor to the local PM2.5 mass 

21 value, it would not have been possible for the results to indicate a significant 

22 coarse PM contribution to PM light extinction even if one existed at this site. 

23 However, exceedances of the PM10 NAAQS are rare in Pittsburgh suggesting that 

24 coarse PM likely is not a frequent significant contributor to PM light extinction.

25 3.5 POLICY RELEVANT BACKGROUND

26 Policy relevant background levels of PM light extinction have been estimated for this 

27 assessment by relying on outputs for the 2004 CMAQ run in which anthropogenic emissions in 

28 the U.S., Canada, and Mexico were omitted, as described in the second draft ISA. Estimates of 

29 PRB for PM light extinction were calculated from modeled concentrations of PM2.5 components

30 using the IMPROVE algorithm. The necessary component concentrations were extracted from 

31 the CMAQ output files, as they were not summarized in the final ISA. More detail is provided in 

32 Appendix C.

33 It is also necessary to have estimates of PRB for PM10-2.5, as input to the IMPROVE 

34 algorithm. The final ISA for this review does not present any new information on this subject. 

35 The approach used in the two previous reviews was to present the historical range of annual 
January 2010 3-65 DRAFT - Do Not Quote or Cite

1 means of PM10-2.5 concentrations from IMPROVE monitoring sites selected as being least 

2 influenced by anthropogenic emissions (US EPA, 2004, Table 3E-1). For this assessment, EPA 

3 staff estimated PRB for PM10-2.5 using a contour map based on average 2000-2004 PM10-2.5

4 concentrations from all IMPROVE monitoring sites, found in a recent report from the 

5 IMPROVE program (DeBell, 2006). More detail is provided in Appendix C.

6 The outcome of the procedures for estimating PRB consists of hour-specific estimates of 

7 PRB for PM2.5 components and annual average estimates for PRB for PM10-2.5. Thus, hour8 specific estimates of PM light extinction are possible, using the same hour-specific relative 

9 humidity values as for the estimate of current conditions PM light extinction.

10 The PRB estimates play a role in this assessment (other than allowing confirmation of the 

11 obvious fact that current conditions PM light extinction values are generally well above PRB 

12 conditions) only in the estimation of “what if” scenarios representing compliance with alternative 

13 NAAQS scenarios based on PM light extinction. This role is described in section 4.1.4. 

14

15
January 2010 3-66 DRAFT - Do Not Quote or Cite

1 Figure 3-13 Light Extinction Budgets for the Top 10 Percent of Days for Maximum 

2 Daily 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours

3

4 Tacoma

5

6 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

7

8

9 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

10

11
January 2010 3-67 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Fresno

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12

13
January 2010 3-68 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Los Angeles

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12
January 2010 3-69 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Phoenix

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12
January 2010 3-70 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Salt Lake City

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12

13
January 2010 3-71 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Dallas

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12
January 2010 3-72 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Houston

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12
January 2010 3-73 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 St. Louis

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12

13
January 2010 3-74 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Birmingham

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12
January 2010 3-75 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Atlanta

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12

13
January 2010 3-76 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Detroit

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12

13
January 2010 3-77 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Baltimore

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12
January 2010 3-78 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Pittsburgh

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12

13
January 2010 3-79 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 Philadelphia

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11

12
January 2010 3-80 DRAFT - Do Not Quote or Cite

1 Figure 3-13. Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily 

2 1-hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 

3 continued

4

5 New York

6

7 (a) Top 10 Percent of Days for Maximum Daily 1-hour PM light Extinction

8

9

10 (b) Top 10 Percent of Individual Daylight Hours (Aggregated)

11
January 2010 4-1 DRAFT – Do Note Quote or Cite

1 4 PM LIGHT EXTINCTION UNDER “WHAT IF” CONDITIONS OF 

2 JUST MEETING SPECIFIC ALTERNATIVE SECONDARY 

3 NAAQS

4 4.1 ALTERNATIVE SECONDARY NAAQS BASED ON MEASURED PM 

5 LIGHT EXTINCTION AS THE INDICATOR

6 4.1.1 Indicator and Monitoring Method

7 The indicator considered in this section is PM light extinction, assumed to be measured 

8 by a continuous instrument, or instrument pair, capable of reporting both light scattering and 

9 light absorption. For example, the measurement method could be an Aethalometer® or similar 

10 instrument for measuring light absorption paired with a nephelometer, with both instruments 

11 using a PM10 inlet so that PM light extinction due to PM2.5 and PM10-2.5 combined would be 

12 measured. A measurement of light absorption using an Aethalometer® or similar instrument 

13 based on optical analysis of collected PM would not be affected by ambient NO2 concentrations. 

14 Also, if a nephelometer is calibrated to zero using filtered or zero air and spanned using a light15 scattering span gas with a well characterized scattering coefficient, such as carbon dioxide, 

16 SUVA 134A, Freon 12, or Freon 22, then subsequent measurements of light extinction would 

17 reflect PM light scattering, without including the effect of Rayleigh scattering.

18 4.1.2 Alternative Secondary NAAQS Scenarios based on Measured PM light 

19 extinction

20 Eighteen alternative NAAQS scenarios presented in Table 4-1 are analyzed in this 

21 section. Nine are based on daily maximum daylight 1-hour PM light extinction and nine on light 

22 extinction in all hours without the restriction to daily maxima. Within each set of nine, the 

23 scenarios are ordered from least to most stringent. 

24

25
January 2010 4-2 DRAFT – Do Note Quote or Cite

1 Table 4-1. Alternative Secondary NAAQS Scenarios for PM Light Extinction

Level Annual 

Percentile Form

Scenarios Based on Daily Maximum Daylight 1-hour PM Light Extinction

(a) 191 Mm-1 90 3-year average of percentile value

(b) 191 Mm-1 95 3-year average of percentile value

(c) 191 Mm-1 98 3-year average of percentile value

(d) 112 Mm-1 90 3-year average of percentile value

(e) 112 Mm-1 95 3-year average of percentile value

(f) 112 Mm-1 98 3-year average of percentile value

(g) 64 Mm-1 90 3-year average of percentile value

(h) 64 Mm-1 95 3-year average of percentile value

(i) 64 Mm-1 98 3-year average of percentile value

Scenarios Based on Daylight 1-hour PM Light Extinction (All Daylight Hours)

(j) 191 Mm-1 90 3-year average of percentile value

(k) 191 Mm-1 95 3-year average of percentile value

(l) 191 Mm-1 98 3-year average of percentile value

(m) 112 Mm-1 90 3-year average of percentile value

(n) 112 Mm-1 95 3-year average of percentile value

(o) 112 Mm-1 98 3-year average of percentile value

(p) 64 Mm-1 90 3-year average of percentile value

(q) 64 Mm-1 95 3-year average of percentile value

(r) 64 Mm-1 98 3-year average of percentile value

2

3 4.1.3 Monitoring Site Considerations for Alternative Secondary NAAQS Based on 

4 Measured PM light extinction

5 It is useful to think ahead tentatively to monitor siting aspects of NAAQS 

6 implementation, so that the results presented in the remainder of this chapter based on the 15 

7 specific study sites can be better interpreted in terms of how well they might represent later 

8 findings if these (and other) areas were to deploy PM light extinction measurement instruments 

9 as part of implementing a secondary NAAQS.

10 It is most likely that the instruments that would be used to implement a secondary 

11 NAAQS with an indicator based on measured PM light extinction will be “closed path” 

12 instruments that react only to air quality in their immediate vicinity. However, light paths that 

13 matter to perceived visual air quality are likely to be several kilometers long. Therefore, a 

14 monitoring site should be at least neighborhood in scale, i.e., its relationship to emission sources 

15 and transport should be such that measurements made at the site reasonably reflect 

16 concentrations in an area surrounding the site of at least about 0.5 to 4 kilometers in diameter. 

17 It would be logical to require that in any urban area for which light extinction monitoring 

18 is deemed a necessary requirement, at least one monitoring site would be placed in an area 
January 2010 4-3 DRAFT – Do Note Quote or Cite

1 expected to have the maximum PM light extinction conditions, subject to the above scale of 

2 representation consideration and possibly also subject to the condition that the site be in an area 

3 (or reasonably represent such an area) where valued urban scenes are able to be perceived by 

4 people. i.e., that the site is “population oriented.” It is difficult to imagine a neighborhood scale 

5 monitoring location within the census-defined urbanized area of an urban area which would not 

6 be “population oriented” for purposes of visual air quality, as “neighborhood” size land areas 

7 typically would have residents, workers, etc. somewhere within them during daylight hours.

8 With regard to the monitoring sites used in this assessment, all are reported to be, or 

9 appear to be, neighborhood or larger scale, and all are in areas where people are present during 

10 daylight hours. The sites in Detroit (Dearborn) and New York (Elizabeth, NJ) are, however, 

11 rather close to an industrial source and a major interstate highway interchange/turnpike exit, 

12 respectively. Significantly, most of the study sites are not the highest PM2.5 concentration site in 

13 their urban area, so a “what if” scenario that manipulates the “current conditions” at these sites to 

14 “just meet” an alternative secondary NAAQS might implicitly leave other parts of their urban 

15 areas with PM light extinction above the NAAQS.

16 Probe height is another consideration. For purposes of a secondary NAAQS aimed to 

17 protect visibility, monitoring probes logically should be placed so that the sampled air is 

18 reasonably representative of the air along the sight path to the valued scene, which may be 

different than the probe heights of the monitors that provided data for this assessment.43

21 4.1.4 Approach to Modeling “What If” Conditions for Alternative Secondary 

22 NAAQS Based on Measured PM Light Extinction

19 We 

20 have not yet studied this issue further.

23 Before modeling “what if” conditions, EPA staff augmented the data set described in 

24 Table 4 so that the sets of study days for Houston and Phoenix were seasonally balanced despite 

25 the lack of actual monitoring data for one quarter in each city. For the first quarter of 2005 in 

26 Phoenix, we substituted the available 12 days from the first quarter of 2006. For the fourth 

27 quarter of 2007 in Houston, we substituted 13 randomly drawn days from the fourth quarters of 

28 2005 and 2006.

29 Also, Tacoma (originally) and Phoenix (after this augmentation) each have only two 

30 calendar years of suitable data, while the form of the alternative NAAQS scenarios requires the 

averaging of the 90th, 95th, or 98th 31 percentile values from three years. In Tacoma and Phoenix, 

32 for every step in the analysis at which a design value is used as an input or reported as an output, 

33 we averaged the percentile values from the only two available years.

 43 Probe height influence on measured PM light extinction might alternatively be taken into consideration in setting 

the level of a NAAQS.
January 2010 4-4 DRAFT – Do Note Quote or Cite

1 We modeled daylight and daily maximum daylight 1-hour PM light extinction under each 

2 of the “what if” scenarios (in which each study area “just meets” one of the 18 alternative 

3 secondary NAAQS listed in section 4.1.2) via the following steps. These steps are essentially the 

4 same as the “proportional rollback” steps that have been used in the health risk assessment 

5 modeling of “what if” conditions in several previous NAAQS reviews for PM and other criteria 

6 pollutants. The steps are described here for the nine scenarios based on daily maximum daylight 

7 1-hour PM light extinction; similar steps were followed for the nine scenarios based on 

8 percentiles of all daylight 1-hour PM light-extinction. The referenced tables present results for 

9 both sets of scenarios.

10

11 1. After excluding hours with relative humidity greater than 90 percent, identify the 

appropriate percentile (90th, 95th, or 98th 12 ) daily maximum daylight 1-hour light 

13 extinction value in each year, noting the day and hour each occurred, and average 

14 these values across years to calculate the light extinction design value for each site 

consistent with the percentile form of the NAAQS scenario.44

 44 Annual percentile values were picked from the set of day-specific or hour-specific estimates according to the same 

scheme as used for the current 24-hour secondary PM2.5 standard, as explained in section 4.5(a) of 40 CFR 50 

appendix N. For example, if there are 60 daily maximum values in a year, the second highest value is the 98th

percentile value. Note that this differs from the algorithm used by some spreadsheet and other statistical programs, 

which may interpolate between sample values. Also, this is a different approach than in the Regional Haze program, 

in which conditions in the best and worst 20 percent of days are averaged together, rather than focusing on 

conditions on the specific day at the 80th and 20th percentile points.

15 The three resulting 

design values for each area (for the 90th, 95th, and 98th 16 percentile forms) are shown in 

17 Table 4-2. (Note that in a number of cases, which are identified by a footnote, the 

18 study area meets one or more of the NAAQS scenario under current conditions. In 

19 these cases, the “current conditions” PM light extinction values are not adjusted, i.e., 

20 PM light extinction values are never “rolled up.”) Notice that the design values for 

the 90th 21 percentile maximum daily 1-hour for most cities are generally similar to the 

design values for the 98th 22 percentile of all daylight hours. On average there are about 

23 ten hours defined as daylight per day, so if the light extinction were randomly 

distributed among the daylight hours and days, the 90th 24 percentile maximum daily 1-

hour would correspond to the 99th 25 percentile of all hours; the fact that the point of 

rough equivalency is the 98th 26 percentile indicates a tendency for hours with higher 

27 light extinction to cluster together in the same day. Figure 4-1 presents two scatter 

28 plots that relate the design values based on daily maximum 1-hour PM light 

29 extinction values and the design values based on all daylight 1-hour light extinction 

30 values. In Panel A, design values for the daily maximum and all hours forms are 

paired by the defining percentile, and colors are used to distinguish the 90th, 95th 31 , and 

98th 32 percentile statistical forms. It appears from Panel A that the design values for the 

33 two approaches to defining the NAAQS scenarios are highly correlated but with the 

34 all hours approach resulting in numerically lower design values than the daily 

maximum approach. The correlation breaks down for the 98th 35 percentile form for the 

36 few study areas with the highest levels of PM light extinction. Panel B compares the 
January 2010 4-5 DRAFT – Do Note Quote or Cite

90th 1 percentile design values based on daily maximum PM light extinction with the 

90th, 95th, and 98th 2 percentile design values based on all daylight hours PM light 

extinction. There is close agreement between the 90th 3 percentile design values based 

on daily maximum values and the 98th 4 percentile design value based on all daylight 

5 hours.

6

7 Table 4-2. Current Conditions PM light extinction design values for the study areas.

Study Area

Design Value for 

90th Percentile 

Form (Mm-1)

Design Value for 95th

Percentile Form (Mm-1)

Design Value for 

98th Percentile Form 

(Mm-1

Design Values Based on Daily Maximum Daylight 1-hour PM Light Extinction

Tacoma 140* 157* 210

Fresno 338 463 533

Los Angeles 469 554 624

Phoenix 105* 144* 266

Salt Lake City 164* 252 410

Dallas 183* 239 302

Houston 194 234 291

St. Louis 307 381 467

Birmingham 357 483 562

Atlanta 249 288 331

Detroit 310 473 644

Pittsburgh 278 313 364

Baltimore 246 286 328

Philadelphia 286 339 393

New York 306 355 457

Design Values Based on Daylight 1-hour PM Light Extinction (All Daylight Hours)

Tacoma 76* 106* 136*

Fresno 190* 266 373

Los Angeles 266 349 451

Phoenix 68* 79* 94*

Salt Lake City 93* 142* 225

Dallas 113* 143* 188*

Houston 105* 128* 171*

St. Louis 194 235 290

Birmingham 173* 227 309

Atlanta 166* 195 238

Detroit 212 251 315

Pittsburgh 167* 209 264

Baltimore 172* 227 265

Philadelphia 183* 222 279

New York 186* 244 300

* This design value meets one or more of the NAAQS scenarios based on PM light extinction.

8

9
January 2010 4-6 DRAFT – Do Note Quote or Cite

1 Figure 4-1 Comparison of Daily Max and All Daylight Hour Design Values

2 (A) Comparison of design values matched by percentile form

3

0

100

200

300

400

500

600

700

0 100 200 300 400 500 600 700

Daily Max Daylight Hour Design Value (Mm-1)

All Daylight Hours Design Value (Mm-1)

90th Percentile

95th Percentile

98th Percentile

0

100

200

300

400

500

600

700

0 100 200 300 400 500 600 700

Daily Max Daylight Hour Design Value (Mm-1)

All Daylight Hours Design Value (Mm-1)

90th Percentile

95th Percentile

98th Percentile

4

5

(B) Comparison of 90th percentile daily maximum design values and and 90th, 95th 6 , 

and 98th 7 percentile all daylight hours design values

8

Comparison of 90th percentile Daily Max Form to All Hours 

Forms

0

100

200

300

400

500

0 100 200 300 400 500

Design Value for 90th Percentile Daily Max (Mm-1)

All Hours Design Value 

(Mm-1)

90th percentile

95th percentile

98th percentile

9
January 2010 4-7 DRAFT – Do Note Quote or Cite

1 2. Using the same days and hours, find the three (or two, in the case of Phoenix and 

2 Houston for which there were only two years of suitable data available) 

3 corresponding values of PRB PM light extinction, and average these values across 

4 years to calculate the PRB portion of the design value.

5

6 3. Subtract the value from step 2 from the value from step 1, to determine the non-PRB 

7 portion of the design value.

8

9 4. Calculate the percentage reduction required in non-PRB PM light extinction in order 

10 to reduce the design value to the PM light extinction level that defines the NAAQS 

11 scenario, using the following equation:

12

13 Percent reduction required = 1 – (NAAQS level – PRB portion of the design value)/(non-PRB 

14 portion of the design value)

15

16 The percentage reductions determined in step 4 are shown in Table 4-3. Figure 4-2 

17 presents them graphically in the form of a scatter plot, comparing the required 

18 reductions for scenarios based on daily maximum 1-hour daylight PM light extinction 

19 values to scenarios with the same level and percentile form but based on all daylight 

20 hours 1-hour PM light extinction values. For the NAAQS scenarios involving higher 

21 levels and lower percentile forms, there are some notable differences in the 

22 percentage reductions required for some area to attain. As was the case for the design 

23 values, notice in Table 4-3 that there is generally similar percentage reductions for 

24 each city and level for the 90th percentile maximum daily and 98th percentile of all 

25 daylight hours.

26

27 As already stated, if the study area is meeting a NAAQS scenario in the current 

28 conditions case, no adjustments were made to represent the “just meeting” case. In 

29 effect, negative values for the percent reduction required to meet the NAAQS 

30 scenario calculated by the above equation were re-set to zero.

311.

32 5. Turning to the entire set of day/hour-specific actual and PRB daylight PM light 

33 extinction values for the three (or two) year period, determine the non-PRB portion of 

34 PM light extinction in an hour, reduce it by the percentage determined in step 4, and 

35 add back in the PRB PM light extinction. The result is the “just meets” PM light 

36 extinction value for that day and hour.

37

38 Note that in these steps, it is not necessary to make any explicit or implicit assumption 

39 about what PM components would be reduced to allow the area to meet the NAAQS scenario, as 

40 the NAAQS scenario’s target design value is itself in units of light extinction. One path to 

41 meeting a NAAQS scenario would be to reduce each of the five PM2.5 components (and thus the 

42 annual and 24-hour design values shown in Table 3-2) and PM10-2.5 by the calculated “percent 

43 reduction required”. However, a lesser reduction in one or more of the six PM concentrations 

44 could be offset by a greater reduction in or more of the remaining concentrations. Thus, it is not 
January 2010 4-8 DRAFT – Do Note Quote or Cite

1 possible to associate unique values of annual average and 24-hour average PM2.5 with the “just 

2 meeting” NAAQS scenarios reported in Table 4-3.

3
January 2010 4-9 DRAFT – Do Note Quote or Cite

1 Table 4-3. Percentage reductions in non-PRB light extinction required to “just meet” the 

2 NAAQS scenarios based on measured light extinction.

NAAQS Scenarios Based on Daily Maximum 1-hour Daylight PM light extinction, Average 

of Percentile Value Over Three Years

(a) (b) (c) (d) (e) (f) (g) (h) (i)

Level (Mm-1) 191 191 191 112 112 112 64 64 64

Percentile 

Form 90th 95th 98th 90th 95th 98th 90th 95th 98th

Area

Percentage Reduction Required in

Non-PRB PM light extinction

Tacoma 0 0 10 22 34 53 61 70 80

Fresno 45 60 65 69 77 81 84 88 90

Los Angeles 61 67 71 78 82 84 88 90 91

Phoenix 0 0 0 0 23 60 42 59 78

Salt Lake City 0 25 54 33 57 74 63 76 85

Dallas 0 21 39 42 55 66 70 76 83

Houston 2 20 36 45 57 64 71 79 81

St. Louis 39 51 61 66 73 78 82 86 89

Birmingham 48 61 67 71 78 82 85 88 90

Atlanta 25 35 44 58 64 69 78 81 84

Detroit 39 61 71 66 78 84 82 88 91

Pittsburgh 32 40 48 61 66 70 79 81 84

Baltimore 23 35 43 57 64 68 77 81 83

Philadelphia 34 45 52 63 68 73 80 83 85

New York 39 48 59 65 70 77 81 84 88

NAAQS Scenarios Based on 1-hour Daylight PM light extinction, Average of Percentile 

Value Over Three Years (All Daylight Hours)

(j) (k) (l) (m) (n) (o) (p) (q) (r)

Level (Mm-1) 191 191 191 112 112 112 64 64 64

Percentile 

Form 90th 95th 98th 90th 95th 98th 90th 95th 98th

Area

Percentage Reduction Required in

Non-PRB PM light extinction

Tacoma 0 0 0 0 0 22 19 46 67

Fresno 0 29 50 43 60 72 70 79 85

Los Angeles 29 47 59 60 70 77 78 84 88

Phoenix 0 0 0 0 0 0 6 20 34

Salt Lake City 0 0 15 0 22 51 33 56 73

Dallas 0 0 0 1 23 42 46 59 69

Houston 0 0 0 0 13 38 42 53 70

St. Louis 1 19 35 44 54 63 70 76 80

Birmingham 0 17 39 37 53 66 66 75 82

Atlanta 0 2 21 34 45 55 64 71 77

Detroit 10 25 40 49 57 66 73 77 82

Pittsburgh 0 9 28 34 48 59 64 71 78

Baltimore 0 16 29 37 52 60 66 74 79

Philadelphia 0 15 32 41 52 62 68 74 79

New York 0 22 37 42 56 64 69 76 81

3

4
January 2010 4-10 DRAFT – Do Note Quote or Cite

1 Figure 4-2 Comparison of Required Percentage Reductions in Non-PRB PM Light 

2 Extinction Needed to Meet NAAQS Scenarios

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

Based on Daily Maximum Daylight LE

Based on Daylight LE (all hours)

191/90th 191/95th 191/98th 112/90th 112/95th 112/98th 64/90th

64/95th 64/98th

3

4 4.2 ALTERNATIVE SECONDARY PM2.5 NAAQS BASED ON ANNUAL AND 

5 24-HOUR PM2.5 MASS

6 4.2.1 Secondary NAAQS Scenarios Based on Annual and 24-hour PM2.5 Mass

7 In this second draft version of the assessment, EPA staff have modeled two “what if” 

8 scenarios based on the same indicators and averaging periods as define the current secondary 

9 PM2.5 NAAQS:

• 15 μg/m3 weighted annual average PM2.5 concentration and 35 μg/m3 10 24-hour average 

PM2.5 concentration with a 98th 11 percentile form, both averaged over three years. These 

12 are the current secondary NAAQS for PM2.5.

• 12 μg/m3 weighted annual average PM2.5 concentration and 25 μg/m3 13 24-hour average 

PM2.5 concentration with a 98th 14 percentile form, both averaged over three years.

15 These are the highest and lowest alternative NAAQS scenarios considered in the health 

16 risk assessment, and therefore encompass the full range of alternative primary PM2.5 NAAQS 

17 being analyzed by EPA staff.

18
January 2010 4-11 DRAFT – Do Note Quote or Cite

1 4.2.2 Approach to Modeling Conditions If Secondary PM2.5 NAAQS Based on 

2 Annual and 24-hour PM2.5 Mass Were Just Met

3 Because these NAAQS scenarios are based on PM2.5 mass as the indicator, rather than 

4 light extinction, the steps needed to model “what if” conditions are somewhat different, and 

5 involve explicit consideration of changes in PM2.5 components.

6

7 1. Apply proportional rollback to all the PM2.5 monitoring sites in each study area, 

8 taking into account PRB PM2.5 mass, to “just meet” the NAAQS scenario for the area 

9 as a whole, not just at the visibility assessment study site. The health risk assessment 

10 document describes this procedure in detail. The degree of rollback is controlled by 

11 the highest annual or 24-hour design value, which in most study areas is from a site 

12 other than the site used in this visibility assessment. The relevant result from this 

13 analysis is the percentage reduction in non-PRB PM2.5 mass need to “just meet” the 

14 NAAQS scenario, for each study area. These percentage reductions are shown in 

15 Table 4-4. Note that Phoenix and Salt Lake City meet the 15/35 NAAQS scenario 

16 under current conditions, and require no reduction. PM2.5 levels in these two cities 

17 were not “rolled up.”

18

19 2. For each day and hour for each PM2.5 component, subtract the PRB concentration 

20 from the current conditions concentration, to determine the non-PRB portion of the 

21 current conditions concentration.

22

23 3. Apply the percentage reduction from step 1 to the non-PRB portion of each of the 

24 five PM2.5 components. Add back the PRB portion of the component.

25

26 4. Re-apply the IMPROVE algorithm, using the reduced PM2.5 component 

27 concentrations, the current conditions PM10-2.5 concentration for the day and hour, and 

28 relative humidity for the day and hour. Include the term for Rayleigh scattering.

29

30 The results of these steps are shown in Table 4-6.

31
January 2010 4-12 DRAFT – Do Note Quote or Cite

1 Table 4-4. Percentage reductions required in non-PRB PM2.5 mass to “just meet” NAAQS 

2 scenarios based on annual and 24-hour PM2.5 mass

Study Area

Percentage Reduction Required

(s)

Annual PM2.5 NAAQS = 15 μg/m3

24-hour PM2.5 NAAQS = 35 μg/m3

(t)

Annual PM2.5 NAAQS = 12 μg/m3

24-hour PM2.5 NAAQS = 25 μg/m3

Tacoma 19 43

Fresno 45 61

Los Angeles 37 55

Phoenix 0* 22

Salt Lake City 37 56

Dallas 0* 7

Houston 6 27

St. Louis 10 37

Birmingham 22 45

Atlanta 8 30

Detroit 19 43

Pittsburgh 19 43

Baltimore 6 33

Philadelphia 8 35

New York 17 41

* These areas meet this NAAQS scenario under current conditions.

3

4 4.3 RESULTS FOR “JUST MEETING” EACH ALTERNATIVE SECONDARY 

5 NAAQS SCENARIO

6 The modeling described in sections 4.1 and 4.2 resulted in estimates of PM light 

7 extinction for each day and hour in each study area, for each NAAQS scenario. Four summaries 

8 of these conditions are presented here.

9 Figure 4-3 shows two box-and-whisker plots of daily maximum daylight 1-hour PM light 

10 extinction. The top panel (a) is for the single illustrative scenario of a NAAQS based on daily 

maximum daylight 1-hour light extinction with a level of 112 Mm-1 and a 90th 11 percentile form, 

12 which was chosen for this illustration because it is approximately mid-way among the nine

scenarios based on daily maximum light extinction in terms of stringency.45

 45 Plots of the distribution of daily maximum light extinction for all 18 NAAQS scenarios based on daily maximum 

light extinction, and of individual hourly light extinction for all 18 NAAQS scenarios based on individual daylight 

hours, are provided in Appendix F.

13 The bottom panel 

(b) is for the scenario of meeting the current secondary PM2.5 NAAQS of 15 μg/m3 14 for the annual 

average and 35 μg/m3 for the 98th 15 percentile 24-hour average. A notable feature of this 

16 comparison is that in the top panel, all the study areas have a similar distribution of the daily 
January 2010 4-13 DRAFT – Do Note Quote or Cite

1 maximum daylight 1-hour PM light extinction, while in the bottom panel this is not the case. 

2 This is expected, since a NAAQS based on a measured daily maximum PM light extinction

3 indicator will of course result in areas achieving similar daily maximum PM light extinction

4 patterns once each area reaches a “just meets” condition. In areas with generally higher relative 

5 humidity conditions, concentrations of PM2.5 components and/or PM10-2.5 would need to be lower 

6 to achieve the “just meet” condition. In contrast, in the NAAQS scenario represented by the 

7 bottom panel, concentrations of PM2.5 mass will be similar across areas, but concentrations of 

8 PM2.5 components may not be, and levels of PM light extinction will not be similar in areas with 

9 dissimilar levels of relative humidity. The specific differences among areas in the bottom panel 

10 are generally as expected, with the drier study areas having lower levels of PM light extinction.

11 Tables 4-5 and 4-6 summarize the “just meets” conditions in the NAAQS scenarios in 

12 terms of the PM light extinction design values when just meeting. Table 4-5 addresses the 18

13 scenarios of NAAQS based on measured PM light extinction. When an area just meets a 

14 NAAQS scenario, its design value in principle should exactly equal the NAAQS level, so 

15 preparation of this table serves as a check against calculation errors. Note that the design values 

16 in Table 4-5, resulting from the rollback steps described in section 4.1.4, in some cases do not 

17 exactly equal the assumed level of the NAAQS, although all are quite close. Closer investigation 

18 has revealed that this is mostly a result of hours switching their ranking in the rollback process. 

19 Hours can switch rank because the level of PRB PM light extinction varies with each hour, so a 

20 uniform percentage reduction in non-PRB light extinction (step 5) can result in non-uniform 

21 percentage reductions in actual PM light extinction; a lower ranking hour can thereby move up in 

22 the post-rollback ranking. In principle, rollback could be iterated to exactly achieve a design 

23 value equal to the level of the NAAQS for each scenario. However, the discrepancies indicated 

24 in Table 4-5 were judged too small to justify iterative rollback, given other uncertainties in the 

25 analysis.

26 Table 4-6 addresses the two scenarios of NAAQS based on PM2.5 mass, with PM light 

extinction design values shown for the 90th, 95th , and 98th 27 percentile forms.

28 Table 4-7 summarizes all 20 scenarios in terms of the percentage of days (across 2005 to 

29 2007, but after rollback) in which the daily maximum daylight 1-hour PM light extinction under 

30 “just meeting” conditions exceeds each of the CPLs. Part A of the table applies to NAAQS 

31 scenarios based on daily maximum 1-hour PM light extinction values. Part B of the table applies 

32 to the scenarios based on 1-hour PM light extinction values during all daylight hours. Note that 

33 the reported percentages in both Part A and Part B is the percentage of days in which the daily 

34 maximum daylight 1-hour PM light extinction under “just meeting” conditions exceeds each of 

35 the CPLs; this allows comparison of the “effectiveness” of the two NAAQS approaches using a 

36 consistent metric. (The 15/35 and 12/25 NAAQS scenarios are the same in Part A and Part B, 
January 2010 4-14 DRAFT – Do Note Quote or Cite

1 and are repeated only for convenience in making comparisons.) Hours with relative humidity 

2 above 90 percent have been excluded from consideration, consistent with the definition of the 

3 NAAQS scenarios. Also shown at the bottom of the table in each column representing a 

4 NAAQS scenario is the average of these percentages of time across the 15 study areas (this is the 

5 simple column average, not weighted by the number of days available in each area). 

6 Comparisons of these percentages allows a rough indication of how the two scenarios of a 

7 NAAQS based on PM2.5 mass compare to the other 18 scenarios in terms of protecting visual air 

8 quality. Notice that the most restrictive of the two NAAQS scenarios based on PM2.5 mass 

9 would reduce the projected 1-hour maximum daily light extinction above the least restrictive 

CPL (191Mm-1 10 ) to less than 10 percent of the time for most of the urban areas (only L.A., St. 

11 Louis, and Birmingham have values above 10 percent). However at the current PM NAAQS 

12 level (i.e., 15/35) all of the eastern urban areas and Los Angeles exceed the least restrictive CPL 

13 more than 10% of the time. Comparison of Parts A and B of Figure 4-7 indicates that basing a 

14 PM light extinction NAAQS scenario on daily maximum 1-hour light extinction has a lower 

15 percentage in excess of the 1-hour daily maximum versus the NAAQS scenario based on all 

16 daylight hours light extinction for a given level and percentile form of the NAAQS. This is 

17 consistent with the results presented in Table 4-2 and Figure 4-1, which indicated that current 

18 conditions design values are generally lower for the all hours approach. Again there is near 

equivalence between the 90th percentile daily maximum and 98th 19 percentile all daylight hours in 

20 terms of the percent of days exceeding the daily maximum CPL values in Table 4-7.

21

22
January 2010 4-15 DRAFT – Do Note Quote or Cite

Figure 4-3 Distributions of daily maximum daylight 1-hour PM light extinction under two “just meeting” secondary NAAQS 

scenarios (excluding hours with relative humidity greater than 90 percent)

(a) Secondary NAAQS based on daily maximum daylight 1-hour PM light extinction with a level of 112 Mm-1 and a 90th

percentile form
January 2010 4-16 DRAFT – Do Note Quote or Cite

Figure 4-3. Distributions of daily maximum daylight 1-hour PM light extinction under two “just meeting” secondary NAAQS 

scenarios (excluding hours with relative humidity greater than 90 percent), continued

(b) Secondary NAAQS of 15 μg/m3 for the annual average and 35 μg/m3 for the 98th percentile 24-hour average
January 2010 4-17 DRAFT – Do Note Quote or Cite

Table 4-5. PM light extinction design values for “just meeting” secondary NAAQS 

scenarios based on measured PM light extinction (excluding hours with relative humidity 

greater than 90 percent)

Secondary NAAQS Scenarios Based on Daily Maximum

(a) (b) (c) (d) (e) (f) (g) (h) (i)

Level (Mm-1) 191 191 191 112 112 112 64 64 64

Percentile Form 90th 95th 98th 90th 95th 98th 90th 95th 98th

PM light extinction Design Value

(based on same percentile form as the NAAQS scenario)

Tacoma, WA 140 157 191 112 112 108 66 70 60

Fresno, CA 191 191 191 112 112 112 64 64 64

Los Angeles, CA 191 191 191 112 112 112 65 64 64

Phoenix, AZ 105 144 185 105 112 112 64 64 64

Salt Lake City, UT 164 191 191 112 112 112 64 64 64

Dallas, TX 183 191 191 113 113 112 64 66 66

Houston, TX 191 191 191 115 112 112 67 61 67

St. Louis, IL 191 191 191 113 112 112 65 64 64

Birmingham, AL 191 192 191 113 114 112 64 66 64

Atlanta, GA 191 191 191 112 111 112 64 63 65

Detroit, MI 191 191 191 112 112 112 64 64 65

Pittsburgh, PA 191 191 191 112 112 112 64 64 64

Baltimore, MD 191 191 191 111 112 112 63 64 65

Philadelphia, PA 191 191 191 112 112 112 65 64 64

New York, NY 192 191 191 113 112 112 65 64 64

Secondary NAAQS Scenarios Based on All Daylight Hours

(j) (k) (l) (m) (n) (o) (p) (q) (r)

Level (Mm-1) 191 191 191 112 112 112 64 64 64

Percentile Form 90th 95th 98th 90th 95th 98th 90th 95th 98th

PM light extinction Design Value

(based on same percentile form as the NAAQS scenario)

Tacoma, WA 76 106 136 76 106 112 63 64 59

Fresno, CA 190 191 191 113 112 112 65 64 64

Los Angeles, CA 192 191 192 113 112 112 66 64 64

Phoenix, AZ 68 79 94 68 79 94 64 64 64

Salt Lake City, UT 93 142 191 93 112 112 64 64 64

Dallas, TX 113 143 188 112 112 113 66 64 67

Houston, TX 105 128 171 105 113 110 65 66 61

St. Louis, IL 191 191 191 113 112 111 65 65 65

Birmingham, AL 173 191 192 113 112 113 65 64 65

Atlanta, GA 166 191 192 113 111 113 66 63 65

Detroit, MI 191 191 191 112 113 112 64 65 64

Pittsburgh, PA 167 191 191 113 113 112 65 66 64

Baltimore, MD 172 191 191 112 112 112 64 65 64

Philadelphia, PA 183 191 191 112 112 113 64 64 65

New York, NY 186 191 191 112 112 113 64 66 66
January 2010 4-18 DRAFT – Do Note Quote or Cite

Table 4-6. PM light extinction design values for “just meeting” secondary NAAQS

scenarios based on PM2.5 mass (excluding hours with relative humidity greater than 90 

percent)

Annual/1-hour 

PM2.5 NAAQS

(s) 

15μg/m3 / 35μg/m3

(t)

12μg/m3 / 25μg/m3

City Name

90th %tile

Design Value

(Mm-1)

95th %tile 

Design Value 

(Mm-1)

98th %tile 

Design Value 

(Mm-1)

90th %tile 

Design Value 

(Mm-1)

95th %tile 

Design Value 

(Mm-1)

98th %tile 

Design Value 

(Mm-1)

Design Values Based on Daily Maximum Daylight 1-hour PM Light Extinction

Tacoma, WA 120 131 177 94 102 136

Fresno, CA 195 267 306 144 197 225

Los Angeles, CA 323 365 436 239 263 360

Phoenix, AZ 105* 143* 265* 97 135 250

Salt Lake City, UT 110 168 269 83 125 198

Dallas, TX 183* 239* 302* 172 224 282

Houston, TX 185 222 276 148 178 220

St. Louis, IL 286 355 441 253 289 364

Birmingham, AL 285 394 464 213 300 365

Atlanta, GA 230 266 307 181 208 243

Detroit, MI 257 389 536 189 278 401

Pittsburgh, PA 229 258 299 167 188 218

Baltimore, MD 233 272 310 169 202 222

Philadelphia, PA 264 313 364 194 226 269

New York, NY 255 296 381 183 213 272

Design Values Based on Daylight 1-hour PM Light Extinction (All Daylight Hours)

Tacoma, WA 65 88 113 52 70 84

Fresno, CA 112 154 217 84 115 161

Los Angeles, CA 176 233 299 131 172 223

Phoenix, AZ 68* 79* 94* 60 70 86

Salt Lake City, UT 63 95 150 48 72 112

Dallas, TX 113* 143* 188* 106 134 176

Houston, TX 99 122 163 81 99 131

St. Louis, IL 181 221 271 147 183 237

Birmingham, AL 140 183 247 105 138 186

Atlanta, GA 154 180 220 123 144 174

Detroit, MI 176 209 258 130 155 188

Pittsburgh, PA 138 173 218 102 127 159

Baltimore, MD 163 215 251 121 157 184

Philadelphia, PA 169 206 258 123 150 187

New York, NY 156 204 250 113 148 179

* Phoenix and Dallas meet 15 μg/m3/35 μg/m3 under current conditions, so these entries are essentially the same as 

for current conditions.
January 2010 4-19 DRAFT – Do Note Quote or Cite

Table 4-7. Percentage of days across three years (two in the case of Phoenix and Houston) with maximum 1-hour daylight PM light 

extinction above CPLs when “just meeting” the NAAQS scenarios

(A) NAAQS Scenarios Based on Daily Maximum 1-hour PM Light Extinction

Days with max hour above 

64 Mm-1

Days with max hour above 

112 Mm-1

Days with max hour above 

191 Mm-1

(a) (b) (c) (d) (e) (f) (g) (h) (i) (s) (t) (a) (b) (c) (d) (e) (f) (g) (h) (i) (s) (t) (a) (b) (c) (d) (e) (f) (g) (h) (i) (s) (t)

NAAQS 

Level Mm-1 191 191 191 112 112 112 64 64 64 191 191 191 112 112 112 64 64 64 191 191 191 112 112 112 64 64 64

NAAQS 

Percentile 

Form 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98

Annual/24-

hour

15/

35

12/

25

15/

35

12/

25

15/

35

12/

25

Area Percentage of days Percentage of days Percentage of days

Tacoma 52 52 49 40 32 17 11 7 2 43 28 22 22 14 10 7 2 1 1 0 10 5 4 4 3 1 1 0 0 0 0 1 0

Fresno 55 42 34 31 20 15 10 4 3 54 40 30 16 12 10 5 3 1 0 0 30 17 10 5 3 2 0 0 0 0 0 10 5

Los Angeles 74 67 60 44 35 28 11 6 3 85 79 41 32 26 10 6 3 0 0 0 69 53 10 6 3 1 0 0 0 0 0 39 20

Phoenix 44 44 44 44 27 10 10 5 2 44 40 6 6 6 6 5 2 2 1 1 6 6 1 1 1 1 1 1 1 0 0 1 1

Salt Lake 

City 44 27 14 24 11 5 10 5 1 24 15 17 11 5 9 5 1 4 1 0 9 6 8 5 1 4 1 0 1 0 0 4 2

Dallas 80 66 51 49 28 14 11 5 1 81 77 41 23 13 10 5 1 1 0 0 41 37 10 5 1 1 0 0 0 0 0 10 8

Houston 77 65 57 47 31 20 13 4 3 75 64 43 28 16 12 4 3 1 0 0 41 23 11 6 2 1 1 0 0 0 0 11 3

St. Louis 83 72 57 47 35 21 12 6 2 97 89 45 30 19 12 6 2 1 0 0 74 57 11 5 3 2 1 0 0 0 0 36 21

Birmingham 63 51 41 34 20 16 10 6 3 84 70 31 18 12 10 5 3 1 0 0 55 38 10 5 2 1 0 0 0 0 0 24 13

Atlanta 86 81 77 62 52 37 10 5 1 90 85 59 47 31 11 5 1 0 0 0 71 54 11 4 2 0 0 0 0 0 0 25 8

Detroit 74 54 43 48 23 6 11 4 3 80 74 45 18 6 10 4 3 4 1 0 61 50 10 4 3 4 1 0 1 0 0 33 10

Pittsburgh 70 64 54 40 32 24 9 6 2 78 63 38 29 22 9 6 1 0 0 0 48 28 9 6 1 0 0 0 0 0 0 16 5

Baltimore 68 62 55 43 31 27 12 4 2 78 64 40 29 23 12 5 2 1 0 0 48 31 12 6 2 1 0 0 0 0 0 19 8

Philadelphia 73 66 61 43 33 28 8 6 3 85 74 39 30 25 8 5 3 0 0 0 61 38 8 5 3 0 0 0 0 0 0 28 9

New York 63 59 42 35 28 18 9 6 2 76 62 32 25 16 10 6 2 1 0 0 45 30 10 6 2 1 0 0 0 0 0 19 8

Average 67 58 49 42 29 19 10 5 2 72 62 35 24 16 10 5 2 1 0 35 45 32 9 5 2 1 0 0 0 0 0 18 8
January 2010 4-20 DRAFT – Do Note Quote or Cite

(B) NAAQS Scenarios Based on PM Light Extinction During All Daylight Hours*

Days with max hour above 

64 Mm-1

Days with max hour above 

112 Mm-1

Days with max hour above 

191 Mm-1

(j) (k) (l) (m) (n) (o) (p) (q) (r) (s) (t) (j) (k) (l) (m) (n) (o) (p) (q) (r) (s) (t) (j) (k) (l) (m) (n) (o) (p) (q) (r) (s) (t)

NAAQS 

Level Mm-1 191 191 191 112 112 112 64 64 64 191 191 191 112 112 112 64 64 64 191 191 191 112 112 112 64 64 64

NAAQS 

Percentile 

Form 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98

Annual/24-

hour

15/

35

12/

25

15/

35

12/

25

15/

35

12/

25

Area Percentage of days Percentage of days Percentage of days

Tacoma 52 52 52 52 52 40 40 24 10 43 28 22 22 22 22 22 10 10 4 1 10 5 4 4 4 4 4 1 1 0 0 1 0

Fresno 75 65 52 57 42 29 30 18 9 54 40 52 37 26 30 16 8 9 3 1 30 17 30 16 8 10 5 1 2 0 0 10 5

Los Angeles 86 83 76 76 61 47 44 27 13 85 79 73 58 44 42 28 13 10 2 1 69 53 42 27 12 12 3 1 0 0 0 39 20

Phoenix 44 44 44 44 44 44 44 30 17 44 40 6 6 6 6 6 6 6 5 3 6 6 1 1 1 1 1 1 1 1 1 1 1

Salt Lake 

City 44 44 34 44 28 15 24 11 6 24 15 17 17 14 17 11 6 9 5 1 9 6 8 8 5 8 5 2 4 1 1 4 2

Dallas 80 80 80 80 64 48 43 21 11 81 77 41 41 41 41 21 10 9 3 1 41 37 10 10 10 10 4 1 1 0 0 10 8

Houston 77 77 77 77 70 53 51 35 13 75 64 44 44 44 44 34 13 12 8 1 41 23 11 11 11 11 7 2 1 1 0 11 3

St. Louis 98 93 85 78 69 52 40 28 15 97 89 76 62 48 39 28 15 8 3 2 74 57 39 27 15 9 4 2 1 0 0 36 21

Birmingham 89 85 73 74 59 43 42 26 15 84 70 65 56 40 42 25 14 14 7 3 55 38 34 26 14 15 9 3 3 1 0 24 13

Atlanta 91 91 87 82 76 64 52 30 15 90 85 75 73 62 48 30 13 5 1 0 71 54 31 30 12 6 1 0 0 0 0 25 8

Detroit 84 78 73 67 57 47 41 29 11 80 74 64 55 45 40 26 9 5 4 4 61 50 40 26 9 6 4 4 1 1 1 33 10

Pittsburgh 85 83 74 69 55 44 36 23 11 78 63 57 53 40 35 22 11 8 1 0 48 28 26 22 11 8 2 0 0 0 0 16 5

Baltimore 80 72 66 61 45 38 29 15 9 78 64 50 43 34 28 14 8 4 2 0 48 31 23 14 8 5 2 1 0 0 0 19 8

Philadelphia 86 83 74 68 60 45 34 23 9 85 74 64 58 41 33 24 8 5 1 0 61 38 31 24 8 7 1 0 0 0 0 28 9

New York 83 74 63 62 46 36 31 19 11 76 62 59 42 33 30 18 10 7 3 1 45 30 28 18 10 8 3 1 0 0 0 19 8

Average 77 74 67 66 55 43 39 24 12 72 62 51 44 36 33 22 10 8 3 1 45 32 24 18 9 8 4 1 1 0 0 18 8

* Note that the table reports results based on daily maximum daylight hour, while the NAAQS scenarios in Panel B are based on all daylight 

hours (in both cases excluding hours with RH>90%).
January 2010 5-1 Do Not Quote or Cite 

5 REFERENCES

Abt Associates Inc., 2001. Assessing Public Opinions on Visibility Impairment due to Air Pollution: Summary 

Report. Prepared for EPA Office of Air Quality Planning and Standards; funded under EPA Contract No. 

68-D-98-001. Abt Associates Inc., Bethesda, MD. January. Available: 

http://www.epa.gov/ttncaaa1/t1/reports/vis_rpt_final.pdf. Accessed 9/16/2008.

Air Resource Specialists. 2003. 2003 WinHaze Air Quality Modeler, Version 2.9. Air Resource Specialists, Inc. 

Available: http://www.air-resource.com/downloads.php. Accessed 9/16/2008.

Arizona Department of Environmental Quality. 2003. Recommendation for a Phoenix Area Visibility Index. 

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Honorable Stephen L. Johnson, Administrator, US EPA. Clean Air Scientific Advisory Committee 

Recommendations Concerning the Proposed National Ambient Air Quality Standards for Particulate 

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Henderson, R. (2008) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee to the 

Honorable Stephen L. Johnson, Administrator, US EPA. Clean Air Scientific Advisory Committee 

Consultation on EPA’s Draft Integrated Review Plan for the National Ambient Air Quality Standards for 

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Jacques Whitford AXYS. 2007. The View Ahead; Identifying Options for a Visibility Management Framework for 

British Columbia. Report for the British Columbia Ministry of Environment. Available: 

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Molenar, J.V., W.C. Malm, and C.E. Johnson. 1994. Visual air quality simulation techniques. Atmospheric 

Environment 28(5):1055-1063.

NARSTO (2004) Particulate Matter Science for Policy Makers: A NARSTO Assessment. P. McMurry, M. 

Shepherd, and J. Vickery, eds. Cambridge University Press, Cambridge, England. ISBN 0 52 184287 5

Pitchford, M. and W. Malm. 1993. Development and applications of a standard visual index. Atmospheric 

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30(15):2705-2716.

RWDI AIR. 2008. Final Report: Establishing a Visibility Goal for Wilderness and Urban areas in British Columbia 

and Canada. Report to the British Columbia Ministry of Environment. Available: 

http://www.env.gov.bc.ca/air/airquality/pdfs/visibility_goal_report_final.pdf. Accessed 8/5/2009.

Samet, J. (2009a) Letter from Dr. Jonathan M. Samet, Chair, Clean Air Scientific Advisory Committee (CASAC) 

to The Honorable Lisa P. Jackson, Administrator, U.S. EPA. Consultation on EPA’s Particulate Matter 

National Ambient Air Quality Standards: Scope and Methods Plan for Urban Visibility Impact Assessment. 

May 21, 2009. Available: http://yosemite.epa.gov/sab/sabpeople.nsf/WebCommittees/CASAC.

Samet, J. (2009b) Letter from Dr. Jonathan M. Samet, Chair, Clean Air Scientific Advisory Committee (CASAC) to 

The Honorable Lisa P. Jackson, Administrator, U.S. EPA. Review of the Particulate Matter UrbanFocused Visibility Assessment (External Review Draft); November 24, 2009. - Available: 

http://yosemite.epa.gov/sab/sabpeople.nsf/WebCommittees/CASAC.

Schmidt. M; Frank, N.; Mintz, D.; Rao, T.; McCluney, L. (2005). Analyses of particulate matter (PM) data for the 

PM NAAQS review. Memorandum to PM NAAQS review docket EPA-HQ-OAR-2001-0017. June 30, 

2005. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_td.html

Smith, A. 2009. Comments on the First External Review Draft of EPA’s “Integrated Science Assessment for 

Particulate Matter.” CRA International, Washington, DC. March 30. Prepared for the Utility Air 

Regulatory Group. Submitted as public comment to the public meeting EPA Clean Air Science Advisory 

Council. April 2.

Smith, A.E. and S. Howell. 2009. An Assessment of the Robustness of Visual Air Quality Preference Study Results. 

CRA International, Washington, DC. March 30. Prepared for the Utility Air Regulatory Group. Submitted 

as supplemental material to presentation by Anne Smith to the public meeting of the EPA Clean Air 

Science Advisory Council. April 2.

Statistics Canada. 2009a. Population and Dwelling Count Highlight Tables, 2006 Census. Available: 

http://www12.statcan.gc.ca/census-recensement/2006/dp-pd/hlt/97-550/Index.cfm?Page = INDX&LANG 

= Eng. Accessed 7/13/2009.

Statistics Canada. 2009b. Population: Chilliwack, British Columbia (Census Agglomeration). Available: 

http://www12.statcan.ca/census-recensement/2006/dp-pd/prof/92-591/details/page.cfm?Lang = E&Geo1 = 

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= Begins&SearchPR = 01&B1 = Population&Custom. Accessed 7/13/2009.

Stratus Consulting Inc., 2009. Review of Urban Visibility Public Preference Studies: Final Report. Prepared for EPA 

Office of Air Quality Planning and Standards; funded under EPA Contract No. EP-D-08-100 with Abt 

Associates Inc., Bethesda, MD. September. Available:

US EPA (1996a). Air Quality Criteria for Particulate Matter. Research Triangle Park, NC: National Center for 

Environmental Assessment-RTP Office; report no. EPA/600/P-95/001aF-cF. 3v. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_pr_cd.html. 

US EPA (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 27711: Office 

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http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_pr_sp.html. 
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US EPA (2004). Air Quality Criteria for Particulate Matter. National Center for Environmental Assessment, Office 

of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; 

report no. EPA/600/P-99/002aF and EPA/600/P-99/002bF. October 2004. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_cd.html

US EPA (2005). 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 27711: Office 

of Air Quality Planning and Standards; report no. EPA EPA-452/R-05-005a. December 2005. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html

U.S. Environmental Protection Agency. (2007). Draft Integrated Review Plan for the National Ambient Air Quality 

Standards for Particulate Matter. October 2007. U.S. Environmental Protection Agency, Research 

Triangle Park, NC, EPA 452/P-08-006. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html

US EPA (2008a). Integrated Review Plan for the National Ambient Air Quality Standards for Particulate Matter. 

National Center for Environmental Assessment and Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. Report No. EPA 452/R-08-004. March 

2008. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/data/2008_03_final_integrated_review_plan.pdf

U.S. EPA (2008b). Integrated Science Assessment for Particulate Matter: First External Review Draft. National 

Center for Environmental Assessment-RTP Division, Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. EPA/600/R-08/139 and 139A. December 2008. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html.

US EPA (2009a). Integrated Science Assessment for Particulate Matter: Final Report. National Center for 

Environmental Assessment-RTP Division, Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. EPA/600/R-08/139F. December 2009. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html.

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Visibility Assessment. Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC. EPA-452/P-09-001. February 2009. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html.

US EPA (2009c). Particulate Matter Urban-Focused Visibility Assessment: First External Review Draft. Office of 

Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

EPA-452/P-09-005. September 2009. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html.
January 2010 Do Not Quote or Cite 

APPENDICES

A. PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Analysis of PM 

light extinction in the 15 Study Areas

B. Distributions of Estimated PM2.5 and Other Components under Current Conditions

C. Development of PRB Estimates of PM2.5 components, PM10-2.5, and PM light extinction

D. Relationships between PM Mass Concentration and PM Light Extinction under Current 

Conditions

E. Differences in Daily Patterns of Relative Humidity and Light Extinction between Areas 

and Seasons

F. Distributions of Maximum Daily Daylight PM light extinction under “Just Meets” 

Conditions

G. Additional Information on the Exclusion of Daylight Hours with Relative Humidity 

Greater than 90 Percent

H. Inter-Year Variability

I. Daylight Hours
January 2010 A-1 Do Not Quote or Cite

APPENDIX A - PM2.5 MONITORING SITES AND

MONITORS PROVIDING 2005-2007 DATA FOR THE 

ANALYSIS OF PM LIGHT EXTINCTION IN THE 15 STUDY 

AREAS

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Tacoma AQS ID 530530029

State: Washington 

City: Tacoma 

MSA: Tacoma, WA 

Local Site Name: TACOMA - L 

STREET 

Address: 7802 SOUTH L 

STREET, TACOMA

0.5 miles east of I-5

2005-2007 annual DV = 10.2

2005-2007 24-hr DV = 43

This is the highest 24-hour PM2.5 

DV site in the Seattle-TacomaOlympia, WA annual PM2.5

nonattainment area

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; one-inthree sampling schedule)

• PM2.5 speciation (one-in-six 

sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass)

Correlated Radiance Research 

M903 Nephelometry

No continuous PM10 monitoring at 

this site, see right hand column..

NA AQS ID 530530031

State: Washington 

City: Tacoma 

MSA: Tacoma, WA 

Local Site Name: TACOMA - ALEXANDER 

AVE 

Address: 2301 ALEXANDER AVE, TACOMA, 

WA 

6.4 miles NNE of PM2.5 site

Neighborhood Scale

Parameters taken from this site: 

• 1-hour PM10 STP mass (AQS parameter 

81102)

• Sample Collection Method: 

INSTRUMENTAL-R&P SA246B-INLET 

• Sample Analysis Method: TEOMGRAVIMETRIC 

7% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-2 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Fresno AQS ID 060190008

State: California 

City: Fresno 

MSA: Fresno, CA 

Local Site Name: None given

Address: 3425 N FIRST ST, 

FRESNO 

2.5 miles west of the airport, 3 

miles NNE of central Fresno

2005-2007 annual DV = 17.4

2005-2007 24-hr DV = 63

This is not the highest annual or 

24-hr PM2.5 DV site in the San 

Joaquin nonattainment area.

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88501, PM2.5 Raw 

Data) Met-One BAM

No continuous PM10 monitoring at 

this site, see right hand column..

NA PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-3 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Los Angeles AQS ID 060658001

State: California 

City: Rubidoux (West Riverside)

MSA: Riverside-San Bernardino,

CA 

Local Site Name: None given

Address: 5888 MISSION BLVD., 

RUBIDOUX

Eastern SCAB, 0.4 miles from 

Pomona Freeway.

2005-2007 annual DV = 19.6

2005-2007 24-hr DV = 55

This site is not the highest DV site 

in the LA-South Coast 

nonattainment area.

Neighborhood scale.

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

• 1-hour PM2.5 (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass) 

R&P 1400 TEOM

No continuous PM10 monitoring at 

this site, see right hand column..

NA AQS ID 060710306

State: California 

City: Victorville 

MSA: Riverside-San Bernardino, CA 

Local Site Name: MOVED FROM 060710014 

Address: 14306 PARK AVE., VICTORVILLE, 

CA

36 miles north of PM2.5 site, on the other side 

of a range of hills. 0.4 miles from I-15

Measurement Scale not given in AQS, but 

appears Neighborhood by aerial image.

Parameters taken from this site: 

• 1-hour PM10 STP mass (AQS parameter 

81102)

• Sample Collection Method: 

INSTRUMENTAL-R&P SA246B-INLET 

• Sample Analysis Method: TEOMGRAVIMETRIC

6% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-4 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Phoenix AQS ID 040137020 (FRM 

& CSN)

State: Arizona 

City: Scottsdale 

MSA: Phoenix-Mesa, AZ 

Local Site Name: 

Address: 10844 EAST OSBORN 

ROAD SCOTTSDALE' AZ

Reporting Agency: Salt River 

Pima-Maricopa Indian Community 

of Salt River Reservation

Eastern edge of the metro area, 

largely surrounded by agricultural 

fields.

2005-2007 annual DV = 7.9

2005-2007 24-hr DV = 15

This site is not the highest DV site 

in the Phoenix-Mesa CBSA.

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; one-insix sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

No continuous PM10 monitoring at 

this site, see right hand column.

AQS ID 040139998

(Continuous)

State: Arizona 

City: Phoenix

MSA: Phoenix-Mesa, AZ 

Local Site Name: Vehicle 

Emissions Laboratory

Address: 600 N 40th St & 

Fillmore St

Measurement Scale not 

available; 0.75 miles from 

intersection of two 

freeways, 1 mile from 

Phoenix airport.

Parameters taken from this 

site:

• 1-hour PM2.5 mass. 

Nephelometer.

AQS ID 040133002

State: Arizona 

City: Phoenix 

MSA: Phoenix-Mesa, AZ 

Local Site Name: CENTRAL PHOENIX 

Address: 1645 E ROOSEVELT ST-CENTRAL 

PHOENIX STN 

1.8 miles NE of central Phoenix

Neighborhood Scale

Parameters taken from this site: 

• 1-hour PM10 STP mass (AQS parameter 

81102)

Sample Collection Method: INSTRUMENTALR&P SA246B-INLET 

Sample Analysis Method: TEOMGRAVIMETRIC 

2% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-5 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Salt Lake City AQS ID490353006

State: Utah 

City: Salt Lake City 

MSA: Salt Lake City-Ogden, UT 

Local Site Name: UTM 

COORDINATES = PROBE 

LOCATION 

Address: 1675 SOUTH 600 EAST, 

SALT LAKE CITY 

2.5 miles SSE of central Salt Lake 

City

2005-2007 annual DV = 10.7

2005-2007 24-hr DV = 48

This is not the highest DV site in 

the Salt Lake City CSA.

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88501, PM2.5 Raw 

Data) FDMS-Gravimetric

No continuous PM10 monitoring at 

this site, see right hand column.

NA PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper

Dallas AQS ID 481130069

State: Texas 

City: Dallas 

MSA: Dallas, TX 

Local Site Name: DALLAS 

HINTON 

Address: 1415 HINTON STREET

4.5 miles NE of central Dallas

2005-2007 annual DV = 11.5

2005-2007 24-hr DV = 25

This is not the highest DV site in 

the Dallas-Ft. Worth CSA.

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass)

TEOM Gravimetric 50 deg C

No continuous PM10 monitoring at 

this site, see right hand column..

NA PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-6 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Houston AQS ID 482010024

State: Texas 

City: Not in a city 

MSA: Houston, TX 

Local Site Name: HOUSTON 

ALDINE 

Address: 4510 1/2 ALDINE MAIL 

RD

10 miles NNE of central Houston

2005-2007 annual DV = 13.1

2005-2007 24-hr DV = 25

This is not the highest DV site in 

the 'Houston-Baytown-Huntsville, 

TX CSA.

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; one-in-six 

day sampling schedule)

• PM2.5 speciation (one-in-six 

sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass)

TEOM Gravimetric 50 deg C

No continuous PM10 monitoring at 

this site, see right hand column.

NA PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-7 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

St. Louis AQS ID 295100085

State: Missouri 

City: St. Louis 

MSA: St, Louis, MO-IL 

Local Site Name: BLAIR STREET 

CATEGORY A CORE SLAM 

PM2.5. 

Address: BLAIR S

2 miles north of central St. Louis

2005-2007 annual DV = 14.5

2005-2007 24-hr DV = 34

This is not the highest DV site in 

the St. Louis nonattainment area.

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass) 

TEOM Gravimetric 30 deg C

No continuous PM10 monitoring at 

this site, see right hand column.

NA AQS ID 295100092 (2005 and 2006 

data)

State: Missouri 

City: St. Louis 

MSA: St, Louis, MO-IL 

Local Site Name: 

Address: 3 NORTH MARKET

0.7 miles ESE of PM2.5 site, across the street 

from the eastern edge of what appears to be 

a recycling/municipal works yard.

Middle Scale

Parameters taken from this site: 

• 1-hour PM10 STP mass (AQS parameter 

81102)

• Sample Collection Method: 

INSTRUMENTAL-R&P SA246B-INLET 

• Sample Analysis Method: TEOMGRAVIMETRIC Site was on the other 

(western) side of the recycling/municipal 

works yard as site 295100093, below.

295100093 (2007 data)

State: Missouri 

City: St. Louis 

MSA: St, Louis, MO-IL 

Local Site Name: None given

Address: Branch Street

0.6 miles ESE of PM2.5 site, across the street 

from the western edge of what appears to be 

a recycling/municipal works yard.

Middle Scale

Parameters taken from this site: 

• 1-hour PM10 STP mass (AQS parameter 

81102)

• Sample Collection Method: 

INSTRUMENTAL-R&P SA246B-INLET 

• Sample Analysis Method: TEOMGRAVIMETRIC

•

4% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-8 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Birmingham AQS ID 010730023

State: Alabama 

City: Birmingham 

MSA: Birmingham, AL 

Local Site Name: 

Address: NO. B'HAM,SOU R.R., 

3009 28TH ST. NO

2.3 miles north of central 

Birmingham

2005-2007 annual DV = 18.7

2005-2007 24-hr DV = 44

This is the highest DV site in the 

Birmingham nonattainment area

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass) 

TEOM Gravimetric 50 deg C

• 1-hour PM10 STP mass (AQS 

parameter 81102)

Sample Collection Method: 

INSTRUMENTAL-R&P SA246BINLET 

Sample Analysis Method: TEOMGRAVIMETRIC

NA Same as PM2.5 site.

0.3% of PM10-2.5 values were determined 

using regional average PM10-2.5: PM2.5 ratios 

from 2005 Staff Paper
January 2010 A-9 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Atlanta AQS ID 130890002

State: Georgia 

City: Decatur 

MSA: Atlanta, GA 

Local Site Name: 2390-B 

WILDCAT ROAD, DECATUR, GA 

Address: SOUTH DEKALB 

About 7 miles SE of central Atlanta

2005-2007 annual DV = 15.7

2005-2007 24-hr DV = 33

This is not the highest DV site in 

the Atlanta nonattainment area.

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass) 

TEOM Gravimetric 30 deg C

No continuous PM10 monitoring at 

this site, see right hand column.

NA AQS ID 131210048

State: Georgia 

City: Atlanta 

MSA: Atlanta, GA 

Local Site Name: Georgia Tech, Ford 

Environmental Science and Technology Bldg, 

roof 

Address: GA. TECH., Ford ES&T Bldg, 311 

Ferst St NW, Atlanta GA 

8.6 miles NW of PM2.5 site

Neighborhood Scale

Parameters taken from this site:

• 1-hour PM10 STP mass (AQS parameter 

81102)

Sample Collection Method: INSTRUMENT 

MET ONE 4 MODELS 

Sample Analysis Method: BETA 

ATTENUATION 

8% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-10 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Detroit AQS ID 261630033

State: Michigan 

City: Dearborn 

MSA: Detroit, MI 

Local Site Name: PROPERTY 

OWNED BY DEARBORN PUBLIC 

SCHOOLS 

Address: 2842 WYOMING 

About 0.2 miles from Ford River 

Rouge auto plant

2005-2007 annual DV = 17.2

2005-2007 24-hr DV = 43

This is the highest annual and 24-

hr DV site in the Detroit 

nonattainment area

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-in-six 

sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88501, PM2.5 Raw 

Data) TEOM Gravimetric 50 deg C

• 1-hour PM10 STP mass (AQS 

parameter 81102)

Sample Collection Method: 

INSTRUMENTAL-R&P SA246BINLET 

Sample Analysis Method: TEOMGRAVIMETRIC

NA Same as PM2.5 site.

2% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-11 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Pittsburgh AQS ID 420030008

State: Pennsylvania 

City: Pittsburgh 

MSA: Pittsburgh, PA 

Local Site Name: None given

Address: BAPC 301 39TH 

STREET BLDG #7

3 miles NE of central Pittsburgh, 

0.5 miles from Allegheny River

2005-2007 annual DV = 15.0

2005-2007 24-hr DV = 40

This site is not the highest DV site 

in the Pittsburgh nonattainment 

area.

Urban Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass)

TEOM Gravimetric 50 deg C

No continuous PM10 monitoring at 

this site, see right hand column.

NA PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper

Baltimore AQS ID 240053001 (FRM 

& CSN)

State: Maryland 

City: Essex 

MSA: Baltimore, MD 

Local Site Name: Essex 

Address: 600 Dorsey Avenue

7 miles east of central Baltimore

2005-2007 annual DV = 14.5

2005-2007 24-hr DV = 35

This is not the highest DV site in 

the Baltimore nonattainment area.

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

• 1-hour PM10 LC mass (AQS 

parameter 85101

AQS ID 245100040

(Continuous)

State: Maryland 

City: Baltimore 

MSA: Baltimore, MD 

Local Site Name: Oldtown 

Address: Oldtown Fire 

Station, 1100 Hillen Street

1 mile NNE of Inner Harbor 

area

Middle Scale

Parameters taken from this 

site:

• 1-hour PM2.5 mass 

(AQS parameter 88502, 

Acceptable PM2.5 AQI & 

Speciation Mass) TEOM 

Gravimetric 50 deg C

Same as PM2.5 site.

5% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-12 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Philadelphia AQS ID100032004 (DE)

State: Delaware 

City: Wilmington 

MSA: Wilmington-Newark, DE-MD 

Local Site Name: CORNER OF 

MLK BLVD AND JUSTISON ST

2.5 miles NE of central 

Wilmington, 0.25 miles from the 

Delaware River, 22 miles SW from 

central Philadelphia

2005-2007 annual DV = 14.7

2005-2007 24-hr DV = 37

This is not the highest DV site in 

the Philadelphia nonattainment 

area

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-in-six 

sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88501, PM2.5 Raw 

Data) Beta Attenuation

• 1-hour PM10 STP mass (AQS 

parameter 81102)

NA Same as PM2.5 site.

3% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper

New York AQS ID 340390004 (NJ)

State: New Jersey 

City: Elizabeth 

MSA: Newark, NJ 

Local Site Name: ELIZABETH 

LAB 

Address: NEW JERSEY 

TURNPIKE INTERCHANGE 13 

1.75 miles south of Elizabeth, at 

the I-95 interchange with I-278

2005-2007 annual DV = 14.4

2005-2007 24-hr DV = 42

This is not the highest DV site in 

the New York nonattainment area

Neighborhood Scale

Parameters taken from this site:

• 24-hour FRM PM2.5 mass 

(AQS parameter 88101; every 

day sampling schedule)

• PM2.5 speciation (one-inthree sampling schedule)

• 1-hour PM2.5 mass (AQS 

parameter 88502, Acceptable 

PM2.5 AQI & Speciation Mass)

TEOM Gravimetric 30 deg C

No continuous PM10 monitoring at 

this site, see right hand column.

NA AQS ID 360610125

State: New York 

City: New York 

MSA: New York, NY 

Local Site Name: PARK ROW 

Address: 1 PACE PLAZA

Near the on-ramp to the Brooklyn Bridge, 

Manhattan end

Measurement scale not stated.

Parameters taken from this site: 

• 1-hour PM10 STP mass (AQS parameter 

81102)

Sample Collection Method: INSTRUMENTALR&P SA246B-INLET 

Sample Analysis Method: TEOMGRAVIMETRIC

2% of PM10-2.5 values were determined using 

regional average PM10-2.5: PM2.5 ratios from 

2005 Staff Paper
January 2010 A-13 Do Not Quote or Cite

PM2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the 

Analysis of PM light extinction in the 15 Study Areas

Study Area First PM2.5 Monitoring Site Second PM2.5

Monitoring Site (if 

applicable)

PM10 data source for PM10-2.5

Notes:

• In this Table, the 1-hour concentration parameter “88502, Acceptable PM2.5 AQI & Speciation Mass” is the 

same as the ISA refers to as “FRM-like” PM2.5 mass. An entry of “88501, PM2.5 Raw Data” indicates that 

the monitoring agency makes no representation as to the degree of correlation with FRM PM2.5 mass. The 

latter type of continuous PM2.5 data were used only when the former were unavailable.

• Where PM10 was reported in STP, it was converted to LC before PM10-2.5 was calculated.

• All continuous PM2.5 data were obtained through the AirNow data system rather than from AQS, as an 

initial exploration indicated that not all the desired 1-hour data from all sites had been submitted to AQS.

Data are submitted to the AirNow system within hours of collection and may not be subject to as much data 

validation review as is typical for data in AQS, despite the opportunity offered by the AirNow system for 

monitoring agencies to correct data after initial submission. 
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B-1

APPENDIX B - DISTRIBUTIONS OF ESTIMATED PM2.5

AND OTHER COMPONENTS
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B-2

Figure B-1 – Distribution of daily maximum PM2.5 and PM10-2.5 across the 2005-2007 period, by study area

(a) Daily maximum daylight PM2.5
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B-3

(b) Daily maximum daylight PM10-2.5
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B-4

Figure B-2 – Distribution of hourly PM2.5 components across the 2005-2007 period, by study area

(a) 1-hour daylight sulfate (dry, fully neutralized)
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B-5

Figure B-2 – Distribution of hourly PM2.5 components across the 2005-2007 period, by study area, continued

(b) 1-hour daylight nitrate (dry, fully neutralized, CSN method consistent)
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B-6

Figure B-2 – Distribution of hourly PM2.5 components across the 2005-2007 period, by study area, continued

(c) 1-hour daylight elemental carbon
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B-7

Figure B-2 – Distribution of hourly PM2.5 components across the 2005-2007 period, by study area, continued

(d) 1-hour daylight organic carbonaceous material (by SANDWICH method)
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B-8

Figure B-2 – Distribution of hourly PM2.5 components across the 2005-2007 period, by study area, continued

(e) 1-hour daylight fine soil
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C-1

APPENDIX C - DEVELOPMENT OF PRB ESTIMATES OF 

PM2.5 COMPONENTS, PM10-2.5, AND PM LIGHT 

EXTINCTION

Policy relevant background levels of PM light extinction have been estimated for this 

assessment by relying on outputs for the 2004 CMAQ run in which anthropogenic emission 

in the U.S., Canada, and Mexico were omitted, as described in the second draft ISA. 

Estimates of PRB for PM light extinction were calculated from modeled concentrations of 

PM2.5 components using the IMPROVE algorithm. The necessary component concentrations 

were extracted from the CMAQ output files, as they were not summarized in the second draft 

ISA.

More specifically, for each study area, EPA staff overlaid CMAQ grid cells over 

shapes representing the Census-defined urbanized area for each study area, and visually 

identified the CMAQ grid cells that had a substantial portion of their area coincident with the 

urbanized area. For each such grid cell, for each of the 12 months of the year, we obtained 

the 24 values of the hour-specific average concentrations of the five PM2.5 components. We 

then averaged these across the selected grid cells. Thus, a given hour of the day has the same 

PRB estimate for a component on all days within a month, but months and study areas differ. 

We generally observed that PRB concentrations did not vary greatly across the several grid 

cells overlaying the urbanized area of a given study area; this is reasonable given the 

exclusion of local anthropogenic sources from this CMAQ model run. CMAQ estimates of 

PRB for the five PM2.5 components averaged across grid cells and months were not adjusted 

in any.46

There too many values of PRB to present or illustrate them comprehensively in this 

document. Table C-1 presents annual average concentrations by study area to summarize 

these PRB estimates for the PM2.5 components (including the specific form assumed for 

sulfate, nitrate, and organic carbon). The right hand column of the table shows the PM2.5 

mass calculated from the CMAQ-estimated components, including factors to fully neutralize 

sulfate and nitrate (but with no water mass added). One notable feature of the annual average 

of the PRB estimates is the relatively high values for elemental and organic carbon PRB for 

the Tacoma study area. This area is often affected by wildfires for extended periods in the 

 46 This approach to estimation of PRB for PM2.5 shares the same information source but is more 

disaggregated than the approach used in the health risk assessment for this review of the PM NAAQS. In the health 

risk assessment, PRB estimates for PM2.5 mass concentration are taken from the same CMAQ model run, but is 

averaged by calendar quarter and by region of the country.
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C-2

autumn months, and such fires were included in the 2004 emissions scenario for the PRB 

CMAQ run. A cursory review of information on fire events in 2005-2007 confirmed that the 

fire situation in this part of the country in 2004 was not an anomaly.

Another notable feature of the PRB estimates is that the values for nitrate and fine 

soil/crustal are low relative to previous estimates of natural background concentrations of 

these fine PM components in Class I areas. These previous estimates by Trijonis, repeated in 

the 2003 EPA guidance document “Guidance for Estimating Natural Visibility Conditions 

Under the Regional Haze Program”, are 0.10 μg/m3 for (neutralized) nitrate and 0.50 μg/m3

for fine soil. These estimates are based largely on data from the earliest of the IMPROVE 

monitoring stations, and thus may include some influence from non-PRB emissions. On the 

other hand, it is understandable that the unadjusted output from the PRB CMAQ scenario 

would underestimate nitrate and fine soil. CMAQ is known to underestimate actual nitrate in 

many situations when provided with a complete NOx inventory, and the nonanthropogenic 

emission inventory for NOx itself has uncertainties. The non-anthropogenic emission 

inventory for the PRB CMAQ run may also quite easily underestimate nonanthropogenic 

emissions of fine soil. However, even if the estimates for PRB nitrate and fine soil were 

increased to match the Trijonis estimates, the resulting values for PRB PM light extinction 

would increase only a little. Even at 90 percent relative humidity, the contribution to PM 

light extinction calculated from the Trijonis estimates is 1.7 Mm-1, versus the average of

about 0.5 Mm-1 using the estimates in Table C-1. The increment of 1.2 Mm-1 would be only 

about 10 to 20 percent of the PRB PM light extinction estimates shown in Table C-4, and 

would not significantly affect the calculation of PM light extinction values under the “what 

if” scenarios.
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C-3

Table C-1. Summary of PRB estimates for the five PM2.5 components: average 1-hour 

values across 2005-2007

Study Area Average 1-Hour PRB Concentration Across 2005-2007 (μg/m3)

Sulfate

(dry, no 

ammonium)

Nitrate 

(dry, no 

ammonium)

Elemental 

Carbon

Organic 

Carbonaceous 

Material

Fine 

Soil/Crustal

Calculated 

PM2.5

Tacoma 0.45 0.026 0.15 1.3 0.31 2.4

Fresno 0.4 0.00062 0.08 0.74 0.19 1.6

Los Angeles 0.36 0.0037 0.028 0.3 0.036 0.9

Phoenix 0.31 0.000052 0.02 0.26 0.015 0.7

Salt Lake City 0.25 0.00028 0.025 0.26 0.034 0.7

Dallas 0.27 0.0022 0.055 0.59 0.092 1.1

Houston 0.3 0.0055 0.091 0.86 0.17 1.5

St. Louis 0.31 0.0027 0.047 0.53 0.07 1.1

Birmingham 0.29 0.007 0.099 1.1 0.19 1.8

Atlanta 0.3 0.016 0.1 1.1 0.19 1.8

Detroit-Ann 0.34 0.00062 0.024 0.32 0.018 0.8

Pittsburgh 0.3 0.00052 0.029 0.36 0.034 0.8

Baltimore 0.34 0.0016 0.039 0.44 0.054 1.0

Philadelphia 0.34 0.00097 0.03 0.36 0.032 0.9

New York City 0.36 0.0038 0.026 0.31 0.022 0.9

Average 0.33 0.00 0.06 0.59 0.10 1.20
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C-4

It is also necessary to have estimates of PRB for PM10-2.5, to feed into the IMPROVE 

algorithm. It is not EPA’s practice to rely on coarse PM estimates from CMAQ modeling, so 

other sources of PRB estimates were considered. The final ISA for this review does not 

present any new information on this subject. The approach used in the previous two Criteria 

Documents was to present the historical range of annual means of PM10-2.5 concentrations 

from IMPROVE monitoring sites selected as being least influenced by anthropogenic 

emissions. See Table 3E-1 of the 2004 Criteria Document (reproduced here as Table C-3). 

For sites in the lower 48 states, these annual means ranged from a low of 1.8 μg/m3 to a high 

of 10.8 μg/m3. No cross-year average or median values were provided that could be used as 

the point estimates needed in this assessment. Therefore, for this assessment, EPA staff 

estimated PRB for PM10-2.5 using a contour map based on average 2000-2004 PM10-2.5

concentrations from all IMPROVE monitoring sites, found in a recent report from the 

IMPROVE program. (DeBell, 2006). We located each study area’s position on this map, 

and assigned it the mid-point of the range of concentrations indicated by the contour band for 

that location. The contour map is reproduced here as Figure C-1. Stars show locations of the 

15 study areas. In this reproduction, the midpoints of the contour ranges have been added to 

the legend.

The results for PRB for coarse PM are shown in Table C-2. Lacking any other 

information, these PRB values are taken to apply to every hour of the year. The contour map 

and thus these values are influenced by data from IMPROVE sites that were not considered 

in the 2004 Criteria Document because they are not sufficiently isolated from the influence of 

anthropogenic emissions, including three IMPROVE sites in urban areas which clearly are 

influenced by anthropogenic emissions, and thus may be overestimates of PRB for coarse

PM. Nevertheless, these values are generally within the range of values presented in the 

Criteria Document for the more isolated sites. These values for the more isolated sites are 

reproduced here in Table C-3 for ease of comparison. Further, these PRB values are low 

enough that their exact values have little effect on the results of “what if” estimation of PM 

light extinction levels under possible secondary PM NAAQS.

Table C-4 presents the resulting 2005-2007 average PRB daylight PM light extinction 

by study area, determined by using each daylight hour’s f(RH),47

 47 Hour-specific relative humidity for PRB conditions was assumed to be the same as measured for current 

conditions.

the hour-specific PRB 

PM2.5 component estimates (summarized only as annual averages in Table C-1), the PRB 

PM10-2.5 estimates in Table C-2, and the IMPROVE algorithm. The sulfate and nitrate 

component values in Table C-1 are multiplied by 1.375 and 1.29 to reflect full neutralization, 
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C-5

before being used in the IMPROVE algorithm. While for conciseness Table C-4 presents 

only the annual average PRM for PM light extinction for all daylight hours in 2005-2007 

(excluding hours with relative humidity greater than 90 percent), in the rollback analysis of 

“what if” conditions hour-specific PRB values are retained and used.

The values of PRB PM light extinction in Table C-4 range between 5 and 11 Mm-1. 

For comparison, the default estimates of natural visibility conditions in the 2003 EPA 

guidance document for Class I areas range between about 15 and 20 Mm-1, including the 

Rayleigh contribution of about 10 Mm-1. Thus, on an annual average basis the range of PRB 

estimates for PM light extinction used for this assessment is very consistent with the range of 

total light extinction values recommended in the guidance document.
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C-6

Figure C-1. Selection of PRB values for PM10-2.5 based on contoured IMPROVE 

monitoring data

9.5

8.5 

7.5

6.5 

5.5

4.5

3.5

2.5

1.5

0.5

9.5

8.5 

7.5

6.5 

5.5

4.5

3.5

2.5

1.5

0.5
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C-7

Table C-2. Policy Relevant Background Concentrations of PM10-2.5 Used in This 

Assessment, Based on Measurements at IMPROVE Sites

Study Area PRB PM10-2.5 Mass (μg/m3)

Tacoma 4.5

Fresno 5.5

Los Angeles 4.5

Phoenix 5.5

Salt Lake City 4.5

Dallas 8.5

Houston 5.5

St. Louis 7.5

Birmingham 5.5

Atlanta 5.5

Detroit 9.5

Pittsburgh 3.5

Baltimore 3.5

Philadelphia 6.5

New York 3.5

Table C-3. Ranges of 1990-2002 Annual Mean PM Concentrations at IMPROVE 

Monitoring Sites (μg/m3

)

Source: Table 3E-1 of the 2004 Air Quality Criteria Document for PM
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C-8

Table C-4. 2005-2007 Average Policy Relevant Background Daylight PM light extinction 

(excluding hours with relative humidity above 90%)

Study Area 2005-2007 Average Policy Relevant 

Background Daylight PM light extinction, 

Mm-1

Tacoma 11

Fresno 11

Los Angeles 9

Phoenix 8

Salt Lake City 5

Dallas 8

Houston 10

St. Louis 9

Birmingham 9

Atlanta 10

Detroit 7

Pittsburgh 7

Baltimore 8

Philadelphia 8

New York 8
January 2010

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D-1

APPENDIX D - RELATIONSHIPS BETWEEN PM MASS 

CONCENTRATION AND PM LIGHT EXTINCTION UNDER 

CURRENT CONDITIONS

In the last review, the 2005 Staff Paper examined the correlation between PM light 

extinction and PM2.5 mass concentrations, each defined for various consistent time periods. The 

2005 Staff Paper analysis assumed that the percentage mix of PM2.5 components was the same 

in all 24 hours of each day, equal to that indicated by 24-hour CSN sampling. The modeling of 

1-hour PM light extinction in this new assessment allows these correlations to be re-examined, 

with the more realistic treatment in which the mix of PM2.5 components is modeled to vary 

during the day, based in part on diurnal profiles from CMAQ modeling (see section 3.2.2).

Five scatter plot figures relating PM2.5 mass concentrations and PM light extinction are 

presented here for the individual study areas, using different time periods for the two parameters; 

these time periods are not always matched. In each figure, the solid red curve was estimated by 

applying locally weighted scatter plot smoothing (LOESS) to the data. LOESS is a form of 

locally weighted polynomial regression (see http://support.sas.com/rnd/app/papers/loesssugi.pdf) 

and is a convenient way to visualize whether a dense data cloud in a scatter plot reflects a more 

linear or more nonlinear relationship. The LOESS results in each case indicate a generally linear 

relationship as a central tendency but with considerable variability around that central tendency.

Table D-1 presents squared correlation coefficients between observed and LOESS modelpredicted values for all five figures. Because the LOESS regressions are generally linear, 

comparisons among these correlation coefficients should lead to the same qualitative conclusions 

as if coefficients from linear regressions were compared. All values of PM light extinction 

presented here are based on excluding daylight hours with relative humidity greater than 90 

percent; hence, a nominally 4-hour period might have as few a one 1-hour PM light extinction 

value, although this is rare in this data set (see the tile plots in Figure 3-12). However, values of 

PM2.5 mass concentration do not exclude any hours within the time period specified. Note that 

if several study areas were grouped by region and combined into a single scatter plot and LOESS 

fit, similar to the analysis of this topic in the 2005 Staff Paper, the correlations would be weaker 

than observed here for individual study areas.

Figure D-1 compares 24-hour PM2.5 mass (as measured by the FRM/FEM filter-based 

sampler) to daily maximum daylight PM light extinction. The scatter is due the variations in 

PM2.5 concentration, in the mix of PM2.5 components, and in relative humidity during the day 

and across days. Variations in PM10-2.5 concentrations also contribute to the scatter, in all five 

comparisons presented here, since very high levels of PM10-2.5 substantially influence PM light 
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D-2

extinction. This source of variability in the scatter plots is particularly important for Los 

Angeles, Phoenix, and St. Louis which have many (Phoenix) or some (Los Angeles and St. 

Louis) hours with high PM10-2.5.

Because of the large scatter and low correlation coefficients when using 24-hour PM2.5 

mass concentration to predict daily maximum daylight PM light extinction, it is natural to 

investigate how much the correlation improves when the PM2.5 mass indicator is limited to 

shorter periods of time. The next four figures investigate correlations during such shorter 

periods, both matched and un-matched in time.

Figure D-2 compares hourly PM2.5 mass (as actually measured by the continuous 

instruments) vs. same-hour daylight PM light extinction. Lack of agreement due to mismatch of 

time period is not a factor in this comparison. However, there is still considerable scatter due to 

variations in the mix of PM2.5 components and in relative humidity across hours and days. In 

addition, continuous PM2.5 mass instruments do not register the mass of each component 

consistently with FRM/FEM and CSN samplers and lab analysis methods. This affects the 

scatter in this figure because the estimates of hourly light extinction are linked to the FRM/FEM 

and CSN measurements more strongly than to the continuous PM2.5 measurements. Note that 

the correlation values in Table D-1 for this comparison are better than those for the 24-hour 

comparison in most but not all study areas. An implication of this figure and the information in 

Table D-1 is that a wide range of light extinction levels can prevail in hours that have the same 

PM2.5 mass concentration, even at a single site. Additional variability no doubt exists across 

areas.

Figure D-3 compares 12-4 pm average PM2.5 mass vs. 12-4 pm average PM light 

extinction. The 2005 Staff Paper observed that because this time period is generally the time of 

lowest relative humidity, the relationship between PM2.5 mass and PM light extinction (i.e., the 

ratio of the two or the slope of the regression line) is more uniform across areas during this 

period than the relationship for values of each averaged over all 24 hours in a day. In addition, 

the longer averaging period might be expected to reduce the effect of variability in the 

measurement of hourly PM2.5 mass. However, comparison of Figures D-2 (time-matched single 

hours) and D-3 (time-matched 4 afternoon hours) and the corresponding columns of Table D-1 

indicates that after exclusion of hours with relatively humidity greater than 90 percent the scatter 

in Figure D-3 is about the same as in Figure D-2. This residual scatter is due to composition 

differences from hour-to-hour, as well as to variations in relative humidity during hours with 

relative humidity of 90 percent or less. It can also be observed by comparing Figures D-2 and D3 that the period between 12 pm and 4 pm generally has lower levels of PM light extinction than 

for all daylight hours taken together, even after the exclusion of the hours with the highest 

relative humidity. (Note the change in scale between these two figures.)
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D-3

Figure D-4 compares 12-4 pm average PM2.5 mass vs. daily maximum daylight 1-hour

PM light extinction. This time-unmatched comparison tests the usefulness of a 12-4 pm PM2.5 

mass indicator as a predictor of the daily PM light extinction metric of potentially greatest 

interest. The scatter in Figure D-4 is typically more than in Figure D-3 (4 time-matched 

afternoon hours), because daily maximum daylight 1-hour PM light extinction often occurs 

earlier in the day than the 12-4 pm period used to average the PM2.5 mass, and the time period 

mismatch introduces prediction errors due to changes in PM2.5 concentration and composition 

and relative humidity. An implication is that while a secondary NAAQS based on 12-4 pm 

average PM2.5 mass might achieve a given level of protection across days and areas in avoiding 

high levels of PM light extinction between 12 and 4 pm, with some variation across areas due to 

composition and relative humidity differences, there could be considerable additional variation in 

the level of protection against PM light extinction during the earlier hours of the day when some 

areas often have their highest PM light extinction levels.

Figure D-5 compares 8 am-12 pm average PM2.5 mass vs. daily maximum daylight 1-

hour PM light extinction. This comparison is of interest because it may reduce the number of 

instances of time mismatch, versus the comparison made in Figure D-4, if the daily maximum 

light extinction often occurs between 8 am and 12 pm. The scatter in Figure D-5 is typically less 

than in Figure D-4 and the squared correlation coefficients larger, indicating that this earlier 

averaging period for PM2.5 mass more often encompasses the period of maximum PM light 

extinction. However, the scatter in Figure D-5 is greater than that in Figure D-3 (4 time-matched 

afternoon hours).

Figure D-6 provides another perspective on the possible use of PM2.5 mass concentration 

as an indicator for a secondary PM NAAQS aimed at protecting visual air quality. Figure D-6 

shows in box-and-whisker plot form two versions of the ratios of PM light extinction to PM2.5

mass concentration, allowing a comparison across the 15 study areas of the central tendencies 

and the distributions of these ratios. The Panel A version corresponds to the comparison in 

Figure D-1 (24-hour averages of PM2.5 mass and PM light extinction) and the Panel B version 

corresponds to the comparison in Figure D-2 (time-matched single hour values). The data points 

in Figure D-6 were prepared as follows. In each day for each study area, the value of the 

indicated PM light extinction (24-hour average or 1-hour value) was divided by the indicated 

PM2.5 concentration metric (24-hour average or 1-hour value). Ratios that reflect PM2.5

concentrations less than 5 μg/m3 or PM light extinction less than 64 Mm-1 were eliminated 

before plotting, as such data points represent days or hours that could not play any role in 

determining compliance with any of the NAAQS scenarios considered in this assessment; also, 

some of these low-concentration/extinction data pairs produced extreme ratios that obscured the 

pattern for data pairs of most policy interest. The maximum ratio value for the vertical scale in 
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D-4

these plots is set at 40 to allow closer examination of the portion of the plot representing the bulk 

of the data; this prevents a very small number of daily maximum data points for a few study 

areas from appearing in Panel A and a very small percentage of 1-hour data points for a few 

study areas (Los Angeles and St. Louis in particular) from appearing in Panel B. The notable 

variation in the vertical positions of the 25-75 percentile boxes and the 90 percentile whiskers 

representing the ratios in the 15 areas illustrates the point that because of differences in PM 

composition mix and relative humidity (even after excluding hours with relative humidity greater 

than 90 percent) across study areas, a secondary NAAQS based on PM2.5 mass concentration 

would not give equal protection in terms of PM light extinction levels across cities, days, and 

hours.

In the first public review draft of this assessment, it was notable that the correlation 

values for St. Louis and Philadelphia were much lower than for other areas. In this version 

(reflecting both corrections to relative humidity inputs and exclusion of hours with very high 

relative humidity) the correlation value for Philadelphia is about that for other eastern areas. The 

correlation values for St. Louis remain notably low relative to the average of all areas, for all five 

scatter plots. This is likely due to the influence of the high estimated values for PM10-2.5. In 

several other cases of notably low correlation, the small available range of PM2.5values relative 

to other areas contributes to the lower correlation values, e.g., in Phoenix, Dallas, and Houston.
January 2010

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D-5

Table D-1. Squared correlation coefficients between observed and LOESS 

model-predicted values of PM light extinction

Area

Figure D-1

24-hour 

PM2.5

mass vs. 

daily 

maximum 

daylight 1-

hour PM 

light 

extinction

Figure D-2

1-hour 

PM2.5 mass 

vs. samehour PM 

light 

extinction

Figure D-3

12-4 pm 

average 

PM2.5 mass 

vs. 12-4 pm 

average PM 

light 

extinction

Figure D-4

12-4 pm 

average 

PM2.5 mass 

vs. daily 

maximum 

daylight 1-

hour PM 

light 

extinction

Figure D-5

8 am-12pm 

average PM2.5

mass vs. daily 

maximum 

daylight 1-

hour PM 

light 

extinction

Tacoma 0.48 0.81 0.78 0.29 0.65

Fresno 0.76 0.83 0.9 0.69 0.83

Los Angeles 0.57 0.63 0.67 0.53 0.7

Phoenix 0.22 0.67 0.73 0.18 0.2

Salt Lake City 0.88 0.89 0.95 0.8 0.89

Dallas 0.45 0.59 0.53 0.2 0.35

Houston 0.46 0.61 0.62 0.2 0.3

St. Louis 0.40 0.43 0.2 0.18 0.36

Birmingham 0.61 0.81 0.78 0.34 0.44

Atlanta 0.54 0.72 0.8 0.4 0.7

Detroit 0.62 0.55 0.6 0.11 0.3

Pittsburgh 0.73 0.63 0.65 0.52 0.62

Baltimore 0.78 0.69 0.69 0.58 0.71

Philadelphia 0.60 0.6 0.57 0.38 0.49

New York 0.69 0.76 0.76 0.5 0.62

AVERAGE 0.59 0.68 0.68 0.39 0.54
January 2010

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D-6

Figure D-1. – Relationship between 24-hour PM2.5 mass vs. daily maximum daylight 1-hour PM light extinction. 
January 2010

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D-7

Figure D-2. – Relationship between daylight 1-hour PM2.5 mass vs. same-hour PM light extinction. 
January 2010

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D-8

Figure D-3. Relationship between 12-4 pm average PM2.5 mass vs. 12-4 pm average PM light extinction.
January 2010

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D-9

Figure D-4. Relationship between 12-4 pm average PM2.5 mass vs. daily maximum daylight 1-hour PM light 

extinction.
January 2010

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D-10

Figure D-5. Relationship between 8 am-12pm average PM2.5 mass vs. daily maximum daylight 1-hour PM light extinction
January 2010

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D-11

Figure D-6. Distribution of ratios of 1-hour PM light extinction and PM2.5 mass 

concentration. 

A – Ratios of daily maximum daylight 1-hour PM light extinction to 24-hour average PM2.5

concentration. 

B – Ratios of daylight 1-hour PM light extinction to same-hour PM2.5 concentration 
January 2010

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E-1

APPENDIX E - DIFFERENCES IN DAILY PATTERNS OF 

RELATIVE HUMIDITY AND PM LIGHT EXTINCTION

BETWEEN AREAS AND SEASONS

In the last review of the secondary PM NAAQS, the pattern of PM light extinction

during the day was of particular interest. It was noted, using estimates of hourly PM light 

extinction based on a simpler approach than described for this analysis, that both (1) mid-day 

PM light extinction and (2) the slope of the relationship between PM light extinction and 

PM2.5 concentration varied less among regions of the country that at other times of the day. 

This was attributed to greater homogeneity of relative humidity across regions in the mid-day 

period. This is in contrast to the situation in the morning and later afternoon hours, when

more eastern areas typically experience higher relative humidity levels than the more arid 

western and southwestern areas. The current analysis allows these patterns to be reexamined.

Figures E-1 through E-4 show the diurnal pattern of season-average, hour-specific

PM light extinction and relative humidity for the four “daylight seasons.” These graphics 

exclude hours with relative humidity greater than 90 percent. Light extinction and relative 

humidity for a given clock hour are averaged across the days in the season, across all three 

years. Daylight hours (per the simplified schedule of Table 3-5) are indicated by solid 

circles. Average 1-hour PM light extinction generally is highest in the morning, 

corresponding to higher relatively humidity (mostly due to lower temperature), higher

vehicle traffic, and less dispersive conditions than later in the day. As was observed in the 

last review, there is more variation in average 1-hour PM light extinction among areas in the 

morning than at mid-day, although the morning variation has been reduced (relative to same 

information in the first public review draft of this assessment) by the exclusion of hours with 

relative humidity greater than 90 percent. 
January 2010

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E-2

Figure E-1. Diurnal and seasonal patterns of relative humidity (percent) and PM light extinction (Mm-1) for 2005-2007

(a) November-January 
January 2010

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E-3

Figure E-2. Diurnal and seasonal patterns of relative humidity (percent) and PM light extinction (Mm-1) for 2005-2007, 

continued

(b) February-April 
January 2010

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E-4

Figure E-3. Diurnal and seasonal patterns of relative humidity (percent) and PM light extinction (Mm-1) for 2005-2007, 

continued

(c) May-July 
January 2010

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E-5

Figure E-4. Diurnal and seasonal patterns of relative humidity (percent) and PM light extinction (Mm-1) for 2005-2007, 

continued

(d) August-October 
F-1

January 2010

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APPENDIX F - DISTRIBUTIONS OF MAXIMUM DAILY 

AND HOURLY DAYLIGHT PM LIGHT EXTINCTION -

UNDER “JUST MEET” CONDITIONS
F-2

January 2010

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(a) NAAQS Scenario

Daily Max

191 Mm-1

90th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-3

January 2010

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F-4

January 2010

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(b) NAAQS Scenario

Daily Max

191 Mm-1

95th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-5

January 2010

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(c) NAAQS Scenario

Daily Max

191 Mm-1

98th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-6

January 2010

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(d) NAAQS Scenario

Daily Max

112 Mm-1

90th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-7

January 2010

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(e) NAAQS Scenario

Daily Max

112 Mm-1

95th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-8

January 2010

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(f) NAAQS Scenario

Daily Max

112 Mm-1

98th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-9

January 2010

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(g) NAAQS Scenario

Daily Max

64 Mm-1

90th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-10

January 2010

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(h) NAAQS Scenario

Daily Max

64 Mm-1

95th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-11

January 2010

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(i) NAAQS Scenario

Daily Max

64 Mm-1

98th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-12

January 2010

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(j) NAAQS Scenario

All hours

191 Mm-1

90th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-13

January 2010

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(k) NAAQS Scenario

All hours

191 Mm-1

95th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-14

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(l) NAAQS Scenario

All hours

191 Mm-1

98th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-15

January 2010

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(m) NAAQS Scenario

All hours

112 Mm-1

90th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-16

January 2010

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(n) NAAQS Scenario

All hours

112 Mm-1

95th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-17

January 2010

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(o) NAAQS Scenario

All hours

112 Mm-1

98th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-18

January 2010

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(p) NAAQS Scenario

All hours

64 Mm-1

90th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-19

January 2010

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(q) NAAQS Scenario

All hours

64 Mm-1

95th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-20

January 2010

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(r) NAAQS Scenario

All hours

64 Mm-1

98th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-21

January 2010

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(s) NAAQS Scenario

15 μg/m3 annual

35 μg/m3 24-hour

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
F-22

January 2010

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(t) NAAQS Scenario

12 μg/m3 annual

25 μg/m3 24-hour

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)
G-1

January 2010

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APPENDIX G – ADDITIONAL INFORMATION ON THE 

EXCLUSION OF DAYLIGHT HOURS WITH RELATIVE 

HUMIDITY GREATER THAN 90 PERCENT

This appendix provides detailed information related to the exclusion of daylight hours 

with relative humidity greater than 90 percent from the design value formula for the secondary 

NAAQS scenarios based on PM light extinction, as discussed in section 3.3.5. As described in 

that section, these hours have also been excluded from graphical displays of the distribution of 

PM light extinction under current conditions and the various NAAQS scenarios, and from the 

denominator of percentages of day or hours (as in Table 4-7).

Table G-1 shows how many estimates of 1-hour daylight PM light extinction were 

excluded, based both on individual hours and on days that were affected by the exclusion of one 

or more daylight hours. Phoenix was not affected at all. Among the other areas, Detroit was the 

least affected. For all areas, comparison of the percentage of hours affected to the percentage of 

days affected indicates that several hours with high relatively humidity tend to occur in the same 

day, rather than being evenly distributed across all days. For example, in Atlanta 24 percent of 

daylight hours have relative humidity greater than 90 percent, which corresponds to about 876 

hours per year (assuming there were data for every day of the year and given that on average 

there are about 10 fully daylight hours per day). However, only 80 percent of the days 

(corresponding to 292 days, if there were data for every day of the year) are affected. Thus, on 

average, an affected day in Atlanta has about 3 affected hours. The tile plots in Figure 3-12 also 

illustrate the tendency for hours with high PM light extinction to cluster in some days.

Figure G-1 shows when during the daylight hours these hours with relative humidity 

greater than 90 percent occurred, prior to the their exclusion. Some but not all areas have a 

strong tendency for the affected hours to be in the morning. The counts in this figure are across 

all the days in 2006-2008 that have estimates of PM light extinction, not all the actual calendar 

days in that three year period. Given the regularity of the monitoring schedules, these results 

should represent year-round conditions reasonably well. However, the estimates of PM light 

extinction for Phoenix and Houston are not seasonally balanced due to one calendar quarter with 

no data in each case (see Table 3-4), so the true year-round time-of-day distributions of excluded 

hours for these two areas may be somewhat different than shown here. 

Figure G-2 contrasts the distribution of daylight PM light extinction estimates before and 

after the exclusion, based on both daily maximum values and all daylight hourly values 

individually. The differences observable in the figure are consistent with the information on the 

percentages of hours and day affected in the study areas. In most cases, the highest values of 
G-2

January 2010

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light extinction are notably lower after exclusion, on both a daily maximum basis and individual 

hour basis, indicating that PM concentrations in some of the excluded hours are fairly high. If 

only low-PM hours were excluded by the relative humidity screen, the highest values of light 

extinction would not have been affected.

Finally, Table G-2 contrasts PM light extinction design values before and after the 

exclusion, for the 90th and 95th percentile forms based on daily maximum daylight 1-hour PM 

light extinction, for current conditions. (A similar comparison for the 98th percentile form was 

not generated.) As expected, design values are notably lower after the exclusion. For both 

percentile forms, the largest reduction is in Los Angeles (represented by the Rubidoux site in the 

far eastern part of the South Coast Air Basin). Phoenix had no hours with relative humidity 

greater than 90 percent, and accordingly Table G-2 shows that its PM light extinction design 

values are not affected by the exclusion. Similarly, Detroit and Dallas had only a few hours with 

relative humidity greater than 90 percent, and their design values are affected very little by the 

exclusion.

Table G-1. Percent of daylight hours and days affected by the elimination of 

hours with relative humidity greater than 90 percent

Study Areas

Percent of Daylight Hours 

Excluded

Percent of Days with at 

Least One Daylight Hour 

Excluded

Tacoma 12.3 49.1

Fresno 3.6 15.7

Los Angeles 10.6 49.7

Phoenix 0.0 0.0

Salt Lake City 2.9 13.7

Dallas 2.8 12.8

Houston 9.6 40.9

St. Louis 6.4 21.1

Birmingham 4.4 19.1

Atlanta 24.1 80.7

Detroit 2.3 7.1

Pittsburgh 11.4 41.2

Baltimore 10.6 33.2

Philadelphia 9.6 31.7

New York 9.1 22.4
G-3

January 2010

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Figure G-1. Distribution by time of day of eliminated daylight hours with relative humidity greater than 90 percent.
G-4

January 2010

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Figure G-2. Comparison of distributions of estimated daylight 1-hour PM light extinction

and maximum daily daylight 1-hour PM light extinction across the 2005-2007 period for 

current conditions, by study area, before and after elimination of hours with relative 

humidity greater than 90 percent.

(a) Maximum daily values

Before Elimination

After Elimination
G-5

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(b) Individual 1-hour values

Before Elimination

After Elimination
G-6

January 2010

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Table G-2. Comparison of 90th and 95th percentile PM light extinction design values for the 

2005-2007 period for current conditions based on maximum daily 1-hour daylight PM light 

extinction, before and after elimination of hours with relative humidity greater than 90 

percent

Study 

Areas

PM Light Extinction Design Values Based on Daily Maximum 1-Hour Values

90th Percentile 95th Percentile

Before 

Exclusion

After 

Exclusion

Reduction 

Due to 

Exclusion

Before 

Exclusion

After 

Exclusion

Reduction 

Due to 

Exclusion

Tacoma 244 140 104 371 157 215

Fresno 381 338 43 533 463 70

Los Angeles 919 469 450

114

0 554 586

Phoenix 105 105 0 144 144 0

Salt Lake 

City 176 164 12 266 252 13

Dallas 189 183 5 239 239 0

Houston 253 194 59 279 234 44

St. Louis 359 307 52 423 381 42

Birmingham 366 357 9 496 483 13

Atlanta 380 249 131 462 288 174

Detroit 313 310 3 473 473 0

Pittsburgh 368 278 90 500 313 187

Baltimore 399 246 153 446 286 159

Philadelphia 382 286 96 449 339 110

New York 339 306 33 415 355 61
H-1

January 2010

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APPENDIX H – INTER-YEAR VARIABILITY

One aspect of a NAAQS is whether it is based on the level of the selected indicator for a 

single year, or the average of the level of that indicator over multiple years. The NAAQS 

scenarios examined in this assessment are all based on a three-year average approach. That is, 

design values are based on the average of specified percentile values of PM light extinction from 

2005, 2006, and 2007. Table H-1 presents more detailed information on the variability of these 

percentiles across these three years.

Figure H-1 presents some of the information in Table H-1 in graphical form, specifically 

for the 90th percentile form for both the daily maximum and all hours approaches.
H-2

January 2010

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Table H-1. Year-specific percentile values of PM light extinction for 2005, 2006, and 

2007

Study 

Areas

90th Percentile Form 95th Percentile Form 98th Percentile Form

2005 2006 2007 2005-

2007 

Average

2005 2006 2007 2005-

2007 

Average

2005 2006 2007 2005-

2007 

Average

Based on Daily Maximum 1-Hour Daylight PM Light Extinction

(Excluding hours with relative humidity greater than 90%)

Tacoma NA 121 158 140 NA 140 173 157 NA 214 206 210

Fresno 308 307 398 338 553 364 472 463 658 400 542 533

Los 

Angeles 435 485 486 469 507 535 619 554 606 605 662 624

Phoenix 100 110 NA 105 156 131 NA 144 344 187 NA 266

Salt Lake 

City 217 112 163 164 310 141 306 252 343 191 696 410

Dallas 184 169 197 183 252 223 242 239 313 321 271 302

Houston 217 204 161 194 269 238 196 234 306 319 248 291

St. Louis 350 331 239 307 434 405 303 381 483 572 347 467

Birmingham 438 307 325 357 547 410 493 483 608 513 565 562

Atlanta 235 255 257 249 283 295 286 288 305 338 351 331

Detroit 300 312 318 310 347 408 663 473 391 489 1051 644

Pittsburgh 284 257 292 278 347 272 320 313 360 350 383 364

Baltimore 303 227 208 246 362 258 239 286 417 308 260 328

Philadelphia 257 325 276 286 333 367 318 339 426 376 377 393

New York 333 265 320 306 405 275 384 355 568 352 451 457

Based on 1-Hour Daylight PM Light Extinction (All Hours)

(Excluding hours with relative humidity greater than 90%)

Tacoma NA 73 78 76 NA 102 109 106 NA 120 151 136

Fresno 183 175 212 190 257 262 278 266 395 332 391 373

Los 

Angeles 263 275 259 266 325 362 359 349 408 458 486 451

Phoenix 67 68 NA 68 79 78 NA 79 92 96 NA 94

Salt Lake 

City 115 67 96 93 194 83 148 142 255 116 303 225

Dallas 114 100 125 113 145 126 158 143 184 176 204 188

Houston 116 98 100 105 143 122 119 128 191 174 148 171

St. Louis 229 195 157 194 277 239 188 235 334 309 226 290

Birmingham 191 162 166 173 251 204 226 227 340 267 319 309

Atlanta 166 164 169 166 188 194 202 195 233 233 248 238

Detroit 226 213 198 212 267 253 234 251 320 312 314 315

Pittsburgh 173 153 176 167 217 193 218 209 284 236 272 264

Baltimore 203 163 150 172 290 194 196 227 345 225 225 265

Philadelphia 163 204 183 183 209 234 224 222 280 298 258 279

New York 204 169 186 186 265 222 244 244 313 267 320 300
H-3

January 2010

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Figure H-1. Inter-year variability in 90th percentile 1-hour daylight PM light 

extinction (excluding hours with relative humidity greater than 90 percent)

(a) Daily maximum approach

0

100

200

300

400

500

600

2005 2006 2007

Year

Daily Max Daylight PM Light 

Ext. (Mm-1)

Salt Lake City Dallas Houston St. Louis

Birmingham Atlanta Detroit Pittsburgh

Baltimore Philadelphia New York Phoenix

Los Angeles Fresno Tacoma

(b) All daylight hours approach

0

50

100

150

200

250

300

2005 2006 2007

Year

Hourly Daylight PM Light 

Ext. (Mm-1)

Salt Lake City Dallas Houston St. Louis

Birmingham Atlanta Detroit Pittsburgh

Baltimore Philadelphia New York Phoenix

Los Angeles Fresno Tacoma
I-1

January 2010

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APPENDIX I – DAYLIGHT HOURS

Table 3-5 shows the simple scheme used in this analysis to denote hours as fully daylight 

and thus eligible for consideration in the calculation of design values for the secondary NAAQS 

scenarios based on PM light extinction. This scheme also has been used to select which hours to 

to show in various graphics. The scheme is based on applying a fixed set of fully daylight hours 

for each three-month season (November to January, etc.). In reality, the local time minutes of 

daylight vary continuously during the year, with latitude, and with the east-west position of a city 

within its time zone. The hours that are fully daylight will change in increments rather than 

continuously. This appendix examines how well the simple scheme reflects actual conditions and 

how disparities if any might affect the results presented and the answers to policy relevant 

questions that may be addressed in the subsequent policy assessment document.

Six study areas were selected for this examination: Tacoma, Los Angeles, Phoenix, 

Houston, Detroit, and New York. These areas cover the extremes with regard to latitude and to 

east-west position within time zone. For each area, the times of sunrise (defined by the leading 

or top edge of the sun appearing above the horizon) and of sunset (defined by the leading or 

bottom edge of the sun disappearing below the horizon) were obtained for each day of the year. 

It is several minutes after each of these times that the sun is fully visible in the morning and not 

visible at all in the evening.

Figure I-1 shows the relationship between these sunrise and sunset times and the simple 

scheme used to denote hours as fully daylight. The vertical scale is in hours with zero 

corresponding to local noon. The smooth curves represent the actual times of sunrise (top of 

figure) and sunset (bottom of figure). The stepped lines represent the scheme used to select the 

first and last hour denoted as fully daylight. Months are indicated on the horizontal axis. The 

figure indicates that the simple scheme has the effect of treating some hours as daylight that in 

fact contain minutes prior to sunrise or after sunset, and conversely treating some hours as not 

daylight that include no such minutes. In particular:

• In February, the hours from 7 am to 8 am and from 5 pm to 6 pm are treated as 

daylight but include non-daylight minutes in most of the example areas.

• In April, the hour from 6 am to 7 am is treated as non-daylight but in many areas 

includes only minutes that are after sunrise.

• In most of June and most of July, for Detroit and Tacoma only, the hour of 7 pm to 8 

pm is treated as non-daylight but in fact has no minutes after sunset.
I-2

January 2010

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• In October, the hours of 6 am to 7 am and 5 pm to 6 pm are treated as daylight but 

include non-daylight minutes in all of the example areas.

The tile plots in Figure 3-12 can be used to assess the significance of these disparities, 

i.e., whether they are likely to significantly affect PM light extinction design values. Table I-1 

contains observations for each of the 24 combinations of the four time periods listed above and 

the six example areas. Taken together, these observations make it likely that refining the scheme 

for designating hours as fully daylight would not significantly change conclusions that can be 

drawn from this assessment as it has been performed. Changing the scheme would involve 

considerable effort in updating virtually every table and graphic in the assessment, however.
I-3

January 2010

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Figure I-1. Comparison of Actual Sunrise and Sunset Times to this Assessment’s Scheme to Denote Hours as Fully 

Daylight

Sunrise/Sunset for Six Sites

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

Jan10Feb10Mar10Apr10May10Jun10Jul10Aug10Sep10Oct10Nov10Dec10

Date

Local Time

Assumed daylight start

Assumed daylight end

TAC Rise

TAC Set

LA Rise

LA Set

PHX Rise

PHX Set

HOU Rise

HOU Set

DET Rise

DET Set

NY Rise

NY Set
I-4

January 2010

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Table I-1. Observations from Tile Plots for Hours with Questionable Daylight/Non-Daylight Status in Six Study Areas

Study Area February (AM and PM) April (AM) June-July (PM) October (AM and PM)

Tacoma The morning hour with questionable 

daylight status tends to have RH > 90%. 

The evening hour in question tends to 

either have low PM light extinction or 

to have RH > 90%.

The tile plot does not show data for the 

morning hour that may better be denoted 

daylight, but the instances of high PM light 

extinction that do appear typically last 

multiple hours.

Very late afternoon typically is not a 

period of high PM light extinction.

Instances of high light extinction involving 

the questionable hours are multi-hour and/or 

involve RH > 90%.

Los 

Angeles

Instances of high light extinction 

involving the questionable hours are 

multi-hour and/or involve RH > 90%.

The tile plot does not show data for the 

morning hour that may better be denoted 

daylight, but the instances of high PM light 

extinction that do appear typically last 

multiple hours.

NA Instances of high light extinction involving 

the questionable hours are multi-hour and/or 

involve RH > 90%.

Phoenix Instances of high light extinction 

involving the questionable hours are 

multi-hour.

The tile plot does not show data for the 

morning hour that may better be denoted 

daylight, but early morning in April typically 

is not a time of high PM light extinction.

NA PM light extinction is usually low in 

October; on those days with moderate levels 

in the questionable hours, another hour in 

the central part of the day has a similar level.

Houston Instances of high light extinction 

involving the questionable hours are 

multi-hour and/or involve RH > 90%.

The tile plot does not show data for the 

morning hour that may better be denoted 

daylight, but the instances of high PM light 

extinction that do appear typically last 

multiple hours and/or involve RH >90%.

NA The amount of information is limited due to 

missing data. On those days with moderate 

to high PM light extinction during the 

questionable hours, another hour has a 

similar level, or RH >90% plays a role.

Detroit Instances of high light extinction 

involving the questionable hours are 

multi-hour.

The tile plot does not show data for the 

morning hour that may better be denoted 

daylight, but the instances of high PM light 

extinction that do appear typically last 

multiple hours.

July generally is a time of high PM 

light extinction for the hours currently 

considered daylight. Adding one 

more late afternoon hour likely would 

not affect design values.

Instances of high light extinction involving 

the questionable hours are multi-hour.

New York All but one instance of high light 

extinction involving the questionable 

hours are multi-hour.

The tile plot does not show data for the 

morning hour that may better be denoted 

daylight, but the instances of high PM light 

extinction that do appear typically last 

multiple hours.

NA Instances of high light extinction involving 

the questionable hours are multi-hour and/or 

involve RH > 90%.
January 2010

DRAFT Do Not Quote or Cite
January 2010

DRAFT Do Not Quote or Cite

United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, NC

Publication No. EPA-452/P-10-002

January, 2010