# Particulate Matter Urban-Focused Visibility Assessment

Final DocumentDISCLAIMER

This document has been prepared by staff from the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency (EPA). Any opinions, findings, conclusions, or recommendations are those of the authors and do not necessarily reflect the views of the EPA. Questions related to this document should be addressed to Ms. 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 have been provided to the EPA by Abt Associates, Inc. and Stratus Consulting Inc. in partial fulfillment of Contract No. EP-D-08-100, Work Assignments 0

- 11 and 0-19

. Any opinions, findings, conclusions, or recommendations are those of the authors and do not necessarily reflect the views of the EPA or its contractors.EPA 452/P-10-004

July 2010



## Particulate Matter Urban-Focused Visibility Assessment Final Document

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Health and Environmental Impacts Division Research Triangle Park, NCThis page left intentionally blank.i 

## TABLE OF CONTENTS

List of Tables

................................................................................................ ................................ iv List of Figures

................................................................................................ ................................. v List of Acronyms/Abbreviations

................................................................ ................................. vi

1 INTRODUCTION................................

.......................................................................... 1 -1
1.1 PM NAAQS BACKGROUND ................................................................ ........................ 1 -4
1.2 VISIBILITY EFFECTS SCIENCE OVERVIEW ................................ ........................... 1 -7 1.3 GOALS AND APPROACH ................................................................ .......................... 1 -11
1.4 SCOPE OF URBAN-FOCUSED VISIBILITY ASSESSMENT .................................. 1 -1 2
1.4.1 Background ................................

........................................................................ 1 -1 2 1.4.2 Selection of Alternative Scenarios for First Draft Assessments ........................ 1 -14 1.4.3 Selection of Alternative Scenarios for Second Draft Assessments ................... 1 -1 5 1.5 ORGANIZATION OF DOCUMENT ........................................................................... 1 -1 8

2 URBAN VISIBILITY PREFERENCE STUDIES ................................ ...................... 2 -1
2.1 METHODS USED IN VISIBILITY PREFERENCE STUDIES .................................... 2 -2
2.2 DENVER, COLORADO ................................

................................................................. 2 -3
2.3 VANCOUVER, BRITISH COLUMBIA, CANADA ................................ ..................... 2 -8
2.4 PHOENIX, ARIZONA ................................

.................................................................. 2 -13
2.5 WASHINGTON, DC ................................

..................................................................... 2 -15
2.5.1 Washington, DC 2001 ................................................................ ........................ 2 -1 6 2.5.2 Washington, DC, 2009 ................................................................ ....................... 2 -18
2.6 SUMMARY OF PREFERENCE STUDIES AND SELECTION OF CANDIDATE PROTECTION LEVELS ................................ ............................................................... 2 -27

3 ESTIMATION OF RECENT PM MASS AND SPECIES CONCENTRATIONS AND PM 10 LIGHT EXTINCTION .............................................................................. 3 -1
3.1 SUMMARY OF PREVIOUS CHARACTERIZATIONS OF PM CONCENTRATIONS AND LIGHT EXTINCTION ................................................................ ........................... 3 -1
3.1.1 PM 2.5 and PM 10 -2.5
................................ ................................................................ 3 -1 3.1.2 PM 10 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 -1 0 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 10 light extinction ........ 3 -19
3.3 DETAILED STEPS ................................

....................................................................... 3 -1 9
3.3.1 Hourly PM 2.5 Component Concentrations ................................ ......................... 3 -1 9 3.3.2 Hourly PM 10 -2.5 Concentrations .......................................................................... 3 -2 8ii

3.3.3 Hourly Relative Humidity Data ......................................................................... 3 -2 9 3.3.4 Calculation of Daylight 1-Hour PM 10 Light Extinction .................................... 3 -29 3.3.5 Exclusion of Hours with Relative Humidity Greater than 90 Percent from PM 10 Light Extinction NAAQS Scenarios and Most Results ..................................... 3 -30 3.3.6 Calculation of Daily Maximum 1-Hour PM 10 Light Extinction ........................ 3 -3 4
3.4 SUMMARY OF RESULTS FOR CURRENT CONDITIONS..................................... 3 -3 4
3.4.1 Levels of Estimated PM 2.5 , PM 2.5 C omponents, PM 10

- 2.5 , and Relative Humidity ................................ .............................................................. 3 -3 4 3.4.2 Levels of Estimated PM 10 light extinction ................................ ......................... 3 -37 3.4.3 Patterns of Relative Humidity and Relationship between Relative Humidity and PM 10 light extinction ................................................................ .......................... 3 -43 3.4.4 Tile Plots of Hourly PM 10 Light Extinction ................................ ....................... 3 -46 3.4.5 Extinction Budgets for High PM 10 Light Extinction Conditions ....................... 3 -6 3
3.5 POLICY RELEVANT BACKGROUND ...................................................................... 3 -85

4 PM 10 LIGHT EXTINCTION UNDER “WHAT IF” CONDITIONS OF JUST MEETING SPECIFIC ALTERNATIVE SECONDARY NAAQS ........................... 4 -1
4.1 ALTERNATIVE SECONDARY NAAQS BASED ON PM 10 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 PM 10 light extinction ........ 4 -2 4.1.3 Monitoring Site Considerations for Alternative Secondary NAAQS Based on Measured PM 10 light extinction ........................................................................... 4 -2 4.1.4 Approach to Modeling “What If” Conditions for Alternative Secondary NAAQS Based on Measured PM 10 Light Extinction ................................ ......................... 4 -3
4.2 ALTERNATIVE SECONDARY PM 2.5 NAAQS BASED ON ANNUAL AND 24- HOUR PM 2.5 MASS ................................

...................................................................... 4 -10
4.2.1 Secondary NAAQS Scenarios Based on Annual and 24-hour PM 2.5 Mass ....... 4 -10 4.2.2 Approach to Modeling Conditions If Secondary PM 2.5 NAAQS Based on Annual

an d 24 -hour PM 2.5 Mass Were Just Met................................ ............................. 4 -1 1
4.3 RESULTS FOR EACH “JUST MEET” ALTERNATIVE SECONDARY NAAQS SCENARIO................................................................................................ .................... 4 -12

5 SUMMARY ................................................................................................ ........................ 5 -1

5.1 URBAN VISIBILITY PREFERENCE STUDIES REANALYSIS ........................ 5 -1 5.2 CURRENT VISIBILITY CONDITIONS................................................................ 5 -2 5.3 VISIBILITY CONDITIONS FOR ALTERNATIVE SECONDARY PM NAAQS SCENARIOS ................................

....................................................................... 5 -4

6 REFERENCES ................................................................................................................... 6 -1iii

APPENDICES

A



## . PM 2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Analysis of PM 10 Light Extinction in the 15 Study Areas

B. Distributions of Estimated PM 2.5 and Other C omponen ts under Current Conditions

C. Development of PRB Estimates of PM 2.5 Components, PM 10 -2.5 , and PM 10 Light Extinction

D. Relationships between PM Mass Concentration and PM 10 Light Extinction under Current Conditions

E. Differences in Daily Patterns of Relative Humidity and PM 10 Light Extinction between Areas and Seasons

F. Distributions of Maximum Daily Daylight PM 10 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

J. Logit Memorandumiv 

### LIST OF TABLES

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

participants ................................

.......................................................................... 2 -6 Table 2-2. Summary of photographs used in British Columbia study ................................ 2 -11 Table 2-3 . Logit model estimated VAQ values corresponding to various percent

acceptablilityvalues for the four cities ............................................................... 2 -29 Table 3-1. Annual Mean Reconstructed 24-hour Light Extinction Estimates by Region ..... 3 -7 Table 3-2. Urban Visibility Assessment Study Areas ................................ ......................... 3 -11 Table 3-3. PM 2.5 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 -16 Table 3-5 . Assumed daylight hours by season (Local Standard Time) ............................... 3 -30 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 -32 Table 3-7 . Percentage of daily maximum hourly values and individual hourly values of

daylight PM 10 light extinction exceeding CPLs (excluding hours with relative humidity greater than 90 percent). ..................................................................... 3 -41 Table 3-8 . The numbers of common and unique days selected for each of the 15 urban areas

by the top 10% of daily maximum and the top 2% of all hours form................ 3 -80 Table 4-1. Alternative secondary NAAQS Scenarios for PM 10 light extinction ................... 4 -2 Table 4-2. Current Conditions PM 10 light extinction design values for the study areas. ..... 4 -5 Table 4-3. Percentage reductions in non-PRB PM 10 light extinction required to “just meet”

the NAAQS scenarios based on measured light extinction (Mm -1 ). .................... 4 -9 Table 4-4. Percentage reductions required in non-PRB PM 2.5 mass to “just meet” NAAQS

scenarios based on annual and 24-hour PM 2.5 mass ........................................... 4 -12 Table 4-5. PM 10 light extinction design values for “just meet” secondary NAAQS scenarios

based on measured PM 10 light extinction (excluding hours with relative humidity greater than 90 percent) ................................................................ ..................... 4 -16 Table 4-6. PM 10 light extinction design values for “just meet” secondary NAAQS scenarios

based on PM 2.5 mass (excluding hours with relative humidity greater than 90 percent) .............................................................................................................. 4 -17 Table 4-7. Percentage of d ays with maximum 1-h our d aylight PM 10 light extinction above

CPLs for each NAAQS scenario u nder “just m eet ” conditions across three years (two in the case of Phoenix and Houston) ................................ ........................ 2 -20v 

### 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. ..................... 1-9 Figure 1-2. Progression from PM light extinction to value of visual air quality (VAQ) ..... 1 -10 Figure 2-1. Percent of Denver participants who considered VAQ in each photograph

“ acceptable.” ................................ ........................................................................ 2 -5 Figure 2-2. Photograph time of day information .................................................................... 2 -7 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 -8 Figure 2-4. Composite Chilliwack, BC photograph shows VAQ of 14.1 dv and 34 dv. ....... 2 -9 Figure 2-5. Percent of BC participants who consider VAQ in each photograph

“acceptable.” ................................ ...................................................................... 2 -12 Figure 2-6. Reproduction of image with the best VAQ (15 dv) used in the

P hoenix study. .................................................................................................... 2 -14 Figure 2-7. Percent of Phoenix participants who consider VAQ in each image

“ acceptable.” ................................ ...................................................................... 2 -15 Figure 2-8. Reproduction of the image with the best VAQ (8.8 dv) used in the

W ashington, DC study. ...................................................................................... 2-17 Figure 2-9. Percent of 2001 Washington participants who considered VAQ acceptable in

e ach image ......................................................................................................... 2 -18 Figure 2-10. Percent of 2009 Test 1 study participants who considered VAQ acceptable

in ea ch image, showing the range of the lower and upper bound of 50% acceptability criteria. .......................................................................................... 2-20 Figure 2-11. Combined results of Washington, DC 2001 and 2009 Test 1 (showing 50%

a cceptability criteria from 2009, Test 1). ........................................................... 2-21 Figure 2-12. Comparison of results from Test 1 and Test 2 (Smith and Howell, 2009). ....... 2 -22 Figure 2-13. Average visibility ratings for the Washington, DC WinHaze images by

pa rticipants in Tests 1-3 conducted by Smith and Howell 2009). ..................... 2-23 Figure 2-14. Comparison of results from the Smith and Howell (2009) Test 1 and

Te st 3. ................................................................................................................. 2 -25 Figure 2-15. Composite results from Smith and Howell (2009) Tests 1 and 3, and Abt

(2001 ) Washington, DC pilot study. .................................................................. 2 -26 Figure 2-16. Summary of results of urban visibility studies in four cities, showing the

identifie d range of the 50% acceptance criteria . ............................................. 2 -28 Figure 3-1. Annual average and 24-hour (98 th percentile 24-hour concentrations)

PM 2.5 concentrations in μg/m 3 , 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 PM 10 light extinction

based on 2000-2004 IMPROVE data. ................................................................. 3 -8 Figure 3-4. January and August monthly average diurnal profiles of PM 2 .5 components

derived from the 2004 CMAQ modeling platform, for the Detroit study

area . .................................................................................................................... 3-18vi

Figure 3-5. Sequence of steps used to estimate hourly PM 2.5 components and PM 10 light

extinction............................................................................................................ 3 -2 3 Figure 3-6. Example from Detroit study area. ...................................................................... 3 -25 Figure 3-7. Distribution of PM parameters and relative humidity across the 2005-2007 period,

by study area ................................

...................................................................... 3 -35 Figure 3-8 . Distributions of estimated daylight 1-hour PM 10 light extinction and maximum

daily daylight 1-hour PM 10 light extinction (in Mm -1 units) across the 2005-2007 period, by study area (excluding hours with relative humidity greater than 90 percent). ............................................................................................................. 3 -39 Figure 3-9. Distributions of 1-hour PM 10 light extinction levels by daylight hour (x -axis)

across the 2005-200 7 period, by study area (excluding hours with relative humidity greater than 90 percent). ..................................................................... 3 -42 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 -44 Figure 3-11. Scatter plot of daylight 1-hour relative humidity (percent) vs. reconstructed PM 10

light extinction (Mm -1 ) across the 2005-2007 period, by study area (excluding hours with relative humidity greater than 90 percent). ...................................... 3 -45 Figure 3-12 . Tile Plots of Hourly PM 10 Light Extinction ................................ ....................... 3 -48 Figure 3-13 . Light Extinction Budgets for the Top 10 Percent of Days for Maximum Daily

1 -hour PM 10 light Extinction and for the Top 2 Percent of Individual Daylight Hours ................................................................................................ .................. 3 -65 Figure 3 -14 . Average PM 10 light extinction budgets for the 15 cities for hours in the (a) top

10 percent of the maximum daily PM 10 light extinction and (b) top 2 percent of all daylight hours of PM 10 light extinction ................................ .................... 3 -81 Figure 4-1 . Comparison of daily max and all daylight hour design values for PM 10

light extinction ................................

..................................................................... 4 -6 Figure 4-2 . Comparison of required percentage reductions in non -PRB PM 10 light extinction

needed to meet NAAQS scenarios ..................................................................... 4 -10 Figure 4-3 . Distributions of daily maximum daylight 1-hour PM 10 light extinction under two

“just meet” secondary NAAQS scenarios (excluding hours with relative humidity greater than 90 percent) ................................................................ ..................... 4 -14vii 

### 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 D eciview 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 Administrationviii

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 Assessme nt PM Particulate Matter
PM 2.5 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. PM 10 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. PM 10 -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
R H Relative Humidity
SANDWICH S ulfate, A djusted N itrate, D erived W ater, I nferred C arbonaceous

mass approach SEARCH Southeastern Aerosol Research and Characterization Study
SMOKE Sparse Matrix Operator Kernal Emissions
S Sulfur
SO 2 Sulfur Dioxide
SO x 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 Quality1-1 

### 1 INTRODUCTION

The U.S. Environmental Protection Agency (EPA) is presently conducting a review of the 2006 national ambient air quality standards ( NAAQS) for particulate matter (PM). Sections 108 and 109 of the Clean Air Act (Act) govern th e establishment and pe riodic review of the NAAQS. The NAAQS are to be base d on air quality criteria, which are to accurately reflect the latest scientific knowledge usef ul in indicating the kind and ex tent of identifiable effects on public health or welfare that may be expected from the presence of the pollutant in ambient air. The EPA Administrator is to promulgate and pe riodically review, at no later than five-year intervals, “primary” (health -based) and “secondary” (welfa re-based) NAAQS for such pollutants. Based on periodic reviews of the air quality criteria and standards, the Administrator is to make revisions in the air quality criter ia and standards, and to promulgate any new standards, as may be appropriate . The Act also requires that an independent scientific review committee advise the Administrator as part of th is NAAQS review process, a function performed by the Clean Air Scientific Advisory Committee (CASAC). The current suite of secondary standards for PM 2.5 and PM 10 were set in 2006 to be identical to the primary standards, on the basis that these standards woul d, in conjunction with the Regional Haze Program 1 , provide appropriate protection to address PM-related welfare effects, including visibility impairment, effect s on vegetation and ecosystems, materials damage and soiling, and effects on climate change (71 FR 61144, October 17, 2006). At that time, the
EPA revised the level of the 24-hour PM 2.5 primary standard to 35 μ g/m 3 (calculated as a 3-year average of the 98 th percentile of 24-hour concentrations at each population-oriented monitor),
retained the level of the PM 2.5 annual primary standard at 15 μ g/m 3 (calculated as the 3-year average of the weighted annual mean PM 2.5 concentrations from single or multiple communityoriented monitors), and revi sed the form of the annual PM 2.5 primary standard by narrowing the constraints on the optional use of spatial averaging 2 . With regard to the primary standards for
PM 10 , EPA retained the 24-hour PM 10 standard at 150 μ g/m 3 (not to be exceeded more than once per year on average over 3 years) and revoked the annual standard beca use available evidence generally did not suggest a link between long-term exposure to current ambient levels of coarse particles and health effects. The 2006 primary standards were based primarily on a large body of

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

2 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).1-2 epidemiological evidence relating ambient PM concentrations to various adverse health outcomes. (As noted below, portions of the 2006 decision were reversed and remanded by the Court of Appeals for the Dist rict of Columbia Circuit.)
In the Integrated Review Plan for the National Ambient Air Quality Standards for Particulate Matter, March 2008 (US EPA, 2008a), developed ear ly in the current review of the PM NAAQS, 3 the EPA outlined the science policy questions that frame this review, outlined the process and schedule for the revi ew, and provided descriptions of the purpose, contents, and approach for developing the key documents that will be developed in the review. 4 EPA has completed the process of assessing the latest available policy-relevant scientific information to
inform the review of the PM standards. The fina l assessment is contained in the final Integrated Science Assessment for Particulate Matter (I SA, US EPA, 2009a) which was released in
December 2009. The final PM ISA includes a su mmary of the scientific evidence for the relationship of PM to visibi lity effects, remote area and urban haze conditions, the PM components responsible for visibili ty impacts, and studies of pub lic preference with respect to urban visibility conditions. Building upon the visibility effects evidence pr esented in the PM ISA, as well as CASAC advice (Samet, 2009a and b) and public comments on the plan for and first draft of the UrbanFocused Visibility Assessment (UFVA) (U S EPA, 2009b, c), EPA’s Office of Air Quality Planning and Standards (OAQPS) developed a second draft UFVA (US EPA, 2010a) which described the quantitative assessments conducted by the Agency to support the review of the secondary PM standards. This second draft document presented the methods, key results, observations, and related uncertainties associated with the quantitative analyses performed and was reviewed and discussed by CASAC and the public at a March 10-11 meeting. Based on input received at the March 2010 meeting and in a subsequent letter (Samet, 2010a), this final UFVA document includes the following changes: 1) inclusion of the complete logit memo in Appendix J and streamlining of logit discussion in chapter 2 to reduce redundancy and reflect this addition; 2) Figure 3-13 and associated text was modified to provide a more consistent comparison of speciated PM mass for the top 10 % and 2% of maximum daylight hours and all daylight hours, respectively; 3) addition of footnotes and caveats in the text to acknowledge that

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 r eceived 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 require ments 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.1-3 the St. Louis data is now considered unrealisti cally high and is not carried forward into the
second draft PA 5 ; 4) modification of Table 3-6 to corre ctly omit non-daylight hours which were inadvertently included in the second draft UFVA , report results for four study areas for which results were missing in the second draft UFVA, and separate “mist” from “smoke/haze” to
reflect that “mist” is a natu ral condition while “smoke/haze” is not always a natural condition; and 5) addition of an integrative summary (chapter 5). In addition, a preliminary draf t PA (US EPA, 2009d) was released in September 2009 for informational purposes and to facilitate disc ussion with CASAC at the October 5-6, 2009 meeting on the overall structure, area s of focus, and level of detail to be included in the PA. This preliminary draft PA was discussed in conjuncti on with CASAC review of and public comment on the second draft ISA, first draft UFVA, and first draft health risk assessment documents produced in support of this PM NAAQS rule making. CASAC comments on the preliminary draft PA were considered in developing the first external review draft PA (US EPA, 2010b). The first draft PA, which built upon the information presented in the final ISA and second draft UFVA, was released for CASAC review and public comment in February of 2010 (US EPA, 2010b). EPA presented an overview of the firs t draft PA at the CASAC meeting on March 10, 2010. CASAC and public review of the firs t draft PA was discussed during public teleconferences on April 8-9, 2010 (75 FR 8062, February 23, 2010) and May 7, 2010 (75 FR 19971, April 16, 2010). CASAC (Samet, 2010b) and public comments on the first draft PA were considered in developing the second draft PA which will be reviewed by CASAC at an upcoming meeting scheduled for July 26-27, 2010 (75 FR 32763), June 9, 2010). The PA is intended to help “bridge the gap” between the Agency’s scientific assessments, presented in the ISA and UFVA, and the judgments required of the Administrator in determining whether it is appropriate to reta in or revise the secondary PM standards. The PA is intended to provide a transparent staff analysis of the scie ntific basis for alterna tive policy options for consideration by senior EPA management prior to rulemaking by integrating and interpreting
information from the ISA and the UFVA to fram e policy options and to facilitate CASAC’s advice to the Agency and recommendations on any new standards or revisions to existing standards as may be appropriate, as provided for in the Clean Air Act. A second draft PA (US EPA, 2010c) has been released in conjunc tion with this final UFVA document.

5 Comments concerning unrealistically high PM 10-2.5 values for St. Louis are viewed as credible, but were received too late in the review process to permit reanalysis using an alternate data set or to remove St. Louis from all portions of this document. However, the text has been revised to caution readers with respect to the St. Louis results, and they are not included in the visibility eff ects discussion in the second draft PM Policy Assessment document. Some graphics have been updated to exclude St. Louis results in this final UFVA.1-4 1.1 PM NAAQS BACKGROUND

In the review of the secondary PM NAAQS completed in 2006, EPA took into account that the Regional Haze Program, authorized under sections 169A and 169B of the CAA, was
established to address all huma n-caused visibility impairment in federal Class I areas. The national goal of this program is to prevent a ny future, and remedy any existing, impairment of visibility in mandatory class I Federal areas (C lass I areas) which results from manmade air pollution. This program also mandates that stat es develop SIPs to ensure that reasonable progress is made towards meeting those goals. Because Congress explicitly targeted 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. Browner, 175 F. 3d 1027, 1056-57 (D. C. Cir. 2002) (upholding this posi tion). However, Congress recognized that visibility impairment can and often does occur in areas outside federal Class I areas, including urban areas and judged that protect ion from visibility impairment was important in those areas as well. In this regard, Congress included visibi lity effects in the defi nition of public welfare effects that should be protected under the na tional ambient air quality standards (NAAQS) program authorized in sections 108 and 109 of the CAA. As a result, EPA may establish secondary standards addressing visibility impair ment notwithstanding exis tence of the Regional Haze Program. Under the NAAQS program, it is up to the Administrator to judge the requisite level of public welfare vi sibility protection. Recognizing that efforts were underway to pr ovide increased protection to Class I areas under the Regional Haze Program, EPA focused the 2006 PM NAAQS review on visibility impairment in non-Class I areas. Because most of the available non-Class I PM data came from PM monitoring sites located primarily in urban ar eas, the assessments took on an urban focus. In addition, EPA considered availabl e information on people’s preferen ces for different levels of visual air quality which came fr om studies conducted in urban areas and information regarding existing urban visibility programs and goals.
In an effort to minimize the factors that hi storically had complicated efforts to address visibility impairment nationally, (i.e., the substantial East/West differences in factors contributing to impairment in Class I areas), EPA st aff noted that with respect to fine particles,
East/West differences in urban areas are substan tially smaller than in rural areas. Further, relative humidity levels, though gene rally higher in eastern than western areas, are appreciably lower in both regions during daylight, as compared to nighttime, hours. The PM 2.5 data available at that time in urban areas were obtained using a filter –based Federal Reference Method (FRM) which captures ambient PM 2.5 on a filter and then dries it to get the dry PM 2.5 mass concentration. By drying the sample, most water and to some extent other labile PM compounds evaporate so that the original characteristics (e.g., particle size and composition) of the ambient1-5 PM are altered. Using PM and meteorological data from 161 cities, EPA staff assessed the
correlations between PM 2.5 levels and reconstructed (RE) (i.e ., calculated) light extinction during daylight hours for different regions of the count ry. This assessment showed that the strongest correlation in the relationship of am bient PM light extinction to dry PM 2.5 mass concentration was during afternoon periods when lower relative hu midity conditions generally prevailed in all regions of the country and ambient PM was dr ier (Staff Paper, US EPA, 2005). While EPA recognized that the effect of ambient PM on vi sibility results from the ambient particle characteristics of size, concentration, and comp osition (including associated water) present in the air in the sight path of the obser ver, given the data availability at the time, EPA viewed the FRM
altered PM 2.5 mass concentration as an acceptable indi cator for addressing ambient PM-related visibility effects at the national scale during afternoon hours. Thus, the 2005 Staff Paper chose to address the issue of regional differences in terms of averaging time rather than indicator, discussing the use of a sub-daily afternoon dry PM 2.5 standard, because the generally lower afternoon relative humidity tended to produce a more uniform relati onship between light extinction and dry PM 2.5 mass concentration throughout the c ountry, therefore providing a more uniform level of visibility pr otection nationwide. This more uniform level of visibility protection, however, was limited to the afternoon hours of the day when relative humidity and visibility impairment tend to be the lowest. Based on the above, in the 2005 PM Staff Pape r, EPA staff recommended a separate subdaily secondary standard to address visibility impairment using dried PM 2.5 mass concentration as the indicator, a recommendation endorsed by CASAC. In the 2006 proposal notice, however , EPA proposed to revise the secondary standard s by making them identical to the suite of proposed primary standards for fine and coarse particles, to provide protection against PMrelated public welfare effects including visibility impairment, effects on vegetation and ecosystems, materials damage and soiling, and climate, while soliciting comment on adding a new sub-daily PM 2.5 secondary standard to address visibil ity impairment primarily in urban areas (71 FR 2620). CASAC provided additional advice to EPA in a letter to the Administrator requesting reconsideration of CASAC’s recomme ndations for both the primary and secondary
PM 2.5 standards as well as standards for thorac ic coarse particles (Henderson, 2006). With regard to the secondary standa rd, CASAC reaffirmed “... the recommendation of Agency staff regarding a separate secondary fine particle st andard to protect visibility.... the CASAC wishes to emphasize that continuing to rely on primary standards to protect ag ainst all PM-related
adverse environmental and welfare effects assure s neglect, and will allow substantial continued degradation, of visual air quality over large areas of the c ountry” (Henderson, 2006). On September 21, 2006, EPA announced its fi nal decisions to provide increased protection of public welfare by making the seco ndary NAAQS identical to the revised primary1-6 standards (71 FR 61144, October 17, 2006). This suite of seconda ry standards was designed to address both visibility and other non-visibility we lfare related effects. Specifically, with regard to the secondary welfare effect of visibility im pairment, the Administrato r believed that revising
both the 24-hour and annual PM 2.5 secondary standards to be iden tical to the revised suite of
PM 2.5 primary standards was a reasonable policy approach to address visibility impairment primarily in urban areas. With regard to the other non-visibility PM-related welfare effects such as vegetation and ecosystems, materials damage and soiling, and climate, the Administrator concluded that it was appropria te to address these effects by revising the current suite of PM 2.5 secondary standards, making them identical in all respects to the suite of primary PM 2.5 standards, while retaining the current 24-hour PM 10 secondary standard an d revoking the current
annual PM 10 secondary standard. In particular for coarse particles, EPA retained PM 10 as the indicator for purposes of regula ting the coarse fraction of PM 10 and retained the 24-hour secondary PM 10 standard at 150 μg/m 3 and revoked the annual secondary PM 10 standard. Several parties filed petitions for review following promulgation of the revised PM NAAQS in 2006. These petitions addr essed a number of issues, incl uding the decision to set the secondary PM 2.5 standards identical to the primary sta ndards. On judicial review the court remanded the secondary PM 2.5 NAAQS to EPA because the Agency failed to adequately explain why setting the PM 2.5 secondary standards equal to the primary PM 2.5 standards provided the required protection from visibility impairment. In particular, the Agency failed to identify a target level of visibility impairment that woul d be requisite to protect the public welfare, and improperly relied on a misleading comparison of the number of counties which would be in nonattainment for the revised primary NAAQS comp ared to one alternative secondary standard under consideration. Among other things, this equivalence analysis failed to address the issue of regional differences in humidity-r elated effects on visibility. American Farm Bureau Federation
v. EPA , 559 F. 3d 512, 530-31 (D.C. Cir. 2009). The analyses developed for and described in this document reflect consideration of the issues raised by the court. In pa rticular, a) the reanalysis of public preference studies (described in Chapter 2) provides information useful for the selection of “target leve ls” for urban visibility protection; b) the analyses of the factors contributing to visibility impairment for selected urban areas, including PM species component contributi ons and variations in relative humidity, provide
information useful for better characterization of regional differences im portant for development of a national standard (Chapter 3) ; and c) the analyses of alterna tive standards (Chapter 4) using
different combinations of indicat or, averaging times, levels and forms provide information useful in understanding the degree of visibility prot ection provided by altern ative standards.1-7 1.2 VISIBILITY EFFECTS SCIENCE OVERVIEW

Light extinction is the loss of light per unit of distance and occurs when light is scattered and/or absorbed. Particulate matter and gases can both scatter and absorb light. Light scattering by gases (e.g., nitrogen, oxygen, etc. ) that comprise the pollutant free or clean atmosphere (also known as Rayleigh or clean-air sc attering) is related to the de nsity of the air, which is sufficiently constant with elevation that it can be taken to be a time invariant constant that depends principally on eleva tion above sea level. NO 2 is the only atmospheri c pollutant gas that absorbs light appreciably and its effects are generally small (i.e., less than 5%) compared to PM light extinction. Hereinafter th e phrase “PM light extinction” indicates that the Rayleigh contribution to light extincti on (nominally considered 10 Mm -1 ) has been subtracted out and the
NO 2 contribution is considered ne gligible or is simply excluded due to the measurement approach used. By contrast, the term “light extinction” or “total light extinction” is meant to include both the Rayleigh and NO 2 contributions. Visual air quality (VAQ) is de fined as the visibility effect caused solely by air quality conditions 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 ) or the haziness index (i n terms of deciview, dv) (Pitchford and Malm, 1993).
The haziness index measured in deciview units was developed for use in visibility perception
studies because it has a more lin ear relationship to perceived ch anges in haze compared with light extinction. It is defined as ten times the natural logarith mic of one tenth of the light extinction in inverse megameter units (Mm -1 ) (Pitchford and Malm, 1993). Light extinction and haziness are physical measures of the amount of visibility impairment (e.g., the amount of “haze”), with both increasing as the amount of haze increases. Visual range, defined as the greatest distance that a large dark object can be seen, was developed for military and transportation safety use. Under conditions that meet certain sta ndard assumptions, visual range is inversely related to light extinc tion (Pitchford and Malm, 1993). PM is a heterogeneous mixture of particles of different sizes and chemical compositions. While visibility impairment has been associated most often with PM 2.5 , larger particles such as
those found in PM 10 may be a significant contributor in some areas. Thus, the UFVA considers the visibility impairment caused by all particles 10 microns or smaller. As stated above, the degree of visibility impairment caused by a given mass of PM depends in large part on the size, density and chemical composition of the PM. If the ambient PM has a large number of hygroscopic particles (i.e., particle s that readily absorb moisture from the atmosphere), and also occurs when the relative humidity of the air is higher, those particles will grow larger in size and have a larger haze effect than if those same part icles occurred in ambient air with lower relative humidity.1-8 Ambient PM light extinction is most accura tely determined by direct measurements. However, as shown in Figure 1-1, the ambient PM light extinction can also be estimated from dry PM mass and composition data and relativ e humidity using an algorithm. One well established algorithm, know n as the IMPROVE algorithm 6 , accounts for water present in hygroscopic PM components and uses assumed light extinction efficiencies for each of the major PM species. Because there is limited ambient PM light extinction data av ailable in urban areas, the assessments below will principally use monitored and modeled dry PM mass and species estimates, along with relative humidity measurem ents as input to the IMPROVE algorithm for estimating ambient PM light extinction.

6 Malm, et al., 1994 and DeBell, 2006. (See also ISA, section 9.2.2.2, pgs. 9-7 and 9-8.)1-9 Figure 1-1. Progression from PM Characteristics to PM Ligh t 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

The extent to which any amount of light extin ction affects a person’ s ability to view a scene depends on both scene and light characteristics. For example the appearance of a nearby object (i.e., a building) is generally less sensitiv e to a change in light extinction than the appearance of a similar object at a greater distan ce. For a scene with kn own characteristics, the degradation in the scene associated with a change in light extinction can be determined and the resulting appearance can be realistically displayed on a digital photograph of the scene using the WinHaze system 7 . Figure 1-2 below shows the progr ession from PM light extinction to perceived visual air quality impacts to the valuation of those perceived impacts.

7 Molenar, et al., 1994 

### Measured Derived
Dry PM Mass Concentration

Dry PM Composition

Ambient Relative Humidity Dry PM Characteristics

Hygroscopic Growth Model

Ambient PM Characteristics

Ambient PM Light Extinction Light Extinction Model

Ambient PM Light Extinction1-10 Survey studies have used sets of photographs or computer simula ted images developed from a base photo depicting a range of visibil ity conditions on urban scenes to assess the individual’s opinion on the acceptabi lity of those VAQ conditions. Fo r the specific scenes used
in such studies there is a known or predetermined one-to- one corresponden ce between the amount of ambient or computer generated h aze captured in the photographs or images, respectively, and the associated amount of ambien t PM light extinction. Fo r visibility preference studies, visibility levels are ge nerally characterized using the h aze index in units of deciview (similar to the decibel scale for sound).

Figure 1-2. Progression from PM Light Exti nction to Value of Improved Visual Air Quality (VAQ)



### Relationship Steps from PM

Light Extinction to VAQ Non-Air Quality Information

Ambient PM Light Extinction

Perceived Visual Air Quality (Images) Scene and Lighting Characteristics WinHaze Modeling

Public-Scene Contextual Information

Value of Improved VAQ Valuation Studies1-11 1.3 GOALS AND APPROACH The goal of the UFVA is to characterize visibi lity impairment in 15 selected urban areas under recent air quality conditions, “just meet” air quality scenarios for both current secondary
PM 2.5 standards, and under scenario s using various alternative st andards which utilize different indicators, averaging times, forms and levels to identify those that better reflect the relationship between ambient PM and visibility impairment. In particular, the UFVA focuses on the use of a
PM 10 light extinction-based indicator for a possible secondary PM NAAQS (see Figure 1-1 and 1-2). This is done by compar ing estimates of hourly PM 10 light extinction in 15 major U.S. urban areas over a three-year period (2005-2007) to a range of light extinction values, i.e., candidate protection levels (CPLs), beyond which half of the participants in assessed urban visibility preference studies indicated the haze conditions were unacceptable (see discussion in chapter 2 below, Stratus Consulting Inc., 2009 a nd Appendix J). In addition, the UFVA includes
additional characterizations of th e effectiveness of the current a nd an alternative suite of PM2.5 secondary standards 8

. The previous PM NAAQS review used the results of visibility preference survey studies conducted in Denver (1990), Phoenix (2003), and British Columbia (1993) as the basis for suggesting that a standard set to protect visibility conditions to a level with in a visual range from between about 40 km to about 60 km (corre sponding to light extinction from ~100 Mm -1 to ~67
Mm -1 ) could represent an appropriate de gree of welfare protection from PM 9 . With the exception of a small pilot study conducted in Washington, DC in 2001 (9 participants; Abt Associates Inc., 2001), and a replicate study also conducted for Washington, DC in 2009 (26 participants; Smith and Howell, 2009), there are no additional visibi lity preference survey studies upon which to base the selection of CPLs. The EPA staff, with contractor support, has conducted a more detailed, in-depth assessment of the results from these studies, in cluding the two Washington, DC studies. This assessment includes an analysis that combines da ta from across all stud ies using graphical and logit model analysis to examine the consistenc y of the results between the surveys (Stratus Consulting Inc., 2009 and Appendix J). Based on the results of this analysis, we have been able to refine the range of visibility conditions put forth in the 2006 review, that could represent an appropriate degree of public we lfare visibility prot ection, and to determine a central tendency value for the CPLs. These analyses and re sults are described below in chapter 2.

8 EPA also included an assessment of the sub-daily PM 2.5 mass concentration indicator, which was explored in the 2005 PM staff paper and which was considered a viable option by EPA staff and CASAC in the
2006 review. These latter assessmen ts are summarized in Appendix D.

9 Light extinction is inversely related to visual range.1-12 In the previous PM NAAQS review, the char acterization of urban visibility conditions were based on IMPROVE algorithm estimates using the 2001 to 2003 PM 2.5 mass and speciation data from 161 urban areas by assuming a constant composition for every hour of the day equal to the 24-hour measured composition and by using either actual or monthly average (10-year mean) hour of the day relative humidity. Statistical relationships between hourly light extinction estimates and concurrent hourly PM 2.5 mass concentrations were used to show that daytime and especially afternoon relationships are relatively strong with a si milar linear relationship for both eastern and western urban areas (i.e. R 2 >0.6, slope ~6 m 2 /g). The current assessment of urban visibility c onditions (as described in chapter 3) uses a modeling approach to estimate hourly light extinction using PM 2.5 mass and speciation data with measured relative humidity. However, it diffe rs by replacing the unrealistic assumption of constant composition for PM 2.5 , with composition that is made to vary during the day using urban-specific monthly mean diurnal variations of species concentrations determined from regional air quality model resu lts, while constraining the means of the hourly species concentration for each day to closely match the 24-hour duration measured species
concentrations.

1.4 SCOPE OF URBAN-FOCUSED VISIBILITY ASSESSMENT

This section provides an overview of the sc ope and key design elements of the UFVA, including the process that has been followed to design the anal yses. Following initiation of this PM NAAQS review in 2007, we began the design of the assessments in the UFVA by revisiting the analyses completed during the previous PM NAAQS review (Abt Associates Inc., 2001; US EPA, 2005, chapter 6) with an emphasis on c onsidering key limitations and sources of uncertainty recognized in that review.

1.4.1 Background As an initial step in this review, EPA invited a wide range of external experts as well as EPA staff, representing a variety of areas of expertise to participate in a workshop titled, “Workshop to Discuss Policy-Relevant Science to Inform EPA’s Integrated Plan for the Review of the Secondary PM NAAQS” (72 FR 34005, June 20, 2007). This workshop provided an opportunity for the participants to broadly disc uss the key policy-relev ant issues around which EPA would structure the PM NAAQS review and to discuss the most meaningful new science
that would be available to inform our understa nding of these issues. One session of this workshop centered on issues related to visibi lity impacts associated with ambient PM. Specifically, the discussions focused on the exte nt to which new research and/or improved methodologies were available to info rm how EPA evaluated visibility impairment in this review.1-13 Based in part on these workshop discussions, EPA developed a draft IRP outlining the schedule, the process, and the ke y policy-relevant science issues that would guide the evaluation of the air quality criteria fo r PM and the review of the primary and secondary PM NAAQS, including initial thoughts for conducting quantita tive assessments (US EPA, 2007, chapter 6). On November 30, 2007, CASAC held a teleconferen ce with EPA to provide its comments on the draft IRP (72 FR 63177, November 8, 2007). Pub lic comments were also presented at that teleconference. A final IRP incorporating comments received from CASAC and the general public on the draft plan was issued in March 2008 (US EPA, 2008a). In articulating a rationale for the urban fo cus of this assessment, we reviewed the available information and found the following info rmation compelling: 1) PM levels in urban
areas are often in excess of t hose of the surrounding region sin ce urban haze typically includes both regional and local contributions (US EP A, 2009a; sections 9.2.3.3 and 9.2.3.4), suggesting the potential for higher levels of PM-induced visi bility impairment in urban areas; 2) the
existence of numerous urban vi sibility protection programs and goals demonstrating that urban VAQ is noticed and considered an important value to urban residents (US EPA, 2009a; section 9.2.4); and 3) the existence of large urban populat ions means that potentially more people are routinely affected by poor VAQ than in rural areas. These features of urban areas have led EPA
staff to conclude that urban dwellers represent a susceptibl e population group for adverse PMrelated effects on visibility. However, this conc lusion is not meant to imply that there are not other susceptible populations or in dividuals living in other non-urb an and non-Class I areas that are currently adversely impacted by ambient PM-re lated visibility conditions. Unfortunately, visibility preferences and PM levels in th ese areas have not been well characterized. Although this visibility assessment focuses only on selected urban areas, a new secondary PM standard would apply to all non-Class I areas of the country. On October 6-8, 2008 the EPA sponsored an urban visibility workshop in Denver, Colorado to identify and discuss methods and materi als that could be used in “next step” projects to develop additional information about people’s preferences for reducing existing impairment of urban visibility, and about the value of improvi ng urban visibility. Invited individuals came from a broad array of relevant technical and policy backgrounds, including visual air quality science, sociology, psychology, survey research methods, economics, and EPA’s process of setting NAAQS. The 23 people who attended th e workshop (including one via teleconference line) came from EPA, the National Oceanic a nd Atmospheric Administ ration (NOAA), National Park Service, academia, regional and state ai r pollution planning agencies, and consulting1-14 firms. 10 The information discussed at this Workshop was useful in informing subsequent steps in the process.

1.4.2 Selection of Alternative Scenarios for First Draft Assessments

In designing the quantitative assessments to include in the first draft UFVA, EPA staff developed a planning document outlining the initial design for the PM NAAQS visibility

assessment - Particulate Matter National Ambient Ai r Quality Standards: Scope and Methods Plan for Urban Visibility Impact Assessment, henceforth Scope and Methods Plan (US EPA, 2009b). This planning document was released for CASAC consultation and public review in February 2009. Based on consideration of CASAC and public comments on the Scope and Methods Plan, along with ongoing review of the late st PM-related literature , several aspects of the original scope of the urban visibility conditions assessment, as depicted in Figure 1-1 of section 1.3 of the Scope and Methods document (U S EPA, 2009b), were modified in the first draft UFVA (US EPA, 2009c). Taki ng into account the nature of urban versus more remote area PM composition, and input received at the Ap ril 2, 2009 CASAC meeting, EPA staff concluded that it was unnecessary to develop a new urban- optimized algorithm at this time and that it remained appropriate in the context of this a ssessment to use the original IMPROVE algorithm to relate urban PM to local haze (PM light extin ction). One of the primary reasons for initially considering an urban

- optimized algorithm was a concern that the organic components of PM in urban areas, being generally nearer their emissi on sources, would have a lower ratio to the measured organic carbon mass than the ratio of organic component mass to measured organic carbon mass currently used for the more aged PM organic components found in remote areas. As described below in chapter 3, this concer n has been addressed by using the SANDWICH mass balance approach to estimate the PM organic component mass, which negates the need to estimate organic component mass from measured organic carbon mass. With regard to the urban visual air quality preference assessment described in the Scope and Methods document (US EPA, 2009b, section 1.3) , more significant modifications occurred. EPA staff decided to conduct a reanalysis of the urban visibility preference studies available at the time of the 2006 PM NAAQS review, rather th an conduct new public pref erence studies, as it has become apparent that the results of these st udies would be unlikely to be completed in time to inform this review. Recognition that the in itial plans described in the Scope and Methods document were possibly overly ambitious wa s also shared by members of CASAC (see individual member comments; Sa met, 2009a). The analysis, ther efore, relied on existing, rather than new, urban visibility preference studies and was designed to expl ore the similarities and

10 To view the complete report from the October 2008 urban visibility workshop, see: http://vista.cira.colostate.edu/improve/P ublications/GrayLit/gray_literature.h tm1-15 differences (comparability) among these studies. Information drawn from these results informed the selection of VAQ CPLs (described in chapte r 2 below) to be used in subsequent impact assessments. Further, information presented during the public comment phase of the April 2, 2009 CASAC meeting and later provid ed to EPA staff, led to the inclusion of a recent study by Smith and Howell (2009) for Washi ngton, DC in the reanalysis.

1.4.3 Selection of Alternative Scenarios for Second Draft Assessments
The first draft UFVA was reviewed at an October 2009 CASAC meeting, and a CASAC letter providing its advice and recommendati ons was submitted to the Administrator in November 2009 (Samet, 2009b). In its letter, th e CASAC indicated suppo rt for EPA staff’s approach to evaluating the nature and degree of PM-related visibility impairment, including EPA’s focus on non-Class I areas, including in particular, urban areas as an “ effective complement” to the Regional Haze Rule. In this regard, CASAC expressed support for consideration of a new PM light extinction indicator, a one h our averaging time, and for the range of selected candida te protection levels.

 Indicator: PM 10 Light Extinction There are a number of different ways to m easure ambient PM: par ticle counts, surface area, volume, mass concentrations , and concentration of component s. Each of these different characteristic of ambient PM can be important in the context of different effects. For example, particle count may be important from the perspec tive of cloud formation or to characterize the abundance of ultrafine PM, which is of interest fo r health effects. In a similar way PM light
extinction measures the characteri stic of ambient PM most releva nt and directly related to the effect of PM visibility impairment. Thus, as described in the Scope and Methods document (US EPA, 2009b) and first and second drafts of th e UFVA, EPA staff has continued to focus
assessments in this final document in terms of ambient PM 10 light extinction as the indicator for PM visibility impairment, instead of the traditional PM 2.5 mass concentration. Unlike the current
FRM measurement method for PM mass con centration, which gene rally changes the composition and size of the particles by driving o ff most of the water, direct measurement of ambient PM light extinction captures the PM-induced visibility impairment of the particles as they exist in the atmosphere. PM light extinc tion, like conventional PM mass concentration, is a measurable physical characteristic of atmosphe ric PM. PM light extinction can also be calculated using a simple algorithm such as the IMPROVE algorithm 11 Section 109 (b) (2) of the CAA states that “Any national secondary ambient air quality standard prescribed under subsection (a) of this section shall specify a level of air quality the

11 Malm, et al., 1994 and DeBell, 2006. (See also ISA, section 9.2.2.2, pgs. 9-7 and 9-8.)1-16 attainment and maintenance of which ... is requ isite to protect the public welfare from any known or anticipated adverse effects associated with the presence of such air pollutant in the ambient air....” ( emphasis added). In addition, section 108 (a) (2) states that the air quality criteria “for an air pollutant shall accurately re flect the latest scientific knowledge useful in indicating the kind and extent of all identifiable effects on public he alth or welfare which may be expected from the presence of such pollutant in the ambient air, in va rying quantities. The criteria ... shall include information on (A) those variable factors (including atmospheric conditions) which of themselves or in combina tion with other factors may alter the effects on public health or welfare of such air pollutant;...” ( emphasis added ). Thus, EPA staff believes that the visibility effects of PM important to the public welfare are precisely the visibility effects of PM occurring in the ambien t air, which necessarily enta ils association with ambient atmospheric conditions that affect the nature or magnitude of th e PM visibility effect. These ambient conditions lead to constant changes in the size and composition of particles as these particles come in contact with other pollutants or natural com ponents, become oxidized/age as they are transported great distan ces, and shrink or grow in the absence or presence of water vapor, or other atmospheric gases. The combined effect of all these inte ractions of ambient PM with real time atmospheric conditions and chemis try on the public welfare effect of visibility impairment depends on factors other than dr y PM mass concentrati on alone. Use of PM 10 light extinction as the indicator for a secondary PM NAAQS is thus a more direct measure of the relationship between ambient PM an d the public welfare effect of vi sibility impairment than any dry PM mass concentration (either PM 2.5 or any other dry mass fraction).

 Averaging times: Daylight Daily Ma x. 1 Hour or All Daylight Hours It is necessary to also identify an averagi ng time to apply along with the CPLs in the assessments described in chapters 3 and 4. Because the nature of visibility impairment and its impact on the public welfare is sufficiently di fferent and less well unde rstood at night, this assessment only considers visibility conditions that occur during daylight hours. Though not directly supported by preference or other studies, EPA staff belie ves that a short averaging time (e.g. an hour) may be more appropriate than longer time periods (e.g. multiple hours) since VAQ impacts are instantaneously perceived. This is also consistent with staff’s belief that most
individuals in an urban settin g experience urban VAQ in relative ly short-term incidental and intermittent periods when they have the opportunity to be outdoors (e.g. during commutes to work, school, shopping, etc.). Since this fract ion of the public may experience poor VAQ during a relatively small time period and not have th e opportunity to see it improve later during the same day, it seems appropriate to EPA staff to consider assessing the current and projected conditions in chapters 3 and 4 by comparing the 1-hour daily ma ximum light extinction to each1-17 of the three CPLs supported by th e preference studies. There is uncertainty associated with predicting the duration of the effect associated with such br ief periods of exposur es, i.e., it is not known how long the person remembers the poor VAQ conditions once he/she goes indoors and is removed from the sight. Alternately, a complementary fraction of the public may have multiple or continuing opportunities to experience visibility throughout the day. People in this s ituation can experience a variety of conditions ranging from improveme nt, maintenance, or diminished VAQ throughout the day. For them, a day with several hours that exceed acceptable VAQ levels may represents a greater impact on their wellbeing than on a day with only one such hour. To assess impacts more related to this portion of the population, in which the degree of impact depends upon the conditions present across multiple hours of exposur e, EPA staff has also considered all daylight hours which have light extincti on levels beyond the three CPLs , as well as the 1-hour daily maximum light extinction in the assessm ents described in chapters 3 and 4.

 Level: Candidate Protection Levels (CPLs) In order to identify a range of light extinc tion levels associated with acceptable VAQ to 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 ) based on the composite results and the effective range of 50 th percentile acceptabili ty across the four urban preference study areas shown in Fi gure 2-16. A midpoint of 25 dv (122 Mm -1 ) was also
selected for use in the assessment. These three values provide a low, middle, and high set of light extinction conditions that ar e used in subsequent sections of the UFVA to define daylight
hours with urban haze conditions that have been judged unacceptable by the participants of these preference studies. As discussed in greater detail in section 1. 2 above, 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 ) and NO 2 contribution (assumed to be negligib le), 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 , respectively.

 Forms: Percentiles and Relative Humidity Constraints In considering an appropriate range of forms to consider in the analys es of alternative PM light extinction visibility standa rds analyzed in chapter 4 of this final UFVA, staff considered what frequency of conditions at or below the CPLs should be consider ed acceptable. Again, none of the preference studies provided insight in to this aspect of accep tability. Because the nature of the public welfare effect is one of aes thetics and/or on feelings of wellbeing and not directly related to a phys ical health outcome, EPA staff be lieves that it is not necessary to1-18 eliminate all such exposures and that some nu mber of hours/days with poor VAQ can reasonably
be tolerated. In the first dr aft UFVA, staff selected the 90 th and 95 th percentiles to assess. In the CASAC letter following the review of the first draft UFVA, CASAC recommended that other percentiles be considered, up to and including the 98 th percentile used for the current 24-hour primary and secondary standards. EPA staff has therefore considered the 90 th , 95 th and 98 th percentiles per year in the second and final iterations of this document. Due to inter-annual variability in meteorology and other circumstances that affect air quality, EPA staff is recommending using a three year average form of the standard for purposes of consistency and stability , as is the current 24-hour primary PM 2.5 standard. By considering all of th e combinations of the two hourly forms (i.e. each daylight hour and da ylight 1-hour daily maximum), the three CPLs and the three frequencies, a total of 18 sepa rate alternative secondary PM NAAQS scenarios were generated
for use in the assessments described below in ch apters 3 and 4 (See table 4-1). An additional CASAC recommendation, that the relative humidity (RH) limit be lowered from 95% to 90% and used as a screen (i.e., hours above it should be discarded) rather th an as a cap, to more clearly exclude weather events lik e fog or precipitation and to minimize effects of measurement error and spatial variabil ity, was also incorporated into the second and final iterations of this
document.

1.5 ORGANIZATION OF DOCUMENT

The remainder of this document is organized as follows: Chapter 2 includes an analysis of the urban visibility prefer ence studies with a discussion of similarities and differences regarding the approaches and methods used a nd results obtained for each study. This chapter also includes a summary discussion of the results of a composite assessment of the combined results from the four urban areas (Denver, P hoenix, British Columbia, and Washington, DC, an accompanying logit (statistical) analysis, and us e of these results in the selection of the
alternative levels evaluated in the remainder of the assessment. The complete description of the logit analyses is found in Appendi x J. Chapter 3 describes the an alytical approach, methods, and data used in conducting the assessment of recent ur ban visibility conditions, both in terms of PM mass concentration and PM light extinction for the set of urban case stud ies included in this analysis. Selected results are presented in chapter 3, with additional results found in the Appendices. Chapter 4 presents estimat es of PM mass concentration and PM 10 light extinction conditions generated for the urban ca se studies for two alternative PM 2.5 mass concentration levels and for the three light extinction CPLs . Additional information regarding approaches, results, method validation studies and uncertain ty assessments for both chapters 3 and 4 are presented in Appendices A-J).2-1 

### 2 URBAN VISIBILITY PR EFERENCE STUDIES

The purpose of this chapter is to describe the rean alysis of available urban visibility preference studies conducted by EP A staff with contractor support. The reanalysis covered the three completed urban visibility preference survey studies plus a pair of smaller focus studies designed to explore and further develop urban visibility surv ey instruments. The three completed survey studies (all in the west) incl uded Denver, Colorado (Ely et al., 1991), one in the lower Fraser River valley near Vancouver, British Columbia (BC), Canada (Pryor, 1996), and one in Phoenix, Arizona (BBC Research & Consulting, 2003). The first pilot focus group study was conducted in Washington, DC on beha lf of EPA to inform the 2006 PM NAAQS review (Abt Associates Inc., 2001). In respons e to an EPA request for public comment on the Scope and Methods Plan (74 FR 11580, March 18, 2009) for the current review, Dr. Anne Smith provided comments (Smith, 2009) ab out the results of a new Washington, DC focus group study that had been conducted using methods and appr oaches similar to the method and approach employed in the EPA pilot study (Smith and Howell, 2009). When taken together, these studies
from the four different urban areas included a total of 852 individuals, with each individual responding to a series of questi ons answered while viewing a se t of images of various urban VAQ conditions. The apparent similarity in the methods used across the studies made it appear initially that the studies were comparable. However, in order to ensure th at our basis for selecting an appropriate range of CPLs was sound, we, along with contractor support, undert ook a detailed reanalysis to determine the robustness of the survey study re sults, the appropriaten ess of comparing each study’s results to the others, and the key uncertainties relevant to data interpretation. This reanalysis included a statistical analysis using a logit re gression model to assess the compar ability of different datasets. Limited discussion of logit model results occurs in the body of th is chapter when pertinent to informing staff judgments regarding comparability of study results. A detailed description of the logit assessment is provided in the contractor memo included as Appendix J of this document. The following sections (sections 2.1 to 2.5) examin e in detail the study methods used and results
obtained from each of the available studies.2-2 2.1 METHODS USED IN VISIBILI TY PREFERENCE STUDIES

In all but one 1 of the visibility prefer ence studies assessed in this document, participants were shown a series of different VAQ conditions projected on a large screen using a slide projector. In the earliest two studies (the Denver and lo wer Frazer River Valley British Columbia studies) the range of VAQ conditi ons were presented by projecting photographs (slides) of actual VAQ conditions. The photographs were taken on different days from the same location, and presented the same scene. Photographs were selected to avoi d depicting significant weather events (e.g., rain, snow, or fog), and where measured ex tinction data were available from the time the photograph was taken. The Phoenix study and the Washington, DC projects used computer generated photographic-quality images to present different VAQ conditions. Using an original nearpristine base photograph, addi tional images representing a ra nge of VAQ conditions were generated using the WinHaze software program, which is based on a technique described in Molenar et al. (1994). The Phoenix study and th e 2001 Washington, DC project projected slides of digital images prepared by WinHaze. The 2009 Washington, DC project presented images directly from the desktop version of WinHaze using either a liquid crystal display (LCD) projector or a computer monitor. WinHaze analysis synthetically superimpos es a uniform haze on a digitized, actual photograph. The WinHaze computer algorithm calcu lates how a given extinction level would impair the appearance of each individual por tion of the photograph. A major advantage of presenting WinHaze-generated images is that they provide viewers depictions of alternative VAQ levels, with each image containing exactly the sa me scene, with identical light angle, time of day properties, weather conditions, and specifi c scene content details (e.g., the amount of traffic in a intersection). Add itional details about WinHaze, and a discussion of the applicability of WinHaze images for regulatory purposes, is in the 2004 PM Criteria Document (U.S. EPA, 2004). The desktop version of WinHaze is avai lable online (Air Resources Specialists, 2003). The first urban visibility pr eference study (Denver, CO; El y et al., 1991) developed the basic survey method used in all the subsequent studies. Although th ere are variations in specific details in each study, all the stud ies use a similar overall approach (key variations are discussed in the section on each study later in this chapter). This approach consisted of conducting a series of group interview sessions, where the participants were shown a set of photographs or images of alternative VAQ conditions and as ked a series of questions.

1 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 monito r connected to the computer.2-3 The group interview sessions were conducted mu ltiple times with different participants. Ideally the participants will be a representative sample of the residents of the metropolitan area. While all studies agree that this is the preferre d approach, due to the hi gh cost of organizing and conducting a series of in-person grou p interviews with a large, statistically represen tative sample, only the Phoenix study was able to fully meet this objective. During the group interview sessions, the participants were instructed to consider whet her the VAQ in each photograph or image would meet an urban visibility standa rd, according to their own preferences and considering three factors:

1. The standard would be for their own urban area, not a pristine national park area where the standards might be stricter.

2. The level of an urban visibility standa rd violation should be set at a VAQ level considered to be unreasonable, object ionable, and unacceptable visually.

3. Judgments of standards violations shou ld be based on visibility only, not on health effects.

The photographs (images) were not shown in order of ascending or descending VAQ conditions; the VAQ conditions were shown in a ra ndomized order (with the same order used in each group interview session). In order to ch eck on the consistency of each individual’s answers, the full set of photographs (image s) shown during the group interview included duplicates with the identical VAQ conditions. The participants were initially given a set of “warm up” exercises to familiarize them with how the scene in the photograph or image appears under different VAQ conditions. The participants next were shown 25 randomly ordered photographs (ima ges), and asked to rate each one based on a scale of 1 (poor) to 7 (excellent). They were then shown the same photographs or images again (in the same order), and asked to judge whether each of the photographs (images) would violate what they would c onsider to be an appropriate ur ban visibility standard (i.e., whether the level of impairment was “acceptable” or “unacceptable”). While the studies all asked the basic question, “What level of visi bility degradation is acceptable?”, the term “acceptable” was not defined, so that each person’s response was based on his/her own values and preferences for VAQ.

2.2 DENVER, COLORADO

The Denver urban visibility preference st udy (Ely et al., 1991) was conducted on behalf of the Colorado Department of Public Health and Environment (CDPHE). The study consisted of a series of focus group sessions conducte d in 1989 with participants from 16 civic2-4 associations, community groups, and employees of state and local government organizations. 2 The participants were not select ed to be a fully representative sample of the Denver metropolitan population but were instead selected to take a dvantage of previously scheduled meetings. During the 16 focus group sessions, a total of 214 individuals were asked to rate photographs of varying visibility conditions in Denver. The photographs were taken November 1987 through January 1988 by a camera in Thornton, Colorado. Thornton is suburb of Denver, located approximately six miles north of downtown Denver. The photographs were taken as part of a CDPHE study of Denver’s air quality. The s cene in the photographs was toward the south
from Thornton and included a broad view of dow ntown Denver and the mountains to the south. Each group was shown one of two sets of 20 ra ndomly ordered unique photographs (13 of the sessions included 5 duplicate slides , for a total of 25 photographs, to evaluate consistency of responses). The two sets of different slides were used to investigat e whether the responses between the two sets of photographs were differe nt (no differences were found). Approximately 100 participants viewed each photograph. Projected color slides were used to present the photographs to focus group participants, a nd were projected on a large screen The VAQ conditions in each Denver photograph were recorded when the photograph was taken and measured by a transmissometer yielding hourly average light extinction, b ext . The transmissometer was located in downtown Denver, approximately eight miles from the camera and in the middle of the camera’s view path. Ely et al. (1991) provide the time of day and measured extinction level for each photograph. The extinction levels presented in the Denver photographs ranged from 30 to 596 Mm -1 . This corresponds to 11dv to 41dv, approximating the
10 th to 90 th percentile of wintertime vi sibility conditions in De nver in the late 1980s. The participants first rated the VAQ in each photograph on a 1 to 7 scale, and subsequently were asked if each photograph woul d violate an urban visibility standard. The individual’s rating on the 1 to 7 scale and whether the photograph vi olated a visibility standard were highly correlated (Pearson correlati on coefficient greater than 80%). The percent of participants who found a phot ograph acceptable to them (i.e., would meet an appropriate urban visibility standard) was calculated for each photograph. Figure 2-1 shows the results of the Denver part icipants’ responses, with VAQ measured in deciviews.

2 No preference data were collected at a 17 th focus group session due to a slide projector malfunction.2-5 Figure 2-1. Percent of Denver Partic ipants 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

Ely et al. (1991) introduce a “50% acceptabi lity” criteria analysis of the Denver preference study results. The 50% acceptability criteria is designed to identify the VAQ level that best divides the photographs into two groups: those with a VAQ rated as acceptable by the majority of the participants, and those rated not a cceptable by the majority of participants. While no single VAQ level creates a perfect separation between the two groups, the CDPHE identified a VAQ of 20.3 dv as the point that best separa tes the Denver study res ponses into “acceptable” and “not acceptable” groups. Based in part on the findings of the Denver visibility preference study, the CDPHE established a Denver visibility standard at b ext = 76 Mm -1 (dv = 20.3). Using 20.3 dv as the 50% acceptability crit eria led to six photographs being inconsistently rated by the majority of the vi ewers. A photograph was in consistently rated for two possible reasons; either the photograph’s V AQ was at least 1 dv better than the Denver standard (i.e., dv < 19.3) but was judged to be “u nacceptable” by a majority of the participants rating that photograph, or the VAQ was at least 1 dv worse than the standard (> 21.3 dv) but found to be acceptable by the majority of the partic ipants. This definition of inconsistent rating helps evaluate the robustness of the study results to support the selection of the Denver urban visibility standard at 76 Mm -1 (20.3 dv) by identifying photographs with VAQ a minimum of 1
dv above or below the standard and ignoring “near misses” involving photographs within 1 dv of the standard. A change of 1 or 2 dv in uniform haze under many viewing conditions will be seen2-6 as a small but noticeable change in the appear ance of a scene, regardless of the initial haze condition (U.S. EPA, 2004). Table 2-1 presents information about the six pho tographs that were in consistently rated. All six of the inconsistently rate d photographs were taken at 9:00 a.m. The five inconsistently rated photographs with a VAQ better than the De nver standard have a VAQ at least 2 dv below the standard. The VAQ in the only inconsistently rated photograph with air quality worse than the standard (Photograph #6) is 1.1 dv above th e standard. The study used 18 photographs from 9:00 a.m., so a third of the 9:00 a.m. photographs were inconsistently rated. Conversely, none of the 32 photographs taken at noon or 3:00 p.m. were inconsistently rated.

Table 2-1. VAQ of Denver Photos Substantively Misclassified by Majori ty of Participants

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.

Figure 2-2 shows the same data results abou t percent of participants who rated each photograph acceptable as in Figure 2-1, but with th e time of day of each photograph indicated by different colors. The time of day colors clearl y indicate how inconsistent ly participants rated some of the 9:00 a.m. photographs. Eliminating the 9:00 a.m. photographs crea tes a “hole” in the range of remaining photographs; there are no photographs with a VAQ between 17.7 dv and 20.3 dv. As seen in Figure 2-3, this is a critical rang e in evaluating the responses. All of the photographs with a VAQ equal to or better (i.e., a lower dv value) than 17.7 dv are ra ted acceptable by the majority of the participants, and all photographs with a VAQ at or above 20.3 dv are rated not acceptable. After eliminating the 9:00 a.m. photographs , any VAQ level between 17.7dv and 20.3 dv would completely divide the photographs into two groups with no inconsistent ratings.2-7 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

A modestly broader range of VAQ conditi ons provides an even more unambiguous interpretation of the Denver study results. Every photograph with a VAQ of 17.7 dv or lower was rated acceptable by 89% or more of par ticipants, and every phot ograph with a VAQ of 24.6 or higher was rated not acceptable by 84% or more of the participants. The 17.7 dv to 24.6 dv range separating the results is shown in Figure 2- 3, which also eliminates the 9:00 a.m. results.2-8 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



### Denver Preference Results Showing Broader Range Separating Unambigious Ratings

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

2.3 VANCOUVER, BRITISH COLUMBIA, CANADA

The BC urban visibility pref erence study (Pryor, 1996) was c onducted on behalf of the BC Ministry of Environment following the methods used in the Denver study. Participants were students at the University of British Columbia, who were in one of four focus group sessions with between 7 and 95 participants . A total of 180 participants completed the surveys (29 did not complete the survey). The BC study used photographs (projected as slides) depicting various VAQ conditions in two cities (Chilliwack and Abbot sford) in the lower Fraser Rive r valley in southwestern BC. Abbotsford is located approximately 75 miles east of Vancouver, BC, and had a 2006 population of 159,000 (Statistics Canada, 2009a). Abbotsford has a diverse and successful economy, with approximately 25% of the labor force working in the Vancouver metropolitan area. Chilliwack is adjacent to Abbotsford to the east. Both ci ties have experienced rapid population growth, growing faster than the Vancouver metropolitan area, and are considered suburbs (or exurbs) of
Vancouver. The survey was conducted at the University of British Columbia (UBC) in 1994. The participants were 206 undergradu ate and graduate students enro lled in classes in UBC’s2-9 Department of Geography. Information about stud ent demographics and where they lived prior to enrolling at UBC (which potentially influe nces their knowledge of, and preferences for, Vancouver area visibility) is not available. The BC survey showed 20 unique photographs to the participants in random order. Ten photographs were from Chilliwack, and 10 were from Abbotsford. The Chilliwack photographs were taken at the Chilliwack Hospital, and the scene includes a complex foreground with downtown buildings, with mountains in the ba ckground up to 40 miles away. Figure 2-4 is a composite of two of the Chilliwack photographs us ed in the preference study, showing the scene with a good visibility day (14.1 dv) in the middle and a significantly impa ired day (34 dv) around the border (Jacques Whitford AXYS, 2007). The Abbotsford photographs were taken at the Abbotsford Airport. The Abbot sford scene includes fewer man- made objects in the foreground and is primarily a more rural scene with the mountains in th e background up to 36 miles away.

Figure 2-4. Composite Chilliwack, BC Ph otograph Shows VAQ of 14.1 dv and 34 dv

The photographs were taken in July and August 1993 as part of a VAQ and fine particulate monitoring project s ponsored by the BC Ministry of Environment, Lands and Parks (REVEAL, the Regional Visibility Experimental Assessment in the Lower Fraser Valley). All of the photographs were taken at either 12:00 p.m. or 3:00 p.m. VAQ data were available for each photograph from visibility monitors near the location of each camera. The types of VAQ
measurement data available from the two locations were not id entical. The Chilliwack location2-10 measurement data available from the two locations were not id entical. The Chilliwack location used both an open-chamber nephelometer and a l ong path transmissometer and collected hourly average data on both aeros ol light scattering ( b sp ) and total extinction ( b ext ), respectively. The visibility monitoring at the Abbotsford loca tion had only a nephelometer and collected only b sp data. As explained in section 1.3, to tal light extinction is the sum of scattering by gases ( b sg ) and particles ( b sp ) plus light absorption by gases ( b ag ) and particles ( b ap ). In order to present the preference results from the BC study in comparable terms, b ext for the Abbotsford photographs is estimated by assuming that the average of the ratios of PM light extinction (i.e., b ap + b sp ) to PM light scattering ( b sp ) for all ten of the Chilliwack phot ographs can be multiplied by the Abbotsford nephelometer determined b sp values corresponding to each of its photographs to estimate its PM light extinction value. By assuming that absorption by gases ( b ag ) is zero, total light extinction is equal to th e PM light extinction (i.e., b ap + b sp ) plus particle scattering by gases (i.e., b sg that is approximately equal to 10 Mm -1 ). Table 2-2 presents the data from the photographs used in the BC study, including the estimated b ext for the Abbotsford photographs.
There are two caveats to be noted about the extinction data for th e photographs reported in Pryor, 1996. First, in Table 2 of the original article, two of the Abbotsford photographs are listed with the same date and time (12:00 p.m., 7/26/1993). There is no in formation provided for a 3:00 p.m., 7/26/1993 Abbotsford photograph, although there is a Chilliwack photograph from that time. The preference and VAQ data are pr esumed to be correct for both photographs and
one of the two identical date/time labels is assumed to be a typogr aphic error. The second caveat
is that b sp levels from the same date and time can differ substantially between Abbotsford and Chilliwack, and the relative levels can change rapidly, even though the two cities are only 25 miles apart. For example, at 12:00 p.m. on 8/19/1993, the b sp level in Chilliwack was about onethird of the Abbotsford b sp level. By 3:00 p.m. the situation was reversed, with the Chilliwack
b sp level 50% higher than Abbotsford. In those three hours the Chilliwack b sp level had more than doubled (from 46 Mm -1 to 105 Mm -1 ), and the Abbotsford level had fallen by over half (from 145 Mm -1 to 67 Mm -1 ). Such substantial changes in measured b sp levels occurring across a relatively short period of time and short distance, may reflect an inherent uncertainty introduced by using a single measure of light extinction from a portion of visual scene (where the nephelometer or transmissometer was operating) to assess visibility conditions throughout an actual photographs of a complex scene. Spatia l and temporal non-uniformity of visibility conditions within a scene are an atmospheric condition known to occur on some days, and may contribute to the variability in participant responses in pref erence studies utilizing actual photographs.2-11 Table 2-2. Summary of Photographs Used in British Columbia Study

Date Time b sp b ext Ratio ( b ext -b sg )/b sp Estimated b ext 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

Figure 2-5 presents the results of the BC study. The division corresponding to the Denver “50% acceptable” criteria occurs between 22.6 dv and 23.2 dv. All of the photographs with a VAQ better than 22.6 dv were rated acceptable by the majority of the participants with one exception (47% of the participants judge d the 19.2 dv photograph to be acceptable). All photographs with a VAQ better than 19.2 dv were rated acceptable by over 90% of the2-12 participants. All photographs with a VAQ wors e than 22.6 dv were rated not acceptable by the majority of the participants, and all photographs with a VAQ worse than 28.3 dv were rated not acceptable by over 90% of the participants. Figure 2-5 also suggests that there may be some difference between the preferences expressed for the Chilliwack scene and those fo r the Abbotsford scene. All photographs were rated by the same individuals (st udents at UBC), but the summary of the responses indicate that the participants may have rated as acceptable a worse level of impaired VAQ impairment (e.g., higher dv levels) in photographs showing more of a downtown area (Ch illiwack) than in less congested scenes (Abbotsford). The strongest evidence for this hypothesis, however, is the preference for a single photograph (the 19.0 dv photograph from Abbotsford, rated as acceptable by 47%), previously identified as an outlier observation.

Figure 2-5. Percent of BC Participants Who Consider VAQ in Each Photograph “Acceptable”

The BC Ministry of the Environment is cons idering the BC urban visibility preference study as part of establishing urban an d wilderness visibility goals in BC.2-13 2.4 PHOENIX, ARIZONA

The Phoenix urban visibility preference study (BBC Research & Consulting, 2003), which was conducted on behalf of the Arizona Department of Environmental Quality, used group interviews based on the methods used in the Denver study, with tw o major exceptions: (1) the focus group participants were selected as a representative sample of the Phoenix area population, and (2) the pictures presented in the focus groups were computer-generated images to depict specific uniform haze conditions. The Phoenix study included 385 participants in 27 separate focus group sessions. Participants were recruited using random digit dialing to obtain a sample group designed to be demographically representative of the la rger Phoenix population. During July 2002, group interview sessions took place at six neighborhood locations throughout the metropolitan area to improve the participation rate. Participants received $50 as an inducem ent to participate. Three sessions were held in Spanish in one region of the city with a large Hispanic population (25%), although the final overall part icipation of native Span ish speakers (18%) in the study was below the targeted level. The ag e distribution of the participants corresponded reasonably well to the overall age distribution in the 2000 U.S. Census for the Phoenix area (BBC Research & Consulting, 2003). Participants slightly over-represented the middle-income range ($50,000 to $74,999), compared with 2000 Cens us data, and slightly under-represented very low-income ranges (under $24,9 99). The distribution of part icipant education levels was fairly consistent with the educat ion distribution in the 2000 Census. Photographic-quality slides of the images were developed using the WinHaze software (Molenar et al., 1994). The scene used in th e Phoenix study images was taken at a water treatment plant. The view is toward the southw est, including downtown Phoenix, with the Sierra Estrella Mountains in the backgr ound at a distance of 25 miles. Figure 2-6 shows the image with the best VAQ (15 dv).2-14 Figure 2-6 Reproduction of Image with the Best VAQ (15 dv) Used in the Phoenix Study

The study used a total of 21 unique WinHaze images. Four of the 21 unique images were randomly selected and used twice to evaluate cons istency so that participants viewed a total of 25 images. The 25 images were randomly ordered, with all participants viewing the images in the same order. The WinHaze images used in the Phoenix study do not include layered haze, a frequent and widely recognized form of vi sibility impairment in the Phoenix area. The VAQ levels in the 21 unique images ra nged from 15 dv to 35 dv (the extinction
coefficient b ext ranged from 45 Mm -1 to 330 Mm -1 ). As in the Denver study, participants first individually rated the randomly shown slides on th e same VAQ scale of 1 to 7. Participants were instructed to rate the photographs solely on visi bility and to not base their decisions on either
health concerns or what it would cost to have better visibilit y. Next, the partic ipants individually rated the randomly ordered slides as “acceptable” or “not acceptable,” defined as whether the visibility in the slide is un reasonable or objectionable. Figure 2-7 presents the percent acceptability results from the Phoenix study. The combination of the use of WinHaze images and the larger number of participants than in the Denver study may account for the “smoother” ba ckwards S-shaped pattern of preferences.2-15 Figure 2-7. Percent of Phoenix Particip ants who Consider VAQ in Each Image “Acceptable”

0% 50% 100%

10 15 20 25 30 35 40 45
Deciview% participants rating "acceptable"

Ninety percent or more of the participants ra ted a VAQ of 20 dv or better as acceptable, and 70% rated a VAQ of 22 dv or better as acceptable. The “50% acceptable criteria” was met at approximately 24.3 dv (with 51.3% of the particip ants rating that image as acceptable). The percent acceptability dec lines rapidly as VAQ wors ens; only 27% of the participants rated a 26 DV image as acceptable, and fewer than 10% rated a 29 dv image as acceptable. The Phoenix urban visibility study formed th e basis of the decision of the Phoenix Visibility Index Oversight Committee for a vi sibility index for the Phoenix metropolitan area (Arizona Department of Environmental Quality, 2003). The Phoenix Visibi lity Index establishes an indexed system with 5 categories of visibil ity conditions, ranging from “Excellent” (14 dv or less, which was a better VAQ than any of the imag es used in the Phoenix study) to “Very Poor” (29 dv or greater, which less than 10% of the st udy participants rated as acceptable). The “Good” range is 15 dv to 20 dv (more than 90% of the participants rated images in this VAQ range as acceptable). The environmental goal of the Phoenix ur ban visibility program is to achieve continued progress through 2018 by movi ng the number of days in poorer quality categories into better quality categories.

2.5 WASHINGTON, DC

One of the Washington, DC urban visibility pilot studies was conducted on behalf of EPA (Abt Associates Inc., 2001). It was designed to be a pilo t focus group study, an initial2-16 developmental trial run of a larg er study. The intent of the pilot study was to refine both focus group method design and potential survey questions . Due to funding limitations, only a single focus group session took place, consisting of one ex tended session with nine participants. No further urban visibility focus group sessions were held in Washington, DC on behalf of EPA. In March 2009, Dr. Anne Smith conducte d a separate study of Washington urban visibility, using the same photographs and si milar approach as the 2001 study (Smith and Howell, 2009). On behalf of the Utility Air Regulatory Group, Dr. Smith presented comments (Smith, 2009) to the CASAC at a public meetin g held on April 2, 2009 to review EPA’s plan (US EPA, 2009b) for conducting further urban vi sibility studies in support of PM NAAQS
reviews. Dr. Smith submitted the Smith and Howe ll (2009) report to the CASAC as part of the public comment process. The Smith and Howe ll study conducted three study variations of a Washington, DC preference study, including one experiment invol ving 26 participants designed to replicate the EPA pilot study (Abt Associates Inc., 2001). Both the Abt Associates Inc. (2001) study results and the results of the Sm ith and Howell (2009) study are discussed below.

2.5.1 Washington, DC 2001 The EPA’s Washington, DC study (Abt Associ ates Inc., 2001) adopted the general study methods used in the Denver, BC, and Phoenix st udies, modifying them appropriately to be applicable in an eastern urban setting. Washington’s (and the entire East’s) current visibility conditions are typically substantially worse than western cities and ha ve different characteristics. Washington’s visibility impairment is primar ily a uniform whitish haze dominated by sulfates, and the relative humidity levels are higher compar ed with the western stud y areas. In addition, the relatively low-lying terrain 3 in Washington, DC provides s ubstantially shorter maximum sight distances. The Washington, DC focus group session includ ed questions on valuation, as well as on preferences. The focus group content dealing with preferences for an urban visibility standard was similar to the focus group sess ions in the western studies. A single scene of a panoramic photograph ta ken from Arlington National Cemetery in Virginia was used, and included an iconic view of the Potomac River, the National Mall, and downtown Washington, DC. All of the distinct buildings in the scene are less than four miles from the camera, and the higher elevations in the background are less than 10 miles from the camera. Figure 2-8 presents the photograph with the best VAQ used in the study.

3 The maximum elevation in Wash ington, DC is 409 feet.2-17 Figure 2-8. Reproduction of the Image with the Best VAQ (8.8 dv) Used in the Washington, DC Study

The Washington, DC study used 20 unique imag es generated by WinHaze, each prepared from the same original photograph. Humidity and gaseous light scattering was held constant in preparing the WinHaze images, as was the relative chemical mix of aerosol particulates in the photos (i.e., only the aerosol concentrations were increased to create the images with worse VAQ). Five of the images were repeated as a co nsistency check, so participants viewed a total of 25 slides. The VAQ in the images ranged from 8.8 to 38.3 dv Figure 2-9 presents the percent acceptability results from the 2001 Washington st udy. Because only nine participants were involved in the study, the possible values of “perce nt acceptable” are limited to multiples of 1/9. Figure 2-9 also shows an anomalous result involvi ng one of the five repeated images. Three of the repeat images had the same ranking each time th ey were presented (i.e., all nine participants rated them acceptable or not acceptable both times they rated that slide). One of the images (the image with 8.8 dv, the best VAQ image used in the study) was rated acceptable by all nine participants the first time it was used, but the repe at of that slide was rated not acceptable by one participant. Another image, however, had a substantially diff erent result. The 30.9 dv image was rated acceptable by five of the nine participants the first time it was presented, but the repeat of the slide was only rated acceptable by one of the nine participants. The responses for all five pairs of repeated images are shown in red on Figure 2-9, including the images which were identically rated both time s they were presented.2-18 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

In the 2001 Washington, DC study, all images with a VAQ below 25.9 dv were rated acceptable by the majority of the participants , and all images with a VAQ below 29.2 dv were rated acceptable by at least four of the nine (44 %) participants. All images with a VAQ above 30.9 dv were rated not acceptable. The “50% accepta bility criteria” division occurs in the range of 25.9 dv to 30.9 dv, with the anomalous result of the inconsistent respon ses to the repeated image with 30.9 dv effectively broadening this range and adding uncertainty to identifying a
clear division.

2.5.2 Washington, DC 2009 The Smith and Howell (2009) study conducted additional focus group sessions based on
the methods and materials used in the 2001 Wa shington, DC study. Smith and Howell recreated the WinHaze images used in the 2001 Washingto n, DC urban visibility preference study, using the description in the report on the 2001 study (Abt Associates Inc., 2001), and created images using currently available desktop computer version of WinHaze (Version 2.9.0). Smith and
Howell used a shortened version of the same question protocol as the 2001 study. The WinHaze images were presented to a total of 64 particip ants who were all empl oyees of Charles River Associates (CRA International, Inc). (Smith a nd Howell also are CRA International employees). The CRA employees were based at the firm’s Washington, DC and Houston, Texas offices (442-19 and 20 participants, respectively). The Houston pa rticipants were included to explore whether familiarity with Washington, DC VAQ conditions developed from currently living in the Washington region noticeably infl uenced the responses. As noted by Smith and Howell, the participants were not a representative sample of either metropolitan area’s population; all participants were employed, and the particip ant group included a higher proportion of college educated individuals and higher househol d incomes than the general population. Eight of the Washington-based participants a nd all of the Houston pa rticipants viewed the WinHaze images on a desktop computer monitor. The remaining Washington participants viewed the images projected on a screen. The stated purpose of the Smith and Howell study was to explore the robustness of the 2001 results. To investigate this issue, Smith and Howell conducted three different tests concerning urban visibility preferences. Each participant was involved with only one test. The
three tests were:

 Test 1 - replicated the Abt Associates Inc. (2001) study

 Test 2 - reduced the upper end of the range of VAQ by eliminating the 11 images used in Test 1 with a VAQ above 27.1 dv

 Test 3 - increased the upper e nd of the range of VAQ by including two new images of worse VAQ; the two new images had a VAQ of 42 dv and 45 dv

Sixteen employees from the Washington, DC office and 10 participants from the Houston office took Test 1 (a total of 26 participants). All the participants viewed the same unique 20 Washington, DC WinHaze images as the 2001 study (plus repeated images for a total of 25 images shown to participants). Images were presented in the same random order as in the 2001 study. Figure 2-10 presents the resu lts of Test 1. The results for the 16 Washington participants
are indicated in blue and result s for the 10 Houston participants in red. Although all images used in the study were of Washington, DC , the results suggest that there is not a significant difference in the preferences of participants based in th e two offices. The scene in the images is an immediately recognizable iconic view of th e National Mall and downt own Washington, DC, which may influence the similarity of re sponses by residents of the two cities.2-20 Figure 2-10. Percent of 2009 Test 1 St udy Participants Who Considered VAQ Acceptable in Each Image, Showing th e Range of the Lower and Upper Bound of 50% Acceptability Criteria

Using the combined Test 1 results from the two CRA offices (26 total participants), the majority of participants in the 2009 study rated all VAQ imag es with 25.9 dv or less as acceptable and all VAQ images with 29.2 dv or great er as not acceptable. The image of 27.1 dv was rated as acceptable by 50% of the total partic ipants (56% of the Washington-based and 40% of the Houston-based participan ts). All images with a VAQ less than 22.9 dv were rated acceptable by at least 90% of the participants, and all images with a VAQ greater than 32.3 dv were rated not acceptable by 88% of the participants. Figure 2-11 presents the Abt Associates Inc., 2001 study and Smith and Howell 2009 (Test 1) study results on a singl e graph, representing th e results of 35 total participants of preferences for urban visibility in Washingt on, DC. The results from the 2009 study on Figure 2-11 combine the Test 1 responses from the two CRA offices. Figure 2-11 also shows the 50% acceptability criteria range (22.9 dv to 32.3 dv) from the 2009 Test 1. In comparison, the 2001 study 50% acceptability range was 25.9 dv to 30.9 dv. Inspection of the points in Figure 2-11
indicates that the results from the 2009 study (Test 1) are not a ppreciably different than the results of the 2001 Washington study. This observa tion of similar results is confirmed by a logit regression analysis of the 2001 and 2009 Test 1 data that includes estimates of the 50% criteria2-21 deciview values with confidence intervals and hypot hesis testing of the similarity of the values, as described in Appendix J (see Tables 6, 7 & 8 and Figure 3).

Figure 2-11. Combined Results of Washington, DC 2001 and 2009 Test 1 (showing 50% Acceptability Criteria from 2009, Test 1)

0% 50% 100%

5 10 15 20 25 30 35 40

Deciview% Participants Rating "Acceptable"

2001 Study 2009, Test 1 Lower Bound Upper Bound

In Test 2, Smith and Howell reduced the rang e of VAQ images to images with a VAQ of 27.1 dv or less. The 26 participants in the Test 2 study were different pe ople than the Test 1 participants. Test 2 presented only the nine uni que clearest WinHaze images from the full Test 1 set of 20 images, along with 3 duplicates for a total of 12 images. This constricted the VAQ levels presented to the range that the major ity of participants in the 2001 study rated as acceptable and reduced the upper end of the VAQ range by 11.2 dv. Figure 2-12 presents the Test 1 and Test 2 resu lts. Test 2 found a s ubstantial shift in the responses regarding which VAQ levels are cons idered acceptable. The smaller number of images used in Test 2 made identifying the rang e of the 50% acceptability criteria more difficult
than in Test 1. The lower bound of the ra nge occurs between 15.6 and 18.7 dv, and the upper
bound occurs between 24.5 and 27.1 dv. Smith and Howell conclude that the shift in the acceptability responses between Test 1 and Test 2 suggests that the VAQ le vels identified as acceptable in an urban visibility preference study conducted using the general approach previously used in the all the studies may be influenced by the range of VAQ images presented.2-22 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

In order for the range of images shown to be able to influence the acceptability ratings, participants would need to be aware of the upper and lower b ounds of the range prior to the judging acceptability. However, since they were shown images randomly with respect to the VAQ levels, asked to rate each one before going to the next image, and were not given a chance to revise their acceptability ratings, this was not possible during the acceptability exercise itself. The only other opportunity participants could have to learn the VAQ range is during the VAQ rating exercise done just prior to the acceptabil ity rating. However, in the VAQ rating exercise where the participants were asked to rate the qual ity of visibility for th e shown images on a scale from 1 to 7, the images were also shown in a random order, participants were not aware how many photographs would be shown or the range of conditions, they were asked to rate each one using a value from 1 to 7 before going on to the next image and they did not have the opportunity to revise the ratings of earlier viewed images. Figure 2-13 shows the average visibility rating on the 1 to 7 scale for each image used in each of the three tests conducted by Smith and Ho well (2009). The consistency observed in the relationship between VAQ deciview levels and the average scores assigned across the three tests demonstrates that the participants come to the su rvey with the capability to consistently rate the haze levels shown in the images, regardless of the breadth of the range used or the order or2-23 number of slides shown, and that they are aware of a full range of conditions, even when, as was the case in Test 2, they were not shown the worst haze images.

Figure 2-13. Average Visibility Rating s for the Washington, DC WinHaze Images by Participants in Tests 1-3 Co nducted by Smith and Howell (2009).

Why then did Test 2 participants in the subseque nt part of the survey rate images of haze levels as unacceptable that were rated acceptable by participants in the other tests and the earlier Washington, DC pilot study? In a three sentence script 4 that constituted the only instructions read prior to the acceptability rating, the particip ants were told that they would see the same set of slides that they had just ra ted (i.e., on the 1 to 7 scale), and they were asked to rate them according to whether the VAQ depicted were accepta ble or unacceptable to them. Apparently by directing then to rate the same images for accep tability, the participants understood that their choices of visibility conditions were restricted to a range of conditions shown in the 1 to 7 ratings that they had just completed. For participan ts in Test 2 this would mean that by their own 1 to 7 ratings the range was restricted to incl ude no poor visibility conditions (i.e. only scenes rated from 3 to 7). Smith and Howell (2009) concluded that th e effects of a changed range on the acceptability ratings results dem onstrates that VAQ preference st udies results are not robust and

4 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 ju st rated. Again each image will illustr ate the effects of a different level of visibility. This time, rate the slides according to whether the visibility is acceptable or unacceptable to you.”2-24 do not reflect an enduring view on the “unacceptabil ity” of different levels of VAQ degradation. However, there is an alternative explanation. It seems more likely that the use of such a severely truncated range of VAQ conditions in Test 2, which did not include any of the images of VAQ that previous studies identified as unacceptable, in effect fundamentally changed the implied instructions for the participants. Instead of c onveying that they were to identify VAQ levels that they found acceptable among a full range of VAQ conditions from very poor to very good, the implied message was that they should identify the VAQ levels that they found acceptable among a curtailed range of VAQ conditions that only included average to very good VAQ. By this reasoning, it would be inappropriate to include Test 2 results with those of the other tests as a measure of VAQ preference for Washington, DC. In Test 3, Smith and Howell expanded the VAQ range of WinHaze images shown to the participants, including two new images with a worse VAQ. The new images had a VAQ of 42 dv and 45 dv, raising the upper end of the VAQ range by 6.7 dv. Test 3 al so reduced the total number of images shown to par ticipants to 19 images by eliminating the use of the five repeat images in Test 1, and also eliminated three addi tional images in order to reduce the participants’ time burden. The three deleted images had a VAQ of 11.1, 15.6, and 24.5 dv. The best VAQ image shown to Test 3 participants was 8.8 dv (s ame as the best VAQ image in Tests 1 and 2). However, in Test 3 there were no images with VAQ between 8.8 dv and 18.7 dv, creating a significant “hole” in the distributi on of VAQ conditions presented to the Test 3 participants. Test 3 was conducted with 12 participants fr om the CRA Washington office (none of whom participated in Test 1 or Test 2). No Houston participants were involved with Test 3. Figure 2- 13 shows that the Test 3 average ratings from 1 to 7 during the VAQ rating exercise increased the average participant rating by about 1 at th e low end of the scale (very poor VAQ). The results of Test 3 are shown in Figure 2-14, along with the results of Test 1.
Test 3 resulted in an overall increase in the percent of responde nts rating as acceptable the VAQ images used in both tests. In Test 3 all images with a VAQ below 22.9 dv were rated acceptable by 100% of the participants (similar to the Test 1 results), implying there was no general change in the accepta bility of the images with good VAQ. However, for all VAQ images (that were used in both studies) between 25.9 dv and 33.6 dv, a noticeably larger percentage of the participants in Test 3 rated the image as accep table than in Test 1. At VAQ levels worse than 33.6 dv, the majority of the pa rticipants found the VAQ level not acceptable in
both tests.2-25 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

Given that most of the same images of VAQ conditions were used in all of the tests, composite acceptability ratings (i.e., from the orig inal pilot study (2001) and from Tests 1 and 3) of each image were initially developed to evalua te whether increasing the number of participant ratings for each image influenced the 50% acceptability value. Figure 2-15 shows composite results from the combination of these three gr oups (total of 47 part icipants). The 50% acceptability criteria value for this composite dataset lies unambiguously between the 30.1 dv (at 51.1%) and the 30.9 dv points (at 46.3%),2-26 Figure 2-15. Composite Results from Smit h and Howell (2009) Tests 1 and 3, and Abt (2001) Washington, DC Pilot Study

0% 50% 100%

0 1 0 2 0 3 0 4 0 5 0 % A c c e p t a b l e

Deciview Washington DC Studies Combined 2001 and Smith Tests 1 & 3

To determine whether it would be appropriate to combine all of the results from the Washington, DC studies, in order to increase th e number of data points for Washington, DC, we
considered several factors. Firs t, while the range limitations identif ied with Test 2 (i.e. an overly restrictive range) that resulted in its results bei ng eliminated from consid eration in the selection of appropriate CPLs do not apply to Test 3 results due to its so mewhat more complete coverage of the 1 to 7 rating range in the VAQ rating exerci se, the number of participants in Test 3 (i.e., 12) is small enough that the statisti cal uncertainty of the results ma y be an issue if used alone. Second, it was not clear whether the significant “hole” in the Test 3 VAQ distribution between 8.8 dv and 18.7 dv potentially had an effect on partic ipant acceptability re sponses. Finally, the logit regression analysis which was applied to each of the individual Smith and Howell (2009)
tests as well as subsets of some of these tests to investigate differences in the preference curves and 50% criteria deciview levels (Appendix J) co ncluded in part that Te st 2 and Test 3 response
curves and 50% criteria values are statistica lly dissimilar from those of the 2001 and Test 1 Washington, DC studies. In c ontrast, the logit analysis conc luded that the 2001 and Test 1
results have 50% criteria values that are statistically indistingu ishable. These findings provided2-27 support for combining the 2001 and Test 1 data se ts and for excluding the dissimilar results of
Test 2 and Test 3.

2.6 SUMMARY OF PREFERENCE STUDIES AND SELECTION OF CANDIDATE PROTECTION LEVELS
As described above, because each of the studie s reviewed in this a ssessment investigates a common question and use similar approaches that are all derived from the method first developed for the Denver urban visibility study, we concluded that it is reasonable to compare the results from all four urban areas to identify overall trends in the st udy findings and that this comparison can usefully inform the selection of CPLs for use in further analyses. However, because variations in the specific material s and methods used in each study introduce uncertainties, direct comparison of the study result s should take these factors into account. Key
differences between the studies include:

 Image presentation methods (e.g., projected sl ides of actual photos, projected images generated using WinHaze (a si gnificant technical advance in the method of presenting VAQ conditions), use of computer monitor screen

 Number of participants in each study,

 Participant representativeness of the general population of the relevant metropolitan area, and

 Specific wording used to frame the questions used in the group interview process.

Figure 2-16 presents a graphical summary of the results of the studies in the four cities and draws on results previously pres ented in Figures 2-3, 2-5, 2-7 and 2-11. As described in the separate discussions for each urban area above, the data and curves depicted in Figure 2-16 include the following modifications: 1) the Denver results omit the 9:00 a.m. photograph results; 2) the Chill iwack and Abbotsford results appear as a single set of data for the BC study; 3) the results from 2001 and 2009 (Test 1) studies of VAQ preferences in Washington, DC are presente d as a single combined set of data; 4) the results from the 2009 Washington, DC study Tests 2 and 3 are not included.2-28 Figure 2-16

. Summary of Results of Urban Visibility Studies in Four Cities, Showing the Identified Range of th e 50% Acceptance Criteria 5

Figure 2-16 shows the results of a logistical regression analysis using a logit model of the greater than 19,000 ratings of haze images as accep table or unacceptable. The logit model is a generalized linear model used for binomial regres sion analysis which fits explanatory data about binary outcomes (in this case, a person rati ng a VAQ image as acceptable or not) to a logistic function curve. A more complete description of the logit model application to these data is contained in Appendix J. The results shown in Figure 2-16 are from the more generalized of the two logit assessment models (i.e. model 2) in which both the shape and displacement of the curves for the four cities are permitted to vary independently. The logit analysis city intercept coefficien ts (Appendix J, Table 3) are all positive and statistically significant, indicati ng that the response functions fo r different cities shifted right relative to the function for Denver. However, onl y the Phoenix interaction term is insignificant,

5 Top scale shows light extinction in inverse megameter units; bottom scale in deciviews. Logit analysis estimated response functions are shown as the color-code d curved lines for each of the four urban areas.2-29 indicating that the Phoenix respons e function has a different shape th at is steeper than the other three cities, as can be seen in Figure 2-16. Figure 2-16 also shows the Washington, DC function is modestly less steep than the others, but the decr ease in the slope is not st atistically significant.
The model results can be used to estimate the VAQ deciview values where the estimated response functions cross the 50% acce ptability level, as well as a ny alternative criteria levels. Selected examples of these are shown in Table 2-3. A t-test of these 50% acceptance deciview values for the four cities shows each to be signi ficantly different from the others (Appendix J, Table 5).

Table 2-3. Logit Model Estimated VAQ Va lues Corresponding to Various Percent Acceptability Values for the Four Cities

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

Figure 2-16 also contains lin es at 20 dv and 30 dv that e ffectively and pragmatically identify a range where the 50% acceptance criteria occur across all four of the urban preference studies. Out of the 114 data points shown in Figure 2-16, only one photograph (or image) with a VAQ below 20 dv was rated as acceptable by less than 50% of the participants who rated that photograph. 6 Similarly, only one image with a VAQ above 30 dv was rated acceptable by more than 50% of the partic ipants who viewed it. 7 These upper and lower range values are also supported by the logit model data which estimates 50 th percentile acceptability values near 20 dv for Denver and near 30 dv for Washington, DC (see Table 2-4). There are several hypotheses that may explain why the VAQ acceptability response curves for the four cities are different and why some study results have greater variability than

6 Only 47% of the BC participants rate d a 19.2 dv photograph as acceptable.
7 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 ac ceptable the second time it was shown. The same VAQ level was rated as acceptable by 42% of the particip ants in the 200 9 study (Test 1).2-30 others. 8 First, as mentioned, the use of photogra phs (Denver and BC surveys) versus WinHazegenerated images (Phoenix and Washington, DC surveys) may play a significant role in preference studies, perhaps introducing bias (such as suggested by the responses to the 9:00 a.m. Denver photographs) as well as variability. Furthe r, the use of photographs from different days and times of day that rely on associated ambient measurements of light extinction to characterize their VAQ level can introduce two ot her types of uncertainty. The intrinsic appearance of the scene can change due to the changing shadow patte rn and cloud conditions, and spatial variations in air quality can result in ambient light extinction measurem ents not being representative of the sight-path-averaged light extinction. WinHaze ha s neither of these sources of uncertainty because the same base photograph is used (i.e. no intrinsic change in scene appearance) and the modeled haze that is displayed in the photograph is determined based on uniform light extinction throughout the scene. Second, variation in the degree of representati veness of the participan ts and the sizes of the participant samples involved may also be importa nt factors. The small sample size and fairly uniform population of respondents is a plausible explanation for the noisiness of the combined Washington, DC results (35 participants, includi ng 26 from a single consulting firm and 10 of those from a different city) compared with th e larger and more representative population of responders from Phoenix (385 participants, carefully selected to be representative of the Phoenix population). A third hypothesis put forward by Smith and Howell (2009) is that the range of VAQ images presented in the survey may influence the results. As discussed above, a more plausible explanation it that the range of haze images shown to part icipants in the VAQ 1 to 7 rating exercise was interpreted by participants as a rest riction on acceptability ra ting exercise to confine their rating to the range VAQ conditions shown, wh ich for Test 2 was curtailed to only average to good VAQ conditions. When other evidence is taken into account, the Smith and Howell hypothesis seems an even more un likely explanation for the differe nces in results between the four urban preference studies. For example th e Denver study included photographs with the haziest conditions among the four studies, but resulted in the lowest haze condition for the 50 th percentile preference ratings among th e four, not the highest as might be expected if the range of haze levels were a significant factor influe ncing the results of preference studies. Also , inspection of the average VAQ 1 to 7 ratings fo r the Phoenix and Denver studies showed that they spanned the full ratings range of values similar to those for the Smith and Howell Test 1 and 3, so the participants in those st udies were not presented with a re stricted range within which to select acceptable VAQ conditions, su ggesting that the range itself was not an important factor

8 Variability here refers to the degree of scatter of the average acceptability ratings for each image around the logit curve for that city.2-31 influencing their results. Valu es for the British Columbia 1 to 7 VAQ rating exercise were not readily available. A fourth major hypothesis is th at urban visibility preferen ces may differ by location, and the differences may arise from inherent differences in the cityscape scene used in each city. The key evidence to suggest this hypothesis is that the apparent differen ces between the Denver results (which found the 50% accepta nce criteria occurred in the best VAQ levels among the four cities) and the Washington, DC results (which found the 50% acceptance criteria occurred at the worst VAQ levels among the four cities). This hypothesis suggests that these results may occur because the most prominent and picturesque feature of the city scape of Denver is the clearly
visible snow-covered mountains in the distance, while the prominen t and picturesque features of the Washington, DC cityscape are buildings relatively nearby wit hout prominent and/or valued
scenic features that are more distant. Finally, and perhaps of significan t importance is that the sens itivity of individual scenes to perceived changes in VAQ under ch anging light extinction levels can be quite different. As in the fourth hypothesis, this may in part explain why the Denver study scene, with its long distance to the mountain backdrop, resulted a preference fo r the best VAQ level, with a 50% criteria value of about 20 dv, while the Washington, DC study scen e, with much shorter sight paths yielded a 50% criteria VAQ value at a substantially worse level of about 30 dv. Th e distinction between the last two hypotheses are that the earlier one speaks to the de sirability of seeing distant mountains versus this hypothesis wh ich concerns the ability to perc eive changes in haze at lower light extinction levels. Additional studies, incl uding directly comparable studies using similar methods in diverse cities, would be useful to ga in further understanding of preferences for urban visibility. Based on the composite results and the effective range of 50 th percentile acceptability across the four urban preference studies shown in Figure 2-16, CP Ls have been selected in a range from 20 dv to 30 dv (74 Mm -1 to 201 Mm -1 ) for the purpose of comparing to current and projected conditions in the assessm ent in chapters 3 and 4 of this document. A midpoint of 25 dv (122 Mm -1 ) was also selected for use in the assessm ent. These three values provide a low, middle, and high set of light exti nction conditions that are used in subsequent chapters of the UFVA to provisionally define da ylight hours with urban haze c onditions that have been judged unacceptable by the participants of these preference studies. As discussed earlier (section 1.2), PM light extinction is taken to be light extinction minus the Rayl eigh scatter (i.e. light scattering by atmospheric gases is about 10 Mm -1 ), so the low, middle and high CPL levels correspond to PM light extinction le vels of about 64 Mm -1 , 112 Mm -1 and 191 Mm -1

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### 3 ESTIMATION OF RECENT PM MASS AND SPECIES CONCENTRATIONS AND PM 10 

### LIGHT EXTINCTION

This chapter characterizes hourly PM conditions in terms of both PM 2.5 mass concentration and PM 10 light extinction in a set of ur ban study areas during 2005-2007. This characterization supports the following goals: (1) to improve understa nding of the levels, patterns, and causes of PM-related impairment of urban visibility during daylight hours; (2) to create the basis for projections of PM 2.5 mass and PM 10 light extinction levels under “what if” scenarios; and (3) to exam ine the correlation between PM 10 light extinction and potential alternative indicator(s) based on PM 2.5 mass concentration. These goals are addressed in chapters 3, 4 and Appendix D, respectively. A nu mber of other appendices address related topics of particular interest in more detail.

3.1 SUMMARY OF PREVIOUS CHARACTERIZATIONS OF PM CONCENTRATIONS AND LI GHT EXTINCTION

3.1.1 PM 2.5 and PM 10-2.5

Chapter 2 of the 2005 Staff Paper (US EPA, 2005) from the previous review and chapters 3 (especially section 3.5) and 9 (especially se ction 9.2.3) and Annex A of the final ISA (US EPA, 2009a) from the current review present extensive characteriza tions of the levels, composition, and temporal and spatial patterns of PM 2.5 in U.S. urban areas. Both documents present data summaries base d on the approximately 1000 PM 2.5 monitoring sites in the U.S. The characterizations in the 2005 Staff Paper were based on 2001-2003 data. The characterizations in the ISA are based on 2005-2007 data, which is the same time period used in this visibility assessment. While there generally have been reductions in the concentrations of PM 2.5 in many
areas as a result of em ission reductions of PM 2.5 and its precursors, the general patterns, and the diversity of patterns across area s, noted in the 2005 Staff Pape r still prevaile d in the 2005-2007 period. Using 2005-2007 air quality data, 38 urba n areas violated the annual PM 2.5 NAAQS set at a level of 15μg/m 3 in 1997 and retained in the last revi ew completed in 2006. Seventy-six areas violated the 2006 24-hour NAAQS level of 35μg/m 3 . There is considerable but not complete overlap in the areas not meeting the two NAAQS. It should be not ed that in many parts of the U.S., PM 2.5 concentrations in 2005 were high relative to the next three y ears. Figure 3-1
illustrates PM 2.5 air quality in 2007 by representing each monitor by a symbol whose color
reflects the annual mean of the conc entration at that site or the 98 th percentile 24-hour concentration, in both case s in that one year.3-2 Figure 3-1. Annual Average and 24-hour (98 th Percentile 24-hour Concentrations) PM 2.5 Concentrations in μ g/m 3 , 2007.3-3 Each urban area exhibits its own detailed patterns of observed concentration levels, temporal and spatial variation, a nd composition. These differences ar e due to differences in local and transported emissions and in meteorology. Because of differences in the placement of PM 2.5 monitoring sites in each urban area, the actual levels and spatial pattern of PM 2.5 and PM 2.5 species concentrations may not be consistently discernable in a ll areas. This variability and limited monitoring network make it difficult to offer concise generaliz ations, although some
broad similarities can be drawn among areas. Midwestern, southeastern, and eastern urban ar eas have much higher sulfate levels than do more western areas, attributable to the much higher emissions of SO 2 in and upwind of them. Areas in the upper Midwest and to a lesser extent upper East have notable nitrate concentrations in winter but not in summer, while southeastern areas generally have lower nitrate concentrations even in winter. Many western urban areas have la rge nitrate concentrations year round. In all areas, carbonaceous material is an important component of PM 2.5 and is attributable to many emission sources of organic material in PM form and of organic PM precursor gases. In some areas with high local use of wood for resident ial heating carbonaceous material is dominant during the heating season. PM 2.5 derived from crustal sources is generally a small fraction of total mass, except during local high wind events or due to brief periods of intercontinental transport of dust from Africa or Asia. Comparison of PM 2.5 species concentrations within a nd outside urban areas leads to the conclusion that, in the eastern ar eas with high sulfate concentrations, the large majority of the sulfate affecting any given urban area originates outside that area. Inward transport and local generation of nitrate and carbonaceou s material are more evenly bala nced in eastern areas, with some differences among areas. In western area s, local sources dominate for carbonaceous material and nitrate, with the origins of the small sulfate com ponent being more balanced. See Figure 9-24 of the final ISA (US EPA, 2009a). Southeastern areas have their highest PM 2.5 concentrations in the summer, when
conditions are most conducive to sulfate formati on. More northern areas, being affected by a
more balanced mix of contribu tors, tend not to have such a strongly seasonal pattern. The seasonal patterns in western areas are individual and varied, re lated to differences in local sources and formation and disper sion conditions. In all areas, inversion conditions with low wind speeds are conducive to high concentrations due to the trapping of emissions from local sources. Some western areas, especially t hose with valley or bowl-like topography, are especially affected. There is at present no systematic monitoring network in place for PM 10-2.5 , as states have until January 1, 2011, to implement required monitoring sites for PM 10-2.5 . Consequently,
estimates of PM 10-2.5 must be developed using data from PM 2.5 and PM 10 monitoring sites and3-4 equipment, which are not always collocated and consistent. The 2005 Sta ff Paper presented such estimates in section 2.4.3. The final ISA presents such estimates in Figure 3-10 and Table 3-9 of section 3.5.1.1. The 2005 Staff Paper used a data-inc lusive approach in which the best available
data on PM 2.5 and PM 10 concentrations – in some cases not very robust data – were used to
estimate 2001-2003 PM 10-2.5 concentrations for 351 metropol itan area counties. For these counties, the annual mean PM 10-2.5 concentrations were genera lly estimated to be below 40

μ g/m 3 , with one maximum value as high as 64 μ g/m 3 and a median of about 10-11 μ g/m 3 . The ISA used a much more data-restrictive appro ach based only on paired (collocated) low-volume filter-based samplers for both PM 10 and PM 2.5 . The ISA reports that on ly 40 counties have such paired samplers. Using these available co-l ocated PM measurements from 2005-2007, the mean
24-hr PM 10-2.5 concentration in th ese 40 counties was 13 μ g/m 3 . This urban visibility assessment
has used a data-inclusive approach to estimating PM 10-2.5 concentrations, similar to that used for the 2005 Staff Paper, where needed to obtain hourly PM 10-2.5 estimates for the 15 selected study areas, which are reported below in section 3.3.2.
Additional detail on PM 2.5 , PM 10 , and PM 10-2.5 concentrations, composition, and patterns appears in section 3.5.1.1 of the ISA. Also, chapter 6 of the 2004 PM Assessment by NARSTO
contains more detailed characterizations of PM in different parts of the U.S.

3.1.2 PM 10 Light Extinction

While total light extinction is directly measurable using a transmissometer and PM 10 light
extinction can be measured with other instru ments, there are very few regularly operating monitoring sites measuring either form of light ex tinction in urban areas, a nd generally those that do operate do not submit data to AQS. 1 Consequently, any characterization of PM 10 light
extinction conditions based on actua l measurements is necessarily less comprehensive than for
PM 2.5 and PM 10-2.5 . Many monitoring sites that employ nephelometers, which measure light scattering, operate that equipment in a heat ed mode for purposes of tracking “dry” PM 2.5 mass concentrations, and actual light sc attering due to ambient PM is not reportable. There are many more filter-based Aethalometers® and similar instruments for measuring light absorption in operation and reporting to AQS, but light absorpti on is typically a small fraction of total PM light extinction, so these data alone are not a good indicator of overall PM 10 light extinction in

1 EPA is aware of routine, long-ter m 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 9.2.2.3.3-5 urban areas. Also, there are unres olved issues of data corrections and comparability for the light absorption data from these instruments now residing in AQS.
PM 10 light extinction can be “reconstr ucted” from measurements of PM 2.5 mass components and PM 10-2.5 concentrations, in combination wi th relative humidity values, using
either of two versions of the formula known as the IMPROVE algorithm but excluding its term for Rayleigh scattering by gases in clean air. (Section 9.2.2.2 of the ISA gives an overview of the algorithm and its basis. Section 3.2.3 of this document disc usses the application of the original version of the IMPROVE al gorithm in this assessment. PM 2.5 component measurements are generally available only on a 24-hour average ba sis, so it generally is possible to estimate only 24-hour average PM 10 light extinction, unless additional information on hourly patterns is brought to bear. 2 Because EPA’s Regional Haze Rule (RHR ) currently requires states to address visibility problems in Class I visibility protec tion areas, which are nearly all rural and remote, there is a large body of li terature characterizing PM 10 light extinction in remote rural areas, based
on data from the IMPROVE network’s 24-hour sa mplers and on special studies. Sections 9.2.3.2
and 9.2.3.4 of the ISA summarize this literatu re. Section 9.2.3.3 of the ISA contrasts
concentrations of PM 2.5 and PM 2.5 components between rural and ur ban areas using data from the
rural IMPROVE network and the urban Chemic al Speciation Network (CSN) but does not present estimates of PM 10 light extinction in urban areas. The CSN network provides 24-hour PM 2.5 species measurements at about 200 urban sites, from which mass components can be derived. These sites have a mix of daily, one day in three, and one day in six sampling schedules. The 2005 Staff Paper (and its references) may be the only readily available prior a ssessment to use these urban PM 2.5 speciation monitoring data, along with estimates of PM 10-2.5 concentrations and data on relati ve humidity, to reconstruct daily 24-hour average light extinction in urban areas, for the year 2003. 3 One presentation of the results was in the form of a scatter plot of daily 24-hour reconstructed light extinction versus 24-
hour PM 2.5 concentration. This graphic appears here as Figure 3-2. (For the immediate purpose of this section, it is the distribu tion of the data points along the y-ax is that is of interest, not the relationship between li ght extinction and PM 2.5 concentrations; the latter subject is addressed in

2 When the IMPROVE algorithm is used to estimate 24-hour PM 10 light extinction from 24-hour PM 2.5 species and PM 10-2.5 concentrations, an assumption is made that every hour has the same PM concentrations but its own relative humidity value. Hourly estimates of PM 10 light extinction, including the strongly non-linear effect of relative humidity, are then averaged to get the 24-hour PM 10 light extinction estimate.
3 Estimates of light extinction in the 2005 Staff Paper include Rayleigh scattering of 10 Mm -1 and thus represent “total” light extinction (excluding NO 2 absorption). Adjustment for consistency must be made before any close comparisons to PM 10 light extinction values in this document.3-6 Appendix D.) Generally, most days have light extinction below 200 inverse megameters (Mm -1 ), but a small percentage of valu es were as high as about 750 Mm -1

. 4

Figure 3-2. Reconstructed 24-hour Light Extinction in U.S. Urban Areas in 2003 Source: Schmidt et al., 2005

In addition to this scatter plot, a table de veloped for the previous PM NAAQS review presented the annual average of estimates of 24-hour reconstructed light extinction values, averaged across 161 urban areas grouped into se ven regions (Schmidt, et al., 2005). Table 3-1 reproduces these estimates. For regions exclud ing Southern California, annual average 24-hour

4 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.3-7 light extinction ranged from 73 to 118 Mm -1 . The estimate of the annual average 24-hour light
extinction for Southern California was 168 Mm -1 . These estimates were based on 10-year average 1-hour relative humidity va lues and 2003 PM monitoring data.

Table 3-1. Annual Mean Reconstructed 24-hour Light Extinction Estimates by Region (Mm -1 )



### 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

Source: Output D.3, Schmidt et al., 2005. We note these regions were used to summarize PM 2.5 patterns for the PM NAAQS review 1997 (US EPA, 1996b).

Figure 3-3 is a contour map of annua l average reconstructed 24-hour PM 10 light extinction based on IMPROVE monitoring sites in 2000-2004, nearly all of which are remote and rural (the three urban s ites in Phoenix, AZ, Washington, DC, and Puget Sound, WA are indicated by square symbols). A comparison of the mean urban light extinction levels by region3-8 listed in Table 3-1, with this map of rural light ex tinction indicates that in most parts of the U.S., light extinction levels in urban areas are notably higher than in the surrounding remote rural area,
with the northeast and th e southeast regions having the most similarity between rural and urban light extinction levels. This is consistent with observations of an “urban excess” of PM 2.5 and

Figure 3-3. Isopleth Map of A nnual Total Reconstructed PM 10 Light Extinction Based on 2000-2004 IMPROVE Data.

(Source: Spatial and Season al Patterns and Temporal Variability of Haze and its Co nstituents in the United States Report IV, DeBell 2006)

PM 10-2.5 and with the known high regi onal concentrations of sulfat e in these eastern areas.
One-hour PM 10 light extinction values of course vary above and below the 24-hour average, due to diur nal variations in PM 2.5 component concentrations, PM 10-2.5 concentrations, and relative humidity. Although PM 10 light extinction was formally reconstructed on an hourly basis in the 2005 Staff Paper analysis for the last review cited above, th e actual full strength of the diurnal pattern could not be discerned in th at analysis because component mix was assumed not to vary from hour to hour. Under the unveri fied assumption of constant component mix and using actual hourly relative humidity data, the daily maximum daylight 1-hour PM 10 light extinction values were roughly 50 percen t higher than the 24-hour average PM 10 light extinction3-9 values. 5 The new analysis presented in this documen t includes a closer look at diurnal patterns, for 15 study areas.

3.2 OVERVIEW OF APPROACH AND DATA SOURCES FOR URBAN STUDY ANALYSIS As explained above, there are limited da ta from direct measurements of PM 10 light extinction in urban areas. Consequently, this assessment has reconstructed hourly PM 10 light extinction levels for daylight hours from values of hourly PM 2.5 components, PM 10-2.5 , and relative humidity. Hourly monitoring data for PM 2.5 components and PM 10-2.5 are also generally lacking, so the estimates of these parameters have necessarily been developed from a combination of other available ambient monitoring data and air quality modeling results from a chemical transport model (CTM) run. Specifica lly, the ambient monitoring data starting points
are 24-hour PM 2.5 mass measured by filter-based Federal Reference Method (FRM) or Federal Equivalent Method (FEM) monitors 6 , 24-hour PM 2.5 components measured by the filter-based monitors of the CSN, and hourly PM 2.5 mass measured by continuous instruments such as the Tapered Element Oscillating Microbalance (TEO M), beta attenuation monitors (BAMs), and nephelometers, which were used at different sites. The CTM-base d diurnal profiles for individual components, in conjunction with hourly PM 2.5 measurements, are used to adjust and
allocate the 24-hour PM 2.5 components measurements to individual hours of each day, as described in detail below. In addition, levels of hourly PM 10-2.5 mass are calculated from separate measurements of hourly PM 10 and hourly PM 2.5 if both are availa ble, or by applying
PM 10-2.5 to PM 2.5 ratios to hourly PM 2.5 data if both types of h ourly measurements are not
available. The ambient data are from 2005-2007 and were all obtained from AQS in the first half
of 2009.
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. 7 The CTM modeling is used as one element in the deve lopment of realistic diurnal variations for each of the major PM 2.5 components used to estimate PM 10 light extinction, anchored to site-specific, day-specific measurements of 24-hour concentrations. That is, monthly averaged diurnal profiles for the five major components were gene rated using the CTM results, which were then

5 These observations on diurnal patterns come from examination of “Output D.3 (Relationship RE & PM 2.5 ; Diurnal RE; Timeframe) 8 of 30” and “Output D.3 (Relationship RE & PM 2.5 ; Diurnal RE; Timeframe) 17 of 30”, Analyses of Particulate Matter (PM) Data fo r the PM NAAQS Review, Schmidt et al., 2005.
6 Filter-based Federal Reference Method samplers an d filter-based Federal Equivalent Method samplers will both be referred to as FRM samplers in the remainder of this document.
7 GEOS-Chem is the NASA Goddard Earth Observing System-CHEMistry (global 3-D CTM for atmospheric composition). This modeling platform, with an appropriately different emissi ons scenario, is also the basis for the estimates of policy relevant background concentrations of PM 2.5 presented in section 3.6 of the ISA (US EPA, 2009a).3-10 combined with hour-specific measurements of PM 2.5 to generate hourly c oncentration variations for each of the 24-hour CSN sample days during the 2005-2007 period.

3.2.1 Study Period, Study Areas, Monitoring Sites, and Sources of Ambient PM Data At the time this assessment began, the am bient monitoring data from 2005-2007, but not from 2008, had been certified as accurate and complete by the st ate/local monitoring agencies that collected them, and the data had been extens ively summarized and pres ented in the first draft
ISA. The EPA staff aimed to deve lop estimates of daylight hours PM 10 light extinction for a reasonably representative number of days in ea ch year of 2005-2007, to allow the application of statistical forms based on three years of data. Ho wever, as explained in more detail below, in several study areas the limited availability of starting data for these estimates resulted in estimate sets that do not cover all three year s. Also, even in areas with so me data in all three years, the number of days with valid estimates differs by year and is in some cases not large by typical
standards of monitori ng data completeness. For efficiency in the analysis, this visibi lity assessment uses the same 15 urban study
areas selected for the health risk assessment. These areas are listed in Table 3-2, along with the area-wide (maximum) FRM-base d 2005-2007 annual and 24-hour PM 2.5 design values for each study area based on the highest-reading monitor in each area, and for the specific site used in this

assessment. 8 (See below for an explanation of the “s ite-specific” columns in Table 3-2.)

8 2005-2007 PM 2.5 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” curr ent concentrations to represent achieveme nt of alternative annual and 24-hour PM 2.5 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 th an the highest values from a site with complete data (seethe PM Risk Assessment, US EPA, 2010e, section 3.2.3). For Fresno, the area-wide design value is for the FresnoMadera 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 certa in 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 nonattain ment area; the Liberty-Clairton nonattainment area is within the Pittsburgh CBSA but is distinct for regulatory pur poses, 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.3-11 Table 3-2. Urban Visibility Assessment Study Areas

Study Area Area-wide 2005-2007

Annual Design Value (μg/m 3 ) Area-wide 2005-2007

24-hour Design Value (μg/m 3 ) Site-specific 2005-2007 Annual Design Value (μg/m 3 ) Site-specific 2005-2007

24-hour Design Value (μg/m 3 ) 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 (US EPA 2005) (based on earlier work by others) to separate the Southern California region from the Northwest region.

For time reasons and because it was anticipat ed that some study areas would not contain more than one suitable study site, EPA staff sought to identify the single best study site in each area. In identifying the single best study site in each study area first cons ideration was given to the availability of collocated 24-hour data on PM 2.5 and its components, b ecause the contribution
of PM 2.5 components to PM 10 light extinction will typically dominate the contribution from
PM 10-2.5 . Ideally, within each study area the three types of PM 2.5 data (FRM PM 2.5 , CSN PM 2.5 components, continuous PM 2.5 ) would be available at a common site, and that site would be
located in a manner consistent with reliance on it to characterize visibi lity as it would be perceived by a large number of ar ea residents and visitors. As can be seen in Table 3-2, in 10 of the 15 study areas the site provi ding FRM data for this assessmen t is not the area-wide design value site, because the area-wide design valu e site did not have collocated CSN and/or
continuous PM 2.5 data. Appendix A provides details on the site(s) id entified and used in each study area, including information on the type of monitoring equipment that provided the data and other information that may help interpret the results of the analysis. A portion of this table for a single site – Tacoma – is presented here as Table 3-3 as an example. When viewing this document3-12 electronically, the site IDs in thes e tables are active links and can be used to view the location of the site via GoogleMaps. 9 In 11 of the study areas, the three types of PM 2.5 data were available at a common site. In the remaining four areas, Phoenix, AZ, Pittsburgh, PA, Baltimore, MD, and St. Louis, MO-IL, two types of data were available at one site, but the remaining type of data had to be taken from another site and treated as being re presentative of the former site. The monitoring agencies described all but one of these sites as neighborhood or urban scale, indicating those agencies’ opinion that the s ites represent concentrations in an area at least
0.5 to 4 km across. An aerial view of the remain ing site (in Phoenix) wh ich did not have a scale characterization recorded in AQS suggests that it may be middle or neighborhood scale. As already stated, selected sites are not nece ssarily the locations of th e maximum measured annual
or 24-hour PM 2.5 levels in their urban area. Site days which were missing 1-hour PM 2.5 concentration data points for more than 25 percent of daylight hours were excluded from th e analysis, because such data gaps were judged
to result in too much uncertain ty in estimates of 1-hour PM 2.5 components, 1-hour PM 10 light extinction, and daily maximum PM 10 light extinction. Days w ith fewer missing 1-hour PM 2.5 concentration data points were retained, but no estimate of PM 10 light extinction was made for
hours without 1-hour PM 2.5 concentration data (see below fo r more explanation). Hourly PM 10-

2.5 presented more varied challenges. In f our areas (Birmingham, Detroit, Baltimore, and Philadelphia) the site that provides the continuous PM 2.5 data also hosts a continuous FEM PM 10 monitor, and hourly PM 10-2.5 could be calculated by difference for most hours. In other areas, this was not the case, and either (1) hourly instruments at two diffe rent sites were used in this subtraction (Tacoma, Los Angeles- South Coast Air Basin, Phoenix, St. Louis, Atlanta, and New York-N. New Jersey) or (2) a single regionally applicable PM 10-2.5 to PM 2.5 ratio calculated as part of the last review ba sed on 2001-2003 24-hour FRM/FEM PM 10 and PM 2.5 samples was applied to 2005-2007 hourly PM 2.5 data to estimate hourly PM 10-2.5 (Fresno, Salt Lake City, Dallas, Houston, and Pittsburgh). In the cas e of Los Angeles-South Coast Air Basin, the
continuous PM 10 and PM 2.5 sites were quite distant and separated by a range of hills, so the
estimates of PM 10-2.5 and its contribution to PM 10 light extinction are more uncertain than if the monitors were clearly within the same air mass. Comments on the second review draft of this document from those familiar with the monitoring site s in St. Louis indicate that the site selected to provide continuous PM 10 monitoring, though less than a mile from the site of the PM 2.5 data is

9 Additional meta data on each monitoring site, and access to daily and annual data listings, can be conveniently obtained using GoogleEarth and the PM 2.5 , PM 10 , and CSN monitoring network KML files that can be downloaded from http://www.epa.gov/airexplorer/monitor_kml.htm

.3-13 not representative of the urban area a nd resulted in unrealistically large PM 10-2.5 values. 10 Obviously, for the five study areas for which 1-hour PM 10-2.5 was estimated by application of ratios, PM 10-2.5 estimates can only represent broad tre nds, not hour-specific conditions at the particular site. More description of the methods used for estimating hourly PM 10-2.5 appears in
section 3.3.2.

10 Comments concerning unrealistically high PM 10-2.5 values for St. Louis are viewed as credible, but were received too late in the review process to permit reanalysis using an alternate data set or to remove St. Louis from all portions of this document. However, the text has been revised to caution readers with respect to the St. Louis results, and they will not be included in the visibility effects discussion in the final PM Policy Assessment document. Some graphics have been updated to exclude St. Louis results.3-14 Table 3-3. PM 2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Tacoma Study Area

Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 Data Source for PM 10-2.5

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 PM 2.5 DV site in the SeattleTacoma-Olympia, WA annual PM 2.5 nonattainment area Neighborhood Scale Parameters taken from this site:
 24-hour FRM PM 2.5 mass (AQS parameter 88101; one-in-three sampling schedule)
 PM 2.5 speciation (one-insix sampling schedule)
 1-hour PM 2.5 mass (AQS parameter 88502, Acceptable PM 2.5 AQI & Speciation Mass) Correlated Radiance Research M903 Nephelometry No continuous PM 10 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 PM 2.5 site Neighborhood Scale Parameters taken from this site:
 1-hour PM 10 STP mass (AQS parameter 81102)
 Sample Collection Method:INSTRUMENTAL-R&P SA246BINLET
 Sample Analysis Method: TEOMGRAVIMETRIC 7% of PM 10-2.5 values were determined using regional average PM 10-2.5 :PM 2.5 ratios from 2005 Staff Paper

Additional Explanation

 In this Table, the 1-hour concentration parameter “88502, Acceptable PM 2.5 AQI & Speciation Mass” is the same as the ISA refers to as “FRM-like” PM 2.5 mass. An entry of “88501, PM 2.5 Raw Data” indicates that the monitoring agency makes no representation as to the degree of correlation with FRM PM 2.5 mass. The latter type of continuous PM 2.5 data were used only when the former were unavailable.

 Where PM 10 was reported in STP, it was converted to LC before PM 10-2.5 was calculated.

 For convenience, continuous PM 2.5 data was obtained through the AirNow website rath er than from AQS, as an initial exploration indicated that not all the desired 1- hour data had been submitted to AQS.3-15 The sampling schedule for CSN PM 2.5 speciation monitoring was one-in-six days for Tacoma, Phoenix, Houston, Detroit, and Philadel phia, and one-in-three days for the other study areas. Not every scheduled CSN site day in 2005-2007 had data for all three types of PM 2.5 data, due to missed or invalid samples. Also, for continuous PM 2.5 , values for a small number of hours of an otherwise data-sufficient day were sometimes missing, due to equipment failure or servicing. EPA staff retained only those days in which 75 percent or more of daylight hours had
measurements of PM 2.5 (see section 3.3. for more details). If for isolated hours at a site (or site pair) with collocated measurements, PM 10-2.5 concentrations could not be estimated because of gaps in the same-hour continuous PM 10 and/or PM 2.5 data, EPA staff used the regional ratio approach described above to estimate PM 10-2.5 for those specific hours. Table 3-4 provides more detailed information on the quarterly distributi on of the successfully ma tched and sufficiently complete data available for use. As described la ter, for some parts of this assessment EPA staff substituted data for the single missing quarters of data in Phoenix and Houston, to achieve seasonal balance. For some sites, two CSN sa mplers operated on some days for data quality assessment purposes; when this was the case, the results from the two samplers were averaged. In this assessment, we have not excluded PM concentration data that may have been affected by exceptional events such as wildfi res and wind storms. Under EPA’s Exceptional Events rule, for existing NAAQS states may reque st exclusion of such data from regulatory determinations, and accordingly such data are not reflected in design values for existing NAAQS once exclusion is approved by EPA. A similar ar rangement presumably would apply to a new or revised secondary PM NAAQS. Design values for PM 10 light extinction under current conditions (Table 4-2) and percentage reductions to “just meet” alternative secondary NAAQS
based on PM 10 light extinction (Table 4-3), presente d below in chapter 4, may thus be overestimates. Overestimation is more likely for the western study sites than for the eastern study sites. However, PM 2.5 design values shown in Table 3-2, and associated estimates of the
reductions needed from 2005-2007 PM 2.5 level to just meet alte rnative secondary NAAQS based
on PM 2.5 mass (Table 4-4) do reflect the exclusion of at least some data affected by exceptional

events.3-16 Table 3-4. Number of Days per Quarter in Each Study Area

Study Area Total Number of Days 2005 2006 2007

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Tacoma 109 0 0 0 0 13 15 15 14 12 13 14 13
Fresno 324 19 24 27 27 30 29 29 27 26 28 30 28 Los Angeles 300 26 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 292 26 27 24 27 28 19 27 28 29 25 22 10 Birmingham 350 30 30 29 30 29 29 30 30 30 30 27 26
Atlanta 285 20 25 25 22 26 27 26 24 25 19 26 20
Detroit 141 12 12 10 11 12 13 11 15 11 11 12 11 Pittsburgh 281 25 23 25 21 22 25 24 26 22 22 23 23
Baltimore 186 19 17 15 11 15 16 18 18 12 12 17 16 Philadelphia 145 15 11 13 10 9 13 10 13 13 14 12 12
New York 227 22 23 13 14 23 19 18 21 19 15 19 21 Note: Only days with matched and sufficiently complete data were retained in the assessment.

3.2.2 Use of CMAQ Model Validation Runs for 2004 to Augment Ambient Data

Because systematic monitoring data on hourly PM 2.5 component concentrations are not available for most of the 15 study areas, EPA st aff extracted and applied certain information from the modeling platform for calendar year 200 4 described in section 3.7.1.2 of the ISA, in which the global-scale circula tion model GEOS-Chem was paired with the regional scale air quality model CMAQ. 11 The main use of this platform in the ISA is to estimate policy-relevant background concentrations of PM 2.5 . For the urban-focused visibility assessment described here, however, we used results from the validation r un of the platform, in which emissions for all emission source types and countries are included, to develop realistic diur nal variations of the major PM 2.5 components. EPA staff identified the one or more 36 km-by-36 km CMAQ grid cells generally corresponding to the urbanized area surroundi ng each study site, thus omitting grid cells dominated by rural land uses. 12 We then extracted from the detailed model output for these grid

11 Similar modeling was not available for 2005, 2006, or 2007.
12 Urbanized area here refers to a specific land ar ea 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.3-17 cells the day/hour-specific concentrations of sulfate, nitrate, elemental carbon, organic carbon, and “crustal/unspeciated” PM 2.5 during 2004, and then we averaged across grid cells and then across days within the month for each individual hour of the day. 13 Thus, for each species, we obtained 24 hour-of-day values for a month, for each of the 12 calendar months. We then averaged the 24 hour-of-day values in each mont hly set for each component to obtain the corresponding 24-hour average concentration for the month. We then divided each hour-of-day value by the 24-hour value, to obtain a normalized diurnal profile for the pollutant, which was taken as the initial representa tion of all days in that month for 2005, 2006, and 2007 (but further adjusted day-by-day in a later step ). In total, this resulted in 5 (components) x 12 (months) x 15 (study areas) = 900 profiles. Visual examination of a number of these showed them to be reasonably smooth and generally to show morni ng (and sometimes also late afternoon/evening) peaks which are the anticipated effect of higher vehicle traffic and lowe r mixing heights. The peaks were generally moderate, as would be expect ed in light of the averaging of predictions for multiple large grid cells, the averaging across da ys, and the generally moderate diurnal profiles for SMOKE pre-processing of emissions in the CM AQ modeling platform. (Note, however, that as described below a later step in the estima tion process reduces the smoothness in the diurnal pattern of PM components.) Sulfate, as would be expected for a regionally transported pollutant, generally had a flatter diurnal profile than for ot her components. Hourly nitrate concentrations were low when expected: during warmer mont hs and in warmer areas. Figure 3-4 shows example diurnal profiles for the five PM 2.5 components, for the Detroit study area for the months of January and August. Diurna l profiles like these were applie d to 24-hour CSN measurements of component concentrations, as explained in detail below.

13 For several of the listed components that are not direct CMAQ outputs, concentrations were estimated by post-processing to aggregate the appropriate CMAQ outputs. The “crust al/unspeciated” CMAQ output results from non-reactive dispersion of that portion of the PM 2.5 emission inputs not assigned during SMOKE processing to a more specific CMAQ species, and is consid ered in most EPA analyses to represent the same material as the “soil” component reported for IMPROVE sampling.3-18 Figure 3-4. January and August Mont hly Average Diurnal Profiles of PM 2.5 Components Derived From the 2004 CMAQ Modelin g Platform, for the Detroit Study Area.

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

ocm3-19 3.2.3 Use of Original IMPROVE Algorithm to Estimate PM 10 Light Extinction

The EPA staff used the original IMPROVE light extinction algorithm, rather than the more recent revised version, because the original version is cons idered more representative of urban situations, when emissions are still fresh rather than aged as at remote IMPROVE sites. 14 To maintain consistency with the form of the candidate protection levels (CPLs) for PM 10 light extinction identified in chapter 2, EPA staff excluded from the IMPROVE algorithm for total light extinction the term for Ra yleigh scattering by gases in clean air. The formula for PM 10 light extinction using the traditional IMPROVE algorithm but without the Rayleigh scattering term is
shown below.
b extPM = 3 x f(RH) x [Sulfate] + 3 x f (RH) x [Nitrate] + 4 x [Organic Mass] +10 x [Elemental Carbon] + 1 x {Fine Soil] + 0.6 x [Coarse Mass]
PM 10 light extinction (b extPM ) is in units of Mm -1 , the mass concentrations of the components indicated in brackets are in μ g/m 3 , and f(RH) is the unitless water growth term that depends on relative humidity. We refer to the firs t five terms in this algorithm as the five PM 2.5 components. In this algorithm, the sulfate and nitrate component s are to be expressed as fully
neutralized and as reta ined and measured in the IMPROVE sampling and laboratory methods.
Associated water is to be omitted from all brack eted terms since the water absorption effect is reflected in the f(RH) term. The organic mass co mponent is to include the mass of associated
elements in addition to carbon. As described be low, we included steps in our development of estimates of hourly component concentration to ensure consistency with these aspects of the IMPROVE algorithm.

3.3 DETAILED STEPS

3.3.1 Hourly PM 2.5 Component Concentrations

The task of estimating hourly PM 2.5 component concentrations is in a sense overdetermined, given the four types of available information: 24-hour PM 2.5 mass by filter-based FRM, 24-hour component concentrations by CSN, hourly PM 2.5 mass by continuous instrument,

14 Other differences between the original and revised al gorithms 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 NO 2 light absorption when NO 2 data are available. Their exclusion in this assessment is not expected to make any appreciable difference to th e results or conclusions.3-20 and diurnal profiles of components from the 2004 CMAQ run. There are multiple ways in which
two or three of these four data sour ces could be used to estimate hourly PM 2.5 component concentrations, and the result generally can be expect ed to be at least somewhat inconsistent with the information in the remaining data s ource(s). For example, each 24-hour PM 2.5 component mass from CSN sampling can be apportioned to hours based on the monthly average diurnal profile developed from the 2004 CMAQ run, but then in general the hourly values of PM 2.5 mass determined by summing the components in an hour would not exactly match the data from the
continuous PM 2.5 instrument. EPA staff therefore used a sequence of steps which achieves a prioritized compromise among the data sources. In this sequence, we have given greater weight to the 24-hour FRM, CSN, and continuous PM 2.5 mass data because these are instrument-based and location- and day-specific, than to the CMAQ-based profiles which are CTM-based, averaged to the month, and extrapolated from 2004 to each of 2005, 2006, and 2007. Because of differences in filter materials, sa mple collection, laboratory analysis, and data reporting, there are differences between the contribution of some PM components to PM 2.5 mass as reported by a filter-based 24-hour FRM sampler, and the mass of the same components as reported by CSN (or IMPROVE) sampling. The fo llowing summary of these differences may be helpful in understanding the steps us ed to develop estimates of hourly PM 2.5 components in this analysis. In the IMPROVE algorithm for reconstructing PM 10 light extinction, the light extinction contribution multipliers per unit of mass concentration of components are not all the same for the five principal components. Cons equently, care is requ ired to estimate these components as consistently as possible with th e IMPROVE sampling and analytical methods so that particle mass is correctly assigned to the right component.

 Nitrate: CSN (and IMPROVE) sampling uses a Ny lon filter for purposes of nitrate ion quantification, while FRM sampling uses a Teflon filter for PM 2.5 mass as a whole. The Nylon filter limits the loss of nitrate in th e form of nitric acid vapor which could otherwise occur if the filter temperature ri ses above the temperature at the time of collection, compared to the Teflon filter. The fine particle nitrate ion collected on nylon and Teflon filters are assumed to be associated with ammonium ions, and for this analysis ammonium is assumed to evaporate at the same rate as nitrate on the FRM filters 15

. Hence, the nitrate ion and calculated ammoni um nitrate concentra tions reported by CSN (and IMPROVE) sampling typically will be high er than the nitrate contribution to FRM PM 2.5 mass, particularly under warm ambien t conditions. On the other hand, FRM sampling may result in some wa ter that is associated with nitrate being included in the reported PM 2.5 mass, while the nitrate mass reported by CSN (or IMPROVE) sampling excludes all water. Continuous PM 2.5 samplers employ a variety of methods for measuring PM 2.5 mass, with correspondingly differen t behaviors regarding retention/loss

15 EPA staff recognizes that fine particle nitrate may be in the form of calcium or sodium nitrate, but like the IMPROVE program treats nitr ate as ammonium nitrate.3-21 of nitrate. In this assessment’s approach to estimating actual ambient concentrations and PM 10 light extinction, the FRM measurement of nitrate is us ed in the calculation of the concentration of organic carbonaceous material, but not in estimating ambient concentrations of nitrate or PM 10 light extinction. The CSN-reported nitrate ion concentration and corresponding ammonium nitrate mass is used for the latter purposes.

 Sulfate: Unlike nitrate, sulfate is not subject to loss once collected by a filter, so the sulfate ion mass reported by a CSN (or IMPROVE ) sampler will be about the same as the contribution of sulfate ion to the mass reported by FRM sampling. In FRM sampling, sulfate ion may not be fully neutralized. When IMPROVE data are used to estimate PM 10 light extinction, it is assumed that sulfat e ion is fully neutralized. Even more important than nitrate, FRM sampling results in water that is associated with sulfate being included in the reported PM 2.5 mass. While the water asso ciated with the measured sulfate ion is used in the calculation of the concentra tion of organic carbonaceous material, it is not used in estimating ambient concentrations of sulfate or PM 10 light extinction.

 Elemental and Organic Carbon: Only the mass of carbon atoms is included in the reported elemental carbon and organic car bon for a CSN (or IMPROVE) sampler. In addition, the assignment of carbon atoms betw een the reported elemental and organic amounts is dependent on the specifics of the two different thermo-optical analytical methods used in the CSN vs. the IMPROVE network. 16 Also, the quartz filter used to quantify carbonaceous material in CSN and IMPROVE sampling both absorbs and loses organic vapors during sampling, while the Teflon filter in a FRM sampler does not absorb organic vapors (although PM on the filter may do so). Therefore, some method other than direct measurement must be used to estimate the total mass concentration of organic carbonaceous material in ambient air. The IMPROVE program adjusts for absorption of vapors by subtracting a monthly average backup filter value, and then applies a standard adjustment factor (1.4 in the original IMPR OVE method) to the remaining organic carbon measurement to es timate organic carbonaceous material. In contrast, the standard reports from CSN sampling submitted to AQS do not include these two adjustments, but it is routine for EPA staff to apply adjustments for the same purpose, after reporting of CSN data to AQS. The latter are based on network-wide filter field blanks and are judged as very approximate. For this assessment, the SANDWICH approach to such adjustments (Frank, 2006) is used to estimate the organic mass through a material balance of components m easured on the CSN and FRM samplers.

 Hourly PM 2.5 : The continuous instruments used for measuring hourly PM 2.5 mass were different among sites (as listed in Appendix A) . None of the instrument types that provided hourly data for this assessment, wh en averaged over 24 hours, exactly matches either the measurement of PM 2.5 mass from a FRM sampler or the sum-of-components reportable from CSN sampling. Differences can arise because of differences in water capture and retention, inconsistent absorption and loss of organic va pors and nitric acid

16 While CSN carbon sampling and analysis method s 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.3-22 vapor, etc. Furthermore, comparability between hourly and 24-hour integrated measurements can only be made on a daily av erage basis. Consequently, the continuous instruments providing data to this assessm ent can be assumed to have a range of correlation performance versus the FRM. In light of these consistency issues, the hourly data from the continuous instruments were ta ken to be most indicative of the relative concentrations of PM 2.5 from hour-to-hour, with less relian ce on the absolute accuracy of the continuous instruments. 17

Taking into consideration the above informa tion, EPA staff combined the four types of
available PM 2.5 data in each study area using the followi ng steps. Figure 3-5 provides a flow chart to assist in understanding these steps.

1. The SANDWICH method (Frank, 2006) was used to subdivide the 24-hour PM 2.5 mass reported by the FRM for each day and site into sulfate (including associated ammonium and residual water during filter equilibration and weighing), nitrate (including associated ammonium, but not necessarily enough to fully neutralize th e sulfate ion, and residual water during filter weighing), elemental ca rbon, organic carbonaceous mass, and fine soil/crustal mass. This is done using information from the CSN measurements, physical models, and day-specific temperatures. Th e primary purpose of this SANDWICH step is to estimate organic carbonaceous mass. Significantly, in the SANDWICH method, the component referred to as organic carbonaceous mass is actually a residual whose value is determined as the difference between the PM 2.5 mass determined from weighing the FRM filter and the sum of the estimated masses of the other four mass components as listed above. Therefore, it is not necessary to adju st for organic carbon sampling artifacts or to apply the 1.4 factor commonly used to esti mate organic carbonaceous material from IMPROVE measurements of organic car bon. The SANDWICH procedure did not consider sea salt in the material balance, since this is generally a very small mass constituent for the urban areas considered in this analysis. For the same reason, sea salt was also not considered in the ae rosol based light extinction algorithm. 18

17 In 2006, EPA developed and promulgated criteria for approval of continuous PM 2.5 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.
18 After completion of the second draft UFVA, an error in the ex ecution of the SANDWICH method was discovered. The error had the effect of reducing the estimate of nitrate on the FRM filter for some sample days, generally by no more than 3 or 4 μg/m 3 , and increasing the estimate of organic carbon mass on the FRM filter by the same amount. The effect on estimates for PM 10 light extinction was that light extinction had been overestimated for some hours in some cities. The error has been corrected in th is final version. The effect of the correction is in most cases very small and visually imperceptible in graphics such as box plots.3-23 Figure 3-5. Sequence of Steps Used to Estimate Hourly PM 2.5 Components and PM 10 Light Extinction

2. The CMAQ-derived monthly diurnal profiles fo r the sulfate, nitrat e, elemental carbon, organic carbon and fine soil/cr ustal components, like the exam ples for Detroit in Figure 3-4, were multiplied by the day-specific S ANDWICH-based estimates of the 24-hour average concentrations of these five PM 2.5 components, to get day-specific hourly estimates of these five components (including ammonium and water associated with sulfate and nitrate ion).

3. The hourly concentrations of these five components (including ammonium and water associated with sulfate and nitrate ion when th e filter is weighed) we re added together, to get a sum-of-components estimate of hourly PM 2.5 mass for the day of the FRM sampling.3-24 4. The hourly data from the continuous PM 2.5 instrument on the day of the FRM sampling were normalized by their 24-hour average, to get a diurnal profile. (Recall that days were not used in this assessment if hourly PM 2.5 mass data were missing for more than 25 percent of daylight hours.) This pr ofile was applied to the 24-hour PM 2.5 mass reported by the FRM sampler, to get a preliminar y, FRM-consistent estimate of hourly PM 2.5 mass for the day of the FRM sampling. This is straightforward when all 24 values of 1-hour PM 2.5 mass were available for the day. Howeve r, for some (but not many) days, some values for continuously measured hourly PM 2.5 mass were missing. In such cases, EPA staff used only the hours with valid 1-hour PM 2.5 mass values to develop the diurnal profile and then applied the profile to the FR M value as just descri bed. This keeps the average of the valid 1-hour PM 2.5 values equal to the 24- hour value from the FRM sampler.

5. The two estimates of hourly PM 2.5 mass from steps 3 and 4 were compared, hour-byhour. By virtue of the way they were derive d, the averages of these estimates across all 24 hours of the day will necessari ly be the same (and will be equal to the 24-hour FRM measurement). However, while the diurnal pattern of these two estimates of the same physical parameter should also be generally similar, it can be expected (and it is observed) that the hourly measur ements from the continuous PM 2.5 instruments (after adjustment to be consistent with the FR M data) have more hour-to-hour variability. Figure 3-6 gives an example of this compar ison, for one day for the Detroit study area.3-25 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 24Concentration, 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 PM 2.5 mass on March 24, 2006 as estimated by applying CMAQ-based diurnal pr ofiles to SANDWICH estimates of 24-hour component concentrations versus applying a diurnal profile derive d from continuous PM 2.5 measurements to FRM PM 2.5 mass.3-26 6. Given that the continuous instrument is reac ting to hour-specific local conditions that can vary from hour-to-hour due to real variations in local emi ssions and dispersion/transport conditions, while the CMAQ-based estimates c ontain much less specific information, the diurnal pattern of PM 2.5 mass observed by the continuous instrument (adjusted to be consistent with the FRM value for 24-hour average PM 2.5 ) was taken as more reliable. Within each hour, the estimates of all five co mponents from step 2 were increased or decreased by a common percentage (referred to below as A i where the subscript i indicates the hour) so that the sum of the five components after this adjustment was equal to the estimate of the hourly PM 2.5 mass from step 4. The adju stment percentage varied from hour-to-hour. Necessarily, in some hour s the adjustment is an increase in the concentrations of all compone nts, and in other hours it is a decrease. While this adjustment preserves the consistenc y between the 24 values of hourly PM 2.5 mass and the 24-hour FRM mass, it can disturb the consiste ncy between the daily average of hourly estimates of PM 2.5 components and the SANDWICH-bas ed estimates of 24-hour average component concentrations. This disturbance was generally small, because the adjustments necessarily go in one direction for some hours and the other direction for other hours. For example, for the particular day in Detroit used for illustration purposes in Figure 6, the effect of this step wa s to cause a discrepancy of 3 percent between the SANDWICH-based values of 24-hour sulfate co ncentration and the average of the 24 estimates of 1-hour sulfate concentrations (the positive percent indicates a higher concentration in the result of this step than the SANDWICH-based value). The discrepancies were 1, 1, 2, and 2 percent for nitrate, elemental ca rbon, organic carbon, and fine soil/crustal, respectively.

7. Each hourly estimate of sulfate concentration from step 6 (which includes estimates of associated ammonium and particle bound wate r) was adjusted so that it excludes water and reflects full neutralization and therefore is consistent wi th the reporting practices ofthe IMPROVE program and the IMPROVE algorithm. This was done via these substeps:

a. The 24-hour CSN value for the dry mass of sulfate ion (not SANDWICHed, no ammonium or water) was multiplied by 1.375 to reflect an assumption of full neutralization of dry sulfate mass. 19

b. The ratio of this fully neutralized 24 -hour sulfate mass to the SANDWICH-based 24-hour sulfate value was calculated.

c. This ratio was applied to each individual hour’s sulfate concentration from step 6.
As in Step 6, it is possible for the 24 fi nal hourly sulfate estim ates to no longer be exactly consistent with the 24-hour CSN su lfate measurement, both reported as fully neutralized sulfate ion.

19 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.3-27 8. A similar adjustment as in step 7 (for sulfate) was made to each hour’s nitrate concentration from step 6, so that the estim ate of hourly nitrate would reflect actual atmospheric conditions and be consistent wi th the IMPROVE algorithm. However, the ratio approach used in step 7(b) for sulfat e could not be applied for nitrate, so this adjustment had to be more complicated. B ecause in warm weather the FRM Teflon filter does not retain nitrate, the in itial FRM-consistent nitrate es timate derived by applying the SANDWICH method to the FRM and CSN data can be zero. Such a zero value makes it impossible to use the ratio approach in 7( b). Instead, the adjustment was made as follows:

a. The 24-hour CSN value for nitrate ion (not SANDWICHed, no ammonium or water) was multiplied by 1.29 to reflect an assumption of full neutralization by ammonia.

b. This 24-hour value was then diurnalized using the CMAQ-based profile, similar to step 2.

c. Each resulting hourly value of nitrat e was further multiplied by the A i factor from step 6.

d. This new estimate of hourly nitrate was used to replace the in itial nitrate value that had resulted from step 6.
For cooler areas and days in which the 24- hour SANDWICH results include some nitrate, the effect of these steps for nitrate are exactly the same as the effects of step 7 for sulfate (except for the 1.29 vs. 1.375 neutralization factor ). For warmer areas and days in which the 24-hour SANDWICH results did not incl ude any nitrate even though nitrate was measured on the CSN Nylon filter, the effect of these steps is to assign the CSN nitrate to each hour using a combination of the information in the CMAQ-based profiles and the information provided by the continuous PM 2.5 sampler. As in Step 6, it is possible for the 24 final hourly nitrate estimates to no longer be exactly consistent with the 24-hour CSN nitrate measurement.
The net effect of these steps is believed by EPA staff to result in hourly PM 10 light extinction estimates with the following features with respect to some of the complicating aspects of PM sampling:

 The 24-hour average of the hourly nitrate c oncentrations used to estimate hourly PM 10 light extinction agrees clos ely but not exactly with the 24-hour value provided by the CSN sampling, and generally is higher than the contribution of nitrate to the FRM measure of PM 2.5 mass. In some mid-day hours in so me areas, estimated hourly nitrate is zero which is a more realistic approach than applying a 24-hour species mix to each hour.3-28  The 24-hour average of the hourly organic carb onaceous material concentrations used to estimate hourly PM 10 light extinction achieves FRM ma ss balance closure, taking into account also the difference in nitrate and the possibly partial neutraliz ation of sulfate ion on the FRM filter. Because the Teflon filter used in FRM sampling is less subject to positive artifacts for organic material, this approach sidesteps an area of uncertainty in the IMPROVE sampling method. By relying on ma ss closure as the driving principle for estimating organic material, it is not necessa ry to choose a multiplier to relate organic carbon to organic carbonaceous material. 20

 The 24-hour average of the hourly elemental carbon concentrations used to estimate hourly PM 10 light extinction agrees closely but not exactly with the 24-hour value provided by the CSN sampling, and with the contribution of elem ental carbon to the FRM measure of PM 2.5 mass. Elemental carbon is generally defined by the thermal optical transmission method used in CSN, ra ther than the thermal optical reflectance method used by the IMPROVE network.

3.3.2 Hourly PM 10-2.5 Concentrations

Three different paths were used to estimate hourly PM 10-2.5 concentrations depending on data availability, in the fo llowing order of preference:

1. When hourly data from a collocated PM 10 instruments were available at the continuous PM 2.5 site in a study area, PM 2.5 was subtracted hour-by-hour from PM 10 . Negative values were reset to zero. This was the approach most often used in Birmingham, Detroit, Baltimore, and Philadelphia. This method should result in reliable estimates of actual PM 10-2.5 at the study site. (H ow well the study site represents the study area generally, or the most visibil ity-impacted portions of the stud y area, is a separate issue.)

2. When collocated continuous PM 10 data were not available at the continuous PM 2.5 site in a study area, but continuous PM 10 data were available at anothe r site in or near the same study area, PM 10-2.5 was estimated by subtraction, implicitly assuming that the latter site was also representative of PM 10 at the former site. This was the approach most often used in Los Angeles, Phoenix, St. Louis, Atla nta, and New York. As a result, estimates of PM 10-2.5 for these areas could be affected by site-t o-site differences. In particular, the two sites in Los Angeles were a good distance apart, and the PM 10 site in Victorville may represent influences from agricultural operations rather than typical urban influences. In St. Louis, the PM 10 site may also have been influenced by particular local sources. In both cases, very high estimates of hourly PM 10-2.5 may not represent reality at the PM 2.5 site, although they may be reasonable estimates for the PM 10 site.

20 In other work, EPA staff has observed that when applied to urban sampling data together with CSNnetwork-wide field blanks applied to reported OC measured concentrations, the multipliers that can be backcalculated 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.3-29 3. If neither of the first two methods was possible, a regional average ratio of PM 10-2.5 to PM 2.5 determined from an analysis of 24-hour data for the 2005 Staff Paper was applied to hourly PM 2.5 from the continuous instrument associ ated with the study area. This was the approach used for all hours in Tacoma, Fresno, Salt Lake Cit y, Dallas, Houston, and Pittsburgh. With this approach, it is not po ssible for there to be any particularly high estimates of hourly PM 10-2.5
.

The estimation of PM 10-2.5 was further complicated because some types of data were missing for isolated hours in the 2005-2007 period. As result, even for a single study area more than one method sometimes had to be used to estimate hourly PM 10-2.5 . Appendix A gives more specifics about the estimation of hourly PM 10-2.5 in each study area. The three-path approach described here is sim ilar to that used for the visibility analysis reported in the 2005 Staff Paper. While the second and third path s involve the use of data and assumptions that are not robust compared to the use of paired, collocated, same-method continuous instruments or compared to the use of paired low-volume filter-based samplers, in most areas and periods the contribution to PM 10 light extinction from the resulting PM 10-2.5 concentrations was not large compared to the PM 10 light extinction contribution from PM 2.5 components.

3.3.3 Hourly Relative Humidity Data

Hourly relative humidity (RH) data for each study area’s primary monitoring site were obtained hour-by-hour from the closest availabl e non-missing relative humidity measurement, as reported by either an air monito ring station reporting such data to AQS or a National Weather Service (NWS) station. For the AQS RH data, pa rameter 62201 values were utilized. RH data from both sources are expr essed as percentages.

3.3.4 Calculation of Daylight 1-Hour PM 10 Light Extinction

Because the interest in this analysis is on visibility dur ing daylight hours, EPA staff applied a scheme to denote those hours that would be considered daylight hours. For simplicity, all the days within each “season” in all study areas were consider ed to have the same daylight
hours. 21 Table 3-5 shows the dividing times used to denote daylight hours for the study areas. Unless otherwise stated, all subse quent discussion of the results refers only to the values of parameters during these daylight hours. The original IMPROVE algorithm was a pplied hour-by-hour to estimate PM 10 light extinction in each study area for each daylight h our. When doing so, we capped the value of the

21 This simple approach does not account for the effect s 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, concl uding that it is unlikely to affect later answers to policy relevant questions.3-30 humidity adjustment factor in the IMPROVE algor ithm (“f(RH)”) at the value of 7.4 that it has for a relative humidity of 95 per cent. The effect of measuremen t errors in relative humidity at values above 95 percent on the value of f(RH) and thus on reconstructed PM 10 light extinction is considerable because of the highly nonlinear form of the function in that range. This creates uncertainty as to the representativeness of the extinction values calculate d with high values of relative humidity. 22

.

Table 3-5. Assumed Daylight Hours by Season (Local Standard Time)

NovemberJanuary 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

3.3.5 Exclusion of Hours with Relative Humidity Greater than 90 Percent from PM 10 Light Extinction NAAQS Sc enarios and Most Results

As advised by CASAC as part of its comments on the first pu blic review draft of this assessment, EPA staff considered whether to structure the PM 10 light extinction NAAQS scenarios so that ambient data obtained during daylight hours in which relative humidity was greater than 90 percent w ould play no role in the form of th e NAAQS, i.e., so that those data
would not enter into the calcu lation of the design value. EPA staff obtained hourly meteorological parameters from NWS monitoring sites near the 15 study sites (usually a major airport), for 2005 through 2007, for all days in this period including days for which PM observations to support estimates of PM 10 light extinction are not available. Using these data, EPA staff compared the occurren ce of liquid precipitation, hail, other frozen precipitation, fog, mist, and smoke/haze during daylight hours with humi dity greater than 90 percent and during all other daylight hours. 23 The first five of these conditions ar e generally considered natural causes

22 The IMPROVE program also caps the value of f(RH) at its value for a relative humidity of 95% when reporting visibility in deciviews.
23 The “smoke/haze” category is not an original NWS re porting category. It is a combination of two original NWS weather categories: smoke and haze. The explanation of these categories in the NWS documentation does not allow EPA staff to be confident that these terms ha ve distinct and clear meanings that are uniformly applied across observation sites, so they have been combined in this presentation. As best EPA staff can determine, the combined category reflects some mix of smoke from burning biomass, smoke from industrial processes, dust from3-31 of reduced visibility. Table 3-6 presents this comparison. 24 The percentages of hours with each of these five conditions individually are shown for the two sets of daylight hours.

wind storms, volcanic ash, an d general urban haze. Also, the reported co nditions may be at some distance from the observation site.
24 Compared to the version of this table in the second external review draft, th is version correctly omits non-daylight hours which were inadvertently included in the earlier version, reports results for four study areas for which results were missing in the earlier version, and separa tes “mist” from “smoke/haze” to reflect that “mist” is a natural condition while “smoke/haze” is not always a natural condition.3-32Table 3-6.Comparison of Meteorological Parameters for Daylight Hours with Relative Humidity Greater than 90 Percent andOther Daylight Hours, During 2005 -2007Study AreaDaylight Hours with Relative Humidity <= 90%Daylight Hours with Relative Humidity > 90%NumberofHoursPercentage of Hours with Weather or OtherConditionNumberofHoursPercentage of Hours with Weather or Other ConditionLiquidPrecip.HailOtherFrozenPrecip.FogMistSmoke/HazeAnyLiquidPrecip.HailOtherFrozenPrecip.FogMistSmoke/HazeAnyTacoma10,326 12% 0% 0% 0% 0% 4% 14% 1,756 36% 0% 1% 10% 0% 43% 63%Fresno11,758 3% 0% 0% 1% 0% 15% 17% 342 25% 0% 1% 60% 0% 65% 93%LosAngeles11,419 2% 0% 0% 0% 0% 8% 9% 713 25% 0% 0% 12% 0% 52% 73%Phoenix12,123 1% 0% 0% 0% 0% 0% 1% 43 67% 0% 0% 30% 0% 40% 74%Salt LakeCity11,810 4% 0% 2% 1% 0% 4% 8% 304 28% 0% 40% 42% 0% 69% 85%Dallas11,827 4% 0% 0% 1% 0% 5% 8% 223 68% 0% 2% 20% 0% 82% 91%Houston11,525 6% 0% 0% 1% 0% 6% 9% 645 42% 0% 0% 25% 0% 64% 75%St. Louis11,590 5% 0% 1% 1% 0% 10% 14% 583 56% 0% 8% 48% 0% 82% 91%Birmingham11,590 5% 0% 0% 1% 0% 9% 11% 486 56% 0% 0% 41% 0% 79% 86%Atlanta11,337 5% 0% 0% 1% 0% 10% 13% 867 50% 0% 0% 45% 0% 81% 88%Detroit11,484 5% 0% 3% 1% 0% 9% 14% 676 51% 0% 16% 39% 0% 76% 92%Pittsburgh10,603 5% 0% 3% 1% 0% 9% 14% 1,261 46% 0% 9% 12% 0% 72% 85%Baltimore11,321 4% 0% 1% 2% 0% 12% 14% 858 53% 0% 5% 38% 0% 80% 90%Philadelphia11,125 4% 0% 1% 1% 0% 8% 11% 878 47% 0% 3% 33% 0% 64% 84%New York11,799 7% 0% 1% 1% 0% 10% 14% 397 66% 0% 8% 48% 0% 86% 96%Average11,442 5% 0% 1% 1% 0% 8% 11% 669 48% 0% 6% 34% 0%69% 84%3-33 NWS observations of these conditions are in stantaneous, and are generally made about
50 minutes after the hour. The relative humidity observations are made at the same time. It should be noted that this analys is of the co-occurrence of high relative humidity and these five
conditions uses data from NWS sites other than the AQS sites that provided the data used to
estimate PM 10 light extinction. AQS sites could not be used for this analysis because they generally do not report sim ilar weather condition data. The comparison for the 15 sites shows that in the set of hours with relative humidity above 90 percent, the frequencies of liquid preci pitation (rain), other fr ozen precip itation (snow and sleet), or fog ranged as high as 68 percen t, and were considerably higher for the same
condition than in the set of hours with lower relative humidity. The frequencies of hail and mist were all less than 0.5 percent and thus too low fo r meaningful comparisons. Moreover, except in Tacoma, the frequency of rain or fog at the observation moments duri ng the hours w ith relative humidity less than or equal to 90 percent was less than 8 percent. Also, a separate analysis (not shown) indicated that rainy hours with lower relative humidity experience considerably less accumulation than rainy hours with higher relative humidity. Based on this assessment, the 90% relative humidity cutoff criteria is effective in th at on average less than 6 percent of the daylight
hours are removed from consid eration, yet those hours have on average about 10 times the likelihood of rain, 6 times the likelihood of snow/sleet, and 34 times the likelihood of fog compared to hours with 90% or less relative humidity. Rain, snow/sleet, and fog cause a natural reduction in visibility, independent of PM
concentrations. To reduce th e likelihood that a design value for a secondary PM NAAQS could be affected by measurements made under natural weather conditions that reduce visibility, for
this assessment EPA staff eliminated from th e design value definition any contribution from
PM 10 light extinction values that come from any daylight hours with relative humidity above 90 percent. 25 Also, because PM 10 light extinction during such hour s is not as likely to be the primary cause of adverse effects on the public, all figures and tables in the body of this document and in Appendices that present PM 10 light extinction values or stat istics exclude values for such hours (unless explicitly stated to incl ude them), so that the patterns of PM 10 light extinction during the remaining daylight hours can be seen clearly. Figures and tables that present PM component concentrations and relative humidity va lues are based on all daylight hours, however. More information on this topic can be found in Appendix G, which reports by study area the percentages of daylight hours that were excluded from design values, the distribution of the excluded hours by time of day, and the percentage of days that had one or more daylight hours

25 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.3-34 eliminated. Appendix G also cont ains box plots which contrast th e distributions of daylight 1-
hour PM 10 light extinction values (and maximum daily daylight 1-hour PM 10 light extinction, see section 3.3.6) before and af ter this elimination step. The til e plots in Figure 3-12 also present additional detailed information on the specific ho urs that had relative humidity values above 90 percent, and on the PM 10 light extinction values during those and other daylight hours.

3.3.6 Calculation of Daily Maximum 1-Hour PM 10 Light Extinction

Daily maximum 1-hour PM 10 light extinction is a statistic of interest in this assessment, as briefly discussed in section 1.4.3. The daylight hour with the maximum value of PM 10 light extinction and the corresponding PM 10 light extinction value were identified for each day for each study area. As mentioned in sec tion 3.2.1, days which were missing 1-hour PM 2.5 values for more than 25 percent of daylight hours were not used in this analysis. No further completeness requirement for 1-hour data during a day was applied when se lecting the daylight
hour with the maximum value of PM 10 light extinction.

3.4 SUMMARY OF RESULTS FOR CURRENT CONDITIONS

3.4.1 Levels of Estimated PM 2.5 , PM 2.5 Components, PM 10-2.5 , and Relative Humidity

Figure 3-7 presents box-and-whisker plots to il lustrate the distributions in each study area
of the estimates of 1-hour PM 2.5 (the diurnalized FRM value, resulting from step 4 in section 3.4.1), PM 10-2.5 , and relative humidity over the entire 2005-2007 study period. In the plot for each parameter, areas are ordered by longitude, to make it easier to see East-versus-West regional differences. For these three parameters, th e distributions are given for all the daylight 1- hour estimates, including hours with relative humid ity greater than 90 percent. Similar plots of the daily maximum daylight 1-hour values of PM 2.5 and PM 10-2.5 concentrations and relative humidity are available in Appendix B, as are plots of all daylight 1-hour values for each of the
PM 2.5 component species. 26 From these plots we see th at the distributions of PM 2.5 generally trend toward higher concentrations from West to East except for the two California urban locations which have PM 2.5 concentrations more typical of eastern areas. The lowest median PM 2.5 concentrations are in Tacoma, WA, and Phoenix, AZ. Median PM 10-2.5 concentrations are highest in St. Louis, MO, and Phoenix, AZ, and lower elsewhere. The highest outlier PM 10-2.5 concentrations are in St. Louis, MO, and Los Angeles, CA. Relative humi dity is lowest for the western urban areas except for Tacoma, WA, which is similar to the northeastern urban locati ons with respect to

26 In all box-and-whisker plots in this document, the box represents the 25 th to 75 th percentile range and the whiskers represent the 10 th and 90 th percentile points of the data; individual data points below the 10 th percentile and above the 90 th 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 as an integer).3-35 Figure 3-7. Distribution of PM Parameters and Relative Humidity Across the 2005-2007 Period, by Study Area

(a) Estimates of 1-Hour PM 2.5 Mass, Based on Applying Continuous Instrument-based Diurnal Profiles to 24-hour FRM PM 2.5 Mass3-36 Figure 3-7. Distribution of PM Parameters and Relative Humidity Across the 2005-2007 Period, by Study Area, continued

(b) Estimates of 1-Hour PM 10-2.53-37 Figure 3-7. Distribution of PM Paramet ers and Relative Humidity Across the 2005-2007 Period, by Study Area, continued

(c) 1-Hour Relative Humidity

humidity. These hourly daylight PM concentra tion and relative humidity box and whisker plots are consistent with our expectations based on regional 24-hour PM concentration values and humidity climatology

3.4.2 Levels of Estimated PM 10 Light Extinction

Figure 3-8 presents box-and-whiske r plots to illustra te the distributions of the estimates of daylight 1-hour reconstructed PM 10 light extinction levels in each area in each year (excluding hours with relative humidity greater than 90 percen t). The distribution of (a) the daily maximum 1-hour values and (b) the indivi dual 1-hour values are both show n. The horizontal dashed lines in the plots represent the low, middle, and high CPLs for PM 10 light extinction as discussed in
section 2.6. These benchmarks for PM 10 light extinction are 64, 112, and 191 Mm -1

, corresponding to the benchmark VAQ values of 20 dv, 25 dv and 30 dv. Table 3-7 provides (a) the percentages of days ( across all of 2005-2007, unweighted) in which the daily maximum3-38 daylight 1-hour PM 10 light extinction level was greater than each of the three CPLs (excluding hours with relative humidity greater than 90 percen t), and (b) the similar percentage based on all daylight hours (with the same exclusion). As was also seen in the comparable PM 2.5 concentration box and whisker plots in Figure 3-7, the high percentile hourly PM 10 light extinction values in Figur e 3-8 tend to be higher in the
eastern urban areas and lower in the non-California western urban areas. The distributions of maximum daily PM 10 light extinction values are higher (Fig ure 3-8a), as expected, than for all hours (Figure 3-8b). Both Figure 3-8 and Table 3- 7 indicate that all 15 ur ban areas have daily maximum hourly PM 10 light extinctions that exceed even the highest of the CPLs some of the time. Again, the non-California we stern urban locations have the lowest frequency of maximum hourly PM 10 light extinction with values in excess of the high CPL fo r 8 percent or fewer of the days. Except for the two Texas and the non-California western urban area s, all of the other urban areas exceed that high CPL from about 20 pe rcent to over 60 percent of the days. Based on these estimated maximum hourly PM 10 light extinction estimates, all 15 of the urban areas exceed the low CPL for about 40 percent to over 90 percent of the days. As noted in section 3.2.1, in 10 of the 15 study areas the study site used in this assessmen t is not the site in the study area with the highest concentrations of PM 2.5 . Thus, these estimates may not characterize visibility in the worst-visibil ity portion of each study area. In the last review of the secondary PM NAA QS, the pattern of light extinction during the day was of particular interest. To illustrate the distributions of 1-hour PM 10 light extinction levels in specific daylight hours, Figure 3-9 shows the distributions of 1-hour PM 10 light extinction across the entire three-year study peri od, individually for th e study areas (excluding hours with relative humidity greater than 90 perc ent). (Appendix E provides additional graphics related to temporal/spa tial patterns of light extinction.) These plots show that high PM 10 light extinction can occur during any of the daylight h ours, though for most of these urban areas the morning hours have somewhat higher PM 10 light extinction th an in the afternoon. 27 Urban areas without a pronounced preference for morning high PM 10 light extinction include Phoenix, AZ; Salt Lake City, UT; Tacoma, WA; Fresno, CA; and Philadelphia, PA.

27 If hours with relative humidity greater than 90 percent were not eliminated, the tendency for higher PM 10 light extinction in the morning hours would be stronger.3-39 Figure 3-8. Distributions of Estimated Daylight 1-Hour PM 10 Light Extinction and Maximum Daily Daylight 1-Hour PM 10 Light Extinction (in Mm -1 units) Across the 2005-2007 Period, by Study Area (Excluding Hours with Relative Humidity Greater Than 90 Percent).

(a) Maximum Daily Values3-40 Figure 3-8. Distributions of Estimated Daylight 1-Hour PM 10 Light Extinction and Maximum Daily Daylight 1-Hour PM 10 Light Extinction (in Mm -1 units) Across the 2005-2007 Period, by Study Area (Excluding Hours with Relative Humidity Greater Than 90 Percent), continued.

(b) Individual 1-Hour Values3-41 Table 3-7. Percentage of Daily Maximum Hourly Values and Individual Hourly Values of Daylight PM 10 Light Extinction Exceeding CPLs (Excluding Hours with Relative Humidity 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 76 52 29 Los Angeles 300 90 83 61

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 64 34
Atlanta 279 91 75 31
Detroit 141 87 68 43 Pittsburgh 277 85 57 26
Baltimore 181 80 50 23 Philadelphia 143 86 63 31

New York 225 83 59 28

Average 229 77 52 25 Number of Daylight Hours with Estimates (b) Percentage of Indivi dual Daylight Hours Exceeding CPL Tacoma 1087 14 4 1

Fresno 3533 41 20 10 Los Angeles 3048 68 42 19

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 2398 45 21 73-42Figure 3-9. Distributions of 1-Hour PM10Light Extinction Levels by DaylightHour Across the 2005-2007 Period, by StudyArea (Excluding Hours with Relative Humidity Greater Than 90 Percent).3-43 3.4.3 Patterns of Relative Humidity and Rela tionship between Relative Humidity and PM 10 Light Extinction Figure 3-9 shows the distribution of relative humidity values at each daylight hour, for each study area across 2005-2007 (excluding hours with relative humidity greater than 90 percent). 28 As expected, in every area relative humidity is lowest in the early afterno on, typically the warmest part of the day. Relative humid ity is most similar across areas in the early afternoon, as observed in the 2005 Staff Paper. Ho wever, even in this period there are notable differences among areas. This vari ation was not as evident in th e information presented in the 2005 Staff Paper because only regionally averaged information was presented. In all areas, there is considerable variation in hour-specific rela tive humidity during th e three-year period. To allow closer inspection of the relationship between PM 10 light extinction values and relative humidity values, Figure 3-10 is a scatte r plot of actual 1-hour relative humidity and 1-
hour reconstructed PM 10 light extinction (excluding hours with relative humidity greater than 90 percent). Horizontal lines are included in each of the individual plots corresponding to the three
benchmarks for PM 10 light extinction and a vertical line in each for the 90 percent relative humidity cutoff. There are many instances with PM 10 light extinction grea ter than the CPLs when relative humidity is 90 percent or lower. No tice that in Figure 3-10 th ere also are plenty of high humidity conditions for each ur ban area that correspond to low PM 10 light extinction values. This is because humid air does not by itself contri bute to light extinction. Particles composed of material that absorbs water in high relative humidity conditions (e.g., sulfate and nitrate PM) swell to larger solution droplets that scatter more light than their smaller dry particle counterparts in a less humid environment. The magnitude of the relative humidity effect on light extinction depends directly on the concentration of th ese hygroscopic PM components. (Figure 3-10 reveals skips in reported relative humidity values for some but not all the study areas. This is a result of calculations of relative humidity from dry and wet bulb temperatures reported to the nearest whole Celsius degree.)

28 Similar information on diurnal patterns but broken out by season is given in Appendix E.3-44Figure 3-10. Distributions of 1-Hour Relative Humidity Levels by Daylight Hour(X-axis) Across the 2005-2007 Period, byStudy Area (Excluding Hours with Relative Humidity Greater Than 90 Percent).3-45Figure 3-11. Scatter Plot of Daylight 1-Hour Relative Humidity (Percent) vs. Reconstructed PM10Light Extinction (Mm-1)Across the 2005-2007 Period, by Study Area (Excluding Hours with Relative Humidity Greater Than 90 Percent).3-46 3.4.4 Tile Plots of Hourly PM 10 Light Extinction

Figure 3-12 consists of “tile plots” that show the estimated levels of 1-hour PM 10 light extinction for each daylight hour for each study ar ea. These plots assist in understanding the times of the year and hours of the day in which high relative humidity and high PM 10 light extinction occur, both separately and together. Time runs horizontally with each row of tiles representing a single day from midnight (left site) to midnight (right side), and vertically from January (top) to December (bottom). Each tile represents one hour of the y ear for which data to estimate PM 10 light extinction were sufficient. Sites with 1:3 speciation sampling ha ve more (and smaller) tiles than sites with 1:6 speciation sampling. The tick marks on the vertical axis identify the first av ailable sample day of each month identified by its month number.
PM 10 light extinction is presented in terms of four ranges or bins defined by the two intervals between the three CPLs, a bin above th e high CPL, and a bin below the low CPL. For the hours with relative humidity of 90 percent an d below (referred to as “Low RH bext” in the figure legend), shades of green are used to indi cate the CPL range. Contra sting blue color scales are used for the tiles representi ng hours with relative humidity gr eater than 90 percent (referred to as “High RH bext” in the shading legend), so that the hours excluded from the PM NAAQS scenarios (see section 3.3.5 and Chapter 4) can be distingui shed. Hours with missing PM 2.5 data
from the continuous instrume nt have no estimates of PM 10 light extinction and are white. Such cases are rare, following the prior complete exclusi on of days in which more than 25 percent of daylight hours were missing such data.
Note that for Tacoma and Phoenix there ar e plots for only two years because the third year did not have suitable data, and for Phoeni x and Houston only 9 months are shown for one of the available years because suitable data were not available for the remaining quarter (the
available 9 months of results are stretched over the same vertical distance as the 12 months in the other cases). One observation that can be made in looking at th ese tile plots is that in very many cases, days which have one or more hours with high PM 10 light extinction excluded because of high relative humidity have other hours with high PM 10 light extinction which are not excluded. Although none of the PM 10 light extinction NAAQS scenario s considered in Chapter 4 are based on averaging periods l onger than one hour, these tile pl ots can be used to get a rough sense of whether hours with high PM 10 light extinction tend to be isolated, such that average
values over several hours would be considerably lower, or tend to occur together, such that a longer averaging period would produ ce roughly the same design value. A number of the eastern urban areas have numerous day-long haze episodes throughout the year (e.g. St. Louis, Detroit, Pittsburgh, Philadelphia and New York) or seas onally (e.g. Fresno and Salt Lake City, in the3-47 winter, and Los Angeles and Atlanta in the summer). Some of the urban areas have morning haze levels that diminish later in the day on a year-around basis (e.g. Dallas) or seasonally (e.g. Los Angeles, Birmingham and Atlanta in wint er and Tacoma, Fresno, and St. Louis in the summer). This type of information may be us eful in this regard during the subsequent preparation of the final Policy Assessment Document.3-48Figure 3-12.Tile Plots of Hourly PM10Light Extinction3-49Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-50Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-51Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-52Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-53Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-54Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-55Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-56Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-57Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-58Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-59Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-60Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-61Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-62Figure 3-12. Tile Plots of Hourly PM10Light Extinction, continued3-63 3.4.5 Extinction Budgets for High PM 10 Light Extinction Conditions

An extinction budget for a single period shows the contribution that each PM component
makes to PM 10 light extinction via the a dditive terms of the IMPROVE algorithm. It can be expected that the pattern in th e extinction budgets will vary by tim e of year and by study area. Examination of extinction budgets allows initial insights into what pollu tants cause poor urban visibility and what emission reduction appro ach may be most eff ective in reducing PM 10 light
extinction. Figure 3-13 presents (a) day-specif ic maximum daylight 1-hour PM 10 light extinction budgets for the 10 percent of the days in each st udy 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) light extinction budgets for the greatest 2 percent of a ll individual daylight
hours with the same relative humid ity restrictions. The day a nd hour of each hourly budget are indicated on the horizontal axis, and the hours are arranged chronologica lly. Note that the vertical scale differs from city-to-cit y, to accommodate the wide variation in PM 10 light
extinction values. Since there is an annual average of about 10 daylight hours per day 29 , there are approximately twice the number of hours (i.e., bars in the plots) included in the top 2% of all daylight hours form compared to the number of hours in the top 10% of the daily maximum 1- hour form. The rationale for pairing th e top 10% of the maximum daily 1-hour PM 10 light
extinction form with th e top 2% of all hours PM 10 light extinction form was the similarity of the design values for the 90 th and 98 th percentiles for each form, re spectively,, as discussed in Chapter 4 (see Table 4-2 and Figure 4- 1). In each of these plots the height of the shortest bar is
the PM 10 light extinction desi gn value associated with the selected form (e.g., the smallest value in the top 2% of all hours corres ponds to the design value of the 98 th percentile of all hours form). The largest light extincti on value in each pair of plots is identical, representing the single largest daylight hour value for the city. As a resu lt, the ranges of bar heights for each city’s pair of plots are approximately the same. The paired extinction budget plots in Figur e 3-13 provide a means to examine the
similarities and differences between the PM co mponents that contribute to the daylight hours with the greatest PM 10 light extinction as identified by the two forms. Though for each city there are twice the number of hours selected by the 2% of all hours form co mpared to the 10% of maximum daily form, the relative component contri butions are generally qu ite similar. Much of the reason for this similarity of the extinc tion budgets between forms has to do with the selections of the same hours by both forms and having the additional hours for the 2% form

29 Daylight hours are determined for this assessme nt as described in section 3.3.4 and Appendix I.3-64 coming as multiple (often consecutive) hours in so me of the same days that contained the top 10% maximum daily values. These multiple hours generally have similar relative composition. For example notice that among the Tacoma top 2% of all hours, there are four consecutive hours on November 8, 2007, which includes the largest hourly daylight PM 10 light extinction in the
Tacoma dataset.3-65 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours



### Tacoma

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-66 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### Fresno

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-67 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued


### Los Angeles

(a) Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-68 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### Phoenix

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-69 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### Salt Lake City

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-70 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### Dallas

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-71 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### Houston

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-72 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### St. Louis 30
(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours

30 See footnote 10 above regarding concerns with respect to the St. Louis results.3-73 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### Birmingham

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-74 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### Atlanta

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-75 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### Detroit

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-76 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### Baltimore

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-77 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued


### Pittsburgh

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-78 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued


### Philadelphia

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-79 Figure 3-13. Light Extinction Budgets for th e Top 10 Percent of Days for Maximum Daily 1-Hour PM 10 Light Extinction and for the Top 2 Percent of Individual Daylight Hours, continued



### New York

(a) Top 10 Percent of Days fo r Maximum Daily 1-Hour PM 10 Light Extinction

(b) Top 2 Percent of Individual Daylight Hours3-80 Table 3-8 shows the numbers of common and unique days selected across the two forms as well as the numbers of days selected by each form. All of the cities have more common than unique day selected by the two forms. Salt Lake City has the highest fraction of unique days, so it is likely to have greater differences in th eir PM component composition selected by the two forms. For example, the 2% of all hours form fo r Salt Lake City selected only one hour of one of the days when the PM carbonaceous components was the major contributor compared with the 4 hours with high PM carbonaceous components selected by the top 10% of maximum daily form (see Figure 3-13 for Salt Lake City). By comp arison, for Salt Lake City most of the multiple hours in single days had high contributions from PM nitrate. The overall effects of these differences for Salt Lake City are more easily seen by viewing the av erage extinction budgets using the two forms by city as shown in Figure 3-14. The 2% of all daylight hours form has a greater contribution to light exti nction by PM nitrate and a larger average light extinction than for the 10% of maximum daily 1-hour form. Di fferences between the av erage extinction budgets
for the other urban areas are much smaller than for Salt Lake City.

Table 3-8. The Numbers of Common and Unique Days Selected for Each of the 15 Urban Areas by the Top 10% of Daily Maximum and the Top 2% of all Hours Form. (Also shown are the numbers of days selected for each form.)

10% vs. 2% Number of Days Common Unique 10% 2% Tacoma 11 3 11 14 Fresno 25 7 32 25 Los Angeles 29 9 30 38 Phoenix 9 6 9 14 Salt Lake City 16 15 31 16 Dallas 25 2 27 25 Houston 14 11 14 23 St. Louis 29 4 29 33 Birmingham 35 15 35 47 Atlanta 27 6 27 33 Detroit 13 1 14 13 Baltimore 13 5 18 12 Pittsburgh 28 3 28 31 Philadelphia 14 0 14 14 New York City 22 1 23 233-81 Figure 3-14. Average PM 10 Light Extinction Budgets for the 15 Cities for Hours in the (a) Top10 Percent of the Maximum Daily PM 10 Light Extinction and (b) Top 2 Percent of all Daylight Hours of PM 10 Light Extinction.

(a)

(b)

The patterns of results for individual selected hours as shown in Figure 3-13 and for city averages as shown in Figure 3-14 are generally as expected in light of emissions and climate differences among study areas. Except for the PM 2.5 soil component, each of the components of
PM 10 light extinction is a major contributor to ex treme light extinction events at some time and location. In the West, carbonaceous PM 2.5 (i.e., organic mass and elemental carbon), nitrate, and/or coarse mass (especially in Phoenix) tend to be most responsible for these high haze hours. In the East it tends to be sulfate, nitrate, and the carbonaceous PM 2.5 components that are the large contributors to PM 10 light extinction. From the sample period dates we can determine the seasonal variations in major components. Nitrate and carbonaceous PM 2.5 contribute more to the3-82 extreme PM 10 light extinction periods during winter, while sulfate contributes more in the summer. In many of the more northerly eastern urban areas, a combination of sulfate and nitrate contributes to high PM 10 light extinction year-round. Looking at individual urban areas, the following are some highlights:

 Tacoma has its highest PM 10 light extinction hours in the colder months and primarily due to carbonaceous PM 2.5 components. Because coarse PM was estimated by applying a regional factor to the local PM 2.5 mass value, it would not have been possible for the results to indi cate a significant coarse PM contribution
to PM 10 light extinction even if one existed at this site. However, from what EPA staff know of the area, it is unlikely that there is a significant contribution from
coarse PM.

 Extreme haze hours in the two Californi a urban areas are primarily caused by high nitrate PM 2.5 , though Los Angeles has two ex treme hours associated with
coarse PM and several other hours with moderate contribution from coarse PM.
Recall that estimates of coarse PM in Los Angeles are based in part on hourly
PM 10 measurements in Victorville, and may not represent coarse PM at the PM 2.5 mass and speciation site in Rubidoux or in the larger South Coast Basin. Also, such high coarse PM values may indicat e influence from exceptional winds in Victorville. Figure B-1(b) in Appendix B shows that several other days with high daily maximum PM coarse concentrati ons had concentrations only about 60 percent or less than on the tw o days appearing in Figure 3-13; the fact that these other days do not appear among the top 10 percent indicates that other
contributors to PM 10 light extinction were low on t hose days. Whether or not the
PM 10 measurements in Victorville represent the PM 2.5 mass and speciation site in Rubidoux, it can be concluded that nitrate and to a lesser extent sulfate dominate
PM 10 light extinction on the days likely to be above the CPLs. Because coarse PM for Fresno was estimated by applyi ng a regional factor to the local PM 2.5 mass value, it would not have been possible fo r the results to indicate a significant
coarse PM contribution to PM 10 light extinction even if one existed at the Fresno site. However, given the presence of agricultural operations and occasional high winds in the San Joaquin Valley, the possibi lity of a significant contribution from
coarse PM in some hours cannot not be ruled out.

 Phoenix is unique among the 15 urban ar eas in having most of its extreme PM 10 light extinction caused by coarse PM, t hough there are a few top-10-percent days where the maximum hourly haze is domi nated by carbonaceous, sulfate, and
nitrate PM 2.5 . Unlike for Los Angeles, this domination by coarse PM is no doubt3-83 correct. PM 10 measurements for Phoenix come from a site near the center of the metro area, while the PM 2.5 measurements are from a more peripheral site (see Appendix A) and are probably underestimates of PM 2.5 at the PM 10 measurement site; this would have only a small effect on estimates of coarse PM. While it is quite possible that the very highest coar se PM concentration (indicated in Figure B-1(b) to be about 500 μg/m 3 ) reflects the effect of exceptional winds, and might be excluded under the Exceptional Event ru le, the next-highest values of PM 10 light extinction almost certainly woul d also be dominated by coarse PM concentrations in the range of 150 to 200 μg/m 3 and many might not be
excludable.

 Salt Lake City has extreme haze hours cause d mostly by nitrate in the winter with some periods with carbonaceous PM 2.5 being the major contributor. Because coarse PM in Salt Lake City was estima ted by applying a regional factor to the
local PM 2.5 mass value, it would not have been po ssible for the results to indicate a significant coarse PM contribution to PM 10 light extinction even if one existed at this site. However, from what EPA staff know of the area , it is unlikely that there is a frequent large contribution from coarse PM. The area typically has at most a few days per year with measured 24-hour average PM 10 as high as 150-200 μg/m 3 . If this were all coarse PM, th e contribution to 24- hour average light
extinction would be 90-120 Mm -1 , with the possibility of much higher hourly contributions by coarse mass during these few days.

 Dallas and Houston have high sulfate PM 2.5 contributions to PM 10 light extinction,
but Dallas also has some winter hours with extreme PM 10 light extinction with
substantial contributions from nitrate and organic carbonaceous material, while Houston seems to have less contribution by nitrate. Because coarse PM in both Dallas and Houston was estimated by applyi ng a regional factor to the local PM 2.5 mass value, it would not have been possible for the results to i ndicate a significant
coarse PM contribution to PM 10 light extinction even if one existed at this site. However, from what EPA staff know of th e areas, it is unlikely that there is a frequent large contribution from coarse PM . Houston typically has at most a few days per year with measured 24-hour average PM 10 as high as 150-200 μg/m 3 . If this were all coarse PM, the contributi on to 24-hour average light extinction
would be 90-120 Mm -1 . Dallas typically does not have PM 10 as high as 150 μg/m 3

.

 Sulfate in the summer and nitrate in the fa ll and winter are responsible for most of
the extreme PM 10 light extinction at St. Louis, though there are several maximum3-84 hourly periods where coarse PM is a majo r component. Recall that estimates of coarse PM in St. Louis may be affected by a very local source (see Appendix A), and thus the instances of high PM 10 light extinction due to coarse PM may be limited in geographic scope. 31

 Birmingham and Atlanta are similar in having sulfate year-round and winter
carbonaceous PM 2.5 as major contributors to their extreme PM 10 light extinction periods. Coarse PM for Birmingham was es timated using data from a single site, and the estimates should be reasonably re presentative. Coarse PM for Atlanta was estimated using data from two fairly close sites and the estimates should be reasonably representative.

 Detroit has frequent large light exti nction contributions from nitrate PM 2.5 , mostly in the winter, as well as some contributions from sulfate PM 2.5 year-round and several fall and winter days with high contributions from carbonaceous PM 2.5
. Coarse PM makes a notable contribution on a few days. Coarse PM for Detroit was estimated using data from a single si te near an automobile plant, and the estimates should be reasonably re presentative for that site.

 The remaining four urban locations (Pitts burgh, Baltimore, Philadelphia, and New York) are similar in that most of their extreme PM 10 light extinction is from yearround combinations of sulfate and nitrat e. New York also has some winter elemental and organic carbonaceous contributions to its extreme PM 10 light
extinction. Recall that the PM 2.5 site representing the New York area is actually in Elizabeth, NJ; emissions from diesel truc ks on nearby interstate highways and/or diesel engines associated with port activities might expl ain the carbonaceous
contributions. Coarse PM for Baltimore and Philadelphia was estimated using data from a single site in each area, and the estimates should be reasonably representative. Coarse PM for New Yo rk was estimated using data from two fairly distant sites and the estimates may not be representative of both sites.
Because coarse PM was estimated for Pitt sburgh by applying a re gional factor to
the local PM 2.5 mass value, it would not have been possible for the results to indicate a significant coar se PM contribution to PM 10 light extinction even if one existed at this site. However, exceedances of the PM 10 NAAQS are rare in

31 Comments concerning unrealistically high PM 10-2.5 values for St. Louis are viewed as credible, but were received too late in the review process to permit reanalysis using an alternate data set or to remove St. Louis from all portions of this document. However, the text has been revised to caution readers with respect to the St. Louis results, and they will not be included in the visibility effects discussion in the final PM Policy Assessment document. Some graphics have been updated to exclude St. Louis results3-85 Pittsburgh suggesting that coarse PM likely is not a frequent significant
contributor to PM 10 light extinction.

3.5 POLICY RELEVANT BACKGROUND

Policy relevant background levels of PM 10 light extinction have been estimated for this assessment by relying on outputs for the 2004 CMAQ run in which anthropogenic emissions in the U.S., Canada, and Mexico were omitted, as described in the ISA. Estimates of PRB for PM 10 light extinction were calculated fr om modeled concentrations of PM 2.5 components using the IMPROVE algorithm. The necessary component c oncentrations were extr acted from the CMAQ output files, as they were not summarized in th e final ISA. More deta il is provided in Appendix
C. It is also necessary to ha ve estimates of PRB for PM 10-2.5 , as input to the IMPROVE algorithm. The final ISA for this review does no t present any new information on this subject. The approach used in the two previous reviews was to present the historical range of annual
means of PM 10-2.5 concentrations from IMPROVE monito ring sites selected as being least influenced by anthropogenic emissions (US EPA, 2004, Table 3E-1). For this assessment, EPA
staff estimated PRB for PM 10-2.5 using a contour map based on average 2000-2004 PM 10-2.5 concentrations from all IMPROVE monitori ng sites, found in a recent report from the IMPROVE program (DeBell, 2006). More detail is provided in Appendix C. The outcome of the procedures for estimating PRB consists of hour-specific estimates of
PRB for PM 2.5 components and annual average estimates for PRB for PM 10-2.5 . Thus, hourspecific estimates of PM 10 light extinction are possible, us ing the same hour-specific relative humidity values as for the estimate of current conditions PM 10 light extinction. In addition to allowing confirmation of th e obvious fact that current conditions PM 10 light extinction values are generally well above PRB c onditions, the PRB estimates only play a role in
this assessment in the estimation of “what if” sc enarios representing compliance with alternative NAAQS scenarios based on PM 10 light extinction. This role is described in section 4.1.4.This page left intentionally blank.4-1 

### 4 PM 10 

### LIGHT EXTINCTION UNDER “WHAT IF” CONDITIONS OF JUST MEETING SPECIFIC ALTERNATIVE SECONDARY NAAQS

4.1 ALTERNATIVE SECONDARY NAAQS BASED ON PM 10 LIGHT EXTINCTION AS THE INDICATOR

4.1.1 Indicator and Monitoring Method

The indicator considered for the UFVA is PM 10 light extinction, assumed to be measured by a continuous instrument, or instrument pair, capable of re porting both light scattering and light absorption. EPA staff prep ared a White Paper on Particulat e Matter (PM) Light Extinction Measurements (US EPA, 2010d) for the purpo se of soliciting comments on prospective measurement methods from the public and the Ambient Air Monitoring and Methods Subcommittee (AAMMS). In its review (Russell and Same t, 2010), the AAMMS made the
recommendation to EPA that direct measurements be limited to PM 2.5 light extinction as this can be accomplished by a number of commercia lly available instruments and because PM 2.5 is generally responsible for most of the PM visibility impairment in urban areas. They indicated that it is technically more challenging at this time to accurately measure the PM 10-2.5 component of light extinction. These recommendations we re received subsequent to completion of the assessments described here, so they did not influence the use of PM 10 light extinction as the
indicator.

4.1.2 Alternative Secondary NAAQS Scenarios Based on PM 10 Light Extinction

Eighteen alternative NAAQS scenarios presente d in Table 4-1 are analyzed in this
section. Nine are based on da ily maximum daylight 1-hour PM 10 light extinction and nine on
PM 10 light extinction in all hours without the restriction to dail y maxima. Within each set of nine, the scenarios are ordered from least to most stringent.4-2 Table 4-1. Alternative Secondary NAAQS Scenarios for PM 10 Light Extinction

Level Annual Percentile Form Scenarios Based on Daily Maximum Daylight 1-Hour PM 10 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 10 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

4.1.3 Monitoring Site Considerations for Alternative Secondary NAAQS Based on Measured PM 10 Light Extinction

It is useful to think ahead tentativ ely to monitor siti ng aspects of NAAQS implementation, so that the results presented in the remainder of this chapter based on the 15 specific study sites can be better interpreted in terms of how well they might represent later findings if these (and other) areas were to deploy PM 10 light extinction measurement instruments as part of implementing a secondary NAAQS. In light of the recommendations of the AAMMS (Russell and Samet, 2010), it is most likely that the instruments that woul d be used if directly measured PM 2.5 light extinction were selected as the indicator to implement a sec ondary NAAQS would be “closed path” instruments that react only to air quality in their immediate vicinity. However, light paths that matter to perceived visual air quality are likely to be seve ral kilometers long. Therefore, a monitoring site should be at least neighborhood in scale, i.e., its relationship to emission sources and transport
should be such that measurements made at the site reasonably reflect conc entrations in an area surrounding the site of at least about 0.5 to 4 kilometers in diameter. The AAMMS4-3 recommendations also include ad vice concerning network design, and probe and siting criteria applicable to a program of directly measuring PM 2.5 light extinction. 1 With regard to the monitoring sites used in this assessment, all are reported to be, or appear to be, neighborhood or larg er scale, and all are in areas where people are present during daylight hours. The sites in Detroit (Dearborn) and New York (Elizabeth, NJ) are, however, rather close to an industrial source and a majo r interstate highway interchange/turnpike exit, respectively. Significantly, most of the study sites are not the highest PM 2.5 concentration site in their urban area, so a “what if” scenario that mani pulates the “current cond itions” at these sites to “just meet” an alternative sec ondary NAAQS might implicitly l eave other parts of their urban
areas with PM 2.5 light extinction above the NAAQS.

4.1.4 Approach to Modeling “What If” Condi tions for Alternative Secondary NAAQS Based on Measured PM 10 Light Extinction Before modeling “what if” condi tions, EPA staff augmented the data set described in Table 4 so that the sets of study days for Hous ton and Phoenix were seasonally balanced despite the lack of actual monitoring data for one quarter in each city. For the first quarter of 2005 in Phoenix, we substituted the available 12 days fr om the first quarter of 2006. For the fourth quarter of 2007 in Houston, we substituted 13 rando mly drawn days from the fourth quarters of
2005 and 2006. Also, Tacoma (originally) and Phoenix (aft er this augmentation) each have only two calendar years of suitable data, while the form of the alternative NAAQS scenarios requires the averaging of the 90 th , 95 th , or 98 th percentile values from three years. In Tacoma and Phoenix, for every step in the analysis at which a design va lue is used as an input or reported as an output, we averaged the percentile values from the only two available years. We modeled all daylight and da ily maximum daylight 1-hour PM 10 light extinction under each of the “what if” scenarios (in which each st udy area “just meets” one of the 18 alternative secondary NAAQS listed in secti on 4.1.2) via the following steps. These steps are essentially the same as the “proportional rollback” steps that ha ve been used in the he alth risk assessment modeling of “what if” conditions in several previous NAAQS revi ews for PM and other criteria pollutants. The steps are described here for the nine scenarios based on daily maximum daylight
1-hour PM 10 light extinction; similar steps were followed for the nine scenarios based on percentiles of all daylight 1-hour PM 10 light-extinction. The referenc ed tables present results for
both sets of scenarios.

1 In chapter 4 of the second review draft of the PM Policy Assessment (US EPA, 2010c), EPA staff considers as an alternative to directly measuring PM 2.5 light extinction the use of speciated PM 2.5 mass-calculated light extinction by a method similar in concept to but simpler than the method described in section 3.2. Much of the monitoring infrastructure needed to implement this approa ch is already deployed by state and local air agencies.4-4 1. After excluding hours with relative humidity greater than 90 percent, identify the appropriate percentile (90 th , 95 th , or 98 th ) daily maximum daylight 1-hour PM 10 light extinction value in each year, noting th e day and hour each occurred, and average these values across year s to calculate the PM 10 light extinction design value for each site consistent with the percentile form of the NAAQS scenario. 2 The three resulting design values for each area (for the 90 th , 95 th , and 98 th percentile forms) are shown in Table 4-2. (Note that in a number of cases, which are identified by a footnote, the study area meets one or more of the NAAQS scenarios under current conditions. In these cases, the “current conditions” PM 10 light extinction values are not adjusted, i.e., PM 10 light extinction values are never “rolled up.”) Notice that the design values for the 90 th percentile maximum daily 1-hour for mo st cities are generally similar to the design values for the 98 th percentile of all daylight hours. On average there are about ten hours defined as da ylight per day, so if the PM 10 light extinction were randomly distributed among the da ylight hours and days, the 90 th percentile maximum daily 1-hour would correspond to the 99 th percentile of al l hours; the fact that the point of rough equivalency is the 98 th percentile indicates a tendency for hours with higher PM 10 light extinction to cluster togeth er in the same day. Figure 4- 1 presents two scatter plots that relate the design values based on daily maximum 1- hour PM 10 light extinction values and the desi gn values based on all daylight 1-hour PM 10 light extinction values. In Panel A, design values for the daily maximum and all hours forms are paired by the defining percen tile, and colors are used to distinguish the 90 th , 95 th , and 98 th percentile statistical forms. It appears from Panel A that the design values for the two approaches to defining the NAAQS scenarios are highly correlated but with the all hours approach resulting in numerically lower design values than the daily maximum approach. The correlation breaks down for the 98 th percentile form for the few study area s with the highest levels of PM 10 light extinction. Panel B compares the 90 th percentile design values based on daily maximum PM 10 light extinction with the 90 th , 95 th , and 98 th percentile design values based on all daylight hours PM 10 light extinction. There is close agreement between the 90 th percentile design values based on daily maximum values and the 98 th percentile design value base d on all daylight hours.

2 Annual percentile values were picked from the set of day-specific or hour-sp ecific estimates according to the same scheme as used for the current 24-hour secondary PM 2.5 standard, as explained in section 4.5(a) of 40 CFR 50 Appendix N. For example, if ther e are 60 daily maximum values in a y ear, the second highest value is the 98 th percentile value. Note that this diff ers from the algorithm used by some sp readsheet and other statistical programs, which may interpolate between sample values. Also, this is a different approach than that used in the Regional Haze program, in which conditions in the best and worst 20 percen t of days are averaged together, rather than focusing on conditions on the specific day at the 80 th and 20 th percentile points.4-5 Table 4-2. Current Conditions PM 10 Light Extinction Design Values for the Study Areas.

Study Area Design Value for 90 th Percentile Form (Mm -1 ) Design Value for 95 th Percentile Form (Mm -1 ) Design Value for 98 th Percentile Form (Mm -1 ) Design Values Based on Daily Maximum Daylight 1-Hour PM 10 Light Extinction
Tacoma 140* 157* 211 Fresno 330 460 530 Los Angeles 454 550 611 Phoenix 105* 144* 185 Salt Lake City 163* 252 409 Dallas 184* 239 301 Houston 194 234 291 St. Louis 306 380 467 Birmingham 357 483 562 Atlanta 249 288 331 Detroit 308 471 644 Pittsburgh 278 313 364 Baltimore 246 286 326 Philadelphia 285 334 374 New York 306 354 451 Design Values Based on Daylight 1-Hour PM 10 Light Extinction (All Daylight Hours)
Tacoma 76* 105* 136* Fresno 188* 261 368 Los Angeles 261 341 441 Phoenix 68* 79* 94* Salt Lake City 93* 141* 225 Dallas 113* 143* 188* Houston 105* 128* 171* St. Louis 193 235 290 Birmingham 173* 227 309 Atlanta 166* 195 238 Detroit 212 251 315 Pittsburgh 167* 209 264 Baltimore 171* 225 262 Philadelphia 183* 222 278 New York 186* 243 299 * This design value meets one or more of the NAAQS scenarios based on PM 10 light extinction.4-6 Figure 4-1. Comparison of Daily Max and All Daylight Hour Design Values for PM 10 Light Extinction

(A) Comparison of Design Valu es Matched by Percentile Form

(B) Comparison of 90 th Percentile Daily Maximum Design Values and 90 th , 95 th , and 98 th Percentile All Daylight Hours Design Values4-7 2. Using the same days and hours, find the th ree (or two, in the case of Phoenix and Houston for which there were only two years of suitable data available) corresponding values of PRB PM 10 light extinction, and average these values across years to calculate the PRB portion of the design value.

3. Subtract the value from step 2 from the value from step 1, to determine the non-PRB portion of the design value.

4. Calculate the percentage re duction required in non-PRB PM 10 light extinction in order to reduce the design value to the PM 10 light extinction level that defines the NAAQS scenario, using the following equation:

Percent reduction required = 1 – (NAAQS level – PRB portion of the design value)/ (non-PRB portion of the design value)

The percentage reductions determined in st ep 4 are shown in Table 4-3. Figure 4-2 presents them graphically in the form of a scatter plot, comparing the required reductions for scenarios based on daily maximum 1-hour daylight PM 10 light extinction values to scenarios with the same level and percentile form but based on all daylight hours 1-hour PM 10 light extinction values. For the NAAQS scenarios involving higher levels and lower percentile forms, there are some notable differences in the percentage reductions required for so me area to attain. As was the case for the design values, notice in Tabl e 4-3 that there are gene rally similar percentage reductions for each city and level for th e 90th percentile maximum daily and 98th percentile of all daylight hours.

As already stated, if the study area is meeting a NAAQS scenario in the current conditions case, no adjustments were made to represent the “just meeting” case. In effect, negative values for the per cent reduction required to meet the NAAQS scenario calculated by the above equation were re-set to zero.

5. Turning to the entire set of day/hour -specific actual and PRB daylight PM 10 light extinction values for the thr ee (or two) year period, de termine the non-PRB portion of PM 10 light extinction in that hour, reduce it by the percentage determined in step 4, and add back in the PRB PM 10 light extinction. The result is the “just meets” PM 10 light extinction value for that day and hour.

Note that in these steps, it is not necessary to make any explicit or implicit assumption about what PM components would be reduced to allow the area to meet the NAAQS scenario, as the NAAQS scenario’s target design value is itself in units of light extinction. One path to meeting a NAAQS scenario would be to reduce each of the five PM 2.5 components (and thus the annual and 24-hour design values shown in Table 3-2) and PM 10-2.5 by the calculated “percent reduction required”. However, a le sser reduction in one or more of the six PM concentrations could be offset by a greater reduction in one or mo re of the remaining concentrations. Thus, it is4-8 not possible to associate unique values of annual average and 24-hour average PM 2.5 with the “just meeting” NAAQS scenario s reported in Table 4-3.4-9 Table 4-3. Percentage Reductions in Non-PRB PM 10 Light Extinction Required to “Just Meet” the NAAQS Scenarios Based on Measured Light Extinction (Mm -1 ) 3
NAAQS Scenarios Based on Daily Maximum 1-Hour Daylight PM 10 Light Extinction Scenario (a) (b) (c) (d) (e) (f) (g) (h) (i) Level/Form 191/90 th 191/95 th 191/98 th 112/ 90 th 112/95 th 112/98 th 64/90 th 64/95 th 64/98 th
Area Percentage Reduction Required in Non-PRB PM 10 Light Extinction
Tacoma 0 0 10 22 31 52 59 63 78 Fresno 43 59 64 67 76 79 82 87 89 Los Angeles 59 66 69 76 81 82 87 89 90

Phoenix 0 0 0 0 22 40 39 56 66 Salt Lake City 0 24 54 32 56 73 61 75 85 Dallas 0 21 38 41 54 65 69 75 81
Houston 2 20 35 44 56 63 70 78 80 St. Louis 38 51 60 64 72 77 80 85 88 Birmingham 48 61 67 70 77 81 84 87 90
Atlanta 24 35 44 57 63 68 77 80 83 Detroit 39 60 71 65 77 83 80 87 91 Pittsburgh 32 40 48 60 65 70 78 81 83
Baltimore 23 34 42 56 63 67 76 80 82 Philadelphia 34 43 49 62 67 71 79 82 84 New York 38 47 58 64 69 76 80 83 87 NAAQS Scenarios Based on 1-Hour Daylight PM 10 Light Extinction Scenario (j) (k) (l) (m) (n) (o) (p) (q) (r) Level/Form 191/90 th 191/95 th 191/98 th 112/90 th 112/95 th 112/98 th 64/90 th 64/95 th 64/98 th
Area Percentage Reduction Required in Non-PRB PM 10 Light Extinction Tacoma 0 0 0 0 0 21 18 44 64 Fresno 0 27 49 41 58 70 67 77 84 Los Angeles 27 45 57 58 68 75 77 82 86

Phoenix 0 0 0 0 0 0 5 19 32 Salt Lake City 0 0 15 0 21 51 32 55 72 Dallas 0 0 0 1 23 42 44 58 68 Houston 0 0 0 0 13 37 40 51 67 St. Louis 1 19 35 43 53 62 68 74 79 Birmingham 0 16 39 36 52 65 65 74 81 Atlanta 0 2 20 33 44 55 63 70 75 Detroit 10 24 40 48 56 65 71 75 81 Pittsburgh 0 9 28 34 47 58 63 70 77 Baltimore 0 15 28 35 51 58 64 73 77 Philadelphia 0 14 32 39 51 61 66 73 78 New York 0 22 36 40 55 63 67 75 79

3 As a result of a formula error, the intended values of PRB PM 10 light extinction were not properly used in calculating the entries in this table, generally resulting in the required reductions shown he re to be slightly smaller than they should be. For a more detailed explanation of this issue, see the 2010 Lorang memo, "Explanation of Error in Table 4-3 (Percentage reductions in non-PRB PM 10 light extinction required to 'just meet' the NAAQS scenarios based on measured light extinction) of the fina l Urban Focused Visibility Assessment", July 23, 2010. As discussed in that memo, the only other results presented in this document that were affected by this error were Figure 4-2, Figure 4-3(a), and Panels (a) through (r) of Appendix F. The effect on those figures was judged too negligible to warrant regenerating them for this final version.4-10 Figure 4-2. Comparison of Required Percentage Reductions in Non-PRB PM 10 Light Extinction Needed to Meet NAAQS Scenarios

0 20 40 60 80 100

0 20 40 60 80 100 Based on Daily Maximum Daylight LEBased on Daylight LE (all hours)

191/90th 191/95th 191/98th 112/90th 112/95th 112/98th 64/90th
64/95th 64/98th

4.2 ALTERNATIVE SECONDARY PM 2.5 NAAQS BASED ON ANNUAL AND 24-HOUR PM 2.5 MASS

4.2.1 Secondary NAAQS Scenarios Based on Annual and 24-Hour PM 2.5 Mass

In this final assessment, EPA staff have m odeled two “what if” scenarios using the same indicators and averaging periods as defined in the current suite of PM 2.5 NAAQS set in 2006.
The first scenario uses the current suite of PM 2.5 NAAQS levels and the second a suite of lower
levels considered in the health risk assessment (US EPA, 2010e):

 15 μg/m 3 weighted annual average PM 2.5 concentration and 35 μg/m 3 24-hour average
PM 2.5 concentration with a 98 th percentile form, both aver aged over three years.

 12 μg/m 3 weighted annual average PM 2.5 concentration and 25 μg/m 3 24-hour average
PM 2.5 concentration with a 98 th percentile form, both aver aged over three years.4-11 4.2.2 Approach to Modeling Conditions If Secondary PM 2.5 NAAQS Based on Annual and 24-Hour PM 2.5 Mass Were Just Met Because these NAAQS scenarios are based on PM 2.5 mass as the indicator, rather than light extinction, the steps needed to model “what if ” conditions are somewhat different, and involve explicit consideration of changes in PM 2.5 components.

1. Apply proportional rollb ack to all the PM 2.5 monitoring sites in each study area, taking into account PRB PM 2.5 mass, to “just meet” the NAAQS scenario for the area as a whole, not just at the visibility assessment study site. The health risk assessment document describes this procedure in detail . The degree of rollback is controlled by the highest annual or 24-hour design value, which in most study areas is from a site other than the site used in this visibility assessment. The relevant result from this analysis is the percenta ge reduction in non-PRB PM 2.5 mass need to “just meet” the NAAQS scenario, for each study area. Thes e percentage reduc tions are shown in Table 4-4. Note that Phoenix and Da llas meet the 15/35 NAAQS scenario under current conditions, and require no reduction. PM 2.5 levels in these two cities were not “rolled up.”

2. For each day and hour for each PM 2.5 component, subtract the PRB concentration from the current conditions concentration, to determine the non-PRB portion of the current conditions concentration.

3. Apply the percentage reduction from step 1 to the non-PRB portion of each of the five PM 2.5 components. Add back the PRB portion of the component.

4. Re-apply the IMPROVE algorithm (s ection 3.2.3), using the reduced PM 2.5 component concentrations, the current conditions PM 10-2.5 concentration for the day and hour, and relative humidity for the day and hour. Include the term for Rayleigh scattering.4-12 Table 4-4. Percentage Reducti ons Required in Non-PRB PM 2.5 Mass to “Just Meet” NAAQS Scenarios Based on Annual and 24-Hour PM 2.5 Mass

Study Area Percentage Reduction Required (s) Annual PM 2.5 NAAQS = 15 μg/m 3
24-Hour PM 2.5 NAAQS = 35 μg/m 3 (t) Annual PM 2.5 NAAQS = 12 μg/m 3
24-Hour PM 2.5 NAAQS = 25 μg/m 3
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.

4.3 RESULTS FOR EACH “JUST M EET” ALTERNATIV E SECONDARY NAAQS SCENARIO The modeling described in sections 4. 1 and 4.2 resulted in estimates of PM 10 light extinction for each day and hour in each study ar ea, for each NAAQS scenario. Four summaries of these conditions are presented here. Figur e 4-3 shows two box-and-whisker plots of daily maximum daylight 1-hour PM 10 light extinction. The top panel (a) is for the single illustrative scenario of a NAAQS based on da ily maximum daylight 1-hour PM 10 light extinction with a
level of 112 Mm -1 and a 90 th percentile form, which was chosen fo r this illustration because it is approximately mid-way among the nine scenarios based on daily maximum PM 10 light extinction in terms of stringency. 4 The bottom panel (b) is for the scenario of meeting the current suite of secondary PM 2.5 NAAQS standards: 15 μg/m 3 annual average and 35 μg/m 3 24-hour average (98 th percentile form). A notable feature of this co mparison is that in the top panel, all the study

4 Plots of the distribution of daily maximum PM 10 light extinction for all 18 NAAQS scenarios based on daily maximum PM 10 light extinction, and of individual hourly PM 10 light extinction for all 18 NAAQS scenarios based on individual daylight hours, are provided in Appendix F.4-13 areas have a similar distribution of the daily maximum daylight 1-hour PM 10 light extinction, while in the bottom panel this is not the case. This is expected, since a NAAQS based on a measured daily maximum PM 10 light extinction indicator will of course result in areas achieving similar daily maximum PM 10 light extinction patterns once ea ch area reaches a “just meets” condition. In areas with generally higher relati ve humidity conditions, concentrations of PM 2.5 components and/or PM 10-2.5 would need to be lower to achieve the “just meet” condition. In contrast, in the NAAQS scenario represented by the bottom panel, concentrations of PM 2.5 mass will be similar across areas, but concentrations of PM 2.5 components may not be, and levels of
PM 10 light extinction will not be similar in areas with dissimilar levels of relative humidity. The specific differences among areas in the bottom pa nel are generally as expected, with the drier study areas having lower levels of PM 10 light extinction. Tables 4-5 and 4-6 summarize the “just m eet” conditions in the NAAQS scenarios in
terms of the PM 10 light extinction design va lues. Table 4-5 addre sses the 18 scenarios of NAAQS based on measured PM 10 light extinction. When an area just meets a NAAQS scenario, its design value in principle should exactly equal the NAAQS level, so preparation of this table serves as a check against calculation errors. Note that the design values in Table 4-5, resulting from the rollback steps described in secti on 4.1.4, in some cases do not exactly equal the assumed level of the NAAQS, although all are quite clos e. Closer investiga tion has revealed that this is mostly a result of hours switching their ra nking in the rollback process. Hours can switch
rank because the level of PRB PM 10 light extinction varies with each hour, so a uniform percentage reduction in non-PRB light extinction (step 5) can resu lt in non-uniform percentage
reductions in actual PM 10 light extinction; a lower ranking hour can thereby move up in the postrollback ranking. In principle, rollback could be iterated to exactly achie ve a design value equal to the level of the NAAQS for each scenario. Ho wever, the discrepancies indicated in Table 4-5 were judged too small to justif y iterative rollback, given other uncertainties in the analysis.
Table 4-6 addresses the two s cenarios of NAAQS based on PM 2.5 mass, with PM 10 light extinction design values shown for the 90 th , 95 th , and 98 th percentile forms.4-14Figure 4-3. Distributions of Daily Maximum Daylight 1-Hour PM10Light Extinction Under Two “Just Meet”Secondary NAAQS Scenarios (Excluding Hours with Relative Humidity Greater Than 90 Percent)(a) Secondary NAAQS Based on Daily Maximum Daylight 1-Hour PM10Light Extinction with a Level of 112 Mm-1and a 90thPercentile Form4-15Figure 4-3. Distributions of Daily Maximum Daylight 1-Hour PM10Light Extinction Under Two “Just Meet” SecondaryNAAQS Scenarios (Excluding Hours with RelativeHumidity Greater Than 90 Percent), continued(b) Secondary NAAQS of 15 μg/m3for the Annual Average and 35 μg/m3for the 98thPercentile 24-Hour Average4-16 Table 4-5. PM 10 Light Extinction Design Values for “Just Meet” Secondary NAAQS Scenarios Based on Measured PM 10 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 90 th 95 th 98 th 90 th 95 th 98 th 90 th 95 th 98 th PM 10 Light Extinction Design Value (based on same percentile form as the NAAQS scenario) Tacoma, WA 140 157 191 112 115 108 66 74 58 Fresno, CA 191 191 191 113 112 112 65 64 64 Los Angeles, CA 191 191 191 113 112 112 65 64 64

Phoenix, AZ 105 144 185 105 112 112 64 64 64 Salt Lake City, UT 163 191 191 112 112 112 65 64 64 Dallas, TX 184 191 191 112 113 112 64 65 65 Houston, TX 191 191 191 114 111 112 67 61 67 St. Louis, IL 191 191 191 112 112 112 65 64 64 Birmingham, AL 191 191 191 113 113 112 64 66 64

Atlanta, GA 191 191 191 112 112 112 64 64 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 64 Philadelphia, PA 191 191 191 112 112 112 64 64 64 New York, NY 191 191 191 112 112 112 64 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 90 th 95 th 98 th 90 th 95 th 98 th 90 th 95 th 98 th PM 10 Light Extinction Design Value (based on same percentile form as the NAAQS scenario) Tacoma, WA 76 105 136 76 105 112 63 64 59 Fresno, CA 188 192 191 112 113 111 65 64 63 Los Angeles, CA 191 191 191 112 113 112 64 65 64

Phoenix, AZ 68 79 94 68 79 94 64 64 64 Salt Lake City, UT 93 141 191 93 112 112 64 64 64 Dallas, TX 113 143 188 112 112 113 65 64 66 Houston, TX 105 128 171 105 113 111 65 66 61 St. Louis, IL 191 191 191 112 112 112 65 64 65 Birmingham, AL 173 191 191 112 112 112 65 64 65

Atlanta, GA 166 191 192 113 112 113 65 63 65 Detroit, MI 191 191 191 112 112 112 64 65 64 Pittsburgh, PA 167 191 191 113 113 112 65 65 65 Baltimore, MD 171 191 191 112 112 112 64 65 64 Philadelphia, PA 183 191 191 112 112 113 65 64 65 New York, NY 186 191 192 112 112 113 65 65 654-17 Table 4-6. PM 10 Light Extinction Design Values for “Just Meet” Secondary NAAQS Scenarios Based on PM 2.5 Mass (Excluding Hours with Relative Humidity Greater Than 90 Percent)

Annual/24-Hour PM 2.5 NAAQS (s) 15 μ g/m 3 / 35 μ g/m 3 (t) 12 μ g/m 3 / 25 μ g/m 3

City Name 90 th %tile Design Value (Mm -1 ) 95 th %tile Design Value (Mm -1 ) 98 th %tile Design Value (Mm -1 ) 90 th %tile Design Value (Mm -1 ) 95 th %tile Design Value (Mm -1 ) 98 th %tile Design Value (Mm -1 ) Design Values Based on Daily Maximum Daylight 1-Hour PM 10 Light Extinction Tacoma, WA 119 131 178 93 102 136 Fresno, CA 191 266 304 142 196 224 Los Angeles, CA 312 361 429 231 261 355

Phoenix, AZ 105* 143* 185* 96 135 161 Salt Lake City, UT 110 167 269 83 125 198 Dallas, TX 183* 239* 301* 172 224 282 Houston, TX 185 222 276 148 178 220 St. Louis, IL 286 354 441 252 289 363 Birmingham, AL 285 394 464 213 300 365

Atlanta, GA 230 266 307 181 208 243 Detroit, MI 256 387 536 187 277 401 Pittsburgh, PA 229 258 299 167 188 218 Baltimore, MD 233 272 308 169 202 221 Philadelphia, PA 263 308 346 190 222 254 New York, NY 255 295 376 182 213 268 Design Values Based on Daylight 1-Hour PM 10 Light Extinction (All Daylight Hours) Tacoma, WA 65 88 113 52 70 84 Fresno, CA 110 152 214 83 113 160 Los Angeles, CA 173 228 294 129 169 220

Phoenix, AZ 68* 79* 94* 60 70 86 Salt Lake City, UT 63 95 150 49 72 112 Dallas, TX 113* 143* 188* 106 134 176 Houston, TX 99 122 163 81 99 131 St. Louis, IL 180 221 271 147 183 237 Birmingham, AL 140 183 247 105 138 186

Atlanta, GA 154 180 220 123 144 174 Detroit, MI 175 208 257 130 155 188 Pittsburgh, PA 138 173 218 102 127 159 Baltimore, MD 163 213 248 121 155 184 Philadelphia, PA 169 205 258 123 149 187 New York, NY 155 203 249 113 148 178 * Phoenix and Dallas meet 15 μg/m 3 /35 μg/m 3 under current conditions, so these entries are essentially the same as for current conditions.4-18 Table 4-7 summarizes all 20 scenarios in terms of the percentage of days (across 2005 to 2007, but after rollback) in which th e daily maximum daylight 1-hour PM 10 light extinction under “just meeting” conditions exceeds each of the CPLs. Part A of the table applies to NAAQS scenarios based on daily maximum 1-hour PM 10 light extinction values. Part B of the table applies to the scenarios based on 1-hour PM 10 light extinction values during all daylight hours. Note that the reported pe rcentages in both Part A and Part B is the percentage of days in which the daily maximum daylight 1-hour PM 10 light extinction under “just meet” conditions exceeds each of the CPLs; this allows compar ison of the “effectiveness” of the two NAAQS approaches using a consistent metric. (The 15/35 and 12/25 NAAQS scenarios are the same in Part A and Part B, and are repeated only for convenience in making comparisons.) Hours with relative humidity above 90 percent have been excluded from consid eration, consistent with the definition of the NAAQS scenarios. Also show n at the bottom of the table in each column representing a NAAQS scenario is the average of thes e percentages of time across the 15 study areas (this is the simple column average, not we ighted by the number of days available in each area). Comparisons of these percentages allows a rough indication of how the two scenarios of a NAAQS based on PM 2.5 mass compare to the other 18 scenarios in terms of protecting visual air quality. Notice that the mo st restrictive of the two NAAQS scenarios based on PM 2.5 mass would reduce the projected 1-hour maximum daily PM 10 light extinction above the least restrictive CPL (191Mm -1 ) to less than 10 percent of the time for most of the urban areas (only L.A., St. Louis, and Birmingham have values above 10 percent). 5 However at the current PM NAAQS level (i.e., 15/35) all of the eastern urban areas and Los Angeles exceed the least
restrictive CPL more than 10% of the time. Co mparison of Parts A and B of Figure 4-7 indicates that basing a PM 10 light extinction NAAQS scenar io on daily maximum 1-hour PM 10 light extinction has a lower percentage in excess of the 1-hour daily ma ximum versus the NAAQS scenario based on all daylight hours PM 10 light extinction for a given level and percentile form of the NAAQS. This is consistent with the result s presented in Table 4-2 and Figure 4-1, which
indicated that current conditions design values are generally lowe r for the all hours approach. Again there is near equivalence between the 90 th percentile daily maximum and 98 th percentile all daylight hours in terms of the percent of days exceeding the daily maximum CPL values in
Table 4-7

.

5 Comments were received concerning unrealistically high PM 10-2.5 values for St. Louis and to a lesser extent for Los Angeles. High contributions by PM 10-2.5 would help explain why a PM 2.5 standard would be less effective in reducing visibility impacts. EPA staff view the comments concerning unreliable PM 10-2.5 values for St. Louis as credible, but these comments were received too late in the review process to permit reanalysis using an alternate data set or to remove St. Louis from this document. However, the text has been revised to caution readers with respect to the St. Louis results, and they will not be forwarded to the visibility effects discussion in the PM Policy Assessment document.4-19Table 4-7. Percentage of Days with Maximum 1-Hour Daylight PM10Light Extinction Above CPLs For Each NAAQSScenario Under “Just Meet” Conditions Across Three Years (or Two in the Case of Phoenix and Houston)(A) NAAQS Scenarios Based on Daily Maximum 1-Hour PM10Light ExtinctionDays with Max Hour Above64 Mm-1Days with Max Hour Above112 Mm-1Days with Max Hour Above191 Mm-1Scenarioabcdefghi s tabcdefghi s tabcdefghi s tNAAQSLevelMm-1191191191112112112646464191191191112112112646464191191191112112112646464NAAQSPercentileForm909598909598909598909598909598909598909598909598909598Annual/24-Hour15/3512/2515/3512/2515/3512/25AreaPercentage of daysPercentage of daysPercentage of daysTacoma52 52 48 40 34 16 12 10 2 43 28 22 22 14 11 7 2 1 1 0 11 5 4 4 3 1 1 0 0 0 0 1 0Fresno54 40 32 31 19 13 10 5 3 54 40 29 16 11 9 5 3 2 0 0 29 17 9 5 3 2 0 0 0 0 0 9 5Los Angeles74 64 58 43 32 27 12 6 3 85 79 40 31 26 11 6 3 1 0 0 69 52 11 6 3 1 0 0 0 0 0 37 19Phoenix44 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 1Salt LakeCity44 27 13 24 11 5 10 5 1 24 15 17 11 5 10 5 1 4 1 0 9 6 8 5 1 4 1 0 1 0 0 4 2Dallas80 66 50 48 25 14 10 5 1 81 77 41 22 12 10 5 1 1 0 0 41 37 10 5 1 1 0 0 0 0 0 10 8Houston77 65 55 47 30 18 13 3 3 75 64 43 28 16 12 4 3 1 0 0 41 23 11 6 2 1 1 0 0 0 0 11 3St. Louis81 71 54 46 34 20 11 6 2 97 89 45 30 18 12 6 2 2 1 0 73 57 11 5 3 2 1 0 0 0 0 36 20Birmingham63 49 40 33 19 14 10 6 3 84 70 30 18 12 10 5 3 1 0 0 55 38 10 5 2 1 0 0 0 0 0 24 13Atlanta85 80 76 62 51 34 9 5 1 90 85 59 47 30 11 5 1 0 0 0 71 54 11 4 2 0 0 0 0 0 0 25 8Detroit74 52 41 46 22 6 11 4 3 80 74 45 17 6 11 4 3 4 1 0 61 49 10 4 3 4 1 0 1 0 0 33 10Pittsburgh70 63 54 40 31 23 9 6 2 79 63 37 29 21 9 6 1 0 0 0 48 28 9 5 1 0 0 0 0 0 0 16 5Baltimore67 61 54 41 30 25 11 4 2 78 64 39 29 23 11 5 2 1 0 0 47 31 12 6 2 1 0 0 0 0 0 19 8Philadelphia72 66 60 41 31 27 8 6 3 85 74 38 31 24 8 5 3 0 0 0 61 38 8 5 3 0 0 0 0 0 0 29 9New York63 59 40 33 27 18 9 6 2 76 62 32 24 16 9 6 2 1 0 0 46 30 10 6 2 1 0 0 0 0 0 19 8Average67 57 48 41 28 18 10 6 2 72 62 35 24 16 10 5 2 1 0 0 45 31 9 5 2 1 0 0 0 0 0 18 84-20(B) NAAQS Scenarios Based on PM10Light Extinction DuringAll Daylight Hours*Days with Max Hour Above64 Mm-1Days with Max Hour Above112 Mm-1Days with Max Hour Above191 Mm-1Scenariojklmnopqr s tjklmnopqr s tjklmnopqr s tNAAQSLevelMm-1191191191112112112646464191191191112112112646464191191191112112112646464NAAQSPercentile Form909598909598909598909598909598909598909598909598909598Annual/24-Hour15/3512/2515/3512/2515/3512/25AreaPercentage of daysPercentage of daysPercentage of daysTacoma52 52 52 52 52 40 40 23 10 43 28 22 22 22 22 22 11 12 4 1 11 5 4 4 4 4 4 1 1 0 0 1 0Fresno76 65 51 56 41 27 30 17 7 54 40 52 37 25 30 17 7 9 4 1 29 17 29 17 7 10 5 1 2 0 0 9 5LosAngeles86 83 76 75 61 46 40 27 14 85 79 73 58 42 41 27 14 10 3 1 69 52 42 27 13 12 3 1 1 0 0 37 19Phoenix44 44 44 44 44 44 44 29 17 44 40 6 6 6 6 6 6 6 5 3 6 6 1 1 1 1 1 1 1 1 1 1 1Salt LakeCity44 44 33 44 27 15 24 12 6 24 15 17 17 14 17 12 5 9 5 2 9 6 8 8 5 8 5 2 4 1 1 4 2Dallas80 80 80 80 63 45 42 21 11 81 77 41 41 41 41 21 10 9 4 1 41 37 10 10 10 10 4 1 0 0 0 10 8Houston77 77 77 77 70 53 49 35 13 75 64 44 44 44 44 33 13 13 8 1 41 23 11 11 11 11 7 2 1 1 0 11 3St. Louis98 92 84 78 65 51 39 27 15 97 89 76 62 48 39 27 15 9 4 2 73 57 39 27 14 9 4 2 1 0 0 36 20Birmingham89 85 72 74 59 42 42 25 15 84 70 64 55 39 42 25 14 14 8 3 55 38 34 26 14 15 9 3 3 1 0 24 13Atlanta91 91 87 81 76 63 51 31 14 90 85 75 73 62 48 30 12 5 1 0 71 54 31 30 12 6 2 0 0 0 0 25 8Detroit84 78 72 66 57 45 41 27 10 80 74 63 55 45 41 26 9 6 4 4 61 49 40 26 9 6 4 4 3 1 1 33 10Pittsburgh85 83 73 69 55 42 35 22 11 79 63 57 53 40 34 22 11 8 1 0 48 28 26 22 11 8 2 0 0 0 0 16 5Baltimore80 72 66 60 44 38 28 14 10 78 64 50 43 34 28 14 9 4 2 1 47 31 23 14 9 5 2 1 0 0 0 19 8Philadelphia86 82 73 68 59 43 34 23 9 85 74 63 58 41 33 24 8 6 1 0 61 38 31 24 8 8 1 0 0 0 0 29 9New York83 74 64 62 44 34 31 18 11 76 62 59 42 32 30 18 10 8 3 1 46 30 28 18 10 8 3 1 0 0 0 19 8Average77 73 67 66 54 42 38 23 12 72 62 51 44 36 33 22 10 9 4 1 45 31 24 18 9 8 4 1 1 0 0 18 8* Note that the table reports results based on daily maximum daylight hour, while theNAAQS scenarios in Panel B are based on all daylighthours (in both cases excluding hours with RH>90%).5-1 

### 5 SUMMARY

This chapter integrates the key information on the purpose, approac h, principal results, and significant technical issues of the assessment e fforts that are characterize d in greater detail in chapters 2, 3 and 4 and the appendices of this final Urban Focused Visibility Assessment (UFVA). Earlier versions of the UFVA 1 document the original asse ssment and its evolution in response to Clean Air Science Advisory Comm ittee (CASAC) and public comments. This chapter is organized by the three separate assessments that are described in greater detail in chapters 2 through 4.

5.1 Urban Visibility Preference Studies Reanalysis

Purpose: The overall purpose in cond ucting a reanalysis of urba n preference studies is to
determine whether there is a cr edible range of acceptable visu al air quality conditions above which the national public welfare is adversely affected. Similar, though not identical, visibility preference studies were conducted in four metr opolitan areas including Denver, CO; Vancouver, BC (Canada); Phoenix, AZ; and Washington, DC. These studies were performed separately to support the development of local visibility prot ection efforts, except for Washington, DC which was the subject of two separate pilot studies designed to better understand visibility preference
studies. The common feature in each of these stud ies was that study participants were asked to individually rate the acceptability of scenic imag es shown to them one at a time in random order that depicted visibility impairment over a range of conditions from nearly pristine to highly impaired. Approach: The methodology used in the reanalysis involved a critical review of data generated for each study to identif y issues of consistency and to identify whether and when it might be appropriate to compare/combine the st udies’ results. The results were displayed as points on plots of percent of participants that rated each visibility condition acceptable versus the amount of visibility impairment as measured in deciview units (i.e., a l ogarithmic transformation of light extinction). Logit regression analysis wa s used to develop best fit curves for each of the four urban area study results and to determine whether they differed significantly from each
other. Principal Results: Logit regression applied to the resu lts for each of the four urban areas defined statistically significant relationships for ea ch that are similarly sh aped but with different visibility impairment threshold value, defined here as the 50 th percentile acceptabi lity criteria in

1 Available at http://www.epa.gov/ttn/naaqs/sta ndards/pm/s_pm_2007_risk.html5-2 each urban area study. The range of 50 th percentile values is from ~20 dv to ~30 dv, which when expressed in terms of PM 10 light extinction corresponds to 64 Mm -1 to 191 Mm -1 . The upper and lower bounds of the range and a mid-point value of 25 dv (co rresponding to 112 Mm -1 ) were selected as Candidate Protective Levels (CPLs) to compare with the distributions of current conditions for 15 urban areas (chapter 3) and to define alternative standards to be included in the assessment (chapter 4).
Technical Issues: Each of the four urban prefer ence study areas produced well defined though statistically significant differe nt results with respect to what visibility impairment level divides acceptable from unacceptable conditions. A number of hypotheses concerning why the results differed for each area are discussed in chap ter 2. However, additional research to better understand why the response distributions differed by location could usefully inform future PM
reviews.

5.2 Current Visibility Conditions

Purpose: The goal of this assessment is to develop a daylight hourly averaged PM 10 light extinction dataset for several large urban areas to characterize current visibility conditions and compare them to the CPLs in order to determin e the extent, frequency and causes of visibility impairment in cases where the CPLs are exceeded. Approach: A simple linear algorit hm, the IMPROVE algorithm, 2 was used to estimate
PM 10 light extinction for 15 urban areas for the period from 2005 to 2007 from hourly PM 2.5 and
PM 10-2.5 mass and PM 2.5 component concentrations and rela tive humidity data (used to estimate the water component of the PM under ambient conditions). While PM 2.5 mass concentration and relative humidity data are available from conti nuous instruments on an hourly averaged basis,
PM 2.5 composition data available from the Chemi cal Speciation Network (CSN) is based on 24- hour average filter samples which are only coll ected on a one-day-in-three or one-day-in-six basis. The methodology used to estimate hourly daylight PM 2.5 components involved use of CMAQ regional air quality modeling to generate monthly averaged species-specific diurnal patterns (ratio of each hour to the 24-hour mean) fo r each of the urban areas. These are used to calculate the hourly relative mix of species that are then scal ed so that the sum of the PM 2.5 component concentrations e quals the measured hourly PM 2.5 mass concentration. PM 10 continuous monitoring data was available at a few of the urban areas permitting hourly PM 10-2.5 concentrations to be determin ed by subtracting the hourly PM 2.5 concentration. Elsewhere PM 10-

2.5 was estimated using ratios of PM 2.5 to PM 10 . The resulting estimates of hourly averaged PM 2.5 component concentrations, PM 10-2.5 mass concentration and the meas ured relative humidity were

2 Malm, et al., 1994 and DeBell, 2006. (See also ISA, section 9.2.2.2, pgs. 9-7 and 9-8.)5-3 used as input to the IMPROVE algorithm to estimate hourly PM 10 light extinction to be calculated for daylight hours with relative humid ity no greater than 90%. Resulting daylight 1- hour averaged PM 10 light extinction values were comp iled on the basis of all hours and maximum daily values for relative humidity conditi ons less than or equal to 90% to examine the frequency that they exceed CPLs by urban area. Tables and plots of the total and component
contributions to PM 10 light extinction were produced to ch aracterize the nature and causes of visibility impairment. Principal Results: All of the results are for 1-hour PM 10 light extinction during daylight hours when relative humidity is no greater than 90% for the 15 selected urban areas.

 The use of this relative humidity cap signi ficantly reduced the occurrence of visibility impairment caused by meteorological c onditions like fog a nd precipitation.

 Maximum daily hourly PM light extinction va lues exceeded the low, middle and high CPL 77%, 52% and 26% of the days, respectively, when averaged across the 15 urban areas. Eastern and California urban areas have the highest frequencies and nonCalifornia Western urban areas have th e lowest frequencies above each CPL.

 All hours PM 10 light extinction values exceeded the low, middle and high CPLs 45%, 22% and 7% of the hours, respectively, averag ed across the 15 urban areas, with the Eastern and California urban areas having the highest fre quencies and non-California Western urban areas having the lowe st frequencies above each CPL.

 The range of PM 10 light extinction values a nd relative contributions by PM 2.5 components for the most impaired 10% of maximum da ily 1-hour hours are similar to the most impaired 2% of all hours, because they include hours from a large number of days in

common.

 During the most visibility impaired hours PM 2.5 nitrate is the domina nt light extinction contributor for several wester n urban areas (Fresno, LA, and Salt Lake City) while sulfate tends to be the largest contributor in the Eastern urban areas. Carbonaceous PM 2.5 (i.e., organic mass plus elemental carbon) is a majo r contributor at Tacoma and a significant contributor at several othe r urban areas. Phoenix has significant light extinction contribution by PM 10-2.5 . Thus, regional differences in the dominant component contributing to visibility impairment are apparent.

Technical Issues: The approach used to determine the hourly PM 10-2.5 concentrations varied among the sites. Four of th e sites had collocated continuous PM 2.5 and PM 10 measurements, while six urban areas used da ta from separate sites to determine the PM 10-2.5 by difference, and the remaining five urban area s used regionally determined ratios of PM 10-2.5 to
PM 2.5 to infer the PM 10-2.5 . The quality of the PM 10-2.5 data inferred from separate sites for St. Louis have been called into question in review comments on an earlier version of the UFVA and are no longer viewed as credible. Therefore, the St. Louis results were ap propriately labeled in5-4 the subsequent analyses in the final UFVA. A si milar concern was raised regarding the LA data,
but the PM 10-2.5 values are low during most of the visibili ty impaired days so they are less of an issue compared with that of St. Louis and t hus, were retained in these and subsequent
assessments. PM 10-2.5 is a small component of the light exti nction for all of the other urban areas except for Phoenix, where high values are consid ered more plausible. Collocated continuous
PM 10 and PM 2.5 monitoring at a greater number of urban ar eas would better address this issue in
the future. While yielding generally reasonable results, the process employed to develop the hourly
PM 10 component information used as input to th e IMPROVE algorithm is complex and subject to comments that there are alternate approaches that could have been employed. The use of regional modeling to generate PM 2.5 species specific monthly averag ed diurnal patterns would be unnecessary if continuous PM 2.5 speciation monitoring were more readily available. Use of an algorithm to estimate PM 10 light extinction would be unnecessa ry if direct measurements of
continuous PM 10 light extinction were commonly available.

5.3 Visibility Conditions for Alternat ive Secondary PM NAAQS Scenarios

Purpose: The goal is to evaluate the eff ectiveness of alternative secondary PM 2.5 NAAQS, including 2 that use the PM 2.5 mass concentration indica tor (i.e., the current 15 μ g/m 3 annual, 35 μ g/m 3 24-hour PM 2.5 NAAQS and a more restrictive 12 μ g/m 3 /25 μ g/m 3 alternative), and 18 that use a 1-hour daylight PM 10 light extinction indicator (i.e ., all combinations of both a maximum daily 1-hr indicator a nd a 1-hr indicator based on all daylight hours with 3 percentile forms (90 th , 95 th and 98 th ) and 3 levels (CPLs) for the 15 urban areas). Approach: The hourly averaged PM 10 light extinction data set developed to characterize
current conditions for the 15 urban areas was used as the starting point for a rollback adjustment to simulate just meeting the various alternativ e PM NAAQS scenarios. Rollback is not applied to the PRB portion of the PM 2.5 as estimated using CMAQ modeling, though it is applied uniformly to all other PM 2.5 components. This process produces adjusted daylight hourly PM 10 light extinction data sets for each urban area that would just m eet each of the alternative NAAQS scenarios that can be assessed with respect to their visibility prot ection effectiveness. Principal Results: Each of the PM NAAQS scenarios that used PM 10 light extinction as the indicator produced similar distributions of hourly PM 10 light extinction across the 15 urban
areas. This is not the case for the two scenarios that used PM 2.5 mass concentration as the indicator, where for example the 90 th percentile PM 10 light extinction values vary among the 15 urban areas by as much as a factor of 2 to 3. The maximum daily 1-hour form of the alternative NAAQS is more restrictive for any percentile than the all-hours form. In fact the 90 th percentile of the maximum daily form is nearly identical with respect to design values for current5-5 conditions and the percent reduction re quired to just meet it as the 98 th percentile for the allhours form.
Technical Issues: The rollback approach implicitly assumes all non-PRB PM 2.5 components will be uniformly reduced to meet any of the alternative standards. In practice, emission control programs that would be developed to meet standards wi ll not operate in this manner, nor will they be so fine tuned that each urban area would just meet the PM NAAQS. In that sense, the rollback assessment produces idealized results which for the PM 10 light extinction based NAAQS scenarios are more uniform across urban areas than is likely should such a standard be implemented. The use of this na tionally uniform emissions rollback approach is justified by it providing a common basis for assessing the variations in the magnitude of emissions controls required to meet NAAQS scenarios for urban areas across the country.This page left intentionally blank.

### 6 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 Associat es Inc., Bethesda, MD. January. Available: http://www.epa.gov/ttn caaa 1/ t1/reports/v is_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. Visibility Index Oversight Committee. Available: http://www.phoenixvis.net/PDF/vis_031403final.pdf

. Accessed 9/16/2008. BBC Research & Consulting. 2002. Phoenix Area Visibility Survey. Draft Report. Available: http://www.azdeq.gov/environ/air/download/vis_021903f.pdf . Accessed 9/16/2008.

BBC Research & Consulting. 2003. Phoenix Area Visibility Survey. Draft Report . Available: http://www.azdeq.gov/e nviron/air/download/vis_0219 03f.pdf. Accesse d 9/16/2008

.
DeBell L. (2006). Spatial and seasonal patt erns and temporal variability of haze a nd its constituents in the United States: Rep ort IV. 1Cooperative Institute for Research in the Atmos phere. Colorado State University Fort Collins, CO. 156388

Ely, D.W., J.T. Leary, T.R. Stewart, and D.M. Ross. 1991. The Establishm ent of the Denver Visibility Standard. Presented at the 84th Annual Meeting & Exhibition of the Air and Waste Management Association. June 16-21.

Frank NH. (2006). Retain ed nitrate, hydrated sulfat es, and carbonaceous mass in federal reference method fine particulate matter for six eastern U.S. c ities. J Air Waste Manag Assoc, 56: 500-11

Henderson, R. (2006) 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 Recommendations Concerning the Proposed National Ambient Air Quality Standards for Particulate Matter. March 21, 2006. Available: http://www.epa.gov/sab/pdf/casac-ltr-06-002.pdf

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 Particulate Matter January 3, 2008

. 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: http://www.env.gov .bc.ca/air/airquality/pdfs/view _a hea d.pdf. Accessed 8/5/2009

.
Malm WC; Trijonis J; Sisler J; Pitchf ord M; Dennis RL (1994). Assessing the eff ect of SO2 emission changes on visibility. Atmos Environ, 28: 1023-1034.

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 Environment 28(5):1049-1054.

6-16-2 Pryor, S.C. 1996. Assessing pub lic perception of visibility fo r standard setting exercises. Atmospheric Environment 30(15):2705-2716.

Russell, A; Samet, J.M. (2010). Letter from the Clean Air Science Advisory Committee (CASAC) Ambient Air Monitoring and Methods Subcommittee (AAMMS) to the Honorable Lisa P, Jackson, Administrator, US EPA. Review of the White Paper on Particulate Ma tter (PM) Light Extinction Measurements. April 29, 2010, Available at: http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/92C9F5AA09A76A938 52577150004A782/$File/EPA-CASAC-10-010-unsigned.pdf

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 Clean Air Scientific Adviso ry Committee (CASAC) to the Honorable Lisa P. Jackson, Administrator, US. 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 Clean Air Scientific Adviso ry Committee (CASAC) to the Honorable Lisa P. Jackson, Administrator, US. EPA. Review of the Particulate Matter Urban-Focused Visibility Assessment (First External Review Draft); November 24, 2009. - Available: http://yosemite.epa.gov/sab/sabpeople.nsf/WebCommittees/CASAC

.
Samet, J. (2010a). Letter from Clean Air Scientific Advisory Committee (CASAC) to the Honorable Lisa P. Jackson, Administrator, US EPA. Review of Pa rticulate Matter Urban-Focused Visibility Assessment (Second External Review Draft, January 2010). April 20, 2010. Available: http://yosemite.epa.gov/sab/sabproduct.nsf/0 D5CB76AFE7FA77C8525770D004EED55/$File/EPACASAC-10-009-unsigned.pdf

Samet, J. (2010b). Letter from Clean Air Scientific A dvisory Committee to the Honorable Lisa P. Jackson, Administrator, US EPA. CASAC Review of Policy Assessment for the Review of the PM NAAQS (First External Revew Draft, March 2010). May 17, 2010. Available: http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/E504EE3276D87A9E8 525772700647AFB/$File/EPA-CASAC-10-011-unsigned.pdf/.

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 Revi ew Draft of EPA’s “Integrated Science Assessment for Particulate Matter.” CRA International, Washingt on, 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 Ro bustness of Visual Air Quality Preference Study Results. CRA International, Washington, DC. March 30. Prepar ed 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-rece n s ement/2006/dp-pd/prof/92-591/details/page.cfm?Lang = E&Geo1 = CMA&Code1 = 930__&Geo2 = PR&Code2 = 59&Data = Count&SearchText = Chilliwack&SearchType = Begins&SearchPR = 01&B1 = Population&Custom . Accessed 7/13/2009.

Stratus Consulting Inc., 2009. Review of Urban Visibility P ublic Preference Studies: Fi nal Report. Prepared for EPA Office of Air Quality Planning and Standards; fu nded under EPA Contract No. EP-D-08-100 with Abt Associates Inc., Bethesda, MD. September. Available: http://www.regulations .gov/search/Regs/hom e .html#doc umentDetail?R= 0900006480a296e2

Trijonis, J. C. 1990. Natural Background Conditions fo r Visibility/Aerosols. Washington (DC): National Acid Precipitation Assessment Program. NAPAP State of Science and Technology Report no. 24. http://www.epa.gov/ttncaaa1/t1/m e m oranda/rh_envcurhr_gd.pdf

US EPA (1996a). Air Quality Criteria for Particulate Ma tter. 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 of Air Quality Planning and Standards; report no. EPA-452/R-96-013. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_pr_sp.html

. U.S. EPA. (2003) Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Program. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-454/B-03-005

US EPA (2004). Air Quality Criteria for Particulate Matte r. National Center for Envi ronmental 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 Qua lity 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 Standa rds; report no. EPA EPA-452/R-05-00 5a. December 2005. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html

U.S. Environmental Protection Agency. (2 007). 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 Nationa l 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 Par ticulate 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 Scien ce Assessment for Particulate Matter: Final Report. National Center for Environmental Assessment-RTP Division, Office of Air Quality Planning and Standards, Research

6-36-4 Triangle Park, NC. EPA/600/R-08/139 F. December 2009. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html.

US EPA (2009b). Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan for Urban 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 Visib ilityAssessment: 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

US EPA (2009d). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards – Preliminary Draft. Office of Air Qua lity Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/P-09-007. September 2009. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html

. US EPA (2010a). Particulate Matter Urban-Focused Visibility Assessment – Second External Review Draft. Office of Air Quality Planning and Standards, U.S. Enviro nmental Protection Agency, Research Triangle Park, NC. EPA-452/P-10-002. January 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html

. US EPA (2010b). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards-First External Review Draft. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA 452/P-10-003. March 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html

.
US EPA (2010c). Particulate Matter Urban- 1 Focused Visibility Assessment – Second External Review Draft. Office of Air Quality Planning and Standards, U.S. En vironmental Protection Agency, Research Triangle Park, NC. EPA-452/P-10-002. January 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html

. US EPA (2010d). White Paper on PM Light Extinctio n Measurements. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. January 2010. Available
at: http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/823a6c8842610e76852 5764900659b22!OpenDocument

US EPA (2010e). Quantitative Risk Assessment for Pa rticulate Matter – Final Report. Office of Air QualityPlanning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/R10-005. June 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html

.0 B 

### APPENDICES

F 1

A



## . PM 2.5 Monitoring Sites and Monitors Providing 2005-2007 Data for the Analysis of PM 10 Light Extinction in the 15 Study Areas

B. Distributions of Estimated PM 2.5 and Other Components under Current Conditions

C. Development of PRB Estimates of PM 2.5 Components, PM 10-2.5 , and PM 10 Light Extinction

D. Relationships between PM Mass Concentration and PM 10 Light Extinction under Current Conditions

E. Differences in Daily Patterns of Relative Humidity and PM 10 Light Extinction between Areas and Seasons

F. Distributions of Maximum Daily Daylight PM 10 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

J. Logit Memorandum

1 When viewing St. Louis results throughout these appendi ces, the reader should keep in mind that credible comments concerning unrealistically high PM 10-2.5 values were received but too late in the review process to permit reanalysis using an alternate data set or to remove St. Louis from all portions of this document. However, the text in the body of this document has been revised to caution reader s with respect to the St. Louis results, and they will not be included in the visibility effects discussion in the fi nal PM Policy Assessment document. Some graphics have been updated to exclude St. Louis results.This page left intentionally blank.A-1 1 B 

## APPENDIX A

2 B 

## PM 2.5 

## MONITORING SITES AND MONITORS PROVIDING 2005-2007 DATA FOR THE ANALYSIS OF PM 10 

## LIGHT EXTINCTION IN THE 15 STUDY AREASA-2 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Tacoma AQS ID

H U 530530029

U 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 PM 2.5DV site in the Seattle-TacomaOlympia, WA annual PM 2.5 nonattainment area

Neighborhood Scale

Parameters taken from this site:

 24-hour FRM PM 2.5 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 PM 2.5 AQI & Speciation Mass) Correlated Radiance Research M903 Nephelometry

No continuous PM 10 monitoring at this site, see right hand column

.. NA AQS ID

H U 530530031

U State: Washington City: Tacoma MSA: Tacoma, WA Local Site Name: TACOMA - ALEXANDER AVE Address: 2301 ALEXANDER AVE, TACOMA, WA 6.4 miles NNE of PM 2.5 site

Neighborhood Scale

Parameters taken from this site:

 1-hour PM 10 STP mass (AQS parameter 81102)

 Sample Collection Method: INSTRUMENTAL-R&P SA246B-INLET

 Sample Analysis Method: TEOMGRAVIMETRIC

7% of PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-3 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Fresno AQS ID

H U 060190008

U 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 PM 2.5 DV site in the San Joaquin nonattainment area.

Neighborhood Scale

Parameters taken from this site:

 24-hour FRM PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88501, PM 2.5 Raw Data) Met-One BAM

No continuous PM 10 monitoring at this site, see right hand column

.. NA PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-4 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Los Angeles AQS ID

H U 060658001

U 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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

 1-hour PM 2.5 (AQS parameter 88502, Acceptable PM 2.5 AQI & Speciation Mass) R&P 1400 TEOM

No continuous PM 10 monitoring at this site, see right hand column

.. NA AQS ID

H U 060710306

U 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 PM 2.5 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 PM 10 STP mass (AQS parameter 81102)

 Sample Collection Method: INSTRUMENTAL-R&P SA246B-INLET

 Sample Analysis Method: TEOMGRAVIMETRIC

6% of PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-5 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Phoenix AQS ID

H U 040137020

U H (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 PM 2.5 mass(AQS parameter 88101; one-insix sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

No continuous PM 10 monitoring at this site, see right hand column

. AQS ID

H U 040139998

U H (Continuous) State: Arizona City: Phoenix MSA: Phoenix-Mesa, AZ Local Site Name: Vehicle Emissions Laboratory Address: 600 N 40 th 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 PM 2.5 mass. Nephelometer. AQS ID

H U 040133002

U 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 PM 10 STP mass (AQS parameter 81102)Sample Collection Method: INSTRUMENTALR&P SA246B-INLETSample Analysis Method: TEOMGRAVIMETRIC

2% of PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-6 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Salt Lake City AQS ID

H U 490353006

U 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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88501, PM 2.5 Raw Data) FDMS-Gravimetric

No continuous PM 10 monitoring at this site, see right hand column

. NA PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff Paper

Dallas AQS ID

H U 481130069

U 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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88502, Acceptable PM 2.5 AQI & Speciation Mass) TEOM Gravimetric 50 deg C

No continuous PM 10 monitoring at this site, see right hand column

.. NA PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-7 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Houston AQS ID

H U 482010024

U 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 PM 2.5 mass (AQS parameter 88101; one-in-six day sampling schedule)

 PM 2.5 speciation (one-in-six sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88502, Acceptable PM 2.5 AQI & Speciation Mass) TEOM Gravimetric 50 deg C

No continuous PM 10 monitoring at this site, see right hand column

. NA PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-8 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

St. Louis AQS ID

H U 295100085

U State: Missouri City: St. Louis MSA: St, Louis, MO-IL Local Site Name: BLAIR STREET CATEGORY A CORE SLAM PM 2.5 . 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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88502, Acceptable PM 2.5 AQI & Speciation Mass) TEOM Gravimetric 30 deg C

No continuous PM 10 monitoring at this site, see right hand column

. NA AQS ID

H U 295100092

U H (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 PM 2.5 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 PM 10 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.

H U 295100093

U H (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 PM 2.5 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 PM 10 STP mass (AQS parameter 81102)

 Sample Collection Method: INSTRUMENTAL-R&P SA246B-INLET

 Sample Analysis Method: TEOMGRAVIMETRIC

 4% of PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-9 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Birmingham AQS ID

H U 010730023

U 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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88502, Acceptable PM 2.5 AQI & Speciation Mass) TEOM Gravimetric 50 deg C

 1-hour PM 10 STP mass (AQS parameter 81102) Sample Collection Method:INSTRUMENTAL-R&P SA246BINLETSample Analysis Method: TEOMGRAVIMETRIC NA Same as PM 2.5 site.

0.3% of PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-10 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Atlanta AQS ID

H U 130890002

U 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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88502, Acceptable PM 2.5 AQI & Speciation Mass) TEOM Gravimetric 30 deg C

No continuous PM 10 monitoring at this site, see right hand column

. NA AQS ID

H U 131210048

U State: Georgia City: Atlanta MSA: Atlanta, GA Local Site Name: Georgia Tech, Ford Environmental Scienc e and Technology Bldg, roof Address: GA. TECH., Ford ES&T Bldg, 311 Ferst St NW, Atlanta GA 8.6 miles NW of PM 2.5 site

Neighborhood Scale

Parameters taken from this site:

 1-hour PM 10 STP mass (AQS parameter 81102) Sample Collection Method: INSTRUMENT MET ONE 4 MODELS Sample Analysis Method: BETA ATTENUATION

8 % of PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-11 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Detroit AQS ID

H U 261630033

U 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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-in-six sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88501, PM 2.5 Raw Data) TEOM Gravimetric 50 deg C

 1-hour PM 10 STP mass (AQS parameter 81102) Sample Collection Method:INSTRUMENTAL-R&P SA246BINLETSample Analysis Method: TEOMGRAVIMETRIC NA Same as PM 2.5 site.

2% of PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-12 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Pittsburgh AQS ID

H U 420030008

U 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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88502, Acceptable PM 2.5 AQI & Speciation Mass) TEOM Gravimetric 50 deg C

No continuous PM 10 monitoring at this site, see right hand column

. NA PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff Paper

Baltimore AQS ID

H U 240053001

U H (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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

 1-hour PM 10 LC mass (AQS parameter 85101 AQS ID

H U 245100040

U H (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 PM 2.5 mass (AQS parameter 88502, Acceptable PM 2.5 AQI & Speciation Mass) TEOM Gravimetric 50 deg C Same as PM 2.5 site.

5% of PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-13 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Philadelphia AQS ID

H U 100032004

U H (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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-in-six sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88501, PM 2.5 Raw Data) Beta Attenuation

 1-hour PM 10 STP mass (AQS parameter 81102) NA Same as PM 2.5 site.

3% of PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff Paper

New York AQS ID

H U 340390004

U H (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 PM 2.5 mass (AQS parameter 88101; every day sampling schedule)

 PM 2.5 speciation (one-inthree sampling schedule)

 1-hour PM 2.5 mass (AQS parameter 88502, Acceptable PM 2.5 AQI & Speciation Mass) TEOM Gravimetric 30 deg C

No continuous PM 10 monitoring at this site, see right hand column

. NA AQS ID

H U 360610125

U 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 PM 10 STP mass (AQS parameter 81102)Sample Collection Method: INSTRUMENTALR&P SA246B-INLETSample Analysis Method: TEOMGRAVIMETRIC

2% of PM 10-2.5 values were determined using regional average PM 10-2.5 : PM 2.5 ratios from 2005 Staff PaperA-14 

## PM 2.5 

## Monitoring Sites and Monitors Pr oviding 2005-2007 Data for the Analysis of PM 10 

## Light Extinction in the 15 Study Areas Study Area First PM 2.5 Monitoring Site Second PM 2.5 Monitoring Site (if applicable) PM 10 data source for PM 10-2.5

Notes:

 In this Table, the 1-hour concentr ation parameter “88502, Acceptable PM 2.5 AQI & Speciation Mass” is the same as the ISA refers to as “FRM-like” PM 2.5 mass. An entry of “88501, PM 2.5 Raw Data” indicates that the monitoring agency makes no representation as to the degree of correlation with FRM PM 2.5 mass. The latter type of continuous PM 2.5 data were used only when the former were unavailable.

 Where PM 10 was reported in STP, it was converted to LC before PM 10-2.5 was calculated.

 All continuous PM 2.5 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, desp ite the opportunity offered by the AirNow system for monitoring agencies to correct data after initial submission.B-1 3 B 

## APPENDIX B

4 B 

## DISTRIBUTIONS OF ESTIMATED PM 2.5 

## AND OTHER COMPONENTSB-2Figure B-1 – Distribution of Daily Maximum PM2.5and PM10-2.5Across the 2005-2007 Period, by Study Area(a) Daily Maximum Daylight PM2.5B-3(b) Daily Maximum Daylight PM10-2.5B-4Figure B-2 – Distribution of Hourly PM2.5Components Across the 2005-2007 Period, by Study Area(a) 1-Hour Daylight Sulfate (dry, fully neutralized)B-5Figure B-2 – Distribution of Hourly PM2.5Components Across the 2005-2007 Period, by Study Area, continued(b) 1-Hour Daylight Nitrate (dry, fully neutralized, CSN method consistent)B-6Figure B-2 – Distribution of Hourly PM2.5Components Across the 2005-2007 Period, by Study Area, continued(c) 1-Hour Daylight Elemental CarbonB-7Figure B-2 – Distribution of Hourly PM2.5Components Across the 2005-2007 Period, by Study Area, continued(d) 1-Hour Daylight Organic Carbonaceous Material (by SANDWICH method)B-8Figure B-2 – Distribution of Hourly PM2.5Components Across the 2005-2007 Period, by Study Area, continued(e) 1-Hour Daylight Fine SoilC-1 5 B 

## APPENDIX C

6 B 

## DEVELOPMENT OF PRB ESTIMATES OF PM 2.5 

## COMPONENTS, PM 10-2.5 

## , AND PM 10 

## LIGHT EXTINCTION

Policy relevant background levels of PM 10 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 ISA (US EPA, 2009a). Estimates of PRB for PM 10 light extinction were calculated from modeled
concentrations of PM 2.5 components using the IMPROVE algorithm. The necessary component concentrations were extracted from the CMAQ output files, as they were not summarized in the ISA (US EPA, 2009a). More specifically, for each study area, EPA staff overlaid CM AQ grid cells over shapes representing the Census-defined urba nized area for each study area, and visually identified the CMAQ grid cells that had a substa ntial 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 PM 2.5 components. We then averaged these across the selected grid cell s. Thus, a given hour of the day has the same PRB estimate for a component on all days within a month, but months a nd study areas differ. We generally observed that PRB concentrations did not vary greatly ac ross the several grid cells overlaying the urbanized area of a give n study area; this is reasonable given the exclusion of local anthropogenic sources from this CMAQ model run. CMAQ estimates of
PRB for the five PM 2.5 components averaged across grid cells and months were not adjusted in any.

F 2 There are too many values of PRB to presen t or illustrate them comprehensively in this document. Table C-1 presents annua l average concentrations by study area to
summarize these PRB estimates for the PM 2.5 components (including the specific form assumed for sulfate, nitrate, and organic car bon). The right hand column of the table shows
the PM 2.5 mass calculated from the CMAQ-estimated components, including factors to fully neutralize sulfate and nitrate (b ut with no water mass added). One notable feature of the annual average of the PRB estimates is the re latively high values for elemental and organic carbon PRB for the Tacoma study area. This area is often affected by wildfires for extended

2 This approach to estimation of PRB for PM 2.5 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 (US EPA, 2010e). In the health risk assessment, PRB estimates for PM 2.5 mass concentration are taken from the same CMAQ model run, but are averaged by calendar quarter and by region of the country.C-2 periods in the autumn months, and such fire s 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 situa tion in this part of the count ry in 2004 was not an anomaly.
Another notable feature of th e PRB estimates is that the values for nitrate and fine
soil/crustal are low relative to previous estimates of natura l background concentrations of these fine PM components in Class I areas. These previous estimates by Trijonis (1990), repeated in the 2003 EPA guidance document “G uidance for Estimating Natural Visibility Conditions Under the Regional Haze Program” (US EPA, 2003), are 0.10 μ g/m 3 for (neutralized) nitrate and 0.50 μ g/m 3 for fine soil. These estimate s are based largely on data from the earliest of the IMPROVE monitoring st ations, 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 underestim ate nitrate and fine soil. CMAQ is known to underestimate actual nitrate in many situa tions when provided with a complete NOx inventory, and the nonanthropogenic emission inventory for NOx itself has uncertainties. The non-anthropogenic emission inventory for th e 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 (1990) estimates, the resulting
values for PRB PM 10 light extinction would increase only a little. Even at 90 percent relative humidity, the contribution to PM 10 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 10 light extinction estimates shown in Table C-4, and w ould not significantly aff ect the calculation of
PM 10 light extinction values unde r the “what if ” scenarios.C-3 Table C-1. Summary of PRB Estimates for the Five PM 2.5 Components: Average 1-Hour Values Across 2005-2007 Study Area Average 1-Hour PRB Concentration Across 2005-2007 ( μ g/m 3 )
Sulfate (dry, no ammonium) Nitrate (dry, no ammonium) Elemental Carbon Organic Carbonaceous Material Fine Soil/Crustal Calculated PM 2.5

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 Atlant a 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

Pittsbur g h 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 Philadelphi a 0.34 0.00097 0.03 0.36 0.032 0.9 N ew Yor k City 0.36 0.0038 0.026 0.31 0.022 0.9 Average 0.33 0.00 0.06 0.59 0.10 1.20C-4 It is also necessary to ha ve estimates of PRB for PM 10-2.5 , 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 consider ed. The final ISA for this review does not present any new information on this subject. Th e approach used in th e previous two Criteria Documents was to present the histor ical range of annual means of PM 10-2.5 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 μ g/m 3 to a high
of 10.8 μ g/m 3 . No cross-year average or median valu es were provided that could be used as the point estimates needed in this assessment. Therefore, for this assessment, EPA staff
estimated PRB for PM 10-2.5 using a contour map based on average 2000-2004 PM 10-2.5 concentrations from all IMPROVE monitori ng 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 indica ted by the contour band for that location. The contour map is reproduced here as Figure C-1. Stars s how locations of the 15 study areas. In this reproducti on, 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 infl uenced by data from IMPROVE s ites that were not considered in the 2004 Criteria Document because they are no t 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 co mparison. Further, these PRB values are low enough that their exact values ha ve little effect on the results of “what if” estimation of PM 10 light extinction levels under possible secondary PM NAAQS. Table C-4 presents the resulting 2005-2007 average PRB daylight PM 10 light extinction by study area, determined by using each daylight hour’s f(RH),

F 3

F the hour-specific
PRB PM 2.5 component estimates (summarized only as annual averages in Table C-1), the
PRB PM 10-2.5 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, before being used in the IMPROVE algorithm. While for conciseness Table C-4 presents

3 Hour-specific relative humidity for PRB conditions was assumed to be the same as measured for current conditions.C-5 only the annual average PRM for PM 10 light extinction for all daylight hours in 2005-2007 in the rollback analysis of “what if” conditions ho ur-specific PRB values are retained and used.
The values of PRB PM 10 light extinction in Table C-4 range between 5 and 11 Mm -1

. For comparison, the default estimates of na tural 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 10 light extinction used for this assessmen t is very consistent with the range of total light extinction values r ecommended in the guidance document.C-6 Figure C-1. Selection of PRB Values for PM 10-2.5 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 0.5C-7 Table C-2. Policy Relevant Background Concentrations of PM 10-2.5 Used in This Assessment, Based on Measurements at IMPROVE Sites Study Area PRB PM 10-2.5 Mass ( μ g/m 3 )

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/m 3 )

Source: Table 3E-1 of the 2004 Air Quality Criteria Document for PM (US EPA, 2004)C-8 Table C-4. 2005-2007 Average Policy Re levant Background Daylight PM 10 Light Extinction

Study Area 2005-2007 Average Policy Relevant Background Daylight PM 10 Light Extinction, Mm -1
Tacoma 11 Fresno 11 Los An g eles 9 Phoenix 8 Salt Lake Cit y 5 Dallas 8 Houston 10 St. Louis 9 Birmin g ham 9 Atlant a 10 Detroit 7 Pittsburgh 7 Baltimo r e 8 Philadelphi a 8

New York 8D-1 7 B 

## APPENDIX D

8 B 

## RELATIONSHIPS BETWEEN PM MASS CONCENTRATION AND PM 10 

## LIGHT EXTINCTION UNDER CURRENT CONDITIONS

In the last review, the 2005 Staff Paper (US EPA, 2005) examined the correlation
between PM 10 light extinction and PM 2.5 mass concentrations, each defined for various consistent time periods. The 2005 Staff Paper an alysis assumed that th e percentage mix of PM 2.5 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 10 light extinction in this new assessment allows these correlations to be re-examined, with the more realistic treatment in which the mix of PM 2.5 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 PM 2.5 mass concentrations and PM 10 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 e ach figure, the solid red curve was estimated by applying locally weighted scatte r plot smoothing (LOESS) to the data. LOESS is a form of locally weighted polynomial regression (see

H http://support.sas.com/rnd/app/papers/loesssugi.pdf

H ) and is a convenient way to visua lize whether a dense data cloud in a scatter plot reflects a more
linear or more nonlinear relationshi p. The LOESS results in each case indicate a generally linear relationship as a central tendency but with consid erable variability around that central tendency. Table D-1 presents squared correlation coe fficients 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 10 light extinction presented here are based on excluding daylight hours with relative hum idity greater than 90 percent; hence, a nominally 4-hour period might have as few a one 1-hour PM 10 light extinction value, although this is rare in th is data set (see the tile plots in Figure 3-12). However, values of
PM 2.5 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 th e 2005 Staff Paper, the corre lations would be weaker than observed here for individual study areas. Figure D-1 compares 24-hour PM 2.5 mass (as measured by the FRM/FEM filter-based sampler) to daily maximum daylight PM 10 light extinction. The scatte r is due the variations inD-2 PM 2.5 concentration, in the mix of PM 2.5 components, and in relativ e humidity during the day and across days. Variations in PM 10-2.5 concentrations also contribute to the scatter, in all five comparisons presented here, since very high levels of PM 10-2.5 substantially influence PM 10 light
extinction. This source of variab ility in the scatter plots is particularly important for Los Angeles, Phoenix, and St. Louis which have ma ny (Phoenix) or some (Los Angeles and St. Louis) hours with high PM 10-2.5
. Because of the large scatter and low corre lation coefficients when using 24-hour PM 2.5 mass concentration to predict daily maximum daylight PM 10 light extinction, it is natural to investigate how much the corr elation improves when the PM 2.5 mass indicator is limited to shorter periods of time. The next four figures investigate correlati ons during such shorter periods, both matched and un-matched in time. Figure D-2 compares hourly PM 2.5 mass (as actually measured by the continuous instruments) vs. same-hour daylight PM 10 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 PM 2.5 components and in relative humi dity across hours and days. In addition, continuous PM 2.5 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 becaus e the estimates of hourly PM 10 light extinction are linked to the
FRM/FEM and CSN measurements more strongly than to the continuous PM 2.5 measurements. Note that the correlation values in Table D-1 fo r 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 PM 10 light extinction levels can prevail in hours
that have the same PM 2.5 mass concentration, even at a singl e site. Additional variability no
doubt exists across areas. Figure D-3 compares 12-4 pm average PM 2.5 mass vs. 12-4 pm average PM 10 light extinction. The 2005 Staff Paper observed that becau se this time period is generally the time of lowest relative humidity, the relationship between PM 2.5 mass and PM 10 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 av eraged 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 PM 2.5 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. Th is 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 obser ved by comparing FiguresD-3 D-2 and D-3 that the period between 12 pm a nd 4 pm generally has lower levels of PM 10 light
extinction than for all da ylight hours taken together, even af ter the exclusion of the hours with the highest relative humidity. (Note the ch ange in scale between these two figures.) Figure D-4 compares 12-4 pm average PM 2.5 mass vs. daily maximum daylight 1-hour
PM 10 light extinction. This time-unmatched comp arison tests the useful ness of a 12-4 pm PM 2.5 mass indicator as a predictor of the daily PM 10 light extinction metric of potentially greatest interest. The scatter in Figur e D-4 is typically more than in Figure D-3 (4 time-matched afternoon hours), because daily maximum daylight 1-hour PM 10 light extinction often occurs earlier in the day than the 12-4 pm period used to average the PM 2.5 mass, and the time period mismatch introduces prediction errors due to changes in PM 2.5 concentration and composition and relative humidity. An implication is that while a secondary NAAQS based on 12-4 pm average PM 2.5 mass might achieve a given level of pr otection across days and areas in avoiding high levels of PM 10 light extinction between 12 and 4 pm, with some variation across areas due to composition and relative humidity differences, th ere could be considerab le additional variation in the level of pr otection against PM 10 light extinction during the earlier hours of the day when some areas often have their highest PM 10 light extinction levels. Figure D-5 compares 8 am-12 pm average PM 2.5 mass vs. daily maximum daylight 1-
hour PM 10 light extinction. This comparison is of in terest because it may reduce the number of instances of time mismatch, versus the compar ison made in Figure D-4, if the daily maximum
PM 10 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 square d correlation coefficients larger, indicating that this earlier averaging period for PM 2.5 mass more often encompasses the period of maximum
PM 10 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 perspe ctive on the possible use of PM 2.5 mass concentration as an indicator for a secondary PM NAAQS aimed at protecting visual ai r quality. Figure D-6 shows in box-and-whisker plot form two versions of the ratios of PM 10 light extinction to PM 2.5 mass concentration, allowing a comparison across the 15 study areas of the central tendencies
and the distributions of these ratios. The Pa nel A version corresponds to the comparison in Figure D-1 (24-hour averages of PM 2.5 mass and PM 10 light extinction) an d the Panel B version corresponds to the comparison in Figure D-2 (tim e-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 10 light extinction (24-hour average or 1- hour value) was divi ded by the indicated
PM 2.5 concentration metric (24-hour average or 1-hour value). Ratios that reflect PM 2.5 concentrations less than 5 μg/m 3 or PM 10 light extinction less than 64 Mm -1 were eliminated before plotting, as such data points represent da ys or hours that could not play any role inD-4 determining compliance with any of the NAAQS s cenarios considered in this assessment; also, some of these low-concentration/extinction data pairs produced extreme ra tios that obscured the pattern for data pairs of most policy interest. The maximum ratio value for the vertical scale in these plots is set at 40 to allow closer examina tion of the portion of the plot representing the bulk of the data; this prevents a very small numb er of daily maximum data points for a few study areas from appearing in Panel A and a very sm all percentage of 1-hour data points for a few study areas (Los Angeles and St. Louis in particul ar) from appearing in Panel B. The notable variation in the vertical positions of the 25- 75 percentile boxes and th e 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 rela tive humidity greater than 90 percent) across study area s, a secondary NAAQS based on PM 2.5 mass concentration would not give equal protection in terms of PM 10 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 excl usion of hours with very high relative humidity) the correlation value for Philadelphi a is about that for other eastern areas. The correlation values for St. Louis remain notably low re lative to the average of all areas, for all five scatter plots. This is likely due to the influence of the high estimated values for PM 10-2.5 . In
several other cases of no tably low correlation, the small available range of PM 2.5 values relative
to other areas contributes to the lower correla tion values, e.g., in Phoe nix, Dallas, and Houston.D-5 Table D-1. Squared Correlation Coeffici ents between Observed and LOESS Model-predicted Values of PM 10 Light Extinction

Area Figure D-1 24-Hour PM 2.5 Mass

vs. Daily Maximum Daylight 1- Hour PM 10 Light Extinction Figure D-2 1-Hour PM 2.5 Mass

vs. SameHour PM 10 Light Extinction Figure D-3 12-4 pm Average PM 2.5 Mass vs. 12-4 pm

Average PM 10 Light Extinction Figure D-4 12-4 pm Average PM 2.5 Mass

vs. Daily Maximum Daylight 1- Hour PM 10 Light Extinction Figure D-5 8 am-12pm

Average PM 2.5 Mass

vs. Daily Maximum Daylight 1- Hour PM 10 Light Extinction Tacoma 0.48 0.80 0.78 0.29 0.65 Fresno 0.76 0.83 0.90 0.69 0.83 Los Angeles 0.57 0.63 0.66 0.52 0.69 Phoenix 0.22 0.67 0.73 0.18 0.20 Salt Lake City 0.88 0.89 0.95 0.80 0.89 Dallas 0.45 0.59 0.54 0.20 0.36 Houston 0.46 0.61 0.62 0.20 0.30 St. Louis 0.40 0.43 0.20 0.18 0.36 Birmingham 0.61 0.81 0.78 0.34 0.44 Atlanta 0.54 0.72 0.80 0.40 0.70 Detroit 0.62 0.55 0.61 0.11 0.30 Pittsburgh 0.73 0.63 0.66 0.52 0.62 Baltimore 0.78 0.69 0.69 0.58 0.71 Philadelphia 0.61 0.61 0.57 0.39 0.50

New York 0.69 0.77 0.76 0.51 0.62 AVERAGE 0.59 0.68 0.68 0.39 0.54D-6Figure D-1. Relationship Between 24-Hour PM2.5Mass vs. Daily MaximumDaylight 1-Hour PM10Light Extinction.D-7Figure D-2. Relationship Between Daylight 1-Hour PM2.5Mass vs. Same-Hour PM10Light Extinction.D-8Figure D-3. Relationship Between 12-4 pm Average PM2.5Mass vs. 12-4 pm Average PM10Light Extinction.D-9Figure D-4. Relationship Between 12-4 pm Average PM2.5Mass vs. Daily Maximum Daylight 1-Hour PM10LightExtinction.D-10Figure D-5. Relationship Between 8 am-12 pm Average PM2.5Mass vs. Daily Maximum Daylight 1-Hour PM10LightExtinctionD-11 Figure D-6. Distribution of Ratios of 1-Hour PM 10 Light Extinction and PM 2.5 Mass Concentration.

A – Ratios of Daily Maximum Daylight 1-Hour PM 10 Light Extinction to 24-Hour Average PM 2.5 Concentration.D-12 B – Ratios of Daylight 1-Hour PM 10 Light Extinction to Same-Hour PM 2.5 ConcentrationE-1 9 B 

## APPENDIX E

1 0 B 

## DIFFERENCES IN DAILY PATTERNS OF RELATIVE HUMIDITY AND PM 10 

## LIGHT EXTINCTION BETWEEN AREAS AND SEASONS

In the last review of the sec ondary PM NAAQS, the pattern of PM 10 light extinction during the day was of particular interest. It was noted, using estimates of hourly PM 10 light extinction based on a simpler approach than desc ribed for this analysis, that both (1) mid-day
PM 10 light extinction and (2) the slop e of the relationship between PM 10 light extinction and
PM 2.5 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 pa ttern of season-aver age, hour-specific
PM 10 light extinction and relative humidity for th e four “daylight seasons.” These graphics
exclude hours with relative hum idity 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 sc hedule of Table 3-5) are indicated by solid circles. Average 1-hour PM 10 light extinction generally is highest in the morning, corresponding to higher relatively humidity (mostly due to lower temperature), higher vehicle traffic, and less dispersi ve conditions than later in th e day. As was observed in the last review, there is more variation in average 1-hour PM 10 light extinction among areas in the morning than at mid-day, although the morn ing variation has been reduced (relative to same information in the first pub lic review draft of this asse ssment) by the exclusion of hours with relative humidity great er than 90 percent.E-2Figure E-1. Diurnal and Seasonal Patterns of Relative Humidity (percent) and PM10Light Extinction (Mm-1) for 2005-2007(a) November-JanuaryE-3Figure E-2. Diurnal and Seasonal Patterns of Relative Humidity (percent) and PM10Light Extinction (Mm-1) for 2005-2007,continued(b) February-AprilE-4Figure E-3. Diurnal and Seasonal Patterns of Relative Humidity (percent) and PM10Light Extinction (Mm-1) for 2005-2007,continued(c) May-JulyE-5Figure E-4. Diurnal and Seasonal Patterns of Relative Humidity (percent) and PM10Light Extinction (Mm-1) for 2005-2007,continued(d) August-OctoberThis page left intentionally blank.F-1 1 B 

## APPENDIX F

1 2 B 

## DISTRIBUTIONS OF MAXIMUM DAILY AND HOURLY DAYLIGHT PM 10 

## LIGHT EXTINCTION - UNDER “JUST MEET” CONDITIONSF-2 (a) NAAQS Scenario Daily Max 191 Mm -1 90 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-3 (b) NAAQS Scenario Daily Max 191 Mm -1 95 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-4 (c) NAAQS Scenario Daily Max 191 Mm -1 98 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (exc luding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (exc luding hours >90% RH)F-5 (d) NAAQS Scenario Daily Max 112 Mm -1 90 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (exc luding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (exc luding hours >90% RH)F-6 (e) NAAQS Scenario Daily Max 112 Mm -1 95 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (exc luding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (exc luding hours >90% RH)F-7 (f) NAAQS Scenario Daily Max 112 Mm -1 98 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-8 (g) NAAQS Scenario Daily Max 64 Mm -1 90 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-9 (h) NAAQS Scenario Daily Max 64 Mm -1 95 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-10 (i) NAAQS Scenario Daily Max 64 Mm -1 98 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-11 (j) NAAQS Scenario All hours 191 Mm -1 90 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-12 (k) NAAQS Scenario All hours 191 Mm -1 95 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-13 (l) NAAQS Scenario All hours 191 Mm -1 98 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-14 (m) NAAQS Scenario All hours 112 Mm -1 90 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-15 (n) NAAQS Scenario All hours 112 Mm -1 95 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-16 (o) NAAQS Scenario All hours 112 Mm -1 98 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-17 (p) NAAQS Scenario All hours 64 Mm -1 90 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-18 (q) NAAQS Scenario All hours 64 Mm -1 95 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-19 (r) NAAQS Scenario All hours 64 Mm -1 98 th percentile

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-20 (s) NAAQS Scenario 15 μ g/m 3 annual 35 μ g/m 3 24-hour

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight PM 10 Light Extinction (excluding hours >90% RH)F-21 (t) NAAQS Scenario 12 μ g/m 3 annual 25 μ g/m 3 24-hour

Displayed: Daily Max Daylight PM 10 Light Extinction (excl uding hours >90% RH)

Displayed: Hourly Daylight Light Ex tinction (excluding hours >90% RH)This page left intentionally blank.G-1 1 3 B 

## APPENDIX G

1 4 B 

## ADDITIONAL INFORMATION ON THE EXCLUSION OF DAYLIGHT HOURS WITH RELATIVE HUMIDITY GREATER THAN 90 PERCENTG-2 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 10 light extinction, as discussed in section 3.3.5. As described in that section, these hours have also been excluded from graphical di splays of the distribution of
PM 10 light extinction under curren t conditions and the various NAA QS scenarios, and from the denominator of percentages of day or hours (as in Table 4-7). 4 Table G-1 shows how many esti mates of 1-hour daylight PM 10 light extinction were excluded, based both on individual h ours and on days that were aff ected by the exclusion of one or more daylight hours. Phoenix was not affected at all. Amo ng 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 seve ral hours with high relatively humi dity tend to occur in the same day, rather than being evenly distributed across al l 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 ev ery day of the year and given that on average there are about 10 fully daylight hours per da y). 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 Atla nta has about 3 affected hours. The tile plots in Figure 3-12 also illustrate the tendency for hours with high PM 10 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 o ccurred, prior to the their exclusi on. 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 10 light extinction, not a ll the actual calendar days in that three year period. Given the regu larity of the monitoring schedules, these results should represent year-round conditions reasonably well. However, the estimates of PM 10 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 some what different than shown here. Figure G-2 contrasts the di stribution of daylight PM 10 light extinction estimates before and after the exclusion, based on both daily maxi mum values and all daylight hourly values individually. The differences obs ervable in the figure are consistent with the information on the percentages of hours and day affected in the stud y areas. In most cases, the highest values of light extinction are notably lowe r after exclusion, on bot h a daily maximum basis and individual

4 This appendix was prepared prior to the discovery of the SANDWICH processing error noted in the footnote on page 3-22 of the main document, and has not been updated to incorporate that correction. Values for
PM 10 light extinction in Figure G-2 and Ta ble G-2 are therefore slig htly inconsistent with values presented in the main report, but this should have a neglig ible effect on the comparisons presented.G-3 hour basis, indicating that PM c oncentrations in some of the ex cluded hours are fairly high. If only low-PM hours were excluded by the relative humidity screen, the highest values of PM 10 light extinction would not have been affected. Finally, Table G-2 contrasts PM 10 light extinction design values before and after the exclusion, for the 90 th and 95 th percentile forms based on daily maximum daylight 1-hour PM 10 light extinction, for current conditions . (A similar comparison for the 98 th percentile form was not generated.) As expecte d, design values are notably lowe r 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 accord ingly Table G-2 shows that its PM 10 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 thei r design values are aff ected 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 22.4G-4Figure G-1.Distribution by Time of Day of Eliminated Daylight Hours with Relative Humidity Greater Than 90 Percent.G-5 Figure G-2. Comparison of Distributions of Estimated Daylight 1-Hour PM 10 Light Extinction and Maximum Daily Daylight 1-Hour PM 10 Light Extinction Across the 2005- 2007 Period for Current Conditions, by Study A rea, Before and After Elimination of Hours with Relative Humidity Greater Than 90 Percent.

(a) Maximum Daily Values:
Before Elimination

After EliminationG-6 (b) Individual 1-Hour Values:
Before Elimination

After EliminationG-7 Table G-2. Comparison of 90 th and 95 th Percentile PM 10 Light Extinction Design Values for the 2005-2007 Period for Current Conditions Based on Maximum Daily 1-Hour Daylight PM 10 Light Extinction, Before and After Elimination of Hours with Relative Humidity Greater Than 90 Percent

Study
Areas PM 10 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 61This page left intentionally blank.H-1 1 5 B 

## APPENDIX H

1 6 B 

## 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 ba sed on a three-year average approach. That is, design values are based on the average of specified percentile values of PM 10 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 90 th percentile form for both the daily maximum and all hour approaches.H-2 Table H-1. Year-specific Percentile Values of PM 10 Light Extinction for 2005, 2006,
and 2007
Study Areas 90 th Percentile Form 95 th Percentile Form 98 th 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 10 Light Extinction (Excluding hours with relativ e humidity greater than 90%) Tacoma NA 121 158 140 NA 141 173 157 NA 214 207 211 Fresno 298 293 398 330 549 363 467 460 653 398 540 530 Los Angeles 424 461 477 454 507 523 619 550 582 594 658 611 Phoenix 100 110 NA 105 156 131 NA 144 182 187 NA 185 Salt Lake City 216 112 161 163 309 142 305 252 341 191 696 409 Dallas 184 170 197 184 252 223 242 239 312 321 271 301 Houston 217 204 161 194 269 238 196 234 306 319 248 291 St. Louis 350 329 239 306 432 405 303 380 483 572 347 467 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 300 312 313 308 347 401 664 471 391 490 1051 644 Pittsburgh 284 257 292 278 347 272 320 313 360 350 382 364 Baltimore 303 227 208 246 362 258 239 286 415 302 260 326 Philadelphia 257 325 274 285 331 352 318 334 426 375 320 374 New York 333 265 320 306 405 272 384 354 559 353 441 451 Based on 1-Hour Daylight PM 10 Light Extinction (All Hours) (Excluding hours with relativ e humidity greater than 90%) Tacoma NA 73 78 76 NA 101 109 105 NA 120 151 136 Fresno 181 172 211 188 255 254 273 261 391 326 387 368 Los Angeles 258 267 257 261 314 353 357 341 393 451 478 441 Phoenix 67 68 NA 68 79 78 NA 79 92 96 NA 94 Salt Lake City 116 67 97 93 193 83 148 141 255 116 304 225 Dallas 114 100 125 113 145 126 158 143 184 176 203 188 Houston 116 98 100 105 143 122 119 128 191 174 148 171 St. Louis 227 195 157 193 276 240 188 235 334 309 226 290 Birmingham 191 162 166 173 251 204 226 227 340 267 319 309 Atlanta 166 164 168 166 188 194 202 195 233 233 248 238 Detroit 226 212 198 212 268 252 234 251 320 312 313 315 Pittsburgh 173 153 176 167 217 193 218 209 284 237 272 264 Baltimore 203 161 150 171 290 190 196 225 342 225 218 262 Philadelphia 163 203 182 183 209 234 223 222 279 298 258 278 New York 203 169 187 186 264 222 244 243 313 267 317 299H-3 Figure H-1. Inter-ye ar Variability in 90 th Percentile 1-Hour Daylight PM 10 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

YearDaily Max Daylight PM LightExt. (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

YearHourly Daylight PM LightExt. (Mm-1)

Salt Lake City Dallas Houston St. Louis Birmingham Atlanta Detroit Pittsburgh
Baltimore Philadelphia New York Phoenix Los Angeles Fresno TacomaThis page left intentionally blank.I-1 1 7 B 

## APPENDIX I

1 8 B 

## DAYLIGHT HOURS

Table 3-5 shows the simple scheme used in th is analysis to denote hours as fully daylight and thus eligible for considera tion in the calculation of design values for the secondary NAAQS
scenarios based on PM 10 light extinction. This scheme also has been used to select which hours to show in various graphics. The scheme is ba sed on applying a fixed set of fully daylight hours for each three-month season (November to January, et c.). In reality, the local time minutes of daylight vary continuously during th e 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 result s presented and the answers to policy relevant questions that may be addressed in th e subsequent policy assessment document. Six study areas were selected for this ex amination: 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 appeari ng above the horizon) and of suns et (defined by the leading or bottom edge of the sun disappearing below the hori zon) 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 thes e 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 (botto m of figure). The stepped lines repres ent the scheme used to select the first and last hour denoted as fully daylight. Months are indicated on th e horizontal axis. The figure indicates that the simple sc heme has the effect of treating some hours as daylight that in fact contain minutes prior to s unrise or after sunset, and conversely treating some hours as not daylight that include no su ch 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 minu tes 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 De troit and Tacoma only, the hour of 7 pm to 8 pm is treated as non-day light but in fact has no minutes after sunset.I-2  In October, the hours of 6 am to 7 am and 5 pm to 6 pm are treat ed as daylight but include non-daylight minutes in all of the example areas.

The tile plots in Figure 3-12 can be used to as sess the significance of these disparities, i.e., whether they are likely to significantly affect PM 10 light extinction design values. Table I-1
contains observations for each of the 24 combinati ons of the four time periods listed above and the six example areas. Taken toge ther, these observations make it likely that refining the scheme for designating hours as fully daylight would not significantly change conc lusions that can be drawn from this assessment as it has been pe rformed. Changing the scheme would involve considerable effort in updating virtually every table and graphic in the assessment, however.I-3Figure I-1. Comparison of Actual Sunrise and Sunset Times to this Assessment’sScheme to Denote Hours as FullyDaylightSunrise/Sunset for Six Sites020040060080010001200140016001800200022002400Jan10Feb-10Mar-10Apr-10May-10Jun-10Jul-10Aug-10Sep-10Oct-10Nov-10Dec-10Date

Local TimeAssumed daylight startAssumed daylight endTAC RiseTAC SetLA RiseLA SetPHX RisePHX SetHOU RiseHOU SetDET RiseDET SetNY RiseNY SetI-4Table I-1. Observations from Tile Plots for Hours with Questionable Daylight/Non-Daylight Status in Six Study AreasStudy AreaFebruary (AM and PM)April (AM)June-July (PM)October (AM and PM)TacomaThe morning hour with questionabledaylight status tends to have RH > 90%.The evening hour in question tends toeither have low PM light extinction or tohave RH > 90%.The tile plot does not show data for themorning hour that may better be denoteddaylight, but the instances of high PM lightextinction that do appear typically lastmultiple hours.Very late afternoon typically is not aperiod of high PM light extinction.Instances of high light extinction involvingthe questionable hours are multi-hour and/orinvolve RH > 90%.Los AngelesInstances of high light extinctioninvolving the questionable hours aremulti-hour and/or involve RH > 90%.The tile plot does not show data for themorning hour that may better be denoteddaylight, but the instances of high PM lightextinction that do appear typically lastmultiple hours.NA Instances of high light extinction involvingthe questionable hours are multi-hour and/orinvolve RH > 90%.PhoenixInstances of high light extinctioninvolving the questionable hours aremulti-hour.The tile plot does not show data for themorning hour that may better be denoteddaylight, but early morning in April typicallyis not a time of high PM light extinction.NA PM light extinction is usually low inOctober; on those days with moderate levelsin the questionable hours, another hour in thecentral part of the day has a similar level.HoustonInstances of high light extinctioninvolving the questionable hours aremulti-hour and/or involve RH > 90%.The tile plot does not show data for themorning hour that may better be denoteddaylight, but the instances of high PM lightextinction that do appear typically lastmultiple hours and/or involve RH >90%.NA The amount of information is limited due tomissing data. On those days with moderate tohigh PM light extinction during thequestionable hours, another hour has asimilar level, or RH >90% plays a role.DetroitInstances of high light extinctioninvolving the questionable hours aremulti-hour.The tile plot does not show data for themorning hour that may better be denoteddaylight, but the instances of high PM lightextinction that do appear typically lastmultiple hours.July generally is a time of high PMlight extinction for the hours currentlyconsidered daylight. Adding one morelate afternoon hour likely would notaffect design values.Instances of high light extinction involvingthe questionable hours are multi-hour.New YorkAll but one instance of high lightextinction involving the questionablehours are multi-hour.The tile plot does not show data for themorning hour that may better be denoteddaylight, but the instances of high PM lightextinction that do appear typically lastmultiple hours.NA Instances of high light extinction involvingthe questionable hours are multi-hour and/orinvolve RH > 90%.J- 1 

## APPENDIX J

1 9 B 

### LOGIT MEMORANDUMSC11979 

# Memorandum

To: Vicki Sandiford, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency

From: Leland Deck and Megan Lawson, Stratus Consulting Inc.

Date: 2/3/2010 Subject: Statistical analysis of existing urban visibility prefer ence studies

During the CASAC meeting on October 5-6, 2009, Dr. Bill Malm and other CASAC members suggested that a limited dependent variable statistical analysis could be used to analyze the acceptability criteria responses in the four cities for which there are existing urban visibility preference studies. It was the view of those Panel members that successful statistical analyses of the studies results would provide an estimate of a “best fit” central tendency function describing the results of the p reference studies, as well as confidence intervals around the estimated functions. Such analyses would also make it possible to conduct hypothesis testing, such as examining whether the estimated 50% criteria level in one study is statistically different than the 50% criteria level in another study.

On the basis of the CASAC comments and the information available in the previous St ratus Report (Stratus Consulting, 2009), EPA concluded it was appropriate to conduct further statistical analyses on the available urban visibility preference studies. Subsequently, EPA asked Stratus Consulting to re- examine the data from these studies an d identify several methods for statistical analyses along the lines CASAC members suggested. This memorandum provides a description of the statistical analyses we conducted, and summarizes the results.

Data

While we do not have complete original response data from each preference study, certain data available in all four studies can be used to derive a set of data for an analysis comparing the results from each of the four 1

1 In the initial set of analyses discussed in this memorandum we combine the results from the 2001 Washington, DC focus group study with all 26 participants in the “Te st 1” analysis from Smith and Howell (2009). “Test 1” was designed to replicate the 2001 focus group study, with a goal of making two sets of results directly comparable. Additional analysis described later in this memorandum uses a different set of stati stical techniques to examine the Washington, DC studies in more detail. cities. This available data is the percentage of respondents that rated each indiv idual photograph (or image) as acceptable. We also know the total number of individuals that rated each photograph, as well as the haziness level in each photograph, measured in deciviews (dv). Using these pieces of information we were able to assemble a master data set of 19,280 observations from the original data. Each observation is associatedStratus Consulting Memorandum (2/ 3/2010)

Page 2
SC11979 with an individual binary “yes” or “no” acceptability answer, the dv level, and the city location for a single photograph.

For example, in the Phoenix study 385 participants rated each of 21 different WinHaze images. Hence the Phoenix study contributes 8,085 (385 × 21) observations, nearly 41.9% of the total set of 19,280 observations in the master data set. The 32 photographs used in the Denver study

con tribute 6,848 observations (35.5% of the total), the 20 photographs in the British Columbia contribute 3,600 observations (18.7% of the total), and the combined Washington, DC studies (combining data from the DC-2001 study with the Test 1 data from the DC-2009 study) contribute 747 (3.9% of the total). The 19,280 observations are fairly evenly split, with 9,452 “yes” observations, and 9,828 “no” responses.

The participants in each study viewed a series of images with different dv levels. While the data collected by the original researchers included information linking each individual with their ratings on each picture, such detailed information is currently only available for the Washington, DC study conducted in 2009. Access to this additional level of information in the 2009 Washington study allows us to conduct an additional type of analysis accounting for individual heterogeneity of preferences regarding acceptable levels of visibility.

Statistical Analysis Models

All of the analyses described i n this memorandum are logistic regressions using the logit model. The logit model is a generalized linear model used for binomial regression analysis which fits explanatory data about binary outcomes (in this case, a person rating a photograph acceptable or not) to a logistic function curve.

In the context of the preference studies, the logit model estimates the function that best approximates the percentage of respondents that will rate a photograph acceptable based on a set of explanatory variables. The observations on the dependent variable have one of two discrete values: 1 (the person rated the photograph acceptable) or 0 (unacceptable). In our context, the logit model estimates the proportion of participants who will find any particular dv level acceptable. In our analysis, there were two basic types of explanatory (independent) variables; one continuous numerical variable (the photograph’s haziness level in dv), and a set of discrete variables that identify which city the observation is from. We es timate two variations of the logit model, using the basic explanatory variables in different ways.

The fundamental form of a logistic function is:

. 1 1 )()"("

z e z f yes ypr obabilit

− + ==

where the variable z, known as the logit, is the influence of all the explanatory var iables:Stratus Consulting Memorandum (2/ 3/2010)

Page 3
SC11979 .... 2211 εβββ ++++= xxz o In our analysis the estimated logistic function f (z) is the estimated probability of the participants in the study rating a photograph acceptable, given the dv value of the photograph and what city the observation came from.

We conducted the logit analysis using two alternative forms of the logit model.

Model 1 is a simple form of the logit model, and includes the dv value and uses the city information to create a set of categorical indicator variables. This analysis assumes that all respondents have a similar shape to their response function (the probability function of responding “yes” given the dv level of a photograph), but investigates whether the location of the response function differs in the four cities.

The logit f or Model 1 is:

ε ββββ + ++++ = Phoenix DC BC dv Intercept z 4 3 2 1 .
The variables BC (British Columbia), DC (Washington), and Phoenix are the indicator (or “dummy” variables. For example, the BC variable is set equal to one if the observation is from the BC study, and set to zero if that observation is from a study in a different city study. Denver is used as the omitted city indicator variable, allowing the estimated coefficients on the other three city indicator variables to estimate if the response function is different in those cities than in Denver. The term ε represents the error with which the model was estimated, or the difference between the actual and predicted values of z. The logit model assumes that ε has a mean of zero.

The Model 1 form of the logit model estimates a s ingle “slope” for the response function in all cities as β 1 , the coefficient for haziness (dv). The other terms shift the intercept. The intercept for Denver is simply the estimated parameter Intercept

. The effective intercept for the other cities becom es the sum of Intercept plus the coefficient on the city’s indicator variable, for example the intercept for Washington is Intercept + β 3
. Model 1 creates one test of the hypothesis that the responses in each city are the same. If the estimated coefficie nt on a particular city variable is statistically significant, the analysis would imply that the city’s response function is likely shifted relative to the Denver function, and that city would have a different dv value for the 50% criteria. A positive and significant city coefficient shifts that city’s response function to the right, resulting in the dv level where 50% criteria level in that particular city is higher than Denver’s.

Model 2 is a more general model than Model 1, and relaxes the assumption in Model 1 that the slope of the response function is the same in every city. Model 2 includes not only dv and theStratus Consulting Memorandum (2/ 3/2010)

Page 4
SC11979 city indicator variables as in Model 1, but also a set of interaction terms, where each city dummy variable is multiplied by the dv level. Th e logit for Model 2 is:

.) ( )( )(

7 6 5 4 3 2 1

ε β β β β βββ

+ ×+ +×+ +×+++ =
Phoenix dv Phoenix DC dv DC BC dv BC dv Intercept z

For example, in Model 2 the estimated total intercept for Washington becomes Intercept + β 4 , and the estimated slope of the Washington function is β 4 + β 5 . In the fully interacted Model 2 a statistically significant estimate of the city indicator variable coefficients ( β 2 , β 4 , or β 6 ) has the same implication as in Model 1; the response functio n is likely shifted relative to the Denver function. A statistically significant estimate of the interaction term coefficient ( β 3 , β 5 , or β 7 ) for a particular city implies that the response function has a different slope than the Denver function.

The full y interacted model produces the same results as conducting a separate logit analysis for each of the four cities. The interacted model, however, makes it easier to conduct hypothesis testing on the estimated mean response functions.

The predicted mean dv values at each of the acceptance criteria presented here are a function of the coefficients on dv and the other explanatory variables, each of which have their mean and standard deviation. Therefore , a confidence interval constructed around this predicted mean must account for both the variance and covariance of the parameter estimates. Using a Monte Carlo estimation approach, we made 1000 random draws from the joint distribution of the coefficients using the mean vector and variance

- covariance matrix of the parameter estimates for the distribution parameters. For each of these draws we then calculated the predicted mean dv. After removing the lower and upper 5% of the simulated values, the lower and upper end of the range of predicted values represent th e lower and upper range of the 95% confidence interval. Confidence intervals calculated using this procedure are known as Krinsky-Robb confidence intervals (Krinsky and Robb, 1986). Because estimating Krinsky -Robb confidence intervals requires a separate Monte Carlo analysis for each acceptability criteria dv level, we only estimate confidence intervals for five different acceptability levels: 90%, 75%, 50%, 25%, and 10%.

The Krinsky-Robb procedure assumes that the estimated parameters are normally distributed, which may or may not be true. To explore the potential impact of this assumption, for one logit analysis we also conducted an alternative procedure that does not assume a normal distribution. This alternative procedure (Hole, 2007) uses a bootstrap method to estimate the confidence intervals for the estimated mean 50% criteria. The confidence intervals using the bootstrap were within 1% of the confidence intervals using the Krinsky-Robb procedure, indicating that the multivariate normal assumption i mposed by the Krinsky-Robb procedure is not unreasonable. We also conducted hypothesis tests using the median dv values estimated using theStratus Consulting Memorandum (2/ 3/2010)

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SC11979 bootstrapping procedure. The conclusions from these hypothesis tests were identical to the conclusions from the other hypothesis tests.

Statistical Analysis Results, Inter -City analyses

We conducted all the logit analyses described in this document using STATA ® Data Analysis and Statistical Software (Release ES 10.1), using the LOGIT procedure. The Krinsky-Robb analy sis used STATA’s “wtpcikr” module. The bootstrap method (Hole, 2007) was conducted using STATA’s “bootstrap” module.

Model 1 Results, Inter- City Analysis

Table 1 presents the parameter estimates from the logit analysis with city indicators (Model 1) which effectively shift the intercept. The Washington, DC data in this analysis includes both DC -2001 and DC-2009 (Test 1) data. The Denver study is the omitted indicator city in this analysis, so the intercept term coefficient for Denver is equal to the Cons tant. The intercept for the other cities is the sum of the constant plus the coefficient for the respective city. The coefficient for variable dv is the estimated slope for all four cities.

Table 1. Model 1 logit analysis results

Variable Coefficient ( β) Standard

error z- statistic Pr |β| = 0 5% confidence estimate 95% confidence estimate
dv -0.4187 0.0059 -71.09 < 0.001 -0.430 -0.407
British Columbia 1.1164 0.0630 17.72 < 0.001 0.993 1.240 Washington, DC 3.8743 0.1325 29.25 < 0.001 3.615 4.134
Phoen ix 1.8021 0.0576 31.31 < 0.001 1.689 1.915
Constant 8.3073 0.1186 70.07 < 0.001 8.075 8.540

McFadden’s pseudo-R 2 for the Model 1 estimate 2

2 While pseudo -R 2 is, like traditional R 2 , bounded between zero and one, it does not have the same interpretation. R 2 can be interpreted as the percentage of the variation in the depen dent variable explained by variation in the independent variables. Pseudo -R 2 , on the other hand, is the percent improvement in log likelihood from using the full set of explanatory variables, relative to a model that uses only a constant. It offers a sen se for how much better the model fits when the explanatory variables are added, but cannot tell us the percentage of variation we are explaining. Pseudo R 2 , instead of traditional R 2 , must be used in evaluating logit and other maximum likelihood estimatio n models. Similar to R 2 , a higher pseudo-R 2 indicates a model with a better fit. was 0.474.Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 The Log likelihood chi 2 test strongly rejects the null hypothesis there is no effect of explanatory variables on the probability that a respondent would find a photograph acceptable (Pr (chi 2 ) = 0 < 0.000).

The z

- statistic (also known as the Wald z

- statistic) in a logit analysis is analogous to the t

statistic in a conventional linear regression. The z

- st atistic is simply the ratio of the estimated coefficient to its standard error, and can be used to estimate the probability that the estimated coefficient is equal to zero. The column in Table 1 labeled “Pr |β| = 0” is the 2

- tailed p

- value used in testing the null hypothesis that the estimated parameter is zero. The Pr |β| values shown in Table 1 are all less than 0.005 ( “~0”), indicating that all of the estimated coefficients are very statistically significant. Because the city dummy variables are significant, in Model 1 we reject the hypothesis that the four studies have an identical response function.

Figure 1 shows the estimated response functions in each city for the logit analysis with city indicators, as well as the underlying data as was shown in Figure 14 of the Stratus Consulting final report (Stratus Consulting, 2009). While Model 1 estimates the shape of a response function that is identical in each city, the positive and significant coefficients on the city variables in Model 1 result in the response functions for the different cities to shift to the right of the Denver function.

The logit analysis results also support estimating the dv value where the 50% acceptability criteria are met in each city. The 50% acceptability criteria occur at the level of haziness where half the survey participants said the vis ibility is acceptable, and half said it was not acceptable. In Figure 1, the 50% criteria level is the dv value where the estimated response function crosses the 50% response level on the y axis.

As a sensitivity analysis, it is also possible to calculat e the dv levels that meet alternative decision criteria. For example, one can calculate the estimated dv level at which 75% of the participants said the visibility was acceptable. This 75% criterion would occur at better visibility (i.e., lower dv values) than the 50% criteria. Similarly, one can also calculate the estimated the dv level that any desired percentage of the participants said was acceptable. The Model 1 estimates of alternative acceptability criteria dv values for each city are shown in Table 2.Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 Table 2. Model 1 estimated haziness (dv) levels of alternative acceptability criteria

Denver British Columbia Washington, DC Phoenix 90% acceptability criteria 14.59 17.26 23.85 18.90 75% acceptability criteria 17.22 19.88 26.47 21.52
50% acceptability criteria 19.84 22.51 29.10 24.15 25% acceptability criteria 22.47 25.13 31.72 26.77 10% acceptability criteria 25.09 27.76 34.34 29.39 0% 50% 100%

5 10 15 20 25 30 35 40 45
Deciview% Participants Rating "Acceptable"

Denver Phoenix BC Washington Denver Logit Phoenix Logit BC Logit DC Logit

Figure 1. Estimated response functions for full cities using the Model 1 logit analysis.Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 The range of the Model 1 estimates of the 50% acceptability criteria is very consistent with the Cand idate Protection Level (CPL) range of 20 dv to 30 dv identified in the U.S. EPA (2009) report Particulate Matter Urban- Focused Visibility Assessment; External Review Draft (UFVA)

. Model 2 Results, Inter- City Results

Table 3 presents the parameter estimates from the fully interacted logit analysis, which investigates whether both slope and the intercept of the estimated response function differ between cities. Denver was again used as the omitted city in the fully interacted model.

Table 3. Model 2 logit a nalysis results

Variable Coefficient ( β) Standard

error z- statistic Pr |β| = 0 5% confidence estimate 95% confidence estimate
dv -0.3862 0.0094 -41.16 < 0.001 -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 < 0.001 1.2873 4.6026
Pho enix 3.5682 0.3015 11.84 < 0.001 2.9773 4.1591
BC × dv -0.0029 0.0162 -0.18 0.860 -0.0345 0.0288
Wash. × dv 0.0200 0.0293 0.68 0.495 -0.0374 0.0774
Phoenix × dv -0.0797 0.0136 -5.88 < 0.001 -0.1063 -0.0531
Constant 7.6844 0.1830 41.99 < 0.001 7.3257 8.0431

The pseudo-R 2 for the Model 2 estimate was 0.4756 (very similar to the Model 1 results), and the Model 2 log likelihood chi 2 test also strongly rejects the null hypothesis there is no effect of the explanatory variables on the probability that a respondent would find a photograph acceptable (Pr (chi 2 ) = 0 < 0.000).

The city indicator coefficients in this full interaction model are all positive and statistically significant, as they were in Model 1, indicating that the response functions for different cities shifted right (relative to Denver). However, of all the interactions only the Phoenix interaction term is significant, indicating that the Phoenix response function has a different slope than the other three cities.

Figure 2 shows the estimated r esponse functions in each city for Model 2, as well as the underlying data.Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 The significantly different slope of the Phoenix response function is clearly visible in Figure 2. The negative estimated coefficient on the Phoenix interaction term results in the Phoenix response function being steeper than the other cities’ functions. In other words, Phoenix respondents’ acceptance probabilities were more sensitive to changes in dv levels. Figure 2 also shows the Washington, DC function is modestly less steep than the others, but the decrease in the slope is not statistically significant. Therefore , while Washington, DC respondents are more likely to accept worse visibility overall, they are just as responsive to changes in dv as respondents in Denver a nd British Columbia

. As with Model 1, it is possible to use the Model 2 results to estimate the dv values where the estimated response functions cross the 50% acceptability level, as well as any alternative criteria levels. The Model 2 estimates of alternative acceptability dv values for each city are shown in Table 4. 0% 50% 100%

5 10 15 20 25 30 35 40 45
Deciview% Participants Rating "Acceptable"

Denver Phoenix BC Washington Denver Logit Phoenix Logit BC Logit DC Logit

Figure 2. Estimated res ponse functions for four cities using the fully interacted logit analysis.Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 Table 4. Model 2 estimated haziness (dv) levels of alternative acceptability criteria

Denver British Columbia Washington, DC Phoenix 90% acceptability criteria 14.21 16.80 23.03 24.15 75% acceptability criteria 17.05 19.63 26.03 21.80 50% acceptability criteria 19.90 22.45 29.12 24.15 25% acceptability criteria 22.74 25.28 32.03 26.51 10% acceptability criteria 25.59 28.10 35.03 28.87

The Model 2 estimates of the 50% acceptability criteria are nearly identical to the Model 1 estimates; the biggest difference is a 0.07 dv decrease in the Washington, DC 50% acceptability criteria. The essentially identical estimates of the 50% acceptability criteria in Models 1 (city indicato r only) and Model 2 (full interaction) indicates the choice of model form does not change the conclusion that the logit results are consistent with the 20 to 30 dv CPL range identified in the draft UFVA (EPA, 2009).

We also conducted hypothesis testing with the four city data used in this section to examine the probability that the 50% acceptance criteria in the four different cities are the same. We used the full interaction model results for the hypothesis testing. Our approach estimated the mean 50% criteria dv levels and standard error (based on the Krinsky -Robb confidence intervals) for each of the four cities. We then conducted a hypothesis testing using a t- test to estimate the probability the mean 50% criteria dv levels are the same in each pair of cities. The null hypothesis in this hypothesis test is that the means are the same. As shown in Table 5, the null hypothesis is strongly rejected for all pairs of cities, indicating that the mean 50% criteria dv levels differ for all four cities.

Tab le 5. Hypothesis testing on whether the full interaction model mean 50% criteria dv levels are the same

British Columbia Mean dv = 22.45 Phoenix Mean = 24.15 Washington, DC Mean dv = 29. 12

Denver Mean dv = 19.90 t-stat = 16.89 Pr(Den = BC) ~0 t-stat = 35.15 Pr(Den = Ph) ~ 0 t-stat = 30.21 Pr(Den = DC) ~ 0
British Columbia – t-stat =12.08 Pr(BC = Ph)~0 t-stat = 21.23 Pr(BC = DC) ~ 0
Phoenix – t-stat = 16.53 Pr(Ph = DC) ~ 0Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 Analysis of Washington, DC Preference Studies

There are two related st udies of visibility preferences in Washington, DC. In 2001, in a project sponsored by the U.S. Environmental Protection Agency, Abt Associates conducted a pilot focus group study (DC-2001) of urban visibility preferences in Washington, DC. In 2009, in a s tudy for the Utility Air Regulatory Group , Smith and Howell conducted a series of three tests of urban visibility preferences in Washington, DC. In their first test (DC

- Test 1) , Smith and Howell used all the images used in the DC -2001 study, trying to rep licate the DC

- 2001 study. Their second test (DC

- Test 2) used fewer of the Washington images, restricting the study to the 12 images with better visibility (images with visibility of 27.1 dv or better). In the third test (DC -Test 3), they expanded the range of images to include two hazier images (adding a 42 and 45 dv images, and deleting images at 11.1, 15.6, and 24.5 dv).

An important question is whether the participant responses obtained in the DC-2001 study are similar to the responses in Test 1, which was designed to replicate the DC -2001 study. A related question is whether the responses in Tests 2 and 3 are similar to Test 1. To investigate these questions we estimated logit response functions using the data from the four different Washington, DC d ata sets (DC -2001, DC-Test 1, DC-Test 2, and DC-Test 3), using the full interaction logit model specification.

The estimated coefficients from a full interacted model are presented in Table 6. The DC -2001 test is used as the omitted interaction variable.

Table 6. Logit regression results with full interacted model of Washington, DC studies

Variable Coefficient (β) Standard

error z- statistic Pr |β| = 0 5% confidence estimate 95% confidence estimate
dv -0.4035 0.0567 -7.12 < 0.001 -0.5146 -0.2925
Test 1 -1.5425 1.8785 -0.82 0.412 -5.2242 2.1392
Test 2 -0.7431 2.0737 -0.36 0.720 -4.8075 3.3212
Test 3 3.4109 2.6980 1.26 0.206 -1.8772 8.6990
Test 1 × dv 0.0616 0.0632 0.97 0.330 -0.0624 0.1855
Test 2 × dv -0.1043 0.0804 -1.30 0.194 -0.2618 0.0532
Test 3 × dv -0.0607 0.0868 -0.70 0.485 -0.2309 0.1095
Constant 11.5621 1.6777 6.89 < 0.001 8.2739 14.8504

Figure 3 shows the estimated full interaction logit function for the separate Washington, DC Test data, including the DC-2001 data.Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 Figure 3 suggests that while the logit functions from DC -2001 and Test 1 appear to be similar, Test 2 and Test 3 appea r to be substantially different. Estimating the 50% criteria levels, along with the Krinsky -Robb confidence intervals, confirms this observation. Table 7 presents the estimated mean 50% criteria levels and the Krinsky -Robb confidence intervals.

Table 7. Mean 50% criteria levels, and Krinsky

- Robb confidence intervals

Mean dv 5% confidence estimate 95% confidence estimate
Test 1 29.30 28.59 29.97
Test 2 21.30 20.57 22.03
Test 3 32.26 31.37 33.16
DC -2001 28.65 27.46 29.70 0% 50% 100%

10 15 20 25 30 35 40

dv% Acceptable

DC-2001 Test 1 Test 2 Test 3

Figure 3. Full interaction logit results for four separate DC data sets.Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 Hypothesis testing using t he predicted mean dv distribution from the Krinsky-Robb procedure provides statistical support for the conclusion that the Test 1 and DC-2001 results are similar, but Test 2 and Test 3 results are different. The hypothesis testing results are presented in Table 8.

Table 8. Hypothesis testing on the individual coefficients in the full interaction model is the same for the four different Washington, DC experiments

Test 2 Mean dv = 21.30 Test 3 Mean = 32.26 DC -2001 Mean dv = 28 .65
Test 1 Mean dv = 2 9.30 Reject hypothesis that Test 1 = Test 2 (pr < 0.001) Reject hypothesis that Test 1 = Test 3 (pr < 0.001) Cannot reject hypothesis that Test 1 = DC -2001 (pr = 0.15)
Test 2 – Reject hypothesis that Test 2 = Test 3 (pr < 0.001) Reject hypothesis that Test 3 = DC -2001 (pr < 0.001)
Test 3 – Reject hypothesis that Test 3 = DC -2001 (pr < 0.001)

As shown in Table 8, we cannot reject (at the 5% confidence level) the hypothesis that the mean 50% criteria level in the DC -2001 data and the Test 1 data are the same. In other words, it is likely that the mean dv in Test 1 is the same as the mean dv in DC -2001. Thus, this hypothesis test supports combining those two data sets together, as we did in the four city analysis presented above. The results in Tab le 8 reject the hypothesis that Test 2 and Test 3 are the same as either the Test 1 or DC-2001 results.

Further Analysis of the Washington, DC Test 1 Data

Smith and Howell conducted Test 1 using three distinct groups of respondents. Four of the respondents in Test 1 were Washington, DC area residents that were used in a pilot test of the testing procedure. Twelve of the respondents were CRA International employees who live in the Washington, DC area, and ten of the respondents were CRA International employees who live in the Houston, Texas area. The Test 1 participants were all shown the same images of Washington, DC haze levels as the DC-2001 participants, and were asked about their preferences for urban visibility in Washington, DC.

We investigated he terogeneity among these three groups’ responses by conducting a full interaction logit analysis using information about which of the three groups (pilot, DC area or Houston area) the respondents were in. We also included the DC-2001 respondents (who were all DC area residents) in this analysis to conduct hypothesis tests on whether the Test 1 groups were different than the DC -2001 respondents. We used the pilot test respondents as the omitted group in a full interaction model analysis. The results of the logit analysis are presented in Table 9.Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 Table 9. Logit regression results with full interacted model of the 3 Test 1 groups and the DC -2001 participants

Variable Coefficient (β) Standard

error z- statistic Pr |β| = 0 5% confidence estimate 95% confidence estimate
dv -0.5719 0.1310 -4.36 0.000 -0.8287 -0.3151
Test 1/DC -0.8344 3.7361 -0.22 0.823 -8.1570 6.4881
Test 1/ Houston -4.8831 3.5486 -1.38 0.169 -11.8382 2.0719
DC -2001 -2.4042 3.7273 -0.65 0.519 -9.7095 4.9012
Test 1/DC × dv 0.1439 0.1420 1.01 0.311 -0.1344 0.4222
Test 1/Houston × dv 0.2643 0.1372 1.93 0.054 -0.0047 0.5332
DC

- 2001 × dv 0.1684 0.1428 1.18 0.238 -0.1114 0.4482
Constant 13.9663 3.3284 4.20 0.000 7.4428 20.4898

Using the estimated coefficients in Table 9, we calculated estimate d 50% criteria levels for each group, along with the Krinsky-Robb confidence intervals, which are shown in Table 10.

Table 10. Mean 50% criteria levels, and Krinsky-Robb intervals for the Test 1 groups and the DC -2001 participants

Mean dv 5% confidence level 95% confidence level
Test 1/DC 30.68 29.79 31.51
Test 1/Houston 29.52 28.30 30.66
Test 1/Pilot 24.42 22.37 25.97
DC -2001 28.65 27.46 29.70

Table 10 suggests that the mean 50% acceptance criteria level for the Washington, DC area residents in th e 2001 study are closest to the mean 50% criteria level for the Test 1 Houston area residents, and differ to a greater degree from the mean 50% criteria level for the Test 1 Washington area residents. Hypothesis testing confirms this finding, as shown in Table 11.Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 Table 11. Hypothesis tests of the mean 50% acceptance criteria level for the three groups in the Test 1 data and the DC -2001

Houston Mean dv = 29.52 Pilot Mean dv = 24.42 DC -2001 Mean dv = 28.65
Test 1/DC area Mean dv = 30.68 Reject hyp othesis Houston = Test 1/DC (pr = 0.06) Reject hypothesis Pilot = Test 1/DC (pr < 0.001) Reject Test 1/DC = 2001

- DC (pr < 2%)

Test 1/Houston

area – Reject Houston = Pilot (pr < 0.001) Cannot reject Houston = DC-2001 at 5% confidence (pr = 14%)
Test 1/Pilot – Reject Pilot = DC -2001 (pr <0.001)

These hypothesis test results in Table 11 provide some insight into the hypothesis tests in Table 8, which found the 50% mean criteria level (mean = 29.30 dv) estimated using the combined Test 1 data is similar to the 50% criteria level from the DC -2001 data (mean = 28.65 dv). The Table 11 results suggest that the Table 8 results could be the result of the Test 1 pilot participants (mean = 24. 42 dv) offsetting the Test 1/DC area participants (mean = 30.6 8 dv), giving us a mean estimate for the combined sample closest to the Houston area participants (mean = 29.52 dv).

Individual Heterogeneity

Individual respondents will likely have different general attitudes regarding haze than other respondents, reflecting their individual preferences about urban visibility. An individual’s preferences may affect how they rate the acceptability of different dv levels. In the Smith and Howell (2009) Washington, DC study we can track an individual’s responses over all dv l evels. 3

We conducted a logit analy sis on Test 1 data using individuals as the indicator variable. We included slope interaction terms for the Washington and Houston area residents (with the pilot slope interaction term omitted). Each individual This enables us to account for individual heterogeneity in our estimation procedure using individual-specific indicators. These are called fixed -effect models and control for unobserved differences between respondents.

4

3 While this level of data was originally collected for the studies in Denver, Phoenix, British Columbia and the 2001 Washington, DC study, the original data is not availabl e at this time. also has an indicator which becomes the

4 Respondents 1 and 13 are dropped in the individual heterogeneity analysis because they had identical responses, accepting every dv level. The form of the logit model used in this analysis cannot be estimated

w hen all the responses are identical

.Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 intercept term for that individual. The terms for Respondents 2 through 12 are intercept shifters for DC respondents. Respondents 14 through 22 were Houston respondents, and Respondents 23 through 25 were pilot respondents. The results from this model are presented in Table 12.

Table 12. Logit analysis results of individual heterogeneity analysis

Variable Coefficient (β) Standard

error z- statistic Pr |β| = 0 5% confidence

estimate 95% confidence

estimate
dv

- 0.7315 0.1911

- 3.83 0

- 1.1060

- 0.3569 Houston × dv 0.1207 0.2139 0.56 0.573 -0.2986 0.5399 DC × dv -0.3588 0.2658 -1.35 0.177 -0.8799 0.1622 Respondent 2 (DC) 35.2050 5.9847 5.88 0 23.4752 46.9349 Respondent 3 (DC) 35.2050 5.9847 5.88 0 23.4752 46.9349 Respondent 4 (DC) 29.9950 5.2578 5.7 0 19.69 00 40.3001 Respondent 5 (DC) 34.3924 5.8635 5.87 0 22.9002 45.8846 Respondent 6 (DC) 32.0347 5.5755 5.75 0 21.1070 42.9624 Respondent 7 (DC) 31.0845 5.4326 5.72 0 20.4369 41.7322 Respondent 8 (DC) 25.7365 4.5956 5.6 0 16.7293 34.7438 Respondent 9 (DC) 36.1200 6.1617 5.86 0 24.0434 48.1966 Respondent 10 (DC) 34.3924 5.8635 5.87 0 22.9002 45.8846 Respondent 11 (DC) 28.7572 5.0615 5.68 0 18.8369 38.6775 Respondent 12 (DC) 35.2050 5.9847 5.88 0 23.4752 46.9349 Respondent 14 (H) 16.6104 2.8047 5.92 0 11 .1133 22.1075 Respondent 15 (H) 15.9236 2.7170 5.86 0 10.5984 21.2488 Respondent 16 (H) 15.9236 2.7170 5.86 0 10.5984 21.2488 Respondent 17 (H) 18.3145 2.9999 6.11 0 12.4348 24.1942 Respondent 18 (H) 20.8740 3.3153 6.3 0 14.3761 27.3719 Respondent 19 (H) 13.3405 2.3443 5.69 0 8.7457 17.9353 Respondent 20 (H) 19.8140 3.1722 6.25 0 13.5966 26.0315 Respondent 21 (H) 16.6104 2.8047 5.92 0 11.1133 22.1075 Respondent 22 (H) 18.8166 3.0538 6.16 0 12.8313 24.8019 Respondent 23 (P) 16.0044 4.3526 3.68 0 7.4736 24.5353 Respondent 24 (P) 17.1746 4.6884 3.66 0 7.9854 26.3637 Respondent 25 (P) 19.9838 5.4132 3.69 0 9.3742 30.5933 Respondent 26 (P) 18.2301 4.9705 3.67 0 8.4882 27.9720

As in the analyses previously described, we used the logit analysis coefficients in Table 12 to estimate the mean value for the 50% acceptance criteria. We also estimated the Krinsky -Robb confidence intervals for each data subset using the fixed effects model. Because three of the TestStratus Consulting Memorandum (2/ 3/2010)

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SC11979 1 participants were deleted in the individual heterogeneity analyses, for comparison purposes we also re

- estimated a model without accounting for individual heterogeneity using the same data set (i.e., with the t wo individuals deleted). The results are presented in Table 13.

Table 13. Estimated mean 50% criteria levels, and Krinsky -Robb intervals for the Test 1 data accounting for individual heterogeneity

Mean dv Lower bound 95% Upper bound 95% Washington area residents 30.57 29.97 31.18
Houston area residents 29.40 28.41 30.33
Pilot (DC res idents) 24.40 22.60 25.91
Mean dv estimates without individual heterogeneity (using same data) Washington area residents 30.02 29.19 30.77
Houston area residents 28.50 27.25 29.58
Pilot (DC area residents) 24.42 22.37 25.97

Table 13 shows that including individual heterogeneity in the model modestly increased the estimated mean 50% criteria levels.

Table 14 shows the results of h ypothesis testing on the individual heterogeneity results. Modeling with individual heterogeneity leads to rejecting the hy pothesis that the mean dv levels are the same in any of the three respondent groups.

Table 1 4 . Hypothesis tests of the mean 50% acceptance criteria level for the three groups in the Test 1 data modeled with individual heterogeneity

Houston area Mean dv = 29.40 Pilot (DC area) Mean dv = 24.40
DC area
Mean dv = 30.57 Reject hypothesis
Houston = DC (pr = 0.02 ) Reject hypothesis
Pilot = DC (pr < 0.001)

Houston area – Reject Houston = Pilot (pr < 0.001)Stratus Consulting Memorandum (2/ 3/2010)

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SC11979 Summary

This memorandum describes a series of logit regression analyses that estimated the percentage of respondents that rated a haze (dv) level acceptable in four different studies of urban visibility. The first analysis in this report estimated a separate logit function for each of the four studies: Denver, British Columbia, Phoenix and Washington, DC (combining the data from the DC-2001 study and all Test 1 data from the DC-2009 study). The estimated mean 50% criteria levels in the four cities (Table 4) are different, with the mean estimate ranging from 19.9 0 dv (Denver) to 29.03 dv (Washington, DC). The hypothesis tests presented in Table 5 found that there is a statistically different logit function in each city (rejecting the null hypothesis that there was a single function that applies to more than one city). The range of mean estimates from the 4 city logit analysis is similar to the Candidate Protection Level range of 20 dv to 30 dv described in the draft UFVA (EPA, 2009).

The remainder of this memorandum examined in more detail the data from the two Washington, DC studies. In the first analysis focusing on only the Washington, DC data, we compared the estimated mean 50% criteria levels from the 2001 study to the mean estimates from each of the three tests in the 2009 study. Figure 3 and Table 7 show the estimated mean levels in the 2001 (mean = 28.65 dv) and 2009, Test 1 (mean = 29.30 dv) studies were similar, while the Test 2 (21.30 dv) and Test 3 (32.26) mean levels were quite different. The hypothesis tests presented in Table 8 support that overall observation. The only hypothesis not rejected was the hypothesis that the DC -2001 and Test 1 are the same (i.e., we cannot reject the hypothesis that they have the

same mean 50% criteria level ). This finding supports our approach of combining the DC-2001 and the DC-2009, Test 1 data in the four city analysis.

In t he second analysis of the Washington, DC data, we investigated whether the study participants who lived in the Washington, DC Metro area had the same mean 50% criteria levels as the participants who lived in the Houston metro area. This analysis involved three groups ofWashington, DC residents (the DC-2001 participants, the pilot project participants in the DC2009 study, and participants 1 through 12 in Test 1 of DC-2009). The hypothesis tests results inTable 11 show that the participants in the DC-2001 and the Houston area residents in the DC2009 study are similar (i.e., we cannot reject the hypothesis they have the same mean 50% criteria level). Our hypothesis testing further found however, that the DC -2001 participants had statistically significantly different mean 50% criteria levels than either of the two groups of Washington, DC area residents included in the DC-2009, Test 1 results.

The third analysis of the Washington, DC data investigated the effect of individual heterogeneity of preferences. This analysis was limited to the DC -2009 data because it required more complete information on the responses of each participant. The individual heterogeneity analysis found modestly higher mean 50% criteria levels than the second analysis of th e Washington, DC areaStratus Consulting Memorandum (2/ 3/2010)

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SC11979 residents . The hypothesis testing in this analysis rejected the hypothesis tha t the mean dv levels were the same for the three groups who participated in Test 1.

This apparent inconsistency with the two hypothesis tests of analyses of subsets the Washington, DC studies with the results of the hypothesis tests comparing the DC

- 2001 d ata with all of the DC -2009, Test 1data may be due to having subdivid ed the participants of Test 1 into subsets with too few member s to provide stable results. Combining the DC -2001 data with all the Test 1 data provides the largest sampl e size available to estimate the logit preference function for Washington, DC.

References

Hole, A.R. 2007. A comparison of approaches to estimating confidence intervals for willingness to pay measures. Health Economics 16:827–840.

Krinsky, I. and L. Robb . 1986. On approximating the statistical properties of elasticities. The Review of Economics and Statistics 68(4):715–719.

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.

Stratus Consulting. 2009. Review of Urban Visibility Preference Studies. Prepared for Vicki Sandiford, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. Stratus Consulting Inc., Boulder, CO. September 21.

U.S. EPA. 2009. Particulate Matter Urban- Focused Visibilit y Assessment; External Review Draft. U.S. Environmental Protection Agency. September.This page left intentionally blank.United StatesEnvironmental ProtectionAgencyOffice of Air Quality Planning and StandardsHealth and Environmental Impacts DivisionResearch Triangle Park, NCPublication No. EPA 452/P-10-004July, 2010