# Particulate Matter

Urban

- Focused Visibility Assessment

Second External Review Draft

January 20 1 0DISCLAIMER

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

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

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

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

Clean Air Scientific A dv isory Comm ittee (CASAC) and the general public. Comments on this

draft document should be addressed to Vicki Sandiford

, U.S. Environmental Protection Agency,

Office of Air Quality Planning and Standards, C504

- 06, Research Triangle Park, North Carolina

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

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

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

partial fulfillment of Contract No. EP

- D

- 08

- 100, Wor k Assignment 0

- 11

. Any opinions,

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

the views of the EPA or its contractors

.

ACKNOWLEDGEMENTS

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

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

Stratus Consulting Inc. made contributions is chapter 2 (Urban Visibility Preference Studies).EPA 45 2 /P

- 1 0

- 00 2

January 20 10



## P articulate Matter

Urban

- Focused Visibility Assessment

Second External Review Draft

U

. S

. Environmental Protection Agency

Office of Air and Radiation

Office of Air Quality Planning and Standards

Research Triangle Park, North Carolina 27711Th is page left intentionally blank.i 

## TABLE OF CONTENTS

List of Tables

................................ ................................ ................................ ................................ ............ iv

List of Figures

................................ ................................ ................................ ................................ ............ v

List of Acronyms/Abbreviations

................................ ................................ ................................ ............ vii

1 Introduction

................................ ................................ ................................ ................................ 1

- 1

1.1 PM NAAQS BACKGROUND

................................ ................................ ................................ .... 1

- 3

1.2 VISIBILITY EFFECTS S CIENCE OVERVIEW

................................ ................................ ....... 1

- 6

1.3 GOALS AND APPROACH

................................ ................................ ................................ ........ 1

- 9

1.4 SCOPE OF URBAN

- FO CUSED VISIBILITY ASS ESSMENT

................................ .............. 1

- 11

1.4.1 Background

................................ ................................ ................................ .................... 1

- 11

1.4.2 Selection of Alternative Scenarios for First Draft Assessments

................................ .... 1

- 12

1.4.3 Selection of Alternative Scenarios for Second Draft Assessments

............................... 1

- 13

1.5 ORGANIZATION OF DOCU MENT

................................ ................................ ....................... 1

- 16

2 Urban Visibility Preference Studies

................................ ................................ ......................... 2

- 1

2.1 METHODS USED IN PREV IOUS STUDIES

................................ ................................ ........... 2

- 1

2.2 DENVER, COLORADO

................................ ................................ ................................ ............. 2

- 3

2.3 VANCOUVER, BRITISH C OLUMBIA, CANADA

................................ ................................ . 2

- 7

2.4 PHOENIX, ARIZONA

................................ ................................ ................................ .............. 2

- 11

2.5 WASHINGTON, DC

................................ ................................ ................................ ................. 2

- 14

2.5.1 Washington, DC 2001

................................ ................................ ................................ .... 2

- 15

2.5.2 Washington, DC, 2009

................................ ................................ ................................ ... 2

- 17

2.6 SUMMARY OF PREFERENC E STUDIES AND SELECT ION OF CANDIDATE

PROTECTION LEVELS

................................ ................................ ................................ ........... 2

- 25

3 Estimation of Current PM Concentrations and PM Ligh t Extinction

................................ . 3

- 1

3.1 SUMMARY OF PREVIOUS CHARACTERIZATIONS OF PM CONCENTRATIONS AN D

LIGHT EXTINCTION

................................ ................................ ................................ ................ 3

- 1

3.1.1 PM 2.5 and PM 10

- 2. 5

................................ ................................ ................................ ............ 3

- 1

3.1.2 PM light extinction

................................ ................................ ................................ .......... 3

- 4

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

- 9

3.2.1 Study Period, Study Areas, Monitoring Sites, and Sources of Ambient PM Data

........ 3

- 10

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

............... 3

- 16

3.2.3 Use of Original IMPROVE Algorithm to Estimate PM light extinction

....................... 3

- 17

IMPROVE ALGORITHM.

................................ ................................ ................................ .................... 3

- 19

3.3 DETAILED STEPS

................................ ................................ ................................ ................... 3

- 19

3.3.1 Hourly PM 2.5 Component Concentrations

................................ ................................ ..... 3

- 19

3. 3.2 Hourly PM 10

- 2.5 Concentrations

................................ ................................ ...................... 3

- 26

3.3.3 Hourly Relative Humidity Data

................................ ................................ ..................... 3

- 27

3.3.4 Calculation of Daylight 1

- Hour PM Light E xtinction

................................ ................... 3

- 27ii 3.3.5 Exclusion of Hours with Relative Humidity Greater than 90 Percent from PM Light

Extinction NAAQS Scenarios and Most Results

................................ ........................... 3

- 28

3.3.6 Calculation of Daily Maximum 1

- Hour PM Light Extinction

................................ ....... 3

- 32

3.4 SUMMARY OF RESULTS F OR CURRENT CONDITION S

................................ ................. 3

- 32

3.4.1 Levels of Estimated PM 2.5 , PM 2.5 C omponents, PM 10

- 2.5 , and Relative Humidity

........ 3

- 32

3.4.2 Levels of Estimated PM light extinction

................................ ................................ ....... 3

- 35

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

extinction

................................ ................................ ................................ ........................ 3

- 41

3.4.4 Tile Plots of Hourly PM Light Extinction

................................ ................................ ..... 3

- 44

3.4.5 Extinction Budgets for High PM Light Extinction Conditions

................................ ..... 3

- 61

3.5 POLICY RELEVANT BACK GROUND

................................ ................................ .................. 3

- 64

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

Secondary NAAQS

................................ ................................ ................................ ..................... 4

- 1

4.1 ALTERNATIVE SECONDAR Y NAAQS BASED ON MEA SURED PM LIGHT

EXTINCTION AS THE IN DICATOR

................................ ................................ ....................... 4

- 1

4.1.1 Indicator and Monitoring Method

................................ ................................ .................... 4

- 1

4.1.2 Alternative Secondary NAAQS Scenarios base d on Measured PM light extinction

...... 4

- 1

4.1.3 Monitoring Site Considerations for Alternative Secondary NAAQS Based on Measured

PM light extinction

................................ ................................ ................................ .......... 4

- 2

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

on Measured PM Light Extinction

................................ ................................ ................... 4

- 3

4.2 ALTERNATIVE SECONDAR Y PM 2.5 NAAQS BASED ON AN NUAL 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 and 24

- hour PM 2.5 Mass Were Just Met

................................ ................................ ..................... 4

- 11

4.3 RESULTS FOR “JUST ME ETING” EACH ALTERNAT IVE SECONDARY NAAQS

SCENARIO

................................ ................................ ................................ ................................ 4

- 12

5 References

................................ ................................ ................................ ................................ ... 5

- 1

Appendices

................................ ................................ ................................ ................................ .............. 5

- 1

Appendix A

- PM 2.5 Monitoring Sites and Mon itors Providing 2005

- 2007 Data for the Analysis of

PM light extinction in the 15 Study Areas

................................ ................................ .............. A

- 1

Appendix B

- Distributions of Estimated PM 2.5 And Other Components

................................ ...... B

- 1

Appendix C

- Development of PRB Estimates of PM 2.5 components, PM 10

- 2.5 , and PM light

extinction

................................ ................................ ................................ ................................ .... C

- 1

Appendix D

- Relationships between PM Mass Concent ration and PM light extinction under

Current Conditions

................................ ................................ ................................ ................... D

- 1

Appendix E

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

Areas and Seasons

................................ ................................ ................................ ..................... E

- 1

Appendix F

- Distributions of Maximum Daily and Hourly Daylight PM light extinction

- Under

“Just Meet” Conditions

................................ ................................ ................................ ............. F

- 1iii Appendix G

– ADditional Information on the Exclusion of Daylight Hours with Relative Humidity

Greater Than 90 Percent

................................ ................................ ................................ .......... G

- 1

APPENDIX H

– Inter

- Year Variability

................................ ................................ .............................. H

- 1

APPENDIX I

– Daylight Hours

................................ ................................ ................................ ............ I

- 1January 2010 iv DRAFT

- Do Not Quote or Cite 

### LIST OF TABLES

Table 2

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

...... 2

- 5

Table 2

- 2. Summary of photographs used in British Columbia study

................................ ........ 2

- 9

Table 2

- 3 Logit Analysis Results

................................ ................................ .............................. 2

- 28

Table 2

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

values for the four cities.

................................ ................................ .................... 2

- 29

Table 3

- 1. Annual Mean Reconstructed 24

- hour Light Extinction Estimates

............................ 3

- 7

Table 3

- 2. Urban Visibility Assessment Study Areas

................................ .............................. 3

- 11

Table 3

- 3. PM 2.5 Monitoring Si tes and Monitors Providing 2005

- 2007 Data for the Tacoma

Study Area

................................ ................................ ................................ ......... 3

- 14

Table 3

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

................................ ....................... 3

- 15

Table 3

- 5 Assumed daylight hours by season (Local Standard Time)

................................ ..... 3

- 28

Table 3

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

Greater than 90 Perc ent and Other Daylight Hours, During 2005

- 2007

.......... 3

- 31

Table 3

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

PM light extinction exceeding CPLs (excluding hours with relative humidity

greater than 90 percent).

................................ ................................ .................... 3

- 39

Table 4

- 1. Alternative Secondary NAAQS Scenarios for PM Light Extinction

........................ 4

- 2

Table 4

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

............... 4

- 5

Table 4

- 3. Percentage reductions in non

- PRB light extinction required to “just meet” the

NA AQS scenarios based on measured light extinction.

................................ ...... 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 light extinction design values for “just meeting” secondary NAAQS scenarios

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

greater than 90 perce nt)

................................ ................................ ..................... 4

- 17

Table 4

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

based on PM 2.5 mass (excluding hours with relative humidity greater than 90

percent)

................................ ................................ ................................ .............. 4

- 18

Table 4

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

maximum 1

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

the NAAQS scenarios

................................ ................................ ........................ 4

- 19January 2010 v DRAFT

- Do Not Quote or Cite 

### LIST OF FIGURES

Figure 1

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

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

blue) as alternative ways to estimate PM light extinction.

................................ .. 1

- 7

Figure 1

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

............. 1

- 9

Figure 2

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

“acceptable.”

................................ ................................ ................................ ........ 2

- 4

Figure 2

- 2 Photograph time of day infor mation

................................ ................................ ......... 2

- 6

Figure 2

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

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

... 2

- 7

Figure 2

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

............. 2

- 8

Figure 2

- 5 Percent of BC participants who consid er VAQ in each photograph “acceptable.” 2

- 11

Figure 2

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

..... 2

- 13

Figure 2

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

. 2

- 14

Figure 2

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

study.

................................ ................................ ................................ .................. 2

- 16

Figure 2

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

image

................................ ................................ ................................ .................. 2

- 17

Figure 2

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

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

criteria.

................................ ................................ ................................ ............... 2

- 19

Figure 2

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

acceptability criteria from 2009, Test 1).

................................ ........................... 2

- 20

Figure 2

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

.......... 2

- 21

Figure 2

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

participants in Tests 1

- 3 conducted by Smith and Howell (2009).

.................... 2

- 22

Figure 2

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

..... 2

- 24

Figure 2

- 15 Composite results from Smith and Howell (2009) Tes ts 1 and 3, and Abt (2001)

Washington, DC pilot study.

................................ ................................ .............. 2

- 25

Figure 2

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

range of the 50% acceptance crite ria .

................................ ............................. 2

- 26

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 particulate 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

- 18

Figure 3

- 5. Sequence of steps used to estimate hourly PM 2.5 components and PM light extinction

................................ ................................ ................................ ............................ 3

- 22

Figure 3

- 6. Example from Detroit study area.

................................ ................................ .......... 3

- 23

Figure 3

- 7. Distribution of PM parameters and relative humidity across the 2005

- 2007 per iod,

by study area

................................ ................................ ................................ ...... 3

- 33January 2010 vi DRAFT

- Do Not Quote or Cite Figure 3

- 8. Distributions of estimated daylight 1

- hour PM light extinction and maximum daily

daylight 1

- hour PM light extinction across the 2005

- 2007 period, by study a rea (excluding hours with relative humidity greater than 90 percent).

.................... 3

- 37

Figure 3

- 9. Distributions of 1

- hour PM light extinction levels by daylight hour across the 2005

- 2007 period, by stud y area (excluding hours with relative humidity greater than

90 percent).

................................ ................................ ................................ ........ 3

- 40

Figure 3

- 10. Distributions of 1

- hour relative humidity levels by daylight hour across the 2005

- 2007 period, by st udy area (excluding hours with relative humidity greater than

90 percent).

................................ ................................ ................................ ........ 3

- 42

Figure 3

- 11. Scatter plot of daylight 1

- hour relative humidity (percent) vs. reconstructed PM

light extinction (Mm - 1 ) across the 2005

- 2007 period, by study area (excluding

hours with relative humidity greater than 90 percent).

................................ ...... 3

- 43

Figure 3

- 12 Tile Plots of Hourly PM Light Extinction

................................ ............................ 3

- 46

Figure 3

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

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

................................ ................................ ................................ ............................ 3

- 66

Figure 4

- 1 Comparison of Daily Max and All Daylight Hour Design Values

........................... 4

- 6

Figure 4

- 2 Comparison of Required Percentage Reductions in Non

- PRB PM Light Exti nction

Needed to Meet NAAQS Scenarios

................................ ................................ ... 4

- 10

Figure 4

- 3 Distributions of daily maximum daylight 1

- hour PM light extinction under two “just

meeting” secondary NAAQS scenarios (excluding hours wi th relative humidity

greater than 90 percent)

................................ ................................ ..................... 4

- 15January 2010 vii DRAFT

- Do Not Quote or Cite 

### LIST OF ACRONYMS/ABB REVIATIONS

AQS EPA’s Air Quality System

BAM Beta Attenuation Mass Monitor

BC British Columbia

CAA Clean Air Act

CAIR Clean Air Inters tate Rule

CASAC Clean Air Scientific Advisory Committee

CBSA Consolidated Business Statistical Area

CCN Cloud Condensation Nuclei

CDPHE Colorado Department of Public Health and Environment

CMAQ Community Multiscale Air Quality

CONUS CMAQ simulations covering continental US

CPL Candidate Protection Level

CRA Charles River Associates

CSA Consolidated Statistical Area

CSN Chemical Speciation Network

CTM Chemical Transport Model

DRE Direct Radiative Effects

dv deciview

EPA United States Environmental Protection Agency

FEM Federal Equivalent Method

FRM Federal Reference Method

GEOS Global Scale Air Circulation Model

IMPROVE Interagency Monitoring of Protected Visual Environment

ISA Integrated Science Assessment

Km Kilometer

LCD Liquid Crystal Display

LOESS Locally weighted Scatter Plot Smoothing

Mm Megameter

MSA Metropolitan Statistical Area

N Nitrogen

NAAQS National Ambient Air Quality Standards

NARSTO North American Researc h Strategy for Tropospheric Ozone

NCEA National Center for Environmental Assessment

NOAA National Oceanic and Atmospheric AdministrationJanuary 2010 viii DRAFT

- Do Not Quote or Cite NOx Nitrogen oxides

NPS National Park Service

NRC National Research Council

NWS National Weather Service

OAQPS Office of Air Quality Planning and Standards

OAR Office of Air and Radiation

OMB Office of Management and Budget

ORD Office of Research and Development

PA Policy Assessment

PM Particulate Matter

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

F ederal 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

RH 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 Dio xide

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 QualityJanuary 2010 1

- 1 DRAFT

- Do Not Quote or Cite 

### 1 INTRODUCTION 1

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

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

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

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

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

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

EPA Administrator is to promulgate and periodically review, at no later than five

- year intervals, 8

“primary” (health

- based) and “secondary” (welfare

- based) NAAQS for such pollutants. Based 9

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

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

be appropriate. The Act also requires that an independent scientific review committee a dv ise the 12

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

Scientific A dv isory Committee (CASAC). 14

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

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

particles

, using PM 2.5 and PM 10 as indicators, respectively (71 FR 61144, October 17, 2006). 17

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

- 18

hour PM 2.5 stan dard to 35 μg/m 3 (calculated as a 3

- year average of the 98 th percentile of 24

- hour 19

concentrations at each population

- oriented monitor), retained the level of the annual PM 2.5 annual 20

standard at 15 μg/m 3 (calculated as the 3

- year average of the weighted ann ual mean PM 2.5 21

concentrations from single or multiple community

- oriented monitors), and revised the form of 22

the annual PM 2.5 standard by narrowing the constraints on the optional use of spatial averaging 1

The 2006 s econdary standards for PM 2.5 and PM 10 were set to be identic al to the primary 30

standards , on the basis that these standards would, in conjunction with the Regional Haze 31 . 23

With regard to the primary standards for PM 10 , EP A retained the 24

- hour PM 10 standard at 150 24

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

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

- term 26

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

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

concentrations to various a dv erse health outcomes

. 29

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

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

- oriented

monitors to determine compliance (see 71 FR 61165

- 61167, October 17, 2006).January 2010 1

- 2 DRAFT

- Do Not Quote or Cite Program 2

The next periodic review of the PM NAAQS is now underway. , provide appropriate protection to address PM

- related welfare effects, including 1

visibility impairment, effects on vegetation and ecosyste ms, materials damage and soiling, and 2

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

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

3 In the Int egrated 5

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

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

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

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

the review. 4

Building upon the vis ibility effects evidence presented in the PM ISA

, as well as CASAC 17

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

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

develope d this second draft Urban

- Focused Visibility Assessment ( UFV A) describing the 20

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

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

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

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

first draft UFVA, as described in section 1.2 below

. 25 EPA ha s rec ently complet ed the process of assessing the latest available policy

10

relevant scientific informa tion to inform the review of the PM standards. The final assessment is 11

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

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

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

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

preference with respect to urban visibility conditions. 16

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

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

is now being prepared by OAQPS staff t o provide a transparent staff analysis of the scient ific 28

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

3 See http://www.epa.gov/ttn/naaqs/standard s/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 A dv isory Committee (CASAC) on

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

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

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

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

. See

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

information on the CASAC PM Review Panel activities.January 2010 1

- 3 DRAFT

- Do Not Quote or Cite basis for alternative policy options for consideration by senior EPA management prior to 1

rulemaking

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

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

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

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

to facilitate CASAC’s a dv ice to the Agency an d recommendations on any new standards or 6

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

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

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

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

2010

. This preliminary draft PA was discussed i n conjunction with CASAC review of and 11

publi c comment o n the second draft ISA

, first draft UFVA, and first draft health risk assessment 12

documents produced in support of this PM NAAQS rulemaking. 13

1.1 PM NAAQS BACKGROUND 14

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

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

established to address all human

- caused visibility impairment in federal Class I areas. The 17

national goal of this program is to p revent any future, and r emedy any existing , impairment of 18

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

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

reasonable progress is made towards meeting those goals

. Because Congress explicitly targeted 21

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

envision such a stringent goal in non

- Class I areas . See American Trucking Ass’n v.

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

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

impairment in non

- Class I areas. Because most of the available non

- Class I PM data came from 35 Browner, 23

175 F. 3d 1027, 1056

- 57 (D. C . Cir. 2002) (upholding this position). However, Congress 24

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

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

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

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

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

establish secondary standards addressing visibility impairment notwithstanding existence of the 30

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

is the requisite level of public welfare visib ility protection. 32January 2010 1

- 4 DRAFT

- Do Not Quote or Cite PM monitoring sites located primarily in urban areas , the assessments took on an urban focus. In 1

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

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

visibility programs and goals

. 4

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

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

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

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

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

nighttime hours. The PM 2.5 data available at that time in urban areas were obtained using a filter 10

– based Federal Reference Method (FRM) which captures ambient PM 2.5 on a filter and then dr ies 11

it to get the dry PM 2.5 mass concentration. By drying the sample, most water and to some extent 12

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

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

EPA staff assessed the correlations between PM 2.5 levels and reconstructed light extinction (RE) 15

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

strongest correlation in the relationship of ambient PM light extinction to dry PM 2.5 mass 17

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

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

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

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

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

altered PM 2.5 mass concentration as a permissible indicator for addressing ambient PM

- related 23

visibility effects at the national scale during afternoon hours

. Thus, t he 200 5 S taff P aper chose 24

to address the issue in terms of averag ing time rather than indicator, discussing the use of a sub

25

daily afternoon dry PM 2.5 standard, because the generally lower afternoon relative humidity 26

tended to produce a more uniform relationship between light extinction and dry PM 2.5 mass 27

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

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

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

lowest. 31

Based on the above, in the 2005 PM Staff Paper, EPA staff recommended a separate sub

32

daily secondary standard to address visibility impairment using dried PM 2.5 mass concentration 33

as the indicator, a recommendation endorsed by CASAC

. In the 2006 proposal notice, h owever , 34

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

proposed primary standards for fine and coarse particles, to provid e protection against PM

- 36January 2010 1

- 5 DRAFT

- Do Not Quote or Cite related public welfare effects inclu ding visibility impairment, effects on vegetation and 1

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

new sub

- daily PM 2.5 secondary standard to address visibility impairment primarily in urban areas 3

(71 FR 2620)

. CASAC provided additional a dv ice to EPA in a letter to the Administrator 4

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

PM 2.5 standards as well as standards for thoracic coarse particles (Henderson, 2006). With 6

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

regarding a separate secondary fine particle standard to protect visibility

... . t he CASAC wishes 8

to emphasize that continuing to rely on primary standards to protect aga inst all PM

- related 9

a dv erse environmental and welfare effects assures neglect, and will allow substantial continued 10

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

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

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

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

address both visibility and other non

- visibil ity welfare related effects. Specifically, with regard 15

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

both the 24

- hour and annual PM 2.5 secondary standards to be identical to the revised suite of 17

PM 2.5 p rimary standards was a reasonable policy approach to address visibility impairment 18

primarily in urban areas. In particular , EPA revised the level of the 24

- hour PM 2.5 standard to 35 19

μg/m 3 , retained the level of the annual PM 2.5 standard at 15 μg/m 3 , and r evised the form of the 20

annual PM 2.5 standard by narrowing the constraints on the optional use of spatial averaging. 21

With regard to the other non

- visibility PM

- related welfare effects such as vegetation and 22

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

appropriate to address these effects by revising the current suite of PM 2.5 secondary standard s, 24

making them identical in all respects to the suite of primary PM 2.5 standards, while retaining the 25

current 24

- hou r PM 10 secondary standard and revoking the current annual PM 10 secondary 26

standard. In particular for coarse particles, EPA retained PM 10 as the indicator for purposes of 27

regulating the coarse fraction of PM 10 and retained the 24

- hour secondary PM 10 standa rd at 150 28

μg/m 3 and revoked the annual secondary PM 10 standard

. 29

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

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

secondary P M 2.5 standards identical to the primary standards. On judicial review the court 32

remanded the secondary PM 2.5 NAAQS to EPA because the Agency failed to adequately explain 33

why setting the PM 2.5 secondary standards equal to the primary PM 2.5 standards provid ed the 34

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

target level of visibility impairment that would be requisite to protect the public welfare, and 36January 2010 1

- 6 DRAFT

- Do Not Quote or Cite improperly relied on a misleading comparison of the numb er of counties which would be in 1

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

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

regional differences in humidity

- related effects on visibility

. American Farm Bureau Federation 4

v. EPA

, 559 F. 3d 512, 530

- 31 (D.C. Cir. 2009). 5

1.2 VISIBILITY EFFECTS S CIENCE OVERVIEW 6

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

and/ or absor b ed . P articulate matter and gases can both scatter and absorb light. Light scattering 8

by gases (e.g.

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

known as Rayleigh or clean

- air scattering) is related to the density o f the air, which is 10

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

depends principally on elevation above sea level . NO 2 is the only atmospheric pollutant gas that 12

absorbs light appreciably and its effects a re generally small (i.e.

, less than 5%) compared to PM 13

light extinction

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

contribution to light extinction (nominally considered 10 Mm

- 1 ) has been subtracted out and the 15

NO 2 contribut ion is considered negligible or is simply excluded due to the measurement 16

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

include both the Rayleigh and NO 2 contributions. 18

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

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

commonly measured as either light extinction (in terms of inverse megameters, Mm

- 1 ) or the 21

haziness index (in terms of deciview

, dv ) (Pitchford and Malm, 1993). The haziness index 22

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

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

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

megameter units (Mm

- 1 ) (Pitchford and Malm, 1993)

. Light e xtinction and haziness are physical 26

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

increasing as the amount of haze increases. 28

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

While visibility impairment has been associated most often with PM 2.5 , larger parti cle s such as 30

those found in PM 10 may be a significant contributor in some areas. Thus, UFVA considers the 31

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

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

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

particles, and also occurs when the relative humidity of the air is higher, those particles will be 35January 2010 1

- 7 DRAFT

- Do Not Quote or Cite larger in size so tha t the PM will have a larger haze effect than if PM with the same 1

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

relative humidity. 3

A s shown in Figure 1

- 1

, the ambient PM light extinction can be estimated from dry P M 4

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

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

7

Figure 1

- 1 Progre ssion from PM c haracteristics to PM l ight e xtinction that shows the 8

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

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

11

12

t he major PM species. Ambient PM l ight extinction is most accurately determined by direct 13

measure ments. However, because there is limited ambient PM light extinction data available in 14January 2010 1

- 8 DRAFT

- Do Not Quote or Cite urban areas, the assessments below will principally use monitored and modeled dry PM mass 1

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

for estimating ambient PM light extinction. 3

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

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

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

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

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

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

WinHaze system. Figure 1

- 2 below shows the progression from PM light extinction to perceived 10

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

S urvey studies have used s ets of photographs or computer simulated images developed 12

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

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

studies there is a known or predetermined one

- to

- one correspondence between the computer 15

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

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

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

. 18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36January 2010 1

- 9 DRAFT

- Do Not Quote or Cite Figure 1

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

(VAQ) 2

3

1.3 GOALS AND APPROACH 4

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

urban areas, as well as “just meet” scena rios for both the current secondary PM 2.5 standards, as 6

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

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

part icular, this UFVA focuses on the use of a PM light extinction

- based indicator for a possible 9

secondary PM NAAQS (see Figure 1

- 1 and 1

- 2). This is done by comparing estimates of hourly 10

PM light extinction in 15 major U.S. urban areas over the three

- year pe riod 2005

- 2007 to the 11

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

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

conditions were unacceptable (see discussion in chapter 2 below and Stratus Consulting Inc., 14January 2010 1

- 10 DRAFT

- Do Not Quote or Cite 2009)

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

effectiveness of a sub

- daily PM 2.5 mass concentration indicator, which was explored in the 2005 2

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

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

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

conducted in Denver (1990) , Phoenix (200 3), and British Columbia (1993) as the basis for 6

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

between about 40 km to about 60 km (corresponding to light extinction from ~100 Mm

- 1 to ~67 8

Mm - 1 ) could r epresent an appropriate degree of welfare protection from PM 5

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

- depth 14

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

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

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

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

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

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

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

9

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

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

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

upon which to base the selection of CPLs. 13

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

were based on IMPROVE algorithm estimates using the 2001 to 2003 PM 2.5 mass and speciation 23

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

the 24

- hour measured composition and by using either actual or monthly average (10

- year mean) 25

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

estimates and concurrent hourly PM 2.5 mass concentrations were used to show that daytime and 27

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

eastern an d western urban areas (i.e. R 2 >0.6, slope ~6 m 2 /g). 29

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

modeling approach to estimate hourly light extinction using PM 2.5 mass and speciation data with 31

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

constant composition for PM 2.5 , with composition that is made to vary during the day using 33

urban

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

5 Light extinction is inversely related to visual range.January 2010 1

- 11 DRAFT

- Do Not Quote or Cite r egional air quality model results, while constraining the means of the hourly species 1

concentration for each day to closely match the 24

- hour duration measured species 2

concentrations. 3

1.4 SCOPE OF URBAN

- FOCUSED VISIBILITY A SSESSMENT 4

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

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

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

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

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

uncertainty recognized in that review. 10

1.4.1 Background 11

As an initial step in this r eview, E PA invited a wide range of external experts as well as 12

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

“Workshop to Discuss Policy

- Relevant Science to Inform EPA’s Integrated Plan for the Review 14

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

opportunity for the participants to broadly discuss the key policy

- relevant issues around which 16

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

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

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

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

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

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

schedule, the process, and the key policy

- relevant science issues t hat would guide the evaluation 23

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

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

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

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

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

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

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

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

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

both regional and local contributions ( US EPA

, 2009a; sections 9.2.3.3 and 9.2.3.4 ), suggesting 33

the potential for higher levels of PM

- induced visibility impairment in urban areas; 2) the 34January 2010 1

- 12 DRAFT

- Do Not Quote or Cite existence of num erous urban visibility protection programs and goals demonstrat ing that urban 1

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

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

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

staff to conclude that urban dwellers represent a susceptible population group for adverse PM

5

related effects on visibility. However, this conclusion is not meant to imply that there are not 6

other susceptible populations or individuals living in other non

- urban and non

- Class I areas that 7

are currently adversely impacted by ambient PM

- related visibility conditions. Unfortunately, 8

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

this v isibility a ssessment focuses only on selected urban areas, a new secondary PM standard would 10

apply to all non

- Class I areas of the country. 11

On October 6

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

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

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

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

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

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

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

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

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

1.4.2 Selection of Alternative Scenarios for First Draft Assessments 23 20

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

process. 22

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

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

assessment

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

Plan for Urban Visibility Impact Assessment

, hencef orth Scope and Methods Plan (US EPA, 27

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

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

Methods Plan, along with ongoing review of th e latest PM

- related literature, several aspects of 30

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

- 1 of 31

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

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

6 To view the complete report from the October 2008 urban visibility workshop, see : http://vista.cira.colostate.edu/impr ove/Publications/GrayLit/gray_literature.h tmJanuary 2010 1

- 13 DRAFT

- Do Not Quote or Cite PM composition, and input received at the April 2, 2009 CASAC meeting, EPA staff concluded 1

that it was unnecessary to develop a new urban

- optimized algorithm at this time and that it 2

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

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

considering an urban

- optimized algorithm was a concern that the o rganic components of PM in 5

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

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

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

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

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

estimate organic component mass fr om measured organic carbon mass. 11

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

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

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

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

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

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

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

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

- existi ng, 19

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

and differences (comparability) among the se studies . I nformation drawn from these results 21

inform ed the selection of VAQ candidate protection levels (CPL s ) (described in chapter 2 below) 22

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

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

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

1. 4 .3 Selection of Alternative Scenarios for Second Draft Assessments 26

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

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

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

approach to evaluating the nature and degree of PM

- related visibility impairment, including 30

EPA’s focus on non

- Class I areas, i ncluding in particular, urban areas as an “ effective 31

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

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

range of selected candidat e light extinction levels. 34

**•** Indicator: PM Light Extinction 35January 2010 1

- 14 DRAFT

- Do Not Quote or Cite There are a number of different ways to measure ambient PM: particle counts, surface 1

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

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

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

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

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

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

EPA, 2009b) and first draft UFVA

, EPA staff is contin uing to focus assessments in this second 8

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

impairment, instead of the traditional PM 2.5 mass concentration. Unlike PM mass concentration, 10

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

water, ambient PM light extinction captures the PM

- induced visibility impairment of the 12

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

extinction as the indictor for a secondary PM NAAQS is thus a more appropriate and direct 35January 2010 1

- 15 DRAFT

- Do Not Quote or Cite measure of t he relationship between ambient PM and the public welfare effect of visibility 1

impairment than any dry PM mass concentration (either PM 2.5 or any other dry mass fraction). 2

3

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

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

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

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

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

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

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

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

experience urban VAQ in relatively short

- term incidental and intermittent periods when they 12

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

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

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

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

comparing the 1

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

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

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

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

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

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

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

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

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

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

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

hours which have light extinction levels beyond the three CPLs, as well as the 1

- hour daily 28

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

30

**•** Level: Candidate Protection Levels (CPLs) 31

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

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

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

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

urban preference study areas shown in Figure 2

- 16. A midpoint of 25 dv (122 Mm - 1 ) was a lso 36January 2010 1

- 16 DRAFT

- Do Not Quote or Cite selected for use in the assessment. These three values provide a low, middle, and high set of 1

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

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

participants of these preference studies. As discussed in greater detail in section 1. 2 above , PM 4

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

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

middle and high CPLs are about 64 Mm

- 1 , 112 Mm - 1 and 191 Mm

- 1 , respectively. 7

8

**•** Forms: Percentiles and Relative Humidity Constraints 9

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

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

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

Again, none of the preference stud ies provided insight into this aspect of acceptability. B ecause 13

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

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

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

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

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

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

- hour primary 19

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

year in this docum ent. Due to inter

- annual variability in meterology and other circumstances 21

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

standard for purposes of consistency and stability

, as is the current 24

- hour primary PM s tandard. 23

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

daylight 1

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

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

below in chapters 3 and 4 (See table 4

- 1). An additional CASAC recommendation, that the 27

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

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

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

incorpora ted in this draft. 31

1.5 ORGANIZATION OF DOCU MENT 32

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

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

regarding the approaches and methods used and results obtained for each study. This chapter 35January 2010 1

- 17 DRAFT

- Do Not Quote or Cite also includes a summary discussion of the results of a composite assessment of the combined 1

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

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

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

analytical approach

, methods

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

visibility conditions, both in terms of PM 2.5 and PM light extinction indicator s for the set of 6

urban case stu dies included in this analysis

. Selected results are presented in chapter 3, with 7

additional results found in the Appendices. Chapter 4 pr esents estimates of PM 2.5 and PM light 8

extinction conditions gener ated for the urban case studies for six alternative PM 2.5 and light 9

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

studies and uncertainty assessments for both chapters 3 and 4 are presented in Appendices A

- I ). 11

12January 2010 2

- 1 DRAFT

- Do Not Quote or Cite 

### 2 URBAN VISIBI LITY PREFERENCE STUD IES 1

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

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

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

subsequent UFVA assessments of current and alternative VAQ conditions

. T he available u rban 5

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

level of visibility im pairment that participants described as “acceptable. While e ach study asked 7

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

, t he term “acceptable” 8

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

for VAQ. 10

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

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

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

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

, and 14

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

also conducted for Washingt on, DC (Abt Associates Inc., 2001). In response to a n EPA request 16

for public comment on the Scope and Methods Plan ( 74 FR 11580, March 18, 200 9 ), Dr. Anne 17

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

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

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

from the four different urban areas included a total of 852 individuals

, wit h each individual 21

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

VAQ conditions. 23

2.1 METHOD S USED IN PREVIOUS S TUDIES 24

In all but one 7

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

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

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

images on a computer monitor connected to the computer. of the visibility preference studies assessed in this document, participants 25

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

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

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

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

location, and presented the same scene. Photographs were selected to avoid depicting significant 30January 2010 2

- 2 DRAFT

- Do Not Quote or Cite weather events (e.g., rain, snow, or fog), and where m easured extinction data were available 1

from the time the photograph was taken. 2

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

photographic

- quality images to present different VAQ conditions. Using an original near

4

pristine base photograph , additional images representing a range of VAQ conditions were 5

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

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

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

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

projector or a computer monitor. 10

WinHaze analysis synthetically superimpose s a uniform haze on a digitized, actual 11

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

impair the appearance of each individual portion of the photograph. A major a dv antage of 13

presenting WinHaze

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

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

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

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

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

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

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

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

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

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

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

alterna tive VAQ conditions and asked a series of questions. The group interview sessions we re 25

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

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

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

- person 28

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

able to fully meet this objective. Duri ng the group interview session s , the participants were 30

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

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

33

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

where the standards might be strict er

. 35January 2010 2

- 3 DRAFT

- Do Not Quote or Cite 2. The level of an urban visibility standard violation should be set at a VAQ level 1

considered to be unreasonable, objectionable, and unacceptable visually

. 2

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

health effects. 4

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

conditions; the VAQ conditions we re shown in a randomized order (with the same order use d in 6

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

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

duplicates with the identical VAQ conditions. 9

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

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

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

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

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

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

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

2.2 DENVER, COLORADO 17

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

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

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

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

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

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

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

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

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

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

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

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

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

between the two sets of photographs were different (no differ ences were found). Approximately 33 21

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

population but were instead selected to take a dv antage of previously scheduled meetings. 23

8 No preference data were collected at a 17 th focus group session due to a s lide projector malfunction.January 2010 2

- 4 DRAFT

- Do Not Quote or Cite 100 participants viewed each photograph. Projected color slides were used to present the 1

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

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

taken and measured by a transmissometer yielding hourly average light extinction, b ext

. The 4

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

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

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

photographs ranged from 30 to 596 Mm - 1

. This corresponds to 11 dv to 41 dv , approximating the 8

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

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

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

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

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

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

an appro priate urban visibility standard) was calculated for each photograph. Figure 2

- 1 shows 15

the r esults of the Denver participants’ responses, with VAQ measured in deciview s

. 16

17

Figure 2

- 1 Percent of Denver part icipants who considered VAQ in each

photograph “acceptable.”

0% 50% 100%

10 15 20 25 30 35 40 45

Deciview% participants rating"acceptable"

% acceptable Denver standard

18

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

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

that best di vides the photographs into two groups : th os e with a VAQ rated as acceptable by the 21January 2010 2

- 5 DRAFT

- Do Not Quote or Cite majority of the participants, and th os e rated not acceptable by the majority of participants. While 1

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

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

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

study, the CDPHE established a Denver visib ility standard at b

ext = 76 Mm - 1 ( dv = 20.3). 5

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

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

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

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

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

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

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

visibility standard at 76 Mm

- 1 (20.3 dv ) by identifying photographs with VAQ a minimum of 1 13

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

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

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

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

Table 2

- 1 presents information about the six photographs that were inconsistently rated

. 18

A ll six of the inconsistently rated photographs were taken at 9:00 a.m. The five inconsist ently 19

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

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

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

9:00 a.m., so a third of the 9:00 a.m. photographs were inconsistently rated

. Conversely, none of 23

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

. 24

25

Table 2

- 1

. VAQ of Denver photos substantively misclassified by majority of participants 26

27

Photograph # VAQ in photograph

in extinction

(Mm - 1 ) VAQ in

photograph ( dv ) % of participants

who rated the photo

“acceptable” Time of day of

photograph

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

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

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

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

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

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

28January 2010 2

- 6 DRAFT

- Do Not Quote or Cite Figure 2

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

photograph acceptable as in Figure 2

- 1, but with the time of day of each photograph indicated by 2

different colors. The time of day colors clearly indicate how inconsistently participants rated 3

some of the 9:00 a.m. photographs. 4

Eliminating the 9:00 a.m. photographs creates a “hole” in the range of remaining 5

photographs; there are no photographs with a VAQ between 17.7 dv and 20.3 dv . As seen in 6

Figure 2

- 3 , this is a critical range in evaluating the responses. All of the photographs with a 7

VAQ equal to or better (i.e., a lower dv va lue) than 17.7 dv are rated acceptable by the majority 8

of the participants, and all photographs with a VAQ at or above 20.3 dv are rated not acceptable. 9

After eliminating the 9:00 a.m. photographs, any VAQ level between 17.7 dv and 20.3 dv would 10

completely divide the photographs into two groups with no inconsistent ratings

. 11

12

Figure 2

- 2 Photograph time of day information

0% 50% 100%

10 15 20 25 30 35 40 45

Deciview% participants rating"acceptable"

9:00 AM 12:00 PM 3:00 PM Denver standard

13

A modestly broader range of VAQ conditions provides an even more unambiguous 14

interpre tation of the Denver study results. Every photograph with a VAQ of 17.7 dv or lower 15

was rated acceptable by 89% or more of participants, and every photograph with a VAQ of 24.6 16

or higher was rated not acceptable by 84% or more of the participants. The 17 .7 dv to 24.6 dv 17

range separating the results is shown in Figure 2

- 3, which also eliminates the 9:00 a.m. results. 18

19

20

21

22January 2010 2

- 7 DRAFT

- Do Not Quote or Cite 1

Figure 2

- 3 Denver photograph time of day results (9:00 a.m. photographs

eliminated), wi th the broader range (17.7 dv and 24.6 dv) of the 50% acceptability criteria

shown.

0% 50% 100%

10 15 20 25 30 35 40 45

Deciview% Participants Rating"Acceptable"

12:00 PM 3:00 PM Denver Standard Lower Bound Upper Bound

2.3 VANCOUVER, BRITISH C OLUMBIA, CANADA 2

The BC urban visibility preference study (Pryor, 1996) was conducted on behalf of the 3

BC Ministry of Environment following the meth ods used in the Denver study. Participants were 4

students at the University of British Columbia, who were in one of four focus group sessions 5

with between 7 and 95 participants. A total of 180 participants completed the surveys (29 did 6

not complete the su rvey). 7

The BC study used photographs (projected as slides) depicting various VAQ conditions 8

in two cities (Chilliwack and Abbotsford) in the lower Fraser River valley in southwestern BC. 9

Abbotsford is located approximately 75 miles east of Vancouver, BC, and had a 2006 population 10

of 159,000 (Statistics Canada, 2009a). Abbotsford has a diverse and successful economy, with 11

approximately 25% of the labor force working in the Vancouver metropolitan area. Chilliwack 12

is adjacent to Abbotsford to the east. Bot h cities have experienced rapid population growth, 13

growing faster than the Vancouver metropolitan area, and are considered suburbs (or exurbs) of 14

Vancouver. 15

The survey was conducted at the University of British Columbia (UBC) in 1994. The 16

participants we re 206 undergraduate and graduate students enrolled in classes in UBC’s 17

Department of Geography. Information about student demographics and where they lived prior 18

to enrolling at UBC (which potentially influences their knowledge of, and preferences for, 19

V ancouver area visibility) is not available. 20January 2010 2

- 8 DRAFT

- Do Not Quote or Cite The BC survey showed 20 unique photographs to the participants in random order. Ten 1

photographs were from Chilliwack, and 10 were from Abbotsford. The Chilliwack photographs 2

were taken at the Chilliwack Hospit al, and the scene includes a complex foreground with 3

downtown buildings, with mountains in the background up to 40 miles away. Figure 2

- 4 is a 4

composite of two of the Chilliwack photographs used in the preference study, showing the scene 5

with a good visib ility day (14.1 dv ) in the middle and a significantly impaired day (34 dv ) around 6

the border (Jacques Whitford AXYS, 2007). The Abbotsford photographs were taken at the 7

Abbotsford Airport. The Abbotsford scene includes fewer man

- made objects in the foreg round 8

and is primarily a more rural scene with the mountains in the background up to 36 miles away. 9

10

11

Figure 2

- 4 Composite Chilliwack, BC photograph show s VAQ of 14.1 dv and 34 dv

.

12

The photographs were t aken in July and August 1993 as part of a VAQ and fine 13

particulate monitoring project sponsored by the BC Ministry of Environment, Lands and Parks 14

(REVEAL, the Regional Visibility Experimental Assessment in the Lower Fraser Valley). All of 15

the photographs were taken at either 12:00 p.m. or 3:00 p.m. VAQ data were available for each 16

photograph from visibility monitors near the location of each camera. The types of VAQ 17

measurement data available from the two locations were not identical. The Chilliwack lo cation 18

used both an open

- chamber nephelometer and a long path transmissometer and collected hourly 19

average data on both aerosol light scattering ( b

sp ) and total extinction ( b

ext ), respectively. The 20January 2010 2

- 9 DRAFT

- Do Not Quote or Cite visibility monitoring at the Abbotsford location had only a nephelometer and collected only b

sp 1

data. 2

As explained in section 1.3, t otal light extinction is the sum of scattering by gases ( b

sg ) 3

and particles ( b

sp ) plus light absorption by gases ( b

ag ) and particles ( b

ap ). In order to present the 4

preference resul ts from the BC study in comparable terms, b

ext for the Abbotsford photographs is 5

estimated by assuming that the average of the ratios of PM light extinction (i.e., b

ap + b

sp ) to PM 6

light scattering ( b

sp ) for all ten of the Chilliwack photographs can be mul tiplied by the 7

Abbotsford nephelometer determined b

sp values corresponding to each of its photographs to 8

estimate its PM light extinction value. By assuming that absorption by gases ( b

ag ) is zero, total 9

light extinction is equal to the PM light extinction (i.e., b

ap + b

sp ) plus particle scattering by gases 10

(i.e., b

sg that is approximately equal to 10 Mm - 1 ). Table 2

- 2 presents the data from the 11

photographs used in the BC study, including the estimated b

ext for the Abbotsford photographs. 12

There are two cav eats to be noted about the extinction data for the photographs reported 13

in Pryor, 1996. First, in Table 2 of the original article, two of the Abbotsford photographs are 14

listed with the same date and time (12:00 p.m., 7/26/1993). There is no information p rovided for 15

a 3:00 p.m., 7/26/1993 Abbotsford photograph, although there is a Chilliwack photograph from 16

that time. The preference and VAQ data are presumed to be correct for both photographs and 17

one of the two identical date/time labels is assumed to be a typographic error. The second caveat 18

is that b

sp levels from the same date and time can differ substantially between Abbotsford and 19

Chilliwack, and the relative levels can change rapidly, even though the two cities are only 25 20

miles apart. For example, at 12:00 p.m. on 8/19/1993, the b

sp level in Chilliwack was about one

21

third of the Abbotsford b

sp level. By 3:00 p.m. the situation was reversed, with the Chilliwack 22

b

sp level 50% higher than Abbotsford. In those three hours the Chilliwack b

sp level ha d more 23

than doubled (from 46 Mm

- 1 to 105 Mm - 1 ), and the Abbotsford level had fallen by over half 24

(from 145 Mm

- 1 to 67 Mm - 1 ). Such substantial changes in measured b

sp levels occurring across a 25

relatively short period of time and short distance, may reflect an inherent uncertainty introduced 26

by using a single measure of light extinction from a portion of visual scene (where the 27

nephelometer or transmissometer was operating) to assess visibility conditions throughout an 28

actual photographs of a complex scene. Spatial and temporal non

- uniformity of visibility 29

conditions within a scene are an atmospheric condition known to occur on some days, and may 30

contribute to the variability in participant responses in preference studies utilizing actual 31

photographs. 32

33

34

35

Ta ble 2

- 2

. Summary of photographs used in British Columbia study 36January 2010 2

- 10 DRAFT

- Do Not Quote or Cite 1

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 11 2 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

Averag e 76 122 23.67

2

3

Figure 2

- 5 presents t he results of the BC study. The division corresponding to the 4

Denver “50% acceptable” criteria occurs between 22.6 dv and 23. 2 dv

. All of the photographs 5

with a VAQ better than 22.6 dv were rated acceptable by the majority of the participants with 6

one exception (47% of the participants judged the 19. 2 dv photograph to be acceptable). All 7

photographs with a VAQ better than 19.2 dv were rated acceptable by over 90% of the 8

participants. All photographs with a VAQ wo rse than 2 2

. 6 dv were rated not acceptable by the 9January 2010 2

- 11 DRAFT

- Do Not Quote or Cite majority of the participants, and all photographs with a VAQ worse than 28.3 dv were rated not 1

acceptable by over 90% of the participants. 2

Figure 2

- 5 also suggests that there may be some difference between the preferences 3

expressed for the Chilliwack scene and those for the Abbotsford scene. All photographs were 4

rated by the same individuals (students at UBC), but the summary of the responses indicate that 5

the participants may have rated as acceptable a wor se level of impaired VAQ impairment (e.g., 6

higher dv levels) in photographs showing more of a downtown area (Chilliwack) than in less 7

congested scenes (Abbotsford). The strongest evidence for this hypothesis, however, is the 8

preference for a single photog raph (the 19.0 dv photograph from Abbotsford, rated as acceptable 9

by 47%), previously identified as an outlier observation. 10

11

Figure 2

- 5 Percent of BC participants who consider VAQ in each photograph 12

“accept able.” 13

14

0% 50% 100%

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

Deciview Results of British Columbia Visiblity Study

Chilliwack Abbotsford 15

16

The BC Ministry of the Environment is considering the BC urban visibility preference 17

study as part of establishing urban and wilderness visibility goals in BC. 18

2.4 PHOENIX, ARIZONA 19

The Phoenix urban visibility preference study ( BBC Research & Consu lting , 2002)

, 20

which was conducted on behalf of the Arizona Department of Environmental Quality, us ed 21January 2010 2

- 12 DRAFT

- Do Not Quote or Cite group interviews based on the methods used in the Denver stud y, with two major exceptions : (1) 1

the focus group participants were selected as a representati ve sample of the Phoenix area 2

population, and (2) the pictures presented in the focus groups were computer

- generated images 3

to depict specific uniform haze conditions. 4

The Phoenix study included 385 participants in 27 separate focus group sessions. 5

Parti cipants were recruited using random digit dialing to obtain a sample group designed to be 6

demographically representative of the larger Phoenix population. During July 2002, group 7

interview sessions took place at six neighborhood locations throughout the m etropolitan area to 8

improve the participation rate. Participants received $50 as an inducement to participate. 9

Three sessions were held in Spanish in one region of the city with a large Hispanic 10

population (25%), although the final overall participation o f native Spanish speakers (18%) in 11

the study was below the targeted level. The age distribution of the participants corresponded 12

reasonably well to the overall age distribution in the 2000 U.S. Census for the Phoenix area 13

(BBC Research & Consulting, 2002)

. Participants slightly over

- represented the middle

- income 14

range ($50,000 to $74,999)

, compared with 2000 Census data, and slightly under

- represented 15

very low

- income ranges (under $24,999). The distribution of participant education levels was 16

fairly cons istent with the education distribution in the 2000 Census. 17

Photographic

- quality slides of the images were developed using the WinHaze software 18

(Molenar et al., 1994). The scene used in the Phoenix study images was taken at a water 19

treatment plant. The vi ew is toward the southwest, including downtown Phoenix, with the Sierra 20

Estrella Mountains in the background at a distance of 25 miles. Figure 2

- 6 shows t he image with 21

the best VAQ (15 dv ). 22

23January 2010 2

- 13 DRAFT

- Do Not Quote or Cite Figure 2

- 6 Rep roduction of image with the best VAQ (15 dv ) used in the Phoenix

study.

1

The study used a total of 21 unique WinHaze images. Four of the 21 unique images were 2

randomly selected and used twice to evaluate consistency; participants viewed a total of 3

25 i mages. The 25 images were randomly ordered, with all participants viewing the images in 4

the same order. The WinHaze images used in the Phoenix study do not include layered haze, a 5

frequent and widely recognized form of visibility impairment in the Phoeni x area. 6

The VAQ levels in the 21 unique images ranged from 15 dv to 35 dv (the extinction 7

coefficient b

ext range d from 45 Mm - 1 to 330 Mm - 1 ). As in the Denver study, participants first 8

individually rated the randomly shown slides on the same VAQ scale of 1 to 7. Participants were 9

instructed to rate the photographs solely on visibility and to not base their decisions on either 10

health concerns or what it would cost to have better visibility. Next, the participants individually 11

rated the randomly ordered sli des as “acceptable” or “not acceptable,” defined as whether the 12

visibility in the slide is unreasonable or objectionable. 13

Figure 2

- 7 presents the percent acceptability results from the Phoenix study. The 14

combination of the use of WinHaze images and the l arger number of participants than in the 15

Denver study may account for the “smoother” backwards S

- shaped pattern of preferences. 16

17

18

19

20

21

22January 2010 2

- 14 DRAFT

- Do Not Quote or Cite 1

Figure 2

- 7 Percent of Phoenix participants who consider VAQ in each imag e

“acceptable.”

0% 50% 100%

10 15 20 25 30 35 40 45

Deciview% participants rating "acceptable"

2

Ninety percent or more of the participants rated a VAQ of 20 dv or better as acceptable, and 70% 3

rated a VAQ of 22 dv or better as acceptable. The “50% acceptable criteria” was met at 4

approximately 24.3 dv (with 51.3% of the participa nts rating that image as acceptable). The 5

percent acceptability declines rapidly as VAQ worsens; only 27% of the participants rated a 6

26 DV image as acceptable, and fewer than 10% rated a 29 dv image as acceptable. 7

The Phoenix urban visibility study forme d the basis of the decision of the Phoenix 8

Visibility Index Oversight Committee for a visibility index for the Phoenix metropolitan area 9

(Arizona Department of Environmental Quality, 2003). The Phoenix Visibility Index establishes 10

an indexed system with 5 categories of visibility conditions, ranging from “Excellent” (14 dv or 11

less, which was a better VAQ than any of the images used in the Phoenix study) to “Very Poor” 12

(29 dv or greater, which less than 10% of the study participants rated as acceptable). T he 13

“Good” range is 15 dv to 20 dv (more than 90% of the participants rated images in this VAQ 14

range as acceptable). The environmental goal of the Phoenix urban visibility program is to 15

achieve continued progress through 2018 by moving the number of days i n poor er quality 16

categories into better quality categories. 17

2.5 WASHINGTON, DC 18

One of the Washington, DC urban visibility pilot stud ies was conducted on behalf of 19

EPA (Abt Associates Inc., 2001)

. It was designed to be a pilot focus group study, an initial 20January 2010 2

- 15 DRAFT

- Do Not Quote or Cite dev elopmental trial run of a larger study. The intent of the pilot study was to refine both focus 1

group method design and potential survey questions. Due to funding limitations, only a single 2

focus group session took place , consisting of one extended sessio n with nine participants. No 3

further urban visibility focus group sessions were held in Washington, DC on behalf of EPA. 4

In March 2009, Dr. Anne Smith conducted a separate study of Washington urban 5

visibility, using the same photographs and similar approac h as the 2001 study (Smith and 6

Howell, 2009). On behalf of the Utility Air Regulatory Group, Dr. Smith presented comments 7

(Smith, 2009) to the CASAC at a public meeting held on April 2, 2009 to r eview EPA’s plan 8

(US EPA, 2009 b ) for conducting further urba n visibility studies in support of PM NAAQS 9

reviews

. Dr. Smith submitted the Smith and Howell (2009) report to the CASAC as part of the 10

public comment process. The Smith and Howell study conducted three study variations of a 11

Washington, DC

, preference st udy, including one experiment involving 26 participants designed 12

to replicate the EPA 2001 preference study. 13

Both the Abt Associates Inc. ( 2001 ) study results and the results of the Smith and Howell 14

( 2009 ) study are discussed below. 15

2.5.1 Washington, DC 2001 16

T he EPA’s Washington, DC study (Abt Associates Inc. , 2001) adopted the general study 17

methods used in the Denver, BC, and Phoenix studies, modifying them appropriately to be 18

applicable in an eastern urban setting. Washington’s (and the entire East’s) curren t visibility 19

conditions are typically substantially worse than western cities and have different characteristics. 20

Washington’s visibility impairment is primarily a uniform whitish haze dominated by sulfates, 21

and the relative humidity levels are higher com pared with the western study areas

. In addition, 22

the relatively low

- lying terrain 9

The Washington

, DC focus group session included questions on valuation, as well as on 26

preferences. The focus group content dealing with preferences for an urban visibility standard 27

was similar to the focus group sessions in the western studies. 28 in Washington, DC provides substantially shorter maximum 23

sight distances. M any residents are not well informed that anthropogenic emissions impair 24

visibility on hazy days. 25

A single scene of a panoramic photograph taken from Arlington National Cemetery in 29

Virginia was used, and included an iconic view of the Potomac River, the National Mall, and 30

downtown Washington, DC

. All of the distinct buildings in the scene are less than four m iles 31

from the camera, and the higher elevations in the background are less than 10 miles from the 32

camera. Figure 2

- 8 presents t he photograph used in the study. 33

34

9 The maximum elevation in Washington, DC is 409 feet.January 2010 2

- 16 DRAFT

- Do Not Quote or Cite Figure 2

- 8 Reproduction of the image with t he best VAQ (8.8 dv) used in the

Washington, DC study.

1

The Washington

, DC study used 20 unique images generated by WinHaze, each prepared 2

from the same original photograph. Humidity and gaseous light scattering was held constant in 3

preparing the WinH aze images, as was the relative chemical mix of aerosol particulates in the 4

photos (i.e., only the aerosol concentrations were increased to create the images with worse 5

VAQ). Five of the images were repeated as a consistency check, so participants viewed a total 6

of 25 slides. The range of VAQ in the images ranged from 8.8 to 38.3 dv

. 7

Figure 2

- 9 presents the percent acceptability results from the 2001 Washington study. 8

Because only nine participants were involved in the study, the possible values of “perce nt 9

acceptable” are limited to multiples of 1/9. Figure 2

- 9 also shows an anomalous result involving 10

one of the five repeated images. Three of the repeat images had the same ranking each time they 11

were presented (i.e., all nine participants rated them acc eptable or not acceptable both times they 12

rated that slide). One of the images (the image with 8.8 dv , the best VAQ image used in the 13

study) was rated acceptable by all nine participants the first time it was used, but the repeat of 14

that slide was rated n ot acceptable by one participant. Another image, however, had a 15

substantially different result. The 30.9 dv image was rated acceptable by five of the nine 16

participants the first time it was presented, but the repeat of the slide was only rated acceptable 17

by one of the nine participants. The responses for all five pairs of repeated images are shown in 18January 2010 2

- 17 DRAFT

- Do Not Quote or Cite red on Figure 2

- 9

, including the images which were identically rated both times they were 1

presented. 2

3

4

Figure 2

- 9 Percent of 2001 Washington participants who consider ed VAQ

acceptable in each image

Results of 2001 Washington DC Preference Study

0% 50% 100%

5 10 15 20 25 30 35 40 45

Deciview% Participants Rating"Acceptable"

Unique Images 5 Repeated Images

5

In the 2001 Washington

, DC study, all images with a VAQ below 25.9 dv were rated 6

acceptable by the majority of the participants, and all images with a VAQ below 29.2 dv were 7

rated acceptable by at least four of the nine (44%) participants. All images with a VAQ above 8

30.9 dv were rated not acceptable. The “50% acceptability criteria” division occurs in the range 9

of 25.9 dv to 30.9 dv , with the anomalous result o f the inconsistent responses to the repeated 10

image with 30.9 dv effectively broadening this range and adding uncertainty to identifying a 11

clear division. 12

2.5.2 Washington, DC , 2009 13

The Smith and Howell (2009) study conducted additional focus group sessions base d on 14

the methods and materials used in the 2001 Washington, DC study. Smith and Howell recreated 15

the WinHaze images used in the 2001 Washington, DC urban visibility preference study, using 16

the description in the report on the 2001 study (Abt Associates In c. , 2001), and created images 17

using currently available desktop computer version of WinHaze (Version 2.9.0). Smith and 18January 2010 2

- 18 DRAFT

- Do Not Quote or Cite Howell used a shortened version of the same question protocol as the 2001 study. The WinHaze 1

images were presented to a total of 64 par ticipants who were all employees of Charles River 2

Associates ( CRA International, Inc )

. (Smith and Howell also are CRA International employees). 3

The CRA employees were based at the firm’s Washington, DC and Houston, Texas offices (44 4

and 20 participants, respectively). The Houston participants were included to explore whether 5

familiarity with Washington, DC VAQ conditions developed from currently living in the 6

Washington region noticeably influenced the responses. As noted by Smith and Howell, the 7

partic ipants were not a representative sample of either metropolitan area’s population; all 8

participants were employed, and the participant group included a higher proportion of college 9

educated individuals and higher household incomes than the general populatio n. 10

Eight of the Washington

- based participants and all of the Houston participants viewed the 11

WinHaze images on a desktop computer monitor. The remaining Washington participants 12

viewed the images projected on a screen. 13

The stated purpose of the Smith and Howell study was to explore the robustness of the 14

2001 results. To investigate this issue, Smith and Howell conducted three different tests 15

concerning urban visibility preferences. Each participant was involved with only one test. The 16

three tests were: 17

**♦** Test 1

- replicated the Abt Associates Inc. (2001) study 18

19

**♦** Test 2

- reduced the upper end of the range of VAQ by eliminating the 11 images 20

used in Test 1 with a VAQ above 27.1 dv 21

22

**♦** Test 3

- increased the upper end of the range of VAQ by including two new ima ges 23

of worse VAQ; the two new images had a VAQ of 42 dv and 45 dv 24

25

Sixteen employees from the Washington, DC office and 10 participants from the Houston 26

office took Test 1 (a total of 26 participants)

. All the participants viewed the same unique 20 27

Washin gton, DC WinHaze images as the 2001 study (plus repeated images for a total of 25 28

images shown to participants). Images were presented in the same random order as in the 2001 29

study. Figure 2

- 10 presents the results of Test 1. The results for the 16 Wash ington participants 30

are indicated in blue and results for the 10 Houston participants in red. Although all images used 31

in the study were of Washington, DC, the results suggest that there is not a significant difference 32

in the preferences of participants b ased in the two offices. The scene in the images is an 33

immediately recognizable iconic view of the National Mall and downtown Washington, DC, 34

which may influence the similarity of responses by residents of the two cities. 35

36

37January 2010 2

- 19 DRAFT

- Do Not Quote or Cite 1

Figure 2

- 10 Percent of 2009 Test 1 study participants who considered VAQ

acceptable in each image, showing the range of the lower and upper bound of 50%

acceptability criteria.

0% 50% 100%

5 10 15 20 25 30 35 40

Deciview% Participants Rating "Acceptable"

Washington Houston Lower Bound Upper Bound

2

Using the combined Test 1 results from the two CRA offi ces (2 6 total participants), the 3

majority of participants in the 2009 study rated all VAQ images with 25.9 dv or less as 4

acceptable and all VAQ images with 29.2 dv or greater as not acceptable. The image of 27.1 dv 5

was rated as acceptable by 50% of the to tal participants (56% of the Washington

- based and 40% 6

of the Houston

- based participants). All images with a VAQ less than 22.9 dv were rated 7

acceptable by at least 90% of the participants, and all images with a VAQ greater than 32.3 dv 8

were rated not acce ptable by 88% of the participants. 9

Figure 2

- 11 presents the Abt 2001 study and Smith and Howell 2009 (Test 1) study 10

results on a single graph, representing the results of 35 total participants of preferences for urban 11

visibility in Washington, DC

. The res ults from the 2009 study on Figure 2

- 1 1 combine the Test 12

1 responses from the two CRA offices. Figure 2

- 11 also shows the 50% acceptability criteria 13

range (22.9 dv to 32.3 dv ) from the 2009 Test 1. In comparison, the 2001 study 50% 14

acceptability range wa s 25.9 dv to 30.9 dv

. Inspection of the points in Figure 2

- 1 1 indicate s that 15

the results from the 2009 study (Test 1) are not appreciably different than the results of the 2001 16

Washington study. 17January 2010 2

- 20 DRAFT

- Do Not Quote or Cite In Test 2, Smith and Howell reduced the range of VAQ images to images with a VAQ of 1

27.1 dv or less. The 26 participants in the Test 2 study were different people than the Test 1 2

participants. Test 2 presented only the nine unique clearest WinHaze images from the full Test 1 3

set of 20 images , along with 3 duplica tes for a total of 12 images

. This constricted the VAQ 4

levels presented to the range that the majority of participants in the 2001 study rated as 5

acceptable and reduced the upper end of the VAQ range by 11.2 dv

. 6

7

Figure 2

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

acceptability criteria from 2009, Test 1).

0% 50% 100%

5 10 15 20 25 30 35 40

Deciview% Participants Rating "Acceptable"

2001 Study 2009, Test 1 Lower Bound Upper Bound

8

9

Figure 2

- 1 2 presents the Test 1 and Test 2 results. Test 2 found a substantial shift in the 10

responses regarding which V AQ level s are considered acceptable. The smaller number of 11

images used in Test 2 ma de identifying the range of the 50% acceptability criteria more difficult 12

than in Test 1. The lower bound of the range occurs between 15.6 and 18.7 dv , and the upper 13

bound occurs between 24.5 and 27.1 dv

. Smith and Howell conclude that the shift in the 14

acceptability responses between Test 1 and Test 2 suggests that the VAQ levels identified as 15

acceptable in an urban visibility preference study conducted using the general a pproach 16

previously used in the all the studies may be influenced by the range of VAQ images presented. 17

18

19

20January 2010 2

- 21 DRAFT

- Do Not Quote or Cite Figure 2

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

.

Results of Smith and Howell Test 1 and Test 2

0% 50% 100%

5 10 15 20 25 30 35 40

Deciview% Participants Rating "Acceptable"

Test 2 Test 1

In order f or th e range of images shown to be able to influence the acceptability ratings, 1

participants would need to be aware of the upper and lower bounds of the range prior to the 2

judging acceptability . However , since they were shown images randomly with respect to th e 3

VAQ levels, asked to rate each one before going to the next image

, and wer e not given a chance 4

to revise their acceptability ratings , this was not possible during the acceptability exercise itself

. 5

The only other opportunity participants could have to l earn the VAQ range is during the VAQ 6

rating exercise done just prior to the acceptability rating. However, i n the VAQ rating exercise 7

where the participants were asked to rate the quality of visibility for the shown images on a scale 8

from 1 to 7 , the imag es were also shown in a random order, participants were not aware how 9

many photographs would be shown or the range of conditions, they were asked to rate each one 10

using a value from 1 to 7 before going on to the next image and they did not have the opportu nity 11

to revise the rating s of earlier viewed images. 12

Figure 2

- 13 shows the average visibility rating on the 1 to 7 scale for each image used in 13

each of the three tests conducted by Smith and Howell (2009). The consisten cy observed in the 14

relationship be tween VAQ deciview levels and the average scores assigned across the three test s 15

demonstrates that the participants come to the survey with the capability to consistently rate the 16

haze levels shown in the images , regardless of the breadth of the range used or the order or 17

number of slides shown, and that they are aware of a full range of conditions, even when, as was 18

the case in Test 2, they were not shown the worst haze images. 19January 2010 2

- 22 DRAFT

- Do Not Quote or Cite 1

Figure 2

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

images by participants in Tests 1

- 3 conducted by Smith and Howell (2009). 3

4

5

Why then did Test 2 participants in the subsequent part of the survey rate images of haze 6

levels as unacceptable that were ra ted acceptable by participants in the other tests and the earlier 7

Washington, DC pilot study? In a three sentence script 10

Smith and Howell (2009) concluded that the effects of a changed range on the 17

acceptability ratings results demonstrates that VAQ preference studies results are not robust and 18

do not reflect an enduring view on the “unacceptability” of different levels of VAQ degradation. 19 that constituted the only instructions

8

read prior to the acceptability rating, the participants were told that they would see the sam e set 9

of slides that they had just rated (i.e., on the 1 to 7 scale), and they were asked to rate them 10

according to whether the VAQ depicted were acceptable or unacceptable to them. Apparently by 11

directing then to rate the same images for acceptability, t he participants understood that their 12

choices of visibility conditions were restricted to a range of conditions shown in the 1 to 7 13

ratings that they had just completed. For participants in Test 2 this would mean that by their own 14

1 to 7 ratings the range was restricted to include no poor visibility conditions (i.e. only scenes 15

rated from 3 to 7). 16

10 The complete script for the acceptability/unacceptability part of the study is as follows. “Now you will

be shown the same set of slides that you just rated. Again each image will illustrate the effects of a different level of

visibility. This time, rate the slides according to whether the visibility is acceptable or unacceptable to you.”January 2010 2

- 23 DRAFT

- Do Not Quote or Cite However, there is an alternative explanation. It seems more likely that the use of such a severely 1

truncated range of VAQ conditions in Test 2, which did not include any of the images of VAQ 2

that previous studies identified as unacceptable, in effect fundamentally changed the implied 3

instructions for the participants. Instead of conveying that they were to identify VAQ levels that 4

they found acceptable among a full range of VAQ conditions from very poor to very good, the 5

implied message was that they should identify the VAQ levels that they found acceptable among 6

a curtailed range of VAQ conditions that only included average to very good VAQ. By this 7

reasoning, it would be inappropriate to include Test 2 results with those of the other tests as a 8

measure of VAQ preference for Washington, DC

. 9

In Test 3, Smith and Howell expanded the VAQ range of WinHaze images shown to the 10

participants, includi ng two new images with a worse VAQ. The new images had a VAQ of 42 11

dv and 45 dv , raising the upper end of the VAQ range by 6.7 dv

. Test 3 also reduced the total 12

number of images shown to participants to 19 images by eliminating the use of the five repeat 13

images in Test 1, and also eliminated three additional images in order to reduce the participants’ 14

time burden. The three deleted images had a VAQ of 11.1, 15.6, and 24.5 dv

. The best VAQ 15

image shown to Test 3 participants was 8.8 dv (same as the best V AQ image in Tests 1 and 2). 16

However, in Test 3 there were no images with VAQ between 8.8 dv and 18.7 dv , creating a 17

significant “hole” in the distribution of VAQ conditions presented to the Test 3 participants. 18

Test 3 was conducted with 12 participants from the CRA Washington office (none of whom 19

participated in Test 1 or Test 2). No Houston participants were involved with Test 3. Figure 2

- 20

13 shows that the T est 3 average ratings from 1 to 7 during the VAQ rating exercise increased 21

the average particip ant rating by about 1 at the low end of the scale (very poor VAQ). The 22

results of Test 3 are shown in Figure 2

- 1 4 , al ong with the results of Test 1. 23

Test 3 resulted in an overall increase in the percent of respondents rating as acceptable 24

the VAQ images u sed in both tests. In Test 3 all images with a VAQ below 22.9 dv were rated 25

acceptable by 100% of the participants (similar to the Test 1 results), implying there was no 26

general change in the acceptability of the images with good VAQ. However, for all VA Q 27

images (that were used in both studies) between 25.9 dv and 33.6 dv , a noticeably larger 28

percentage of the participants in Test 3 rated the image as acceptable than in Test 1. At VAQ 29

levels worse than 33.6 dv , the majority of the participants found the VAQ level not acceptable in 30

both tests. While the differences are noticeable, the small number of participants in Test 3 (i.e. 31

12) makes the significance of the difference unclear. 32

33

34

35

36January 2010 2

- 24 DRAFT

- Do Not Quote or Cite Figure 2

- 14 Compariso n of results from the Smith and Howell (2009) Test 1 and

Test 3.

0% 50% 100%

5 10 15 20 25 30 35 40 45

Deciview% Participants Rating "Acceptable"

Test 3 Test 1

1

The range limitations identified with Test 2 (i.e. an overly restrictive range) that resulted 2

in its results being eliminated from consideration in the selection of appropriate CPLs, do not 3

apply to Test 3 results due to its somewhat more complete coverage of the 1 to 7 rating range in 4

the VAQ rating exercise. In that sense, T est 3 results may be considered somewhat more reliable 5

than those from T est 1 and the original Washington

, DC pil ot study. However the number of 6

participants in Test 3 (i.e., 12) is small enough that the statistical uncertainty of the results may 7

be an issue if used alone. Given that most of the same images of VAQ conditions were used in 8

all of the tests, composite acceptability ratings (i.e., from the original pilot study and from Tests 9

1and 3) of each image were developed to increase the number of participant ratings for each 10

image . Figure 2

- 15 shows the composite results of the se three groups involving a total o f 47 11

participants

. The 50% acceptability criteria value for this composite dataset lies unambiguously 12

between the 30. 1 dv (at 51.1%) and the 30

. 9 dv points (at 46.3%)

. This analysis does not address 13

the question of whether a significant “hole” in the Tes t 3 VAQ distribution bet ween 8.8 dv and 14

18.7 dv potentially had an effect on participant acceptability responses. 15

16

17

18

19January 2010 2

- 25 DRAFT

- Do Not Quote or Cite Figure 2

- 15 Composite results from Smith and Howell (2009) Tests 1 and 3, and 1

Abt (2001) Washington, DC pilot study. 2

3

4

5

2.6 SUMMARY OF PREFERENC E STUDIES AND SELECTION OF 6

C ANDIDATE PROTECTION LEVELS 7

Each of the studies reviewed in this assessment investigate s the common question, “What 8

level of visibility degradation is acceptable?” The approac h es used in the four studies are similar 9

and are all derived from the method first developed for the Denver urban visibility study. As a 10

result, EPA staff ha s concluded that it is reasonable to compare the results to identify overall 11

trends in the study f indings and that this comparison can usefully inform the selection of CPLs 12

for use in further analyses. However, because variations in t he specific materials and methods 13

used in each study introduce uncertainties

, direct comparison of the study results sh ould take 14

these factors into account

. Key differences between the studies include: 15

16

**♦** Image presentation methods (e.g., projected slides of actual photos, projected images 17

generated using WinHaze (a significant technical a dv ance in the method of presenting 18

VAQ conditions), use of computer monitor screen 19

20January 2010 2

- 26 DRAFT

- Do Not Quote or Cite **♦** N umber of participants in each study, 1

2

**♦** P articipant representative ness of the general population of the relevant metropolitan 3

area, and 4

5

**♦** S pecific wording used to frame the questions used in the group inter view process. 6

7

Figure 2

- 16 presents a graphical summary of the results of the studies in the four cities 8

and draws on results previously presented in Figures 2

- 3, 2

- 5, 2

- 7 and 2

- 11. 9

10

Figure 2

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

identified range of the 50% acceptance criteria

. 11

11

For clarity in Figure 2

- 16, the Denver results omit the 9:00 a.m. photograph results, the 12

Chilliwack and Abbotsford photographs app ear as a single set of data for the BC study, and the 13

results from 2001 and 2009 (Test 1) studies of VAQ preferences in Washington, DC are 14

presented as a single combined set of data. The results from the 2009 Washington, DC study 15

Tests 2 and 3 are not inc luded on Figure 2

- 16; Test 2 is not a comparable study because it 16

restricted the range of VAQ conditions to only those rated average to best (e.g., 3

- 7) visibility in 17

11 Top scale shows light extinction in inverse megameter units; bottom scale in deci views. Logit analysis estimated

response functions are shown as the color

- coded curved lines for each of the four urban areasJanuary 2010 2

- 27 DRAFT

- Do Not Quote or Cite the VAQ ratings , and Test 3 is not included because of concerns for the effects on having a 1

significant “hole” in the VAQ distributions shown to participants between 8.8 dv and 18.7 dv

. 2

Figure 2

- 16 shows the results of a logistical regression analysis using a logit model of the 3

greater than 19,000 ratings of haze images as acceptable or unacc eptable. The logit model is a 4

generalized linear model used for binomial regression analysis which fits explanatory data about 5

binary outcomes (in this case, a person rating a VAQ image as acceptable or not) to a logistic 6

function curve. 7

In the context o f the preference studies, the logit model determines a function that best 8

estimates the percentage of respond e nt s that rate an image acceptable based on a set of 9

explanatory variables. The observations on the dependent variable ha ve one of two discrete 10

va lues: 1 (the person rated the image acceptable) or 0 (unacceptable). For this application, the 11

logit model determines an equation estimating the proportion of participants who will find any 12

particular deviciew level acceptable. There were two basic types of explanatory (independent) 13

variables used : one continuous numerical variable (the image’s haziness level or VAQ in 14

deciviews), and a set of discrete variables that identify which city the observation is from. 15

The fundamental form of a logistic function is; 16

z e z f yes y probabilit

**− +**  **= =**  1 1 ) ( ) " (" 17

where the variable z, known as the logit, is the influence of all the explanatory variables; 18

... 2 2 1 1 **+ +**  **+ =**  x x z

o **β β**  **β** 19

In this analysis the estimated logistic function f(z) is the estimated probability of the 20

participants in the study rating an image as acceptable, given the dv value of the image and what 21

city the observation came from. In this application the logit is 22

) ( ) ( ) (

7 6 5 4 3 2 1 Phoenix dv Phoenix DC dv DC BC dv BC dv Intercept z **× +**  **+ ×**  **+ +**  **× +**  **+ +**  **= β**  **β β**  **β β**  **β β**  23

24

The variables BC (British Columbia), DC (Washington

, DC), and Phoenix are “dummy” 25

varia bles. For example, the BC variable is set equal to one if the observation is from the BC 26

study, and set to zero if that observation is from a study for a different city. Denver is used as 27

the omitted city dummy variable, allowing the estimated coefficien ts on the other three city 28

dummy variables to estimate if the response function is different in those cities than in Denver

. 29

For example the estimated total intercept for Washington becomes Intercept + β 4 , and the 30

estimated slope of the function is β 4 + β 5

. A statistically significant estimate of the interaction 31January 2010 2

- 28 DRAFT

- Do Not Quote or Cite term coefficient ( β 3 , β 5 , or β 7 ) for a particular city implies that the response function has a 1

different slope than the Denver fun ction. 2

The logit analysis was conducted using STATA ® Data Analysis and Statistical Software 3

(Release ES 10.1), using the LOGIT procedure. Table 2

- 3 presents the parameter estimates from 4

the logit analysis, which investigates whether both slope and the int ercept of the estimated 5

response function differ between cities. The pseudo

- R 2 estimate was 0.4756 and the 6

loglikelihood chi 2 test also strongly rejects the null hypothesis that there is no effect of the 7

explanatory variables on the probability that a res pondent would find a image acceptable 8

(Pr(chi 2 )=0 < 0.000). In other words, the acceptability ratings depend both on the deciview value 9

and city. 10

Table 2

- 3 Logit Analysis Results 11

Variable Coefficient Standar d

Error z

- statistic Pr /z/ = 0 5% confidence

estimate 95% confidence

estimate

Dv

- 0.3862 0.0094

- 41.16 ~0

- 0.4045

- 0.3678

British Columbia 1.0496 0.3589 2.92 0.003 0.3463 1.7530

Washington, DC 2.9450 0.8458 3.48 ~0 1.2873 4.6026

Phoenix 3.5682 0.3015 1 1.84 ~0 2.9773 4.1591

BC x dv

- 0.0029 0.0162

- 0.18 0.86

- 0.0345 0.0288

Wash. X dv 0.0200 0.0293 0.68 0.495

- 0.0374 0.0774

Phoenix x dv

- 0.0797 0.0136

- 5.88 ~0

- 0.1063

- 0.0531

Constant 7.6844 0.1830 41.99 ~0 7.3257 8.0431

12

The city intercept coefficien ts are all positive and statistically significant indicating that 13

the response functions for different cities shifted right relative to the function for Denver. 14

However, only the Phoenix interaction term is insignificant, indicating that the Phoenix respo nse 15

function has a different slope than the other three cities, as can be seen in Figure 2

- 16. The 16

negative estimated coefficient on the Phoenix interaction term results in the Phoenix response 17

function being steeper than the other cities’ functions. Fig ure 2

- 16 also shows the Washington, 18

DC function is modestly less steep than the others, but the decrease in the slope is not 19

statistically significant. 20

The model results can be used to estimate the VAQ deciview values where the estimated 21

response functions cross the 50% acceptability level, as well as any alternative criteria levels. 22

Selected examples of these are shown in Table 2

- 4. 23

24

25January 2010 2

- 29 DRAFT

- Do Not Quote or Cite Table 2

- 4 Logit model estimated VAQ values corresponding to various percen t 1

acceptability values for the four cities. 2

3

Denver British

Columbia Phoenix Washington,

DC

90% Acceptability criteria 14.21 16.80 24.15 23.03

75% Acceptability criteria 17.05 19.63 21.80 26.03

50% Acceptability criteria 19.90 22.45 24.15 29.03

25 % Acceptability criteria 22.74 25.28 26.51 32.03

10% Acceptability criteria 25.59 28.10 28.87 35.03

4

Figure 2

- 1 6 also contains lines at 20 dv and 30 dv that effectively and pragmatically 5

identif y a range where the 50% acceptance criteri a occur across a ll four of the urban preference 6

studies. Out of the 114 data points shown in Figure 2

- 1 6 , only one photograph (or image) with a 7

VAQ below 20 dv was rated as acceptable by less than 50% of the participants who rated that 8

photograph. 12 Similarly, only one i mage with a VAQ above 30 dv was rated acceptable by more 9

than 50% of the participants who viewed it. 13

There are several hypotheses that may explain why the VAQ acceptability response 13

curves for the four cities are different and why some study results have greater variabil i t y than 14

others. These upper and lower range values are also 10

supported by the logit model data which estimates 50 th percentile acceptability values near 20 dv 11

for Denver and near 30 dv for Washington

, DC (see Table 2

- 4). 12

14

12 Only 47% of the BC participants rated a 19.2 dv photograph as acceptable. First, a s ment ioned, the use of photographs (Denver and BC surveys) versus WinHaze

15

generated images (Phoenix and Washington

, DC surveys) may play a significant role in 16

preference studies, perhaps introducing bias (such as suggested by the responses to the 9:00 a.m. 17

Denv er photographs) as well as variability. Further, the u se of photographs from different days 18

and times of day that rely on associated ambient measurements of light extinction to characterize 19

their VAQ level can introduce two other types of uncertainty. Th e intrinsic appearance of the 20

scene can change due to the changing shadow pattern and cloud conditions, and spatial variations 21

in air quality can result in ambient light extinction measurements not being representative of the 22

sight

- path

- averaged light exti nction. WinHaze has neither of these sources of uncertainty 23

13 In the 2001 Washington, D

. C

. study, a 30.9 dv image was used as a repeated slide. The first time it was shown

56% of the participants rated it as acceptable, and 11% rated it as acceptable the second time it was shown. The

same VAQ level was rated as acceptable by 42% of the participants in the 2009 study (Test 1).

14 Variability here refers to the degree of scatter of the average acceptability ratings for each image around the logit

curve for that city.January 2010 2

- 30 DRAFT

- Do Not Quote or Cite because the same base photograph is used (i.e. no intrinsic change in scene appearance) and the 1

modeled haze that is displayed in the photograph is determined based on uniform light extinction 2

th roughout the scene. 3

Second, variation in the degree of representativeness of the participants and the sizes of 4

the participant sample s involved may also be important factors. The small sample size and fairly 5

uniform population of respondents is a plausibl e explanation for the noisiness of the combined 6

Washington, DC results (35 participants, including 26 from a single consulting firm and 10 of 7

those from a different city) compared with the larger and more representative population of 8

responders from Phoeni x (385 participants, carefully selected to be representative of the Phoenix 9

population). 10

A third hypothesis promoted by Smith and Howell (2009) is that the range of VAQ 11

images presented in the survey may influence the results. As discussed above, a more p lausible 12

explanation it that the range of haze images shown to participants in the VAQ 1 to 7 rating 13

exercise was interpreted by participants as a restriction on acceptability rating exercise to confine 14

their rating to the range VAQ conditions shown, which for Test 2 was curtailed to only average 15

to good VAQ conditions. When other evidence is taken into account, the Smith and Howell 16

hypothesis see m s an even more unlikely explanation for the differences in results between the 17

four urban preference studies. For example the Denver study included photographs with the 18

haziest conditions among the four studies, but resulted in the lowest haze condition for the 50 th 19

percentile preference ratings among the four, not the highest as might be expected if the range of 20

haze levels were a significant factor influencing the results of preference studies. Also

, 21

i nspection of the average VAQ 1 to 7 ratings for the Phoenix and Denver studies showed that 22

they spanned the full ratings range of values similar to those for the Smith and Howell Test 1 and 23

3, so the participants in those studies were not presented with a restricted range within which to 24

select acceptable VAQ conditions , suggesting that the range itself was not an important factor 25

influencing their results

. Values for the British Columbia 1 to 7 VAQ rating exercise were not 26

readily available. 27

A fourth major hypothesis is that urban visibility preferences may differ by location, and 28

the differences may arise from inherent differences in the cityscape scene used in e ach city

. The 29

key evidence to suggest this hypothesis is that the apparent differences between the Denver 30

results (which found the 50% acceptance criteria occurred in the best VAQ levels among the four 31

cities) and the Washington, DC results (which found t he 50% acceptance criteria occurred at the 32

worst VAQ levels among the four cities). This hypothesis suggests that these results may occur 33

because the most prominent and picturesque feature of the cityscape of Denver is the clearly 34

visible snow

- covered mou ntains in the distance, while the prominent and picturesque features of 35January 2010 2

- 31 DRAFT

- Do Not Quote or Cite the Washington, DC cityscape are buildings relatively nearby without prominent and/or valued 1

scenic features that are more distant. 2

Finally, and perhaps of significant importance is t hat the sensitivity of individual scenes 3

to perceived changes in VAQ under changing light extinction levels can be quite different. As in 4

the fourth hypothesis, this may in part explain why the Denver study scene, with its long distance 5

to the mountain ba ckdrop, result ed a preference for the best VAQ level

, with a 50% criteria value 6

of about 20 dv , while the Washington, DC study scene, with much shorter sight paths yielded a 7

50% criteria VAQ value at a substantially worse level of about 30 dv . The distinc tion between 8

the last two hypotheses are that the earlier one speaks to the desirability of seeing distant 9

mountains versus this hypothesis which concerns the ability to perceive changes in haze at lower 10

light extinction levels. Additional studies, includ ing directly comparable studies using similar 11

methods in diverse cities, would be useful to gain further understanding of preferences for urban 12

visibility. 13

Based on the composite results and the effective range of 50 th percentile acceptability 14

across the f our urban preference studies shown in Figure 2

- 16 , CPL s have been selected in a 15

range from 20 dv to 30 dv (74 Mm - 1 to 201 Mm - 1 ) for the purpose of comparing to current and 16

projected conditions in the assessment in chapters 3 and 4 of this document. A midp oint of 25 17

dv (122 Mm - 1 ) was also selected for use in the assessment. These three values provide a low, 18

middle, and high set of light extinction conditions that are used in subsequent chapters of the 19

UFVA to provisionally define daylight hours with urban haze conditions that have been judged 20

unacceptable by the participants of these preference studies. As discussed earlier (section 1. 2 ) 21

PM light extinction is taken to be light extinction minus the Rayleigh scatter (i.e. light scattering 22

by atmospheric gas es is about 10 Mm

- 1 ), so the low, middle and high CPL levels correspond to 23

PM light extinction levels of about 64 Mm

- 1 , 112 Mm - 1 and 191 Mm

- 1

. 24January 2010 3

- 1 DRAFT

- Do Not Quote or Cite 

### 3 ESTIMATION OF CURREN T PM CONCENTRATIONS AND PM 1



### LIGHT EXTINCTION 2

The goals of the “current conditions” portion of this urban

- focused visibility impact 3

assessment are to characterize hourly PM light extinction conditions in a set of urban study areas 4

during 2005

- 2007, in order (1) to improve understanding of the levels, patterns, and causes of 5

daylight hours PM light extinction; (2) to provide the starting point for projections of PM light 6

extinction levels under “what if” scenarios; and (3) to examine the correlation between PM light 7

extinction and potential alternative indicator(s) based on PM 2.5 mass concentration. This chapter 8

addresses the first goal. Chapter 4 addresses the second goal regarding “what if” scenarios. 9

Appendix D addresses the third goal. A number of other appendices address related topics of 10

particular interest in more detail. 11

3.1 SUMMARY OF PREVIOU S CHARACTERIZATIONS OF PM 12

CONCENTRATIONS AND L IGHT EXTINCTION 13

3.1.1 PM 2.5 and PM 10

- 2.5 14

Chapter 2 of the 2005 Staff Paper from the previous review and chapters 3 (especially 15

section 3.5) and 9 (especially section 9.2.3) and Annex A of the final ISA (US EPA, 2009 a) from 16

the current review present extensive characterizations of the levels, composition, and temporal 17

and spatial patterns of PM 2.5 in U.S. urban areas. Both documents present data summaries based 18

on the approximately 1000 PM 2.5 monitoring sites in the U.S. The characterizations in the 2005 19

Staff Paper were based on 2001

- 2003 data. The characterizations in the ISA are based on 2005

- 20

2007 data, which is the same time period used in this visibility assessment. While there 21

generally have been reductions i n the concentrations of PM 2.5 in many areas as a result of 22

emission reductions of PM 2.5 and its precursors, the general patterns, and the diversity of patterns 23

across areas, noted in the 2005 Staff Paper still prevailed in the 2005

- 2007 period. 24

Using 200 5

- 2007 air quality data , 38 urban areas violated the annual PM 2.5 NAAQS set at 25

a level of 15 μg/m 3 in 1997 and retained in the last review completed in 2006. Seventy

- six areas 26

violated the 2006 24

- hour NAAQS level of 35 μg/m 3 . There is considerable but n ot complete 27

overlap in the areas not meeting the two NAAQS. It should be noted that in many parts of the 28

U.S., PM 2.5 concentrations in 2005 were high relative to the next three years. Figure 3

- 1 29

illustrates PM 2.5 air quality in 2007 by representing each monitor by a symbol whose color 30

reflects the annual mean of the concentration at that site or the 98 th percentile 24

- hour 31

concentration, in both cases in that one year. 32

33January 2010 3

- 2 DRAFT

- Do Not Quote or Cite Figure 3

- 1

. Annual average and 24

- ho ur (98 th percentile 24

- hour concentrations) PM 2.5 1

concentrations in μg/m 3 , 2007. 2

3

4

5

6

7January 2010 3

- 3 DRAFT

- Do Not Quote or Cite Each urban area exhibits its own d etailed patterns of observed concentration levels, 1

temporal and spatial variation, and composition. These differences are due to differences in local 2

and transported emissions and in meteorology. Because of differences in the placement of PM 2.5 3

monitorin g sites in each urban area, the actual levels and spatial pattern of PM 2.5 and PM 2.5 4

species concentrations may not be consistently discernable in all areas. This variability and 5

limited monitoring network make it difficult to offer concise generalization s, although some 6

broad similarities can be drawn among areas. 7

Midwestern, southeastern, and eastern urban areas have much higher sulfate levels than 8

do more western areas, attributable to the much higher emissions of SO 2 in and upwind of them. 9

Upper midwe stern areas and to a lesser extent upper eastern areas have notable nitrate 10

concentrations in winter but not in summer, while southeastern areas generally lack notable 11

nitrate even in winter. Many western urban areas have notable nitrate year round. In a ll areas, 12

carbonaceous material is an important component of PM 2.5 and is attributable to many emission 13

sources of organic material in PM form and of organic PM precursor gases. In some areas with 14

high local use of wood for residential heating carbonaceou s material is dominant during the 15

heating season. PM 2.5 derived from crustal sources is generally a small f r action of total mass, 16

except during local high wind events or due to brief periods of intercontinental transport of dust 17

from Africa or Asia. 18

Com parison of PM 2.5 species concentrations within and outside urban areas leads to the 19

conclusion that, in the eastern areas with high sulfate concentrations, the large majority of the 20

sulfate affecting any given urban area originates outside that area. Inwa rd transport and local 21

generation of nitrate and carbonaceous material are more evenly balanced in eastern areas, with 22

some differences among areas. In western areas, local sources dominate for carbonaceous 23

material and nitrate, with the origins of the sm all sulfate component being more balanced. See 24

Figure 9

- 24 of the final ISA (US EPA, 2009a). 25

Southeastern areas have their highest PM 2.5 concentrations in the summer, when 26

conditions are most conducive to sulfate formation. More northern areas, being aff ected by a 27

more balanced mix of contributors, tend not to have such a strongly seasonal pattern. The 28

seasonal patterns in western areas are individual and varied, related to differences in local 29

sources and formation and dispersion conditions. In all are as, inversion conditions with low 30

wind speeds are conducive to high concentrations due to the trapping of emissions from local 31

sources. Some western areas, especially those with valley or bowl

- like topography, are 32

especially affected. 33

There is at present no systematic monitoring network in place for PM 10

- 2.5 , as states have 34

until January 1, 2011, to implement required monitoring sites for PM 10

- 2.5 . Consequently, 35

estimates of PM 10

- 2.5 must be developed using data from PM 2.5 and PM 10 monitoring sites and 36January 2010 3

- 4 DRAFT

- Do Not Quote or Cite eq uipment, which are not always collocated and consistent. The 2005 Staff Paper presented such 1

estimates in section 2.4.3. The final ISA presents such estimates in Figure 3

- 10 and Table 3

- 9 of 2

section 3.5.1.1. The 2005 Staff Paper used a data

- inclusive ap proach in which the best available 3

data on PM 2.5 and PM 10 concentrations

– in some cases not very robust data

– were used to 4

estimate 2001

- 2003 PM 10

- 2.5 concentrations for 351 metropolitan area counties. For these 5

counties, the annual mean PM 10

- 2.5 conce ntrations were generally estimated to be below 40 6

μg/m 3 , with one maximum value as high as 64 μg/m 3 and a median of about 10

- 11 μg/m 3

. The 7

ISA used a much more data

- restrictive approach based only on paired (collocated) low

- volume 8

filter

- based samplers for both PM 10 and PM 2.5 . The ISA reports that only 40 counties have such 9

paired samplers. Using these available co

- located PM measurements from 2005

- 2007, the me an 10

24

- hr PM 10

- 2.5 concentration in these 40 counties was 13 μg/m 3 . This urban visibility assessment 11

has used a data

- inclusive approach to estima ting PM 10

- 2.5 concentrations, similar to that used for 12

the 2005 Staff Paper, where needed to obtain hourly PM 10

- 2.5 estimates for 15 study areas, which 13

are reported below in section 3.3.2. 14

Additional detail on PM 2.5 , PM 10 , and PM 10

- 2.5 concentrations, comp osition, and patterns 15

appears in section 3.5.1.1 of the ISA. Also, chapter 6 of the 2004 PM Assessment by NARSTO 16

contains more detailed characterizations of PM in different parts of the U.S. 17

3.1.2 PM light extinction 18

While total light extinction is directly measurable using a transmissometer and PM light 19

extinction can be measured with other instruments, there are very few regularly operating 20

monitoring sites measuring either form of light extinction in urban areas, and generally those that 21

do operate do not submit data to AQS. 15

15 EPA is aware of routine, long

- term direct measurement of light extinction using transmissometers only i n 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, t here 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 operation s are not individually reported) th e 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. Consequently, any characterization of PM light 22

extinction conditions based on actual measurements is necessarily less comprehensive than for 23

PM 2.5 and PM 10

- 2.5 . Many monitoring sites that employ nephelometer s, which measure light 24

sca ttering, operate that equipment in a heated mode for purposes of tracking “dry” PM 2.5 mass 25

concentrations, and actual light scattering due to ambient PM is not reportable. There are many 26

more filter

- based Aethalometers® and similar instruments for measuri ng light absorption in 27

operation and reporting to AQS, but light absorption is typically a small fraction of total PM 28

light extinction, so these data alone are not a good indicator of overall PM light extinction in 29January 2010 3

- 5 DRAFT

- Do Not Quote or Cite urban areas. Also, there are unresolved issues of data corrections and comparability for the light 1

absorption data from these instruments now residing in AQS. 2

PM light extinction can be “reconstructed” from measurements of PM 2.5 mass 3

components and PM 10

- 2.5 concentrations, in combination with re lative humidity values, using 4

either of two versions of the formula known as the IMPROVE algorithm but excluding its term 5

for Rayleigh scattering by gases in clean air. (Section 9.2.2.2 of the ISA gives an overview of 6

the algorithm and its basis. Section 3.2.3 of this document discusses the application of the 7

original version of the IMPROVE algorithm in this assessment. PM 2.5 component measurements 8

are generally available only on a 24

- hour average basis, so it generally is possible to estimate 9

only 24

- ho ur average PM light extinction, unless additional information on hourly patterns is 10

brought to bear. 16

The CSN network provides 24

- hour PM 2.5 species measurements at about 200 urban 19

sites, from which mass components can be derived. These sites have a mix of daily, one day in 20

three, and one day in six sampling schedules. The 2005 Staff Pape r (and its references) may be 21

the only readily available prior assessment to use these urban PM 2.5 speciation monitoring data, 22

along with estimates of PM 10

- 2.5 concentrations and data on relative humidity, to reconstruct daily 23

24

- hour average light extinct ion in urban areas, for the year 2003. Because EPA’s Regional Haze Rule (RHR) currently requires states to 11

address visibility problems in Class I visibility protection areas, which are nearly all rural and 12

remote, there is a large body of literature characterizing light extinction in remote rural areas, 13

based on data from the IMPROVE network’s 24

- hour samplers and on special studies. Sections 14

9.2.3.2 and 9.2.3.4 of the ISA summarize this liter ature. Section 9.2.3.3 of the ISA contrasts 15

concentrations of PM and PM components between rural and urban areas using data from the 16

rural IMPROVE network and the urban Chemical Speciation Network (CSN), but does not 17

present estimates of light extinction in urban areas. 18

17

16 When the IMPROVE algorithm is used to estimate 24

- hour light extinction from 24

- hour PM 2.5 species and One presentation of the 24

results was in the form of a scatter plot of daily 24

- hour reconstructed light extinction versus 24

- 25

hour PM 2.5 concentration. This graphic appears here as Figure 3

- 2. (For the immediate purpose 26

of this section, it is the distribution of the data points along the y

- axis that is of interest, not the 27

relationship between light extinction and PM 2.5 concentrations; the latter subject is addressed in 28

PM 10

- 2.5 concen trations, an assumption is made that every hour has the same PM concentrations but its own relative

humidity value. Hourly estimates of light extinction, including the strongly non

- linear effect of relative humidity,

are then averaged to get the 24

- hour l ight extinction estimate.

17 Estimates of light extinction in the 2005 Staff Paper include Rayleigh scattering of 10 Mm

- 1 and thus represent

“total” light extinction (excluding NO 2 absorption). Adjustment for consistency must be made before any close

compa risons to PM light extinction values in this document.January 2010 3

- 6 DRAFT

- Do Not Quote or Cite Appendix D.) Generally, most days have light e xtinction below 200 inverse megameters (Mm

- 1 ), 1

but a small percentage of values were as high as about 750 Mm

- 1

. 18

3 2

Figure 3

- 2

. Reconstructed 24

- hour light extinction in U.S. urban areas in 2003 4

Source: Sch midt et al., 2005 5

6

18 Unfortunately, the file of paired data used to create this scatter plot is no longer available, so the actual

distribution of light extinction values cannot be described more specifically.January 2010 3

- 7 DRAFT

- Do Not Quote or Cite In addition to this scatter plot, a table developed for the previous PM NAAQS review 1

presented the annual average of estimates of 24

- hour reconstructed light extinction values, 2

averaged across 161 urban areas grouped into seven regions (Schmidt, et al., 2005). Table 3

- 1 3

reproduces these estimates. For regions excluding Southern California, annual average 24

- hour 4

light extinction ranged from 73 to 118 Mm

- 1 . The estimate of the annual average 24

- hour light 5

extinction for Southern Califo rnia was 168 Mm

- 1 . These estimates were based on 10

- year 6

average 1

- hour relative humidity values and 2003 PM monitoring data. 7

8

Table 3

- 1

. Annual Mean Reconstructed 24

- hour Light Extinction Estimates 9

by Reg ion (Mm - 1 ) 10

11



### Region Reconstructed 24

- hour Light

Extinction in 2003

Northeast 108

Southeast 98

Industrial Midwest 118

Upper Midwest 80

Southwest 73

Northwest 76

Southern California 168

Source: Output D.3, Schmidt et al., 2005

. We note t hese regio ns were used to summarize PM 2.5 patterns for the PM 12

NAAQS review 1997 (US EPA, 1996)

. 13

14January 2010 3

- 8 DRAFT

- Do Not Quote or Cite Figure 3

- 3 is a contour map of annual average reconstructed 24

- hour total light extinction 1

based on IMPROVE monitoring sites in 2000

- 2004, nearly all of which are remote and rural (the 2

three urban sites in Phoenix, AZ, Washington, DC, and Puget Sound, WA are indicated by 3

square symbols). A comparison of the mean urban light extinction levels by region listed in 4

Table 3

- 1, with this map of rural light extinction indicates that in most parts of the U.S., light 5

extinction levels in urban areas are notably higher than in the surrounding remote rural area, with 6

the northeast and the southeast regions having the most similarity between rural and urban light 7

extinction levels. This is consistent with observations of an “urban excess” of PM 2.5 and 8

9

Figure 3

- 3

. Isopleth map of annual total reconstructed particulate extinction based 10

on 2000

- 2004 IMPROVE data. 11

12

13

(Source: Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the 14

United States Report IV, November 2006.) 15

16

PM 10

- 2.5

, and with the known high regional concentrations of sulfate in these eastern areas. 17

One

- hour light extinction values of course vary above and below the 24

- hour average, 18

due to diurnal variations in PM 2.5 component concentrations, PM 10

- 2.5 concentrations, and 19

relative humidity. Although light extinction was formally reconstructed on an hourly basis in the 20

2005 Staff Paper analysi s for the last review cited above, the actual full strength of the diurnal 21January 2010 3

- 9 DRAFT

- Do Not Quote or Cite pattern could not be discerned in that analysis because component mix was assumed not to vary 1

from hour to hour. Under the unverified assumption of constant component mix and using actual 2

hourly relative humidity data, the daily maximum daylight 1

- hour light extinction values were 3

roughly 50 percent higher than the 24

- hour average light extinction values. 19

3.2 OVERVIEW OF APPROACH AND DATA SOURCES FOR URBAN STUDY 6

ANALYSIS 7 The new analysis 4

presented in this document includes a closer look at diurna l patterns, for 15 study areas. 5

As explained above, there are limited data from direct measurements of light extinction in 8

urban areas. Consequently, this assessment has reconstructed hourly P M light extinction levels 9

for daylight hours from values of hourly PM 2.5 components, PM 10

- 2.5 , and relative humidity. 10

Hourly monitoring data for PM 2.5 components and PM 10

- 2.5 are also generally lacking, so the 11

estimates of these parameters necessarily in turn have been developed from a combination of 12

other available ambient monitoring data and air quality modeling results from a chemical 13

transport model (CTM) run. Specifically, the ambient monitoring data starting points are 24

- 14

hour PM 2.5 mass measured by filter

- based Federal Reference Method (FRM) or Federal 15

Equivalent Method (FEM) monitors 20

The CTM run was the “actual emissions” or “validation” run of the 2004 CMAQ 27

modeling platform with boundary conditions provided by GEOS

- Chem global scale CTM. , 24

- hour PM 2.5 components measured by the filter

- based 16

monitors of the Chemical Speciation Network, and hourly PM 2.5 mass measured by continuous 17

instruments such as t he Tapered Element Oscillating Microbalance (TEOM), beta attenuation 18

monitors (BAMs), and nephelometers, which were used at different sites. The CTM

- based 19

diurnal profiles for individual components, in conjunction with hourly PM 2.5 measurements, are 20

used to adjust and allocate the 24

- hour PM 2.5 components measurements to individual hours of 21

each day, as described in detail below. In addition, levels of hourly PM 10

- 2.5 mass are calculated 22

from separate measurements of hourly PM 10 and hourly PM 2.5 if both a re available, or by 23

applying PM 10

- 2.5 to PM 2.5 ratios to hourly PM 2.5 data if both types of hourly measurements are 24

not available. The ambient data are from 2005

- 2007 and were all obtained from AQS in the first 25

half of 2009. 26

21

19 These o bservations 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 for the PM N AAQS Review, Schmidt et al., 2005. The 28

CTM modeling is used as one element in the development of realistic diurnal variations for each 29

20 Filter

- based Federal Reference Method samplers and filter

- based Federal Equivalent Method samplers will both

be referred to as FRM samplers in the remainder of this document.

21 GEOS

- Chem is the NASA Goddard Earth Observ ing System

- CHEMistry (global 3

- D CTM for atmospheric

composition). This modeling platform, with an appropriately different emissions scenario, is also the basis for the

estimates of policy relevant background concentrations of PM 2.5 presented in section 3 .6 of the ISA (US EPA,

2009a)

.January 2010 3

- 10 DRAFT

- Do Not Quote or Cite of the major PM 2.5 compo nents used to estimate PM light extinction, anchored to site

- specific, 1

day

- specific measurements of 24

- hour concentrations. That is, monthly averaged diurnal 2

profiles for the five major components were generated using the CTM results, which were then 3

comb ined with hour

- specific measurements of PM 2.5 to generate hourly concentration variations 4

for each of the 24

- hour CSN sample days during the 2005

- 2007 period. 5

3.2.1 Study Period

, Study Areas , Monitoring Sites

, and Sources of Ambient PM Data 6

At the time this asse ssment began, the ambient monitoring data from 2005

- 2007, but not 7

from 2008, had been certified as accurate and complete by the state/local monitoring agencies 8

that collected them, and the data had been extensively summarized and presented in the first dra ft 9

ISA. The EPA staff aimed to develop estimates of daylight hours PM light extinction for a 10

reasonably representative number of days in each year of 2005

- 2007, to allow the application of 11

statistical forms based on three years of data. However, as expla ined in more detail below, in 12

several study areas the limited availability of starting data for these estimates resulted in estimate 13

sets that do not cover all three years. Also, even in areas with some data in all three years, the 14

number of days with val id estimates differs by year and is in some cases not large by typical 15

standards of monitoring data completeness. 16

For efficiency in the analysis, this visibility assessment uses the same 15 urban study 17

areas selected for the health risk assessment. These areas are listed in Table 3

- 2, along with the 18

area

- wide (maximum) FRM

- based 2005

- 2007 annual and 24

- hour PM 2.5 design values for each 19

study area based on the highest

- reading monitor in each area, and for the specific site used in this 20

assessment. 22

22 (See bel ow for an explanation of the “site

- specific” columns in Table 3

- 2.) 21

22 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 th e health risk

assessment to “roll back” current concentrations to represent achievement 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 des ignated (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 s omewhat higher than the highest values from a site with complete data (see

the draft PM R isk A ssessment, US EPA, 2009c, section 3.2.3 ) For Fresno, the area

- wide design value is for the

Fresno

- Madera CSA, which is only a portion of the San Joaquin Valley n onattainment area. Also, note that there

are three cases in which the nonattainment area does not include certain areas sometimes thought of as being part of

the area named in Table 2; monitors in these non

- included areas were not considered in this asses sment. (1) The

design value shown for Pittsburgh is for the Pittsburgh

- Beaver nonattainment area; the Liberty

- Clairton

nonattainment area is within the Pittsburgh CBSA but is distinct for regulatory purposes, and was not considered in

this assessment. (2) Baltimore was treated separately, although part of a CSA with Washington DC. (3) Berks Co.,

PA is part of the Philadelphia

- Camden

- Vineland CSA, but not part of the Philadelphia

- Wilmington nonattainment

area.January 2010 3

- 11 DRAFT

- Do Not Quote or Cite Table 3

- 2

. Urban Visibility Assessment Study Areas 1

Study Area Area

- wide 2005

- 2007

Annual

Design Value

(μg/m 3 ) Area

- wide

2005

- 2007

24

- h o ur Design Value

(μg/m 3 ) Site

- specific

2005

- 2007

Annual

Design Value

(μg/m 3 ) Site

- specific

2005

- 2007

24

- h our 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

Cali fornia*

Los Angeles 19.6 55 Same Same Southern

California

Phoenix 12.6 32 7.9 15 Southwest

Salt Lake City 11.6 55 10.7 48 Northwest

Dallas 12.8 26 11.5 25 Southeast

Houston 15.8 31 13.1 25 Southeast

St. Louis 16.5 39 14.5 34 Midwest

Birmingham 18.7 44 Same Same Southeast

Atlanta 16.2 35 15.7 33 Southeast

Detroit 17.2 43 Same Same Midwest

Pittsburgh 16.5 43 15.0 40 Industrial

Midwest

Baltimore 15.6 37 14.5 35 Northeast

Philadelphia 15.0 38 14.7 37 Northeast

New York 15.9 42 14.4 42 Northeast

* While not generally considered to be part of Southern California as the term is commonly used, Fresno lies just

south of the line used in the 2005 Staff Paper (based on earlier work by others) to separate the Southern California

region from the Northwest region.

2

For time reasons and because it was anticipated that some study areas would not contain 3

more than one suitable study site, EPA staff sought to identify the single best study site in each 4

area. In identifying the single best study site in each st udy area first consideration was given to 5

the availability of collocated 24

- hour data on PM 2.5 and its components, because the contribution 6

of PM 2.5 components to PM light extinction will typically dominate the contribution from PM 10

- 7

2.5

. Ideally, within each study area the three types of PM 2.5 data (FRM PM 2.5 , CSN PM 2.5 8

components , continuous PM 2.5 ) would be available at a common site, and that site would be 9

located in a manner consistent with reliance on it to characterize visibility as it would be 10

perce ived by a large number of area residents and visitors. As can be seen in Table 3

- 2, in 10 of 11

the 15 study areas the site providing FRM data for this assessment is not the area

- wide design 12

value site, because the area

- wide design value site did not have co llocated CSN and/or 13

continuous PM 2.5 data. 14

Appendix A provides details on the site(s) identified and used in each study area, 15

including information on the type of monitoring equipment that provided the data and other 16

information that may help interpret the results of the analysis. A portion of this table for a single 17January 2010 3

- 12 DRAFT

- Do Not Quote or Cite site

– Tacoma

– is presented here as Table 3

- 3 as an example. When viewing this document 1

electronically, the site IDs in these tables are active links and can be used to view the location of 2

the site via GoogleMaps. 23

In 11 of the study areas, the three types of PM 2.5 data were available at a common site. In 4

the remaining four areas, Phoenix

, AZ, Pittsburgh

, PA, Baltimore

, MD, and St. Louis

, MO

- IL

, 5

two types of data were available at one sit e, but the remaining type of data had to be taken from 6

another site and treated as being representative of the former site. 7 3

The monitoring agencies described all but one of these sites as neighborhood or urban 8

scale, indicating those agencies’ opinion th at the sites represent concentrations in an area at least 9

0.5 to 4 km across. An aerial view of the remaining site (in Phoenix) which did not have a scale 10

characterization recorded in AQS suggests that it may be middle or neighborhood scale. As 11

already s tated, selected sites are not necessarily the locations of the maximum measured annual 12

or 24

- hour PM 2.5 levels in their urban area. 13

Site days which were missing 1

- hour PM 2.5 concentration data points for more than 25 14

percent of daylight hours were excluded from the analysis, because such data gaps were judged 15

to result in too much uncertainty in estimates of 1

- hour PM 2.5 components, 1

- hour light 16

extinction, and daily maximum light extinction. Days with fewer missing 1

- hour PM 2.5 17

concentration data points w ere retained, but no estimate of light extinction was made for hours 18

without 1

- hour PM 2.5 concentration data (see below for more explanation). 19

Hourly PM 10

- 2.5 presented more varied challenges. In four areas (Birmingham, Detroit, 20

Baltimore, and Philadelphi a) the site that provides the continuous PM 2.5 data also hosts a 21

continuous FEM PM 10 monitor, and hourly PM 10

- 2.5 could be calculated by difference for most 22

hours. In other areas, this was not the case, and either (1) hourly instruments at two different 23

s ites were used in this subtraction (Tacoma, Los Angeles

- South Coast Air Basin, Phoenix, St. 24

Louis, Atlanta, and New York

- N. New Jersey) or (2) a single regionally applicable PM 10

- 2.5 to 25

PM 2.5 ratio calculated as part of the last review based on 2001

- 2003 2 4

- hour FRM/FEM PM 10 26

and PM 2.5 samples was applied to 2005

- 2007 hourly PM 2.5 data to estimate hourly PM 10

- 2.5 27

(Fresno, Salt Lake City, Dallas, Houston, and Pittsburgh). In the case of Los Angeles

- South 28

Coast Air Basin, the continuous PM 10 and PM 2.5 sites w ere quite distant and separated by a range 29

of hills, so the estimates of PM 10

- 2.5 and its contribution to PM light extinction are more uncertain 30

than if the monitors were clearly within the same air mass. Obviously, for the five study areas 31

for which 1

- ho ur PM 10

- 2.5 was estimated by application of ratios, PM 10

- 2.5 estimates can only 32

23 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.ep a.gov/airexplorer/monitor_kml.htm

.January 2010 3

- 13 DRAFT

- Do Not Quote or Cite represent broad trends, not hour

- specific conditions at the particular site. More description of the 1

methods used for estimating hourly PM 10

- 2.5 appears in section 3.3.2. 2

The sampling schedule for CSN PM 2.5 speciation monitoring was one

- in

- six days for 3

Tacoma, Phoenix, Houston, Detroit, and Philadelphia, and one

- in

- three days for the other study 4

areas. Not every scheduled CSN site day in 2005

- 2007 had data for all three types of PM 2.5 data, 5

due to missed or invalid samples. Also, for continuous PM 2.5 , values for a small number of hours 6

of an otherwise data

- sufficient day were sometimes missing, due to equipment failure or 7

servicing. EPA staff retained only those days in which 75 percent or more of daylight hours had 8

measurements of PM 2.5 (see section 3.3. for more details). If for isolated hours at a site (or site 9

pair) with collocated measurements, PM 10

- 2.5 concentrations could not be estimated because of 10

gaps in the same

- ho ur continuous PM 10 and/or PM 2.5 data, EPA staff used the regional ratio 11

approach described above to estimate PM 10

- 2.5 for those specific hours. Table 3

- 4 provides more 12

detailed information on the quarterly distribution of the successfully matched and suff iciently 13

complete data available for use. As described later, for some parts of this assessment EPA staff 14

substituted data for the single missing quarters of data in Phoenix and Houston, to achieve 15

seasonal balance. 16

In this assessment, we have not exclude d PM concentration data that may have been 17

affected by exceptional events such as wildfires and wind storms. Under EPA’s Exceptional 18

Events rule, for existing NAAQS states may request exclusion of such data from regulatory 19

determinations, and accordingly such data are not reflected in design values for existing NAAQS 20

once exclusion is approved by EPA. A similar arrangement presumably would apply to a new or 21

revised secondary PM NAAQS. Design values for PM light extinction under current conditions 22

(Table 4

- 2) and percentage reductions to “just meet” alternative secondary NAAQS based on PM 23

light extinction (Table 4

- 3), presented below, may thus be overestimates. Overestimation is 24

more likely for the western study sites than for the eastern study sites. Ho wever, PM 2.5 design 25

values shown in Table 3

- 2, and associated estimates of the reductions needed from 2005

- 2007 26

PM 2.5 level to just meet alternative secondary NAAQS based on PM 2.5 mass (Table 4

- 4) do 27

reflect the exclusion of at least some data affected by exceptional events. 28January 2010 3

- 14 DRAFT

- Do Not Quote or Cite Table 3

- 3 . PM 2.5 Monitoring Sites and Monitors Providing 2005

- 2007 Data for the Tacoma 1

Study Area 2

3

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

STR EET, 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 Seattle

Tacoma

- Olympia, WA annual

PM 2.5 nonattainment area

Neighborhood Scale

Parameters taken from this

site:

**♦** 24

- hour FR M PM 2.5 mass (AQS parameter 88101;

one

- in

- three 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)

Correlated Radiance

Research M903

Nephelometry

No continuou s 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 SA246B

INLET

**♦** Sample Analysis Method: TEOM

GRAVIMETRIC

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 t he 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 whe n 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 rather than from AQS, as an initial exploration

indicated t hat not all the desired 1

- hour data had been submitted to AQS.January 2010 3

- 15 DRAFT

- Do Not Quote or Cite Table 3

- 4 . Number of days per quarter in each study area 1

2

Study Area Total Number

of Days 2005 2006 2007

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q 4

Tacoma 110 0 0 0 0 13 15 15 14 13 13 14 13

Fresno 324 19 24 27 27 30 29 29 27 26 28 30 28

Los Angeles 302 28 28 22 28 26 26 27 22 21 26 24 24

Phoenix 86 0 13 11 14 12 13 11 12 0 0 0 0

Salt Lake City 306 27 28 30 26 20 28 31 20 23 25 19 29

Dallas 27 4 22 24 26 22 23 23 24 24 18 23 24 21

Houston 149 21 20 10 14 14 12 8 12 15 14 9 0

St. Louis 294 27 27 24 27 28 19 27 29 29 25 22 10

Birmingham 350 30 30 29 30 29 29 30 30 30 30 27 26

Atlanta 295 22 25 25 24 28 27 26 27 25 19 26 21

Detroi t 141 12 12 1 0 11 12 13 11 15 11 11 12 11

Pittsburgh 284 26 23 25 23 22 25 24 26 22 22 23 23

Baltimore 187 19 17 15 11 15 16 19 18 12 12 17 16

Philadelphia 145 15 11 13 10 9 13 10 13 13 14 12 12

New York 228 22 23 13 15 23 19 18 21 19 15 19 21

Note : O nly days wit h matched and sufficiently complete data were retained in the assessment

.January 2010 3

- 16 DRAFT

- Do Not Quote or Cite 3.2.2 Use of CMAQ Model Validation Runs for 2004 to Augment Ambient Data 1

Because systematic monitoring data on hourly PM 2.5 component concentrations are not 2

available for most of the 15 study areas

, EPA staff extracted and applied certain information 3

from the modeling platform for calendar year 2004 described in section 3.7.1.2 of the ISA , in 4

which the global

- scale circulation model GEOS

- Chem was paired with the regional scale air 5

qualit y model CMAQ

. 24

The EPA staff identified the one or more 36 km

- by

- 36 km CMAQ grid cells generally 11

corresponding to the urbanized area surrounding each study site, thus omitting grid cells 12

dominated by rural land uses. The main use of this platform in the ISA is to estimate policy

- relevant 6

background concentrations of PM 2.5

. For the urban

- focused visibility assessment described here, 7

however, we used results from the validation run of the platform

, in wh ich emissions for all 8

emission source type s and countries are included , to develop realistic diurnal variations of the 9

major PM 2.5 components

. 10

25 We then extracted from the detailed model output for these grid 13

cells the day/hour

- specific concentrations of sulfate, nitrate, elemental carbon, organic carbon, 14

an d “crustal/u nspeciated ” PM 2.5 during 2004, and then we averaged across grid cells and then 15

across days within the month for each individual hour of the day. 26

24 Similar modeling was not available for 2005, 2006, or 2007. Thus, for each species, EPA 16

staff obtained 24 hour

- of

- day values for a month, for each of the 12 calendar months. We then 17

averaged the 24 hour

- of

- day values in each monthly set for each component to obtain the 18

corresponding 24

- hour average concentration for the month. We then divided each hour

- of

- day 19

value by the 24

- hour value, to obtain a normalize d diurnal profile for the pollutant, which was 20

taken as the initial representation of all days in that month for 2005, 2006, and 2007 (but further 21

adjusted day

- by

- day in a later step). In total, this resulted in 5 (components) x 12 (months) x 15 22

(study ar eas) = 900 profiles. Visual examination of a number of these showed them to be 23

reasonably smooth and generally to show morning (and sometimes also late afternoon/evening) 24

peaks which are the anticipated effect of higher vehicle traffic and lower mixing he ights. The 25

peaks were generally moderate, as would be expected in light of the averaging of predictions for 26

multiple large grid cells, the averaging across days, and the generally moderate diurnal profiles 27

for SMOKE pre

- processing of emissions in the CMAQ modeling platform. (Note, however, that 28

25 Urbanized area here refers to a specific land area identified by the U.S. Census Bureau based on population

density and other factors. Shape files for thes e areas were compared to the CMAQ grid to identify the grid cells to

be used.

26 For several of the listed components that are not direct CMAQ outputs, concentrations were estimated by post

processing to aggregate the appropriate CMAQ outputs. The “crustal /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 considered in most EPA analyses to represent the same material as the “ soil”

component reported for IMPROVE sampling.January 2010 3

- 17 DRAFT

- Do Not Quote or Cite as described below a later step in the estimation process reduces the smoothness in the diurnal 1

pattern of PM components.) Sulfate, as would be expected for a regionally transported pollutant, 2

generally had a flat ter diurnal profile than for other components. Hourly nitrate concentrations 3

were low when expected: during warmer months and in warmer areas. Figure 3

- 4 shows 4

example diurnal profiles for the five PM 2.5 components, for the Detroit study area for the mon ths 5

of January and August. Diurnal profiles like these were applied to 24

- hour CSN measurements 6

of component concentrations, as explained in detail below. 7

3.2.3 Use of Original IMPROVE Algorithm to Estimate PM light extinction 8

The EPA staff used the original IM PROVE light extinction algorithm

, rather than the 9

more recent revised version, because the original version is considered more representative of 10

urban situations, when emissions are still fresh rather than aged as at remote IMPROVE sites. 27

b extPM = 3 x f(RH) x [Sulfate] 17 11

To maintain con sistency with the form of the candidate protection levels (CPLs) for PM light 12

extinction identified in chapter 2, EPA staff excluded from the IMPROVE algorithm for total 13

light extinction the term for Rayleigh scattering by gases in clean air. The formula for PM light 14

extinction using the traditional IMPROVE algorithm but without the Rayleigh scattering term is 15

shown below. 16

+ 3 x f (RH) x [Nitrate] 18

+ 4 x [Organic Mass] 19

+10 x [Elemental Carbon] 20

+ 1 x {Fine Soil] 21

+ 0.6 x [Coarse Mass] 22

PM light extinction (b ext PM ) is in units of Mm

- 1 , the mass concentrations of the 23

components indicated in brackets are in μg/m 3 , and f(RH) is the unitless water growth term that 24

depends on relative humidity. We refer to the first five terms in this algorithm as the five PM 2.5 25

components. In this algorithm, the sulfate and nitrate components are to be expressed as fully 26

neutralized and as retained and measured in the IMPROVE sampling and laboratory methods. 27

Associated water is to be omitted from all bracketed terms since the water absorption effect is 28

reflected in the f(RH) term. The organic mass component is to include the mass of associated 29

elements in addition to carbon. As described below, we included steps in our development of 30

estimates of ho urly component concentration to ensure consistency with these aspects of the 31

32

27 Other differences between the original and revised algorithms include estimates of sea salt contributions which

can be important for near

- coastal locations, inclusion of site

- elevation specific Rayleigh ligh t 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 the results or conclusions.January 2010 3

- 18 DRAFT

- Do Not Quote or Cite Figure 3

- 4 . January and August monthly average diurnal profiles of PM 2.5 components 1

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

. 2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20 CMAQ Relative Patterns (JANUARY)

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 soil

ec

no3

so4

ocm

CMAQ Relative Patterns (AUGUST)

0 0.5

1 1.5

2 2.5

3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 soil

ec

no3

so4

ocmJanuary 2010 3

- 19 DRAFT

- Do Not Quote or Cite IMPROVE algorithm. 1

3.3 DETAILED STEPS 2

3.3.1 Hourly PM 2.5 Component Concentrations 3

The task of estimating hourly PM 2.5 component concentrations is in a sense over

4

determined, given the four types of available informat ion: 24

- hour PM 2.5 mass by filter

- based 5

FRM , 24

- hour component concentrations by CSN, hourly PM 2.5 mass by continuous instrument, 6

and diurnal profiles of components from the 2004 CMAQ run. There are multiple ways in which 7

two or three of these four data s ources could be used to estimate hourly PM 2.5 component 8

concentrations, and the result generally can be expected to be at least somewhat inconsistent with 9

the information in the remaining data source(s). For example, each 24

- hour PM 2.5 component 10

mass from CSN sampling can be apportioned to hours based on the monthly average diurnal 11

profile developed from the 2004 CMAQ run, but then in general the hourly values of PM 2.5 mass 12

determined by summing the components in an hour would not exactly match the data fr om the 13

continuous PM 2.5 instrument. EPA staff therefore used a sequence of steps which achieves a 14

prioritized compromise among the data sources. In this sequence, we have given greater weight 15

to the 24

- hour FRM, CSN, and continuous PM 2.5 mass data becaus e these are instrument

- based 16

and location

- and day

- specific, than to the CMAQ

- based profiles which are CTM

- based, 17

averaged to the month, and extrapolated from 2004 to each of 2005, 2006, and 2007. 18

Because of differences in filter materials, sample collec tion, laboratory analysis, and data 19

reporting, there are differences between the contribution of some PM components to PM 2.5 mass 20

as reported by a filter

- based 24

- hour FRM sampler, and the mass of the same components as 21

reported by CSN (or IMPROVE) samplin g. The following summary of these differences may be 22

helpful in understanding the steps used to develop estimates of hourly PM 2.5 components in this 23

analysis. In the IMPROVE algorithm for reconstructing light extinction, the light extinction 24

contribution multipliers per unit of mass concentration of components are not all the same for the 25

five principal components. Consequently, care is required to estimate these components as 26

consistently as possible with the IMPROVE sampling and analytical methods so t hat particle 27

mass is correctly assigned to the right component. 28

**•** Nitrate: CSN (and IMPROVE) sampling uses a Nylon filter for purposes of nitrate ion 29

quantification, while FRM sampling uses a Teflon filter for PM 2.5 mass as a whole. The 30

Nylon filter limit s the loss of nitrate in the form of nitric acid vapor which could 31

otherwise occur if the filter temperature rises above the temperature at the time of 32

collection, compared to the Teflon filter. The fine particle nitrate ion collected on nylon 33

and Teflon filters are assumed to be associated with ammonium ion s , and for this analysis 34January 2010 3

- 20 DRAFT

- Do Not Quote or Cite ammonium is assumed to evaporate at the same rate as nitrate on the FRM filters 28

**•** . 1

Hence, the nitrate ion and calculated ammonium nitrate concentrations reported by CSN 2

(and IMP ROVE) sampling typically will be higher than the nitrate contribution to FRM 3

PM 2.5 mass, particularly under warm ambient conditions. The latter steps make nitrate 4

mass as reported for a CSN (or IMPROVE) site higher than the nitrate contribution to 5

PM 2.5 m ass reported by a FRM sampler at the same site. On the other hand, FRM 6

sampling may result in some water that is associated with nitrate being included in the 7

reported PM 2.5 mass, while the nitrate mass reported by CSN (or IMPROVE) sampling 8

excludes all w ater. Continuous PM 2.5 samplers employ a variety of methods for 9

measuring PM 2.5 mass, with correspondingly different behaviors regarding retention/loss 10

of nitrate. In this assessment’s approach to estimating actual ambient concentrations and 11

PM light ext inction, the FRM measurement of nitrate is used in the calculation of the 12

concentration of organic carbonaceous material, but not in estimating ambient 13

concentrations of nitrate or PM light extinction. The CSN

- reported nitrate ion 14

concentration and corres ponding ammonium nitrate mass is used for the latter purposes. 15

Sulfate:

**•** Unlike nitrate, sulfate is not subject to loss once collected by a filter, so the 16

sulfate ion mass reported by a CSN (or IMPROVE) sampler will be about the same as the 17

contribution of sulfate ion to the mass reported by FRM sampling. In FRM sampling, 18

sulfate ion may not be fully neutralized. When IMPROVE data are used to estimate light 19

extinction, it is assumed that sulfate ion is fully neutralized. Even more important than 20

nitrate, FRM sampling results in water that is associated with sulfate being included in 21

the reported PM 2.5 mass. While the water associated with the measured sulfate ion is 22

used in the calculation of the concentration of organic carbonaceous material, it is not 23

u sed in estimating ambient concentrations of sulfate or PM light extinction. 24

Elemental and Organic Carbon: Only the mass of carbon atoms is included in the 25

reported elemental carbon and organic carbon for a CSN (or IMPROVE) sampler. In 26

addition, the assi gnment of carbon atoms between the reported elemental and organic 27

amounts is dependent on the specifics of the two different thermo

- optical analytical 28

methods used in the CSN vs. the IMPROVE network. 29

28 EPA staff recognizes that fine particle nitr ate may be in the form of calcium or sodium nitrate, but like the

IMPROVE program treats nitrate as ammonium nitrate. Also, the quartz filter used to 29

quantify carbonaceous m aterial in CSN and IMPROVE sampling both absorbs and loses 30

organic vapors during sampling, while the Teflon filter in a FRM sampler does not 31

absorb organic vapors (although PM on the filter may do so). Therefore, some method 32

other than direct measurement must be used to estimate the total mass concentration of 33

organic carbonaceous material in ambient air. The IMPROVE program adjusts for 34

absorption of vapors by subtracting a monthly average backup filter value, and then 35

applies a standard adjustment factor (1.4 in the original IMPROVE method) to the 36

remaining organic carbon measurement to estimate organic carbonaceous material. In 37

contrast, the standard reports from CSN sampling submitted to AQS do not include these 38

two adjustments, but it is routine for E PA staff to apply adjustments for the same 39

29 While CSN carbon sampling and analysis methods have recently been harmonized with IMPROVE methods at

many CSN sites, it was not until mi d

- 2007 that the first 57 sites were using the harmonized methods. Consequently,

most of the elemental and organic carbon data used in this assessment were obtained with the original CSN methods.January 2010 3

- 21 DRAFT

- Do Not Quote or Cite purpose, after reporting of CSN data to AQS. The latter are based on network

- wide filter 1

field blanks and are judged as very approximate. For this assessment, the SANDWICH 2

approach to such adjustments (Frank, 20 06) is used to estimate the organic mass through 3

a material balance of components measured on the CSN and FRM samplers. 4

**•** Hourly PM 2.5 : The continuous instruments used for measuring hourly PM 2.5 mass were 5

different among sites (as listed in Appendix A). Non e of the instrument types that 6

provided hourly data for this assessment, when averaged over 24 hours, exactly matches 7

either the measurement of PM 2.5 mass from a FRM sampler or the sum

- of

- components 8

reportable from CSN sampling. Differences can arise beca use of differences in water 9

capture and retention, inconsistent absorption and loss of organic vapors and nitric acid 10

vapor, etc. Furthermore, comparability between hourly and 24

- hour integrated 11

measurements can only be made on a daily average basis. Cons equently, the continuous 12

instruments providing data to this assessment can be assumed to have a range of 13

correlation performance versus the FRM. In light of these consistency issues, the hourly 14

data from the continuous instruments were taken to be most in dicative of the relative 15

concentrations of PM 2.5 from hour

- to

- hour, with less reliance on the absolute accuracy of 16

the continuous instruments. 30

Taking into consideration the above information, EPA staff combined the four types of 18

available PM 2.5 data in e ach study area using the following step s . Figure 3

- 5 provides a flow 19

chart to assist in understanding these steps. 20 17

1. T he SANDWICH method (Frank, 2006) was used to subdivide the 24

- hour PM 2.5 mass 21

reported by the FRM for each day and site into sulfate (inclu ding associated ammonium 22

and residual water during filter equilibration and weighing), nitrate (including associated 23

ammonium, but not necessarily enough to fully neutralize the sulfate ion, and residual 24

water during filter weighing), elemental carbon, org anic carbonaceous mass, and fine 25

soil/crustal mass. This is done using information from the CSN measurements, physical 26

models, and day

- specific temperatures. The primary purpose of this SANDWICH step is 27

to estimate organic carbonaceous mass. Significant ly, in the SANDWICH method, the 28

component referred to as organic carbonaceous mass is actually a residual whose value is 29

determined as the difference between the PM 2.5 mass determined from weighing the FRM 30

filter and the sum of the estimated masses of the other four mass components as listed 31

above. Therefore, it is not necessary to adjust for organic carbon sampling artifacts or to 32

apply the 1.4 factor commonly used to estimate organic carbonaceous material from 33

IMPROVE measurements of organic carbon. The SANDWICH procedure did not 34

consider sea salt in the material balance, since this is generally a very small mass 35

constituent for the urban areas considered in this analysis. For the same reason, sea salt 36

was also not considered in the aerosol based light extinction algorithm. 37

38

30 In 2006, EPA developed and promulgated criteria for approv al 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.January 2010 3

- 22 DRAFT

- Do Not Quote or Cite Figure 3

- 5 . Sequence of steps used to estimate hourly PM 2.5 components and PM light 1

extinction 2

3

2. The CMAQ

- derived monthly diurnal profiles for the sulfate, nitrate, elemental carbo n, 4

organic carbon and fine soil/crustal components, like the examples for Detroit in Figure 5

3

- 4, were multiplied by the day

- specific SANDWICH

- based estimates of the 24

- hour 6

average concentrations of these five PM 2.5 components, to get day

- specific hourly 7

e stimates of these five components (including ammonium and water associated with 8

sulfate and nitrate ion). 9

3. The hourly concentrations of these five components (including ammonium and water 10

associated with sulfate and nitrate ion when the filter is weighed) w ere added together, to 11

get a sum

- of

- components estimate of hourly PM 2.5 mass for the day of the FRM 12

sampling. 13

4. The hourly data from the continuous PM 2.5 instrument on the day of the FRM sampling 14

were normalized by their 24

- hour average, to get a diurnal pro file. (Recall that days were 15

not used in this assessment if hourly PM 2.5 mass data were missing for more than 25 16

percent of daylight hours.) This profile was applied to the 24

- hour PM 2.5 mass reported 17

by the FRM sampler, to get a preliminary, FRM

- consist ent estimate of hourly PM 2.5 mass 18

for the day of the FRM sampling. This is straightforward when all 24 values of 1

- hour 19January 2010 3

- 23 DRAFT

- Do Not Quote or Cite PM 2.5 mass were available for the day. However, for some (but not many) days, some 1

values for continuously measured hourly PM 2.5 mass were missing. In such cases, EPA 2

staff used only the hours with valid 1

- hour PM 2.5 mass values to develop the diurnal 3

profile and then applied the profile to the FRM value as just described. This keeps the 4

average of the valid 1

- hour PM 2.5 values equal t o the 24

- hour value from the FRM 5

sampler. 6

5. The two estimates of hourly PM 2.5 mass from steps 3 and 4 were compared, hour

- by

7

hour. By virtue of the way they were derived, the averages of these estimates across all 8

24 hours of the day will necessarily be the same (and will be equal to the 24

- hour FRM 9

measurement). However, while the diurnal pattern of these two estimates of the same 10

physical parameter should also be generally similar, it can be expected (and it is 11

observed) that the hourly measurements from the continuous PM 2.5 instruments (after 12

adjustment to be consistent with the FRM data) have more hour

- to

- hour variability. 13

Figure 3

- 6 gives an example of this comparison, for one day for the Detroit study area. 14

Figure 3

- 6 . Example from Detroit study area. 15

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 profiles to SANDWICH estimates of 24

- hour

component concentrations versus applying a diurnal profile derived from continuous PM 2.5

measurements to FRM PM 2.5 mass

.

16

6. Given that the continuous instrument is reacting to hour

- specific local conditions that can 17

vary from hour

- to

- hour due to real variations in local emissions and dis persion/transport 18

conditions, while the CMAQ

- based estimates contain much less specific information, the 19

diurnal pattern of PM 2.5 mass observed by the continuous instrument (adjusted to be 20January 2010 3

- 24 DRAFT

- Do Not Quote or Cite consistent with the FRM value for 24

- hour average PM 2.5 ) was taken as more reliable. 1

Within each hour, the estimates of all five components from step 2 were increased or 2

decreased by a common percentage (referred to below as A i where the subscript i 3

indicates the hour) so that the sum of the five components after this ad justment was equal 4

to the estimate of the hourly PM 2.5 mass from step 4. The adjustment percentage varied 5

from hour

- to

- hour. Necessarily, in some hours the adjustment is an increase in the 6

concentrations of all components, and in other hours it is a decr ease. While this 7

adjustment preserves the consistency between the 24 values of hourly PM 2.5 mass and the 8

24

- hour FRM mass, it can disturb the consistency between the daily average of hourly 9

estimates of PM 2.5 components and the SANDWICH

- based estimates of 24

- hour average 10

component concentrations. This disturbance was generally small, because the 11

adjustments necessarily go in one direction for some hours and the other direction for 12

other hours. For example, for the particular day in Detroit used for illus tration purposes 13

in Figure 6, the effect of this step was to cause a discrepancy of 3 percent between the 14

SANDWICH

- based values of 24

- hour sulfate concentration and the average of the 24 15

estimates of 1

- hour sulfate concentrations (the positive percent indi cates a higher 16

concentration in the result of this step than the SANDWICH

- based value). The 17

discrepancies were 1, 1, 2, and 2 percent for nitrate, elemental carbon, organic carbon, 18

and fine soil/crustal, respectively. 19

7. Each hourly estimate of sulfate conc entration from step 6 (which includes estimates of 20

associated ammonium and particle bound water) was adjusted so that it excludes water 21

and reflects full neutralization and therefore is consistent with the reporting practices of 22

the IMPROVE program and the IMPROVE algorithm. This was done via these sub

23

steps: 24

a. The 24

- hour CSN value for the dry mass of sulfate ion (not SANDWICHed, no 25

ammonium or water) was multiplied by 1.375 to reflect an assumption of full 26

neutralization of dry sulfate mass. 31

b. The ratio of this fully neutralized 24

- hour sulfate mass to the SANDWICH

- based 28

24

- hour sulfate value was calculated. 29 27

c. This ratio was applied to each individual hour’s sulfate concentration from step 6. 30

As in Step 6, it is possible for the 24 final hourly sulfate estimat es to no longer be 31

exactly consistent with the 24

- hour CSN sulfate measurement, both reported as fully 32

neutralized sulfate ion. 33

34

8. A similar adjustment as in step 7 (for sulfate) was made to each hour’s nitrate 35

concentration from step 6, so that the estimate of hourly nitrate would reflect actual 36

atmospheric conditions and be consistent with the IMPROVE algorithm. However, the 37

ratio approach used in step 7(b) for sulfate could not be applied for nitrate, so this 38

adjustment had to be more complicated. Becaus e in warm weather the FRM Teflon filter 39

31 While it would have been possible to develop a more realistic estimate of partially neutralized sulfate, the

assumption of full neutralization was used to main tain consistency with the basis for the f(RH) term in the

IMPROVE algorithm.January 2010 3

- 25 DRAFT

- Do Not Quote or Cite does not retain nitrate, the initial FRM

- consistent nitrate estimate derived by applying the 1

SANDWICH method to the FRM and CSN data can be zero. Such a zero value makes it 2

impossible to use the ratio approach in 7( b). Instead, the adjustment was made as 3

follows: 4

a. The 24

- hour CSN value for nitrate ion (not SANDWICHed, no ammonium or 5

water) was multiplied by 1.29 to reflect an assumption of full neutralization by 6

ammonia. 7

b. This 24

- hour value was then diurnalized using the CMAQ

- based profile, similar 8

to step 2. 9

c. Each resulting hourly value of nitrate was further multiplied by the Ai factor from 10

step 6. 11

d. This new estimate of hourly nitrate was used to replace the initial nitrate value 12

that had resulted from step 6. 13

For coo ler areas and days in which the 24

- hour SANDWICH results include some nitrate, 14

the effect of these steps for nitrate are exactly the same as the effects of step 7 for sulfate 15

(except for the 1.29 vs. 1.375 neutralization factor). For warmer areas and days in which 16

the 24

- hour SANDWICH results did not include any nitrate even though nitrate was 17

measured on the CSN Nylon filter, the effect of these steps is to assign the CSN nitrate to 18

each hour using a combination of the information in the CMAQ

- based profil es and the 19

information provided by the continuous PM 2.5 sampler. As in Step 6, it is possible for the 20

24 final hourly nitrate estimates to no longer be exactly consistent with the 24

- hour CSN 21

nitrate measurement. 22

The net effect of these steps is believed by EPA staff to result in hourly PM light 23

extinction estimates with the following features with respect to some of the complicating aspects 24

of PM sampling: 25

**•** The 24

- hour average of the hourly nitrate concentrations used to estimate hourly PM light 26

extinction agrees closely but not exactly with the 24

- hour value provided by the CSN 27

sampling, and generally is higher than the contribution of nitrate to the FRM measure of 28

PM 2.5 mass. In some mid

- day hours in some areas, estimated hourly nitrate is zero which 29

is a more realistic approach than applying a 24

- hour species mix to each hour. 30

**•** The 24

- hour average of the hourly organic carbonaceous material concentrations used to 31

estimate hourly PM light extinction achieves FRM mass balance closure, taking into 32

account al so the difference in nitrate and the possibly partial neutralization of sulfate ion 33

on the FRM filter. Because the Teflon filter used in FRM sampling is less subject to 34

positive artifacts for organic material, this approach sidesteps an area of uncertaint y in the 35

IMPROVE sampling method. By relying on mass closure as the driving principle for 36January 2010 3

- 26 DRAFT

- Do Not Quote or Cite estimating organic material, it is not necessary to choose a multiplier to relate organic 1

carbon to organic carbonaceous material. 32

**•** The 24

- hour average of the hourly elemental carbon concentrations used to estimate 3

hourly PM light extinction agrees closely but not exactly with the 24

- hour value provided 4

by the CSN sampling, and with the contribution of elemental carbon to the FRM measure 5

of PM 2.5 mass. Elemental carb on is generally defined by the thermal optical transmission 6

method used in CSN, rather than the thermal optical reflectance method used in 7

IMPROVE. 8 2

3.3.2 Hourly PM 10

- 2.5 Concentrations 9

Three different paths were used to estimate hourly PM 10

- 2.5 concentrations de pending on 10

data availability , in the following order of preference : 11

12

1. When hourly data from a collocated PM 10 instruments were available at the continuous 13

PM 2.5 site in a study area, PM 2.5 was subtracted hour

- by

- hour from PM 10 . Negative 14

values were reset t o zero. This was the approach most often used in Birmingham, 15

Detroit, Baltimore, and Philadelphia. This method should result in reliable estimates of 16

actual PM 10

- 2.5 at the study site. (How well the study site represents the study area 17

generally, or the most visibility

- impacted portions, of the study area is a separate issue.) 18

19

2. W hen collocated continuous PM 10 data were not available at the continuous PM 2.5 site in 20

a study area, but continuous PM 10 data we re available at another site in or near the same 21

st udy area, PM 10

- 2.5 was estimated by subtraction, implicitly assuming that the latter site 22

was also representative of PM 10 at the former site. This was the approach most often 23

used in Los Angeles, Phoenix, St. Louis, Atlanta, and New York. As a result, es timates 24

of PM 10

- 2.5 for these areas could be affected by site

- to

- site differences. In particular, the 25

two sites in Los Angeles were a good distance apart, and the PM 10 site in Victorville may 26

represent influences from agricultural operations rather than t ypical urban influences. In 27

St. Louis, the PM 10 site may also have been influenced by particular local sources. In 28

both cases, very high estimates of hourly PM 10

- 2.5 may not represent reality at the PM 2.5 29

site , although they may be reasonable estimates f or the PM 10 site. 30

31

3. If neither of the first two methods was possible, a regional average ratio of PM 10

- 2.5 to 32

PM 2.5 determined from an analysis of 24

- hour data for the 2005 Staff Paper was applied 33

to hourly PM 2.5 from the continuous instrument associated w ith the study area. This was 34

the approach used for all hours in Tacoma, Fresno, Salt Lake City, Dallas, Houston, and 35

Pittsburgh. With this approach, it is not possible for there to be any particularly high 36

estimates of hourly PM 10

- 2.5

. 37

38

32 In other work, EPA staff has observed that when applied to urban sampling data together with CSN network

- wide

field blanks applied to reported OC measured concentrations, the mu ltipliers that can be back

- calculated from the

results of the SANDWICH method tend to be nearer to 1.4 than to the higher value used in the new IMPROVE

algorithm.January 2010 3

- 27 DRAFT

- Do Not Quote or Cite The estimation o f PM 10

- 2.5 was further complicated because some types of data were 1

missing for isolated hours in the 2005

- 2007 period. As result, e ven for a single study area more 2

than one method sometimes had to be used to estimate hourly PM 10

- 2.5 . Appendix A gives mor e 3

specifics about the estimation of hourly PM 10

- 2.5 in each study area. 4

The three

- path approach described here is similar to that used for the visibility analysis 5

reported in the 2005 Staff Paper. While the second and third paths involve the use of data a nd 6

assumptions that are not robust compared to the use of paired, collocated , same

- method 7

continuous instrument s or compared to the use of paired low

- volume filter

- based samp lers, in 8

most areas and periods the contribution to PM light extinction from the r esulting PM 10

- 2.5 9

concentrations was not large compared to the PM light extinction due to PM 2.5 components

. 10

3.3.3 Hourly Relative Humidity Data 11

Hourly relative humidity (RH) data for each study area ’s primary monitoring site were 12

obtained hour

- by

- hour from the c losest available non

- missing relative humidity measurement, as 13

reported by either an a ir m onitoring station reporting such data to AQS or a National Weather 14

Service (NWS) station. For the AQS RH data, parameter 62201 values were utilized. RH data 15

from bo th sources are expressed as percentages. 33

3.3.4 Calculation of Daylight 1

- Hour PM Light Extinction 17 16

Because the interest in this analysis is on visibility during daylight hours, EPA staff 18

applied a scheme to denote those hours that would be considered daylight h ours. For simplicity, 19

all the days within each “season” in all study areas were considered to have the same daylight 20

hours. 34

The original IMPROVE algorithm was applied hour

- by

- hour to estimate PM light 24

extinction in each study area for each daylight hour. When doing so, we capped the va lue of the 25

humidity adjustment factor in the IMPROVE algorithm (“f(RH)”) at the value of 7.4 that it has 26

for a relative humidity of 95 percent. The effect of measurement errors in relative humidity at 27

values above 95 percent on the value of f(RH) and thus on reconstructed PM light extinction is 28 Table 3

- 5 shows the dividing times used to denote daylight hours for the study areas. 21

Unless otherwise stated, all subsequent di scussion of the results refers only to the values of 22

parameters during these daylight hours. 23

33 After release of the first public review draft of this assessment, and error was discovered in the data processing

that assembled the relative humidity data base, such that the nearest site was not used as described here. That error

has been corrected in this second public review draft. See also “ Corrections to Relative Humidity Values Used in

the Draft UFVA, Corrected Graphics, Tables, and Availability of Detailed Da ta File for Current Conditions”., P.

Lorang

, November 10, 2009.

34 This simple approach does not account for the effects of the actual date within a three

- month season, latitude, or

east

- west position within a time zone on the actual local hours that are entirely daylight. Appendix I examines the

possible impact of this simplification, concluding that it is unlikely to affect later answers to policy relevant

questions.January 2010 3

- 28 DRAFT

- Do Not Quote or Cite considerable because of the highly nonlinear form of the function in that range . This creates 1

uncertainty as to the representativeness of the extinction values calculated with high values of 2

relative humidity. 35

4 . 3

Table 3

- 5 Assumed daylight hours by season (Local Standard Time) 5

November

- January February

- April May

- July August

- October

First hour that is

entirely daylight 8:00

- 9:00 AM 7:00

- 8:00 AM 5:00

- 6:00 AM 6:00

- 7:00 AM

Last hour that is

entirely daylight 3:00

- 4:00 PM 5:00

- 6:00 PM 6:00

- 7:00 PM 5:00

- 6:00 PM

Number of daylight

hours 8 11 14 12

6

3.3.5 Exclusion of Hours with Relative Humidity Greater than 90 Percent from PM 7

Light Extinction NAAQS Scenarios and Most Results 8

9

As advised by CASAC as part of its comments on the first public review draft of this 10

assessment, EPA staff considered whether to structure the PM light extinction NAAQS scenarios 11

so that ambient data obtained during daylight hours in which relative humidi ty was greater than 12

90 percent would play no role in the indicator/form of the NAAQS, i.e., so that those data would 13

not enter into the calculation of the design value. EPA staff obtained hourly meteorological 14

parameters from National Weather Service moni toring sites near 11 of the 15 study sites (usually 15

a major airport), for 2005 through 2007, for all days in this period including days for which PM 16

observations to support estimate of PM light extinction are not available 36

35 The IMPROVE program also caps the value of f(RH) at its value for a relative humidity of 95% when reporting

visibility in deciviews. . For these sites, we 17

compared t he occurrence of liquid precipitation, hail, other frozen precipitation, fog, and 18

haze/mist during daylight hours with humidity greater than 90 percent and during all other 19

daylight hours. These five conditions are generally considered natural causes of r educed 20

visibility. Table 3

- 6 presents this comparison. The percentages of hours with each of these five 21

conditions individually and for any one or more of the five conditions together are shown for the 22

two sets of daylight hours. NWS observations of the se conditions are instantaneous, and are 23

generally made about 50 minutes after the hour. The relative humidity observations are made at 24

the same time. It should be noted that this analysis of the co

- occurrence of high relative 25

humidity and these five con ditions uses data from NWS sites other than the AQS sites that 26

36 Th r ough an oversight, EPA staff did not obtain NWS data for Los Angeles, St. Louis, Houston, and Detroit in

time for processing and incorporation of results into Table 3

- 6. These data will be added in the final version of this

assessment.January 2010 3

- 29 DRAFT

- Do Not Quote or Cite provided the relative humidity value for the light extinction estimate. AQS sites could not be 1

used for this analysis because they generally do not report similar weather condition data. 2

The c omparison for the 11 sites shows that in the set of hours with relative humidity 3

above 90 percent, the frequencies of liquid precipitation (rain), fog and haze/mist individually 4

and the frequency of any one or more of them together were considerably higher than in the set 5

of hours with lower relative humidity. 37

Rain, fog, and mist cause a natural reduction in visibility, independent of PM 15

concentrations. To reduce the likelihood of a secondary PM NAAQS based on an indicator/f orm 16

that could be affected by measurements made under natural weather conditions that reduce 17

visibility, for this assessment EPA staff eliminated the estimates of PM light extinction from any 18

daylight hours with relative humidity above 90 percent from desi gn value calculations. The frequencies of hail and other frozen precipitation 6

were too low for meaningful comparisons. Moreover, except in Tacoma, the frequency of rain or 7

fog at the observation moments during the hours with relative humidity less than or equal to 90 8

percent was less than 6 percent. Also, a separate analysis (not shown) indicated that rainy hours 9

with lower relative humidity experience considerably less accumulation than rainy hours with 10

higher relative humidity. Based on this assessment, the 90% relative humidity cutoff criteria is 11

effective in that on average less than 6% of the hours are removed from consideration, yet those 12

hours have on average over twelve times the likelihood of weather conditions that directly reduce 13

visibility compared to hours with 90% or less relative humidity. 14

38

More information on this topic can be found in Appendix G, which reports by study area 26

the percentages of daylight hours that were excluded from design values, the distribution of the 27

e xcluded hours by time of day, and the percentage of days that had one or more daylight hours 28 Also, 19

because PM light extinction during such hours is not as likely to be the primary cause of adverse 20

effects on the public, all figures and tables in the body of this document and in Appendices that 21

present PM light extinction v alues or statistics exclude values for such hours (unless explicitly 22

stated to include them), so that the patterns of PM light extinction during the remaining daylight 23

hours can be seen clearly. Figures and tables that present PM component concentrations and 24

relative humidity values are based on all daylight hours, however. 25

37 The “haze/mist” category is not an original NWS reporting category. It is a combination of three original NWS

weather categories: mist, smoke, a nd haze that were prepared earlier by EPA staff for another purpose. EPA staff

was unable to separate the occurrence of these three conditions in time for this version of this assessment. Of these,

only mist, defined as fog

- like conditions that do not im pair visibility below 0.5 nautical miles, is clearly a natural

condition during which people would not consider limited visibility to be aesthetically undesirable. Consequently,

the columns of Table 3

- 6 for “haze/mist” and “any” must be interpreted accord ingly.

38 Another consideration is that instruments used to measure light extinction could be adversely affected if allowed

to operate without heating or other protective method (such as diffusion drying of incoming air) when relative

humidity is very high. If protected, however, the measured light scattering would not reflect actual ambient

conditions.January 2010 3

- 30 DRAFT

- Do Not Quote or Cite eliminated. Appendix G also contains box plots which contrast the distributions of daylight 1

- 1

hour PM light extinction values (and maximum daily daylight 1

- hour P M light extinction, see 2

section 3.3.6) before and after this elimination step. The tile plots in Figure 3

- 12 also present 3

additional detailed information on the specific hours that had relative humidity values above 90 4

percent, and on the PM light extinct ion values during those and other daylight hours. 5January 2010 3

- 31 DRAFT

- Do Not Quote or Cite Table 3

- 6 Comparison of Meteorological Parameters for Daylight Hours with Relative Humidity Greater than 90 1

Percent and Other Daylight Hours , During 2005

- 20 07 2

Study Area Daylight Hours with Relative Humidity <= 90% Daylight Hours with Relative Humidity > 90%

Number

of

Hours Percentage of Hours with Weather Condition Number

of

Hours Percentage of Hours with Weather Condition

Liquid

Precip. Hail Other

Froz en

Precip. Fog Haze/Mist Any Liquid

Precip. Hail Other

Frozen

Precip. Fog Haze/Mist Any

Tacoma 18293 12 0 0 0 3 13 7987 24 0 1 7 26 45

Fresno 24245 3 0 0 2 16 18 1615 12 0 0 44 60 79

Los

Angeles

Phoenix 26045 1 0 0 0 0 1 235 50 0 0 11 27 56

Salt Lake

City 24989 4 0 2 1 4 8 1291 21 0 30 33 57 69

Dallas 25519 3 0 0 1 4 6 761 47 0 1 18 71 80

Houston

St. Louis

Birmingham 23826 4 0 0 1 8 11 2454 30 0 0 32 55 61

Atlanta 23696 5 0 0 1 7 10 2584 39 0 0 34 61 71

Detroit

Pittsburgh 22254 5 0 7 1 8 17 4026 36 0 9 26 54 69

Baltimore 22867 4 0 1 2 9 12 3413 36 0 3 30 64 74

Philadelphia 24302 6 0 0 1 6 11 1978 44 0 4 26 64 80

New York 24963 6 0 1 1 9 13 1317 52 0 8 41 79 89January 2010 3

- 32 DRAFT

- Do Not Quote or Cite 3.3.6 Calculation of Daily Maxi mum 1

- Hour PM Light Extinction 1

Daily maximum 1

- hour PM light extinction is a statistic of interest in this assessment, as 2

briefly discussed in section 1. 4 .3. The daylight hour with the maximum value of PM light 3

extinction and the corresponding PM light ex tinction value were identified for each day for each 4

study area. As mentioned in section 3.2.1, days which were missing 1

- hour PM 2.5 values for 5

more than 25 percent of daylight hours were not used in this analysis. No further completeness 6

requirement for 1

- hour data during a day was applied when selecting the daylight hour with the 7

maximum value of PM light extinction. 8

3.4 SUMMARY OF RESULTS F OR CURRENT CONDITION S 9

3.4.1 Levels of Estimated PM 2.5 , PM 2.5 C omponents, PM 10

- 2.5 , and Relative Humidity 10

Figure 3

- 7 presen ts box

- and

- whisker plots to illustrate the distribution s in each study area 11

of the estimates of 1

- hour PM 2.5 (the diurnalized FRM value, resulting from step 4 in section 12

3.4.1), PM 10

- 2.5 , and relative humidity over th e entire 2005

- 2007 study period. In th e plot for 13

each parameter, areas are ordered by longitude, to make it easier to see east

- versus

- west regional 14

differences. For these three parameters, the distributions are given for all the daylight 1

- hour 15

estimates, including hours with relative humidit y greater than 90 percent. Similar plots of the 16

daily maximum daylight 1

- hour values of PM 2.5 and PM 10

- 2.5 concentrations and relative 17

humidity are available in Appendix B, as are plots of all daylight 1

- hour values for each of the 18

PM 2.5 component species

. 39

From these plots we see that the distributions of PM 2.5 generally trend toward higher 20

concentrations from west to east except for the two California urban locations which have PM 2.5 21

concentrations more typical of eastern areas. The lowest median PM 2.5 concentrations are in 22

Tacoma, WA, and Phoenix, AZ. Median PM 10

- 2 .5 concentrations are highest in St. Louis, MO, 23

and Phoenix, AZ, and lower elsewhere. The highest outlier PM 10

- 2.5 concentrations are in St. 24

Louis, MO, and Los Angeles, CA. Relative humidit y is lowest for the western urban areas 25

except for Tacoma, WA, which is similar to the northeastern urban locations with respect to 26

humidity. These hourly daylight PM concentration and relative humidity box and whisker plots 27

are consistent with our expect ations based on regional 24

- hour PM concentration values and 28

humidity climatology. 29 19

30

39 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 on as an interger.January 2010 3

- 33 DRAFT

- Do Not Quote or Cite Figure 3

- 7

. Distribution of PM parameters and relative humidity across the 2005

- 2007 1

period, by study area 2

3

(a) Estimates o f 1

- hour PM 2.5 mass, based on applying continuous instrument

- based 4

diurnal profiles to 24

- hour FRM PM 2.5 mass 5

6

7

(b) Estimates of 1

- hour PM 10

- 2.5 8

9

10January 2010 3

- 34 DRAFT

- Do Not Quote or Cite Figure 3

- 7. (cont.)

. Distribution of PM parameters and relative humidity across the 200 5

- 1

2007 period, by study area 2

3

(c) 1

- hour relative humidity 4

5

6January 2010 3

- 35 DRAFT

- Do Not Quote or Cite 3.4.2 Levels of Estimated PM light extinction 1

Figure 3

- 8 presents box

- and

- whisker plots to illustrate the distributions of the estimates 2

of daylight 1

- hour reconstructed PM light extinction levels in each area in each year (excluding 3

hours with relative humidity greater than 90 percent). The distribution of (a) the daily maximum 4

1

- hour values and (b) the individual 1

- hour values are both shown. The horizontal dashed lines 5

in the plots represent the low, middle, and high candidate protection levels (CPLs) for PM light 6

extinction as discussed in section 2.6. T hese benchmarks for PM light extinction are 64, 112

, 7

and 191 Mm - 1

, corresponding to the benchmark VAQ values of 20 dv, 25 dv and 30 dv. Table 3

- 8

7 provides (a) the percentage s of days (across all of 2005

- 2007, unweighted) in which the daily 9

maximum daylight 1

- hour PM light extinction level was greater than each of the three candidate 10

protection levels (excluding hours with relative humidity greate r than 90 percent), and (b) the 11

similar percentage based on all daylight hours (with the same exclusion). 12

As was also seen in the comparable PM 2.5 concentration box and whisker plots in Figure 13

3

- 7, the high percentile hourly PM light extinction values in F igure 3

- 8 tend to be higher in the 14

eastern urban areas and lower in the non

- California western urban areas. The distributions of 15

maximum daily PM light extinction values are higher (Figure 3

- 8b), as expected, than for all 16

hours (Figure 3

- 8a). Both Figure 3

- 8 and Table 3

- 7 indicate that all 15 urban areas have daily 17

maximum hourly PM light extinctions that exceed even the highest of the CPL s some of the 18

time. Again, the non

- California western urban locations have the lowest frequency of maximum 19

hourly PM light extinction with values in excess of the high CPL for 8 percent or fewer of the 20

days. Except for the two Texas and the non

- California western urban areas, all of the other 21

urban areas exceed that high CPL from about 20 percent to over 60 percent of t he days. Based 22

on these estimated maximum hourly PM light extinction estimates, all 15 of the urban areas 23

exceed the low CPL for about 40 percent to over 90 percent of the days. As noted in section 24

3.2.1, in 10 of the 15 study areas the study site used i n this assessment is not the site in the study 25

area with the highest concentrations of PM 2.5 . Thus, these estimates may not characterize 26

visibility in the worst

- visibility portion of each study area. 27

In the last review of the secondary PM NAAQS, the patte rn of light extinction during the 28

day was of particular interest. To illustrate the distributions of 1

- hour PM light extinction levels 29

in specific daylight hours, Figure 3

- 9 shows the distributions of 1

- hour PM light extinction 30

across the entire three

- yea r study period, individually for the study areas (excluding hours with 31

relative humidity greater than 90 percent). (Appendix E provides additional graphics related to 32

temporal/spatial patterns of light extinction.) These plots show that high PM light ext inction can 33

occur during any of the daylight hours, though for most of these urban areas the morning hours 34January 2010 3

- 36 DRAFT

- Do Not Quote or Cite have somewhat higher PM light extinction than in the afternoon

. 40

4 Urban areas without a 1

preference for morning high PM light extinction include Phoen ix, AZ; Salt Lake City, UT; 2

Tacoma, WA; Fresno, CA; and Philadelphia, PA. 3

40 If hours with relative humidity greater than 90 percent were not eliminated, the tendency for higher PM light

extinction in the morning hours would be stronger.January 2010 3

- 37 DRAFT

- Do Not Quote or Cite Figure 3

- 8 . Distributions of estimated daylight 1

- hour PM light extinction and maximum 1

daily daylight 1

- hour PM light extinction acro ss the 2005

- 2007 period, by study area 2

(excluding hours with relative humidity greater than 90 percent)

. 3

4

(a) Maximum daily values 5

6

7

8

9

10

11

12

13

14January 2010 3

- 38 DRAFT

- Do Not Quote or Cite (b) Individual 1

- hour values 1

2

3

4

5

6January 2010 3

- 39 DRAFT

- Do Not Quote or Cite 1

Table 3

- 7 Percentage of daily maximum hourly values and individual hourly values 2

of daylight PM light extinction exceeding CPL s (excluding hours with relative humidity 3

greater than 90 percent)

. 4

Study Area

Number of Days with

Estimates Candidate Protecti on Level

64Mm - 1 112 Mm - 1 191 Mm - 1

(a) Percentage of Daily Maximum Hourly Values

Exceeding CPL

Tacoma 109 52 22 4

Fresno 324 75 52 30

Los Angeles 300 90 83 62

Phoenix 86 42 7 1

Salt Lake City 306 44 17 8

Dallas 273 80 41 10

Houston 148 79 45 11

St. Louis 289 98 78 40

Birmingham 349 89 65 34

Atlanta 279 91 75 31

Detroit 141 87 68 43

Pittsburgh 277 85 57 26

Baltimore 181 80 50 23

Philadelphia 143 86 64 31

New York 225 83 59 28

Average **229 77**  **52 26** 

Number of Daylight Hours

with Estimates (b) Percentage of Individual Daylight H ours Exceeding CPL

Tacoma 1087 14 4 1

Fresno 3533 41 21 10

Los Angeles 3048 68 43 20

Phoenix 988 11 1 0

Salt Lake City 3366 17 7 3

Dallas 3043 33 10 2

Houston 1504 35 8 1

St. Louis 3096 66 36 11

Birmingham 3763 57 25 8

Atlanta 2507 60 28 5

Detro it 1547 62 36 14

Pittsburgh 2842 53 25 7

Baltimore 1873 55 24 7

Philadelphia 1468 55 28 9

New York 2296 53 28 9

Average 2397 45 22 7January 2010 3

- 40 DRAFT

- Do Not Quote or Cite Figure 3

- 9 . Distributions of 1

- hour PM l ight extinction levels by daylight hour across the 2005

- 2007 period, by study area 1

(excluding hours with relative humidity greater than 90 percent)

. 2

3

4

5January 2010 3

- 41 DRAFT

- Do Not Quote or Cite 3.4.3 Patterns of Relative Humidity and Relationship between Relative Humidity and 1

PM light extinction 2

Figur e 3

- 10 shows the distribution of relative humidity values at each daylight hour, for 3

each study area across 2005

- 2007 (excluding hours with relative humidity greater than 90 4

percent)

. 41

To allow closer inspec tion of the relationship between PM light extinction values and 11

relative humidity values, Figure 3

- 11 is a scatter plot of actual 1

- hour relative humidity and 1

- 12

hour reconstructed PM light extinction (excluding hours with relative humidity greater than 90 13

percent) . Horizontal lines are included in each of the individual plots corresponding to the three 14

benchmarks for PM light extinction and a vertical line in each for the 90 percent relative 15

humidity cutoff. There are many instances with PM light extincti on greater than the candidate 16

protecti on levels when relative humidity is 90 percent or lower. Notice that in Figure 3

- 11 there 17

also are plenty of high humidity conditions for each urban area that correspond to low PM light 18

extinction values. This is bec ause humid air does not by itself contribute to light extinction. 19

Particles composed of material that absorbs water in high relative humidity conditions (e.g., 20

sulfate and nitrate PM) swell to larger solution droplets that scatter more light than their sm aller 21

dry particle counterparts in a less humid environment. The magnitude of the relative humidity 22

effect on light extinction depends directly on the concentration of these hygroscopic PM 23

components. (Figure 3

- 11 reveals skips in reported relative humid ity values for some but not all 24

the study areas. This is a result of calculations of relative humidity from dry and wet bulb 25

temperatures reported to the nearest whole Celsius degree.) 26 As expected, in every area relative humidity is lowest in the early af ternoon, 5

typically the warmest part of the day. Relative humidity is most similar across areas in the early 6

afternoon, as observed in the 2005 Staff Paper. However, even in this period there are notable 7

differences among areas. This variation was not as evident in the information presented in the 8

2005 Staff Paper because only regionally averaged information was presented. In all areas, there 9

is considerable variation in hour

- specific relative humidity during the three

- year period. 10

41 Similar information on diurnal p atterns but broken out by season is given in Appendix E.January 2010 3

- 42 DRAFT

- Do Not Quote or Cite Figure 3

- 10 . Distrib utions of 1

- hour relative humidity levels by daylight hour across the 2005

- 2007 period, by study area 1

(excluding hours with relative humidity greater than 90 percent)

. 2

3

4

5January 2010 3

- 43 DRAFT

- Do Not Quote or Cite Figure 3

- 11 . Scatter plot of dayli ght 1

- hour relative humidity (percent) vs. reconstructed PM light extinction (Mm - 1 ) across 1

the 2005

- 2007 period, by study area (excluding hours with relative humidity greater than 90 percent)

. 2

3

4

5January 2010 3

- 44 DRAFT

- Do Not Quote or Cite 3.4.4 Tile Plots of Hourly PM Light Extinction 1

Figure 3

- 12 consi sts of “tile plots” that show the estimated levels of 1

- hour PM light 2

extinction for each daylight hour for each study area. These plots assist in understanding the 3

times of the year and hours of the day in which high relative humidity and high PM light 4

e xtinction occur, both separately and together. 5

Time runs horizontally with each row of tiles representing a single day from midnight 6

(left site) to midnight (right side), and vertically from January (top) to December (bottom). Each 7

tile represents one hou r of the year for which data to estimate PM light extinction were 8

sufficient. Sites with 1:3 speciation sampling have more (and smaller) tiles than sites with 1:6 9

speciation sampling. The tick marks on the vertical axis identify the first available sampl e day of 10

each month identified by its month number. 11

PM light extinction is presented in terms of four ranges or bins defined by the two 12

intervals between the three CPLs, a bin above the high CPL, and a bin below the low CPL. For 13

the hours with relative humidity of 90 percent and below (referred to as “Low RH bext” in the 14

figure legend), shades of green are used to indicate the CPL range. Contrasting blue color scales 15

are used for the tiles representing hours with relative humidity greater than 90 percen t (referred 16

to as “High RH bext” in the shading legend), so that the hours excluded from the PM NAAQS 17

scenarios (see section 3.3.5 and Chapter 4) can be distinguished. Hours with missing PM 2.5 data 18

from the continuous instrument have no estimates of PM li ght extinction and are white

. S uch 19

cases are rare, following the prior complete exclusion of days in which more than 25 percent of 20

daylight hours were missing such data. 21

Note that for Tacoma and Phoenix there are plots for only two years because the thir d 22

year did not have suitable data, and for Phoenix and Houston only 9 months are shown for one of 23

the available years because suitable data were not available for the remaining quarter (the 24

available 9 months of results are stretched over the same vertical distance as the 12 months in the 25

other cases). 26

One observation that can be made in looking at these tile plots is that in very many cases, 27

days which have one or more hours with high PM light extinction excluded because of high 28

relative humidity have ot her hours with high PM light extinction which are not excluded. 29

Although none of the PM light extinction NAAQS scenarios considered in Chapter 4 are 30

based on a averaging period longer than one hour, these tile plots can be used to get a rough 31

sense of whet her hours with high PM light extinction tend to be isolated, such than average 32

values over several hours would be considerably lower, or tend to occur together, such that a 33

longer averaging period would produce roughly the same design value. A number of t he eastern 34

urban areas have numerous day

- long haze episodes throughout the year (e.g. St. Louis, Detroit, 35

Pittsburgh, Philadelphia and New York) or seasonally (e.g. Fresno and Salt Lake City, in the 36January 2010 3

- 45 DRAFT

- Do Not Quote or Cite winter, and Los Angeles and Atlanta in the summer). Some of the urban areas have morning 1

haze levels that diminish later in the day on a year

- around basis (e.g. Dallas) or seasonally (e.g. 2

Los Angeles, Birmingham and Atlanta in winter and Tacoma, Fresno, and St. Louis in the 3

summer). This type of information m ay be useful in this regard during the subsequent 4

preparation of the Policy Assessment Document. 5

6January 2010 3

- 46 DRAFT

- Do Not Quote or Cite Figure 3

- 12 Tile Plots of Hourly PM Light Extinction 1

2

3January 2010 3

- 47 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extincti on, continued 1

2January 2010 3

- 48 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 49 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 50 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 51 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly P M Light Extinction, continued 1

2January 2010 3

- 52 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 53 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 54 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 5 5 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile P lots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 56 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 57 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 58 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 59 DRAFT

- Do Not Quote or Cite Figu re 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 60 DRAFT

- Do Not Quote or Cite Figure 3

- 12. Tile Plots of Hourly PM Light Extinction, continued 1

2January 2010 3

- 61 DRAFT

- Do Not Quote or Cite 3.4.5 Extinction Budgets for High PM Light Extinction Conditions 1

An extinction budget for a single period shows the contribution that each PM component 2

makes to PM light extinction via the additive terms of the IMPROVE algorithm

. It can be 3

expected that the pattern in the extinction budgets will vary by time of year and by study area. 4

Examination of extinction budgets allows initi al insights into what pollutants cause poor urban 5

visibility and what emission reduction approach may be most effective in reducing PM light 6

extinction. 7

Figure 3

- 13 prese nts (a) day

- specific maximum daylight 1

- hour light extinction budgets 8

for the 1 0 perce nt of the days in each study area that have the highest daily maximum 1

- hour PM 9

light extinction levels (excluding hours with relative humidity greater than 90 percent) , and (b) 10

similar but more aggregated information based on all individual daylight hours . For the 11

maximum daily budgets, the day and hour of each hourly budget are indicated on the horizontal 12

axis, and the hours are arranged chronologically. There are too many individual daylight hours 13

within the top 10 percent group to display separately, so component concentrations for all days 14

within 10 one

- percentile

- point

- range “bins” have been averaged together for display. 42

Looking at individu al urban areas, the following are some highlights: 28 Note that 15

the vertical scale differs from figure to figure, to accommodate the wide variation in PM light 16

extinction values. The pattern of results shown in Figures 3

- 13 is generally as expected in light 17

of emissions and climate differences among study areas. Except for the PM 2.5 soil component, 18

each of the components of PM light extinction is a major contributor to extreme light extinction 19

events at some time and location. In the West, carbonaceous PM 2.5 (i.e., organic mass and 20

elemental carbon), nitrate, and/or coarse mass (especially in Phoenix) tend to be most 21

responsible for these high haze hours. In the East it tends to be sulfate, nitrate, and the 22

carbonaceous PM 2.5 components that are the large contributors to PM light extinction. From the 23

sample period dates we can determine the seasonal variations in major components. Nitrate and 24

carbonaceous PM 2.5 contribute more to t he extreme light extinction periods during winter, while 25

sulfate contributes more in the summer. In many of the more northerly eastern urban areas, a 26

combination of sulfate and nitrate contributes to high light extinction year

- round. 27

**•** Tacoma has its highest light extinction hours in the colder months and primarily 29

due to carbonaceous PM 2.5 components. Because coarse PM was estimated by 30

applying a regional factor to the local PM 2.5 mass value, it would not have been 31

possible for the results to indicate a significant coarse PM contribution to PM 32

light extinction even if one existed at this site. However, from what EPA staff 33

42 Note that this binning approach may combine days with dissimilar extinction budgets into one bin because their

PM light extinction values are similar, obscuring some of the heterogeniety among hour s.January 2010 3

- 62 DRAFT

- Do Not Quote or Cite know of the area, it is unlikely that there is a significant con tribution from coarse 1

PM. 2

**•** Extreme haze hours in the two California urban areas are primarily caused by 3

high nitrate PM 2.5 , though Los Angeles has two extreme hours associated with 4

coarse PM and several other hours with moderate contribution from coarse PM. 5

Recall that estimates of coarse PM in Los Angeles are based in part on hourly 6

PM 10 measurements in Victorville, and may not represent coarse PM at the PM 2.5 7

mass and speciation site in Rubidoux or in the larger South Coast Basin. Also, 8

such high coarse PM values may indicate influence from exceptional winds in 9

Victorville. Figure B

- 1(b) in Appendix B shows that next several other days with 10

high daily maximum PM coarse concentrations had concentrations only about 60 11

percent or less than on the two days a ppearing in Figure 3

- 13; the fact that these 12

other days do not appear among the top 10 percent indicates that other 13

contributors to PM light extinction were low on those days. Whether or not the 14

PM 10 measurements in Victorville represent the PM 2.5 mass an d speciation site in 15

Rubidoux, it can be concluded that nitrate and to a lesser extent sulfate dominate 16

PM light extinction on the days likely to be above the CPLs . Because coarse PM 17

for Fresno was estimated by applying a regional factor to the local PM 2. 5 mass 18

value, it would not have been possible for the results to indicate a significant 19

coarse PM contribution to PM light extinction even if one existed at the Fresno 20

site. However, given the presence of agricultural operations and occasional high 21

winds in the San Joaquin Valley, the possibility of a significant contribution from 22

coarse PM in some hours cannot not be ruled out. 23

**•** Phoenix is unique among the 15 urban areas in having most of its extreme light 24

extinction caused by coarse PM, though there are a few top

- 10

- percent days 25

where the maximum hourly haze is dominated by carbonaceous, sulfate, and 26

nitrate PM 2.5 . Unlike for Los Angeles, this domination by coarse PM is no doubt 27

correct. PM 10 measurements for Phoenix come from a site near the center of t he 28

metro area, while the PM 2.5 measurements are from a more peripheral site (see 29

Appendix A) and are probably underestimates of PM 2.5 at the PM 10 measurement 30

site, this would have only a small effect on estimates of coarse PM. While it is 31

quite possible t hat the very highest coarse PM concentration (indicated in Figure 32

B

- 1(b) to be about 500 μg/m 3 ) reflects the effect of exceptional winds, and might 33

be excluded under the Exceptional Event rule, the next

- highest non

- excludable 34

values of PM light extinction almost certainly would also be dominated by coarse 35January 2010 3

- 63 DRAFT

- Do Not Quote or Cite PM concentrations in the range of 150 to 200 μg/m 3 and many might not be 1

excludable. 2

**•** Salt Lake City has extreme haze hours caused mostly by nitrate in the winter with 3

some periods with carbonaceous PM 2.5 b eing the major contributor. Because 4

coarse PM in Salt Lake City was estimated by applying a regional factor to the 5

local PM 2.5 mass value, it would not have been possible for the results to indicate 6

a significant coarse PM contribution to PM light extinct ion even if one existed at 7

this site. However, from what EPA staff know of the area, it is unlikely that there 8

is a frequent large contribution from coarse PM. The area typically has at most a 9

few days per year with measured 24

- hour average PM 10 as high as of 150

- 200 10

μg/m 3 . If this were all coarse PM, the contribution to 24

- hour average light 11

extinction would be 90

- 120 Mm - 1 , with the possibility of much higher hourly 12

contributions by coarse mass during these few days

. 13

**•** Dallas and Houston have high contrib utions to PM light extinction by sulfate 14

PM 2.5 , but Dallas has some winter hours with extreme PM light extinction with 15

substantial contributions from nitrate and organic carbonaceous material, while 16

Houston seems to have less contribution by nitrate. Beca use coarse PM in both 17

Dallas and Houston was estimated by applying a regional factor to the local PM 2.5 18

mass value, it would not have been possible for the results to indicate a significant 19

coarse PM contribution to PM light extinction even if one existed at this site. 20

However, from what EPA staff know of the areas, it is unlikely that there is a 21

frequent large contribution from coarse PM. Houston typically has at most a few 22

days per year with measured 24

- hour average PM 10 as high as of 150

- 200 μg/m 3

. 23

If this were all coarse PM, the contribution to 24

- hour average light extinction 24

would be 90

- 120 Mm - 1 . Dallas typically does not have PM 10 as high as 150 μg/m 3 25

**•** Sulfate in the summer and nitrate in the fall and winter are responsible for most of 26

the extreme l ight extinction at St. Louis, though there are several maximum 27

hourly periods where coarse PM is a major component. Recall that estimates of 28

coarse PM in St. Louis may be affected by a very local source (see Appendix A), 29

and thus the instances of high PM light extinction due to coarse PM may be 30

limited in geographic scope. 31

**•** Birmingham and Atlanta are similar in having sulfate year

- round and winter 32

carbonaceous PM 2.5 as major contributors to their extreme light extinction periods. 33

Coarse PM for Birmingham w as estimated using data from a single site, and the 34

estimates should be reasonably representative. Coarse PM for Atlanta was 35January 2010 3

- 64 DRAFT

- Do Not Quote or Cite estimated using data from two fairly close sites and the estimates should be 1

reasonably representative. 2

**•** Detroit has frequent large light extinction contributions from nitrate PM 2.5 , mostly 3

in the winter, as well as some contributions from sulfate PM 2.5 year

- round and 4

several fall and winter days with high contributions from carbonaceous PM 2.5

. 5

Coarse PM makes a notable contribution on a few days. Coarse PM for Detroit 6

was estimated using data from a single site near an automobile plant, and the 7

estimates should be reasonably representative for that site. 8

**•** The remaining four urban locations (Pittsburgh, Baltimore, Philadelphia, and New 9

York) are similar in that most of their extreme light extinction is from year

- round 10

combinations of sulfate and nitrate. New York also has some winter elemental 11

and organic carbonaceous contributions to its extreme light extinction. Recall that 12

the PM 2.5 site representing the New York area is actually in Elizabeth, NJ; 13

emissions from diesel trucks on nearby interstate highways and/or diesel engines 14

associated with port activities might explain the carbonaceous contributions. 15

Coarse PM for Baltimore an d Philadelphia was estimated using data from a single 16

site in each area, and the estimates should be reasonably representative. Coarse 17

PM for New York was estimated using data from two fairly distant sites and the 18

estimates may not be representative of bo th sites. Because coarse PM was 19

estimated for Pittsburgh by applying a regional factor to the local PM 2.5 mass 20

value, it would not have been possible for the results to indicate a significant 21

coarse PM contribution to PM light extinction even if one exist ed at this site. 22

However, exceed a nces of the PM 10 NAAQS are rare in Pittsburgh suggesting that 23

coarse PM likely is not a frequent significant contributor to PM light extinction. 24

3.5 POLICY RELEVANT BACK GROUND 25

Policy relevant background levels of PM light exti nction have been estimated for this 26

assessment by relying on outputs for the 2004 CMAQ run in which anthropogenic emission s in 27

the U.S., Canada, and Mexico were omitted, as described in the second draft ISA. Estimates of 28

PRB for PM light extinction were c alculated from modeled concentrations of PM 2.5 components 29

using the IMPROVE algorithm

. The necessary component concentrations were extracted from 30

the CMAQ output files, as they were not summarized in the final ISA

. More detail is p rovided in 31

Appendix C

. 32

It is also necessary to have estimates of PRB for PM 10

- 2.5 , as input to the IMPROVE 33

algorithm. The final ISA for this review does not present any new information on this subject. 34

The approach used in the two previous reviews was to present the historical range of annual 35January 2010 3

- 65 DRAFT

- Do Not Quote or Cite means of PM 10

- 2.5 concentrations from IMPROVE monitoring sites selected as being least 1

influenced by anthropogenic emissions (US EPA, 2004, Table 3E

- 1). For this assessment, EPA 2

staff estimated PRB for PM 10

- 2.5 using a contour map based o n average 2000

- 2004 PM 10

- 2.5 3

concentrations from all IMPROVE monitoring sites, found in a recent report from the 4

IMPROVE program (DeBell, 2006). More detail is p rovided in Appendix C

. 5

The outcome of the procedures for estimating PRB consists of hour

- speci fic estimates of 6

PRB for PM 2.5 components and annual average estimates for PRB for PM 10

- 2.5

. Thus, hour

7

specific estimates of PM light extinction are possible, using the same hour

- specific relative 8

humidity values as for the estimate of current conditions PM light extinction. 9

The PRB estimates play a role in this assessment (other than allowing confirmation of the 10

obvious fact that current conditions PM light extinction values are generally well above PRB 11

conditions) only in the estimation of “what if” sce narios representing compliance with alternative 12

NAAQS scenarios based on PM light extinction. This role is described in section 4.1.4. 13

14

15January 2010 3

- 66 DRAFT

- Do Not Quote or Cite Figure 3

- 13 Light Extinction Budgets for the Top 10 Percent of Da ys for Maximum 1

Daily 1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours 2

3



### Tacoma 4

5

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 6

7

8 (b) Top 10 Percent of Individual Daylight Hours (Aggregated) 9

10

11January 2010 3

- 67 DRAFT

- Do Not Quote or Cite Figu re 3

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

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4



### Fresno 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Percent of Individual Daylight Hours (Aggregated) 10

11

12

13January 2010 3

- 68 DRAFT

- Do Not Quote or Cite Figure 3

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

1

- hour PM light Extinction and for the Top 10 Percent of Indivi dual Daylight Hours, 2

continued 3

4


### Los Angeles 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9 (b) Top 10 Percent of Individual Daylight Hours (Aggregated) 10

11

12January 2010 3

- 69 DRAFT

- Do Not Quote or Cite Figure 3

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

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4



### Phoenix 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Percent of Individual Daylight H ours (Aggregated) 10

11

12January 2010 3

- 70 DRAFT

- Do Not Quote or Cite Figure 3

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

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4



### Salt Lake City 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Percent of Individual Daylight Hours (Aggregated) 10

11

12

13January 2010 3

- 71 DRAFT

- Do Not Quote or Cite Figure 3

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

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4



### Dallas 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Percent of Individual Daylight Hours (Aggregated) 10

11

12January 2010 3

- 72 DRAFT

- Do Not Quote or Cite Figure 3

- 13 . Light Extinct ion Budgets for the Top 10 Percent of Days for Maximum Daily 1

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4



### Houston 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Perce nt of Individual Daylight Hours (Aggregated) 10

11

12January 2010 3

- 73 DRAFT

- Do Not Quote or Cite Figure 3

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

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4



### St. Loui s 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9 (b) Top 10 Percent of Individual Daylight Hours (Aggregated) 10

11

12

13January 2010 3

- 74 DRAFT

- Do Not Quote or Cite Figure 3

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

1

- hour P M light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4


### Birmingham 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Percent of Individual Daylight Hours (Aggregated) 10

11

12January 2010 3

- 75 DRAFT

- Do Not Quote or Cite Figure 3

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

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4



### Atlanta 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Exti nction 7

8

9

(b) Top 10 Percent of Individual Daylight Hours (Aggregated) 10

11

12 13January 2010 3

- 76 DRAFT

- Do Not Quote or Cite Figure 3

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

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4



### Detroit 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Percent of Individual Daylight Hours (Aggregated) 10

11

12

13January 2010 3

- 77 DRAFT

- Do Not Quote or Cite Figure 3

- 13 . Light Extinction Budgets for the Top 10 Percent of Days f or Maximum Daily 1

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4



### Baltimore 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Percent of Individual Daylight Hours (Aggregate d) 10

11 12January 2010 3

- 78 DRAFT

- Do Not Quote or Cite Figure 3

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

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4


### Pittsburgh 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Percent of Individual Daylight Hours (Aggregated) 10

11

12

13January 2010 3

- 79 DRAFT

- Do Not Quote or Cite Figure 3

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

1

- hour PM light Extinction and for the Top 10 Per cent of Individual Daylight Hours, 2

continued 3

4


### Philadelphia 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Percent of Individual Daylight Hours (Aggregated) 10

11 12January 2010 3

- 80 DRAFT

- Do Not Quote or Cite Figure 3

- 13 . Light Extinction Budgets f or the Top 10 Percent of Days for Maximum Daily 1

1

- hour PM light Extinction and for the Top 10 Percent of Individual Daylight Hours, 2

continued 3

4



### New York 5

6

(a) Top 10 Percent of Days for Maximum Daily 1

- hour PM light Extinction 7

8

9

(b) Top 10 Percent of Indivi dual Daylight Hours (Aggregated) 10

11January 2010 4

- 1 DRAFT

– Do Note Quote or Cite 

### 4 PM LIGHT EXTINCTION UNDER “WHAT IF” CONDITIONS OF 1



### JUST MEETING SPECIFI C ALTERNATIVE SECOND ARY 2



### NAAQS 3

4.1 ALTERNATIVE SECONDAR Y NAAQS BASED ON MEASURED PM 4

LIGHT EXTINCTION AS THE INDICATOR 5

4.1.1 Indicator and Monitoring Method 6

T he indicator considered in this section is PM light extinction , assumed to be measured 7

by a continuous instrument, or instrument pair, capable of reporting both light scattering and 8

light absorption

. For example, the measurement method could be an Aethalome ter® or similar 9

instrument for measuring light absorption paired with a nephelometer, with both instruments 10

using a PM 10 inlet so that PM light extinction due to PM 2.5 and PM 10

- 2.5 combined w ould be 11

measured. A measurement of light absorption using an Aet halometer® or similar instrument 12

based on optical analysis of collected PM would not be affected by ambient NO 2 concentrations. 13

Also, if a nephelometer is calibrated to zero using filtered or zero air and spanned using a light

14

scattering span gas with a w ell characterized scattering coefficient, such as carbon dioxide, 15

SUVA 134A, Freon 12, or Freon 22, then subsequent measurements of light extinction would 16

reflect PM light scattering, without including the effect of Rayleigh scattering. 17

4.1.2 Alternative Seconda ry NAAQS Scenarios based on Measured PM light 18

extinction 19

Eighteen alternative NAAQS scenarios presented in Table 4

- 1 are analyzed in this 20

section. Nine are based on daily maximum daylight 1

- hour PM light extinction and nine on light 21

extinction in all hour s without the restriction to daily maxima. Within each set of nine, the 22

scenarios are ordered from least to most stringent. 23

24

25January 2010 4

- 2 DRAFT

– Do Note Quote or Cite Table 4

- 1 . Alternative Secondary NAAQS Scenarios for PM Light Extinction 1

Level Annual

Percentile Form

Scenarios Based on Daily Maximum Daylight 1

- hour PM Light Extinction

(a) 191 Mm - 1 90 3

- year average of percentile value

(b) 191 Mm - 1 95 3

- year average of percentile value

(c) 191 Mm - 1 98 3

- year average of percentile value

(d) 1 12 Mm - 1 90 3

- year average of percentile value

(e) 112 Mm - 1 95 3

- year average of percentile value

(f) 112 Mm - 1 98 3

- year average of percentile value

(g) 64 Mm - 1 90 3

- year average of percentile value

(h) 64 Mm - 1 95 3

- year average of percentile value

(i) 64 Mm - 1 98 3

- year average of percentile value

Scenarios Based on Daylight 1

- hour PM Light Extinction (All Daylight Hours)

(j) 191 Mm - 1 90 3

- year average of percentile value

(k) 191 Mm - 1 95 3

- year average of percentile value

(l) 191 Mm - 1 98 3

- year aver age 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 av erage of percentile value

(r) 64 Mm - 1 98 3

- year average of percentile value

2

4.1.3 Monitoring Site Considerations for Alternative Secondary NAAQS B ased on 3

Measured PM light extinction 4

It is useful to think ahead tentatively to monitor siting aspects of NAAQS 5

i mplementation, so that the results presented in the remainder of this chapter based on the 15 6

specific study sites can be better interpreted in terms of how well they might represent later 7

findings if these (and other) areas were to deploy PM light extinct ion measurement instruments 8

as part of implementing a secondary NAAQS

. 9

It is most likely that the instruments that would be used to implement a secondary 10

NAAQS with a n indicator based on measured PM light extinction will be “closed path” 11

instruments that r eact only to air quality in their immediate vicinity

. However, light paths that 12

matter to perceived visual air quality are likely to be several kilometers long

. Therefore, a 13

monitoring site should be at least neighborhood in scale, i.e., its relationship to emission sources 14

and transport should be such that measurements made at the site reasonably reflect 15

concentrations in an area surrounding the site of at least about 0.5 to 4 kilometers in diameter. 16

It would be logical to require that in any urban are a for which light extinction monitoring 17

is deemed a necessary requirement, at least one monitoring site would be placed in an area 18January 2010 4

- 3 DRAFT

– Do Note Quote or Cite expected to have the maximum PM light extinction conditions, subject to the above scale of 1

representation consideration and p ossibly also subject to the condition that the site be in an area 2

(or reasonably represent such an area) where valued urban scenes are able to be perceived by 3

people

. i.e., that the site is “population oriented.” It is difficult to imagine a neighborhood scale 4

monitoring location within the census

- defined urbanized area of an urban area which would not 5

be “population oriented” for purposes of visual air quality, as “neighborhood” size land areas 6

typically would have residents, workers, etc. somewhere with in them during daylight hours. 7

With regard to the monitoring sites used in this assessment, all are reported to be, or 8

appear to be, neighborhood or larger scale, and all are in areas where people are present during 9

daylight hours

. The sites in Detroit (D earborn) and New York (Elizabeth, NJ) are, however, 10

rather close to an industrial source and a major interstate high way interchange/ turnpike exit, 11

respectively. Significantly, most of the study sites are not the highest PM 2.5 concentration site in 12

their u rban area, so a “what if” scenario that manipulates the “current conditions” at these sites to 13

“just meet” an alternative secondary NAAQS might implicitly leave other parts of their urban 14

areas with PM light extinction above the NAAQS. 15

Probe height is anot her consideration. For purposes of a secondary NAAQS aimed to 16

protect visibility, monitoring probes logically should be placed so that the sampled air is 17

reasonably representative of the air along the sight path to the valued s cene , which may be 18

different than the probe heights of the monitors that provided data for this assessment. 43

4.1.4 Approach to Modeling “What If” Conditions for Alternative Secondary 21

NAAQS B ased on Measured PM Light Extinction 22 We 19

have not yet studied this issue further. 20

Before modeling “wh at if” conditions, EPA staff augmented the data set described in 23

Table 4 so that the sets of study days for Houston and Phoenix were seasonally balanced despite 24

the lack of actual monitoring data for one quarter in each city

. For the first quarter of 2005 in 25

Phoenix, we substituted the available 12 days from the first quarter of 2006. For the fourth 26

quarter of 2007 in Houston, we substitute d 13 randomly drawn days from the fourth quarters of 27

2005 and 2006. 28

Also, Tacoma (originally) and Phoenix (after this augmentation) each have only two 29

calendar years of suitable data, while the form of the alternative NAAQS scenarios requires the 30

averaging of the 90 th

, 95 th , or 98 th percentile values from three years. In Tacoma and Phoenix, 31

for every step in the analys is at which a design value is used as an input or reported as an output, 32

we averaged the percentile values from the only two available years. 33

43 Probe height influence on measured PM light extinction might alternatively be taken into consideration in setting

the level of a NAAQS.January 2010 4

- 4 DRAFT

– Do Note Quote or Cite We modeled daylight and daily maximum daylight 1

- hour PM light extinction under each 1

of the “what if” scenarios (i n which each study area “just meets” one of the 18 alternative 2

secondary NAAQS listed in section 4.1.2) via the following steps

. These steps are essentially the 3

same as the “proportional rollback” steps that have been used in the health risk assessment 4

mo deling of “what if” conditions in several previous NAAQS reviews for PM and other criteria 5

pollutants. The steps are described here for the nine scenarios based on daily maximum daylight 6

1

- hour PM light extinction; similar steps were followed for the nine scenarios based on 7

percentiles of all daylight 1

- hour PM light

- extinction. The referenced tables present results for 8

both sets of scenarios. 9

10

1. After excluding hours with relative humidity greater than 90 percent, i dentify the 11

appropriate percentile (90 th

, 95 th , or 98 th ) daily maximum daylight 1

- hour light 12

extinction value in each year, noting the day and hour each occurred, and average 13

these values across years to calculate the light extinction design value for each site 14

consistent with the percentile form of the NAAQS scenario. 44

44 Annual percentile values were picked from the set of day

- specific or hour

- specific estimates according to the sam e

scheme as used for the current 24

- hour secondary PM2.5 standard, as explained in section 4.5(a) of 40 CFR 50

appendix N. For example, if there are 60 daily maximum values in a year, the second highest value is the 98 th

percentile value. Note that this differs from the algorithm used by some spreadsheet and other statistical programs,

which may interpolate between sample values. Also, this is a different approach than in the Regional Haze program,

in which conditions in the best and worst 20 percent of days are averaged together, rather than focusing on

conditions on the specific day at the 80 th and 20 th percentile points. The three resulting 15

design values for each area (for the 90 th , 95 th , and 98 th percentile forms) are shown in 16

Table 4

- 2 . (Note that in a number of cases, which are identified by a footnote , the 17

study area meets one or more of the NAAQS scenario under current conditions. In 18

these cases, the “current conditions” PM light extinction values are not adjusted, i.e., 19

PM light extinction values are never “rolled up.”) Notice that the design values for 20

the 90 th percentile maximum daily 1

- hour for most cities are generally similar to the 21

design values for the 98 th percentile of all daylight hours. On average there are about 22

ten hours defined as daylight per day, so if the light extinction were randomly 23

distributed among the daylight hours and days, the 90 th percentile maximum daily 1

- 24

hour would correspond to the 99 th percentile of all hours; the fact that the point of 25

rough equivalency is the 98 th percentile indicates a tendency for hours with higher 26

light extinction to cluster together in the same day. Figure 4

- 1 presents two scatter 27

plots that relate the design values based on daily maximum 1

- hour PM light 28

extinction values and the design values based on all daylight 1

- hour light extinction 29

values. In Panel A, design values for the daily maximum and all hours forms are 30

paired by the defining percentile, and colors are used to distinguish the 90 th , 95 th , and 31

98 th percentile statistical forms. It appears from Panel A that the design values for the 32

two approaches to defining the NAAQS scena rios are highly correlated but with the 33

all hours approach resulting in numerically lower design values than the daily 34

maximum approach. The correlation breaks down for the 98 th percentile form for the 35

few study areas with the highest levels of PM light e xtinction. Panel B compares the 36January 2010 4

- 5 DRAFT

– Do Note Quote or Cite 90 th percentile design values based on daily maximum PM light extinction with the 1

90 th , 95 th , and 98 th percentile design values based on all daylight hours PM light 2

extinction

. T here is close agreement between the 90 th per centile design values based 3

on daily maximum values and the 98 th percentile design value based on all daylight 4

hours. 5

6

Table 4

- 2 . Current Conditions PM light extinction design values for the study areas. 7

Stud y Area Design Value for

90 th Percentile

Form (Mm

- 1 ) Design Value for 95 th

Percentile Form (Mm

- 1 ) Design Value for

9 8 th Percentile Form

(Mm

- 1

Design Values Based on Daily Maximum Daylight 1

- hour PM Light Extinction

Tacoma 140 * 157 * 210

Fresno 338 463 533

Los Angeles 469 554 624

Phoenix 105 * 144 * 266

Salt Lake City 164 * 252 410

Dallas 183 * 239 302

Houston 194 234 291

St. Louis 307 381 467

Birmingham 357 483 562

Atlanta 249 288 331

Detroit 310 473 644

Pittsburgh 278 313 364

Baltimore 246 286 328

Philadelphi a 286 339 393

New Yor k 306 355 457

Design Values Based on Daylight 1

- hour PM Light Extinction (All Daylight Hours)

Tacoma 76* 106* 136*

Fresno 190* 266 373

Los Angeles 266 349 451

Phoenix 68* 79* 94*

Salt Lake City 93* 142* 225

Dallas 1 13* 143* 188*

Houston 105* 128* 171*

St. Louis 194 235 290

Birmingham 173* 227 309

Atlanta 166* 195 238

Detroit 212 251 315

Pittsburgh 167* 209 264

Baltimore 172* 227 265

Philadelphia 183* 222 279

New York 186* 244 300

* This design value meets o ne or more of the NAAQS scenarios based on PM light extinction.

8

9January 2010 4

- 6 DRAFT

– Do Note Quote or Cite Figure 4

- 1 Comparison of Daily Max and All Daylight Hour Design Values 1

(A) Comparison of design values matched by percentile form 2

3

0 100 200 300 400 500 600 700

0 100 200 300 400 500 600 700

Daily Max Daylight Hour Design Value (Mm-1)All Daylight Hours Design Value (Mm-1)

90th Percentile

95th Percentile

98th Percentile

0 100 200 300 400 500 600 700

0 100 200 300 400 500 600 700

Daily Max Daylight Hour Design Value (Mm-1)All Daylight Hours Design Value (Mm-1)

90th Percentile

95th Percentile

98th Percentile

4

5

(B) Co mparison of 90 th percentile daily maximum design values and and 90 th , 95 th

, 6

and 98 th percentile all daylight hours design values 7

8



### Comparison of 90th percentile Daily Max Form to All Hours

Forms

0 100 200 300 400 500

0 100 200 300 400 500

Design Value for 90th Percentile Daily Max (Mm-1)All Hours Design Value(Mm-1) 90th percentile

95th percentile

98th percentile

9January 2010 4

- 7 DRAFT

– Do Note Quote or Cite 2. Using the same days and hours, find the three (or two, in the case of Phoenix and 1

Houston for which there were only two ye ar s of suitable data available) 2

corresponding values of PRB PM light extinction , and average these values across 3

years to calculate the PRB portion of the design value. 4

5

3. Subtract the value from step 2 from the value from step 1, to determine the non

- PRB 6

port ion of the design value. 7

8

4. Calculate the percentage reduction required in non

- PRB PM light extinction in order 9

to reduce the design value to the PM light extinction level that defines the NAAQS 10

scenario, using the following equation: 11

12

Percent reduction requ ired = 1

– (NAAQS level

– PRB portion of the design value)/(non

- PRB 13

portion of the design value) 14

15

The percentage reductions determined in step 4 are shown in Table 4

- 3. Figure 4

- 2 16

presents them graphically in the form of a scatter plot, comparing the requ ired 17

reductions for scenarios based on daily maximum 1

- hour daylight PM light extinction 18

values to scenarios with the same level and percentile form but based on all daylight 19

hours 1

- hour PM light extinction values. For the NAAQS scenarios involving highe r 20

levels and lower percentile forms, there are some notable differences in the 21

percentage reductions required for some area to attain. As was the case for the design 22

values, notice in Table 4

- 3 that there is generally similar percentage reductions for 23

eac h city and level for the 90 th percentile maximum daily and 98 th percentile of all 24

daylight hours. 25

26

As already stated, if the study area is meeting a NAAQS scenario in the current 27

conditions case, no adjustments were made to represent the “just meeting” cas e. In 28

effect, negative values for the percent reduction required to meet the NAAQS 29

scenario calculated by the above equation were re

- set to zero. 30

1. 31

5. Turning to the entire set of day/hour

- specific actual and PRB daylight PM light 32

extinction values for the th ree (or two) year period, determine the non

- PRB portion of 33

PM light extinction in an hour , reduce it by the percentage determined in step 4, and 34

add back in the PRB PM light extinction. The result is the “ just meets ” PM light 35

extinction value for that day and hour. 36

37

Note that in these steps, it is not necessary to make any explicit or implicit assumption 38

about what PM components would be reduced to allow the area to meet the NAAQS scenario, as 39

the NAAQS scenario’s target design value is itself in units of light extinction. One path to 40

meeting a NAAQS scenario would be to reduce each of the five PM 2.5 components (and thus the 41

annual and 24

- hour design values shown in Table 3

- 2) and PM 10

- 2.5 by the calculated “percent 42

reduction required”. However, a lesser reduction in one or more of the six PM concentrations 43

could be offset by a greater reduction in or more of the remaining concentrations. Thus, it is not 44January 2010 4

- 8 DRAFT

– Do Note Quote or Cite possible to associate unique values of annual average and 24

- hour average PM 2.5 with the “just 1

meeting ” NAAQS scenarios reported in Table 4

- 3. 2

3January 2010 4

- 9 DRAFT

– Do Note Quote or Cite Table 4

- 3 . Percentage reductions in non

- PRB light extinction required to “just meet” the 1

NAAQS scenarios based on measured light extinction. 2

NAAQS Scenarios Based o n Daily Maximum 1

- hour Daylight PM light extinction, Average

of Percentile Value Over Three Years

(a) (b) (c) (d) (e) (f) (g) (h) (i)

Level (Mm

- 1 ) 191 191 191 112 112 112 64 64 64

Percentile

Form 90 th 95 th 98th 90 th 95 th 98th 90 th 95 th 98th

Area Perce ntage Reduction Required in

Non

- PRB PM light extinction

Tacoma 0 0 10 22 34 53 61 70 80

Fresno 45 60 65 69 77 81 84 88 90

Los Angeles 61 67 71 78 82 84 88 90 91

Phoenix 0 0 0 0 23 60 42 59 78

Salt Lake City 0 25 54 33 57 74 63 76 85

Dallas 0 21 39 42 55 66 70 76 83

Houston 2 20 36 45 57 64 71 79 81

St. Louis 39 51 61 66 73 78 82 86 89

Birmingham 48 61 67 71 78 82 85 88 90

Atlanta 25 35 44 58 64 69 78 81 84

Detroit 39 61 71 66 78 84 82 88 91

Pittsburgh 32 40 48 61 66 70 79 81 84

Baltimore 23 35 43 57 64 68 77 81 83

Philadelphia 34 45 52 63 68 73 80 83 85

New York 39 48 59 65 70 77 81 84 88

NAAQS Scenarios Based on 1

- hour Daylight PM light extinction, Average of Percentile

Value Over Three Years (All Daylight Hours)

(j) (k) (l) (m) (n) (o) ( p) (q) (r)

Level (Mm

- 1) 191 191 191 112 112 112 64 64 64

Percentile

Form 90th 95th 98th 90th 95th 98th 90th 95th 98th

Area Percentage Reduction Required in

Non

- PRB PM light extinction

Tacoma 0 0 0 0 0 22 19 46 67

Fresno 0 29 50 43 60 72 70 79 85

Los Angeles 29 47 59 60 70 77 78 84 88

Phoenix 0 0 0 0 0 0 6 20 34

Salt Lake City 0 0 15 0 22 51 33 56 73

Dallas 0 0 0 1 23 42 46 59 69

Houston 0 0 0 0 13 38 42 53 70

St. Louis 1 19 35 44 54 63 70 76 80

Birmingham 0 17 39 37 53 66 66 75 82

Atlanta 0 2 2 1 34 45 55 64 71 77

Detroit 10 25 40 49 57 66 73 77 82

Pittsburgh 0 9 28 34 48 59 64 71 78

Baltimore 0 16 29 37 52 60 66 74 79

Philadelphia 0 15 32 41 52 62 68 74 79

New York 0 22 37 42 56 64 69 76 81

3

4January 2010 4

- 10 DRAFT

– Do Note Quote or Cite Figure 4

- 2 Comparison of Required Percentage Reductions in Non

- PRB PM Light 1

Extinction Needed to Meet NAAQS Scenarios 2

0 10 20 30 40 50 60 70 80 90 100

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

3

4.2 ALTERNATIVE SECONDARY PM 2.5 NAAQS BASED ON ANNUAL AND 4

24

- HOUR PM 2.5 MASS 5

4.2.1 Secondary NAAQS Scenarios Based on Annual and 24

- hour PM 2.5 Mass 6

In this second draft version of the assessment, EPA staff have modeled two “what if” 7

scenarios based on the same indicators and averaging periods as define the current secondary 8

PM 2.5 NAAQS: 9

**•** 15 μg/m 3 weighted annual average PM 2.5 concentration and 35 μg/m 3 24

- hour average 10

PM 2.5 concentration with a 98 th percentile form, both averaged over three years . These 11

are the current secondary NAAQS for PM 2.5

. 12

**•** 12 μg/m 3 weighted annual average PM 2.5 concentration and 25 μg/m 3 24

- hour average 13

PM 2.5 concentration with a 98 th percentile form, both averaged over three years. 14

These are the highest and lowest alternative NAAQS scenarios considered in the health 15

risk assessment , and therefore encompass the full range of alternative primary PM 2.5 NAAQS 16

being analyzed by EPA staff. 17

18January 2010 4

- 11 DRAFT

– Do Note Quote or Cite 4.2.2 Approach to Modeling Conditions If Secondary PM 2.5 NAAQS Based on 1

Annual and 24

- hour PM 2.5 Mass Were Just Met 2

Because these NAAQS scenarios are based on PM 2.5 mass as the indicator, rather than 3

light extinction, the steps needed to model “what if” conditions are somewhat different, and 4

involve explicit consideration of changes in PM 2.5 components. 5

6

1. Apply p roportional rollback to all the PM 2.5 monitoring sites in each study area, 7

taking into account PRB PM 2.5 mass, to “just meet” the NAAQS scenar io for the area 8

as a whole, not just at the visibility assessment study site. The health risk assessment 9

document describes this procedure in detail . The degree of rollback is controlled by 10

the highest annual or 24

- hour design value, which in most study areas is from a site 11

other than the site used in this visibility assessment. The relevant result from this 12

analysis is the percentage reduction in non

- PRB PM 2.5 mass need to “just meet” the 13

NAAQS scenario, for each study area. These percentage reductions are shown in 14

Table 4

- 4. Note that Phoenix and Salt Lake City meet the 15/35 NAAQS scenario 15

under current conditions, and require no reduction. PM 2.5 levels in these two cities 16

were not “rolled up.” 17

18

2. For each day and hour for each PM 2.5 component, subtra ct the PRB concentration 19

from the current conditions concentration, to determine the non

- PRB portion of the 20

current conditions concentration. 21

22

3. Apply the percentage reduction from step 1 to the non

- PRB portion of each of the 23

five PM 2.5 components. Add back the PRB portion of the component. 24

25

4. Re

- apply the IMPROVE algorithm, using the reduced PM 2.5 component 26

concentrations, the current conditions PM 10

- 2.5 concentration for the day and hour, and 27

relative humidity for the day and hour

. Include the term for Rayl eigh scattering. 28

29

The results of these steps are shown in Table 4

- 6. 30

31January 2010 4

- 12 DRAFT

– Do Note Quote or Cite Table 4

- 4 . Percentage reductions required in non

- PRB PM 2.5 mass to “just meet” NAAQS 1

scenarios based on annual and 24

- hour PM 2.5 mass 2

Stud y 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.

3

4.3 RESULTS FOR “JUST MEETING” E A CH ALTERNATIVE SECON DARY 4

NAAQS SCENARIO 5

The modeling described in section s 4.1 and 4.2 resulted in estimates of PM light 6

extinction for each day and hour in each study area , for each NAAQS scenario . Four summaries 7

of these conditions are presented here. 8

F igure 4

- 3 shows two box

- and

- whisker plots of daily maximum daylight 1

- hour PM light 9

extinction . The top panel (a) is for the single illustrative scenario of a NAAQS based on daily 10

maximum daylight 1

- hour light extinction with a level of 112 Mm - 1 and a 90 t h percentile form, 11

which was chosen for this illustration because it is approximately mid

- way among the nine 12

scenarios based on daily maximum light extinction in terms of stringency. 45

45 Plots of the distribution of daily maximum light extinction for all 18 NAAQS scenarios based on daily maximum

light extinction, and of individual hourly light extinction for all 18 NAAQS scenarios based on individual daylight

hours, are provided in Appendix F. The bottom panel 13

(b) is for the scenario of meeting the current seconda ry PM 2.5 NAAQS of 15 μg/m 3 for the annual 14

average and 35 μg/m 3 for the 98 th percentile 24

- hour average. A notable feature of this 15

comparison is that in the top panel, all the study areas have a similar distribution of the daily 16January 2010 4

- 13 DRAFT

– Do Note Quote or Cite maximum daylight 1

- hour PM light extinction , while in the bott om panel this is not the case. 1

This is expected, since a NAAQS based on a measured daily maximum PM light extinction 2

indicator will of course result in areas achieving similar daily maximum PM light extinction 3

patterns o nce each area reaches a “just meets” condition

. I n areas with generally higher relative 4

humidity conditions, concentrations of PM 2.5 components and/or PM 10

- 2.5 would need to be lower 5

to achieve the “just meet” condition

. In contrast, in the NAAQS scenari o represented by the 6

bottom panel, concentrations of PM 2.5 mass will be similar across areas, but concentrations of 7

PM 2.5 components may not be, and levels of PM light extinction will not be similar in areas with 8

dissimilar levels of relative humidity. Th e specific differences among areas in the bottom panel 9

are generally as expected, with the drier study areas having lower levels of PM light extinction

. 10

Tables 4

- 5 and 4

- 6 summarize the “just meets” conditions in the NAAQS scenarios in 11

terms of the PM ligh t extinction design value s when just meeting. Table 4

- 5 addresses the 18 12

scenarios of NAAQS based on measured PM light extinction. When an area just meets a 13

NAAQS scenario, its design value in principle should exactly equal the NAAQS level, so 14

preparatio n of this table serves as a check against calculation errors. Note that the design values 15

in Table 4

- 5, resulting from the rollback steps described in section 4.1.4, in some cases do not 16

exactly equal the assumed level of the NAAQS, although all are quite close. Closer investigation 17

has revealed that t his is mostly a result of hours switching their ranking in the rollback process

. 18

Hours can switch rank because the level of PRB PM light extinction varies with each hour, so a 19

uniform percentage reduction i n non

- PRB light extinction (step 5) can result in non

- uniform 20

percentage reductions in actual PM light extinction; a lower ranking hour can thereby move up in 21

the post

- rollback ranking. In principle, rollback could be iterated to exactly achieve a design 22

value equal to the level of the NAAQS for each scenario

. However, the discrepancies indicated 23

in Table 4

- 5 were judged too small to justify iterative rollback, given other uncertainties in the 24

analysis

. 25

Table 4

- 6 addresses the two scenarios of NAAQS based on PM 2.5 mass, with PM light 26

extinction design values shown for the 90 th , 95 th , and 98 th percentile forms. 27

Table 4

- 7 summarizes all 20 scenarios in terms of the percentage of days (across 2005 to 28

2007, but after rollback) in which the daily maximum dayl ight 1

- hour PM light extinction under 29

“just meeting” conditions exceeds each of the CPLs. Part A of the table applies to NAAQS 30

scenarios based on daily maximum 1

- hour PM light extinction values. Part B of the table applies 31

to the scenarios based on 1

- hou r PM light extinction values during all daylight hours. Note that 32

the reported percentages in both Part A and Part B is the percentage of days in which the daily 33

maximum daylight 1

- hour PM light extinction under “just meeting” conditions exceeds each of 34

t he CPLs; this allows comparison of the “effectiveness” of the two NAAQS approaches using a 35

consistent metric. (The 15/35 and 12/25 NAAQS scenarios are the same in Part A and Part B, 36January 2010 4

- 14 DRAFT

– Do Note Quote or Cite and are repeated only for convenience in making comparisons.) Hours with relative humidity 1

above 90 percent have been excluded from consideration, consistent with the definition of the 2

NAAQS scenarios. Also shown at the bottom of the table in each column representing a 3

NAAQS scenario is the average of these percentages of tim e across the 15 study areas (this is the 4

simple column average, not weighted by the number of days available in each area). 5

Comparisons of these percentages allows a rough indication of how the two scenarios of a 6

NAAQS based on PM 2.5 mass compare to the o ther 18 scenarios in terms of protecting visual air 7

quality. Notice that the most restrictive of the two NAAQS scenarios based on PM 2.5 mass 8

would reduce the projected 1

- hour maximum daily light extinction above the least restrictive 9

CPL (191Mm - 1 ) to less than 10 percent of the time for most of the urban areas (only L.A., St. 10

Louis, and Birmingham have values above 10 percent )

. However at the current PM NAAQS 11

level (i.e., 15/35) all of the eastern urban areas and Los Angeles exceed the least restrictive C PL 12

more than 10% of the time. Comparison of Parts A and B of Figure 4

- 7 indicates that basing a 13

PM light extinction NAAQS scenario on daily maximum 1

- hour light extinction has a lower 14

percentage in excess of the 1

- hour daily maximum versus the NAAQS scena rio based on all 15

daylight hours light extinction for a given level and percentile form of the NAAQS. This is 16

consistent with the results presented in Table 4

- 2 and Figure 4

- 1, which indicated that current 17

conditions design values are generally lower for t he all hours approach. Again there is near 18

equivalence between the 90 th percentile daily maximum and 98 th percentile all daylight hours in 19

terms of the percent of days exceeding the daily maximum CPL values in Table 4

- 7. 20

21

22January 2010 4

- 15 DRAFT

– Do Note Quote or Cite Figure 4

- 3 Distributions of daily maximum daylight 1

- hour PM light extinction under two “just meeting” secondary NAAQS

scenarios (excluding hours with relative humidity greater than 90 percent)

( a ) Secondary NAAQS based on daily maximum daylight 1

- hour PM light extinction with a level of 112 Mm

- 1 and a 90 th

percentile formJanuary 2010 4

- 16 DRAFT

– Do Note Quote or Cite Figure 4

- 3 . Distributions of daily maximum daylight 1

- hour PM light extinction under two “just meeting” secondary NAAQS

scenarios (excluding hour s with relative humidity greater than 90 percent), continued

( b ) Secondary NAAQS of 15 μg/m 3 for the annual average and 35 μg/m 3 for the 98 th percentile 24

- hour averageJanuary 2010 4

- 17 DRAFT

– Do Note Quote or Cite Table 4

- 5

. PM light extinction des ign values for “just meeting” secondary NAAQS

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

greater than 90 percent)

Secondary NAAQS Scenario s Based on Daily Maximum

(a) (b) (c) (d) (e) (f) (g) (h) (i)

Level ( M m - 1 ) 191 191 191 112 112 112 64 64 64

Percentile Form 90 th 95 th 98th 90 th 95 th 98th 90 th 95 th 98th

PM light extinction Design Value

(based on same percentile form as the NAAQS scenario)

Tacoma, WA 140 157 191 112 112 108 66 70 60

Fresno, CA 191 191 19 1 112 112 112 64 64 64

Los Angeles, CA 191 191 191 112 112 112 65 64 64

Phoenix, AZ 105 144 185 105 112 112 64 64 64

Salt Lake City, UT 164 191 191 112 112 112 64 64 64

Dallas, TX 183 191 191 113 113 112 64 66 66

Houston, TX 191 191 191 115 112 112 67 61 67

St. Louis, IL 191 191 191 113 112 112 65 64 64

Birmingham, AL 191 192 191 113 114 112 64 66 64

Atlanta, GA 191 191 191 112 111 112 64 63 65

Detroit, MI 191 191 191 112 112 112 64 64 65

Pittsburgh, PA 191 191 191 112 112 112 64 64 64

Baltimore, MD 191 191 191 111 112 112 63 64 65

Philadelphia, PA 191 191 191 112 112 112 65 64 64

New York, NY 192 191 191 113 112 112 65 64 64

Secondary NAAQS Scenario s Based on All Daylight Hours

(j) (k) (l) (m) (n) (o) (p) (q) (r)

Level ( Mm

- 1 ) 191 191 191 1 12 112 112 64 64 64

Percentile Form 90 th 95 th 98th 90 th 95 th 98th 90 th 95 th 98th

PM light extinction Design Value

(based on same percentile form as the NAAQS scenario)

Tacoma, WA 76 106 136 76 106 112 63 64 59

Fresno, CA 190 191 191 113 112 112 65 64 64

Los Angeles, CA 192 191 192 113 112 112 66 64 64

Phoenix, AZ 68 79 94 68 79 94 64 64 64

Salt Lake City, UT 93 142 191 93 112 112 64 64 64

Dallas, TX 113 143 188 112 112 113 66 64 67

Houston, TX 105 128 171 105 113 110 65 66 61

St. Louis, IL 191 19 1 191 113 112 111 65 65 65

Birmingham, AL 173 191 192 113 112 113 65 64 65

Atlanta, GA 166 191 192 113 111 113 66 63 65

Detroit, MI 191 191 191 112 113 112 64 65 64

Pittsburgh, PA 167 191 191 113 113 112 65 66 64

Baltimore, MD 172 191 191 112 112 112 64 65 64

Philadelphia, PA 183 191 191 112 112 113 64 64 65

New York, NY 186 191 191 112 112 113 64 66 66January 2010 4

- 18 DRAFT

– Do Note Quote or Cite Table 4

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

scenarios based o n PM 2.5 mass (excluding hours with relative humidity greater than 90

percent)

Annual/1

- hour

PM 2.5 NAAQS (s)

15μg/m3 / 35μg/m3 (t)

12μg/m3 / 25μg/m3

City Name 90 th %tile

Design Value

( Mm - 1 ) 95 th %tile

Design Value

( Mm - 1 ) 9 8 th %tile

Design Value

( Mm - 1 ) 90 th %tile

Design Value

( Mm - 1 ) 95 th %tile

Design Value

( Mm - 1 ) 9 8 th %tile

Design Value

( Mm - 1 )

Design Values Ba sed on Daily Maximum Daylight 1

- hour PM Light Extinction

Tacoma, WA 120 131 177 94 102 136

Fresno, CA 195 267 306 144 197 225

Los Angeles, CA 323 365 436 239 263 360

Phoenix, AZ 105* 143* 265* 97 135 250

Salt Lake City, UT 110 168 269 83 125 198

Dall as, TX 183* 239* 302* 172 224 282

Houston, TX 185 222 276 148 178 220

St. Louis, IL 286 355 441 253 289 364

Birmingham, AL 285 394 464 213 300 365

Atlanta, GA 230 266 307 181 208 243

Detroit, MI 257 389 536 189 278 401

Pittsburgh, PA 229 258 299 167 188 218

Baltimore, MD 233 272 310 169 202 222

Philadelphia, PA 264 313 364 194 226 269

New York, NY 255 296 381 183 213 272

Design Values Based on Daylight 1

- hour PM Light Extinction (All Daylight Hours)

Tacoma, WA 65 88 113 52 70 84

Fresno, CA 112 154 217 84 115 161

Los Angeles, CA 176 233 299 131 172 223

Phoenix, AZ 68* 79* 94* 60 70 86

Salt Lake City, UT 63 95 150 48 72 112

Dallas, TX 113* 143* 188* 106 134 176

Houston, TX 99 122 163 81 99 131

St. Louis, IL 181 221 271 147 183 237

Birmingha m, AL 140 183 247 105 138 186

Atlanta, GA 154 180 220 123 144 174

Detroit, MI 176 209 258 130 155 188

Pittsburgh, PA 138 173 218 102 127 159

Baltimore, MD 163 215 251 121 157 184

Philadelphia, PA 169 206 258 123 150 187

New York, NY 156 204 250 113 1 48 179

* 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.January 2010 4

- 19 DRAFT

– Do Note Quote or Cite Table 4

- 7 . Percentage of days across three years (two in the case of Phoenix and Houston) with maximum 1

- hour daylight PM light

extinction above CPLs when “just meeting” the NAAQS scenarios

(A) NAAQS Scenarios Based on Daily Maximum 1

- hour PM Light Extinction

Days with max hour above

64 Mm - 1 Days with max hour above

112 Mm - 1 Days with max hour above

191 Mm - 1

(a) (b) (c) (d) (e) (f) (g) (h) (i) (s) (t) (a) (b) (c) (d) (e) (f) (g) (h) (i) (s) (t) (a) (b) (c) (d) (e) (f) (g) (h) (i) (s) (t)

NAAQS

Level Mm

- 1 191 191 191 112 112 112 64 64 64 191 191 191 112 112 112 64 64 64 191 191 191 112 112 112 64 64 64

NAAQS

Percentile

Form 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98

Annual/24

- hour 15/

35 12/

25 15/

35 12/

25 15/

35 12/

25

Area P ercentage of days Percentage of days Percentage of days

Tacoma 52 52 49 40 32 17 11 7 2 43 28 22 22 14 10 7 2 1 1 0 10 5 4 4 3 1 1 0 0 0 0 1 0

Fresno 55 42 34 31 20 15 10 4 3 54 40 30 16 12 10 5 3 1 0 0 30 17 10 5 3 2 0 0 0 0 0 10 5

Los Angeles 74 67 60 44 35 28 11 6 3 85 79 41 32 26 10 6 3 0 0 0 69 53 10 6 3 1 0 0 0 0 0 39 20

Phoenix 44 44 44 44 27 10 10 5 2 44 40 6 6 6 6 5 2 2 1 1 6 6 1 1 1 1 1 1 1 0 0 1 1

Salt Lake

City 44 27 14 24 11 5 10 5 1 24 15 17 11 5 9 5 1 4 1 0 9 6 8 5 1 4 1 0 1 0 0 4 2

Dallas 80 66 51 49 28 14 11 5 1 81 77 41 23 13 10 5 1 1 0 0 41 37 10 5 1 1 0 0 0 0 0 10 8

Houston 77 65 57 47 31 20 13 4 3 75 64 43 28 16 12 4 3 1 0 0 41 23 11 6 2 1 1 0 0 0 0 11 3

St. Louis 83 72 57 47 35 21 12 6 2 97 89 45 30 19 12 6 2 1 0 0 74 57 11 5 3 2 1 0 0 0 0 36 21

Birmingham 63 51 41 34 20 16 10 6 3 84 70 31 18 12 10 5 3 1 0 0 55 38 10 5 2 1 0 0 0 0 0 24 13

Atlanta 86 81 77 62 52 37 10 5 1 90 85 59 47 31 11 5 1 0 0 0 71 54 11 4 2 0 0 0 0 0 0 25 8

Detroit 74 54 43 48 23 6 11 4 3 80 74 45 18 6 10 4 3 4 1 0 61 50 10 4 3 4 1 0 1 0 0 33 10

Pittsburgh 70 64 54 40 32 24 9 6 2 78 63 38 29 22 9 6 1 0 0 0 48 28 9 6 1 0 0 0 0 0 0 16 5

Baltimore 68 62 55 43 31 27 12 4 2 78 64 40 29 23 12 5 2 1 0 0 48 31 12 6 2 1 0 0 0 0 0 19 8

Philadelphia 73 66 61 43 33 28 8 6 3 85 74 39 30 25 8 5 3 0 0 0 61 38 8 5 3 0 0 0 0 0 0 28 9

New York 63 59 42 35 28 18 9 6 2 76 62 32 25 16 10 6 2 1 0 0 45 30 10 6 2 1 0 0 0 0 0 19 8

Average 67 58 49 42 29 19 10 5 2 72 62 35 24 16 10 5 2 1 0 35 45 32 9 5 2 1 0 0 0 0 0 18 8January 2010 4

- 20 DRAFT

– Do Note Quote or Cite (B) NAAQS Scenarios Based on PM Light Extinction During All Daylight Hours*

Days with max hour above

64 Mm - 1 Days with max hour above

112 Mm - 1 Days with max hour above

191 Mm - 1

(j) (k) (l) (m) (n) (o) (p) (q) (r) (s) (t) (j) (k) (l) (m) (n) (o) (p) (q) (r) (s) (t) (j) (k) (l) (m) (n) (o) (p) (q) (r) (s) (t)

NAAQS

Level Mm

- 1 191 191 191 112 112 112 64 64 64 191 191 191 112 112 112 64 64 64 191 191 191 112 112 112 64 64 64

NAAQS

Percentile

Form 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98 90 95 98

Annual/24

- hour 15/

35 12/

25 15/

35 12/

25 15/

35 12/

25

Area Percentage of days Percentage of days Percentage of days

Tacoma 52 52 52 52 52 40 40 24 10 43 28 22 22 22 22 22 10 10 4 1 10 5 4 4 4 4 4 1 1 0 0 1 0

Fresno 75 65 52 57 42 29 30 18 9 54 40 52 37 26 30 16 8 9 3 1 30 17 30 16 8 10 5 1 2 0 0 10 5

Los Angeles 86 83 76 76 61 47 44 27 13 85 79 73 58 44 42 28 13 10 2 1 69 53 42 27 12 12 3 1 0 0 0 39 20

Phoenix 44 44 44 44 44 44 44 30 17 44 40 6 6 6 6 6 6 6 5 3 6 6 1 1 1 1 1 1 1 1 1 1 1

Salt Lake

City 44 44 34 44 28 15 24 11 6 24 15 17 17 14 17 11 6 9 5 1 9 6 8 8 5 8 5 2 4 1 1 4 2

Dallas 80 80 80 80 64 48 43 21 11 81 77 41 41 41 41 21 10 9 3 1 41 37 10 10 10 10 4 1 1 0 0 10 8

H ouston 77 77 77 77 70 53 51 35 13 75 64 44 44 44 44 34 13 12 8 1 41 23 11 11 11 11 7 2 1 1 0 11 3

St. Louis 98 93 85 78 69 52 40 28 15 97 89 76 62 48 39 28 15 8 3 2 74 57 39 27 15 9 4 2 1 0 0 36 21

Birmingham 89 85 73 74 59 43 42 26 15 84 70 65 56 40 42 25 14 14 7 3 55 38 34 26 14 15 9 3 3 1 0 24 13

Atlanta 91 91 87 82 76 64 52 30 15 90 85 75 73 62 48 30 13 5 1 0 71 54 31 30 12 6 1 0 0 0 0 25 8

Detroit 84 78 73 67 57 47 41 29 11 80 74 64 55 45 40 26 9 5 4 4 61 50 40 26 9 6 4 4 1 1 1 33 10

Pittsburgh 85 83 74 69 55 44 36 23 11 78 63 57 53 40 35 22 11 8 1 0 48 28 26 22 11 8 2 0 0 0 0 16 5

Baltimore 80 72 66 61 45 38 29 15 9 78 64 50 43 34 28 14 8 4 2 0 48 31 23 14 8 5 2 1 0 0 0 19 8

Philadelphia 86 83 74 68 60 45 34 23 9 85 74 64 58 41 33 24 8 5 1 0 61 38 31 24 8 7 1 0 0 0 0 28 9

New York 83 74 63 62 46 36 31 19 11 76 62 59 42 33 30 18 10 7 3 1 45 30 28 18 10 8 3 1 0 0 0 19 8

Average 77 74 67 66 55 43 39 24 12 72 62 51 44 36 33 22 10 8 3 1 45 32 24 18 9 8 4 1 1 0 0 18 8

* Note that the table reports results based on daily maximum daylight hour, while the NAAQS scenarios in Panel B are based on all daylight

hours (in both cases excluding hours with RH>90%).January 2010 5

- 1 Do Not Quote or Cite 

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.January 2010 Do Not Quote or Cite 

### APPENDICES

A



## . PM 2.5 Monitoring Sites and Monitors Providing 2005

- 2007 Data for the Analysis of PM

light extinction in the 15 Study Areas

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

C. Development of PRB Estimates of PM 2.5 components, PM 10

- 2.5 , and PM light extinction

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

Conditions

E. Differences in Daily Patterns of Relative Humidity and Light Extinction between Are as

and Seasons

F. Distributions of Maximum Daily Daylight PM light extinction under “Just Meets”

Conditions

G. Ad ditional Information on the Exclusion of Daylight Hours with Relative Humidity

Greater t han 90 Percent

H. Inter

- Year Variability

I . Dayligh t HoursJanuary 2010 A

- 1 Do Not Quote or Cite 

## APPENDIX A

- PM 2.5 

## MONITORING SITES AND

MONITORS PROVIDING 2 005

- 2007 DATA FOR THE

ANALYSIS OF PM LIGHT EXTINCTION IN THE 15 STUDY

AREAS

PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 530530029

State: W ashington

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 Seattle

- Tacoma

Oly mpia, 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

- in

- six

sampling schedule)

**•** 1

- hour PM2.5 mass (AQS

paramet er 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 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 SA246B

- INLET

**•** Sample Analysis Method: TEOM

GRAVIMETRIC

7% of PM 10

- 2.5 values were determined using

regional average PM 10

- 2.5 : PM 2.5 ratios from

2005 Staff PaperJanuary 2010 A

- 2 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 060190008

State: California

City: Fresno

MSA: Fresno, CA

Local Site Name: None given

Address: 3425 N FIRST ST,

FRESNO

2.5 miles west of the airport, 3

miles NNE of central Fresno

2005

- 2007 annual DV = 17.4

2005

- 2007 24

- hr DV = 63

This is not the highest annual or

24

- hr 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 paramete r 88101; every

day sampling schedule)

**•** PM 2.5 speciation (one

- in

three 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 wer e determined using

regional average PM 10

- 2.5 : PM 2.5 ratios from

2005 Staff PaperJanuary 2010 A

- 3 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 060658001

State: California

City: Rubidoux (West Riverside )

MSA: Riverside

- San Bernardino,

CA

Local Site Name: None given

Address: 5888 MISSION BLVD.,

RUBIDOUX

Eastern SCAB, 0.4 miles from

Pomona F reeway

.

2005

- 2007 annual DV = 19.6

2005

- 2007 24

- hr DV = 55

This site is not the highest DV site

in the LA

- South Co ast

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

three sampling schedule)

**•** 1

- hour PM 2.5 (AQS

parameter 88502, Acceptable

P M 2.5 AQI & Speciation Mass)

R&P 1400 TEOM

No continuous PM 10 monitoring at

this site, see right hand column

.. NA AQS ID 060710306

State: California

City: Victor ville

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

ap pears 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: TEOM

GRAVIMETRIC

6% of PM 10

- 2.5 values were determined using

regional average PM 10

- 2.5 : PM 2.5 ratios from

2005 Staff PaperJanuary 2010 A

- 4 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 040137020 (FRM

& CSN)

State: Arizona

City: Scottsdale

MSA: Phoenix

- Mesa, AZ

Loc al 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

- 2 007 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

- in

six sampling schedule)

**•** PM 2.5 speciation (one

- in

three sampling sch edule)

No continuous PM 10 monitoring at

this site, see right hand column

. AQS ID 040139998

(Continuous)

State: Arizona

City: Phoenix

MSA: Phoenix

- Mesa, AZ

Local Site Name: Vehicle

Emissions Laboratory

Address: 600 N 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. Nephelomet er. AQS ID 040133002

State: Arizona

City: Phoenix

MSA: Phoenix

- Mesa, AZ

Local Site Name: CENTRAL PHOENIX

Address: 1645 E ROOSEVELT ST

- CENTRAL

PHOENIX STN

1 .8 miles NE of central Phoenix

Neighborhood Scale

Parameters taken from this site:

**•** 1

- hour PM 10 STP mass (AQS parameter

81102)

Sample Collection Method: INSTRUMENTAL

R&P SA246B

- INLET

Sample Analysis Method: TEOM

GRAVIMETRIC

2% of PM 10

- 2.5 values were determined using

regional average PM 10

- 2.5 : PM 2.5 ratios from

2005 Staff PaperJanuary 2010 A

- 5 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 490353006

State: Utah

City: Salt Lake City

MSA: Salt La ke 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

- in

three 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 PM10

- 2.5 values were determined using

regional average PM 10

- 2.5 : PM 2.5 ratios from

2005 Staff Paper

Dallas AQS ID 481130069

State: Texas

City: Dallas

MSA: Dallas, TX

Local Site Name: DALLAS

HINTON

Address: 1415 HINTON STREET

4.5 miles NE of central Dallas

2005

- 2007 annual DV = 11.5

2005

- 2007 24

- hr DV = 25

This is not the h ighest 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

- in

three sampling schedule)

**•** 1

- hour PM 2.5 m ass (AQS

parame ter 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 PaperJanuary 2010 A

- 6 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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

Hous ton AQS ID 482010024

State: Texas

City: Not in a city

MSA: Houston, TX

Local Site Name: HOUSTON

ALDINE

Address: 4510 1/2 ALDINE MAIL

RD

10 miles NNE of cent ral 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 PaperJanuary 2010 A

- 7 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 295100085

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.

Neighborh ood 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

three sampling schedule)

**•** 1

- hour PM 2.5 mass (AQS

parameter 88502, Acceptable

PM 2.5 AQI & Speciation Mass)

TE OM Gravimetric 30 deg C

No continuous PM 10 monitoring at

this site, see right hand column

. NA AQS ID 295100092 (2005 and 2006

data)

State: Missouri

City: St. Lo uis

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 1 0 STP mass (AQS parameter

81102)

**•** Sample Collection Method:

INSTRUMENTAL

- R&P SA246B

- INLET

**•** Sample Analysis Method: TEOM

GRAVIMETRIC Site was on the other

(western) side of the recycling/municipal

works yard as site 295100093, below.

295100093 (2007 data)

State: Missouri

City: St. Louis

MSA: St, Louis, MO

- IL

Local Site Name: None given

Address: Branch Street

0.6 miles ESE of 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: TEOM

GRAVI METRIC

**•**

4% of PM 10

- 2.5 values were determined using

regional average PM 10

- 2.5 : PM 2.5 ratios from

2005 Staff PaperJanuary 2010 A

- 8 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 010730023

State: Alabama

Cit y: 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

- in

three 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 SA246B

INLET

Sample Analysis Method: TEOM

GRAVIMETRIC NA Same as PM 2.5 site.

0.3% of PM 10

- 2.5 values were determined

using regional av erage PM 10

- 2.5 : PM 2.5 ratios

from 2005 Staff PaperJanuary 2010 A

- 9 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 130890002

State: Georgia

City: Decatur

MSA: Atlanta, GA

Local Site Name: 2390

- B

WILDCAT R OAD, 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

- hou r FRM PM 2.5 mass (AQS parameter 88101; every

day sampling schedule)

**•** PM 2.5 speciation (one

- in

three 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 monito ring at

this site, see right hand column

. NA AQS ID 131210048

State: Georgia

City: Atlanta

MSA: Atlanta, GA

Local Site Name: Georgia Tech, Ford

Environmental Science and Technology Bldg,

roof

Address: GA. TECH., Ford ES&T Bldg, 311

Ferst St NW, Atlanta GA

8.6 miles NW of PM 2.5 site

Neighborhood Scale

Parameters taken from this site:

**•** 1

- hour PM 10 STP mass (AQS parameter

81102)

Sample Collection Method: INST RUMENT

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 PaperJanuary 2010 A

- 10 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 261630033

State: Michigan

City: Dearborn

MSA: Detroit, MI

Local Site Name: PROPERTY

OWNED BY DEARBORN PUBLIC

SCHOOLS

Address: 2842 WYOMING

About 0.2 miles from Ford River

Rouge auto plant

2005

- 2007 annual DV = 17.2

200 5

- 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 SA246B

INLET

Sample Analysis Method: TEOM

GRAVIMETRIC NA Sam e 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 PaperJanuary 2010 A

- 11 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 420030008

State : Pennsylvania

City: Pittsburgh

MSA: Pittsburgh, PA

Local Site Name: None given

Address: BAPC 301 39TH

STREET BLDG #7

3 miles NE of central Pittsburgh,

0.5 miles from Allegheny River

2005

- 2007 annual DV = 15.0

2005

- 2007 24

- hr DV = 40

This site is not t he 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

- in

three 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 240053001 (FRM

& CSN)

State: Maryland

City: Essex

MSA: Baltimore, MD

Local Site Name: Essex

Address: 600 Dorsey Avenue

7 miles east of cen tral 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

- in

three sampling schedule)

**•** 1

- hour PM 10 LC mass (AQS

parameter 85101 AQS ID 245100040

(Continuous)

State: Maryland

City: Balt imore

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 &

Speciat ion 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 PaperJanuary 2010 A

- 12 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 100032004 (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 340390004 (NJ)

State: New Jersey

City: Elizabeth

MSA: Newark, NJ

Local Site Name: ELIZAB ETH

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 Sca le

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

three sampling schedule)

**•** 1

- hour PM 2.5 mass (AQS

parameter 88502, Acceptable

PM 2.5 AQI & Speciation Mass)

TEOM Grav imetric 30 deg C

No continuous PM 10 monitoring at

this site, see right hand column

. NA AQS ID 360610125

State: New York

City: New York

MSA: New York, NY

Local Site Name: PARK ROW

Address: 1 PACE PLAZA

Near the on

- ramp to the Brooklyn Bridge,

Manhattan end

Measurement scale not stated.

Parameters taken from this site:

**•** 1

- hour PM 10 STP mass (AQS parameter

81102)

Sample Collection Method: INSTRUMENTAL

R&P SA246 B

- INLET

Sample Analysis Method: TEOM

GRAVIMETRIC

2% of PM 10

- 2.5 values were determined using

regional average PM 10

- 2.5 : PM 2.5 ratios from

2005 Staff PaperJanuary 2010 A

- 13 Do Not Quote or Cite 

## PM 2.5 

## Monitoring Sites and Monitors Providing 2005

- 2007 Data for the

Analysis of PM 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 concentration parameter “88502, Acceptable PM 2.5 AQI & Speciatio n 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 d ata 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 c ontinuous PM 2.5 data were obtained through the AirNow data system rather than from AQS, as an

initial explorati on indicated that not all the desired 1

- hour data from all sites had been submitted to AQS.

Data are submitted to the AirNow system within hours of collection and may not be subject to as much data

validation review as is typical for data in AQS, despite t he opportunity offered by the AirNow system for

monitoring agencies to correct data after initial submission.January 2010

DRAFT Do Not Quote or Cite B

- 1 

## APPENDIX B

- DISTRIBUTIONS OF EST IMATED PM 2.5 

## AND OTHER COMPONENTSJanuary 2010

DRAFT Do Not Quote or Cite B

- 2 Figure B

- 1

– Distribution of daily maximum PM 2.5 and PM 10

- 2.5 across the 2005

- 200 7 period, by study area

(a) Daily maximum daylight PM 2.5January 2010

DRAFT Do Not Quote or Cite B

- 3 (b) Daily maximum daylight PM 10

- 2.5January 2010

DRAFT Do Not Quote or Cite B

- 4 Figure B

- 2

– Distribution of hourly PM 2.5 components across the 2005

- 200 7 period, by study area

(a) 1

- hour daylight sulfate (dry, fully neutralize d)January 2010

DRAFT Do Not Quote or Cite B

- 5 Figure B

- 2

– Distribution of hourly PM 2.5 components across the 2005

- 200 7 period, by study area , continued

(b) 1

- hour daylight nitrate (dry, fully neutralized, CSN method consistent)January 2010

DRAFT Do Not Quote or Cite B

- 6 Figure B

- 2

– Distribution of hourly PM 2.5 components across the 2005

- 200 7 period, by study area , continued

(c) 1

- hour daylight elemental carbonJanuary 2010

DRAFT Do Not Quote or Cite B

- 7 Figure B

- 2

– Distribution of hourly PM 2.5 components across the 2005

- 200 7 period, by study area , continued

(d) 1

- hour daylight organic carbonaceous material (by SAN DWICH method)January 2010

DRAFT Do Not Quote or Cite B

- 8 Figure B

- 2

– Distribution of hourly PM 2.5 components across the 2005

- 200 7 period, by study area , continued

(e) 1

- hour daylight fine soilJanuary 2010

DRAFT

– Do Not Quote or Cite C

- 1 

## APPENDIX C

- DEVELOPMENT OF PRB E STIMATES OF

PM 2.5 

## COMPONENTS, PM 10

- 2.5 

## , AND PM LIGHT

EXTIN CTION

Policy relevant background levels of PM light extinction have been estimated for this

assessment by relying on outputs for the 2004 CMAQ run in which anthropogenic emission

in the U.S., Canada, and Mexico were omitted, as described in the second draf t ISA.

Estimates of PRB for PM 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 second draft

ISA.

More specifically, for each study area, EPA staff overlaid CMAQ grid cells over

shapes representing the Census

- defined urbanized area for each study area, and visually

identified the CMAQ grid cells that had a substantial portion of t heir 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 g rid cells. Thus, a given hour of the day has the same

PRB estimate for a component on all days within a month, but months and study areas differ.

We generally observed that PRB concentrations did not vary greatly across the several grid

cells overlaying the urbanized area of a given study area; this is reasonable given the

exclusion of local anthropogenic sources from this CMAQ model run. CMAQ estimates of

PRB for the five PM 2.5 components averaged across grid cells and months were not adjusted

in any. 46

There too many values of PRB to present or illustrate them comprehensively in this

document. Table C

- 1 presents annual average concentrations by study area to summarize

these PRB estimates for the PM 2.5 components (including the specific form assumed for

sulfate, nitrate, and organic carbon). 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 (but with no water mass added). One notable feature o f the annual average

of the PRB estimates is the relatively high values for elemental and organic carbon PRB for

the Tacoma study area. This area is often affected by wildfires for extended periods in the

46 This approach to estimation of PRB for PM2.5 shares the same information source but is more

disaggregated than the approach used in the health risk assessment for this review of the PM NAAQS. In the health

risk assessment, PRB estimates for PM2.5 mass concentration are taken from the same CMAQ model run, but is

averaged by calendar quarter and by region of the country.January 2010

DRAFT

– Do Not Quote or Cite C

- 2 autumn months, and such fires were included in the 2004 emissions scenario for the PRB

CMAQ run. A cursory review of information on fire events in 2005

- 2007 confirmed that the

fire situation in this part of the country in 2004 was not an anomaly.

Another notable feature of the PRB estimates is that the v alues for nitrate and fine

soil/crustal are low relative to previous estimates of natural background concentrations of

these fine PM components in Class I areas. These previous estimates by Trijonis, repeated in

the 2003 EPA guidance document “Guidance fo r Estimating Natural Visibility Conditions

Under the Regional Haze Program”, are 0.10 μg/m 3 for (neutralized) nitrate and 0.50 μg/m 3

for fine soil. These estimates are based largely on data from the earliest of the IMPROVE

monitoring stations, and thus may include some influence from non

- PRB emissions. On the

other hand, it is understandabl e that the unadjusted output from the PRB CMAQ scenario

would underestimate nitrate and fine soil. CMAQ is known to underestimate actual nitrate in

many situations when provided with a complete NOx inventory, and the nonanthropogenic

emission inventory fo r NOx itself has uncertainties. The non

- anthropogenic emission

inventory for the PRB CMAQ run may also quite easily underestimate nonanthropogenic

emissions of fine soil. However, even if the estimates for PRB nitrate and fine soil were

increased to matc h the Trijonis estimates, the resulting values for PRB PM light extinction

would increase only a little. Even at 90 percent relative humidity, the contribution to PM

light extinction calculated from the Trijonis estimates is 1.7 Mm

- 1 , versus the average of

about 0.5 Mm

- 1 using the estimates in Table C

- 1. The increment of 1.2 Mm

- 1 would be only

about 10 to 20 percent of the PRB PM light extinction estimates shown in Table C

- 4, and

would not significantly affect the calculation of PM light extinction values under the “what

if” scenarios.January 2010

DRAFT

– Do Not Quote or Cite 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 (μg/m 3 )

Sulfate

(dry, no

ammonium ) Nitrate

(dry, no

ammonium ) Elemental

Carbon Organic

Carbon aceous

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 0.0 15 0.7

Salt Lake City 0.25 0.00028 0.025 0.26 0.034 0.7

Dallas 0.27 0.0022 0.055 0.59 0.092 1.1

Houston 0.3 0.0055 0.091 0.86 0.17 1.5

St. Louis 0.31 0.0027 0.047 0.53 0.07 1.1

Birmingham 0.29 0.007 0.099 1.1 0.19 1.8

Atlanta 0.3 0.016 0.1 1.1 0.19 1 .8

Detroit

- Ann 0.34 0.00062 0.024 0.32 0.018 0.8

Pittsburgh 0.3 0.00052 0.029 0.36 0.034 0.8

Baltimore 0.34 0.0016 0.039 0.44 0.054 1.0

Philadelphia 0.34 0.00097 0.03 0.36 0.032 0.9

New York City 0.36 0.0038 0.026 0.31 0.022 0.9

Average 0.33 0. 00 0.06 0.59 0.10 1.20January 2010

DRAFT

– Do Not Quote or Cite C

- 4 It is also necessary to have 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 considered. The fi nal ISA for this review does not

present any new information on this subject. The approach used in the previous two Criteria

Documents was to present the historical range of annual means of 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 o f 1.8 μg/m 3 to a high

of 10.8 μg/m 3 . No cross

- year average or median values were provided that could be used as

the point estimates needed in this assessment. Therefore, for this assessment, EPA staff

estimated PRB for PM 10

- 2.5 using a contour map based on average 2000

- 2004 PM 10

- 2.5

concentrations from all IMPROVE monitoring sites, found in a recent report from the

IMPROVE program. (DeBell, 2006). We located each study area’s position on this map,

and assigned it the mid

- point of the range of concentrat ions indicated by the contour band for

that location. The contour map is reproduced here as Figure C

- 1. Stars show locations of the

15 study areas

. In this reproduction, the m idpoint s of the contour ranges have been added to

the legend.

The results for PRB for coarse PM are shown in Table C

- 2. Lacking any other

information, these PRB values are taken to apply to every hour of the year. The contour map

and thus these values are influenced by data from IMPROVE sites that were not considered

in the 2004 C riteria Document because they are not sufficiently isolated from the influence of

anthropogenic emissions, including three IMPROVE sites in urban areas which clearly are

influenced by anthropogenic emissions, and thus may be overestimates of PRB for coarse

PM. Nevertheless, these values are generally within the range of values presented in the

Criteria Document for the more isolated sites. These values for the more isolated sites are

reproduced here in Table C

- 3 for ease of comparison. Further, these PRB values are low

enough that their exact values have little effect on the results of “what if” estimation of PM

light extinction levels under possible secondary PM NAAQS.

Table C

- 4 presents the resulting 2005

- 2007 average PRB daylight PM light extinction

by study area, determined by using each daylight hour’s f(RH), 47

47 Hour

- s pecific relative humidity for PRB conditions was assumed to be the same as measured for current

conditions. 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

co mponent values in Table C

- 1 are multiplied by 1.375 and 1.29 to reflect full neutralization,January 2010

DRAFT

– Do Not Quote or Cite C

- 5 before being used in the IMPROVE algorithm. While for conciseness Table C

- 4 presents

only the annual average PRM for PM light extinction for all daylight hours in 2005

- 2007

(excluding hours with relative humidity greater than 90 percent), in the rollback analysis of

“what if” conditions hour

- specific PRB values are retained and used.

The values of PRB PM light extinction in Table C

- 4 range between 5 and 11 Mm

- 1

.

F or comparison, the default estimates of natural visibility conditions in the 2003 EPA

guidance document for Class I areas range between about 15 and 20 Mm

- 1 , including the

Rayleigh contribution of about 10 Mm

- 1 . Thus, on an annual average basis the range o f PRB

estimates for PM light extinction used for this assessment is very consistent with the range of

total light extinction values recommended in the guidance document.January 2010

DRAFT

– Do Not Quote or Cite C

- 6 Figure C

- 1

. Selection of PRB values for PM 10

- 2.5 based on contoured IMPROVE

monitor ing 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

0.5January 2010

DRAFT

– Do Not Quote or Cite C

- 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 (μg/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 PMJanuary 2010

DRAFT

– Do Not Quote or Cite C

- 8

Table C

- 4. 2005

- 2007 Average Policy Relevant Background D aylight PM light extinction

(excluding hours with relative humidity above 90%)

Study Area 2005

- 2007 Average Policy Relevant

Background Daylight PM light extinction

, Mm - 1

Tacoma 11

Fresno 11

Los Angeles 9

Phoenix 8

Salt Lake City 5

Dallas 8

Houston 10

St. Louis 9

Birmingham 9

Atlanta 10

De troit 7

Pittsburgh 7

Baltimore 8

Philadelphia 8

N ew York 8January 2010

DRAFT

– Do Not Quote or Cite D

- 1 

## APPENDIX D

- RELATIONSHIPS BETWEE N PM MASS

CONCENTRATION AND PM LIGHT EXTINCTION UND ER

CURRENT CONDITIONS

In the last review, the 2005 Staff Paper examined the correlation between PM light

ex tinction and PM 2.5 mass concentrations, each defined for various consistent time periods. The

2005 Staff Paper analysis assumed that the 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 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 pro files from CMAQ modeling (see section 3.2.2).

Five scatter plot figures relating PM 2.5 mass concentrations and PM light extinction are

presented here for the individual study areas, using different time periods for the two parameters;

these time periods a re not always matched. In each figure, the solid red curve was estimated by

applying locally weighted scatter plot smoothing (LOESS) to the data. LOESS is a form of

locally w eighted polynomial regression (see http://support.sas.com/rnd/app/papers/loesssugi.pdf )

and is a convenient way to visualize whether a dense data cloud in a scatter plot reflects a more

linear or more nonlinear relationship. The LOESS results in each case indicate a generally linear

relationship as a central tendency but with considerable variability around that central tendency.

Table D

- 1 presents squared correlation coefficient s between observed and LOESS model

predicted values for all five figures. Because the LOE SS regressions are generally linear,

comparisons among these correlation coefficients should lead to the same qualitative conclusions

as if coefficients from linear regressions were compared. All values of PM light extinction

presented here are based on e xcluding daylight hours with relative humidity greater than 90

percent; hence, a nominally 4

- hour period might have as few a one 1

- hour PM light extinction

value, although this is rare in this data set (see the tile plots in Figure 3

- 12). However, values of

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 the 2005 Staff Paper, the correlations 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 light extinction. The scatter is due the variatio ns in

PM 2.5 concentration, in the mix of PM 2.5 components, and in relative 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 P M 10

- 2.5 substantially influence PM lightJanuary 2010

DRAFT

– Do Not Quote or Cite D

- 2 extinction. This source of variability in the scatter plots is particularly important for Los

Angeles, Phoenix, and St. Louis which have many (Phoenix) or some (Los Angeles and St.

Louis) hours with high PM 10

- 2.5

. Because of the large scatter and low correlation coefficients when using 24

- hour PM 2.5

mass concentration to predict daily maximum daylight PM light extinction, it is natural to

investigate how much the correlation improves when the PM 2.5 mass indicator is limited to

shorter periods of time. The next four figures investigate correlations during such shorter

periods, both matched and un

- matched in time.

Figure D

- 2 compares hourly PM 2.5 mass (as actually measured by the continuous

instruments) vs. same

- ho ur daylight PM light extinction. Lack of agreement due to mismatch of

time period is not a factor in this comparison. However, there is still considerable scatter due to

variations in the mix of PM 2.5 components and in relative humidity 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 because the estimates of hourly light extinctio n 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 for this comparison are better than those for the 24

- hour

comparison in most but not all study areas. An implication of this figure and the information in

Table D

- 1 is that a wide range of light extinction levels can prevail in hours that have the same

PM 2.5 mass concentration, even at a single 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 light

extinction. The 2005 Staff Paper observed that because this time period is generally the time of

lowest relative humidity, the relationship between PM 2.5 mass and PM light ext inction (i.e., the

ratio of the two or the slope of the regression line) is more uniform across areas during this

period than the relationship for values of each averaged over all 24 hours in a day. In addition,

the longer averaging period might be expect ed 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 exclus ion of hours with relatively humidity greater than 90 percent the scatter

in Figure D

- 3 is about the same as in Figure D

- 2. This residual scatter is due to composition

differences from hour

- to

- hour, as well as to variations in relative humidity during hou rs with

relative humidity of 90 percent or less. It can also be observed by comparing Figures D

- 2 and D

3 that the period between 12 pm and 4 pm generally has lower levels of PM light extinction than

for all daylight hours taken together, even after the e xclusion of the hours with the highest

relative humidity. (Note the change in scale between these two figures.)January 2010

DRAFT

– Do Not Quote or Cite D

- 3 Figure D

- 4 compares 12

- 4 pm average PM 2.5 mass vs. daily max imum daylight 1

- hour

PM light extinction. This time

- unmatched comparison tests th e usefulness of a 12

- 4 pm PM 2.5

mass indicator as a predictor of the daily PM light extinction metric of potentially greatest

interest. The scatter in Figure D

- 4 is typically more than in Figure D

- 3 (4 time

- matched

afternoon hours), because daily maximu m daylight 1

- hour PM 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 hum idity. An implication is that while a secondary NAAQS based on 12

- 4 pm

average PM 2.5 mass might achieve a given level of protection across days and areas in avoiding

high levels of PM light extinction between 12 and 4 pm, with some variation across areas due to

composition and relative humidity differences, there could be considerable additional variation in

the level of protection against PM light extinction during the earlier hours of the day when some

areas often have their highest PM light extinction levels.

Figure D

- 5 compares 8 am

- 12 pm average PM 2.5 mass vs. daily maximum daylight 1

-

hour PM light extinction. This comparison is of interest because it may reduce the number of

instances of time mismatch, versus the comparison made in Figure D

- 4, if th e daily maximum

light extinction often occurs between 8 am and 12 pm. The scatter in Figure D

- 5 is typically less

than in Figure D

- 4 and the squared correlation coefficients larger, indicating that this earlier

averaging period for PM 2.5 mass more often e ncompasses the period of maximum PM light

extinction . However, the scatter in Figure D

- 5 is greater than that in Figure D

- 3 (4 time

- matched

afternoon hours).

Figure D

- 6 provides another perspective on the possible use of PM 2.5 mass concentration

as an i ndicator for a secondary PM NAAQS aimed at protecting visual air quality. Figure D

- 6

shows in box

- and

- whisker plot form two versions of the ratios of PM light extinction to PM 2.5

mass concentration, allowing a comparison across the 15 study areas of the c entral tendencies

and the distributions of these ratios. The Panel A version corresponds to the comparison in

Figure D

- 1 (24

- hour averages of PM 2.5 mass and PM light extinction) and the Panel B version

corresponds to the comparison in Figure D

- 2 (time

- ma tched single hour values). The data points

in Figure D

- 6 were prepared as follows. In each day for each study area, the value of the

indicated PM light extinction (24

- hour average or 1

- hour value) was divided by the indicated

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 light extinction less than 64 Mm

- 1 were eliminated

before plotting, as such data points represent days or hours that could not play any role in

determining compliance with any of the NAAQS scenarios considered in this assessment; also,

some of these low

- concentration/extinction data pairs produced extreme ratios that obscured the

pattern for data pairs of most policy interest. The maximum ratio value for the vertical scale inJanuary 2010

DRAFT

– Do Not Quote or Cite D

- 4 these plots is set at 40 to allow closer examination of the portion of the plot representing the bulk

of the data; this prevents a very small number of daily maximum data points for a few study

areas from appearing in Panel A and a very s mall percentage of 1

- hour data points for a few

study areas (Los Angeles and St. Louis in particular) from appearing in Panel B. The notable

variation in the vertical positions of the 25

- 75 percentile boxes and the 90 percentile whiskers

representing the r atios in the 15 areas illustrates the point that because of differences in PM

composition mix and relative humidity (even after excluding hours with relative humidity greater

than 90 percent) across study areas, a secondary NAAQS based on PM 2.5 mass concen tration

would not give equal protection in terms of PM light extinction levels across cities, days, and

hours.

In the first public review draft of this assessment, it was notable that the correlation

values for St. Louis and Philadelphia were much lower th an for other areas. In this version

(reflecting both corrections to relative humidity inputs and exclusion of hours with very high

relative humidity) the correlation value for Philadelphia is about that for other eastern areas. The

correlation values for St. Louis remain notably low relative to the average of all areas, for all five

scatter plots. This is likely due to the influence of the high estimated values for PM 10

- 2.5

. In

several other cases of notably low correlation, the small available range of PM 2.5 values relative

to other areas contributes to the lower correlation values, e.g., in Phoenix, Dallas, and Houston.January 2010

DRAFT

– Do Not Quote or Cite D

- 5 Table D

- 1

. Squared correlation coefficients between observed and LOESS

model

- predicted values of PM light extinction

Area Figure D

- 1

24

- hour

PM 2.5

mass vs.

daily

maximum

daylight 1

-

hour PM

light

extinction Figure D

- 2

1

- hour

PM 2.5 mass

vs. same

hour PM

light

extinction Figure D

- 3

12

- 4 pm

average

PM 2.5 mass

vs. 12

- 4 pm

average PM

light

extinction Figure D

- 4

12

- 4 pm

average

PM 2.5 mass

vs. daily

maximum

daylight 1

-

hour PM

light

extinction Figure D

- 5

8 am

- 12pm

average PM 2.5 mass vs. daily

maximum

daylight 1

-

hour PM

light

extinction

Tacoma 0.48 0.81 0.78 0.29 0.65

Fresno 0.76 0.83 0.9 0.69 0.83
Los Angeles 0.57 0.63 0.67 0.53 0.7

Phoenix 0 .22 0.67 0.73 0.18 0.2
Salt Lake City 0.88 0.89 0.95 0.8 0.89

Dallas 0.45 0.59 0.53 0.2 0.35

Houston 0.46 0.61 0.62 0.2 0.3

St. Louis 0.40 0.43 0.2 0.18 0.36
Birmingham 0.61 0.81 0.78 0.34 0.44

Atlanta 0.54 0.72 0.8 0.4 0.7

Detroit 0.62 0.55 0.6 0.1 1 0.3
Pittsburgh 0.73 0.63 0.65 0.52 0.62

Baltimore 0.78 0.69 0.69 0.58 0.71
Philadelphia 0.60 0.6 0.57 0.38 0.49

New York 0.69 0.76 0.76 0.5 0.62

AVERAGE 0.59 0.68 0.68 0.39 0.54January 2010

DRAFT

– Do Not Quote or Cite D

- 6 Figure D

- 1.

– Relationship between 24

- hour PM 2.5 mass vs. daily maximum daylight 1

- hour PM light extinction.January 2010

DRAFT

– Do Not Quote or Cite D

- 7 Figure D

- 2.

– Relationship between daylight 1

- hour PM 2.5 mass vs. same

- hour PM light extinction

.January 2010

DRAFT

– Do Not Quote or Cite D

- 8 Figure D

- 3. Relations hip between 12

- 4 pm average PM 2.5 mass vs. 12

- 4 pm average PM light extinction

.January 2010

DRAFT

– Do Not Quote or Cite D

- 9 Figure D

- 4. Relationship between 12

- 4 pm average PM 2.5 mass vs. daily maximum daylight 1

- hour PM light

extinction

.January 2010

DRAFT

– Do Not Quote or Cite D

- 10 Figure D

- 5. Relationship between 8 am

- 12pm aver age PM 2.5 mass vs. daily maximum daylight 1

- hour PM light extinctionJanuary 2010

DRAFT

– Do Not Quote or Cite D

- 11 Figure D

- 6. Distribution of ratios of 1

- hour PM light extinction and PM 2.5 mass

concentration.

A

– Ratios of daily maximum daylight 1

- hour PM light extinction to 24

- hour average P M 2.5
concentration.

B

– Ratios of daylight 1

- hour PM light extinction to same

- hour PM 2.5 concentrationJanuary 2010

DRAFT

– Do Not Quote or Cite E

- 1 

## APPENDIX E

- DIFFERENCES IN DAILY PATTERNS OF

RELATIVE HUMIDITY AN D PM LIGHT EXTINCTION

BETWEEN AREAS AND SE ASONS

In the last review of the sec ondary PM NAAQS, the pattern of PM light extinction

during the day was of particular interest. It was noted, using estimates of hourly PM light

extinction based on a simpler approach than described for this analysis, that both (1) mid

- day

PM light extinct ion and (2) the slope of the relationship between PM 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 acros s regions in the mi d

- day

period. This is in contrast to the situation in the morning and later afternoon hours , when

more eastern areas typically experienc e higher relative humidity levels than the more arid

western and southwestern areas

. The current analysis allows these patterns to be re

examined.

Figures E

- 1 through E

- 4 show the diurnal pattern of season

- average, hour

- specific

PM light extinction and relative humidity for the four “daylight seasons.” These graphics

exclude hours with relative humidity greater than 90 p ercent. Light extinction and relative

humidity for a given clock hour are averaged across the days in the season , across all three

years . Daylight hours (per the simplified schedule of Table 3

- 5) are indicated by solid

circles. Average 1

- hour PM light e xtinction generally is highest in the morning,

corresponding to high er relatively humidity (mostly due to lower temperature), higher

vehicle traffic

, and less dispersive conditions than later in the day. As was observed in the

last review, there is more v ariation in average 1

- hour PM light extinction among areas in the

morning than at mid

- day , although the morning variation has been reduced (relative to same

information in the first public review draft of this assessment) by the exclusion of hours with

rel ative humidity greater than 90 percent

.January 2010

DRAFT

– Do Not Quote or Cite E

- 2 Figure E

- 1. Diurnal and seasonal patterns of relative humidity (percent) and PM light extinction (M m - 1 ) for 200 5

- 2007

(a) November

- JanuaryJanuary 2010

DRAFT

– Do Not Quote or Cite E

- 3 Figure E

- 2. Diurnal and seasonal patterns of relative humidity (perce nt) and PM light extinction (M m - 1 ) for 200 5

- 2007

, continued

(b) February

- AprilJanuary 2010

DRAFT

– Do Not Quote or Cite E

- 4 Figure E

- 3. Diurnal and seasonal patterns of relative humidity (percent) and PM light extinction (M m - 1 ) for 200 5

- 2007

, continued

(c) May

- JulyJanuary 2010

DRAFT

– Do Not Quote or Cite E

- 5 Figure E

- 4. Diurnal and sea sonal patterns of relative humidity (percent) and PM light extinction (M m - 1 ) for 200 5

- 2007

, continued

(d) August

- OctoberF

- 1

January 2010

DRAFT Do Not Quote or Cite 

## APPENDIX F

- DISTRIBUTIONS OF MAX IMUM DAILY

AND HOURLY DAYLIGHT PM LIGHT EXTINCTION

- UNDER “JUST MEET” CO NDITIONSF

- 2

January 2010

DRAFT Do Not Quote or Cite (a) NAAQS Scenar io

Daily Max

191 Mm - 1

90 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 3

January 2010

DRAFT Do Not Quote or CiteF

- 4

January 2010

DRAFT Do Not Quote or Cite (b) NAAQS Scenario

Daily Max

191 Mm - 1

95 th percentile

Displayed: Da ily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 5

January 2010

DRAFT Do Not Quote or Cite (c) NAAQS Scenario

Daily Max

191 Mm - 1

98 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 6

January 2010

DRAFT Do Not Quote or Cite (d) NAAQS Scenario

Daily Max

112 Mm - 1

90 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinctio n (excluding hours >90% RH)F

- 7

January 2010

DRAFT Do Not Quote or Cite (e) NAAQS Scenario

Daily Max

112 Mm - 1

95 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 8

January 2010

DRAFT Do Not Quote or Cite (f) NAAQS Scenario

Dail y Max

112 Mm - 1

98 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 9

January 2010

DRAFT Do Not Quote or Cite (g) NAAQS Scenario

Daily Max

64 Mm - 1

90 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 10

January 2010

DRAFT Do Not Quote or Cite (h) NAAQS Scenario

Daily Max

64 Mm - 1

95 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 11

January 2010

DRAFT Do Not Quote or Cite (i) NAAQS Scenario

Daily Max

64 Mm - 1

98 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 12

January 2010

DRAFT Do Not Quote or Cite (j) NAAQS Scenario

All hours

191 Mm - 1

90 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 13

January 2010

DRAFT Do Not Quote or Cite (k) NAAQS Scenario

All hours

191 Mm - 1

95 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 14

January 2010

DRAFT Do Not Quote or Cite (l) NAAQS Scenario

All hours

191 Mm - 1

98 th percentile

Displayed: Daily Max Daylight Li ght Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 15

January 2010

DRAFT Do Not Quote or Cite (m) NAAQS Scenario

All hours

112 Mm - 1

90 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 16

January 2010

DRAFT Do Not Quote or Cite (n) NAAQS Scenario

All hours

112 Mm - 1

95 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >9 0% RH)F

- 17

January 2010

DRAFT Do Not Quote or Cite (o) NAAQS Scenario

All hours

112 Mm - 1

98 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 18

January 2010

DRAFT Do Not Quote or Cite (p) NAAQS Scenario

All hours

64 Mm - 1

90 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 19

January 2010

DRAFT Do Not Quote or Cite (q) NAAQS Scenario

All hours

64 Mm - 1

95 th percentile

Displayed: Daily Max Daylight Light Extinc tion (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 20

January 2010

DRAFT Do Not Quote or Cite (r) NAAQS Scenario

All hours

64 Mm - 1

98 th percentile

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Day light Light Extinction (excluding hours >90% RH)F

- 21

January 2010

DRAFT Do Not Quote or Cite (s) NAAQS Scenario

15 μg/m 3 annual

35 μg/m 3 24

- hour

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)F

- 22

January 2010

DRAFT Do Not Quote or Cite (t) N AAQS Scenario

12 μg/m 3 annual

25 μg/m 3 24

- hour

Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH)

Displayed: Hourly Daylight Light Extinction (excluding hours >90% RH)G

- 1

January 2010

DR AFT Do Not Quote or Cite 

## APPENDIX G

– ADDITIONAL INFORMATI ON ON THE

EXCLUSION OF DAYLI GHT HOURS WITH RELAT IVE

HUMIDITY GREATER THA N 90 PERCENT

This appendix provides detailed information related to the exclusion of daylight hours

with relative humidity greater than 90 percent from the design value formula for the secondary

NAAQS scenarios b ased on PM light extinction, as discussed in section 3.3.5. As described in

that section, these hours have also been excluded from graphical displays of the distribution of

PM light extinction under current conditions and the various NAAQS scenarios, and f rom the

denominator of percentages of day or hours (as in Table 4

- 7).

Table G

- 1 shows how many estimates of 1

- hour daylight PM light extinction were

excluded, based both on individual hours and on days that were affected by the exclusion of one

or more day light hours. Phoenix was not affected at all. Among the other areas, Detroit was the

least affected. For all areas, comparison of the percentage of hours affected to the percentage of

days affected indicates that several hours with high relatively humid ity tend to occur in the same

day, rather than being evenly distributed across all days. For example, in Atlanta 24 percent of

daylight hours have relative humidity greater than 90 percent, which corresponds to about 876

hours per year (assuming there wer e data for every day of the year and given that on average

there are about 10 fully daylight hours per day). However, only 80 percent of the days

(corresponding to 292 days, if there were data for every day of the year) are affected. Thus, on

average, an affected day in Atlanta has about 3 affected hours. The tile plots in Figure 3

- 12 also

illustrate the tendency for hours with high PM light extinction to cluster in some days.

Figure G

- 1 shows when during the daylight hours these hours with relative humi dity

greater than 90 percent occurred, prior to the their exclusion. Some but not all areas have a

strong tendency for the affected hours to be in the morning. The counts in this figure are across

all the days in 2006

- 2008 that have estimates of PM light extinction, not all the actual calendar

days in that three year period. Given the regularity of the monitoring schedules, these results

should represent year

- round conditions reasonably well. However, the estimates of PM light

extinction for Phoenix and Houston are not seasonally balanced due to one calendar quarter with

no data in each case (see Table 3

- 4), so the true year

- round time

- of

- day distributions of excluded

hours for these two areas may be somewhat different than shown here.

Figure G

- 2 contra sts the distribution of daylight PM light extinction estimates before and

after the exclusion, based on both daily maximum values and all daylight hourly values

individually. The differences observable in the figure are consistent with the information on the

percentages of hours and day affected in the study areas. In most cases, the highest values ofG

- 2

January 2010

DR AFT Do Not Quote or Cite light extinction are notably lower after exclusion, on both a daily maximum basis and individual

hour basis, indicating that PM concentrations in some of th e excluded hours are fairly high. If

only low

- PM hours were excluded by the relative humidity screen, the highest values of light

extinction would not have been affected.

Finally, Table G

- 2 contrasts PM light extinction design values before and after the

exclusion, for the 90 th and 95 th percentile forms based on daily maximum daylight 1

- hour PM

light extinction, for current conditions. (A similar comparison for the 98 th percentile form was

not generated.) As expected, design values are notably lower afte r 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 accordin gly Table G

- 2 shows that its PM light extinction design

values are not affected by the exclusion. Similarly, Detroit and Dallas had only a few hours with

relative humidity greater than 90 percent, and their design values are affected very little by the

ex clusion.

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 1 2.3 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

Philadelp hia 9.6 31.7

New York 9.1 22.4G

- 3

January 2010

DR AFT Do Not Quote or Cite Figure G

- 1

. Distribution by time of day of eliminated daylight hours with relative humidity greater than 90 percent.G

- 4

January 2010

DR AFT Do Not Quote or Cite

Figure G

- 2

. Comparison of d istributions of estimated daylight 1

- hour PM light extinction

and maxi mum daily daylight 1

- hour PM light extinction across the 2005

- 2007 period for

current conditions , by study area , before and after elimination of hours with relative

humidity greater than 90 percent.

(a) Maximum daily values

Before Elimination

After El iminationG

- 5

January 2010

DR AFT Do Not Quote or Cite (b) Individual 1

- hour values

Before Elimination

After EliminationG

- 6

January 2010

DR AFT Do Not Quote or Cite Table G

- 2. Comparison of 90 th and 95 th percentile PM light extinction design values for the

2005

- 2007 period for current conditions based on maximum daily 1

- hour dayligh t PM light

extinction, before and after elimination of hours with relative humidity greater than 90

percent

Study

Areas PM Light Extinction Design Values Based on Daily Maximum 1

- Hour Values

90th Percentile 95th Percentile

Before

Exclusion After

Excl usion 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 61H

- 1

January 2010

DRAFT Do Not Quote or Cite 

## APPENDIX H

– I NTER

- 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 ind icator over multiple years. The NAAQS

scenarios examined in this assessment are all based on a three

- year average approach. That is,

design values are based on the average of specified percentile values of PM light extinction from

2005, 2006, and 2007. Table H

- 1 presents more detailed information on the variability of these

percentiles across these three years.

Figure H

- 1 presents some of the information in Table H

- 1 in graphical form, specifically

for the 90 th percentile form for both the daily maximum and all hours approaches.H

- 2

January 2010

DRAFT Do Not Quote or Cite Table H

- 1. Year

- specific p ercentile values of PM 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

A verage 2005 2006 2007 2005

- 2007

Average

Based on Daily Maximum 1

- Hour Daylight PM Light Extinction

(Excluding hours with relative humidity greater than 90%)

Tacoma NA 121 158 140 NA 140 173 157 NA 214 206 210

Fresno 308 307 398 338 553 364 472 463 658 400 542 533

Los

Angeles 435 485 486 469 507 535 619 554 606 605 662 624

Phoenix 100 110 NA 105 156 131 NA 144 344 187 NA 266

Salt Lake

City 217 112 163 164 310 141 306 252 343 191 696 410

Dallas 184 169 197 183 252 223 242 239 313 321 271 302

Houston 217 204 161 194 269 238 196 234 306 319 248 291

St. Louis 350 331 239 307 434 405 303 381 483 572 347 467

Birmingham 438 307 325 357 547 410 493 483 608 513 565 562

Atlanta 235 255 257 249 283 295 286 288 305 338 351 331

Detroit 300 312 318 310 347 408 663 473 391 489 1051 644

Pittsburgh 284 257 292 278 347 272 320 313 360 350 383 364

Baltimore 303 227 208 246 362 258 239 286 417 308 260 328

Philadelphia 257 325 276 286 333 367 318 339 426 376 377 393

New York 333 265 320 306 405 275 384 355 568 352 451 457

Based on 1

- Hour Daylight PM Light Extinction (All Hours)

(Excluding hours with relative humidity greater than 90%)

Tacoma NA 73 78 76 NA 102 109 106 NA 120 151 136

Fresno 183 175 212 190 257 262 278 266 395 332 391 373

Los

Angeles 263 275 259 266 325 362 359 349 408 458 486 451

Phoenix 67 68 NA 68 79 78 NA 79 92 96 NA 94

Salt Lake

City 115 67 96 93 194 83 148 142 255 116 303 225

Dallas 114 100 125 113 145 126 158 143 184 176 204 188

Houston 116 98 100 105 143 122 119 128 191 174 148 171

S t. Louis 229 195 157 194 277 239 188 235 334 309 226 290

Birmingham 191 162 166 173 251 204 226 227 340 267 319 309

Atlanta 166 164 169 166 188 194 202 195 233 233 248 238

Detroit 226 213 198 212 267 253 234 251 320 312 314 315

Pittsburgh 173 153 176 1 67 217 193 218 209 284 236 272 264

Baltimore 203 163 150 172 290 194 196 227 345 225 225 265

Philadelphia 163 204 183 183 209 234 224 222 280 298 258 279

New York 204 169 186 186 265 222 244 244 313 267 320 300H

- 3

January 2010

DRAFT Do Not Quote or Cite Figure H

- 1. Inter

- year variability in 90th percentile 1

- hour daylight PM light

extinction (excluding hours with relative humidity greater than 90 percent)

(a) Daily maximum approach

0 100 200 300 400 500 600

2005 2006 2007

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 TacomaI

- 1

January 2010

DRAFT Do Not Quote or Cite 

## APPENDIX I

– D AYLIGHT HOURS

Table 3

- 5 shows the simple scheme used in t his analysis to denote hours as fully daylight

and thus eligible for consideration in the calculation of design values for the secondary NAAQS

scenarios based on PM light extinction. This scheme also has been used to select which hours to

to show in vario us graphics. The scheme is based on applying a fixed set of fully daylight hours

for each three

- month season (November to January, etc.). In reality, the local time minutes of

daylight vary continuously during the year, with latitude, and with the east

- w est 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 res ults presented and the answers to policy relevant

questions that may be addressed in the subsequent policy assessment document.

Six study areas were selected for this examination: Tacoma, Los Angeles, Phoenix,

Houston, Detroit, and New York. These areas co ver the extremes with regard to latitude and to

east

- west position within time zone. For each area, the times of sunrise (defined by the leading

or top edge of the sun appearing above the horizon) and of sunset (defined by the leading or

bottom edge of th e sun disappearing below the horizon) were obtained for each day of the year.

It is several minutes after each of these times that the sun is fully visible in the morning and not

visible at all in the evening.

Figure I

- 1 shows the relationship between 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 (bottom of f igure). The stepped lines represent the scheme used to select the

first and last hour denoted as fully daylight. Months are indicated on the horizontal axis. The

figure indicates that the simple scheme has the effect of treating some hours as daylight th at in

fact contain minutes prior to sunrise or after sunset, and conversely treating some hours as not

daylight that include no such minutes. In particular:

**•** In February, the hours from 7 am to 8 am and from 5 pm to 6 pm are treated as

daylight but include non

- daylight minutes in most of the example areas.

**•** In April, the hour from 6 am to 7 am is treated as non

- daylight but in many areas

includes only minutes that are after sunrise.

**•** In most of June and most of July, for Detroit and Tacoma only, the hour of 7 pm to 8

pm is treated as non

- daylight but in fact has no minutes after sunset.I

- 2

January 2010

DRAFT Do Not Quote or Cite **•** In October, the hours of 6 am to 7 am and 5 pm to 6 pm are treated as daylight but

include non

- daylight minutes in all of the example areas.

The tile plots in Figure 3

- 12 can b e used to assess the significance of these disparities,

i.e., whether they are likely to significantly affect PM light extinction design values. Table I

- 1

contains observations for each of the 24 combinations of the four time periods listed above and

the six example areas. Taken together, these observations make it likely that refining the scheme

for designating hours as fully daylight would not significantly change conclusions that can be

drawn from this assessment as it has been performed. Changing the scheme would involve

considerable effort in updating virtually every table and graphic in the assessment, however.I

- 3

January 2010

DRAFT Do Not Quote or Cite Figure I

- 1. Comparison of Actual Sunrise and Sunset Times to this Assessment’s Scheme to Denote Hours as Fully

Daylight



# Sunrise/Sunset for Six Sites

0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400

Jan10 Feb10 Mar10 Apr10 May10 Jun10 Jul10 Aug10 Sep10 Oct10 Nov10 Dec10



## DateLocal Time Assumed daylight start

Assumed daylight end

TAC Rise

TAC Set

LA Rise

LA Set

PHX Rise

PHX Set

HOU Rise

HOU Set

DET Rise

DET Set

NY Rise

NY SetI

- 4

January 2010

DRAFT Do Not Quote or Cite Table I

- 1. Obs ervations from Tile Plots for Hours with Questionable Daylight/Non

- Daylight Status in Six Study Areas

Study Area February (AM and PM) April (AM) June

- July (PM) October (AM and PM)

Tacoma The morning hour with questionable

daylight status tends to have RH > 90%.

The evening hour in question tends to

either have low PM light extinction or

to have RH > 90%. The tile plot does not show data for the

morning hour that may better be denoted

daylight, but the instances of high PM light

extinction that do appear t ypically last

multiple hours. Very late afternoon typically is not a

period of high PM light extinction. Instances of high light extinction involving

the questionable hours are multi

- hour and/or

involve RH > 90%.

Los

Angeles Instances of high light extinc tion

involving the questionable hours are

multi

- hour and/or involve RH > 90%. The tile plot does not show data for the

morning hour that may better be denoted

daylight, but the instances of high PM light

extinction that do appear typically last

multiple ho urs. NA Instances of high light extinction involving

the questionable hours are multi

- hour and/or

involve RH > 90%.

Phoenix Instances of high light extinction

involving the questionable hours are

multi

- hour. The tile plot does not show data for the

mornin g hour that may better be denoted

daylight, but early morning in April typically

is not a time of high PM light extinction. NA PM light extinction is usually low in

October; on those days with moderate levels

in the questionable hours, another hour in

the central part of the day has a similar level.

Houston Instances of high light extinction

involving the questionable hours are

multi

- hour and/or involve RH > 90%. The tile plot does not show data for the

morning hour that may better be denoted

daylight, but the instances of high PM light

extinction that do appear typically last

multiple hours and/or involve RH >90%. NA The amount of information is limited due to

missing data. On those days with moderate

to high PM light extinction during the

questionable hou rs, another hour has a

similar level, or RH >90% plays a role.

Detroit Instances of high light extinction

involving the questionable hours are

multi

- hour. The tile plot does not show data for the

morning hour that may better be denoted

daylight, but the i nstances of high PM light

extinction that do appear typically last

multiple hours. July generally is a time of high PM

light extinction for the hours currently

considered daylight. Adding one

more late afternoon hour likely would

not affect design values. Instances of high light extinction involving

the questionable hours are multi

- hour.

New York All but one instance of high light

extinction involving the questionable

hours are multi

- hour. The tile plot does not show data for the

morning hour that may bet ter be denoted

daylight, but the instances of high PM light

extinction that do appear typically last

multiple hours. NA Instances of high light extinction involving

the questionable hours are multi

- hour and/or

involve RH > 90%.January 2010

DRAFT Do Not Quote or CiteJanuary 2010

DRAFT Do Not Quote or Cite United States

Environ mental Protection

Agency Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, NC Publication No. EPA

- 452/P

- 10

- 002

January, 2010