Risk and Exposure Assessment for the Review 

of the Primary National Ambient Air Quality 

Standard for Sulfur Oxides

EPA-452/R-18-003

May 2018

Risk and Exposure Assessment for the Review of the Primary National Ambient Air Quality 

Standard for Sulfur Oxides

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, NC
i

DISCLAIMER

This document has been prepared by staff in the Health and Environmental Impacts 

Division, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency 

(EPA). Any findings and conclusions are those of the authors and do not necessarily reflect the 

views of the Agency. Mention of trade names or commercial products is not intended to 

constitute endorsement or recommendation for use. Questions or comments related to this 

document should be addressed to Dr. Stephen Graham, U.S. Environmental Protection Agency, 

Office of Air Quality Planning and Standards, C539-07, Research Triangle Park, North Carolina 

27711 (email: graham.stephen@epa.gov) and Dr. Nicole Hagan, U.S. Environmental Protection 

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

Carolina 27711 (email: hagan.nicole@epa.gov). 
ii

TABLE OF CONTENTS

LIST OF APPENDICES................................................................................................................ iv

LIST OF FIGURES ........................................................................................................................ v

LIST OF TABLES........................................................................................................................ vii

LIST OF ACRONYMS AND ABBREVIATIONS ...................................................................... xi

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

1.1 Background ................................................................................................................... 1-2

1.2 Previous Reviews and Assessments.............................................................................. 1-4

1.3 Current Review, CASAC Advice and Public Comment............................................... 1-7

1.3.1 REA Aspects Updated Since 2009......................................................................... 1-7

1.3.2 CASAC Advice and Public Comment................................................................... 1-9

REFERENCES .......................................................................................................................... 1-11

2 OVERVIEW OF ASSESSMENT APPROACH.............................................................. 2-1

2.1 Conceptual Model for SO2 Exposure and Risk ............................................................. 2-1

2.1.1 Sources of SO2 ....................................................................................................... 2-3

2.1.2 Exposure Pathways and Route............................................................................... 2-4

2.1.3 At-Risk Populations............................................................................................... 2-5

2.1.4 Health Endpoints.................................................................................................... 2-6

2.1.5 Risk Metrics........................................................................................................... 2-6

2.2 Assessment Approach................................................................................................... 2-8

REFERENCES .......................................................................................................................... 2-11

3 AMBIENT AIR CONCENTRATIONS............................................................................ 3-1

3.1 Characterization of Study Areas ................................................................................... 3-2

3.2 Air Quality Modeling .................................................................................................... 3-4

3.2.1 General Model Inputs............................................................................................. 3-9

3.2.2 Stationary Sources Emissions Preparation........................................................... 3-13

3.2.3 Air Quality Receptor Locations........................................................................... 3-15

3.2.4 Concentrations Associated with Sources Not Explicitly Modeled ...................... 3-16

3.2.5 Hourly Concentrations at Air Quality Model Receptors ..................................... 3-19

3.3 Selection of Air Quality Receptors for Exposure Modeling Domain ......................... 3-23

3.4 Air Quality Adjustment to Conditions Meeting the Current Standard........................ 3-24

3.5 Five-Minute Concentrations........................................................................................ 3-31
iii

3.5.1 Preparing Monitoring Data: Assessing Completeness & Filling Missing Values......

.............................................................................................................................. 3-31

3.5.2 Estimating Continuous 5-minute Concentrations at Monitor Having Only 1-hour 

Average and Hourly Maximum 5-minute Data ................................................... 3-34

3.5.3 Estimating 5-minute Concentrations Across Study Areas................................... 3-38

REFERENCES .......................................................................................................................... 3-50

4 POPULATION EXPOSURE AND RISK ........................................................................ 4-1

4.1 Populations Simulated................................................................................................... 4-2

4.1.1 Demographics ........................................................................................................ 4-3

4.1.2 Asthma Prevalence................................................................................................. 4-6

4.1.3 Personal Attributes............................................................................................... 4-13

4.2 Meteorological Data.................................................................................................... 4-20

4.3 Construction of Human Activity Sequences............................................................... 4-21

4.3.1 Consolidated Human Activity Database .............................................................. 4-21

4.3.2 Commuting Activity Pattern Data........................................................................ 4-22

4.3.3 Assigning Activity Pattern Data to Individuals ................................................... 4-23

4.3.4 Method for Longitudinal Activity Sequences...................................................... 4-26

4.4 Microenvironmental Concentrations........................................................................... 4-28

4.4.1 Air Exchange Rates for Indoor Residential Microenvironments......................... 4-33

4.4.2 Air Conditioning Prevalence for Indoor Residential Microenvironments........... 4-35

4.4.3 AER Distributions for All Other Indoor Microenvironments.............................. 4-36

4.4.4 Removal Rate for Indoor Microenvironments..................................................... 4-36

4.4.5 Factors for Estimating In-Vehicle/Near-Road Microenvironmental Concentrations.

.............................................................................................................................. 4-37

4.5 Estimating Exposure ................................................................................................... 4-38

4.6 Risk Metrics ................................................................................................................ 4-39

4.6.1 Comparison to Benchmark Concentrations ......................................................... 4-39

4.6.2 Lung Function Risk.............................................................................................. 4-42

4.7 Approach for Characterizing Uncertainty and Variability.......................................... 4-48

4.7.1 Assessment of Variability and Co-variability...................................................... 4-49

4.7.2 Characterization of Uncertainty ........................................................................... 4-49

REFERENCES .......................................................................................................................... 4-51

5 POPULATION EXPOSURE AND RISK RESULTS..................................................... 5-1
iv

5.1 Characteristics of the Simulated Population and Study Areas...................................... 5-2

5.2 Exposures at or above Benchmark Concentrations....................................................... 5-5

5.3 Lung Function Decrements Associated with 5-minute SO2 Exposures...................... 5-11

5.4 Study Area Differences and Population Distribution.................................................. 5-14

5.4.1 Derivation of DV&POP Metric ........................................................................... 5-14

5.4.2 Comparing the Study Areas with the DV&POP Metric ...................................... 5-15

5.5 Comparison with 2009 REA Results............................................................................. 5-20

6 VARIABILITY ANALYSIS AND UNCERTAINTY CHARACTERIZATION ......... 6-1

6.1 Treatment of Variability and Co-Variability................................................................. 6-1

6.2 Characterization of Uncertainty .................................................................................... 6-6

6.2.1 Characterizing Sources of Uncertainty .................................................................. 6-7

6.2.2 Exposure Model Sensitivity Analyses ................................................................. 6-23

REFERENCES .......................................................................................................................... 6-38

LIST OF APPENDICES

A. Surface characteristic values and meteorological data preparation for input to air 

quality modeling

B. Development of hourly emissions profiles

C. Air quality modeling domains for study areas

D. Modeled air quality evaluation

E. Asthma prevalence

F. Description of the Air Pollutants Exposure Model (APEX)

G. ICF Final Memo: Joint distributions of body weight and height for use in APEX

H. ICF Final Memo: Resting metabolic rate (RMR) and ventilation rate (V̇ E) 

algorithm refinements

I. Consolidated Human Activity Database (CHAD) data

J. Detailed exposure and risk results

K. Daytime hourly concentration estimates and measurements by season
v

LIST OF FIGURES

Figure 2-1. Conceptual model for exposure and associated health risk of SO2 in ambient air. ..

.............................................................................................................................. 2-2

Figure 2-2. Overview of the assessment approach. .............................................................. 2-10

Figure 3-1. Location of surface and upper air meteorological stations, SO2 emissions sources, 

and ambient monitors used to predict ambient air quality in the Fall River study 

area. ..................................................................................................................... 3-6

Figure 3-2. Location of surface and upper air meteorological stations, SO2 emissions sources, 

and ambient monitors used to predict ambient air quality in the Indianapolis study 

area. Also included is source type and 2011 NEI SO2 emissions........................ 3-7

Figure 3-3. Location of surface and upper air meteorological stations, SO2 emissions sources, 

and ambient monitors used to predict ambient air quality in the Tulsa study area. 

Also included is source type and 2011 NEI SO2 emissions................................. 3-8

Figure 3-4. Comparison of AERMOD predicted SO2 concentrations (y-axis) with observed 

air monitor SO2 concentrations (x-axis) during daytime of the three warmer 

seasons at the highest design value monitor in each study area. ....................... 3-22

Figure 3-5. Comparison of ambient air measurements from high concentration years (x-axis) 

to low concentration years (y-axis) in the Fall River (top row), Indianapolis 

(middle row), and Tulsa (bottom row) study areas. Left column contains the year 

having the highest 99th percentile daily maximum concentration. Right column 

contains the year having the 2

nd highest 99th percentile daily maximum 

concentration...................................................................................................... 3-26

Figure 3-6. Location of air quality receptors, emission sources, and ambient monitors in the 

Fall River exposure modeling domain and receptor design values calculated from 

modeled hourly SO2 concentrations adjusted to just meet the current standard.......

............................................................................................................................ 3-28

Figure 3-7. Location of air quality receptors, emission sources, and ambient monitors in the 

Indianapolis exposure modeling domain and receptor design values calculated 

from modeled hourly SO2 concentrations adjusted to just meet the current 

standard.............................................................................................................. 3-29

Figure 3-8. Location of air quality receptors, emission sources, and ambient monitors in the 

Tulsa exposure modeling domain and receptor design values calculated from 

modeled hourly SO2 concentrations adjusted to just meet the current standard.......

............................................................................................................................ 3-30

Figure 3-9. Comparison of estimated to measured SO2 concentrations in ambient air in Fall 

River monitor 250051004: 1-hour average (top panels), maximum 5-minute 

(middle panels) and continuous 5-minute (bottom panels) for 2011 (left panels) 

and 2012 (right panels). ..................................................................................... 3-37

Figure 4-1. Influence of age, race, obesity, sex and family income on adult asthma prevalence 

(based on NHIS 2011-2015 for four U.S. regions)............................................ 4-11
vi

Figure 4-2. Influence of age, race, obesity, sex and family income on child asthma prevalence 

(based on NHIS 2011-2015 for four U.S. regions)............................................ 4-12

Figure 4-3. Illustration of the mass balance model used by APEX...................................... 4-31

Figure 4-4. Percent of individuals experiencing changes in sRaw ≥ 100% (top panel) and 

sRaw ≥ 200% (bottom panel) using controlled human exposure study data (Table 

4-12) fit using a probit regression (solid lines). Dashed lines indicate a 90 percent 

confidence interval for the mean response......................................................... 4-45

Figure 5-1. Population in the Fall River study area considering 2010 U.S. Census tracts..... 5-4

Figure 5-2. Population in the Indianapolis study area considering 2010 U.S. Census tracts. 5-4

Figure 5-3. Population in the Tulsa study area considering 2010 U.S. Census tracts............ 5-5

Figure 5-4. Percent of children’s time in indoor, outdoor, and vehicle MEs while exposed to 

SO2 in Fall River (top), Indianapolis (middle), and Tulsa study areas. ............. 5-10

Figure 5-5. Values of the DV&POP exposure metric in the Fall River study area.............. 5-17

Figure 5-6. Values of the DV&POP exposure metric in the Indianapolis study area. ......... 5-18

Figure 5-7. Values of the DV&POP exposure metric in the Tulsa study area. .................... 5-19

Figure 6-1. Spatial pattern of design values using an adjustment based on the maximum 

design value (left panel) and an adjustment based on the 99th percentile design 

value (right panel) in the Fall River study area.................................................. 6-26

Figure 6-2. Spatial pattern of design values using an adjustment based on the maximum 

design value (left panel) and an adjustment based on the 99th percentile design 

value (right panel) in the Indianapolis study area.............................................. 6-27

Figure 6-3. Spatial pattern of design values using an adjustment based on the maximum 

design value (left panel) and an adjustment based on the 99th percentile design 

value (right panel) in the Tulsa study area......................................................... 6-27
vii

LIST OF TABLES

Table 3-1. General features of the study areas selected for the exposure and risk assessment..

.............................................................................................................................. 3-4

Table 3-2. National Weather Service surface stations for meteorological input data in study 

areas. .................................................................................................................. 3-10

Table 3-3. National Weather Service upper air stations for meteorological input data in study 

areas. .................................................................................................................. 3-10

Table 3-4. Monthly seasonal assignments input to AERSURFACE. ................................. 3-11

Table 3-5. Monthly surface moisture categorizations for the three study areas.................. 3-12

Table 3-6. Facilities with point sources included in the air quality modeling domain for each 

study area. .......................................................................................................... 3-13

Table 3-7. SO2 concentrations (ppb) used to account for source emissions not explicitly 

modeled in the three study areas, stratified by season and hour of day............. 3-18

Table 3-8. Maximum SO2 design values modeled at air quality receptors and associated 

proportional adjustment factors applied to primary source concentrations in each 

study area. .......................................................................................................... 3-28

Table 3-9. Percent of missing values in the hourly and 5-minute ambient air monitoring data 

sets for the three study areas (2011-2013). ........................................................ 3-34

Table 3-10. Descriptive statistics and correlations associated with measured and estimated 1-

hour average, maximum 5-minute, and continuous 5-minute SO2 concentrations, 

Fall River (monitor 250051004), 2011-2012..................................................... 3-38

Table 3-11. Descriptive statistics for concentrations at monitors and concentrations estimated 

at air quality receptor locations, Fall River study area 2011-2013. ................... 3-43

Table 3-12. Descriptive statistics for concentrations at monitors and concentrations estimated 

at model receptor locations, Indianapolis study area 2011-2013....................... 3-44

Table 3-13. Descriptive statistics for concentrations at monitors and concentrations estimated 

at model receptor locations, Tulsa study area 2011-2013.................................. 3-45

Table 3-14. Percent of air quality receptors and monitors at which 5-minute SO2

concentrations (for conditions just meeting standard) exceed concentrations of 

interest on single and multiple days, Fall River study area 2011-2013. ............ 3-47

Table 3-15. Percent of air quality receptors and monitors at which 5-minute SO2

concentrations (for conditions just meeting standard) exceed concentrations of 

interest on single and multiple days, Indianapolis study area 2011-2013. ........ 3-48

Table 3-16. Percent of air quality receptors and monitors at which 5-minute SO2

concentrations (for conditions just meeting standard) exceed concentrations of 

interest on single and multiple days, Tulsa study area 2011-2013. ................... 3-49

Table 4-1. Distribution of the percent of total population that are children residing in the 

census blocks comprising each study area........................................................... 4-5
viii

Table 4-2. Estimated asthma prevalence for children and adults in census blocks of three 

study areas, summary statistics............................................................................ 4-8

Table 4-3. Regression parameters used to estimate RMR by sex and age groups. ............. 4-16

Table 4-4. Study area meteorological stations, locations, and hours of missing data......... 4-21

Table 4-5. Comparison of outdoor time expenditure and exertion level by asthma status for 

children and adult CHAD diaries used by APEX.............................................. 4-25

Table 4-6. Microenvironments modeled and calculation method used............................... 4-30

Table 4-7. AERs for indoor residential microenvironments (ME-1) with A/C by study area 

and temperature.................................................................................................. 4-34

Table 4-8. AERs for indoor residential microenvironments (ME-1) without A/C by study 

area and temperature. ......................................................................................... 4-35

Table 4-9. American Housing Survey A/C prevalence from 2013 Current Housing Reports 

for selected urban areas...................................................................................... 4-35

Table 4-10. Parameter estimates of SO2 removal rate distributions in two indoor 

microenvironments modeled by APEX. ............................................................ 4-37

Table 4-11. Responses reported in controlled human exposure studies at a given benchmark 

concentration...................................................................................................... 4-41

Table 4-12. Summary of controlled human exposure studies containing individual response 

data: number and percent of exercising individuals with asthma who experienced 

greater than or equal to a 100 or 200 percent increase in specific airway resistance 

(sRaw), adjusted for effects of exercise in clean air. ......................................... 4-44

Table 4-13. Example of risk calculation using estimated daily maximum 5-minute exposures 

of children with asthma in the Fall River study area. ........................................ 4-47

Table 5-1. Summary of study area features and the simulated population. .......................... 5-3

Table 5-2. Percent and number of children and adults with asthma estimated to experience at 

least one day per year with a SO2 exposure at or above 5-minute benchmark 

concentrations while breathing at elevated rate, air quality adjusted to just meet 

the existing standard. ........................................................................................... 5-8

Table 5-3. Percent of children and adults with asthma estimated to experience multiple days 

per year with a SO2 exposure at or above 5-minute benchmark concentrations 

while breathing at elevated rate, air quality adjusted to just meet the existing 

standard................................................................................................................ 5-9

Table 5-4. Percent and number of children and adults with asthma estimated to experience at 

least one day per year with a SO2-related increase in sRaw of 100% or more while 

breathing at an elevated rate, air quality adjusted to just meet the existing 

standard.............................................................................................................. 5-12

Table 5-5. Percent of children and adults with asthma estimated to experience multiple days 

per year with a SO2-related increase in sRaw of 100% or more while breathing at 

elevated rate, air quality adjusted to just meet the existing standard................. 5-13
ix

Table 5-6. Contribution of different magnitudes of 5-minute SO2 exposures to lung function 

risk (sRaw increase of at least 100%) estimated for children with asthma in Fall 

River................................................................................................................... 5-13

Table 6-1. Summary of how variability was incorporated into the exposure and risk 

assessment............................................................................................................ 6-4

Table 6-2. Important components of co-variability in exposure modeling. .......................... 6-6

Table 6-3. Characterization of Key Uncertainties in Exposure and Risk Assessments using 

APEX. .................................................................................................................. 6-8

Table 6-4. Comparison of measured and estimated continuous 5-minute SO2 concentrations 

in ambient air, Fall River monitor 250051004, 2011......................................... 6-24

Table 6-5. Comparison of simulated exposures, for children with asthma, at or above 

benchmarks using measured versus estimated continuous 5-minute SO2

concentrations from monitor 250051004, Fall River, 2011............................... 6-24

Table 6-6. Comparison of simulated lung function decrements in children with asthma using 

measured versus estimated 5-minute continuous SO2 concentrations, Fall River 

2011.................................................................................................................... 6-25

Table 6-7. Air quality adjustment factors for main body REA and sensitivity analysis. .... 6-26

Table 6-8. Comparison of two approaches used to adjust ambient air concentrations to just 

meet the existing standard (2011-2013): Percent of children with asthma 

estimated to experience at least one day per year with a SO2 exposure at or above 

5-minute benchmark concentrations while at elevated exertion........................ 6-29

Table 6-9. Comparison of two approaches used to adjust ambient air concentrations to just 

meet the existing standard (2011-2013): Percent of children with asthma 

estimated to experience multiple days per year with a SO2 exposure at or above 5-

minute benchmark concentrations while at elevated exertion. .......................... 6-30

Table 6-10. Percent of children with asthma estimated to experience at least one day per year 

with a SO2-related increase in sRaw of 100% or more while breathing at elevated 

rates, air quality adjusted to just meet the existing standard.............................. 6-30

Table 6-11. Percent of children with asthma estimated to experience multiple days per year 

with a SO2-related increase in sRaw of 100% or more while breathing at elevated 

rates, air quality adjusted to just meet the existing standard.............................. 6-31

Table 6-12. Comparison of three approaches for using continuous 5-minute monitoring data to 

estimate 5-minute concentrations associated with modeled 1-hour concentrations 

at receptor locations: Air quality adjusted to just meet the existing standard, Fall 

River study area 2011. ....................................................................................... 6-33

Table 6-13. Comparison of three approaches for using continuous 5-minute ambient air 

monitoring data to estimate 5-minute concentrations associated with modeled 1-

hour concentrations: Estimated exposures for air quality adjusted to just meet the 

existing standard, Fall River, 2011. ................................................................... 6-34
x

Table 6-14. Comparison of three approaches for using continuous 5-minute monitoring data to 

estimate 5-minute concentrations associated with modeled 1-hour concentrations: 

Estimated lung function decrements associated with exposure to air quality 

adjusted to just meet the existing standard, Fall River 2011. ............................ 6-35

Table 6-15. Comparison of estimated lung function risk using mean, lower bound and upper 

bound of the fitted E-R function: Percent of children with asthma estimated to 

experience at least one or multiple days per year with a SO2-related increase in 

sRaw of 100% or more while breathing at elevated rates, air quality adjusted to 

just meet the existing standard, 2011-2013........................................................ 6-36
xi

LIST OF ACRONYMS AND ABBREVIATIONS

A/C air conditioner

ACS American Community Survey

AER air exchange rate

AHR airway hyperresponsiveness

AHS American Housing Survey

APEX Air Pollutants Exposure model

AQS Air Quality System

ASOS Automated Surface Observing Stations

BASE Building Assessment Survey and Evaluation

BSA body surface area

CAA Clean Air Act

CASAC Clean Air Scientific Advisory Committee

CHAD Consolidated Human Activity Database

DV design value

EGU Electricity generating unit

EPA Environmental Protection Agency

E-R exposure-response

EVR equivalent ventilation rate

FEV1 forced expiratory volume in one minute

IRP Integrated Review Plan

ISA Integrated Science Assessment

ISH Integrated Surface Hourly

km kilometer

lat latitude

lon longitude

m meter

MCC Markov-chain clustering

ME microenvironment

MER mixed-effects regression

MLR multiple linear regression

MRLC Multi-Resolution Land Characteristics

MSA Metropolitan Statistical Area

NAAQS National Ambient Air Quality Standard

NCEI National Centers for Environmental Information

NED National Elevation Data
xii

NEI National Emissions Inventory

NHIS National Health Interview Survey

NLCD National Land Cover Dataset

NO2 nitrogen dioxide

NWS National Weather Service

O3 ozone

OAQPS Office of Air Quality Planning and Standards

ppb parts per billion

PA Policy Assessment

PM particulate matter

PMR peak-to-mean ratio

PSD Prevention of Significant Deterioration

REA Risk and Exposure Assessment

RMR resting metabolic rate

SIP State Implementation Plan

SOX oxides of sulfur

sRaw specific airway resistance

V̇ E activity-specific ventilation rate

WHO World Health Organization
1-1

1 INTRODUCTION

This document, Risk and Exposure Assessment for the Review of the Primary National 

Ambient Air Quality Standard for Sulfur Oxides (hereafter referred to as REA), describes the 

quantitative human exposure and risk characterization conducted to inform the U.S. 

Environmental Protection Agency’s (EPA’s) current review of the primary (health-based)1

national ambient air quality standard (NAAQS) for sulfur oxides (SOX). This document presents 

the methods, key results, observations, and related uncertainties associated with the quantitative 

analyses performed. The REA draws upon the Integrated Science Assessment (ISA; U.S. EPA 

2017a) and reflects consideration of the Clean Air Scientific Advisory Committee’s (CASAC)

advice and public comments on the draft REA. 

In this review, as in each NAAQS review, the policy implications of the REA results are 

considered in the policy assessment prepared separately for the review. The policy assessment 

presents analyses and staff conclusions regarding the policy implications of the key scientific and 

technical information that informs the review. The policy assessment is intended to “bridge the 

gap” between the relevant scientific evidence and technical information and the judgments 

required of the Administrator in his consideration of the adequacy of the current standards. The 

policy assessment for this review of the primary NAAQS for SOX is titled, Policy Assessment for 

the Review of the Primary National Ambient Air Quality Standard for Sulfur Oxides (PA; U.S. 

EPA, 2018).

The remainder of this chapter summarizes the legislative requirements (section 1.1), 

provides an overview of the history of the primary NAAQS for SOX (section 1.2), and describes 

aspects of the REA that have been updated since the 2009 REA, and revisions made from the 

draft to the final REA in consideration of CASAC recommendations and public comments

(section 1.3). Following Chapter 1, the REA presents an overview of the assessment approach 

(Chapter 2), describes the study areas and air quality modeling (Chapter 3), describes the 

exposure modeling and risk characterization (Chapter 4), presents the exposure and risk 

estimates (Chapter 5), and describes the analysis of variability and characterization of 

uncertainty (Chapter 6).

 

1 The EPA is separately reviewing the welfare effects associated with sulfur oxides and the public welfare protection 

provided by the secondary SO2 standard, in conjunction with a review of the secondary standards for nitrogen 

oxides and particulate matter with respect to their protection of the public welfare from adverse effects related to 

ecological effects (U.S. EPA, 2017b).
1-2

1.1 BACKGROUND

Sections 108 and 109 of the Clean Air Act (CAA) govern the establishment and periodic 

review of the NAAQS. Section 108 [42 U.S.C. 7408] directs the Administrator to identify and 

list certain air pollutants and then to issue air quality criteria for those pollutants. The 

Administrator is to list those air pollutants “emissions of which, in his judgment, cause or 

contribute to air pollution which may reasonably be anticipated to endanger public health or 

welfare,” “the presence of which in the ambient air results from numerous or diverse mobile or 

stationary sources;” and “for which...[the Administrator] plans to issue air quality criteria....” 

CAA section 108(a)(1). The NAAQS are established for the pollutants listed. The CAA requires 

that NAAQS are to be based on air quality criteria, which are intended to “accurately reflect the 

latest scientific knowledge useful in indicating the kind and extent of all identifiable effects on 

public health or welfare which may be expected from the presence of [the] pollutant in the 

ambient air...” CAA section 108(a)(2). Under CAA section 109 [42 U.S.C. 7409], the EPA 

Administrator is to propose, promulgate, and periodically review, at five-year intervals, 

“primary” (health-based) and “secondary” (welfare-based)2 NAAQS for such pollutants for 

which air quality criteria are issued.3 Based on periodic reviews of the air quality criteria and 

standards, the Administrator is to make revisions in the criteria and standards, and promulgate 

any new standards, as may be appropriate. The CAA also requires that an independent scientific 

review committee review the air quality criteria and standards and recommend to the 

Administrator any new standards and revisions of existing air quality criteria and standards as 

may be appropriate, a function now performed by the CASAC.

The current primary NAAQS for SOX is a 1-hour standard set at a level of 75 parts per 

billion (ppb), based on the 3-year average of the annual 99th percentile of 1-hour daily maximum 

SO2 concentrations. This standard was set in the last review of the primary NAAQS for SOX,

which was completed in 2010 (75 FR 35520, June 22, 2010). In comparison to the standards 

existing at that time, establishment of the 1-hour standard was determined to provide increased 

protection for people with asthma and other at-risk populations against an array of respiratory 

 

2 Section 302(h) of the CAA provides that all language referring to effects on welfare includes but is not limited to, 

“...effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility, and 

climate, damage to and deterioration of property, and hazards to transportation, as well as effects on economic 

values and on personal comfort and well-being....”

3 Section 109(b)(1) [42 U.S.C. 7409] of the CAA defines a primary standard as one “the attainment and maintenance 

of which in the judgment of the Administrator, based on such criteria and allowing an adequate margin of safety, 

are requisite to protect the public health.” Section 109(b)(2) of the CAA directs that a secondary standard is to 

“specify a level of air quality the attainment and maintenance of which, in the judgment of the Administrator, 

based on such criteria, is requisite to protect the public welfare from any known or anticipated adverse effects 

associated with the presence of [the] pollutant in the ambient air.”
1-3

effects related to short-term exposures (as short as 5 minutes) and to maintain longer-term 

concentrations below those specified by the then-existing standards (75 FR 35550, June 22, 

2010).4

The EPA initiated the current review of the primary NAAQS for SOX in May 2013, with 

a call for information from the public (78 FR 27387, May 10, 2013). The EPA held a workshop 

on June 12-13, 2013 to discuss policy-relevant scientific and technical information to inform the 

EPA’s planning for the review. Following the workshop, the EPA developed the plan for the 

review, which is described in the Integrated Review Plan for the Primary National Ambient Air 

Quality Standard for Sulfur Dioxide (U.S. EPA, 2014; hereafter referred to as the IRP). The IRP 

includes policy-relevant questions for the review, the process and schedule for conducting the 

review, and descriptions of the purpose, contents and approach for developing the key 

documents for the review.

The key documents in the review include an Integrated Science Assessment (ISA), a 

REA (as warranted), and a PA. In general terms, the ISA is to provide a critical assessment of the 

latest available scientific information upon which the NAAQS are to be based, and the PA is to 

evaluate the policy implications of the information contained in the ISA and of any policyrelevant quantitative analyses, such as a quantitative REA performed for the current review or, as 

applicable, for past reviews. Based on that evaluation, the PA presents staff conclusions 

regarding policy options for the Administrator to consider in reaching decisions on the NAAQS.5

The EPA has developed this REA describing the quantitative risk and exposure 

assessment being conducted by the Agency to support this review of the primary SOX standard. 

This document is intended to be a concise presentation of the methods, key results, observations, 

and related uncertainties associated with the analyses performed. The REA builds upon the 

health effects evidence presented in the ISA, as well as CASAC advice and public comments on 

the REA planning document (Review of the Primary National Ambient Air Quality Standard for 

Sulfur Oxides: Risk and Exposure Assessment Planning Document, REA Planning Document, 

U.S. EPA, 2017c) following a consultation with the CASAC at a public meeting in March 2017 

(82 FR 11449). In consideration of CASAC comments at that consultation and public comments, 

the EPA developed the draft REA (U.S. EPA, 2017d) and the draft PA (U.S. EPA 2017e), which 

 

4

In the 2010 decision to establish a new 1-hour standard, the EPA revoked the then-existing 24-hour and annual 

primary standards.

5 The basic elements of a standard include the indicator, averaging time, form, and level. The indicator defines the 

pollutant to be measured in the ambient air for the purpose of determining compliance with the standard. The 

averaging time defines the time period over which air quality measurements are to be obtained and averaged or 

cumulated. The form of a standard defines the air quality statistic that is to be compared to the level of the 

standard in determining whether an area attains the standard. The level of a standard defines the air quality 

concentration used (i.e., an ambient air concentration of the indicator pollutant).
1-4

were released on August 4, 2017 (82 FR 43756, September 19, 2017). The draft REA and draft 

PA were reviewed by the CASAC on September 18-19, 2017 (82 FR 37213, August 9, 2017). 

Following a CASAC teleconference on April 20, 2018 (83 FR 14638, April 5, 2018), the 

CASAC’s recommendations, based on its review of the draft REA and draft PA, were provided 

in a letter to the EPA Administrator (Cox and Diez Roux, 2018a,b). The EPA staff considered

these recommendations, as well as public comments provided on the draft REA and draft PA, 

when developing this REA.

The ISA and REA informed the development of the PA and will inform the subsequent 

rulemaking steps that will lead to final decisions on the primary NAAQS for SOX. The PA 

document includes staff analysis of the scientific basis for policy options for consideration by the 

Administrator prior to rulemaking. The PA integrates and interprets information from the ISA 

and the REA to frame policy options for consideration by the Administrator. The PA is intended 

to help “bridge the gap” between the Agency’s scientific and technical assessments, presented in 

the ISA and REA and the judgments required of the Administrator in determining whether it is 

appropriate to retain or revise the standards. The PA is also intended to facilitate the CASAC’s 

advice to the Administrator on the adequacy of existing standards, and any new standards or 

revisions to existing standards as may be appropriate. Concurrent with the release of this REA, 

the PA (U.S. EPA, 2018) is also being released.

The schedule for completion of this review is governed by a court order, which resulted 

from the entry of consent decree resolving a lawsuit that was filed in July 2016 and that 

concerned, in relevant part, the timing of completion of this review. Center for Biological 

Diversity et al. v. McCarthy (No. 4:16-cv-07396-VC, N.D. Cal.). The order specifies that the 

Administrator shall sign a notice setting forth his proposed decision concerning the review of the 

primary NAAQS for SOX no later than May 25, 2018; and sign a notice setting forth his final 

decision concerning the review of the primary NAAQS for SOX no later than January 28, 2019. 

1.2 PREVIOUS REVIEWS AND ASSESSMENTS

Reviews of the primary NAAQS for SOX completed in 1996 and 2010 included analyses 

of potential exposure to SO2 in ambient air (61 FR 25566, May 22, 1996; 75 FR 35520, June 22, 

2010). These analyses pertained to the then-existing 24-hour and annual standards, but primarily 

focused on whether additional protection was necessary to protect at-risk populations (people 

with asthma) against short-term (e.g., 5-minute) peak exposures while at elevated breathing rates 

(e.g., while exercising). The analyses that informed the review completed in 1996 focused on 

potential exposures to 5-minute concentrations at or above 600 ppb for several air quality 

scenarios (61 FR 2556, May 22, 1996). The 2010 review analyses estimated the number of 

individuals and percent of the modeled at-risk population that would be expected to experience 
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5-minute exposures above several concentrations of potential concern extending down to 100 

ppb (“benchmark concentrations” based on findings from controlled human exposure studies)

and also the number of individuals and percent of the population expected to experience a 

doubling or greater increase in specific airway resistance (sRaw) or a reduction in forced 

expiratory volume in one second (FEV1) of at least 15% (U.S. EPA, 2009 [hereafter referred to 

as the 2009 REA]). As summarized in more detail in the PA, the analyses in the 2009 REA 

informed the 2010 decision to establish a new 1-hour standard to protect at-risk populations from 

short-term (e.g., 5-minute) peak exposures (75 FR 35520, June 22, 2010).

The multiple quantitative analyses that informed the 1996 review decision are described 

in the 1986 Addendum to the 1982 OAQPS Staff Paper (U.S. EPA, 1986), the 1994 Supplement 

to the 1986 OAQPS Staff Paper Addendum (U.S. EPA, 1994) and the final decision notice (61 

FR 25566, May 22, 1996). A key aspect of the design for those analyses was the focus on 5-

minute concentrations at or above 600 ppb, an exposure level that the Agency judged could pose 

an immediate significant health risk for a substantial portion of asthmatics at elevated breathing

rates, e.g., while exercising (61 FR 25573, May 22, 1996). The available ambient monitoring 

data from 1988-1995 were analyzed to estimate the frequency of 5-minute peak concentrations 

above 500, 600, and 700 ppb, the number of repeated exceedances of these concentrations, and 

the sequential occurrences of peak concentrations within a given day (U.S. EPA, 1994; SAI, 

1996). The analysis indicated that during that period a substantial number of 5-minute 

concentrations at or above 600 ppb occurred in several locations in the vicinity of certain sources 

(61 FR 25574, May 22, 1996). The probability of at-risk individuals breathing at elevated levels

with the probability of encountering such peak concentrations was assessed in several exposure 

analyses (U.S. EPA, 1986, 1994; Burton et al., 1987; Rosenbaum et al., 1992; Stoeckenius et al., 

1990; Sciences International, Inc., 1995).

A series of exposure analyses informed the 1994 proposed decision. These analyses 

focused on exposures of interest associated with coal-fired power utilities, all power utility 

boilers, non-utility sources of SO2 emissions and such exposures associated with the projected 

reduction in emissions from fossil-fueled power plants following implementation of the acid 

deposition provisions (Title IV) of the 1990 Clean Air Act Amendments (U.S. EPA, 1986; 

Burton et al., 1987; Stoeckenius et al., 1990; Rosenbaum et al., 1992). Subsequent to the 1994 

proposal, an additional exposure analysis of non-utility sources was submitted to the rulemaking 

docket (Sciences International, Inc., 1995). Together these analyses provided a range of 

estimates of the number of individuals with asthma and the percent of the population with 

asthma to be exposed to 5-minute concentrations of 500 and 600 ppb while at elevated exertion, 

as well as estimates of such individuals to be exposed on multiple occasions in a year. These 
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analyses generally employed the time-activity exposure modeling approaches and underlying 

data that were available at that time.

Quantitative analyses performed for the review completed in 2010, and documented in 

the 2009 REA, included analyses of the limited then-available ambient air monitoring data for 5-

minute concentrations in 40 U.S. counties and a population exposure assessment (75 FR 35520, 

June 22, 2010; 2009 REA). The air quality analyses provided estimates of the annual number of

days that daily 5-minute maximum SO2 concentrations at a monitor exceeded 5-minute

concentrations of interest or benchmark concentrations

6

(2009 REA, Chapter 7). In the exposurebased approach, population-based estimates of human exposure were developed using an 

exposure model in order to account for the time people spend in different microenvironments, as 

well as for time spent at elevated breathing rates while exposed to peak 5-minute SO2

concentrations (2009 REA, Chapter 8). The analyses were performed for recent ambient air 

concentrations (unadjusted, “as is” air quality), and with ambient air concentrations adjusted to 

just meet the then-existing annual and daily standards and several potential alternative standards.

The 2009 REA simulated population exposure using version 4.3 of the Air Pollutant 

Exposure (APEX) model, a probabilistic model that simulates the movement of individuals 

through time and space and estimates their exposure to a given pollutant in indoor, outdoor, and 

in-vehicle microenvironments.7 The model was used to simulate population exposures in two 

study areas: Greene County, MO and a three-county portion of the St. Louis Metropolitan 

Statistical Area (MSA). The simulated population included all people with asthma, with results 

also presented for the subset of those who were children. Health risk was characterized by 

estimating, for each air quality scenario: (1) the number and percent of people with asthma 

exposed, while breathing at elevated rates, to 5-minute daily maximum SO2 concentrations that 

exceeded the benchmark concentrations; and (2) the number and percent of exposed people with 

asthma estimated to experience moderate or greater lung function responses (in terms of FEV1

and sRaw) at least once per year and the total number of such lung function responses estimated 

to occur per year (2009 REA, Chapter 8 and 9). An extensive analysis of variability and 

 

6 The benchmark concentrations are concentrations chosen to represent “exposures of potential concern” which were 

used in the analyses to estimate exposures and risks associated with 5-minute concentrations of SO2 (75 FR 

35527, June 22, 2010). Based on the evidence in the 2008 ISA and recommendations from the CASAC, staff 

concluded that it was appropriate to examine 5-minute benchmark concentrations in the range of 100-400 ppb 

(2009 REA, chapter 7). The comparisons of SO2 concentrations to benchmark concentrations provided 

perspective on the extent to which, under various air quality scenarios, there was the potential for at-risk 

populations to experience SO2 exposures that could be of concern.

7 The APEX model is designed to account for sources of variability that affect people’s exposures. It stochastically 

generates simulated individuals using census-derived probability distributions for demographic characteristics 

based on the information from the Census at the tract, block-group, or block-level (2009 REA).
1-7

characterization of uncertainty accompanied the exposure estimates (2009 REA, sections 8.11 

and 9.4). 

1.3 CURRENT REVIEW, CASAC ADVICE AND PUBLIC COMMENT

In preparing the planning document for this REA, we considered the scientific evidence 

presented in the second draft ISA (U.S. EPA, 2016) and the key science policy issues raised in 

the IRP (U.S. EPA, 2014). In February 2017, the REA Planning Document was released to the

CASAC and made available for public comment (82 FR 11356, February 22, 2017). The EPA 

held a consultation with the CASAC and solicited comments on the REA Planning Document 

during a March 2017 public meeting at which the CASAC also reviewed the second draft ISA 

(82 FR 11356, February 22, 2017). The consultative advice from the CASAC and public 

comments were considered in developing the draft REA (U.S. EPA, 2017d), which implemented 

an exposure-based approach to assess population exposure and risk in three urban study areas 

(Fall River, MA, Indianapolis, IN, and Tulsa, OK). The draft REA was reviewed by the CASAC, 

along with the draft PA (82 FR 37213, August 9, 2017; 83 FR 14638, April 5, 2018). The EPA 

also solicited comment from the public on both documents (82 FR 43756 September 19, 2017; 

82 FR 48507, October 18, 2017). Comments and advice from the CASAC, and public comment 

have been considered in development of this REA and the PA.

1.3.1 REA Aspects Updated Since 2009

As was also the case in the last review of the primary sulfur dioxide (SO2) standards 

completed in 2010, the health effects evidence available in this review indicates that short-term 

exposures to SO2 are causally linked to respiratory effects and that people with asthma are the atrisk population. Specifically, controlled human exposure studies demonstrate an increased risk of 

lung function decrements for people with asthma exposed while at increased breathing rates. The 

quantitative risk and exposure assessment presented in this REA is based on these findings. The

approach to estimating health risk in this REA is similar to that in the REA conducted as part of 

the last review (2009 REA), which included quantitative analyses of both exposure and risk. 

Specifically, the 2009 REA included: analyses focused on short-term (5-minute) SO2

concentrations; an exposure assessment designed to estimate exposures likely to be experienced 

by at-risk populations while at elevated breathing rates; and risk characterization utilizing two 

types of metrics: (1) comparisons of exposures to concentrations of potential concern 

(benchmark levels), and (2) lung function risk estimates. 

The quantitative analyses performed for the current review and presented in this 

document reflect the use of several new pieces of information that address important areas of 

uncertainty identified in the last review. Perhaps most importantly, the REA uses an updated SO2
1-8

ambient air monitoring dataset. Specifically, the data for 5-minute concentrations are greatly 

expanded with regard to both the number of monitoring locations for which hourly maximum 5-

minute concentrations are available and the number for which all 5-minute values for each hour 

are available. Limitations in the 5-minute dataset available at the time of the last review 

influenced the approaches that could be used in the 2009 REA to characterize the potential for atrisk populations to experience exposures of potential concern. The analysis approach for this 

REA is based on linking the health effects information to population exposure estimates which

draws on the improved understanding of 5-minute SO2 concentrations and takes advantage of a 

number of improvements and updates to the air quality, exposure, and risk models, and their 

associated input data.

As in the last review, this REA uses the Air Pollutant Exposure model (APEX) to 

estimate population exposures that account for the time people spend in different 

microenvironments, as well as for time spent at elevated breathing rates while exposed to peak 5-

minute SO2 concentrations. The REA also reflects the new information and model improvements 

that are now available including:

• A SO2 air monitoring dataset that is greatly expanded with regard to both the number 

of monitoring locations for which hourly maximum 5-minute concentrations are 

available and the number for which all 5-minute values for each hour are available;

• Estimated exposures associated with air quality adjusted to just meet the current

standard across a three-year averaging period;

8

• Improvements in the air quality dispersion model, AERMOD, intended to reduce 

uncertainties in 1-hour concentration estimates;

• Greatly expanded database of human activity patterns that provide a stronger 

foundation for inhalation exposure modeling; 

• Improvements to the exposure model, APEX, designed to reduce uncertainties in 

personal attributes of simulated individuals (e.g., breathing rates); and,

• Use of an expanded dataset for development of a lung function exposure-response 

function, intended to reduce uncertainties in the response across the range of the study 

data. 

Based on the new information, model improvements, exploratory data evaluations, and updated 

characterization of uncertainties, the results from this REA provide an improved characterization 

of exposure and risk to inform the EPA’s review of the primary SO2 standard.

 

8 The 2009 REA estimated exposures considering air quality adjusted to just meet several alternative standards 

across a single-year period.
1-9

The exposure-based risk assessment for this review includes an assessment of air quality 

conditions just meeting the current standard in three study areas. The study areas were selected 

based on consideration of the magnitude of recent SO2 concentrations, number of monitors in the 

area, including those with 5-minute monitoring data, and population size. The risk 

characterization is based on comparisons of population 5-minute exposures at elevated breathing 

rates to health-based benchmark levels and estimated population risk of moderate or greater SO2-

related lung function decrements. The analyses and results are documented in this REA and key 

findings of the REA are considered in the broader context of the PA, which also considers the 

current evidence as assessed in the ISA and characterization of SO2 concentrations in ambient air 

across the U.S. based on recent monitoring data, with particular attention to peak 5-minute 

concentrations.

1.3.2 CASAC Advice and Public Comment

After consultation with the CASAC on the REA Planning Document (U.S. EPA, 2017c) 

in March 2017 (Diez Roux, 2017), the EPA developed the draft REA (U.S. EPA, 2017d). The 

CASAC SOX Panel discussed its review of the draft REA at a public meeting on September 20-

21, 2017 and in a public teleconference on April 20, 2018. The CASAC comments and 

recommendations on the draft REA are provided in a May 2018 letter to the Administrator (Cox 

and Diez Roux, 2018a). A number of comments on aspects of the draft REA were also received 

from the public (see the public docket for this review, EPA-HQ-OAR-2013-0566 at 

www.regulations.gov).

This final REA has been produced in consideration of the comments received on the draft 

REA from the CASAC and from the public. The approach used to estimate population exposure 

and risk has remained largely the same as the approach used in the draft REA, with a number of 

adjustments and additions to address comments. Key changes include:

• Clarification regarding key design aspects including the air quality scenario and scope of 

REA (Chapter 2);

• Revised study area maps that show locations of meteorological stations, air quality 

receptors, emissions sources, and ambient air monitors, that also indicate source types 

and SO2 emissions (sections 3.2 and 3.4);

• Improvements in estimating ambient concentrations associated with sources not explicitly 

modeled in the Indianapolis study area (section 3.2.4);

• Additional evaluations of the daytime estimated 1-hour and 5-minute ambient air 

concentrations in the three study areas by season (sections 3.2.5 and section 3.5.3.3);

• Expanded discussion regarding the approach used to adjust air quality to just meet the 

current standard (section 3.4);
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• Use of newly acquired continuous 5-minute ambient air monitoring data to estimate 5-

minute concentrations at modeled air quality receptors in the Indianapolis study area

(section 3.5.1);

• Analysis of asthma prevalence information regarding the influence of body mass index 

and race on populations with asthma (section 4.1.2);

• Reorganized, clarified and expanded discussion regarding exposure model input data (i.e., 

body weight, surface area, energy expenditure) and algorithms (i.e., resting metabolic 

rate, breathing rate) (section 4.1.3);

• Expanded discussion of using activity pattern data from any individual in CHAD, 

regardless of whether their asthma status is known or unknown, to represent the 

simulated individuals with asthma (section 4.3.3);

• Additional analysis and revised study area maps to better indicate where study area 

populations overlap with highest ambient SO2 concentrations (sections 5.1 and 5.4); and

• Updated analysis of the microenvironments where the simulated population experiences 

the highest exposures (section 5.2);

• Inclusion of number (and percentage) of individuals in the estimates of population

exposure and risk of lung function decrements presented in summary tables (section 5.2 

and 5.3);

• Expanded discussion of previously identified uncertainties, as well as identification and 

discussion of additional uncertainties (Table 6-3).
1-11

REFERENCES

Burton CS, Stoeckenius TE, Stocking TS, Carr EL, Austin BS, Roberson RL (1987). Assessment 

of Exposures of Exercising Asthmatics to Short-term SO2 Levels as a Result of 

Emissions from U.S. Fossil-fueled Power Plants. Systems Applications Inc., San Rafael, 

CA. Publication No. 87/176, September 23, 1987. 

Cox, LA; Diez Roux, A. (2018a). Letter from Louis Anthony Cox, Chair, Clean Air Scientific 

Advisory Committee, and Ana Diez Roux, Immediate Past Chair, Clean Air Scientific 

Advisory Committee, to Administrator E. Scott Pruitt. Re: CASAC Review of the EPA’s 

Risk and Exposure Assessment for the Review of the Primary National Ambient Air 

Quality Standard for Sulfur Oxides (External Review Draft - August 2017). April 30, 

2018.

Cox, LA; Diez Roux, A. (2018b). Letter from Louis Anthony Cox, Chair, Clean Air Scientific 

Advisory Committee, and Ana Diez Roux, Immediate Past Chair, Clean Air Scientific 

Advisory Committee, to Administrator E. Scott Pruitt. Re: CASAC Review of the EPA’s 

Policy Assessment for the Review of the Primary National Ambient Air Quality Standard 

for Sulfur Oxides (External Review Draft - August 2017). April 30, 2018.

Diez Roux A. (2017). Letter from Ana Diez Roux, Chair, Clean Air Scientific Advisory 

Committee, to Administrator Gina McCarthy. Re: Consultation on the EPA’s Review of 

the Primary National Ambient Air Quality Standard for Sulfur Oxides: Risk and 

Exposure Assessment Planning Document (External Review Draft – February 2017). 

April 18, 2017.

Rosenbaum AS, Hudischewskyj AB, Roberson RL, Burton CS. (1992). Estimates of Future 

Exposures of Exercising Asthmatics to Short-term Elevated SO2 Concentrations 

Resulting from Emissions of U.S. Fossil-fueled Power Plants: Effects of the 1990 

Amendments to the Clean Air Act and a 5-Minute Average Ambient SO2 Standard. 

Publication No. SYSAPP- 92/016. April 23, 1992. Docket No. A-84-25, IV-K-37.

SAI. (1996). Summary of 1988-1995 Ambient 5-Minute SO2 Concentration Data. Prepared for 

US EPA Office of Air Quality Planning and Standards by Systems Application 

International. Contract #68-D3-0101, May 1996.

Sciences International (1995). Estimate of the Nationwide Exercising Asthmatic Exposure 

Frequency to Short-term Peak Sulfur Dioxide Concentrations in the Vicinity of NonUtility Sources. Prepared for National Mining Association by Sciences International, Inc., 

Alexandria VA. April 1995. Docket No. A-84-25, VIII-D-71.

Stoeckenius TE, Garelick B, Austin BS, O’Connor K, Pehling JR. (1990). Estimates of 

Nationwide Asthmatic Exposures to Short-Term Sulfur Dioxide Concentrations in the 

Vicinity of Non-Utility Sources. Systems Applications Inc., San Rafael, CA. Publication 

No. SYSAPP-90/129, December 6, 1990.
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U.S. EPA. (1986). Review of the National Ambient Air Quality Standards for Sulfur Oxides: 

Updated Assessment of Scientific and Technical Information, Addendum to the 1982 

OAQPS Staff Paper. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC, EPA/450/5-86-13. Available from: NTIS, Springfield, VA; PB87-

200259/XAB.

U.S. EPA. (1994). Supplement to the second addendum (1986) to air quality criteria for 

particulate matter and sulfur oxides (1982): Assessment of new findings on sulfur dioxide 

acute exposure health effects in asthmatic individuals. Environmental Criteria and 

Assessment Office, Office of Health and Environmental Assessment, Office of Research 

and Development, Research Triangle Park, NC, EPA/600/FP-93/002, August 1994. 

Available at: https://www3.epa.gov/ttn/naaqs/standards/so2/s_so2_pr.html

U.S. EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO2 Primary 

National Ambient Air Quality Standard. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC, EPA-452/R-09-007, July 2009. Available at: 

https://www3.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf

U.S. EPA. (2014). Integrated Review Plan for the Primary National Ambient Air Quality 

Standard for Sulfur Dioxide, Final. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC, EPA-452/P-14-007, October 2014. Available at: 

https://www3.epa.gov/ttn/naaqs/standards/so2/data/20141028so2reviewplan.pdf

U.S. EPA. (2016). Integrated Science Assessment (ISA) for Sulfur Oxides – Health Criteria 

(Second External Review Draft). National Center for Environmental Assessment-RTP 

Division, Office of Research and Development, Research Triangle Park, NC, 

EPA/600/R-16/351, December 2016. Available at: 

https://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=326450

U.S. EPA. (2017a). Integrated Science Assessment (ISA) for Sulfur Oxides – Health Criteria 

(Final). National Center for Environmental Assessment-RTP Division, Office of 

Research and Development, Research Triangle Park, NC, EPA/600/R-17/451, December 

2017. Available at: https://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=338596

U.S. EPA. (2017b). Integrated Review Plan for the Secondary National Ambient Air Quality 

Standard for Ecological Effects of Oxides of Nitrogen, Oxides of Sulfur and Particulate 

Matter. Office of Air Quality Planning and Standards, Research Triangle Park, NC, EPA452/R-17-002, January 2017. Available at: https://www.epa.gov/naaqs/nitrogen-dioxideno2-and-sulfur-dioxide-so2-secondarystandards-planning-documents-current

U.S. EPA. (2017c). Review of the Primary National Ambient Air Quality Standard for Sulfur 

Oxides: Risk and Exposure Assessment Planning Document. EPA-452/P-17-001, 

February 2017. Available at: 

https://www3.epa.gov/ttn/naaqs/standards/so2/data/20170216so2rea.pdf

U.S. EPA. (2017d). Review of the Primary National Ambient Air Quality Standard for Sulfur 

Oxides: Risk and Exposure Assessment Planning Document. Office of Air Quality 

Planning and Standards, Research Triangle Park, NC, EPA-452/P-17-001, February 
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2017. Available at: 

https://www3.epa.gov/ttn/naaqs/standards/so2/data/20170216so2rea.pdf

U.S. EPA. (2017e). Policy Assessment for the Review of the Primary National Ambient Air 

Quality Standard for Sulfur Oxides, External Review Draft. Office of Air Quality 

Planning and Standards, Research Triangle Park, NC, EPA-452/P-17-003, August 2017. 

Available at: https://www.epa.gov/naaqs/sulfur-dioxide-so2-primary-air-qualitystandards

U.S. EPA. (2018). Policy Assessment for the Review of the Primary National Ambient Air 

Quality Standard for Sulfur Oxides, Final. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC, EPA-452/R-18-002, May 2018. Available at: 

https://www.epa.gov/naaqs/sulfur-dioxide-so2-primary-air-quality-standards
2-1

2 OVERVIEW OF ASSESSMENT APPROACH

As summarized in the IRP and PA for this review of the NAAQS for SOX, the review 

focuses on the presence in ambient air of sulfur oxides, a group of closely related gaseous 

compounds that include sulfur dioxide and sulfur trioxide and of which sulfur dioxide (the 

indicator for the current standard) is the most prevalent in the atmosphere and the one for which 

there is a large body of scientific evidence on health effects. Sulfur trioxide is known to be 

present in the emissions of coal-fired power plants, factories, and refineries, but it reacts with 

water vapor within emission stacks or immediately after release into the atmosphere within 

seconds to form H2SO4 which quickly condenses onto existing atmospheric particles or 

participates in new particle formation (ISA, p. 2-18). Thus, only SO2 is present at concentrations 

in the gas phase that are relevant for chemistry in the atmospheric boundary layer and 

troposphere, and for human exposures (ISA, p. 2-18). The health effects of particulate 

atmospheric transformation products of SOX, such as sulfates, are addressed in the review of the 

NAAQS for particulate matter (U.S EPA, 2018; U.S. EPA, 2016). For these reasons, this REA is 

focused on SO2.

9 The conceptual model for exposure and associated health risk of SO2 in 

ambient air that guides our assessment in this review is described in this section along with an 

overview of the implemented approach. 

2.1 CONCEPTUAL MODEL FOR SO2 EXPOSURE AND RISK 

The conceptual model for our consideration of exposure and risk associated with SO2 in 

ambient air is illustrated in Figure 2-1. This general model guided our assessment in the last 

review and, as discussed in the REA Planning Document and draft REA, remains appropriate in 

the current review. The unshaded boxes indicate components included in the assessment in this 

review. Current information regarding the individual components specified in the model 

(emissions sources, exposure pathways, routes of exposure, exposed populations, health 

endpoints and risk metrics) is summarized in the following sections. A more detailed 

characterization of this information is presented in the ISA (U.S. EPA, 2017a).

 

9 While there are some toxicological animal studies of SOX in mixtures, such as with co-occurring PM, that indicate 

some enhanced effect on lung function parameters, there are a number of limitations with regard to appropriate 

controls and relevance to ambient air exposures (ISA, pp. 5-143 to 5-144). Thus, the available information does 

not support characterization in this assessment of any potential for modification SO2-related effects by 

copollutants, such as PM. Uncertainties in the exposure and risk estimates generated in this REA with regard to 

the potential for modification of SO2-related effects by co-occurring pollutants, such as PM, are characterized in 

section 6.2.1.
2-2

Note: The grey boxes indicate elements not included.

Figure 2-1.Conceptual model for exposure and associated health risk of SO2 in ambient air.
2-3

2.1.1 Sources of SO2

Sulfur dioxide occurs in ambient air as a result of direct emissions of SO2 as well as 

emissions of other compounds, such as reduced sulfur compounds or sulfides, that are converted 

to SO2 through chemical reactions in the atmosphere. The largest natural sources of SO2 are 

volcanoes and wildfires. Fossil fuel combustion is the main anthropogenic source of SO2 and

industrial chemical production and pulp and paper production are among the sources of reduced 

sulfur compounds that are converted to SO2 in the atmosphere. Anthropogenic sources of SO2

emissions that contribute to SO2 found in ambient air are primarily large facilities and include

coal-fired electricity generating units (EGUs) as well as other industrial facilities (U.S. EPA,

2008 [hereafter referred to as the 2008 ISA], section 2.1; ISA, section 2.2.1). Because such large, 

discrete sources are the primary source of SO2 (e.g., versus more prevalent, widespread sources),

ambient concentrations can vary substantially across an area and can be relatively high in areas 

affected by these large sources.

Coal-fired EGUs are an important emissions source because coal contains sulfur, which 

is present to some degree in all fossil fuels. The sulfur content of the most common types of coal

varies between 0.4 and 4% by mass (ISA, section 2.2). Fuel sulfur is almost entirely converted to 

sulfur oxides during combustion. This makes accurate estimates of SO2 combustion emissions 

possible based on fuel composition and combustion rates (ISA, section 2.2). Fuel combustion by 

electric utilities as well as industrial and other sources is the largest source of anthropogenic SO2

emissions (ISA, Figure 2-1).

Although they may be fewer in number than fossil fuel-fired EGUs, other types of large 

emissions facilities that may impact local air quality include copper smelters, kraft pulp mills, 

Portland Cement plants, iron and steel mill plants, sulfuric acid plants, petroleum refineries, and 

chemical processing plants. For example, the metal processing sector represents less than 2.3% 

of total emissions from the 2014 National Emissions Inventory (NEI),

10 however, monitoring 

sites that have recorded some of the highest 1-hour daily maximum SO2 concentrations in the 

U.S. are located near copper smelters in Arizona (ISA, sections 2.5.2 and 2.5.4, Figure 2-11).

The two smelters in this area emit appreciable quantities of SO2, estimated at 17,000 tons per 

year (tpy) and 5,000 tpy (ISA, p. 2-50), but for added perspective, several EGUs in other areas 

have been estimated to emit well over 50,000 tpy in the 2014 NEI.

The main indoor source of SO2 is indoor combustion of sulfur-containing fuels, such as 

from space heaters that are generally used in the U.S. as emergency or supplemental sources of 

heat. For example, a study in the eastern U.S. reported that kerosene heaters, but not fireplaces, 

 

10 The National Emissions Inventory (NEI) is a comprehensive and detailed estimate of air emissions of criteria 

pollutants, criteria precursors, and hazardous air pollutants from air emissions sources. For additional 

information, see https://www.epa.gov/air-emissions-inventories/national-emissions-inventory-nei. 
2-4

woodstoves, or gas space heaters, resulted in increased indoor concentrations of SO2 (ISA, 

section 3.4.1.1). Personal SO2 exposure measurements, however, have generally been lower than 

ambient air concentrations, indicating personal exposure is generally dominated by ambient air

(outdoor) sources (ISA, section 3.4.1).

The context for the REA is exposure and associated risk of SO2 emitted into ambient air. 

Accordingly, the conceptual model for the REA focuses on sources to ambient air (Figure 2-1). 

2.1.2 Exposure Pathways and Route

Human exposure to SO2 involves the contact between a person and the pollutant in any of 

the various locations (or microenvironments, MEs) in which people spend their time. As SO2 is a 

gas, human exposure occurs through inhalation of air containing SO2. The concentrations of SO2

occurring in each ME and the associated activity performed in the ME at the time of exposure 

both contribute to individual exposure events. Together, these exposure events make up an 

individual’s exposure (ISA, section 3.2.2). 

Exposure microenvironments occur indoors (e.g., in homes, offices or stores), outdoors 

(e.g., yards, parks, sidewalks) and in vehicles (e.g., automobiles, buses). All of these 

microenvironments can receive ambient air that may contain SO2. Thus, the pathways by which 

people are exposed to SO2 in ambient air involve inhaling air while spending time in the various 

MEs. 

While indoors, people can be exposed to SO2 from indoor sources as well as to SO2

associated with outdoor air that has infiltrated into indoor MEs. Studies of personal exposure 

have generally found that the largest portion of a person’s day is spent indoors (ISA, section 

3.4.2.1). As a result of this and indoor SO2 concentrations typically being lower than SO2

concentrations measured outdoors, SO2 exposure concentrations are often much lower than SO2

concentrations in ambient air (ISA, section 3.4.1). As stated in the ISA, high correlations (>0.75) 

between indoor and outdoor SO2 concentrations indicate that variations in outdoor ambient SO2

concentration11 are driving indoor SO2 concentrations, which is considered to be consistent with 

the relative lack of indoor sources of SO2 (ISA, section 3.4.1.2).

Thus, personal SO2 exposure is expected to be dominated by SO2 emitted into ambient air 

in outdoor microenvironments and enclosed microenvironments with high air exchange rates, 

such as buildings with open windows and vehicles. This was found to be the case in exposure 

 

11Concentrations of SO2 in ambient air are spatially highly variable compared to pollutants such as ozone (ISA, 

section 3.2.3); this is due to the point source nature of SO2 emissions. Another factor in the spatial variability is 

the dispersion and oxidation of SO2 in the atmosphere, processes that contribute to decreasing concentrations with 

increasing distance from the source. Point source emissions of SOX create a plume of higher concentrations, 

which may or may not impact large portions of surrounding populated areas depending on meteorological 

conditions and terrain (ISA, section 3.2.3).
2-5

modeling of recent air quality performed for the 2009 REA: more than 80% of the events by 

which simulated individuals experienced elevated 5-minute exposure concentrations of interest 

were in outdoor MEs (2009 REA, Figure 8-21). As was done in the 2009 REA for the last review

of the NAAQS for SOX, exposures to SO2 in ambient air outdoors, as well as to ambient air that 

has infiltrated indoors, are included in the REA for the current review.

2.1.3 At-Risk Populations 

As at the time of the 2009 REA, the current evidence demonstrates that the populations at 

increased risk of effects from SO2 exposure continue to be people with asthma, particularly 

children with asthma (ISA, section 6.3.1). Strong evidence of this comes from the controlled 

human exposure studies of people with asthma exposed to SO2 when their breathing rates are 

increased, such as from exercise (ISA, section 5.2.1.9). Consistent with the controlled human 

exposure study findings of asthma exacerbation-related effects, some epidemiological studies in 

the current evidence report associations between short-term SO2 exposure and increased risk of 

asthma-related emergency department visits and hospital admissions (ISA, section 5.2.1.9). 

The short-term respiratory effects that are the focus of the quantitative assessment, and 

for which the evidence for respiratory effects associated with policy-relevant SO2 exposure 

concentrations is strongest, are asthma exacerbation-related effects (ISA, Table 1-1). Under 

resting conditions, inhaled SO2 is readily removed in the nasal passages (ISA, section 1.5.1). 

However, during activities that result in elevated breathing rates, such as those associated with 

exercise, and/or an increased potential for taking breaths through the mouth (versus the nose), 

there is greater transport of inhaled SO2 past the nasal passages to the tracheobronchial region of 

the airways where it can contribute to bronchoconstriction-related effects and asthma 

exacerbation (ISA, section 1.5.1). Thus, elevated breathing rates and breathing habits that 

include breathing through the mouth (oronasal), such as that occurring during exercise, play 

important roles in eliciting SO2-related effects in at-risk populations.

While some controlled exposure studies involving adolescents with asthma have 

indicated that this age group has similar responsiveness as adults, controlled exposure study data 

are not available for children younger than 12 years (ISA, section 5.2.1.2). However, some 

factors indicate that children (e.g., younger than 13 years) with asthma may be at a greater risk 

than adults with asthma. For example, children, particularly younger than 13 years of age, have a 

greater tendency to breathe through the mouth than do adults (ISA, section 4.1.2.2). Evidence 

also suggests that older adults with asthma may also be at an increased risk compared to younger 

adults with asthma (ISA, section 6.5.1.2). 

The evidence in controlled exposure studies documents the difference in sensitivity to 

SO2-related respiratory effects in individuals with and without asthma. For example, these 
2-6

studies document respiratory effects occurring in exercising study subjects with asthma at 

exposure concentrations below 1000 ppb, while higher concentrations are needed to elicit similar

effects in healthy subjects and in some subjects with asthma (ISA, sections 5.2.1.2 and 5.2.1.7).12

The currently available information does not identify other populations at increased risk beyond 

what is described here (ISA, section 6.6). As indicated in Figure 2-1, people with asthma, 

including both adults and children, are specifically identified as at-risk populations in the REA 

for this review.

2.1.4 Health Endpoints

The health effects causally related to SO2 exposures are effects on the respiratory system 

(ISA, section 1.6). As demonstrated in long-standing evidence from controlled human exposure 

studies and consistent with findings in epidemiological studies, short-term SO2 exposures (as 

short as a few minutes) can result in asthma exacerbation-related effects in people with asthma. 

The controlled human exposure studies have demonstrated a relationship between 5- and 10-

minute peak SO2 exposures and bronchoconstriction-related decrements in lung function in 

exercising individuals with asthma; depending on the exposure level, these decrements are 

accompanied by respiratory symptoms (ISA, section 5.2.1.2). 

Lung function decrements were quantified in these studies by reductions in forced 

expiratory volume in one second, FEV1, and increased specific airway resistance, sRaw. In 

considering the magnitude of these responses, the ISA (as in the 2008 ISA) focuses on 15% or 

greater reductions in FEV1 and increases in sRaw of 100% or more (ISA, sections 1.6.1.1 and

5.2.1.2). Such responses have been reported in some individuals with asthma exposed to 5-

minute concentrations as low as 200 ppb while exercising. Across the range of exposure 

concentrations studied, both the percentage of individuals affected to at least this degree and the 

severity of the response increases with increasing SO2 concentrations. At higher concentrations 

(above 400 ppb), such responses were frequently accompanied by respiratory symptoms (ISA, 

section 5.2.1.2).

2.1.5 Risk Metrics

As was the case in the 2009 REA, the risk metrics included in the current REA (bottom 

panels, Figure 2-1) are based on the SO2-induced bronchoconstriction-related lung function 

 

12 The evidence from controlled exposure studies has long documented the sizeable variation in sensitivity to SO2

among individuals with asthma. This was further characterized in a pooled analysis of data from five such studies 

that is newly available in this review (Johns et al., 2010). This new analysis demonstrates the study population of 

individuals with asthma to fall into one of two subpopulations with regard to airway responsiveness to SO2. One 

subpopulation is insensitive to the bronchoconstrictive effects of SO2 even at concentrations as high as 1.0 ppm, 

and it is the second subpopulation that has an increased risk for bronchoconstriction at the lower concentrations of 

SO2 (ISA, section 5.2.1.2).
2-7

decrements documented in the strong evidence base of controlled human exposure studies of 

exercising individuals with asthma. Bronchoconstriction, an asthma-exacerbation-related effect,

is the “most sensitive indicator of SO2-induced lung function effects” and the evidence for this 

effect is strong (ISA, section 5.2.1.2, p. 5-8). The first of the risk metrics included in this REA 

involves characterization of the extent to which individuals with asthma were estimated to 

experience 5-minute exposures at or above concentrations of potential concern while they are at 

elevated breathing rates. The second metric quantifies the extent to which individuals with 

asthma are estimated to experience lung function responses (in terms of a doubling, or larger 

increase, in sRaw) as a result of 5-minute SO2 exposures while at elevated breathing rates.

In deriving these two risk metrics, the controlled human exposure studies are used in two 

ways: (1) to identify exposure concentrations of potential concern (“benchmark concentrations”) 

and (2) to derive exposure-response (E-R) functions for lung function decrements. As described 

in more detail in section 3.5.1, the benchmark concentrations are 5-minute exposure 

concentrations chosen to represent exposures of potential concern. The first metric, the 

comparison of SO2 exposures to benchmark concentrations, provides perspective on the extent to 

which there is potential for sensitive individuals with asthma to experience SO2 exposures that 

could be of concern at air quality just meeting the current standard.

The second metric relies on the E-R function and exposure estimates to estimate risk of 

decrements in lung function based on sRaw, which is a specific measure of bronchoconstriction. 

The focus on sRaw as the primary indicator of lung function response is consistent with the 

emphasis on this indicator in the REA for the last review. The E-R functions for sRaw are based 

on more observations from individual subjects than were E-R functions based on FEV1 (2009 

REA, p. 332), which provides greater confidence in the resultant quantitative relationship when 

compared with that developed for the FEV1 health endpoint.

Another category of metric shown in the conceptual model figure represents potential 

asthma-exacerbation-related health outcomes that are reported in the epidemiological evidence. 

As indicated by the shading in Figure 2-1, this category of metrics is not included in this REA as 

the current evidence base does not support its inclusion. This was also the case in the 2009 REA 

(REA Planning Document, section 3.2.3). As examined in detail in the ISA, the epidemiological 

evidence includes studies reporting associations between short-term SO2 concentrations and 

asthma-related emergency department visits or hospitalizations. The risk characterization for the 

2009 REA focused on metrics for lung function decrements related to bronchoconstriction, 

concluding that the epidemiological evidence did not support development of an epidemiological 

study-based risk model. In considering support in the evidence available in this review, the REA 

Planning Document for this REA reached the same conclusion (REA Planning Document, 
2-8

section 3.2.3). Thus, as shown in Figure 2-1, the incidence of respiratory health outcomes metric 

is not included in this REA.

2.2 ASSESSMENT APPROACH

The approach employed for this REA generally involves estimating population exposures 

to ambient air-related SO2 concentrations and associated health risk for air quality conditions 

simulated to just meet the current standard (Figure 2-2). This approach, which draws on air 

monitoring data, air quality modeling and exposure modeling, was applied in three study areas 

(section 3.1) selected to be most informative to this review. The focus on air quality conditions 

just meeting the current standard reflects the key overarching question articulated in the IRP for 

this review: Does the currently available scientific evidence- and exposure/risk-based 

information, as reflected in the ISA and REA, support or call into question the adequacy of the 

protection afforded by the current standard (IRP, section 3; PA, section 3.2)? In considering the 

final ISA and the draft REA results, the draft PA reached preliminary conclusions that the 

answer to this question was no and that it is appropriate to consider retaining the current standard 

without revision. The CASAC concurred with this conclusion (Cox and Diez Roux, 2018). 

Accordingly, exposure and risk analyses using alternative air quality conditions were not 

warranted and have not been performed for this REA.

As indicated by the case study approach, the REA analyses are not intended to provide a 

comprehensive national assessment. Rather, they are intended to provide assessments for a small 

varied set of study areas, and the associated exposed at-risk populations, that will be informative 

to EPA’s consideration of potential exposures and risks that may be associated with the air 

quality conditions occurring under the current SO2 standard. The purpose of the REA is to 

assess, based on the currently available, improved and expanded tools and information, the 

potential for exposures and risks beyond those indicated by the information available at the time 

the current standard was established. In this way, the REA can inform the EPA’s conclusions on 

the public health protection afforded by the current standard. 

Consistent with the health effects evidence and the health risk metrics identified in 

section 2.1.5, the focus is on short-term exposures of individuals in the population with asthma 

during times when they are breathing at an elevated rate. Exposure and risk is characterized for 

two population groups: adults (individuals older than 18 years) with asthma and school-aged 

children (aged 5 to 18 years)13 with asthma. The focus on these populations is consistent with the 

ISA’s identification of individuals with asthma as the population at risk of SO2-related effects, 

 

13 As in other NAAQS reviews, this REA does not estimate exposures and risk for children younger than 5 years old 

due to the more limited information contributing relatively greater uncertainty in modeling their activity patterns 

and physiological processes than children between the ages of 5 to 18.
2-9

and its conclusion that within this population, children with asthma may be at greater risk than 

adults with asthma (ISA, section 6.6). 

In order to estimate ambient air concentrations at the needed temporal scale of 5-minute 

increments, the REA employs air quality modeling as informed by additional information from 

5-minute ambient air monitoring data. Air quality modeling is used in order to adequately 

capture the spatial variation in ambient SO2 concentrations across an urban area, which can be 

relatively high in areas affected by large point sources and which the limited number of 

monitoring locations in each area are unlikely to capture. Continuous 5-minute ambient air 

monitoring data are used to reflect the fine-scale temporal variation in SO2 concentrations 

documented by these data and for which air quality modeling is limited, e.g., by limitations in 

currently available input data such as emissions estimates. Thus, 5-minute concentrations in 

ambient air were estimated using a combination of 1-hour concentrations from the EPA’s 

preferred near-field dispersion model, the American Meteorological Society/EPA regulatory 

model (AERMOD), and relationships between 1-hour and 5-minute concentrations occurring in 

the local ambient air monitoring data.14

The Air Pollutants Exposure (APEX) model, a probabilistic human exposure model that 

simulates the activity of individuals in the population, including their exertion levels and 

movement through time and space, was then used to estimate 5-minute exposure concentrations 

for individuals based on exposures in indoor, outdoor, and in-vehicle microenvironments. The 

use of APEX for estimating exposures allows for consideration of factors that affect exposures 

that are not addressed by consideration of ambient air concentrations alone. These factors include 

1) attenuation in SO2 concentrations expected to occur in some indoor microenvironments, 2) the 

influence of human activity patterns on the time series of exposure concentrations, and 3) 

accounting for human physiology and the occurrence of elevated breathing rates concurrent with 

SO2 exposures, all key to appropriately characterizing health risk for SO2. 

The estimated exposures were then combined with findings of the controlled human 

exposure studies to characterize health risk using two approaches. The first approach compares 

estimated exposures to benchmark concentrations of interest and the second combines exposures 

with an E-R function to estimate the expected occurrences of decrements in lung function. 

 

14 The current information continues to support the use of an air dispersion model such as AERMOD over the use of 

other models, such as photochemical models, for modeling of directly emitted SO2 concentrations for use in 

assessing risk and exposure for this pollutant. Unlike dispersion models, photochemical models cannot capture 

the sharp concentration gradients that can occur near SO2 sources. Also, SO2 emissions to ambient air are 

dominated by point sources, such as large coal-fired utilities, and AERMOD is the EPA’s preferred air quality 

model for SO2 for State Implementation Plans (SIPs) and new source permitting purposes. For all of these 

reasons, AERMOD remains the most appropriate model for predicting SO2 concentrations in ambient air.
2-10

Thus, two types of risk metrics were derived from the simulated individual exposure 

profiles: (1) the number and percent of the simulated subpopulation that had at least one 5-

minute exposure above the benchmark concentrations of 100, 200, 300, and 400 ppb and (2) the 

number and percent per year of simulated at-risk individuals that would experience moderate or 

greater lung function decrements in response to 5-minute daily maximum peak exposures while 

engaged in moderate or greater exertion. Estimates were developed for three study areas. The 

details and basis for each of these aspects of the assessment are described in Chapters 3 and 4.

Figure 2-2.Overview of the assessment approach.
2-11

REFERENCES

Cox, LA; Diez Roux, A. (2018). Letter from Louis Anthony Cox, Chair, Clean Air Scientific 

Advisory Committee, and Ana Diez Roux, Immediate Past Chair, Clean Air Scientific 

Advisory Committee, to Administrator E. Scott Pruitt. Re: CASAC Review of the EPA’s 

Policy Assessment for the Review of the Primary National Ambient Air Quality Standard 

for Sulfur Oxides (External Review Draft - August 2017). April 30, 2018.

Johns DO, Svendsgaard D, Linn WS. (2010). Analysis of the concentration-respiratory response 

among asthmatics following controlled short-term exposures to sulfur dioxide. Inhal 

Toxicol. 22:1184-1193. http://dx.doi.org/10.3109/08958378.2010.535220

U.S. EPA. (2008). Integrated Science Assessment (ISA) for Sulfur Oxides – Health Criteria 

(Final Report). National Center for Environmental Assessment-RTP Division, Office of 

Research and Development, Research Triangle Park, NC, EPA-600/R-08/047F, 

September 2008. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843

U.S. EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO2 Primary 

National Ambient Air Quality Standard. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC, EPA-452/R-09-007, July 2009. Available at: 

https://www3.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf

U.S. EPA. (2016). Integrated Review Plan for the Secondary National Ambient Air Quality 

Standards for Particulate Matter. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC, EPA-452/R-16-005, December 2016. Available at: 

https://www.epa.gov/naaqs/particulate-matter-pm-standards-planning-documents-currentreview

U.S. EPA. (2017). Integrated Science Assessment (ISA) for Sulfur Oxides – Health Criteria 

(Final). National Center for Environmental Assessment-RTP Division, Office of 

Research and Development, Research Triangle Park, NC, EPA/600/R-17/451, December 

2017. Available at: https://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=338596

U.S. EPA. (2018). Policy Assessment for the Review of the Primary National Ambient Air 

Quality Standard for Sulfur Oxides, Final. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC, EPA-452/R-18-002, May 2018. Available at: 

https://www.epa.gov/naaqs/sulfur-dioxide-so2-primary-air-quality-standards
3-1

3 AMBIENT AIR CONCENTRATIONS

As summarized in chapter 2, the approach for this REA is based on linking the health 

effects information to estimated population-based exposures that reflect our current 

understanding of 5-minute concentrations of SO2 in the ambient air. This approach is applied to 

three study areas to provide a valuable perspective on exposures and risks for at-risk populations 

that is informative to this review of the SO2 primary standard. This chapter describes the 

methodology for developing the spatial and temporal patterns of 5-minute concentrations in 

ambient air for each of the three study areas. Our overall objective for this methodology is not 

necessarily to develop an air quality surface for each study area that exactly matches one that has 

occurred. Rather, it is to develop a hypothetical air quality scenario that accounts for the spatial 

and temporal pattern of ambient SO2 concentrations in each study area that might be expected to 

occur when the current primary SO2 standard has just been met, is based on the types of SO2

sources that have existed in the area (and local or nearby sources that may also influence ambient 

air concentrations) and considers the expected variability in observed meteorological conditions. 

This hypothetical scenario, however, is not necessarily reflective of a specific calendar year, 

even though data from specific years have been used as a basis for the development of the 

hypothetical scenario. In so doing, we have implemented methods intended to capture the 

appropriate spatial and temporal heterogeneity in SO2 concentrations that occur near and around 

important emissions sources considering this hypothetical air quality scenario and, when 

considering population demographics, to reasonably represent the population groups at risk for 

SO2-related health effects.

The three study areas and time periods simulated are described in section 3.1 below. Air 

quality modeling is used to develop the spatially varying distributions of 1-hour concentrations, 

as described in section 3.2. The definition of the extent and scale of the exposure modeling 

domain and associated air quality receptor grid is described in section 3.3. The next step in the 

approach is the development of an air quality scenario for each study area that reflects conditions 

that just meet the current standard. This step involves adjustment of the estimates resulting from 

the air quality modeling for each area. Section 3.4 summarizes the method used for the 

adjustment of the air quality concentrations to a scenario that just meets the current primary SO2

standard. Development of the temporally varying 5-minute concentrations at each air quality 

receptor site is described in section 3.5.
3-2

3.1 CHARACTERIZATION OF STUDY AREAS

The study areas for this REA are Fall River, MA, Indianapolis, IN, and Tulsa, OK (Table 

3-1). These study areas were selected to meet a number of individual and collective criteria. The 

following list includes the criteria used in considering individual study areas:

• Design value1 near the current standard (75 ppb). Using recent air quality monitoring 

data (2011-2015), design values ranging from 50 ppb to 100 ppb were considered 

preferable in order to minimize the magnitude of the adjustment needed to generate air 

quality just meeting the current standard, therefore potentially minimizing the 

uncertainties in estimates of exposures associated with the adjustment approach. In 

considering areas with regard to this criterion, consecutive 3-year periods as far back as 

2011-2013 were considered. 

• One or more air quality monitors reporting 5-minute SO2 data for the 3-year study 

period. In judging whether monitors provided such a 3-year record, completeness 

requirements (summarized in section 3.5) were applied for all three years to ensure the

availability of adequate data for informing the ambient air concentrations used for 

exposure modeling. Study areas having continuous 5-minute data were preferable to 

those with only hourly maximum 5-minute data. There are no monitoring requirements to 

report continuous 5-minute data at all ambient air monitors, therefore we used this as an 

additional consideration after an initial screen for the top candidate areas. 

• Availability of existing air quality modeling datasets. There are many areas in the U.S. 

that have chosen to model air quality for regulatory purposes, i.e., in designating areas 

with regard to determining the attainment of the current standard. This criterion was 

considered important for efficiency purposes and to maintain consistency between our 

assessment approach and state-level modeling regarding the years selected, sources 

included, emission levels and profiles, and assumptions used to predict ambient air 

concentrations.

• Population size greater than 100,000. Candidate study areas having the larger

populations were given priority to provide a more robust and improved representation of 

exposures and risk to key at-risk populations.

• Significant and diverse emissions sources. Preference was given to study areas with a 

diverse source mix, including EGUs, petroleum refineries, and secondary lead smelting 

(generally reflects battery recycling). A diverse source mix allows for capturing 

 

1 A design value (DV) is a statistic that describes the air quality status of a given area relative to a particular

NAAQS. A design value summarizes the concentrations of a criteria pollutant in terms of the statistical form of 

the standard for that pollutant, thus indicating whether the area meets or exceeds the standard. Consistent with the 

form of the SO2 standard, SO2 design values are calculated as the 3-year average of the annual 99th percentile of 

the daily maximum 1-hour average concentrations (see 40 CFR 50.17). By regulation, design values calculated 

from monitoring data are considered to be valid if they meet specified completeness criteria, which for SO2 are 

data for at least 75 percent of the sampling days in all four quarters of all three years of the period (see Appendix 

T to Part 50).
3-3

exposures to both large sources (e.g., emissions of 10,000-20,000 tons per year [tpy])

2

and small sources (e.g., emissions of hundreds of tpy) distributed about a study area.

In consideration of the above criteria, Fall River, MA, Indianapolis, IN, and Tulsa, OK 

were selected.

3

In identifying this set of study areas, we also concluded it to be desirable for the 

study areas, as a set, to represent different geographical regions of the U.S. The three study areas 

– in Massachusetts, Indiana and Oklahoma –are in three different climate regions of the U.S.: the 

Northeast, Ohio River Valley (Central), and South (Karl and Koss, 1984). These regions, 

particularly the Ohio River Valley, generally have a higher concentration of EGU and non-EGU 

sources of SO2 emissions than other areas of the country (ISA, Figure 2-3). Given the objective 

of assessing air quality conditions that just meet the current standard, our focus, as indicated by 

the first criterion above, is not on the areas in the U.S. with ambient air concentrations 

substantially above the standard.

4 Additionally, we minimized inclusion of study areas near the 

ocean or large water bodies, such as the Great Lakes, given the potential for unusual atmospheric 

chemistry and associated transformation of SO2 in those areas and our limited ability to 

accurately model such events. 

We considered more than one hundred areas and multiple time periods as study area 

candidates. Closer examination of candidate areas and time periods led us to select the three 

study areas identified above and the study period of 2011 to 2013, as they best fit the above 

selection criteria.

5 The study areas and time periods selected – Fall River, MA, Indianapolis, IN, 

and Tulsa, OK (Table 3-1) – together represent an array of differing exposure circumstances for 

5-minute peak SO2 concentrations in ambient air. This array expands on the more limited set of 

study areas, focused in a single region of the U.S., that was addressed in the 2009 SO2 REA. As 

described in subsequent sections, information for the 2011-2013 period in the three study areas 

was used to develop the air quality scenarios that represent conditions just meeting the current 

 

2 While there may be other sources having similar or greater SO2 emissions, design values for the ambient monitors 

surrounding these other sources may not necessarily fall within that particular selection criterion. Again, having 

monitor design values at or near the existing standard is considered important in limiting the magnitude of 

uncertainty associated with adjusting concentrations that just meet the existing standard.

3 Further investigation of available information for potential study area locations with regard to the criteria identified 

above resulted in the identification of the selected three study areas, for two of which existing air quality 

modeling datasets were available. Such datasets were not available for many of the potential study areas referred 

to as candidates in the REA Planning Document (e.g., Detroit and Savannah). 

4 This objective of the REA and, more specifically, the design value criterion used to identify candidate study areas

for the REA differs from the criteria used in selecting the six focus areas in the ISA. The selection criteria used to 

identify focus areas in the ISA did not consider ambient monitoring concentration levels, and as such, four of the 

six ISA focus areas would not meet the above REA design value criterion alone (ISA, section 2.5.2.2).

5 Use of this time period (2011-2013) in these three study areas, in which concentrations were closer to the current 

standard than indicated in more recent data, allowed us to apply a smaller adjustment in developing the air quality 

scenarios for just meeting the current standard, thus reducing any associated uncertainty.
3-4

standard for which this REA has estimated exposures and risks to at-risk populations from SO2

concentrations in ambient air.

Table 3-1. General features of the study areas selected for the exposure and risk 

assessment.

Study Area Geographic 

Region

# of Monitors in 

Exposure 

Modeling Domain a

Reporting 

5-Minute Data 

(# with Continuous 

Data)

2011-2013

DV b

(ppb)

Population in 

Exposure 

Modeling 

Domain a c

# of Sources 

emitting >100 

tons per year d

in Exposure 

Modeling 

Domain

Source Types e

Fall River, MA New England 1 (1) 64 183,874 1 EGU

Indianapolis, 

IN

Ohio River 

Valley 3 (3) 78 547,968 4

EGUs, secondary 

lead smelter, 

airport

Tulsa, OK South 4 (4) 55 257,423 3

EGU, petroleum 

refineries

a Delineation of the exposure modeling domain is described in section 3.4; it includes the area within 10 km of the sources with 

SO2 emissions above 100 tons in 2011, 2012 or 2013 and inclusive of the monitors with 5-minute data. 

b Highest monitor-based design value in exposure modeling domain. 

c Population sizes are drawn from 2010 U.S. Census.

d This reflects information in 2011 National Emissions Inventory. As described in section 3.2, other sources are also reflected in 

the air quality modeling, either explicitly or via the addition of study-area-specific concentrations.

e This reflects sources counted in column to the left of this one. As described in section 3.2, other sources are also reflected in 

the air quality modeling, either explicitly or via the addition of study-area-specific concentrations.

3.2 AIR QUALITY MODELING

The EPA’s preferred model for near-field dispersion, AERMOD (U.S. EPA, 2016a, b), 

was used to generate 1-hour concentrations for the 3-year period, 2011-2013, across the exposure 

modeling domains for the three study areas: Fall River, MA, Indianapolis, IN, and Tulsa, OK. In 

addressing the development of model inputs and specifications, as well as performing the 

modeling runs themselves, the steps listed below were performed for all three study area

modeling domains.

(1) Collected and analyzed general input parameters. Meteorological data, processing 

methodologies used to derive input meteorological fields (e.g., temperature, wind speed, 

precipitation), and information on surface characteristics and land use were needed to 

help determine pollutant dispersion characteristics, atmospheric stability and mixing 

heights (section 3.2.1).

(2) Defined sources and estimated emissions. The modeled emission sources included 

major stationary emission sources within the domain (section 3.2.2).
3-5

(3) Defined air quality receptor locations. Receptor locations were identified for the 

dispersion modeling at varying spatial scale (depending on distance from source to 

receptor) from 2 km to 100 m (section 3.2.3).

(4) Calculated background concentrations. In this context the phrase “background 

concentrations” refers to SO2 concentrations resulting from sources (nearby and distant) 

other than those whose emissions are explicitly modeled. These concentrations were 

calculated based on ambient air monitoring data that exclude hours of the day that were 

most likely influenced by the modeled emission sources (section 3.2.4).

(5) Estimated concentrations at receptors. Full annual time series of hourly concentration 

were estimated for 2011-2013 by summing concentration contributions from each of the 

emission sources at each of the defined air quality receptors (section 3.2.5).

Details regarding both modeling approaches and input data used are provided below with 

supplemental information regarding model inputs and methodology provided in Appendices A,

B, and C. To ensure use of the appropriate local data for the simulated time periods, as well as 

efficiency and consistency for these areas, we drew on information for the Indianapolis and 

Tulsa study areas (e.g., stack locations, building parameters, etc.) that had been developed for 

regulatory purposes.6,7Information for the Fall River study area was developed specifically for 

this assessment in a manner that was technically appropriate and generally consistent with that 

for the other two areas. The sections below summarize development of the information described 

in the steps listed above for all study areas. Figures 3-1 to 3-3 show the locations of the upper 

and surface meteorological stations, the modeled SO2 emission sources, and the ambient 

monitoring sites used for predicting air quality used in this REA. Because some of the 

meteorological stations and emissions sources were located outside of the general study area,

8

two maps are provided for each study area: one map encompassing all of the features and the 

second map focused on those features closest to or within each study area.

 

6 For the Indianapolis study area, we drew on the modeling performed by Indiana Department of Environmental 

Management for Indiana’s State Implementation Plan (SIP) for the Marion County SO2 nonattainment area. This 

documentation is available at: 

http://www.in.gov/idem/airquality/files/attainment_so2_multi_2015_demo_attach_k.pdf.

7 For the Tulsa study area, we drew on the modeling performed by Oklahoma Department of Environmental Quality 

to address regulatory Prevention of Significant Deterioration (PSD) requirements for refineries in the Tulsa area. 

This information is available for Permits 2012-1062-TVR2 M-9 and 2010-599-TVR M-7 at: 

http://www.deq.state.ok.us/aqdnew/permitting/PermitsIssuedDuringPastYear.html.

8 For better visualization of the meteorological stations, emission sources, and the ambient monitors used to estimate 

air quality for this assessment, the area highlighted is an approximation based on census tracts that encompass the 

actual exposure study area (section 3.3) which is comprised of a subset of census blocks within those same census 

tracts.
3-6

Figure 3-1. Location of surface and upper air meteorological stations, SO2 emissions sources, and ambient monitors used to 

predict ambient air quality in the Fall River study area. Also included is source type and 2011 NEI SO2 emissions.
3-7

Figure 3-2. Location of surface and upper air meteorological stations, SO2 emissions sources, and ambient monitors used to 

predict ambient air quality in the Indianapolis study area. Also included is source type and 2011 NEI SO2

emissions.
3-8

Figure 3-3. Location of surface and upper air meteorological stations, SO2 emissions sources, and ambient monitors used to 

predict ambient air quality in the Tulsa study area. Also included is source type and 2011 NEI SO2 emissions.
3-9

3.2.1 General Model Inputs

3.2.1.1 Meteorological Inputs 

All meteorological data used for the AERMOD dispersion model simulations were 

processed with the AERMET meteorological preprocessor, version 16216 (U.S. EPA, 2016c) 

using regulatory options. The National Weather Service (NWS) served as the source of input 

meteorological data for AERMOD. Tables 3-2 and 3-3 list the surface and upper air NWS 

stations chosen for the three study areas. The NWS hourly surface data are archived in the 

Integrated Surface Hourly (ISH) database for which there is a potential concern for a high 

incidence of calms and variable wind conditions. This is due to how the hourly data are reported 

from the Automated Surface Observing Stations (ASOS) in use at most NWS stations. Wind 

speeds less than three knots are assigned a value of zero knots, and the definition used for a

variable wind observation (wind direction that varies more than 60° in a 2-minute observation) 

may include wind speeds up to 6 knots, but with a wind direction that is reported as missing. The 

AERMOD model currently cannot simulate dispersion under these conditions. This issue was 

addressed by reducing the number of calms and missing winds in the surface data for each of the 

three NWS surface stations using separately archived 1-minute averaged wind data from the 

ASOS stations. Low wind speeds and wind direction are retained in the 1-minute ASOS data.

Hourly average wind speeds and directions were calculated using the 1-minute wind data to

supplement the hourly wind data in the ISH format. The 1-minute data were processed with 

AERMINUTE, version 15272 (U.S. EPA, 2015a). AERMINUTE performs quality assurance 

procedures on the 1-minute data files, computes the hourly averages of wind speed and direction, 

and outputs the hourly averages in a data file that can be directly input into AERMET.
3-10

Table 3-2. National Weather Service surface stations for meteorological input data in 

study areas.

Study Area Station Identifier WMO

(WBAN)

Latitude

(degrees)

Longitude

(degrees)

Elevation

(m)

GMT Offset 

(hours)

Fall River, MA Providence PVD 725070

(14765) 41.7225 -71.4325 19 -5

Indianapolis, IN

Indianapolis 

International 

Airport

IND 724380

(93819) 39.725170 -86.281680 241 -5

Tulsa, OK

Tulsa R L 

Jones Jr 

Airport

RVS 723564

(53908) 36.042441 -95.990166 192 -6

Table 3-3. National Weather Service upper air stations for meteorological input data in 

study areas.

Study Area Station Identifier WMO

(WBAN)

Latitude

(degrees)

Longitude

(degrees)

Elevation

(m)

GMT 

Offset 

(hours)

Fall River, MA Chatham, MA CHH 744940

(14684) 41.67 -69.97 12 -5

Indianapolis, IN Lincoln, IL ILX 745600

(04833) 40.15 -89.33 178 -6

Tulsa, OK Norman, OK OUN 723560

(13968) 35.23 -97.47 354 -6

3.2.1.2 Surface Characteristics and Land Use Analysis

The AERSURFACE tool, version 13016 (U.S. EPA, 2013) was used to determine surface 

characteristics (e.g., albedo, Bowen ratio, and surface roughness) for input to AERMET. Surface 

characteristics were calculated for the location of the ASOS meteorological towers, which were 

approximated by using aerial photos and the station history from the National Centers for 

Environmental Information (NCEI). AERSURFACE utilizes 1992 land cover data from the 

National Land Cover Dataset (NLCD). Land cover data was obtained from the Multi-Resolution 

Land Characteristics (MRLC) consortium website.9 Each of the three surface meteorological 

stations are located at an airport and were specified accordingly in AERSURFACE.

Though the current version of AERSURFACE is limited to processing older land cover 

data for input to AERMET, a review of historical and more recent satellite imagery indicates 

there have not been substantial changes in the land cover within the area immediately 

 

9 https://www.mrlc.gov
3-11

surrounding the meteorological towers for the three modeled sites between 1992 and 2011.10 For 

each of the three sites that were modeled, the surface meteorological observations were collected 

at NWS stations located at airports. The meteorological towers at these airports are located in 

grassy areas near or between runways. Surface roughness is derived as an inverse-distance 

weighted average of the land cover within a 1.0 km radius centered on the meteorological tower. 

Thus, the land cover that is nearest to the tower, where there is the least amount of change over 

time, has the greatest influence on the derived roughness value. Bowen ratio and albedo, on the 

other hand, are derived from a 10 km × 10 km area centered on the meteorological tower. Bowen 

ratio and albedo represent an average of the land cover across the 10 km × 10 km area in which 

each land cover pixel is weighted equally. Isolated areas where the land cover has changed 

substantially over time have little effect on the average value of Bowen ratio and albedo within 

the 10 km × 10 km area.

AERSURFACE allows for the surface roughness length to be defined by up to 12 wind 

sectors with a minimum arc of 30 degrees each. For each of the three ASOS stations, roughness 

was estimated for each of 12 sectors, beginning at 0 degrees through 360 degrees (i.e., 0-30, 30-

60, 60-90, etc.). The wind sectors for each of the three surface stations are illustrated in 

Appendix A. The AERSURFACE default month-to-season assignments were used for Tulsa, and 

reassignments were performed for both Indianapolis and Fall River. The monthly seasonal 

assignments input to AERSURFACE for each of the three surface stations are shown in Table 3-

4. Surface characteristics were output by month. Note that there are two winter options: 1) winter 

with no snow (or without continuous snow) on the ground the entire month and 2) winter with 

continuous snow on ground the entire month.

11 A month was considered to have continuous 

snow cover if a snow depth of one inch or more was reported for at least 75% of the days in the 

month.

Table 3-4. Monthly seasonal assignments input to AERSURFACE.

Study Area Winter

(continuous snow)

Winter

(no snow) Spring Summer Fall

Fall River, MA - Dec, Jan, Feb, Mar Apr, May Jun., Jul, Aug Sep, Oct, Nov

Indianapolis, IN - Dec, Jan, Feb, Mar Apr, May Jun., Jul, Aug Sep, Oct, Nov

Tulsa, OK - Dec, Jan, Feb Mar, Apr, May Jun., Jul, Aug Sep, Oct, Nov

Seasonal definitions: Winter - Late fall after frost and harvest, or winter with no snow; Spring - Transitional spring with partial 

green coverage or short annuals; Summer - Midsummer with lush vegetation; Fall - Fall with unharvested cropland

 

10 Google Earth was used to evaluate the land use/land cover in the area immediately surrounding the meteorological 

tower, out to a distance of 1 km, and similarly in the region around the airport, out to a distance of 5 km.

11 For many of the land cover categories in the 1992 NLCD classification scheme, the designation of winter with 

continuous snow on the ground would tend to increase wintertime albedo (reflectivity) and decrease wintertime 

Bowen ratio (sensible to latent heat flux) and surface roughness compared to the winter with no snow or without 

continuous snow designation.
3-12

AERSURFACE also requires information about the climate and surface moisture at the 

surface station. The station has to be categorized as either arid or non-arid. Each of the three 

surface stations were categorized as non-arid in AERSURFACE. Surface moisture is based on 

precipitation amounts and is categorized as either wet, average, or dry. For the three surface 

stations, 2010 local climatological data from the NCEI was used to look at 30 years (1981-2010) 

of monthly precipitation. The 30th and 70th percentiles of precipitation amounts were calculated 

separately for each of 12 months (January through December) based on the 30-year period. The 

precipitation amount for each month in 2011-2013 was then compared to the 30th and 70th

percentiles for the corresponding month. Months during which precipitation was greater than the 

70th percentile were considered wet, while months that were less than the 30th percentile were 

considered dry. Months within the 30th and 70th percentile range were considered average. 

AERSURFACE was run for each moisture condition to obtain monthly values for wet, dry, and 

average conditions. Using the AERSURFACE output for each of the three moisture categories, a 

separate set of monthly surface characteristics was compiled for each of the three years for input 

to AERMET. The monthly categorization of the surface moisture at each of the locations is 

shown in Table 3-5. Refer to Appendix A for a complete listing of the surface characteristic 

values input to AERMET for each surface station and a detailed discussion of the meteorological 

data preparation.

Table 3-5. Monthly surface moisture categorizations for the three study areas.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fall River, MA

2011 Avg. Wet Dry Wet Avg. Wet Wet Wet Wet Wet Wet Avg.

2012 Avg. Dry Dry Avg. Wet Wet Avg. Wet Wet Wet Dry Wet

2013 Dry Wet Dry Dry Avg. Wet Avg. Wet Wet Dry Wet Wet

Indianapolis, IN

2011 Wet Wet Wet Wet Wet Wet Dry Dry Wet Wet Wet Wet

2012 Wet Avg. Wet Avg. Dry Dry Dry Wet Wet Wet Dry Avg.

2013 Wet Wet Wet Wet Wet Wet Dry Dry Wet Wet Wet Wet

Tulsa, OK (Moisture conditions at RVS are based on precipitation data from Tulsa International Airport, TUL)

2011 Dry Wet Dry Wet Dry Dry Dry Wet Dry Dry Wet Avg.

2012 Dry Avg. Wet Avg. Dry Wet Dry Wet Dry Avg. Dry Dry

2013 Wet Wet Dry Avg. Avg. Dry Wet Wet Dry Wet Avg. Avg.

Moisture categories were defined by comparing existing year/month precipitation values with 30-year monthly 

precipitation data set: Wet (>70th percentile); Dry (<30th percentile); Avg. (within 30th and 70th percentile)
3-13

3.2.2 Stationary Sources Emissions Preparation

3.2.2.1 Emitting Sources and Locations

The modeling approach in all three study areas involved modeling key sources as point 

sources and accounting for other sources through the use of additional study-area-specific 

concentrations (see section 3.2.4). The facilities modeled as point sources included all those 

emitting more than 100 tpy of SO2 in 2011, as well as some in Indianapolis that were somewhat 

smaller (Table 3-6). These facilities were selected from version 2 of the 2011 National Emissions 

Inventory (NEI)12 and paired to a representative surface meteorological station. Any stacks listed 

as in the same location with identical temporal profiles and identical release parameters within a 

certain tolerance (typically to the nearest integer value) were aggregated into a single stack to 

simplify modeling, but all emissions were retained. For facilities with an SO2 emission total 

exceeding 1,000 tpy in 2011, every stack emitting more than 1 tpy was included in the modeling 

inventory. 

Table 3-6. Facilities with point sources included in the air quality modeling domain for 

each study area.

Study Area Facility Name NEI ID

Fall River, MA a Brayton Point Energy (EGU) 5058411

Indianapolis, IN

Belmont Advanced Wastewater Treatment Plant b 4885211

Citizens Thermal, formerly Indianapolis Power and Light 4885311

IPL – Harding Street Generating Station 7255211

Rolls Royce Corporation (combustion engine manufacture and testing) b 7972011

Vertellus Specialties, formerly Reilly Industries and Reilly Tar and Chemical

(chemical manufacturing) 7972111

Quemetco (lead battery recycling facility) 8235411

Tulsa, OK c

Public Service Co. of Oklahoma (PSO) Northeastern Power Station 8212411

Sapulpa Glass Plant 7320611

Tulsa Refinery West 8402711

Tulsa Refinery East 8003911

a Contributions to ambient concentrations from another facility emitting more than 100 tpy (SEMASS Partnership municipal 

waste combustor [8127611]), although 30 km away, are accounted for by the additional study-area-specific concentrations for 

Fall River (see section 3.2.4).

b These sources, although having 2011 NEI emissions under 100 tons, were included based on proximity to nearby 

monitoring locations and previous modeling for Indianapolis and Tulsa.

c There are facilities in the region outside of the immediate study area emitting more than 100 tpy (e.g., Oklahoma Gas & 

Electric Company Muskogee Generating Station [8506011], however, they are outside the nominal distance (50 km) used for 

dispersion modeling. Note also, contributions to ambient concentrations from any emission sources not explicitly modeled

and potentially influencing ambient concentrations in the study area are accounted for by the additional study-area-specific 

concentrations for Tulsa (see section 3.2.4).

 

12 See: https://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventory-nei-technical-supportdocument
3-14

The locations of all emitting stacks that were modeled were corrected based on GIS 

analysis or by using locations identified in the local information developed by the state of 

Indiana for modeling for Indianapolis and the state of Oklahoma for Tulsa.13 This was necessary 

because many stacks in the NEI are assigned the same location, which often corresponds to a 

location in the facility rather than the actual stack locations. NEI sources were mapped to 

AERMOD sources based on matching stack parameters and temporal profiles within the same 

facility. The release heights and other stack parameters were taken from the values listed in the 

2011 NEI. Table B-3-1 (in Appendix B) lists all stacks in all domains.

3.2.2.2 Source Terrain Characterization

With the exception of sources at Quemetco and fugitive sources at Rolls Royce in 

Indianapolis, all source elevations for the three study areas were calculated in AERMAP, version 

11103 (U.S. EPA, 2016d). Source elevations at Quemetco and fugitive sources at Rolls Royce 

were determined by ArcGIS overlays of the sources and National Elevation Data (NED).

3.2.2.3 Emissions Data Sources

Data for the parameterization of major facility point sources in the modeling domains 

comes primarily from these sources: the 2011 NEI (U.S. EPA, 2015b),14 point source 

submissions to the NEI database for the years 2012 and 2013,

15 the Air Markets Program data

(CAMD database) (U.S. EPA, 2017a), and temporal emission profile information from the 

EPA’s 2011v6.3 Emissions Modeling Platform (U.S. EPA, 2016e). The NEI database contains 

stack locations, emissions release parameters (i.e., height, diameter, exit temperature, exit 

velocity), and annual SO2 emissions. The CAMD database has information on hourly SO2

emission rates for all the electric generating units (EGUs) in the U.S. where the units are boilers 

or equivalent, each of which can have multiple stacks. For sources that did not have hourly data 

in the CAMD database, annual total emissions data from the NEI were converted into the hourly 

temporal profiles required for AERMOD according to temporal profiles that are part of the 

EPA’s 2011v6.3 emissions modeling platform. 

 

13 As noted in section 3.2 above, local information was provided by these states in documentation developed for SIP 

and PSD-related purposes.

14 We consider the 2011 NEI is the most appropriate emissions data set to use for modeling the 3-years of air quality

in this REA because the exposure period used is based on 2011-2013 ambient monitor data (and the associated 

meteorology). 

15 Annual total emissions for the largest point sources are reported to the NEI each year by the State air agencies. 

Every third year (e.g., 2011, 2014), emissions for all point sources are to be reported to the NEI by the State air 

agencies. Submissions to the NEI may also include any needed changes to the facility information for point 

sources (e.g., locations, stack parameters, control devices), as this information is stored persistently in the NEI 

database between NEI submission cycles and is updated as needed.
3-15

The emissions information needed for running AERMOD was drawn from this array of 

information sources (detailed information is provided in Appendix B). For EGU sources, the 

more detailed information (e.g., hourly emissions values) were drawn from the CAMD database 

and annual estimates from the NEI. For sources other than EGUs for which hourly SO2

emissions estimates were not available in the CAMD database, temporal profiles were used to 

prepare the hourly emissions factors as described in Appendix B. 

The designation of sources in the three study areas as urban or rural reflected information 

about the source and surrounding area. The urban/rural designation of a source is important in 

determining the boundary layer characteristics that affect the model’s prediction of downwind 

concentrations. It is particularly important for SO2 modeling because AERMOD invokes a 4-

hour half-life for urban SO2 sources (U.S. EPA, 2016a, section 7.2.1.1) to account for SO2

removal by conversion to sulfuric acid (catalytic and photochemical) and adsorption on to 

particulate matter (Turner, 1964).16 For Fall River, a rural designation was used based on land 

use data, the fact that the stacks at Brayton were tall, and the AERMOD Implementation Guide

(U.S. EPA, 2016g) recommendation to use a rural designation when modeling tall stacks in 

urban areas. Classifying tall stacks with buoyant releases as urban sources in urban areas may 

artificially limit plume height, thus artificially increasing modeled ground level concentrations. 

The use of the AERMOD urban option for these sources may not be appropriate given that the 

actual plume is likely to be transported over the urban boundary layer. For Indianapolis, all 

sources were classified as urban sources based on having a broadly defined urban population of 

1,000,000, consistent with the classification in the SIP modeling. For Tulsa, all sources were 

classified as urban based on having a broadly defined urban population of 396,466, consistent 

with the classification in the PSD modeling. 

Building downwash parameters for Indianapolis and Tulsa were set based on local 

information available from Indiana and Tulsa state modeling work. Given the lack of building 

information available in Fall River, building downwash was not used in modeling for this study 

area.

3.2.3 Air Quality Receptor Locations

Among the three study areas, the sizes of the air quality modeling domain and receptor 

grid varied in consideration of differences such as number, size, and distribution of the key 

emissions sources. The domains and receptor grids for Indianapolis and Tulsa drew on the 

approach used by Indiana and Oklahoma in modeling these areas for their SIP and for PSD 

 

16 For urban sources, AERMOD accounts for the urban heat island effect on increasing mixing heights for hours 

under atmospheric stable conditions. Details on determining the urban or rural status of sources can be found in 

U.S. EPA (2016a), U.S. EPA (2016f), and U.S. EPA (2016g).
3-16

purposes. Where these domains were larger than the areas of interest for the exposure 

assessments, the receptor grids were subset to receptors that encompassed the census blocks of 

interest for the exposure assessment as described in section 3.3 below. The full air quality 

modeling domain for Indianapolis was 38 km × 32 km with receptor spacing ranging from 2 km 

at the edges, to 1 km, 500 m, 250 m, and 100 m near the emission sources, with fence line 

receptors included.

17 The Tulsa domain was 26 km × 29 km and receptor spacing ranged from 1 

km at the edges to 666.67 m, 250 m, and 100 m near the sources, with fence line receptors also 

included. For Fall River, we generated a domain (20 km × 20 km receptor grid with 500 m

spacing) that specifically fit the needs of the exposure assessment. Receptor elevations and hill 

heights for all three areas were obtained from AERMAP.

3.2.4 Concentrations Associated with Sources Not Explicitly Modeled

Concentrations associated with sources of SO2 that were not explicitly modeled in all 

three study areas (e.g., source emissions from outside the modeling domain in addition to 

emissions from sources within the domain that were not explicitly modeled with AERMOD) 

were separately estimated and added to the AERMOD modeled concentrations to produce the 

hourly concentrations at each receptor. For example, for Fall River these concentrations were

approximated to account for the impacts from SEMASS Partnership given its distance (~30 km) 

from the Fall River emission source of interest (Brayton EGU), rather than including SEMASS 

Partnership as a point source in the AERMOD modeling run.

For all three study areas, these concentrations were calculated in terms of three-year 

averages of seasonal-hour-of-day concentrations.18 This approach generally relied on the use of 

ambient air monitoring data from a designated monitor (i.e., one not receiving direct impact from 

emission sources modeled in the domain). Measurements from this monitor were excluded, as 

recommended in the EPA air quality modeling guidance (U.S. EPA, 2016a, f), during times 

when the sources that were explicitly modeled were potentially impacting monitor 

concentrations and were informed by monitor siting relative to the modeled sources and wind 

direction.

19 For Fall River, monitor 250051004 (see Figure 3-1) was used for this purpose. Hours 

 

17 The air quality modeling receptor grids utilized varying spatial resolution within the grids, as is customary in most 

regulatory modeling applications. The exact placement of receptors usually depends on individual state modeling 

guidance for dispersion modeling for regulatory applications. This accounts for the varying range of receptor 

grids in the assessment for Indianapolis and Tulsa. Receptors are normally placed in locations of ambient air, i.e. 

where the general public has access and along fencelines of the modeled sources. Receptors are usually spaced 

close together near the modeled sources to capture concentration gradients near the sources, and they are spaced 

with decreasing spatial resolution farther away from the sources.

18 This approach was implemented as recommended in the EPA’s modeling guidance for SO2 (U.S. EPA 2016f).

19 Wind direction data was obtained from the surface meteorological stations representing each study area.
3-17

when wind direction was from the west to north (270° to 360°) were excluded from the 

calculation to remove the impacts from the source that was explicitly modeled (Brayton EGU). 

For Indianapolis, monitor 180970078 (northern monitor; see Figure 3-2) was used. Hours with 

wind directions between 170° and 270° were excluded to eliminate impacts from the modeled 

sources in that study area. For Tulsa, monitor 401431127 (located north of the refineries, see 

Figure 3-3) was used. Hours when the wind direction was either 90° to 140° or 270° to 6° were 

excluded to eliminate impacts from the two refineries or the PSO Northeastern power station. 

Table 3-7 shows the seasonal-hour-of-day concentrations estimated to result from source 

emissions not explicitly modeled in the AERMOD runs for the three study areas.

20

 

20 Use of this approach to estimate concentrations associated with source emissions not modeled contributes to 

uncertainty in the exposure and risk estimates and is summarized in Table 6-3 below.
3-18

Table 3-7. SO2 concentrations (ppb) used to account for source emissions not explicitly modeled in the three study areas, 

stratified by season and hour of day.

Hour Fall River Indianapolis Tulsa

Winter Spring Summer Fall Winter Spring Summer Fall Winter Spring Summer Fall

1 4.07 5.47 9.07 9.43 10.93 6.73 5.03 3.67 2.27 1.27 5.50 1.20

2 5.27 8.43 6.37 7.07 10.60 6.63 3.93 4.73 2.33 0.87 2.60 1.50

3 4.77 4.70 9.13 9.13 10.37 6.40 4.20 4.33 1.83 0.40 4.30 0.93

4 7.30 5.40 7.63 12.23 8.87 6.73 4.57 3.43 1.83 0.50 0.70 1.47

5 8.03 4.80 7.40 10.37 8.83 6.87 7.63 4.70 2.03 1.37 0.60 1.70

6 6.23 4.97 8.00 11.03 11.67 5.23 3.83 5.33 1.93 0.47 8.30 1.43

7 9.30 6.83 7.83 11.27 13.40 5.37 5.20 5.40 1.57 1.03 0.80 1.47

8 8.27 6.07 7.47 8.33 10.00 6.07 5.93 6.47 2.33 3.90 1.20 2.63

9 7.17 5.80 7.30 8.20 7.77 7.50 30.7 7.10 1.93 1.23 1.33 1.50

10 8.13 5.43 7.27 9.40 13.07 9.73 25.73 15.13 2.90 2.37 0.93 1.43

11 8.57 9.30 10.50 7.47 10.20 9.07 23.27 40.57 2.80 1.87 1.53 2.63

12 8.43 7.80 18.37 8.90 12.70 8.63 17.63 37.93 5.30 2.17 2.20 2.67

13 8.77 11.83 15.90 7.50 17.63 5.93 14.83 21.83 6.13 2.30 2.40 5.23

14 9.27 8.33 16.93 7.00 13.13 5.60 9.50 11.07 2.80 2.30 3.03 2.90

15 8.00 3.30 6.40 4.00 13.13 16.33 7.40 7.97 1.80 1.67 2.00 2.20

16 6.83 2.33 6.00 3.67 7.53 4.87 9.90 12.53 3.10 1.97 2.47 2.83

17 8.93 3.60 4.33 3.03 6.97 6.30 8.53 25.10 3.30 3.60 2.13 4.17

18 5.80 2.47 3.63 2.70 11.27 11.37 9.97 16.33 4.27 3.67 5.77 4.00

19 4.43 2.30 3.27 2.87 6.77 9.10 7.47 10.83 2.87 1.47 1.50 2.20

20 4.33 2.03 3.20 2.73 9.57 4.93 10.20 7.60 2.33 2.87 1.83 2.53

21 4.07 2.30 3.13 2.67 10.57 5.87 6.13 6.57 2.57 2.67 1.33 2.00

22 3.63 2.10 2.97 2.57 12.17 4.27 10.30 5.47 2.63 1.37 0.93 2.20

23 3.70 2.60 3.07 2.60 6.13 6.13 10.73 4.00 3.67 1.03 0.67 2.30

24 4.80 2.80 6.77 7.93 5.67 6.27 6.63 3.9 3.17 1.43 2.17 1.87
3-19

3.2.5 Hourly Concentrations at Air Quality Model Receptors

Once all model inputs have been created, i.e. hourly meteorology, emissions, building 

parameters, etc., the AERMOD dispersion model is run to estimate hourly concentrations for 

each study area. AERMOD reads the hourly meteorological data files, pairs the hourly 

meteorology with the appropriate emissions and building parameters for each hour and uses 

Gaussian plume theory to calculate an hourly concentration at each receptor. AERMOD then 

outputs the hourly concentrations to a file that can be used in the exposure assessment.

21 An 

initial evaluation of the modeled concentrations based on comparison to the full distribution of 

monitored concentrations can be found in Appendix D.

22 Briefly, modeled concentrations were 

compared to ambient air measurements using two approaches: calculated design values and 

simple Q-Q (quantile-quantile) plots of the 1-hour, 3-hour, and 24-hour average concentrations. 

Overall, for the three modeled areas, the modeled concentrations were comparable to the ambient 

air measurements, although there were instances of over- and under-prediction of concentrations 

at the upper percentiles of the concentration distribution. When evaluating on an annual basis, 

model-to-monitor agreement tended to be best using the 2011 concentrations.

To augment the model-monitor evaluation of hourly SO2 concentrations in ambient air 

presented in Appendix D, we performed an additional evaluation focused on air quality model 

estimates during time periods with relatively greater potential for population exposures.23 The 

context for this air quality modeling performance evaluation24 is particular to the intended 

purpose of the air quality modeling in providing estimates of 1-hour concentrations across the 

exposure modeling domain that are used with spatially limited monitoring data to estimate shortterm exposure concentrations, especially those in outdoor microenvironments. The focus is on 

 

21 For this assessment, AERMOD output the hourly concentrations resulting from emissions from each of the largest 

sources in each study area separately. These concentrations were used to develop a factor for adjusting 

concentrations such that total concentrations (from all sources) just meet the current standard (section 3.4). After 

adjustment, modeled concentrations were combined along with the estimated concentration contribution from 

source emissions not explicitly modeled and were then used in estimating population exposures.

22 In this section, “modeled concentrations” and “unadjusted model estimates” refers to the concentrations derived 

by adding the concentrations estimated to result from sources not explicitly modeled (section 3.2.4) to the 

AERMOD outputs.

23 While the continuous time-series of hourly concentrations estimated by the air quality modeling is not expected to 

precisely reflect that of the monitor measurements, some consistency with regard to when relatively higher 

concentrations occur (e.g., daytime vs nighttime) is particularly desirable for use in exposure modeling and 

provides a measure of confidence with respect to the intended use of the ambient air concentration estimates and 

in estimating population exposures.

24 Given the specialized use for the air quality model predictions, we recognize the importance of performance 

considerations that may differ from those common in evaluations of air quality modeling for regulatory purposes. 

For example, an area of interest in our evaluation described here is consideration of the occurrence of peak 

concentrations during times with greater (versus lesser) population exposure potential.
3-20

outdoor microenvironments (and hence, ambient air concentrations) given the overwhelming 

influence of these MEs on population exposure estimates (see section 5.2). We also recognize 

that participation in outdoor events is typically influenced by seasonal and diurnal variability in 

activity patterns. For example, more people spend time outdoors when the weather is 

comfortable (e.g., temperate spring mornings or autumn afternoons) and during daylight hours 

than when conditions are opposite (e.g., cold winter nights). Accordingly, this evaluation of the 

modeled and measured hourly concentrations considers important time-of-day and time-of-year 

stratifications.25

While the estimated exposures in this REA do not utilize the AERMOD predictions 

without adjustment, the evaluation summarized here (on the unadjusted model estimates and 

monitor measurements) is considered to provide some perspective on the uncertainty that may be 

contributed to the spatial and temporal pattern of hourly concentrations estimated by AERMOD 

that then feeds into the air quality adjustment step (described in section 3.4) and to the 

development of the 5-minute concentrations in each study area (described in section 3.5). 

Because this data evaluation was performed using the unadjusted ambient air quality, 

conclusions drawn from this evaluation, while informative regarding the overall model

performance, are not directly transferrable to the hypothetical air quality scenario simulated for 

the REA main body results, per se. Additionally, we were not able to develop directly 

comparable modeling and monitoring datasets for our hypothetical air quality scenario (i.e., air 

quality adjusted to just meet the current standard) because the adjustment approach applied to the 

model estimates to create this scenario uses a proportional factor to adjust the primary source 

concentration contribution at each receptor, while holding all other source concentration 

contributions unadjusted (section 3.4). Accordingly, this approach cannot be applied to the 

ambient air monitoring concentrations. Thus, the evaluation provided here is simply intended to 

be somewhat informative, particularly with regard to considering the extent to which the 

relatively higher concentration events predicted by the modeling occur in the same seasons and 

portion of the day (daytime vs nighttime) as the relatively higher concentration monitor events.

We focus our evaluation here on the relatively higher monitor concentrations (i.e., the 

upper part of the concentration distribution) that occur during daytime hours in the spring, 

summer and fall seasons, and using the monitors having the highest design value in each study 

area as indicative of events with the potential for 5-minute concentrations of greatest interest in 

 

25 Data were stratified by two times of day (daytime - 6AM to 8PM; nighttime - all other hours) and four seasons 

(winter - December, January, February; spring - March, April, May; summer - June, July, August; and fall -

September, October, November). Additionally, as the interest of this evaluation is occurrences of relatively higher 

concentrations during times of day and seasons when people are most likely to encounter them and is not 

regarding annual variability, the three years of data for each location are pooled before stratification.
3-21

this REA.

26 The model estimates are, for the most part, similar in magnitude to the monitor 

measurements (Figure 3-4). Across the study areas, the closest fit of the higher-concentration 

estimates to the monitor measurements for Fall River occurs in the spring, while concentrations 

for the summer and fall seasons appear to be somewhat over- and under-predicted by the model, 

respectively. In Indianapolis, the highest monitor concentration events in the spring are not 

reflected in the model estimates, while they may be somewhat over-predicted in the summer and 

fall seasons. In Tulsa, the higher concentration events observed at the monitor are not reflected 

by concentrations predicted by the model for any of the three seasons.

 

26 The complete set of graphs for this evaluation considering all seasons and monitors are provided in Appendix K.
3-22

Figure 3-4. Comparison of AERMOD predicted SO2 concentrations (y-axis) with observed air monitor SO2 concentrations (xaxis) during daytime of the three warmer seasons at the highest design value monitor in each study area.
3-23

3.3 SELECTION OF AIR QUALITY RECEPTORS FOR EXPOSURE 

MODELING DOMAIN

As described above, the air quality modeling was done at a fine spatial scale that in some 

locations included receptor cells as small as 100 m by 100 m. Thus, the air quality modeling 

domains (Appendix C) included thousands of air quality receptor points, many more than 

considered practical for use by APEX in estimating exposures. APEX simulations were 

performed at a census block level, which, combined with the thousands of air quality receptors 

each considering the full 5-minute time-series of concentrations, presented computational 

challenges. In addition, the spatial range of the modeled air quality receptors extended outwards 

beyond areas expected to be influenced by the major sources present in each study area. Thus, 

the number of air quality receptors included in the exposure modeling was reduced to a more 

practicable number (i.e., fewer than 2,000) while still retaining the modeled receptors having the 

highest design value in the particular study area. 

The approach used to define the exposure model domain within the air quality modeling 

domain in each study area, along with the number of air quality receptor sites included in the 

exposure modeling domain, is as follows:

• Fall River: Hourly SO2 concentrations in ambient air were estimated at receptor sites 

defined by a 500 m grid. For the exposure modeling, we selected receptor sites that fell 

within 10 km of the Brayton EGU (latitude (lat) 41.709989, longitude (lon) -71.192441) 

and within 10 km of the continuous 5-minute monitor (lat 41.69, lon -71.17), which 

yielded 1,494 air quality receptors covering a land area of approximately 375 km2.

• Indianapolis: Hourly SO2 concentrations in ambient air were estimated at receptors 

defined by a receptor grid ranging from outside to inside at 2 km, then 1 km, 500 m, 250 

m, and 100 m near the two major sources. For the exposure modeling, we selected 

receptor sites that fell within 10 km of the two major sources (Citizen Thermal: lat 

39.762800, lon -86.166800; IP&L Harding: lat 39.7119, lon -86.1975) and all receptors 

within 10 km of Quemetco (lat 39.755391, lon -86.300155) and within 10 km of 

Indianapolis International Airport (lat 39.716809, lon -86.296127).27 The finest scale grid 

concentrations retained were those falling within a 500 m interval, which yielded 1,917 

air quality receptors covering a land area of approximately 675 km2.

• Tulsa: Hourly SO2 concentrations in ambient air were estimated at receptors defined by a 

receptor grid ranging from outside to inside at 1 km, 666.67 m, 500 m, 250 m, and 100 m 

near the two major sources (West Refinery: lat 36.139140 lon -96.025440; East Refinery: 

lat 36.11705271, lon -96.00477176). For the exposure modeling, we selected receptor 

 

27 Emissions from the Indianapolis International Airport were not explicitly modeled to remain consistent with the 

modeling performed for Indiana’s SIP for the Marion County SO2 nonattainment area; however, the exposure 

modeling domain was expanded using this source location to make this study area more representative of a large 

urban population. 
3-24

sites that fell within 10 km of these two sources and receptor sites within 10 km of 

monitor 401431127 (lat 36.20, lon -95.98).28 With the exception of 24 receptors modeled 

at a 100 m scale (retained in order to retain locations with the highest model-estimated 

DVs), the finest scale grid concentrations retained were those falling within a 500 m 

interval, which yielded 1,389 total air quality receptors covering a land area of 

approximately 550 km2.

These exposure modeling domains for the three study areas are shown, with adjusted air quality 

per section 3.4 below, in Figures 3-6 through 3-8.

3.4 AIR QUALITY ADJUSTMENT TO CONDITIONS MEETING THE 

CURRENT STANDARD

The exposure and risk analyses were conducted for air quality adjusted to just meet the 

current primary SO2 standard. Use of this adjusted air quality surface is most appropriate to 

quantitatively evaluate the associated exposures and health risks in this REA (section 2.2). As 

described in the REA Planning Document, a proportional approach was used to adjust ambient 

concentrations (not the modeled emissions) in the 2009 REA. An analysis of ambient 

concentration data at that time demonstrated that the proportional adjustment of ambient 

concentrations is an appropriate approach to use to generate air quality that just meets a 

particular standard (Rizzo 2009). We analyzed recent air quality data in the REA Planning 

Document to evaluate this assumption for several candidate areas for the purpose of justifying 

the selection of this approach for use in this REA (U.S. EPA, 2017b, Figure 4-6 and Appendix 

C). The results of the air quality comparisons shown in the REA Planning Document were 

similar to what was observed previously (Rizzo, 2009). 

We further refined these air quality analyses here to include the monitoring data from the 

three REA study areas. We also extended the time period considered to encompass the most 

recent year in which ambient air monitor concentrations had a 99th percentile daily maximum 1-

hour concentration at or just below the level of the current standard (i.e., 75 ppb, the air quality 

scenario adjustment goal) and the past year in each study area that had the highest daily 

maximum 1-hour concentrations (i.e., evaluate a maximum range in ambient air concentrations 

to reasonably support the use of potentially high adjustment factors, where needed). Thus, 

evaluated data included recent ambient air monitoring data from 2015 and measurements from as 

far back as 1980. We also focused the analysis on the monitor having the highest recent design 

 

28 In addition to SO2 emission from the two refineries and the glass plant, the emissions from the PSO Northeastern

Power Station were used to estimate ambient SO2 concentrations in the Tulsa exposure study area (Table 3-6). 

However, the exposure study area was not expanded to include receptors near the PSO because it is located 

approximately 40 km northeast of Tulsa (see Figure 3-3).
3-25

values in each of the three study areas and paired two years having the two highest design values 

and with two recent years having design values at or just below 75 ppb.

Figure 3-5 presents the results of this ambient air monitoring data comparison.

Concentrations are linearly related across the wide range of concentrations and, in a few 

instances, exhibit proportionality across the majority of the concentration distribution (i.e., in 

addition to exhibiting linearity, the regression intercept equals zero). Based on the paired 99th

percentile daily maximum 1-hour concentrations, the potential upper range of adjustment factors 

supported by these comparisons would range from 2.2 to 3.1. However, there are instances of 

non-proportionality as has been described previously (2009 REA; U.S. EPA 2017b), including 

limited deviation from linearity, particularly at the upper percentiles, and the presence of 

statistically significant linear regression intercepts. Thus, based on these analyses, we used a 

largely proportional adjustment approach in this REA with a variation from the 2009 REA 

approach, as described below to account for deviations from proportionality.

The process of adjusting air quality to just meet a standard of interest begins with 

consideration of the design values (DVs) calculated at the various locations in the study area. 

When using a proportional adjustment approach, the highest DV is used to derive a single factor 

(F) to adjust the monitored concentrations across the study area. In each study area, F is then 

used to adjust all SO2 concentrations in a study area by this factor to simulate just meeting the 

current standard. In the case of the SO2 standard, this adjustment of air quality is based on three 

years of concentrations, which is consistent with the form for the current standard.

A variation of this approach to air quality adjustment is used in this assessment. This new 

approach attempts to better consider relative source contributions to the ambient air 

concentrations that may or may not change given the particular air quality scenario. For instance, 

in the Fall River study area, the influence of the Brayton EGU (a source having >100 tpy SO2

emissions in the area) was accounted for by air quality modeling as a point source and the 

resulting surface of modeled air concentrations was combined with the set of concentrations that 

account for emission sources not modeled in the study area. In considering how to derive a 

concentration surface reflecting the hypothetical scenario of air quality conditions just meeting 

the current standard, we concluded that adjusting just the concentrations resulting from the EGU 

emissions alone (rather than the aggregate concentrations from the EGU and the mix of 

concentrations from the sources not modeled) would create a scenario that better reflected how 

air concentrations would change in response to actions performed to meet air quality standards.
3-26

Figure 3-5. Comparison of ambient air measurements from high concentration years (xaxis) to low concentration years (y-axis) in the Fall River (top row), Indianapolis 

(middle row), and Tulsa (bottom row) study areas. Left column contains the 

year having the highest 99th percentile daily maximum concentration. Right 

column contains the year having the 2nd highest 99th percentile daily maximum 

concentration.
3-27

Accordingly, we applied this approach to the Fall River study area air quality, with the 

concentrations contributed from the EGU adjusted just enough such that the aggregate of these 

modeled concentrations and the concentrations not modeled just met the current standard at the 

air quality receptor having the highest design value. This concentration adjustment approach was 

also applied in a similar manner to the other two study areas, with a primary source (among the 

collection of sources modeled in these areas) identified for the air quality adjustment. Then, 

concentrations at air quality receptors that were contributed from all other sources were left 

unadjusted. In the Indianapolis study area, the air quality receptor concentrations contributed 

from each of the modeled sources were evaluated; the IP&L Harding Street Facility was 

identified as the primary contributor to most of the air quality receptors having the highest 

concentrations, particularly those within 10 km of the facility. A similar evaluation was done for 

the Tulsa study area; the West Refinery was identified as the primary contributor to the highest 

concentrations at air quality receptors in the study area.

The steps involved for this adjustment approach are summarized here. First, the 

maximum DV and associated air quality receptor (rmax) was identified among the DVs from the 

complete collection of modeled air quality receptors in each study area that comprise the 

exposure modeling domain. Then the following formula was used to calculate the single 

adjustment factor to be applied to the primary source concentrations (C1), while considering the 

concentrations associated with the other sources (Coth) as unchanged:

F =

C1,rmax,2011+ C1,rmax,2012+ C1,rmax,2013

{(75 × 3)− (Coth,rmax,2011+ Coth,rmax,2012+ Coth,rmax,2013)}

 Equation 3-1

In order to have air quality just meet the current standard in each study area, the study 

area specific adjustment factor was used to adjust all hourly concentrations at each receptor as 

follows:

Cstd =

C1

(F)

+ Coth Equation 3-2

Table 3-8 contains the air quality receptor design values for each study area and the 

proportional adjustment factor that was applied to the concentrations that reflect the primary 

source emissions in each area in order to have concentrations just meet the current standard. 

Figures 3-6 to 3-8 show the air quality receptors in each study area and their respective design 

values following the above described approach for adjusting the hourly concentrations to just 

meet the current standard.
3-28

Table 3-8. Maximum SO2 design values modeled at air quality receptors and associated 

proportional adjustment factors applied to primary source concentrations in 

each study area.

Study Area

Modeled Air Quality 

Receptor Maximum 

SO2 DV (ppb)

Primary Source in 

Study Area

Proportional 

Adjustment 

Factor a

Fall River, MA 101.4 Brayton EGU 1.46

Indianapolis, IN 311.3 Harding EGU 4.21

Tulsa, OK 73.5 West Refinery 0.98

a The proportional adjustment factor is based on and applied only to the primary source contributing to the highest 

concentrations in the study area, while other source contributions as well as background concentrations are 

assumed to remain unchanged in approximating air quality conditions to just meet the current standard.

Figure 3-6. Location of air quality receptors, emission sources, and ambient monitors in the 

Fall River exposure modeling domain and receptor design values calculated 

from modeled hourly SO2 concentrations adjusted to just meet the current

standard.
3-29

Figure 3-7. Location of air quality receptors, emission sources, and ambient monitors in the 

Indianapolis exposure modeling domain and receptor design values calculated 

from modeled hourly SO2 concentrations adjusted to just meet the current

standard.
3-30

Figure 3-8. Location of air quality receptors, emission sources, and ambient monitors in the Tulsa exposure modeling domain 

and receptor design values calculated from modeled hourly SO2 concentrations adjusted to just meet the current

standard.
3-31

3.5 FIVE-MINUTE CONCENTRATIONS

In this assessment, we combined the fine-scale temporal characteristics of continuous 5-

minute monitoring data local to each study area with the fine-scale spatial characteristics of

hourly concentrations estimated by AERMOD. First, missing values within any monitoring data 

set were interpolated using the measured values immediately bounding the missing values. Then, 

where continuous 5-minute data were not available, an algorithm was constructed to randomly 

sample 5-minute concentrations from lognormal distributions that conform to the existing 1-hour 

average and maximum 5-minute measurements. Finally, the complete year pattern of 5-minute 

monitored concentrations was combined with the complete year pattern of hourly concentrations 

modeled at each receptor, based on matching the rank ordered 1-hour concentration distributions. 

The following section details how this was done, noting specifically where the approach differs 

from that described in the REA Planning Document.

3.5.1 Preparing Monitoring Data: Assessing Completeness & Filling Missing Values

Because there are years when the ambient air monitor did not report every hourly or 5-

minute concentration and because APEX needs the complete time-series of 5-minute ambient air 

concentrations to estimate exposures, an approach was developed to approximate missing 5-

minute values in the ambient air monitor data sets. As described in section 3.1 above, the study 

areas and years selected for this assessment corresponded to years in which the monitor datasets 

met completeness requirements for calculating a design value. This completeness requirement is 

typically applied to the hourly monitor concentrations and used for regulatory purposes. To best 

inform our estimation of 5-minute concentrations, we did not restrict the 5-minute concentrations 

using this completeness requirement for this assessment. Our intent in this REA was to utilize as 

much of the 5-minute measurements as was available in each study area.29 From ambient air 

monitors in the three selected study areas, the following data sets containing 5-minute 

concentrations were available:

 

29 For the hourly measurements, the following steps were taken: (1) a 75% completeness criterion was applied to 

each day monitored, with the monitored day considered valid if it contained measurements for at least 18 of the 

24 hours; (2) the number of days within a quarter of the calendar year were evaluated, also using a 75% 

completeness criterion such that a monitored quarter was considered valid if there were at least 68-69 valid days 

and a year was considered complete if all four quarters were valid; (3) data were screened for outliers, such that 

hours in which a 5-minute max hourly value (AQS parameter 42406 and duration code 1) was reported and fell 

within a factor of 1 and 12 times the AQS hourly value (parameter 42401 and duration code 1) were kept. For the 

continuous 5-minute measurements, the screening for outliers was as follows: only 5-minute data with a 

corresponding hourly value in AQS (parameter 42401 and duration code 1) were kept and only 5-minute values 

with an hourly mean value less than 120% of the hourly value in AQS (parameter 42401 and duration code 1) 

were kept.
3-32

• Fall River: continuous 5-minute data were available for 2011 and 2012. For 2013, the 

maximum 5-minute concentrations within the hour were available.

• Indianapolis: continuous 5-minute data were available for 2011-2013.

• Tulsa: continuous 5-minute data were available for 2011-2013.

A simple approach was selected to estimate any missing 1-hour, maximum 5-minute, 

and continuous 5-minute concentrations within the ambient air monitor data sets listed in Table 

3-9. We used PROC EXPAND (SAS, 2017) to interpolate between missing values, using the 

measured values that bound the missing data to estimate missing concentrations via the JOIN 

method (SAS, 2017). This approach fits a continuous curve to the data by connecting successive 

straight-line segments. While this approach does not directly calculate an average of the 

concentrations surrounding data gaps and generate a single concentration to use for all hours 

within a particular gap, the degree of variability assigned to concentrations within multi-hour 

gaps is limited. While more complex methods exist (e.g., autoregressive models) to perhaps 

increase the representation of variability that might be occurring within multi-hour data gaps, the 

performance of these simple methods is similar to complex methods when filling data sets 

having few (< 5-10%) missing values (Junger and de Leon, 2015).

To support the use of this method to substitute for missing values, we evaluated 

monitoring data available in the three study areas. Table 3-9 provides the number of missing 

values within each 1-hour, maximum 5-minute, or continuous 5-minute across the 3-year period

and the percentage that number is of the number of values in a full dataset. There were very few 

instances where the gap of missing data spanned several hours to days. The percentage of the 

total dataset values that were missing was at or less than 5% in nearly all instances when 

considering the Fall River and Tulsa Study areas. In contrast, the percentage of missing data for 

some of the ambient air monitors in the Indianapolis study area was greater. For example, 

Indianapolis monitor 18090073 had 40-60% of hours missing concentrations in both the 

continuous 5-minute data set and the maximum 5-minute and 1-hour data sets, and thus was not 

considered useful in subsequent assessment calculations (and was not used in further analyses).

Indianapolis monitor 18090057 also had a large percentage of missing continuous 5-minute data 

for two of the years (25-58% missing), although it still had robust reporting of the maximum 5-

minute and 1-hour data (1-3% missing) for each of the three monitor years. Concentration 

reporting from the Indianapolis monitor 18090078 was fairly complete considering either data 

set and for all three years (4-9% missing). We recognize that Indianapolis monitor 180970057 

exceeds the above recommendation of having somewhere between 5 to 10% missing data when 

using the simple interpolation to estimate 5-minute concentration, however, we decided that use 

of continuous 5-minute concentrations from the local monitor was better than use of a surrogate 
3-33

monitor from Detroit to represent variability in 5-minute concentrations, as was done in the draft 

REA.

For each of the three study areas, we used the PROC EXPAND interpolation approach to 

fill the missing continuous 5-minute concentrations, with the exception of the Fall River monitor 

250051004 in 2013 that only reported 5-minute maximum and 1-hour concentrations. Therefore,

a second approach was employed to estimate within hour 5-minute concentration variability for 

this Fall River monitor (section 3.5.2). A complete set of 1-hour and continuous 5-minute data 

was first needed to apply this second approach. To estimate missing 1-hour and continuous 5-

minute data for the 2013 Fall River monitor, PROC EXPAND used their respective measured 

concentrations to interpolate the missing values. Because of the dependence of 1-hour 

concentrations and maximum 5-minute concentrations, 30 the following steps were used for 

estimating missing maximum 5-minute concentrations:

• Using PROC EXPAND, estimate the missing 1-hour concentrations for each monitor and 

year;

• Calculate peak-to-mean ratios (PMRs) using the measured 1-hour and maximum 5-minute 

concentrations;

• Using PROC EXPAND, estimate the missing PMR values for each monitor and year;

• Calculate missing maximum 5-minute concentrations by multiplying the complete set of 

PMRs by their corresponding 1-hour concentrations.

 

30 PROC EXPAND could have been used to estimate the missing maximum 5-minute concentrations based on using 

the measured values; however, this was not done because these simulated 5-minute values would not have been 

entirely consistent with the estimation of missing hourly concentrations. This lack of consistency would lead to 

PMRs that fall outside of the mathematically acceptable range (i.e., 1 ≤ PMR ≤ 12). For this reason, measurement 

related PMRs were used for the interpolation of missing PMR (with a restriction to remain between 1 and 12) to 

ultimately estimate reasonable maximum 5-minute concentrations. The minimum ratio is 1 because the highest 5-

minute concentration in an hour could never be less than the hourly mean. The maximum ratio is 12 because if 

the maximum 5-minute concentration (max5) was the only measured non-zero value (i.e., all other 11 5-minute 

measurements are 0), the hourly mean would be (max5 + (11 × 0))/12 or simply max5/12, thus effectively 

yielding a PMR = max5/(max5/12) = 12.
3-34

Table 3-9. Percent of missing values in the hourly and 5-minute ambient air monitoring

data sets for the three study areas (2011-2013).

Study Area Monitor ID Year

Continuous 5-minute data 1-hour and 5-minute 

maximum data

% Missing Days/Year < 

75% complete % Missing Days/Year < 75% complete

Fall River 250051004

2011 3.5 4 - -

2012 2.9 2 - -

2013 - - 4.7 7

Indianapolis

18090057

2011 58.0 33 1.2 2

2012 25.5 10 2.1 5

2013 11.6 25 2.8 9

18090078

2011 9.2 32 8.0 31

2012 4.5 10 4.3 9

2013 8.2 26 7.4 22

18090073

2011 42.6 208 41.4 202

2012 52.6 281 51.4 272

2013 64.6 311 63.8 304

Tulsa

401430175

2011 1.2 2 - -

2012 1.1 3 - -

2013 2.6 9 - -

401430179

2011 - - - -

2012 - - - -

2013 3.2 12 - -

401430235

2011 2.7 10 - -

2012 3.3 12 - -

2013 1.6 4 - -

401431127

2011 1.3 5 - -

2012 7.3 31 - -

2013 2.3 7 - -

The symbol “-“ indicates there were no data needed for this evaluation because there were adequate continuous 

5-minute data available or there were no data available.

3.5.2 Estimating Continuous 5-minute Concentrations at Monitor Having Only 1-hour

Average and Hourly Maximum 5-minute Data

In this assessment, we are interested in estimating 5-minute exposures using the complete 

time-series of 5-minute ambient air concentrations for each year. We are also interested in 

utilizing, to the maximum extent possible, the local ambient air measurements to inform this 

estimation. As described above, there were no 5-minute continuous measurements available for 

one year (2013) for the Fall River study area. Based on the ambient air monitoring data that were 

available (i.e., 1-hour average and maximum 5-minute concentrations within each hour) and 

knowing that air pollutant concentrations are typically lognormally distributed (Kahn, 1973), an 

approach was developed to estimate the eleven other 5-minute concentrations occurring within 

each hour in the year for which continuous 5-minute measurements were not available. While
3-35

early studies (e.g., Larsen, 1977) have developed models to estimate a few of the upper 

percentiles of a concentration distribution using relationships between peak concentrations and 

time-averaging (e.g., estimate a 2

nd highest 1-hour from the 2nd highest 8-hour), they are not 

considered directly applicable to estimating a complete time-series of continuous 5-minute 

concentrations in a year (i.e., 105,120 values). We also note that there are maximum 5-minute 

monitored concentrations associated with instances where the hourly concentrations are reported, 

which already provides appropriate values for important peak 5-minute concentrations. Because 

the Fall River study area had continuous 5-minute data available for two of the years of interest, 

while also needing an approach to estimate continuous 5-minute concentrations for 2013, the

2011-2012 Fall River continuous 5-minute data served as a case study for developing and 

evaluating this approach. 

We first evaluated the 5-minute data set to confirm lognormal distributions would be 

appropriate to fit the twelve measured 5-minute values in each hour and to determine the 

parameters associated with that distribution. Using the set of continuous 5-minute monitor data 

in Fall River (2011-2012), where all twelve31 5-minute measurements within an hour were 

available, data were categorized by their 1-hour average concentrations and their peak to mean 

ratios (i.e., PMRs, the maximum 5-minute concentration divided by the 1-hour average). This 

categorization was done because the 2009 REA analyses indicated a relationship between the 

magnitude of hourly SO2 concentrations and the magnitude of the PMRs, consistent with

conclusions made regarding this relationship (Singer, 1961). For the hourly concentrations, bins 

of 10 ppb increments were used to categorize hourly concentrations upwards from 0 through 80 

ppb, with a final bin containing all concentrations above 80 ppb (yielding a total of 9 hourly 

concentration bins). PMR was categorized by 0.5 increments from 1 to 2, then in whole units 

from 2 to 4, ending with a final PMR bin of ≥ 4 (yielding a total of 5 PMR bins). 

Then, we used PROC CAPABILITY (SAS, 2017) to evaluate the fit of eight statistical 

distribution forms32 for both the varying hourly concentration and PMR binned continuous 5-

minute data. Distribution fits were evaluated using four goodness-of-fit statistics: Kolmogorov 

Smirnov, Cramer von Mises, Anderson Darling, and Chi-Square (SAS, 2017). Best fit

distributions were selected based on having the lowest p-value (or highest critical value) in the 

collection of fit statistics. For the low 1-hour concentration binned data (e.g., 0 to <10 ppb, 10 to 

<20ppb), normal distributions were found to have the best statistical fit, while for higher 1-hour 

concentration binned data, lognormal distributions had the best statistical fit (along with a few 

 

31 One hour has 12 five-minute periods (60/5=12), thus there are a total of twelve 5-minute concentrations possible 

within an hour.

32 Distributions evaluated were normal, lognormal, Weibull, gamma, Pareto, exponential, beta, and Rayleigh.
3-36

having gamma and Weibull distributions as the most reasonable fit). This was not entirely 

unexpected given that some of the distribution types could not be fit to the binned data set (e.g., 

the number of samples in some of the bins was too small, the prevalence of concentration values 

of 0). Overall, the results indicate the within-hour 5-minute concentrations are generally 

consistent with a lognormal distribution, particularly considering high concentrations of interest, 

and that a lognormal distribution can be used to reasonably approximate the missing eleven 

within-hour 5-minute concentrations.

To do so, the parameters of all the fitted normal distributions were transformed to 

lognormal terms (geometric means and standard deviations) (Casella and Berger, 2002) and 

combined with the suite of parameters estimated for all of the fitted lognormal distributions. 

Series of twelve 5-minute concentrations were randomly sampled from these distributions for 

thousands of iterations, creating a new data set consisting of a distribution of thousands of 

datasets of twelve 5-minute concentrations, each lognormally distributed and having their own 

hourly average concentration and PMR. Individual sets of twelve 5-minute concentrations were 

then divided by their respective 1-hour average concentrations to create sets of normalized 5-

minute concentrations (estimated concentrations), and then categorized by their PMR in 0.1 

increments. For method validation, a test data set was created from the 2011-2012 Fall River 

monitor data, using only the observed 1-hour average and maximum 5-minute concentrations. 

From the data set of estimated concentrations, a set of twelve mean normalized33 5-minute 

concentrations were then randomly assigned to each 1-hour/maximum 5-minute concentration in 

the test data set and were linked using the same categorization of PMR in 0.1 increments. 

Finally, the within-hour continuous 5-minute concentrations were calculated for each hour by 

multiplying the observed 1-hour average by the normalized twelve 5-minute concentrations.

34

The complete set of estimated 1-hour mean, 5-minute maximum, and continuous 5-

minute concentrations were compared with the respective metric in the monitoring dataset. 

Figure 3-9 illustrates the relationship, indicating excellent reproducibility of the original 1-hour 

(top panels) and maximum 5-minute concentrations (middle panels) and reasonable agreement 

between the estimated and measured 5-minute continuous concentrations (bottom panels). Table 

3-10 provides summary statistics for comparison to further support the relationship.

 

33 All twelve 5-minute concentrations occurring within an hour were divided by that hourly 1-hour average 

concentration. 

34 Where needed, a small downward or upward adjustment was applied to the suite of 5-minute concentrations to 

ensure the modeled values had a 1-hour average and maximum 5-minute concentration consistent with the 

monitoring measurements. The approach was designed to precisely replicate the 1-hour average and its associated 

variability of all 12 within hour 5-minute concentrations, thus there are a few instances where the estimated and 

measured 5-minute maximum deviated slightly from one another (Figure 3-9, middle panel). 
3-37

Figure 3-9. Comparison of estimated to measured SO2 concentrations in ambient air in Fall 

River monitor 250051004: 1-hour average (top panels), maximum 5-minute 

(middle panels) and continuous 5-minute (bottom panels) for 2011 (left panels) 

and 2012 (right panels).
3-38

Table 3-10. Descriptive statistics and correlations associated with measured and estimated

1-hour average, maximum 5-minute, and continuous 5-minute SO2

concentrations, Fall River (monitor 250051004), 2011-2012.

Variable Year Data Set SO2 Concentrations (ppb) Correlation (r) N Mean Std Dev Minimum Maximum

1-hour 

average

2011 Estimated 7728 3.01 5.97 0.09 93.4 1.00000 Measured 7728 3.01 5.97 0.09 93.4

2012 Estimated 8404 2.43 4.27 0.11 86.3 1.00000 Measured 8404 2.43 4.27 0.11 86.3

Maximum 

5- minute

2011 Estimated 7728 5.62 14.11 0.2 205.7 0.99999 Measured 7728 5.59 14.11 0.2 205.7

2012 Estimated 8404 4.04 9.71 0.2 155.5 0.99996 Measured 8404 4.01 9.70 0.2 155.5

Continuous 

5-minute

2011 Estimated 92736 3.01 7.03 0.02 205.7 0.97516 Measured 92736 3.01 7.24 0 205.7

2012 Estimated 100848 2.43 4.94 0.03 155.5 0.97922 Measured 100848 2.43 5.11 0 155.5

3.5.3 Estimating 5-minute Concentrations Across Study Areas

In the following sections we discuss the approach used to estimate 5-minute 

concentrations across the exposure modeling domain encompassing each study area (section 

3.5.3.1), summarize the estimated 5-minute concentrations in relation to available ambient air 5-

minute measurements (section 3.5.3.2), and include a comparison of estimated concentrations 

with ambient air monitor measurements considering the occurrence of concentrations at or above 

concentrations of interest during times of greater exposure potential (section 3.5.3.3). 

3.5.3.1 Combining 5-minute Monitor Measurements with 1-hour AERMOD Receptor 

Estimates

The complete temporal profile of each of the three years of continuous 5-minute monitor 

data developed using the above approach(es) was used to approximate the within-hour variation 

in 5-minute concentrations at each AERMOD air quality receptor site in each study area. The 

approach used in this REA to combine the monitor data with the modeled hourly estimates is a 

slight variation of that described in the REA Planning Document.35 We have adjusted the 

proposed approach in the REA Planning Document to better reflect instances where the ambient 

 

35 For the REA Planning Document, we originally proposed to match by consecutive hour, i.e., using the complete 

calendar years of hourly concentrations for both the ambient monitor and each air quality receptor. Then, each 

within-hour distribution of twelve 5-minute concentrations from the monitor would be adjusted using a 

multiplicative factor derived from the ratio of the 1-hour average concentrations (i.e., modeled divided by 

measured) (see REA Planning Document, Equation 4-4).
3-39

air monitor may capture a high concentration event that may not necessarily occur at the same 

clock time at a modeled air quality receptor that is located at a distance from the monitor. Events 

such as these would result from varying lateral or vertical transport of pollutant plumes that may 

not necessarily be captured by the air quality modeling,

36 affecting both the temporal and spatial 

characteristics of the air quality surface. 

Considering this, the calendar-based approach (described in the REA Planning 

Document) could result in a mismatching of times when peak concentration occurs across the 

spatial domain and thus lead to potentially erroneous distributions of 5-minute concentrations. 

For this REA, we linked the high concentration events occurring in both the monitor data set and 

the modeled hourly estimates at air quality receptors by ranking their respective 1-hour 

concentration distributions. Thus, all low 1-hour concentrations at each modeled air quality 

receptor will be linked to the distribution of 5-minute concentrations that occur during low 1-

hour concentrations measured at the monitor, and, in a similar fashion, all high hourly

concentration events will be appropriately linked, irrespective of clock hour. A similar equation 

to that provided in the REA Planning Document that replicates the pattern of the monitored 5-

minute values in an hour by scaling the 5-minute values so their hourly averages are equal to the 

AERMOD predictions for that hour (Equation 3-3) is described here:

Ys,r,i =

Ys,r

1

12

∑ Xr,i

12

i=1

Xr,i Equation 3-3

where,

Xr,i = the ith 5-minute value (ppb) at the monitor, having 1-hour ranked concentration r

Ys,r = the 1-hour AERMOD value (ppb) at location s, having 1-hr ranked concentration r

Ys,r,i = the ith 5-minute value (ppb) having 1-hr ranked concentration r, at location s

s = AERMOD prediction point in space

r = rank ordered 1-hour concentration, r = 1, 2, ..., 8760 (or 8784 for leap years)

i = sequence of 5-minute values within the hour, i = 1, 2,..., 12.

Thus, the complete year distribution of continuous 5-minute concentrations was applied 

to the modeled receptors using the complete time-series of hourly scaling factors (unique to each 

receptor) to yield the time-series of 5-minute SO2 concentrations (e.g., n= 12×24×365 = 105,120 

 

36 There is variation in the emissions and meteorological data input to the model relative to the actual emissions and 

meteorology. For example, it is possible that, given the limited number of meteorological stations and their 

geographic locations relative to the hundreds of receptors modeled across a 200 km2study area, the actual local 

fine scale weather patterns will not all coincide in time and space.
3-40

values) at every air quality receptor in each study area. Effectively, all spatial gradients that may 

exist for each hour across the study area are maintained; the 5-minute monitoring data only add a 

finer scale to the within-hour temporal variability. Because the ranked concentration distributions 

for each modeled air quality receptor may have a differing order of actual clock hours, it is likely 

that the within-hour 5-minute concentration variability (and hence maximum 5-minute 

concentrations) differs across the air quality receptors when considering the same clock hour. 

This is considered a reasonable and realistic outcome of using this approach. 

For instances where a study area has more than one ambient air monitor (i.e., 

Indianapolis and Tulsa), modeled receptors were linked with 5-minute concentration data from 

the nearest monitor. Again, all spatial gradients that may exist within each hour across the study 

area are maintained and it is likely that there is differing within-hour 5-minute concentration 

variability and occurrence of maximum 5-minute concentrations across the air quality receptors 

when considering the same clock hour. The assignment of monitor to modeled air quality 

receptors is as follows:

• Fall River: all air quality receptors were linked to 5-minute concentrations from the 

single ambient air monitor in the study area (250051004). 

• Indianapolis: monitor 180970057 is located between the two largest sources (Harding 

and Citizens Thermal) and is considered to best represent local source related 5-minute 

concentration variability. The 5-minute concentrations from this monitor were linked to 

air quality receptors within 10 km of Harding and 5 km within Citizens Thermal, i.e., 

those receptors potentially having a strong local source influence. All other receptors 

used monitor 180970078 to represent air quality receptors not having a strong local 

source influence on 5-minute concentrations. Monitor 180970073 is considered outside 

of the exposure modeling domain and had a large percent of missing data, thus these data 

were not used at this time.

• Tulsa: monitor 401430175 is closest to the West Refinery and monitor 401430235 is 

closest to the East Refinery. These monitors are considered to best represent local source 

related 5-minute concentration variability. Based on the spatial pattern of DVs, 

concentrations from monitor 401430175 were linked to air quality receptors within 10 km 

of the West Refinery and concentrations from monitor 401430235 were linked to 

receptors within 5 km of the East Refinery. All other receptors used monitor 401431127 

to represent air quality receptors not having a strong local source influence on 5-minute 

concentrations. Monitor 401430179 is proximal to monitor 401430175, although further 

from the West Refinery. This monitor only has data for 2013 and was not used to 

estimate 5-minute concentrations at this time.

3.5.3.2 Summary of Estimated 5-minute Concentrations Across Study Areas

After estimating the continuous 5-minute concentrations at each air quality receptor 

location, the distributions of these 5-minute concentrations were compared to those of the 5-

minute ambient air measurements in each study area. To do so for this comparison, the ambient 
3-41

air monitor concentrations in each study area were first adjusted proportionally using the single 

factor derived from the maximum monitor design value to reflect conditions that would just meet 

the current standard. As such, the adjusted ambient air concentrations from the monitor having 

the highest design value would hypothetically represent a distribution of the highest 

concentrations in a study area among the monitored data set.

37

We summarized the monitor continuous 5-minute concentrations by identifying the 90th

and 99th percentiles of the distribution and selecting the maximum 5-minute concentration. The 

estimated continuous 5-minute concentrations at the air quality receptor sites were also 

summarized by considering the upper percentiles of the distribution. The 90th and 99th percentiles 

of the distribution, along with the maximum 5-minute concentration, were identified at each 

modeled receptor location. Because there were over a thousand air quality receptors within each 

study area, we consolidated each of these statistics to a new set of statistics. We still focused on 

the 90th and 99th percentiles of the distribution and the maximum 5-minute concentration, 

however now we considered the distribution of each of these upper percentile concentrations 

across the entire set of air quality receptors. For example when considering the maximum 5-

minute concentrations, the maximum of all the maximum 5-minute concentrations (i.e., the single 

highest air quality receptor concentration considering the entire study area), the 99th percentile of 

all maximum 5-minute concentrations (i.e., 1% of the complete set of modeled receptors have a 

maximum 5-minute concentration greater than this value), and the 90th percentile of all maximum

5-minute concentrations (10% of the complete set of modeled receptors have a maximum 5-

minute concentration greater than this value) would be presented. This summary sequence would 

then follow for the other two statistics (the upper percentile distribution of all 90th and 99th

percentile 5-minute concentrations from the collection of receptors) generated from the 

collection of air quality receptors, which are provided in Tables 3-11 through 3-13.

There is reasonable agreement at the upper percentiles between the adjusted monitored 

concentrations and the estimates developed for the receptor sites, particularly considering the 

99th percentile and maximum values in the Fall River and Tulsa study areas (Table 3-11 and 3-

13). For example, the range in particular percentile concentrations (e.g., the 90th, 99th, and 

maximum of the estimated maximum percentile 5-minute concentrations across all receptors) 

estimated for the model receptor locations bound the measured 5-minute concentrations quite 

well (e.g., maximum 5-minute concentrations for 2011 and 2012 in the Fall River study area). In 

some instances, the range of upper percentile concentrations for the model receptor sites extends 

above the monitor upper percentile concentrations (e.g., the 99th percentile concentrations in Fall 

 

37 Therefore, the maximum hourly design value for both the ambient monitor and modeled receptor would be 75 

ppb, making the two sets of data more compatible.
3-42

River for 2012 and 2013). In other cases, the range of the receptor upper percentile 

concentrations is below the monitor upper percentile concentrations (e.g., the maximum 5-

minute concentrations in Fall River for 2013). 

For most percentiles of the concentration distribution for the Indianapolis study area, the 

receptor concentrations are greater than the monitor-based concentrations. This may be related to 

the approach for estimating concentrations associated with source emissions not explicitly 

modeled (Table 3-7).38 Given that the range of maximum 5-minute concentrations estimated at

the receptor locations (e.g., 452-642 ppb for the first year) extends above that of the monitor 

(355 ppb), it is possible that, even when adjusted for just meeting the current standard, these 

maximum 5-minute concentrations at modeled receptor sites appear somewhat high. However, 

we also note that there are situations where the estimated maximum 5-minute concentrations at 

receptor sites were well below that of the monitor (e.g., receptor concentrations peaked between 

167-205 ppb compared to monitor concentration of 369 ppb for year two of the simulation). It 

may also be that the numerous receptors situated in close proximity to the largest emissions 

sources in the area are representing hourly (and hence 5-minute) variability not reflected by the 

monitors. In the absence of having monitors at all the receptor sites to confirm this, the upper 

range of predicted concentrations across each of the study areas remain as an important 

uncertainty.

 

38 In this same analysis performed for the draft REA (which relied on a different approach for estimating 

concentrations associated with source emissions not explicitly modeled), concentrations estimated at the lesser 

primary source influenced receptors were less than that observed for monitor 180970078. While the draft REA 

used a surrogate monitor to approximate 5-minute variability in Indianapolis and this REA used the continuous 5-

minute data from study area monitors, this change in approach does not appear to be a significant contributor to 

the differences between the concentration distributions (data evaluation not shown).
3-43

Table 3-11. Descriptive statistics for concentrations at monitors and concentrations 

estimated at air quality receptor locations, Fall River study area 2011-2013.

Unadjusted or 

Adjusted 

Values

Type of Statistic 2011 2012 2013 a

Monitor (250051004) 5-minute SO2 Concentrations (ppb)

unadjusted

p90 4 3 4

p99 31 21 12

max 206 156 206

adjusted b

p90 5 4 4

p99 37 25 14

max 241 182 241

Estimated 5-minute SO2 Concentrations (ppb) at Air Quality Receptors

adjusted c

p90p90 11 10 11

p99p90 11 10 11

maxp90 11 10 11

p90p99 32 27 22

p99p99 41 31 24

maxp99 48 35 26

p90max 183 129 121

p99max 247 187 150

maxmax 268 214 180

a For 2013, only the maximum 5-minute measurement concentrations were available in Fall River, even 

though this evaluation includes estimated continuous 5-minute concentrations for monitor 250051004. 

b Adjusted concentrations were based on a monitor-based design value (adjustment factor =64/75 = 0.85).

c Adjusted concentrations were based on highest modeled air quality receptor and the primary source 

contribution to concentrations at that receptor (see section 3.4).

Abbreviations: pN= Nth percentile of 5-minute concentrations at monitor; pNpN = Nth percentile of the 

distribution of all study area receptor Nth percentile 5-minute concentrations. For example, p90 = 90th

percentile of 5-minute concentrations at monitor and p90p99 = 90th percentile of the distribution of all study 

area receptor 99

th percentile 5-minute concentrations.
3-44

Table 3-12. Descriptive statistics for concentrations at monitors and concentrations 

estimated at model receptor locations, Indianapolis study area 2011-2013.

Unadjusted 

or Adjusted 

Values

Type of 

Statistic 2011 2012 2013 2011 2012 2013

Monitor 5-minute SO2 Concentrations (ppb) a

Local Primary Source Influence

(monitor 180970057)

Less Primary Source Influence 

(monitor 180970078)

unadjusted

p90 4 5 5 5 6 5

p99 22 38 34 29 31 37

max 370 384 255 99 108 107

adjusted b

p90 3 4 5 5 6 5

p99 21 37 33 29 30 36

max 355 369 245 99 104 103

Estimated 5-minute SO2 Concentrations (ppb) at Model Receptors

Local Primary Source Influence Less Primary Source Influence

adjusted c

p90p90 21 19 20 18 18 18

p99p90 23 21 22 20 19 19

maxp90 25 23 23 20 19 20

p90p99 52 54 53 44 45 44

p99p99 61 62 61 45 46 45

maxp99 68 66 67 45 47 45

p90max 452 167 239 132 157 145

p99max 534 188 286 132 157 145

maxmax 642 205 343 135 157 145

a For all years monitored, continuous 5-minute measurement concentrations were available.

b Adjusted concentrations were based on a monitor-based design value (adjustment factor =78/75 = 1.04).

c Adjusted concentrations were based on highest modeled air quality receptor and the primary source contribution to 

concentrations at that receptor (see section 3.4).

Abbreviations: p90 = 90th percentile of 5-minute concentrations at monitor. p90p90 = 90th percentile of the distribution of all 

study area receptor 90th percentile 5-minute concentrations.
3-45

Table 3-13. Descriptive statistics for concentrations at monitors and concentrations 

estimated at model receptor locations, Tulsa study area 2011-2013.

Adjusted or 

Unadjusted 

Values

statistic 2011 2012 2013 2011 2012 2013 2011 2012 2013

Monitor 5-minute SO2 Concentrations (ppb) a

Local Primary Source 

Influence (401430175)

Local Primary Source 

Influence (401430235)

Less Primary Source 

Influence (401431127)

unadjusted

p90 15 11 7 2 1 1 2 2 1

p99 50 42 33 17 5 7 8 5 4

max 154 152 123 114 77 50 67 33 84

adjusted b

p90 20 15 10 3 1 1 2 2 2

p99 68 57 45 23 7 10 11 7 5

max 210 207 168 155 105 68 92 46 114

Estimated 5-minute SO2 Concentrations (ppb) at Model Receptor Locations

Local Primary Source 

Influence

Local Primary Source 

Influence

Local Primary Source 

Influence

adjusted c

p90p90 10 10 8 7 7 6 5 5 5

p99p90 29 24 14 12 10 7 6 6 5

maxp90 41 37 17 13 11 8 6 6 5

p90p99 41 34 23 35 28 22 16 13 9

p99p99 95 84 40 48 34 24 20 16 10

maxp99 118 108 49 53 39 26 24 18 11

p90max 126 116 64 170 207 96 99 59 57

p99max 239 238 118 199 270 109 127 73 65

maxmax 297 345 157 221 311 116 163 96 75

a For all years monitored, continuous 5-minute measurement concentrations were available.

b Adjusted concentrations were based on a monitor-based design value (adjustment factor =55/75 = 0.73).

c Adjusted concentrations were based on highest modeled air quality receptor and the primary source contribution to 

concentrations at that receptor (see section 3.4).

Abbreviations: p90 = 90th percentile of 5-minute concentrations at monitor. p90p90 = 90th percentile of the distribution of all 

study area receptor 90th percentile 5-minute concentrations

3.5.3.3 Estimated Peak 5-minute Concentrations at Air Quality Receptor Sites During 

Times of Greater Exposure Potential

Similar to the evaluation conducted on the hourly concentrations (section 3.2.5 and 

Appendix K), we were interested in understanding how well the estimated 5-minute 

concentrations corresponded with the available ambient measurements, while focusing on times 

most likely associated with population exposure and considering all modeled receptors and the 

estimated 5-minute concentrations used as input to the exposure model. Accordingly, we 
3-46

stratified both the estimated and measurement data sets by time-of-day and season and have 

focused on the daytime hours during the three warmer seasons.39

This analysis used the set of estimated 5-minute concentrations for all air quality 

receptors in each study area and compared these with the available monitor data, with both sets 

based on an adjustment intended to reflect the hourly concentrations just meeting the current 

standard. The 5-minute estimated concentrations for the air quality receptor sites are derived 

from the hourly concentrations estimated for the current standard scenario (adjustment for hourly 

concentrations described in section 3.4). As that same approach (which is based on adjusting 

concentrations associated with emissions from the primary source) could not be applied for the 

monitor concentrations, monitor concentrations were adjusted by a different approach. 

Concentrations at the highest monitor in each study area were adjusted such that the 3-year DV 

just equaled the level of the standard (75 ppb). For areas with more than one monitor, the 

concentrations at all monitors were adjusted by the same factor (based on the DV monitor). This 

difference in deriving somewhat conceptually comparable datasets affects our ability to precisely 

judge the implications of these comparisons with regard to potential bias in the receptor 

estimates and limits our conclusions accordingly.

Calculated for each data set were instances where 5-minute concentrations were at or 

above 100, 200, 300, and 400 ppb, at each individual air quality receptor and for each year. 

These counts developed for each air quality receptor location were then binned using the number 

of days per year, i.e., a receptor had at least 1 day, 2 or more days, 5 or more days, and 10 or 

more days at or above a selected level. Then the number of air quality receptor locations in each 

bin was summed, indicating how many air quality receptor locations in a study area had 

estimated concentrations at or above the levels of interest. Then we calculated the percentages 

these numbers were of the total number of receptor sites in each study area. Similar counts and 

percentages were also calculated for the monitor data. Results generated for each of the three 

study areas are provided in Table 3-14 to Table 3-16.

In general, there is consistency between the estimated and measured concentrations 

regarding the number of days per year that concentrations are at or above 5-minute 

concentrations of interest considering the years and seasons simulated. For example, in Fall 

River, 6 of the 9 season/years had at least one day with a concentration at or above 100 ppb at 

the ambient air monitor, while 5 of the 9 season/years were above the same level for more than 

40% of air quality receptors in the study area (Table 3-14). The occurrence of these upper 

percentile concentrations seems more frequent than would be observed when considering the 

 

39 Data were stratified by two times of day (daytime and nighttime) and four seasons (winter, spring, summer and 

fall) as described in section 3.2.5.
3-47

ambient air monitor alone, particularly for the Indianapolis study area (Table 3-15). Again, this 

could be a function of the model representing variability in ambient concentrations not observed 

at the ambient air monitors due to the siting of many modeled receptors in close proximity to the 

important emission sources in each study area. Comparisons of the estimated and monitor 5-

minute concentrations in the Tulsa study area (Table 3-16) were similar to that observed for the 

other two study areas, although they differed by having a much smaller percent of receptors at or 

above the concentrations of interest.

Table 3-14. Percent of air quality receptors and monitors at which 5-minute SO2

concentrations (for conditions just meeting standard) exceed concentrations of 

interest on single and multiple days, Fall River study area 2011-2013.

5-minute 

Concentrations 

of Interest a

Season b Year

Percent of Receptors Exceeding 

Concentration of Interest on 

Specified Number of Days in Year c

Percent of Monitors Exceeding 

Concentration of Interest on 

Specified Number of Days in Year c

Number of Days Number of Days

≥1 ≥2 ≥5 ≥10 ≥1 ≥2 ≥5 ≥10

100

Fall

2011 41.7 7.0 0 0 100 100 0 0

2012 1.6 0.1 0 0 100 100 0 0

2013 2.3 0.1 0 0 0 0 0 0

Spring

2011 86.3 60.0 14.1 1.3 100 100 0 0

2012 0.9 0 0 0 100 100 0 0

2013 3.9 0.7 0 0 100 0 0 0

Summer

2011 100 99.9 71.2 12.2 100 100 100 0

2012 100 100 26.2 4.8 0 0 0 0

2013 100 100 7.0 0.1 0 0 0 0

200

Fall

2011 0 0 0 0 0 0 0 0

2012 0 0 0 0 0 0 0 0

2013 0 0 0 0 0 0 0 0

Spring

2011 1.3 0.3 0 0 100 0 0 0

2012 0 0 0 0 0 0 0 0

2013 0 0 0 0 0 0 0 0

Summer

2011 1.1 0.8 0 0 100 100 0 0

2012 0.1 0 0 0 0 0 0 0

2013 0 0 0 0 0 0 0 0

a There were no estimated or measured concentrations at or above 300 ppb.

b Daytime hours (6 AM to 8 PM) only.

c There were 1,494 receptors modeled and 1 monitor.
3-48

Table 3-15. Percent of air quality receptors and monitors at which 5-minute SO2

concentrations (for conditions just meeting standard) exceed concentrations of 

interest on single and multiple days, Indianapolis study area 2011-2013.

5-minute 

Concentrations 

of Interest

Season a Year

Percent of Receptors Exceeding 

Concentration of Interest on 

Specified Number of Days in Year b

Percent of Monitors Exceeding 

Concentration of Interest on 

Specified Number of Days in Year b

Number of Days Number of Days

≥1 ≥2 ≥5 ≥10 ≥1 ≥2 ≥5 ≥10

100

Fall

2011 100 99.7 60.6 60.5 0 0 0 0

2012 100 100 63.5 60.5 100 67 33 0

2013 100 100 60.5 60.5 67 33 33 0

Spring

2011 60.4 57.3 25.4 2.0 33 33 0 0

2012 54.6 38.4 5.2 0.7 67 33 0 0

2013 36.9 16.0 3.0 0.5 33 33 0 0

Summer

2011 100 100 94.3 60.2 33 33 0 0

2012 100 100 62.1 60.5 67 33 0 0

2013 61.0 60.5 60.5 60.5 67 33 0 0

200

Fall

2011 59.5 57.1 0 0 0 0 0 0

2012 0 0 0 0 33 0 0 0

2013 60.4 58.2 0 0 0 0 0 0

Spring

2011 1.3 0.1 0 0 0 0 0 0

2012 0 0 0 0 33 0 0 0

2013 0.9 0 0 0 0 0 0 0

Summer

2011 5.4 1.6 0 0 0 0 0 0

2012 0.3 0 0 0 33 0 0 0

2013 25.2 0.6 0 0 33 0 0 0

300

Fall

2011 57.3 0.1 0 0 0 0 0 0

2012 0.1 0 0 0 0 0 0 0

2013 0.2 0 0 0 0 0 0 0

Spring

2011 0.5 0.1 0 0 0 0 0 0

2012 0.2 0 0 0 33 0 0 0

2013 0.1 0 0 0 0 0 0 0

Summer

2011 1.9 0.1 0 0 0 0 0 0

2012 0.1 0.1 0 0 33 0 0 0

2013 0 0 0 0 0 0 0 0

400

Fall

2011 2.8 0 0 0 0 0 0 0

2012 0 0 0 0 0 0 0 0

2013 0 0 0 0 0 0 0 0

Spring

2011 0.5 0 0 0 0 0 0 0

2012 0 0 0 0 0 0 0 0

2013 0 0 0 0 0 0 0 0

Summer

2011 0.9 0 0 0 0 0 0 0

2012 0 0 0 0 0 0 0 0

2013 0 0 0 0 0 0 0 0

a Daytime hours (6 AM to 8 PM) only.

b There were 1,917 receptors modeled and 3 monitors.
3-49

Table 3-16. Percent of air quality receptors and monitors at which 5-minute SO2

concentrations (for conditions just meeting standard) exceed concentrations of 

interest on single and multiple days, Tulsa study area 2011-2013.

5-minute 

Concentrations 

of Interest a

Season b Year

Percent of Receptors Exceeding 

Concentration of Interest on 

Specified Number of Days in Year c

Percent of Monitors Exceeding 

Concentration of Interest on 

Specified Number of Days in Year c

Number of Days Number of Days

≥1 ≥2 ≥5 ≥10 ≥1 ≥2 ≥5 ≥10

100

Fall

2011 8.2 3.5 2.0 0.7 67 33 0 0

2012 8.0 2.8 0.9 0.6 33 0 0 0

2013 0.6 0.4 0.1 0 33 0 0 0

Spring

2011 5.3 2.1 1.2 0.8 33 33 33 0

2012 4.5 1.3 0.6 0.6 33 33 33 0

2013 0.6 0.4 0.1 0 67 33 0 0

Summer

2011 3.5 1.4 0.9 0.6 33 33 33 33

2012 2.7 1.0 0.6 0.6 67 33 33 0

2013 0.6 0.5 0.2 0.1 33 33 0 0

200

Fall

2011 0.6 0.4 0 0 33 0 0 0

2012 0.5 0.4 0.1 0 0 0 0 0

2013 0 0 0 0 0 0 0 0

Spring

2011 0.6 0.4 0.1 0 0 0 0 0

2012 0.7 0.4 0.3 0.2 33 0 0 0

2013 0 0 0 0 0 0 0 0

Summer

2011 0.6 0.4 0.1 0.1 0 0 0 0

2012 0.5 0.5 0.2 0.1 0 0 0 0

2013 0 0 0 0 0 0 0 0

a There were no estimated or measured concentrations at or above 300 ppb.

b Daytime hours (6 AM to 8PM) only.

c There were 1,389 receptors modeled and 3 monitors.
3-50

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https://www3.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf

U.S. EPA. (2013). AERSURFACE User’s Guide. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC, EPA-454/B-08-001, Revised January 2013. Available at: 

https://www3.epa.gov/scram001/7thconf/aermod/aersurface_userguide.pdf
3-51

U.S. EPA. (2015a). AERMINUTE User’s Guide. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC, EPA-454/B-15-006, October 2015. Available at: 

https://www3.epa.gov/ttn/scram/7thconf/aermod/aerminute_userguide.pdf

U.S. EPA. (2015b). 2011 National Emissions Inventory (NEI) Technical Support Document, 

version 2. Office of Air Quality Planning and Standards, Research Triangle Park, NC, 

August 2015. Available at: https://www.epa.gov/sites/production/files/2015-

10/documents/nei2011v2_tsd_14aug2015.pdf

U.S. EPA. (2016a). Guideline on Air Quality Models. 40 CFR Part 51 Appendix W.

U.S. EPA. (2016b). User's Guide for the AMS/EPA Regulatory Model – AERMOD. EPA454/B-16-011. Office of Air Quality Planning and Standards, Research Triangle Park, 

NC.

U.S. EPA. (2016c). User’s Guide for the AERMOD Meteorological Processor (AERMET). 

Office of Air Quality Planning and Standards, Research Triangle Park, NC, EPA-454/B16-010.

U.S. EPA. (2016d). User's Guide for the AERMOD Terrain Preprocessor (AERMAP). Office of 

Air Quality Planning and Standards, Research Triangle Park, NC, EPA-454/B-16-012, 

December 2016.

U.S. EPA. (2016e). Technical Support Document (TSD): Preparation of Emissions Inventories 

for the Version 6.3, 2011 Emissions Modeling Platform. Office of Air Quality Planning 

and Standards, Research Triangle Park, NC, August 2016. Available at: 

https://www.epa.gov/sites/production/files/2016-

09/documents/2011v6_3_2017_emismod_tsd_aug2016_final.pdf

U.S. EPA. (2016f). SO2 NAAQS Designations Modeling Technical Assistance Document, Draft. 

Office of Air Quality Planning and Standards, Research Triangle Park, NC, August 2016. 

Available at: https://www.epa.gov/sites/production/files/2016-

06/documents/so2modelingtad.pdf

U.S. EPA. (2016g). AERMOD Implementation Guide. Office of Air Quality Planning and 

Standards, Research Triangle Park, NC, EPA-454/B-16-013, December 2016.

U.S. EPA. (2017a). Air Markets Program Data. Available at: https://ampd.epa.gov/ampd/

U.S. EPA. (2017b). Review of the Primary National Ambient Air Quality Standard for Sulfur 

Oxides: Risk and Exposure Assessment Planning Document. Office of Air Quality 

Planning and Standards, Research Triangle Park, NC, EPA-452/P-17-001, February 

2017. Available at: 

https://www3.epa.gov/ttn/naaqs/standards/so2/data/20170216so2rea.pdf
4-1

4 POPULATION EXPOSURE AND RISK 

This chapter describes the methods used to characterize exposure and health risk 

associated with SO2 emitted into ambient air under conditions just meeting the current primary 

standard. As summarized in section 2.2, the overall analysis approach is based on linking the 

health effects information to estimated population-based exposures that reflect our current 

understanding of 5-minute concentrations of SO2 in the ambient air.

Population exposures were estimated using the EPA’s Air Pollution Exposure Model 

(APEX), version 5. The APEX model is a multipollutant, population-based, stochastic, 

microenvironmental model that can be used to estimate human exposure via inhalation for 

criteria and toxic air pollutants. APEX is designed to estimate human exposure to these 

pollutants at the local, urban, and consolidated metropolitan level. In this REA we have used 

APEX to estimate exposures in three study areas, the details of which are provided in the 

following subsections. Additional information not provided here regarding all of APEX modules, 

algorithms, and model options can be found in the APEX User’s Guide (U.S. EPA, 2017a, b).

Briefly, APEX calculates the exposure time-series for a user-specified exposure duration

and number of individuals. Collectively, these simulated individuals are intended to be a 

representative random sample of the population in a given study area. To this end, demographic 

data from the decennial census are used so that appropriate model sampling probabilities can be 

derived, considering personal attributes such as age and sex, and used to properly weigh the 

distribution of individuals in any given geographical area. For this REA, the core demographic 

geographical units for estimating exposure are census blocks. Because AERMOD predicted SO2

concentrations at air quality receptor sites in a regular spaced grid (chapter 3), APEX matches 

the centroid of each census block (which are irregularly spaced due to varying size) in the study 

area with the closest receptor to estimate exposures for simulated individuals residing in each

census block.

For each simulated person, the following general steps are performed:

• Select attribute variables and choose values to characterize the person (e.g. age, sex, body 

weight, disease status);

• Construct the activity event sequence (minute by minute time series) by selecting a 

sequence of appropriate daily activity diaries for the person (using demographic and other 

influential variables);

• Calculate the concentrations in the microenvironments (MEs) that simulated individuals 

visit;

• Calculate the person’s simultaneous breathing rate and exposure for each event and

summarize for the selected exposure metric.
4-2

A simulated individual’s complete time-series of exposures (i.e., exposure profile), 

representing intra-individual variability in exposures, is combined with the exposure profiles for 

all simulated individuals in each study area and summarized to generate the population 

distribution of exposures, representing inter-individual variability in exposures. As described 

above regarding air quality and in the sections that follow describing APEX model inputs and 

approaches to estimating exposure, the overarching goal of the REA is to account for the most 

significant factors contributing to inhalation exposure, i.e., the temporal and spatial distribution 

of people and pollutant concentrations throughout the study area and among the 

microenvironments. The population distributions of exposures are combined with the health 

effects information to characterize associated risk via two types of metrics: comparison to 

benchmark concentrations and lung function risk. The details of the methods for exposure and 

risk estimation are described in the sections that follow.

4.1 POPULATIONS SIMULATED

APEX stochastically generates a user-specified number of simulated persons to represent 

the population in the study area. The number of simulated individuals can vary and is dependent

on the size of the population to be represented. In these analyses, the number of simulated 

individuals was set at 100,000 in each area, a more than adequate number of individuals to 

represent the geographically-restricted population residing within the exposure modeling 

domains (approximately 180,000 – 500,000). Each simulated person is represented by a 

“personal profile.” The personal profile includes characteristics such as a specific age, a specific 

home sector, a specific work sector (or does not work), specific housing characteristics, specific 

physiological parameters, and so on. The profile does not correspond to any particular individual 

in the study area, but rather represents a simulated person. Accordingly, while a single profile 

does not, in isolation, provide information about the study population, a distribution of profiles 

represents a random sample drawn from the study area population. This means that the modeling 

objective is for the statistical properties of the distribution of profiles to reflect statistical 

properties of the population in the study area. 

APEX generates population-based exposures using several population databases. Based 

on the geographic boundaries defining the study areas and the study groups of interest, APEX 

will simulate representative individuals using appropriate geographic, demographic, and health 

status information provided by existing population-based surveys. In this REA, there is variation 

in the geographic units by which some of the input data sets are organized (e.g., U.S. census 

tracts or a smaller subdivision such as census blocks). For example, employment status data are 

provided at the tract level while population demographics are available at the block level. 

Regardless of the geographic unit of the input data, all population-based data sets were applied at 
4-3

the block level in the exposure simulations. Where only tract level data were available, we 

assigned the tract specific information directly to the blocks that comprise a particular tract.

Several updates were made to the APEX model inputs and algorithms for use in 

simulating the populations of interest in this REA and are described in the following sections: (1) 

population demographic data that are based on the 2010 census (section 4.1.1), (2) asthma 

prevalence rates based on the 2011-2015 National Health and Nutrition Examination Survey 

(NHANES) and vary by age, sex and geographic location (section 4.1.2), and data and equations 

used to approximate personal attributes such as body weight, resting metabolic rate, and 

breathing rate (section 4.1.3).

4.1.1 Demographics

As described in section 3.2.3, ambient air concentrations were modeled to a fine-scale 

grid (100 m – 2 km) in each study area to better capture spatial heterogeneity in ambient air SO2

concentrations. We used U.S. Census blocks, the finest geographical scale available for the 

population data,1to take full advantage of this fine-scale air quality surface and best match the 

potential at-risk populations with areas having the highest SO2 concentrations. Block-level 

population counts were obtained from the 2010 Census of Population and Housing Summary 

File 1.2 Summary Files 1 (SF1) contains what the Census program calls “the 100-percent data,” 

which is the information compiled from the questions asked of all (100% of) people and housing 

units in the U.S. Three standard APEX input files3are used for the current assessment. For the 

purposes of having a more tractable analysis, we restricted these population demographic files to 

include the census blocks within the five states that encompass the three study areas (i.e., 

Connecticut, Indiana, Massachusetts, Oklahoma, and Rhode Island) rather than use a nationalbased file that would include all 50 U.S. states.

• PopGeoLocs2010_3StudyAreas.txt: census block identifiers (ID’s), latitudes and 

longitudes in degrees.

 

1 The minimum size for census block is between 30,000 to 40,000 ft2 or approximating a grid cell of about 55 – 60 

meters (see https://www.census.gov/geo/reference/garm.html). The next larger sized census geographic unit is a 

census block group which is comprised of multiple blocks. When considering that, on average, there are about 30 

to 85 blocks per block group in the states where the study areas are located, it is likely that census block groups 

would be more amenable to a modeled air quality surface having a grid cell size of about 1.6 – 8.1 Km (see 

https://www.census.gov/geo/maps-data/data/tallies/tractblock.html).

2 Technical documentation - 2010 Census Summary File 1—Technical Documentation/prepared by the U.S. Census 

Bureau, Revised 2012 - available at: http://www.census.gov/prod/cen2010/doc/sf1.pdf.

3 The names of all APEX files are provided here to link the brief description with the appropriate input file.
4-4

• PopBlockFemale2010_3StudyAreas.txt: census block identifiers, block-level population 

counts for females, stratified by 23 age groups.

4

• PopBlockMale2010_3StudyAreas.txt: census block identifiers, block-level population 

counts for males, stratified by 23 age groups.

We evaluated the spatial distribution of the population in each study area, focusing on

children, a study group identified as an important at-risk population (section 2.1.3). First, we 

subset this APEX input data set to include the blocks that are a part of the study areas. Then, 

because there are wide ranging numbers of people in each block, we stratified these data into 

three population groups: blocks having at least 100 people, blocks having greater than 100 to at 

least 500 people, and blocks having greater than 500 people. Finally, we calculated the percent of 

the total population that were children (aged 0-17) in each census block of each study area and 

population group, and calculated the percentiles of that distribution providing perspective on the 

spatial distribution of children (and adults) in each study area (Table 4-1).

In each study area there are a number of blocks having no people residing in them (i.e., 

non-residential blocks). While these blocks are retained in the exposure simulations as they could 

still serve as an area where an individual might visit and be exposed to SO2 (e.g., a workplace 

location within a study area), these blocks were not used to calculate the population spatial 

distribution statistics. The majority of the residential blocks (84-93%) in each study area have 

fewer than 100 people, with very few blocks (<1%) having a total population greater than 500 

people. From a relative perspective, the Indianapolis study area had approximately double the 

percent of residential blocks having a total population greater than 500 people compared to the 

other two study areas. Further, while the overall distribution of the percent of children in each 

study area is general similar (comprising 15-20% on average per residential block), the 

Indianapolis study area consistently has a greater percent of children across most of the 

percentiles of the distribution, most notably so for the residential blocks with a population 

greater than 500 people.

 

4 The age groups in this file are: 0-4, 5-9, 10-14, 15-17, 18-19, 20-20, 21-21, 22-24, 25-29, 30-34, 35-39, 40-44, 45-

49, 50-54, 55-59, 60-61, 62-64, 65-66, 67-69, 70-74, 75-79, 80-84, >84.
4-5

Table 4-1. Distribution of the percent of total population that are children residing in the 

census blocks comprising each study area.

Percent of Population that are Children Residing in Study Area Census Blocks

Census blocks with ≤100 

people per block

Census blocks with >100 

to ≤500 people per block

Census blocks with >500 

people per block

Study area a FR IN OK FR IN OK FR IN OK

Mean (%) 14.7 16.9 16.7 16.4 19.7 17.7 14.6 18.4 11.9

Standard deviation 

(%) 10.0 11.0 11.2 7.2 9.0 9.8 8.2 8.9 8.7

Minimum (%) 0 0 0 0 0 0 0.5 0 0.5

5

th percentile (%) 0 0 0 4.3 2.6 0.9 0.5 0.2 0.65

10th percentile (%) 0 0 0 8.3 7.8 3.8 3.05 2.8 1.2

25th percentile (%) 7.75 9.1 8.8 12.3 14.5 11.4 8.9 14 3

50th percentile (%) 14.3 17 16.7 16.1 19.7 18 14.8 19.5 11.1

75th percentile (%) 20.3 24 23.9 19.9 25.4 23.8 22 25.8 17.7

90th percentile (%) 27.3 30.4 30.9 24.5 30.5 30 23.9 28.5 24.95

95th percentile (%) 31.9 34.3 35.1 28.2 33.9 34.9 25.3 30.6 27.9

99th percentile (%) 42.3 46.2 47.5 39.4 42.3 41.7 25.3 34.5 28.5

Maximum (%) 86.2 100 83.3 51.7 61.6 61.3 25.3 34.5 28.5

Number of blocks 3883 11130 7319 471 1123 355 10 57 20

Number of blocks 

with non-zero 

population 2588 7650 5106 471 1123 355 10 57 20

a FR = Fall River, IN = Indianapolis, OK = Tulsa.

The employment file for APEX contains the probability of employment separately for 

males and females by age group (starting at age 16) and by Census tract (the only census unit 

available for this type of data). The 2010 Census collected basic population counts and other data 

using the short form, but collected more detailed socioeconomic data (including employed 

persons) from a relatively small subset of people using the 5-year American Community Survey 

(ACS).5 The ACS dataset provides the number of people in the labor force, which we stratified 

by sex/age/tract, considering both civilian workers and workers in the Armed Forces. The data 

were stratified by sex and age group, and were processed so that each sex-age group combination 

is given an employment probability fraction (ranging from 0 to 1) within each census tract. 

Children under 16 years of age were assumed to be unemployed. To match the population 

 

5 2010 U.S. Census American FactFinder: http://factfinder2.census.gov/. For instance, to obtain the table ID B23001 

“Sex by age by employment status for the population 16 years and over”, the following steps were performed. 

First, select the “guided search option”, choose “information about people” and select “employment (labor force) 

status”, “sex” and “age”. For geography type select “census tract - 140” for each state. Tables containing the 

employment numbers were downloaded and used to calculate the employment probabilities for each age group. 
4-6

demographic files, we included only the census blocks within the five states that encompass the

three study areas. To use the file at a block level, all blocks were assigned the same employment 

probabilities as the parent tract. One standard APEX input file is used for the current assessment:

• EmpBlock2010_3StudyAreas.txt: census block IDs, employment probabilities (in 

fractional form), stratified by 13 age groups.

6

4.1.2 Asthma Prevalence

The population groups included in this exposure assessment are adults with asthma (> 18 

years old) and children with asthma (5 to 18 years old),

7

based on their identification in this REA 

as an at-risk population (section 2.1.3). To best approximate the number (and percent) of 

individuals comprising each of these population groups in each study area, we considered several 

influential variables that could affect asthma prevalence. It is widely recognized that there are 

significant differences in asthma prevalence based on age, sex, U.S. region, and family income 

level, among other factors.

8 There is spatial heterogeneity in family income level across census 

geographic areas (and also across age groups)

9

and spatial variability in local scale ambient air 

concentrations of SO2 (e.g., Figures 3-6 to 3-8). Thus, we have developed an approach to better 

estimate the variability in population-based SO2 exposures by accounting for these particular 

attributes of this study group and their spatial distribution across each of the study areas. 

With regard to asthma prevalence, the data are used to identify if a simulated individual 

residing within a modeled census geographic area has asthma – and are not used for selection of 

any other personal attribute nor in the selection of activity pattern data. Thus, our primary 

objective with these data was to generate census block level prevalence estimates that reflect 

variability in asthma prevalence contributed by several known influential attributes (e.g., age, 

sex, geographic location). Two data sets were identified and linked together to estimate asthma 

prevalence used for this REA. First, asthma prevalence data were obtained from the 2011-2015

National Health Interview Survey (NHIS) and are stratified by NHIS defined regions (Midwest, 

Northeast, South, and West), age, and sex.

10 We explored other variables that were available in 

 

6 The age groups in this file are: 16-19, 20-21, 22-24, 25-29, 30-34, 35-44, 45-54, 55-59, 60-61, 62-64, 65-69, 70-74,

and >75.

7 As in other NAAQS reviews, this REA does not estimate exposures and risk for children younger than 5 years old 

due to the more limited information contributing relatively greater uncertainty in modeling their activity patterns 

and physiological processes than children between the ages of 5 to 18.

8 For example, see the Center for Disease Control report “National Surveillance of Asthma: United States, 2001–

2010”, available at: https://www.cdc.gov/nchs/data/series/sr_03/sr03_035.pdf.

9 For example, see the U.S. Census report “Income and Poverty in the United States: 2016”, available at: 

https://www.census.gov/content/dam/Census/library/publications/2017/demo/P60-259.pdf.

10 Information about the NHIS is available at: http://www.cdc.gov/nchs/nhis.htm.
4-7

the NHIS data set that contributed to variability in asthma prevalence and that could be used to 

extrapolate the asthma prevalence to a finer geographic scale than the NHIS-provided four 

regions. The linking variable had to be common with variables available in the population 

demographic data. Based on this criterion, we selected family income level to poverty thresholds

(i.e., whether the family income was considered below or at/above the US Census estimate of 

poverty level for the given year) and used that as an additional variable to stratify the NHIS 

asthma prevalence. Then, we obtained information from the 2013 Census ACS to estimate 

family income level to poverty thresholds at the census tract level and stratified by several ages 

and age groups.

11 By combining these two data sets, we developed census tract level asthma 

prevalence estimates for children (by age in years) and adults (by age groups), also stratified by 

sex (male, female) that were weighted by the individual census tract populations and family 

income level. To match the population demographic and employment files, we included only the 

census blocks within the same five states that encompass the three study areas. The census tract 

sex- and age-specific prevalence were extrapolated to census blocks using the 11-character 

identifier shared between census tracts and blocks. A detailed description of how the NHIS data 

were processed to create the data set used for input to APEX is provided in Appendix E. One 

standard APEX input file is used for the current SO2 assessment:

• asthma_prev_1115_block_3StudyAreas.txt: census block identifiers, block-level asthma 

prevalence (in fractional form) stratified by sex, 18 single year ages (for ages <18),

12 and 

7 age groups (for ages > 17).

The asthma prevalence estimates vary for the different ages and sexes of children and 

adults13 that reside in each census block of each study area. We evaluated the spatial distribution 

of the asthma prevalence using the specific blocks that comprise the exposure modeling domain 

in each study area. We first separated the estimates for children from those for adults and 

calculated the distribution of asthma prevalence for the blocks, stratified by sex (Table 4-2). By 

design (i.e., the use of age, sex, and family income variables), there is spatial variability in the 

 

11 Census tract level data is the finest scale geographical unit having family income information. The family 

income/poverty ratio threshold used was 1.5, that is the surveyed person’s family income was considered either ≤ 

or > than a factor of 1.5 of the U.S. Census estimate of poverty level for the given year.

12 The census data set used only had children for single years up to and including age 17, after that year they are 

provided in groups. The upper portion of this age range differs from those considered as children in estimating 

exposures i.e., in our exposure assessment children are considered upwards to 18 years old. To simulate the 

number of children with asthma age 18, estimated prevalence from the first adult group were used (i.e., 

individuals age 18-24).

13 While prevalence rates were estimated for all ages of children (in single years 5 - 17), for adults they were 

estimated for seven age groups: 18-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, 65-74 years, 

and, ≥75 years old (see Appendix E for more information).
4-8

prevalence estimates. Consistent with broadly defined national asthma prevalence (e.g., Table 3-

2 of SO2 PA), children have higher estimated rates than adults,14 male children have higher rates 

than female children,

15 and adult females have higher rates than adult males (e.g., compare with 

mean values of Table 4-2). However, when evaluating variability contributed by study area, age, 

sex, and family income level on a spatial scale, an additional degree of variability emerges across 

the study areas (as presented in Tables 4-2). The Fall River study area has some of the highest 

asthma prevalence for children considering most of the statistics with rates as high as 21.5% in 

one or more census blocks for males of a given year of age. The Tulsa study area exhibits some 

of the lowest asthma prevalence when considering adults (both sexes) with rates as low as 4.0% 

in one or more blocks for males within a given age group. These summary statistics represent the 

range of age- and sex-specific values for the census blocks used in each APEX simulation to 

estimate the number of individuals that have asthma.

Table 4-2. Estimated asthma prevalence for children and adults in census blocks of three

study areas, summary statistics. 

Study Area

(# census blocks) 

and

Population group

Sex

Asthma Prevalence across all ages (or age groups) for all census blocks a

Mean Standard 

Deviation Minimum Median Maximum

Fall River

(4,364)

child female 9.3% 2.4% 5.7% 9.2% 18.6%

male 13.3% 2.3% 8.4% 13.3% 21.5%

adult female 9.9% 1.5% 7.2% 9.8% 17.6%

male 6.3% 1.0% 5.1% 5.8% 9.0%

Indianapolis

(12,310)

child female 9.1% 2.0% 5.8% 8.6% 19.4%

male 10.8% 2.3% 6.6% 10.7% 16.8%

adult female 9.9% 1.8% 6.8% 10.0% 17.6%

male 6.1% 1.5% 2.5% 5.9% 10.4%

Tulsa

(7,694)

child female 10.2% 1.5% 7.3% 10.2% 13.9%

male 12.0% 2.0% 7.5% 12.3% 16.1%

adult female 8.8% 1.7% 5.5% 8.8% 14.4%

male 5.1% 0.6% 4.0% 5.0% 6.9%

a As described in text above this table, prevalence estimates are based on age (or age group) and sex-specific prevalence 

estimates for each census block derived from CDC NHIS asthma prevalence and U.S. census income/poverty ratio information.

 

14 Asthma prevalence, when not separated by sex, is greater for children (mean of 11.3%, 10.0%, and 11.1%) than 

that of adults (mean of 8.1%, 8.0%, 7.0%) for the Fall River, Indianapolis, and Tulsa study areas, respectively. 

Nationally, asthma prevalence for children is 8.4% and for adults is 7.6% (Table 3-2 of SO2 PA). 

15 Asthma prevalence, when not separated by the three study areas evaluated, is greater in boys (mean of 11.6%) 

than that of girls (mean of 9.4%). Nationally, asthma prevalence for boys is 9.9%, for girls is 6.9% (Table 3-2 of 

SO2 PA).
4-9

There are many other personal attributes that have been shown to influence asthma 

prevalence, such as race, ethnicity, obesity, smoking, health insurance, and activity level (e.g., 

Zahran and Bailey, 2013). The set of variables chosen to stratify asthma prevalence for use in 

this REA (i.e., age, sex, and family income level) was based on 1) maximizing the potential 

range in asthma prevalence variability, 2) maximizing the number of survey respondents 

comprising a representative subset study group, and 3) having the ability to link the set of 

attributes to variables within the Census population demographic data sets. Many of the 

additional potential influential factors identified here are not available in the census data and/or 

have limited representation in the asthma prevalence data (e.g., the survey participant has health 

insurance or they provide a response to a question regarding their body weight). Race is perhaps 

the only attribute common to both the prevalence and population data sets that could be an 

important influential factor and was not directly used in this REA to calculate asthma prevalence. 

However, the use of race in calculating asthma prevalence, either alone or in combination with 

family income level, would further stratify the NHIS analytical data set and appreciably reduce 

the number of individuals of specific age, sex, race, and family income level, potentially 

reducing the confidence in calculated asthma prevalence based on so few data. Because family 

income level already strongly influences asthma prevalence across all races and stratifies the 

NHIS data into only two subgroups (i.e., above or below the poverty threshold) rather than the 

larger number of subgroups a race variable might yield, family income was chosen as the next 

most important variable beyond age and sex to rely on for weighting the spatial distribution of 

asthma prevalence. 

That said, there is some uncertainty in our estimates caused by not utilizing race as a 

influential variable to spatially weight asthma prevalence (e.g., in addition to family income 

level). Therefore, we evaluated the influence race and obesity (as indicated by body mass index 

or BMI) might have on asthma prevalence.

16 While considering this, we also note that while 

census demographic data are available on the spatial distribution of variables such as race, age, 

sex and income level, we are unaware of useful data for spatially allocating obesity prevalence 

across a geographic area.

We first evaluated the number of NHIS adult and child participants that provided race 

and BMI information and compared them to the numbers of these groups that provided the 

information (age, sex, family income) used in the REA approach. The adult data set had few 

missing values when considering the new variables race and BMI (totaling about 160,000 

 

16 The processing of the prevalence data is described in Appendix E and considered age, sex, and family income 

level for each of four regions (i.e., Northeast- NE, Midwest-MW, South-S, and West-W). We evaluated these 

same variables here, however, we also included data responses from the variable RACERPI2 (a value of “2” 

represented black African Americans) and the variable BMI (or BMI_SC) for race and obesity, respectively.
4-10

adults); however, only one-third of children had values for BMI. Thus, the analytical data for 

children set was substantially smaller in survey sample size compared to the NHIS data set used 

for the REA approach (i.e., 20,000 vs 60,000 participants). The smaller sample size of children 

responding to this survey question indicates a potentially greater source of uncertainty with the 

use of this data set as compared to the relatively more complete adult data set for these variables.

A logistic model was constructed using PROC SURVEYLOGISTIC (SAS, 2017) and 

included the newly expanded list of potentially influential variables. Age was considered as a 

continuous variable while four other variables were evaluated using a dichotomous form: sex

(female to not); family-income ratio (below poverty to not); race (black African American to 

not); BMI (obese or BMI≥ 30 to not). Models were then applied to this data set using the same 

four regional stratifications. We first evaluated the independent effect each variable had on 

asthma prevalence (i.e., the individual responded “yes” to the question about still having 

asthma). Odds ratios were calculated and plotted along with 95% confidence intervals.17 The 

odds ratios can be interpreted as the percent difference between the two conditions being 

compared and the variable’s impact on the estimated asthma prevalence. Results of this analysis 

for each of the variables is presented in Figure 4-1 (adults) and Figure 4-2 (children). We also 

evaluated all possible variable interactions in the statistical model. While the results are 

somewhat variable across the data sets and complicated to interpret, in general many of the

variable independent effects remained statistically significant, and there were few significant 

interactions (Appendix E, Attachment 4).

Overall, the results for the adult data were more consistent across the different U.S.

regions than the child data, possibly due to having a less complete data set for the children. 

Obesity, rather than race, appears to play a more important role in influencing the asthma 

prevalence in adults (Figure 4-1) compared to children, while both obesity and race appear to 

play an important role in explaining asthma prevalence in children (Figure 4-2). Family income 

level was important alone and in interactions with other influential variables (e.g., BMI, race) 

considering both children and adults and for most of the four U.S. regions. Further, family 

income level consistently exhibited greater influence than race on adult asthma prevalence in 

three of the four regions, while race exhibited a greater influence than family income when 

considering child asthma prevalence. Sex also had a greater influence on asthma prevalence in 

 

17 The odds ratios can be interpreted as the percent difference between the two conditions being compared and the 

variable’s impact to the estimated asthma prevalence. For instance, Figure 4-1 shows that in the Northeast, an 

odds ratio of 1.85 was calculated for adult asthma prevalence considering the BMI variable (i.e., obese - BMI ≥30 

vs. not obese - BMI<30). This suggests that if an individual is obese, they are 85% more likely to have asthma 

than an individual that is not obese. In addition, the 95% confidence intervals that include a value of 1.00 are not 

considered statistically significant, thus when considering the BMI variable, statistical significance (p<0.05) can 

be assigned to the effect this variable has on the asthma prevalence.
4-11

adults compared to children, though women are more likely to have asthma than men while boys 

are more likely to have asthma than girls. Age was generally not a large factor as assessed in the 

constructed model (i.e., the statistical test evaluates year-to-year comparative differences), 

though clearly asthma varies across the full lifetime of ages (e.g., see Appendix E, Attachments 1 

and 2) and is an important variable for spatially linking at risk populations and ambient air 

concentrations in the exposure model.

This evaluation indicates that use of the family income variable in this exposure 

assessment as an influential factor to estimate variation in asthma prevalence provides 

reasonably similar estimates as race, particularly for adults. For children, while family income 

was shown as an important influential variable and, in a few instances, could serve as a surrogate 

variable to approximate the degree of race-related influence, family income alone may not 

entirely explain spatial variability in asthma prevalence across urban areas such as those in this 

REA. 

Figure 4-1. Influence of age, race, obesity, sex and family income on adult asthma 

prevalence (based on NHIS 2011-2015 for four U.S. regions).
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Figure 4-2. Influence of age, race, obesity, sex and family income on child asthma 

prevalence (based on NHIS 2011-2015 for four U.S. regions).

We note, however, the need for caution in interpreting the influence of race on child 

asthma prevalence due to uncertainty associated with the sharply reduced dataset for which both

BMI and race of the child is specified. It would be possible, however, to generate asthma 

prevalence using both race and family income as influential variables. If then used in an 

exposure assessment with the same approach and other inputs in this REA, it is possible that 

there could be a greater percent and number of people with asthma exposed to concentrations at 

or above a concentration of interest if both high concentrations and high asthma prevalence 

coincide spatially to a greater extent than represented here. 

Finally, while obesity appears to influence asthma prevalence in both children and adults, 

currently, it is not a personal attribute that can be used in selecting activity patterns nor do data 

exist to allow for spatial allocation across a study area. Note also that the body mass (and height 

if needed) of simulated individuals is estimated by APEX through random sampling of statistical 

distributions derived from recent NHANES data (section 4.1.4.2), which appropriately reflects

an overall distribution of body mass for a given population, and includes simulated individuals 
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that would be characterized as obese. However, the resulting distribution of body mass in the 

simulated population is not likely to reflect body mass variation at a local-scale in any particular 

study area.

4.1.3 Personal Attributes

In addition to using the above demographic information to construct the simulated 

individuals, each modeled person is assigned anthropometric and physiological attributes. All of 

these variables are treated probabilistically in APEX, taking into account interdependencies 

where possible, and reflecting variability in the population. It is not the intention of this 

document to provide detailed description of all the model inputs in each of the files and the data 

used in their derivation, however there are a few that have been recently updated for use in this 

REA, namely new statistical distributions for estimating body weight, equations for estimating 

resting metabolic rate, and equations for estimating activity specific ventilation rate. Brief 

descriptions of the data used to develop the input files are provided in the sections below. For

additional detail, see Appendices F through H and the data within the APEX input files.

4.1.3.1 Body Weight and Surface Area

Anthropometric attributes utilized by APEX in various assessments for estimating 

pollutant-specific exposures or doses include height, body weight (BW), and body surface area

(BSA). Two key personal attributes determined for each individual in this assessment are BW

and BSA, both of which are used in the calculation of a number of other variables associated 

with estimating exposures (e.g., ventilation rate). 

Regarding the estimation of body weight, a new APEX input file was generated using 

2009-2014 NHANES data.18 Briefly, body weight and height data for surveyed individuals were 

obtained and stratified by sex and single years for ages 0 – 79; all ages above 80 were combined 

as a single age group. Statistical form of the age- and sex-specific body weight and height 

distributions were evaluated using a log-likelihood statistic. Body weight was found to best fit a 

lognormal distribution; height was found to best fit a normal distribution. Because height and 

body weight are not independent, the joint distributions of height and logarithm of body weight 

were fit assuming a bivariate normal distribution. Then, parameters defining the joint 

distributions19 were smoothed using a natural cubic spline to have them represent continuous 

functions of age rather than vary discontinuously. In addition, having the smoothed parameters 

 

18 Original data are available in the form of the questionnaire datasets for 2009-2010, 2011-2012, 2013-2014 

NHANES from pages reached from this main page: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. Details 

regarding the data used and the derivation of the distributions is provided in Appendix G.

19 Five parameters were used for each age and sex: mean log(BW), standard deviation of log (BW), mean (height), 

standard deviation of (height), and body weight height correlation coefficients. 
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could be used to extrapolate information obtained from the single age year distributions (ages 0 –

79) to approximate statistical distributions of body weight for ages ≥ 80. A linear function was 

fitted to ages 70 and above to extrapolate the parameter values (and hence the statistical 

distributions of body weight) up to age 100. These distributions are randomly sampled to 

estimate an age and sex-specific body weight for each simulated individual. Comparison of the 

new distributions to the body weight distributions previously used by APEX and developed from 

the 1999-2004 NHIS indicate, for both sexes and across all ages, simulated body weight is about 

two percent greater using the updated distributions. This difference is expected given the 

consistent trend of increasing body weight that has occurred over the past few decades.

Finally, age- and sex-specific body surface area, a variable used in conjunction with 

breathing rate to approximate moderate or greater exertion (section 4.1.3.3) is estimated for each 

simulated individual as shown in Equation 4-1, and is based on an analysis provided by 

Burmaster (1998).

BSA = e-2.2781 × BW0.6821 Equation 4-1

One standard APEX input file is used for the SO2 assessment:

• Physiology040617_noHT_Graham_Glen_QA.txt: Provides parameters for estimating 

body weight (log BW, standard deviation of BW, lower and upper bounds of BW, by 

single age years 0-100 and by two sexes) and regression coefficients used in estimating 

BSA for all sexes and ages.

4.1.3.2 Energy Expenditure and Oxygen Consumption

Energy expended by different individuals engaged in different activities can have an 

important role in pollutant-specific exposure and/or dose. For example, energy expenditure is 

related to ventilation rate, which is an important variable in this REA given that the SO2-induced 

lung function response has been documented to occur under conditions of elevated ventilation

(section 2.1.4 above). In addition, because we are also interested in exposures that occur over 

short durations (i.e., 5-minutes), estimating activity-specific ventilation rate (V̇ E) has been an 

important motivation behind the development of the algorithm used by APEX. The fundamental 

basis for V̇ E algorithm is founded in energy expenditure which, for our modeling purposes here, 

can be related to an individual’s resting metabolic rate (RMR) or the energy expended while an 

individual is at complete rest, along with the energy expended while an individual performs 

activities involving greater exertion, termed here as metabolic equivalents of work (METs). The 

approaches used by APEX for estimating RMR and METs are described below, beginning first 

with the update to the equations used for estimating an individual’s RMR. 
4-15

To estimate RMR for the 2009 REA, the previous version of APEX (version 4.3) had 

used an algorithm originally based on analyses by Schofield (1985). Because all of the clinical 

subject data used by Schofield (1985) were from studies conducted as far back as 60 years prior 

to that publication, we felt it was important to search for newly available study data to better 

represent the simulated population in this REA. In addition, while using the Schofield (1985) 

equations there were occasional abrupt discontinuities in the estimated RMR observed at some of 

the equation boundaries (e.g., between age 59 and 60), which were largely a function of how the 

data were stratified (six age groups and two sexes) and the resulting equation parameters.

Since the 2009 REA, we have reviewed recent RMR literature and other published 

sources containing individual data and have compiled the associated individual RMR 

measurements, along with associated influential attributes such as age, sex, and body weight, 

where available. Data from these individual studies were then combined with RMR data reported 

in the Oxford-Brookes database (Henry, 2005; IOM, 2005) and screened for duplicate entries. In 

addition, observations missing values for RMR, BW, age, or sex were deleted, resulting in a

dataset containing 16,254 observations (9,377 males and 6,877 females).

Using this new RMR dataset, and having a goal of updating the previous RMR equations

and reducing discontinuities in RMR between age groups, new equations were developed. The 

equations follow the general format of a multiple linear regression (MLR) model, using age and 

body weight as independent variables to estimate each simulated individual’s RMR, along with a 

residual error term (ε).

20 It is known that RMR and BW, as well as RMR and age, are not exactly 

linearly related; the algorithms developed here use BW (in kg), age (in years), and the natural 

logarithms of BW and (age+1)21 as follows in Equation 4-2, with their parameter estimates

provided in Table 4-3.

RMR = β0 + β1BW + β2 log(BW) + β3Age + β3log(Age) + εi Equation 4-2

When comparing observed versus predicted values, the new RMR equations have a bias 

of less than 0.5%, compared to the previously used APEX equations which had a bias of between 

1-2%. Further, the discontinuities in RMR seen across particular age group boundaries using the 

previous equations have been reduced when using these updated equations in APEX. Additional 

 

20 The residual error term largely accounts for the estimation of inter-personal variability in RMR for individuals 

having the same body weight and age. There are other potentially influential sources of variability that are not 

explicitly accounted for by the equation (e.g., seasonal influences on RMR) and thus remain as an uncertainty.

21 The “+1” modifier allows APEX to round age upwards instead of downwards to whole years, which is necessary 

to avoid undefined log(0) values.
4-16

details regarding the derivation of the updated RMR equation and performance evaluation are 

provided in Appendix H. One standard APEX input file is used for the SO2 assessment:

• Physiology040617_noHT_Graham_Glen_QA.txt: Regression coefficients used to 

estimate RMR (kcal day-1) for two sexes and six age groups.

Table 4-3. Regression parameters used to estimate RMR by sex and age groups.

Sex Age 

Group n BW log(BW) Age log(Age) Intercept Std dev

male

0–5 625 13.19 270.2 -18.34 131.3 -208.5 69.10

6–13 1355 10.21 260.2 13.04 -205.7 333.4 115.3

14–24 4123 0.207 1078. 115.1 -2794.0 3360.6 161.1

25–54 2531 2.845 729.6 3.181 -191.6 -1067 178.2

55–99 743 9.291 264.8 -5.288 181.5 -705.9 163.6

female

0–5 625 11.94 261.5 -22.31 120.9 -183.6 64.16

6–13 1618 5.296 409.1 40.37 -524.9 392.7 99.43

14–29 2657 0.968 676.9 40.89 -1002 772.7 143.1

30–53 1346 4.935 355.4 16.28 -896.0 2225 145.3

54–99 631 2.254 445.9 5.464 -489.9 944.2 124.5

Units: RMR = kilocalories/day; BW = kilograms; Age = years

Following the estimation of an age- and sex-specific RMR for simulated individuals, the 

next variable used for estimating ventilation rate involved an approximation of the energy 

expended for activities an individual performs throughout their day. As mentioned above, 

activity-specific energy expenditure is highly variable and can be estimated using metabolic 

equivalents of work (METs), or the ratios of the rate of energy consumption for non-rest 

activities to the resting metabolic rate of energy consumption, as follows:

EE  MET RMR

Equation 4-3

where,

EE = Energy expenditure (kcal/minute)

MET = Metabolic equivalent of work (unitless)

RMR = Resting metabolic rate (kcal/minute)

Statistical distributions of METs were developed for simulated activities using the 

physical-activity compendium (Ainsworth et al., 2011; hereafter “the compendium”). The 

compendium contains a point value for the MET associated with each of several hundred

different activities. Activity-specific MET distributions were developed by cross-walking the 

activities described in the compendium with the descriptions of activities in the activity pattern 

data base used by APEX (US EPA, 2017c). The shape of the statistical distribution (e.g., normal, 
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lognormal, triangular, point) for each activity was assigned based on the number of 

corresponding activities in the compendium and goodness-of-fit statistics. When simulating 

individuals, APEX randomly samples from the activity-specific METs distributions to obtain 

values for every activity performed. Two standard APEX input files are used for the SO2

assessment:

• MET_Distributions_092915.txt: MET distribution number, statistical form, distribution 

parameters, lower and upper bounds, activity description

• MET_mapping_current_APEX_file.txt: activity codes, age group (where applicable), 

occupation group, MET distribution number, and activity description used to link of MET 

distributions to activities performed

The rate of oxygen consumption (V̇ O2, Liters min-1) for each activity is then calculated 

from the energy expended (kcal min-1) using an energy conversion factor (ECF, Liters O2 kcal-1)

as follows in Equation 4-4:

V̇O2 = EE × ECF Equation 4-4

The value of the ECF is randomly selected from a uniform distribution for each person, 

U[0.20, 0.21] (Johnson et al., 2002, adapted from Esmail et al., 1995). One standard APEX input 

file is used for the SO2 assessment:

• Physiology040617_noHT_Graham_Glen_QA.txt: Parameters of the uniform distribution 

representing the ECF used for all ages and both sexes.

4.1.3.3 Ventilation Rate

Human activities are variable over time, with a wide range of activities possible within 

only a single hour of the day. The type of activity an individual performs, such as sleeping or 

jogging (as well as individual-specific factors such as age, weight, RMR) will influence their 

ventilation rate. APEX estimates minute-by-minute ventilation rates that account for the 

expected variability in the activities performed by simulated individuals. Ventilation rate is 

important in this assessment because the lung function responses associated with short-term peak 

SO2 exposures coincide with moderate or greater exertion (ISA, section 5.2.1.2). In our exposure 

modeling approach, we used APEX to generate the complete time-series of activity-specific 

ventilation rates and the corresponding time-series of estimated SO2 exposures. APEX then 

aggregates both the ventilation rate and exposure concentration to the averaging time of interest 

(a 5-minute average). Thus, the model provides exposure estimates for the simulated individuals 

that pertain to specific target levels for both ventilation rate and exposure concentration. The 

approach to estimating activity-specific energy expenditure and associated ventilation rate 
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involves several algorithms and physiological variables, with details found in the APEX User’s 

Guide (U.S. EPA, 2017a, b). 

Using the existing measurement V̇ E dataset from Graham and McCurdy (2005), new V̇ E

algorithms were developed for predicting activity specific V̇ E in the individuals simulated by 

APEX. The new V̇ E algorithms do not directly employ previously used variables to stratify the

data (age groups, sex) and explain variability (age, body weight, height) in ventilation rate, 

effectively simplifying and reducing the number of equations. The new algorithms utilize a new 

variable, the maximum volume of oxygen consumed (V̇ O2m) as an input.22 Body weight, height 

and sex – as well as fitness level (which is often represented by V̇ O2m) - influence oxygen 

consumption for a particular activity. However, variability for each of these influential variables 

are already captured in the algorithm used to estimate each simulated individual’s RMR, and 

subsequently, the estimation of their activity specific V̇ O2.

23 Thus, the only input variables 

needed for the new V̇ E algorithm are V̇ O2 and V̇ O2m,24 both of which are estimated by APEX.

Details for the derivation of and performance evaluation of the new equation that APEX 

uses to estimate ventilation rate are provided in Appendix H. Briefly, the V̇ E dataset contains 

6,636 observations, with 4,565 males and 2,071 females. Similar to the earlier ventilation 

equation by Graham and McCurdy (2005), a mixed-effects regression (MER) model was fit 

because the MER separates residuals into within-person (ew) and between-person (eb) effects, 

known as intrapersonal and interpersonal effects, respectively.25 It was found that the actual 

values of V̇ O2 and V̇ O2m are less relevant than the fraction of maximum capacity, represented by 

f1 = V̇ O2/ V̇ O2m. The variable f1 may operate non-linearly (for example, f1 = 0.9 is likely more

than twice as encumbering as f1 = 0.45). A transformation regression approach (PROC 

TRANSREG – SAS, 2017) was used to determine the most appropriate variable transformation, 

indicating a power of 4 to 5 be used when only the log transformed V̇ O2 was used as the 

independent variable and described in Equation 4-5.

 

22 Use of V̇ O2m as an explanatory variable in separate related research on metabolic equivalents of task (MET) 

values for persons with unusual maximum capacity for work suggests that their MET distributions are modified in 

a predictable way by their maximum MET (or, equivalently, by V̇ O2m), thus providing support for use of this 

variable in the new V̇ E algorithms.

23 Oxygen consumption associated with activities performed is based on the activity specific metabolic equivalents 

for work (METs), an individual’s estimated RMR, and an energy to oxygen conversion factor (U.S. EPA, 2017b).

24 Distributions of VO2m used by APEX were derived from 20 published studies reporting individual data and 

grouped mean (and standard deviation) data obtained from 136 published studies. Details are provided in Isaacs 

and Smith (2005).

25 N(0, eb) is a normal distribution with mean zero and standard deviation eb=0.09866 meant to capture interpersonal 

variability, which is sampled once per person. N(0, ew) is an intrapersonal residual with standard deviation of 

ew=0.07852, which is resampled daily due to natural intrapersonal fluctuations in V̇

E that occur daily.
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V̇

E = e

(3.300 + 0.8128×ln(V̇O2)+ 0.5126 × (V̇O2÷V̇O2m)

4+N(0,eb)+N(0,ew)) Equation 4-5

In comparing the statistical fit of the new equation with the equations used by APEX 

previously to estimate ventilation rate, the resulting coefficient of determination (r2values) for 

the new equation (r2 = 0.94) indicates an improved fit compared to that of the previous equations 

(r2 = 0.89-0.92). Further, because the data were not stratified by age groups (or any other 

groupings), there are no discontinuities in predictions made across age boundaries as was 

observed when employing the previous equation. Information used in estimating ventilation rate 

is found in the following APEX two input files:

• Physiology040617_noHT_Graham_Glen_QA.txt: parameters describing statistical 

distributions of normalized maximum oxygen consumption rate (NV̇ O2m) for two sexes 

by single age years (0-100) (see, Isaacs et al., 2005).

• Ventilation_VEMethod2_102816_new.txt: minimum and maximum age ranges, regression 

coefficients, between and within error terms used to estimate individual activity-specific 

ventilation.

To use this information to estimate health risks for children, the ventilation rates observed 

for the adult study subjects need to be converted into rates that best reflect the different 

physiology of children. Consistent with prior REAs (U.S. EPA, 2009, 2014b; Whitfield et al.,

1996) we used an equivalent ventilation rate (EVR), which is essentially an allometrically 

normalized ventilation rate, to estimate instances when a simulated individual reaches a 

ventilation rate relatively as high as that of the study subjects (i.e., termed here as moderate or 

greater exertion).

EVR =

V̇E

BSA

Equation 4-6

In the controlled human exposure studies evaluating the health effects of SO2, the 

ventilation rates for study subjects (i.e., male and female adults) experiencing effects from 5- to 

10-minute SO2 exposures are generally within 40-50 L/min, with most set at or around 40 L/min

(ISA, Table 5-2 and Table 4-12 below).26 However, body surface area was not measured in the 

controlled human exposure studies and the relevant ventilation data were not separated by sex. 

We approximated BSA of the study subjects as 1.82 m2based on data for adult males and 

 

26 In these studies, subjects were breathing freely during exercise; thus, it is expected that there was a mixture of 

nasal, oral, and oro-nasal breathing that occurred across the study subjects. Without information regarding the 

precise breathing method used by any subject corresponding with their health response, we assumed that the 

mixture in breathing method used by study subjects is representative for the simulated population.
4-20

females from U.S. EPA (1989).

27 Based on these data, we estimate EVR for the study subjects to 

be 40/1.82 ≈ 22 L/min-m2. Accordingly, we have used this EVR as the target EVR in this 

assessment and simulated individuals at or above an EVR of 22 L/min-m2(children or adult) 

during a 5-minute exposure event were characterized as performing activities at or above 

moderate exertion. This is essentially the same target EVR value as that used in the 2009 REA 

(i.e., ≥ 22 L/min-m2), approximated at that time based on data from U.S. EPA (1997). This value 

used for EVR would represent a mean value based on the data used in its estimation and is 

considered reasonable to apply and approximate when, on average, individuals in a population

might experience periods of moderate or greater exertion. There is uncertainty in this mean based 

on the broad scale of data used in it derivation, as well as by not having any information on how 

to characterize intra- or inter-personal variability, where existing, and in its direct extrapolation 

from adults to children. 

4.2 METEOROLOGICAL DATA

Temperature data are used by APEX in selecting human activity data and in estimating 

AERs for indoor residential MEs. Hourly surface temperature measurements were obtained from 

the National Weather Service Integrated Surface Hourly (ISH) data files (described in section 

3.2.1.1). The weather stations used for each study area are given in Table 4-4. Given the limited 

geographic area of each study area, data from a single station was used to represent the ambient 

air temperature in each study area. The occurrence of missing temperature data was limited to a 

few instances (Table 4-4). Temperature values for the hours missing data were estimated using 

SAS PROC EXPAND, a simple linear interpolation technique. Because of the small number of 

missing values, the impact of the filled values to estimated exposures is assumed negligible.

Multiple unique APEX input files are used, one for each year and study area, and generally in 

two formats:

• METdata[studyarea]Y[year].txt: MET station IDs, hour of day, hourly temperature (°F) 

for each MET station, by study area and year

• METlocs[studyarea]Y[year].txt: MET station ID’s, latitudes and longitudes, start and stop 

dates of temperature data

 

27 Most of the controlled human exposure studies were conducted in the 1980s, thus use of the 1989 EPA Exposure 

Factors Handbook is considered the most representative source to use in estimating BSA for the study subjects 

compared with the 1997 and 2011 versions of that document given that body weight distributions (and hence 

BSA) have changed over time.
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Table 4-4. Study area meteorological stations, locations, and hours of missing data.

Study Area Station Name Station 

Number Latitude Longitude

Number of hours with 

missing temperature

2011 2012 2013

Fall River, MA PROVIDENCE T F 

GREEN ARPT 14765 41.7225 -71.4325 0 0 5

Indianapolis, IN INDIANAPOLIS 

INTERNATIONAL APT 93819 39.72517 -86.28168 0 0 0

Tulsa, OK RICHARD LLOYD 

JONES JR APT 53908 36.0396 -95.9846 10 0 0

4.3 CONSTRUCTION OF HUMAN ACTIVITY SEQUENCES

Exposure models use human activity pattern data to predict and estimate exposure to 

pollutants. Different human activities, such as outdoor exercise, indoor reading, or driving a 

motor vehicle can lead to different pollutant exposures. This may result from differences in the 

amount of the pollutant in the different locations where the activities are performed as well as 

from differences in the energy expended in performing the different activities (because energy 

expenditure influences inhalation and ingestion and thus may influence pollutant intake). To 

accurately model exposures to ambient air pollutants, it is critical to have a firm understanding of 

the locations where people spend time and the activities performed in such locations. The 

following subsections describe the activity pattern data, population commuting data, and the 

approaches used to simulate where individuals might be and what they might be doing. 

After the basic demographic variables are identified by APEX for a simulated individual 

in the study area, values for the other variables are selected as well as the development of the 

activity patterns that account for the places the simulated individual visits and the activities they 

perform. The following subsections describe the population data we used in the assessment to 

assign key features of the simulated individuals, and approaches used to simulate the basic 

physiological functions important to the exposure estimates for this REA.

4.3.1 Consolidated Human Activity Database

The Consolidated Human Activity Database (CHAD) provides time series data on human 

activities through a database system of collected human diaries, or daily time location activity 

logs (U.S. EPA, 2017c). The purpose of CHAD is to provide a basis for conducting multi-route, 

multi-media exposure assessments (McCurdy et al., 2000). The data contained within CHAD 

come from multiple surveys with variable study-specific structure (e.g., real time minute-byminute recording of diary events versus a recall method using time-block-averaging). Common 
4-22

to all studies, individuals provided information on their locations visited and activities performed 

for each survey day. Personal attribute data for these surveyed individuals, such as age and 

gender, are included in CHAD as well. The latest version of CHAD contains data for nearly 

180,000 person-days, however for this assessment, APEX uses about 55,000 of these.28 Most of 

the CHAD data are from studies conducted since 2000, several of which are newly included 

since the 2009 REA. See Appendix I for a list of the studies available, study dates, and number 

of diaries included from each. Three standard APEX input files are used for the current 

assessment to create the activity pattern profiles for all simulated individuals.

• Activity_diaries_events_no_ATUS_BLS.txt: CHAD ID, clock hour (hhmm), duration of 

event (minutes), CHAD activity code, and CHAD location code, serving as a daily 

sequence of locations visited, activities performed, and their duration for individuals in

CHAD

• Activity_diaries_questionnaire_no_ATUS_BLS.txt: CHAD ID, day-of-week, sex, race, 

employment status, age, maximum daily temperature, average temperature, occupation, 

missing time (minutes), record count, commute time (see also section 4.3.2)

• Activity_diaries_statistics_no_ATUS_BLS.txt: CHAD ID, total daily time spent outdoors 

(minutes) (see also section 4.3.4)

4.3.2 Commuting Activity Pattern Data

Exposures can vary across a study area based on spatial heterogeneity in ambient air 

concentrations and how that corresponds with a simulated individual’s activity pattern and 

geographic location. APEX approximates home-to-work commuting flows between census 

designated areas for each employed individual, and thus accounts for differing ambient air 

concentrations that may occur in these geographic locations. APEX has a national commuting 

database originally derived from 2010 Census tract level data collected as part of the U.S. DOT 

Census Transportation Planning Package. The data used to generate the APEX commuting file

are from the “Part 3-The Journey to Work” files.29 The Census files contain counts of individuals 

commuting from home to work locations at a number of geographic scales. These data have been 

 

28 Data from the U.S. Bureau of Labor Statistics American Time Use Survey (ATUS) are in CHAD master 071113, 

but they are not used by APEX in our simulations because of an important survey coding issue. Time spent at 

home for ATUS participants was not distinguished as indoors or outdoors, an important distinction for accurately 

estimating SO2 exposures. It could be possible to approximate the time expenditure of the ATUS diaries using an 

independent source of information, such as using the other CHAD diaries that recorded indoor and outdoor time 

(e.g., the 55,000 CHAD diaries used for estimating exposure would be the best source of information). However, 

it is unlikely that the representation of time expenditure would change/improve nor would the estimated 

exposures differ when including modified ATUS diaries that would reflect the same pattern in indoor and outdoor 

time as the 55,000 CHAD diaries already used in our exposure simulations.

29 These data are available from the U.S. DOT Bureau of Transportation Statistics http://transtats.bts.gov/) at the web 

site: https://www.transtats.bts.gov/Fields.asp. 
4-23

processed to calculate fractions (and hence commute probabilities) for each tract-to-tract flow to 

create the national commuting data distributed with APEX. This database contains commuting 

data for each of the 50 states and Washington, D.C. This data set does not differentiate people 

that work at home from those that commute within their home tract. A companion file to the 

commuting flow file is the commuting times file, i.e., an estimate of the usual amount of time in 

minutes it takes for commuters to get from home to work each day and tract-to-tract commuting 

distances.

30 The commuting times file information is used to select CHAD activity pattern data

from individuals having time spent inside vehicles similar to the census commute times and 

associated distances travelled. To use these tract level files at the block level for this REA, all 

blocks were assumed by APEX to have the same commuting probabilities as the parent tract for 

commuting to the blocks within other tracts by using the 11-character identifier common to both

IDs. Intra-block (within a tract) commuting is unknown and thus not simulated. Two standard 

APEX input files are used for the current assessment, as listed here.

• CommutingTimesBlock2010_3StudyAreas.txt: census block ID’s, count of all employed 

individuals, count of employed individuals that do not work at home, 7 groups of blocklevel one-way commuting times (in minutes)31

• Commuting_flow_US_2010_tracts.txt: census tract IDs, tract-to-tract commute cumulative 

probabilities (in fractional form), commute distance (km)

4.3.3 Assigning Activity Pattern Data to Individuals

Once APEX identifies the basic personal attributes of a simulated individual (section 4.1) 

and daily temperatures (section 4.2), activity pattern data from CHAD are selected based on 

age,32 sex, temperature category, and day of the week. These attributes are considered first-order

attributes in selecting CHAD diaries when modeling human exposures (Graham and McCurdy, 

2004). The maximum daily temperature range is used to select activity pattern data that best 

match the study area meteorological data for the simulated individual. This information is found 

in the following APEX input file, varying by study area and simulation year:

• Functions_ [studyarea]Y[year].txt: probabilities and interval definitions associated with a 

few input variables. For activity diary selection - day of week intervals (weekend or 

weekday) by three temperature ranges (<55, 55-83, or >83 °F).

 

30 These data are from the U.S. Census data portal (http://dataferrett.census.gov/) and are found in Table P31, 

variables P031001-P031015.

31 The nine commuting time groups in this file are: 0-4, 5-14, 15-19, 20-29, 30-44, 45-59, and >60 minutes.

32 Rather than select using exact ages, APEX allows the user to expand the pool of available diaries using the 

variable ‘AgeCutpct’ which allows for diaries to meet the simulated individuals required age within a certain 

percent of that age. A value of 15% was selected (with a default minimum of 1 year). For instance, CHAD diaries 

from people ages 51 to 69 would be available to simulate a person aged 60.
4-24

While there may be other important attributes that may influence activity patterns (e.g., 

obesity, disease status), there are limits to our ability to link to all the possible personal attributes 

that may be of interest in modeling an individual’s activities to the CHAD data. This is largely 

because CHAD is a compilation of data collected from numerous individual activity pattern 

studies conducted over several decades, many of which had a unique survey design. As a result, 

there is a varying amount of missing personal attribute data for the CHAD diaries. For instance, 

there are only a limited number of CHAD diaries with survey-requested health information (e.g., 

the health status of respondents). Specifically regarding whether or not a survey participant had

asthma, about 70% of the available diaries used by APEX in this REA had either a ‘yes’ or ‘no’ 

response to this health condition, of which there were 5,107 diary days representing individuals 

having asthma (of which 3,734 were children). This may appear to be a large number of diaries, 

however, following a grouping of the diaries by their first-order attributes (i.e., stratifying and 

reducing the available data for these diary groups of ‘like individuals’ by about a factor of 20 or 

so), there would be fewer than 200 diaries available for simulating a single day for that particular 

individual. Accordingly, the selection of diaries to use for APEX-simulated individuals does not 

consider health status (e.g., whether they were for people specifying they did or did not have

asthma, or whether such information was indicated by the survey participant).

This restriction in the number of diaries from individuals having asthma is not considered 

to be a significant limitation for estimating exposures for simulated individuals with asthma in 

this REA. In general, modeling people with asthma similarly to healthy individuals (i.e., using 

the same time-location-activity profiles) is supported by the activity analyses reported by van 

Gent et al. (2007) and Santuz et al. (1997). Other researchers, for example, Ford et al. (2003), 

have shown significantly lower leisure time activity levels in asthmatics when compared with 

individuals who have never had asthma. Based on these conflicting conclusion, we evaluated this 

issue in the 2014 O3 REA33 and, using the available activity pattern data in the CHAD database,

we compared participation in afternoon outdoor activities at elevated exertion levels among 

people having asthma, people not having asthma, and unknown health status. The 2014 O3 REA

analysis and associated conclusions are described below.

As is of interest in this current SO2 REA, we wanted to focus on instances when 

individuals would experience their highest O3 exposures. As has been shown in SO2 and O3

exposure assessments (U.S. EPA, 2009; U.S. EPA, 2014), the highest exposures occur when 

 

33 See 2014 O3 REA sections 5.4.1.5 and 5G-1.4 for details (U.S. EPA, 2014). While there are about 8,300 more 

diaries in the CHAD used for this SO2 REA, about 5,800 of the additional diaries added since the 2014 O3 REA 

have an unknown health status. Note, the percent of diaries from people with asthma is nearly identical in both 

data sets: children with asthma - 20.6% in 2014 O3 CHAD vs 20.3% in 2018 SO2 CHAD; adults with asthma –

7.5% in 2014 O3 CHAD vs 7.5% in 2018 SO2 CHAD. Therefore, rather than generate a new evaluation in this 

REA, conclusions drawn from the prior analysis are considered reasonable for this REA. 
4-25

individuals spend time outdoors, particularly during the afternoon hours. To prepare the data set

for analysis, afternoon hours were characterized as the time between 12 PM and 8 PM and only 

those persons that spent some time outdoors were retained. As is done by APEX in simulating 

individuals, level of exertion was estimated by sampling from the specific METS distributions

assigned for each person’s activity performed. Then, we identified activities having a METS 

value of greater than 3 as times where a person was at moderate or greater exertion levels (US 

DHHS, 1999). Afternoon outdoor time was then stratified by exertion level, summed for two 

study groups of interest (children and adults), and presented in percent form within Table 4-5.

Of the CHAD diaries for children, about 18% are from an individual with asthma and 

69% are from those who do not have asthma. About 5% of CHAD diaries for adults are from 

individuals with asthma, and about 65% are from those who do not have asthma. Far fewer 

children’s diaries are from persons whose asthma status is unknown (12%) compared to adults 

(30%), and the proportions are smaller still in terms of the total available person-days. On 

average, about 43% of all children of known asthma status spent some afternoon time outdoors, 

and the percent is actually higher for children with asthma (48.5%) than for children not having 

asthma (41.2%). About half of the adults whose asthma status was known spent afternoon time 

outdoors with a participation rate generally similar for adults having asthma and adults not 

having asthma. Participation in outdoor events for persons having unknown asthma status varied 

from that of persons with known asthma status; about 60% of the children’s diaries with 

unknown asthma status and 31% of the adult diaries indicate some afternoon time was spent 

outdoors.

Table 4-5. Comparison of outdoor time expenditure and exertion level by asthma status for

children and adult CHAD diaries used by APEX.

CHAD: Children (4 to 18) a CHAD: Adults (19 to 95) b

Has Asthma? Yes No Unknown Yes No Unknown

Total Person Days (n) 3,206 12,346 2,128 1,254 15,465 7,075

Number of Person Days with Time 

Spent Outdoors (% participation)

1,564

(48.8%)

5,092

(41.2%)

1,267

(59.5%)

602

(48.0%)

7,949

(51.4%)

2,176

(30.8%)

Percent of Afternoon Hours Spent 

Outdoors (%) 28.5% 27.5% 28.9% 26.2% 27.2% 22.2%

Percent of Afternoon Time Outdoors at 

Moderate or Greater Exertion (%) 80.3% 78.2% 79.2% 62.7% 63.8% 60.3%

From Table 5G-2 of 2014 O3 REA (U.S. EPA, 2014)

a CHAD studies for where a survey questionnaire response of whether or not child was asthmatic include CIN, ISR, NHA, 

NHW, OAB, and SEA (see Appendix I for study names).

b CHAD studies for where survey a questionnaire response of whether or not adult was asthmatic include CIN, EPA, ISR, 

NHA, NHW, NSA, and SEA.
4-26

The amount of time spent outdoors by the persons that did so varied little across the two 

study groups and three asthma classifications. On average, diaries from children indicate 

approximately 21⁄4 hours of afternoon time is spent outdoors, 80% of which is at a moderate or 

greater exertion level, regardless of their asthma status. For individuals whose asthma status is 

known, slightly less afternoon time is spent outdoors by adults (about 125-130 minutes) than 

children and the percent of afternoon time adults perform moderate or greater exertion level 

activities is also lower (about 63%). As noted above regarding the reduced participation in 

outdoor events for adults whose asthma status is unknown, diaries for these adults also have 

about 20 fewer minutes of afternoon time spent outdoors compared with those persons whose 

asthma status is known. Based on this analysis and additional comparisons of CHAD diary days 

with literature reported values of outdoor time participation at varying activity levels (see U.S. 

EPA, 2014), the 2014 O3 REA evaluation of the CHAD data indicates there are strong 

similarities in outdoor time, outdoor event participation, and activity levels among the three 

study groups and with those reported in independent studies of people with asthma. Thus, we 

conclude the use of any CHAD diary, regardless of asthma status, is reasonable for purposes of

simulating people with asthma in this exposure assessment.

4.3.4 Method for Longitudinal Activity Sequences

In order to estimate population exposure over a full year, a year-long activity sequence 

needed to be created for each simulated individual based on CHAD, which is largely a crosssectional activity database of 24-hour records. The typical surveyed subject in the time location 

activity studies in CHAD provided about two days of diary data. For this reason, the construction 

of a season-long activity sequence for each individual requires some combination of repeating 

the same data from one subject and using data from multiple subjects. The best approach would 

reasonably account for the day-to-day and week-to-week repetition of activities common to 

individuals, and recognizing even these diary sequences are not entirely correlated, while 

maintaining realistic variability among individuals comprising each study group. 

APEX provides three methods of assembling composite diaries. We have selected the 

method for this assessment based on our consideration of the assessment objectives, 

consideration of an evaluation of differences in results produced by the three methods and 

consideration of flexibility provided by each approach with regard to specifying key variable 

values. Based on all of these considerations, we have selected the D&A method.

The D&A method is a complex algorithm for assembling longitudinal diaries that 

attempts to realistically simulate day-to-day (within-person correlations) and between-person 

variation in activity patterns (and thus exposures). This method was designed to capture the 

tendency of individuals to repeat activities, based on reproducing realistic variation in a key 
4-27

diary variable, which is a user selected function of diary variables. The method targets two 

statistics: a population diversity statistic (D) and a within-person autocorrelation statistic (A). 

The D statistic reflects the relative importance of within and between-person variance in the key 

variable. The A statistic quantifies the lag-one (day-to-day) key variable autocorrelation. Values 

of D and A for the key variable are selected by the model user and set in the APEX parameters 

file, and the method algorithm constructs longitudinal diaries that preserve these parameters. 

Further details regarding this methodology can be found in Glen et al. (2008). 

Besides the D&A method, there are two additional methods of compiling diaries

provided by APEX: a more basic method and a similarly complex method. The more basic 

method involves randomly selecting an appropriate activity diary for the simulated individual 

from the available diary pool. While this more basic method is adequate for providing a mean 

short-term exposure estimate, it is less useful for this assessment for which the objective is to 

estimate how often individuals may experience particular peak SO2 exposures over a year. The 

more complex method uses a Markov-chain clustering (MCC) approach in attempting to recreate 

realistic patterns of day-to-day variability. First, cluster analysis is employed to divide the daily 

activity pattern records into three groups based on time spent in five microenvironments: indoorresidence, other indoors, outdoor-near roads, other outdoors, and inside vehicles. For each 

simulated individual, a single time-activity record is randomly selected from each cluster. Then 

the Markov process determines the probability of a given time-activity pattern occurring on a 

given day based on the time-activity pattern of the previous day and cluster-to-cluster transition 

probabilities (and are estimated from the available multi-day time-activity records), thus 

constructing a long-term sequence for a simulated individual. Details regarding the MCC method 

and supporting evaluations are provided in the 2009 REA Appendix B, Attachments 4 and 5.

Che et al. (2014) performed an evaluation of the impact of the three APEX methods on 

PM2.5 exposure estimates. As expected, little difference was observed across the methods with 

regard to estimates of the mean exposures of simulated individuals. Differences were observed, 

however, in the number of multiday exposures exceeding a selected benchmark concentration. 

With regard to the number of simulated individuals experiencing 3 or more days above 

benchmark concentrations, the MCC method estimates were approximately 12-14% greater than 

either the random or D&A methods. For the number of persons experiencing at least one 

exposure of concern, however, the MCC method estimates were approximately 4% lower than 

those of the other two methods. For additional context, we note that, using all methods, there is 

an order of magnitude difference in the number of persons exposed at least once versus three or 

more times, indicating that, overall, the occurrence of simulated multiday exposures are rare 

events regardless of method selection. 
4-28

Che et al. (2014) concludes that while the MCC method produces a higher number of 

multiday exposures, there remains a question whether the MCC method has greater accuracy 

relative to the other two methods. We note this conclusion applies to both the estimations of 

single day and multiday exposures, as there is an inverse relationship between the two when 

simulating exposures using APEX and a finite set of activity pattern data. Thus, the MCC 

method produces a smaller number of single day exposures above benchmarks relative to the 

other two methods, estimations also subject to a degree of uncertainty. 

In the absence of having a robust data set (e.g., multiday/week personal exposure 

information from a random population) to better evaluate the accuracy of any of the methods, we 

considered selection of the longitudinal approach for this assessment from a practical 

perspective, guided by a balancing of the single day and multiday exposures that can be 

estimated by each method. In so doing, we selected the D&A approach, recognizing that the

D&A method allows for flexibility in the selection of the key influential variable and its setting 

values, and also the ability to directly observe the impact of changes to these values on model 

outputs. 

The key variable selected for this REA is the amount of time an individual spends 

outdoors each day, as that is one of the most important determinants of exposure to high levels of 

SO2 (see section 2.1.2 above). In their evaluation, Che et al. (2014) varied the values of D and A 

for this variable to determine the impact to estimated exposures. Compared to the base level 

simulation (i.e., D=0.19 and A=0.22),34 increasing both D and A by 100% increased the number 

of persons having at least three exposures above the selected benchmark by about 4%, while also 

reducing the percent of persons experiencing at least one day above benchmarks by less than 1% 

(Che et al., 2014). In recognizing uncertainty in the parameterization of D and A (i.e., based on a 

limited field study of a small subset of the population, children 7-12) and that the base level 

simulation D&A values produced a lower estimate of repeated exposures compared with the 

MCC method, we have used values of 0.38 for D and 0.44 for A for all ages to potentially 

increase representation of multiday exposures without significant reducing the percent of the 

population experiencing at least one day at or above benchmark concentrations.

4.4 MICROENVIRONMENTAL CONCENTRATIONS

In APEX, exposure of simulated individuals occurs in microenvironments. To best 

estimate personal exposures, it is important to maintain the spatial and temporal sequence of 

microenvironments people inhabit and appropriately represent the time series of concentrations 

 

34 Longitudinal diary data from a limited field study of children ages 7-12 (Geyh et al. 2000; Xue et al. 2004) 

provide support for estimates of approximately 0.19 for D and 0.22 for A for the amount of time spent outdoors.
4-29

that occur within them. Two methods available in APEX for calculating pollutant concentrations 

within microenvironments are a mass balance model and a transfer factor approach. In both 

approaches, ME concentrations depend on the ambient (outdoor) air SO2 concentrations and 

temperatures, as well as distributions of the key parameters for each approach. Further, the 

distributions of some of the key parameters depend on values of other variables in the model. For 

example, the distribution of air exchange rates inside an individual’s residence depends on the 

type of heating and air conditioning present, which are also stochastic inputs to the model. The 

value of a stochastic parameter can be set as a constant for the entire simulation (e.g., house 

volume would remain identical throughout the exposure period), or APEX can be directed to 

sample a new value hourly, daily, or seasonally from specified distributions. APEX also allows 

the user to specify diurnal, weekly, or seasonal patterns for certain ME parameters.

Based on findings from the 2009 REA, we have specified five MEs for use in this 

assessment, largely based on two factors: the expectation of an ME having exposure 

concentrations of interest and the availability of factors to reasonably model the ME. The 2009 

REA results indicated that the majority (70-90%) of 5-minute daily maximum SO2 exposures

between 100 and 800 ppb35 occurred while individuals were within outdoor microenvironments 

(2009 REA, Figure 8-21). Given that finding and the objective for this assessment (i.e., 

understanding how often and where short-term peak SO2 exposures occur), we recognized the 

added efficiency of minimizing the number of MEs, particularly lower-exposure indoor MEs, 

that were parameterized and included in the modeling. 

Accordingly, we aggregated the number of MEs to address exposures of ambient air 

origin that occur within a core group of indoor, outdoor, and vehicle MEs. It was expected that 

the exposures occurring near roads would also be associated with high exposures as they would

be modeled identically to all of the other outdoor MEs, only that these outdoor events occur near 

a road. Thus, the near road ME was modeled separately in case time spent in that ME and its 

associated exposures was of specific interest. An inside-vehicle ME was also modeled based on 

the expectation that it would lead to some instances of high exposures, particularly considering 

the high air exchange rate that occurs inside vehicles while moving and having a limited SO2

decay rate, effectively reflecting similar concentration levels as in outdoor MEs. Two indoor 

MEs (indoor-residence and indoor-other) were modeled individually based on having specific air 

exchange rate data available for each (4.4.1 and 4.4.3, respectively). The indoor-other ME is 

comprised of all non-residential MEs, thus could include workplaces or office buildings, stores 

 

35 Although these results were associated with a different air quality scenario than is evaluated in this REA, the 

similarity in the scenario leads us to conclude the results are relevant for judgments made here. Air quality in the 

2009 REA results referenced here was adjusted to just meet a 99th percentile 1-hour daily maximum single year 

standard level of 150 ppb (U.S. EPA, 2009).
4-30

for shopping, medical offices, and so on. Table 4-6 lists the five microenvironments selected for 

this analysis and the exposure calculation method for each. The variables used and their 

associated parameters to calculate ME concentrations are summarized in subsequent subsections 

below. Details on the calculation of ME concentrations in APEX are presented in Appendix F, 

section F.7.

Table 4-6. Microenvironments modeled and calculation method used.

Microenvironment (ME) APEX ME 

Number

Calculation 

Method Variables a

Indoor – Residence 1 Mass balance AER & RM

Indoor – Other 2 Mass balance AER & RM

Outdoor 3 Factors None

Near-road 4 Factors None

Vehicle 5 Factors PE

a AER = air exchange rate, RM = removal rate, PE = fraction of ambient 

pollutant entering microenvironment, 

None = ME concentration is equal to ambient concentration

The mass balance method, used for the indoor MEs, assumes that an enclosed 

microenvironment (e.g., a room within a home) is a single well-mixed volume in which the air 

concentration is approximately spatially uniform (Figure 4-3). The concentration of an air 

pollutant in such a microenvironment is estimated using (1) inflow of air into the 

microenvironment, (2) outflow of air from the microenvironment, (3) removal of a pollutant 

from the microenvironment due to deposition, filtration, and chemical degradation, and (4) 

emissions from sources of a pollutant inside the microenvironment (not used for this REA).
4-31

Figure 4-3. Illustration of the mass balance model used by APEX.

Considering the microenvironment as a well-mixed fixed volume of air, the mass balance 

equation for a pollutant in the microenvironment can be written in terms of concentration:

dC(t)

dt

= C

in − Cout − Cremoval Equation 4-7

where,

C(t) = Concentration in the microenvironment at time t

C

in = Rate of change in C(t) due to air entering the microenvironment

C out = Rate of change in C(t) due to air leaving the microenvironment

C

removal = Rate of change in C(t) due to all internal removal processes

The method used for the outdoor MEs uses a factors model and is simpler than the mass 

balance model. In this method, the value of the concentration in a microenvironment is not 

dependent on the concentration during the previous time step. Rather, this model uses the 

Equation 4-8 to calculate the concentration in a microenvironment from the user-provided hourly 

air quality data:

Air

inflow

Indoor sources

Removal due to:

•Chemical reactions

•Deposition

•Filtration

Air

outflow

Microenvironment

Air

inflow

Indoor sources

Removal due to:

•Chemical reactions

•Deposition

•Filtration

Air

outflow

Microenvironment
4-32

Cmean = Cambient × fproximity × fpollutant Equation 4-8

where,

Cmean = Mean concentration over the time step in a microenvironment (ppb)

Cambient = The concentration in the ambient (outdoor) environment (ppb)

fproximity = Proximity factor (unitless)

fpollutant = fraction of ambient pollutant entering microenvironment (unitless)

The five microenvironments were mapped to the 115 CHAD location codes,36 many of 

which go beyond the scale of the microenvironmental modeling. The ambient air concentration 

used in calculating ME concentration for each event varies temporally and spatially. For 

example, commuters (i.e., employed individuals who do not work at home) are assigned to either 

their home grid or work grid concentrations, depending on whether the population probabilities 

and commuting data base produce either a home or work event. Additionally, depending on the 

particular microenvironment (i.e., other than home or work), the mapping of CHAD locations to 

the five microenvironments also uses an identifier that designates the relative location in the air 

quality surface from which the ambient air concentration (used to calculate the ME 

concentration) is selected. For this assessment, such locations would include the blocks from a 

simulated individual’s home (H), work (W), near work (NW), near home (NH), last (L, either 

NH or NW), other (O, average of all), or unknown (U, last ME determined) census block 

location. Specific designations are provided in the ME mapping file, with selection based on 

known factors and professional judgement. For example, when an individual is in their home, the 

ambient concentration in the home block is used to calculate their ME concentration. When the 

individual is at work, the block the individual commuted to is used to calculate their ME 

concentration. Travel inside vehicles used the ambient concentration data from the block used to 

calculate the prior ME concentration. Most other MEs (both indoor and outdoor) used ambient 

concentration data selected from near home blocks.

Status attribute variables are also important in estimating ME concentrations, and can 

include, but are not limited to, housing type, whether the house has air conditioning, and whether 

the car has air conditioning. Because outdoor MEs are expected to contribute the most to an 

individuals’ highest SO2 exposure (and potential health risk) and the status attribute variables 

pertain to indoor MEs, the setting of these particular variables will have limited impact to this 

REA’s exposure results. In this assessment, a number of temperature ranges are used in selecting

 

36 The location codes indicate specific MEs that extend beyond simple aggregations of indoor, in-vehicle, and 

outdoor locations where people spend time. For example, CHAD has a location code for when individuals spent 

time inside their residence while in the kitchen.
4-33

the particular distribution for estimating air exchange rates (AERs). Maximum daily temperature 

is also used in diary selection to best match the study area meteorological data for the simulated 

individual (Graham and McCurdy, 2004), and air conditioning use prevalence data. 

Multiple APEX ME input files (the first and third in the list below), of the same general 

format, are used for each study area in the REA. A single ME mapping file is used for all study 

areas. These files contain the specific setting of all variables described in this section.

• ME_descriptions_[studyarea]_5MEs.txt: defines ME calculation method, conditional 

variables used (e.g., temperature categories – see functions file), distribution type, 

distribution parameters (mean, standard deviation, minimum, maximum) used for 

estimating AER, decay rates, proximity factors, and PE fraction to estimate

microenvironmental concentrations.

• MicroEnv_Mapping_CHAD_to_APEX_5MEs.txt: maps 115 CHAD locations to 5 APEX 

simulated microenvironments and assigns block-level ambient concentrations to use for 

each location. Contains CHAD location code, CHAD description, APEX ME number, 

and ambient concentration location identifier 

• Functions_ [studyarea]Y[year].txt: variables used for selecting air exchange rates (AER) 

- air conditioning (A/C) prevalence (home has A/C, does not have A/C) by five 

temperature ranges for air exchange rate (<50, 50-67, 68-76, 77-85, or >85 °F). (see 

section 4.4.1 and 4.4.2) 

4.4.1 Air Exchange Rates for Indoor Residential Microenvironments

Distributions of AERs for the indoor residential MEs were developed previously using 

data from several studies. The analysis of these data and the development of most of the 

distributions used in the modeling, originally described in detail in U.S. EPA (2007) Appendix A 

and recently updated by Cohen et al. (2012), are provided in U.S. EPA (2014) Appendix 5E. 

Briefly, these prior analyses indicated that the AER distributions for the residential MEs

depend on the presence or absence of mechanical air conditioning (A/C) and the outdoor 

temperature (and a few other variables37 for which sufficient data are not available). Further, the 

AER distributions vary across the U.S. study locations,

38 such that the selected AER 

distributions for the modeled study areas should also depend on these influential factors. For 

each combination of air conditioner (A/C) prevalence, U.S. geographic region (and hence 

climate zone), and temperature (where data were available), lognormal distributions were fit.

There were a number of limitations in generating study-area-specific AERs, stratified by 

temperature range and A/C type. For example, the AER data collected and the distributions

 

37 For example, there were insufficient data available across the studies to indicate the specific A/C unit type 

(central, window, or both), whether windows were closed or open, or whether a mechanical fan was in operation.

38 The studies were conducted in several U.S. cities (e.g., Detroit, Houston, Los Angeles, New York), likely 

accounting for AER differences due to local climate and variability in overall housing stock (e.g., types of 

residences, year built).
4-34

subsequently derived from them were available only for selected cities that had limited numbers 

of samples collected at varying ambient air temperatures, and yet the summary statistics and 

comparisons demonstrate that the AER distributions depend upon the city as well as the 

temperature range and A/C type. Because specific AER data are not available for the study areas 

in this assessment, we used AER data from Cohen et al. (2012) for a city within the same 

geographic region as the particular study area, and considering the same temperature ranges on 

which the AER distributions were originally based. The AER distributions used for the exposure 

modeling are given in Table 4-7 (for residences with A/C) and Table 4-8 (for residences without 

A/C). Upper and lower bounds were selected to guard against the generation of extreme AER 

values. In general, the AER distributions are highest for the Fall River study area, while the AER 

distributions used for the Tulsa study area are lowest. This implies indoor residential exposures 

would tend to be greatest for the simulated individuals in the Fall River study area when 

compared with the other two study areas, though again, the expectation is that outdoor exposures 

would contribute to the highest exposures in all three study areas and limit the importance of 

these observed differences in AER.

Table 4-7. AERs for indoor residential microenvironments (ME-1) with A/C by study area 

and temperature.

Study Area Daily Mean 

Temperature (°C)

Lognormal Distribution

{GM, GSD, min, max} Original AER Study Data Used

Fall River, MA

< 10 {0.711, 2.108, 0.1, 10}

10 - 25 {1.139, 2.677, 0.1, 10} New York, NY

> 25 {1.244, 2.177, 0.1, 10}

Indianapolis, IN

< 10 {0.744, 1.982, 0.1, 10}

Detroit, MI and New York, NY 10 - 20 {0.811, 2.653, 0.1, 10}

20 - 25 {0.785, 2.817, 0.1, 10}

> 25 {0.916, 2.671, 0.1, 10}

Tulsa, OK

< 20 {0.407, 2.113, 0.1, 10}

Houston, TX 20 - 25 {0.467, 1.938, 0.1, 10}

25 - 30 {0.422, 2.258, 0.1, 10}

> 30 {0.499, 1.717, 0.1, 10}
4-35

Table 4-8. AERs for indoor residential microenvironments (ME-1) without A/C by study 

area and temperature.

4.4.2 Air Conditioning Prevalence for Indoor Residential Microenvironments

The selection of an AER distribution is dependent on the presence or absence of A/C. We

assigned this housing attribute to indoor residential microenvironments using A/C prevalence 

data from the 2013 American Housing Survey (AHS).39 A/C prevalence (specified in terms of 

does or does not have mechanical air conditioning) is distinct from usage (i.e., mechanical air 

conditioning is on or off), the latter ultimately represented by values selected from the AER 

distribution (relatively lower values would be associated with greater recirculation of indoor air) 

and dependent on the daily temperature. The A/C prevalence data were assigned to our study 

areas where the AHS data best matched our exposure simulation years (Table 4-9). In all three

study areas, the sum of room unit and central A/C prevalence was used.

Table 4-9. American Housing Survey A/C prevalence from 2013 Current Housing Reports 

for selected urban areas.

Study Area a

Total Occupied 

Housing Units 

(x1000)

Number of Occupied Housing Units (x1000) % of Occupied Housing Units

Central 

A/C

>1 Central 

A/C

1 Room 

Unit

2 Room 

Units

3+ Room 

Units

Central 

A/C

Window 

Units

Central & 

Window A/C

Fall River, MA 780.3 296.6 20.1 129.6 131.0 146.0 38 52 90

Indianapolis, IN 359.7 319.3 21.5 11.9 14.7 8.4 89 10 99

Tulsa, OK 262.0 233.3 7.1 12.1 6.9 61.2 89 10 99

a Data used were from the 2013 Metropolitan Area using a geography filter of ‘not in central cities’. Because there were no data for the 

study areas data reported for nearby cites was used as follows: Fall River, MA - Boston, MA; Indianapolis - Louisville, KY; Tulsa, OK –

Oklahoma City OK.

 

39 Available at https://www.census.gov/programs-surveys/ahs/data/interactive/ahstablecreator.html.

Study Area Daily Mean 

Temperature (°C)

Lognormal Distribution

{GM, GSD, min, max} Original AER Study Data Used

Fall River, MA

< 10 {1.016, 2.138, 0.1, 10}

10 - 20 {0.791, 2.042, 0.1, 10} New York, NY

> 20 {1.606, 2.119, 0.1, 10}

Indianapolis, IN

< 0 {1.074, 1.772, 0.1, 10}

Detroit, MI and New York, NY

0 - 10 {0.760, 1.747, 0.1, 10}

10 - 20 {1.447, 2.950, 0.1, 10}

20 - 25 {1.531, 2.472, 0.1, 10}

> 25 {1.901, 2.524, 0.1, 10}

Tulsa, OK

< 10 {0.656, 1.679, 0.1, 10}

10 - 20 {0.625, 2.916, 0.1, 10} Houston, TX

> 20 {0.916, 2.451, 0.1, 10}
4-36

4.4.3 AER Distributions for All Other Indoor Microenvironments

To estimate AER distributions for all non-residential, indoor environments (e.g., offices, 

libraries, schools, etc.), we relied on data generated as part of the U.S. EPA Building Assessment 

Survey and Evaluation (BASE) study (Persily and Gorfain, 2004; Persily et al., 2005), as was 

also done for the 2009 REA and REAs for other recent NAAQS reviews (e.g., U.S. EPA, 2014). 

In the BASE study, a total of 390 AER measurements were collected from 96 randomly selected 

office buildings throughout the U.S. using two methods, a volumetric and a carbon dioxide ratio

method. In the vast majority of cases, the reported best estimate was generated using the

volumetric method. The AER values for each office space were averaged, rather than using the 

individual measurements, because of the limited degree of variability in AER measurements for 

the same office space over a relatively short sampling period. We fitted exponential, lognormal, 

normal, and Weibull distributions to the 96 office space average AER values, and the best fitting 

of these was the lognormal. The fitted parameters for this distribution are a geometric mean of 

1.109, geometric standard deviation of 3.015, and bounded by the lower and upper values of the 

sample data set {0.07, 13.8}.

4.4.4 Removal Rate for Indoor Microenvironments

To estimate pollutant removal rates from air within indoor microenvironments, we first 

evaluated the removal rates that had been estimated in the 2009 REA using data collected by 

Grontoft and Raychaudhuri (2004) on SO2 deposition to a variety of building material surfaces 

under differing conditions of relative humidity. In the 2009 REA, this information was used to 

derive estimates for five indoor microenvironments: residences, office buildings, schools, 

restaurants, and other buildings (see 2009 REA Appendix B section 4.1). For the current REA,

we simulated only two indoor microenvironments: residences and an aggregate ME representing 

all other indoor microenvironments. Therefore, we used the same removal rates that were 

derived for the 2009 REA for the residential ME and aggregated the estimated removal rates 

from the other four indoor MEs as follows. One thousand values were randomly sampled from 

the geometric means and standard deviations representing the removal rates for each of the four 

indoor MEs. Parameters describing a lognormal distribution for the new aggregate ME (for other 

indoor locations) were calculated using the 4,000 sampled values and are provided in Table 4-10.
4-37

Table 4-10. Parameter estimates of SO2 removal rate distributions in two indoor 

microenvironments modeled by APEX.

Microenvironment

Removal (hr-1) when Heating or Air 

Conditioning in Use

Removal (hr-1) when Heating or Air 

Conditioning Not in Use

Geometric

Mean

Standard 

Deviation

Lower

Limit a

Upper 

Limit a

Geometric

Mean

Standard 

Deviation

Lower

Limit

Upper

Limit

Indoor Residence 3.14 1.11 2.20 5.34 13.4 1.11 10.3 26.0

Indoor Other 3.32 1.37 1.53 5.07 N/A N/A N/A N/A

a Lower and Upper Limits were approximated by the 10th and 90th percentile values.

b N/A not applicable, assumed to always have mechanical building ventilation in operation.

c From Table B.4-6 of 2009 REA.

d Derived from 4,000 values sampled from removal distributions representing four indoor microenvironments (Table B.4-6 of 

2009 REA).

4.4.5 Factor for Estimating In-Vehicle/Near-Road Microenvironmental Concentrations

As was the case for the 2009 REA, there are no SO2 measurement data available to 

develop a factor for estimating SO2 concentrations inside vehicles resulting from the ambient air 

pollutant entering the microenvironment (and termed PE factor). The ratio of inside-vehicle ME 

concentrations to outdoor concentrations is commonly used to develop this PE factor. Thus, 

based on the outdoor concentration, one can estimate the inside-vehicle concentrations. 

Therefore, as was done for the 2009 REA, the PE factors used were developed from NO2 data 

provided in Chan and Chung (2003) and used in the 2008 NO2 REA (U.S. EPA, 2008a). As both 

SO2 and NO2 are gaseous, and data for PE factors are not broadly available for other gases, this 

was concluded to be a reasonable approach. 

We note that pollutant removal rates inside vehicles might be different because SO2 is 

more water soluble than NO2, although we could not find removal rate data specific to motor 

vehicles. A comparison of indoor residential removal rates used for NO2 (2008 NO2 REA) to that 

of the 2009 SO2 REA suggests that there might be greater removal of SO2 within indoor 

microenvironments, indicating that use of the same PE factor for SO2 as was used for NO2 could 

lead to overestimation of inside-vehicle SO2 concentrations.

40 Further, however, the in-vehicle 

NO2 measurements on which the in-vehicle-to-outdoor-ratios were based might have included a 

small amount of in-vehicle emissions, potentially yielding a discrepancy between effective PE

factors for NO2 and SO2. The additional uncertainty from this influential factor is expected to be 

 

40 NO2 removal rates for the 2010 REA were assumed to range from 1.02 to 1.45 h-1

, based on six measurements 

obtained from a single house provided by Spicer et al. (1993). SO2 removal rates for the 2009 REA were 

approximated by using SO2 deposition collected data Grontoft and Raychaudhuri (2004) for a variety of building 

material surfaces under differing conditions of relative humidity and configured to five indoor 

microenvironments. The lower and upper limits of the removal rates ranged from 1.64 to 5.34 h-1.
4-38

small compared to the overall uncertainty implied by using a uniform distribution that assumes 

all factors that influence variability and that are not directly accounted for have the same impact.

Chan and Chung (2003) measured inside-vehicle and outdoor NO2 concentrations for 

three ventilation conditions: air-recirculation, fresh air intake, and with windows open. Mean invehicle-to-outdoor ratio values ranged from about 0.6 to just over 1.0, with higher values 

associated with increased ventilation (i.e., window open). A uniform distribution U{0.6, 1.0} 

was selected for the PE factor due to the simplified manner it is applied in this REA. For 

example, we could not consider influential characteristics such as use of vehicle ventilation 

systems due to the lack of data available to reasonably assign values for each study area.

4.5 ESTIMATING EXPOSURE

Based on the event-specific exposures estimated for each individual as described in the 

preceding sections, APEX identifies the occurrence of daily maximum 5-minute SO2 exposures 

at or above specific levels, while at or above the target ventilation rate (i.e., an EVR ≥ 22 L/minm2

). More specifically, this is the count of individuals (with asthma) experiencing a specific 

number of days per year (e.g., one or more, two or more, etc.) with exposures at or above

specified 5-minute SO2 concentrations (i.e., falling within bins representing different magnitudes 

of exposure) while at elevated ventilation.

The daily maximum 5-minute exposure concentrations (of people with asthma at elevated 

ventilation) are binned considering the overall features expected for the distribution of ambient 

SO2 concentrations and population-based SO2 exposures. Observed ambient concentrations are 

generally lognormally distributed – on average, 1-hour daily maximum concentrations are about 

5 ppb, 90th percentile 1-hour daily maximum concentrations are typically below 20 ppb, while 

99th and maximum 1-hour daily maximum concentrations can be a factor of 10 to 20 times

higher than the mean (ISA, Table 2-13). It follows that because of this distribution of ambient air 

concentrations, it is likely that most simulated individuals will experience low daily maximum 

exposures (between 5 and 20 ppb), some will experience a daily maximum exposure between 20 

and 100 ppb, while few will experience exposures above 100 ppb. Considering this and the 

relationship documented in the controlled human exposure studies between exposure

concentration and percent of individuals estimated to experience a lung function decrement 

(section 4.6.2), exposure bins were as follows. 

For exposure concentrations below 150 ppb, the exposure bins are set at 10 ppb 

increments (e.g., 10–20 ppb, 20–30 ppb, etc.); exposure concentrations at or above 150 and 

below 250 ppb are at 20 ppb increments though also including a bin for 200 ppb; and exposure 

concentrations at or above 250 are at 50 ppb increments, totaling 29 exposure bins. The smaller 

bin increments are used for lower exposure concentrations given the relatively greater number of 
4-39

exposure events expected to occur in that range and the desire to reduce potential for 

overestimation through use of larger size bins (see REA Planning Document, section 4.2.4.2). 

From this we summarize the number of days with maximum exposures within each exposure bin, 

such that the exposure model outputs are summarized as (1) counts of people exposed at least 

one day per year to a range of short-term peak SO2 concentrations while at or above the target 

exertion level, and (2) counts of people experiencing multiple days per year with the maximum 

5-minute exposure at or above a particular level while at or above the target exertion level.

4.6 RISK METRICS

Using the population exposure estimates, we derived two types of metrics to characterize 

potential population health risk: (1) comparison to benchmark concentrations; and, (2) lung 

function risk. As in the last review, these approaches are based on the body of evidence from the 

controlled human exposure studies reporting lung function decrements (as measured by changes 

in sRaw), as well as changes in other measures of lung function, respiratory symptoms, and 

various markers of inflammation, in adult study subjects having asthma. For both approaches,

estimates are developed for two groups of individuals with asthma living in the study areas: 

adults with asthma (individuals older than 18 years), and children with asthma (individuals aged

5 to 18 years).

4.6.1 Comparison to Benchmark Concentrations

One of the two types of risk metrics in this assessment is based on comparison of 

estimated 5-minute exposures experienced while at an elevated ventilation rate to benchmark 

concentrations based on the controlled human exposure studies. In addition to its use in the 2009 

SO2 REA, the benchmark approach was used in past NO2 and O3 REAs (e.g., U.S. EPA, 2014), 

although ventilation rate does not play a role in the approach for the NO2 REA. For this metric, 

the time-series of exposures for each APEX-simulated individual is used to identify the daily 

maximum 5-minute SO2 concentrations that occur while at moderate or greater exertion. Based 

on all of the instances a daily maximum 5-minute exposure (while at or above the target EVR) is

at or above a benchmark concentration, summaries of the individual-level exposures are 

produced and combined to generate a statistic for the simulated at-risk population in each study 

area. This statistic indicates the number (and percent) of simulated persons experiencing 

exposures at or above the benchmark concentrations, while at moderate or greater exertion.41

 

41 A ‘person-day’ metric can be generated, indicating the total number of exceedances across the study area as a 

whole, but this metric is less informative for this review. The metric conflates variability in individual exposures, 

which can vary widely depending on the occurrence of peak concentrations and the distribution of time spent 

outdoors, and from a physiological perspective, creates an uninterpretable aggregate population exposure metric.
4-40

As in the 2009 REA, we have identified a set of benchmark concentrations to represent 

“exposures of potential concern” (75 FR 35527, June 22, 2010), 5-minute exposure 

concentrations for which there is potential for a respiratory response indicative of some level of 

bronchoconstriction to occur in an exposed individual, with the potential and the severity varying 

with the magnitude of the benchmark concentration. These levels are derived solely from the 

controlled human exposure studies, which can examine the health effects of SO2 in the absence 

of copollutants that typically can confound results in epidemiologic analyses; thus, health effects 

observed in such controlled studies can confidently be attributed to a defined SO2 exposure level.

Considering this information on variation in SO2 exposures and severity of respiratory 

response, as described in the ISA and summarized in section 2.2.3 of the REA Planning 

Document, we concluded that it is appropriate, as in the last review, to use four benchmark 

concentrations: 100, 200, 300 and 400 ppb. As recognized in the last review, we consider 

exposures with respect to the 200 and 400 ppb 5-minute benchmark concentrations to be of 

particular interest because: (1) 400 ppb represents the lowest exposure concentration in 

controlled human exposure studies where moderate or greater lung function decrements occurred 

that were often statistically significant at the group mean level and frequently accompanied by 

respiratory symptoms; and (2) 200 ppb is the lowest exposure concentration in controlled human 

exposure studies at which moderate or greater lung function decrements were found in some 

individuals, although these lung function changes were not statistically significant when 

evaluated at the group mean level (75 FR 35527, June 22, 2010) (Table 4-11). Additionally, 

analyses of pooled datasets for study subjects with asthma that are responsive to SO2 at 

concentrations below 1000 ppb found statistically significant increases in lung function 

decrements at 300 ppb (ISA, p. 5-19 to 5-20, 5-153; Johns et al., 2010). The lowest benchmark 

concentration (100 ppb) is one half the lowest exposure concentration tested by studies in which 

the exposure conditions allowed the study subjects to breathe freely.42 We have included this 

benchmark concentration in consideration of the nonzero, albeit low (fewer than 10%),

percentage of subjects with asthma experiencing moderate decrements in lung function at the 

200 ppb exposure concentration and the lack of specific study data for some groups of 

individuals with asthma, such as primary-school-age children (ages 5 to 11) and those with 

severe asthma.43

 

42 Studies of free-breathing subjects generally make use of small rooms in which the atmosphere is experimentally 

controlled such that study subjects are exposed by freely breathing the surrounding air (e.g., Linn et al., 1987).

43 We have considered the evidence with regard to the response of individuals with severe asthma that are not 

generally represented in the full set of controlled human exposure studies. There is no evidence to indicate such 

individuals would experience moderate or greater lung function decrements at lower SO2 exposure concentrations 

than individuals with moderate asthma. With regard to the severity of the response, the limited data that are 
4-41

Table 4-11. Responses reported in controlled human exposure studies at a given 

benchmark concentration.

Benchmark 

Concentration

(ppb)

Responses Reported in Controlled Human Exposure Studies a

Decrements in Lung Function Respiratory Symptoms, 

Supporting Studies

400

Across studies of exposures at/above this concentration (400-

500 ppb), 13-60% of exposed exercising study subjects with 

asthma experienced moderate decrements in lung function, 

and 4-40% experienced more severe responses a b c

“Stronger evidence, with 

some statistically significant 

increases in respiratory 

symptoms” (ISA, Table 5-2) d

300

Across studies of exposure at this concentration, 10-33% of 

exposed exercising study subjects with asthma experienced 

moderate decrements in lung function, and 0-40% 

experienced more severe responses a e f

“Limited evidence of SO2-

induced increases in 

respiratory symptoms in 

some people with asthma” 

200 (ISA, Table 5-2)

Across studies of exposures at this concentration, 7-9% of 

exposed exercising study subjects with asthma experienced 

moderate decrements in lung function, and up to 3% 

experienced more severe responses a g

100 This is one half the lowest concentration tested in free-breathing exposure conditions h

a Drawn from Table 5-2 of the ISA.

b Bronchoconstriction in individuals with asthma is the most sensitive indicator of SO2-induced lung function effects and is 

characteristic of an asthma attack, and airway hyperresponsiveness (AHR) is a characteristic feature of individuals with 

asthma (ISA, section 5.2.1.2). As in the last review, the ISA describes as moderate decrements in lung function that involve at 

least a doubling in sRaw or at least a 15% reduction in FEV1; increases in sRaw of 200% or more and FEV1 reductions of 

20% or more are indicated as more severe (ISA, section 1.6.1.1 and Table 5-2).

c Linn et al., 1983, 1987; Bethel et al.,1983; Roger et al., 1985; Magnussen et al., 1990; Horstman et al., 1986; ISA, Table 5-2.

d Lowest exposure finding both statistically significant lung decrements and respiratory symptoms (2008 ISA, section 3.1.3.1).

e Linn et al., 1988, 1990; ISA, Table 5-2.

f Statistically significant increases in lung function decrements in study subjects with asthma that are responsive to SO2 at 

concentrations below 1000 ppb (ISA, pp. 5-19 to 5-20; Johns et al 2010).

g Linn et al., 1983, 1987; ISA, Table 5-2.

h Very limited data are available for this exposure concentration from five studies utilizing a mouthpiece to deliver pollutant 

concentrations (PA, section 3.2.1.3). In these studies, nasal absorption of SO2 is bypassed during oral breathing, thus 

allowing a greater fraction of inhaled SO2 to reach the tracheobronchial airways. As a result, individuals exposed to SO2

through a mouthpiece are likely to experience greater respiratory effects from a comparable SO2 exposure using a free 

breathing protocol (ISA, p. 5-23).Although few of these studies included an exposure to clean air while exercising that would 

have allowed for determining the effect of SO2 versus that of exercise in causing bronchoconstriction, in those cases, the 

magnitudes of change in affected subjects appeared to be smaller than responses reported from studies at 200 ppb or more, 

with none indicating as much as a doubling in sRaw (PA, section 3.2.1.3).

 

available indicate a similar magnitude SO2-specific response (in sRaw) as that for individuals with less severe 

asthma, although the individuals with more severe asthma are indicated to have a greater response to exercise 

prior to SO2 exposure, indicating that those individuals “may have more limited reserve to deal with an insult 

compared with individuals with mild asthma” (ISA, p. 5-22).
4-42

4.6.2 Lung Function Risk

For lung function risk, we have focused on estimating the risk of experiencing SO2-

related increases in sRaw44 that correspond to moderate decrements in lung function as described 

in the ISA.

45 The assessment estimates the number of people (and percent of the population) 

expected to experience such a decrement and the total number of occurrences of these effects per 

individual across the simulation period. Results include the number of people (and percent of 

population) estimated to experience at least one such decrement in a year and the number 

estimated to experience multiple decrements. Estimates are generated for each of two lung 

function response definitions: an increase in sRaw by at least 100% (Δ sRaw ≥ 100%), and an 

sRaw increase of at least 200% (Δ sRaw ≥ 200%). These measures of lung function risk are 

derived from the E-R function (discussed below) and the number of exposures (concomitant with 

moderate or greater exertion) among the population that are at or above each of a set of exposure 

concentrations estimated from the exposure modeling. 

The E-R function is based on the controlled human exposure studies of decrements in 

lung function experienced by exercising individuals exposed to a range of 5-minute SO2

concentrations. Table 4-12 presents all study summary data for changes in sRaw from all 

references from which individual study data are available (ISA, Table 5-2). Because the health 

response variable is binary, a generalized linear model (GLiM) was used to construct the E-R 

function (SAS, 2017), represented by the following

g(μ) = β0 + β1X Equation 4-9

Briefly explained, one important feature of GLiM is the function (g) used to link the

structural component (i.e., the standard portion of a linear model, β0 + β1X) to the mean of a 

conditional response distribution (μ). There are several types of link functions to use in fitting 

these regression models, the selection of which is generally guided by the empirical fit of the 

data, practical considerations, and knowledge of the form of the response distribution.

Two link functions (i.e., probit and logistic) were evaluated for developing the E-R 

function used in the 2009 REA (2009 REA, section 9.2 and Appendix C). Both functions are 

symmetrical, yielded similar model fits and had nearly similar functional shape, indicating either 

 

44 Although risk of lung function decrements in terms of both FEV1 and sRaw were estimated in the REA for the last

review, the risk related to increases in sRaw, a direct indicator of bronchoconstriction for which data are available 

across a more extensive set of exposure concentrations than FEV1, was given greater emphasis and is the focus 

here.

45 The ISA describes a doubling in sRaw (or a 15% reduction in FEV1) to be a moderate lung function decrement 

(ISA, p. 1-17).
4-43

link function could be used to approximate risk. However, as is commonly observed for logistic

functions, the lower and upper tails tend to be flatter when compared to a probit function, that is, 

the approach of the curve towards the horizontal axis is more gradual (see Figures 9-2 to 9-5 of 

2009 REA). It followed that, when using the logistic function, the estimated health risk differed 

by a factor of 2 to 3 from that estimated using the probit function (see Tables 9-5 and 9-8 of 2009 

REA). These differences in estimated risk were largely the result of combining the slightly 

higher probability of risk at low exposure concentrations when using the logistic function 

combined with the large number of simulated individuals having low 5-minute exposures; this is

particularly the case for exposures less than 100 ppb (2009 REA, Figures 9-7 and 9-8). Thus, the 

probit model reduces the contribution of these exposures to risk estimates, for which E-R 

information is lacking, relative to that provide by the logistic function, thus better addressing the 

uncertainty in the E-R extrapolation to such low concentrations that appears magnified when 

using a logistic function. Further, as noted by the CASAC comments on a draft of the 2009 REA, 

assumptions regarding the distribution of individual thresholds for response support use of a 

probit function, which is based on the inverse of the cumulative standard normal distribution 

function, rather than a logistic function which assumes a logistic distribution, for estimating risk 

associated with population-based SO2 exposures (Samet, 2009, pp. 14 and 60-63).

Based on the above factors, we used a probit model for this risk analysis as in the 2009 

REA.

46 We used all of the data available47 to fit the two separate E-R functions (for ΔsRaw ≥ 

100% and ΔsRaw ≥ 200%), generating both the best fit regression as well as using variability 

associated with the predicted regression coefficients to provide lower and upper bounds of the 

risk estimation. To illustrate the E-R relationship indicated by these data, the percent of the study 

populations experiencing increases in sRaw is plotted in Figure 4-4. Further details regarding the 

E-R function, its application, and the interpretation of the estimated risk is provided below. 

 

46 The SAS procedure, PROC LOGISTIC, is used to fit the discrete response data by the method of maximum 

likelihood and using link=probit model option (SAS, 2017).

47 As mentioned in the REA Planning Document, the concentration levels included in the regression can influence 

the model fit, in particular the area of particular interest in this REA (low concentration related predicted 

responses). 
4-44

Table 4-12. Summary of controlled human exposure studies containing individual response 

data: number and percent of exercising individuals with asthma who 

experienced greater than or equal to a 100 or 200 percent increase in specific 

airway resistance (sRaw), adjusted for effects of exercise in clean air.

SO2

(ppb)

Exposure 

duration

(minutes)

N

Ventilation

sRaw sRaw sRaw sRaw

≥100 ≥200 ≥100 ≥200 Reference

(l/min) (N) (N) (%) (%)

200 5 23 ~48 2 0 8.7% 0.0% Linn et al. (1983)a

200 10 40 ~40 3 1 7.5% 2.5% Linn et al. (1987)b

250 5 19 ~50-60 6 3 31.6% 15.8% Bethel et al. (1985)

250 5 9 ~80-90 2 0 22.2% 0.0% Bethel et al. (1985)

250 10 27 ~42 0 0 0.0% 0.0% Horstman et al. (1986)a

250 10 28 ~40 1 0 3.6% 0.0% Roger et al. (1985)

300 10 20 ~50 2 1 10.0% 5.0% Linn et al. (1988)

300 10 21 ~50 7 2 33.3% 9.5% Linn et al. (1990)

400 5 23 ~48 3 1 13.0% 4.3% Linn et al. (1983)a

400 10 40 ~40 9.5 3.5 23.8% 8.8% Linn et al. (1987)b

500 5 10 ~50-60 6 4 60.0% 40.0% Bethel et al. (1983)

500 10 27 ~42 6 1 22.2% 3.7% Horstman et al. (1986)a

500 10 28 ~40 5 1 17.9% 3.6% Roger et al. (1985)

600 5 23 ~48 9 6 39.1% 26.1% Linn et al. (1983)a

600 10 40 ~40 13.5 9.5 33.8% 23.8% Linn et al. (1987)b

600 10 20 ~50 12 7 60.0% 35.0% Linn et al. (1988)

600 10 21 ~50 13 6 61.9% 28.6% Linn et al. (1990)

1000 10 10 ~40 6 2 60.0% 20.0% Kehrl et al. (1987)

1000 10 28 ~40 14 7 50.0% 25.0% Roger et al. (1985)

1000 10 27 ~42 15 7 55.6% 25.9% Horstman et al. (1986)a

Data presented are from all studies from which individual data were available (ISA Table 5-2 and Figure 5-1) on percentage of 

individuals who experienced greater than or equal to a 100 or 200% increase in specific airway resistance (sRaw). Lung 

function decrements are adjusted for the effects of exercise in clean air (calculated as the difference between the percent 

change relative to baseline with exercise|SO2 and the percent change relative to baseline with exercise|clean air).

a Data were not available for use in developing the E-R function for the 2009 SO2 REA.

b Responses of mild and moderate asthmatics reported in Linn et al. (1987) are the average of the first and second round 

exposure responses following the first 10 min period of exercise.
4-45

Figure 4-4. Percent of individuals experiencing changes in sRaw ≥ 100% (top panel) and 

sRaw ≥ 200% (bottom panel) using controlled human exposure study data 

(Table 4-12) fit using a probit regression (solid lines). Dashed lines indicate a 90 

percent confidence interval for the mean response.
4-46

The intent of the REA approach described in this section is to calculate population risk, 

not individual risk. Thus, it is considered appropriate to focus on the mean response calculated 

from the limited number of subjects in the collection of independently performed controlled 

human exposure studies. Using the study subject data, we approximated a best fit function that 

represents a mean response for any daily maximum 5-minute exposure concentration and derived 

a confidence interval for it in order to present a range of estimated population response. We 

applied the best fit function to the exposures estimated for the entire simulated population, 

assuming the simulated at-risk population (people with asthma in the three study areas) is 

comprised of individuals that have a similar response frequency as the controlled human 

exposure study test subjects. The 90% confidence interval for the mean response was used to 

approximate lower and upper bounds of the E-R function and used estimate lower and upper 

bounds of the population risk as part of the uncertainty characterization (section 6.2.2.4). Given 

the objective of estimating risk associated with population exposures, this confidence interval is 

considered a reasonable approach for estimating the range of the estimated population risk. 

An alternative approach for developing a range for the estimated risk might be a 

prediction interval that incorporates the spread of the individual study responses at each exposure 

concentration.48 We concluded that such an approach – that can be biased by one particular study 

having responses outside of the curves representing the 90% confidence interval (e.g., see Figure 

4-4) – would not provide an appropriate representation of population risk for sRaw responses. 

Given the wide-ranging responses in the individual studies, the very small number of subjects 

tested in each study leads us to conclude that such an approach that emphasized one or a few 

individual studies would be less likely to represent the response frequency for the entire 

population of people with asthma in each study area.

Using the exposure model counts of individuals with daily maximum 5-minute 

concentrations falling into the different bins (as described in section 4.5 above), the number of 

occurrences of lung function response is calculated by multiplying the number of exposures in an 

exposure bin by the response probability (given by the probit E-R function for the specified 

definition of lung function response) associated with the midpoint of that bin. Provided in Table 

4-13 are single-year exposure estimates for children with asthma in the Fall River study area to 

demonstrate this calculation.

 

48 In general, a prediction interval for a regression is useful in estimating a random future value of the dependent 

variable (y), while a confidence interval is useful in estimating the average (or expected) value of y variable given 

the same value of the independent variable (x).
4-47

Table 4-13. Example of risk calculation using estimated daily maximum 5-minute 

exposures of children with asthma in the Fall River study area.

Exposure Results ER Function Estimated Risk

5-minute SO2

Exposure Bin a

(ppb)

Number of 

Children b

(n)

Bin 

Midpoint

(ppb)

Calculated Response

(fraction of population)

Number of Individuals 

Responding c

(n)

0 44 5 2.49E-07 0

10 103 15 4.02E-05 0

20 149 25 2.92E-04 0

30 143 35 9.45E-04 0

40 190 45 2.12E-03 0

50 249 55 3.90E-03 0

60 345 65 6.28E-03 2

70 346 75 9.26E-03 3

80 477 85 1.28E-02 6

90 396 95 1.69E-02 6

100 379 105 2.15E-02 8

110 271 115 2.66E-02 7

120 196 125 3.21E-02 6

130 149 135 3.80E-02 5

140 70 145 4.41E-02 3

150 75 160 5.40E-02 4

170 36 180 6.80E-02 2

190 8 195 7.90E-02 0

200 5 205 8.65E-02 0

210 3 220 9.80E-02 0

230 0 240 1.14E-01 0

250 0 275 1.42E-01 0

300 0 325 1.82E-01 0

350 0 375 2.22E-01 0

400 0 425 2.60E-01 0

Total 3633 52

a The exposure bin includes daily maximum 5-minute exposures of at least that value, but less than that of the 

next exposure bin.

b This is the number of children with asthma experiencing the exposure while at moderate or greater exertion. In 

the Fall River study area, the total population of children with asthma is 3,641. 

c Multiplying number of children by the calculated response, then rounded down to the nearest integer, gives the 

number of individuals responding.

For example, the midpoint of the 10-20 ppb bin is 15 ppb (Table 4-13). The 15 ppb 

exposure bin contains a total of 103 individuals who experienced a daily maximum 5-minute 

concentration in the simulated year of at least 10 ppb, but less than 20 ppb. The 

frequency/probability obtained from the probit function at 15 ppb (i.e., 4.02 E-05) is then used to 
4-48

estimate the number of the 103 persons that respond. To avoid accounting for and summing 

(numerically calculated) fractions of people, all risk estimates obtained by combining the number 

of individuals with the percent responding within each bin (i.e., the count of individuals 

responding) are truncated at the integer level. Therefore, in the Table 4-13 example for the 10-20 

ppb bin, the number of individuals estimated to experience a response (i.e., 0.004 persons) is 

zero. After calculating the number of whole individuals estimated to respond in each bin, these

are summed to generate the total estimated population risk (i.e., 52 children with asthma). Thus, 

1.4% of children with asthma (52 divided by 3641) are estimated to experience at least one day 

in the simulated year with an sRaw increase of 100% or more) as a result of their daily maximum 

5-minute SO2 exposure.

Additionally, the contribution to risk estimates from each exposure bin is developed 

based on the apportionment of the risk estimates to the exposure bins. In this example, nearly 

90% of the estimated risk is attributed to 5-minute concentrations at or above 50 ppb and less 

than 150 ppb. No children were estimated to experience a response at a 5-minute concentration 

below 50 ppb.49

4.7 APPROACH FOR CHARACTERIZING UNCERTAINTY AND 

VARIABILITY

An important issue associated with any population exposure and risk assessment is the 

assessment of variability and characterization of uncertainty. Variability refers to the inherent 

heterogeneity in a population or variable of interest (e.g., residential air exchange rates). The 

degree of variability cannot be reduced through further research, only better characterized with 

additional measurement. Uncertainty refers to the lack of knowledge regarding the values of 

model input variables (i.e., parameter uncertainty), the physical systems or relationships used 

(i.e., use of input variables to estimate exposure or risk or model uncertainty), and in specifying 

the scenario that is consistent with purpose of the assessment (i.e., scenario uncertainty). 

Uncertainty is, ideally, reduced to the maximum extent possible through improved measurement 

of key parameters and iterative model refinement. The following two sections describe the 

approaches we have used to assess variability (section 4.7.1) and to characterize uncertainty 

(section 4.7.2) in this REA. The primary outcome is a summary of variability and uncertainty 

evaluations conducted to date of our SO2 exposure assessments and APEX exposure modeling, 

and the identification of the elements or areas of the assessment with which is associated the 

greatest uncertainty. 

 

49 Thus, it can be observed that in this example for this study area and air quality scenario, 50 ppb represents a 

limiting value for the response function among the lowest exposure level bins. Such values would be expected to 

differ with population and air quality scenario characteristics.
4-49

4.7.1 Assessment of Variability and Co-variability

The goal in addressing variability in the REA is to ensure that the estimates of exposure 

and risk reflect the variability of SO2 concentrations in ambient air, population characteristics, 

associated SO2 exposures, physiological characteristics of simulated individuals, and potential 

health risk across the study areas and for the simulated at-risk populations. In the REA, there are 

several algorithms that are used to account for variability when generating the two risk metrics. 

For example, variability may arise from differences in the population residing within census 

tracts (e.g., age distribution) and the activities that may affect population exposure to SO2 (e.g., 

time spent outdoors, performing moderate or greater exertion level activities outdoors). The 

range of exposure and associated risk estimates are intended to reflect such sources of variability, 

although we note that the range of values obtained reflects the input parameters, algorithms, and 

modeling system used, and may not necessarily reflect the complete range of the true exposure or 

risk values.

We note also that correlations and non-linear relationships between variables input to the 

model can result in the model producing inaccurate results if the inherent relationships between 

these variables are not preserved. APEX is designed to account for co-variability, or linear and 

nonlinear correlation among the model inputs, provided that enough is known about these 

relationships to specify them. This is accomplished by providing inputs that enable the 

correlation to be modeled explicitly within APEX. For example, there is a non-linear relationship 

between the outdoor temperature and air exchange rate in homes. One factor that contributes to 

this non-linear relationship is that windows tend to be closed more often when temperatures are 

at either low or high extremes than when temperatures are moderate. This relationship is 

explicitly modeled in APEX by specifying different probability distributions of air exchange 

rates for different ambient air temperatures.

Important sources of the variability and co-variability accounted for by APEX and used 

for this SO2 exposure analysis have been identified and summarized in section 6.1. Where 

possible, we identified and incorporated the observed variability in input data sets rather than 

employing standard default assumptions and/or using point estimates to describe model inputs. 

4.7.2 Characterization of Uncertainty

While it may be possible to capture a range of exposure or risk values by accounting for 

variability inherent to influential factors, the true exposure or risk for any given individual within 

a study area may be unknown, although it can be estimated. To characterize health risks, 

exposure and risk assessors commonly use an iterative process of gathering data, developing 

models, and estimating exposures and risks, given the goals of the assessment, scale of the 

assessment performed, and limitations of the input data available. However, significant 
4-50

uncertainty often remains and emphasis is then placed on characterizing the nature of that 

uncertainty and its impact on exposure and risk estimates.

In section 6.2, we have summarized the most important uncertainties potentially affecting 

the exposure estimates derived for this assessment. In so doing, we recognize that uncertainties 

associated with APEX exposure modeling are also characterized in the REAs conducted for 

recent reviews of the primary NAAQS for NO2, carbon monoxide, and O3, along with other 

pollutant-specific issues (U.S. EPA, 2008a, 2010, 2014). Conclusions drawn from each of these 

characterizations are considered in light of new information and of the approaches used in this

REA. Additionally, the new evaluations performed in the current REA have been synthesized 

following the approach outlined by WHO (2008) and used to identify, evaluate, and prioritize the 

most important uncertainties relevant to the estimated exposure and risk outcomes. The 

characterization presented in section 6.2 uses a predominantly qualitative approach 

supplemented by various model sensitivity analyses and input data evaluations, all 

complementary to quantitative uncertainty characterizations conducted for the 2007 O3 REA by 

Langstaff (2007).

The approach used for this REA varies from that described by WHO (2008) in that a 

greater focus has been placed on evaluating the direction and the magnitude50 of the uncertainty. 

This refers to qualitatively rating how the source of uncertainty, in the presence of alternative 

information, may affect the estimated exposures and health risk results. Following the 

identification of key uncertainties, we have subjectively scaled the overall impact of the 

uncertainty by considering the relationship between the source of uncertainty and the exposure 

concentrations (e.g., low, moderate, or high potential impact). Also to the extent possible, we 

have included an assessment of the direction of influence, indicating how the source of 

uncertainty may be affecting exposure or risk estimates (e.g., the uncertainty could lead to overor under-estimates). Further, and consistent with the WHO (2008) guidance, section 6.2 

discusses the uncertainty in the knowledge base (e.g., the accuracy of the data used, 

acknowledgement of data gaps) and, where possible, particular assessment design decisions (e.g., 

selection of particular model forms). The output of the uncertainty characterization is the 

summary in section 6.2 that describes, for each identified source of uncertainty, the magnitude of 

the impact and the direction of influence the uncertainty may have on the exposure and risk 

characterization results.

 

50 This is synonymous with the “level of uncertainty” discussed in WHO (2008), section 5.1.2.2.
4-51

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5-1

5 POPULATION EXPOSURE AND RISK RESULTS

Exposure and risk results are presented here for simulated populations residing in the 

three study areas – Fall River, MA, Indianapolis, IN, and Tulsa OK – for a three-year air quality 

scenario in which air quality conditions just meet the current primary SO2 standard. The 

approaches used to link air quality modeling, ambient concentration measurements, exposure 

modeling, and controlled human exposure study data in this assessment are summarized in 

Figure 2-2. Briefly, and as described in more detail in chapter 3, first AERMOD predicts hourly 

SO2 concentrations at air quality receptors within a spatial grid for each study area. Then, the 

complete annual temporal pattern of 5-minute continuous ambient monitor concentrations local 

to each study area was combined with the AERMOD-predicted 1-hour concentrations to generate 

5-minute concentrations at every air quality receptor. As described in Chapter 4, APEX used the

5-minute air quality surface in each study area along with U.S. census block population 

demographics to estimate the number of days per year each simulated individual in a particular 

study area experiences a daily maximum 5-minute SO2 exposure at or above 5-minute 

benchmark levels of 100, 200, 300, and 400 ppb. These short-term exposures were evaluated for 

children (5-18 years old) and adults (>18 years old) with asthma when the exposure 

corresponded with moderate or greater exertion (i.e., the individual’s EVR was ≥22 L/minutem2

). And finally, simulated individuals expected to experience a lung function decrement (i.e., 

doubling or larger increase in sRaw) were estimated by linking the population-based daily 

maximum 5-minute exposures with an exposure-response function derived from controlled 

human exposure study data (section 4.6.2)

Study area characteristics and the composition of the simulated population are provided 

in section 5.1. Exposure results are presented in a series of tables that allow for simultaneous 

comparison of the exposure and risk metrics across the three study areas and three simulation 

years. Two types of results are provided for each modeling domain: (1) the percent of the 

simulated subpopulation exposed at or above selected benchmarks, stratified by the number of 

occurrences (i.e., days) in a year (section 5.2) and (2) the percent of the simulated subpopulation 

experiencing a doubling or larger increase in sRaw, also stratified by the number of days in a 

year (section 5.3). Tables summarizing all of the exposure and risk results for each study area, 

exposure and response level, 1and simulated at-risk population are provided in Appendix J.

 

1 As described in section 4.5, exposure model output includes the number and percent of individuals at or above the 

benchmark levels and several other exposure levels used for estimating lung function risk.
5-2

5.1 CHARACTERISTICS OF THE SIMULATED POPULATION AND 

STUDY AREAS

The three study areas differ in population, geographic size, and demographic features (as 

summarized in Table 5-1 and Figures 5-1 through 5-3).2In each study area, APEX simulated 

SO2 exposures for thousands of individuals,

3

the demographic features of which were based on 

the information associated with the thousands of census blocks within each area (as described in 

section 4.1 above). 

Asthma prevalence in each modeling domain was estimated based on the NHIS asthma

prevalence data and the demographic characteristics for each study area (e.g., age, sex and family 

income) using the methodology summarized in section 4.1.2. Accordingly, the percent of the 

simulated populations with asthma within the exposure modeling domain varied by study area

(Table 5-1). The exposure modeling domain for Tulsa had the lowest percent of adults with 

asthma (7.2%), while Indianapolis had the lowest percent of children with asthma (9.7%). Fall 

River had the highest percent of children with asthma (11.2%), while Indianapolis had the 

highest percent of adults with asthma (8.3%). The statistics presented here are the aggregate of 

the study area as a whole, within which asthma prevalence varied widely as the modeling 

approach fully accounted for the variation in asthma prevalence across census blocks with 

demographic factors such as poverty, age, and sex (described in section 4.1.2).4 Nationally, 

asthma prevalence is 7.8%; for children it is 8.4% and for adults it is 7.6% (PA, Table 3-2). The 

asthma prevalence for children, adults, and the total population estimated for each of the three 

study areas are all greater than that of the National asthma prevalence, except for adults in Tulsa 

which has a slightly lower asthma prevalence. This suggest that overall, the at-risk population 

simulated in the three study areas could represent at-risk populations in other U.S. area that have 

a similarly above average asthma prevalence.

 

2 Specific census block (or tract) identifiers used for the simulations are documented in the APEX ‘sites’ files for 

these simulations.

3 While precisely 30,000 children and 70,000 adults were simulated as part of each APEX model run, the number of 

individuals estimated to be exposed are appropriately weighted to reflect the actual population residing within the 

census blocks that comprise each respective study area.

4 Representing the variation in asthma prevalence that occurs at the census block level provides a level of resolution 

for identification of at-risk individuals that is generally comparable with the resolution of the spatially variable 

ambient air concentrations at air quality receptors. In this way, the population in census blocks with higherconcentration air quality receptors is represented appropriately with regard to asthma prevalence and exposures of 

the at-risk individuals with asthma are not under-represented.
5-3

Table 5-1. Summary of study area features and the simulated population.

Study Area

(# census tracts | #

census blocks)

Population 

Group 

(age range)

Total

Population

Population with 

Asthma

% of Population 

with Asthma

Fall River

(56 | 4,364)

Children (5-18) 32,424 3,641 11.2 %

Adults (19-95) 151,450 12,304 8.1 %

All (5-95) 183,874 15,945 8.7 %

Indianapolis

(172 | 12,310)

Children (5-18) 112,366 10,851 9.7 %

Adults (19-95) 435,602 36,217 8.3 %

All (5-95) 547,968 47,068 8.6 %

Tulsa

(114 | 7,694)

Children (5-18) 49,482 5,484 11.1 %

Adults (19-95) 207,941 15,049 7.2 %

All (5-95) 257,423 20,533 8.0 %

There are also differences among the study areas with regard to the spatial distribution of 

the population (Figures 5-1 to 5-3).

5

In the Fall River study area (Figure 5-1), the most highly 

populated census tracts (6,000 to 9,000 people per tract) were generally toward the outer edges 

of the study area, with the exception of one highly populated tract encompassing the primary 

source. Most census tracts in the Fall River study area (86%) had a population of fewer than 

6,000 people per tract, with a few tracts (23%) having fewer than 3,000 people per tract. In the 

Indianapolis study area (Figure 5-2), most tracts also had fewer than 6,000 people per tract

(84%), though several tracts had greater than 9,000 people, one of which is located just south of 

the collection of modeled emission sources. The census tracts in the Tulsa study area were the 

least populated when compared to tract populations in the other two study areas, with all but one 

tract having fewer than 6,000 people and nearly 60% of tracts having fewer than 3,000 people 

per tract (Figure 5-3).

 

5 Data used for these figures were obtained from https://www.census.gov/geo/maps-data/data/gazetteer2010.html. 

An identical scale was used for the three figures, progressing by increments of 3,000 people to allow for 

appropriate comparisons. For illustrative purposes, census tract population data were used for these maps to better 

view the overall population distribution across the study area rather than using census block level data because 

many of the smallest sized blocks were not viewable.
5-4

Figure 5-1.Population in the Fall River study area considering 2010 U.S. Census tracts.

Figure 5-2.Population in the Indianapolis study area considering 2010 U.S. Census tracts.
5-5

Figure 5-3.Population in the Tulsa study area considering 2010 U.S. Census tracts.

5.2 EXPOSURES AT OR ABOVE BENCHMARK CONCENTRATIONS

There were few simulated individuals estimated to experience 5-minute exposures at or 

above the three highest benchmark levels (200, 300, and 400 ppb), in any of the study areas

(Tables 5-2 and 5-3). Regarding the two highest benchmarks of 300 ppb and 400 ppb, neither 

children nor adults with asthma had any 5-minute exposures at or above these levels in the Fall 

River and Tulsa study areas. In the Indianapolis study area, a small fraction (<1%) of the 

simulated population of children with asthma was estimated to experience exposures at or above 

300 ppb and 400 ppb in the first year of the 3-year simulation. The relatively few exposures at or 

above 300 ppb is consistent with the limited number of occurrences of these high 5-minute 

concentrations in the air quality data set (Tables 3-14 to 3-16) for the air quality scenario 

modeled.

6 The next highest benchmark, 200 ppb, was also rarely exceeded, and when it was, a

daily maximum 5-minute exposure only occurred at or above this level on no more than one day 

in the year and for a small fraction of the simulated at-risk population (<0.1% to 1.0%).

 

6 Air quality was adjusted to just meet the existing standard of 75 ppb, as a 3-year average of 99th percentile annual 

daily maximum 1-hour concentrations.
5-6

Sensitivity analyses described in section 6.2.2 illustrate some variation from the estimates 

presented in this section. The use of alternative exposure model inputs, such as an alternative 

approach to adjust ambient concentrations to meet the existing standard and an alternative 

method to combine patterns of monitored 5-minute concentrations with modeled receptors can,

in some instances, contribute to somewhat higher estimates. Overall, such differences based on 

the use of alternative approaches evaluated are not large and mainly affected estimated exposures 

at or above the 100 ppb benchmark (i.e., ranging from no difference to a few percentage points).

Given the findings noted above for the higher benchmark levels, discussion here of 

differences across air quality years and simulated populations focuses on the lowest benchmark 

level. Regarding this benchmark level (100 ppb), the Tulsa study area did not have more than 

0.2% of either simulated at-risk population estimated to experience one or more days with a 5-

minute exposure at or above 100 ppb (Tables 5-2 and Table 5-3). Thus, the discussion here

focuses primarily on the Fall River and Indianapolis study area results. 

Across the three years modeled, the highest population exposures were estimated in the 

first year. This is seen with the yearly estimates of the percent of the simulated populations

expected to experience one, two or more days with exposures above benchmark levels (Tables 5-

2 and Table 5-3). For example, in considering exposure results for children with asthma having 

at least one daily maximum 5-minute exposure at or above 100 ppb in Fall River during the first 

year, the percent was 32.7%, while air quality for the subsequent years yielded a lower percent 

(13.2% and 12.3%, respectively). Such year-to-year variability in the estimated exposures can be 

expected given variability in ambient concentrations across sequential years (e.g., Table 3-11 to 

Table 3-13), largely resulting from actual variability in emissions and meteorology in the air 

quality modeling.

7 Year-to-year variability was also observed for the Indianapolis results for 100 

ppb, although the range (nine percentage points) was smaller.

Across the three areas, a greater proportion of simulated children with asthma were 

estimated to experience exposures at or above benchmark levels compared to adults with asthma.

For example, for the three years in the Fall River study area, as many as 12.3 to 32.7% of 

children with asthma were estimated to experience at least one daily maximum 5-minute 

exposure at or above 100 ppb, while the range in the percent of adults with asthma exposed was 

from 1.3 to 5.1% (Table 5-2). The number of days per year with exposure above benchmarks 

was also greater for children with asthma compared to adults with asthma. For example, no

 

7 The emissions for the main source in Fall River declined appreciably over the 3-year simulation period, also likely 

contributing to the variation observed in the annual exposure estimates. Note, the air quality adjustment used to 

create the hypothetical air quality scenario of conditions just meeting the existing standard (with its three-year 

form) maintains the year-to-year variability in emissions and meteorology, yielding high and low ambient 

concentration years within the 3-year period.
5-7

simulated adults with asthma in the Fall River study area were estimated to have more than three 

days in a year with a daily maximum 5-minute exposure at or above 100 ppb, while on average 

across the 3-year period, 0.9% of children with asthma were estimated to have four or more days 

at or above that benchmark (Table 5-3). Such differences between these two populations are

expected given that higher exposures are more frequent outdoors (see sections 2.1.2 and below) 

and that children spend more time outdoors and at a greater frequency compared to adults.
5-8

Table 5-2. Percent and number of children and adults with asthma estimated to 

experience at least one day per year with a SO2 exposure at or above 5-minute 

benchmark concentrations while breathing at elevated rate, air quality 

adjusted to just meet the existing standard.

Study area Population

group

Benchmark

concentration

(ppb)

Percent (and number) of population with asthma 

having at least one day per year when 5-minute 

SO2 exposure > benchmark 

Year 1 Year 2 Year 3 Average

Fall River

children

100 32.7

(1,192)

13.2

(480)

12.3

(447)

19.4

(706)

200 0.2

(8) 0

a 0

<0.1 a

(3)

300 No exposures at or above this benchmark

adults

100 5.1

(625)

1.9

(229)

1.3

(162)

2.8

(339)

200 <0.1

(2) 0 0

<0.1

(1)

300 No exposures at or above this benchmark

Indianapolis

children

100 27.0

(2,932)

22.3

(2,419)

18.0

(1,947)

22.4

(2,433)

200 1.0

(112) 0

0.9

(101)

0.7

(71)

300 0.8

(89) 0 0

0.3

(30)

400 0.3

(33) 0 0

0.1

(11)

adults

100 4.3

(1,549)

3.8

1,369)

2.9

(1,051)

3.7

(1,323)

200 0.1

(43) 0

0.2

(62)

0.1

(35)

300 <0.1

(31) 0 0

<0.1

(10)

400 <0.1

(24) 0 0

<0.1

(8)

Tulsa

children

100 0.2

(13)

0.2

(8)

<0.1

(1)

0.1

(7)

200 No exposures at or above this benchmark

adults

100 0.1

(14)

<0.1

(8) 0

<0.1

(7)

200 No exposures at or above this benchmark

a < 0.1 represents nonzero estimates below 0.1%. A zero (0) indicates there were no individuals having the 

specified exposure.
5-9

Table 5-3. Percent of children and adults with asthma estimated to experience multiple 

days per year with a SO2 exposure at or above 5-minute benchmark 

concentrations while breathing at elevated rate, air quality adjusted to just 

meet the existing standard.

Benchmark 

concentration

(ppb)

Percent of population with asthma having multiple days per year when 5-minute SO2

exposure > benchmark a

Fall River Indianapolis Tulsa

>2 days >4 days >6 days >2 days >4 days >6 days >2 days >4 days >6 days

Children, aged 5 to 18 years

100 5.5

(1.6 - 12.2)

0.9

(<0.1b- 2.6)

0.2

(0 - 0.6)

6.8

(4.7 - 8.0)

0.8

(0.3 - 1.0) 

0.1

(<0.1 - 0.2) 0

b 0 0

200 no study area results included multiple days per year at or above this benchmark level

Adults, aged 19 to 95 years

100 0.2

(<0.1 – 0.4)

0 0

0.5

(0.4 – 0.6)

<0.1

(0 - <0.1)

<0.1

(0 - <0.1) 0 0 0

200 no study area results included multiple days per year at or above this benchmark level

a These estimates are summarized from the single year data provided in Appendix J. The first value in each cell is the average 

across the three years; the range is provided in parentheses.

b < 0.1 represents nonzero estimates below 0.1%. Zero (0) indicates there were no individuals having the specified exposure.

We also evaluated the microenvironments where the highest exposures occurred in the 

three study areas, as was done for the 2009 REA. With the summary information APEX provides 

for the simulated population is the total time spent in each microenvironment and for every 

exposure level across the entire simulation period.

8 9 We summed the total time that simulated 

individuals in the population spent at or above each of the exposure levels and calculated the 

percent of this time occurring in each of the microenvironments (Figure 5-4). Consistent with 

findings from the 2009 REA, the majority (about 90% or more) of the time that the population is 

exposed at or above 100 ppb occurs in outdoor MEs for all three study areas.

 

8 For all of these APEX simulations, children are the simulated population group, of which a subset are children with 

asthma. The APEX ME summary output is directly for that base population (i.e., all children) and cannot be 

edited to reflect a subset of that population (e.g., specific age groups of children). Because there are no 

modifications made to simulate children with asthma (i.e., all children use the same physiological and activity 

pattern data), inferences made regarding exposures for the total population of children in this analysis are 

applicable to the subset of simulated children with asthma.

9 This default ME summary output summarizes all exposure time for the entire simulation, thus it reflects instances 

where individuals are at any exertion level (e.g., resting, vigorous, etc.). Nevertheless, the presentation here 

remains informative in this assessment, particularly considering that it is likely the vigorous exertion level 

activities are also linked to particular MEs such as those outdoors.
5-10

Figure 5-4.Percent of children’s time in indoor, outdoor, and vehicle MEs while exposed to 

SO2 in Fall River (top), Indianapolis (middle), and Tulsa study areas.
5-11

5.3 LUNG FUNCTION DECREMENTS ASSOCIATED WITH 5-MINUTE 

SO2 EXPOSURES

There were few simulated individuals estimated to experience SO2-related increases in 

sRaw of at least 100% in any of the three study areas under air quality conditions just meeting

the existing standard (Tables 5-4 and 5-5). We additionally note that as mentioned above for the 

benchmark comparisons, sensitivity analyses using alternative exposure model inputs described 

in section 6.2.2 indicate that the percent of individuals estimated to experience lung function 

decrements of interest can vary from these estimates, although such differences are not large. 

Additionally, as discussed in section 5.4 below, differences among the three study areas with 

regard to the extent of areas within each study area with higher DVs and greater population 

appears to contribute to the relatively higher estimates for the Fall River and Indianapolis study 

areas. As recognized in the PA, such exposure circumstances are particularly informative to 

consideration of public health protection provided by the current SO2 standard.

In the Fall River and Indianapolis study areas, on average across the three-year period, as 

many as 1.3% of children with asthma were estimated to experience at least one day per year 

with an SO2-related increase in sRaw of 100% or more; in a single year, the percent is as high as 

1.5% (Table 5-4). The percent of children with asthma estimated to experience two or more such 

days with an SO2-related increase in sRaw of 100% or more ranged as high as 0.8% in a single 

year, while on average across the three years it was as high as 0.7% of children with asthma 

(Table 5-5). When considering SO2-related increases in sRaw of 200% or more, on average as 

many as 0.3% of children with asthma were estimated to experience this lung function 

decrement. The percent of adults estimated to experience lung function decrements was lower 

than that of children, due to adults having a lesser amount of time spent outdoors and lower 

frequency of outdoor events, leading to lower exposures relative to those estimated for children. 

Based on the design of the exposure assessment and how estimated exposures are 

summarized for the risk calculation (i.e., use of exposure concentration bins), the number of 

individuals falling within each exposure concentration bin is used to derive the number of 

individuals estimated to experience the lung function decrement from their daily maximum 5-

minute exposure estimates based on the E-R function (see section 4.6.2). The extent to which 

differing magnitudes of exposure concentrations contribute to the total risk estimates in each 

year is shown in Table 5-6 for the children with asthma in Fall River and Indianapolis study 

areas and days with a SO2-related increase in sRaw of 100% or more. The majority (83-100%) of 

the simulated individuals estimated to experience at least one day with such a lung function 

decrement had their 5-minute daily maximum exposure between 50 and 150 ppb. In all three 

study areas, there were no simulated individuals with a SO2-related increase in sRaw of 100% or 
5-12

more when 5-minute daily maximum exposures were less than 40 ppb, effectively serving as a 

threshold for the ER function at this exposure level.

Table 5-4. Percent and number of children and adults with asthma estimated to 

experience at least one day per year with a SO2-related increase in sRaw of 

100% or more while breathing at an elevated rate, air quality adjusted to just 

meet the existing standard.

Study area

Population

group

Increase 

in sRaw 

(%)

Percent (and number) of population with asthma 

having at least one day per year with specified 

increase in sRaw

Year 1 Year 2 Year 3 Average

Fall River

children 100

1.4

(52)

0.8

(28)

0.5

(20)

0.9

(33)

200

0.2

(9)

0.1

(5)

<0.1 a

(2)

0.1

(5)

adults 100

0.3

(42)

0.2

(21)

<0.1

(9)

0.2

(24)

200

<0.1

(6)

<0.1

(1) 0

a

<0.1

(2)

Indianapolis

children

100 1.5

(161)

1.3

(140)

1.1

(121)

1.3

(141)

200 0.4

(39)

0.3

(35)

0.3

(29)

0.3

(34)

adults

100 0.4

(158)

0.4

(147)

0.4

(128)

0.4

(144)

200 0.1

(36)

<0.1

(32)

<0.1

(26)

<0.1

(31)

Tulsa

children 100 0

<0.1

(1)

<0.1

(1)

<0.1

(1)

200 0 0 0 0

adults 100 <0.1

(1)

<0.1

(2)

<0.1

(1)

<0.1

(1)

200 0 0 0 0

a < 0.1 represents nonzero estimates below 0.1%. A zero (0) indicates there were no individuals having the 

increase in sRaw.
5-13

Table 5-5. Percent of children and adults with asthma estimated to experience multiple 

days per year with a SO2-related increase in sRaw of 100% or more while 

breathing at elevated rate, air quality adjusted to just meet the existing 

standard.

Lung function 

decrement 

(increase in 

sRaw)

Percent (and number) of population with asthma having multiple days per year 

with specified increase in sRaw a

Average per year (minimum/year – maximum/year)

Fall River, MA Indianapolis, IN Tulsa, OK

# Days # Days # Days

>2 >4 >6 >2 >4 >6 >2 >4 >6

Children, aged 5 to 18 years

> 100% 0.4

(<0.1b- 0.7)

0.2

(<0.1-0.4)

0.1

(0 - 0.2) 

0.7

(0.6 – 0.8)

0.4

(0.4)

0.3

(0.3)

no individuals 

experiencing multiple

days with this size 

increase in sRaw > 200% <0.1

(0 - 0.1) 0

b 0

0.2

(0.1 – 0.2)

<0.1

(<0.1)

<0.1

(<0.1)

Adults, aged 19 to 95 years

> 100% <0.1

(0 - <0.1) 0 0

0.2

(0.1 – 0.2)

<0.1

(<0.1)

<0.1

(<0.1) no individuals 

experiencing multiple

days with this size 

increase in sRaw > 200%

no individuals experiencing 

multiple days with this size 

increase in sRaw

<0.1

(<0.1)

<0.1

(<0.1) 0

a These estimates are summarized from the single year data provided in Appendix J.

b < 0.1 represents nonzero estimates below 0.1%. Zero (0) indicates there were no individuals having the specified 

increase in sRaw.

Table 5-6. Contribution of different magnitudes of 5-minute SO2 exposures to lung 

function risk (sRaw increase of at least 100%) estimated for children with 

asthma in Fall River.

5-minute SO2 exposure 

concentration bins

Percent contribution of exposure concentration to total risk estimate a

Fall River Indianapolis

Year 1 Year 2 Year 3 Year 1 Year 2 Year 3

0 to <50 ppb 0.0% 0.0% 5.0% 0.6% 0.7% 0.8%

50 to <100 ppb 32.7% 67.9% 45.0% 37.9% 47.1% 51.2%

100 to <150 ppb 55.8% 32.1% 50.0% 45.3% 39.3% 38.8%

150 to <200 ppb 11.5% 0.0% 0.0% 2.5% 12.9% 1.7%

>=200 ppb 0.0% 0.0% 0.0% 13.7% 0.0% 7.4%

a These results are generated from the same data used to estimate the percent of children experiencing at least one day with 

an increase in sRaw ≥ 100% provided in Table 5-4.
5-14

5.4 STUDY AREA DIFFERENCES AND POPULATION DISTRIBUTION

To gain a better understanding of the role of two study area characteristics in differences 

of exposure estimates among the three study areas, we derived a metric that combines the census 

tract population counts (Figures 5-1 to 5-3) with the spatial distribution of the modeled air 

quality receptor design values for the air quality scenario assessed (Figures 3-6 to 3-8).10 By 

merging the two variables that most influence population-based exposures – ambient 

concentrations and number of people – this metric can be used to indicate spatial variability in 

exposures and be useful in broadly comparing relative differences across the three study areas.

The first section below summarizes how the exposure metric (labeled DV&POP) was derived 

and the subsequent section describes use of the metric in comparing the study areas.

5.4.1 Derivation of DV&POP Metric

First, the air quality receptors used for estimating exposures were identified using the 

APEX sites file, a file that contains the IDs for both the air quality receptors and the population 

census blocks used in each exposure simulation. This set of IDs was then used to link the air 

quality receptor design values with the census tract population data (i.e., the first 11 characters of 

the block IDs are the tract IDs). Second, all design values were normalized to the maximum 

design value in each study area, creating a set of data ranging in value of 0 to 1, with a value of 1 

given to the receptor that had an original design value of 75 ppb. Because there can be multiple 

census blocks (and air quality receptors) within each census tract, two new ambient 

concentration variables were calculated using this normalized data set - the arithmetic average of 

all normalized receptor design values falling within each tract (nDVavg) and the maximum 

normalized design value within each tract (nDVmax). This was done to represent the overall 

relative concentration in each tract while also recognizing the importance of the upper percentile 

concentrations. Third, two new population variables were created. In each study area, the tract 

populations were normalized by its own maximum tract population to generate values for the 

first population variable (and thus having a value ranging from 0 to 1 for tracts in each study 

area). The purpose of this population variable (nPOPintra) was to discern intra-study area spatial 

differences in population. The second population variable was created similarly, though the tract 

populations in each study area were normalized using the maximum population in any of the 

three study areas (nPOPinter). The Indianapolis study area had the tract with the greatest 

population, thus for this study area, the values for this second population variable were identical 

to values for the first population variable. However, in the other two study areas, the second 

 

10 Note that the degree of spatial heterogeneity of SO2 concentrations across each study area is a function of the 

emission source(s) characteristics (e.g. emission rate, stack height), meteorological conditions (e.g., wind speed), 

among other factors.
5-15

population variable had its values relative to that of Indianapolis, thus accounting for inter-study 

area differences in population.

These four variables were then combined to represent the final metric (DV&POP) in each 

tract and weighted such that its range of values for this new metric extends from 0 to 1, as 

follows in equation 5-1:

DV&POP = 0.333 × nDVavg + 0.333 × nDVmax + 0.222 × nPOPintra + 0.112 × nPOPinter

Equation 5-1

We note that the weighting scheme gives more weight to the design value information 

(2/3) than the population data (1/3) given the significance of high concentrations for this 

exposure assessment. More weight was given to the intra-population variable than to the interpopulation variable to allow for between study area comparisons, but focus more on within study 

area population variability.

5.4.2 Comparing the Study Areas with the DV&POP Metric

Census tract values of the DV&POP metric are presented in Figures 5-5 to 5-7 for the 

three study areas. A scale of 0 to 1 is used, varying by equivalent increments of 0.2. The highest 

values indicate census tracts where the confluence of population and ambient concentrations is

greatest, the lowest values indicate tracts having little influence from either the population and 

ambient concentrations. Therefore, of greatest interest are DV&POP values at the upper end of 

the scale.

As a general observation, it can be seen that DV&POP values are similar in the Fall River 

and Indianapolis study areas and the values for those two areas differ from Tulsa (Figures 5-5

through 5-7). For example, all of the census tracts in the Fall River and Indianapolis study areas

(Figures 5-5 and 5-6) have a DV&POP value greater than 0.4, while 89% of the tracts in Tulsa 

have DV&POP values less than 0.4, indicating that most tracts in the Fall River and Indianapolis 

study areas have relatively higher design values and/or populations than Tulsa census tracts. This 

overall observation using the metric reflects the study area-specific design value and population 

information for the three areas. For example, in Fall River, over 70% of receptors had hourly 

design values between 31 to 45 ppb (Figure 3-6) and 77% of tracts have a population greater

than 3,000 people (Figure 5-1).

11 In Tulsa, by comparison, 83% of receptor sites have hourly 

design values less than 30 ppb (Figure 3-8) and 59% of tracts have fewer than 3,000 people 

(Figure 5-3).

 

11 Similarly, in the Indianapolis study area, over 63% of receptors had moderately high hourly design values 

between 31 to 45 ppb (Figure 3-7) and 58% of tracts have a population greater than 3,000 people (Figure 5-2).
5-16

Most importantly to the exposure/risk results are tracts having the higher DV&POP 

values (e.g., greater than 0.6), as these are most likely locations within the study area where the 

highest exposures occur for the greatest number of simulated people. In Fall River, a total of 13 

tracts (comprising about 73,000 people) have DV&POP values greater than 0.6, with one of 

these encompassing the primary source and having the highest DV&POP value of 0.82 (Figure 

5-5). Similarly, in the Indianapolis study area (Figure 5-6), there are a total of 10 tracts 

(comprising about 86,000 people) with DV&POP values above 0.6, with the one that 

encompasses one of the largest sources (IPL-Harding) having the highest DV&POP value of 

0.88. In contrast, the Tulsa study area (Figure 5-7) has only one tract with a DV&POP value 

above 0.6, and its value is 0.61.

These broad spatial differences in population size and where that might overlap with 

higher DVs likely contribute to the greater number of exposures at or above the benchmark 

levels in the Fall River and Indianapolis study areas compared with the Tulsa study area. 

Additionally, both Fall River and Indianapolis had a greater spatial extent of air quality receptor 

sites with 5-minute ambient air concentrations at or above 100 ppb than the Tulsa study area 

(Tables 3-14 to 3-16).
5-17

1

2 Figure 5-5.Values of the DV&POP exposure metric in the Fall River study area.
5-18

1

2 Figure 5-6.Values of the DV&POP exposure metric in the Indianapolis study area.
5-19

1

2 Figure 5-7.Values of the DV&POP exposure metric in the Tulsa study area.
5-20

1 5.5 COMPARISON WITH 2009 REA RESULTS 

2 The results presented in this chapter and discussed above provide estimates for air quality 

3 conditions associated with just meeting the now-current 1-hour standard of 75 ppb (evaluated as 

3-year average of annual 99th 4 percentiles), an air quality scenario that was not included in the 

5 2009 REA. As summarized in section 1.2 above, the 2009 REA included single-year air quality 

scenarios for 99th 6 percentile levels of 50 ppb and 100 ppb in two study areas (St. Louis and 

7 Greene County, Missouri). For each air quality scenario, the exposure estimates for these two 

8 areas differed, and it is plausible that population and spatial heterogeneity explain those observed 

9 differences, although the type of analysis of these factors discussed in section 5.4 above was not 

10 done in the 2009 REA. Further, while the range of the exposures at or above benchmark levels 

11 estimated here is roughly consistent with the range of estimates in the 2009 REA study areas for 

12 the air quality scenarios bracketing the current standard, there are complications associated with 

13 a direct comparison of these results given the many ways in which these analyses differ from 

14 those available in the last review. In addition to the expansion in the number, type, and 

15 geographic regions of study areas assessed, there have been many improvements to input data 

16 and modeling approaches used in this assessment compared to the prior assessment, including 

17 the availability of continuous 5-minute air monitoring data at monitors within each of the three 

18 study areas. The air quality scenario in the current REA extends the time period of exposure 

19 simulations by covering a 3-year period, consistent with the statistical form established for the 

20 now-current standard. The current air quality scenario additionally focuses on the existing 

21 standard level of 75 ppb. Further, there are also differences between the current REA and the 

22 2009 REA with regard to the air quality adjustment approach, and the methods for estimating 5-

23 minute concentrations. Also, the years simulated in this assessment reflect more recent emissions 

24 and circumstances subsequent to adoption of the standard in 2010.

25 As described in section 2.2, these REA analyses are intended to be informative to EPA’s 

26 consideration of potential exposures and risks that may be associated with the air quality 

27 conditions occurring under the current SO2 standard. This is reflected in the attributes of the 

28 study areas, including the criteria used in their selection (section 3.1), the identification of 

29 specific source emissions and characteristics, local meteorological conditions, and distribution of 

30 at-risk populations. The presence in the U.S. of these areas and others having similar attributes

31 make the findings reported here important in considering the protection provided by the SO2

32 standard, as discussed in the PA.
6-1

6 VARIABILITY ANALYSIS AND UNCERTAINTY 

CHARACTERIZATION

An important issue associated with any population exposure or risk assessment is the 

characterization of variability and uncertainty. Variability refers to the inherent heterogeneity in 

a population or variable of interest (e.g., residential air exchange rates). The degree of variability 

cannot be reduced through further research, only better characterized with additional 

measurement. Uncertainty refers to the lack of knowledge regarding the values of model input 

variables (i.e., parameter uncertainty), the physical systems or relationships used (i.e., use of 

input variables to estimate exposure or risk or model uncertainty), and in specifying the scenario 

that is consistent with purpose of the assessment (i.e., scenario uncertainty). Uncertainty is, 

ideally, reduced to the maximum extent possible through improved measurement of key 

parameters and iterative model refinement.

This chapter focuses on the general characteristics of the assessment performed, 

including the data and approaches used to evaluate exposures and risk associated with air quality 

conditions that just meet the existing standard in the three study areas. The approaches used to 

assess variability and to characterize uncertainty in this REA are discussed in the following two 

sections. The primary purpose of this characterization is to provide a summary of variability and 

uncertainty evaluations conducted to date regarding our SOR2R exposure assessments and APEX 

exposure modeling and to identify the most important elements of uncertainty in need of further 

characterization. Each section contains a concise tabular summary of the identified components 

and how, for elements of uncertainty, each source may affect the estimated exposures.

6.1 TREATMENT OF VARIABILITY AND CO-VARIABILITY

The purpose for addressing variability in this REA is to ensure that the estimates of 

exposure and risk reflect the variability of ambient SOR2R concentrations, population 

characteristics, associated SOR2R exposure, and potential health risk across the study area and for 

the simulated at-risk populations. In this REA, there are numerous algorithms that account for 

variability of input data when generating the exposures or risk estimates of interest. For example, 

variability may arise from differences in the population residing within census blocks (e.g., age 

distribution) and the activities that may influence population exposure to SOR2R (e.g., time spent 

outdoors, performing moderate exertion-level activities outdoors). A complete range of potential 

exposure levels and associated risk estimates can be generated when appropriately addressing 

variability in exposure and risk assessments; note however that the range of values obtained 
6-2

would be within the constraints of the input parameters, algorithms, or modeling system used, 

not necessarily the complete range of the true exposure or risk values.

Where possible, we identified and incorporated the observed variability in input data sets 

rather than employing standard default assumptions and/or using point estimates to describe 

model inputs. The details regarding many of the variability distributions used in data inputs are 

described in Chapter 4, while details regarding the variability addressed within its algorithms and 

processes are found in the APEX User Guides (U.S. EPA, 2017a, b).

Briefly, APEX has been designed to account for variability in most of the input data, 

including the physiological variables that are important inputs to determining exertion levels and 

associated ventilation rates. APEX simulates individuals and then calculates SOR2R exposures for 

each of these simulated individuals. The simulated individuals are selected to represent a random 

sample from a defined population. The collection of individuals represents the variability of the 

target population, and accounts for several types of variability, including demographic, 

physiological, and human behavior. In this assessment, APEX simulated 100,000 individuals 

(70,000 adults and 30,000 children) to reasonably capture the variability expected in the 

population exposure distribution for each study area. APEX incorporates stochastic processes 

representing the natural variability of personal profile characteristics, activity patterns, and 

microenvironment parameters. In this way, APEX is able to represent much of the variability in 

the exposure estimates resulting from the variability of the factors effecting human exposure.

We note also that correlations and non-linear relationships between variables input to the 

model can result in the model producing incorrect results if the inherent relationships between 

these variables are not preserved. That is why APEX is also designed to account for covariability, or linear and nonlinear correlation among several of the model inputs, provided that 

enough is known about these relationships to specify them. This is accomplished by providing 

inputs that enable the correlation to be modeled explicitly within APEX. For example, there is a 

non-linear relationship between the outdoor temperature and air exchange rate in homes. One 

factor that contributes to this non-linear relationship is that windows tend to be closed more often 

when temperatures are at either low or high extremes than when temperatures are moderate. This 

relationship is explicitly modeled in APEX by specifying different probability distributions of air 

exchange rates for different ambient temperatures. In any event, APEX models variability and 

co-variability in two ways:

• UStochasticallyU. The user provides APEX with probability distributions characterizing the 

variability of many input parameters. These are treated stochastically in the model and 

the estimated exposure distributions reflect this variability. For example, the rate of SOR2R

removal in houses can depend on a number of factors which we are not able to explicitly 

model at this time, due to a lack of data. However, we can specify a distribution of 

removal rates that reflects observed variations in SOR2R decay. APEX randomly samples 
6-3

from this distribution to obtain values that are used in the mass balance model. Further, 

co-variability can be modeled stochastically through the use of conditional distributions. 

If two or more parameters are related, conditional distributions that depend on the values 

of the related parameters are input to APEX. For example, the distribution of air 

exchange rates (AERs) in a house depends on the outdoor temperature and whether or not 

air conditioning (A/C) is in use. In this case, a set of AER distributions is provided to 

APEX for different ranges of temperatures and A/C use, and the selection of the 

distribution in APEX is driven by the temperature and A/C status at that time.

• UExplicitlyU. For some variables used in modeling exposure, APEX models variability and 

co-variability explicitly and not stochastically. For example, the complete series of 5-

minute ambient air SOR2R concentrations for each hour and hourly temperatures are used in 

model calculations. These are input to the model continuously in the time period modeled 

at different spatial locations, and in this way the variability and co-variability of 5-minute 

concentrations and hourly temperatures are modeled explicitly.

Important sources of the variability and co-variability accounted for by APEX and used 

for this exposure analysis are summarized in Table 6-1 and Table 6-2 below, respectively.
6-4

Table 6-1. Summary of how variability was incorporated into the exposure and risk 

assessment. 

Component Variability Source Summary

Ambient Input

Meteorological data

Spatial: local surface and upper air NWS stations used. 

Temporal: 1-hour NWS wind data for 2011-2013, supplemented 

with 1-minute ASOS wind data (Appendix A). 

Emission source types 

and profiles

Important SOR2R emission sources include EGUs and petroleum 

refineries. Hourly emission profiles derived from CEMS data, 

where available or using EPA’s 2011v6.3 emissions modeling 

platform combined with the SMOKE modeling system (Appendix 

B).

AERMOD modeled 1-hour 

ambient SOR2R

concentrations

Spatial: ambient SOR2R predicted to 1,400 – 1,900 air quality 

receptors in three geographically representative study areas

Temporal: hourly SOR2 Rfor each of three years (2011-2013).

Ambient air monitor 5-

minute concentrations

Spatial: local ambient air monitors used. Where multiple monitors 

available, receptors used 5-minute patterns from the closest 

monitor.

Temporal: patterns of 5-minute continuous SOR2R concentrations 

within each hour used to estimate 5-minute continuous SOR2R

concentrations at modeled air quality receptors.

Simulated Individuals

Population data

Individuals are randomly sampled from U.S. census blocks used in 

each model study area, stratified by age (single years) and sex 

probability distributions (U.S. Census Bureau, 2012).

Employment Work status is randomly generated from U.S. census data at the 

tract level by age and sex (U.S. Census Bureau, 2012).

Activity pattern data

Data diaries used to represent locations visited and activities 

performed by simulated individuals are randomly selected from 

CHAD master (>55,000 diaries) using six diary pools stratified by 

two day-types (weekday, weekend) and three temperature ranges 

(< 55.0 °F, between 55.0 and 83.9 °F, and ≥84.0 °F). CHAD 

diaries capture real locations that people visit and the activities 

they perform, ranging from 1 minute to 1 hour in duration (U.S.

EPA, 2017c).

Commuting data

Employed individuals are probabilistically assigned ambient air 

concentrations originating from either their home or work block 

based on U.S. Census derived tract-level commuter data (U.S.

DOT, 2012; U.S. Census Bureau, 2012).

Longitudinal profiles

A sequence of diaries is linked together for each individual that 

preserves both the inter- and intra-personal variability in human 

activities (Glen et al., 2008).

Asthma prevalence

Asthma prevalence is stratified by sex, single age years for 

children (5-17), seven adult age groups, (18-24, 25-34, 35-44, 45-

54, 55-64, 65-74, and, ≥75), three regions (Midwest, Northeast, 

and South), and U.S. Census tract level poverty ratios (Appendix 

E).

Physiological Factors 

Relevant to Ventilation 

Rate

Resting metabolic rate Five age-group and two sex-specific regression equations, use 

body mass and age as independent variables (Appendix H).

Metabolic equivalents by 

activity (METS)

Randomly sampled from distributions developed for specific 

activities (some age-specific) (U.S. EPA, 2017c).

Oxygen uptake per unit of 

energy expended

Randomly sampled from a uniform distribution to convert energy 

expenditure to oxygen consumption (U.S. EPA, 2017a, b).
6-5

Component Variability Source Summary

Body mass

Randomly selected from population-weighted lognormal 

distributions with age- and sex-specific geometric mean (GM) and 

geometric standard deviation (GSD) derived from the National 

Health and Nutrition Examination Survey (NHANES) for the years 

2009-2014 (Appendix G).

Body surface area Sex-specific exponential equations using body mass as an 

independent variable (Burmaster, 1998).

Height

Randomly sampled from population-weighted normal distributions 

stratified by single age years and two sexes developed from 2009-

2014 NHANES data (Appendix G).

Ventilation rate

Event-level activity-specific regression equation using oxygen 

consumption rate (VOR2R) and maximum VOR2R as independent 

variables, and accounting for intra and interpersonal variability 

(Appendix H).

Fatigue and EPOC 

APEX approximates the onset of fatigue, controlling for unrealistic 

or excessive exercise events in an individual’s activity time-series 

while also estimating excess post-exercise oxygen consumption 

(EPOC) that may occur following vigorous exertion activities using 

several equations and input variable distributions (Isaacs et al., 

2007; U.S. EPA, 2017a, b).

Microenvironmental 

Approach

Microenvironments: 

General

Five total microenvironments are represented, including those 

expected to be associated with high exposure concentrations (i.e., 

outdoors and outdoor near-road). Where this type of variability is 

incorporated within particular microenvironmental algorithm inputs, 

this results in differential exposure estimates for each individual 

(and event) as persons spend varying time frequency within each 

microenvironment and ambient air concentrations vary spatially 

within and between study areas.

Microenvironments: 

Spatial Variability

Ambient air concentrations used in microenvironmental algorithms 

vary spatially within and among study areas.

Microenvironments: 

Temporal Variability

All exposure calculations are performed at the event-level when 

using either factors or mass balance approach (durations can be 

as short as one minute). For the indoor microenvironments, using 

a mass balance model accounts for SOR2R concentrations occurring 

during a previous hour (and of ambient origin) to calculate a 

current event’s indoor SOR2R concentrations.

Air exchange rates

Several lognormal distributions are sampled based on five daily 

mean temperature ranges, study area region (Chapter 4) and 

study-area specific A/C prevalence rates from AHS survey data 

(U.S. Census Bureau, 2013).

Removal rates Values randomly selected for microenvironment-specific 

distributions, stratified by air conditioning usage (Chapter 4).

Penetration factors Indoor/outdoor ratios randomly sampled from a uniform distribution 

(Chapter 4).

Exposure Response 

Function

Regression estimates

A central tendency, along with upper and lower confidence 

intervals were derived using a probit function to generate a range 

of risk estimates.

Exposure bins Fine-scale bins (10-50 ppb) stratifying the population exposures 

were linked to the continuous E-R function.
6-6

Table 6-2. Important components of co-variability in exposure modeling.

Type of Co-variability Modeled 

by APEX? Treatment in APEX / Comments

Within-person correlations P

a Yes Sequence of activities performed, microenvironments 

visited, and general physiological parameters (body 

mass, height, ventilation rates).

Between-person correlations No Perhaps not important, assuming the same likelihood of 

the population of individuals either avoiding or 

experiencing an exposure event based on a social 

(group) activity.

Correlations between profile variables and 

microenvironment parameters

Yes Profiles are assigned microenvironment parameters.

Correlations between demographic 

variables and activities

Yes Census block demographic variables, appropriately 

weighted and stratified by age and sex, are used in 

activity diary selection.

Correlations between activities and

microenvironment parameters

No Perhaps important, but do not have data. For example, 

frequency of opening windows when cooking or smoking 

tobacco products.

Correlations among microenvironment 

parameters in the same microenvironment

Yes Modeled with joint conditional variables.

Correlations between demographic 

variables and air quality

Yes Modeled with the spatially varying census block 

demographic variables (age and sex) and fine-scale (100 

m to 2 km) air quality input to APEX.

Correlations between meteorological 

variables and activities

Yes Temperature is used in activity diary selection.

Correlations between meteorological 

variables and microenvironment parameters

Yes The distributions of microenvironment parameters can be 

functions of temperature.

Correlations between drive times in CHAD 

and commute distances traveled

Yes CHAD diary selection is weighted by commute times for 

employed persons during weekdays.

Consistency of occupation/school 

microenvironmental time and time spent 

commuting/busing for individuals from one 

working/school day to the next.

No Simulated individuals are assigned activity diaries 

longitudinally without regard to occupation or school 

schedule (note though, longitudinal variable used to 

develop annual profile is time spent outdoors).

P

a

P The term correlation is used to represent linear and nonlinear relationships.

6.2 CHARACTERIZATION OF UNCERTAINTY

While it may be possible to capture a range of exposure or risk estimates by accounting 

for variability inherent to influential factors, the true exposure or risk for any given individual 

within a study area is unknown. To characterize health risks, exposure and risk assessors 

commonly use an iterative process of gathering data, developing models, and estimating 

exposures and risks, given the goals of the assessment, scale of the assessment performed, and 

limitations of the input data available. However, uncertainty remains and emphasis is then placed 
6-7

on characterizing the nature and potential magnitude of that uncertainty and its impact on 

exposure and risk estimates. A summary of the overall characterization is provided in section 

6.2.1. The summary is followed by exposure model sensitivity analyses in section 6.2.2 that 

provide additional support to the characterization of four elements of uncertainty: (1) the 

proportional approach applied to the primary emission source to adjust ambient air 

concentrations to just meet the current standard, (2) estimating continuous 5-minute 

concentrations at ambient air monitors, (3) estimating 5-minute concentrations at modeled air 

quality receptors, and (4) estimating exposure and risk estimated using upper and lower bounds 

of the E-R function.

6.2.1 Characterizing Sources of Uncertainty 

The REAs for the previous OR3R, NOR2R, SOR2R, and CO NAAQS reviews each presented a 

characterization of uncertainty of exposure modeling (Langstaff, 2007; U.S. EPA, 2008, 2009a, 

2010, 2014). The qualitative approach used in this and other REAs, also informed by quantitative 

sensitivity analyses, is described by WHO (2008). Briefly, we identified the key aspects of the 

assessment approach that may contribute to uncertainty in the exposure and risk estimates and 

provided the rationale for their inclusion. Then, we characterized the magnitude and direction of 

the influence on the assessment results for each of these identified sources of uncertainty.

Consistent with the WHO (2008) guidance, we scaled the overall impact of the 

uncertainty by considering the degree of uncertainty as implied by the relationship between the 

source of uncertainty and the exposure concentrations. A qualitative characterization of low, 

moderate, and high was assigned to the magnitude of influence and knowledge base uncertainty 

descriptors, using quantitative observations relating to understanding the uncertainty, where 

possible. Where the magnitude of uncertainty was rated low, it was judged that large changes 

within the source of uncertainty would have only a small effect on the assessment results. A 

designation of moderate implies that a change within the source of uncertainty would likely have 

a moderate (or proportional) effect on the results. A characterization of high implies that a small 

change in the source would have a large effect on results. We also included the direction of 

influence, indicating how the source of uncertainty was judged to potentially affect the 

exposure/risk estimates; this included whether the estimates were likely over-estimated (“over”)

or under-estimated (“under”) or the direction was unknown. A summary of the key findings of 

those prior uncertainty characterizations that are most relevant to the current SOR2R exposure 

assessment are also provided in Table 6-3 (i.e., Langstaff, 2007; U.S. EPA, 2008, 2009a, 2010, 

2014).
6-8

Table 6-3. Characterization of Key Uncertainties in Exposure and Risk Assessments using APEX.

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

Aspects of 

Assessment Design

Representation of 

SOR2R emission 

source types

having substantial 

emissions

Unknown Low -

Moderate Moderate

The three study areas include the most prevalent source type (i.e., EGUs) 

emitting at least 15,000 tons of SOR2R per year (95% of all U.S. facilities emitting 

SOR2R in 2011; U.S. EPA, 2015). There are only three other source types having 

emissions at least as large: copper/lead smelters (2 facilities), pulp and paper 

mills (2 facilities), and chemical plants (1 facility) (U.S. EPA, 2015). The limited 

occurrence of these large non-EGU facilities and their occurrence in locations 

with small populations and/or for which ambient air monitoring data for SOR2R are 

not available hampered their selection as study areas evaluated in this REA. To 

the extent that the temporal patterns of emissions and/or emissions 

characteristics for these source types differ in a way that would lead to greater 

variability in ambient SOR2R concentrations than that associated with EGU 

emissions, it is possible the risk/exposure estimates associated with these 

particular sources (if having substantial emissions) could vary from those 

estimated in this REA. However, risk and exposure estimates for areas with 

such sources would likely have limited applicability nationally due to limited 

prevalence of such areas across the U.S.

No

Representation of 

population 

subgroups with 

asthma

Unknown Low -

Moderate Moderate

Consistent with the ISA identification of people with asthma (and children with 

asthma in particular) as an important at-risk population for SOR2R in ambient air, 

risk estimates are developed for people with asthma and are reported 

separately for children and adults. Exposure and risk were not estimated for 

more targeted population groups with asthma based on additional personal 

attributes associated with increased asthma prevalence (e.g., obesity or African 

American or Hispanic ethnicity) generally due to limitations in the data needed 

to simulate such subgroups. Such data limitations affect our ability to 

characterize SOR2R exposure and associated health risks for different population 

subgroups of children and adults with asthma, some of which may have higher 

exposure/risk and others lower. 

No
6-9

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

AERMOD

Inputs and 

Algorithms

Algorithms 

(section 3.2) Unknown Low Low

Multiple historical model evaluations consistently demonstrate unbiased 

ambient air concentrations under variety of conditions. Some potential 

dispersion scenarios may not be adequately represented and in some 

instances, concentration variability could be under-represented by model 

algorithms, however, it is largely unknown as to how this uncertainty might 

apply in this application.

No

Meteorological 

Data

(section 3.2.1.1 

and Appendix A)

Unknown Low –

Moderate Low

A limited number of missing hours of wind data remain in dataset, potentially 

leading to under-estimation. Model predictions have low to medium sensitivity 

to surface roughness characteristics, as long as they are appropriate for the site 

of the meteorological data inputs. Data are from a well-known and qualityassured source. One minute ASOS wind data used to supplement 1-hour data 

for improved completeness, reducing the number of calms and missing data.

Two meteorological stations (one upper and one surface) are used to represent 

meteorological conditions in each study area, some of which are located a few 

to several km from ambient air monitor sites and the modeled air quality 

receptors. There is uncertainty in the extent to which conditions measured at 

these stations represent study area meteorological conditions, particularly wind 

speed and direction, and how this could affect the estimation of hourly and 5-

minute concentration variability.

No

Stationary Source 

Emissions and 

Profiles (section 

3.2.2 and 

Appendix B)

Both Low Low

Temporal emission characteristics are well represented for most modeled point 

sources. Most temporal data are from a well-known quality-assured source of 

direct measurements. 

No

Ambient Air Monitor 

Concentrations Database Quality Both Low Low

All ambient pollutant measurements available from AQS are comprehensive 

and subject to quality control. Completeness criteria applied to hourly 

concentrations ensure air quality representativeness.

No
6-10

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

Missing Data 

Substitution 

(section 3.5.1)

Under Low Low

Missing ambient air concentration values (hourly, 5-minute maximum, 5-minute 

continuous) were interpolated using a statistical technique. Use of this type of 

approach is appropriate for data sets having a limited missing number of total 

values (<5-10%), though will constrain substituted values within the bounds of 

the measured concentrations. In addition, there are a few monitors missing 

concentrations for several hours/minutes per day and for several days in the 

year, most notably in the Indianapolis study area (Table 3-9), potentially 

missing a few high concentration events (if actually occurred) that would not be 

estimated using the interpolation technique. However, completeness of the 

maximum 5-minute concentrations was reasonable (<10%) for all monitors

used in estimating 5-minute concentrations, thus the missing within hour 5-

minute concentrations are of lesser importance and likely contribute less 

uncertainty. 

No

Estimation of 

Continuous 5-

minute 

Concentrations

(section 3.5.2) 

Under Low Low

For one year in Fall River (2013), only the 5-minute maximum measurements 

within each hour were reported. A series of lognormal distributions were used to 

estimate the 5-minute continuous patterns occurring with each hour for these 

monitors (Section 3.5.2). Excellent agreement was observed comparing the 

estimated versus the measured values for each of the hourly and 5-minute 

maximum concentrations. Agreement between the estimated and measured 5-

minute continuous concentrations was also excellent, though exhibiting some 

deviations (Figure 3-6). In addition, the estimated 5-minute continuous 

concentrations had less overall variability compared to the measurement data 

(Table 3-10). However, there was negligible difference in exposures when 

comparing an APEX simulation that used measured continuous 5-minute 

concentrations versus one that used estimated values.

Yes, section 

6.2.2.1

Temporal 

Representation 

(section 3.5.2 and 

3.5.3)

Both Low Low

Temporal scale (5-minutes) is appropriate for analysis performed. Monitored

hourly and 5-minute maximum data are screened for temporal completeness 

and considered appropriate. While 5-minute continuous data were not screened 

for completeness, the number of missing values were limited in most study 

areas and for most years (Table 3-9).

No
6-11

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

Spatial 

Representation 

(section 3.5.3.1)

Both Moderate Moderate

There were few ambient air monitors available to approximate 5-minute 

patterns across study area: Fall River, one monitor available and used to 

estimate 5-minute concentrations; Indianapolis, three monitors available (two 

were used); Tulsa, four monitors available (three were used). Where more than 

one monitor was available, the air quality receptors used 5-minute 

concentration patterns from closest monitor.

No

Air Quality 

Receptor 

Concentrations

Concentration 

Used to Represent 

Sources Not 

Modeled 

(section 3.2.4)

Both Moderate Low -

Moderate

There is uncertainty in the estimates of hourly concentration associated with 

SOR2R emission sources not explicitly modeled in the three study areas. While 

temporal variability in these estimates is accounted for by calculating diurnal 

and seasonal values, year to year variability is not considered, thus not 

accurately accounting for instances where the contribution may vary by year. 

The value used for each hour/season is the 3-year average of the 99P

th

P

percentile concentration (section 3.2.4), an approach that at most times would 

generally tend to overestimate these concentrations. Further, monitor hours that 

may have concentrations influenced by modeled sources were identified for 

exclusion using wind direction data from nearby airports. This provided a 

consistent approach across study areas as local wind direction data were not 

reported at all monitors. However, uncertainty is contributed in circumstances 

where the airport wind direction does not reflect conditions occurring at a 

monitor. The magnitude of such uncertainty may be sizeable at some monitors. 

No
6-12

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

AERMOD 

Predicted Hourly 

Concentrations

Both Low -

Moderate Moderate

Overall, comparisons of model predicted hourly values and ambient air 

measurements in each study area indicate good agreement when considering 

concentration magnitude alone. The first of the three years tended to exhibit the 

highest concentrations in addition to having the best agreement across all three 

study areas. In the Fall River study area, AERMOD over-predicted low to mid 

percentile concentrations and under-predicted upper percentile concentrations. 

In the Indianapolis study area, AERMOD over-predicted all percentiles of the 

concentration distribution at the monitor closest to the primary emissions 

source, while under-predicting mid to high percentile concentrations at the 

monitors more distant from the primary emissions source. In the Tulsa study 

area, AERMOD under-predicted mid to high percentile concentrations at the 

monitor closest to the primary emissions source, while over-predicting most 

percentile concentrations at monitors more distant from the primary emissions 

source (Appendix D). Such differences are of lesser importance to the 

assessment estimates given the focus on air quality after adjustment to just 

meet the existing standard.

No

Hourly Ambient Air 

Concentration 

Estimates during 

Times of 

Relatively Greater 

Exposure Potential

Both Low -

Moderate

Low -

Moderate

As separately concluded for the generalized performance evaluation 

summarized in Appendix D, these comparisons that consider both spatial and 

temporal variability (i.e., where and when peak concentrations occur) also 

indicate reasonable agreement between the model estimates and 

measurements at the nearby monitor site(s). Similarity in the paired 

concentrations across much of the respective distributions for times when 

exposure potential may be greatest provide additional positive support for 

concluding the modeled air quality surfaces are likely useful for estimating 

exposures in this REA (section 3.2.5 and Appendix K). However, having limited 

monitoring data available in each study area and the inability to directly 

evaluate the concentrations for the air quality scenario that is the focus of this 

REA limits the extent by which conclusions can be made regarding model 

performance in estimating spatial variability in hourly concentrations for that 

scenario.

No
6-13

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

Adjustment of 

Hourly 

Concentrations to 

Just Meet the 

Existing Standard: 

Proportional 

Approach for 

Primary Source 

(section 3.4)

Under Low Moderate

Performance of this approach in this REA depends in part on the degree of 

proportionality in the air quality distribution and the magnitude of the ambient air 

concentration adjustment. A proportional approach was judged adequate for 

such a use (section 3.4 above; REA Planning Document; Rizzo, 2008). The 

approach used in this REA is a modification of 2009 REA adjustment approach 

in that the proportional adjustment was applied only to the concentration 

contribution from the primary emission source in each study area, holding 

concentrations contributed from all other sources as is. The sharpness of the 

concentration gradient from the primary emission source relative to the other 

emission sources could be an important factor in determining the impact to the 

adjusted air quality surface. However, sensitivity analyses that modified the air 

quality receptor having the maximum design value (section 6.2.2.2) indicate 

there was negligible (in two areas) or somewhat limited (in the third area) 

impact to the estimated exposures by varying the magnitude of the adjustment 

and the number of receptors to which the adjustment was applied.

Yes, section 

6.2.2.2

Approach Used to 

Estimate 5-minute 

Concentrations:

Linking 5-minute 

Monitor to Hourly 

Receptor 

Concentrations 

(section 3.5.3)

Both Low -

Moderate Moderate

Hourly concentrations modeled at the air quality receptors were linked to the 5-

minute monitor concentrations using the rank order of the hourly 

concentrations. Two alternative approaches were developed and evaluated. 

The first, a calendar based approach, linked the modeled receptor 

concentrations to the monitor by date and hour of day. The second used hourly 

concentration bins (i.e., 5 ppb increments). There were differences when 

comparing the upper percentiles of the 5-minute concentration distributions, 

particularly when comparing the calendar based approach to the rank order and 

binning approaches. There were also notable differences to the percent of the 

at-risk population exposed at or above benchmarks when comparing results 

from the three adjustment approaches. However, little difference was observed 

when comparing risk of lung function decrements estimated using each of these 

three approaches.

Yes, section 

6.2.2.3
6-14

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

Estimated Peak 5-

minute 

Concentrations 

during Times of 

Relatively Greater 

Exposure Potential

Both Low-Moderate Moderate

As concluded in section 3.5.3.3, there is reasonable agreement between the 5-

minute concentrations estimated at air quality receptors and measurement 

concentrations at the local ambient air monitor site(s), considering 

concentrations of interest and the number of days at or above these 

concentrations. Having limited monitoring data available in each study area 

however limits our ability to assess the reasonableness of the degree to which 

these concentrations may be present spatially. It appears that the spatial extent 

of receptors having the highest 5-minute concentrations could be overestimated in the Indianapolis study area and less so (to possibly not at all) in 

the Fall River and Tulsa study areas.

No

APEX: General 

Input Databases

Population 

Demographics and 

Commuting

(sections 4.1.1 

and 4.3.2)

Both Low Low

Comprehensive and subject to quality control. Differences in 2010 population 

data versus modeled years (2011-2013) are likely small when estimating 

percent of population exposed. 

No

Activity Patterns 

(CHAD)

(sections 4.3.1 

and 4.3.3)

Both Low -

Moderate

Low -

Moderate

Comprehensive and subject to quality control. Increased number of diaries 

used to estimate exposure from 2009 SOR2R REA. Thoroughly evaluated trends 

and patterns in historical activity pattern data – no major issues noted with use 

of historical data to represent current patterns (Figures 5G-1 and 5G-2 of U.S. 

EPA, 2014). Compared outdoor event participation and outdoor time of CHAD 

diary data with larger American Time Use Survey (ATUS) data – CHAD 

participation is higher than ATUS, likely due to ATUS survey methods. 

Comparison of activity data (outdoor events and exertion level) for people with 

asthma generally similar to individuals without asthma (Table 4-5) (see also 

Tables 5G2-to 5G-5 of U.S. EPA, 2014). There is little indication of differences 

in time spent outdoors comparing activity patterns across U.S. regions, though 

sample size may be a limiting factor in drawing significant conclusions (U.S. 

EPA, 2014). Remaining uncertainty exists for other influential factors that 

cannot be accounted for (e.g., SES, region/local participation in outdoor events 

and associated amount of time).

No
6-15

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

Meteorological 

(NWS)

(section 4.2)

Both Low Low

Comprehensive and subject to quality control, having very few missing values. 

Limited use in selecting CHAD diaries for simulated individuals and AERs that 

may vary with temperature. However, while using three years of varying 

meteorological conditions, the 2011-2013 MET data set may not reflect the full 

suite of conditions that could exist in future hypothetical air quality scenarios or 

across periods greater than 3-years.

No

Asthma 

Prevalence 

Weighted by 

Poverty Status 

(section 4.1.2 and 

Appendix E)

Both Low Low -

Moderate

Data used are from peer-reviewed quality controlled sources. Use of these data 

accounts for variability in important influential variables (poverty status, as well 

as age, sex, and region). It is possible that variability in microscale prevalence 

is not entirely represented when considering other potentially influential 

variables such as race and obesity, two attributes that can influence asthma 

prevalence and can vary spatially (section 4.1.2). Family income level was used 

in this REA to represent spatial variability in asthma prevalence and may, in 

some instances, capture spatial variability in race and obesity (Ogden et al., 

2010), and thus to some extent, reasonably represent the potential influence 

race and obesity have on asthma prevalence. However, instances where these 

influential variables are not fully represented in simulating the at-risk population, 

and where populations identified by such variables are associated with 

increased asthma prevalence that may spatially intersect with the highest 

ambient concentrations, could lead to uncertainty in estimated exposures and 

health risk. Further characterization could be appropriate by comparing with 

local prevalence rates stratified by a similar collection of influential variables, 

where such data exist.

No

APEX: 

Microenvironmental 

Concentrations

Vehicle PE 

Factors 

(Section 4.4.5)

Both Low Moderate

Input distribution is from an older measurement study and for a different 

pollutant (section 4.4.5). Considering that the exposures of interest need to be 

concomitant with elevated exertion, the accurate estimation of 5-minute 

exposures occurring inside vehicles is considered relatively unimportant.

No
6-16

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

Indoor: Air 

Exchange Rates

(sections 4.4.1 

and 4.4.3)

Both Low Moderate

Uncertainty due to random sampling variation via bootstrap distribution analysis 

indicated the AER geometric mean (GM) and standard deviation (GSD) 

uncertainty for a given study area tends to range from ±1.0 GM and ± 0.5 GSD 

hrP

-1

P (Langstaff, 2007). Each of the three study areas (Fall River, Indianapolis, 

and Tulsa) used AER from a geographically similar city (New York, Detroit/New 

York, and Houston, respectively). Non-representativeness remains an important 

issue as city-to-city variability can be wide ranging (GM/GSD pairs can vary by 

factors of 2-3) and data available for city-specific evaluation are limited 

(Langstaff, 2007). There is uncertainty associated with the use of an AER 

derived from a different city than the REA study areas. That said, indoor 

microenvironments are considered less likely to contribute to an individual’s 

daily maximum 5-minute SOR2R exposure while at elevated exertion levels and 

likely does not contribute substantially to uncertainty in the exposure and risk 

estimates.

No

Indoor: A/C 

Prevalence

(section 4.4.2)

Both Low Low

Data were obtained from a reliable source, are comprehensive, and subject to 

quality control (US Census Bureau, 2013). For two of the three study areas 

(Fall River and Indianapolis), data from a geographically related city were used 

(Boston and Louisville, respectively). There is uncertainty associated with the 

use of an AC prevalence derived from a different city than the REA study areas. 

That said, indoor microenvironments are considered less likely to contribute to 

an individual’s daily maximum 5-minute SOR2R exposure while at elevated 

exertion levels and likely does not contribute substantially to uncertainty in the 

exposure and risk estimates.

No

Indoor: Removal 

Rate

(section 4.4)

Unknown Low Moderate

In the 2009 REA it was found that indoor exposures may be underestimated 

when not using all 5-minute concentrations within the hour, an issue resolved in 

this current REA by using estimates of all 5-minute values. Data used to 

develop removal rates were obtained from a comprehensive review, though 

many assumptions were needed in developing the distributions. However, most 

peak exposures concomitant with elevated exertion are expected to occur 

outdoors, thus accurate estimation of indoor concentrations is of reduced 

importance.

No
6-17

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

APEX: Simulated 

Activity Profiles 

Longitudinal 

Profiles

(section 4.3.4)

Under Low -

Moderate Moderate

The magnitude of potential influence for this uncertainty would be mostly 

directed toward estimates of multiday exposures. Simulations indicate the 

number of single day and multiday exposures of interest can vary based on the 

longitudinal approach selected (Che et al., 2014). As discussed in chapter 4, 

the D&A method provides a reasonable balance of this exposure feature. Note 

however, long-term diary profiles (i.e., monthly, annual) do not exist for a 

population, thus limiting the evaluation. Further, the general population-based 

modeling approach used for main body REA results does not assign rigid 

schedules, for example explicitly representing a 5-day work week for employed 

people.

No

Commuting

(section 4.3.2) Both Low Moderate

Method used in this assessment is designed to link Census commute distances 

with CHAD vehicle drive times. Considered an improvement over the prior 

approach that did not match commute distance and activity time. While vehicle 

time is accounted for through diary selection, it is not rigidly scheduled. 

However, accurate estimation of exposures occurring while inside vehicles is 

considered unimportant because it is unlikely to occur at elevated exertion.

No

Activity Patterns 

for At-Risk 

Population 

(section 4.3.3)

Both Low Low -

Moderate

Analyses of activity patterns of people with asthma are similar to that of 

individuals not having asthma (section 4.3.3; see also Tables 5G-2 to 5G-5 of 

U.S. EPA, 2014).

No

APEX: 

Physiological 

Processes

Body Weight 

(NHANES)

(section 4.1.3.1 

and Appendix G)

Unknown Low Low

Comprehensive and subject to quality control, appropriate years (2009-2014) 

selected for simulated population, though possible small regional variation is 

not represented by national data. 

No
6-18

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

RMR

(section 4.1.3.2, 

Appendix H)

Unknown Low Low

New, improved algorithm used for this assessment. Comprehensive literature 

review resulted in construction of large data base used to derive algorithm. 

Algorithm considers variables most influential to RMR (i.e., age, body weight, 

and sex). There are other factors that could affect intra-personal variability in 

RMR such as time-of-day (Haugen et al., 2003) or seasonal/temperature 

influences (van Ooijen et al., 2004; Leonard et al., 2014). Variability from these 

and other potentially influential factors may be indirectly accounted for by the 

residual error term used in the RMR Equation 4-2 depending on the extent to 

which these influential factors varied across the clinical study data that were 

used to create the RMR analytical data set. However, because there is 

inadequate information regarding the presence of multiple RMR measurements 

for individual study subjects, we could not estimate intra-personal variability nor 

could we use these influential factors, other than age and sex, as explanatory 

variables in the RMR equation. Therefore, any influences on spatial variability in 

RMR, both within and among the three study areas, would largely be driven by 

the spatial distribution of age and sex.

No

METS 

Distributions

(section 4.1.3.2)

Over Low -

Moderate Moderate

APEX estimated daily mean METs range from about 0.1 to 0.2 units (between 

about 5-10%) higher than independent literature reported values (Table 15 of 

Langstaff, 2007). However, shorter-term values are of greater importance in this 

assessment, thus METs could be better characterized where short-term METS 

data are available.

No

Ventilation Rates

(section 4.1.3.3 

and Appendix H)

Unknown Low Low -

Moderate

Predictions made using the prior algorithm showed excellent agreement with 

independent measurement data, particularly when considering simulated study 

group (Graham and McCurdy, 2005; Figure 5-23 and Figure 5-24 of U.S. EPA, 

2014). New algorithm derived using the same data observed to have improved 

predictability (Appendix H). However, a shorter-term comparison (5-minutes or 

a single hour rather than daily) of predicted versus measured ventilation rates, 

while more informative, cannot be performed due to lack of ventilation rate data 

at this duration and considering influential factors (e.g., age, particular activity 

performed).

No
6-19

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

EVR 

Characterization of 

Moderate or 

Greater Exertion

(section 4.1.3.3)

Both Moderate Moderate

Given that the EVR serves as a cut point for selecting individuals performing moderate 

or greater exertion activities and is an approximated mean value applied to the 

population as a whole, the simulated number of people achieving this level of exercise 

(along with having a response) could be either under or overestimated. This is because 

EVR is calculated as a function of body surface area as a means to extrapolate the 

ventilation rates achieved by adults in the controlled human exposure studies to that of 

our simulated children. Fundamentally, the EVR assumes that differences between 

adults and children with regard to the target ventilation rate denoting ventilation for 

moderate or greater exertion can be described by differences in body surface area 

(and hence body weight). On average, body surface area for adults is approximately 

1.95 mP

2

P and for children is approximately 1.33 mP

2

P (U.S. EPA, 2011), indicating that on 

average, the ventilation rate required to meet an EVR of 22 (i.e., moderate or greater 

exertion) is about 43 L/min and 29 L/min for adults and children, respectively. 

Recommended ventilation rates representing moderate intensity exercise (3<METS<6) 

in children is about 88% that of adults (U.S. EPA, 2011) and is close to that estimated 

using the above VE or body surface area differences (68%), Based on this, simulated 

children use a lower ventilation threshold to reach moderate or greater exertion relative 

to that of adults, possibly leading to overestimation of health risks, holding all other 

potential influential factors constant. Additionally, it is possible that there is a 

distribution of EVRs within a particular age (or age group) that vary based on body 

weight or perhaps even fitness level (e.g., individuals having the same body weight but 

different fat free mass). For example, among people with asthma, an appreciable 

portion are obese. Obesity is an important personal attribute that can affect activity 

patterns and EVR, both influential variables in estimating SOR2R risk. Based on the role 

of body weight in energy expenditure alone (e.g., without consideration of the role interindividual variation in fitness level has on VE for different activities), simulated obese 

people (BMI≥30) would need to have higher VE than non-obese people performing the

same activity to achieve an EVR of 22 given their relatively greater body surface area 

(on average, approximately 30-60% greater). APEX estimates ventilation rates for 

obese individuals that are approximately 10-30% greater than non-obese individuals 

largely the result of obese individuals having, on average, a 10-40% greater resting 

metabolic rate than non-obese individuals. Accordingly, SOR2R-related health risk 

estimates derived for simulated obese people with asthma could be underestimated,

holding all other potential influential factors constant.

No
6-20

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

Lung Function Risk 

Estimation

(section 4.6.2)

Risk Estimation for 

Exposures Below 

100-200 ppb

Over Low -

Moderate

Low -

Moderate

While there is very strong support for SOR2R being causally linked to lung function 

responses within the range of tested exposure levels (i.e., ≥ 200 ppb), data are limited 

or lacking for lower concentrations. Data available at 100 ppb are limited to studies in 

which SOR2R was administered by mouthpiece, some of which also do not include a 

control exposure to clean air while exercising (Sheppard et al. 1981; Sheppard et al., 

1984; Koenig et al., 1989; Koenig et al., 1990; Trenga et al., 2001). These studies 

indicate smaller responses (in adults and adolescents) than is observed in the 200 ppb 

chamber exposures. No data are available at lower exposure levels below 100 ppb. 

Since this assessment assumes there is a causal relationship at levels below 100 ppb, 

the influence of this source of uncertainty would be to over-estimate risk. 

No

Probit Model Used 

to Estimate E-R 

Function

Unknown Low Low

It was necessary to estimate responses at SOR2R levels both within the range of 

exposure levels tested (i.e., 200 to 1,000 ppb) as well as below the lowest exposure 

levels used in free-breathing controlled human exposure studies (i.e., below 200 ppb). 

We have developed probabilistic exposure-response relationships using a probit form, 

considered appropriate for this assessment. However, the regression model assumes 

a positive response occurring at any exposure concentration, of particular relevance to 

the lowest exposures. 

No

Use of E-R data 

from Studies of 

Individuals having 

Mild/Moderate 

Asthma to 

Represent Any 

Asthma Severity

Unknown Unknown Moderate

The data set that was used to estimate exposure-response relationships included 

people with mild and/or moderate asthma. There is uncertainty with regard to how well 

the population of people with mild and moderate asthma included in the series of SOR2R

controlled human exposure studies represent responses that might be expected across 

the entire distribution of people with asthma in the U.S. population. As indicated in the 

ISA (section 5.2.1.2), the subjects studied do not generally include people with asthma 

that would be classified as severe by today’s classification standards. The available 

studies “suggest that adults with moderate/severe asthma may have more limited 

reserve to deal with an insult compared with individuals with mild asthma” (ISA, p. 5-

22).

No
6-21

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

Reproducibility of 

SOR2R-induced Lung 

Function 

Response

Unknown Unknown Low

The risk assessment assumes that the SOR2R-induced responses for individuals are 

reproducible. We note that this assumption has some support in that one study (Linn et 

al., 1987) exposed the same subjects on two occasions to 0.6 ppm and the authors 

reported a high degree of correlation (r > 0.7 for people with mild asthma and r > 0.8 

for people with moderate asthma, p < 0.001), while observing much lower and 

nonsignificant correlations (r = 0.0 – 0.4) for the lung function response observed in the 

clean air with exercise exposures. 

No

Use of E-R

Derived from 

Adults for Children 

Unknown Unknown Low -

Moderate

Because the vast majority of controlled human exposure studies investigating lung 

function responses were conducted with adult subjects, the risk assessment relies on 

data from adult subjects with asthma to estimate exposure-response relationships that 

have been applied to all individuals with asthma, including children aged 5-18. The 

available evidence includes some studies of adolescents (aged 12-18) with asthma 

that indicate generally similar effects as observed for adults, although precise 

comparisons are not feasible with the available data (ISA, pp. 5-22 to 5-23). The 

studies involving adolescents administered SOR2R via inhalation through a mouthpiece 

rather than an exposure chamber. This technique bypasses nasal absorption of SOR2R

and can result in an increase in lung SOR2R uptake. Given this is a limited dataset and 

the lack of any such studies for children younger than 12, , the uncertainty in the risk 

estimates for children with asthma is greater than those for adults.

No

SOR2R Exposure 

History Both Low Moderate

The risk assessment assumes that the SOR2R-induced response on any given day is 

independent of previous SOR2R exposures and only the highest daily 5-minute exposure 

(under moderate or greater exertion) is assessed. The limited evidence related to this 

source indicates effects from a subsequent-day exposure to not be statistically 

significantly different from the first day. Further, responses to repeated exposures 

within an hour have been found to be diminished responses from initial ones, although 

data are limited or lacking regarding exposures repeated after multiple hours but within 

the same 24-hour period (ISA, section 5.2.1.2). 

No
6-22

Sources of Uncertainty

Uncertainty Characterization

Sensitivity 

Analysis 

Performed?

Influence of Uncertainty on 

Exposure | Risk Estimates

Knowledgebase 

Uncertainty

Comments

Category Element Direction Magnitude

Assumed No 

Interaction of other 

Co-pollutants on 

SOR2R-related Lung 

Function 

Responses

Under Low Moderate

There are a few studies regarding the potential for an increased response to SOR2R when 

exposure is in the presence of other common pollutants such as PM (potentially 

including particulate sulfur compounds), nitrogen dioxide and ozone, although the 

studies are limited (e.g., with regard to relevance to ambient exposure concentrations) 

and/or provide inconsistent results (ISA, p. 5-25; 2008 ISA, section 3.1.4.7; ISA, pp. 5-

143 to 5-144). For example, “studies of mixtures of particles and sulfur oxides indicate 

some enhanced effects on lung function parameters, airway responsiveness, and host 

defense,” however, “some of these studies lack appropriate controls and others involve 

[sulfur-containing species] that may not be representative of ambient exposures” (ISA, 

p.5-144).

No
6-23

6.2.2 Exposure Model Sensitivity Analyses

6.2.2.1 Continuous 5-minute Concentrations – Estimated versus Measured

Analyses evaluating the approach used to estimate the twelve 5-minute concentrations for 

each hourly concentration in the assessment is summarized in section 3.5.2. These analyses 

utilized datasets at monitors for which continuous 5-minute data are available; the analyses 

indicate reasonable agreement between the estimated and measured concentrations. By design, 

the estimated hourly and within-hour 5-minute maximum concentrations were identical to the 

measured hourly and 5-minute maximum concentrations, though sampling from lognormal 

distributions led to instances where the within-hour pattern of the eleven other estimated withinhour 5-minute concentrations varied from that measured (Figure 3-6). 

We evaluated the impact this difference may have on exposures in the Fall River study 

area, the only study area that used this method to estimate continuous 5-minute concentrations 

for the single year that continuous 5-minute measurements were not available (2013). Two 

identical APEX simulations were performed in the Fall River study area that differed only by the 

ambient air concentrations used for input to the model. Both simulations used a single air quality 

district, the center of which was the location of monitor 250051004, and employed a 10 km 

radius of influence to select the census blocks comprising the exposure modeling domain. One 

simulation used the continuous 5-minute concentrations measured in 2011 at the ambient air 

monitor and the other using the pattern of 5-minute continuous concentrations estimated for that 

same year and location (and initiated by the monitor’s measured hourly and daily maximum 5-

minute concentrations). All other model settings were the same as that used for the APEX 

simulations performed for the main REA, though only children with asthma were simulated.

We first evaluated statistics of interest beyond those presented in Table 3-10. Of interest 

were the upper percentile concentrations and number of times the 5-minute ambient air 

concentrations were at or above the benchmark concentrations. Table 6-4 provides the results of 

this analysis. Consistent with results provided in chapter 3, there are differences between 

estimated and measured values at the upper percentile concentrations shown here (i.e., 99P

th

P

percentile of the distribution and the number of values at or above 100 ppb), with the estimated 

percentile concentrations slightly lower than the percentile concentrations for the measured 

values. However, in the APEX simulation results there is little to no difference in either the 

estimated exposures at or above the benchmarks (Table 6-5) or in the percent of the children 

expected to experience a lung function decrement (Table 6-6) when considering the varying 

concentration input.
6-24

Table 6-4. Comparison of measured and estimated continuous 5-minute SOR2 

Rconcentrations in ambient air, Fall River monitor 250051004, 2011. 

Monitor ID 250051004

Continuous 5-minute SOR2R concentrations (ppb)

Percentile of 

distribution Estimated Measured

p0 0.0 0.0

p1 0.0 0.0

p5 0.1 0.1

p10 0.4 0.5

p25 1.0 1.1

P50 1.8 1.9

p75 2.8 2.7

p90 5.5 5.2

p95 9.4 9.0

p99 34.1 36.6

p100 241.1 241.1

Number of times per year 5-minute concentration at or above benchmark

Benchmark 

Concentration (ppb) Estimated Measured

100 144 147

200 5 5

300 0 0

400 0 0

Table 6-5. Comparison of simulated exposures, for children with asthma, at or above 

benchmarks using measured versus estimated continuous 5-minute SOR2R

concentrations from monitor 250051004, Fall River, 2011. 

benchmark 

(ppb)

5-minute 

ambient air 

concentrations

Percent of children with asthma having exposures at or above 5-

minute benchmark concentration

number of days per year at or above benchmark concentration

≥ 1 ≥ 2 ≥ 3 ≥ 4 ≥ 5 ≥ 6

100 Measured 43.9 20.0 9.0 3.9 1.6 0.7

Estimated 43.2 19.2 8.4 3.7 1.5 0.7

200 Measured 8.3 0.6 <0.1 P

a 0 Pa 0 0

Estimated 8.3 0.6 <0.1 0 0 0

300 Measured no individuals estimated to experience any days at or above 300 ppb Estimated

P

a

P < 0.1 represents nonzero estimates below 0.1%. A zero (0) indicates there were no individuals having the specified 

exposure.
6-25

Table 6-6. Comparison of simulated lung function decrements in children with asthma 

using measured versus estimated 5-minute continuous SOR2R concentrations, 

Fall River 2011.

sRaw

5-minute 

ambient air 

concentration 

input

Percent of children with asthma estimated to experience one or 

more days with an increase of sRaw of specified amount

number of days per year

≥ 1 ≥ 2 ≥ 3 ≥ 4 ≥ 5 ≥ 6

100% Measured 2.4 0.8 0.4 0.3 0.2 0.1

Estimated 2.3 0.9 0.4 0.3 0.2 <0.1 P

a

200% Measured 0.6 0.1 0 P

a 0 0 0

Estimated 0.6 0.1 0 0 0 0

P

a

P < 0.1 represents nonzero estimates below 0.1%. A zero (0) indicates there were no individuals having the specified 

exposure.

6.2.2.2 Adjustment of Hourly Concentrations to Just Meet the Existing Standard

In this assessment, a proportional approach was used to adjust air quality to just meet the 

current standard. For the exposure and risk results presented in Chapter 5, as described in section 

3.4, we adjusted concentrations for the source contributing the most to the air quality receptor 

concentrations, and that single receptor having the maximum design value in each study area. 

Thus, all other design values calculated for the modeled receptors in the study area following the 

air quality adjustment were less than 75 ppb, with one receptor having a design value of 75 ppb. 

In light of the variation in adjustment factors (Table 3-8), the fact that the factor is 

derived from the highest design value, and the finding that, while the model predicted hourly 

concentrations were found generally comparable with monitor measurements, there were a few 

instances where the highest upper percentile concentrations could be overestimated (see 

Appendix D, Table D-3), we have evaluated the impact on the estimated population exposures of 

an alternative adjustment approach. The alternative approach is intended to address the potential 

for overestimation at the few highest-concentration receptors that could result in the application 

of an overly large adjustment factor for a number of the receptors in the modeling domain. This 

alternative adjustment procedure modifies the selection of the receptor that is used to calculate 

the adjustment factor. Rather than select the single maximum design value to determine the 

adjustment factor for all receptor concentrations within a study area, we chose the 99P

th

P percentile 

design value to determine the adjustment factor for the receptor with that design value, and for 

receptors with lower values. Thus, all receptors having design values less than the 99P

th

P percentile 

following the air quality adjustment would have a design value less than 75 ppb. All study area 

receptors having design values above the 99P

th

P percentile design value were adjusted using their 

own individual adjustment factors that resulted in each of them having adjusted concentrations 

that also yielded a design value of 75 ppb. 
6-26

Table 6-7 summarizes the adjustment factors used in this alternative approach. The air 

quality scenario created by this alternative approach, just like the base approach used for the 

exposure and risk results in Chapter 5, reflects air quality conditions that just meet the existing 

standard. However, this alternative adjustment procedure using the 99P

th

P percentile design value 

results in a greater spatial distribution of relatively higher concentrations across the study area 

compared with the scenario created using the maximum design value, which leads to higher 

percentages of children with asthma having exposures above benchmark concentrations and lung 

function decrements. Figures 6-1 to 6-3 illustrate this in each of the study areas, showing the 

overlay of the population distribution and the design values resulting from the two different 

adjustment approaches.

Table 6-7. Air quality adjustment factors for main body REA and sensitivity analysis.

Study area

Approach for Main body REA Alternative Approach for Sensitivity Analysis

Maximum 

Design value

(ppb)

Factor 

applied to all 

receptors

99P

th

P

percentile

design 

value (ppb)

Factor applied to 

Receptors < 99P

th

P

percentile design value

Factor applied to 

Receptors > 99P

th

P

percentile design

Fall River 101.4 1.46 83.2 1.12 1.14 – 1.46

Indianapolis 311.3 4.21 205.2 2.77 2.85 – 4.21

Tulsa 73.5 0.98 63.1 0.82 0.81 - 0.98

Figure 6-1. Spatial pattern of design values using an adjustment based on the 

maximum design value (left panel) and an adjustment based on the 99P

th

P

percentile design value (right panel) in the Fall River study area.
6-27

Figure 6-2. Spatial pattern of design values using an adjustment based on the 

maximum design value (left panel) and an adjustment based on the 99P

th

P

percentile design value (right panel) in the Indianapolis study area.

Figure 6-3. Spatial pattern of design values using an adjustment based on the 

maximum design value (left panel) and an adjustment based on the 99P

th

P

percentile design value (right panel) in the Tulsa study area.
6-28

We performed APEX simulations using these air quality data sets derived with the 

alternative adjustment approach, and holding all model settings identical to those used to 

generate the exposures presented in Chapter 5. Exposures and risk of lung function decrements 

were estimated for children with asthma in the three study areas for all three years. Tables 6-8

through 6-11 present the results of these alternative simulations, including a comparative 

summary of the results provided in Chapter 5. In general, there is a greater percent of children 

expected to experience at least one daily maximum exposure at or above the benchmark 

concentrations when using the alternative adjustment based on the 99P

th

P percentile design value 

compared to that estimated using the adjustment based on the maximum design value (Table 6-

8). The difference was most noticeable for the Fall River study area, particularly considering the 

100 ppb benchmark (i.e., 7 to 14 percentage points at the mean and maximum, respectively). The 

difference was smaller when considering the 200 ppb benchmark in the Fall River study area and 

both benchmarks in the two other study areas (i.e., mainly fractions of a percentage point

difference for any simulation). Further, there was also a greater percent of multiple exposures at 

or above the 100 ppb benchmark in the Fall River study area using the alternative adjustment 

approach, although the difference was limited to a few percentage points (Table 6-9). Only a 

fractional difference in the percent of children experiencing multiple exposures at or above the 

100 ppb benchmark was observed for the Indianapolis study area, and there was little to no 

difference observed in any study area or when considering multiple exposures at or above the 

200 ppb benchmark.

When considering lung function risk estimated using the two different adjusted air quality 

surfaces, results using the 99P

th

P percentile design value for the adjustment are similar to those 

estimated using the adjustment approach employing the maximum design value, although 

differing slightly for the Fall River study area (Table 6-10 and 6-11). On average, about 1% of 

children are estimated to experience at least one or multiple days with an increase in sRaw at or 

above 100% in the Fall River and Indianapolis study areas, regardless of the adjustment 

approach. Results for the Tulsa study area indicate few (<0.1%) to no children estimated to 

experience any increase in sRaw of interest, neither single nor multiple days. Little to no 

difference was observed for increases in sRaw at or above 200% in any study area when 

considering the alternative adjustment approach, neither single nor multiple days.
6-29

Table 6-8. Comparison of two approaches used to adjust ambient air concentrations to 

just meet the existing standard (2011-2013): Percent of children with asthma 

estimated to experience at least one day per year with a SOR2R exposure at or 

above 5-minute benchmark concentrations while at elevated exertion. 

Study area

Benchmark 

Concentration

(ppb) P

a

Percent of children with asthma having at least one day per 

year U> Ubenchmark concentration: mean (min – max)

Max DV used to adjust air 

quality P

b

Max 99P

th

P DV used to adjust air 

quality

Fall River

100 19.4

(12.3 – 32.7)

26.7

(13.8 – 46.8)

200 <0.1 P

c

(0 P

c

P – 0.2)

0.7

(0 – 2.2)

Indianapolis

100 22.4

(18.0 - 27.0)

23.0

(18.8 – 26.1)

200 0.7

(0 – 1.0)

0.6

(0 – 1.0)

300 0.3

(0 – 0.8)

0.2

(0 – 0.7)

400 0.1

(0 – 0.3)

<0.1

(0 – 0.2)

Tulsa

100 0.1

(<0.1 – 0.2)

0.4

(0 – 0.8)

200 0 0

P

a

P There were no daily maximum 5-minute exposures at or above 300 ppb benchmark in any study area.

P

b

P Data from Table 5-2.

P

c

P < 0.1 represents nonzero estimates below 0.1%. A value of zero (0) indicates there were no individuals having the 

selected exposure in any year.
6-30

Table 6-9. Comparison of two approaches used to adjust ambient air concentrations to 

just meet the existing standard (2011-2013): Percent of children with asthma 

estimated to experience multiple days per year with a SOR2R exposure at or 

above 5-minute benchmark concentrations while at elevated exertion.

Study area

Benchmark 

Concentration

(ppb)

Percent of children with asthma having multiple days per year > benchmark 

concentration: mean (min – max)

Max DV used to adjust air quality P

a Max 99Pth

P DV used to adjust air quality

>2 days >4 days >6 days >2 days >4 days >6 days

Fall River

100 5.5

(1.6 – 12.2)

0.9

(<0.1 P

b

P – 2.6)

0.2

(0 P

b

P – 0.6)

10.5

(2.0 – 24.0)

2.8

(0.1 – 7.7)

1.0

(0 – 2.8)

200 no results included multiple days per year 

at or above this benchmark concentration

<0.1

(0 – <0.1) 0 0

Indianapolis

100 6.8

(4.7 – 8.0)

0.8

(0.3 – 1.0)

0.1

(<0.1 – 0.2)

6.9

(5.3 – 7.9)

1.0

(0.6 – 1.3)

0.2

(0.1 – 0.3)

200 no results included multiple days per year at or above this benchmark concentration

Tulsa

100 no results included multiple days per year 

at or above this benchmark concentration

<0.1

(0 - <0.1) 0 0

200 no results included multiple days per year at or above this benchmark concentration

P

a

P Data from Table 5-3.

P

b

P < 0.1 represents nonzero estimates below 0.1%. A value of zero (0) indicates there were no individuals having the selected 

exposure in any year.

Table 6-10. Percent of children with asthma estimated to experience at least one day per 

year with a SOR2R-related increase in sRaw of 100% or more while breathing at 

elevated rates, air quality adjusted to just meet the existing standard.

Study area sRaw 

(%)

Percent of children with asthma having at least 

one day per year U> UsRaw level: mean (min – max)

Max DV used to adjust air 

quality P

a

Max 99P

th

P DV used to 

adjust air quality

Fall River

100 0.9

(0.5 – 1.4)

1.1

(0.6 – 1.9)

200 0.1

(<0.1P

b

P – 0.2)

0.2

(<0.1 – 0.4)

Indianapolis

100 1.3 

(1.1 – 1.5)

1.3

(1.1 – 1.5)

200 0.3

(0.3 – 0.4)

0.3

(0.3 – 0.4)

Tulsa

100 <0.1

(0 P

b

P – <0.1)

<0.1

(<0.1 – <0.1)

200 There were no individuals that experienced a day with 

this size increase in sRaw

P

a

P Data from Table 5-4.

P

b

P < 0.1 represents nonzero estimates below 0.1%. A value of zero (0) indicates there were 

no individuals having the selected exposure in any year.
6-31

Table 6-11. Percent of children with asthma estimated to experience multiple days per 

year with a SOR2R-related increase in sRaw of 100% or more while breathing at 

elevated rates, air quality adjusted to just meet the existing standard.

Study area

Lung function 

decrement 

(increase in 

sRaw)

Percent of children with asthma having multiple days per year U> UsRaw

level: mean (minP P– max)

Max DV used to adjust air 

quality P

a

Max 99P

th

P DV used to adjust air 

quality

>2 >4 >6 >2 >4 >6

Fall River

> 100% 0.4

(<0.1P

b 

P– 0.7)

0.2

(<0.1 – 0.4)

0.1

(0P

b

P – 0.2)

0.6

(0.2 – 1.0)

0.2

(<0.1 – 0.4)

0.1

(<0.1 – 0.3)

> 200% <0.1

(0 – 0.1) 0 0

<0.1

(0 – 0.2) 0 0

Indianapolis

> 100% 0.7

(0.6 – 0.8)

0.4

(0.4)

0.3

(0.3)

0.7

(0.7 – 0.8)

0.4

(0.4 – 0.5)

0.3

(0.3)

> 200% 0.2

(0.1 – 0.2)

<0.1

(<0.1) 

<0.1

(<0.1)

0.2

(0.1 – 0.2)

<0.1

(<0.1)

<0.1

(<0.1)

Tulsa There were no individuals experiencing an sRaw at or above any level of interest for multiple 

days

P

a

P Data from Table 5-5.

P

b

P < 0.1 represents nonzero estimates below 0.1%. A value of zero (0) indicates there were no individuals having the 

selected exposure in any year.

6.2.2.3 Estimating 5-minute Concentrations at Air Quality Receptors

The approach that has been used to relate the continuous 5-minute concentrations based 

on ambient air measurement data to the 1-hour modeled air quality receptor concentrations is the 

rank order of the hourly concentration distributions (rank-order distribution approach), as 

summarized in section 3.5.2 (and also evaluated in section 6.2.2.1). To inform our consideration 

of uncertainty associated with this approach, we have also evaluated two alternative approaches: 

a calendar-based and concentration bin-based approach. Sensitivity analyses comparing this 

approach to the two alternatives that were considered are described here.

The calendar-based approach uses the actual date and time of each of the two 

concentration datasets (monitor and modeled) as the linking variable. Thus, the temporal patterns 

in hourly (and hence 5-minute patterns) would be the same at all the modeled air quality 

receptors, though normalized by their respective hourly concentrations that occur during that 

same hour (effectively employing equation 3-3, though instead of the rank order to match hourly 

concentrations, the consecutive calendar date and hour-of-day are used). We did not use the 

calendar-based approach to develop the air quality surfaces used in generating the main body 

exposure and risk estimates because we felt it would not appropriately represent the patterns in 

5-minute concentrations, given the relationship between the within-hour 5-minute concentration 

variability and the magnitude of the hourly concentrations. Often times, there is greater 
6-32

variability in the 5-minute concentrations occurring at low hourly concentrations (particularly 

hourly values less than 1 ppb) than at higher hourly concentrations (U.S EPA, 2009, section 

7.2.3.2). Further, we also expected that the monitor(s) would not necessarily reflect the exact 

temporal pattern that could occur at all receptors simultaneously, given the generally sporadic 

nature of peak concentrations driven by temporal and spatial variability in meteorology. That 

said, this mismatching of the temporal patterns observed at the monitor with the air quality 

receptors using the calendar-based approach would likely lead to instances where the 5-minute 

concentrations at the upper percentiles of the distribution are overestimated (i.e., assigning 

greater variability in 5-minute concentrations obtained from low hourly ambient air monitor 

concentrations to the highest hourly modeled concentrations). Alternatively, 5-minute 

concentrations at the lower percentiles would tend to be underestimated in certain instances. As

the estimated risk is largely a function of the highest exposures, avoiding the potential 

assignment of increased variability to higher modeled concentrations was a factor in not 

selecting this approach for the REA. Nevertheless, how this selection affected the exposure and 

risk results warranted additional evaluation as given here.

The second alternative approach to assigning 5-minute variability to the hourly 

concentrations, the concentration bin-based approach, used the actual concentration levels in

each of the two concentration datasets. Both monitor and modeled hourly concentrations were 

binned by 5 ppb increments, except for the lowest hourly concentrations (i.e., three bins were 

used – a 0 concentration bin, the second for hourly concentrations between 0 and 1 ppb, then a 

third for hourly concentrations between 1 and 5). This concentration bin-based approach is 

similar to that using the rank order distribution approach, though likely improves the matching of 

the hourly concentrations between the two concentration data sets, where different (i.e., 

structurally the monitor hourly concentration distribution becomes more like the receptor hourly 

concentration distribution). One limitation to the concentration bin-based approach is that there 

could be limits to the monitor data set in providing measurement data to all of the bins, 

particularly the highest hourly concentrations in the air quality scenario of interest for this REA 

(conditions just meeting the existing standard). This was the case in the monitoring data for the 

Fall River and Tulsa study areas, where the 2011-2013 monitor design values were 64 and 55 

ppb, respectively. Hence, nearly all the hourly concentrations were also below the existing 

standard level of 75 ppb in these two study areas. Therefore, the pattern of the 5-minute 

concentrations associated with the highest hourly concentrations in those areas would all rely on 

very few measurements, leading to uncertainty in their estimation.

Table 6-12 provides the statistics calculated for the upper percentiles of the 5-minute 

concentrations, for air quality adjusted to just meet the existing standard, derived using each of 

the three methods: (1) the rank order distribution approach (used in the assessment); (2) the 
6-33

calendar-based approach; and (3) the concentration bin-based approach. The table presents the 5-

minute concentrations estimated at all air quality receptor locations, along with statistics 

calculated using the ambient air monitor measurement data (also adjusted to meet the standard). 

Consistent with what was described above, the calendar-based approach results in unusually high 

5-minute concentrations, with several receptors exhibiting concentrations at or above 300 ppb. 

Neither the monitor nor the receptors using the rank order distribution-based approach had

concentrations at or above 300 ppb, while the concentration bin-based approach yielded a few 

receptors (i.e., about 15 or more) with estimated 5-minute concentrations at or above that level.

Table 6-12. Comparison of three approaches for using continuous 5-minute monitoring 

data to estimate 5-minute concentrations associated with modeled 1-hour 

concentrations at receptor locations: Air quality adjusted to just meet the 

existing standard, Fall River study area 2011.

Statistic

5-minute SOR2R Concentrations in Ambient Air (ppb)

Estimation Approach Adjusted 

monitoring data 

at monitor 

location

Calendar Rank Order 

Distribution Binned

p90p90 12 11 11

p99p90 12 11 11 5

maxp90 12 11 11

p90p99 29 32 31

p99p99 38 41 40 37

maxp99 45 48 48

p90max 303 183 236

p99max 459 247 338 241

maxmax 662 268 386

Abbreviations: p90 = 90P

th

P percentile of 5-minute concentrations at monitor. 

p90p90 = 90P

th

P percentile of the distribution of all study area receptor 90P

th

P percentile 

5-minute concentrations. Etc.

For this sensitivity analysis, all three of these approaches were used to generate an air 

quality surface of 5-minute concentrations in the Fall River study area and used to simulate 

exposures of children with asthma for 2011. All other model settings and input data were held 

the same as in the main analysis in Chapter 5; the only difference among these three simulations 

was the 5-minute concentration input described above. Table 6-13 shows the resulting estimated 

exposures at or above the selected benchmarks. The largest differences among the three 

approaches are estimates for the 100 ppb benchmark. There are greater percentages of children 

with asthma estimated to experience at least one day with an exposure at or above 100 ppb using 

the calendar-based and concentration bin-based approaches than using the rank order distribution 
6-34

approach. There is less variability across the three approaches when considering three or more 

days with exposures at or above this benchmark. Consistent with the greater number of estimated 

5-minute ambient concentrations at or above the higher benchmarks (200 through 400 ppb), the 

calendar-based approach is the only approach estimating any days with exposures above these 

benchmarks. Given the discussion provided above regarding this particular approach, these

results using the calendar-based approach are likely overestimates of exposure.

Table 6-13. Comparison of three approaches for using continuous 5-minute ambient air 

monitoring data to estimate 5-minute concentrations associated with modeled 

1-hour concentrations: Estimated exposures for air quality adjusted to just 

meet the existing standard, Fall River, 2011.

benchmark 

concentration

(ppb)

5-minute concentration 

approach

Percent of children with asthma estimated to experience one or 

more days with exposures at or above 5-minute benchmark 

concentration, while breathing at elevated rates

Number of days per year

>1 >2 >3 >4 >5 >6

100

Calendar-based 37.2 15.4 6.9 3.6 1.8 1.0

Rank order distribution P

a 32.7 12.2 5.5 2.6 1.3 0.6

Concentration bin-based 36.9 14.7 6.6 2.9 1.4 0.8

200

Calendar-based 4.7 0.5 0.1 0 P

b 0 0

Rank order distribution 0.2 0 0 0 0 0

Concentration bin-base 1.2 <0.1 P

b 0 0 0 0

300

Calendar 1.4 <0.1 0 0 0 0

Rank order distribution 0 0 0 0 0 0

Concentration bin-based 0 0 0 0 0 0

400

Calendar-based 0.3 0 0 0 0 0

Rank order distribution 0 0 0 0 0 0

Concentration bin-based 0 0 0 0 0 0

P

a

P Data from Table 5-2, Table 5-3, and Appendix J.

P

b

P < 0.1 represents nonzero estimates below 0.1%. A value of zero (0) indicates there were no individuals having the selected 

exposure in any year.

Table 6-14 shows the percent of children with asthma estimated to experience at least one 

or more days per year with a SOR2R-related increase in sRaw of 100% or more while breathing at 

elevated rates, using the three different approaches. The general pattern of results is similar as for 

the benchmark comparison, and indicates low frequency of occurrence of lung function 

decrements on at least one day or multiple days (all ≤ 2%), at both levels of interest.
6-35

Table 6-14. Comparison of three approaches for using continuous 5-minute monitoring 

data to estimate 5-minute concentrations associated with modeled 1-hour 

concentrations: Estimated lung function decrements associated with exposure 

to air quality adjusted to just meet the existing standard, Fall River 2011.

Lung function 

decrement

(increase in sRaw)

5-minute concentration 

approach

Percent of children with asthma estimated to experience 

one or more days with specified response P

a

number of days per year

>1 >2 >3 >4 >5 >6

100%

Calendar-based 2.0 0.7 0.4 0.2 0.2 0.1

Rank order distribution 1.4 0.7 0.5 0.4 0.3 0.2

Concentration bin-based 1.6 0.7 0.4 0.3 0.2 0.2

200%

Calendar-based 0.4 <0.1 P

b 0 Pb 0 0 0

Rank order distribution 0.2 0.1 <0.1 0 0 0

Concentration bin-based 0.3 0.1 <0.1 0 0 0

P

a

P Data from Table 5-4, Table 5-5, and Appendix J.

P

b

P < 0.1 represents nonzero estimates below 0.1%. A value of zero (0) indicates there were no individuals having the selected 

exposure in any year.

6.2.2.4 E-R Function for Lung Function Risk Estimates

The E-R functions for lung function risk were generated from the controlled human study 

data provided in Table 4-12 using a probit regression (as described in section 4.6.2 above). In 

addition to estimates for the risks of increases in sRaw of at least 100% and 200% based on the 

best fit (mean) probit model regression coefficients, we also generated lower and upper bounds 

for estimated risks using the 5P

th

P and 95P

th

P percentile predictions of the regression coefficients (see 

section 4.6.2 and Appendix J, Table J-28).

For the presentation here, the lower and upper bound E-R functions were combined with 

the distribution of exposures estimated in each study area, as was done using the mean regression 

estimates to generate the risk estimates presented in section 5.3. As for many of the sensitivity 

analyses in this chapter, the focus of this presentation is on risks for children with asthma 

experiencing 5-minute exposures while breathing at elevated rates. The estimated risks using 

each of the three E-R functions (for each of the two severities of response) averaged across the 3-

year study period are provided in Table 6-15.

The risks estimated for the three functions vary as expected. The highest risks (both for 

single occurrences as well as multiple occurrences) are derived using the upper bound function 

and the lowest with the lower bound function. With regard to the Fall River and Indianapolis risk 

estimates, the differences in risk estimated using the upper bound function versus that estimated 

using the mean E-R function, in terms of percent of the population, are about 2 to 3 percentage 

points when considering the estimate of children experiencing at least one day per year with an 

increase in sRaw of at least 100%. The differences are smaller for multiple such occurrences 
6-36

(e.g., a 1.4 to 1.8 percentage point difference at most considering two or more days in a year), 

and also for occurrences of a 200% increase in sRaw (at most a 1.2 percentage point difference 

considering at least one day per year). 

Table 6-15. Comparison of estimated lung function risk using mean, lower bound and 

upper bound of the fitted E-R function: Percent of children with asthma 

estimated to experience at least one or multiple days per year with a SOR2Rrelated increase in sRaw of 100% or more while breathing at elevated rates, 

air quality adjusted to just meet the existing standard, 2011-2013.

Study Area

Lung function 

decrement

(increase in 

sRaw)

E-R FunctionP

a

Percent of children with asthma estimated to experience 

one or more days with an increase of sRaw of specified 

amount (average across 3-year period)

Number of days per year

>1 >2 >3 >4 >5 >6

Fall River

100%

LB 0.2 <0.1 P

b <0.1 0 Pb 0 0

MeanP

c 0.9 0.4 0.3 0.2 0.1 0.1

UB 2.7 1.8 1.3 1.1 0.9 0.8

200%

LB

There were no children that experienced a day with an 

increase in sRaw of at least 100% using this E-R function

Mean 0.1 <0.1 <0.1 0 0 0

UB 1.1 0.7 0.5 0.4 0.4 0.3

Indianapolis

100%

LB 0.5 0.2 0.1 <0.1 <0.1 <0.1

Mean 1.3 0.7 0.5 0.4 0.4 0.3

UB 3.5 2.5 2.0 1.8 1.6 1.4

200%

LB <0.1 0 0 0 0 0

Mean 0.3 0.2 0.1 <0.1 <0.1 <0.1

UB 1.5 1.1 0.9 0.8 0.7 0.7

Tulsa

100%

LB

There were no children that experienced a day with an 

increase in sRaw of at least 100% using either E-R function

Mean <0.1 0 0 0 0 0

UB 0.5 0.3 0.2 0.2 0.2 0.1

200%

LB There were no children that experienced a day with an 

Mean increase in sRaw of at least 200% using either E-R function

UB 0.2 0.1 0.1 <0.1 <0.1 <0.1

P

a

P LB is a lower bound E-R function derived using the 5P

th

P percentile for the mean regression coefficient, Mean is the 

E-R function representing the best fit (mean) regression estimate, UB is an upper bound E-R function derived using 

the 95P

th

P percentile for the mean regression coefficient, each derived using the controlled human exposure-response 

study data in Table 4-12. See also section 4.6.2 and Figure 4-4.

P

b

P < 0.1 represents nonzero estimates below 0.1%. A value of zero (0) indicates there were no individuals having the selected 

exposure in any year.

P

c

P From main body REA results Tables 5-4 and 5-5 and Appendix J.
6-37

Risk estimated using the lower bound E-R function yields a smaller percent of children 

compared to that using the mean E-R function, with at most a 0.8 percentage point difference for

children experiencing at least one day per year with an increase in sRaw of at least 100% in the 

Fall River and Indianapolis study areas.

Regarding the Tulsa study area, there were no children estimated to experience a 100% 

increase in lung function decrement when using the lower bound function; with the mean E-R 

functions, fewer than 0.1% were estimated to experience at least one day with an SOR2R-related 

increase in sRaw of 100%. When using the upper bound function to estimate risk, a fraction of a 

percent (all ≤0.5%) of children with asthma were estimated to experience at least one or multiple 

days per year with a SOR2R-related increase in sRaw of 100%.
6-38

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A-1

APPENDIX A

SURFACE CHARACTERISTIC VALUES AND METEOROLOGICAL DATA 

PREPARATION FOR INPUT TO AIR QUALITY MODELING

A.1 Introduction

Air quality dispersion modeling was performed for three study areas to support the SO2

Risk and Exposure Assessment, including: Fall River, MA; Indianapolis, IN; and Tulsa, OK. 

Each of the three study areas was modeled for the same three-year period, 2011-2013. National 

Weather Service (NWS) meteorological data were used as meteorological input to AERMOD 

(U.S. EPA, 2016a), preprocessed with AERMET (v.16216) (U.S. EPA, 2016b), the 

meteorological preprocessor for AERMOD. 

AERMET requires continuous hourly surface meteorological observations and concurrent 

twice daily upper air sounding data. The surface and upper air data should be representative of 

the modeling domain. The NWS and the Federal Aviation Administration (FAA) jointly operate 

and maintain a network of Automated Surface Observing Systems (ASOS) at airports throughout 

the U.S. Upper air data are collected by the NWS at 69 stations across the conterminous U.S. 

Table A-1 and Table A-2 lists the NWS surface and upper air stations selected for each of the 

study areas. Figure A-1 through Figure A-5 show the locations of the ASOS and upper air 

stations selected for each study area, relative to emission sources that were modeled. 

Table A-1. National Weather Service surface stations.

Study Area Station Identifier WMO

(WBAN)

Latitude

(degrees)

Longitude

(degrees)

Elevation

(m)

GMT Offset 

(hours)

Fall River Providence PVD 725070

(14765) 41.7225 -71.4325 19 -5

Indianapolis

Indianapolis 

International 

Airport

IND 724380

(93819) 39.725170 -86.281680 241 -5

Tulsa Tulsa R. L. 

Jones Jr. Airport RVS 723564(53908) 36.042441 -95.990166 192 -6
A-2

Table A-2. National Weather Service upper air stations.

Study Area Station Identifier WMO

(WBAN)

Latitude

(degrees)

Longitude

(degrees)

Elevation

(m)

GMT Offset 

(hours)

Fall River Chatham, MA CHH 744940

(14684) 41.67 -69.97 12 -5

Indianapolis Lincoln, IL ILX 745600

(04833) 40.15 -89.33 178 -6

Tulsa Norman, OK OUN 723560

(13968) 35.23 -97.47 354 -6

Figure A-1. Location of surface and upper air meteorological stations and emission sources 

for Fall River, MA.
A-3

Figure A-2. Location of surface and upper air meteorological stations selected for 

Indianapolis, IN.

Figure A-3. Location of emission sources for Indianapolis, IN.
A-4

Figure A-4. Location of surface and upper air meteorological stations selected for Tulsa, 

OK.

Figure A-5. Location of emission sources for Tulsa, OK.
A-5

In addition to surface and upper air meteorological data, AERMET also requires the user 

to input values of surface albedo, Bowen ratio, and roughness length that are representative of

the location where the surface observations are taken. Surface characteristic values were 

estimated using the AERSURFACE (v.13016) (U.S. EPA, 2013).

The remainder of this document describes the preparation of the meteorological data files 

input to AERMOD for each of the three study areas. Section A.2 describes the preparation of the 

surface and upper air data for input to AERMET. Section A.3 describes the estimation of surface 

characteristic values using AERSURFACE, and Section A.4 describes the AERMET processing 

with a brief analysis of the AERMET output for each of the study areas.

A.2 Preparation of the Surface and Upper Air Meteorological Data

A.2.1 Surface Data

Three years of surface data for 2011-2013 were downloaded from the Integrated Surface 

Hourly (ISH) archive maintained by the National Oceanic and Atmospheric Administration 

(NOAA) National Centers for Environmental Information (NCEI), formerly the National 

Climatic Data Center (NCDC). The data are accessible for download via File Transfer Protocol 

(FTP) at ftp://ftp.ncdc.noaa.gov.

A potential concern related to the use of NWS meteorological data for dispersion 

modeling is the often high incidence of calms and variable wind conditions in the Integrated 

Surface Hourly (ISH) data. This is due to the implementation of the ASOS program to replace 

observer-based data beginning in the mid-1990’s, and the adoption of the METAR standard for 

reporting NWS observations in July 1996. Currently, the wind speed and direction used to 

represent the hour in AERMOD is based on a single two-minute average, usually reported about 

10 minutes before the hour. The METAR system reports winds of less than three knots as calm 

(coded as 0 knots), and winds up to six knots will be reported as variable when the variation in 

the 2-minute wind direction is more than 60 degrees. This variable wind is reported as a non-zero 

wind speed with a missing wind direction. The number of calms and variable winds can 

influence concentration calculations in AERMOD because concentrations are not calculated for 

calms or variable wind hours. Significant numbers of calm and variable hours may compromise 

the representativeness of NWS surface data for AERMOD applications. This is especially of 

concern for applications involving low-level releases since the worst-case dispersion conditions 

for such sources are associated with low wind speeds, and the hours being discarded as calm or 

variable are biased toward this condition.

The NCEI maintains a separate archive of 1-minute wind data for each of the ASOS 

surface stations. These wind data represent 2-minute average wind speeds calculated for each 

minute of the hour. To reduce the number of calms and missing winds, these wind data were 
A-6

used to calculate hourly average wind speed and direction to replace the standard archive of 

winds in the ISH dataset. The 1-minute data were processed with AERMINUTE (v.15272) (U.S. 

EPA, 2015), which calculates the hourly wind speed and wind direction and generates a file 

formatted for input directly to AERMET, where the ISH wind data are replaced during 

processing. The NCEI archives the1-minute ASOS wind data as monthly files. Monthly 1-minute 

data files were downloaded for the 2011-2013 period for each ASOS surface stations listed in 

Table A-1.

A.2.2 Upper Air Data

Three years (2011-2013) of upper air sounding data were downloaded for each of the 

upper air stations listed in Table A-2 from the NOAA/ Earth System Research Laboratory 

(ESRL) Radiosonde Database (https://ruc.noaa.gov/raobs/). The upper air data are archived in 

the Forecast System Laboratory (FSL) format and maintained by the Global Systems Division, 

formerly the FSL. Data for each station was downloaded as a separate file as required by 

AERMET.

A.3 Estimation of Surface Characteristics Using AERSURFACE

As previously stated, surface values for albedo, Bowen ratio, and roughness length were 

estimated using the AERSURFACE tool. As noted in the AERSURFACE User’s Guide (U.S. 

EPA, 2013), surface characteristics that are input to AERMET should be representative of the 

location of the meteorological tower. AERSURFACE was run for the location of each of the 

three ASOS stations using the geographic coordinates of the meteorological towers in Table A-1.

The current version of AERSURFACE utilizes 1992 land cover data from the National 

Land Cover Database (NLCD) in GeoTIFF format. NLCD data files for the three ASOS stations 

were downloaded from the Multi-Resolution Land Characteristics consortium website 

(https://www.mrlc.gov).

AERSURFACE can generate annual, seasonal, or monthly surface characteristic values 

in a format for input directly into AERMET. Monthly values were generated for each of the 

locations. To properly interpret some of the land cover categories in the 1992 NLCD data, 

AERSURFACE requires the user to specify whether or not the location of the weather station is 

at an airport. All three ASOS stations were specified as airport locations. AERSURFACE also 

allows for the surface roughness length to be defined by up to 12 wind sectors with a minimum 

arc of 30 degrees each. For each of the three locations, roughness was estimated for each of 12 

sectors, beginning at 0 degrees through 360 degrees (i.e., 0-30, 30-60, 60-90, etc.). The 

roughness length sectors at each of the three ASOS stations are illustrated in Figure A-6 through 
A-7

Figure A-8. The sectors extend from the location of the meteorological tower out to 1 km, the 

distance over which the roughness length is estimated.

Figure A-6. Surface roughness sectors for Providence Airport (PVD).
A-8

Figure A-7. Surface roughness sectors for Indianapolis International Airport (IND).

Figure A-8. Surface roughness sectors for Tulsa R. L. Jones Airport (RVS).

Values for the three surface characteristics are defined within AERSURFACE by season 

but are computed monthly based on the assignment of months to seasons. Monthly values are 
A-9

then rolled up to seasonal or annual values based on the option specified by the user. The user 

has the option to use default month-to-season assignments or input user-defined assignments. 

Seasonal surface characteristic values are defined based on five season definitions: spring, 

summer, autumn, winter with no snow, and winter with continuous snow cover. Note, there are 

two winter options: 1) winter with no snow (or without continuous snow) on the ground the 

entire month and 2) winter with continuous snow on ground the entire month.1 AERSURFACE 

was run for Tulsa using the default month-to-season assignments, while months were reassigned 

for both Indianapolis and Fall River. The month-to-season assignments used for each of the three 

surface stations are shown in Table A-3, along with the seasonal definitions. A month was 

considered to have continuous snow cover if a snow depth of one inch or more was reported for 

at least 75% of the days in the month.

Table A-3. AERSURFACE month-to-season assignments.

Station

Winter

(continuous snow)

Winter

(no snow) Spring Summer Autumn

PVD Feb (2015 only) Dec, Jan, Feb, Mar Apr, May Jun., Jul, Aug Sep, Oct, Nov

IND Dec, Jan, Feb, Mar Apr, May Jun., Jul, Aug Sep, Oct, Nov

RVS Dec, Jan, Feb Mar, Apr, May Jun., Jul, Aug Sep, Oct, Nov

Seasonal definitions: Winter: Late autumn after frost and harvest, or winter with no snow; Spring: Transitional spring with 

partial green coverage or short annuals; Summer: Midsummer with lush vegetation; Autumn: Autumn with unharvested 

cropland

AERSURFACE also requires information about the climate and surface moisture at the 

surface station. The climate at the station location is categorized as either arid or non-arid. Each 

of the three surface station locations was categorized as non-arid in AERSURFACE. Surface 

moisture is based on precipitation amounts and is categorized as either wet, average, or dry. For 

the three surface stations, 2010 local climatological data from the NCEI was used to look at 30 

years (1981-2010) of monthly precipitation. The 30th and 70th percentiles of precipitation 

amounts were calculated for each of 12 months (Jan. – Dec.) based on the 30-year period. The 

precipitation amount for each month in 2011-2013 was then compared to the 30th and 70th

percentiles for the corresponding month. Months during which precipitation was greater than the 

70th percentile were considered wet while months that were less than the 30th percentile were 

considered dry. Months within the 30th and 70th percentile range were considered average. 

AERSURFACE was run for each moisture condition to obtain monthly values for wet, dry, and 

average conditions. Using the AERSURFACE output for each of the three moisture categories, a 

 

1 For many of the land cover categories in the 1992 NLCD classification scheme, the designation of winter with 

continuous snow on the ground would tend to increase wintertime albedo (reflectivity) and decrease wintertime 

Bowen ratio (sensible to latent heat flux) and surface roughness compared to the winter with no snow or without 

continuous snow designation.
A-10

separate set of monthly surface characteristics was compiled for each of the three years for input 

to AERMET. The monthly categorization of the surface moisture at each of the locations is 

shown in Table A-4. The resulting surface characteristic values input to AERMET, by sector, 

month, and year, are listed in Table A-6 through Table A-8 at the end of this document.

Table A-4. Monthly surface moisture categorizations.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

PVD

2011 Avg Wet Dry Wet Avg Wet Wet Wet Wet Wet Wet Avg

2012 Avg Dry Dry Avg Wet Wet Avg Wet Wet Wet Dry Wet

2013 Dry Wet Dry Dry Avg Wet Avg Wet Wet Dry Wet Wet

IND

2011 Wet Wet Wet Wet Wet Wet Dry Dry Wet Wet Wet Wet

2012 Wet Avg Wet Avg Dry Dry Dry Wet Wet Wet Dry Avg

2013 Wet Wet Wet Wet Wet Wet Dry Dry Wet Wet Wet Wet

RVS (Moisture conditions at RVS are based on precipitation data from Tulsa International Airport, TUL)

2011 Dry Wet Dry Wet Dry Dry Dry Wet Dry Dry Wet Avg

2012 Dry Avg Wet Avg Dry Wet Dry Wet Dry Avg Dry Dry

2013 Wet Wet Dry Avg Avg Dry Wet Wet Dry Wet Avg Avg

A.4 AERMET Processing

The meteorological data files (upper air, ISH data, and 1-minute hourly averaged wind 

data) for each station were processed in AERMET. Each year was processed separately using the 

monthly surface characteristics specific to each year. AERMET processes the meteorological 

data in three “Stages.” Stage 1 reads in the upper air and ISH data files and performs an initial 

QA on the values. Stage 2 reads the 1-minute averaged wind data and merges the three data sets 

into a single file. Stage 3 performs data replacements and substitutions as specified by the user, 

computes the boundary layer parameters, and generates data files formatted for input to 

AERMOD. Surface characteristics were input during Stage 3. When 1-minute hourly averaged 

winds were available, those winds were used for the hour, while all other surface data are from 

the ISH data (temperature, cloud cover, precipitation, etc.). 

Table A-5 shows the percentage of calm and missing winds in the AERMET output for 

the combined three years (2011-2013) for each of the surface stations. These values take into 

account the replacement of the ISH wind data with the 1-minute hourly averaged wind data 

during the AERMET Stage 3 processing. Figure A-9 through Figure A-11 are wind roses 

generated from the 2011-2013 surface data files output by AERMET for three surface stations.
A-11

Table A-5. Percent calm and missing winds in AERMET surface file.

Station % Calm % Missing

PVD 0.49 0.06

IND 0.37 0.10

RVS 3.90 0.22

Figure A-9. Wind rose for Providence Airport (PVD), 2011-2013 (direction blowing from).
A-12

Figure A-10. Wind rose for Indianapolis International Airport (IND), 2011-2013 (direction 

blowing from).

Figure A-11. Wind rose for Tulsa R. L. Jones Jr. Airport (RVS), 2011-2013 (direction 

blowing from).
A-13

Table A-6. Surface characteristics for Providence Airport (PVD) by month and year.

Station = PVD 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Jan 0-30 0.16 0.64 0.023 0.16 0.64 0.023 0.16 1.24 0.023

Jan 30-60 0.16 0.64 0.022 0.16 0.64 0.022 0.16 1.24 0.022

Jan 60-90 0.16 0.64 0.026 0.16 0.64 0.026 0.16 1.24 0.026

Jan 90-120 0.16 0.64 0.036 0.16 0.64 0.036 0.16 1.24 0.036

Jan 120-150 0.16 0.64 0.041 0.16 0.64 0.041 0.16 1.24 0.041

Jan 150-180 0.16 0.64 0.027 0.16 0.64 0.027 0.16 1.24 0.027

Jan 180-210 0.16 0.64 0.018 0.16 0.64 0.018 0.16 1.24 0.018

Jan 210-240 0.16 0.64 0.038 0.16 0.64 0.038 0.16 1.24 0.038

Jan 240-270 0.16 0.64 0.038 0.16 0.64 0.038 0.16 1.24 0.038

Jan 270-300 0.16 0.64 0.053 0.16 0.64 0.053 0.16 1.24 0.053

Jan 300-330 0.16 0.64 0.081 0.16 0.64 0.081 0.16 1.24 0.081

Jan 330-360 0.16 0.64 0.030 0.16 0.64 0.030 0.16 1.24 0.030

Feb 0-30 0.16 0.40 0.023 0.16 1.24 0.023 0.16 0.40 0.023

Feb 30-60 0.16 0.40 0.022 0.16 1.24 0.022 0.16 0.40 0.022

Feb 60-90 0.16 0.40 0.026 0.16 1.24 0.026 0.16 0.40 0.026

Feb 90-120 0.16 0.40 0.036 0.16 1.24 0.036 0.16 0.40 0.036

Feb 120-150 0.16 0.40 0.041 0.16 1.24 0.041 0.16 0.40 0.041

Feb 150-180 0.16 0.40 0.027 0.16 1.24 0.027 0.16 0.40 0.027

Feb 180-210 0.16 0.40 0.018 0.16 1.24 0.018 0.16 0.40 0.018

Feb 210-240 0.16 0.40 0.038 0.16 1.24 0.038 0.16 0.40 0.038

Feb 240-270 0.16 0.40 0.038 0.16 1.24 0.038 0.16 0.40 0.038

Feb 270-300 0.16 0.40 0.053 0.16 1.24 0.053 0.16 0.40 0.053

Feb 300-330 0.16 0.40 0.081 0.16 1.24 0.081 0.16 0.40 0.081

Feb 330-360 0.16 0.40 0.030 0.16 1.24 0.030 0.16 0.40 0.030

Mar 0-30 0.16 1.24 0.023 0.16 1.24 0.023 0.16 1.24 0.023

Mar 30-60 0.16 1.24 0.022 0.16 1.24 0.022 0.16 1.24 0.022

Mar 60-90 0.16 1.24 0.026 0.16 1.24 0.026 0.16 1.24 0.026

Mar 90-120 0.16 1.24 0.036 0.16 1.24 0.036 0.16 1.24 0.036

Mar 120-150 0.16 1.24 0.041 0.16 1.24 0.041 0.16 1.24 0.041

Mar 150-180 0.16 1.24 0.027 0.16 1.24 0.027 0.16 1.24 0.027

Mar 180-210 0.16 1.24 0.018 0.16 1.24 0.018 0.16 1.24 0.018

Mar 210-240 0.16 1.24 0.038 0.16 1.24 0.038 0.16 1.24 0.038

Mar 240-270 0.16 1.24 0.038 0.16 1.24 0.038 0.16 1.24 0.038

Mar 270-300 0.16 1.24 0.053 0.16 1.24 0.053 0.16 1.24 0.053

Mar 300-330 0.16 1.24 0.081 0.16 1.24 0.081 0.16 1.24 0.081

Mar 330-360 0.16 1.24 0.030 0.16 1.24 0.030 0.16 1.24 0.030

Apr 0-30 0.15 0.37 0.029 0.15 0.53 0.029 0.15 1.05 0.029

Apr 30-60 0.15 0.37 0.029 0.15 0.53 0.029 0.15 1.05 0.029
A-14

Station = PVD 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Apr 60-90 0.15 0.37 0.034 0.15 0.53 0.034 0.15 1.05 0.034

Apr 90-120 0.15 0.37 0.047 0.15 0.53 0.047 0.15 1.05 0.047

Apr 120-150 0.15 0.37 0.052 0.15 0.53 0.052 0.15 1.05 0.052

Apr 150-180 0.15 0.37 0.036 0.15 0.53 0.036 0.15 1.05 0.036

Apr 180-210 0.15 0.37 0.025 0.15 0.53 0.025 0.15 1.05 0.025

Apr 210-240 0.15 0.37 0.051 0.15 0.53 0.051 0.15 1.05 0.051

Apr 240-270 0.15 0.37 0.045 0.15 0.53 0.045 0.15 1.05 0.045

Apr 270-300 0.15 0.37 0.062 0.15 0.53 0.062 0.15 1.05 0.062

Apr 300-330 0.15 0.37 0.088 0.15 0.53 0.088 0.15 1.05 0.088

Apr 330-360 0.15 0.37 0.037 0.15 0.53 0.037 0.15 1.05 0.037

May 0-30 0.15 0.53 0.029 0.15 0.37 0.029 0.15 0.53 0.029

May 30-60 0.15 0.53 0.029 0.15 0.37 0.029 0.15 0.53 0.029

May 60-90 0.15 0.53 0.034 0.15 0.37 0.034 0.15 0.53 0.034

May 90-120 0.15 0.53 0.047 0.15 0.37 0.047 0.15 0.53 0.047

May 120-150 0.15 0.53 0.052 0.15 0.37 0.052 0.15 0.53 0.052

May 150-180 0.15 0.53 0.036 0.15 0.37 0.036 0.15 0.53 0.036

May 180-210 0.15 0.53 0.025 0.15 0.37 0.025 0.15 0.53 0.025

May 210-240 0.15 0.53 0.051 0.15 0.37 0.051 0.15 0.53 0.051

May 240-270 0.15 0.53 0.045 0.15 0.37 0.045 0.15 0.53 0.045

May 270-300 0.15 0.53 0.062 0.15 0.37 0.062 0.15 0.53 0.062

May 300-330 0.15 0.53 0.088 0.15 0.37 0.088 0.15 0.53 0.088

May 330-360 0.15 0.53 0.037 0.15 0.37 0.037 0.15 0.53 0.037

Jun 0-30 0.15 0.36 0.035 0.15 0.36 0.035 0.15 0.36 0.035

Jun 30-60 0.15 0.36 0.035 0.15 0.36 0.035 0.15 0.36 0.035

Jun 60-90 0.15 0.36 0.040 0.15 0.36 0.040 0.15 0.36 0.040

Jun 90-120 0.15 0.36 0.056 0.15 0.36 0.056 0.15 0.36 0.056

Jun 120-150 0.15 0.36 0.061 0.15 0.36 0.061 0.15 0.36 0.061

Jun 150-180 0.15 0.36 0.043 0.15 0.36 0.043 0.15 0.36 0.043

Jun 180-210 0.15 0.36 0.031 0.15 0.36 0.031 0.15 0.36 0.031

Jun 210-240 0.15 0.36 0.059 0.15 0.36 0.059 0.15 0.36 0.059

Jun 240-270 0.15 0.36 0.050 0.15 0.36 0.050 0.15 0.36 0.050

Jun 270-300 0.15 0.36 0.068 0.15 0.36 0.068 0.15 0.36 0.068

Jun 300-330 0.15 0.36 0.094 0.15 0.36 0.094 0.15 0.36 0.094

Jun 330-360 0.15 0.36 0.042 0.15 0.36 0.042 0.15 0.36 0.042

Jul 0-30 0.15 0.36 0.035 0.15 0.49 0.035 0.15 0.49 0.035

Jul 30-60 0.15 0.36 0.035 0.15 0.49 0.035 0.15 0.49 0.035

Jul 60-90 0.15 0.36 0.040 0.15 0.49 0.040 0.15 0.49 0.040

Jul 90-120 0.15 0.36 0.056 0.15 0.49 0.056 0.15 0.49 0.056

Jul 120-150 0.15 0.36 0.061 0.15 0.49 0.061 0.15 0.49 0.061
A-15

Station = PVD 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Jul 150-180 0.15 0.36 0.043 0.15 0.49 0.043 0.15 0.49 0.043

Jul 180-210 0.15 0.36 0.031 0.15 0.49 0.031 0.15 0.49 0.031

Jul 210-240 0.15 0.36 0.059 0.15 0.49 0.059 0.15 0.49 0.059

Jul 240-270 0.15 0.36 0.050 0.15 0.49 0.050 0.15 0.49 0.050

Jul 270-300 0.15 0.36 0.068 0.15 0.49 0.068 0.15 0.49 0.068

Jul 300-330 0.15 0.36 0.094 0.15 0.49 0.094 0.15 0.49 0.094

Jul 330-360 0.15 0.36 0.042 0.15 0.49 0.042 0.15 0.49 0.042

Aug 0-30 0.15 0.36 0.035 0.15 0.36 0.035 0.15 0.36 0.035

Aug 30-60 0.15 0.36 0.035 0.15 0.36 0.035 0.15 0.36 0.035

Aug 60-90 0.15 0.36 0.040 0.15 0.36 0.040 0.15 0.36 0.040

Aug 90-120 0.15 0.36 0.056 0.15 0.36 0.056 0.15 0.36 0.056

Aug 120-150 0.15 0.36 0.061 0.15 0.36 0.061 0.15 0.36 0.061

Aug 150-180 0.15 0.36 0.043 0.15 0.36 0.043 0.15 0.36 0.043

Aug 180-210 0.15 0.36 0.031 0.15 0.36 0.031 0.15 0.36 0.031

Aug 210-240 0.15 0.36 0.059 0.15 0.36 0.059 0.15 0.36 0.059

Aug 240-270 0.15 0.36 0.050 0.15 0.36 0.050 0.15 0.36 0.050

Aug 270-300 0.15 0.36 0.068 0.15 0.36 0.068 0.15 0.36 0.068

Aug 300-330 0.15 0.36 0.094 0.15 0.36 0.094 0.15 0.36 0.094

Aug 330-360 0.15 0.36 0.042 0.15 0.36 0.042 0.15 0.36 0.042

Sep 0-30 0.15 0.40 0.029 0.15 0.40 0.029 0.15 0.40 0.029

Sep 30-60 0.15 0.40 0.029 0.15 0.40 0.029 0.15 0.40 0.029

Sep 60-90 0.15 0.40 0.034 0.15 0.40 0.034 0.15 0.40 0.034

Sep 90-120 0.15 0.40 0.048 0.15 0.40 0.048 0.15 0.40 0.048

Sep 120-150 0.15 0.40 0.053 0.15 0.40 0.053 0.15 0.40 0.053

Sep 150-180 0.15 0.40 0.036 0.15 0.40 0.036 0.15 0.40 0.036

Sep 180-210 0.15 0.40 0.025 0.15 0.40 0.025 0.15 0.40 0.025

Sep 210-240 0.15 0.40 0.051 0.15 0.40 0.051 0.15 0.40 0.051

Sep 240-270 0.15 0.40 0.045 0.15 0.40 0.045 0.15 0.40 0.045

Sep 270-300 0.15 0.40 0.062 0.15 0.40 0.062 0.15 0.40 0.062

Sep 300-330 0.15 0.40 0.088 0.15 0.40 0.088 0.15 0.40 0.088

Sep 330-360 0.15 0.40 0.037 0.15 0.40 0.037 0.15 0.40 0.037

Oct 0-30 0.15 0.40 0.029 0.15 0.40 0.029 0.15 1.24 0.029

Oct 30-60 0.15 0.40 0.029 0.15 0.40 0.029 0.15 1.24 0.029

Oct 60-90 0.15 0.40 0.034 0.15 0.40 0.034 0.15 1.24 0.034

Oct 90-120 0.15 0.40 0.048 0.15 0.40 0.048 0.15 1.24 0.048

Oct 120-150 0.15 0.40 0.053 0.15 0.40 0.053 0.15 1.24 0.053

Oct 150-180 0.15 0.40 0.036 0.15 0.40 0.036 0.15 1.24 0.036

Oct 180-210 0.15 0.40 0.025 0.15 0.40 0.025 0.15 1.24 0.025

Oct 210-240 0.15 0.40 0.051 0.15 0.40 0.051 0.15 1.24 0.051
A-16

Station = PVD 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Oct 240-270 0.15 0.40 0.045 0.15 0.40 0.045 0.15 1.24 0.045

Oct 270-300 0.15 0.40 0.062 0.15 0.40 0.062 0.15 1.24 0.062

Oct 300-330 0.15 0.40 0.088 0.15 0.40 0.088 0.15 1.24 0.088

Oct 330-360 0.15 0.40 0.037 0.15 0.40 0.037 0.15 1.24 0.037

Nov 0-30 0.15 0.40 0.029 0.15 1.24 0.029 0.15 0.40 0.029

Nov 30-60 0.15 0.40 0.029 0.15 1.24 0.029 0.15 0.40 0.029

Nov 60-90 0.15 0.40 0.034 0.15 1.24 0.034 0.15 0.40 0.034

Nov 90-120 0.15 0.40 0.048 0.15 1.24 0.048 0.15 0.40 0.048

Nov 120-150 0.15 0.40 0.053 0.15 1.24 0.053 0.15 0.40 0.053

Nov 150-180 0.15 0.40 0.036 0.15 1.24 0.036 0.15 0.40 0.036

Nov 180-210 0.15 0.40 0.025 0.15 1.24 0.025 0.15 0.40 0.025

Nov 210-240 0.15 0.40 0.051 0.15 1.24 0.051 0.15 0.40 0.051

Nov 240-270 0.15 0.40 0.045 0.15 1.24 0.045 0.15 0.40 0.045

Nov 270-300 0.15 0.40 0.062 0.15 1.24 0.062 0.15 0.40 0.062

Nov 300-330 0.15 0.40 0.088 0.15 1.24 0.088 0.15 0.40 0.088

Nov 330-360 0.15 0.40 0.037 0.15 1.24 0.037 0.15 0.40 0.037

Dec 0-30 0.16 0.64 0.023 0.16 0.40 0.023 0.16 0.40 0.023

Dec 30-60 0.16 0.64 0.022 0.16 0.40 0.022 0.16 0.40 0.022

Dec 60-90 0.16 0.64 0.026 0.16 0.40 0.026 0.16 0.40 0.026

Dec 90-120 0.16 0.64 0.036 0.16 0.40 0.036 0.16 0.40 0.036

Dec 120-150 0.16 0.64 0.041 0.16 0.40 0.041 0.16 0.40 0.041

Dec 150-180 0.16 0.64 0.027 0.16 0.40 0.027 0.16 0.40 0.027

Dec 180-210 0.16 0.64 0.018 0.16 0.40 0.018 0.16 0.40 0.018

Dec 210-240 0.16 0.64 0.038 0.16 0.40 0.038 0.16 0.40 0.038

Dec 240-270 0.16 0.64 0.038 0.16 0.40 0.038 0.16 0.40 0.038

Dec 270-300 0.16 0.64 0.053 0.16 0.40 0.053 0.16 0.40 0.053

Dec 300-330 0.16 0.64 0.081 0.16 0.40 0.081 0.16 0.40 0.081

Dec 330-360 0.16 0.64 0.030 0.16 0.40 0.030 0.16 0.40 0.030
A-17

Table A-7. Surface characteristics for Indianapolis Int’l (IND) by month and year.

Station = IND 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Jan 0-30 0.18 0.52 0.032 0.18 0.52 0.032 0.18 0.52 0.032

Jan 30-60 0.18 0.52 0.033 0.18 0.52 0.033 0.18 0.52 0.033

Jan 60-90 0.18 0.52 0.046 0.18 0.52 0.046 0.18 0.52 0.046

Jan 90-120 0.18 0.52 0.030 0.18 0.52 0.030 0.18 0.52 0.030

Jan 120-150 0.18 0.52 0.031 0.18 0.52 0.031 0.18 0.52 0.031

Jan 150-180 0.18 0.52 0.040 0.18 0.52 0.040 0.18 0.52 0.040

Jan 180-210 0.18 0.52 0.027 0.18 0.52 0.027 0.18 0.52 0.027

Jan 210-240 0.18 0.52 0.016 0.18 0.52 0.016 0.18 0.52 0.016

Jan 240-270 0.18 0.52 0.022 0.18 0.52 0.022 0.18 0.52 0.022

Jan 270-300 0.18 0.52 0.022 0.18 0.52 0.022 0.18 0.52 0.022

Jan 300-330 0.18 0.52 0.019 0.18 0.52 0.019 0.18 0.52 0.019

Jan 330-360 0.18 0.52 0.041 0.18 0.52 0.041 0.18 0.52 0.041

Feb 0-30 0.18 0.52 0.032 0.18 0.89 0.032 0.18 0.52 0.032

Feb 30-60 0.18 0.52 0.033 0.18 0.89 0.033 0.18 0.52 0.033

Feb 60-90 0.18 0.52 0.046 0.18 0.89 0.046 0.18 0.52 0.046

Feb 90-120 0.18 0.52 0.030 0.18 0.89 0.030 0.18 0.52 0.030

Feb 120-150 0.18 0.52 0.031 0.18 0.89 0.031 0.18 0.52 0.031

Feb 150-180 0.18 0.52 0.040 0.18 0.89 0.040 0.18 0.52 0.040

Feb 180-210 0.18 0.52 0.027 0.18 0.89 0.027 0.18 0.52 0.027

Feb 210-240 0.18 0.52 0.016 0.18 0.89 0.016 0.18 0.52 0.016

Feb 240-270 0.18 0.52 0.022 0.18 0.89 0.022 0.18 0.52 0.022

Feb 270-300 0.18 0.52 0.022 0.18 0.89 0.022 0.18 0.52 0.022

Feb 300-330 0.18 0.52 0.019 0.18 0.89 0.019 0.18 0.52 0.019

Feb 330-360 0.18 0.52 0.041 0.18 0.89 0.041 0.18 0.52 0.041

Mar 0-30 0.15 0.36 0.038 0.15 0.36 0.038 0.15 0.36 0.038

Mar 30-60 0.15 0.36 0.039 0.15 0.36 0.039 0.15 0.36 0.039

Mar 60-90 0.15 0.36 0.051 0.15 0.36 0.051 0.15 0.36 0.051

Mar 90-120 0.15 0.36 0.036 0.15 0.36 0.036 0.15 0.36 0.036

Mar 120-150 0.15 0.36 0.038 0.15 0.36 0.038 0.15 0.36 0.038

Mar 150-180 0.15 0.36 0.046 0.15 0.36 0.046 0.15 0.36 0.046

Mar 180-210 0.15 0.36 0.034 0.15 0.36 0.034 0.15 0.36 0.034

Mar 210-240 0.15 0.36 0.022 0.15 0.36 0.022 0.15 0.36 0.022

Mar 240-270 0.15 0.36 0.029 0.15 0.36 0.029 0.15 0.36 0.029

Mar 270-300 0.15 0.36 0.028 0.15 0.36 0.028 0.15 0.36 0.028

Mar 300-330 0.15 0.36 0.025 0.15 0.36 0.025 0.15 0.36 0.025

Mar 330-360 0.15 0.36 0.046 0.15 0.36 0.046 0.15 0.36 0.046

Apr 0-30 0.15 0.36 0.038 0.15 0.53 0.038 0.15 0.36 0.038

Apr 30-60 0.15 0.36 0.039 0.15 0.53 0.039 0.15 0.36 0.039
A-18

Station = IND 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Apr 60-90 0.15 0.36 0.051 0.15 0.53 0.051 0.15 0.36 0.051

Apr 90-120 0.15 0.36 0.036 0.15 0.53 0.036 0.15 0.36 0.036

Apr 120-150 0.15 0.36 0.038 0.15 0.53 0.038 0.15 0.36 0.038

Apr 150-180 0.15 0.36 0.046 0.15 0.53 0.046 0.15 0.36 0.046

Apr 180-210 0.15 0.36 0.034 0.15 0.53 0.034 0.15 0.36 0.034

Apr 210-240 0.15 0.36 0.022 0.15 0.53 0.022 0.15 0.36 0.022

Apr 240-270 0.15 0.36 0.029 0.15 0.53 0.029 0.15 0.36 0.029

Apr 270-300 0.15 0.36 0.028 0.15 0.53 0.028 0.15 0.36 0.028

Apr 300-330 0.15 0.36 0.025 0.15 0.53 0.025 0.15 0.36 0.025

Apr 330-360 0.15 0.36 0.046 0.15 0.53 0.046 0.15 0.36 0.046

May 0-30 0.15 0.36 0.038 0.15 1.47 0.038 0.15 0.36 0.038

May 30-60 0.15 0.36 0.039 0.15 1.47 0.039 0.15 0.36 0.039

May 60-90 0.15 0.36 0.051 0.15 1.47 0.051 0.15 0.36 0.051

May 90-120 0.15 0.36 0.036 0.15 1.47 0.036 0.15 0.36 0.036

May 120-150 0.15 0.36 0.038 0.15 1.47 0.038 0.15 0.36 0.038

May 150-180 0.15 0.36 0.046 0.15 1.47 0.046 0.15 0.36 0.046

May 180-210 0.15 0.36 0.034 0.15 1.47 0.034 0.15 0.36 0.034

May 210-240 0.15 0.36 0.022 0.15 1.47 0.022 0.15 0.36 0.022

May 240-270 0.15 0.36 0.029 0.15 1.47 0.029 0.15 0.36 0.029

May 270-300 0.15 0.36 0.028 0.15 1.47 0.028 0.15 0.36 0.028

May 300-330 0.15 0.36 0.025 0.15 1.47 0.025 0.15 0.36 0.025

May 330-360 0.15 0.36 0.046 0.15 1.47 0.046 0.15 0.36 0.046

Jun 0-30 0.18 0.44 0.045 0.18 1.76 0.045 0.18 0.44 0.045

Jun 30-60 0.18 0.44 0.045 0.18 1.76 0.045 0.18 0.44 0.045

Jun 60-90 0.18 0.44 0.056 0.18 1.76 0.056 0.18 0.44 0.056

Jun 90-120 0.18 0.44 0.046 0.18 1.76 0.046 0.18 0.44 0.046

Jun 120-150 0.18 0.44 0.048 0.18 1.76 0.048 0.18 0.44 0.048

Jun 150-180 0.18 0.44 0.063 0.18 1.76 0.063 0.18 0.44 0.063

Jun 180-210 0.18 0.44 0.051 0.18 1.76 0.051 0.18 0.44 0.051

Jun 210-240 0.18 0.44 0.040 0.18 1.76 0.040 0.18 0.44 0.040

Jun 240-270 0.18 0.44 0.043 0.18 1.76 0.043 0.18 0.44 0.043

Jun 270-300 0.18 0.44 0.042 0.18 1.76 0.042 0.18 0.44 0.042

Jun 300-330 0.18 0.44 0.032 0.18 1.76 0.032 0.18 0.44 0.032

Jun 330-360 0.18 0.44 0.055 0.18 1.76 0.055 0.18 0.44 0.055

Jul 0-30 0.18 1.76 0.045 0.18 1.76 0.045 0.18 1.76 0.045

Jul 30-60 0.18 1.76 0.045 0.18 1.76 0.045 0.18 1.76 0.045

Jul 60-90 0.18 1.76 0.056 0.18 1.76 0.056 0.18 1.76 0.056

Jul 90-120 0.18 1.76 0.046 0.18 1.76 0.046 0.18 1.76 0.046

Jul 120-150 0.18 1.76 0.048 0.18 1.76 0.048 0.18 1.76 0.048
A-19

Station = IND 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Jul 150-180 0.18 1.76 0.063 0.18 1.76 0.063 0.18 1.76 0.063

Jul 180-210 0.18 1.76 0.051 0.18 1.76 0.051 0.18 1.76 0.051

Jul 210-240 0.18 1.76 0.040 0.18 1.76 0.040 0.18 1.76 0.040

Jul 240-270 0.18 1.76 0.043 0.18 1.76 0.043 0.18 1.76 0.043

Jul 270-300 0.18 1.76 0.042 0.18 1.76 0.042 0.18 1.76 0.042

Jul 300-330 0.18 1.76 0.032 0.18 1.76 0.032 0.18 1.76 0.032

Jul 330-360 0.18 1.76 0.055 0.18 1.76 0.055 0.18 1.76 0.055

Aug 0-30 0.18 1.76 0.045 0.18 0.44 0.045 0.18 1.76 0.045

Aug 30-60 0.18 1.76 0.045 0.18 0.44 0.045 0.18 1.76 0.045

Aug 60-90 0.18 1.76 0.056 0.18 0.44 0.056 0.18 1.76 0.056

Aug 90-120 0.18 1.76 0.046 0.18 0.44 0.046 0.18 1.76 0.046

Aug 120-150 0.18 1.76 0.048 0.18 0.44 0.048 0.18 1.76 0.048

Aug 150-180 0.18 1.76 0.063 0.18 0.44 0.063 0.18 1.76 0.063

Aug 180-210 0.18 1.76 0.051 0.18 0.44 0.051 0.18 1.76 0.051

Aug 210-240 0.18 1.76 0.040 0.18 0.44 0.040 0.18 1.76 0.040

Aug 240-270 0.18 1.76 0.043 0.18 0.44 0.043 0.18 1.76 0.043

Aug 270-300 0.18 1.76 0.042 0.18 0.44 0.042 0.18 1.76 0.042

Aug 300-330 0.18 1.76 0.032 0.18 0.44 0.032 0.18 1.76 0.032

Aug 330-360 0.18 1.76 0.055 0.18 0.44 0.055 0.18 1.76 0.055

Sep 0-30 0.18 0.52 0.040 0.18 0.52 0.040 0.18 0.52 0.040

Sep 30-60 0.18 0.52 0.040 0.18 0.52 0.040 0.18 0.52 0.040

Sep 60-90 0.18 0.52 0.053 0.18 0.52 0.053 0.18 0.52 0.053

Sep 90-120 0.18 0.52 0.041 0.18 0.52 0.041 0.18 0.52 0.041

Sep 120-150 0.18 0.52 0.043 0.18 0.52 0.043 0.18 0.52 0.043

Sep 150-180 0.18 0.52 0.059 0.18 0.52 0.059 0.18 0.52 0.059

Sep 180-210 0.18 0.52 0.046 0.18 0.52 0.046 0.18 0.52 0.046

Sep 210-240 0.18 0.52 0.033 0.18 0.52 0.033 0.18 0.52 0.033

Sep 240-270 0.18 0.52 0.037 0.18 0.52 0.037 0.18 0.52 0.037

Sep 270-300 0.18 0.52 0.036 0.18 0.52 0.036 0.18 0.52 0.036

Sep 300-330 0.18 0.52 0.027 0.18 0.52 0.027 0.18 0.52 0.027

Sep 330-360 0.18 0.52 0.051 0.18 0.52 0.051 0.18 0.52 0.051

Oct 0-30 0.18 0.52 0.040 0.18 0.52 0.040 0.18 0.52 0.040

Oct 30-60 0.18 0.52 0.040 0.18 0.52 0.040 0.18 0.52 0.040

Oct 60-90 0.18 0.52 0.053 0.18 0.52 0.053 0.18 0.52 0.053

Oct 90-120 0.18 0.52 0.041 0.18 0.52 0.041 0.18 0.52 0.041

Oct 120-150 0.18 0.52 0.043 0.18 0.52 0.043 0.18 0.52 0.043

Oct 150-180 0.18 0.52 0.059 0.18 0.52 0.059 0.18 0.52 0.059

Oct 180-210 0.18 0.52 0.046 0.18 0.52 0.046 0.18 0.52 0.046

Oct 210-240 0.18 0.52 0.033 0.18 0.52 0.033 0.18 0.52 0.033
A-20

Station = IND 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Oct 240-270 0.18 0.52 0.037 0.18 0.52 0.037 0.18 0.52 0.037

Oct 270-300 0.18 0.52 0.036 0.18 0.52 0.036 0.18 0.52 0.036

Oct 300-330 0.18 0.52 0.027 0.18 0.52 0.027 0.18 0.52 0.027

Oct 330-360 0.18 0.52 0.051 0.18 0.52 0.051 0.18 0.52 0.051

Nov 0-30 0.18 0.52 0.040 0.18 2.26 0.040 0.18 0.52 0.040

Nov 30-60 0.18 0.52 0.040 0.18 2.26 0.040 0.18 0.52 0.040

Nov 60-90 0.18 0.52 0.053 0.18 2.26 0.053 0.18 0.52 0.053

Nov 90-120 0.18 0.52 0.041 0.18 2.26 0.041 0.18 0.52 0.041

Nov 120-150 0.18 0.52 0.043 0.18 2.26 0.043 0.18 0.52 0.043

Nov 150-180 0.18 0.52 0.059 0.18 2.26 0.059 0.18 0.52 0.059

Nov 180-210 0.18 0.52 0.046 0.18 2.26 0.046 0.18 0.52 0.046

Nov 210-240 0.18 0.52 0.033 0.18 2.26 0.033 0.18 0.52 0.033

Nov 240-270 0.18 0.52 0.037 0.18 2.26 0.037 0.18 0.52 0.037

Nov 270-300 0.18 0.52 0.036 0.18 2.26 0.036 0.18 0.52 0.036

Nov 300-330 0.18 0.52 0.027 0.18 2.26 0.027 0.18 0.52 0.027

Nov 330-360 0.18 0.52 0.051 0.18 2.26 0.051 0.18 0.52 0.051

Dec 0-30 0.18 0.52 0.032 0.18 0.89 0.032 0.18 0.52 0.032

Dec 30-60 0.18 0.52 0.033 0.18 0.89 0.033 0.18 0.52 0.033

Dec 60-90 0.18 0.52 0.046 0.18 0.89 0.046 0.18 0.52 0.046

Dec 90-120 0.18 0.52 0.030 0.18 0.89 0.030 0.18 0.52 0.030

Dec 120-150 0.18 0.52 0.031 0.18 0.89 0.031 0.18 0.52 0.031

Dec 150-180 0.18 0.52 0.040 0.18 0.89 0.040 0.18 0.52 0.040

Dec 180-210 0.18 0.52 0.027 0.18 0.89 0.027 0.18 0.52 0.027

Dec 210-240 0.18 0.52 0.016 0.18 0.89 0.016 0.18 0.52 0.016

Dec 240-270 0.18 0.52 0.022 0.18 0.89 0.022 0.18 0.52 0.022

Dec 270-300 0.18 0.52 0.022 0.18 0.89 0.022 0.18 0.52 0.022

Dec 300-330 0.18 0.52 0.019 0.18 0.89 0.019 0.18 0.52 0.019

Dec 330-360 0.18 0.52 0.041 0.18 0.89 0.041 0.18 0.52 0.041
A-21

Table A-8. Surface characteristics for Tulsa R. L. Jones Jr. (RVS) by month and year.

Station = RVS 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Jan 0-30 0.18 0.48 0.055 0.18 0.87 0.055 0.18 1.96 0.055

Jan 30-60 0.18 0.48 0.031 0.18 0.87 0.031 0.18 1.96 0.031

Jan 60-90 0.18 0.48 0.043 0.18 0.87 0.043 0.18 1.96 0.043

Jan 90-120 0.18 0.48 0.039 0.18 0.87 0.039 0.18 1.96 0.039

Jan 120-150 0.18 0.48 0.030 0.18 0.87 0.030 0.18 1.96 0.030

Jan 150-180 0.18 0.48 0.059 0.18 0.87 0.059 0.18 1.96 0.059

Jan 180-210 0.18 0.48 0.048 0.18 0.87 0.048 0.18 1.96 0.048

Jan 210-240 0.18 0.48 0.110 0.18 0.87 0.110 0.18 1.96 0.110

Jan 240-270 0.18 0.48 0.083 0.18 0.87 0.083 0.18 1.96 0.083

Jan 270-300 0.18 0.48 0.057 0.18 0.87 0.057 0.18 1.96 0.057

Jan 300-330 0.18 0.48 0.083 0.18 0.87 0.083 0.18 1.96 0.083

Jan 330-360 0.18 0.48 0.044 0.18 0.87 0.044 0.18 1.96 0.044

Feb 0-30 0.18 0.48 0.055 0.18 0.87 0.055 0.18 1.96 0.055

Feb 30-60 0.18 0.48 0.031 0.18 0.87 0.031 0.18 1.96 0.031

Feb 60-90 0.18 0.48 0.043 0.18 0.87 0.043 0.18 1.96 0.043

Feb 90-120 0.18 0.48 0.039 0.18 0.87 0.039 0.18 1.96 0.039

Feb 120-150 0.18 0.48 0.030 0.18 0.87 0.030 0.18 1.96 0.030

Feb 150-180 0.18 0.48 0.059 0.18 0.87 0.059 0.18 1.96 0.059

Feb 180-210 0.18 0.48 0.048 0.18 0.87 0.048 0.18 1.96 0.048

Feb 210-240 0.18 0.48 0.110 0.18 0.87 0.110 0.18 1.96 0.110

Feb 240-270 0.18 0.48 0.083 0.18 0.87 0.083 0.18 1.96 0.083

Feb 270-300 0.18 0.48 0.057 0.18 0.87 0.057 0.18 1.96 0.057

Feb 300-330 0.18 0.48 0.083 0.18 0.87 0.083 0.18 1.96 0.083

Feb 330-360 0.18 0.48 0.044 0.18 0.87 0.044 0.18 1.96 0.044

Mar 0-30 0.16 0.36 0.088 0.16 0.56 0.088 0.16 1.37 0.088

Mar 30-60 0.16 0.36 0.056 0.16 0.56 0.056 0.16 1.37 0.056

Mar 60-90 0.16 0.36 0.070 0.16 0.56 0.070 0.16 1.37 0.070

Mar 90-120 0.16 0.36 0.072 0.16 0.56 0.072 0.16 1.37 0.072

Mar 120-150 0.16 0.36 0.052 0.16 0.56 0.052 0.16 1.37 0.052

Mar 150-180 0.16 0.36 0.068 0.16 0.56 0.068 0.16 1.37 0.068

Mar 180-210 0.16 0.36 0.063 0.16 0.56 0.063 0.16 1.37 0.063

Mar 210-240 0.16 0.36 0.219 0.16 0.56 0.219 0.16 1.37 0.219

Mar 240-270 0.16 0.36 0.133 0.16 0.56 0.133 0.16 1.37 0.133

Mar 270-300 0.16 0.36 0.095 0.16 0.56 0.095 0.16 1.37 0.095

Mar 300-330 0.16 0.36 0.133 0.16 0.56 0.133 0.16 1.37 0.133

Mar 330-360 0.16 0.36 0.083 0.16 0.56 0.083 0.16 1.37 0.083

Apr 0-30 0.16 0.36 0.088 0.16 0.56 0.088 0.16 1.37 0.088

Apr 30-60 0.16 0.36 0.056 0.16 0.56 0.056 0.16 1.37 0.056
A-22

Station = RVS 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Apr 60-90 0.16 0.36 0.070 0.16 0.56 0.070 0.16 1.37 0.070

Apr 90-120 0.16 0.36 0.072 0.16 0.56 0.072 0.16 1.37 0.072

Apr 120-150 0.16 0.36 0.052 0.16 0.56 0.052 0.16 1.37 0.052

Apr 150-180 0.16 0.36 0.068 0.16 0.56 0.068 0.16 1.37 0.068

Apr 180-210 0.16 0.36 0.063 0.16 0.56 0.063 0.16 1.37 0.063

Apr 210-240 0.16 0.36 0.219 0.16 0.56 0.219 0.16 1.37 0.219

Apr 240-270 0.16 0.36 0.133 0.16 0.56 0.133 0.16 1.37 0.133

Apr 270-300 0.16 0.36 0.095 0.16 0.56 0.095 0.16 1.37 0.095

Apr 300-330 0.16 0.36 0.133 0.16 0.56 0.133 0.16 1.37 0.133

Apr 330-360 0.16 0.36 0.083 0.16 0.56 0.083 0.16 1.37 0.083

May 0-30 0.16 0.36 0.088 0.16 0.56 0.088 0.16 1.37 0.088

May 30-60 0.16 0.36 0.056 0.16 0.56 0.056 0.16 1.37 0.056

May 60-90 0.16 0.36 0.070 0.16 0.56 0.070 0.16 1.37 0.070

May 90-120 0.16 0.36 0.072 0.16 0.56 0.072 0.16 1.37 0.072

May 120-150 0.16 0.36 0.052 0.16 0.56 0.052 0.16 1.37 0.052

May 150-180 0.16 0.36 0.068 0.16 0.56 0.068 0.16 1.37 0.068

May 180-210 0.16 0.36 0.063 0.16 0.56 0.063 0.16 1.37 0.063

May 210-240 0.16 0.36 0.219 0.16 0.56 0.219 0.16 1.37 0.219

May 240-270 0.16 0.36 0.133 0.16 0.56 0.133 0.16 1.37 0.133

May 270-300 0.16 0.36 0.095 0.16 0.56 0.095 0.16 1.37 0.095

May 300-330 0.16 0.36 0.133 0.16 0.56 0.133 0.16 1.37 0.133

May 330-360 0.16 0.36 0.083 0.16 0.56 0.083 0.16 1.37 0.083

Jun 0-30 0.17 0.38 0.250 0.17 0.57 0.250 0.17 1.33 0.250

Jun 30-60 0.17 0.38 0.114 0.17 0.57 0.114 0.17 1.33 0.114

Jun 60-90 0.17 0.38 0.131 0.17 0.57 0.131 0.17 1.33 0.131

Jun 90-120 0.17 0.38 0.138 0.17 0.57 0.138 0.17 1.33 0.138

Jun 120-150 0.17 0.38 0.098 0.17 0.57 0.098 0.17 1.33 0.098

Jun 150-180 0.17 0.38 0.075 0.17 0.57 0.075 0.17 1.33 0.075

Jun 180-210 0.17 0.38 0.107 0.17 0.57 0.107 0.17 1.33 0.107

Jun 210-240 0.17 0.38 0.389 0.17 0.57 0.389 0.17 1.33 0.389

Jun 240-270 0.17 0.38 0.318 0.17 0.57 0.318 0.17 1.33 0.318

Jun 270-300 0.17 0.38 0.265 0.17 0.57 0.265 0.17 1.33 0.265

Jun 300-330 0.17 0.38 0.325 0.17 0.57 0.325 0.17 1.33 0.325

Jun 330-360 0.17 0.38 0.244 0.17 0.57 0.244 0.17 1.33 0.244

Jul 0-30 0.17 0.38 0.250 0.17 0.57 0.250 0.17 1.33 0.250

Jul 30-60 0.17 0.38 0.114 0.17 0.57 0.114 0.17 1.33 0.114

Jul 60-90 0.17 0.38 0.131 0.17 0.57 0.131 0.17 1.33 0.131

Jul 90-120 0.17 0.38 0.138 0.17 0.57 0.138 0.17 1.33 0.138

Jul 120-150 0.17 0.38 0.098 0.17 0.57 0.098 0.17 1.33 0.098
A-23

Station = RVS 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Jul 150-180 0.17 0.38 0.075 0.17 0.57 0.075 0.17 1.33 0.075

Jul 180-210 0.17 0.38 0.107 0.17 0.57 0.107 0.17 1.33 0.107

Jul 210-240 0.17 0.38 0.389 0.17 0.57 0.389 0.17 1.33 0.389

Jul 240-270 0.17 0.38 0.318 0.17 0.57 0.318 0.17 1.33 0.318

Jul 270-300 0.17 0.38 0.265 0.17 0.57 0.265 0.17 1.33 0.265

Jul 300-330 0.17 0.38 0.325 0.17 0.57 0.325 0.17 1.33 0.325

Jul 330-360 0.17 0.38 0.244 0.17 0.57 0.244 0.17 1.33 0.244

Aug 0-30 0.17 0.38 0.250 0.17 0.57 0.250 0.17 1.33 0.250

Aug 30-60 0.17 0.38 0.114 0.17 0.57 0.114 0.17 1.33 0.114

Aug 60-90 0.17 0.38 0.131 0.17 0.57 0.131 0.17 1.33 0.131

Aug 90-120 0.17 0.38 0.138 0.17 0.57 0.138 0.17 1.33 0.138

Aug 120-150 0.17 0.38 0.098 0.17 0.57 0.098 0.17 1.33 0.098

Aug 150-180 0.17 0.38 0.075 0.17 0.57 0.075 0.17 1.33 0.075

Aug 180-210 0.17 0.38 0.107 0.17 0.57 0.107 0.17 1.33 0.107

Aug 210-240 0.17 0.38 0.389 0.17 0.57 0.389 0.17 1.33 0.389

Aug 240-270 0.17 0.38 0.318 0.17 0.57 0.318 0.17 1.33 0.318

Aug 270-300 0.17 0.38 0.265 0.17 0.57 0.265 0.17 1.33 0.265

Aug 300-330 0.17 0.38 0.325 0.17 0.57 0.325 0.17 1.33 0.325

Aug 330-360 0.17 0.38 0.244 0.17 0.57 0.244 0.17 1.33 0.244

Sep 0-30 0.17 0.48 0.250 0.17 0.87 0.250 0.17 1.96 0.250

Sep 30-60 0.17 0.48 0.114 0.17 0.87 0.114 0.17 1.96 0.114

Sep 60-90 0.17 0.48 0.131 0.17 0.87 0.131 0.17 1.96 0.131

Sep 90-120 0.17 0.48 0.138 0.17 0.87 0.138 0.17 1.96 0.138

Sep 120-150 0.17 0.48 0.098 0.17 0.87 0.098 0.17 1.96 0.098

Sep 150-180 0.17 0.48 0.075 0.17 0.87 0.075 0.17 1.96 0.075

Sep 180-210 0.17 0.48 0.107 0.17 0.87 0.107 0.17 1.96 0.107

Sep 210-240 0.17 0.48 0.389 0.17 0.87 0.389 0.17 1.96 0.389

Sep 240-270 0.17 0.48 0.318 0.17 0.87 0.318 0.17 1.96 0.318

Sep 270-300 0.17 0.48 0.265 0.17 0.87 0.265 0.17 1.96 0.265

Sep 300-330 0.17 0.48 0.325 0.17 0.87 0.325 0.17 1.96 0.325

Sep 330-360 0.17 0.48 0.244 0.17 0.87 0.244 0.17 1.96 0.244

Oct 0-30 0.17 0.48 0.250 0.17 0.87 0.250 0.17 1.96 0.250

Oct 30-60 0.17 0.48 0.114 0.17 0.87 0.114 0.17 1.96 0.114

Oct 60-90 0.17 0.48 0.131 0.17 0.87 0.131 0.17 1.96 0.131

Oct 90-120 0.17 0.48 0.138 0.17 0.87 0.138 0.17 1.96 0.138

Oct 120-150 0.17 0.48 0.098 0.17 0.87 0.098 0.17 1.96 0.098

Oct 150-180 0.17 0.48 0.075 0.17 0.87 0.075 0.17 1.96 0.075

Oct 180-210 0.17 0.48 0.107 0.17 0.87 0.107 0.17 1.96 0.107

Oct 210-240 0.17 0.48 0.389 0.17 0.87 0.389 0.17 1.96 0.389
A-24

Station = RVS 2011 2012 2013

Month Sector

(degrees) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m) Albedo BowenRatio

Roughness

(m)

Oct 240-270 0.17 0.48 0.318 0.17 0.87 0.318 0.17 1.96 0.318

Oct 270-300 0.17 0.48 0.265 0.17 0.87 0.265 0.17 1.96 0.265

Oct 300-330 0.17 0.48 0.325 0.17 0.87 0.325 0.17 1.96 0.325

Oct 330-360 0.17 0.48 0.244 0.17 0.87 0.244 0.17 1.96 0.244

Nov 0-30 0.17 0.48 0.250 0.17 0.87 0.250 0.17 1.96 0.250

Nov 30-60 0.17 0.48 0.114 0.17 0.87 0.114 0.17 1.96 0.114

Nov 60-90 0.17 0.48 0.131 0.17 0.87 0.131 0.17 1.96 0.131

Nov 90-120 0.17 0.48 0.138 0.17 0.87 0.138 0.17 1.96 0.138

Nov 120-150 0.17 0.48 0.098 0.17 0.87 0.098 0.17 1.96 0.098

Nov 150-180 0.17 0.48 0.075 0.17 0.87 0.075 0.17 1.96 0.075

Nov 180-210 0.17 0.48 0.107 0.17 0.87 0.107 0.17 1.96 0.107

Nov 210-240 0.17 0.48 0.389 0.17 0.87 0.389 0.17 1.96 0.389

Nov 240-270 0.17 0.48 0.318 0.17 0.87 0.318 0.17 1.96 0.318

Nov 270-300 0.17 0.48 0.265 0.17 0.87 0.265 0.17 1.96 0.265

Nov 300-330 0.17 0.48 0.325 0.17 0.87 0.325 0.17 1.96 0.325

Nov 330-360 0.17 0.48 0.244 0.17 0.87 0.244 0.17 1.96 0.244

Dec 0-30 0.18 0.48 0.055 0.18 0.87 0.055 0.18 1.96 0.055

Dec 30-60 0.18 0.48 0.031 0.18 0.87 0.031 0.18 1.96 0.031

Dec 60-90 0.18 0.48 0.043 0.18 0.87 0.043 0.18 1.96 0.043

Dec 90-120 0.18 0.48 0.039 0.18 0.87 0.039 0.18 1.96 0.039

Dec 120-150 0.18 0.48 0.030 0.18 0.87 0.030 0.18 1.96 0.030

Dec 150-180 0.18 0.48 0.059 0.18 0.87 0.059 0.18 1.96 0.059

Dec 180-210 0.18 0.48 0.048 0.18 0.87 0.048 0.18 1.96 0.048

Dec 210-240 0.18 0.48 0.110 0.18 0.87 0.110 0.18 1.96 0.110

Dec 240-270 0.18 0.48 0.083 0.18 0.87 0.083 0.18 1.96 0.083

Dec 270-300 0.18 0.48 0.057 0.18 0.87 0.057 0.18 1.96 0.057

Dec 300-330 0.18 0.48 0.083 0.18 0.87 0.083 0.18 1.96 0.083

Dec 330-360 0.18 0.48 0.044 0.18 0.87 0.044 0.18 1.96 0.044
A-25

REFERENCES

U.S. EPA. (2013). AERSURFACE User’s Guide. U.S. Environmental Protection Agency. EPA

454/B-08-001. Revised January 16, 2013. 

U.S. EPA. (2015). AERMINUTE User’s Guide. U.S. Environmental Protection Agency. EPA

454/B-15-006.

U.S. EPA. (2016a). User's Guide for the AMS/EPA Regulatory Model – AERMOD. U.S. 

Environmental Protection Agency. 454/B-16-011.

U.S. EPA. (2016b). User’s Guide for the AERMOD Meteorological Processor (AERMET). U.S. 

Environmental Protection Agency. EPA-454/B-16-010.
B-1

APPENDIX B

DEVELOPMENT OF HOURLY EMISSIONS PROFILES

Preface: The source type influenced how the hourly emissions profiles were developed. The 

methods followed are summarized below separately for EGU and other sources.

B.1 EGU Sources

The NEI stores references to the Office of Regulatory Information Systems (ORIS) 

identification code for most sources that have Continuous Emissions Monitoring System 

(CEMS) data in the CAMD database. For these stacks the relative hourly profiles were derived 

from the hourly values in the CAMD database, and the annual emissions totals were taken from 

the NEI (Table B-1). EGU emissions came from the NEI for their respective years. Where 

CEMS data was available, the CEMS emissions values were used and the emissions in the 

annual inventory were adjusted to match the temporal pattern of the year-specific CEMS data. 

The EGU units with more than 20 tons of SO2 emissions in at least one year for which CEMS 

data are available are listed in Table B-1 along with their annual SO2 emissions for 2011, 2012, 

and 2013. Sources at the SEMASS Partnership facility (county 25023 and facility ID 8127611) 

and IP&L – Harding Street (county 18097 and facility ID 7255211) are designated as EGUs but 

are not matched to sources in the CAMD database. These sources were temporalized to hourly 

values using average temporal profiles that were derived based on other EGU units in their 

respective regions.
B-2

Table B-1. SO2 emissions each year for EGUs included in the air quality modeling.

FIPS Facility Name Facility ID Unit ID 2011 2012 2013

25005 BRAYTON POINT ENERGY LLC 5058411 87339613 3,535 1,228 1,625

25005 BRAYTON POINT ENERGY LLC 5058411 87339713 45 12 118

25005 BRAYTON POINT ENERGY LLC 5058411 87340713 4,298 1,859 1,383

25005 BRAYTON POINT ENERGY LLC 5058411 87340813 10,769 6,033 4,479

18097 IP&L - HARDING STREET 7255211 91188613 8,634 10,531 13,324

18097 IP&L - HARDING STREET 7255211 91188713 7,941 10,270 12,603

18097 IP&L - HARDING STREET 7255211 91188813 681 632 1,846

18097 IP&L - HARDING STREET 7255211 91188813 1,739 109 200

40131 PSO NORTHEASTERN PWR STA 8212411 6698813 8,039 9,008

40131 PSO NORTHEASTERN PWR STA 8212411 6698813 8,879

40131 PSO NORTHEASTERN PWR STA 8212411 6698813 20 38

40131 PSO NORTHEASTERN PWR STA 8212411 6698813 26

40131 PSO NORTHEASTERN PWR STA 8212411 6698313 7,402 9,337

40131 PSO NORTHEASTERN PWR STA 8212411 6698313 9,008

40131 PSO NORTHEASTERN PWR STA 8212411 6698313 27 22

40131 PSO NORTHEASTERN PWR STA 8212411 6698313 26

B.2 Non-EGU Sources 

For non-EGU sources that did not have hourly SO2 data in the CAMD database, SCCspecific temporal profiles from EPA’s 2011v6.3 emissions modeling platform were used to 

prepare the hourly factors. Stacks with emissions greater than 20 tons of SO2 in 2011, 2012, or 

2013 for which temporal profiles were used are listed in Table B-2 below. The allocation of the 

sources to the hourly factors needed for AERMOD was done using tools available within the 

Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system version 4.5 (UNC, 2017). 

The tools support the generation of “helper files” from which the AERMOD input files can be 

derived. The temporal values output from SMOKE were renormalized from scalars to factors 

that sum to 1 to aid with quality assurance and usability of the factors.
B-3

Table B-2. SO2 emissions each year for non-EGU release points included in the air quality 

modeling1.

FIPS Facility Name Facility ID Unit ID Release 

Point ID

2011 2012 2013

18097 Citizens Thermal 4885311 100805413 30985212 2,094 1,849 1,575

18097 Citizens Thermal 4885311 100805713 30985012 1,029 853 855

18097 Citizens Thermal 4885311 100805813 30985012 1,225 1,150 1,375

40037 SAPULPA 7320611 72251213 66374812 79 79 98

40037 SAPULPA 7320611 8331413 8217312 33 33 34

40037 SAPULPA 7320611 8331213 8217212 100 100 108

18097 VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC

7972111 65408713 60023412 20 17 20

18097 QUEMETCO, INC. 8235411 65358713 5022512 49 49 16

18097 QUEMETCO, INC. 8235411 65358713 5022612 71 71 69

40143 TULSA RFNRY WEST 8402711 654613 655312 103 42 26

40143 TULSA RFNRY WEST 8402711 654413 660012 45 20 9

40143 TULSA RFNRY WEST 8402711 654313 659912 380 237 169

40143 TULSA RFNRY WEST 8402711 654113 663512 36 18 11

40143 TULSA RFNRY WEST 8402711 651713 655012 59 65 24

40143 TULSA RFNRY WEST 8402711 651413 661212 270 210 125

40143 TULSA RFNRY WEST 8402711 651313 658812 43 41 11

40143 TULSA RFNRY WEST 8402711 651113 662812 39

40143 TULSA RFNRY WEST 8402711 651113 662812 43 17

40143 TULSA RFNRY WEST 8402711 651013 658912 157 150 37

40143 TULSA RFNRY WEST 8402711 650913 654912 74 55 34

40143 TULSA RFNRY WEST 8402711 650813 656012 38 46 8

40143 TULSA RFNRY WEST 8402711 663113 651512 866 688 360

40143 TULSA RFNRY WEST 8402711 658713 651412 460 370 211

The emissions factors developed for non-EGU sources were monthly, hour-of-day, or 

month-hour-of-day, where day was weekday, Saturday, or Sunday. These emission factors 

correspond to the MONTH, HROFDY, and MHRDOW emission factors used in AERMOD 

(U.S. EPA, 2016). These emission factors are set to sum to 1 for each source. For example, for a 

source using the MONTH emission factors, the 12 monthly factors sum to 1. This means that a 

particular month’s factor allocates a portion of the annual emissions to that month. Further 

processing is needed to create hourly emissions for the sources. For monthly factors, the monthly 

factor is divided by the number of hours in the month (number of days x 24 hours) and this ratio 

is multiplied by the annual emissions to get an hourly emission rate and this rate is then 

converted to a g/s rate. This rate is then input into AERMOD as the MONTH emission factor, 

and the reference emission rate in AERMOD (emission rate on the SRCPARAM line in the 

 

1 Based on units emitting over 20 tons of SO2.
B-4

AERMOD input file) is set to 1.0. This method creates an hourly emission rate while conserving 

the annual emissions. 

Consider a source with the following monthly factors (Table B-3) output from SMOKE 

for 2011 and annual emissions of 100.32 tons. The factors divide the emissions equally across 

the months, resulting in the monthly emissions (in tons) shown for each month. To convert the 

monthly emissions for a given month, to g/s, the following equation is used:

251.9957778

24

1 1





























 

month

hour annual Days

E E

Equation B-1

Where Ehour is the hourly emission rate in g/s, Eannual are the annual emissions in tons, Daysmonth

are the number of days in the month (31 days for January, etc.), 1/24 is the reciprocal of the 

number of hours in a day, and 251.9957778 is the conversion factor to convert from tons/hour to 

g/s. The resulting hourly emissions rates are also shown in Table B-3. Figure B-1 shows how the 

hourly emissions are input into AERMOD using the SRCPARAM and EMISFACT keywords. 

Equation 1 is also used to calculate the MHRDOW emissions and a similar form of Equation 1 is 

used for HROFDY emissions, with the exception that 1/Daysmonth is 1/365 (number of days in the 

year).

Table B-3. Example calculation of hourly emissions using the SMOKE MONTH temporal 

factors for 2011.

Month SMOKE factor Daysmonth Ehour (g/s)

January 0.083333 31 2.831565

February 0.083333 28 3.134947

March 0.083333 31 2.831565

April 0.083333 30 2.925951

May 0.083333 31 2.831565

June 0.083333 30 2.925951

July 0.083333 31 2.831565

August 0.083333 31 2.831565

September 0.083333 30 2.925951

October 0.083333 31 2.831565

November 0.083333 30 2.925951

December 0.083333 31 2.831565
B-5

SO SRCPARAM SAP_SN1 1.000000E+00 28.35000 530.37000 9.60000 1.86000

SO EMISFACT SAP_SN1 MONTH 2.831565E+00 3.134947E+00 

SO EMISFACT SAP_SN1 MONTH 2.831565E+00 2.925951E+00 

SO EMISFACT SAP_SN1 MONTH 2.831565E+00 2.925951E+00

SO EMISFACT SAP_SN1 MONTH 2.831565E+00 2.831565E+00 

SO EMISFACT SAP_SN1 MONTH 2.925951E+00 2.831565E+00 

SO EMISFACT SAP_SN1 MONTH 2.925951E+00 2.831565E+00

Figure B-1. Example AERMOD input emission lines for monthly emissions.

B.3 AERMOD inputs

Tables B-4 through B-41 list the cross walks between facility unit identifiers and 

AERMOD source identifiers and the 2011-2013 AERMOD inputs for each of the three study 

areas. Note that the AERMOD source identifiers are unique to each year. In some cases, a 

particular emission release point may not have an AERMOD source identifier for one year but 

may have an identifier for other years. Years in which a release point does not have an 

AERMOD identifier are left as blanks.
B-6

Table B-4. Fall River 2011-2013 AERMOD source identifier crosswalk.

Facility Name Unit ID Process ID Release Point ID AERMOD 2011 AERMOD 2012 AERMOD 2013

BRAYTON POINT ENERGY LLC 87339613 83612912 118371314 BRAY_SE1 BRAY_SE1 BRAY_SE1

BRAYTON POINT ENERGY LLC 87339613 83612912 118371714 BRAY_SE2 BRAY_SE2 BRAY_SE2

BRAYTON POINT ENERGY LLC 87339713 83613312 118371814 BRAY_SE3 BRAY_SE3 BRAY_SE3

BRAYTON POINT ENERGY LLC 87339713 83613312 118371914 BRAY_SE4 BRAY_SE4 BRAY_SE4

BRAYTON POINT ENERGY LLC 87339713 83613312 118372014 BRAY_SE5

BRAYTON POINT ENERGY LLC 87339713 83613312 118372114 BRAY_SE5 BRAY_SE6 BRAY_SE5

BRAYTON POINT ENERGY LLC 87340713 83612812 118373214 BRAY_SE6 BRAY_SE7 BRAY_SE6

BRAYTON POINT ENERGY LLC 87340713 83612812 118373514 BRAY_SE7 BRAY_SE8 BRAY_SE7

BRAYTON POINT ENERGY LLC 87340813 83612612 118373614 BRAY_SE8 BRAY_SE9 BRAY_SE8

BRAYTON POINT ENERGY LLC 87340813 83612612 118373714 BRAY_SE9 BRAY_SE10 BRAY_SE9

BRAYTON POINT ENERGY LLC 90543213 83613612 122762214 BRAY_SN1 BRAY_SN1 BRAY_SN1

BRAYTON POINT ENERGY LLC 90543413 83613612 122762414 BRAY_SN1 BRAY_SN1 BRAY_SN1

BRAYTON POINT ENERGY LLC 87341513 83613212 118374814 BRAY_SN2 BRAY_SN2 BRAY_SN2

BRAYTON POINT ENERGY LLC 87341613 83612512 118374914 BRAY_SN2 BRAY_SN2 BRAY_SN2
B-7

Table B-5. 2011 Fall River point source emissions, locations, and stack parameters.

Facility Name AERMDOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

BRAYTON POINT 

ENERGY LLC BRAY_SE1 3534.90 HOURLY 317613.67 4620047.98 5.07 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE2 0.01 HOURLY 317613.67 4620047.98 5.07 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE3 45.24 HOURLY 317536.89 4620117.91 4.72 152.40 432.04 21.73 5.64

BRAYTON POINT 

ENERGY LLC BRAY_SE4 0.77 HOURLY 317536.89 4620117.91 4.72 152.40 432.04 21.73 5.64

BRAYTON POINT 

ENERGY LLC BRAY_SE5 0.11 HOURLY 317536.89 4620117.91 4.72 152.40 432.04 21.73 5.64

BRAYTON POINT 

ENERGY LLC BRAY_SE6 4298.40 HOURLY 317639.35 4620024.01 5.56 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE7 0.01 HOURLY 317639.35 4620024.01 5.56 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE8 0.02 HOURLY 317577.42 4620064.54 4.81 107.29 405.37 24.93 5.94

BRAYTON POINT 

ENERGY LLC BRAY_SE9 10769.00 HOURLY 317577.42 4620064.54 4.81 107.29 405.37 24.93 5.94

BRAYTON POINT 

ENERGY LLC BRAY_SN2 0.0004 MONTH 317600.47 4619900.00 8.20 3.66 783.15 24.66 0.30

Table B-6. 2011 Fall River area source emissions, locations, and stack parameters.

Facility Name AERMDOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Release 

height 

(m)

Xdimension 

(m)

Ydimensio

n (m)

Angle σz 

(m)

BRAYTON POINT 

ENERGY LLC BRAY_SN1 3534.90 MONTH 317600.47 4619900.00 8.20 3.05 10.0 10.0 0.0 0.0
B-8

Table B-7. 2012 Fall River point source emissions, locations, and stack parameters.

Facility Name AERMDOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

BRAYTON POINT 

ENERGY LLC BRAY_SE1 1228.40 HOURLY 317613.67 4620047.98 5.07 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE2 0.08 HOURLY 317613.67 4620047.98 5.07 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE3 12.14 HOURLY 317536.89 4620117.91 4.72 152.40 432.04 21.73 5.64

BRAYTON POINT 

ENERGY LLC BRAY_SE4 1.81 HOURLY 317536.89 4620117.91 4.72 152.40 432.04 21.73 5.64

BRAYTON POINT 

ENERGY LLC BRAY_SE5 1.53 HOURLY 317536.89 4620117.91 4.72 152.40 432.04 21.73 5.64

BRAYTON POINT 

ENERGY LLC BRAY_SE6 0.33 HOURLY 317639.35 4620024.01 5.56 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE7 1859.40 HOURLY 317639.35 4620024.01 5.56 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE8 0.17 HOURLY 317577.42 4620064.54 4.81 107.29 405.37 24.93 5.94

BRAYTON POINT 

ENERGY LLC BRAY_SE9 0.13 HOURLY 317577.42 4620064.54 4.81 107.29 405.37 24.93 5.94

BRAYTON POINT 

ENERGY LLC BRAY_SE10 6033.0 HOURLY 317577.42 4620064.54 4.81 107.29 405.37 24.93 5.94

BRAYTON POINT 

ENERGY LLC BRAY_SN2 0.0014 MONTH 317600.47 4619900.00 8.20 3.66 783.15 24.66 0.30

Table B-8. 2012 Fall River area source emissions, locations, and stack parameters.

Facility Name AERMDOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Release 

height 

(m)

Xdimension 

(m)

Ydimensio

n (m)

Angle σz 

(m)

BRAYTON POINT 

ENERGY LLC BRAY_SN1 0.008 MONTH 317600.47 4619900.00 8.20 3.05 10.0 10.0 0.0 0.0
B-9

Table B-9. 2013 Fall River point source emissions, locations, and stack parameters.

Facility Name AERMDOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

BRAYTON POINT 

ENERGY LLC BRAY_SE1 1625.20 HOURLY 317613.67 4620047.98 5.07 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE2 0.01 HOURLY 317613.67 4620047.98 5.07 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE3 118.06 HOURLY 317536.89 4620117.91 4.72 152.40 432.04 21.73 5.64

BRAYTON POINT 

ENERGY LLC BRAY_SE4 0.77 HOURLY 317536.89 4620117.91 4.72 152.40 432.04 21.73 5.64

BRAYTON POINT 

ENERGY LLC BRAY_SE5 0.11 HOURLY 317536.89 4620117.91 4.72 152.40 432.04 21.73 5.64

BRAYTON POINT 

ENERGY LLC BRAY_SE6 1383.00 HOURLY 317639.35 4620024.01 5.56 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE7 0.01 HOURLY 317639.35 4620024.01 5.56 107.29 383.15 20.45 4.42

BRAYTON POINT 

ENERGY LLC BRAY_SE8 0.02 HOURLY 317577.42 4620064.54 4.81 107.29 405.37 24.93 5.94

BRAYTON POINT 

ENERGY LLC BRAY_SE9 4479.30 HOURLY 317577.42 4620064.54 4.81 107.29 405.37 24.93 5.94

BRAYTON POINT 

ENERGY LLC BRAY_SN2 0.0004 MONTH 317600.47 4619900.00 8.20 3.66 783.15 24.66 0.30

Table B-10. 2013 Fall River area source emissions, locations, and stack parameters.

Facility Name AERMDOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Release 

height 

(m)

Xdimension 

(m)

Ydimensio

n (m)

Angle σz 

(m)

BRAYTON POINT 

ENERGY LLC BRAY_SN1 0.0005 MONTH 317600.47 4619900.00 8.20 3.05 10.0 10.0 0.0 0.0
B-10

Table B-11. Indianapolis, IN Indianapolis Belmont WWTP 2011-2013 AERMOD source identifier crosswalk.

Facility Name Unit ID Process ID Release Point ID AERMOD 2011 AERMOD 2012 AERMOD 2013

INDIANAPOLIS BELMONT WWTP 68272413 64154812 124267014 BELL_SN1 BELL_SN1

INDIANAPOLIS BELMONT WWTP 68272613 64155012 124267214 BELL_SN1 BELL_SN1

INDIANAPOLIS BELMONT WWTP 32403713 30985312 123964514 BELL_SN1 BELL_SN1 BELL_SN1

INDIANAPOLIS BELMONT WWTP 32403813 30985312 123964614 BELL_SN1 BELL_SN1 BELL_SN1

INDIANAPOLIS BELMONT WWTP 32403913 30985312 123964814 BELL_SN1 BELL_SN1 BELL_SN1

INDIANAPOLIS BELMONT WWTP 32404013 30985312 123964714 BELL_SN1 BELL_SN1 BELL_SN1

Table B-12. Indianapolis, IN Citizens Thermal 2011-2013 AERMOD source identifier crosswalk.

Facility Name Unit ID Process ID Release Point ID AERMOD 2011 AERMOD 2012 AERMOD 2013

Citizens Thermal 100805713 30985012 141379114 CIT_SN1 CIT_SN1 CIT_SN1

Citizens Thermal 100805813 30985012 141379414 CIT_SN1 CIT_SN1 CIT_SN1

Citizens Thermal 100805413 30985212 141378314 CIT_SN2 CIT_SN2 CIT_SN2

Citizens Thermal 100805713 30985012 141379214 CIT_SN3 CIT_SN3

Citizens Thermal 100805813 30985012 141379514 CIT_SN3 CIT_SN3

Citizens Thermal 100805313 30984812 141378114 CIT_SN3 CIT_SN4 CIT_SN4

Citizens Thermal 100805413 30984812 141378414 CIT_SN3 CIT_SN4 CIT_SN4

Citizens Thermal 100805513 30985212 141378714 CIT_SN4 CIT_SN5 CIT_SN5

Citizens Thermal 100805613 30985212 141378914 CIT_SN4 CIT_SN5 CIT_SN5

Citizens Thermal 100805913 30984812 141379714 CIT_SN5 CIT_SN6 CIT_SN6

Citizens Thermal 100806013 30984812 141379914 CIT_SN5 CIT_SN6 CIT_SN6
B-11

Table B-13. Indianapolis, IN IP&L Harding Street 2011-2013 AERMOD source identifier crosswalk.

Facility Name Unit ID Process ID Release Point ID AERMOD 2011 AERMOD 2012 AERMOD 2013

IP&L - HARDING STREET 91608313 87281612 124834714 IPL_SE1 IPL_SE1 IPL_SE1

IP&L - HARDING STREET 91608413 87281712 124834814 IPL_SE1 IPL_SE1 IPL_SE1

IP&L - HARDING STREET 91608213 87281512 124834614 IPL_SE2 IPL_SE2 IPL_SE2

IP&L - HARDING STREET 91188213 87281812 123965914 IPL_SE3 IPL_SE3

IP&L - HARDING STREET 91188313 87281912 123966114 IPL_SE4

IP&L - HARDING STREET 91188613 87281212 123966614 IPL_SE4 IPL_SE3 IPL_SE5

IP&L - HARDING STREET 91188713 87281312 123966814 IPL_SE5 IPL_SE4 IPL_SE6

IP&L - HARDING STREET 91188813 87281412 123966914 IPL_SE6 IPL_SE5 IPL_SE7

IP&L - HARDING STREET 91188813 101276612 123967114 IPL_SE7 IPL_SE6 IPL_SE8

IP&L - HARDING STREET 91608513 88573012 124834914 IPL_SE8 IPL_SE7 IPL_SE9
B-12

Table B-14. Indianapolis, IN Rolls Royce 2011-2013 AERMOD source identifier crosswalk.

Facility Name Unit ID Process ID Release Point ID AERMOD 2011 AERMOD 2012 AERMOD 2013

ROLLS ROYCE CORPORATION 68294413 64180912 124304714 RR_SN1 RR_SN1 RR_SN1

ROLLS ROYCE CORPORATION 68294313 64180812 124304614 RR_SN2

ROLLS ROYCE CORPORATION 2995413 2866112 124164914 RR_SN2

ROLLS ROYCE CORPORATION 2996413 2865012 124166414 RR_SN2 RR_SN3 RR_SN3

ROLLS ROYCE CORPORATION 2996513 2865912 124166514 RR_SN3 RR_SN4 RR_SN4

ROLLS ROYCE CORPORATION 2996613 2865112 124166614 RR_SN3 RR_SN4 RR_SN4

ROLLS ROYCE CORPORATION 2995313 2864812 124164814 RR_SN4 RR_SN5 RR_SN5

ROLLS ROYCE CORPORATION 2995813 64180712 124165414 RR_SN5 RR_SN6 RR_SN6

ROLLS ROYCE CORPORATION 2995413 2866112 124165114 RR_SN6 RR_SN7 RR_SN7

ROLLS ROYCE CORPORATION 2995313 2864812 124164714 RR_SN7

ROLLS ROYCE CORPORATION 2995413 2866112 124165014 RR_SN8 RR_SN8 RR_SN8

ROLLS ROYCE CORPORATION 2997413 2865812 41165514 RR_SN9 RR_SN9 RR_SN9

ROLLS ROYCE CORPORATION 2994913 2866312 124166214 RR_SN10 RR_SN10 RR_SN10

ROLLS ROYCE CORPORATION 2996113 2866812 124166014 RR_SN10 RR_SN10 RR_SN10

ROLLS ROYCE CORPORATION 2994913 2866312 124166114 RR_SN11 RR_SN11 RR_SN11

ROLLS ROYCE CORPORATION 2996113 2866812 124165914 RR_SN11 RR_SN11 RR_SN11

ROLLS ROYCE CORPORATION 2997513 2865612 124165814 RR_SN12 RR_SN12 RR_SN12

ROLLS ROYCE CORPORATION 2997613 2864712 124165714 RR_SN12 RR_SN12 RR_SN12

ROLLS ROYCE CORPORATION 2995913 2866412 124165614 RR_SN13 RR_SN13 RR_SN13

ROLLS ROYCE CORPORATION 2996213 2865412 124165514 RR_SN13 RR_SN13 RR_SN13

ROLLS ROYCE CORPORATION 2997413 2865812 124167114 RR_SN14 RR_SN14 RR_SN14

ROLLS ROYCE CORPORATION 2996713 2866912 124166714 RR_SN15 RR_SN15 RR_SN15
B-13

Table B-15. Indianapolis, IN Vertellus 2011-2013 AERMOD source identifier crosswalk.

Facility Name Unit ID Process ID Release Point ID AERMOD 2011 AERMOD 2012 AERMOD 2013

VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC 65408713 60023312 90663014 VERT_SN1 VERT_SN1

VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC 65408713 60023312 141512314 VERT_SN1 VERT_SN1

VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC 65408713 60023412 90662914 VERT_SN2 VERT_SN2 VERT_SN1

VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC 65408713 60023412 90663314 VERT_SN2 VERT_SN2 VERT_SN1

VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC 65408713 60023412 90663414 VERT_SN2 VERT_SN2 VERT_SN1

VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC 65408713 101303012 90662214 VERT_SN4 VERT_SN4

VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC 65408613 2863012 90661214 VERT_SN12 VERT_SN12 VERT_SN10

VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC 65408613 2861612 141511014 VERT_SN13 VERT_SN13 VERT_SN11

VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC 65408613 2864112 90660614 VERT_SN14 VERT_SN14 VERT_SN12

VERTELLUS AGRICULTURE & 

NUTRITION SPECIALTIES LLC 65408613 2863312 90660214 VERT_SN15 VERT_SN15 VERT_SN13
B-14

Table B-16. Indianapolis, IN Quemetco 2011-2013 AERMOD source identifier crosswalk.

Facility Name Unit ID Process ID Release Point ID AERMOD 2011 AERMOD 2012 AERMOD 2013

QUEMETCO, INC. 65358713 5022612 90566814 QUE_SN1 QUE_SN1 QUE_SN1

QUEMETCO, INC. 65358913 5022612 90567014 QUE_SN1 QUE_SN1 QUE_SN1

QUEMETCO, INC. 65358713 5022612 90566714 QUE_SN1

QUEMETCO, INC. 109197013 112719612 154715314 QUE_SN2

QUEMETCO, INC. 65358713 5022512 90566614 QUE_SN2 QUE_SN2 QUE_SN3

QUEMETCO, INC. 65359113 5022512 90567214 QUE_SN2 QUE_SN2 QUE_SN3
B-15

Table B-17. 2011 Indianapolis Belmont WWTP, Citizens Thermal, and IP&L Harding Street point source emissions, locations, 

and stack parameters.

Facility Name AERMOD 

source ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height (m)

Stack 

temperature (K)

Stack 

velocity

(m s-1)

Stack 

diameter (m)

INDIANAPOLIS 

BELMONT WWTP BELL_SN1 24.90 MONTH 568970.00 4397879.00 208.61 45.72 297.59 0.64 3.20

Citizens Thermal CIT_SN1 2254.90 MONTH 571351.00 4401766.00 216.45 82.91 566.48 4.60 4.42

Citizens Thermal CIT_SN2 2093.70 MONTH 571396.00 4401766.00 217.46 82.91 463.71 4.72 4.64

Citizens Thermal CIT_SN3 0.16 MONTH 571380.00 4401766.00 217.35 82.91 488.71 5.33 4.63

Citizens Thermal CIT_SN4 0.08 MONTH 571396.00 4401766.00 217.46 82.91 463.71 4.72 4.64

Citizens Thermal CIT_SN5 0.0005 MONTH 571380.00 4401766.00 217.35 82.91 488.71 5.33 4.63

IP&L - HARDING 

STREET IPL_SE1 0.11 HOURLY 569200.00 4396339.00 208.02 9.45 791.48 7.16 3.81

IP&L - HARDING 

STREET IPL_SE2 0.10 HOURLY 569180.00 4396327.00 207.98 9.75 791.48 7.16 3.81

IP&L - HARDING 

STREET IPL_SE3 0.10 HOURLY 568867.00 4396303.00 208.00 20.12 827.59 57.39 4.21

IP&L - HARDING 

STREET IPL_SE4 8633.50 HOURLY 568749.00 4396008.00 208.08 79.55 440.93 65.84 1.98

IP&L - HARDING 

STREET IPL_SE5 7940.50 HOURLY 568752.00 4395965.00 208.32 79.55 449.82 63.52 1.98

IP&L - HARDING 

STREET IPL_SE6 680.70 HOURLY 568984.00 4395792.00 206.56 172.21 329.26 14.33 6.10

IP&L - HARDING 

STREET IPL_SE7 1739.00 HOURLY 568984.00 4395792.00 206.56 172.21 414.82 23.44 6.10

IP&L - HARDING 

STREET IPL_SE8 0.20 HOURLY 569050.00 4396339.00 208.26 22.86 810.93 36.58 5.49
B-16

Table B-18. 2011 Rolls Royce, Vertellus, and Quemetco point source emissions, locations, and stack parameters.

Facility Name AERMOD 

source ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

ROLLS ROYCE RR_SN1 0.02 MONTH 567493.00 4398570.00 212.29 4.57 866.48 32.34 0.30

ROLLS ROYCE RR_SN2 0.89 HROFDY 567428.00 4398870.00 212.70 17.37 588.71 33.04 1.22

ROLLS ROYCE RR_SN3 16.17 HROFDY 567402.00 4398886.00 212.70 19.81 755.37 45.51 0.91

ROLLS ROYCE RR_SN9 3.60 MHRDOW 567435.00 4398899.00 212.72 9.14 866.48 21.21 1.52

ROLLS ROYCE RR_SN10 6.19 MONTH 567551.00 4399165.00 212.10 15.24 677.59 17.47 1.98

ROLLS ROYCE RR_SN11 0.06 MONTH 567551.00 4399165.00 212.10 15.24 677.59 17.47 1.98

ROLLS ROYCE RR_SN12 23.36 MONTH 567544.50 4399165.00 212.24 15.24 677.59 17.47 1.98

ROLLS ROYCE RR_SN13 1.56 MONTH 567512.00 4399163.00 212.51 18.29 533.15 6.52 1.22

ROLLS ROYCE RR_SN14 0.04 MHRDOW 567513.00 4399174.00 212.61 9.14 866.48 21.21 1.52

ROLLS ROYCE RR_SN15 0.002 MONTH 567439.00 4398911.00 212.70 15.24 755.37 13.53 1.68

VERTELLUS VERT_SN1 3.98 MONTH 566836.00 4399683.00 214.94 9.14 453.71 6.28 1.22

VERTELLUS VERT_SN2 26.43 MONTH 566981.00 4399746.00 215.16 9.14 504.26 7.53 1.22

VERTELLUS VERT_SN4 0.19 MONTH 566995.00 4399731.00 214.89 10.97 422.04 5.49 0.81

VERTELLUS VERT_SN12 0.04 MONTH 566851.06 4399666.50 214.85 20.42 823.15 5.09 1.07

VERTELLUS VERT_SN13 0.02 MONTH 566901.00 4399710.00 215.15 20.73 823.15 5.09 1.07

VERTELLUS VERT_SN14 0.05 MONTH 566866.94 4399637.00 214.79 21.64 633.15 6.10 1.52

VERTELLUS VERT_SN15 0.03 MONTH 566864.94 4399640.00 214.79 24.69 823.15 6.07 1.07

QUEMETCO QUE_SN1 70.78 MONTH 559977.54 4400993.45 235.78 30.48 327.04 16.86 1.22

QUEMETCO QUE_SN2 53.59 MONTH 559993.31 4400853.53 235.10 50.29 321.48 14.84 3.35
B-17

Table B-19. 2011 Indianapolis, IN Rolls Royce area source emissions, locations, and release parameters.

Facility 

Name

AERMOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Release 

height (m)

X-dimension 

(m)

Y-dimension 

(m) Angle σz (m)

ROLLS 

ROYCE RR_SN4 1.30 HROFDY 567593.31 4398478.50 211.876 3.05 10 10 0 0

ROLLS 

ROYCE RR_SN5 0.33 HROFDY 567359.62 4398742.50 212.987 3.05 10 10 0 0

ROLLS 

ROYCE RR_SN6 4.58 HROFDY 567492.69 4399179.00 212.8 3.05 10 10 0 0

ROLLS 

ROYCE RR_SN7 0.0003 MONTH 567593.31 4398478.50 211.876 3.05 10 10 0 0

ROLLS 

ROYCE RR_SN8 0.0007 MONTH 567492.69 4399179.00 212.8 3.05 10 10 0 0
B-18

Table B-20. 2012 Indianapolis Belmont WWTP, Citizens Thermal, and IP&L Harding Street point source emissions, locations, 

and stack parameters.

Facility Name AERMOD 

source ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height (m)

Stack 

temperature (K)

Stack 

velocity

(m s-1)

Stack 

diameter (m)

INDIANAPOLIS 

BELMONT WWTP BELL_SN1 24.90 MONTH 568970.00 4397879.00 208.61 45.72 297.59 0.64 3.20

Citizens Thermal CIT_SN1 2002.70 MONTH 571351.00 4401766.00 216.45 82.91 566.48 4.60 4.42

Citizens Thermal CIT_SN2 1849.50 MONTH 571396.00 4401766.00 217.46 82.91 463.71 4.72 4.64

Citizens Thermal CIT_SN3 0.0000004 MONTH 571351.00 4401766.00 216.45 82.91 566.48 4.60 4.42

Citizens Thermal CIT_SN4 0.18 MONTH 571380.00 4401766.00 217.35 82.91 488.71 5.33 4.63

Citizens Thermal CIT_SN5 0.07 MONTH 571396.00 4401766.00 217.46 82.91 463.71 4.72 4.64

Citizens Thermal CIT_SN6 0.001 MONTH 571380.00 4401766.00 217.35 82.91 488.71 5.33 4.63

IP&L - HARDING 

STREET IPL_SE1 0.19 HOURLY 569200.00 4396339.00 208.02 9.45 791.48 7.16 3.81

IP&L - HARDING 

STREET IPL_SE2 0.16 HOURLY 569180.00 4396327.00 207.98 9.75 791.48 7.16 3.81

IP&L - HARDING 

STREET IPL_SE3 10531.00 HOURLY 568749.00 4396008.00 208.08 79.55 440.93 65.84 1.98

IP&L - HARDING 

STREET IPL_SE4 10270.00 HOURLY 568752.00 4395965.00 208.32 79.55 449.82 63.52 1.98

IP&L - HARDING 

STREET IPL_SE5 632.10 HOURLY 568984.00 4395792.00 206.56 172.21 329.26 14.33 6.10

IP&L - HARDING 

STREET IPL_SE6 109.00 HOURLY 568984.00 4395792.00 206.56 172.21 414.82 23.44 6.10

IP&L - HARDING 

STREET IPL_SE7 0.20 HOURLY 569050.00 4396339.00 208.26 22.86 810.93 36.58 5.49
B-19

Table B-21. 2012 Rolls Royce, Vertellus, and Quemetco point source emissions, locations, and stack parameters.

Facility Name AERMOD 

source ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

ROLLS ROYCE RR_SN1 1.74 MONTH 567493.00 4398570.00 212.29 4.57 866.48 32.34 0.30

ROLLS ROYCE RR_SN2 0.23 HROFDY 567402.00 4398886.00 212.70 19.81 755.37 45.51 0.91

ROLLS ROYCE RR_SN3 0.70 HROFDY 567428.00 4398870.00 212.70 17.37 588.71 33.04 1.22

ROLLS ROYCE RR_SN4 13.98 HROFDY 567402.00 4398886.00 212.70 19.81 755.37 45.51 0.91

ROLLS ROYCE RR_SN9 3.49 MHRDOW 567435.00 4398899.00 212.72 9.14 866.48 21.21 1.52

ROLLS ROYCE RR_SN10 6.08 MONTH 567551.00 4399165.00 212.10 15.24 677.59 17.47 1.98

ROLLS ROYCE RR_SN11 0.03 MONTH 567551.00 4399165.00 212.10 15.24 677.59 17.47 1.98

ROLLS ROYCE RR_SN12 7.29 MONTH 567544.50 4399165.00 212.24 15.24 677.59 17.47 1.98

ROLLS ROYCE RR_SN13 1.57 MONTH 567512.00 4399163.00 212.51 18.29 533.15 6.52 1.22

ROLLS ROYCE RR_SN14 0.02 MHRDOW 567513.00 4399174.00 212.61 9.14 866.48 21.21 1.52

ROLLS ROYCE RR_SN15 0.0001 MONTH 567439.00 4398911.00 212.70 15.24 755.37 13.53 1.68

VERTELLUS VERT_SN1 1.38 MONTH 566836.00 4399683.00 214.94 9.14 453.71 6.28 1.22

VERTELLUS VERT_SN2 22.18 MONTH 566981.00 4399746.00 215.16 9.14 504.26 7.53 1.22

VERTELLUS VERT_SN4 0.90 MONTH 566995.00 4399731.00 214.89 10.97 422.04 5.49 0.81

VERTELLUS VERT_SN12 0.06 MONTH 566851.06 4399666.50 214.85 20.42 823.15 5.09 1.07

VERTELLUS VERT_SN13 0.01 MONTH 566901.00 4399710.00 215.15 20.73 823.15 5.09 1.07

VERTELLUS VERT_SN14 0.03 MONTH 566866.94 4399637.00 214.79 21.64 633.15 6.10 1.52

VERTELLUS VERT_SN15 0.02 MONTH 566864.94 4399640.00 214.79 24.69 823.15 6.07 1.07

QUEMETCO QUE_SN1 70.78 MONTH 559977.54 4400993.45 235.78 30.48 327.04 16.86 1.22

QUEMETCO QUE_SN2 53.59 MONTH 559993.31 4400853.53 235.10 50.29 321.48 14.84 3.35
B-20

Table B-22. 2012 Indianapolis, IN Rolls Royce area source emissions, locations, and release parameters.

Facility 

Name

AERMOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Release 

height (m)

X-dimension 

(m)

Y-dimension 

(m) Angle σz (m)

ROLLS 

ROYCE RR_SN5 1.33 HROFDY 567593.31 4398478.50 211.88 3.05 10 10 0 0

ROLLS 

ROYCE RR_SN6 0.47 HROFDY 567359.63 4398742.50 212.99 3.05 10 10 0 0

ROLLS 

ROYCE RR_SN7 2.49 HROFDY 567492.69 4399179.00 212.80 3.05 10 10 0 0

ROLLS 

ROYCE RR_SN8 0.001 MONTH 567492.69 4399179.00 212.80 3.05 10 10 0 0
B-21

Table B-23. 2013 Indianapolis Belmont WWTP, Citizens Thermal, and IP&L Harding Street point source emissions, locations, 

and stack parameters.

Facility Name AERMOD 

source ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height (m)

Stack 

temperature (K)

Stack 

velocity

(m s-1)

Stack 

diameter (m)

INDIANAPOLIS 

BELMONT WWTP BELL_SN1 20.10 MONTH 568970.00 4397879.00 208.61 45.72 297.59 0.64 3.20

Citizens Thermal CIT_SN1 2229.80 MONTH 571351.00 4401766.00 216.45 82.91 566.48 4.60 4.42

Citizens Thermal CIT_SN2 1575.00 MONTH 571396.00 4401766.00 217.46 82.91 463.71 4.72 4.64

Citizens Thermal CIT_SN3 0.0000001 MONTH 571351.00 4401766.00 216.45 82.91 566.48 4.60 4.42

Citizens Thermal CIT_SN4 0.28 MONTH 571380.00 4401766.00 217.35 82.91 488.71 5.33 4.63

Citizens Thermal CIT_SN5 0.24 MONTH 571396.00 4401766.00 217.46 82.91 463.71 4.72 4.64

Citizens Thermal CIT_SN6 0.002 MONTH 571380.00 4401766.00 217.35 82.91 488.71 5.33 4.63

IP&L - HARDING 

STREET IPL_SE1 0.02 HOURLY 569200.00 4396339.00 208.02 9.45 791.48 7.16 3.81

IP&L - HARDING 

STREET IPL_SE2 0.01 HOURLY 569180.00 4396327.00 207.98 9.75 791.48 7.16 3.81

IP&L - HARDING 

STREET IPL_SE3 0.20 HOURLY 568867.00 4396303.00 208.00 20.12 827.59 57.39 4.21

IP&L - HARDING 

STREET IPL_SE4 0.20 HOURLY 568910.00 4396306.00 208.01 20.12 822.04 62.15 4.21

IP&L - HARDING 

STREET IPL_SE5 13324.00 HOURLY 568749.00 4396008.00 208.08 79.55 440.93 65.84 1.98

IP&L - HARDING 

STREET IPL_SE6 12603.00 HOURLY 568752.00 4395965.00 208.32 79.55 449.82 63.52 1.98

IP&L - HARDING 

STREET IPL_SE7 1846.10 HOURLY 568984.00 4395792.00 206.56 172.21 329.26 14.33 6.10

IP&L - HARDING 

STREET IPL_SE8 200.30 HOURLY 568984.00 4395792.00 206.56 172.21 414.82 23.44 6.10

IP&L - HARDING 

STREET IPL_SE9 0.30 HOURLY 569050.00 4396339.00 208.26 22.86 810.93 36.58 5.49
B-22

Table B-24. 2013 Rolls Royce, Vertellus, and Quemetco point source emissions, locations, and stack parameters.

Facility Name AERMOD 

source ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

ROLLS ROYCE RR_SN1 0.48 MONTH 567493.00 4398570.00 212.29 4.57 866.48 32.34 0.30

ROLLS ROYCE RR_SN3 1.15 HROFDY 567428.00 4398870.00 212.70 17.37 588.71 33.04 1.22

ROLLS ROYCE RR_SN4 12.39 HROFDY 567402.00 4398886.00 212.70 19.81 755.37 45.51 0.91

ROLLS ROYCE RR_SN9 2.78 MHRDOW 567435.00 4398899.00 212.72 9.14 866.48 21.21 1.52

ROLLS ROYCE RR_SN10 7.24 MONTH 567551.00 4399165.00 212.10 15.24 677.59 17.47 1.98

ROLLS ROYCE RR_SN11 0.05 MONTH 567551.00 4399165.00 212.10 15.24 677.59 17.47 1.98

ROLLS ROYCE RR_SN12 2.80 MONTH 567544.50 4399165.00 212.24 15.24 677.59 17.47 1.98

ROLLS ROYCE RR_SN13 4.77 MONTH 567512.00 4399163.00 212.51 18.29 533.15 6.52 1.22

ROLLS ROYCE RR_SN14 0.02 MHRDOW 567513.00 4399174.00 212.61 9.14 866.48 21.21 1.52

ROLLS ROYCE RR_SN15 0.001 MONTH 567439.00 4398911.00 212.70 15.24 755.37 13.53 1.68

VERTELLUS VERT_SN1 25.01 MONTH 566981.00 4399746.00 215.16 9.14 504.26 7.53 1.22

VERTELLUS VERT_SN10 0.07 MONTH 566851.06 4399666.50 214.85 20.42 823.15 5.09 1.07

VERTELLUS VERT_SN11 0.02 MONTH 566901.00 4399710.00 215.15 20.73 823.15 5.09 1.07

VERTELLUS VERT_SN12 0.02 MONTH 566866.94 4399637.00 214.79 21.64 633.15 6.10 1.52

VERTELLUS VERT_SN13 0.02 MONTH 566864.94 4399640.00 214.79 24.69 823.15 6.07 1.07

QUEMETCO QUE_SN1 68.77 MONTH 559977.54 4400993.45 235.78 30.48 327.04 16.86 1.22

QUEMETCO QUE_SN2 3.97 MONTH 559993.31 4400853.53 235.10 50.29 297.04 10.70 3.35

QUEMETCO QUE_SN3 23.82 MONTH 559993.31 4400853.53 235.10 50.29 321.48 14.84 3.35
B-23

Table B-25. 2013 Rolls Royce area source emissions, locations, and release parameters.

Facility 

Name

AERMOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Release 

height (m)

X-dimension 

(m)

Y-dimension 

(m) Angle σz (m)

ROLLS 

ROYCE RR_SN2 0.001 HROFDY 567492.69 4399179.00 212.80 3.05 10 10 0 0

ROLLS 

ROYCE RR_SN5 1.39 HROFDY 567593.31 4398478.50 211.88 3.05 10 10 0 0

ROLLS 

ROYCE RR_SN6 0.61 HROFDY 567359.63 4398742.50 212.99 3.05 10 10 0 0

ROLLS 

ROYCE RR_SN7 3.02 HROFDY 567492.69 4399179.00 212.80 3.05 10 10 0 0

Table B-26. Tulsa Refinery-East 2011-2013 AERMOD source identifier crosswalk.

Facility Name Unit ID Process ID Release Point ID AERMOD 2011 AERMOD 2012 AERMOD 2013

TULSA RFNRY-EAST 5070713 4882912 15790214 REFEAST_SN1 REFEAST_SN1 REFEAST_SN1

TULSA RFNRY-EAST 72309613 66435812 100082714 REFEAST_SN2 REFEAST_SN2 REFEAST_SN2

TULSA RFNRY-EAST 5070913 4882512 15790014 REFEAST_SN4 REFEAST_SN4 REFEAST_SN4

TULSA RFNRY-EAST 5070813 4882812 15790114 REFEAST_SN5 REFEAST_SN5 REFEAST_SN5

TULSA RFNRY-EAST 72309513 66435712 100082614 REFEAST_SN6 REFEAST_SN6 REFEAST_SN6

TULSA RFNRY-EAST 72309413 66435612 100082514 REFEAST_SN7 REFEAST_SN7 REFEAST_SN7

TULSA RFNRY-EAST 72308613 66437212 100081514 REFEAST_SN8 REFEAST_SN8 REFEAST_SN8

TULSA RFNRY-EAST 5070213 4883812 15790914 REFEAST_SN9 REFEAST_SN9 REFEAST_SN9

TULSA RFNRY-EAST 5066913 4883712 15659614 REFEAST_SN11 REFEAST_SN11 REFEAST_SN11

TULSA RFNRY-EAST 5070613 4882412 15790314 REFEAST_SN12 REFEAST_SN12 REFEAST_SN12

TULSA RFNRY-EAST 72310013 66436712 100083414 REFEAST_SN14 REFEAST_SN14 REFEAST_SN14

TULSA RFNRY-EAST 5064513 4880712 15786414 REFEAST_SN15 REFEAST_SN15 REFEAST_SN15

TULSA RFNRY-EAST 5064313 4884112 15786714 REFEAST_SN16 REFEAST_SN16 REFEAST_SN16

TULSA RFNRY-EAST 5071113 4882612 15789714 REFEAST_SN17 REFEAST_SN18 REFEAST_SN17

TULSA RFNRY-EAST 5071913 4883912 15788214 REFEAST_SN18 REFEAST_SN19 REFEAST_SN18
B-24

Table B-27. Tulsa Refinery-West 2011-2013 AERMOD source identifier crosswalk.

Facility Name Unit ID Process ID Release Point ID AERMOD 2011 AERMOD 2012 AERMOD 2013

TULSA RFNRY WEST 72317213 66440812 100094814 REFWEST_SN1 REFWEST_SN1

TULSA RFNRY WEST 651913 655212 15606514 REFWEST_SN2 REFWEST_SN2

TULSA RFNRY WEST 652113 657112 15606314 REFWEST_SN3 REFWEST_SN3 REFWEST_SN1

TULSA RFNRY WEST 663913 654212 16298114 REFWEST_SN2

TULSA RFNRY WEST 72311713 66439312 100085314 REFWEST_SN4 REFWEST_SN4

TULSA RFNRY WEST 664013 654712 16298014 REFWEST_SN3

TULSA RFNRY WEST 660813 654812 16303714 REFWEST_SN4

TULSA RFNRY WEST 107042213 110579312 151543514 REFWEST_SN5 REFWEST_SN5

TULSA RFNRY WEST 654413 660012 15477714 REFWEST_SN5 REFWEST_SN6 REFWEST_SN6

TULSA RFNRY WEST 654313 659912 15477814 REFWEST_SN6 REFWEST_SN7 REFWEST_SN7

TULSA RFNRY WEST 654613 655312 15477514 REFWEST_SN7 REFWEST_SN8 REFWEST_SN8

TULSA RFNRY WEST 663113 651512 16299414 REFWEST_SN8 REFWEST_SN9 REFWEST_SN9

TULSA RFNRY WEST 651113 662812 15607614 REFWEST_SN9 REFWEST_SN11 REFWEST_SN11

TULSA RFNRY WEST 650813 656012 15607914 REFWEST_SN10 REFWEST_SN10 REFWEST_SN10

TULSA RFNRY WEST 653513 659612 15478614 REFWEST_SN11 REFWEST_SN12 REFWEST_SN12

TULSA RFNRY WEST 654213 662012 15477914 REFWEST_SN12 REFWEST_SN13 REFWEST_SN13

TULSA RFNRY WEST 651013 658912 15607714 REFWEST_SN13 REFWEST_SN14 REFWEST_SN14

TULSA RFNRY WEST 653613 659012 15478514 REFWEST_SN14 REFWEST_SN15 REFWEST_SN15

TULSA RFNRY WEST 651713 655012 15606714 REFWEST_SN15 REFWEST_SN16 REFWEST_SN16

TULSA RFNRY WEST 654113 663512 15478014 REFWEST_SN16 REFWEST_SN17 REFWEST_SN17

TULSA RFNRY WEST 651313 658812 15607314 REFWEST_SN17 REFWEST_SN18 REFWEST_SN18

TULSA RFNRY WEST 650913 654912 15607814 REFWEST_SN18 REFWEST_SN19 REFWEST_SN19

TULSA RFNRY WEST 651413 661212 15607214 REFWEST_SN19 REFWEST_SN20 REFWEST_SN20

TULSA RFNRY WEST 663813 651712 16298214 REFWEST_SN21

TULSA RFNRY WEST 654513 656112 15477614 REFWEST_SN22

TULSA RFNRY WEST 653713 659112 15478414 REFWEST_SN23

TULSA RFNRY WEST 658713 651412 16408914 REFWEST_SN20 REFWEST_SN21 REFWEST_SN24
B-25

Table B-28. PSO Northeastern Power Station and Sapulpa 2011-2013 AERMOD source identifier crosswalk.

Facility Name Unit ID Process ID Release Point ID AERMOD 2011 AERMOD 2012 AERMOD 2013

PSO NORTHEASTERN 6698313 6664412 15999814 PSO_SE1 PSO_SE1 PSO_SE1

PSO NORTHEASTERN 6698313 6664412 15999914 PSO_SE2 PSO_SE2 PSO_SE2

PSO NORTHEASTERN 6698513 6664212 15999514 PSO_SE3 PSO_SE3 PSO_SE3

PSO NORTHEASTERN 6698813 6664412 15999114 PSO_SE4 PSO_SE4 PSO_SE4

PSO NORTHEASTERN 6698813 6664412 15999214 PSO_SE5 PSO_SE5 PSO_SE5

PSO NORTHEASTERN 6698913 6664012 15999014 PSO_SE6 PSO_SE6 PSO_SE6

PSO NORTHEASTERN 6699113 6664712 15998814 PSO_SE7 PSO_SE7 PSO_SE7

SAPULPA 8331213 8217212 17068814 SAP_SN1 SAP_SN1 SAP_SN2

SAPULPA 8331413 8217312 17068514 SAP_SN2 SAP_SN2 SAP_SN3

SAPULPA 72251213 66374812 100009614 SAP_SN3 SAP_SN3 SAP_SN4

SAPULPA 8331113 66375112 17068914 SAP_SN4 SAP_SN4 SAP_SN5

SAPULPA 8331113 66375212 17068914 SAP_SN4 SAP_SN4 SAP_SN5

SAPULPA 8331113 66375312 17068914 SAP_SN4 SAP_SN4 SAP_SN5

SAPULPA 8331113 66375412 17068914 SAP_SN4 SAP_SN4 SAP_SN5

SAPULPA 8331113 66375512 17068914 SAP_SN4 SAP_SN4 SAP_SN5

SAPULPA 8331113 66375612 17068914 SAP_SN4 SAP_SN4 SAP_SN5

SAPULPA 8331113 66375712 17068914 SAP_SN4 SAP_SN4 SAP_SN5

SAPULPA 8331113 66376612 17068914 SAP_SN4 SAP_SN4 SAP_SN5

SAPULPA 8331113 66375812 17068914 SAP_SN5 SAP_SN5 SAP_SN6

SAPULPA 8331113 66375912 17068914 SAP_SN5 SAP_SN5 SAP_SN6

SAPULPA 8331113 66376012 17068914 SAP_SN5 SAP_SN5 SAP_SN6

SAPULPA 8331113 66376112 17068914 SAP_SN5 SAP_SN5 SAP_SN6

SAPULPA 8331113 66376212 17068914 SAP_SN5 SAP_SN5 SAP_SN6

SAPULPA 8331113 66376312 17068914 SAP_SN5 SAP_SN5 SAP_SN6

SAPULPA 8331113 66376412 17068914 SAP_SN5 SAP_SN5 SAP_SN6

SAPULPA 8331113 66376512 17068914 SAP_SN5 SAP_SN5 SAP_SN6

SAPULPA 72251313 66375012 100009714 SAP_SN6 SAP_SN6 SAP_SN7

SAPULPA 108757113 112230012 153985314 SAP_SN1
B-26

Table B-29. 2011 Tulsa East Refinery point source emissions, locations, and stack parameters.

Facility Name AERMOD source 

ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

TULSA 

RFNRY-EAST REFEAST_SN1 2.00 MONTH 230409.02 4000701.87 192.12 73.15 1088.71 43.34 0.49

TULSA 

RFNRY-EAST REFEAST_SN2 0.25 MONTH 229761.77 4000607.68 192.00 30.78 317.59 6.68 0.76

TULSA 

RFNRY-EAST REFEAST_SN4 0.83 MONTH 229823.09 4000610.90 192.00 60.96 444.26 5.88 0.61

TULSA 

RFNRY-EAST REFEAST_SN5 15.21 MONTH 229944.74 4000860.87 194.00 58.22 572.59 22.92 1.52

TULSA 

RFNRY-EAST REFEAST_SN6 0.12 MONTH 229658.38 4000653.14 192.00 29.26 313.71 8.23 1.13

TULSA 

RFNRY-EAST REFEAST_SN7 0.13 MONTH 229663.74 4000658.82 192.00 30.48 311.48 7.86 1.13

TULSA 

RFNRY-EAST REFEAST_SN8 0.04 MONTH 229954.38 4001000.54 192.90 13.72 570.37 16.06 1.07

TULSA 

RFNRY-EAST REFEAST_SN9 0.25 MONTH 229946.71 4000617.28 194.83 42.67 583.15 14.60 1.46

TULSA 

RFNRY-EAST REFEAST_SN11 0.44 MONTH 229945.36 4000870.85 194.00 46.02 624.82 3.99 1.77

TULSA 

RFNRY-EAST REFEAST_SN12 1.83 MONTH 229956.32 4001096.60 194.47 53.34 449.82 3.47 3.51

TULSA 

RFNRY-EAST REFEAST_SN14 0.66 MONTH 229971.12 4000687.91 194.77 38.10 466.48 3.84 2.53

TULSA 

RFNRY-EAST REFEAST_SN15 0.74 MONTH 229950.17 4000673.17 194.97 37.80 560.93 10.00 1.77

TULSA 

RFNRY-EAST REFEAST_SN16 0.16 MONTH 229950.84 4000700.18 195.00 37.80 533.15 7.04 1.37

TULSA 

RFNRY-EAST REFEAST_SN17 1.44 MONTH 229912.85 4001441.17 192.39 21.64 449.82 6.25 2.13

TULSA 

RFNRY-EAST REFEAST_SN18 1.43 MONTH 229940.84 4001441.17 192.31 21.64 449.82 6.19 2.13
B-27

Table B-30. 2011 Tulsa West Refinery point source emissions, locations, and stack parameters.

Facility Name AERMOD source 

ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

TULSA RFNRY 

WEST REFWEST_SN1 0.03 MONTH 228617.00 4003889.00 195.00 5.49 616.48 5.06 0.15

TULSA RFNRY 

WEST REFWEST_SN2 0.005 MONTH 228750.30 4003806.26 195.10 6.71 588.71 13.20 0.15

TULSA RFNRY 

WEST REFWEST_SN3 5.73 MONTH 228706.00 4002861.00 195.00 43.89 477.59 11.83 0.30

TULSA RFNRY 

WEST REFWEST_SN4 0.007 MONTH 228658.38 4003859.03 195.10 7.62 547.04 7.25 0.21

TULSA RFNRY 

WEST REFWEST_SN5 44.78 MONTH 229176.29 4003711.77 195.10 30.48 637.59 1.92 1.62

TULSA RFNRY 

WEST REFWEST_SN6 380.27 MONTH 229185.32 4003728.24 195.10 38.10 548.15 5.15 1.62

TULSA RFNRY 

WEST REFWEST_SN7 103.02 MONTH 229202.04 4003723.20 195.20 18.90 505.93 2.99 1.07

TULSA RFNRY 

WEST REFWEST_SN8 866.22 MONTH 228262.29 4003837.45 194.30 41.15 522.04 4.88 2.26

TULSA RFNRY 

WEST REFWEST_SN9 39.26 MONTH 228236.99 4003995.32 194.20 15.24 471.48 4.11 0.85

TULSA RFNRY 

WEST REFWEST_SN10 37.86 MONTH 228237.62 4003989.27 194.20 15.24 683.15 2.99 1.37

TULSA RFNRY 

WEST REFWEST_SN11 0.006 MONTH 228251.07 4004028.52 193.90 25.91 768.71 4.05 1.52

TULSA RFNRY 

WEST REFWEST_SN12 0.01 MONTH 228262.17 4004029.83 193.90 27.43 736.48 2.19 2.13

TULSA RFNRY 

WEST REFWEST_SN13 157.00 MONTH 228246.58 4004020.78 193.90 27.74 922.04 4.82 2.13

TULSA RFNRY 

WEST REFWEST_SN14 18.64 MONTH 228246.08 4004012.79 193.90 30.78 877.59 2.04 1.13

TULSA RFNRY 

WEST REFWEST_SN15 59.37 MONTH 228239.18 4003982.16 194.30 23.47 523.15 26.33 0.61

TULSA RFNRY 

WEST REFWEST_SN16 36.35 MONTH 229175.91 4003721.81 195.10 27.43 560.93 3.20 0.91

TULSA RFNRY 

WEST REFWEST_SN17 43.23 MONTH 228239.37 4003969.12 194.60 20.12 594.26 2.38 1.37

TULSA RFNRY 

WEST REFWEST_SN18 74.03 MONTH 228279.45 4003823.37 194.50 38.10 726.48 2.26 2.13

TULSA RFNRY 

WEST REFWEST_SN19 270.43 MONTH 228279.45 4003823.37 194.50 38.10 738.71 4.88 2.26

TULSA RFNRY 

WEST REFWEST_SN20 460.16 MONTH 228688.88 4003894.68 195.19 33.53 394.26 3.41 3.20
B-28

Table B-31. 2011 PSO Northeastern and Sapulpa point source emissions, locations, and stack parameters.

Facility Name AERMOD 

source ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

PSO 

NORTHEASTERN PSO_SE1 9007.70 HOURLY 258002.59 4034618.88 195.67 182.88 419.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE2 26.14 HOURLY 258002.59 4034618.88 195.67 182.88 419.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE3 2.36 HOURLY 257841.41 4035283.44 195.41 55.78 393.71 16.28 5.49

PSO 

NORTHEASTERN PSO_SE4 8879.30 HOURLY 258002.59 4034618.88 195.67 182.88 419.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE5 25.54 HOURLY 258002.59 4034618.88 195.67 182.88 419.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE6 0.18 HOURLY 257850.92 4035160.78 195.23 45.72 366.48 19.69 5.74

PSO 

NORTHEASTERN PSO_SE7 0.20 HOURLY 257850.92 4035160.78 195.23 45.72 366.48 21.55 5.49

SAPULPA SAP_SN1 100.32 MONTH 220648.04 3989373.19 215.01 28.35 530.37 9.60 1.86

SAPULPA SAP_SN2 33.08 MONTH 220621.83 3989378.25 215.62 32.31 498.71 19.39 1.29

SAPULPA SAP_SN3 78.85 MONTH 220621.83 3989378.25 215.62 29.87 515.37 10.27 1.71

SAPULPA SAP_SN4 0.02 MONTH 220667.19 3989381.92 214.54 26.52 310.93 2.13 2.29

SAPULPA SAP_SN5 0.03 MONTH 220667.19 3989381.92 214.54 29.26 310.93 2.13 2.29

Table B-32. 2011 Sapulpa area source emissions, locations, and release parameters.

Facility 

Name

AERMOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Release 

height (m)

X-dimension 

(m)

Y-dimension 

(m) Angle σz (m)

SAPULPA SAP_SN6 0.03 MONTH 220691.84 3989080 218.06 10.67 2.74 2.74 0 2.48
B-29

Table B-33. 2012 Tulsa East Refinery point source emissions, locations, and stack parameters.

Facility Name AERMOD source 

ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

TULSA 

RFNRY-EAST REFEAST_SN1 3.91 MONTH 230409.02 4000701.87 192.12 73.15 1088.71 43.34 0.49

TULSA 

RFNRY-EAST REFEAST_SN2 1.15 MONTH 229761.77 4000607.68 192.00 30.78 317.59 6.68 0.76

TULSA 

RFNRY-EAST REFEAST_SN4 0.38 MONTH 229823.09 4000610.90 192.00 60.96 444.26 5.88 0.61

TULSA 

RFNRY-EAST REFEAST_SN5 11.19 MONTH 229944.74 4000860.87 194.00 58.22 572.59 22.92 1.52

TULSA 

RFNRY-EAST REFEAST_SN6 0.14 MONTH 229658.38 4000653.14 192.00 29.26 313.71 8.23 1.13

TULSA 

RFNRY-EAST REFEAST_SN7 0.15 MONTH 229663.74 4000658.82 192.00 30.48 311.48 7.86 1.13

TULSA 

RFNRY-EAST REFEAST_SN8 0.04 MONTH 229954.38 4001000.54 192.90 13.72 570.37 16.06 1.07

TULSA 

RFNRY-EAST REFEAST_SN9 0.26 MONTH 229946.71 4000617.28 194.83 42.67 583.15 14.60 1.46

TULSA 

RFNRY-EAST REFEAST_SN11 0.58 MONTH 229945.36 4000870.85 194.00 46.02 624.82 3.99 1.77

TULSA 

RFNRY-EAST REFEAST_SN12 1.35 MONTH 229956.32 4001096.60 194.47 53.34 449.82 3.47 3.51

TULSA 

RFNRY-EAST REFEAST_SN14 0.62 MONTH 229971.12 4000687.91 194.77 38.10 466.48 3.84 2.53

TULSA 

RFNRY-EAST REFEAST_SN15 0.72 MONTH 229950.17 4000673.17 194.97 37.80 560.93 10.00 1.77

TULSA 

RFNRY-EAST REFEAST_SN16 0.16 MONTH 229950.84 4000700.18 195.00 37.80 533.15 7.04 1.37

TULSA 

RFNRY-EAST REFEAST_SN18 1.46 MONTH 229912.85 4001441.17 192.39 21.64 449.82 6.25 2.13

TULSA 

RFNRY-EAST REFEAST_SN19 1.20 MONTH 229940.84 4001441.17 192.31 21.64 449.82 6.19 2.13
B-30

Table B-34. 2012 Tulsa West Refinery point source emissions, locations, and stack parameters.

Facility Name AERMOD source 

ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

TULSA RFNRY 

WEST REFWEST_SN1 0.007 MONTH 228617.00 4003889.00 195.00 5.49 616.48 5.06 0.15

TULSA RFNRY 

WEST REFWEST_SN2 0.005 MONTH 228750.30 4003806.26 195.10 6.71 588.71 13.20 0.15

TULSA RFNRY 

WEST REFWEST_SN3 7.66 MONTH 228706.00 4002861.00 195.00 43.89 477.59 11.83 0.30

TULSA RFNRY 

WEST REFWEST_SN4 0.007 MONTH 228658.38 4003859.03 195.10 7.62 547.04 7.25 0.21

TULSA RFNRY 

WEST REFWEST_SN5 0.017 MONTH 228617.00 4003889.00 195.00 5.49 616.48 5.06 0.15

TULSA RFNRY 

WEST REFWEST_SN6 20.44 MONTH 229176.29 4003711.77 195.10 30.48 637.59 1.92 1.62

TULSA RFNRY 

WEST REFWEST_SN7 237.06 MONTH 229185.32 4003728.24 195.10 38.10 548.15 5.15 1.62

TULSA RFNRY 

WEST REFWEST_SN8 41.63 MONTH 229202.04 4003723.20 195.20 18.90 505.93 2.99 1.07

TULSA RFNRY 

WEST REFWEST_SN9 687.65 MONTH 228262.29 4003837.45 194.30 41.15 522.04 4.88 2.26

TULSA RFNRY 

WEST REFWEST_SN10 45.53 MONTH 228237.62 4003989.27 194.20 15.24 683.15 2.99 1.37

TULSA RFNRY 

WEST REFWEST_SN11 43.48 MONTH 228236.99 4003995.32 194.20 15.24 471.48 4.11 1.52

TULSA RFNRY 

WEST REFWEST_SN12 0.004 MONTH 228251.07 4004028.52 193.90 25.91 768.71 4.05 1.52

TULSA RFNRY 

WEST REFWEST_SN13 0.007 MONTH 228262.17 4004029.83 193.90 27.43 736.48 2.19 2.13

TULSA RFNRY 

WEST REFWEST_SN14 150.00 MONTH 228246.58 4004020.78 193.90 27.74 922.04 4.82 2.13

TULSA RFNRY 

WEST REFWEST_SN15 18.25 MONTH 228246.08 4004012.79 193.90 30.78 877.59 2.04 1.13

TULSA RFNRY 

WEST REFWEST_SN16 65.03 MONTH 228239.18 4003982.16 194.30 23.47 523.15 26.33 0.61

TULSA RFNRY 

WEST REFWEST_SN17 18.27 MONTH 229175.91 4003721.81 195.10 27.43 560.93 3.20 0.91

TULSA RFNRY 

WEST REFWEST_SN18 41.40 MONTH 228239.37 4003969.12 194.60 20.12 594.26 2.38 1.37

TULSA RFNRY 

WEST REFWEST_SN19 54.57 MONTH 228279.45 4003823.37 194.50 38.10 726.48 2.26 2.13

TULSA RFNRY 

WEST REFWEST_SN20 210.11 MONTH 228279.45 4003823.37 194.50 38.10 738.71 4.88 2.26

TULSA RFNRY 

WEST REFWEST_SN21 370.21 MONTH 228688.88 4003894.68 195.19 33.53 394.26 3.41 3.20
B-31

Table B-35. 2012 PSO Northeastern and Sapulpa point source emissions, locations, and stack parameters.

Facility Name AERMOD 

source ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

PSO 

NORTHEASTERN PSO_SE1 7401.70 HOURLY 258002.59 4034618.88 195.67 182.88 394.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE2 26.69 HOURLY 258002.59 4034618.88 195.67 182.88 394.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE3 3.08 HOURLY 257841.41 4035283.44 195.41 55.78 393.71 16.28 5.49

PSO 

NORTHEASTERN PSO_SE4 8038.60 HOURLY 258002.59 4034618.88 195.67 182.88 394.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE5 19.99 HOURLY 258002.59 4034618.88 195.67 182.88 394.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE6 2.27 HOURLY 257850.92 4035160.78 195.23 45.72 366.48 19.69 5.74

PSO 

NORTHEASTERN PSO_SE7 2.42 HOURLY 257850.92 4035160.78 195.23 45.72 366.48 21.55 5.49

SAPULPA SAP_SN1 100.32 MONTH 220648.04 3989373.19 215.01 28.35 530.37 9.60 1.86

SAPULPA SAP_SN2 33.08 MONTH 220621.83 3989378.25 215.62 32.31 498.71 19.39 1.29

SAPULPA SAP_SN3 78.85 MONTH 220621.83 3989378.25 215.62 29.87 515.37 10.27 1.71

SAPULPA SAP_SN4 0.02 MONTH 220667.19 3989381.92 214.54 26.52 310.93 2.13 2.29

SAPULPA SAP_SN5 0.03 MONTH 220667.19 3989381.92 214.54 29.26 310.93 2.13 2.29

Table B-36. 2012 Sapulpa area source emissions, locations, and release parameters.

Facility 

Name

AERMOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Release 

height (m)

X-dimension 

(m)

Y-dimension 

(m) Angle σz (m)

SAPULPA SAP_SN6 0.03 MONTH 220691.84 3989080 218.06 10.67 2.74 2.74 0 2.48
B-32

Table B-37. 2013 Tulsa East Refinery point source emissions, locations, and stack parameters.

Facility Name AERMOD source 

ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

TULSA 

RFNRY-EAST REFEAST_SN1 11.85 MONTH 230409.02 4000701.87 192.12 73.15 1088.71 43.34 0.49

TULSA 

RFNRY-EAST REFEAST_SN2 0.34 MONTH 229761.77 4000607.68 192.00 30.78 317.59 6.68 0.76

TULSA 

RFNRY-EAST REFEAST_SN4 0.26 MONTH 229823.09 4000610.90 192.00 60.96 444.26 5.88 0.61

TULSA 

RFNRY-EAST REFEAST_SN5 5.08 MONTH 229944.74 4000860.87 194.00 58.22 572.59 22.92 1.52

TULSA 

RFNRY-EAST REFEAST_SN6 0.10 MONTH 229658.38 4000653.14 192.00 29.26 313.71 8.23 1.13

TULSA 

RFNRY-EAST REFEAST_SN7 0.10 MONTH 229663.74 4000658.82 192.00 30.48 311.48 7.86 1.13

TULSA 

RFNRY-EAST REFEAST_SN8 0.05 MONTH 229954.38 4001000.54 192.90 13.72 570.37 16.06 1.07

TULSA 

RFNRY-EAST REFEAST_SN9 0.22 MONTH 229946.71 4000617.28 194.83 42.67 583.15 14.60 1.46

TULSA 

RFNRY-EAST REFEAST_SN11 0.45 MONTH 229945.36 4000870.85 194.00 46.02 624.82 3.99 1.77

TULSA 

RFNRY-EAST REFEAST_SN12 0.83 MONTH 229956.32 4001096.60 194.47 53.34 449.82 3.47 3.51

TULSA 

RFNRY-EAST REFEAST_SN14 0.44 MONTH 229971.12 4000687.91 194.77 38.10 466.48 3.84 2.53

TULSA 

RFNRY-EAST REFEAST_SN15 0.57 MONTH 229950.17 4000673.17 194.97 37.80 560.93 10.00 1.77

TULSA 

RFNRY-EAST REFEAST_SN16 0.11 MONTH 229950.84 4000700.18 195.00 37.80 533.15 7.04 1.37

TULSA 

RFNRY-EAST REFEAST_SN17 0.99 MONTH 229912.85 4001441.17 192.39 21.64 449.82 6.25 2.13

TULSA 

RFNRY-EAST REFEAST_SN18 1.02 MONTH 229940.84 4001441.17 192.31 21.64 449.82 6.19 2.13
B-33

Table B-38. 2013 Tulsa West Refinery point source emissions, locations, and stack parameters.

Facility Name AERMOD source 

ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

TULSA RFNRY 

WEST REFWEST_SN1 8.22 MONTH 228706.00 4002861.00 195.00 43.89 477.59 11.83 0.30

TULSA RFNRY 

WEST REFWEST_SN2 0.15 MONTH 228659.61 4003895.03 195.10 18.29 433.15 8.23 1.52

TULSA RFNRY 

WEST REFWEST_SN3 0.26 MONTH 228660.10 4003903.01 195.10 18.29 440.37 6.49 1.52

TULSA RFNRY 

WEST REFWEST_SN4 0.10 MONTH 228658.38 4003859.03 195.10 24.38 425.93 6.25 1.52

TULSA RFNRY 

WEST REFWEST_SN5 0.02 MONTH 228617.00 4003889.00 195.00 5.49 616.48 5.06 0.15

TULSA RFNRY 

WEST REFWEST_SN6 9.09 MONTH 229176.29 4003711.77 195.10 30.48 637.59 1.92 1.62

TULSA RFNRY 

WEST REFWEST_SN7 169.39 MONTH 229185.32 4003728.24 195.10 38.10 548.15 5.15 1.62

TULSA RFNRY 

WEST REFWEST_SN8 26.45 MONTH 229202.04 4003723.20 195.20 18.90 505.93 2.99 1.07

TULSA RFNRY 

WEST REFWEST_SN9 360.29 MONTH 228262.29 4003837.45 194.30 41.15 522.04 4.88 2.26

TULSA RFNRY 

WEST REFWEST_SN10 8.45 MONTH 228237.62 4003989.27 194.20 15.24 683.15 2.99 1.37

TULSA RFNRY 

WEST REFWEST_SN11 16.96 MONTH 228236.99 4003995.32 194.20 15.24 471.48 4.11 1.52

TULSA RFNRY 

WEST REFWEST_SN12 0.002 MONTH 228251.07 4004028.52 193.90 25.91 768.71 4.05 1.52

TULSA RFNRY 

WEST REFWEST_SN13 0.003 MONTH 228262.17 4004029.83 193.90 27.43 736.48 2.19 2.13

TULSA RFNRY 

WEST REFWEST_SN14 36.95 MONTH 228246.58 4004020.78 193.90 27.74 922.04 4.82 2.13

TULSA RFNRY 

WEST REFWEST_SN15 4.42 MONTH 228246.08 4004012.79 193.90 30.78 877.59 2.04 1.13

TULSA RFNRY 

WEST REFWEST_SN16 23.79 MONTH 228239.18 4003982.16 194.30 23.47 523.15 26.33 0.61

TULSA RFNRY 

WEST REFWEST_SN17 10.56 MONTH 229175.91 4003721.81 195.10 27.43 560.93 3.20 0.91

TULSA RFNRY 

WEST REFWEST_SN18 10.76 MONTH 228239.37 4003969.12 194.60 20.12 594.26 2.38 1.37

TULSA RFNRY 

WEST REFWEST_SN19 34.20 MONTH 228279.45 4003823.37 194.50 38.10 726.48 2.26 2.13

TULSA RFNRY 

WEST REFWEST_SN20 124.53 MONTH 228279.45 4003823.37 194.50 38.10 738.71 4.88 2.26

TULSA RFNRY 

WEST REFWEST_SN21 0.03 MONTH 228524.37 4004105.79 195.40 27.74 555.37 3.02 1.22
B-34

Table B-39. 2013 Tulsa West Refinery point source emissions, locations, and stack parameters.

Facility Name AERMOD source 

ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

TULSA RFNRY 

WEST REFWEST_SN22 0.14 MONTH 229194.24 4003726.69 195.20 34.14 478.71 3.20 1.83

TULSA RFNRY 

WEST REFWEST_SN23 0.07 MONTH 228527.85 4004113.59 195.10 34.14 610.93 2.47 1.68

TULSA RFNRY 

WEST REFWEST_SN24 211.21 MONTH 228688.88 4003894.68 195.19 33.53 394.26 3.41 3.20

Table B-40. 2013 PSO Northeastern and Sapulpa point source emissions, locations, and stack parameters.

Facility Name AERMOD 

source ID

Emissions 

(tons year-1)

Emission 

factor UTM-x (m) UTM-y (m) Elevation (m)

Stack 

height 

(m)

Stack 

temperature 

(K)

Stack 

velocity

(m s-1)

Stack 

diameter 

(m)

PSO 

NORTHEASTERN PSO_SE1 9337.20 HOURLY 258002.59 4034618.88 195.67 182.88 394.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE2 22.32 HOURLY 258002.59 4034618.88 195.67 182.88 394.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE3 1.38 HOURLY 257841.41 4035283.44 195.41 55.78 393.71 16.28 5.49

PSO 

NORTHEASTERN PSO_SE4 9007.50 HOURLY 258002.59 4034618.88 195.67 182.88 394.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE5 38.16 HOURLY 258002.59 4034618.88 195.67 182.88 394.26 13.81 8.23

PSO 

NORTHEASTERN PSO_SE6 2.88 HOURLY 257850.92 4035160.78 195.23 45.72 366.48 19.69 5.74

PSO 

NORTHEASTERN PSO_SE7 3.11 HOURLY 257850.92 4035160.78 195.23 45.72 366.48 21.55 5.49

SAPULPA SAP_SN1 0.01 MONTH 220685.88 3989163.75 216.57 2.44 755.37 21.73 0.10

SAPULPA SAP_SN2 108.29 MONTH 220648.04 3989373.19 215.01 28.35 530.37 9.60 1.86

SAPULPA SAP_SN3 33.74 MONTH 220621.83 3989378.25 215.62 32.31 498.71 19.39 1.29

SAPULPA SAP_SN4 98.48 MONTH 220621.83 3989378.25 215.62 29.87 515.37 10.27 1.71

SAPULPA SAP_SN5 0.02 MONTH 220667.19 3989381.92 214.54 26.52 310.93 2.13 2.29

SAPULPA SAP_SN6 0.03 MONTH 220667.19 3989381.92 214.54 29.26 310.93 2.13 2.29
B-35

Table B-41. 2013 Sapulpa area source emissions, locations, and release parameters.

Facility 

Name

AERMOD 

source ID

Emissions

(tons year-1)

Emission 

factor

UTM-x

(m)

UTM-y

(m)

Elevation 

(m)

Release 

height (m)

X-dimension 

(m)

Y-dimension 

(m) Angle σz (m)

SAPULPA SAP_SN7 0.03 MONTH 220691.84 3989080 218.06 10.67 2.74 2.74 0 2.48
B-36

REFERENCES

UNC (University of North Carolina). (2017). Sparse Matrix Operator Kernel Emissions 

modeling system User Manual. Available at: 

https://www.cmascenter.org/help/documentation.cfm?MODEL=smoke&VERSION=4.5

U.S. EPA. (2016). User's Guide for the AMS/EPA Regulatory Model – AERMOD. EPA-454/B16-011. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
C-1

APPENDIX C

AIR QUALITY MODELING DOMAINS FOR STUDY AREAS

Preface: The modeling domains, including receptors and modeled sources, for the three study 

areas are shown in Figures C-1 and C-2, for Fall River, Figures C-3 and C-4 for Indianapolis, 

and Figures C-5 and C-6 for Tulsa. Sources are denoted by stars, monitors by triangles, and 

gridded receptors by small dots. The blue airport symbol denotes the location of the NWS station 

used in the modeling. 
C-2

Figure C-1. Fall River study area air quality modeling domain.
C-3

Figure C-2. Detailed view of Fall River study area air quality modeling domain.
C-4

Figure C-3. Indianapolis study area air quality modeling domain.
C-5

Figure C-4. Detailed view of Indianapolis study area air quality modeling domain.
C-6

Figure C-5. Tulsa study area air quality modeling domain.
C-7

Figure C-6. Detailed view of Tulsa study area air quality modeling domain.
D-1

APPENDIX D

MODELED AIR QUALITY EVALUATION

AERMOD output for the three study areas was evaluated using three methods. First, 

comparison of the 99th percentile of daily 1-hour maximum concentrations for each and 

subsequent 3-year design values were compared at each monitor. Second, simple QQ-plots were 

generated to provide a quick visual performance of the model for 1-hour, 3-hour, and 24-hour 

averages. The QQ-plots are comparisons of the observed and modeled concentrations, unpaired 

in time and space, consistent with regulatory evaluations of AERMOD (U.S. EPA, 2003; 

Venkatram et al., 2001). Third, for a more rigorous comparison, the EPA Protocol for 

determining best performing model, or sometimes called the Cox-Tikvart method (U.S. EPA,

1992; Cox and Tikvart, 1990) was used. Normally, this protocol is used to determine which 

model or model scenarios among a suite of models or scenario is the better performer for 

regulatory application and focuses on the higher concentrations in the concentration distribution 

as these are the concentrations of interest in most regulatory applications (State Implementation 

Plans and Prevention of Significant Deterioration). For example, U.S. EPA (2016) used the 

protocol to determine which was a better performer in terms of meteorological data, observed or 

prognostic data. For the study presented here, we are only evaluating one model and one 

scenario, i.e., AERMOD for 2011-2013. Therefore, the protocol will not be used to its full 

extent, but rather to provide information regarding the performance of the model for these study 

areas. An explanation of the protocol follows.

The protocol uses fractional bias (equation D-1) for evaluating model performance.

FB = 2 [

OB−PR

OB+PR] Equation D-1

Where FB is the fractional bias, OB is the average of the highest 25 observed concentrations and 

PR is the average of the highest 25 predicted averages. 

In the evaluation, air quality models are subjected to a comprehensive statistical 

comparison that involves both an operational and scientific component. The operational 

component is to measure the model’s ability to estimate concentration statistics most directly 

used for regulatory purposes and the scientific component evaluates the model’s ability to 

perform accurately throughout the range of meteorological conditions and the geographic area of 

concern (U.S. EPA, 1992). The test statistic used for the comparison is the robust highest 

concentration (RHC) statistic and is given by:

RHC = Χ(N) + [Χ̅ − Χ(N)] × ln [

3N−1

2

] Equation D-2
D-2

Where X(N) is the Nth largest value, X is the average of N-1 values, and N is the number of 

values exceeding the threshold value, usually 26. 

The operational component of the evaluation compares performance in terms of the 

largest network-wide RHC test statistic. The RHC is calculated separately for each monitor 

within the network for both observed and modeled values. The absolute fractional bias (AFB) is 

calculated for both 3 and 24-hour averages using the absolute value of the results of equation 1. 

The inputs to the AFB calculation are the highest observed RHC and the highest modeled RHC. 

The scientific component of the evaluation is also based on absolute fractional bias but 

the bias is calculated using the RHC for each meteorological condition and monitor. The 

meteorological conditions are a function of atmospheric stability and wind speed. For the 

purposes of these studies, six unique conditions were defined based on two wind speed 

categories (below and above 2.0 m/s) and three stability categories: unstable, neutral, and stable. 

1

In scientific evaluation, only 1-hour concentrations are used and the AFB is based on RHC 

values paired in space and stability/wind speed combination. 

A composite performance measure (CPM) is calculated from the 1-hour, 3-hour, and 24-

hour AFB’s:

CPM =

1

3

× (AFBi,j) +

2

3

× [

AFB3−AFB24

2

] Equation D-3

Where AFBi,j is the absolute fractional bias for monitor i and meteorological condition j, AFBi,j

is the average absolute fractional bias across all monitors and meteorological conditions, AFB3 is 

the absolute fractional bias for the 3-hour average, and AFB24 is the absolute fractional bias for 

the 24-hour average. The closer the CPM is to zero, the better the performance of the model. 

Also, since the absolute fraction biases are calculated using equation 1, which is bounded by 2 

(U.S. EPA, 1992), then the maximum value for the CPM is also 2.

Both the QQ-plots and the EPA protocol are applied to the model output in two ways. 

First, evaluations were conducted by comparing model output and observations unpaired in time 

and space, consistent with regulatory evaluations of AERMOD (U.S. EPA, 2003; Venkatram et

al., 2001). In regulatory applications, the emphasis is not on where potential modeled NAAQS 

violations occur, but whether they occur. Second, given the nature of this particular study as an 

exposure analysis, where individual receptors are being used on an hourly basis, the QQ-plots 

and the EPA protocol were both applied to model output at individual monitors. This would be a 

pairing in space but not necessarily time. This would help answer the question, is the model 

 

1

In U.S. EPA (1992), the three stability categories are related to the Pasquill-Gifford categories, unstable being A, 

B, and C, neutral being D, and stable being E and F. Since AERMOD does not use the stability categories, the 

stability class was determined using Monin-Obukhov length and surface roughness using methodology from 

AERMOD subroutine LTOPG.
D-3

performing well at predicting the locations of concentrations of interest. Also, since the monitors 

in each of the study areas are located near populations, if the model performs well near these 

monitors then reasonable performance in the population areas, or areas of interest for exposure, 

can be expected. For all three areas, QQ-plots and the EPA protocol were performed for the 

entire three-year period, 2011-2013, and for each year individually to see if individual years were 

driving the total period comparisons.

Fall River: Modeled Air Quality Evaluation

Only one monitor (Figure C-1, Figure C-2) was located in the vicinity of Brayton Power 

Station. Table D-1 shows the monitored and modeled annual 99th percentile daily 1-hour 

maximum concentration and the three-year design value. With the exception of 2011, the model 

under-predicts the 99th percentile of the daily 1-hour maximum concentration and under-predicts 

the 3-year design value.

Table D-1. Fall River monitored and modeled annual 99th percentile daily 1-hour 

maximum concentrations (g m-3) and 3-year design value (g m-3).

Year Monitor Observed Model

2011 250051004 169.8 177.1

2012 250051004 171.1 138.2

2013 250051004 161.9 84.9

Design Value 250051004 167.6 133.4

Figures D-1 through D-3 show the QQ-plots for 1-hour, 3-hour, and 24-hour averages 

respectively. In each figure, panel a is the ranked comparisons for the entire 3-year period, while 

panels b-d are the individual years’ ranked pairings. For the 1-hour comparison across all three 

years, the model is over predicting at the lower end of the concentration distributions (less than 

50 g m-3), predicts very well at the middle of the distribution (50 -125 g m-3) and then shifts to 

under-prediction from 150 to 250 g m-3. At the very high end, i.e. the last three observations, 

the model over-predicts, under-predicts and is almost equal to the highest monitored 

concentration. Analyzing the three individual years, the model appears to perform the best in 

2011. The 3-hour QQ-plots exhibit similar patterns as the 1-hour plots. The 24-hour plots exhibit 

a pattern of over-prediction at the low to mid-range of the distributions and then under prediction 

at the high ends.
D-4

Figure D-1. Fall River 1-hour QQ plots.
D-5

Figure D-2. Fall River 3-hour QQ plots.
D-6

Figure D-3. Fall River QQ-plots.

In addition to the QQ-plots, composite performance metrics, CPM, were calculated for 

the entire period and each of the individual years. 

Table D-2 lists the CPM values for 2011-13 and CPM values for the individual years. 

Also shown are the absolute fractional biases for 1-hour, 3-hour, and 24-hours. Overall, 

considering impacts from the three averaging periods, 2011 was the better performing year of the 

three years and the 2011-2013 CPM shows the influence of 2013.
D-7

Table D-2. Fall River composite performance metrics (CPM) and absolute fractional biases

for 1-hour, 3-hour, and 24-hour averages.

Period CPM AFB1-hr AFB3-hr AFB24-hr

2011-2013 0.45 0.68 0.30 0.38

2011 0.29 0.56 0.21 0.10

2012 0.35 0.43 0.22 0.41

2013 0.49 0.75 0.52 0.20

Indianapolis: Modeled Air Quality Evaluation

Three monitors were available for model evaluation in Indianapolis (Figure C-3). Table 

D-3 lists the annual 99th percentile daily 1-hour maximum concentration and 3-year design value 

for each monitor. The model is over-predicting at monitor 180970057 (the nearest monitor to the 

sources) and generally under-predicting each year and the design values at the other monitors. 

The modeled design value at 180970057 is within 10% of the monitored design value while at 

the other monitors, the modeled design values are within 3% of the monitored design values.

Table D-3. Indianapolis monitored and modeled annual 99th percentile daily 1-hour 

maximum concentrations (g m-3) and 3-year design value (g m-3).

Monitor Year Observed Modeled

180970057

2011 164.8 268.7

2012 239.4 330.8

2013 204.3 367.5

Design Value 202.8 322.4

180970073

2011 155.6 122.1

2012 146.8 129.9

2013 110.7 151.4

Design Value 137.7 134.4

180970078

2011 156.2 153.9

2012 159.9 162.2

2013 182.4 168.7

Design Value 166.1 161.6

One-hour, 3-hour, and 24-hour QQ-plots across all three monitors are shown in Figures 

D-4 through D-6, respectively. For 1-hour averages, the 3-year QQ-plot and 2012 and 2013 QQplots show an over-prediction trend except at the higher concentrations for the 3-year period and 

2012, where there is under-prediction. Analysis of the 2012 higher 1-hour concentrations (Figure 

D-4) showed very high observations for those years which the model did not simulate while 

2011 actually shows very good model performance. For the 3-hour averages (Figure D-5), all 

three years and the entire period show good model to monitor agreement with some overprediction in 2013. The 24-hour averages (Figure D-6), the 3-year period and each individual 

year exhibit over-prediction. Overall, 2011 appeared to show the better performance among the 

years among all averaging periods. Figures D-7 through D-9 show the 1-hour, 3-hour, and 24-
D-8

hour QQ-plots for the individual monitors for the 3-year period and by year. Results were mixed 

among the three monitors. For the 1-hour averages, monitor 180970057, the closest monitor to 

the modeled sources (Figure C-3, Figure C-4), the modeled concentrations were higher than 

monitored values except at the highest concentrations for 2012. The annual 99th percentile daily 

1-hour maxima and design value in Table D-3 reflect the over-prediction. For 2013, the model 

overestimated throughout the distribution. For the other two monitors, the modeled values 

showed good agreement through most of the concentration distribution and then tended toward 

underestimation at the higher end of the distributions. The same general trend was seen with the 

3-hour average concentrations (Figure D-8) for monitor 180970073. All three monitors exhibit 

over-prediction for the 24-hour period (Figure D-9) which could be a consequence of the 

seasonal background included in the Indianapolis modeling as 3-year average background 

concentrations are added to each individual hour in the modeling. The inclusion of an average 

background could be over-estimated for some individual hours and when calculating a multihour average, e.g. 3 or 24-hour, the overestimates could accumulate over time.
D-9

Figure D-4. Indianapolis 1-hour QQ-plots.
D-10

Figure D-5. Indianapolis 3-hour QQ-plots.
D-11

Figure D-6. Indianapolis 24-hour QQ-plots.
D-12

Figure D-7. 1-hour QQ plots for individual monitors in Indianapolis.
D-13

Figure D-8. 3-hour QQ-plots for individual monitors in Indianapolis.
D-14

Figure D-9. 24-hour QQ-plots for individual monitors in Indianapolis.
D-15

CPM values were calculated for 2011, 2012, and 2013 and the entire 3-year period and 

are shown in Table D-4 across all monitors and each individual monitor. 

Table D-4. Indianapolis composite performance metrics (CPM) and absolute fractional 

biases for 1-hour, 3-hour, and 24-hour averages.

Period Monitor CPM AFB1-hr AFB3-hr AFB24-hr

2011-2013

All 0.21 0.41 0.03 0.19

180970057 0.33 0.62 0.03 0.34

180970073 0.28 0.28 0.40 0.17

180970078 0.25 0.35 0.37 0.03

2011

All 0.32 0.45 0.14 0.38

180970057 0.48 0.61 0.26 0.57

180970073 0.34 0.44 0.49 0.07

180970078 0.26 0.29 0.20 0.30

2012

All 0.32 0.53 0.01 0.43

180970057 0.37 0.66 0.01 0.43

180970073 0.44 0.60 0.16 0.56

180970078 0.23 0.34 0.21 0.15

2013

All 0.29 0.53 0.17 0.16

180970057 0.45 0.74 0.17 0.43

180970073 0.44 0.44 0.26 0.61

180970078 0.24 0.41 0.29 0.03

The CPM values based on all monitors indicates relatively good model performance, for 

each individual year, as well as the entire 3-year period. Monitor 180970057 tends to have higher 

CPM values than the other monitors, possibly due to the inclusion of background increasing 

concentration while the monitor is impacted by most of the modeled sources as well. The one 

outlier in the CPM values is monitor 180970073 for 2011, with a CPM value of 0.74, much 

higher than the other monitors in 2011 or the CPM based on all three monitors. The high CPM 

appears to be due to the high AFB values for the 3-hour and 24-hour periods for the monitor as 

the monitor under-predicts compared to the other monitors for 2011 (Figures 3-11f and 3-12f). 

Tulsa: Modeled Air Quality Evaluation

Three monitors were available for model evaluation in Tulsa (Figures C-5 and C-6). 

Table D-5 shows the annual 99th percentile of the daily 1-hour maximum concentrations and 

design values for each monitor. The model under-predicts the design value for 401430175 but 

does very well at the design value predictions for the other two monitors.
D-16

Table D-5. Tulsa monitored and modeled annual 99th percentile daily 1-hour maximum 

concentrations (g m-3) and 3-year design value (g m-3).

Monitor Year Observed Modeled

401430175

2011 177.9 141.3

2012 143.9 117.7

2013 109.9 63.9

Design Value 143.9 107.6

401430235

2011 88.9 122.8

2012 62.8 99.7

2013 49.8 52.6

Design Value 67.1 91.7

401431127

2011 66.2 63.9

2012 40.5 56.6

2013 51.8 36.8

Design Value 52.8 52.4

One-hour, 3-hour, and 24-hour average QQ-plots are shown in Figures D-10 through D12 respectively across all monitors and QQ-plots by monitor are shown in Figures D-13 through 

D-15. For the 1-hour averages (Figure D-10), the model tends to over-predict for much of the 

concentration distribution for the total 3-year period as well as 2011 and 2012. 2013 shows a 

trend to more of the distribution being under-predicted. The 3-hour averages (Figure D-11) also 

show a trend of over-prediction and then under-prediction at the high end of the concentration 

distributions but perhaps less pronounced over-prediction than for the 1-hour averages. The 24-

hour averages (Figure D-12) for the 3-year period show slight over-prediction at the lower ends 

of the distribution with good agreement in the middle followed by under-prediction but overprediction at the very top of the distribution. 2011 shows slight over-prediction for much of the 

distribution, followed by under-prediction and over-prediction for the top three concentrations. 

2012 and 2013 show mostly under-prediction, except at the lower end of the concentration 

distributions.

With regards to individual monitor performance, monitor 401430175 (located just north 

of the West Refinery in Figure C-5 and Figure C-6, appeared to have better model performance 

for the 1-hour averages based on the 1-hour QQ-plots (Figure D-13a) when considering the 

entire 3-year period. Monitor 401430175 under-predicted for 2011, a mix of under-prediction 

and slight over-prediction for 2012 and mostly over-prediction 2013. The other two monitors 

mostly over-predicted for the 3-year period and each individual year. For the 3-hour averages, 

monitor 4011431127 appeared to be the better performer (Figure D-14i-l) while monitor 

401430175 tended toward over-prediction at the low end of the concentrations and underprediction at the higher end. Monitor 401430235 mostly over-predicted. Similar trends for the 

monitors are seen in the 24-hour averages (Figure D-15). 
D-17

Figure D-10. Tulsa 1-hour QQ-plots.
D-18

Figure D-11. Tulsa 3-hour QQ-plots.
D-19

Figure D-12. Tulsa 24-hour QQ-plots.
D-20

Figure D-13. 1-hour QQ-plots for individual monitors in Tulsa, OK.
D-21

Figure D-14. 3-hour QQ-plots for individual monitors in Tulsa, OK.
D-22

Figure D-15. 24-hour QQ-plots for individual monitors in Tulsa, OK.
D-23

CPM values were calculated for 2011, 2012, and 2013 and the entire 3-year period (Table 

D-6) across all monitors and each individual monitor. The CPM values among the individual 

monitors and the CPM based on all monitors tend to be very close to one another. The model 

with best agreement is 410431127 which tends to have the lower CPM with the exception of 

4010432035 in 2013. Based on the CPM values, the model appears to do reasonably well against 

the monitored values, with the exception of 2013, where the high CPM of 401430175 is driving 

the overall CPM value across all monitors.

Table D-6. Tulsa composite performance metrics (CPM) and absolute fractional biases for 

1-hour, 3-hour, and 24-hour averages.

Period Monitor CPM AFB1-hr AFB3-hr AFB24-hr

2011-2013

All 0.29 0.42 0.29 0.16

401430175 0.34 0.57 0.29 0.16

401432035 0.36 0.27 0.34 0.47

410431127 0.31 0.42 0.18 0.33

2011

All 0.28 0.36 0.33 0.17

401430175 0.34 0.52 0.33 0.17

401432035 0.31 0.24 0.24 0.45

410431127 0.29 0.32 0.14 0.41

2012

All 0.43 0.42 0.37 0.51

401430175 0.49 0.59 0.37 0.51

401432035 0.42 0.54 0.30 0.41

410431127 0.34 0.13 0.34 0.55

2013

All 0.72 0.63 0.84 0.68

401430175 0.83 0.97 0.84 0.68

401432035 0.33 0.42 0.18 0.36

410431127 0.37 0.50 0.37 0.24

Overall Model Performance Summary

Overall, for the three modeled areas, given uncertainties in emissions and meteorology 

and temporal resolution of the emissions for many of the sources (i.e., monthly, hour-of-day, 

month-hour-of-day, not individual hours), AERMOD appears to show adequate model 

performance, both from a regulatory evaluation standpoint, and the narrower analysis on a 

monitor-by-monitor-basis. When evaluating on an annual basis, 2011 tended to be the better 

performing year, which is not surprising given that 2011 is one of the triennial emissions 

inventory years. Also, as noted, given the temporal resolution of the most of the emissions, the 

model performance is quite good. With some of the sources using a monthly temporal profile, 

emissions for each hour for a given month would be the same (See Appendix B of this document 

for an example). Given the lack of temporal variability of source emissions in the model and the 

fact that a monitor does pick up temporal variability of emissions not seen by the model, the 

performance of AERMOD is acceptable for the purposes of this exposure assessment.
D-24

REFERENCES

Cox, W.M. and J.A. Tikvart. (1990). A statistical Procedure for Determining the Best Performing 

Air Quality Simulation Model. Atmos. Environ., 24A (9): 2387-2395.

U.S. EPA. (1992). Protocol for Determining the Best Performing Model, EPA-454/R-92-025. 

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

U.S. EPA. (2003). AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-003. U.S. 

Environmental Protection Agency, Research Triangle Park, NC 27711.

U.S. EPA. (2016). Evaluation of Prognostic Meteorological Data in AERMOD Applications, 

EPA-454/R-16-004. U.S. Environmental Protection Agency, Research Triangle Park, 

North Carolina 27711.

Venkatram, A., R. W. Brode, A. J. Cimorelli, J. T. Lee, R. J. Paine, S. G. Perry, W. D. Peters, J. 

C. Weil, and R. B. Wilson. (2001). A complex terrain dispersion model for regulatory 

applications. Atmos.Environ., 35, 4211-4221.
E-1

APPENDIX E

ASTHMA PREVALENCE

E.1 Overview

This appendix describes the development of the most recent asthma prevalence file used 

by EPA’s Air Pollution Exposure Model (APEX) to estimate individuals (e.g., children, adults) 

having asthma. This development involved three basic steps: 1) processing National Health 

Interview Survey (NHIS) asthma prevalence data, 2) processing U.S. Census poverty/income 

status data, and 3) combining the two sets considering variables known to influence asthma (e.g., 

age, sex, poverty status, U.S. region) to estimate asthma prevalence stratified by age and sex for 

all US Census tracts.

E.2 General History

The current processing approach is based on work originally performed by Cohen and 

Rosenbaum (2005) and then revised and extended by U.S. EPA (2014). Briefly for the earlier 

APEX asthma prevalence file development, Cohen and Rosenbaum (2005) calculated asthma 

prevalence for children aged 0 to 17 years for each age, sex, and four U.S. regions using 2003 

NHIS survey data. The regions defined by NHIS were ‘Midwest’, ‘Northeast’, ‘South’, and 

‘West’. The asthma prevalence was defined as the probability of a ‘Yes’ response to the question 

“EVER been told that [the child] had asthma?”1among those persons that responded either ‘Yes’ 

or ‘No’ to this question.2 The responses were weighted to take into account the complex survey 

design of the NHIS.3 Standard errors and confidence intervals for the prevalence were calculated 

using a logistic model (PROC SURVEY LOGISTIC). A scatterplot technique (LOESS 

smoother) was applied to smooth the prevalence curves and compute the standard errors and 

confidence intervals for the smoothed prevalence estimates. Logistic analysis of the raw and 

smoothed prevalence curves showed statistically significant differences in prevalence by gender 

and region, supporting their use as stratification variables in the final data set. These smoothed 

prevalence estimates were used as an input to APEX to estimate air pollutant exposure in 

children with asthma (U.S. EPA 2007; 2008; 2009). 

 

1 The response was recorded as variable “CASHMEV” in the downloaded dataset. Data and documentation are 

available at http://www.cdc.gov/nchs/nhis/quest_data_related_1997_forward.htm.

2

If there were another response to this variable other than “yes” or “no” (i.e., refused, not ascertained, don’t know, 

and missing), the surveyed individual was excluded from the analysis data set.

3

In the SURVEY LOGISTIC procedure, the variable “WTF_SC” was used for weighting, “PSU” was used for 

clustering, and “STRATUM” was used to define the stratum.
E-2

In the revision documented in U.S. EPA (2014), several years of NHIS survey data 

(2006-2010) were combined and used to calculate asthma prevalence for that period. Asthma 

prevalence for children (by age in years) as was estimated as described above but also included 

an estimate of adult asthma prevalence (by age groups). In addition, two sets of asthma 

prevalence for each adults and children were estimated. The first data set, as was done 

previously, was based on responses to the question “EVER been told that [the child] had 

asthma”. The second data set was developed using the probability of a ‘Yes’ response to a 

question that followed those that answered ‘Yes’ to the first question regarding ever having 

asthma, specifically, do those persons “STILL have asthma?”. And finally, in addition to the 

nominal variables region and sex, the asthma prevalence in this new analysis were further 

stratified by a family income/poverty ratio (i.e., whether the family income was considered 

below or at/above the US Census estimate of poverty level for the given year).

These updated asthma prevalence data were linked to U.S. census tract level poverty 

ratios probabilities, also stratified by age. Staff considered the variability in population exposures 

to be better represented when accounting for and modeling these newly refined attributes of this 

susceptible population. This is because of the 1) significant observed differences in asthma 

prevalence by age, sex, region, and poverty status, 2) the variability in the spatial distribution of 

poverty status across census tracts, stratified by age, and 3) the potential for spatial variability in 

local scale ambient concentrations. 

It is in this spirit that staff update the asthma prevalence files used by APEX, using the 

most recent data available that reasonably bound the exposure assessment period of interest.

Step 1: NHIS Data Set Description and Processing

The objective of this first processing step was to estimate asthma prevalence for children 

and adults considering several influential variables. First, raw 2011-2015 data and associated 

documentation were downloaded from the Center for Disease Control (CDC) and Prevention’s 

NHIS website.4 The ‘Sample Child’ and ‘Sample Adult’ files were selected because of the 

availability of person-level attributes of interest within these files, i.e., age in years (‘age_p’), sex 

(‘sex’), U.S. geographic region (‘region’), coupled with the response to questions of whether or 

not the surveyed individual ever had and still has asthma. In total, five years of recent survey 

data were obtained, comprising over 64,000 children and 170,000 children for years 2011-2015 

(Table E-1).

 

4 See http://www.cdc.gov/nchs/nhis.htm (accessed April 11, 2017).
E-3

Information regarding personal and family income and poverty ranking are also provided 

by the NHIS in separate files. Five files (‘INCIMPx.dat’) are available for each survey year, each 

containing either the actual responses (where recorded or provided by survey participant) or 

imputed values for the desired financial variable.5 For this current analysis, the ratio of income to 

poverty was provided as a continuous variable (‘POVRATI3’) and used to develop a nominal 

variable for this evaluation: either the survey participant was below or above a selected poverty 

threshold. This was done in this manner to be consistent with data generated as part of the second 

data set processing step, i.e., a table containing census tract level poverty ratio probabilities 

stratified by age (step 2).

When considering the number of stratification variables, the level of asthma prevalence, 

and poverty distribution among the survey population, sample size was an important issue. For 

the adult data, there were insufficient numbers of persons available to stratify the data by single 

ages (for some years of age there were no survey persons). Therefore, the adult survey data were 

grouped as follows: ages 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, and, ≥75.

6 To increase the 

number of persons within the age, gender, and four region groupings of our characterization of 

‘below poverty’ asthmatics persons, the poverty ratio threshold was selected as <1.5, therefore 

including persons that were within 50% above the poverty threshold. If the mean of the five 

imputed/recorded values were <1.5, the person’s family income was categorized ‘below’ the 

poverty threshold, if the mean of the 5 values were ≥1.5, the person’s family income was 

categorized ‘above’ the poverty threshold.

The person-level income files were then merged with the sample adult and child files 

using the ‘HHX’ (a household identifier), ‘FMX’ (a family identifier), and ‘FPX’ (an individual 

identifier) variables. Note, all persons within the sample adult and child files had corresponding 

financial survey data. 

Two asthma survey response variables were of interest in this analysis and were used to 

develop the two separate prevalence data sets for each children and adults. The response to the 

first question “Have you EVER been told by a doctor or other health professional that you [or 

your child] had asthma?” was recorded as variable name ‘CASHMEV’ for children and 

‘AASMEV’ for adults. Only persons having responses of either ‘Yes’ or ‘No’ to this question 

were retained to estimate the asthma prevalence. This assumes that the exclusion of those 

 

5 Financial information was not collected from all persons; therefore, the NHIS provides imputed data. Details into 

the available variables and imputation method are provided with each year’s data set. For example, see “Multiple 

Imputation of Family Income and Personal Earnings in the National Health Interview Survey: Methods and 

Examples” at https://www.cdc.gov/nchs/data/nhis/tecdoc15.pdf.

6 These same age groupings were used to create the companion file containing the census tract level poverty ratio 

probabilities (section 2).
E-4

responding otherwise, i.e., those that ‘refused’ to answer, instances where it was “not 

ascertained’, or the person ‘does not know’, does not affect the estimated prevalence rate if either 

‘Yes’ or ‘No’ answers could actually be given by these persons. There were very few persons 

providing an unusable response (Table E-1), thus the above assumption is reasonable. A second 

question was asked as a follow to persons responding “Yes” to the first question, specifically, 

“Do you STILL have asthma?” and noted as variables ‘CASSTILL’ and ‘AASSTILL’ for 

children and adults, respectively. Again, while only persons responding ‘Yes’ and ‘No’ were 

retained for further analysis, the representativeness of the screened data set is assumed 

unchanged from the raw survey data given the few persons having unusable data.

Table E-1. Number of total surveyed persons from NHIS (2006-2010) sample adult and 

child files and the number of those responding to asthma survey questions.

CHILDREN 2011 2012 2013 2014 2015 TOTAL

All Persons 12,844 13,275 12,860 13,380 12,281 64,640

Yes/No Asthma 12,831 13,263 12,851 13,366 12,269 64,580

Yes/No to Still Have + No Asthma 12,831 13,248 12,844 13,359 12,269 64,551

ADULTS 2011 2012 2013 2014 2015 TOTAL

All Persons 33,014 34,525 34,557 36,697 33,672 172,465

Yes/No Asthma 32,982 34,505 34,525 36,667 33,651 172,330

Yes/No to Still Have + No Asthma 32,953 34,468 34,498 36,615 33,614 172,148

Logistic Models

As described in the previous section, four person-level analytical data sets were created 

from the raw NHIS data files, generally containing similar variables: a ‘Yes’ or ‘No’ asthma 

response variable (either ‘EVER’ or ‘STILL’), an age (or age group for adults), their sex (‘male’ 

or ‘female’), US geographic region (‘Midwest’, ‘Northeast’, ‘South’, and ‘West’), and poverty 

status (‘below’ or above’). One approach to calculate prevalence rates and their uncertainties for 

a given gender, region, poverty status, and age is to calculate the proportion of ‘Yes’ responses 

among the ‘Yes’ and ‘No’ responses for that demographic group, appropriately weighting each 

response by the survey weight. This simplified approach was initially used to develop ‘raw’ 

asthma prevalence rates however this approach may not be completely appropriate. The two 

main issues with such a simplified approach are that the distributions of the estimated prevalence 

rates would not be well approximated by normal distributions and that the estimated confidence 

intervals based on a normal approximation would often extend outside the [0, 1] interval. A 

better approach for such survey data is to use a logistic transformation and fit the model:

Prob (asthma) = exp(beta) / (1 + exp(beta)),
E-5

where beta may depend on the explanatory variables for age, sex, poverty status, or region. This 

is equivalent to the model:

Beta = logit {prob (asthma)} = log {prob (asthma) / [1 – prob (asthma)]}.

The distribution of the estimated values of beta is more closely approximated by a normal 

distribution than the distribution of the corresponding estimates of prob (asthma). By applying a 

logit transformation to the confidence intervals for beta, the corresponding confidence intervals 

for prob (asthma) will always be inside [0, 1]. Another advantage of the logistic modeling is that 

it can be used to compare alternative statistical models, such as models where the prevalence 

probability depends upon age, region, poverty status, and sex, or on age, region, poverty status 

but not sex.

In previous analyses using the 2006-2010 NHIS asthma prevalence data, a variety of 

logistic models and compared them for use in estimating asthma prevalence, where the 

transformed probability variable beta is a given function of age, gender, poverty status, and 

region (Cohen and Rosenbaum, 2005; U.S. EPA, 2014). The SAS procedure 

SURVEYLOGISTIC was used to fit the various logistic models, taking into account the NHIS 

survey weights and survey design (using both stratification and clustering options), as well as 

considering various combinations of the selected explanatory variables.

As an example, Table E-2 lists the models fit and their log-likelihood goodness-of-fit 

measures using the sample child data and for the “EVER” asthma response variable using the 

2006-2010 NHIS data. A total of 32 models were fit, depending on the inclusion of selected 

explanatory variables and how age was considered in the model. The ‘Strata’ column lists the 

eight possible stratifications: no stratification, stratified by gender, by region, by poverty status, 

by region and gender, by region and poverty status, by gender and poverty status, and by region, 

gender and poverty status. For example, “5. region, gender” indicates that separate prevalence 

estimates were made for each combination of region and gender. As another example, “2. 

gender” means that separate prevalence estimates were made for each gender, so that for each 

gender, the prevalence is assumed to be the same for each region. Note the prevalence estimates 

are independently calculated for each stratum.

The ‘Description’ column of Table E-2 indicates how beta depends upon the age:

Linear in age Beta =  +  × age, where  and  vary with strata.

Quadratic in age Beta =  +  × age +  × age2 where   and  vary with strata.
E-6

Cubic in age Beta =  +  × age +  × age2 +  × age3 where  , , and  vary 

with the strata.

f(age) Beta = arbitrary function of age, with different functions for 

different strata

The category f(age) is equivalent to making age one of the stratification variables, and is 

also equivalent to making beta a polynomial of degree 17 in age (since the maximum age for 

children is 17), with coefficients that may vary with the strata.

The fitted models are listed in order of complexity, where the simplest model (1) is a 

non-stratified linear model in age and the most complex model (model 32) has a prevalence that 

is an arbitrary function of age, gender, poverty status, and region. Model 32 is equivalent to 

calculating independent prevalence estimates for each of the 288 combinations of age, sex, 

poverty status, and region. 
E-7

Table E-2. Alternative logistic models for estimating child asthma prevalence using the 

“EVER” asthma response variable and goodness of fit test results using the 

2006-2010 NHIS data.

Model Description Strata - 2 Log Likelihood DF

1 1. logit(prob) = linear in age 1. none 288740115.1 2

2 1. logit(prob) = linear in age 2. gender 287062346.4 4

3 1. logit(prob) = linear in age 3. region 288120804.1 8

4 1. logit(prob) = linear in age 4. poverty 287385013.1 4

5 1. logit(prob) = linear in age 5. region, gender 286367652.6 16

6 1. logit(prob) = linear in age 6. region, poverty 286283543.6 16

7 1. logit(prob) = linear in age 7. gender, poverty 285696164.7 8

8 1. logit(prob) = linear in age 8. region, gender, poverty 284477928.1 32

9 2. logit(prob) = quadratic in age 1. none 286862135.1 3

10 2. logit(prob) = quadratic in age 2. gender 285098650.6 6

11 2. logit(prob) = quadratic in age 3. region 286207721.5 12

12 2. logit(prob) = quadratic in age 4. poverty 285352164 6

13 2. logit(prob) = quadratic in age 5. region, gender 284330346.1 24

14 2. logit(prob) = quadratic in age 6. region, poverty 284182547.5 24

15 2. logit(prob) = quadratic in age 7. gender, poverty 283587631.7 12

16 2. logit(prob) = quadratic in age 8. region, gender, poverty 282241318.6 48

17 3. logit(prob) = cubic in age 1. none 286227019.6 4

18 3. logit(prob) = cubic in age 2. gender 284470413 8

19 3. logit(prob) = cubic in age 3. region 285546716.1 16

20 3. logit(prob) = cubic in age 4. poverty 284688169.9 8

21 3. logit(prob) = cubic in age 5. region, gender 283662673.5 32

22 3. logit(prob) = cubic in age 6. region, poverty 283404487.5 32

23 3. logit(prob) = cubic in age 7. gender, poverty 282890785.3 16

24 3. logit(prob) = cubic in age 8. region, gender, poverty 281407414.3 64

25 4. logit(prob) = f(age) 1. none 285821686.2 18

26 4. logit(prob) = f(age) 2. gender 283843266.2 36

27 4. logit(prob) = f(age) 3. region 284761522.8 72

28 4. logit(prob) = f(age) 4. poverty 284045849.2 36

29 4. logit(prob) = f(age) 5. region, gender 282099156.1 144

30 4. logit(prob) = f(age) 6. region, poverty 281929968.5 144

31 4. logit(prob) = f(age) 7. gender, poverty 281963915.7 72

32 4. logit(prob) = f(age) 8. region, gender, poverty 278655423.1 288
E-8

Table E-2 also includes the -2 Log Likelihood statistic, a goodness-of-fit measure, and 

the associated degrees of freedom (DF), which is the total number of estimated parameters. Any 

two models can be compared using their -2 Log Likelihood values: models having lower values 

are preferred. If the first model is a special case of the second model, then the approximate 

statistical significance of the first model is estimated by comparing the difference in the -2 Log 

Likelihood values with a chi-squared random variable having r degrees of freedom, where r is 

the difference in the DF (hence a likelihood ratio test). For all pairs of models from Table E-2, all 

the differences in the -2 Log Likelihood statistic are at least 600,000 and thus significant at pvalues well below 1 percent. Based on its having the lowest -2 Log Likelihood value, the last 

model fit (model 32: retaining all explanatory variables and using f(age)) was preferred and used 

to estimate the asthma prevalence in the prior analyses7as well as employed for this updated 

2011-2015 NHIS data analysis.

The SURVEYLOGISTIC procedure produces estimates of the beta values and their 95% 

confidence intervals for each combination of age, region, poverty status, and gender. By 

applying the inverse logit transformation,

Prob (asthma) = exp( beta) / (1 + exp(beta) ),

one can convert the beta values and associated 95% confidence intervals into predictions and 

95% confidence intervals for the prevalence. The standard error for the prevalence was estimated 

as:

Std Error {Prob (asthma)} = Std Error (beta) × exp(- beta) / (1 + exp(beta) )2,

which follows from the delta method (i.e., a first order Taylor series approximation). 

Estimated asthma prevalence using this approach and termed here as ‘unsmoothed’ are 

provided in Attachment 1. Graphical representation is provided in a series of figures 

incorporating the following variables:

• Region

• Gender

• Age (in years) or Age_group (age categories) 

 

7 Similar results were obtained when estimating prevalence using the ‘STILL’ have asthma variable as well as when 

investigating model fit using the adult data sets. In the Cohen and Rosenbaum (2005) analysis, adult data were not 

used and the poverty to income ratio was not a variable in their models. Also, because age was a categorical 

variable in the adult data sets in U.S. EPA (2014) and analyses conducted here, it could only be evaluated using 

f(age_group).
E-9

• Poverty Status

• Prevalence = predicted prevalence

• SE = standard error of predicted prevalence

• LowerCI = lower bound of 95 % confidence interval for predicted prevalence

• UpperCI = upper bound of 95 % confidence interval for predicted prevalence

A series of 8 plots are provided per figure that vary by region and poverty status (i.e., 4 x 

2 = 8). Results for children are given in Figures 1 (‘EVER’ had Asthma) and 2 (‘STILL’ have 

asthma) while adults are provided in Figures 3 (‘EVER’ had Asthma) and 4 (‘STILL’ have 

asthma) within Attachment 1. Data used for each figure/plot can be provided upon request.

Loess Smoother

The estimated prevalence curves show that the prevalence is not necessarily a smooth 

function of age. The linear, quadratic, and cubic functions of age modeled by 

SURVEYLOGISTIC were identified as a potential method for smoothing the curves, but they 

did not provide the best fit to the data. One reason for this might be due to the attempt to fit a 

global regression curve to all the age groups, which means that the predictions for age A are 

affected by data for very different ages. A local regression approach that separately fits a 

regression curve to each age A and its neighboring ages was used, giving a regression weight of 

1 to the age A, and lower weights to the neighboring ages using a tri-weight function:

Weight = {1 – [ |age – A| / q ] 3}, where | age – A| <= q.

The parameter q defines the number of points in the neighborhood of the age A. Instead 

of calling q the smoothing parameter, SAS defines the smoothing parameter as the proportion of 

points in each neighborhood. A quadratic function of age to each age neighborhood was fit 

separately for each gender and region combination. These local regression curves were fit to the 

beta values, the logits of the asthma prevalence estimates, and then converted them back to 

estimated prevalence rates by applying the inverse logit function exp(beta) / (1 + exp(beta)). In 

addition to the tri-weight variable, each beta value was assigned a weight of 

1 / [std error (beta)]2, to account for their uncertainties.

In this application of LOESS, weights of 1 / [std error (beta)] 2 were used such that 

2 = 

1. The LOESS procedure estimates 

2

from the weighted sum of squares. Because it is assumed 



2 = 1, the estimated standard errors are multiplied by 1 / estimated  and adjusted the widths of 

the confidence intervals by the same factor.
E-10

There are several potential values that can be selected for the smoothing parameter; the 

optimum value was determined by evaluating three regression diagnostics: the residual standard 

error, normal probability plots, and studentized residuals. To generate these statistics, the LOESS 

procedure was applied to estimated smoothed curves for beta, the logit of the prevalence, as a 

function of age, separately for each region, gender, and poverty classification. For the children 

data sets, curves were fit using the choices of 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 for the 

smoothing parameter. This selected range of values was bounded using the following 

observations. With only 18 points (i.e., the number of single year ages for children), a smoothing 

parameter of 0.2 cannot be used because the weight function assigns zero weights to all ages 

except age A, and a quadratic model cannot be uniquely fit to a single value. A smoothing 

parameter of 0.3 also cannot be used because that choice assigns a neighborhood of 5 points only 

(0.3 × 18 = 5, rounded down), of which the two outside ages have assigned weight zero, making 

the local quadratic model fit exactly at every point except for the end points (ages 0, 1, 16 and 

17). Usually one uses a smoothing parameter below 1 so that not all the data are used for the 

local regression at a given x value. Note also that a smoothing parameter of 0 can be used to 

generate the raw, unsmoothed, prevalence. The selection of the smoothing parameter used for the 

adult curves would follow a similar logic, although the lower bound could effectively be 

extended only to 0.9 given the number of age groups. This limits the selection of smoothing 

parameter applied to the two adult data sets to a value of 0.9, though values of 0.8 – 1.0 were 

nevertheless compared for good measure.

The first regression diagnostic used was the residual standard error, which is the LOESS 

estimate of . As discussed above, the true value of  equals 1, so the best choice of smoothing 

parameter should have residual standard errors as close to 1 as possible. For children ‘EVER’ 

having asthma and when considering the best models (of the 112 possible, those having 

0.95<RSE<1.05) using this criterion, the best choice varies with gender, region, and poverty 

status between smoothing parameters of 0.4, 0.7, and 1.0 (Table E-3). For the ‘STILL’ data set, a 

value of 0.5 or 0.6 would be slightly preferred. The ‘EVER’ adult data set could be smoothed 

using a value of 0.8 – 1.0 given the limited selection of smoothing values (of the 48 possible 

models), though 0.8 appears a better value for the ‘STILL’ data set.

Table E-3. Top model smoothing fits where residual standard error at or a value of 1.0.

Data Set Asthma Smoothing Parameter

0.4 0.5 0.6 0.7 0.8 0.9 1.0

Children EVER 4 2 2 4 3 3 4

STILL 3 5 4 2 3 2 2

Adults EVER n/a n/a n/a n/a 3 3 5

STILL n/a n/a n/a n/a 3 1 1
E-11

The second regression diagnostic was developed from an approximate studentized 

residual. The residual errors from the LOESS model were divided by standard error (beta) to 

make their variances approximately constant. These approximately studentized residuals should 

be approximately normally distributed with a mean of zero and a variance of 

2 = 1. To test this 

assumption, normal probability plots of the residuals were created for each smoothing parameter, 

combining all the studentized residuals across genders, regions, poverty status, and ages. The 

results for the children data indicate little distinction or affect by the selection of a particular 

smoothing parameter (e.g., see Figure E-1), although linearity in the plotted curve is best 

expressed with smoothing parameters generally between 0.6 and 0.9. When considering the adult 

data sets, the appropriate value would generally be 0.9.

Figure E-1. Normal probability plot of studentized residuals generated using logistic model, 

smoothing set to 0.6, and the children ‘STILL’ asthmatic data set.
E-12

The third regression diagnostic are plots of the studentized residuals against the smoothed 

beta values. All the studentized residuals for a given smoothing parameter are plotted together 

within the same graph. Also plotted is a LOESS smoothed curve fit to the same set of points, 

with SAS’s optimal smoothing parameter choice, to indicate the typical pattern. Ideally there 

should be no obvious pattern and an average studentized residual close to zero with no regression 

slope (e.g., see Figure E-2). For the children data sets, these plots generally indicate no unusual 

patterns, and the results for smoothing parameters 0.4 through 0.6 indicate a fit LOESS curve 

closest to the studentized residual equals zero line. When considering the adult data sets, 0.9 –

1.0 appear to be appropriate values. 

Figure E-2. Studentized residuals versus model predicted betas generated using a logistic 

model and using the children ‘STILL’ asthmatic data set, with smoothing set to 0.6.

When considering both children asthma prevalence responses evaluated, the residual 

standard error (estimated values for sigma) suggests the choice of smoothing parameter as 
E-13

varied, ranging from 0.4 to 0.7. The normal probability plots of the studentized residuals suggest 

preference for smoothing at or above 0.6. The plots of residuals against smoothed predictions 

suggest the choices of 0.4 through 0.6. We therefore chose the final value of 0.6 to use for 

smoothing the children’s asthma prevalence. For the adults, there were small differences in the 

statistical metrics used to evaluate the smoothing. A value of 0.9 was selected for smoothing, 

consistent with what was used in my prior analysis (U.S. EPA, 2014).

The smoothed asthma prevalence and associated graphical presentation are provided in 

Attachment 2 following a similar format to that presented in Attachment 1.

Step 2: U.S. Census Tract Poverty Ratio Data Set Description and Processing

This section briefly describes the approach used to generate census tract level poverty 

ratios for all U.S. census tracts, stratified by age and age groups where available. Details 

regarding the data processing is provided below in Attachment 3.8 Data used was from 2013 U.S.

Census 5-year American Community Survey (ACS).

First, ACS internal point latitudes and longitudes were obtained from the 2013 Gazetteer 

files.9 Next, the individual state level ACS sequence files (SF-56) were downloaded,10 retaining 

the number of persons across the variable “B17024” for each state considering the appropriate 

logical record number.11 The data provided by the B17024 variable is stratified by age or age 

groups (ages <5, 5, 6-11, 12-14, 15, 16-17, 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, and ≥75) 

and income/poverty ratios, given in increments of 0.25. We calculated two new variables for 

each age using the number of persons from the B17024 stratifications; the fraction of those 

persons having poverty ratios < 1.5 and ≥ 1.5 by summing the appropriate B17024 variable and 

dividing by the total number of persons in that age/age group. Then, individual state level 

geographic data (“geo” files) and their associated documentation were downloaded12 and 

 

8 Code has been adapted from ACS 2012 SAS programs and from ACS 2012 SAS Macros available at 

http://www2.census.gov/acs2012_5yr/summaryfile/UserTools/SF20125YR_SAS.zip and 

http://www2.census.gov/acs2012_5yr/summaryfile/UserTools/SF_All_Macro.sas

9 Data set and content description is available at: http://www.census.gov/geo/maps-data/data/gazetteer2013.html.

10 We used the summary tables (B17024), giving census tract populations by poverty income ratio and age group 

downloaded from http://www2.census.gov/acs2013_5yr/summaryfile/2009-2013_ACSSF_By_State_All_Tables/. We 

unzipped each state's ACS2013 5-yr table zip, then gathered sequence file 56.

11 Information regarding variable names is available at 

https://www2.census.gov/acs2013_5yr/summaryfile/ACS_2013_SF_Tech_Doc.pdf. A file for the appropriate logical 

record number, “Sequence_Number_and_Table_Number_Lookup.xls”, can be found at 

https://www2.census.gov/acs2013_5yr/summaryfile/.

12 Geographic data were obtained from obtained from http://www2.census.gov/acs2013_5yr/summaryfile/2009-

2013_ACSSF_By_State_All_Tables/b. Unzipped were each state's ACS2013 5-yr table ("g2013" file names).
E-14

screened for tract level information using the “sumlev” variable equal to ‘140’. Also identified 

was the US Region for each state, consistent with that used for the NHIS asthma prevalence 

data.13

Finally, the poverty ratio data were combined with the above described census tract level 

geographic data using the “stusab” and “logrecno” variables. Because APEX requires the input 

data files to be complete, additional processing of the poverty probability file was needed. For 

where there was missing tract level poverty information,14 we substituted an age-specific value 

using the average for the particular county the tract was located within, or the state-wide average. 

The percent of tracts substituted using county averaged values varied by age group though, on 

average, was approximately 1.7% of the total tracts (Table E-4). Only a handful of tracts in six of 

the age groups were substituted using state averaged values.

Table E-4. Percent of tracts substituted with county average or state average poverty 

status.

Percent 

Substituted

Age Groups

≤5 6-11 12-17 18-24 25-34 35- 44 45-54 55-64 65-74 ≥75 all

Filled with 

County Avg. 1.9 2.1 2.0 1.5 1.4 1.4 1.3 1.4 1.7 2.0 1.7

Filled with 

State Avg. 0.004 0.003 0.004 0.001 0 0 0 0 0 0.001 0.001

The final output was a single file containing relevant tract level poverty probabilities 

(pov_prob) by age groups for all U.S. census tracts.

Step 3: Combining Census Tract Poverty Ratios with the Asthma Prevalence Data

The two data sets were merged considering the region identifier and stratified by age and 

sex. The final asthma prevalence was calculated using the following weighting scheme:

Asthma prevalence=round((pov_prob*prev_belowpov)+((1-pov_prob)*prev_abovepov),0.0001);

whereas each U.S. census tract value now expresses a tract specific poverty-weighted 

asthma prevalence, stratified by ages (children 0-17), age groups (adults), and two sexes. These 

 

13 https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf

14 Whether there were no data collected by the Census for the selected poverty status or whether there were simply 

no persons in that age group is relatively inconsequential to estimating the asthmatic persons exposed, particularly 

considering latter case as no persons in that age group would be modeled by APEX when using the same Census 

population data set. 
E-15

final asthma prevalence data used for the assessment are found within the APEX 

asthmaprevalence.txt file.
E-16

REFERENCES

Cohen J and Rosenbaum A. (2005). Analysis of NHIS Asthma Prevalence Data. Memorandum 

to John Langstaff by ICF Incorporated. For US EPA Work Assignment 3-08 under EPA 

contract 68D01052. Available in US EPA (2007) Appendix G.

U.S. EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas (July 2007). 

Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R07-010. Available at: http://epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html.

U.S. EPA. (2008). Risk and Exposure Assessment to Support the Review of the NO2 Primary 

National Ambient Air Quality Standard. Report no. EPA-452/R-08-008a. November 

2008. Available at: http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf.

U.S. EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO2 Primary 

National Ambient Air Quality Standard. Report no. EPA-452/R-09-007. August 2009. 

Available at: 

http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf.

U.S. EPA. (2014). Health Risk and Exposure Assessment for Ozone, Final Report. Chapter 5 

Appendices. Report no. EPA-452/R-14-004c. August 2014. Available at 

https://nepis.epa.gov/Exe/ZyPDF.cgi/P100KCI7.PDF?Dockey=P100KCI7.PDF.
E-17

Attachment 1 – Non-Smoothed Asthma Prevalence (Figures 1 - 4)

Figure 1 - Children (Ever Have Asthma)

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Figure 1, cont. - Children (Ever Have Asthma)

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Figure 2 – Children (Still Have Asthma)

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Figure 2, cont. – Children (Still Have Asthma)

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Figure 3 – Adults (Ever Have Asthma)

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Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals-2011-2015

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Figure 3, cont. – Adults (Ever Have Asthma)

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Figure 4 – Adults (Still Have Asthma)

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Figure 4, cont. – Adults (Still Have Asthma)

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Attachment 2 –Smoothed Asthma Prevalence (Figures 1-4)

Figure 1 – Children (Ever Have Asthma)

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Figure 1, cont. – Children (Ever Have Asthma)

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Figure 2 – Children (Still Have Asthma)

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Figure 2, cont. – Children (Still Have Asthma)

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Figure 3 – Adults (Ever Have Asthma)

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Figure 3, cont. – Adults (Ever Have Asthma)

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18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals-2011-2015

region=South pov_rat=Below Poverty Level

gender Female Male

prev

0.0

0.1

0.2

0.3

0.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals-2011-2015

region=West pov_rat=Above Poverty Level

gender Female Male

prev

0.0

0.1

0.2

0.3

0.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals-2011-2015

region=West pov_rat=Below Poverty Level

gender Female Male
E-31

Figure 4 – Adults (Still Have Asthma)

prev

0.0

0.1

0.2

0.3

0.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals-2011-2015

region=Midwest pov_rat=Above Poverty Level

gender Female Male

prev

0.0

0.1

0.2

0.3

0.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals-2011-2015

region=Midwest pov_rat=Below Poverty Level

gender Female Male

prev

0.0

0.1

0.2

0.3

0.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals-2011-2015

region=Northeast pov_rat=Above Poverty Level

gender Female Male

prev

0.0

0.1

0.2

0.3

0.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals-2011-2015

region=Northeast pov_rat=Below Poverty Level

gender Female Male
E-32

Figure 4, cont. – Adults (Still Have Asthma)

prev

0.0

0.1

0.2

0.3

0.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals-2011-2015

region=South pov_rat=Above Poverty Level

gender Female Male

prev

0.0

0.1

0.2

0.3

0.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals-2011-2015

region=South pov_rat=Below Poverty Level

gender Female Male

prev

0.0

0.1

0.2

0.3

0.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals-2011-2015

region=West pov_rat=Above Poverty Level

gender Female Male

prev

0.0

0.1

0.2

0.3

0.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals-2011-2015

region=West pov_rat=Below Poverty Level

gender Female Male
E-33

Attachment 3 – Processing Code for US Census Poverty Status Data from 2013 ACS

options mlogic;

LIBNAME sas 'F:\SGRAHAM\NHIS\NHIS_1115_Process'; run; *location of sas data library;

*imports ACS2013_5yr internal point Latitude and Longitude;

PROC IMPORT OUT= acs2013_5yr_tract_lat_long 

 DATAFILE= "F:\SGRAHAM\NHIS\NHIS_1115_Process\2013_Gaz_tracts_national.txt"

 DBMS=TAB REPLACE;

 GETNAMES=YES;

 DATAROW=2; 

RUN;

*formats a new variable GEOID_merge using LAT LONs GEOID in order to merge LAT and LON to geography dataset by GEOID_merge;

data sas.acs2013_5yr_tract_lat_long (keep = GEOID_merge LAT LON);

set work.acs2013_5yr_tract_lat_long(rename=(GEOID=GEOID_char INTPTLAT=LAT INTPTLONG=LON));

length GEOID_merge $12.;

GEOID_merge = put(GEOID_char,Best12.); *STATE COUNTY and TRACT from ACS2013 Sequence File data make up GEOID in Lat Lon file;

run;

%macro Read_poverty(geo); *Imports ACS2013_5yr sequence file 56, income/poverty data (Table B17024) by state (geo);

DATA work.SFe0056&geo;

LENGTH FILEID $6

 FILETYPE $6

 STUSAB $2

 CHARITER $3

 SEQUENCE $4

 LOGRECNO $7;

INFILE "F:\SGRAHAM\NHIS\NHIS_1115_Process\e20135&geo.0056000.txt" DSD TRUNCOVER DELIMITER =',' LRECL=3000;

LABEL 

FILEID ='File Identification'

FILETYPE='File Type' 

STUSAB ='State/U.S.-Abbreviation (USPS)'

CHARITER='Character Iteration'

SEQUENCE='Sequence Number'

LOGRECNO='Logical Record Number'

/*AGE BY RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 12 MONTHS */

/*Universe: Population for whom poverty status is determined */

B17024e1='Total:'

B17024e2='Under 6 years:'

B17024e3='Under .50'

B17024e4='.50 to .74'

B17024e5='.75 to .99'

B17024e6='1.00 to 1.24'

B17024e7='1.25 to 1.49'

B17024e8='1.50 to 1.74'

B17024e9='1.75 to 1.84'

B17024e10='1.85 to 1.99'

B17024e11='2.00 to 2.99'

B17024e12='3.00 to 3.99'

B17024e13='4.00 to 4.99'

B17024e14='5.00 and over'

B17024e15='6 to 11 years:'

B17024e16='Under .50'

B17024e17='.50 to .74'

B17024e18='.75 to .99'

B17024e19='1.00 to 1.24'

B17024e20='1.25 to 1.49'

B17024e21='1.50 to 1.74'

B17024e22='1.75 to 1.84'

B17024e23='1.85 to 1.99'

B17024e24='2.00 to 2.99'

B17024e25='3.00 to 3.99'

B17024e26='4.00 to 4.99'

B17024e27='5.00 and over'

B17024e28='12 to 17 years:'

B17024e29='Under .50'

B17024e30='.50 to .74'

B17024e31='.75 to .99'

B17024e32='1.00 to 1.24'

B17024e33='1.25 to 1.49'

B17024e34='1.50 to 1.74'

B17024e35='1.75 to 1.84'

B17024e36='1.85 to 1.99'

B17024e37='2.00 to 2.99'

B17024e38='3.00 to 3.99'

B17024e39='4.00 to 4.99'

B17024e40='5.00 and over'

B17024e41='18 to 24 years:'
E

-34

B17024e42='Under .50'

B17024e43='.50 to .74'

B17024e44='.75 to .99'

B17024e45='1.00 to 1.24'

B17024e46='1.25 to 1.49'

B17024e47='1.50 to 1.74'

B17024e48='1.75 to 1.84'

B17024e49='1.85 to 1.99'

B17024e50='2.00 to 2.99'

B17024e51='3.00 to 3.99'

B17024e52='4.00 to 4.99'

B17024e53='5.00 and over'

B17024e54='25 to 34 years:'

B17024e55='Under .50'

B17024e56='.50 to .74'

B17024e57='.75 to .99'

B17024e58='1.00 to 1.24'

B17024e59='1.25 to 1.49'

B17024e60='1.50 to 1.74'

B17024e61='1.75 to 1.84'

B17024e62='1.85 to 1.99'

B17024e63='2.00 to 2.99'

B17024e64='3.00 to 3.99'

B17024e65='4.00 to 4.99'

B17024e66='5.00 and over'

B17024e67='35 to 44 years:'

B17024e68='Under .50'

B17024e69='.50 to .74'

B17024e70='.75 to .99'

B17024e71='1.00 to 1.24'

B17024e72='1.25 to 1.49'

B17024e73='1.50 to 1.74'

B17024e74='1.75 to 1.84'

B17024e75='1.85 to 1.99'

B17024e76='2.00 to 2.99'

B17024e77='3.00 to 3.99'

B17024e78='4.00 to 4.99'

B17024e79='5.00 and over'

B17024e80='45 to 54 years:'

B17024e81='Under .50'

B17024e82='.50 to .74'

B17024e83='.75 to .99'

B17024e84='1.00 to 1.24'

B17024e85='1.25 to 1.49'

B17024e86='1.50 to 1.74'

B17024e87='1.75 to 1.84'

B17024e88='1.85 to 1.99'

B17024e89='2.00 to 2.99'

B17024e90='3.00 to 3.99'

B17024e91='4.00 to 4.99'

B17024e92='5.00 and over'

B17024e93='55 to 64 years:'

B17024e94='Under .50'

B17024e95='.50 to .74'

B17024e96='.75 to .99'

B17024e97='1.00 to 1.24'

B17024e98='1.25 to 1.49'

B17024e99='1.50 to 1.74'

B17024e100='1.75 to 1.84'

B17024e101='1.85 to 1.99'

B17024e102='2.00 to 2.99'

B17024e103='3.00 to 3.99'

B17024e104='4.00 to 4.99'

B17024e105='5.00 and over'

B17024e106='65 to 74 years:'

B17024e107='Under .50'

B17024e108='.50 to .74'

B17024e109='.75 to .99'

B17024e110='1.00 to 1.24'

B17024e111='1.25 to 1.49'

B17024e112='1.50 to 1.74'

B17024e113='1.75 to 1.84'

B17024e114='1.85 to 1.99'

B17024e115='2.00 to 2.99'

B17024e116='3.00 to 3.99'

B17024e117='4.00 to 4.99'

B17024e118='5.00 and over'

B17024e119='75 years and over:'

B17024e120='Under .50'

B17024e121='.50 to .74'

B17024e122='.75 to .99'

B17024e123='1.00 to 1.24'

B17024e124='1.25 to 1.49'

B17024e125='1.50 to 1.74'

B17024e126='1.75 to 1.84'

B17024e127='1.85 to 1.99'

B17024e128='2.00 to 2.99'

B17024e129='3.00 to 3.99'
E-35

B17024e130='4.00 to 4.99'

B17024e131='5.00 and over'

;

INPUT

FILEID $ 

FILETYPE $ 

STUSAB $ 

CHARITER $ 

SEQUENCE $ 

LOGRECNO $ 

B17024e1-B17024e131

;

if B17024e1 >=0;

RUN;

%mend;

%macro AnyGeo(geo); *Imports geo data file, assigns a census region, limits to 2013ACS_5yr census tracts by state ('geo'), assigns lat lon;

data work.g20135&geo (drop = AIANHH AIANHHFP AIHHTLI AITS AITSCE ANRC BLKGRP CBSA

CDCURR CNECTA

COMPONENT CONCIT COUSUB CSA

DIVISION FILEID MACC MEMI METDIV NAME

NECTA NECTADIV PCI

PLACE PUMA1 PUMA5 REGION SDELM SDSEC SDUNI

SLDL SLDU STATECE

SUBMCD SUMLEVEL TAZ UA UACP UGA UR

US VTD

ZCTA3 ZCTA5

);

/*Location of geo data file for import*/

 INFILE "F:\SGRAHAM\NHIS\NHIS_1115_Process\g20135&geo..txt" MISSOVER TRUNCOVER LRECL=500; /*change directory*/

 LABEL FILEID ='File Identification' STUSAB ='State Postal Abbreviation'

SUMLEVEL='Summary Level' COMPONENT='geographic Component'

LOGRECNO='Logical Record Number' US ='US'

REGION ='Region' DIVISION ='Division'

STATECE ='State (Census Code)' STATE ='State (FIPS Code)'

COUNTY ='County' COUSUB ='County Subdivision (FIPS)'

PLACE ='Place (FIPS Code)' TRACT ='Census Tract'

BLKGRP ='Block Group' CONCIT ='Consolidated City'

CSA ='Combined Statistical Area' METDIV ='Metropolitan Division'

UA ='Urban Area' UACP ='Urban Area Central Place'

VTD ='Voting District' ZCTA3 ='ZIP Code Tabulation Area (3-digit)'

SUBMCD ='Subbarrio (FIPS)' SDELM ='School District (Elementary)'

SDSEC ='School District (Secondary)' SDUNI ='School District (Unified)'

UR ='Urban/Rural' PCI ='Principal City Indicator'

TAZ ='Traffic Analysis Zone' UGA ='Urban Growth Area'

GEOID ='geographic Identifier' NAME ='Area Name' 

AIANHH ='American Indian Area/Alaska Native Area/Hawaiian Home Land (Census)'

AIANHHFP='American Indian Area/Alaska Native Area/Hawaiian Home Land (FIPS)'

AIHHTLI ='American Indian Trust Land/Hawaiian Home Land Indicator'

AITSCE ='American Indian Tribal Subdivision (Census)'

AITS ='American Indian Tribal Subdivision (FIPS)'

ANRC ='Alaska Native Regional Corporation (FIPS)'

CBSA ='Metropolitan and Micropolitan Statistical Area'

MACC ='Metropolitan Area Central City'

MEMI ='Metropolitan/Micropolitan Indicator Flag'

NECTA ='New England City and Town Combined Statistical Area'

CNECTA ='New England City and Town Area'

NECTADIV='New England City and Town Area Division'

CDCURR ='Current Congressional District'

SLDU ='State Legislative District Upper'

SLDL ='State Legislative District Lower'

ZCTA5 ='ZIP Code Tabulation Area (5-digit)'

PUMA5 ='Public Use Microdata Area - 5% File'

PUMA1 ='Public Use Microdata Area - 1% File' ;

 INPUT

 FILEID $ 1-6 STUSAB $ 7-8 SUMLEVEL $ 9-11

COMPONENT $ 12-13 LOGRECNO $ 14-20 US $ 21-21 

REGION $ 22-22 DIVISION $ 23-23 STATECE $ 24-25

STATE $ 26-27 COUNTY $ 28-30 COUSUB $ 31-35

PLACE $ 36-40 TRACT $ 41-46 BLKGRP $ 47-47

CONCIT $ 48-52 AIANHH $ 53-56 AIANHHFP $ 57-61

AIHHTLI $ 62-62 AITSCE $ 63-65 AITS $ 66-70

ANRC $ 71-75 CBSA $ 76-80 CSA $ 81-83

METDIV $ 84-88 MACC $ 89-89 MEMI $ 90-90

NECTA $ 91-95 CNECTA $ 96-98 NECTADIV $ 99-103

UA $ 104-108 UACP $ 109-113 CDCURR $ 114-115

 

SLDU $ 116-118 SLDL $ 119-121 VTD $ 122-127

ZCTA3 $ 128-130 ZCTA5 $ 131-135 SUBMCD $ 136-140

 
E-36

SDELM $ 141-145 SDSEC $ 146-150 SDUNI $ 151-155

UR $ 156-156 PCI $ 157-157 TAZ $ 158-163

UGA $ 164-168 PUMA5 $ 169-173 PUMA1 $ 174-178

GEOID $ 179-218 /* GEOID is 40 char in length */

NAME $ 219-418 

 ;

IF sumlevel='140'; *imports data for tracts only, similar to WHERE tract IS NOT NULL ;

run;

data work.g20135&geo (keep = STUSAB CENSUS_REGION LOGRECNO GEOID_merge STATE COUNTY TRACT);

set work.g20135&geo;

length CENSUS_REGION $12.;

if STUSAB = 'CT' OR STUSAB = 'ME' OR STUSAB = 'MA' OR STUSAB = 'NH' OR STUSAB = 'RI'

OR STUSAB = 'VT' OR STUSAB = 'NJ' OR STUSAB = 'NY' OR STUSAB = 'PA'

then do;

CENSUS_REGION = 'Northeast'; *assign census region;

end;

else if STUSAB = 'IN' OR STUSAB = 'IL' OR STUSAB = 'MI' OR STUSAB = 'OH' OR STUSAB = 'WI'

OR STUSAB = 'IA' OR STUSAB = 'KS' OR STUSAB = 'MN' OR STUSAB = 'MO' OR STUSAB = 'NE'

OR STUSAB = 'ND' OR STUSAB = 'SD'

then do;

CENSUS_REGION = 'Midwest';

end;

else if STUSAB = 'DE' OR STUSAB = 'DC' OR STUSAB = 'FL' OR STUSAB = 'GA' OR STUSAB = 'MD'

OR STUSAB = 'NC' OR STUSAB = 'SC' OR STUSAB = 'VA' OR STUSAB = 'WV' OR STUSAB = 'AL'

OR STUSAB = 'KY' OR STUSAB = 'MS' OR STUSAB = 'TN' OR STUSAB = 'AR' OR STUSAB = 'LA'

OR STUSAB = 'OK' OR STUSAB = 'TX'

then do;

CENSUS_REGION = 'South';

end;

else if STUSAB = 'AZ' OR STUSAB = 'CO' OR STUSAB = 'ID' OR STUSAB = 'NM' OR STUSAB = 'MT'

OR STUSAB = 'UT' OR STUSAB = 'NV' OR STUSAB = 'WY' OR STUSAB = 'AK' OR STUSAB = 'CA'

OR STUSAB = 'HI' OR STUSAB = 'OR' OR STUSAB = 'WA'

then do;

CENSUS_REGION = 'West';

end;

else CENSUS_REGION = 'Other';

where tract ne '';*limit to 2013ACS_5yr census tracts only;

length GEOID_char $12.;

GEOID_char = CATS(STATE,COUNTY,TRACT); *format GEOID_merge to match LAT LONs GEOID_merge;

GEOID_merge = put(input(GEOID_char,12.),12.);

run;

proc sort data=sas.Acs2013_5yr_tract_lat_long; 

by GEOID_merge;

run;

proc sort data=work.g20135&geo;

by GEOID_merge;

run;

data work.g20135&geo.coord (keep = STUSAB CENSUS_REGION LOGRECNO STATE COUNTY TRACT GEOID_merge LAT LON); *adds internal point lat lon;

merge work.g20135&geo(in=a) sas.Acs2013_5yr_tract_lat_long;

by GEOID_merge;

if a;

run;

%mend;

%macro pov_ratio_calc(geo);*calculates ratios above or below 1.5 income/poverty ratio by age group by tract. *fills tracts with 0 persons in an age class with the county-level ratio;

proc means data=work.SFe_g_0056&geo noprint;*creates a sum by county of each census poverty/income variable (for the entire county);

class county;

output out = work.pov_ratio_county_sum_&geo

sum = CountySum_B17024e1-CountySum_B17024e131

;

run;

proc sort data =work.pov_ratio_county_sum_&geo;

by county;

run;

proc sort data =work.SFe_g_0056&geo;

by county;

run;

data work.SFe_g_filled_co_0056&geo (drop = _TYPE_ _FREQ_);

merge work.SFe_g_0056&geo (in=a) work.pov_ratio_county_sum_&geo;

by county;

if a;

run;

proc means data=work.SFe_g_0056&geo noprint;*creates a sum by state of each census poverty/income variable (for the entire state);

class state;

output out = work.pov_ratio_state_sum_&geo

sum = StateSum_B17024e1-StateSum_B17024e131
E-37

;

run;

proc sort data =work.pov_ratio_state_sum_&geo;

by state;

run;

proc sort data =work.SFe_g_filled_co_0056&geo;

by state;

run;

data work.SFe_g_filled_st_co_0056&geo (drop = _TYPE_ _FREQ_);

merge work.SFe_g_filled_co_0056&geo (in=a) work.pov_ratio_state_sum_&geo;

by state;

if a;

run;

data work.pov_pct_&geo; 

set work.SFe_g_filled_st_co_0056&geo;

length filled_e2 $26 filled_e15 $26 filled_e28 $26 filled_e41 $26 filled_e54 $26 filled_e67 $26 filled_e80 $26 filled_e93 $26

filled_e106 $26 filled_e119 $26;

IF B17024e2 ^= 0 then do;

*where age group population in a tract is not equal to zero, calculate below/above poverty ratio based on income/poverty variables for the tract using tract-level data;

filled_e2 = 'Tract Values Used';

pctB17024e3=B17024e3/B17024e2;

pctB17024e4=B17024e4/B17024e2;

pctB17024e5=B17024e5/B17024e2;

pctB17024e6=B17024e6/B17024e2;

pctB17024e7=B17024e7/B17024e2;

pctB17024e8=B17024e8/B17024e2;

pctB17024e9=B17024e9/B17024e2;

pctB17024e10=B17024e10/B17024e2;

pctB17024e11=B17024e11/B17024e2;

pctB17024e12=B17024e12/B17024e2;

pctB17024e13=B17024e13/B17024e2;

pctB17024e14=B17024e14/B17024e2;end;

ELSE IF CountySum_B17024e2 ^= 0 then do;

*where age group population in a tract is zero, but the county is not equal to zero, calculate below/above poverty ratio based on income/poverty variables using county-level 

data;

filled_e2 = 'Filled with County Values';

pctB17024e3=CountySum_B17024e3/CountySum_B17024e2;

pctB17024e4=CountySum_B17024e4/CountySum_B17024e2;

pctB17024e5=CountySum_B17024e5/CountySum_B17024e2;

pctB17024e6=CountySum_B17024e6/CountySum_B17024e2;

pctB17024e7=CountySum_B17024e7/CountySum_B17024e2;

pctB17024e8=CountySum_B17024e8/CountySum_B17024e2;

pctB17024e9=CountySum_B17024e9/CountySum_B17024e2;

pctB17024e10=CountySum_B17024e10/CountySum_B17024e2;

pctB17024e11=CountySum_B17024e11/CountySum_B17024e2;

pctB17024e12=CountySum_B17024e12/CountySum_B17024e2;

pctB17024e13=CountySum_B17024e13/CountySum_B17024e2;

pctB17024e14=CountySum_B17024e14/CountySum_B17024e2;end;

ELSE IF CountySum_B17024e2 = 0 then do;

*where age group population in a county and tract are both zero, calculate below/above poverty ratio based on income/poverty variables using state-level data for children 17 

and under;

filled_e2 = 'Filled with State Values';

pctB17024e3=sum(StateSum_B17024e3,StateSum_B17024e16,StateSum_B17024e29)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e4=sum(StateSum_B17024e4,StateSum_B17024e17,StateSum_B17024e30)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e5=sum(StateSum_B17024e5,StateSum_B17024e18,StateSum_B17024e31)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e6=sum(StateSum_B17024e6,StateSum_B17024e19,StateSum_B17024e32)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e7=sum(StateSum_B17024e7,StateSum_B17024e20,StateSum_B17024e33)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e8=sum(StateSum_B17024e8,StateSum_B17024e21,StateSum_B17024e34)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e9=sum(StateSum_B17024e9,StateSum_B17024e22,StateSum_B17024e35)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e10=sum(StateSum_B17024e10,StateSum_B17024e23,StateSum_B17024e36)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e11=sum(StateSum_B17024e11,StateSum_B17024e24,StateSum_B17024e37)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e12=sum(StateSum_B17024e12,StateSum_B17024e25,StateSum_B17024e38)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e13=sum(StateSum_B17024e13,StateSum_B17024e26,StateSum_B17024e39)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e14=sum(StateSum_B17024e14,StateSum_B17024e27,StateSum_B17024e40)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);end;

IF B17024e15 ^= 0 then do; 

filled_e15 = 'Tract Values Used';

pctB17024e16=B17024e16/B17024e15;

pctB17024e17=B17024e17/B17024e15;

pctB17024e18=B17024e18/B17024e15;

pctB17024e19=B17024e19/B17024e15;

pctB17024e20=B17024e20/B17024e15;

pctB17024e21=B17024e21/B17024e15;

pctB17024e22=B17024e22/B17024e15;

pctB17024e23=B17024e23/B17024e15;

pctB17024e24=B17024e24/B17024e15;

pctB17024e25=B17024e25/B17024e15;

pctB17024e26=B17024e26/B17024e15;

pctB17024e27=B17024e27/B17024e15;end;

ELSE IF CountySum_B17024e15 ^= 0 then do;
E-38

filled_e15 = 'Filled with County Values';

pctB17024e16=CountySum_B17024e16/CountySum_B17024e15;

pctB17024e17=CountySum_B17024e17/CountySum_B17024e15;

pctB17024e18=CountySum_B17024e18/CountySum_B17024e15;

pctB17024e19=CountySum_B17024e19/CountySum_B17024e15;

pctB17024e20=CountySum_B17024e20/CountySum_B17024e15;

pctB17024e21=CountySum_B17024e21/CountySum_B17024e15;

pctB17024e22=CountySum_B17024e22/CountySum_B17024e15;

pctB17024e23=CountySum_B17024e23/CountySum_B17024e15;

pctB17024e24=CountySum_B17024e24/CountySum_B17024e15;

pctB17024e25=CountySum_B17024e25/CountySum_B17024e15;

pctB17024e26=CountySum_B17024e26/CountySum_B17024e15;

pctB17024e27=CountySum_B17024e27/CountySum_B17024e15;end;

ELSE IF CountySum_B17024e15 = 0 then do;

filled_e15 = 'Filled with State Values';

pctB17024e16=sum(StateSum_B17024e3,StateSum_B17024e16,StateSum_B17024e29)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e17=sum(StateSum_B17024e4,StateSum_B17024e17,StateSum_B17024e30)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e18=sum(StateSum_B17024e5,StateSum_B17024e18,StateSum_B17024e31)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e19=sum(StateSum_B17024e6,StateSum_B17024e19,StateSum_B17024e32)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e20=sum(StateSum_B17024e7,StateSum_B17024e20,StateSum_B17024e33)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e21=sum(StateSum_B17024e8,StateSum_B17024e21,StateSum_B17024e34)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e22=sum(StateSum_B17024e9,StateSum_B17024e22,StateSum_B17024e35)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e23=sum(StateSum_B17024e10,StateSum_B17024e23,StateSum_B17024e36)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e24=sum(StateSum_B17024e11,StateSum_B17024e24,StateSum_B17024e37)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e25=sum(StateSum_B17024e12,StateSum_B17024e25,StateSum_B17024e38)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e26=sum(StateSum_B17024e13,StateSum_B17024e26,StateSum_B17024e39)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e27=sum(StateSum_B17024e14,StateSum_B17024e27,StateSum_B17024e40)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);end;

IF B17024e28 ^= 0 then do;

filled_e28 = 'Tract Values Used';

pctB17024e29=B17024e29/B17024e28;

pctB17024e30=B17024e30/B17024e28;

pctB17024e31=B17024e31/B17024e28;

pctB17024e32=B17024e32/B17024e28;

pctB17024e33=B17024e33/B17024e28;

pctB17024e34=B17024e34/B17024e28;

pctB17024e35=B17024e35/B17024e28;

pctB17024e36=B17024e36/B17024e28;

pctB17024e37=B17024e37/B17024e28;

pctB17024e38=B17024e38/B17024e28;

pctB17024e39=B17024e39/B17024e28;

pctB17024e40=B17024e40/B17024e28;end;

ELSE IF CountySum_B17024e28 ^= 0 then do;

filled_e28 = 'Filled with County Values';

pctB17024e29=CountySum_B17024e29/CountySum_B17024e28;

pctB17024e30=CountySum_B17024e30/CountySum_B17024e28;

pctB17024e31=CountySum_B17024e31/CountySum_B17024e28;

pctB17024e32=CountySum_B17024e32/CountySum_B17024e28;

pctB17024e33=CountySum_B17024e33/CountySum_B17024e28;

pctB17024e34=CountySum_B17024e34/CountySum_B17024e28;

pctB17024e35=CountySum_B17024e35/CountySum_B17024e28;

pctB17024e36=CountySum_B17024e36/CountySum_B17024e28;

pctB17024e37=CountySum_B17024e37/CountySum_B17024e28;

pctB17024e38=CountySum_B17024e38/CountySum_B17024e28;

pctB17024e39=CountySum_B17024e39/CountySum_B17024e28;

pctB17024e40=CountySum_B17024e40/CountySum_B17024e28;end;

ELSE IF CountySum_B17024e28 = 0 then do;

filled_e28 = 'Filled with State Values';

pctB17024e29=sum(StateSum_B17024e3,StateSum_B17024e16,StateSum_B17024e29)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e30=sum(StateSum_B17024e4,StateSum_B17024e17,StateSum_B17024e30)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e31=sum(StateSum_B17024e5,StateSum_B17024e18,StateSum_B17024e31)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e32=sum(StateSum_B17024e6,StateSum_B17024e19,StateSum_B17024e32)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e33=sum(StateSum_B17024e7,StateSum_B17024e20,StateSum_B17024e33)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e34=sum(StateSum_B17024e8,StateSum_B17024e21,StateSum_B17024e34)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e35=sum(StateSum_B17024e9,StateSum_B17024e22,StateSum_B17024e35)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e36=sum(StateSum_B17024e10,StateSum_B17024e23,StateSum_B17024e36)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e37=sum(StateSum_B17024e11,StateSum_B17024e24,StateSum_B17024e37)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e38=sum(StateSum_B17024e12,StateSum_B17024e25,StateSum_B17024e38)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e39=sum(StateSum_B17024e13,StateSum_B17024e26,StateSum_B17024e39)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);

pctB17024e40=sum(StateSum_B17024e14,StateSum_B17024e27,StateSum_B17024e40)/sum(StateSum_B17024e2,StateSum_B17024e15,StateSum_B17024e28);end;

IF B17024e41 ^= 0 then do;

filled_e41 = 'Tract Values Used';

pctB17024e42=B17024e42/B17024e41;

pctB17024e43=B17024e43/B17024e41;

pctB17024e44=B17024e44/B17024e41;

pctB17024e45=B17024e45/B17024e41;

pctB17024e46=B17024e46/B17024e41;

pctB17024e47=B17024e47/B17024e41;

pctB17024e48=B17024e48/B17024e41;
E-39

pctB17024e49=B17024e49/B17024e41;

pctB17024e50=B17024e50/B17024e41;

pctB17024e51=B17024e51/B17024e41;

pctB17024e52=B17024e52/B17024e41;

pctB17024e53=B17024e53/B17024e41;end;

ELSE IF CountySum_B17024e41 ^= 0 then do;

filled_e41 = 'Filled with County Values';

pctB17024e42=CountySum_B17024e42/CountySum_B17024e41;

pctB17024e43=CountySum_B17024e43/CountySum_B17024e41;

pctB17024e44=CountySum_B17024e44/CountySum_B17024e41;

pctB17024e45=CountySum_B17024e45/CountySum_B17024e41;

pctB17024e46=CountySum_B17024e46/CountySum_B17024e41;

pctB17024e47=CountySum_B17024e47/CountySum_B17024e41;

pctB17024e48=CountySum_B17024e48/CountySum_B17024e41;

pctB17024e49=CountySum_B17024e49/CountySum_B17024e41;

pctB17024e50=CountySum_B17024e50/CountySum_B17024e41;

pctB17024e51=CountySum_B17024e51/CountySum_B17024e41;

pctB17024e52=CountySum_B17024e52/CountySum_B17024e41;

pctB17024e53=CountySum_B17024e53/CountySum_B17024e41;end;

ELSE IF CountySum_B17024e41 = 0 then do;

*where age group population in a county and tract are both zero, calculate below/above poverty ratio based on income/poverty variables using state-level data for adults 18 

and over;

filled_e41 = 'Filled with State Values';

pctB17024e42=sum(StateSum_B17024e42,StateSum_B17024e55,StateSum_B17024e68,StateSum_B17024e81,StateSum_B17024e94,StateSum_B17024e107,StateSum_B17024

e120)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e43=sum(StateSum_B17024e43,StateSum_B17024e56,StateSum_B17024e69,StateSum_B17024e82,StateSum_B17024e95,StateSum_B17024e108,StateSum_B17024

e121)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e44=sum(StateSum_B17024e44,StateSum_B17024e57,StateSum_B17024e70,StateSum_B17024e83,StateSum_B17024e96,StateSum_B17024e109,StateSum_B17024

e122)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e45=sum(StateSum_B17024e45,StateSum_B17024e58,StateSum_B17024e71,StateSum_B17024e84,StateSum_B17024e97,StateSum_B17024e110,StateSum_B17024

e123)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e46=sum(StateSum_B17024e46,StateSum_B17024e59,StateSum_B17024e72,StateSum_B17024e85,StateSum_B17024e98,StateSum_B17024e111,StateSum_B17024

e124)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e47=sum(StateSum_B17024e47,StateSum_B17024e60,StateSum_B17024e73,StateSum_B17024e86,StateSum_B17024e99,StateSum_B17024e112,StateSum_B17024

e125)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e48=sum(StateSum_B17024e48,StateSum_B17024e61,StateSum_B17024e74,StateSum_B17024e87,StateSum_B17024e100,StateSum_B17024e113,StateSum_B1702

4e126)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e49=sum(StateSum_B17024e49,StateSum_B17024e62,StateSum_B17024e75,StateSum_B17024e88,StateSum_B17024e101,StateSum_B17024e114,StateSum_B1702

4e127)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e50=sum(StateSum_B17024e50,StateSum_B17024e63,StateSum_B17024e76,StateSum_B17024e89,StateSum_B17024e102,StateSum_B17024e115,StateSum_B1702

4e128)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e51=sum(StateSum_B17024e51,StateSum_B17024e64,StateSum_B17024e77,StateSum_B17024e90,StateSum_B17024e103,StateSum_B17024e116,StateSum_B1702

4e129)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e52=sum(StateSum_B17024e52,StateSum_B17024e65,StateSum_B17024e78,StateSum_B17024e91,StateSum_B17024e104,StateSum_B17024e117,StateSum_B1702

4e130)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e53=sum(StateSum_B17024e53,StateSum_B17024e66,StateSum_B17024e79,StateSum_B17024e92,StateSum_B17024e105,StateSum_B17024e118,StateSum_B1702

4e131)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);end;

IF B17024e54 ^= 0 then do;

filled_e54 = 'Tract Values Used';

pctB17024e55=B17024e55/B17024e54;

pctB17024e56=B17024e56/B17024e54;

pctB17024e57=B17024e57/B17024e54;

pctB17024e58=B17024e58/B17024e54;

pctB17024e59=B17024e59/B17024e54;

pctB17024e60=B17024e60/B17024e54;

pctB17024e61=B17024e61/B17024e54;

pctB17024e62=B17024e62/B17024e54;

pctB17024e63=B17024e63/B17024e54;

pctB17024e64=B17024e64/B17024e54;

pctB17024e65=B17024e65/B17024e54;

pctB17024e66=B17024e66/B17024e54;end;

ELSE IF CountySum_B17024e54 ^= 0 then do;

filled_e54 = 'Filled with County Values';

pctB17024e55=CountySum_B17024e55/CountySum_B17024e54;

pctB17024e56=CountySum_B17024e56/CountySum_B17024e54;

pctB17024e57=CountySum_B17024e57/CountySum_B17024e54;

pctB17024e58=CountySum_B17024e58/CountySum_B17024e54;

pctB17024e59=CountySum_B17024e59/CountySum_B17024e54;

pctB17024e60=CountySum_B17024e60/CountySum_B17024e54;

pctB17024e61=CountySum_B17024e61/CountySum_B17024e54;

pctB17024e62=CountySum_B17024e62/CountySum_B17024e54;

pctB17024e63=CountySum_B17024e63/CountySum_B17024e54;

pctB17024e64=CountySum_B17024e64/CountySum_B17024e54;

pctB17024e65=CountySum_B17024e65/CountySum_B17024e54;

pctB17024e66=CountySum_B17024e66/CountySum_B17024e54;end;

ELSE IF CountySum_B17024e54 = 0 then do;
E-40

filled_e54 = 'Filled with State Values';

pctB17024e55=sum(StateSum_B17024e42,StateSum_B17024e55,StateSum_B17024e68,StateSum_B17024e81,StateSum_B17024e94,StateSum_B17024e107,StateSum_B17024

e120)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e56=sum(StateSum_B17024e43,StateSum_B17024e56,StateSum_B17024e69,StateSum_B17024e82,StateSum_B17024e95,StateSum_B17024e108,StateSum_B17024

e121)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e57=sum(StateSum_B17024e44,StateSum_B17024e57,StateSum_B17024e70,StateSum_B17024e83,StateSum_B17024e96,StateSum_B17024e109,StateSum_B17024

e122)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e58=sum(StateSum_B17024e45,StateSum_B17024e58,StateSum_B17024e71,StateSum_B17024e84,StateSum_B17024e97,StateSum_B17024e110,StateSum_B17024

e123)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e59=sum(StateSum_B17024e46,StateSum_B17024e59,StateSum_B17024e72,StateSum_B17024e85,StateSum_B17024e98,StateSum_B17024e111,StateSum_B17024

e124)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e60=sum(StateSum_B17024e47,StateSum_B17024e60,StateSum_B17024e73,StateSum_B17024e86,StateSum_B17024e99,StateSum_B17024e112,StateSum_B17024

e125)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e61=sum(StateSum_B17024e48,StateSum_B17024e61,StateSum_B17024e74,StateSum_B17024e87,StateSum_B17024e100,StateSum_B17024e113,StateSum_B1702

4e126)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e62=sum(StateSum_B17024e49,StateSum_B17024e62,StateSum_B17024e75,StateSum_B17024e88,StateSum_B17024e101,StateSum_B17024e114,StateSum_B1702

4e127)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e63=sum(StateSum_B17024e50,StateSum_B17024e63,StateSum_B17024e76,StateSum_B17024e89,StateSum_B17024e102,StateSum_B17024e115,StateSum_B1702

4e128)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e64=sum(StateSum_B17024e51,StateSum_B17024e64,StateSum_B17024e77,StateSum_B17024e90,StateSum_B17024e103,StateSum_B17024e116,StateSum_B1702

4e129)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e65=sum(StateSum_B17024e52,StateSum_B17024e65,StateSum_B17024e78,StateSum_B17024e91,StateSum_B17024e104,StateSum_B17024e117,StateSum_B1702

4e130)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e66=sum(StateSum_B17024e53,StateSum_B17024e66,StateSum_B17024e79,StateSum_B17024e92,StateSum_B17024e105,StateSum_B17024e118,StateSum_B1702

4e131)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);end;

IF B17024e67 ^= 0 then do;

filled_e67 = 'Tract Values Used';

pctB17024e68=B17024e68/B17024e67;

pctB17024e69=B17024e69/B17024e67;

pctB17024e70=B17024e70/B17024e67;

pctB17024e71=B17024e71/B17024e67;

pctB17024e72=B17024e72/B17024e67;

pctB17024e73=B17024e73/B17024e67;

pctB17024e74=B17024e74/B17024e67;

pctB17024e75=B17024e75/B17024e67;

pctB17024e76=B17024e76/B17024e67;

pctB17024e77=B17024e77/B17024e67;

pctB17024e78=B17024e78/B17024e67;

pctB17024e79=B17024e79/B17024e67;end;

ELSE IF CountySum_B17024e67 ^= 0 then do;

filled_e67 = 'Filled with County Values';

pctB17024e68=CountySum_B17024e68/CountySum_B17024e67;

pctB17024e69=CountySum_B17024e69/CountySum_B17024e67;

pctB17024e70=CountySum_B17024e70/CountySum_B17024e67;

pctB17024e71=CountySum_B17024e71/CountySum_B17024e67;

pctB17024e72=CountySum_B17024e72/CountySum_B17024e67;

pctB17024e73=CountySum_B17024e73/CountySum_B17024e67;

pctB17024e74=CountySum_B17024e74/CountySum_B17024e67;

pctB17024e75=CountySum_B17024e75/CountySum_B17024e67;

pctB17024e76=CountySum_B17024e76/CountySum_B17024e67;

pctB17024e77=CountySum_B17024e77/CountySum_B17024e67;

pctB17024e78=CountySum_B17024e78/CountySum_B17024e67;

pctB17024e79=CountySum_B17024e79/CountySum_B17024e67;end;

ELSE IF CountySum_B17024e67 = 0 then do;

filled_e67 = 'Filled with State Values';

pctB17024e68=sum(StateSum_B17024e42,StateSum_B17024e55,StateSum_B17024e68,StateSum_B17024e81,StateSum_B17024e94,StateSum_B17024e107,StateSum_B17024

e120)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e69=sum(StateSum_B17024e43,StateSum_B17024e56,StateSum_B17024e69,StateSum_B17024e82,StateSum_B17024e95,StateSum_B17024e108,StateSum_B17024

e121)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e70=sum(StateSum_B17024e44,StateSum_B17024e57,StateSum_B17024e70,StateSum_B17024e83,StateSum_B17024e96,StateSum_B17024e109,StateSum_B17024

e122)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e71=sum(StateSum_B17024e45,StateSum_B17024e58,StateSum_B17024e71,StateSum_B17024e84,StateSum_B17024e97,StateSum_B17024e110,StateSum_B17024

e123)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e72=sum(StateSum_B17024e46,StateSum_B17024e59,StateSum_B17024e72,StateSum_B17024e85,StateSum_B17024e98,StateSum_B17024e111,StateSum_B17024

e124)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e73=sum(StateSum_B17024e47,StateSum_B17024e60,StateSum_B17024e73,StateSum_B17024e86,StateSum_B17024e99,StateSum_B17024e112,StateSum_B17024

e125)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e74=sum(StateSum_B17024e48,StateSum_B17024e61,StateSum_B17024e74,StateSum_B17024e87,StateSum_B17024e100,StateSum_B17024e113,StateSum_B1702

4e126)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);
E-41

pctB17024e75=sum(StateSum_B17024e49,StateSum_B17024e62,StateSum_B17024e75,StateSum_B17024e88,StateSum_B17024e101,StateSum_B17024e114,StateSum_B1702

4e127)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e76=sum(StateSum_B17024e50,StateSum_B17024e63,StateSum_B17024e76,StateSum_B17024e89,StateSum_B17024e102,StateSum_B17024e115,StateSum_B1702

4e128)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e77=sum(StateSum_B17024e51,StateSum_B17024e64,StateSum_B17024e77,StateSum_B17024e90,StateSum_B17024e103,StateSum_B17024e116,StateSum_B1702

4e129)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e78=sum(StateSum_B17024e52,StateSum_B17024e65,StateSum_B17024e78,StateSum_B17024e91,StateSum_B17024e104,StateSum_B17024e117,StateSum_B1702

4e130)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e79=sum(StateSum_B17024e53,StateSum_B17024e66,StateSum_B17024e79,StateSum_B17024e92,StateSum_B17024e105,StateSum_B17024e118,StateSum_B1702

4e131)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);end;

IF B17024e80 ^= 0 then do;

filled_e80 = 'Tract Values Used';

pctB17024e81=B17024e81/B17024e80;

pctB17024e82=B17024e82/B17024e80;

pctB17024e83=B17024e83/B17024e80;

pctB17024e84=B17024e84/B17024e80;

pctB17024e85=B17024e85/B17024e80;

pctB17024e86=B17024e86/B17024e80;

pctB17024e87=B17024e87/B17024e80;

pctB17024e88=B17024e88/B17024e80;

pctB17024e89=B17024e89/B17024e80;

pctB17024e90=B17024e90/B17024e80;

pctB17024e91=B17024e91/B17024e80;

pctB17024e92=B17024e92/B17024e80;end;

ELSE IF CountySum_B17024e80 ^= 0 then do;

filled_e80 = 'Filled with County Values';

pctB17024e81=CountySum_B17024e81/CountySum_B17024e80;

pctB17024e82=CountySum_B17024e82/CountySum_B17024e80;

pctB17024e83=CountySum_B17024e83/CountySum_B17024e80;

pctB17024e84=CountySum_B17024e84/CountySum_B17024e80;

pctB17024e85=CountySum_B17024e85/CountySum_B17024e80;

pctB17024e86=CountySum_B17024e86/CountySum_B17024e80;

pctB17024e87=CountySum_B17024e87/CountySum_B17024e80;

pctB17024e88=CountySum_B17024e88/CountySum_B17024e80;

pctB17024e89=CountySum_B17024e89/CountySum_B17024e80;

pctB17024e90=CountySum_B17024e90/CountySum_B17024e80;

pctB17024e91=CountySum_B17024e91/CountySum_B17024e80;

pctB17024e92=CountySum_B17024e92/CountySum_B17024e80;end;

ELSE IF CountySum_B17024e80 = 0 then do;

filled_e80 = 'Filled with State Values';

pctB17024e81=sum(StateSum_B17024e42,StateSum_B17024e55,StateSum_B17024e68,StateSum_B17024e81,StateSum_B17024e94,StateSum_B17024e107,StateSum_B17024

e120)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e82=sum(StateSum_B17024e43,StateSum_B17024e56,StateSum_B17024e69,StateSum_B17024e82,StateSum_B17024e95,StateSum_B17024e108,StateSum_B17024

e121)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e83=sum(StateSum_B17024e44,StateSum_B17024e57,StateSum_B17024e70,StateSum_B17024e83,StateSum_B17024e96,StateSum_B17024e109,StateSum_B17024

e122)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e84=sum(StateSum_B17024e45,StateSum_B17024e58,StateSum_B17024e71,StateSum_B17024e84,StateSum_B17024e97,StateSum_B17024e110,StateSum_B17024

e123)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e85=sum(StateSum_B17024e46,StateSum_B17024e59,StateSum_B17024e72,StateSum_B17024e85,StateSum_B17024e98,StateSum_B17024e111,StateSum_B17024

e124)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e86=sum(StateSum_B17024e47,StateSum_B17024e60,StateSum_B17024e73,StateSum_B17024e86,StateSum_B17024e99,StateSum_B17024e112,StateSum_B17024

e125)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e87=sum(StateSum_B17024e48,StateSum_B17024e61,StateSum_B17024e74,StateSum_B17024e87,StateSum_B17024e100,StateSum_B17024e113,StateSum_B1702

4e126)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e88=sum(StateSum_B17024e49,StateSum_B17024e62,StateSum_B17024e75,StateSum_B17024e88,StateSum_B17024e101,StateSum_B17024e114,StateSum_B1702

4e127)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e89=sum(StateSum_B17024e50,StateSum_B17024e63,StateSum_B17024e76,StateSum_B17024e89,StateSum_B17024e102,StateSum_B17024e115,StateSum_B1702

4e128)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e90=sum(StateSum_B17024e51,StateSum_B17024e64,StateSum_B17024e77,StateSum_B17024e90,StateSum_B17024e103,StateSum_B17024e116,StateSum_B1702

4e129)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e91=sum(StateSum_B17024e52,StateSum_B17024e65,StateSum_B17024e78,StateSum_B17024e91,StateSum_B17024e104,StateSum_B17024e117,StateSum_B1702

4e130)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e92=sum(StateSum_B17024e53,StateSum_B17024e66,StateSum_B17024e79,StateSum_B17024e92,StateSum_B17024e105,StateSum_B17024e118,StateSum_B1702

4e131)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);end;

IF B17024e93 ^= 0 then do;

filled_e93 = 'Tract Values Used';

pctB17024e94=B17024e94/B17024e93;

pctB17024e95=B17024e95/B17024e93;

pctB17024e96=B17024e96/B17024e93;

pctB17024e97=B17024e97/B17024e93;

pctB17024e98=B17024e98/B17024e93;
E-42

pctB17024e99=B17024e99/B17024e93;

pctB17024e100=B17024e100/B17024e93;

pctB17024e101=B17024e101/B17024e93;

pctB17024e102=B17024e102/B17024e93;

pctB17024e103=B17024e103/B17024e93;

pctB17024e104=B17024e104/B17024e93;

pctB17024e105=B17024e105/B17024e93;end;

ELSE IF CountySum_B17024e93 ^= 0 then do;

filled_e93 = 'Filled with County Values';

pctB17024e94=CountySum_B17024e94/CountySum_B17024e93;

pctB17024e95=CountySum_B17024e95/CountySum_B17024e93;

pctB17024e96=CountySum_B17024e96/CountySum_B17024e93;

pctB17024e97=CountySum_B17024e97/CountySum_B17024e93;

pctB17024e98=CountySum_B17024e98/CountySum_B17024e93;

pctB17024e99=CountySum_B17024e99/CountySum_B17024e93;

pctB17024e100=CountySum_B17024e100/CountySum_B17024e93;

pctB17024e101=CountySum_B17024e101/CountySum_B17024e93;

pctB17024e102=CountySum_B17024e102/CountySum_B17024e93;

pctB17024e103=CountySum_B17024e103/CountySum_B17024e93;

pctB17024e104=CountySum_B17024e104/CountySum_B17024e93;

pctB17024e105=CountySum_B17024e105/CountySum_B17024e93;end;

ELSE IF CountySum_B17024e93 = 0 then do;

filled_e93 = 'Filled with State Values';

pctB17024e94=sum(StateSum_B17024e42,StateSum_B17024e55,StateSum_B17024e68,StateSum_B17024e81,StateSum_B17024e94,StateSum_B17024e107,StateSum_B17024

e120)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e95=sum(StateSum_B17024e43,StateSum_B17024e56,StateSum_B17024e69,StateSum_B17024e82,StateSum_B17024e95,StateSum_B17024e108,StateSum_B17024

e121)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e96=sum(StateSum_B17024e44,StateSum_B17024e57,StateSum_B17024e70,StateSum_B17024e83,StateSum_B17024e96,StateSum_B17024e109,StateSum_B17024

e122)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e97=sum(StateSum_B17024e45,StateSum_B17024e58,StateSum_B17024e71,StateSum_B17024e84,StateSum_B17024e97,StateSum_B17024e110,StateSum_B17024

e123)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e98=sum(StateSum_B17024e46,StateSum_B17024e59,StateSum_B17024e72,StateSum_B17024e85,StateSum_B17024e98,StateSum_B17024e111,StateSum_B17024

e124)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e99=sum(StateSum_B17024e47,StateSum_B17024e60,StateSum_B17024e73,StateSum_B17024e86,StateSum_B17024e99,StateSum_B17024e112,StateSum_B17024

e125)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e100=sum(StateSum_B17024e48,StateSum_B17024e61,StateSum_B17024e74,StateSum_B17024e87,StateSum_B17024e100,StateSum_B17024e113,StateSum_B170

24e126)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e101=sum(StateSum_B17024e49,StateSum_B17024e62,StateSum_B17024e75,StateSum_B17024e88,StateSum_B17024e101,StateSum_B17024e114,StateSum_B170

24e127)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e102=sum(StateSum_B17024e50,StateSum_B17024e63,StateSum_B17024e76,StateSum_B17024e89,StateSum_B17024e102,StateSum_B17024e115,StateSum_B170

24e128)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e103=sum(StateSum_B17024e51,StateSum_B17024e64,StateSum_B17024e77,StateSum_B17024e90,StateSum_B17024e103,StateSum_B17024e116,StateSum_B170

24e129)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e104=sum(StateSum_B17024e52,StateSum_B17024e65,StateSum_B17024e78,StateSum_B17024e91,StateSum_B17024e104,StateSum_B17024e117,StateSum_B170

24e130)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e105=sum(StateSum_B17024e53,StateSum_B17024e66,StateSum_B17024e79,StateSum_B17024e92,StateSum_B17024e105,StateSum_B17024e118,StateSum_B170

24e131)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);end;

IF B17024e106 ^= 0 then do;

filled_e106 = 'Tract Values Used';

pctB17024e107=B17024e107/B17024e106;

pctB17024e108=B17024e108/B17024e106;

pctB17024e109=B17024e109/B17024e106;

pctB17024e110=B17024e110/B17024e106;

pctB17024e111=B17024e111/B17024e106;

pctB17024e112=B17024e112/B17024e106;

pctB17024e113=B17024e113/B17024e106;

pctB17024e114=B17024e114/B17024e106;

pctB17024e115=B17024e115/B17024e106;

pctB17024e116=B17024e116/B17024e106;

pctB17024e117=B17024e117/B17024e106;

pctB17024e118=B17024e118/B17024e106;end;

ELSE IF CountySum_B17024e106 ^= 0 then do;

filled_e106 = 'Filled with County Values';

pctB17024e107=CountySum_B17024e107/CountySum_B17024e106;

pctB17024e108=CountySum_B17024e108/CountySum_B17024e106;

pctB17024e109=CountySum_B17024e109/CountySum_B17024e106;

pctB17024e110=CountySum_B17024e110/CountySum_B17024e106;

pctB17024e111=CountySum_B17024e111/CountySum_B17024e106;

pctB17024e112=CountySum_B17024e112/CountySum_B17024e106;

pctB17024e113=CountySum_B17024e113/CountySum_B17024e106;

pctB17024e114=CountySum_B17024e114/CountySum_B17024e106;

pctB17024e115=CountySum_B17024e115/CountySum_B17024e106;

pctB17024e116=CountySum_B17024e116/CountySum_B17024e106;

pctB17024e117=CountySum_B17024e117/CountySum_B17024e106;

pctB17024e118=CountySum_B17024e118/CountySum_B17024e106;end;

ELSE IF CountySum_B17024e106 = 0 then do;
E-43

filled_e106 = 'Filled with State Values';

pctB17024e107=sum(StateSum_B17024e42,StateSum_B17024e55,StateSum_B17024e68,StateSum_B17024e81,StateSum_B17024e94,StateSum_B17024e107,StateSum_B1702

4e120)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e108=sum(StateSum_B17024e43,StateSum_B17024e56,StateSum_B17024e69,StateSum_B17024e82,StateSum_B17024e95,StateSum_B17024e108,StateSum_B1702

4e121)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e109=sum(StateSum_B17024e44,StateSum_B17024e57,StateSum_B17024e70,StateSum_B17024e83,StateSum_B17024e96,StateSum_B17024e109,StateSum_B1702

4e122)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e110=sum(StateSum_B17024e45,StateSum_B17024e58,StateSum_B17024e71,StateSum_B17024e84,StateSum_B17024e97,StateSum_B17024e110,StateSum_B1702

4e123)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e111=sum(StateSum_B17024e46,StateSum_B17024e59,StateSum_B17024e72,StateSum_B17024e85,StateSum_B17024e98,StateSum_B17024e111,StateSum_B1702

4e124)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e112=sum(StateSum_B17024e47,StateSum_B17024e60,StateSum_B17024e73,StateSum_B17024e86,StateSum_B17024e99,StateSum_B17024e112,StateSum_B1702

4e125)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e113=sum(StateSum_B17024e48,StateSum_B17024e61,StateSum_B17024e74,StateSum_B17024e87,StateSum_B17024e100,StateSum_B17024e113,StateSum_B170

24e126)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e114=sum(StateSum_B17024e49,StateSum_B17024e62,StateSum_B17024e75,StateSum_B17024e88,StateSum_B17024e101,StateSum_B17024e114,StateSum_B170

24e127)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e115=sum(StateSum_B17024e50,StateSum_B17024e63,StateSum_B17024e76,StateSum_B17024e89,StateSum_B17024e102,StateSum_B17024e115,StateSum_B170

24e128)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e116=sum(StateSum_B17024e51,StateSum_B17024e64,StateSum_B17024e77,StateSum_B17024e90,StateSum_B17024e103,StateSum_B17024e116,StateSum_B170

24e129)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e117=sum(StateSum_B17024e52,StateSum_B17024e65,StateSum_B17024e78,StateSum_B17024e91,StateSum_B17024e104,StateSum_B17024e117,StateSum_B170

24e130)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e118=sum(StateSum_B17024e53,StateSum_B17024e66,StateSum_B17024e79,StateSum_B17024e92,StateSum_B17024e105,StateSum_B17024e118,StateSum_B170

24e131)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);end;

IF B17024e119 ^= 0 then do;

filled_e119 = 'Tract Values Used';

pctB17024e120=B17024e120/B17024e119;

pctB17024e121=B17024e121/B17024e119;

pctB17024e122=B17024e122/B17024e119;

pctB17024e123=B17024e123/B17024e119;

pctB17024e124=B17024e124/B17024e119;

pctB17024e125=B17024e125/B17024e119;

pctB17024e126=B17024e126/B17024e119;

pctB17024e127=B17024e127/B17024e119;

pctB17024e128=B17024e128/B17024e119;

pctB17024e129=B17024e129/B17024e119;

pctB17024e130=B17024e130/B17024e119;

pctB17024e131=B17024e131/B17024e119;end;

ELSE IF CountySum_B17024e119 ^= 0 then do;

filled_e119 = 'Filled with County Values';

pctB17024e120=CountySum_B17024e120/CountySum_B17024e119;

pctB17024e121=CountySum_B17024e121/CountySum_B17024e119;

pctB17024e122=CountySum_B17024e122/CountySum_B17024e119;

pctB17024e123=CountySum_B17024e123/CountySum_B17024e119;

pctB17024e124=CountySum_B17024e124/CountySum_B17024e119;

pctB17024e125=CountySum_B17024e125/CountySum_B17024e119;

pctB17024e126=CountySum_B17024e126/CountySum_B17024e119;

pctB17024e127=CountySum_B17024e127/CountySum_B17024e119;

pctB17024e128=CountySum_B17024e128/CountySum_B17024e119;

pctB17024e129=CountySum_B17024e129/CountySum_B17024e119;

pctB17024e130=CountySum_B17024e130/CountySum_B17024e119;

pctB17024e131=CountySum_B17024e131/CountySum_B17024e119;end;

ELSE IF CountySum_B17024e119 = 0 then do;

filled_e119 = 'Filled with State Values';

pctB17024e120=sum(StateSum_B17024e42,StateSum_B17024e55,StateSum_B17024e68,StateSum_B17024e81,StateSum_B17024e94,StateSum_B17024e107,StateSum_B1702

4e120)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e121=sum(StateSum_B17024e43,StateSum_B17024e56,StateSum_B17024e69,StateSum_B17024e82,StateSum_B17024e95,StateSum_B17024e108,StateSum_B1702

4e121)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e122=sum(StateSum_B17024e44,StateSum_B17024e57,StateSum_B17024e70,StateSum_B17024e83,StateSum_B17024e96,StateSum_B17024e109,StateSum_B1702

4e122)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e123=sum(StateSum_B17024e45,StateSum_B17024e58,StateSum_B17024e71,StateSum_B17024e84,StateSum_B17024e97,StateSum_B17024e110,StateSum_B1702

4e123)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e124=sum(StateSum_B17024e46,StateSum_B17024e59,StateSum_B17024e72,StateSum_B17024e85,StateSum_B17024e98,StateSum_B17024e111,StateSum_B1702

4e124)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e125=sum(StateSum_B17024e47,StateSum_B17024e60,StateSum_B17024e73,StateSum_B17024e86,StateSum_B17024e99,StateSum_B17024e112,StateSum_B1702

4e125)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e126=sum(StateSum_B17024e48,StateSum_B17024e61,StateSum_B17024e74,StateSum_B17024e87,StateSum_B17024e100,StateSum_B17024e113,StateSum_B170

24e126)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);
E-44

pctB17024e127=sum(StateSum_B17024e49,StateSum_B17024e62,StateSum_B17024e75,StateSum_B17024e88,StateSum_B17024e101,StateSum_B17024e114,StateSum_B170

24e127)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e128=sum(StateSum_B17024e50,StateSum_B17024e63,StateSum_B17024e76,StateSum_B17024e89,StateSum_B17024e102,StateSum_B17024e115,StateSum_B170

24e128)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e129=sum(StateSum_B17024e51,StateSum_B17024e64,StateSum_B17024e77,StateSum_B17024e90,StateSum_B17024e103,StateSum_B17024e116,StateSum_B170

24e129)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e130=sum(StateSum_B17024e52,StateSum_B17024e65,StateSum_B17024e78,StateSum_B17024e91,StateSum_B17024e104,StateSum_B17024e117,StateSum_B170

24e130)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);

pctB17024e131=sum(StateSum_B17024e53,StateSum_B17024e66,StateSum_B17024e79,StateSum_B17024e92,StateSum_B17024e105,StateSum_B17024e118,StateSum_B170

24e131)/sum(StateSum_B17024e41,StateSum_B17024e54,StateSum_B17024e67,StateSum_B17024e80,StateSum_B17024e93,StateSum_B17024e106,StateSum_B17024e119);end;

run;

data work.pov_ratio_&geo /*calculates percents at or above poverty defined as +/- 1.5 income/poverty ratio */

(keep= STUSAB CENSUS_REGION LOGRECNO STATE COUNTY TRACT GEOID_merge LAT LON

/*B17024e2 B17024e15 B17024e28 B17024e41 B17024e54 B17024e67 B17024e80 B17024e93 

B17024e106 B17024e119*/

filled_e2 filled_e15 filled_e28 filled_e41 filled_e54 filled_e67 filled_e80 filled_e93 filled_e106 

filled_e119

p0-p17 np0-np17 

p5u p6to11 p12to17 p18to24 p25to34 p35to44 p45to54 p55to64 p65to74 p75plus

np5u np6to11 np12to17 np18to24 np25to34 np35to44 np45to54 np55to64 np65to74 np75plus);

 set work.pov_pct_&geo;

/*The first sum provides the prob < 1.5 pov ratio with 'p' meaning poverty. The second sum is > 1.5 pov with 'np' meaning not poverty. */

p5u=sum(pctB17024e3,pctB17024e4,pctB17024e5,pctB17024e6,pctB17024e7); 

np5u=sum(pctB17024e8,pctB17024e9,pctB17024e10,pctB17024e11,pctB17024e12,pctB17024e13,pctB17024e14);

p6to11=sum(pctB17024e16,pctB17024e17,pctB17024e18,pctB17024e19,pctB17024e20); 

np6to11=sum(pctB17024e21,pctB17024e22,pctB17024e23,pctB17024e24,pctB17024e25,pctB17024e26,pctB17024e27);

p12to17=sum(pctB17024e29,pctB17024e30,pctB17024e31,pctB17024e32,pctB17024e33); 

np12to17=sum(pctB17024e34,pctB17024e35,pctB17024e36,pctB17024e37,pctB17024e38,pctB17024e39,pctB17024e40);

p18to24=sum(pctB17024e42,pctB17024e43,pctB17024e44,pctB17024e45,pctB17024e46); 

np18to24=sum(pctB17024e47,pctB17024e48,pctB17024e49,pctB17024e50,pctB17024e51,pctB17024e52,pctB17024e53);

p25to34=sum(pctB17024e55,pctB17024e56,pctB17024e57,pctB17024e58,pctB17024e59); 

np25to34=sum(pctB17024e60,pctB17024e61,pctB17024e62,pctB17024e63,pctB17024e64,pctB17024e65,pctB17024e66);

p35to44=sum(pctB17024e68,pctB17024e69,pctB17024e70,pctB17024e71,pctB17024e72); 

np35to44=sum(pctB17024e73,pctB17024e74,pctB17024e75,pctB17024e76,pctB17024e77,pctB17024e78,pctB17024e79);

p45to54=sum(pctB17024e81,pctB17024e82,pctB17024e83,pctB17024e84,pctB17024e85); 

np45to54=sum(pctB17024e86,pctB17024e87,pctB17024e88,pctB17024e89,pctB17024e90,pctB17024e91,pctB17024e92);

p55to64=sum(pctB17024e94,pctB17024e95,pctB17024e96,pctB17024e97,pctB17024e98); 

np55to64=sum(pctB17024e99,pctB17024e100,pctB17024e101,pctB17024e102,pctB17024e103,pctB17024e104,pctB17024e105);

p65to74=sum(pctB17024e107,pctB17024e108,pctB17024e109,pctB17024e110,pctB17024e111); 

np65to74=sum(pctB17024e112,pctB17024e113,pctB17024e114,pctB17024e115,pctB17024e116,pctB17024e117,pctB17024e118);

p75plus=sum(pctB17024e120,pctB17024e121,pctB17024e122,pctB17024e123,pctB17024e124); 

np75plus=sum(pctB17024e125,pctB17024e126,pctB17024e127,pctB17024e128,pctB17024e129,pctB17024e130,pctB17024e131);

/*copy the percents +/- 1.5 income/poverty ratio for ages 5 and under, 6to11, and 12to17 to separate ages 1-17 for which asthma 

prevalence data are available*/

p0=p5u; p1=p5u; p2=p5u; p3=p5u; p4=p5u; p5=p5u;

np0=np5u; np1=np5u; np1=np5u; np2=np5u; np3=np5u; np4=np5u; np5=np5u;

p6=p6to11; p7=p6to11; p8=p6to11; p9=p6to11; p10=p6to11; p11=p6to11; 

np6=p6to11; np7=p6to11; np8=p6to11; np9=np6to11; np10=np6to11; np11=np6to11; 

p12=p12to17; p13=p12to17; p14=p12to17; p15=p12to17; p16=p12to17; p17=p12to17; 

np12=np12to17; np13=np12to17; np14=np12to17; np15=np12to17; np16=np12to17; np17=np12to17; 

run;

data work.QA_pov_ratio_&geo /* checks that all calculated percents sum to 1 where they exist*/

(keep= STUSAB CENSUS_REGION LOGRECNO STATE COUNTY TRACT GEOID_merge LAT LON

filled_e2 filled_e15 filled_e28 filled_e41 filled_e54 filled_e67 filled_e80 filled_e93 filled_e106 

filled_e119

/*B17024e2 B17024e15 B17024e28 B17024e41 B17024e54 B17024e67 B17024e80 B17024e93 

B17024e106 B17024e119*/

sum5u sum6to11 sum12to17 sum18to24 sum25to34 sum35to44 sum45to54 sum55to64 sum65to74 

sum75plus);

set work.pov_ratio_&geo;

sum5u=p5u+np5u;

sum6to11=p6to11+np6to11;

sum12to17=p12to17+np12to17;

sum18to24=p18to24+np18to24;

sum25to34=p25to34+np25to34;

sum35to44=p35to44+np35to44;

sum45to54=p45to54+np45to54;

sum55to64=p55to64+np55to64;

sum65to74=p65to74+np65to74;

sum75plus=p75plus+np75plus;

run;

data work.pov_ratio_&geo; *changes order of columns (variables);

 retain STUSAB CENSUS_REGION LOGRECNO STATE COUNTY TRACT GEOID_merge LAT LON 

p0-p17 

p5u p6to11 p12to17 p18to24 p25to34 p35to44 p45to54 p55to64 p65to74 p75plus

np0-np17

np5u np6to11 np12to17 np18to24 np25to34 np35to44 np45to54 np55to64 np65to74 np75plus 
E-45

filled_e2 filled_e15 filled_e28 filled_e41 filled_e54 filled_e67 filled_e80 filled_e93 filled_e106 

filled_e119;

 set work.pov_ratio_&geo; 

run;

%mend;

%macro Import_Pov_Calc_Ratio(geo); *Runs macros that imports and merges income/poverty data with geographic data (by state) then calculates ratios above or below 1.5 income/poverty 

ratio (by age group);

%AnyGeo(&geo);

%Read_poverty(&geo);

proc sort data=work.SFe0056&geo; *sort estimate data;

 by logrecno;

run;

proc sort data=work.g20135&geo.coord; *sort geo data;

 by logrecno;

run;

data work.SFe_g_0056&geo ; *merges estimate and geo data;

 merge work.SFe0056&geo(in=a) work.g20135&geo.coord;

 by logrecno;

 retain STUSAB STATE COUNTY TRACT LAT LON;

 if a;

run;

%pov_ratio_calc(&geo);

proc append base=sas.pov_acs2013_5yr data=work.pov_ratio_&geo; run;

proc append base=sas.QA_pov_acs2013_5yr data=work.QA_pov_ratio_&geo; run;

%mend;

*runs macro for 50 United States, District of Columbia, and Puerto Rico;

%Import_Pov_Calc_Ratio(al);

%Import_Pov_Calc_Ratio(ak);

%Import_Pov_Calc_Ratio(az);

%Import_Pov_Calc_Ratio(ar);

%Import_Pov_Calc_Ratio(ca);

%Import_Pov_Calc_Ratio(co);

%Import_Pov_Calc_Ratio(ct);

%Import_Pov_Calc_Ratio(de);

%Import_Pov_Calc_Ratio(dc);

%Import_Pov_Calc_Ratio(fl);

%Import_Pov_Calc_Ratio(ga);

%Import_Pov_Calc_Ratio(hi);

%Import_Pov_Calc_Ratio(id);

%Import_Pov_Calc_Ratio(il);

%Import_Pov_Calc_Ratio(in);

%Import_Pov_Calc_Ratio(ia);

%Import_Pov_Calc_Ratio(ks);

%Import_Pov_Calc_Ratio(ky);

%Import_Pov_Calc_Ratio(la);

%Import_Pov_Calc_Ratio(me);

%Import_Pov_Calc_Ratio(md);

%Import_Pov_Calc_Ratio(ma);

%Import_Pov_Calc_Ratio(mi);

%Import_Pov_Calc_Ratio(mn);

%Import_Pov_Calc_Ratio(ms);

%Import_Pov_Calc_Ratio(mo);

%Import_Pov_Calc_Ratio(mt);

%Import_Pov_Calc_Ratio(ne);

%Import_Pov_Calc_Ratio(nv);

%Import_Pov_Calc_Ratio(nh);

%Import_Pov_Calc_Ratio(nj);

%Import_Pov_Calc_Ratio(nm);

%Import_Pov_Calc_Ratio(ny);

%Import_Pov_Calc_Ratio(nc);

%Import_Pov_Calc_Ratio(nd);

%Import_Pov_Calc_Ratio(oh);

%Import_Pov_Calc_Ratio(ok);

%Import_Pov_Calc_Ratio(or);

%Import_Pov_Calc_Ratio(pa);

%Import_Pov_Calc_Ratio(ri);

%Import_Pov_Calc_Ratio(sc);

%Import_Pov_Calc_Ratio(sd);

%Import_Pov_Calc_Ratio(tn);

%Import_Pov_Calc_Ratio(tx);

%Import_Pov_Calc_Ratio(ut);

%Import_Pov_Calc_Ratio(vt);

%Import_Pov_Calc_Ratio(va);

%Import_Pov_Calc_Ratio(wa);

%Import_Pov_Calc_Ratio(wv);

%Import_Pov_Calc_Ratio(wi);

%Import_Pov_Calc_Ratio(wy);

%Import_Pov_Calc_Ratio(pr);
E-46

Attachment 4 – Proc Survey Logistic Model Results - Evaluating Influence of 

Personal Attributes and their Interaction

Table 1. Logistic model parameter estimates, coefficient variation, and statistical 

significance for personal attributes that influence asthma prevalence in adults.

US REGION

(degrees of 

freedom) Variable

Parameter 

Estimate Std Err

Statistical 

Significance

(ProbT)

Northeast

(51)

Intercept -2.610 0.114 <0.001

family income 0.045 0.188 0.810

Black African American -0.184 0.232 0.432

BMI 0.367 0.136 0.009

sex 0.418 0.091 <0.001

age -0.006 0.002 0.002

family income*sex 0.345 0.236 0.150

Black African American*sex -0.038 0.314 0.905

BMI*sex 0.296 0.181 0.107

family income*BMI 0.567 0.276 0.045

Black African American*BMI -0.163 0.290 0.577

family income*Black African American 0.675 0.499 0.183

family income*Black African American*BMI -0.071 0.667 0.915

family income*BMI*sex -0.401 0.375 0.290

Black African American*BMI*sex 0.257 0.389 0.512

family income*Black African American*sex -0.298 0.648 0.647

family income *Black African American*BMI*sex -0.349 0.862 0.688

Midwest

(66)

Intercept -2.849 0.100 <0.001

family income 0.543 0.137 <0.001

Black African American 0.347 0.265 0.195

BMI 0.280 0.118 0.020

sex 0.420 0.072 <0.001

age -0.004 0.001 0.012

family income*sex -0.219 0.141 0.125

Black African American*sex 0.109 0.315 0.732

BMI*sex 0.380 0.130 0.005

family income*BMI 0.122 0.237 0.609

Black African American*BMI -0.223 0.357 0.534

family income*Black African American -0.295 0.374 0.434

family income*Black African American*BMI 0.133 0.481 0.784

family income*BMI*sex 0.080 0.277 0.774

Black African American*BMI*sex -0.551 0.427 0.202

family income*Black African American*sex 0.093 0.428 0.829

family income *Black African American*BMI*sex 0.390 0.533 0.466
E-47

US REGION

(degrees of 

freedom) Variable

Parameter 

Estimate Std Err

Statistical 

Significance

(ProbT)

South

(115)

Intercept -3.147 0.092 <0.001

family income 0.325 0.123 0.010

Black African American 0.217 0.150 0.151

BMI 0.341 0.102 0.001

sex 0.540 0.076 <0.001

age -0.001 0.001 0.492

family income*sex -0.039 0.152 0.797

Black African American*sex -0.220 0.205 0.286

BMI*sex 0.262 0.130 0.045

family income*BMI 0.123 0.202 0.546

Black African American*BMI -0.084 0.235 0.721

family income*Black African American 0.076 0.218 0.728

family income*Black African American*BMI -0.345 0.404 0.394

family income*BMI*sex -0.050 0.236 0.832

Black African American*BMI*sex 0.119 0.273 0.664

family income*Black African American*sex 0.057 0.286 0.841

family income *Black African American*BMI*sex 0.268 0.460 0.561

West

(70)

Intercept -3.042 0.086 <0.001

family income 0.206 0.111 0.068

Black African American 0.432 0.295 0.147

BMI 0.467 0.090 <0.001

sex 0.532 0.069 <0.001

age 0.001 0.001 0.652

family income*sex -0.150 0.138 0.282

Black African American*sex -0.812 0.314 0.012

BMI*sex 0.235 0.113 0.040

family income*BMI -0.039 0.193 0.841

Black African American*BMI 0.107 0.420 0.799

family income*Black African American 0.164 0.441 0.711

family income*Black African American*BMI -0.451 0.730 0.539

family income*BMI*sex -0.022 0.257 0.932

Black African American*BMI*sex 0.383 0.495 0.442

family income*Black African American*sex 0.998 0.491 0.046

family income *Black African American*BMI*sex 0.025 0.937 0.979
E-48

Table 2. Logistic model parameter estimates, coefficient variation, and statistical 

significance for personal attributes that influence asthma prevalence in children.

US REGION

(degrees of 

freedom) Variable

Parameter 

Estimate Std Err

Statistical 

Significance

(ProbT)

Northeast

(51)

Intercept -1.141 0.734 0.127

family income -0.378 0.298 0.210

Black African American 0.176 0.289 0.544

BMI 0.633 0.406 0.125

sex -0.314 0.202 0.127

age -0.063 0.047 0.188

family income*sex 0.716 0.492 0.152

Black African American*sex -0.032 0.441 0.943

BMI*sex 0.237 0.692 0.733

family income*BMI 0.310 0.856 0.718

Black African American*BMI 0.620 0.833 0.460

family income*Black African American 0.300 0.459 0.517

family income*Black African American*BMI -0.895 1.583 0.574

family income*BMI*sex -0.416 1.264 0.743

Black African American*BMI*sex -0.647 1.320 0.626

family income*Black African American*sex -0.047 0.878 0.958

family income *Black African American*BMI*sex 0.689 2.715 0.801

Midwest

(66)

Intercept -3.116 0.539 <0.001

family income 0.626 0.275 0.026

Black African American 0.648 0.308 0.039

BMI -0.047 0.338 0.889

sex -0.195 0.173 0.263

age 0.052 0.032 0.114

family income*sex -0.313 0.352 0.378

Black African American*sex 0.410 0.533 0.445

BMI*sex 0.397 0.533 0.459

family income*BMI -0.038 0.554 0.945

Black African American*BMI 0.740 0.827 0.374

family income*Black African American -0.351 0.577 0.545

family income*Black African American*BMI -0.679 1.303 0.604

family income*BMI*sex 0.416 0.820 0.614

Black African American*BMI*sex -1.402 1.114 0.213

family income*Black African American*sex -0.010 0.919 0.991

family income *Black African American*BMI*sex 1.394 1.607 0.389

South

(115)

Intercept -1.786 0.350 <0.001

family income 0.358 0.158 0.025

Black African American 1.078 0.181 <0.001

BMI 0.735 0.241 0.003
E-49

US REGION

(degrees of 

freedom) Variable

Parameter 

Estimate Std Err

Statistical 

Significance

(ProbT)

sex 0.119 0.141 0.398

age -0.052 0.025 0.039

family income*sex 0.210 0.243 0.390

Black African American*sex -1.027 0.299 0.001

BMI*sex 0.086 0.384 0.823

family income*BMI -0.740 0.434 0.091

Black African American*BMI -0.634 0.427 0.140

family income*Black African American -0.515 0.285 0.074

family income*Black African American*BMI 1.153 0.637 0.073

family income*BMI*sex 0.330 0.616 0.593

Black African American*BMI*sex 0.767 0.806 0.343

family income*Black African American*sex 0.346 0.446 0.439

family income *Black African American*BMI*sex -1.095 1.035 0.292

West

(70)

Intercept -1.786 0.448 <0.001

family income 0.252 0.189 0.186

Black African American 0.315 0.283 0.270

BMI 0.123 0.335 0.714

sex -0.362 0.180 0.049

age -0.022 0.029 0.456

family income*sex -0.290 0.270 0.287

Black African American*sex -0.406 0.569 0.478

BMI*sex 0.682 0.458 0.141

family income*BMI -0.528 0.588 0.372

Black African American*BMI -0.166 0.958 0.863

family income*Black African American 0.612 0.439 0.167

family income*Black African American*BMI -10.865 1.411 <0.001

family income*BMI*sex -0.137 0.795 0.864

Black African American*BMI*sex -1.933 1.603 0.232

family income*Black African American*sex 0.486 0.812 0.551

family income *Black African American*BMI*sex 12.284 2.211 <0.001
F-1

APPENDIX F

DESCRIPTION OF THE AIR POLLUTANTS EXPOSURE MODEL (APEX)

Purpose: This Appendix briefly describes the EPA’s Air Pollutants Exposure (APEX) model.

Table of Contents

F.1 Overview........................................................................................................................F-2

F.2 Model Inputs.................................................................................................................F-4

F.3 Demographic Characteristics......................................................................................F-5

F.4 Attributes of Individuals..............................................................................................F-5

F.5 Construction of Longitudinal Diary Sequence ..........................................................F-6

F.6 Key Physiological Processes Modeled.........................................................................F-8

F.7 Estimating Microenvironmental Concentrations......................................................F-9

F.7.1 Mass Balance Model....................................................................................F-10

F.7.2 Factors Model ..............................................................................................F-16

F.8 Exposure and Dose Time Series Calculations..........................................................F-17

F.9 Model Output..............................................................................................................F-19
F-2

F.1 Overview

APEX is the human inhalation exposure model within the Total Risk Integrated 

Methodology (TRIM) framework (U.S. EPA, 2017a, b). APEX is conceptually based on the 

probabilistic NAAQS Exposure Model (pNEM) that was used to estimate population exposures 

for the 1996 O3 NAAQS review (Johnson et al., 1996a, b, c). Since that time the model has been 

restructured, improved, and expanded to reflect conceptual advances in the science of exposure 

modeling and newer input data available for the model. Key improvements to algorithms include 

replacement of the cohort approach with a probabilistic sampling approach focused on 

individuals, accounting for fatigue and oxygen debt after exercise in the calculation of ventilation 

rates (Isaacs et al., 2008), new approaches for construction of longitudinal activity patterns for 

simulated persons (Glen et al., 2008; Rosenbaum et al., 2008), and new equations for estimating 

resting metabolic rate (RMR) and ventilation rate (see Appendix H). Major improvements to data 

input to the model include updated air exchange rates (AERs), population census and commuting 

data, distributions of body mass and height (Appendix G), and the daily time-location-activities 

database (Appendix I).

APEX estimates human exposure to criteria and toxic air pollutants at local, urban, or 

regional scales using a stochastic, microenvironmental approach. That is, the model randomly 

selects data on a sample of hypothetical individuals in an actual population database and 

simulates each individual’s movements through time and space (e.g., at home, in vehicles) to 

estimate their exposure to the pollutant. APEX can assume people live and work in the same 

general area (i.e., that the ambient air quality is the same at home and at work) or optionally can 

model commuting and thus exposure at the work location for individuals who work.

The APEX model is a microenvironmental, longitudinal human exposure model for 

airborne pollutants. It is applied to a specified study area, which is typically a metropolitan area. 

The time period of the simulation is typically one year, but can easily be made either longer or 

shorter. APEX uses census data, such as gender and age, to generate the demographic 

characteristics of simulated individuals. It then assembles a composite activity diary to represent 

the sequence of activities and microenvironments that the individual experiences. Each 

microenvironment has a user-specified method for determining air quality. The inhalation 

exposure in each microenvironment is simply equal to the air concentration in that 

microenvironment. When coupled with breathing rate information and a physiological model, 

various measures of dose can also be calculated.

The term microenvironment is intended to represent the immediate surroundings of an 

individual, in which the pollutant of interest is assumed to be well-mixed. Time is modeled as a 

sequence of discrete time steps called events. In APEX, the concentration in a microenvironment 

may change between events. For each microenvironment, the user specifies the method of 
F-3

concentration calculation (either mass balance or regression factors, described later in this 

paper), the relationship of the microenvironment to the ambient air, and the strength of any 

pollutant sources specific to that microenvironment. Because the microenvironments that are 

relevant to exposure depend on the nature of the target chemical and APEX is designed to be 

applied to a wide range of chemicals, both the total number of microenvironments and the 

properties of each are free to be specified by the user.

The ambient air data are provided as input to the model in the form of time series at a list 

of specified locations. Typically, hourly air concentrations are used, although temporal 

resolutions as small as one minute may be used. The spatial range of applicability of a given 

ambient location is called an air district. Any number of air districts can be accommodated in a 

model run, subject only to computer hardware limitations. In principle, any microenvironment 

could be found within a given air district. Therefore, to estimate exposures as an individual 

engages in activities throughout the period it is necessary to determine both the 

microenvironment and the air district that apply for each event.

An exposure event is determined by the time reported in the activity diary; during any 

event the district, microenvironment, ambient air quality, and breathing rate are assumed to 

remain fixed. Since the ambient air data change every hour, the maximum duration of an event is 

limited to one hour. The event duration may be less than this (as short as one minute) if the 

activity diary indicates that the individual changes microenvironments or activities performed 

within the hour.

An APEX simulation includes the following steps:

(1) Characterize the study area - APEX selects sectors (e.g., census tracts) within a study 

area based on user-defined criteria and thus identifies the potentially exposed population 

and defines the air quality and weather input data required for the area.

(2) Generate simulated individuals - APEX stochastically generates a sample of simulated 

individuals based on the census data for the study area and human profile distribution 

data (such as age-specific employment probabilities). The user must specify the size of 

the sample. The larger the sample, the more representative it is of the population in the 

study area and the more stable the model results are (but also the longer the computing 

time).

(3) Construct a long-term sequence of activity events and determine breathing rates - APEX 

constructs an event sequence (activity pattern) spanning the period of simulation for each 

simulated person. The model then stochastically assigns breathing rates to each event, 

based on the type of activity and the physical characteristics of the simulated person.

(4) Calculate pollutant concentrations in microenvironments - APEX enables the user to 

define any microenvironment that individuals in a study area would visit. The model then 

calculates concentrations of each pollutant in each of the microenvironments.
F-4

(5) Calculate pollutant exposures for each simulated individual - Microenvironmental 

concentrations are time weighted based on individuals’ events (i.e., time spent in the 

microenvironment) to produce a sequence of time-averaged exposures (or minute by 

minute time series) spanning the simulation period.

(6) Estimate dose - APEX can also calculate the dose time series for each of the simulated 

individuals based on the exposures and breathing rates for each event. However, dose is 

not needed for the SO2 assessment and thus will not be discussed further.

(7) Estimate a health response – APEX can link an exposure-response (E-R) function 

generated from controlled human exposure study data with the modeled exposures to 

estimate the fraction of the population that could experience and adverse health outcome 

(e.g., lung function decrements).

The model simulation continues until exposures are determined for the user-specified 

number of simulated individuals. APEX then calculates population exposure statistics (such as 

the number of exposures exceeding user-specified levels) for the entire simulation and writes out 

tables of distributions of these statistics.

F.2 Model Inputs

APEX requires certain inputs from the user. The user specifies the geographic area and 

the range of ages and age groups to be used for the simulation. Hourly (or shorter) ambient air 

quality and hourly temperature data must be furnished for the entire simulation period. Other 

hourly meteorological data (humidity, wind speed, wind direction, precipitation) can be used by 

the model to estimate microenvironmental concentrations, but are optional.

In addition, most variables used in the model algorithms are represented by user-specified 

probability distributions which capture population variability. APEX provides great flexibility in 

defining model inputs and parameters, including options for the frequency of selecting new 

values from the probability distributions. The model also allows different distributions to be used 

at different times of day or on different days, and the distribution can depend conditionally on 

values of other parameters. The probability distributions available in APEX include beta, binary, 

Cauchy, discrete, exponential, extreme value, gamma, logistic, lognormal, loguniform, normal, 

off/on, Pareto, point (constant), triangle, uniform, Weibull, and nonparametric distributions. 

Minimum and maximum bounds can be specified for each distribution if a truncated distribution 

is appropriate. There are two options for handling truncation. The generated samples outside the 

truncation points can be set to the truncation limit; in this case, samples “stack up” at the 

truncation points. Alternatively, new random values can be selected, in which case the 

probability outside the limits is spread over the specified range, and thus the probabilities inside 

the truncation limits will be higher than the theoretical untruncated distribution.
F-5

F.3 Demographic Characteristics

The starting point for constructing a simulated individual is the population census 

database; this contains population counts for each combination of age, gender, race, and sector.

The user may decide what spatial area is represented by a sector, but the default input file defines 

a sector as a census tract. Census tracts are variable in both geographic size and population 

number, though usually have between 1,500 and 8,000 persons. Currently, the default file 

contains population counts from the 2010 census for every census tract in the United States, thus 

the default file should be sufficient for most exposure modeling purposes. The combination of 

age, gender, race, and sector are selected first. The sector becomes the home sector for the 

individual, and the corresponding air district becomes the home district. The probabilistic 

selection of individuals is based on the sector population and demographic composition, and 

taken collectively, the set of simulated individuals constitutes a random sample from the study 

area.

The second step in constructing a simulated individual is to determine their employment 

status. This is determined by a probability which is a function of age, gender, and home sector.

An input file is provided which contains employment probabilities from the 2010 census for 

every combination of age (16 and over), gender, and census tract. APEX assumes that persons 

under age 16 do not commute. For persons who are determined to be workers, APEX then 

randomly selects a work sector, based on probabilities determined from the commuting matrix.

The work sector is used to assign a work district for the individual that may differ from the home 

district, and thus different ambient air quality may be used when the individual is at work.

The commuting matrix contains data on flows (number of individuals) traveling from a 

given home sector to a given work sector. Based on commuting data from the 2000 census, a 

commuting data base for the entire United States has been prepared. This permits the entire list 

of non-zero flows to be specified on one input file. Given a home sector, the number of 

destinations to which people commute varies anywhere from one to several hundred other tracts.

F.4 Attributes of Individuals

In addition to the above demographic information, each individual is assigned status and 

physiological attributes. The status variables are factors deemed important in estimating 

microenvironmental concentrations, and are specified by the user. Status variables can include, 

but are not limited to, people’s housing type, whether their home has air conditioning, whether 

they use a gas stove at home, whether the stove has a gas pilot light, and whether their car has air 

conditioning. Physiological variables are important when estimating pollutant specific dose. 

These variables could include height, weight, blood volume, pulmonary diffusion rate, resting 
F-6

metabolic rate, energy conversion factor (liters of oxygen per kilocalorie energy expended), 

hemoglobin density in blood, maximum limit on metabolic equivalents of work (MET) ratios 

(see below), and endogenous CO production rate. All of these variables are treated 

probabilistically taking into account interdependencies where possible, and reflecting variability 

in the population.

Two key personal attributes determined for each individual in this assessment are body 

mass (BM) and body surface area (BSA). Each simulated individual’s body mass was randomly 

sampled from age- and gender-specific body mass distributions generated from National Health 

and Nutrition Examination Survey (NHANES) data for the years 2009-2014.

1 Details in their 

development and the parameter values are provided in Appendix G. Then age- and genderspecific body surface area can be estimated for each simulated individual. Briefly, the BSA 

calculation is based on logarithmic relationships developed by Burmaster (1998) that use body 

mass as an independent variable as follows:

2.2781 0.6821 BSA e BM 



Equation F-1

where,

BSA = body surface area (m2)

BM = body mass (kg)

F.5 Construction of Longitudinal Diary Sequence

The activity diary determines the sequence of microenvironments visited by the 

simulated person. A longitudinal sequence of daily diaries must be constructed for each 

simulated individual to cover the entire simulation period. The default activity diaries in APEX 

are derived from those in the EPA's Consolidated Human Activity Database (CHAD) (McCurdy 

et al., 2000; U.S. EPA 2002; 2017c), although the user could provide area specific diaries if 

available. There are over 55,000 CHAD diaries used for the current SO2 assessment, each 

covering a 24-hour period, that have been compiled from several studies. CHAD is essentially a 

cross-sectional database that, for the most part, only has one diary per person. Therefore, APEX 

must assemble each longitudinal diary sequence for a simulated individual from many single-day 

diaries selected from a pool of similar people.

 

1 Demographic (Demo) and Body Measurement (BMX) datasets for each of the NHANES studies were obtained 

from http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm.
F-7

APEX selects diaries from CHAD by matching gender and employment status, and by 

requiring that age falls within a user-specified range on either side of the age of the simulated 

individual. For example, if the user specifies plus or minus 20%, then for a 40-year old simulated 

individual, the available CHAD diaries are those from persons aged 32 to 48. Each simulated 

individual therefore has an age window of acceptable diaries; these windows can partially 

overlap those for other simulated individuals. This differs from a cohort-based approach, where 

the age windows are fixed and non-overlapping. The user may optionally request that APEX 

allow a decreased probability for selecting diaries from ages outside the primary age window, 

and also for selecting diaries from persons of missing gender, age, or employment status. These 

options allow the model to continue the simulation when diaries are not available within the 

primary window.

The available CHAD diaries are classified into diary pools, based on the temperature and 

day of the week. The model will select diaries from the appropriate pool for days in the 

simulation having matching temperature and day type characteristics. The rules for defining 

these pools are specified by the user. For example, the user could request that all diaries from 

Monday to Friday be classified together, and Saturday and Sunday diaries in another class.

Alternatively, the user could instead create more than two classes of weekdays, combine all 

seven days into one class, or split all seven days into separate classes.

The temperature classification can be based either on daily maximum temperature, daily 

average temperature, or both. The user specifies both the ranges and numbers of temperatures 

classes. For example, the user might wish to create four temperature classes and set their ranges 

to below 50 °F, 50-69 °F, 70-84 °F, and above a daily maximum of 84 °F. Then day type and 

temperature classes are combined to create the diary pools. For example, if there are four 

temperature classes and two-day type classes, then there will be eight diary pools.

APEX then determines the day-type and the applicable temperature for each person’s 

simulated day. APEX allows multiple temperature stations to be used; the sectors are 

automatically mapped to the nearest temperature station. This may be important for study areas 

such as the greater Los Angeles area, where the inland desert sectors may have very different 

temperatures from the coastal sectors. For selected diaries, the temperature in the home sector of 

the simulated person is used. For each day of the simulation, the appropriate diary pool is 

identified and a CHAD dairy is randomly drawn. When a diary for every day in the simulation 

period has been selected, they are concatenated into a single longitudinal diary covering the 

entire simulation for that individual. APEX contains three algorithms for stochastically selecting 

diaries from the pools to create the longitudinal diary. The first method selects diaries at random 

after stratification by age, gender, and diary pool; the second method selects diaries based on 

metrics related to exposure (e.g., time spent outdoors) with the goal of creating longitudinal 
F-8

diaries with variance properties designated by the user (Glen et al., 2008); and the third method 

uses a clustering algorithm to obtain more realistic recurring behavioral patterns (Rosenbaum 

2008).

The final step in processing the activity diary is to map the CHAD location codes into the 

set of APEX microenvironments, supplied by the user as an input file. The user may define the 

number of microenvironments, from one up to the number of different CHAD location codes.

F.6 Key Physiological Processes Modeled

Ventilation is a general term describing the movement of air into and out of the lungs. 

The rate of ventilation is determined by the type of activity an individual performs which in turn 

is related to the amount of oxygen required to perform the activity. Minute or total ventilation 

rate is used to describe the volume of air moved in or out of the lungs per minute. Quantitatively, 

the volume of air breathed in per minute (

•

VI

) is slightly greater than the volume expired per 

minute (

V E

•

). Clinically, however, this difference is not important, and by convention, the 

ventilation rate is always measured by the expired volume.

The rate of oxygen consumption (

V O2

•

) is related to the rate of energy usage in 

performing activities as follows:

V O EEECF

•

2

Equation (F-2)

where,

V O2

•

= Oxygen consumption rate (liters O2/minute)

EE = Energy expenditure (kcal/minute)

ECF = Energy conversion factor (liters O2/kcal).

The ECF shows little variation and typically, commonly a value between 0.20 and 0.21 is 

used to represent the conversion from energy units to oxygen consumption. APEX can randomly 

sample from a uniform distribution defined by these lower and upper bounds to estimate an ECF 

for each simulated individual. The activity-specific energy expenditure is highly variable and can 

be estimated using metabolic equivalents (METs), or the ratios of the rate of energy consumption 

for non-rest activities to the resting rate of energy consumption, as follows

EE  MET RMR

Equation F-3
F-9

where,

EE = Energy expenditure (kcal/minute)

MET = Metabolic equivalent of work (unitless)

RMR = Resting metabolic rate (kcal/minute)

APEX contains distributions of METs for all activities that might be performed by 

simulated individuals. APEX randomly samples from the various METs distributions to obtain 

values for every activity performed by each individual. Age- and sex-specific RMR are estimated 

once for each simulated individual using a linear regression model developed based on use BM, 

age, and the natural logarithms of BM and (age+1) (Equation F-4).2 Details regarding the model 

derivation, ergression coefficient values, and performance evaluation are provided in Appendix 

H.

RMR = β0 + β1BM + β2 log(BM) + β3Age + β4log(Age) + εi Equation F-4

APEX also contains an algorithm that accounts for variability in ventilation rate (

V E

•

) 

due to variation in oxygen consumption (

V O2

•

). The approach indirectly considers influential 

variables such as age, sex, and body mass by use of an individual’s maximum MET (or, 

equivalently, by VO2m), thus the variability within age groups, and both inter- and intra-personal 

and variability are also accounted for. Appendix H describes this new algorithm, derived using 

the same clinical study data used in developing the former APEX algorithm (Graham and 

McCurdy, 2005), though as 

VE = e

(3.300 + 0.8128×ln_vo2+ 0.5126 × (VO2÷VO2m)

4+N(0,eb)+N(0,ew)) Equation F-5

F.7 Estimating Microenvironmental Concentrations

The user provides rules for determining the pollutant concentration in each 

microenvironment. There are two available models for calculating microenvironmental 

concentrations: mass balance and regression factors. Any indoor microenvironment may use 

 

2 The “+1” modifier allows APEX to round age upwards instead of downwards to whole years, which is necessary to 

avoid undefined log(0) values.
F-10

either model; for each microenvironment, the user specifies whether the mass balance or factors 

model will be used.

F.7.1 Mass Balance Model

The mass balance method assumes that an enclosed microenvironment (e.g., a room 

within a home) is a single well-mixed volume in which the air concentration is approximately 

spatially uniform. The concentration of an air pollutant in such a microenvironment is estimated 

using the following four processes (and illustrated in Figure F-1):

• Inflow of air into the microenvironment;

• Outflow of air from the microenvironment;

• Removal of a pollutant from the microenvironment due to deposition, filtration, and 

chemical degradation; and

• Emissions from sources of a pollutant inside the microenvironment.

Figure F-1. Illustration of the mass balance model used by APEX.

Considering the microenvironment as a well-mixed fixed volume of air, the mass balance 

equation for a pollutant in the microenvironment can be written in terms of concentration:

 

Cin Cout Cremoval Csource dt

dC t

       

Equation F-6

Air

inflow

Indoor sources

Removal due to:

•Chemical reactions

•Deposition

•Filtration

Air

outflow

Microenvironment

Air

inflow

Indoor sources

Removal due to:

•Chemical reactions

•Deposition

•Filtration

Air

outflow

Microenvironment
F-11

where,

C(t) = Concentration in the microenvironment at time t

C

in = Rate of change in C(t) due to air entering the microenvironment

C out = Rate of change in C(t) due to air leaving the microenvironment

C

removal = Rate of change in C(t) due to all internal removal processes

C source = Rate of change in C(t) due to all internal source terms

Concentrations are calculated in the same units as the ambient air quality data, e.g., ppm, 

ppb, ppt, or μg/m3. In the following equations concentration is shown only in μg/m3for brevity.

The change in microenvironmental concentration due to influx of air, 

C

in, is given by:

i n outdoor penetration Rairexchange C  C f 

 Equation F-7

where,

Coutdoor = Ambient concentration at an outdoor microenvironment or outside an 

indoor microenvironment (μg/m3)

fpenetration = Penetration factor (unitless)

Rair exchange = Air exchange rate (hr-1)

Because the air pressure is approximately constant in microenvironments that are 

modeled in practice, the flow of outside air into the microenvironment is equal to that flowing 

out of the microenvironment, and this flow rate is given by the air exchange rate. The air 

exchange rate (hr

-1

) can be loosely interpreted as the number of times per hour the entire volume 

of air in the microenvironment is replaced. For some pollutants (especially particulate matter), 

the process of infiltration may remove a fraction of the pollutant from the outside air. The 

fraction that is retained in the air is given by the penetration factor fpenetration.

A proximity factor (fproximity) and a local outdoor source term are used to account for 

differences in ambient concentrations between the geographic location represented by the 

ambient air quality data (e.g., a regional fixed-site monitor) and the geographic location of the 

microenvironment. That is, the outdoor air at a particular location may differ systematically from 

the concentration input to the model representing the air quality district. For example, a 

playground or house might be located next to a busy road in which case the air at the playground 

or outside the house would have elevated levels for mobile source pollutants such as carbon 

monoxide and benzene. The concentration in the air at an outdoor location or directly outside an 

indoor microenvironment (Coutdoor) is calculated as: 
F-12

outdoor proximity Cambient CLocalOutdo orSources C  f 

Equation F-8

where,

Cambient = Ambient air district concentration (μg/m3)

fproximity = Proximity factor (unitless)

CLocalOutdoorSources = the contribution to the concentration at this location from local 

sources not represented by the ambient air district concentration (μg/m3)

During exploratory analyses, the user may examine how a microenvironment affects 

overall exposure by setting the microenvironment’s proximity or penetration factor to zero, thus 

effectively eliminating the specified microenvironment. Change in microenvironmental 

concentration due to outflux of air is calculated as the concentration in the microenvironment 

C(t) multiplied by the air exchange rate:

C R Ct out air exchange 

 Equation F-9

The third term (

C

removal) in the mass balance calculation (Equation F-6) represents 

removal processes within the microenvironment. There are three such processes in general: 

chemical reaction, deposition, and filtration. Removal can be important for pollutants such as O3

and SO2, for example, but not for carbon monoxide. The amount lost to chemical reactions will 

generally be proportional to the amount present, which in the absence of any other factors would 

result in an exponential decay in the concentration with time. Similarly, deposition rates are 

usually given by the product of a (constant) deposition velocity and a (time-varying) 

concentration, also resulting in an exponential decay. The third removal process is filtration, 

usually as part of a forced air circulation or HVAC system. Filtration will normally be more 

effective at removing particles than gases. In any case, filtration rates are also approximately 

proportional to concentration. Change in concentration due to deposition, filtration, and chemical 

degradation in a microenvironment is simulated based on the first-order equation:

   

R Ct

C R R R C t

removal

removal deposition filtration chemical

 

   



Equation F-10

where,
F-13 C

removal = Change in microenvironmental concentration due to removal processes 

(μg/m3/hr)

Rdeposition = Removal rate of a pollutant from a microenvironment due to deposition 

(hr-1)

Rfiltration = Removal rate of a pollutant from a microenvironment due to filtration 

(hr-1)

Rchemical = Removal rate of a pollutant from a microenvironment due to chemical 

degradation (hr-1)

Rremoval = Removal rate of a pollutant from a microenvironment due to the 

combined effects of deposition, filtration, and chemical degradation (hr-1)

The fourth term in the mass balance calculation represents pollutant sources within the 

microenvironment. This is the most complicated term, in part because several sources may be 

present. APEX allows two methods of specifying source strengths: emission sources and 

concentration sources. Either may be used for mass balance microenvironments, and both can be 

used within the same microenvironment. The source strength values are used to calculate the 

term 

C source (μg/m3

/hr).

Emission sources are expressed as emission rates in units of μg/hr, irrespective of the 

units of concentration. To determine the rate of change of concentration associated with an 

emission source SE, it is divided by the volume of the microenvironment:

V

S

C

E

source,SE 

 Equation F-11

where,

C

source,SE = Rate of change in C(t) due to the emission source SE (μg/m3

/hr)

SE = The emission rate (μg/hr)

V = The volume of the microenvironment (m3)

Concentration sources (SC) however, are expressed in units of concentration. These must 

be the same units as used for the ambient concentration (e.g., μg/m3). Concentration sources are 

normally used as additive terms for microenvironments using the factors model. Strictly 

speaking, they are somewhat inconsistent with the mass balance method, since concentrations 

should not be inputs but should be consequences of the dynamics of the system. Nevertheless, a 

suitable meaning can be found by determining the rate of change of concentration (

C source) that 
F-14

would result in a mean increase of SC in the concentration, given constant parameters and 

equilibrium conditions, in this way:

Assume that a microenvironment is always in contact with clean air (ambient = zero), and 

it contains one constant concentration source. Then the mean concentration over time in this 

microenvironment from this source should be equal to SC. The mean source strength expressed in 

ppm/hr or μg/m3/hr is the rate of change in concentration (

C

source,SC). In equilibrium, 

air exchange removal

source,SC

S R R

C

C







Equation F-12

where, Cs is the mean increase in concentration over time in the microenvironment due to 

the source 

C

source,SC . Thus, 

C

source,SC can be expressed as

Csource,SC CS  Rmean

 Equation F-13

where Rmean is the chemical removal rate. From Equation (F-13), Rmean is the sum of the 

air exchange rate and the removal rate (Rair exchange + Rremoval) under equilibrium conditions. In 

general, however, the microenvironment will not be in equilibrium, but in such conditions there 

is no clear meaning to attach to 

C

source,SC since there is no fixed emission rate that will lead to a 

fixed increase in concentration. The simplest solution is to use Rmean = Rair exchange + Rremoval. 

However, the user is given the option of specifically specifying Rmean (see discussion below). 

This may be used to generate a truly constant source strength 

C

source,SC by making SC and Rmean

both constant in time. If this is not done, then Rmean is simply set to the sum of (Rair exchange + 

Rremoval). If these parameters change over time, then 

C

source,SC also changes. Physically, the 

reason for this is that in order to maintain a fixed elevation of concentration over the base 

conditions, then the source emission rate would have to rise if the air exchange rate were to rise.

Multiple emission and concentration sources within a single microenvironment are 

combined into the final total source term by combining Equations (F-11) and (F-13):

 

 

   

e nc

i 1

mean Si

n

i 1

source source,SE source,SC ESi R C

V

1

C C C Equation F-14

where,
F-15

SEi = Emission source strength for emission source i (μg/hr, irrespective 

of the concentration units)

SCi = Emission source strength for concentration source i (μg/m3)

ne = Number of emission sources in the microenvironment

nc = Number of concentration sources in the microenvironment

In Equations (F-11) and (F-14), if the units of air quality are ppm rather than μg/m3, 1/V

is replaced by f/V, where f = ppm / μg/m3 = gram molecular weight / 24.45 (i.e., 24.45 being the 

volume (liters) of a mole of the gas at 25°C and 1 atmosphere pressure). Equations (F-7), (F-9), 

(F-10), and (F-14) can now be combined with Equation (F-6) to form the differential equation for 

the microenvironmental concentration C(t). Within the time period of a time step (at most 1 

hour), 

C source and C

in are assumed to be constant. Using 

C

combined = 

C source + C

in leads to:

 

   

C - R Ct

C - R C t R C t

dt

dC t

combined mean

combined air exchange removal







 

Equation F-15

Solving this differential equation leads to:

   

( ) 0t t

t



















  

Rmean -

mean

combined

0

mean

combined e

R

C

C

R

C

C t

 

Equation F-16

where,

C(t0) = Concentration of a pollutant in a microenvironment at the 

beginning of a time step (μg/m3)

C(t) = Concentration of a pollutant in a microenvironment at time t within 

the time step (μg/m3).

Based on Equation (F-16), the following three concentrations in a microenvironment are 

calculated:

 

airexchange removal

source i n

mean

combined

equil R R

C C

R

C

C C t





    

  

Equation F-17

   

Rmean T C(t0 T Cequil C t0 Cequil e

 )    Equation F-18
F-16  

R T

1 e

C(t)dt C C(t C

T

C

mean

R

mean equil 0 equil

t T

mean T

t

 



    )

1

0

0

Equation F-19

where,

Cequil = Concentration in a microenvironment (μg/m3) if t  

(equilibrium state).

C(t0) = Concentration in a microenvironment at the beginning of the time 

step (μg/m3)

C(t0+T) = Concentration in a microenvironment at the end of the time step 

(μg/m3)

C mean = Mean concentration over the time step in a microenvironment 

(μg/m3)

Rmean = Rair exchange + Rremoval (hr-1)

At each time step of the simulation period, APEX uses Equations (F-17), (F-18), and 

(5A-19) to calculate the equilibrium, ending, and mean concentrations, respectively. The 

calculation continues to the next time step by using C(t0+T) for the previous hour as C(t0).

F.7.2 Factors Model

The factors model is simpler than the mass balance model. In this method, the value of 

the concentration in a microenvironment is not dependent on the concentration during the 

previous time step. Rather, this model uses the following equation to calculate the concentration 

in a microenvironment from the user-provided hourly air quality data:



 

nc

i 1

mean ambient proximity penetratio n SCi C C f f

Equation F-20

where,

Cmean = Mean concentration over the time step in a microenvironment (μg/m3)

Cambient = The concentration in the ambient (outdoor) environment (μg/m3)

fproximity = Proximity factor (unitless)

fpenetration = Penetration factor (unitless)

SCi = Mean air concentration resulting from source i (μg/m3)

nc = Number of concentration sources in the microenvironment
F-17

The user may specify distributions for proximity, penetration, and any concentration 

source terms. All of the parameters in Equation (F-20) are evaluated for each time step, although 

these values might remain constant for several time steps or even for the entire simulation.

The ambient air quality data are supplied as time series over the simulation period at 

several locations across the modeled region. The other variables in the factors and mass balance 

equations are randomly drawn from user-specified distributions. The user also controls the 

frequency and pattern of these random draws. Within a single day, the user selects the number of 

random draws to be made and the hours to which they apply. Over the simulation, the same set 

of 24 hourly values may either be reused on a regular basis (for example, each winter weekday), 

or a new set of values may be drawn. The usage patterns may depend on day of the week, on 

month, or both. It is also possible to define different distributions that apply if specific conditions 

are met. The air exchange rate is typically modeled with one set of distributions for buildings

with air conditioning and another set of distributions for those which do not. The choice of a 

distribution within a set typically depends on the outdoor temperature and possibly other 

variables. In total there are eleven such conditional variables which can be used to select the 

appropriate distributions for the variables in the mass balance or factors equations.

For example, the hourly emissions of CO from a gas stove may be given by the product 

of three random variables: a binary on/off variable that indicates if the stove is used at all during 

that hour, a usage duration sampled from a continuous distribution, and an emission rate per 

minute of usage. The binary on/off variable may have a probability for on that varies by time of 

day and season of the year. The usage duration could be taken from a truncated normal or 

lognormal distribution that is resampled for each cooking event, while the emission rate could be 

sampled just once per stove.

F.8 Exposure and Dose Time Series Calculations

The activity diaries provide the time sequence of microenvironments visited by the 

simulated individual and the activities performed by each individual. The pollutant concentration 

in the air in each microenvironment is assumed to be spatially uniform throughout the 

microenvironment and unchanging within each diary event and is calculated by either the factors 

or the mass balance method, as specified by the user. The exposure of the individual is given by 

the time sequence of airborne pollutant concentrations that are encountered in the 

microenvironments visited. Figure F-2 illustrates the exposures for one simulated 12-year old 

child over a 2-day period. On both days the child travels to and from school in an automobile, 

goes outside to a playground in the afternoon while at school, and spends time outside at home in 

the evening.
F-18

Figure F-2. Example of microenvironmental and exposure concentrations for a simulated 

individual over a 48-hour duration. (H: home, A: automobile, S: school, P: playground, O: 

outdoors at home).

In addition to exposure, APEX models breathing rates based on the physiology of each 

individual and the exertion levels associated with the activities performed. For each activity type 

in CHAD, a distribution is provided for a corresponding normalized metabolic equivalent of 

work or METs (McCurdy, 2000). METs are derived by dividing the metabolic energy 

requirements for the specific activity by a person’s resting, or basal, metabolic rate. The MET 

ratios have less interpersonal variation than do the absolute energy expenditures. Based on age 

and sex, the resting metabolic rate, along with other physiological variables is determined for 

each individual as part of their anthropometric characteristics. Because the MET ratios are 

sampled independently from distributions for each diary event, it would be possible to produce 

time-series of MET ratios that are physiologically unrealistic. APEX employs a MET adjustment 

algorithm based on a modeled oxygen deficit to prevent such overestimation of MET and 

breathing rates (Isaacs et al., 2008). The relationship between the oxygen deficit and the applied 

limits on MET ratios are nonlinear and are derived from published data on work capacity and 

oxygen consumption. The resulting combination of microenvironmental concentration and 

breathing ventilation rates provides a time series of inhalation intake dose for most pollutants.
F-19

F.9 Model Output

APEX calculates the exposure and dose time series based on the events as listed on the 

activity diary with a minimum of one event per hour but usually more during waking hours.

APEX can aggregate the event level exposure and dose time series to output hourly, daily, 

monthly, and annual averages. The types of output files are selected by the user, and can be as 

detailed as event-level data for each simulated individual (note, Figure F-3 was produced from 

an APEX event output file). A set of summary tables are produced for a variety of exposure and 

dose measures. These could include tables of person-minutes at various exposure levels, by 

microenvironment, a table of person-days at or above each average daily exposure level, and 

tables describing the distributions of exposures for different groups. An example of how APEX 

results can be depicted is given Figure F-3 which shows the percent of children with at least one 

5-minute maximum exposure at or above different exposure levels, concomitant with moderate 

or greater exertion. These are results from a simulation of SO2 exposures for Fall River, MA 

during 2011. From this graph it can be observed, for example, that APEX estimates 15 percent of 

the children in this area experienced a daily maximum 5-minute SO2 exposure above 100 ppb 

while exercising, at least once during the year.

Figure F-3. The percent of simulated children (ages 5-18) experiencing at least one daily 

maximum 5-minute SO2 exposure during 2011, while at moderate or greater exertion.
F-20

REFERENCES

Burmaster, D.E. (1998). LogNormal distributions for skin area as a function of body weight. Risk 

Analysis. 18(1):27-32.

Glen, G., Smith, L., Isaacs, K., McCurdy, T., Langstaff, J. (2008). A new method of longitudinal 

diary assembly for human exposure modeling. J Expos Sci Environ Epidem. 18:299-311.

Graham, S.E., McCurdy, T. (2005). Revised ventilation rate (VE) equations for use in inhalationoriented exposure models. Report no. EPA/600/X-05/008 is Appendix A of US EPA 

(2009). Metabolically Derived Human Ventilation Rates: A Revised Approach Based 

Upon Oxygen Consumption Rates (Final Report). Report no. EPA/600/R-06/129F.

Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=202543.

Isaacs, K., Glen, G., McCurdy, T., Smith, L. (2008). Modeling energy expenditure and oxygen 

consumption in human exposure models: accounting for fatigue and EPOC. J Expos Sci 

Environ Epidemiol. 18:289-298.

Johnson, T., Capel, J., McCoy, M. (1996a). Estimation of Ozone Exposures Experienced by 

Urban Residents Using a Probabilistic Version of NEM and 1990 Population Data. 

Prepared by IT Air Quality Services for the Office of Air Quality Planning and 

Standards, U.S. Environmental Protection Agency, Research Triangle Park, North 

Carolina, September.

Johnson, T., Capel, J., Mozier, J., McCoy, M. (1996b). Estimation of Ozone Exposures 

Experienced by Outdoor Children in Nine Urban Areas Using a Probabilistic Version of 

NEM. Prepared for the Air Quality Management Division under Contract No. 68-DO30094, April.

Johnson, T., Capel, J., McCoy, M., Mozier, J. (1996c). Estimation of Ozone Exposures 

Experienced by Outdoor Workers in Nine Urban Areas Using a Probabilistic Version of 

NEM. Prepared for the Air Quality Management Division under Contract No. 68-DO30094, April.

McCurdy, T. (2000). Conceptual basis for multi-route intake dose modeling using an energy 

expenditure approach. J Expo Anal Environ Epidemiol.10:1-12.

McCurdy T, Glen G, Smith L, Lakkadi Y. (2000). The National Exposure Research Laboratory’s 

Consolidated Human Activity Database. J Expos Anal Environ Epidemiol. 10:566-578.

Rosenbaum, A. S. (2008). The Cluster-Markov algorithm in APEX. Memorandum prepared for 

Stephen Graham, John Langstaff. USEPA OAQPS by ICF International.

U.S. EPA. (2002). Consolidated Human Activities Database (CHAD) Users Guide. Database and 

documentation available at: http://www.epa.gov/chadnet1/.

U.S. EPA. (2017a). Air Pollutants Exposure Model Documentation (APEX, Version 5) 

Volume I: User’s Guide. Office of Air Quality Planning and Standards, U.S. 
F-21

Environmental Protection Agency, Research Triangle Park, NC, 27711. EPA-452/R-17-

001a. Available at: https://www.epa.gov/fera/apex-user-guides

U.S. EPA. (2017b). Air Pollutants Exposure Model Documentation (APEX, Version 5) 

Volume II: Technical Support Document. Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711. EPA-452/R17-001b. Available at: https://www.epa.gov/fera/apex-user-guides

U.S. EPA. (2017c). The Consolidated Human Activity Database – Master Version (CHADMaster). Technical Memorandum. U.S. Environmental Protection Agency, National 

Exposure Research Laboratory, Research Triangle Park, NC, 27711. In preparation. 

Previous version (09/15/2014) available at: 

https://www.epa.gov/healthresearch/consolidated-human-activity-database-chad-usehuman-exposure-and-health-studies-and
G-1

APPENDIX G

ICF FINAL MEMO: JOINT DISTRIBUTIONS OF BODY WEIGHT

AND HEIGHT FOR USE IN APEX
2635 Meridian Pkwy., Suite 200 ▪ Durham, NC 27713 ▪ 919.293.1620 ▪ 919.293.1645 fax ▪ icf.com

Draft Memorandum

To: John Langstaff, Stephen Graham, Kristin Isaacs, U.S. Environmental Protection 

Agency

From: Jonathan Cohen, Graham Glen, John Hader, Chris Holder, ICF

Date: April 20, 2017

Re: Joint Distributions of Body Weight and Height for use in APEX (Revised from October 

26, 2016 version to add Section 6).

1. Introduction and Summary

The current version of APEX uses fitted distributions for body weight (BW; also referred to as 

body mass) based on an analysis of the data from the National Health and Nutrition 

Examination Survey (NHANES) for the years 1999–2004. These distributions were developed in 

2005.

1 The current version of APEX also uses fitted distributions for height (HT) based on fitted 

regressions for HT against age for children under 18 years of age and fitted regressions for HT

against the logarithm of BW for adults 18 years and older. The regression coefficients for 

children depend upon the age group and gender.2ICF was tasked with updating these BW 

fitted distributions to use more recent NHANES data and to compute parameters for the 

joint distribution of BW and HT.

We downloaded and analyzed BW and HT data from NHANES for the years 2003–2014. We 

fitted distributions for the entire period 2003–2014 and also for the more recent period 2009–

2014. As shown in Section 5, the final fitted models were very similar for the 2003–2014 and 

2009–2014 periods. In this memorandum, we present detailed results for the 2009–2014 

analysis. We provide the final parameter estimates for both groups of years in accompanying 

Excel spreadsheets. We can provide the detailed analyses for 2003–2014 upon request.

In Section 2, we present histograms and summary tables for the marginal distributions of BW

and HT for each gender and single year of age. We compared fitted normal and log-normal 

distributions using the histograms and log-likelihoods and determined that the best overall 

choice was a log-normal distribution for BW and a normal distribution for HT. To allow a 

smooth set of parameters for different ages, we chose the same distributional forms (but 

different parameters) for each combination of gender and age.

In Section 3, we model the joint distribution of BW and HT as a bivariate normal 

distribution for the HT and the logarithm of the BW, with different parameters for each 

age and gender. We present scatter plots for selected single years of age.

 

1 Kristin Isaacs and Luther Smith, Alion Science and Technology, “New Values for Physiological Parameters for the 

Exposure Model Input File Physiology.txt”. Memorandum to Tom McCurdy, EPA. December 20, 2005.

2

Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J, Rosenbaum A, Cohen J, Stiefer P. 2000. Estimation of 

carbon monoxide exposures and associated carboxyhemoglobin levels for residents of Denver and Los Angeles 

using pNEM/CO. Appendices. EPA contract 68-D6-0064.
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 2

As shown in Section 4, the estimated parameters for each age do not vary smoothly across the 

ages. Therefore, we used a natural cubic spline model to smooth each of the five 

parameters across the different ages for each gender. This approach also allowed us to 

smoothly extrapolate the parameters for ages 80 to 100, since the NHANES data for recent 

periods combines all ages 80 and above into a single age group. 

In Section 5 we compare the fitted parameters between the NHANES periods 2009–2014 and 

2003–2014 and show that, after smoothing the parameters, the maximum unsigned percentage 

difference is 11 percent for the correlation coefficient and less than 1 percent for the means. 

Finally, in Section Error! Reference source not found. we compare summaries of the HT, BW, 

and body mass index from the Personal Summary files generated by running APEX with the old 

and updated method for calculating height and weight. There is now a better correlation 

between HT, WT, and age for young children and older adults. Average BW values tend to be 

larger with the new method, likely reflecting ongoing trends in BW of the U.S. population, and 

simulated body mass indices are roughly in line with NHANES data. 

2. Marginal Distributions of BW and HT

2.1.NHANES Data

For each of the NHANES cycles (2-year periods), we downloaded the age, HT, BW, and survey 

weights for each sampled person by merging the demographic file with the body-measurements 

file. We selected the variables discussed below.

Age

For 2003–2004 and 2005–2006, RIDAGEEX is the age in months at the time of examination for 

individuals of ages 0–84 years, and RIDAGEYR is the age in years at the time of screening for 

all individuals. We used RIDAGEEX to calculate the age in years for individuals under 84 

(integer part of RIDAGEEX/12) and RIDAGEYR for individuals 85 and over. We assigned the 

age group code “1000” to all individuals 80 and over.

For 2007–2008 and 2009–2010, RIDAGEEX is the age in months at the time of examination for 

individuals of ages 0–79 years, and RIDAGEYR is the age in years at the time of screening for 

all individuals. We used RIDAGEEX to calculate the age in years for individuals under 80 

(integer part of RIDAGEEX/12) and RIDAGEYR for individuals 80 and over. We assigned the 

age group code “1000” to all individuals 80 and over. 

For 2009–2010 and 2011–2012, RIDEXAGM is the age in months at the time of examination for 

individuals of ages 0–19 years, and RIDAGEYR is the age in years at the time of screening for 

all individuals. We used RIDEXAGM to calculate the age in years for individuals under 20 

(integer part of RIDEXAGM/12) and RIDAGEYR for individuals 20 and over. We assigned the 

age group code “1000” to all individuals 80 and over.

Gender
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 3

NHANES codes gender using Males = 1 and Females = 2.

HT

For individuals of ages 2 years and older, we used the NHANES variable BMXHT, which is the 

standing HT (cm). For children of ages 0 or 1 years, we used the NHANES variable 

BMXRECUM, which is the recumbent HT (cm); for programming convenience we renamed this 

variable as BMXHT.

BW

For all individuals, we used the NHANES variable BMXWT, which is the BW (kg).

Survey Weight

The NHANES survey weight variable for each 2-year period is WTMEC2YR, which estimates 

the number of people in the U.S. population at the mid-year of the survey period represented by 

the sampled individual. Since the NHANES survey was designed to over-sample certain 

demographic groups (e.g., Mexican-Americans from 2003–2006 and Hispanics from 2006–

2014), the survey weights are needed to adjust the data to represent the U.S. population. 

With two exceptions, all of the analyses in this memorandum used the survey weights to adjust 

the data. One of these exceptions is for the histogram plots in the next sub-section, which used 

the survey weights rounded to the nearest integer because SAS does not allow fractional 

weights for those plots. A second exception is for the natural cubic spline smoothing of the 

parameter estimates described in Section 4; the survey weights were used in the calculations of 

the unsmoothed parameters but it would not have been appropriate to use them for the final 

smoothing step.

2.2.Histograms

Figure 2-1 and Figure 2-2 below are histograms of the BW (kg) and HT (cm; standing HT for 

ages 2 and over, recumbent HT for ages 0 and 1), respectively, for each gender and selected

single years of age (the selected ages shown are 1, 5, 10, 15, 20, 25, 30, 40, 60, 70, and 79

years). Superimposed on each histogram are fitted normal and log-normal distributions. The 

calculations use the survey weights rounded to the nearest integer (making a negligible error, 

since the survey weights are usually several thousand). For BW (Figure 2-1), the distributions

are generally right-skewed and the log-normal distribution appears to fit the data better 

than the normal distribution. For HT (Figure 2-2), the distributions are almost symmetric 

and it is hard to distinguish the two fitted distributions on the plots. We provide larger 

versions of the histograms in Figure 2-1 and Figure 2-2 in Attachment A and Attachment B, 

respectively.
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 4

Figure 2-1. Distributions of BW Figure 2-2. Distributions of HT

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

0 5 10 15 20 25 30 35 40 45 50 55 0 5 10 15 20 25 30 35 40 45 50 55

0

25

50

75

100

125

150

0

25

50

75

100

125

150

Gender = 1 Gender = 2

Age = 5 Age = 1

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

0 40 80 120 160 200 0 40 80 120 160 200

0

20

40

60

80

100

0

20

40

60

80

100

Gender = 1 Gender = 2

Age = 15 Age = 10

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

20 60 100 140 180 220 20 60 100 140 180 220

0

10

20

30

40

50

60

0

10

20

30

40

50

60

Gender = 1 Gender = 2

Age = 25 Age = 20

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

0 40 80 120 160 200 0 40 80 120 160 200

0

10

20

30

40

50

60

0

10

20

30

40

50

60

Gender = 1 Gender = 2

Age = 40 Age = 30

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

20 60 100 140 180 220 20 60 100 140 180 220

0

10

20

30

40

50

0

10

20

30

40

50

Gender = 1 Gender = 2

Age = 60 Age = 50

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

0 40 80 120 160 200 0 40 80 120 160 200

0

20

40

60

80

0

20

40

60

80

Gender = 1 Gender = 2

Age = 79 Age = 70

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

P

e

rc

e

n

t

0 40 80 120 160 200 0 40 80 120 160 200

0

20

40

60

80

0

20

40

60

80

Gender = 1 Gender = 2

A

ge

=

7

9

A

ge

=

7

0

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

50 70 90 110 130 150 50 70 90 110 130 150

0

20

40

60

80

100

0

20

40

60

80

100

Gender = 1 Gender = 2

Age = 5 Age = 1

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

110 130 150 170 190 210 110 130 150 170 190 210

0

10

20

30

40

50

60

0

10

20

30

40

50

60

Gender = 1 Gender = 2

Age = 15 Age = 10

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

140 150 160 170 180 190 140 150 160 170 180 190

0

10

20

30

40

0

10

20

30

40

Gender = 1 Gender = 2

Age = 25 Age = 20

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

130 150 170 190 210 230 130 150 170 190 210 230

0

10

20

30

40

50

60

0

10

20

30

40

50

60

Gender = 1 Gender = 2

Age = 40 Age = 30

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

120 140 160 180 200 220 120 140 160 180 200 220

0

20

40

60

80

0

20

40

60

80

Gender = 1 Gender = 2

Age = 60 Age = 50

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

120 140 160 180 200 220 120 140 160 180 200 220

0

20

40

60

80

0

20

40

60

80

Gender = 1 Gender = 2

Age = 79 Age = 70

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

P

e

rc

e

n

t

120 140 160 180 200 220 120 140 160 180 200 220

0

20

40

60

80

0

20

40

60

80

Gender = 1 Gender = 2

A

ge

=

7

9

A

ge

=

7

0
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 5

2.3.Summary Statistics

Table 1 below contains the estimated means (“Mean”) and standard deviations (“Std Dev”) for 

the BW and its natural logarithm (“Log”) for each age group and gender. The row for age “1000” 

corresponds to ages 80 and older; the summary statistics for this group are shown for 

comparison purposes but are not used for the final set of distributions which are only based on 

the data for ages 0–79 years. Distributions are fitted separately to each combination of gender 

and either a single year of age from 0 to 79 years or the age group 80 years and older. We 

weighted the means and standard deviations across the sampled individuals using the exact 

survey weights.

To compare the fit of the normal and log-normal distributions, we tabulated the likelihood values. 

If f(x) is the probability density function for x (either a log-normal or normal distribution), then 

−2LL = −2 × Σ SWi × log{f(xi)}, where SWi and xi are the survey weight and BW, respectively,

for the i’th individual of the given age group and gender. (We omitted the constant term 1

√2π

from 

f(x)). The value −2LL estimates the corresponding value of minus twice the log-likelihood for the 

population. Based on the likelihood method, the better of the two models (normal or log-normal) 

will have a lower value of −2LL; this determination is shown in the column “Best.” 

For the vast majority of cases, the log-normal model is preferred for BW. This pattern is 

consistent with the histograms shown above. Since the results of the APEX simulations should 

not be too sensitive to the exact ages of the modeled population, it is better to use the same 

distribution for all ages and genders, which suggests that BW should be modeled as a lognormal distribution for all demographic groups.

Table 1. Summary Statistics for BW

Age Gender

Mean

BW

Mean Log

BW

Std Dev

BW

Std Dev

Log BW Best

−2LL 

Normal

−2LL 

Log-Normal

0 1 7.815 2.024 1.933 0.261 Normal 1.30E+07 1.32E+07

1 1 11.443 2.429 1.451 0.126 Lognormal 1.02E+07 9.99E+06

2 1 14.130 2.640 1.850 0.126 Lognormal 1.32E+07 1.26E+07

3 1 16.162 2.773 2.436 0.139 Lognormal 1.94E+07 1.81E+07

4 1 18.693 2.915 3.152 0.157 Lognormal 2.24E+07 2.13E+07

5 1 21.347 3.045 4.002 0.172 Lognormal 2.14E+07 2.02E+07

6 1 23.789 3.149 5.344 0.191 Lognormal 2.81E+07 2.57E+07

7 1 27.870 3.298 7.526 0.234 Lognormal 3.34E+07 3.11E+07

8 1 31.112 3.407 8.244 0.241 Lognormal 3.62E+07 3.45E+07

9 1 34.679 3.513 9.531 0.249 Lognormal 3.38E+07 3.22E+07

10 1 40.133 3.656 11.645 0.263 Lognormal 3.49E+07 3.33E+07

11 1 48.057 3.832 14.351 0.280 Lognormal 3.48E+07 3.36E+07

12 1 50.746 3.894 13.498 0.252 Lognormal 3.99E+07 3.88E+07

13 1 60.002 4.060 16.631 0.256 Lognormal 4.08E+07 3.94E+07

14 1 65.258 4.143 18.467 0.259 Lognormal 5.00E+07 4.82E+07

15 1 71.356 4.234 19.846 0.255 Lognormal 4.14E+07 4.00E+07

16 1 74.894 4.289 18.367 0.226 Lognormal 4.57E+07 4.43E+07

17 1 77.237 4.317 20.101 0.235 Lognormal 4.23E+07 4.07E+07

18 1 81.164 4.363 23.222 0.248 Lognormal 4.39E+07 4.18E+07

19 1 79.636 4.350 19.629 0.229 Lognormal 4.71E+07 4.57E+07
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 6

Age Gender

Mean

BW

Mean Log

BW

Std Dev

BW

Std Dev

Log BW Best

−2LL 

Normal

−2LL 

Log-Normal

20 1 79.206 4.341 20.898 0.246 Lognormal 5.21E+07 5.07E+07

21 1 79.075 4.342 20.585 0.231 Lognormal 4.61E+07 4.41E+07

22 1 81.032 4.368 20.166 0.224 Lognormal 4.43E+07 4.26E+07

23 1 86.142 4.418 25.256 0.269 Lognormal 4.57E+07 4.41E+07

24 1 82.705 4.396 16.561 0.192 Lognormal 4.27E+07 4.19E+07

25 1 85.955 4.422 22.691 0.248 Lognormal 4.40E+07 4.29E+07

26 1 86.496 4.437 19.619 0.213 Lognormal 3.59E+07 3.50E+07

27 1 86.016 4.433 18.552 0.207 Lognormal 3.80E+07 3.73E+07

28 1 88.812 4.459 21.574 0.230 Lognormal 4.74E+07 4.62E+07

29 1 89.171 4.467 20.015 0.215 Lognormal 4.63E+07 4.54E+07

30 1 88.645 4.458 21.090 0.233 Lognormal 4.87E+07 4.80E+07

31 1 88.916 4.465 19.163 0.211 Lognormal 3.86E+07 3.81E+07

32 1 91.226 4.486 22.585 0.230 Lognormal 4.54E+07 4.41E+07

33 1 92.027 4.500 19.719 0.208 Lognormal 3.85E+07 3.79E+07

34 1 87.439 4.451 17.985 0.194 Lognormal 3.33E+07 3.26E+07

35 1 88.897 4.461 21.560 0.228 Lognormal 3.94E+07 3.84E+07

36 1 92.644 4.498 25.114 0.240 Lognormal 4.54E+07 4.36E+07

37 1 93.184 4.512 21.813 0.204 Lognormal 4.11E+07 3.92E+07

38 1 93.366 4.514 20.963 0.210 Lognormal 3.89E+07 3.79E+07

39 1 90.726 4.483 20.780 0.219 Lognormal 4.24E+07 4.16E+07

40 1 92.532 4.504 20.717 0.212 Lognormal 4.58E+07 4.48E+07

41 1 94.364 4.522 22.769 0.218 Lognormal 4.73E+07 4.56E+07

42 1 90.804 4.491 17.670 0.189 Lognormal 3.59E+07 3.54E+07

43 1 92.679 4.510 19.518 0.192 Lognormal 4.57E+07 4.43E+07

44 1 93.069 4.512 20.205 0.202 Lognormal 4.53E+07 4.41E+07

45 1 88.197 4.463 16.018 0.182 Lognormal 3.79E+07 3.77E+07

46 1 90.498 4.485 18.381 0.200 Lognormal 4.43E+07 4.38E+07

47 1 90.870 4.493 17.327 0.180 Lognormal 4.41E+07 4.31E+07

48 1 90.708 4.482 21.347 0.221 Lognormal 4.08E+07 3.98E+07

49 1 90.907 4.488 19.250 0.208 Lognormal 4.00E+07 3.95E+07

50 1 94.131 4.524 20.593 0.199 Lognormal 4.70E+07 4.55E+07

51 1 86.258 4.432 20.135 0.221 Lognormal 3.66E+07 3.57E+07

52 1 92.086 4.501 19.609 0.205 Lognormal 4.26E+07 4.19E+07

53 1 90.250 4.479 19.589 0.215 Lognormal 4.25E+07 4.21E+07

54 1 93.833 4.521 19.125 0.204 Lognormal 4.32E+07 4.30E+07

55 1 90.353 4.483 18.593 0.203 Lognormal 4.56E+07 4.52E+07

56 1 90.006 4.481 17.833 0.192 Lognormal 4.20E+07 4.13E+07

57 1 89.277 4.474 17.028 0.190 Lognormal 3.66E+07 3.63E+07

58 1 89.392 4.474 18.265 0.195 Lognormal 3.85E+07 3.78E+07

59 1 91.403 4.491 20.709 0.217 Lognormal 4.75E+07 4.66E+07

60 1 90.917 4.488 20.306 0.206 Lognormal 3.96E+07 3.85E+07

61 1 93.150 4.506 22.700 0.233 Lognormal 3.16E+07 3.10E+07

62 1 90.499 4.487 18.053 0.192 Lognormal 3.11E+07 3.06E+07

63 1 91.326 4.486 23.270 0.234 Lognormal 3.80E+07 3.68E+07

64 1 89.615 4.467 23.395 0.230 Lognormal 3.13E+07 3.00E+07

65 1 91.754 4.493 20.739 0.229 Lognormal 3.88E+07 3.86E+07

66 1 89.407 4.471 18.910 0.210 Lognormal 2.57E+07 2.55E+07

67 1 90.274 4.482 18.677 0.207 Lognormal 1.96E+07 1.95E+07

68 1 88.174 4.447 22.562 0.256 Lognormal 2.67E+07 2.64E+07

69 1 88.345 4.461 17.487 0.204 Normal 2.12E+07 2.13E+07
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 7

Age Gender

Mean

BW

Mean Log

BW

Std Dev

BW

Std Dev

Log BW Best

−2LL 

Normal

−2LL 

Log-Normal

70 1 88.508 4.465 16.451 0.190 Normal 2.32E+07 2.32E+07

71 1 86.951 4.442 19.122 0.218 Lognormal 1.23E+07 1.22E+07

72 1 85.011 4.427 14.707 0.184 Normal 2.07E+07 2.10E+07

73 1 82.985 4.401 16.298 0.189 Lognormal 1.48E+07 1.45E+07

74 1 87.057 4.452 15.113 0.172 Lognormal 1.71E+07 1.69E+07

75 1 84.965 4.418 18.599 0.219 Lognormal 1.54E+07 1.53E+07

76 1 84.242 4.418 15.364 0.173 Lognormal 1.45E+07 1.42E+07

77 1 87.413 4.457 14.289 0.166 Normal 1.19E+07 1.19E+07

78 1 86.227 4.437 17.646 0.199 Lognormal 1.08E+07 1.06E+07

79 1 79.399 4.361 13.595 0.160 Lognormal 7.74E+06 7.54E+06

1000 1 79.526 4.360 14.305 0.182 Lognormal 7.06E+07 7.04E+07

0 2 7.370 1.963 1.848 0.270 Normal 1.19E+07 1.23E+07

1 2 11.090 2.394 1.754 0.152 Lognormal 1.14E+07 1.09E+07

2 2 13.219 2.573 1.838 0.133 Lognormal 1.43E+07 1.36E+07

3 2 15.640 2.739 2.510 0.145 Lognormal 1.70E+07 1.56E+07

4 2 18.059 2.879 3.247 0.168 Lognormal 2.17E+07 2.06E+07

5 2 20.679 3.012 4.027 0.181 Lognormal 2.12E+07 2.02E+07

6 2 23.793 3.147 5.253 0.205 Lognormal 2.36E+07 2.26E+07

7 2 26.881 3.261 7.211 0.238 Lognormal 2.92E+07 2.75E+07

8 2 32.029 3.433 9.019 0.253 Lognormal 2.99E+07 2.84E+07

9 2 36.699 3.566 10.701 0.264 Lognormal 3.46E+07 3.30E+07

10 2 41.050 3.681 11.396 0.256 Lognormal 3.30E+07 3.17E+07

11 2 47.362 3.818 13.982 0.278 Lognormal 4.43E+07 4.29E+07

12 2 54.672 3.963 15.597 0.273 Lognormal 4.31E+07 4.20E+07

13 2 56.288 4.000 14.933 0.242 Lognormal 3.57E+07 3.44E+07

14 2 59.807 4.069 13.215 0.209 Lognormal 4.03E+07 3.92E+07

15 2 63.838 4.126 16.980 0.240 Lognormal 4.48E+07 4.30E+07

16 2 64.978 4.140 18.345 0.251 Lognormal 4.31E+07 4.12E+07

17 2 65.573 4.151 18.055 0.244 Lognormal 4.11E+07 3.92E+07

18 2 67.681 4.177 20.459 0.263 Lognormal 4.15E+07 3.94E+07

19 2 68.713 4.193 20.005 0.266 Lognormal 3.53E+07 3.40E+07

20 2 67.242 4.175 18.889 0.250 Lognormal 4.92E+07 4.70E+07

21 2 68.518 4.194 18.688 0.253 Lognormal 4.11E+07 3.98E+07

22 2 73.589 4.263 21.062 0.257 Lognormal 4.77E+07 4.57E+07

23 2 73.890 4.269 19.737 0.258 Lognormal 4.70E+07 4.61E+07

24 2 74.087 4.270 20.804 0.259 Lognormal 3.92E+07 3.79E+07

25 2 71.664 4.235 22.042 0.261 Lognormal 4.91E+07 4.63E+07

26 2 74.947 4.278 22.693 0.268 Lognormal 4.46E+07 4.26E+07

27 2 76.495 4.300 21.714 0.272 Lognormal 4.47E+07 4.37E+07

28 2 76.115 4.293 22.452 0.274 Lognormal 4.54E+07 4.40E+07

29 2 76.079 4.305 17.674 0.234 Lognormal 3.79E+07 3.77E+07

30 2 77.839 4.318 22.534 0.262 Lognormal 4.31E+07 4.14E+07

31 2 77.715 4.316 22.610 0.264 Lognormal 5.03E+07 4.84E+07

32 2 79.498 4.331 26.226 0.289 Lognormal 3.77E+07 3.59E+07

33 2 80.160 4.353 21.345 0.243 Lognormal 4.10E+07 3.96E+07

34 2 79.954 4.341 24.352 0.278 Lognormal 4.26E+07 4.11E+07

35 2 76.240 4.309 17.070 0.221 Lognormal 3.04E+07 3.01E+07

36 2 76.700 4.304 22.247 0.259 Lognormal 5.09E+07 4.88E+07

37 2 79.289 4.333 23.794 0.276 Lognormal 4.06E+07 3.92E+07

38 2 79.992 4.354 19.236 0.236 Lognormal 4.41E+07 4.36E+07
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 8

Age Gender

Mean

BW

Mean Log

BW

Std Dev

BW

Std Dev

Log BW Best

−2LL 

Normal

−2LL 

Log-Normal

39 2 76.566 4.305 21.337 0.251 Lognormal 4.81E+07 4.62E+07

40 2 76.974 4.303 23.274 0.279 Lognormal 4.61E+07 4.46E+07

41 2 76.441 4.301 21.868 0.260 Lognormal 4.68E+07 4.51E+07

42 2 76.145 4.298 20.347 0.264 Lognormal 4.63E+07 4.57E+07

43 2 76.903 4.311 20.853 0.243 Lognormal 4.84E+07 4.65E+07

44 2 75.614 4.290 22.250 0.260 Lognormal 4.77E+07 4.55E+07

45 2 75.209 4.290 20.478 0.238 Lognormal 4.98E+07 4.74E+07

46 2 79.677 4.348 21.220 0.240 Lognormal 3.92E+07 3.77E+07

47 2 80.825 4.360 21.865 0.249 Lognormal 4.76E+07 4.60E+07

48 2 78.180 4.324 21.616 0.260 Lognormal 4.68E+07 4.56E+07

49 2 78.804 4.338 19.602 0.240 Lognormal 4.61E+07 4.53E+07

50 2 79.090 4.345 18.574 0.221 Lognormal 5.30E+07 5.17E+07

51 2 77.540 4.320 20.179 0.244 Lognormal 4.67E+07 4.54E+07

52 2 73.712 4.267 20.579 0.252 Lognormal 5.12E+07 4.93E+07

53 2 77.885 4.325 19.474 0.243 Lognormal 3.77E+07 3.70E+07

54 2 81.799 4.368 23.266 0.262 Lognormal 4.49E+07 4.35E+07

55 2 81.660 4.364 23.736 0.270 Lognormal 4.30E+07 4.17E+07

56 2 78.463 4.332 19.938 0.245 Lognormal 5.21E+07 5.11E+07

57 2 77.206 4.320 19.414 0.225 Lognormal 4.11E+07 3.95E+07

58 2 82.906 4.372 25.218 0.306 Lognormal 3.27E+07 3.24E+07

59 2 75.924 4.305 17.461 0.223 Lognormal 4.32E+07 4.25E+07

60 2 80.438 4.349 23.023 0.276 Lognormal 4.03E+07 3.95E+07

61 2 81.177 4.374 17.290 0.215 Lognormal 4.17E+07 4.15E+07

62 2 81.189 4.373 18.224 0.216 Lognormal 3.11E+07 3.05E+07

63 2 74.279 4.282 17.151 0.229 Lognormal 3.96E+07 3.92E+07

64 2 78.502 4.333 20.131 0.243 Lognormal 4.00E+07 3.91E+07

65 2 74.259 4.284 16.038 0.219 Lognormal 3.21E+07 3.20E+07

66 2 76.788 4.320 15.800 0.207 Lognormal 2.52E+07 2.51E+07

67 2 77.607 4.318 20.286 0.259 Lognormal 2.64E+07 2.61E+07

68 2 71.134 4.237 17.438 0.232 Lognormal 2.51E+07 2.45E+07

69 2 74.826 4.288 16.942 0.237 Normal 2.21E+07 2.22E+07

70 2 80.651 4.361 19.520 0.243 Lognormal 2.93E+07 2.91E+07

71 2 77.613 4.318 20.636 0.259 Lognormal 2.55E+07 2.51E+07

72 2 75.780 4.295 19.888 0.254 Lognormal 2.38E+07 2.33E+07

73 2 76.332 4.307 18.416 0.234 Lognormal 2.22E+07 2.18E+07

74 2 73.923 4.280 16.136 0.216 Lognormal 2.19E+07 2.16E+07

75 2 73.693 4.276 15.862 0.222 Normal 1.45E+07 1.45E+07

76 2 77.133 4.324 16.505 0.209 Lognormal 1.55E+07 1.53E+07

77 2 73.587 4.270 18.167 0.238 Lognormal 1.30E+07 1.27E+07

78 2 72.360 4.258 16.423 0.216 Lognormal 1.29E+07 1.26E+07

79 2 69.868 4.224 15.927 0.208 Lognormal 1.45E+07 1.40E+07

1000 2 64.634 4.148 13.273 0.205 Lognormal 1.13E+08 1.12E+08

Note: Age 1000 = 80 years or older.

Table 2 below is the same as Table 1 above but for HT. In this case, the preferred distribution is 

less consistent since 64 percent of the HT cases have “Normal” for the “Best” distribution and 

36 percent of the cases have “Lognormal.” The histograms also did not show a strong 

preference for one of those two distributions. Since the results of the APEX simulations should 
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 9

not be too sensitive to the exact ages of the modeled population, it is better to use the same 

distribution for all ages and genders, which suggests that HT should be modeled as a normal 

distribution for all demographic groups. 

Table 2. Summary Statistics for HT

Age Gender

Mean

HT

Mean Log

HT

Std Dev

HT

Std Dev

Log HT Best

−2LL 

Normal

−2LL LogNormal

0 1 66.348 4.190 6.538 0.101 Normal 2.66E+07 2.68E+07

1 1 81.551 4.400 4.495 0.055 Lognormal 2.33E+07 2.32E+07

2 1 91.720 4.518 4.508 0.049 Normal 2.32E+07 2.32E+07

3 1 98.932 4.593 4.763 0.048 Normal 2.86E+07 2.86E+07

4 1 106.749 4.669 4.795 0.045 Lognormal 2.81E+07 2.81E+07

5 1 114.047 4.735 5.750 0.050 Lognormal 2.55E+07 2.54E+07

6 1 119.584 4.783 5.647 0.047 Normal 2.87E+07 2.88E+07

7 1 126.274 4.837 6.172 0.049 Normal 3.08E+07 3.08E+07

8 1 131.387 4.877 6.487 0.050 Normal 3.28E+07 3.28E+07

9 1 137.145 4.920 6.989 0.051 Lognormal 3.00E+07 2.99E+07

10 1 142.600 4.959 6.965 0.049 Normal 2.88E+07 2.89E+07

11 1 150.274 5.011 8.441 0.056 Lognormal 2.89E+07 2.88E+07

12 1 155.594 5.046 7.455 0.048 Lognormal 3.23E+07 3.23E+07

13 1 163.822 5.097 8.320 0.051 Normal 3.23E+07 3.24E+07

14 1 168.833 5.128 7.825 0.047 Normal 3.74E+07 3.75E+07

15 1 173.395 5.155 7.224 0.042 Normal 2.94E+07 2.95E+07

16 1 174.662 5.162 6.608 0.038 Normal 3.20E+07 3.21E+07

17 1 175.483 5.166 8.067 0.046 Normal 3.13E+07 3.13E+07

18 1 175.871 5.169 7.309 0.042 Normal 3.00E+07 3.00E+07

19 1 176.655 5.173 7.524 0.043 Lognormal 3.41E+07 3.41E+07

20 1 175.034 5.164 7.566 0.044 Normal 3.72E+07 3.73E+07

21 1 176.763 5.174 8.403 0.048 Normal 3.49E+07 3.50E+07

22 1 176.195 5.171 6.516 0.037 Lognormal 3.00E+07 3.00E+07

23 1 174.777 5.162 8.261 0.047 Lognormal 3.20E+07 3.19E+07

24 1 176.734 5.174 7.498 0.042 Lognormal 3.25E+07 3.24E+07

25 1 176.400 5.172 6.713 0.038 Normal 2.92E+07 2.93E+07

26 1 176.482 5.172 6.841 0.039 Normal 2.50E+07 2.51E+07

27 1 176.625 5.173 6.835 0.039 Normal 2.70E+07 2.70E+07

28 1 177.668 5.179 7.591 0.043 Normal 3.35E+07 3.35E+07

29 1 176.629 5.173 7.984 0.045 Lognormal 3.41E+07 3.40E+07

30 1 177.154 5.176 7.644 0.044 Normal 3.48E+07 3.49E+07

31 1 176.424 5.172 6.393 0.036 Normal 2.63E+07 2.63E+07

32 1 176.506 5.172 8.069 0.046 Normal 3.25E+07 3.26E+07

33 1 177.685 5.179 7.686 0.043 Lognormal 2.81E+07 2.81E+07

34 1 176.909 5.175 7.629 0.043 Normal 2.49E+07 2.49E+07

35 1 175.465 5.166 8.162 0.047 Normal 2.87E+07 2.88E+07

36 1 175.886 5.169 7.555 0.043 Normal 3.08E+07 3.08E+07

37 1 176.134 5.170 7.465 0.043 Normal 2.88E+07 2.88E+07

38 1 176.737 5.174 7.627 0.043 Normal 2.78E+07 2.78E+07

39 1 176.688 5.173 8.195 0.047 Normal 3.13E+07 3.14E+07

40 1 177.188 5.176 8.246 0.046 Lognormal 3.41E+07 3.40E+07

41 1 177.129 5.176 8.370 0.047 Normal 3.42E+07 3.43E+07

42 1 175.377 5.166 7.477 0.043 Lognormal 2.67E+07 2.67E+07

43 1 177.690 5.179 7.330 0.041 Lognormal 3.28E+07 3.28E+07
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 10

Age Gender

Mean

HT

Mean Log

HT

Std Dev

HT

Std Dev

Log HT Best

−2LL 

Normal

−2LL LogNormal

44 1 176.112 5.170 7.903 0.045 Lognormal 3.32E+07 3.31E+07

45 1 174.981 5.164 7.396 0.042 Normal 2.89E+07 2.90E+07

46 1 176.634 5.173 6.562 0.038 Normal 3.09E+07 3.10E+07

47 1 175.600 5.167 6.753 0.038 Lognormal 3.17E+07 3.17E+07

48 1 176.122 5.170 7.434 0.043 Normal 2.87E+07 2.88E+07

49 1 177.033 5.176 6.807 0.039 Normal 2.78E+07 2.79E+07

50 1 176.496 5.172 7.690 0.043 Lognormal 3.39E+07 3.38E+07

51 1 174.912 5.163 7.901 0.045 Lognormal 2.69E+07 2.69E+07

52 1 176.530 5.173 6.804 0.039 Normal 2.96E+07 2.96E+07

53 1 176.744 5.174 7.201 0.041 Lognormal 3.02E+07 3.02E+07

54 1 176.288 5.171 7.453 0.042 Normal 3.16E+07 3.16E+07

55 1 175.405 5.166 6.225 0.035 Lognormal 3.10E+07 3.10E+07

56 1 176.729 5.174 7.468 0.043 Normal 3.09E+07 3.10E+07

57 1 175.733 5.168 8.368 0.048 Normal 2.88E+07 2.89E+07

58 1 176.871 5.174 8.038 0.046 Normal 2.93E+07 2.93E+07

59 1 176.603 5.173 6.358 0.036 Normal 3.16E+07 3.17E+07

60 1 175.322 5.166 7.743 0.044 Lognormal 2.90E+07 2.89E+07

61 1 175.231 5.165 7.553 0.044 Normal 2.20E+07 2.20E+07

62 1 174.979 5.164 7.231 0.042 Normal 2.27E+07 2.28E+07

63 1 177.680 5.179 8.229 0.046 Lognormal 2.69E+07 2.69E+07

64 1 173.887 5.158 7.268 0.042 Normal 2.13E+07 2.14E+07

65 1 175.770 5.168 7.209 0.042 Normal 2.72E+07 2.73E+07

66 1 175.376 5.166 8.807 0.051 Normal 2.00E+07 2.01E+07

67 1 173.978 5.158 6.767 0.039 Lognormal 1.38E+07 1.38E+07

68 1 174.040 5.159 6.660 0.039 Normal 1.81E+07 1.82E+07

69 1 173.767 5.157 8.313 0.048 Normal 1.66E+07 1.66E+07

70 1 173.764 5.157 6.780 0.039 Normal 1.69E+07 1.69E+07

71 1 171.952 5.146 7.098 0.041 Lognormal 8.79E+06 8.75E+06

72 1 173.617 5.156 7.523 0.044 Normal 1.64E+07 1.64E+07

73 1 171.815 5.145 7.548 0.044 Normal 1.14E+07 1.14E+07

74 1 173.762 5.157 6.224 0.036 Lognormal 1.23E+07 1.22E+07

75 1 172.609 5.150 7.212 0.042 Lognormal 1.12E+07 1.12E+07

76 1 172.734 5.151 6.328 0.037 Lognormal 1.05E+07 1.05E+07

77 1 172.442 5.149 7.440 0.043 Normal 9.47E+06 9.48E+06

78 1 174.156 5.159 7.499 0.043 Normal 7.98E+06 7.98E+06

79 1 172.635 5.150 6.417 0.037 Lognormal 5.87E+06 5.86E+06

1000 1 171.292 5.143 6.915 0.041 Normal 5.32E+07 5.32E+07

0 2 64.997 4.169 6.275 0.100 Normal 2.50E+07 2.52E+07

1 2 80.615 4.388 4.947 0.062 Normal 2.25E+07 2.25E+07

2 2 89.528 4.493 4.204 0.046 Lognormal 2.50E+07 2.49E+07

3 2 98.281 4.587 4.248 0.044 Normal 2.29E+07 2.30E+07

4 2 105.404 4.657 4.857 0.046 Normal 2.69E+07 2.70E+07

5 2 112.415 4.721 5.787 0.052 Lognormal 2.53E+07 2.53E+07

6 2 118.957 4.778 5.654 0.048 Normal 2.44E+07 2.44E+07

7 2 124.658 4.824 5.843 0.047 Lognormal 2.68E+07 2.67E+07

8 2 131.786 4.880 6.950 0.052 Lognormal 2.70E+07 2.69E+07

9 2 137.722 4.924 6.500 0.047 Lognormal 2.86E+07 2.86E+07

10 2 144.426 4.971 7.298 0.050 Lognormal 2.80E+07 2.79E+07

11 2 150.574 5.013 7.670 0.052 Normal 3.58E+07 3.60E+07

12 2 156.583 5.052 7.295 0.047 Normal 3.30E+07 3.31E+07
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 11

Age Gender

Mean

HT

Mean Log

HT

Std Dev

HT

Std Dev

Log HT Best

−2LL 

Normal

−2LL LogNormal

13 2 158.923 5.068 6.149 0.039 Lognormal 2.58E+07 2.58E+07

14 2 160.849 5.080 6.429 0.040 Normal 3.09E+07 3.09E+07

15 2 161.704 5.085 6.674 0.042 Normal 3.22E+07 3.23E+07

16 2 162.002 5.087 6.219 0.038 Lognormal 2.94E+07 2.94E+07

17 2 162.805 5.092 6.661 0.041 Normal 2.95E+07 2.95E+07

18 2 162.208 5.088 6.344 0.039 Lognormal 2.77E+07 2.77E+07

19 2 163.320 5.095 6.174 0.038 Normal 2.35E+07 2.35E+07

20 2 163.411 5.095 7.485 0.046 Normal 3.59E+07 3.60E+07

21 2 161.858 5.086 6.643 0.041 Lognormal 2.87E+07 2.86E+07

22 2 162.038 5.087 6.058 0.037 Lognormal 3.09E+07 3.09E+07

23 2 161.916 5.086 7.447 0.046 Normal 3.38E+07 3.39E+07

24 2 162.774 5.091 7.195 0.044 Lognormal 2.74E+07 2.73E+07

25 2 162.763 5.092 6.405 0.039 Lognormal 3.22E+07 3.21E+07

26 2 163.198 5.094 6.312 0.039 Normal 2.90E+07 2.91E+07

27 2 163.593 5.096 7.471 0.046 Normal 3.14E+07 3.14E+07

28 2 163.380 5.095 6.569 0.040 Normal 2.99E+07 3.00E+07

29 2 162.909 5.093 5.527 0.034 Normal 2.49E+07 2.49E+07

30 2 163.515 5.096 7.695 0.047 Normal 3.03E+07 3.03E+07

31 2 164.013 5.099 6.712 0.041 Normal 3.34E+07 3.34E+07

32 2 163.674 5.097 7.194 0.044 Normal 2.48E+07 2.48E+07

33 2 163.856 5.098 6.710 0.041 Normal 2.77E+07 2.78E+07

34 2 163.344 5.095 7.496 0.046 Lognormal 2.90E+07 2.90E+07

35 2 163.531 5.096 6.544 0.041 Normal 2.17E+07 2.18E+07

36 2 163.211 5.094 7.656 0.047 Normal 3.58E+07 3.58E+07

37 2 164.099 5.100 6.902 0.043 Normal 2.69E+07 2.70E+07

38 2 162.956 5.092 7.860 0.048 Lognormal 3.27E+07 3.26E+07

39 2 162.702 5.091 7.675 0.047 Normal 3.44E+07 3.44E+07

40 2 162.678 5.091 7.397 0.045 Lognormal 3.16E+07 3.16E+07

41 2 161.638 5.085 6.643 0.041 Lognormal 3.13E+07 3.12E+07

42 2 163.154 5.094 7.131 0.043 Lognormal 3.28E+07 3.27E+07

43 2 162.756 5.091 6.773 0.042 Normal 3.30E+07 3.30E+07

44 2 162.821 5.092 6.921 0.043 Normal 3.22E+07 3.23E+07

45 2 162.737 5.092 5.720 0.035 Normal 3.19E+07 3.19E+07

46 2 162.146 5.087 7.539 0.047 Normal 2.79E+07 2.80E+07

47 2 163.495 5.096 7.326 0.045 Normal 3.31E+07 3.31E+07

48 2 163.566 5.096 6.311 0.039 Normal 3.07E+07 3.08E+07

49 2 162.858 5.092 6.338 0.039 Normal 3.11E+07 3.13E+07

50 2 162.498 5.090 6.919 0.043 Normal 3.76E+07 3.77E+07

51 2 162.610 5.091 5.990 0.037 Normal 3.06E+07 3.07E+07

52 2 161.654 5.084 7.879 0.051 Normal 3.73E+07 3.80E+07

53 2 163.379 5.095 6.657 0.041 Normal 2.60E+07 2.61E+07

54 2 162.049 5.087 7.027 0.043 Lognormal 3.02E+07 3.01E+07

55 2 162.694 5.091 6.633 0.041 Normal 2.81E+07 2.81E+07

56 2 162.638 5.091 6.787 0.041 Lognormal 3.60E+07 3.59E+07

57 2 160.512 5.077 7.084 0.044 Lognormal 2.92E+07 2.91E+07

58 2 160.963 5.080 7.017 0.044 Normal 2.15E+07 2.15E+07

59 2 160.849 5.080 6.991 0.043 Lognormal 3.15E+07 3.14E+07

60 2 161.262 5.082 6.422 0.040 Normal 2.62E+07 2.63E+07

61 2 163.010 5.093 7.148 0.044 Lognormal 3.07E+07 3.07E+07

62 2 160.395 5.077 6.512 0.041 Lognormal 2.17E+07 2.17E+07
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 12

Age Gender

Mean

HT

Mean Log

HT

Std Dev

HT

Std Dev

Log HT Best

−2LL 

Normal

−2LL LogNormal

63 2 161.629 5.084 6.589 0.041 Lognormal 2.83E+07 2.82E+07

64 2 160.269 5.076 6.028 0.038 Normal 2.69E+07 2.70E+07

65 2 161.070 5.081 6.539 0.040 Lognormal 2.33E+07 2.32E+07

66 2 159.425 5.071 5.689 0.036 Normal 1.74E+07 1.75E+07

67 2 160.241 5.076 6.903 0.043 Lognormal 1.83E+07 1.83E+07

68 2 158.931 5.067 7.056 0.045 Normal 1.82E+07 1.83E+07

69 2 159.863 5.073 6.687 0.043 Normal 1.59E+07 1.60E+07

70 2 160.263 5.076 6.986 0.044 Normal 2.07E+07 2.07E+07

71 2 159.678 5.072 7.340 0.046 Normal 1.80E+07 1.80E+07

72 2 158.699 5.066 6.225 0.039 Lognormal 1.59E+07 1.59E+07

73 2 159.618 5.072 7.187 0.045 Normal 1.61E+07 1.61E+07

74 2 159.042 5.068 6.425 0.040 Lognormal 1.57E+07 1.57E+07

75 2 158.332 5.064 7.461 0.047 Normal 1.11E+07 1.11E+07

76 2 159.769 5.073 5.740 0.036 Normal 1.05E+07 1.05E+07

77 2 158.186 5.063 5.841 0.037 Normal 8.57E+06 8.58E+06

78 2 158.001 5.062 7.098 0.045 Normal 9.55E+06 9.57E+06

79 2 158.586 5.065 7.097 0.045 Normal 1.12E+07 1.12E+07

1000 2 155.746 5.047 6.564 0.042 Normal 8.63E+07 8.64E+07

Note: Age 1000 = 80 years or older.

For an overall comparison, we calculated the values of -2LL for the entire population ages 0–79 

years by summing the values of -2LL across all ages and genders. For BW, the -2LL totals were 

5.91×109for the normal distribution and 5.75×109for the log-normal distribution—again 

supporting the log-normal distribution. For HT, the -2LL totals were 4.42×109for the normal 

distribution and 4.43×109for the log-normal distribution, which provides some small support for 

the normal distribution. The unrounded summary statistics from Table 1 and Table 2 above are 

shown in the tabs “Mean”, “Weights”, and “HTs” of the accompanying Excel file “means.2009 to 

2014.102016.xlsx”; the tab “Read Me” gives the content and formats for each tab.

To summarize these results, the recommended distributions are a normal distribution for HTs 

and a log-normal distribution for BWs. The parameters vary by age (in years) and gender. 

The same conclusion was reached by Brainard and Burmaster (1992)3. Note that in 2002, 

the CDC developed growth charts for children by fitting more complicated Box-Cox models to 

earlier NHANES data.4 The Box-Cox model uses a power of the normal distribution, which tends 

to a log-normal distribution when the power tends to zero. Those approaches would be harder 

to implement for APEX, particularly when developing joint distributions for BW and HT. 

3. Joint Distributions for BW and HT

The conclusion from Section 2 was that, for each age and gender, we should model BW by a 

log-normal distribution and HT by a normal distribution. To fit a joint distribution, it is important to 

 

3 Brainard, J., Burmaster, D.E. “Bivariate distributions for height and weight of men and women in the United States”. 

Risk Analysis 1992, 12(2) 267-275.

4

http://www.cdc.gov/growthcharts/cdc_charts.htm
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 13

realize that HT and BW are not independent. Therefore, we fit the joint distribution of HT and 

BW by assuming that the HT and the logarithm of the BW have a bivariate normal 

distribution. Table 1 and Table 2 above contain the means and standard deviations of the HT 

and the logarithm of the BW. Table 3 below contains the correlations between the HT and the 

logarithm of the BW, calculated using the survey weights. The “Mean” tab of the accompanying 

Excel file “means.2009 to 2014.102016.xlsx” contains the unrounded values of the correlation 

coefficient.

Table 3. Correlation Between Log BW and HT

Age Gender

Correlation Between Log 

BW and HT Age Gender

Correlation Between Log 

BW and HT

0 1 0.934 0 2 0.933

1 1 0.804 1 2 0.789

2 1 0.751 2 2 0.765

3 1 0.742 3 2 0.733

4 1 0.755 4 2 0.761

5 1 0.741 5 2 0.744

6 1 0.758 6 2 0.734

7 1 0.706 7 2 0.753

8 1 0.768 8 2 0.720

9 1 0.721 9 2 0.676

10 1 0.685 10 2 0.729

11 1 0.697 11 2 0.606

12 1 0.671 12 2 0.558

13 1 0.563 13 2 0.391

14 1 0.585 14 2 0.344

15 1 0.485 15 2 0.461

16 1 0.430 16 2 0.364

17 1 0.416 17 2 0.359

18 1 0.451 18 2 0.228

19 1 0.312 19 2 0.227

20 1 0.504 20 2 0.294

21 1 0.426 21 2 0.397

22 1 0.299 22 2 0.086

23 1 0.423 23 2 0.294

24 1 0.391 24 2 0.236

25 1 0.388 25 2 0.288

26 1 0.396 26 2 0.325

27 1 0.515 27 2 0.356

28 1 0.337 28 2 0.354

29 1 0.174 29 2 0.269

30 1 0.597 30 2 0.269

31 1 0.298 31 2 0.212

32 1 0.482 32 2 0.248

33 1 0.528 33 2 0.269

34 1 0.292 34 2 0.283

35 1 0.279 35 2 0.200

36 1 0.519 36 2 0.362

37 1 0.434 37 2 0.391

38 1 0.453 38 2 0.328

39 1 0.373 39 2 0.396
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 14

Age Gender

Correlation Between Log 

BW and HT Age Gender

Correlation Between Log 

BW and HT

40 1 0.546 40 2 0.302

41 1 0.357 41 2 0.367

42 1 0.339 42 2 0.300

43 1 0.367 43 2 0.233

44 1 0.470 44 2 0.301

45 1 0.453 45 2 0.240

46 1 0.227 46 2 0.245

47 1 0.405 47 2 0.254

48 1 0.357 48 2 0.042

49 1 0.496 49 2 0.262

50 1 0.590 50 2 0.248

51 1 0.534 51 2 0.167

52 1 0.338 52 2 0.347

53 1 0.510 53 2 0.260

54 1 0.441 54 2 0.235

55 1 0.363 55 2 0.178

56 1 0.292 56 2 0.115

57 1 0.437 57 2 0.301

58 1 0.324 58 2 0.287

59 1 0.472 59 2 0.266

60 1 0.380 60 2 0.414

61 1 0.387 61 2 0.380

62 1 0.475 62 2 0.266

63 1 0.520 63 2 0.310

64 1 0.534 64 2 0.248

65 1 0.372 65 2 0.240

66 1 0.408 66 2 0.331

67 1 0.627 67 2 0.351

68 1 0.490 68 2 0.300

69 1 0.510 69 2 0.287

70 1 0.434 70 2 0.257

71 1 0.413 71 2 0.275

72 1 0.527 72 2 0.262

73 1 0.578 73 2 0.302

74 1 0.220 74 2 0.237

75 1 0.503 75 2 0.083

76 1 0.161 76 2 0.297

77 1 0.400 77 2 0.248

78 1 0.524 78 2 0.292

79 1 0.195 79 2 0.461

1000 1 0.491 1000 2 0.419

Note: Age 1000 = 80 years or older.

Figure 3-1 below illustrates the fitted joint distributions for selected ages (5, 15, 25, 40, 60, and 

79 years) and both genders. Each data point shows the HT and the logarithm of the BW for a 

single NHANES subject. The red prediction ellipse includes 95 percent of the fitted joint 

distribution (which is not necessarily 95 percent of the sampled data). The blue prediction ellipse 

includes 80 percent of the fitted joint distribution (which is not necessarily 80 percent of the 
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 15

sampled data). The ellipses and correlations were computed using the survey weights, even 

though there is only a single point shown for each NHANES subject. The elliptical shapes of the 

scatter plot data support the use of a bivariate normal distribution with a non-zero correlation. A 

zero correlation would imply that HT and BW are independent. We provide larger versions of the 

plots in Figure 3-1 in Attachment C.
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 16

Figure 3-1. Scatter Plots of Log BW versus HT, Years 2009–2014

100 110 120 130

Standing Height (cm)

2.75

3.00

3.25

3.50

3.75

logweight

Correlation 0.7408 Observations 295

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=5 Gender=1

90 100 110 120 130

Standing Height (cm)

2.50

2.75

3.00

3.25

3.50

logweight

Correlation 0.7436 Observations 279

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=5 Gender=2

150 160 170 180 190

Standing Height (cm)

3.75

4.00

4.25

4.50

4.75

5.00

logweight

Correlation 0.4851 Observations 237

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=15 Gender=1

140 150 160 170 180

Standing Height (cm)

3.75

4.00

4.25

4.50

4.75

logweight

Correlation 0.4608 Observations 223

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=15 Gender=2

160 170 180 190

Standing Height (cm)

4.00

4.25

4.50

4.75

5.00

logweight

Correlation 0.3881 Observations 131

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=25 Gender=1

150 160 170 180

Standing Height (cm)

4.0

4.5

5.0

logweight

Correlation 0.2878 Observations 152

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=25 Gender=2

160 170 180 190

Standing Height (cm)

4.00

4.25

4.50

4.75

5.00

5.25

logweight

Correlation 0.5456 Observations 159

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=40 Gender=1

150 160 170 180

Standing Height (cm)

4.0

4.5

5.0

logweight

Correlation 0.3016 Observations 165

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=40 Gender=2

160 170 180 190 200

Standing Height (cm)

4.00

4.25

4.50

4.75

5.00

logweight

Correlation 0.3803 Observations 174

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=60 Gender=1

140 150 160 170

Standing Height (cm)

3.5

4.0

4.5

5.0

logweight

Correlation 0.4144 Observations 182

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=60 Gender=2

160 170 180 190

Standing Height (cm)

4.0

4.2

4.4

4.6

4.8

logweight

Correlation 0.1948 Observations 51

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=79 Gender=1

140 150 160 170

Standing Height (cm)

3.8

4.0

4.2

4.4

4.6

4.8

logweight

Correlation 0.4614 Observations 63

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=79 Gender=2

height (cm) height (cm)

90 100 110 120 130

Standing Height (cm)

2.50

2.75

3.00

3.25

3.50

lo

g

w

e

ig

ht

Correlation 0.7436

Observations 279

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=5 Gender=2

gender = 1 gender = 2 age = 5 age = 15 age = 25 age = 40 age = 60 age = 79
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 17

4. Smoothing the Parameters

4.1.Smooth Parameters Using Natural Cubic Spline

The last step for fitting the joint distributions of BW and HT is to smooth the parameter values 

to make them continuous functions of the age rather than varying discontinuously. 

Otherwise, a small change in the age of one of the simulated persons can lead to a large 

change in the simulated distribution of that person’s HT and BW and thus other exposure 

parameters. The five parameters for each age and gender are

 mean log BW, 

 standard deviation log BW, 

 mean HT, 

 standard deviation HT, and 

 correlation. 

Figure 4-1 below illustrates how the five parameters vary by age for the same gender. Also 

shown are the smoothed curves created with a natural cubic spline, without applying any 

weighting. For each parameter, we chose the same set of eight knots for the spline function: 0, 

10, 20, 30, 40, 50, 60, and 70. Between each two consecutive knots, we fitted a cubic 

polynomial so that the curve and its first two derivatives are continuous at the knot. For values 

above 70, we fitted a straight line so that the curve and its first derivative are continuous at 70. 

(A similar linear curve applies below zero but those values are not needed since age cannot be 

negative). The straight line fitted to ages 70 and above is used to extrapolate the 

parameter values up to age 100. We provide larger versions of the plots in Figure 4-1 in 

Attachment D.
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 18

Figure 4-1. Unsmoothed and Smoothed Values for the Five Joint-distribution Parameters, Years 

2009–2014

4.2.Final Parameter Values

Table 4 below, and the tab “Parameters” of the accompanying Excel file “means.2009 to 

2014.102016.xlsx”, contain the unsmoothed and smoothed parameter values. The values in the 

Excel file should be used for APEX implementation, as they are provided with full numeric 

precision there.

Cubic spline of mean of log body weight (kg) versus age

NHANES 2009 to 2014

the mean, logweight Linear Predictor

AGE

Gender = 1 Gender = 2

0 20 40 60 80 100 0 20 40 60 80 100

2

3

4

the mean, logweight

Cubic spline of mean of height (cm) versus age

NHANES 2009 to 2014

the mean, BMXHT Linear Predictor

AGE

Gender = 1 Gender = 2

0 20 40 60 80 100 0 20 40 60 80 100

75

100

125

150

175

the mean, BMXHT

Cubic spline of standard deviation of log body weight (kg) versus age

NHANES 2009 to 2014

the standard deviation, logweight Linear Predictor

AGE

Gender = 1 Gender = 2

0 20 40 60 80 100 0 20 40 60 80 100

0.15

0.20

0.25

0.30

the standard deviation, logweight

Cubic spline of standard deviation of height (cm) versus age

NHANES 2009 to 2014

the standard deviation, BMXHT Linear Predictor

AGE

Gender = 1 Gender = 2

0 20 40 60 80 100 0 20 40 60 80 100

4

5

6

7

8

9

the standard deviation, BMXHT

Cubic spline of correlation between log body weight and height versus age

NHANES 2009 to 2014

corr Linear Predictor

AGE

Gender = 1 Gender = 2

0 20 40 60 80 100 0 20 40 60 80 100

0.0

0.2

0.4

0.6

0.8

corr
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 19

For simulating the joint distribution of BW and HT in APEX, we propose the following 

approach. 

First, simulate the values of log BW from a normal distribution. We show the mean and 

standard deviation of the log BW for each age and gender in the “SMOOTHED” columns of 

Table 4. Truncate the distribution at the lower and upper bounds as shown in the “BOUNDS 

FOR LOG BW” columns, which we calculated as

BOUNDS FOR LOG BW = Mean Log BW ± (z0.99 × Std Dev Log BW).

z0.99 is the 99th percentile of a standard normal distribution. Resampling should be done, so 

that a new value should be selected if the simulated value is outside these bounds. Thus, the 

probability of being outside these two bounds is 0.02. Let w be the simulated value of log BW.

Second, simulate the values of HT from the conditional distribution of HT given that the 

log of the BW is w. The simulated value of HT is

Simulated HT = mh + (sh × corr ×

w−mw

sw ) + (sh × √1 − corr2 × z),

where

mh = Mean HT,

sh = Std Dev HT,

corr = Correlation coefficient (between log BW and HT),

w = Simulated log BW,

mw = Mean Log BW,

sw = Std Dev Log BW, and

z = Simulated and truncated standard normal variate.

The z-score “z” is randomly generated from a standard normal distribution. Analogously to the 

truncation of the BW distribution, z should be resampled if its absolute value is greater than 

z0.99.
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 20

Table 4. Unsmoothed and Smoothed Parameter Values

UNSMOOTHED SMOOTHED

BOUNDS FOR 

LOG BW

Age Gender Mean HT Mean Log BW Std Dev HT

Std Dev

Log BW

Correlation Mean HT Mean Log BW Std Dev HTStd Dev

Log BW

CorrelationLower 

Bound

Upper 

Bound

0 1 66.348 2.024 6.538 0.261 0.934 71.149 2.189 4.541 0.144 0.827 1.855 2.524

1 1 81.551 2.429 4.495 0.126 0.804 79.700 2.362 4.830 0.156 0.817 1.999 2.725

2 1 91.720 2.640 4.508 0.126 0.751 88.191 2.533 5.117 0.168 0.807 2.141 2.924

3 1 98.932 2.773 4.763 0.139 0.742 96.564 2.701 5.399 0.180 0.795 2.283 3.120

4 1 106.749 2.915 4.795 0.157 0.755 104.761 2.867 5.673 0.191 0.783 2.421 3.312

5 1 114.047 3.045 5.750 0.172 0.741 112.722 3.027 5.937 0.202 0.770 2.557 3.498

6 1 119.584 3.149 5.647 0.191 0.758 120.388 3.182 6.188 0.212 0.755 2.689 3.676

7 1 126.274 3.298 6.172 0.234 0.706 127.701 3.330 6.424 0.221 0.738 2.816 3.845

8 1 131.387 3.407 6.487 0.241 0.768 134.601 3.470 6.642 0.229 0.719 2.937 4.004

9 1 137.145 3.513 6.989 0.249 0.721 141.030 3.601 6.840 0.236 0.698 3.052 4.150

10 1 142.600 3.656 6.965 0.263 0.685 146.928 3.721 7.014 0.241 0.673 3.160 4.283

11 1 150.274 3.832 8.441 0.280 0.697 152.251 3.831 7.164 0.245 0.646 3.260 4.401

12 1 155.594 3.894 7.455 0.252 0.671 157.006 3.929 7.290 0.248 0.616 3.352 4.505

13 1 163.822 4.060 8.320 0.256 0.563 161.217 4.016 7.393 0.249 0.585 3.437 4.596

14 1 168.833 4.143 7.825 0.259 0.585 164.906 4.094 7.474 0.250 0.553 3.514 4.675

15 1 173.395 4.234 7.224 0.255 0.485 168.094 4.162 7.535 0.249 0.521 3.583 4.741

16 1 174.662 4.289 6.608 0.226 0.430 170.804 4.222 7.578 0.248 0.491 3.646 4.798

17 1 175.483 4.317 8.067 0.235 0.416 173.059 4.272 7.604 0.246 0.462 3.701 4.844

18 1 175.871 4.363 7.309 0.248 0.451 174.881 4.315 7.613 0.243 0.437 3.749 4.882

19 1 176.655 4.350 7.524 0.229 0.312 176.292 4.350 7.608 0.241 0.415 3.790 4.911

20 1 175.034 4.341 7.566 0.246 0.504 177.314 4.379 7.590 0.238 0.398 3.824 4.933

21 1 176.763 4.342 8.403 0.231 0.426 177.974 4.401 7.561 0.236 0.385 3.852 4.950

22 1 176.195 4.368 6.516 0.224 0.299 178.320 4.417 7.523 0.233 0.378 3.874 4.960

23 1 174.777 4.418 8.261 0.269 0.423 178.401 4.429 7.481 0.231 0.375 3.891 4.967

24 1 176.734 4.396 7.498 0.192 0.391 178.270 4.437 7.437 0.229 0.375 3.904 4.969

25 1 176.400 4.422 6.713 0.248 0.388 177.977 4.441 7.395 0.227 0.377 3.914 4.969

26 1 176.482 4.437 6.841 0.213 0.396 177.575 4.444 7.359 0.225 0.382 3.921 4.967

27 1 176.625 4.433 6.835 0.207 0.515 177.113 4.445 7.333 0.223 0.388 3.926 4.964

28 1 177.668 4.459 7.591 0.230 0.337 176.643 4.446 7.319 0.222 0.394 3.930 4.961

29 1 176.629 4.467 7.984 0.215 0.174 176.217 4.446 7.322 0.220 0.401 3.934 4.959

30 1 177.154 4.458 7.644 0.233 0.597 175.885 4.449 7.344 0.219 0.407 3.939 4.958

31 1 176.424 4.465 6.393 0.211 0.298 175.688 4.452 7.388 0.218 0.411 3.946 4.959
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 21

UNSMOOTHED SMOOTHED

BOUNDS FOR 

LOG BW

32 1 176.506 4.486 8.069 0.230 0.482 175.614 4.458 7.450 0.217 0.414 3.953 4.963

33 1 177.685 4.500 7.686 0.208 0.528 175.643 4.465 7.523 0.216 0.416 3.962 4.967

34 1 176.909 4.451 7.629 0.194 0.292 175.752 4.472 7.603 0.215 0.418 3.971 4.973

35 1 175.465 4.461 8.162 0.228 0.279 175.920 4.480 7.683 0.215 0.418 3.980 4.979

36 1 175.886 4.498 7.555 0.240 0.519 176.124 4.487 7.757 0.214 0.417 3.990 4.985

37 1 176.134 4.512 7.465 0.204 0.434 176.344 4.495 7.821 0.213 0.416 3.999 4.990

38 1 176.737 4.514 7.627 0.210 0.453 176.556 4.501 7.867 0.212 0.415 4.008 4.994

39 1 176.688 4.483 8.195 0.219 0.373 176.740 4.506 7.891 0.211 0.413 4.015 4.996

40 1 177.188 4.504 8.246 0.212 0.546 176.874 4.509 7.886 0.209 0.411 4.022 4.996

41 1 177.129 4.522 8.370 0.218 0.357 176.941 4.510 7.850 0.208 0.410 4.027 4.993

42 1 175.377 4.491 7.477 0.189 0.339 176.945 4.509 7.786 0.206 0.408 4.030 4.988

43 1 177.690 4.510 7.330 0.192 0.367 176.899 4.507 7.701 0.204 0.407 4.033 4.982

44 1 176.112 4.512 7.903 0.202 0.470 176.813 4.504 7.602 0.202 0.406 4.034 4.974

45 1 174.981 4.463 7.396 0.182 0.453 176.697 4.500 7.496 0.200 0.405 4.034 4.966

46 1 176.634 4.485 6.562 0.200 0.227 176.561 4.495 7.389 0.199 0.405 4.033 4.958

47 1 175.600 4.493 6.753 0.180 0.405 176.417 4.491 7.288 0.198 0.406 4.031 4.951

48 1 176.122 4.482 7.434 0.221 0.357 176.276 4.487 7.199 0.197 0.407 4.029 4.945

49 1 177.033 4.488 6.807 0.208 0.496 176.147 4.483 7.130 0.197 0.409 4.025 4.941

50 1 176.496 4.524 7.690 0.199 0.590 176.042 4.481 7.087 0.197 0.412 4.022 4.940

51 1 174.912 4.432 7.901 0.221 0.534 175.968 4.480 7.074 0.199 0.416 4.018 4.942

52 1 176.530 4.501 6.804 0.205 0.338 175.922 4.480 7.089 0.200 0.421 4.013 4.946

53 1 176.744 4.479 7.201 0.215 0.510 175.897 4.480 7.126 0.203 0.427 4.009 4.952

54 1 176.288 4.521 7.453 0.204 0.441 175.887 4.482 7.180 0.205 0.433 4.004 4.959

55 1 175.405 4.483 6.225 0.203 0.363 175.885 4.484 7.246 0.208 0.439 4.000 4.967

56 1 176.729 4.481 7.468 0.192 0.292 175.885 4.486 7.319 0.211 0.444 3.996 4.976

57 1 175.733 4.474 8.368 0.190 0.437 175.880 4.488 7.393 0.213 0.449 3.992 4.984

58 1 176.871 4.474 8.038 0.195 0.324 175.865 4.489 7.463 0.216 0.454 3.988 4.991

59 1 176.603 4.491 6.358 0.217 0.472 175.831 4.490 7.524 0.217 0.457 3.985 4.996

60 1 175.322 4.488 7.743 0.206 0.380 175.774 4.491 7.571 0.219 0.460 3.982 4.999

61 1 175.231 4.506 7.553 0.233 0.387 175.688 4.490 7.600 0.219 0.461 3.980 5.000

62 1 174.979 4.487 7.231 0.192 0.475 175.574 4.488 7.612 0.219 0.460 3.979 4.998

63 1 177.680 4.486 8.229 0.234 0.520 175.436 4.486 7.608 0.218 0.459 3.978 4.993

64 1 173.887 4.467 7.268 0.230 0.534 175.277 4.482 7.591 0.217 0.456 3.977 4.987

65 1 175.770 4.493 7.209 0.229 0.372 175.099 4.478 7.561 0.215 0.453 3.978 4.979

66 1 175.376 4.471 8.807 0.210 0.408 174.906 4.474 7.523 0.213 0.448 3.978 4.970
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 22

UNSMOOTHED SMOOTHED

BOUNDS FOR 

LOG BW

67 1 173.978 4.482 6.767 0.207 0.627 174.701 4.469 7.476 0.211 0.444 3.979 4.959

68 1 174.040 4.447 6.660 0.256 0.490 174.487 4.464 7.424 0.208 0.438 3.980 4.948

69 1 173.767 4.461 8.313 0.204 0.510 174.267 4.458 7.368 0.205 0.433 3.981 4.936

70 1 173.764 4.465 6.780 0.190 0.434 174.043 4.453 7.310 0.203 0.427 3.982 4.924

71 1 171.952 4.442 7.098 0.218 0.413 173.819 4.447 7.252 0.200 0.421 3.983 4.912

72 1 173.617 4.427 7.523 0.184 0.527 173.595 4.442 7.193 0.197 0.416 3.984 4.900

73 1 171.815 4.401 7.548 0.189 0.578 173.371 4.436 7.135 0.194 0.410 3.985 4.888

74 1 173.762 4.452 6.224 0.172 0.220 173.148 4.431 7.076 0.191 0.404 3.986 4.875

75 1 172.609 4.418 7.212 0.219 0.503 172.924 4.425 7.018 0.188 0.399 3.987 4.863

76 1 172.734 4.418 6.328 0.173 0.161 172.700 4.420 6.960 0.185 0.393 3.989 4.851

77 1 172.442 4.457 7.440 0.166 0.400 172.476 4.414 6.901 0.183 0.387 3.990 4.839

78 1 174.156 4.437 7.499 0.199 0.524 172.252 4.409 6.843 0.180 0.381 3.991 4.827

79 1 172.635 4.361 6.417 0.160 0.195 172.028 4.403 6.785 0.177 0.376 3.992 4.814

80 1 171.804 4.398 6.726 0.174 0.370 3.993 4.802

81 1 171.580 4.392 6.668 0.171 0.364 3.994 4.790

82 1 171.357 4.387 6.610 0.168 0.359 3.995 4.778

83 1 171.133 4.381 6.551 0.165 0.353 3.996 4.766

84 1 170.909 4.376 6.493 0.162 0.347 3.998 4.754

85 1 170.685 4.370 6.434 0.160 0.341 3.999 4.741

86 1 170.461 4.365 6.376 0.157 0.336 4.000 4.729

87 1 170.237 4.359 6.318 0.154 0.330 4.001 4.717

88 1 170.013 4.353 6.259 0.151 0.324 4.002 4.705

89 1 169.789 4.348 6.201 0.148 0.319 4.003 4.693

90 1 169.565 4.342 6.143 0.145 0.313 4.004 4.680

91 1 169.342 4.337 6.084 0.142 0.307 4.006 4.668

92 1 169.118 4.331 6.026 0.140 0.301 4.007 4.656

93 1 168.894 4.326 5.968 0.137 0.296 4.008 4.644

94 1 168.670 4.320 5.909 0.134 0.290 4.009 4.632

95 1 168.446 4.315 5.851 0.131 0.284 4.010 4.620

96 1 168.222 4.309 5.792 0.128 0.279 4.011 4.607

97 1 167.998 4.304 5.734 0.125 0.273 4.012 4.595

98 1 167.774 4.298 5.676 0.122 0.267 4.013 4.583

99 1 167.550 4.293 5.617 0.120 0.262 4.015 4.571

100 1 167.327 4.287 5.559 0.117 0.256 4.016 4.559

0 2 64.997 1.963 6.275 0.270 0.933 68.702 2.113 4.597 0.164 0.848 1.731 2.495
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 23

UNSMOOTHED SMOOTHED

BOUNDS FOR 

LOG BW

1 2 80.615 2.394 4.947 0.152 0.789 77.867 2.301 4.849 0.173 0.831 1.898 2.705

2 2 89.528 2.573 4.204 0.133 0.765 86.943 2.488 5.097 0.183 0.813 2.063 2.912

3 2 98.281 2.739 4.248 0.145 0.733 95.842 2.671 5.339 0.192 0.794 2.225 3.116

4 2 105.404 2.879 4.857 0.168 0.761 104.476 2.849 5.571 0.200 0.775 2.383 3.314

5 2 112.415 3.012 5.787 0.181 0.744 112.756 3.020 5.790 0.208 0.754 2.535 3.505

6 2 118.957 3.147 5.654 0.205 0.734 120.594 3.184 5.993 0.216 0.731 2.681 3.686

7 2 124.658 3.261 5.843 0.238 0.753 127.901 3.337 6.177 0.223 0.706 2.818 3.857

8 2 131.786 3.433 6.950 0.253 0.720 134.589 3.479 6.338 0.230 0.679 2.944 4.014

9 2 137.722 3.566 6.500 0.264 0.676 140.569 3.608 6.474 0.236 0.649 3.060 4.156

10 2 144.426 3.681 7.298 0.256 0.729 145.754 3.722 6.581 0.240 0.616 3.163 4.281

11 2 150.574 3.818 7.670 0.278 0.606 150.083 3.820 6.657 0.244 0.580 3.252 4.389

12 2 156.583 3.963 7.295 0.273 0.558 153.611 3.904 6.705 0.247 0.541 3.328 4.479

13 2 158.923 4.000 6.149 0.242 0.391 156.424 3.974 6.730 0.250 0.501 3.393 4.555

14 2 160.849 4.069 6.429 0.209 0.344 158.606 4.032 6.737 0.251 0.460 3.447 4.617

15 2 161.704 4.126 6.674 0.240 0.461 160.241 4.079 6.728 0.253 0.420 3.491 4.667

16 2 162.002 4.140 6.219 0.251 0.364 161.413 4.118 6.710 0.254 0.382 3.528 4.708

17 2 162.805 4.151 6.661 0.244 0.359 162.208 4.149 6.687 0.254 0.347 3.558 4.740

18 2 162.208 4.177 6.344 0.263 0.228 162.709 4.174 6.662 0.255 0.315 3.582 4.766

19 2 163.320 4.193 6.174 0.266 0.227 163.000 4.195 6.640 0.255 0.288 3.602 4.788

20 2 163.411 4.175 7.485 0.250 0.294 163.167 4.213 6.626 0.255 0.266 3.619 4.807

21 2 161.858 4.194 6.643 0.253 0.397 163.281 4.229 6.624 0.256 0.252 3.634 4.825

22 2 162.038 4.263 6.058 0.257 0.086 163.358 4.244 6.632 0.257 0.243 3.647 4.842

23 2 161.916 4.269 7.447 0.258 0.294 163.405 4.258 6.649 0.258 0.239 3.658 4.857

24 2 162.774 4.270 7.195 0.259 0.236 163.425 4.270 6.675 0.259 0.240 3.667 4.872

25 2 162.763 4.235 6.405 0.261 0.288 163.423 4.280 6.707 0.260 0.244 3.676 4.885

26 2 163.198 4.278 6.312 0.268 0.325 163.404 4.289 6.744 0.261 0.251 3.683 4.896

27 2 163.593 4.300 7.471 0.272 0.356 163.372 4.297 6.786 0.262 0.260 3.689 4.906

28 2 163.380 4.293 6.569 0.274 0.354 163.332 4.304 6.829 0.262 0.271 3.694 4.914

29 2 162.909 4.305 5.527 0.234 0.269 163.288 4.309 6.874 0.263 0.281 3.698 4.920

30 2 163.515 4.318 7.695 0.262 0.269 163.246 4.314 6.919 0.263 0.292 3.702 4.925

31 2 164.013 4.316 6.712 0.264 0.212 163.208 4.316 6.962 0.263 0.301 3.705 4.927

32 2 163.674 4.331 7.194 0.289 0.248 163.176 4.318 7.002 0.262 0.309 3.708 4.928

33 2 163.856 4.353 6.710 0.243 0.269 163.148 4.319 7.039 0.262 0.315 3.711 4.928

34 2 163.344 4.341 7.496 0.278 0.283 163.124 4.319 7.072 0.261 0.320 3.713 4.926

35 2 163.531 4.309 6.544 0.221 0.200 163.103 4.319 7.100 0.260 0.323 3.715 4.923
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 24

UNSMOOTHED SMOOTHED

BOUNDS FOR 

LOG BW

36 2 163.211 4.304 7.656 0.259 0.362 163.085 4.318 7.122 0.259 0.325 3.717 4.920

37 2 164.099 4.333 6.902 0.276 0.391 163.070 4.317 7.137 0.257 0.324 3.719 4.916

38 2 162.956 4.354 7.860 0.236 0.328 163.056 4.316 7.145 0.256 0.322 3.720 4.913

39 2 162.702 4.305 7.675 0.251 0.396 163.043 4.316 7.144 0.255 0.318 3.722 4.909

40 2 162.678 4.303 7.397 0.279 0.302 163.031 4.315 7.134 0.254 0.311 3.724 4.906

41 2 161.638 4.301 6.643 0.260 0.367 163.018 4.315 7.114 0.253 0.302 3.726 4.904

42 2 163.154 4.298 7.131 0.264 0.300 163.004 4.315 7.085 0.252 0.291 3.729 4.902

43 2 162.756 4.311 6.773 0.243 0.233 162.987 4.316 7.050 0.251 0.280 3.731 4.901

44 2 162.821 4.290 6.921 0.260 0.301 162.965 4.317 7.010 0.251 0.267 3.734 4.901

45 2 162.737 4.290 5.720 0.238 0.240 162.937 4.319 6.967 0.250 0.255 3.736 4.901

46 2 162.146 4.348 7.539 0.240 0.245 162.902 4.320 6.923 0.250 0.243 3.739 4.901

47 2 163.495 4.360 7.326 0.249 0.254 162.858 4.322 6.879 0.249 0.233 3.742 4.902

48 2 163.566 4.324 6.311 0.260 0.042 162.803 4.324 6.837 0.249 0.224 3.745 4.903

49 2 162.858 4.338 6.338 0.240 0.262 162.737 4.326 6.800 0.249 0.218 3.748 4.904

50 2 162.498 4.345 6.919 0.221 0.248 162.657 4.328 6.768 0.248 0.215 3.750 4.906

51 2 162.610 4.320 5.990 0.244 0.167 162.563 4.330 6.743 0.248 0.215 3.753 4.907

52 2 161.654 4.267 7.879 0.252 0.347 162.456 4.332 6.725 0.248 0.218 3.756 4.908

53 2 163.379 4.325 6.657 0.243 0.260 162.336 4.334 6.713 0.247 0.223 3.758 4.909

54 2 162.049 4.368 7.027 0.262 0.235 162.207 4.335 6.706 0.247 0.231 3.761 4.910

55 2 162.694 4.364 6.633 0.270 0.178 162.068 4.337 6.703 0.247 0.240 3.763 4.911

56 2 162.638 4.332 6.787 0.245 0.115 161.922 4.338 6.703 0.246 0.249 3.764 4.911

57 2 160.512 4.320 7.084 0.225 0.301 161.770 4.338 6.705 0.246 0.259 3.766 4.910

58 2 160.963 4.372 7.017 0.306 0.287 161.613 4.338 6.708 0.245 0.269 3.767 4.909

59 2 160.849 4.305 6.991 0.223 0.266 161.454 4.338 6.712 0.245 0.278 3.768 4.907

60 2 161.262 4.349 6.422 0.276 0.414 161.293 4.337 6.716 0.244 0.286 3.769 4.905

61 2 163.010 4.374 7.148 0.215 0.380 161.131 4.335 6.718 0.243 0.292 3.769 4.901

62 2 160.395 4.373 6.512 0.216 0.266 160.970 4.333 6.719 0.242 0.296 3.769 4.897

63 2 161.629 4.282 6.589 0.229 0.310 160.808 4.330 6.719 0.241 0.299 3.769 4.892

64 2 160.269 4.333 6.028 0.243 0.248 160.647 4.327 6.718 0.240 0.300 3.768 4.886

65 2 161.070 4.284 6.539 0.219 0.240 160.485 4.324 6.716 0.239 0.301 3.768 4.880

66 2 159.425 4.320 5.689 0.207 0.331 160.324 4.320 6.713 0.238 0.300 3.766 4.873

67 2 160.241 4.318 6.903 0.259 0.351 160.163 4.316 6.710 0.237 0.299 3.765 4.866

68 2 158.931 4.237 7.056 0.232 0.300 160.001 4.311 6.707 0.235 0.297 3.764 4.858

69 2 159.863 4.288 6.687 0.237 0.287 159.839 4.307 6.703 0.234 0.295 3.763 4.851

70 2 160.263 4.361 6.986 0.243 0.257 159.678 4.302 6.699 0.233 0.292 3.761 4.843
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 25

UNSMOOTHED SMOOTHED

BOUNDS FOR 

LOG BW

71 2 159.678 4.318 7.340 0.259 0.275 159.516 4.298 6.695 0.231 0.290 3.760 4.836

72 2 158.699 4.295 6.225 0.254 0.262 159.355 4.293 6.691 0.230 0.287 3.758 4.828

73 2 159.618 4.307 7.187 0.234 0.302 159.193 4.288 6.687 0.229 0.285 3.757 4.820

74 2 159.042 4.280 6.425 0.216 0.237 159.032 4.284 6.683 0.227 0.282 3.755 4.812

75 2 158.332 4.276 7.461 0.222 0.083 158.870 4.279 6.679 0.226 0.280 3.754 4.805

76 2 159.769 4.324 5.740 0.209 0.297 158.709 4.275 6.675 0.225 0.277 3.752 4.797

77 2 158.186 4.270 5.841 0.238 0.248 158.547 4.270 6.671 0.223 0.275 3.751 4.789

78 2 158.001 4.258 7.098 0.216 0.292 158.386 4.266 6.667 0.222 0.272 3.750 4.782

79 2 158.586 4.224 7.097 0.208 0.461 158.224 4.261 6.663 0.220 0.270 3.748 4.774

80 2 158.063 4.257 6.659 0.219 0.267 3.747 4.766

81 2 157.901 4.252 6.655 0.218 0.265 3.745 4.759

82 2 157.740 4.247 6.651 0.216 0.262 3.744 4.751

83 2 157.578 4.243 6.648 0.215 0.260 3.742 4.743

84 2 157.417 4.238 6.644 0.214 0.257 3.741 4.736

85 2 157.255 4.234 6.640 0.212 0.255 3.739 4.728

86 2 157.094 4.229 6.636 0.211 0.252 3.738 4.720

87 2 156.932 4.225 6.632 0.210 0.250 3.737 4.712

88 2 156.771 4.220 6.628 0.208 0.247 3.735 4.705

89 2 156.609 4.215 6.624 0.207 0.245 3.734 4.697

90 2 156.448 4.211 6.620 0.206 0.242 3.732 4.689

91 2 156.286 4.206 6.616 0.204 0.240 3.731 4.682

92 2 156.125 4.202 6.612 0.203 0.237 3.729 4.674

93 2 155.963 4.197 6.608 0.202 0.235 3.728 4.666

94 2 155.802 4.193 6.604 0.200 0.232 3.727 4.659

95 2 155.640 4.188 6.600 0.199 0.230 3.725 4.651

96 2 155.479 4.183 6.596 0.198 0.227 3.724 4.643

97 2 155.317 4.179 6.592 0.196 0.225 3.722 4.636

98 2 155.156 4.174 6.588 0.195 0.222 3.721 4.628

99 2 154.994 4.170 6.584 0.194 0.220 3.719 4.620

100 2 154.833 4.165 6.580 0.192 0.217 3.718 4.613
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 26

5. Comparison between 2009–2014 and 2003–2014

The fitted models for 2009–2014 are contained in Table 4 and in the tab “Parameters” of the

accompanying Excel file “means.2009 to 2014.102016.xlsx”. We give unsmoothed and 

smoothed parameters for each age and gender. Using the same approach, the fitted 

parameters for 2003–2014 are contained in the tab “Parameters” of the accompanying Excel file 

“means.2003 to 2014.102016.xlsx”. 

The following Table 5 contains a comparison of the parameters between the two sets of years.

The differences and percentage differences are relative to the baseline of 2003–2014:

Difference = Value for 2009–2014 - Value for 2003–2014

Percentage Difference = Difference / Value for 2003–2014 × 100

The tabulated means and maxima are for each gender across all ages 0–79 years, for both the 

unsmoothed and smoothed parameters.

The mean differences are between -0.14 and 0.07 across all parameters, so there is only a 

small trend in the parameters. (Note that the two periods overlap, but any difference between 

the overlapping periods implies a difference between 2003–2008 and 2009–2014.)

The differences are small for the mean parameters: the maximum unsigned percentage 

differences are at most 1.7 percent for the unsmoothed mean parameters and at most 0.6 

percent for the smoothed mean parameters. 

The differences are much higher for the standard deviations and the correlations. For the 

unsmoothed data, the maximum unsigned percentage difference is 17 percent for the standard 

deviation of the HT and 69 percent for the correlation. For the smoothed data, the differences 

are much smaller: the maximum unsigned percentage difference is 5.4 percent for the standard 

deviation of the HT and 10.7 percent for the correlation.

The mean unsigned percentage difference is at most 13.7 percent across all unsmoothed 

parameters and at most 3.4 percent across all smoothed parameters.

The lack of a large trend between the two time periods, and the small percentage differences for 

the smoothed parameters, suggest that it will not make very much difference which set of years 

is used for the APEX model inputs. We recommend using the more recent data from 2009–

2014. 
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 27

Table 5. Differences between Parameters for 2009–2014 and 2003–2014 (Baseline)

Statistic Gender Mean Difference

Mean 

Percentage 

Difference

Mean Unsigned 

Percentage 

Difference

Maximum 

Unsigned 

Percentage 

Difference

Unsmoothed

Mean HT 1 -0.12 -0.07 0.22 0.86

Mean HT 2 -0.14 -0.09 0.23 0.67

Mean Log BW 1 0.00 0.05 0.27 0.91

Mean Log BW 2 0.01 0.16 0.34 1.65

Std Dev HT 1 -0.04 -0.57 4.19 17.42

Std Dev HT 2 0.07 0.96 4.47 10.08

Std Dev Log 

BW

1 0.00 0.49 3.82 11.59

Std Dev Log 

BW

2 0.00 1.04 4.09 13.49

Correlation 1 -0.01 -1.67 10.65 51.40

Correlation 2 0.00 0.22 13.71 68.67

Smoothed

Mean HT 1 -0.12 -0.07 0.12 0.32

Mean HT 2 -0.14 -0.09 0.12 0.40

Mean Log BW 1 0.00 0.05 0.08 0.21

Mean Log BW 2 0.01 0.17 0.19 0.61

Std Dev HT 1 -0.04 -0.58 1.69 5.41

Std Dev HT 2 0.07 1.00 1.48 4.42

Std Dev Log 

BW

1 0.00 0.50 1.27 2.98

Std Dev Log 

BW

2 0.00 1.06 1.20 4.28

Correlation 1 -0.01 -1.16 2.19 7.00

Correlation 2 0.00 -1.21 3.37 10.71

6. Effect on HT and WT in APEX using Updated 

Algorithm

6.1.Description of APEX Runs and Analysis

To summarize the effect of the new algorithm on simulated HT and WT values, we conducted 

two separate APEX runs: one employing the HT and BW calculations based on the 1999–2004

NHANES data (referred to as the “old method” in this section) and one employing the HT and 

BW calculation method based on the 2009–2014 NHANES data as proposed in this 

memorandum (the “new method”). Apart from this difference, the two APEX runs were identical. 

Both APEX runs employed 100,000 profiles and modeled ages 0–99 years old. This produced a 

set of 100,000 HT, WT, and body mass index (BMI) values (one of each for each profile).

We analyzed statistics of the HT, WT, and BMI of the profiles generated in APEX for each of 14 

age bins. We created the age bins so that they each (except for the oldest bin) contained a 
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 28

roughly equal number of profiles: 5-year bins ages 0–55 years, then single bins for 55–62 years, 

62–75 years, and 75–99 years. We present in Figure 6-1 the number of profiles in each age bin.

Figure 6-1. Number of Profiles in each Age Bin from APEX Runs (100,000 profiles)

6.2.Comparison of HT, WT, and BMI Results

Table 6 presents a statistical summary and comparison of the HT, WT, and BMI values 

generated in the two APEX runs employing the old and new methods. These statistics were 

calculated only on the basis of gender and not on the basis of age bin. 

We also compared the outputs of the two methods on the basis of age bin. Figure 6-2 through 

Figure 6-7 present the mean and standard deviation of HT, WT, and BMI values from the old 

and new methods in each age bin for the 100,000 profiles generated in APEX. 
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

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Table 6. Statistical Summary of HT, WT, and BMI in APEX using Old and New Methods

Variable Gender N Mean St. Dev Min Max

% 

Difference 

in Mean

Height

(cm)

Old

M

49,473 164.948 25.582 63.058 205.788

-0.108

New 49,473 164.770 26.038 58.240 205.776

Old

F

50,527 154.176 20.525 63.251 187.350 0.126

New 50,527 154.371 21.230 54.668 190.061

Weight 

(kg)

Old

M

49,473 73.943 28.745 3.600 199.198 2.085

New 49,473 75.484 29.782 6.392 148.412

Old

F

50,527 65.056 24.744 3.700 165.998 2.373

New 50,527 66.600 25.885 5.646 138.102

BMI

(kg/m2)

Old

M

49,473 25.611 6.374 5.385 59.404 2.075

New 49,473 26.143 6.637 10.162 54.052

Old

F

50,527 26.189 7.440 5.491 63.184 1.824

New 50,527 26.667 7.690 10.155 61.574

Figure 6-2. Mean ± Standard Deviation of HT for Males
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

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Figure 6-3. Mean ± Standard Deviation of HT for Females

Figure 6-4. Mean ± Standard Deviation of WT for Males
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

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Figure 6-5. Mean ± Standard Deviation of WT for Females

Figure 6-6. Mean ± Standard Deviation of BMI for Males
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

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Figure 6-7. Mean ± Standard Deviation of BMI for Females

We made the following observations based on the information presented above comparing the 

results of the new method to those of the old method:

 When analyzing results irrespective of age bin, the percent differences in the means 

between the old and new methods for all parameters are small: about 0.1 percent for HT 

determination (negative for males, positive for females) and about +2.0 percent for the 

WT and BMI determinations. 

 For both males and females, profiles in the youngest age bin (0–5 years) and in the 

oldest several age bins (from about 55 years and older) are slightly shorter when 

employing the new method. The old method used in APEX was known to occasionally 

generate HTs that were too tall for these age groups—for children because HT was not 

correlated with BW, and for older adults because HT was not correlated with age. This 

average decrease in HT values reflects the expected change that would occur when 

including these dependent variables.

 While not consistent across age bins, profiles of both genders are generally heavier 

using the new method (most apparent with adults, except for males and especially 

females around ages 40–55 years). This increase can be seen in both the mean values 

and in the mean ± standard deviation values. This likely reflects trends in WT for the 

U.S. population (the new method uses newer NHANES data than those of the old 

method). At the far ends of the simulated WT distribution, the new method estimates 

higher WT values for the lightest profiles and lower WT values for the heaviest profiles.
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

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 For BMI values, the new method substantially decreased the standard deviation for ages 

0–15 years, with generally lower BMI means as well (except in the youngest age group).

For adults, there is a general increase in the means and standard deviations of BMI 

values using the new method, especially for males.

 For a previous assessment, we generated the distribution of BMI values shown in Figure 

6-8, from NHANES 2003–2014 data. The distributions of BMI values in these simulations 

are similar to the NHANES BMI distributions. The majority of BMI values from NHANES 

are between about 15 and 35 kg/m2, and the mean BMI values simulated here also fall 

within that range. BMI values below 15 kg/m2 and above 40 kg/m2 are relatively rare in 

the NHANES data, and the same is true of the BMI values simulated here.

Figure 6-8. Distribution of BMI Values (kg/m2) from NHANES 2003–2014
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 34

Attachment A. Distributions of Body Weight

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

0 5 10 15 20 25 30 35 40 45 50 55 0 5 10 15 20 25 30 35 40 45 50 55

0

25

50

75

100

125

150

0

25

50

75

100

125

150

Gender = 1 Gender = 2

Age = 5 Age = 1

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

0 40 80 120 160 200 0 40 80 120 160 200

0

20

40

60

80

100

0

20

40

60

80

100

Gender = 1 Gender = 2

Age = 15 Age = 10
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 35

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

20 60 100 140 180 220 20 60 100 140 180 220

0

10

20

30

40

50

60

0

10

20

30

40

50

60

Gender = 1 Gender = 2

Age = 25 Age = 20

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

0 40 80 120 160 200 0 40 80 120 160 200

0

10

20

30

40

50

60

0

10

20

30

40

50

60

Gender = 1 Gender = 2

Age = 40 Age = 30
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 36

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

20 60 100 140 180 220 20 60 100 140 180 220

0

10

20

30

40

50

0

10

20

30

40

50

Gender = 1 Gender = 2

Age = 60 Age = 50

Distribution of BMXWT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Weight (kg)

Percent

0 40 80 120 160 200 0 40 80 120 160 200

0

20

40

60

80

0

20

40

60

80

Gender = 1 Gender = 2

Age = 79 Age = 70
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 37

Attachment B. Distributions of Height

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

50 70 90 110 130 150 50 70 90 110 130 150

0

20

40

60

80

100

0

20

40

60

80

100

Gender = 1 Gender = 2

Age = 5 Age = 1

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

110 130 150 170 190 210 110 130 150 170 190 210

0

10

20

30

40

50

60

0

10

20

30

40

50

60

Gender = 1 Gender = 2

Age = 15 Age = 10
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 38

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

140 150 160 170 180 190 140 150 160 170 180 190

0

10

20

30

40

0

10

20

30

40

Gender = 1 Gender = 2

Age = 25 Age = 20

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

130 150 170 190 210 230 130 150 170 190 210 230

0

10

20

30

40

50

60

0

10

20

30

40

50

60

Gender = 1 Gender = 2

Age = 40 Age = 30
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 39

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

120 140 160 180 200 220 120 140 160 180 200 220

0

20

40

60

80

0

20

40

60

80

Gender = 1 Gender = 2

Age = 60 Age = 50

Distribution of BMXHT

Curves Normal Lognormal(Theta=0 Sigma=EST Zeta=EST)

Standing Height (cm)

Percent

120 140 160 180 200 220 120 140 160 180 200 220

0

20

40

60

80

0

20

40

60

80

Gender = 1 Gender = 2

Age = 79 Age = 70
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 40

Attachment C. Scatter Plots of Log BW versus HT

100 110 120 130

Standing Height (cm)

2.75

3.00

3.25

3.50

3.75

logweight

Correlation 0.7408

Observations 295

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=5 Gender=1

90 100 110 120 130

Standing Height (cm)

2.50

2.75

3.00

3.25

3.50

logweight

Correlation 0.7436

Observations 279

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=5 Gender=2
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 41

150 160 170 180 190

Standing Height (cm)

3.75

4.00

4.25

4.50

4.75

5.00

logweight

Correlation 0.4851

Observations 237

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=15 Gender=1

140 150 160 170 180

Standing Height (cm)

3.75

4.00

4.25

4.50

4.75

logweight

Correlation 0.4608

Observations 223

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=15 Gender=2
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 42

160 170 180 190

Standing Height (cm)

4.00

4.25

4.50

4.75

5.00

logweight

Correlation 0.3881

Observations 131

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=25 Gender=1

150 160 170 180

Standing Height (cm)

4.0

4.5

5.0

logweight

Correlation 0.2878

Observations 152

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=25 Gender=2
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 43

160 170 180 190

Standing Height (cm)

4.00

4.25

4.50

4.75

5.00

5.25

logweight

Correlation 0.5456

Observations 159

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=40 Gender=1

150 160 170 180

Standing Height (cm)

4.0

4.5

5.0

logweight

Correlation 0.3016

Observations 165

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=40 Gender=2
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 44

160 170 180 190 200

Standing Height (cm)

4.00

4.25

4.50

4.75

5.00

logweight

Correlation 0.3803

Observations 174

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=60 Gender=1

140 150 160 170

Standing Height (cm)

3.5

4.0

4.5

5.0

logweight

Correlation 0.4144

Observations 182

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=60 Gender=2
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 45

160 170 180 190

Standing Height (cm)

4.0

4.2

4.4

4.6

4.8

logweight

Correlation 0.1948

Observations 51

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=79 Gender=1

140 150 160 170

Standing Height (cm)

3.8

4.0

4.2

4.4

4.6

4.8

logweight

Correlation 0.4614

Observations 63

Prediction Ellipses 80% 95%

Scatter Plot

Years=2009-2014 Age=79 Gender=2
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 46

Attachment D. Unsmoothed and Smoothed Values for the Five Joint-distribution 

Parameters

Cubic spline of mean of log body weight (kg) versus age

NHANES 2009 to 2014

the mean, logweight Linear Predictor

AGE

Gender = 1 Gender = 2

0 20 40 60 80 100 0 20 40 60 80 100

2

3

4

the mean, logweight

Cubic spline of standard deviation of log body weight (kg) versus age

NHANES 2009 to 2014

the standard deviation, logweight Linear Predictor

AGE

Gender = 1 Gender = 2

0 20 40 60 80 100 0 20 40 60 80 100

0.15

0.20

0.25

0.30

the standard deviation, logweight
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 47

Cubic spline of mean of height (cm) versus age

NHANES 2009 to 2014

the mean, BMXHT Linear Predictor

AGE

Gender = 1 Gender = 2

0 20 40 60 80 100 0 20 40 60 80 100

75

100

125

150

175

the mean, BMXHT

Cubic spline of standard deviation of height (cm) versus age

NHANES 2009 to 2014

the standard deviation, BMXHT Linear Predictor

AGE

Gender = 1 Gender = 2

0 20 40 60 80 100 0 20 40 60 80 100

4

5

6

7

8

9

the standard deviation, BMXHT
Joint Distributions of Body Weight and Height for use in APEX

April 20, 2017

Page 48

Cubic spline of correlation between log body weight and height versus age

NHANES 2009 to 2014

corr Linear Predictor

AGE

Gender = 1 Gender = 2

0 20 40 60 80 100 0 20 40 60 80 100

0.0

0.2

0.4

0.6

0.8

corr
H-1 

APPENDIX H

ICF FINAL MEMO: RESTING METABOLIC RATE (RMR)

AND VENTILATION RATE (V̇ E) ALGORITHM REFINEMENTS
Memorandum

To: John Langstaff and Stephen Graham, U.S. EPA OAQPS

From: Jessica Levasseur, Graham Glen, and Chris Holder, ICF

Date: February 17, 2017

Re: WA 4-52 Task 4: RMR and VE Algorithm Refinements

1. Introduction

Ventilation rate (VE) and resting metabolic rate (RMR) are two key variables used to assign 

physiological characteristics to individuals in a simulated population in the U.S. Environmental 

Protection Agency (EPA) Air Pollutants Exposure model (APEX). These and other simulated 

aspects of individuals’ physiology, combined with population demographics as well as activity 

data drawn from the EPA Comprehensive Human Activity Database, are used to estimate 

exposure to air pollutants in APEX (Isaacs, 2008). The current implementation of algorithms 

used to estimate RMR and VE in APEX are based on studies that are 30 and 10 years old, 

respectively (Schofield, 1985; Graham and McCurdy, 2005). The algorithm for VE also leads to 

some sharp discontinuities between modeled age groups.

Under this task, ICF (“we”) implemented refinements (i.e., technical improvements) to 

RMR and VE calculations to improve the usefulness or accuracy of APEX simulations. To 

complete this task, we conducted multiple literature searches to identify literature relevant to 

developing appropriate RMR and VE algorithms. We identified additional sources of data to 

augment the RMR dataset provided to us by the EPA. We identified no new data on VE to add to 

the dataset provided by the EPA.

In this memorandum, we describe these literature searches, the datasets used to develop the 

updated RMR and VE algorithms, and the performance in APEX of the updated algorithms for 

RMR and VE compared to the existing algorithms. Using updated datasets, we aimed to improve 

the RMR and VE algorithms.

Note that all references to “log” or “logarithm” refer to the natural logarithm, not the base-10 

logarithm.

2. VE and RMR Literature Search

In McCurdy (2015), titled “Physiological Parameters and Physical Activity Data for Evaluating 

Exposure Modeling Performance: a Synthesis,” the author expounds upon important factors that 

influence physiological parameters and affect exposure and dose modeling. He also provided a 

separate document of “unused references” that contained relevant publications he was unable 

to fully evaluate in the synthesis.

In focusing on sections containing relevant mentions of VE and RMR, we identified 321 

publications as potentially useful sources of literature that warranted further investigation. We 

then scrutinized these publication titles and abstracts for particular relevance to RMR or VE
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page 2

prediction or to refining the algorithms for RMR or VE. Of these 321 publications, we identified 

53 as potentially relevant for our task. 

We identified population gaps within the RMR and VE datasets initially provided by the EPA, 

namely women, children, older adults, and obese people (for the VE dataset) and men and older 

adults (for the RMR dataset). We focused our literature search on publications specifically 

relevant to these underrepresented subpopulations. 

2.1.RMR

Only 13 publications were relevant to addressing the population gaps present within the RMR 

dataset. We conducted a cited-references search on these 13 RMR publications, returning all 

the publications that cite or are cited by these 13 publications. From the RMR cited-references 

search, we focused on publications that contained each of the following characteristics: 

 measured RMR (or an equivalent physiological measurement); 

 contained information on body weight, height, and sex; and 

 used primary data from at least 200 subjects or defined new predictive equations. 

We identified seven publications that had these characteristics. We acquired new RMR data 

from one of these publications—the Oxford-Brookes database (Henry, 2005)—adding 

more than 13,000 unique data points to an RMR dataset provided by EPA.

2.2.VE

We conducted a separate literature search for VE, as requested by the EPA, on those articles

published between 2000 and 2010. Conducting a PubMed search on the following search 

criteria returned 387 publications: 

 “Ventilation Rate” OR “VE” AND (Equation/s OR algorithm/s)

 Humans only 

 English only. 

Assessing these abstracts for new potential sources of data and new potential equations, 16 

articles appeared relevant. After acquiring full articles, we identified two as possible sources of 

data but none had relevant algorithms for VE prediction. We were unable to acquire these new 

datasets for VE.

3. Updated RMR Dataset

3.1.Description of Original Dataset

The initial RMR dataset provided to us by the EPA is described in the research report Analyses 

of Resting Energy Expenditure (REE) data for US residents by Kriti Sharma, Thomas McCurdy, 

and Stephen Graham (no date), which describes a database of 763 individuals ages 4 to 89.
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page 3

3.2.Description of Oxford-Brookes Database

Published in 2005, Dr. Jaya Henry created the Oxford-Brookes (OB) database that combined 

data from a variety of sources, resulting in more than 10,000 RMR values. For a detailed 

summary of the OB database creation, please see Henry (2005) and IOM (2005).

3.3.Merging Datasets 

We removed duplicates between the OB database and the initial RMR dataset (provided by the 

EPA). In addition to information on study author and year of study, this dataset contains 

information on:

 sex, 

 age, 

 BM, 

 height, and 

 RMR. 

We deleted observations missing any of the following values: RMR, BM, age, or sex. The full 

dataset contains 16,254 observations (9,377 males and 6,877 females). Of these, 39 males 

and 33 females were missing reported heights. Therefore, for analyses requiring height (see 

Section 5), we used a smaller dataset of 16,182 observations (9,338 males and 6,844 females).

4. VE Dataset

4.1.Description of Dataset

Dr. William Adams of UC Davis constructed the VE dataset provided to us by the EPA. Graham 

and McCurdy (2005) also used his data. Dr. Adams collected data from 32 panel studies over 

25 years. In addition to information on test exercise parameters, this dataset contains 

information on:

 sex,

 age, 

 BM, 

 height,

 oxygen consumption rate (VO2), and

 VE.

EPA recommended the removal of four data points for quality-assurance reasons. The final VE

dataset, with no new data added (none were identified), contains 6,636 observations, with 

4,565 males and 2,071 females.
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page 4

5. Updated RMR Algorithms

Using the new RMR dataset, and with a goal of improving the RMR algorithm while reducing

discontinuities in RMR between age groups, we developed new algorithms for estimating RMR 

in APEX. The algorithms follow the general format of a multiple linear regression (MLR) model, 

which is described as: 

y = β1x1 + β2x2 + ⋯ βnxn + α + εi(μi, σi) (1)

Where:

y = variable of interest

β = coefficient of input variable 

x = input variable

α = intercept

ε = residual

μ = distribution mean

σ = distribution standard deviation

n = number of independent regression variables

i = person-specific index

It is generally known that RMR and BM, as well as RMR and age, are not exactly linearly 

related; the algorithms developed here use BM, age, and the natural logarithms of BM and 

(age+1). The “+1” modifier allows APEX to round age upwards instead of downwards to whole 

years, which is necessary to avoid undefined log(0)1 values. 

To place all the RMR data on an equal footing, we first rounded all ages down to integer values. 

Instead of dividing the data at preset age boundaries (as was done in the existing APEX 

algorithm), we repeatedly altered the age boundaries until the residual sum of squares was 

minimized. Five age groups were sufficient to capture the data for both males and females, 

though each sex required different age groups. These age groups are shown in Table 1 and 

Table 2 below, along with the optimal regression parameters (not including height) for each age 

group and sex. Note that all people over age 99 are treated as 99 years old by APEX and 

therefore are included in the oldest age groups.

Table 1. Optimal RMR Regression Parameters for Males by Age Group (n = 9,377), Height Not 

Included

Age 

Group n BM log(BM) Age log(Age) Intercept St. Dev.

0–5 625 13.19 270.2 -18.34 131.3 -208.5 69.10

6–13 1355 10.21 260.2 13.04 -205.7 333.4 115.3

14–24 4123 0.207 1078. 115.1 -2794.0 3360.6 161.1

25–54 2531 2.845 729.6 3.181 -191.6 -1067. 178.2

55–99 743 9.291 264.8 -5.288 181.5 -705.9 163.6

Units: RMR = kilocalories/day; BM = kilograms; Age = years

 

1 Note that all references to “log” or “logarithm” refers to the natural logarithm, not base-10 logarithm.
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page 5

Table 2. Optimal RMR Regression Parameters for Females by Age Group (n = 6,877), Height Not 

Included

Age 

Group n BM log(BM) Age log(Age) Intercept St. Dev.

0–5 625 11.94 261.5 -22.31 120.9 -183.6 64.16

6–13 1618 5.296 409.1 40.37 -524.9 392.7 99.43

14–29 2657 0.968 676.9 40.89 -1002. 772.7 143.1

30–53 1346 4.935 355.4 16.28 -896.0 2225. 145.3

54–99 631 2.254 445.9 5.464 -489.9 944.2 124.5

Units: RMR = kilocalories/day; BM = kilograms; Age = years

Input values should be in units of kilograms (kg) for BM and years for age, with the RMR 

estimate in kilocalories/day (kcal/d). For example, using Equation (1) with information from 

Table 1, a 20-year-old male weighing 75 kg would be assigned an RMR as follows:

RMR = 0.207 × 75 + 1078 × log(75) + 115.1 × 20 − 2794 × log(21) + 3360.6

+ 161.1 × N(0,1)

RMR = 1826.4

kcal

day

+ 161.1 × N(0,1) (for any 20-year-old male weighing 75 kg)

While the overall r2 values are fairly high (0.820 males, 0.816 females), the r2for particular age 

groups varies from over 0.9 (for boys and girls ages 0–5 years) to less than 0.6. Transforming 

RMR, and including height and log(height) as input variables, did not improve overall fit. For 

adults in particular, a substantial amount of variation remains in the residual error of the new 

RMR algorithms. To reduce this, more modeling variables would be required than are available 

in the RMR dataset.

When including height, the optimal regression parameters are as shown in Table 3 and Table 4

for males and females, respectively. The overall r2 values are 0.815 for males and 0.816 for 

females when height is included in the regression. These are not appreciably different from the 

regressions without height. Therefore, the proposed updates to RMR regressions do not 

use height.

Table 3. Optimal RMR Regression Parameters for Males by Age Group (n = 9,338), Height Included

Age 

Group n BM log(BM) Age log(Age) HT log(HT) Intercept

St. 

Dev.

0–5 596 17.61 106.3 -17.93 87.37 -368.9 676.3 607.6 68.60

6–13 1355 12.64 149.3 30.91 -417.0 -1498. 2151.5 2344.9 115.0

14–24 4123 0.0309 1098.6 114.3 -2777. 31.45 -101.2 3250.7 161.1

25–54 2522 4.692 481.5 2.422 -136.3 1590. -2014. -1961.3 176.6

55–99 742 12.60 -108.4 -5.151 170.6 -927.2 2405. 982.6 160.7

Units: RMR = kilocalories/day; BM = kilograms; Age = years; Height = meters

Table 4. Optimal RMR Regression Parameters for Females by Age Group (n = 6,844), Height 

Included

Age 

Group n BM log(BM) Age log(Age) HT log(HT) Intercept

St. 

Dev.

0–5 611 21.78 -16.26 -9.014 39.09 -942.8 1259.9 1443.0 61.89
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Age 

Group n BM log(BM) Age log(Age) HT log(HT) Intercept

St. 

Dev.

6–13 1618 7.540 262.8 43.41 -604.3 -338.0 758.7 1209.3 98.85

14–29 2648 4.194 391.6 41.38 -1010.3 152.5 433.1 1298.2 141.1

30–53 1346 6.239 208.5 14.38 -803.3 2854.4 -4066. -180.9 143.9

54–99 621 3.840 284.9 4.510 -400.1 1782.8 -2274. -588.6 123.1

Units: RMR = kilocalories/day; BM = kilograms; Age = years; Height = meters

We tried many variations on the above regressions, including changing the age cutpoints, the 

number of age groups, the list of independent variables, and the transformation of the 

dependent variable RMR. The SAS program provided in Appendix A contains the code that 

produces the regressions in Table 1–Table 4 and some of the plots shown below.

Figure 1 presents scatter plots of observed RMR values (top row) and RMR values predicted by 

the updated algorithms described above (bottom row), as a function of age. These figures use 

“BMR” to mean “RMR.” The updated RMR algorithms have a bias of less than 0.5 percent 

between observed and predicted values, compared to the existing APEX algorithms 

which have a bias of 1–2 percent (10–30 kcal/d; smaller bias for females).

Figure 2 shows the mean RMR values by age: observed (black), predicted by the existing APEX 

algorithms (red), predicted by the updated algorithms (blue), and predicted by the updated 

algorithms with height included as an input variable (green; height-related regression 

parameters not provided in this memorandum). In the red data points (the existing APEX 

algorithms), a discontinuity is seen between ages 59 and 60, particularly for males. For adults 

ages 59 and under, the red points are generally higher than the black points (the observed 

values), whereas the red points are generally below the black points for ages 60 and above.

The same effect is seen in females, but the discontinuity is less pronounced. In the blue data 

points (the updated algorithms), no sizeable discontinuities are seen at the age group 

boundaries. As discussed earlier, the inclusion of height (the green points) does not have a 

dramatic impact on the fit of the new RMR algorithm.
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(a) (b)

(c) (d)

Units: RMR = kilocalories/day, Age = years

Figure 1. Top Row: Observed RMR Values by Age for (a) Males and (b) Females. Bottom Row: Predicted RMR Values 

by Age for (c) Males and (d) Females using the Updated Algorithms (without height). 
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(a)

(b)

Units: RMR = kilocalories/day, Age = years

Figure 2. Mean RMR Values by Age: Observed (Black), Predicted by the 

Existing APEX Algorithms (Red), Predicted by the Updated Algorithms 

(Blue), and Predicted by the Updated Algorithms with Height Included as 

an Input Variable (Green), for (a) Males and (b) Females.
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6. Updated VE Algorithm

Using the existing VE dataset from Graham and McCurdy (2005), we developed updated VE

algorithms for APEX that reduce discontinuities in predicted VE between age groups and that 

also utilize maximum VO2 (VO2m) as an input. VO2m is included because ongoing related work 

on metabolic equivalents of task (MET) values for persons with unusual maximum capacity for 

work suggests that their MET distributions are modified in a predicable way by their maximum 

MET (or, equivalently, by VO2m). One potential limitation of this analysis is that the VO2m 

values might not be well characterized for all people in the dataset.

As discussed earlier with Equation 1 above, we aimed to follow the general format of an MLR

model. In considering VE in particular, the available variables for regression are listed in Table 5

below. As discussed later in this section, we only utilized VO2 and VO2m in the updated VE

algorithms.
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Table 5. Summary of Variables Available in the VE Dataset.

Field Description

Step Stage of exercise regimen at a given work level (0.1–13). <1 indicates resting state 

where 0.1=lay, 0.2=sit, and 0.3=stand (these were not used as they appeared 

consistently unusual with regard to values observed in the exercising dataset).

Age Age (y)

BM Body mass (kg)

Char Special characteristics of the study subject. 1=trained athlete; 2=trained non-athlete; 

3=normally active; 4=sedentary; 5=obese.

ET Cumulative test time at end of step (min). "."=missing.

Gend 1=females; -1=males

Grd Percent grade while on treadmill. "."=missing.

HR Heart rate (b/min) measured during the last minute of each step. "."=missing.

HT Height (cm)

LBM Lean body mass (kg)

Mach Machine used. 1=cycle ergometer; 2=treadmill; "."=missing.

VO2 Oxygen consumption (L/min, STPD) measured during the last minute of each step

Spd Treadmill speed (m/min). "."=missing.

STUD Study number

SUBJ Study subject identifier

TT Total time of test (min). "."=missing.

VE Ventilation (L/min, BTPS) measured during the last minute of each step

VO2m Observed VO2max (L/min, STPD) for the test

Wk Cycle ergometer setting (W). "."=missing.

ln_ve log(VE)

ln_vo2 log(VO2)

VQ VE÷VO2

ln_VQ log(VQ)

ln_bm log(BM)

ve_bm VE÷BM

ln_ve_bm log(ve_bm)

vo2_bm VO2÷BM

ln_vo2_bm log(vo2_bm)

Note: y = years; kg = kilograms; min = minutes; b/min = beats per minute; cm = centimeters; L = liters; m/min 

= meters per minute; log = natural logarithm; STPD = standard temperature and pressure, dry; BTPS = 

body temperature and pressure, saturated.

Out of a total 6,636 observations, 65 had values of VO2m that were less than values of VO2. We 

found that using VO2m as-is, versus using the maximum between VO2m and VO2, made no 

appreciable difference in estimates of VE; we therefore used VO2m as-is. 

Each VE regression took place in two stages. First, all 6,636 data points were used in each 

regression. Then, all the points that were more than 3 studentized residuals away from the fitted 

line were removed, and the regression was repeated. This was done to prevent a few outlier 
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points from having undue influence. In this second step, 43 points were rejected though overall 

they had very little effect on the regression. Note that for a random sample of 6,636 points from 

a true normal distribution, about 18 would be expected to be more than 3 standard deviations 

from the mean. The number of outliers was therefore only modestly above what would be 

expected by chance alone.

The Graham and McCurdy (2005) regressions had four separate age groups (<20, 20–33, 34–

60, and 61+) evaluated independently, so discontinuities appear at the age boundaries. Thus, a 

given person ageing across a boundary would experience a sudden shift in their VE /VO2

relationship. Our new analysis uses the same regression equation for all ages, eliminating this 

issue.

For a given VO2 level, if VO2m decreases, then (VO2/VO2m) increases, and thus VE also 

increases. This relationship eliminated the need to regress upon variables such as age, BM, 

height, and sex. For example, males on average need less VE to support a given VO2, which is 

captured by their having higher VO2m. The only variables needed for the new VE algorithm are 

VO2 and VO2m, both of which are already calculated in APEX.

The actual values of VO2 and VO2m are less relevant than the fraction of maximum capacity, 

represented by f1 = VO2/VO2m. f1 may operate non-linearly (for example, f1 = 0.9 is likely more

than twice as encumbering as f1 = 0.45). A SAS procedure “Proc Transreg” was used to 

determine appropriate transformations. This recommended a power of 4 or 5 be used, that is, y 

= VE

-0.25 or y = VE-0.2, when only the variable ln_vo2 was used as the independent variable. 

Table 6. Reported r2 Statistic Based on Transformation of VE

Transformation

of VE

Variables tr_r2 ve_r2

2 ln_vo2 0.9479 0.7350

3 ln_vo2 0.9566 0.8779

4 ln_vo2 0.9563 0.8873

5 ln_vo2 0.9544 0.8850

6 ln_vo2 0.9523 0.8821

ln_VE ln_vo2 0.9341 0.8561

Note: VO2 = oxygen consumption rate; ln_vo2 = log(VO2) = natural log of VO2; 

transformation of VE is VE

-N when N is an integer; ln_VE= log(VE); tr_r2 = r2 of the 

transformed response variable,ve_r2 = r2 of VE

Table 6 demonstrates that the reported r2for the regression (called tr_r2) of the transformed 

variable Y = VE

(-1/power) is higher than the r2 for VE itself (called ve_r2), but that reflects how well

the regression captures the variation in the transformed variable. Because the transformation is 

intended to “linearize” the data, it is expected that the regression would fit better on the 

transformed variable. Note that the set of variables that produce the optimal r2for the 

transformed variable sometimes is not the same set that is optimal for ve_r2.

When ln_vo2 is the only independent variable, the best transformation (in terms of ve_r2) is 

power=4, or y = VE

-0.25, as seen in Table 7. Table 7 shows that the addition of age, sex, or height 

makes little impact on the prediction of VE. Of these, height is the most effective, but it adds less 

than 0.01 to r2. However, the addition of either VO2m or f1 = VO2/VO2m to the set of independent 
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variables gives a substantial improvement in both tr_r2 and ve_r2. However, note that using f2 

instead of f1 did not improve the fit.

Table 7. Reported r2 Statistic for Variables used with Y=VE

-0.25

Transformation

of VE

Variables tr_r2 ve_r2

4 ln_vo2 0.9563 0.8873

4 ln_vo2, age 0.9566 0.8900

4 ln_vo2, sex 0.9578 0.8923

4 ln_vo2, height 0.9596 0.8938

4 ln_vo2, VO2m 0.9715 0.9213

4 ln_vo2, f1 0.9721 0.9378

4 ln_vo2, f2 0.9712 0.9347

Note: VO2 = oxygen consumption rate; VO2m = maximum VO2; ln_vo2 = log(VO2) = natural log 

of VO2; tr_r2 = r2 of the transformed variable; ve_r2 = r2 of VE; f1 = VO2/VO2m; f2 = 

(VO2/VO2m)2; transformation of VE is VE

-N when N is an integer

Once f1 is added to the list of independent variables, then the optimal transformation of VE

changes. For example, the first line of 
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Table 8 shows that a power of 5 (that is, y = VE

-0.2 ), now outperforms a power of 4 (see the r2

values in the second-to-last line of Table 7), whereas the opposite was true in Table 6. The 

optimal transformation of VE changes and the optimal set of independent variables depend on 

each other. Using the ve_r2 statistic as the measure, then for power=5, f2 provides a better fit 

that f1, but that f3 is worse than f2. The same is true for power = 6, although all the fits (except for 

the one using f1) are better than with power = 5.

Even higher transformation powers can be used, but in practice large powers provide similar 

results to a log transformation2. The last five rows of 

 

2 The SAS Proc Transreg uses the symbolism power=0 to explicitly indicate a log transformation for the response 

variable, although since the Tables report values of (-1/power), it would be more correct to call this power = ∞
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Table 8 examines using the natural logarithm of VE as the dependent variable, with the natural 

logarithm of VO2 and various powers of (VO2/VO2m) as independent variables. Using f1 or f2

provides a worse fit with ln_VE than is obtained with power = 6, but using f4 provides the best 

overall fit.
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Table 8. Reported r2for Combinations of Independent Variables and Transformations of VE

Transformation

of VE

Variables tr_r2 ve_r2

5 ln_vo2, f1 0.9730 0.9402

5 ln_vo2, f2 0.9729 0.9420

5 ln_vo2, f3 0.9723 0.9402

6 ln_vo2, f1 0.9730 0.9397

6 ln_vo2, f2 0.9734 0.9445

6 ln_vo2, f3 0.9731 0.9442

6 ln_vo2, f4 0.9723 0.9427

ln_VE ln_vo2, f1 0.9662 0.9244

ln_VE ln_vo2, f2 0.9714 0.9411

ln_VE ln_vo2, f3 0.9724 0.9466

ln_VE ln_vo2, f4 0.9719 0.9481

ln_VE ln_vo2, f5 0.9711 0.9479

Note: VO2 = oxygen consumption rate; VO2m = maximum VO2; ln_vo2 = log(VO2) = natural log of VO2; f1 = 

VO2/VO2m; fN = (VO2/VO2m)N; transformation of VE is VE

-N when N is an integer; tr_r2 = r2 of the transformed 

variable; ve_r2 = r2 of VE

Using the log transformation with the independent variables ln_vo2 and f4=(VO2/VO2m)4, Table 

9 examines the effects of adding further independent variables; specifically age, gender, and/or 

height.

Table 9. Various Sets of Independent Variables used to Predict log(VE)

Transform Variables tr_r2 ve_r2

ln_VE ln_vo2, f4 0.9719 0.9481

ln_VE ln_vo2, f4, age 0.9720 0.9477

ln_VE ln_vo2, f4, gender 0.9721 0.9483

ln_VE ln_vo2, f4, height 0.9723 0.9481

ln_VE ln_vo2, f4, age gender height 0.9726 0.9477

Note: VO2 = oxygen consumption rate; ln_vo2 = log(VO2) = natural log of VO2; tr_r2 = r2 of 

the transformed variable; ve_r2 = r2 of VE; f4 = (VO2/VO2m)

4

In all cases, the ve_r2 is unchanged to three decimal places, being 0.948 in all cases. Hence, 

the recommendation is to use the simplest version of these regressions, as seen in Equation (2)

below.

VE2 = e

(3.298 + 0.7935×ln_vo2+ 0.53845 × (VO2÷VO2m)

4+0.1253×N(0,1))

(2)

The following two figures show all 6,636 data points from the VE dataset. Figure 3 shows 

measured VE and measured VO2. Figure 4 shows predicted VE (“VE2”) and measured VO2, 

where VE2 is given by Equation (2) (with an r2 of 0.948, as shown in Table 9) which is based on 

the VE dataset with outliers removed (this is not the final updated VE algorithm, as noted later in 

this section). 
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Figure 3. Measured VO2 and Measured VE, from the VE

dataset

Figure 4. Measured VO2 and Predicted VE Using the 

Updated Algorithm (i.e., VE2 shown in Equation 2).

As can be seen in the figures, predicted and observed values of VE are very close. 

In concordance with a request from the EPA WAM, we developed a mixed-effects regression 

(MER) in addition to the above MLR. MER separates residuals into within-person (ew) and 

between-person (eb) effects, known as intrapersonal and interpersonal effects, respectively. 

This analysis, using the same independent variables and the same VE dataset discussed above 

yields another VE algorithm. This algorithm, shown below, is the final version of the 

updated VE algorithm to be incorporated into APEX.
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Pred_VE = e

(3.300 + 0.8128×ln_vo2+ 0.5126 × (VO2÷VO2m)

4+N(0,eb)+N(0,ew))

(3)

N(0,eb) is a normal distribution with mean zero and standard deviation eb=0.09866 meant to 

capture interpersonal variability, which is sampled once per person. N(0,ew) is an intrapersonal 

residual with standard deviation of ew=0.07852, which is resampled daily due to natural 

intrapersonal fluctuations in VE that occur daily. 

Differences between Equations (2) and (3) may be due to the fact that some of the persons in 

the dataset had different numbers of observations. The mean, median, and mode were all 

seven observations per person, with a range from one to 13. With regard to implementation in 

APEX, the cause of the interpersonal variability may not be necessary to determine. It is 

sufficient to specify the size of the two error terms, one sampled once per person and the other 

sampled once per day.

Ultimately, the EPA WAM chose Equation (3) to implement in APEX due to its increased ability 

to account for inter- and intra-personal effects. The resulting r2for VE (0.94) is a substantial 

improvement over the existing VE regressions in APEX (where r2 was 0.892–0.925), with a 

large reduction in discontinuities of VE between ages.

7. Effect of Updated Algorithm(s) on Simulated 

Exposure 

The updated RMR algorithm is based on an MLR with coefficients shown in Table 1 and Table 

2. The updated VE algorithm is shown in Equation (3). 

The existing RMR algorithm in APEX (in units of kilocalories/minute [kcal/min]) is:

RMR = 0.166 × [RMRslope × BM + RMRint + RMRerr] (4)

Where:

0.166 = the conversion factor for converting megajoules (MJ)/d to kcal/min

RMRslope = slope of the regression equation (MJ/(d-kg))

RMRint = intercept of the regression equation (MJ/d) 

RMRerr = variation in the regression equation (MJ/d)

The existing VE algorithm in APEX (in units of milliliters/minute [mL/min]) is:

VE = (1,000 mL

L

) × BM × exp(VEinter + VEslope × ln(VO2

) + Z × VEresid) (5)

Where:

VEinter = intercept of the regression equation

VEslope = slope of the regression equation

Z = random number from normal distribution
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VEresid = variation in the regression equation

And where VO2 (in units L/min/kg) is:

VO2 =

MET×ECF×RMR

BM

(6)

Where:

ECF = energy conversion factor (L O2/kcal)

We compared the effects of the existing and updated RMR and VE algorithms using a sample of 

1000 persons, ages 0 to 95, run for one year each (taken from an APEX run for ozone, in 2010, 

in the Los Angeles area). Four runs were made: R1V1 is the combination of old RMR and old VE

algorithms; R2V1 uses the new RMR and old VE algorithms; R1V2 uses the old RMR and new 

VE algorithms; and finally, R2V2 uses both new algorithms. Each run produced a sample of 

1000 RMR values (one per person), and 8,760,000 VE values (one per hour, per person).

The RMR results did not vary when just the VE method was changed. This was expected, 

because APEX calculates RMR first. The VE calculation is affected by any change in RMR.

Statistics comparing the old and new RMR algorithms are presented in Table 10. The new RMR 

algorithm produces slightly lower values across the board, with larger decreases at the higher 

end of the range. Even then, these differences are below 4 percent. There are fewer extreme 

values using the new algorithm, resulting in a smaller standard deviation.

Table 10. RMR Value Statistics (kcal/min) for 1000 Persons, Using Old and New RMR Algorithms

Statistic Old RMR New RMR % Change

Mean 1.065 1.040 - 2.4 %

Standard deviation 0.292 0.275 - 5.8 %

10th percentile 0.709 0.702 - 1.0 %

Median 1.057 1.034 - 2.2 %

90th percentile 1.443 1.390 - 3.7 %

The VE data below have been analyzed in two ways. First, statistics on the full set of 8,760,000 

VE values are generated. When comparing the same VE algorithm and varying RMR algorithms, 

the old VE algorithm had a drop of 2 percent in mean VE when switching to the new RMR, and 

the new VE algorithm had a similar drop of 1.5 percent (not shown in a table here). These are 

somewhat smaller than the drop in mean RMR of 2.4 percent.

Focusing on the new RMR algorithm, a comparison of VE statistics from the R2V1 and R2V2 

runs is shown in Table 11, using all 8,760,000 VE values. The high-end VE values changed very 

little between the old and new VE algorithms (by 0.5 percent), but the new algorithm predicts 

higher values at lower VE levels (by 17.6 percent), resulting in an increase by 6 percent in mean 

values. These values are effectively time-weighted, so sleeping VE accounts for about one-third 

of the set (that is, at rest or below). By contrast, the Adams dataset was concerned almost 

solely with activities above resting levels. Hence, the regression based on the Adams dataset is 

being extrapolated to sleeping as an activity. One would therefore expect that the new VE
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algorithm would be more robust for the higher activity levels. Note that the new VE algorithm has 

a smaller standard deviation than the old method (by 11.6 percent), resulting in fewer extreme 

values.

Table 11. VE Value Statistics (mL/hr) for 8,760,000 Person-hours, Using the New RMR Algorithm 

with the Old and New VE Algorithms

Statistic Old VE New VE % Change

Mean 19581 20763 + 6.0 %

Standard deviation 10375 9172 - 11.6 %

10th percentile 8778 10319 + 17.6 %

Median 17422 19391 + 11.3 %

90th percentile 33042 32887 - 0.5 %

The second type of analysis is to examine the change in mean VE per person, and the change 

in the 90th percentile of each person’s VE values. First, the 1000 personal means (over the year) 

and 1000 personal 90th percentiles are calculated. Table 12 shows modest increases (in the 

range of 6 percent) in person-mean VE values when using the new VE algorithm, with a 1.8-

percent increase in standard deviation. Table 13 shows that the 90th percentile for each person 

(that is, the VE level that one exceeds for 2.4 hours per day, on average) has changed relatively 

little between the old and new algorithms. The mean has dropped 2 percent, but the standard 

deviation dropped by 9.1 percent because the upper tail does not extend as far as before.

Table 12. Population Statistics on Personal Mean VE (mL/hr), Using the New RMR Algorithm with 

the Old and New VE Algorithms

Statistic Old VE New VE % Change

Mean 19581 20763 + 6.0%

Standard deviation 6187 6296 + 1.8%

10th percentile 12236 12843 + 5.0%

Median 18955 20504 + 8.2%

90th percentile 27822 29164 + 4.8%

Table 13. Population Statistics on Personal 90th Percentile of VE (mL/hr), Using the New RMR 

Algorithm with the Old and New VE Algorithms

Statistic Old VE New VE % Change

Mean 28017 27445 -2.0%

Standard deviation 11094 10087 -9.1%

10th percentile 14205 14415 1.5%

Median 27026 27339 1.2%

90th percentile 42572 40775 -4.2%

In summary, in comparing the updated APEX algorithms for RMR and VE to the existing 

algorithms:

 Average RMR decreases with the updated RMR algorithms, though remains within 3 

percent of RMR predicted by the existing algorithm.

 As expected, the updated VE algorithm has no effect on predicted RMR.
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 The updated RMR algorithm impacts VE predictions less when utilizing the updated VE

algorithm; this impact is greater at the lower end of estimated VE values.

 The upper end (90th percentile) of predicted VE values are similar between the existing 

and updated VE algorithms. This appears to be due to two partially cancelling effects: the 

population 90th percentile of the personal means increased 4.8 percent, but the 

population 90th percentile of the personal 90th percentiles decreased 4.2 percent.

 The lower end of predicted VE values is moderately higher with the updated VE algorithm 

than with the existing VE algorithm (a 17.6-percent change in the 10th percentile, which 

corresponds to sleeping VE)

 Both the updated and existing VE algorithms predict VE values exceeding 100,000 

mL/min for roughly 1 in every 65,000 person-hours, which was the hard-coded maximum 

for VE in APEX. Note that a switch has been added to the APEX Control Options File to 

enable or disable the maximum upper limit. This was disabled for the current comparison 

runs, because truncation of the two tails at the same point would cause the two 

distributions to look more similar than they otherwise would.

8. Summary Discussion and Next Steps

Through extensive literature searches for both RMR and VE algorithms, as well as through 

augmentation of the RMR dataset, ICF has improved upon the RMR and VE physiological 

algorithms within the APEX model. These updated algorithms perform better than the existing 

algorithms in APEX, with reduced discontinuities between APEX age groups and better fits to 

the measured datasets. ICF has created “switches” within the APEX Control Options File that 

allows users to choose between the available RMR or VE algorithms. The coding required to 

completely replace the older algorithms can be done quickly at EPA’s request.
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9. References

Graham, S. and McCurdy, T. (2005) A NERL Internal Research Report: Revised ventilation rate 

(VE) equations for use in inhalation-oriented exposure models. EPA/600/X-05/008.

IOM. (2005). Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, 

Cholesterol, Protein and Amino Acids. Panel on Macronutrients, Panel on the Definition 

of Dietary Fiber, Subcommittee on Upper Reference Levels of Nutrients, Subcommittee 

on Interpretation and Uses of Dietary Reference Intakes, and the Standing Committee 

on the Scientific Evaluation of Dietary Reference Intakes, Food and Nutrition Board. U.S. 

Institute of Medicine. National Academies Press. URL: 

http://www.nationalacademies.org/hmd/Activities/Nutrition/SummaryDRIs/DRITables.aspx. Table: Doubly Labeled Water Data Set: 10_DLW_Database.xls 

(downloaded 8/24/16)

Henry, CJK. (2005). Basal metabolic rate studies in humans: measurement and development of 

new equations. Public Health Nutrition. 8(7A): 1133-1152.

Isaacs, K. (2008). Estimating ventilation in human exposure models: Summary. Internal 

Memorandum. July 28, 2008. 

McCurdy, T. Physiological Parameters and Physical Activity Data for Evaluating Exposure 

Modeling Performance: a Synthesis. U.S. Environmental Protection Agency, 

Washington, DC, EPA/600/R-15/175, 2015.

Schofield, WN (1985). Predicting basal metabolic rate, new standards and review of previous 

work. Human Nutrition: Clinical Nutrition. 39C(Supp. 1): 5-41.
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page A-1

* Written by WGG at ICF, last revised on October 21, 2016

data raw;

infile "C:/main/APEX/WA452/exercise/from_Jess/newrmr_JL_30aug16.csv"

firstobs=2 dsd dlm=',';

length sex $1 author $20 type $6 citation $80 study $40;

input sex author type age bmr ht bm citation year study recno;

yage = floor(age);

run;

data good bad all;

set raw;

logbm = log(bm);

logbmr = log(bmr);

if sex="M" then gender= 1; 

if sex="F" then gender=-1;

bad = 0;

if age=. then bad=1;

if bmr=. then bad=1;

if bm=. then bad=1;

if gender=. then bad=1;

if ht=. then bad=1;

age0 = age;

age = floor(age);

if age>99 then age=99;

logage = log(1+age);

logage0 = log(1+age0);

invage = 1/(1+age);

bmage = bm*age;

bmcage = bm*(1+age);

bmlage = bm*logage;

loght = log(ht);

if bad=0 then output good; else output bad;

output all;

run;

data males females;

set all;

if gender= 1 then output males;

if gender=-1 then output females;

run;

axis1 order = 0 to 3200 by 200;

title 'RMR: All males';

proc gplot data=males;

plot bmr*age /VAXIS=axis1;

run; quit;

title 'RMR: All females';
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page A-2

proc gplot data=females;

plot bmr*age /VAXIS=axis1;

run; quit;

axis1 order = 0 to 3200 by 200;

axis2 order = 0 to 180 by 10;

title 'RMR vs BM: All males';

proc gplot data=males;

plot bmr*bm /VAXIS=axis1 HAXIS=axis2;

run; quit;

title 'RMR vs BM: All females';

proc gplot data=females;

plot bmr*bm /VAXIS=axis1 HAXIS=axis2;

run; quit;

axis1 order = 0 to 3200 by 200;

axis3 order = 0 to 2.0 by 0.1;

title 'RMR vs BM: All males';

proc gplot data=males;

plot bmr*ht /VAXIS=axis1 HAXIS=axis3;

run; quit;

title 'RMR vs BM: All females';

proc gplot data=females;

plot bmr*ht /VAXIS=axis1 HAXIS=axis3;

run; quit;

proc sort data=males; by yage; run;

proc means data=males noprint; 

by yage; 

var bmr;

output out=m1 n=n mean=mean std=std min=min max=max;

run;

proc print data=m1; run;

%macro c(gen,num,test,vars);

%let last=0;

%do i=1 %to # 

%let j=%scan(&test,&i);

%put i=&i j=&j;

title "&gen._&last.-&j";

data a;

%if &gen=M %then set males;;

%if &gen=F %then set females;;

lo = symgetn("last");

hi = &j;

if (age>=lo and age<hi);

* if ht=. then delete;

run;
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page A-3

proc reg data=a;

model bmr=&vars /vif;

output out=z

p =predicted

residual=residual

rstudent=rstudent;

run; quit;

data _null_;

set z end=eof;

retain pertot 0;

retain errtot 0;

pertot = pertot + 1;

errtot = errtot + residual**2; 

if (eof) then do;

call symput("pertot&i",trim(left(pertot)));

call symput("errtot&i",trim(left(errtot)));

end;

run;

%let last = &j;

%end;

%let pertot = 0;

%let errtot = 0;

%do i=1 %to #

%let pertot = %sysevalf(&pertot+&&pertot&i);

%let errtot = %sysevalf(&errtot+&&errtot&i);

%end;

%put test = &test;

%put pertot = &pertot;

%put errtot = &errtot;

%mend;

%c(F,5,6 14 30 54 100,bm logbm age logage); * err = 11052 best;

%c(M,5,6 14 25 55 100,bm logbm age logage); * err = 22753 best;

%macro d(gen,num,test,vars);

%let last=0;

%do i=1 %to # 

%let j=%scan(&test,&i);

%put i=&i j=&j;

title "&gen._&last.-&j";

data a;

%if &gen=M %then set males;;

%if &gen=F %then set females;;

lo = symgetn("last");

hi = &j;

if (age>=lo and age<hi);

if ht=. then delete;

run;

proc reg data=a;

model bmr=&vars /vif;

output out=z

p =predicted

residual=residual

rstudent=rstudent;

run; quit;
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page A-4

data _null_;

set z end=eof;

retain pertot 0;

retain errtot 0;

pertot = pertot + 1;

errtot = errtot + residual**2; 

if (eof) then do;

call symput("pertot&i",trim(left(pertot)));

call symput("errtot&i",trim(left(errtot)));

end;

run;

%let last = &j;

%end;

%let pertot = 0;

%let errtot = 0;

%do i=1 %to #

%let pertot = %sysevalf(&pertot+&&pertot&i);

%let errtot = %sysevalf(&errtot+&&errtot&i);

%end;

%put test = &test;

%put pertot = &pertot;

%put errtot = &errtot;

%mend;

%d(F,5,6 14 30 54 100,bm logbm age logage ht loght); * err = 10767;

%d(M,5,6 14 25 55 100,bm logbm age logage ht loght); * err = 22488;

proc sort data=males; by age; run;

proc means data=males noprint;

by age;

var bm logbm bmr ht loght;

output out=m1 mean=;

run;

data m2;

set m1;

logage = log(1+age);

if (age<=5) then fit = 13.19*bm + 270.2 *logbm - 18.34*age + 131.3*logage -

208.5 ;

if (age>=6 and age<=13) then fit = 10.21*bm + 260.2 *logbm + 13.04*age -

205.7*logage + 333.4 ;

if (age>=14 and age<=24) then fit = 0.207*bm + 1078. *logbm + 115.1*age -

2794.*logage + 3360.6;

if (age>=25 and age<=54) then fit = 2.845*bm + 729.6 *logbm + 3.181*age -

191.6*logage - 1067. ;

if (age>=55) then fit = 9.291*bm + 264.8 *logbm - 5.288*age + 181.5*logage -

705.9 ;

if (fit<50) then fit=50;

if (fit>3000) then fit=3000;

if (age<=5) then fit2 = 17.61*bm + 106.3 *logbm - 17.93*age + 87.37*logage -

368.9*ht + 676.3 *loght + 607.6;

if (age>=6 and age<=13) then fit2 = 12.64*bm + 149.3 *logbm + 30.91*age -

417.0*logage - 1498.*ht + 2151.5*loght + 2344.9;

if (age>=14 and age<=24) then fit2 = .0309*bm + 1098.6*logbm + 114.3*age -

2777.*logage + 31.45*ht - 101.2 *loght + 3250.7;

if (age>=25 and age<=54) then fit2 = 4.692*bm + 481.5 *logbm + 2.422*age -

136.3*logage + 1590.*ht - 2014. *loght - 1961.3;
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page A-5

if (age>=55) then fit2 = 12.60*bm - 108.4 *logbm - 5.151*age + 170.6*logage 

- 927.2*ht + 2405. *loght + 982.6;

if (fit<50) then fit=50;

if (fit>3000) then fit=3000;

if (age<=2) then old = 0.249*bm - 0.127 ;

if (age>=3 and age<=9) then old = 0.095*bm + 2.110 ;

if (age>=10 and age<=17) then old = 0.074*bm + 2.754 ;

if (age>=18 and age<=29) then old = 0.063*bm + 2.896 ;

if (age>=30 and age<=59) then old = 0.048*bm + 3.653 ;

if (age>=60) then old = 0.049*bm + 2.459 ;

old = 238.845 * old;

if (old<144) then old=144;

if (old>2880) then old=2880;

run;

symbol1 color=black;

symbol2 color=red;

symbol3 color=blue;

symbol4 color=green;

title "mean bmr and old and new fits - males";

title2 "data=black, old fit=red, new fit=blue, new with ht=green";

proc gplot data=m2;

plot old*age=2 fit*age=3 bmr*age=1 fit2*age=4 /overlay;

run; quit;

proc gplot data=m2(where=(age>=48 and age<=63));

plot old*age=2 fit*age=3 bmr*age=1 fit2*age=4 /overlay;

run; quit;

proc sort data=females; by age; run;

proc means data=females noprint;

by age;

var bm logbm bmr ht loght;

output out=f1 mean=;

run;

data f2;

set f1;

logage = log(1+age);

if (age<=5) then fit = 11.94*bm + 261.5 *logbm - 22.31*age + 120.9*logage -

183.6;

if (age>=6 and age<=13) then fit = 5.296*bm + 409.1 *logbm + 40.37*age -

524.9*logage + 392.7;

if (age>=14 and age<=29) then fit = 0.968*bm + 676.9 *logbm + 40.89*age -

1002.*logage + 772.7;

if (age>=30 and age<=53) then fit = 4.935*bm + 355.4 *logbm + 16.28*age -

896.0*logage + 2225.;

if (age>=54) then fit = 2.254*bm + 445.9 *logbm + 5.464*age - 489.9*logage + 

944.2;

if (fit<50) then fit=50;

if (fit>3000) then fit=3000;

if (age<=5) then fit2 = 21.78*bm - 16.26 *logbm - 9.014*age + 39.09 *logage 

- 942.8 *ht + 1259.9*loght + 1443.0;

if (age>=6 and age<=13) then fit2 = 7.540*bm + 262.8 *logbm + 43.41*age -

604.3 *logage - 338.0 *ht + 758.7 *loght + 1209.3;

if (age>=14 and age<=29) then fit2 = 4.194*bm + 391.6 *logbm + 41.38*age -

1010.3*logage + 152.5 *ht + 433.1 *loght + 1298.2;
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page A-6

if (age>=30 and age<=53) then fit2 = 6.239*bm + 208.5 *logbm + 14.38*age -

803.3 *logage + 2854.4*ht - 4066. *loght - 180.9;

if (age>=54) then fit2 = 3.840*bm + 284.9 *logbm + 4.510*age - 400.1 *logage 

+ 1782.8*ht - 2274. *loght - 588.6;

if (fit<50) then fit=50;

if (fit>3000) then fit=3000;

if (age<=2) then old = 0.244*bm - 0.130 ;

if (age>=3 and age<=9) then old = 0.085*bm + 2.033 ;

if (age>=10 and age<=17) then old = 0.056*bm + 2.898 ;

if (age>=18 and age<=29) then old = 0.062*bm + 2.036 ;

if (age>=30 and age<=59) then old = 0.034*bm + 3.538 ;

if (age>=60) then old = 0.038*bm + 2.755 ;

old = 238.845 * old;

if (old<144) then old=144;

if (old>2880) then old=2880;

run;

symbol1 color=black;

symbol2 color=red;

symbol3 color=blue;

symbol4 color=green;

title "mean bmr and old and new fits - females";

title2 "data=black, old fit=red, new fit=blue, new with ht=green";

proc gplot data=f2;

plot bmr*age=1 old*age=2 fit*age=3 fit2*age=4 /overlay;

run; quit;

proc gplot data=f2(where=(age>=48 and age <=63));

plot bmr*age=1 old*age=2 fit*age=3 fit2*age=4 /overlay;

run; quit;

data mall;

set males;

z = rannor(0);

if (age<=5) then fit = 13.19*bm + 270.2 *logbm - 18.34*age + 131.3*logage -

208.5 + 69.10*z;

if (age>=6 and age<=13) then fit = 10.21*bm + 260.2 *logbm + 13.04*age -

205.7*logage + 333.4 + 115.3*z;

if (age>=14 and age<=29) then fit = 0.207*bm + 1078. *logbm + 115.1*age -

2794.*logage + 3360.6 + 161.1*z;

if (age>=30 and age<=53) then fit = 2.845*bm + 729.6 *logbm + 3.181*age -

191.6*logage - 1067. + 178.2*z;

if (age>=54) then fit = 9.291*bm + 264.8 *logbm - 5.288*age + 181.5*logage -

705.9 + 163.6*z;

if (fit<50) then fit=50;

if (fit>3000) then fit=3000;

if (age<=5) then fit2 = 11.59*bm + 215.6 *logbm - 29.69*age + 112.9*logage + 

367.1*ht - 332.7 + 68.93*z;

if (age>=6 and age<=13) then fit2 = 10.42*bm + 239.4 *logbm + 11.87*age -

200.3*logage + 42.18*ht + 339.8 + 115.3*z;

if (age>=14 and age<=24) then fit2 = 0.103*bm + 1094. *logbm + 114.4*age -

2781.*logage - 28.7*ht + 3322.1 + 161.1*z;

if (age>=25 and age<=54) then fit2 = 5.022*bm + 457.5 *logbm + 2.370*age -

134.5*logage + 405.3*ht - 939.6 + 176.7*z;

if (age>=55) then fit2 = 11.78*bm - 44.62 *logbm - 3.177*age + 39.95*logage 

+ 490.8*ht + 50.55 + 160.9*z;

if (fit2<50) then fit2=50;

if (fit2>3000) then fit2=3000;
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page A-7

if (age<=5) then fit3 = 17.61*bm + 106.3 *logbm - 17.93*age + 87.37*logage -

368.9*ht + 676.3*loght + 607.6 + 68.60*z;

if (age>=6 and age<=13) then fit3 = 12.64*bm + 149.3 *logbm + 30.92*age -

417.0*logage - 1498.*ht + 2151.*loght + 2344.9 + 115.0*z;

if (age>=14 and age<=24) then fit3 = .0309*bm + 1098.6*logbm + 114.3*age -

2777.*logage + 31.45*ht - 101.2*loght + 3250.7 + 161.1*z;

if (age>=25 and age<=54) then fit3 = 4.692*bm + 481.5 *logbm + 2.422*age -

136.3*logage + 1590.*ht - 2014.*loght - 1961.3 + 176.6*z;

if (age>=55) then fit3 = 12.67*bm - 113.9 *logbm - 3.228*age + 38.95*logage 

- 962.2*ht + 2466.*loght + 1453.5 + 160.9*z;

if (fit3<50) then fit3=50;

if (fit3>3000) then fit3=3000;

if (ht=.) then fit3=.;

if (age<=2) then old = 0.249*bm - 0.127 + 0.29*z;

if (age>=3 and age<=9) then old = 0.095*bm + 2.110 + 0.28*z;

if (age>=10 and age<=17) then old = 0.074*bm + 2.754 + 0.44*z;

if (age>=18 and age<=29) then old = 0.063*bm + 2.896 + 0.64*z;

if (age>=30 and age<=59) then old = 0.048*bm + 3.653 + 0.70*z;

if (age>=60) then old = 0.049*bm + 2.459 + 0.69*z;

old = 238.845 * old;

if (old<144) then old=144;

if (old>2880) then old=2880;

err = BMR-fit;

err2 = BMR-fit2;

err3 = BMR-fit3;

err0 = BMR-old;

run;

axis1 order = 0 to 3000 by 1000;

title "fitted bmr - all males";

proc gplot data=mall;

plot fit*age;

run; quit;

title "fitted bmr with height - all males";

proc gplot data=mall;

plot fit2*age;

run; quit;

title "fitted bmr with ht and loght - all males";

proc gplot data=mall;

plot fit3*age=3;

run; quit;

title "APEX fit for bmr - all males";

proc gplot data=mall;

plot old*age /vaxis=axis1;

run; quit;

title "error statistics - males";

proc means data=mall n mean std var min max;

var bmr err0 err err2 err3;

run;

proc sort data=mall; by age; run;

proc means data=mall noprint;

by age;

var bmr fit fit2 fit3 old err err2 err3 err0;

output out=mstats mean=;

run;

symbol1 color=black;

symbol2 color=red;
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page A-8

symbol3 color=blue;

title "mean bmr and old and new fits - males";

title2 "data=black, old fit=red, new fit=blue";

proc gplot data=mstats;

plot old*age=2 fit*age=3 bmr*age=1 /overlap;

run; quit;

data fall;

set females;

z = rannor(0);

if (age<=5) then fit = 11.94*bm + 261.3 *logbm - 22.14*age + 120.4*logage -

182.9 + 64.62*z;

if (age>=6 and age<=13) then fit = 5.296*bm + 409.1 *logbm + 40.37*age -

524.9*logage + 392.7 + 99.43*z;

if (age>=14 and age<=29) then fit = 1.004*bm + 674.4 *logbm + 41.11*age -

1007.*logage + 790.6 + 143.2*z;

if (age>=30 and age<=53) then fit = 4.935*bm + 355.4 *logbm + 16.29*age -

896.0*logage + 2225.3 + 145.3*z;

if (age>=54) then fit = 2.699*bm + 415.7 *logbm + 8.701*age - 711.6*logage + 

1756.8 + 124.6*z;

if (fit<50) then fit=50;

if (fit>3000) then fit=3000;

if (age<=5) then fit2 = 11.09*bm + 175.3 *logbm - 35.26*age + 98.50 *logage 

+ 449.0*ht - 304.3 + 63.23*z;

if (age>=6 and age<=13) then fit2 = 6.494*bm + 304.9 *logbm + 31.99*age -

483.8 *logage + 209.0*ht + 411.8 + 98.89*z;

if (age>=14 and age<=29) then fit2 = 4.107*bm + 396.9 *logbm + 41.32*age -

1009.3*logage + 423.2*ht + 1049.9 + 141.1*z;

if (age>=30 and age<=53) then fit2 = 6.969*bm + 155.6 *logbm + 14.74*age -

815.2 *logage + 316.4*ht + 2175.2 + 144.0*z;

if (age>=54) then fit2 = 5.038*bm + 198.6 *logbm + 7.630*age - 610.7 *logage 

+ 346.1*ht + 1602.5 + 122.6*z;

if (fit2<50) then fit2=50;

if (fit2>3000) then fit2=3000;

if (age<=5) then fit3 = 21.78*bm - 16.26 *logbm - 9.014*age + 39.09 *logage 

- 942.8 *ht + 1259.9*loght + 1443.0 + 61.89*z;

if (age>=6 and age<=13) then fit3 = 7.540*bm + 262.8 *logbm + 43.41*age -

604.3 *logage - 338.0 *ht + 758.7 *loght + 1209.3 + 98.85*z;

if (age>=14 and age<=29) then fit3 = 4.194*bm + 391.6 *logbm + 41.38*age -

1010.3*logage + 152.5 *ht + 423.1 *loght + 1298.2 + 141.1*z;

if (age>=30 and age<=53) then fit3 = 6.239*bm + 208.5 *logbm + 14.38*age -

803.3 *logage + 2854.4*ht - 4066. *loght - 180.9 + 143.9*z;

if (age>=54) then fit3 = 4.506*bm + 236.4 *logbm + 7.564*age - 605.8 *logage 

+ 1489.9*ht - 1796.6*loght + 475.8 + 122.6*z;

if (fit3<50) then fit3=50;

if (fit3>3000) then fit3=3000;

if (ht=.) then fit3=.;

if (age<=2) then old = 0.244*bm - 0.130 + 0.25*z;

if (age>=3 and age<=9) then old = 0.085*bm + 2.033 + 0.29*z;

if (age>=10 and age<=17) then old = 0.056*bm + 2.898 + 0.47*z;

if (age>=18 and age<=29) then old = 0.062*bm + 2.036 + 0.50*z;

if (age>=30 and age<=59) then old = 0.034*bm + 3.538 + 0.47*z;

if (age>=60) then old = 0.038*bm + 2.755 + 0.45*z;

old = 238.845 * old;

if (old<144) then old=144;

if (old>2880) then old=2880;
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page A-9

err = BMR-fit;

err2 = BMR-fit2;

err3 = BMR-fit3;

err0 = BMR-old;

run;

title "fitted bmr - all females";

proc gplot data=fall;

plot fit*age;

run; quit;

title "fitted bmr with height - all females";

proc gplot data=fall;

plot fit2*age;

run; quit;

title "fitted bmr with ht and loght - all females";

proc gplot data=fall;

plot fit3*age=3;

run; quit;

axis1 order = 0 to 3000 by 1000;

title 'BMR - all males';

proc gplot data=mall;

plot bmr*age /vaxis=axis1;

run; quit;

title 'BMR - all females';

proc gplot data=fall;

plot bmr*age /vaxis=axis1;

run; quit;

proc means data=fall n mean std var min max;

var bmr err0 err err2 err3;

run;

proc sort data=fall; by age; run;

proc means data=fall noprint;

by age;

var bmr fit fit2 fit3 old err err2 err3 err0;

output out=fstats mean=;

run;

symbol1 color=black;

symbol2 color=red;

symbol3 color=blue;

title "mean bmr and old and new fits - females";

title2 "data=black, old fit=red, new fit=blue";

proc gplot data=fstats;

plot old*age=2 fit*age=3 bmr*age=1 /overlap;

run; quit;

proc means data=males(where=(ht NE .)) n mean std var; var bmr; run;

proc means data=females(where=(ht NE .)) n mean std var; var bmr; run;
WA 4-52 Task 4: RMR and VE Algorithm Refinements

February 17, 2017

Page B-1

* August 2, 2016 by WGG, based on program by Jonathan Cohen;

libname apex 'C:\main\APEX\WA342\task4\task4';

data adams4;

set apex.adams4 end=eof;

* The following four obs deleted by JEL email of 3/2/2016;

if STUD = 2 and SUBJ = 32 and step = 1.0 then delete;

if STUD = 2 and SUBJ = 38 and step = 1.0 then delete;

if STUD = 20 and SUBJ = 8 and step = 5.0 then delete;

if STUD = 30 and SUBJ = 114 and step = 0.1 then delete;

if ve=. or ln_vo2=. or vo2m=. or gend=. or age=. then delete;

* VO2 units are L/min;

vo2 = exp(ln_vo2);

* VO2m is personal maximum VO2 in L/min;

retain sum1 0;

sum1 = sum1 + ve;

if (eof) then do;

meanve= sum1/_N_;

call symput ("mean_ve",trim(left(meanve)));

end;

* Macro variable mean_ve is used later in calculating r2 for ve;

drop sum1 meanve;

label vo2='VO2';

run;

proc sort data=adams4 out=sorted; by stud subj; run;

data persons;

set sorted;

by stud subj;

retain vo2max nobs 0;

keep stud subj nobs vo2m vo2max;

if first.subj then do; nobs=0; vo2max=vo2m; end;

nobs = nobs+1;

if vo2max<vo2 then vo2max=vo2;

if last.subj then output;

run;

proc freq data=persons; tables nobs; run;

data base;

merge sorted persons;

by stud subj;

retain reset 0; 

invm = 1/vo2m;

logm = log(vo2m);

* f1 is fraction of personal maximum (unitless);

f1 = vo2/vo2m;

f2 = f1**2;

f3 = f1**3;

f4 = f1**4;

f5 = f1**5;

g1 = vo2/vo2max;

g2 = f1**2;
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g3 = f1**3;

g4 = f1**4;

g5 = f1**5;

* bmi is body mass index;

bmi = bm/(ht/100)**2;

ln_bmi = log(bmi);

* ht is height in cm;

ln_ht = log(ht);

* bm is body mass in kg;

ln_bm = log(bm);

* age in full years - log uses age rounded up to prevent log(0);

ln_age = log(1+age);

id = _N_;

* Gend=-1 are males, gend=1 are females;

run;

***********************************************;

*Box-cox analysis to assess y transformation. Run one model statement at a 

time;

proc transreg data = base;

* model boxcox(ve / lambda= -1 -0.5 -0.3333 -0.25 -0.2 -0.1666 -0.14286 -

0.125 -0.1111 -0.1 0 0.5 1 )= identity(ln_vo2); * -0.2;

* model boxcox(ve / lambda= -1 -0.5 -0.3333 -0.25 -0.2 -0.1666 -0.14286 -

0.125 -0.1111 -0.1 0 0.5 1 )= identity(ln_vo2 f1); * -0.125;

* model boxcox(ve / lambda= -1 -0.5 -0.3333 -0.25 -0.2 -0.1666 -0.14286 -

0.125 -0.1111 -0.1 0 0.5 1 )= identity(ln_vo2 f2); * -0.1;

* model boxcox(ve / lambda= -1 -0.5 -0.3333 -0.25 -0.2 -0.1666 -0.14286 -

0.125 -0.1111 -0.1 0 0.5 1 )= identity(ln_vo2 f3); * 0;

model boxcox(ve / lambda= -1 -0.5 -0.3333 -0.25 -0.2 -0.1666 -0.14286 -0.125

-0.1111 -0.1 0 0.5 1 )= identity(ln_vo2 f4); * 0;

* model boxcox(ve / lambda= -1 -0.5 -0.3333 -0.25 -0.2 -0.1666 -0.14286 -

0.125 -0.1111 -0.1 0 0.5 1 )= identity(ln_vo2 f5); * 0;

run;

/* With just ln_vo2, the best transformation is lambda=-0.2. With higher 

powers of vo2/vo2m included

this shifts to 0, which is the log transform.

*/

%macro regr(power,x);

data a;

set base end=eof;

if (&power>0) then y = ve**(-1/(&power));

else y = log(ve);

run;

*calculate regression coefficients & include VIF;

proc reg data=a noprint;

model y = &x/ vif;

output out=b

p =predicted

residual=residual

rstudent=rstudent;

run; quit;

*remove studentized outliers;

data c;
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set b;

if rstudent = . then delete; 

if abs(rstudent) > 3 then delete;

run;

* Redo regression without outliers;

proc reg data=c plots(maxpoints=6700);

model y = &x/ vif;

output out=d

p =predicted2

residual=residual2

rstudent=rstudent2;

run; quit;

* Calculate and report r2 on the original variable ve;

data e;

set d end=eof;

if (&power>0) then pred = 1/predicted2**(&power);

else pred = exp(predicted2);

retain sumb sum1 0;

db = (ve-&mean_ve)**2;

d1 = (ve-pred)**2;

sumb = sumb + db;

sum1 = sum1 + d1;

if (eof) then do;

vb = sumb / _N_;

v1 = sum1 / _N_;

stat1 = 1 - v1/vb;

put "vars &x ";

put "stats " _N_ sumb sum1 vb v1 stat1;

end;

keep stud subj ve vo2 ln_vo2 vo2m y f1 f2 f3 f4 f5 gend pred;

run;

%mend regr;

%regr(2, ln_vo2) * tr_r2 = 0.9479 ve_r2 = 0.7350;

%regr(3, ln_vo2) * tr_r2 = 0.9566 ve_r2 = 0.8779;

%regr(4, ln_vo2) * tr_r2 = 0.9563 ve_r2 = 0.8873;

%regr(5, ln_vo2) * tr_r2 = 0.9544 ve_r2 = 0.8850;

%regr(6, ln_vo2) * tr_r2 = 0.9523 ve_r2 = 0.8821;

%regr(0, ln_vo2) * tr_r2 = 0.9341 ve_r2 = 0.8561;

%regr(4, ln_vo2) * tr_r2 = 0.9563 ve_r2 = 0.8873;

%regr(4, ln_vo2 age) * tr_r2 = 0.9581 ve_r2 = 0.8900;

%regr(4, ln_vo2 gend) * tr_r2 = 0.9578 ve_r2 = 0.8923;

%regr(4, ln_vo2 ht) * tr_r2 = 0.9596 ve_r2 = 0.8938;

%regr(4, ln_vo2 vo2m) * tr_r2 = 0.9715 ve_r2 = 0.9213;

%regr(4, ln_vo2 f1) * tr_r2 = 0.9721 ve_r2 = 0.9378;

%regr(4, ln_vo2 f2) * tr_r2 = 0.9712 ve_r2 = 0.9347;

%regr(5, ln_vo2 f1) * tr_r2 = 0.9730 ve_r2 = 0.9402;

%regr(5, ln_vo2 f2) * tr_r2 = 0.9729 ve_r2 = 0.9420;

%regr(5, ln_vo2 f3) * tr_r2 = 0.9723 ve_r2 = 0.9402;

%regr(6, ln_vo2 f1) * tr_r2 = 0.9730 ve_r2 = 0.9397;

%regr(6, ln_vo2 f2) * tr_r2 = 0.9734 ve_r2 = 0.9445;

%regr(6, ln_vo2 f3) * tr_r2 = 0.9731 ve_r2 = 0.9442;

%regr(6, ln_vo2 f4) * tr_r2 = 0.9723 ve_r2 = 0.9427;

%regr(0, ln_vo2 f1) * tr_r2 = 0.9662 ve_r2 = 0.9244;
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%regr(0, ln_vo2 f2) * tr_r2 = 0.9714 ve_r2 = 0.9411;

%regr(0, ln_vo2 f3) * tr_r2 = 0.9724 ve_r2 = 0.9466;

%regr(0, ln_vo2 f4) * tr_r2 = 0.9719 ve_r2 = 0.9481; * best;

%regr(0, ln_vo2 f5) * tr_r2 = 0.9711 ve_r2 = 0.9479;

%regr(0, ln_vo2 f4 age) * tr_r2 = 0.9720 ve_r2 = 0.9477;

%regr(0, ln_vo2 f4 gend) * tr_r2 = 0.9721 ve_r2 = 0.9483;

%regr(0, ln_vo2 f4 ht) * tr_r2 = 0.9723 ve_r2 = 0.9481;

%regr(0, ln_vo2 f4 gend age ht) * tr_r2 = 0.9726 ve_r2 = 0.9477;

* For comparison, repeat the near-optimal regression using vo2max instead of 

vo2m;

%regr(0, ln_vo2 g4) * ve_r2 = 0.9481;

/* %regr(0, ln_vo2 f4) seems to be the best choice. While very high powers 

(11+) of 1/ve

give marginally better r2, the log is a more usual choice, especially since 

the primary 

independent variable (vo2) is also log transformed.

Note: ve_r2 is based on the no-outlier data set (3 studentized residuals);

On full Adams data set with (0, ln_vo2 f4), 6636 obs, r2 = 0.9463, which can 

be 

checked by running %stats(adams4) below.;

Macro %stats examines the optimal choice, examining the effects of truncating 

outliers 

on the predicted points. It does not seem to make much difference whether 

the N(0,1) 

is truncated or not, or whether the generated ve values are truncated or 

not. Note that 

%stats may be re-run several times, and the predicted values will change 

because new

random numbers are being drawn. 

*/

%macro stats(ds);

proc sort data=&ds out=s; by stud subj; run;

data cloud;

set s end=eof;

by stud subj;

retain ss vv v1 v1b v2 v2b q1 q1b t1 t1b 0;

ve0 = min(ve,220);

z = rannor(0);

retain zb 0;

if first.subj then zb = rannor(0);

p1 = exp(3.29821+0.79351*ln_vo2+0.53845*f4);

p1b = min(max(p1,4),220);

ve1 = exp(3.29821+0.79351*ln_vo2+0.53845*f4+0.12529*z);

ve1b = min(max(ve1,4),220);

ve2 = exp(3.300+0.8128*ln_vo2+0.5126*f4+0.09866*zb+0.07852*z);

ve2b = min(max(ve2,4),220);

old = 1/(0.163-0.0816*ln_vo2-0.000342*age-0.00348*gend+0.000233*ht)**2;
WA 4-52 Task 4: RMR and VE Algorithm Refinements

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Page B-5

oldb = min(max(old,4),220);

ss = ss + ve**2;

q1 = q1 + (p1-ve)**2;

q1b = q1b + (p1b-ve)**2;

t1 = t1 + (old-ve)**2;

t1b = t1b + (oldb-ve)**2;

vv = vv + (ve-&mean_ve)**2;

v1 = v1 + (ve1-&mean_ve)**2;

v1b = v1b + (ve1b-&mean_ve)**2;

v2 = v2 + (ve2-&mean_ve)**2;

v2b = v2b + (ve2b-&mean_ve)**2;

if (eof) then do;

put "data set = &ds";

put ss vv v1 v1b v2 v2b;

qq1 = 1-q1/vv;

qq1b = 1-q1b/vv;

tt1 = 1-t1/vv;

tt1b = 1-t1b/vv;

put q1 q1b qq1 qq1b tt1 tt1b;

end;

run;

%mend;

%stats(base)

%stats(e)

axis1 order = 0 to 220 by 20;

proc gplot data=cloud;

plot ve0*vo2 /VAXIS=axis1;

plot ve2*vo2 /VAXIS=axis1;

run;quit;

proc means data=cloud N min mean median std max;

var ve ve1 ve2 old;

run;

proc mixed data=e covtest plots(maxpoints=6700);

class stud subj;

model y = ln_vo2 f4 /solution ddfm=kr;

random subj(stud)/ solution ;

title 'data= random statement & ddfm=kr';

ods output covParms=mixedcovm_old;

ods output solutionF=solutions_old;

run;
I-1 

APPENDIX I

CONSOLIDATED HUMAN ACTIVITY DATABASE (CHAD) DATA

A total of 24 Consolidated Human Activity Database (CHAD) studies were included in 

CHAD as of November 2015, with 179,912 diary-days entered. The geographic coverages range 

from specific cities to collections of metropolitan areas to the entire US, and the respondents tend 

to be adults but some studies include (or are limited to) children. CHAD contains human activity 

data from these studies, coded into a harmonized set of location and activity codes. Note, 

however, that the data collected in the original studies differed in level of detail in terms of 

activity, location, and time resolution. In addition, the translation of the original study data into 

CHAD format was performed by different individuals or groups. Therefore, the CHAD data 

themselves will vary in specificity and resolution across the studies. One of the goals of this 

manual is to provide any user with enough information to assess each study within CHAD for 

appropriateness for their application. An overview of the studies is provided in Table I-1 below. 
I-2 

Table I-1. Overview of Activity Studies Included in CHAD-Master (as of November 2015)

Study Name Geographic 

Coverage

Dates (as 

incorporated 

into CHAD)

Respondent Ages 

(years; as 

incorporated into 

CHAD)

Data Gathering

Diary-Days (as 

incorporated 

into CHAD)

Study References

Baltimore Retirement 

Home Study (BAL)

Baltimore County, 

MD

01–02/1997 

07–08/1998

≥65 daily recall data collected by 

study staff over a 3-week 

period

391 Williams et al., 2000

American Time Use 

Survey, Bureau of Labor 

Statistics (BLS)

Whole US 2003–2011 ≥15 24-hour recall data collected 

by telephone interview 

combining structured 

questions and 

conversational interviewing

124,517 BLS, 2014

California Activity Pattern 

Studies (CAA, CAC, CAY)

California CAA and CAY: 

10/1987–09/1988 

CAC: 04/1989–

02/1990

CAA: 18–94

CAY: 12–17

CAC: ≤11

24-hour recall data collected 

by telephone interviews with 

structured questions 

CAA: 1,579 

CAY: 183 

CAC: 1,200

Wiley et al., 1991a; 1991b

Cincinnati Activity 

Patterns Study (CIN)

Cincinnati, OH 08–09/1985 ≤86 activity diary and 

background questionnaire 

2,614 Johnson, 1989

Detroit Exposure and 

Aerosol Research Study 

(DEA)

Detroit, MI 06/2004–10/2007≥18 activities recorded via freeform entry, while location 

data were structured

340 Williams et al., 2008

Denver, Colorado 

Personal Exposure Study 

(DEN)

Denver, CO 11/1982–02/1983 18–70 activity diary and 

background questionnaire

805 Johnson, 1984; Johnson et 

al., 1986

EPA Longitudinal Studies 

(EPA)

Respondents 

residing in Central 

NC (Raleigh, 

Durham, Chapel 

Hill) 

1999–2000, 

2002, 2006–

2008, 2012–2013

0, 35–67 paper diary; free-from 

questionnaire

1,786 Isaacs et al., 2012

Population Study of 

Income Dynamics PSID I, 

II, III (ISR)

Whole US I: 02–12/1997I

I: 2002–2003

III:09/2007–

05/2005

I: ≤12 II and II: <18 interviews; time diaries I: 5,616 II: 4,997 

III: 2,741

Alion Science and 

Technology, 2012; University 

of Michigan, 2014
I-3 

Study Name Geographic 

Coverage

Dates (as 

incorporated 

into CHAD)

Respondent Ages 

(years; as 

incorporated into 

CHAD)

Data Gathering

Diary-Days (as 

incorporated 

into CHAD)

Study References

Los Angeles Ozone 

Exposure Study: 

Elementary School/High 

School (LAE/LAH)

Los Angeles, CA Fall/1989, 

Fall/1990

10–17 real-time diaries 94 Roth Associates, 1988; Spier 

et al., 1992

North Carolina State 

University Study (NCS)

Mostly NC, 9 other 

states also included

09–10/2013, 09–

10/2014

22–58 diaries recorded in real time 662 Hill, 2014

National Human Activity 

Pattern Study (NHAPS): 

Air/Water (NHA/NHW)

48 states 09/1992–10/1994≤93 telephone interview and 

questionnaire

NHA: 4,723 

NHW: 4,663

Klepeis et al., 1995; Tsang 

and Klepeis, 1996

National-scale Activity 

Study (NSA)

7 metropolitan 

areas

06–09/2009 35–92 recall activity diary 

questionnaire

6,862 Knowledge Networks, 2009

RTI Ozone Averting 

Behavior Study (OAB)

35 metropolitan 

areas

07–09/2002, 

08/2003

2–12 no information provided at 

this time 

2,907 Mansfield et al., 2009

RTP Particulate Matter 

Panel Study (RTP)

Wake and Orange 

Counties, NC

06–11/2000,

01–05/2001

55–85 diaries recorded in real time 998 Williams et al., 2001; 2003a,b

Seattle Study (SEA) Seattle, WA 10/1999–05/2001 6–91 diaries recorded in real time 1,692 Liu et al., 2003

Study of Use of Products 

and Exposure-related 

Behaviors (SUP)

California 06/2006–03/2010≤88 24-hour recall data, 

collected by phone interview

9,446 Bennett et al., 2012

Valdez Air Health Study 

(VAL)

Valdez, AK 04–05/1990, 

08/1990,

02–03/1991

11–71 information not provided 397 Goldstein et al., 1992

Washington, DC Study 

(WAS)

Washington, DC 11/1982–02/1983 18–71 activity diary and 

background questionnaire 

699 Hartwell et al., 1984;

Johnson et al., 1986; 

Settergren et al., 1984
I-4 

REFERENCES

Alion Science and Technology. (2012). PSID Integration into CHAD (a description from Alion 

on integrating ISR into CHAD). 

Bennett DH, Teague CH, Lee K, Cassady DL, Ritz B, and Hertz-Picciotto I. (2012). Passive 

sampling methods to determine household and personal care product use. Journal of 

Exposure Science and Environmental Epidemiology 22(2): 148–160.

BLS (Bureau of Labor Statistics). (2014). American Time Use Survey User’s Guide: 

Understanding ATUS 2003 to 2013; Bureau of Labor Statistics, Washington, DC; 

December 2014. Available at: http://www.bls.gov/tus/atususersguide.pdf.

Goldstein B, Tardiff R, Hoffnagle G, and Kester R. (1992). Valdez Air Health Study: Summary 

Report. Prepared for Alyeska Pipeline Service Company, Anchorage, AK.

Hill Z., 2014. Development and Evaluation of Human Longitudinal Time-Location-Activity 

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Hartwell TD, Clayton CA, Michie RM, Whitmore RW, Zelon HS, Jones SM, and Whitehurst 

DA. (1984). Study of Carbon Monoxide Exposure of Residents of Washington, D.C. and 

Denver, Colorado. Prepared for the U.S. Environmental Protection Agency. Research 

Triangle Park, NC.

Isaacs K, McCurdy T, Glen G, Nysewander M, Errickson A, Forbes S, Graham S, McCurdy L, 

Smith L, Tulve N, and Vallero, D. (2012). Statistical properties of longitudinal timeactivity data for use in human exposure modeling. Journal of Exposure Science and 

Environmental Epidemiology 23(3): 328-336.

Johnson, T. (1984). Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. 

Prepared for U.S. Environmental Protection Agency, Environmental Monitoring Systems 

Laboratory, Research Triangle Park, NC.

Johnson, T, Capel J, and Wijnberg L. (1986). Selected Data Analyses Relating to Studies of 

Personal Carbon Monoxide Exposure in Denver and Washington, DC. Prepared for U.S. 

Environmental Protection Agency, Environmental Monitoring Systems Laboratory, 

Research Triangle Park, NC.

Johnson, T. (1989). Human Activity Patterns in Cincinnati, Ohio. Final Report. Prepared for 

Electric Power Research Institute, Health Studies Program, Palo Alto, CA.

Klepeis N, Tsang A, and Behar J. (1995). Analysis of the National Human Activity Pattern 

Survey (NHAPS) Respondents from a Standpoint of Exposure Assessment. Final Report. 

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Laboratory, Las Vegas, NV.

Knowledge Networks. (2009). Field Report: National Scale Activity Survey (NSAS). Conducted 

for Research Triangle Institute. Submitted to Carol Mansfield November 13, 2009.
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Liu L-JS, Box M, Kalman D, Kaufman J, Koenig J, Larson T, Lumley T, Sheppard L, and 

Wallace L. 2003. Exposure assessment of particulate matter for susceptible populations 

in Seattle. Environ Health Perspect 111: 909–918.

Mansfield C, Houtven GV, Johnson F R, and Yang J-C. (2009). Environmental Risks and 

Behavior: Do children spend less time outdoors when ozone pollution is high? ASSA 

annual meeting, January 5, 2009. Update of Houtven et al. (2003) using the OAB CHAD 

data set, and related to Mansfield et al. (2006).

Roth Associates. (1988). LA_part1 and LA_part2 (A Study of Activity Patterns Among a Group 

of Los Angeles Asthmatics). Electric Power Research Institute

Settergren SK, Hartwell TD, and Clayton CA. (1984). Study of Carbon Monoxide Exposure of 

Residents of Washington, DC.: Additional Analyses. Prepared for U.S. Environmental 

Protection Agency, Environmental Monitoring Systems Laboratory, Research Triangle 

Park, NC.

Spier C, Little D, Trim S, Johnson T, Linn W, and Hackney J. (1992). Activity Patterns in 

Elementary and High School Students Exposed to Oxidant Pollution. Journal of Exposure 

Analysis and Environmental Epidemiology 2: 277–293.

Tsang AM and Klepeis NE. (1996). Descriptive Statistics Tables from a Detailed Analysis of the 

National Human Activity Pattern Survey (NHAPS) Data, U.S. Environmental Protection 

Agency, Washington, D.C.

University of Michigan. (2014). The Panel Study of Income Dynamics. 

http://psidonline.isr.umich.edu/Studies.aspx. 

Wiley J, Robinson J, Piazza T, Garrett K, Cirksena K, Cheng Y, and Martin G. (1991a). Activity 

Patterns of California Residents. Final Report. Prepared for California Air Resources 

Board, Research Division, Sacramento, CA.

Wiley J, Robinson J, Cheng Y, Piazza T, Stork L, and Pladsen K. (1991b). Study of Children’s 

Activity Patterns. Final Report under contract no A733-149. Prepared for California Air 

Resources Board, Research Division, Sacramento, CA.

Williams, R, Suggs, J, Creason, J, Rodes, C, Lawless, P, Kwok, R, Zweidinger, R, and Sheldon, 

L. (2000). The 1998 Baltimore particulate matter epidemiology-exposure study: Part 2. 

Personal exposure associated with an elderly population. J Expo Anal Environ

Epidemiol. 10(6): 533–543.

Williams RW, Wallace LA, Suggs JC, Evans EG, Creason JP, Highsmith VR, Sheldon LS, Rea 

AW, Vette AF, Zweidinger RB, Leovic KW, Norris GA, Landis MS, HowardReed C, 

Stevens C, Conner TL, Rodes CE, Lawless PA, Thornburg J, Liu LS, Kalman D, 

Kaufman J, Koenig JQ, Larson TL, Lumley T, Sheppard L, Brown K, Suh H, Wheeler A, 

Gold D, Koutrakis P, and Lippmann M. (2001). Preliminary particulate matter mass 

concentrations associated with longitudinal panel studies: assessing human exposures of 
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high risk subpopulations to particulate matter. Office of Research and Development. 

United States Environmental Protection Agency. EPA/600/R-01/086.

Williams R, Suggs J, Rea A, Leovic K, Vette A, Croghan C, Sheldon L, Rodes C, Thornburg J, 

Ejire A, Herbst M, and Sanders W. (2003a). The Research Triangle Park particulate 

matter panel study: PM mass concentration relationships. Atmospheric Environment 37 

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659.
J-1

APPENDIX J

DETAILED EXPOSURE AND RISK RESULTS

Table J-1. APEX estimates for percent of children and adults with asthma in Fall River

study area, 2011.

Percent of children with asthma at elevated ventilation having exposures at or above 5-minute benchmark

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 32.74 12.17 5.49 2.55 1.31 0.62

200 ppb 0.24 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of children with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB

100% 0.41 0.14 0.05 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 1.43 0.71 0.47 0.38 0.27 0.22

200% 0.25 0.11 0.05 0 0 0

UB

100% 3.68 2.50 1.95 1.57 1.26 1.13

200% 1.48 0.99 0.77 0.60 0.52 0.52

Percent of adults with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 5.08 0.44 0.05 0 0 0

200 ppb 0.02 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of adults with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0.06 0 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.34 0.09 0.03 0 0 0

200% 0.05 0 0 0 0 0

UB 100% 1.28 0.55 0.33 0.21 0.16 0.11

200% 0.56 0.25 0.15 0.10 0.07 0.05
J-2

Table J-2. APEX estimates for percent of children and adults with asthma in Fall River 

study area, 2012.

Percent of children with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 13.21 2.76 0.56 0.12 0.03 0

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of children with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0.14 0.03 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.77 0.44 0.25 0.14 0.08 0.08

200% 0.14 0.03 0 0 0 0

UB 100% 2.55 1.76 1.29 1.04 0.91 0.77

200% 1.10 0.74 0.55 0.44 0.38 0.30

Percent of adults with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 1.86 0.18 0 0 0 0

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of adults with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0.02 0 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.17 0.04 0 0 0 0

200% 0.01 0 0 0 0 0

UB 100% 0.88 0.38 0.22 0.15 0.10 0.08

200% 0.39 0.16 0.10 0.07 0.05 0.03
J-3

Table J-3. APEX estimates for percent of children and adults with asthma in Fall River 

study area, 2013.

Percent of children with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 12.29 1.60 0.33 0.03 0 0

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of children with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0.11 0 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.55 0.08 0.05 0.03 0.03 0

200% 0.05 0 0 0 0 0

UB 100% 1.95 1.04 0.77 0.60 0.52 0.44

200% 0.77 0.44 0.33 0.27 0.25 0.19

Percent of adults with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 1.32 0.07 0 0 0 0

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of adults with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0 0 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.07 0 0 0 0 0

200% 0 0 0 0 0 0

UB 100% 0.54 0.24 0.15 0.11 0.08 0.07

200% 0.24 0.11 0.07 0.06 0.04 0.03
J-4

Table J-4. APEX estimates for percent of children and adults with asthma in Indianapolis 

study area, 2011.

Percent of children with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 27.03 7.97 2.45 0.97 0.41 0.10

200 ppb 1.04 0 0 0 0 0

300 ppb 0.83 0 0 0 0 0

400 ppb 0.31 0 0 0 0 0

Percent of children with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0.57 0.19 0.11 0.07 0.06 0.05

200% 0.07 0 0 0 0 0

MEAN 100% 1.48 0.77 0.57 0.44 0.38 0.32

200% 0.36 0.17 0.11 0.09 0.06 0.06

UB 100% 3.80 2.60 2.11 1.83 1.61 1.47

200% 1.61 1.13 0.94 0.80 0.72 0.66

Percent of adults with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 4.28 0.60 0.10 0 0 0

200 ppb 0.12 0 0 0 0 0

300 ppb 0.09 0 0 0 0 0

400 ppb 0.07 0 0 0 0 0

Percent of adults with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0.11 0.03 0.01 0 0 0

200% 0.01 0 0 0 0 0

MEAN 100% 0.44 0.17 0.10 0.06 0.04 0.03

200% 0.10 0.03 0.02 0.01 0.01 0.00

UB 100% 1.59 0.90 0.64 0.49 0.39 0.33

200% 0.72 0.42 0.31 0.23 0.20 0.17
J-5

Table J-5. APEX estimates for percent of children and adults with asthma in Indianapolis

study area, 2012.

Percent of children with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 22.30 7.66 2.55 0.97 0.41 0.21

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of children with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0.41 0.18 0.11 0.07 0.05 0.04

200% 0.02 0 0 0 0 0

MEAN 100% 1.29 0.74 0.54 0.42 0.37 0.32

200% 0.32 0.17 0.09 0.09 0.06 0.06

UB 100% 3.53 2.51 2.06 1.81 1.62 1.49

200% 1.50 1.09 0.91 0.79 0.74 0.65

Percent of adults with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 3.78 0.55 0.14 0.05 0.03 0.02

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of adults with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0.09 0.02 0.01 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.41 0.17 0.09 0.07 0.05 0.03

200% 0.09 0.03 0.02 0.01 0.01 0

UB 100% 1.54 0.90 0.64 0.48 0.39 0.32

200% 0.69 0.43 0.30 0.24 0.20 0.16
J-6

Table J-6. APEX estimates for percent of children and adults with asthma in Indianapolis

study area, 2013.

Percent of children with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 17.95 4.69 1.73 0.31 0.14 0.07

200 ppb 0.93 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of children with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0.35 0.14 0.08 0.06 0.04 0.03

200% 0.02 0 0 0 0 0

MEAN 100% 1.12 0.64 0.46 0.37 0.32 0.29

200% 0.27 0.13 0.08 0.07 0.06 0.06

UB 100% 3.23 2.27 1.91 1.65 1.48 1.36

200% 1.39 0.99 0.84 0.72 0.67 0.62

Percent of adults with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 2.90 0.36 0.12 0.03 0 0

200 ppb 0.17 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of adults with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0.07 0.02 0.01 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.35 0.14 0.09 0.06 0.04 0.03

200% 0.07 0.03 0.01 0.01 0 0

UB 100% 1.41 0.81 0.58 0.45 0.37 0.31

200% 0.65 0.38 0.28 0.22 0.19 0.16
J-7

Table J-7. APEX estimates for percent of children and adults with asthma in Tulsa study 

area, 2011.

Percent of children with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 0.24 0 0 0 0 0

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of children with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0 0 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0 0 0 0 0 0

200% 0 0 0 0 0 0

UB 100% 0.44 0.24 0.20 0.16 0.11 0.11

200% 0.20 0.11 0.09 0.07 0.05 0.05

Percent of adults with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 0.10 0 0 0 0 0

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of adults with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0 0 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.01 0 0 0 0 0

200% 0 0 0 0 0 0

UB 100% 0.21 0.09 0.06 0.03 0.03 0.01

200% 0.10 0.05 0.03 0.01 0.01 0.01
J-8

Table J-8. APEX estimates for percent of children and adults with asthma in Tulsa study 

area, 2012.

Percent of children with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 0.15 0 0 0 0 0

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of children with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0 0 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.02 0 0 0 0 0

200% 0 0 0 0 0 0

UB 100% 0.53 0.35 0.26 0.22 0.18 0.16

200% 0.22 0.18 0.13 0.11 0.09 0.07

Percent of adults with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 0.06 0 0 0 0 0

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of adults with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0 0 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.01 0 0 0 0 0

200% 0 0 0 0 0 0

UB 100% 0.23 0.11 0.07 0.05 0.03 0.02

200% 0.11 0.06 0.03 0.02 0.02 0.01
J-9

Table J-9. APEX estimates for percent of children and adults with asthma in Tulsa study 

area, 2013.

Percent of children with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 0.03 0 0 0 0 0

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of children with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0 0 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.02 0 0 0 0 0

200% 0 0 0 0 0 0

UB 100% 0.44 0.33 0.27 0.22 0.18 0.16

200% 0.20 0.15 0.15 0.09 0.09 0.07

Percent of adults with asthma at elevated ventilation having exposures at or above 5-minute benchmark 

concentrations

number of days per year

benchmark at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

100 ppb 0 0 0 0 0 0

200 ppb 0 0 0 0 0 0

300 ppb 0 0 0 0 0 0

400 ppb 0 0 0 0 0 0

Percent of adults with asthma estimated to experience at least one day with an increase in sRaw ≥ 100%

number of days per year

E-R Function sRaw at least 1 at least 2 at least 3 at least 4 at least 5 at least 6

LB 100% 0 0 0 0 0 0

200% 0 0 0 0 0 0

MEAN 100% 0.01 0 0 0 0 0

200% 0 0 0 0 0 0

UB 100% 0.19 0.09 0.05 0.04 0.03 0.02

200% 0.09 0.05 0.03 0.02 0.01 0.01
J-10

Table J-10. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Fall River, 2011, 

children.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 44 84 131 159 208 269

10 103 189 255 334 383 437

20 149 233 309 387 477 491

30 143 269 360 438 482 554

40 190 298 422 481 513 559

50 249 436 465 516 549 521

60 345 428 510 503 450 364

70 346 447 427 346 253 219

80 477 463 337 233 182 121

90 396 334 206 129 72 56

100 379 204 118 57 34 21

110 271 106 42 22 13 1

120 196 65 29 12 0 1

130 149 39 6 1 1 0

140 70 14 1 0 0 0

150 75 11 2 1 0 0

170 36 4 1 0 0 0

190 8 0 0 0 0 0

200 5 0 0 0 0 0

210 3 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-11

Table J-11. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Fall River, 2011, 

adults.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 846 1960 3040 4178 5175 6026

10 2399 4026 4673 4712 4524 4225

20 2302 2473 2192 1828 1445 1173

30 1690 1417 1080 768 586 435

40 1257 900 550 376 251 167

50 995 604 333 162 115 104

60 740 327 184 97 76 39

70 554 238 82 69 17 11

80 521 167 52 19 9 2

90 327 71 32 4 2 2

100 236 30 6 0 0 0

110 154 15 0 0 0 0

120 87 9 0 0 0 0

130 63 0 0 0 0 0

140 37 0 0 0 0 0

150 22 0 0 0 0 0

170 24 0 0 0 0 0

190 2 0 0 0 0 0

200 2 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-12

Table J-12. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Fall River, 2012, 

children.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 56 107 163 213 273 334

10 120 252 338 428 490 543

20 183 310 420 510 564 630

30 266 411 552 610 738 828

40 350 518 636 724 694 651

50 375 546 539 479 423 350

60 522 551 495 386 296 191

70 513 465 281 191 98 67

80 400 219 122 53 34 21

90 366 150 58 26 11 5

100 391 93 19 4 1 0

110 66 5 1 0 0 0

120 13 1 0 0 0 0

130 5 1 0 0 0 0

140 3 0 0 0 0 0

150 2 0 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-13

Table J-13. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Fall River, 2012, 

adults.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 1181 2616 4018 5093 6194 7068

10 3038 4422 4656 4604 4189 3734

20 2562 2475 1954 1523 1186 956

30 1770 1287 883 591 398 275

40 1225 666 379 249 143 97

50 764 353 203 102 65 32

60 608 216 91 35 17 17

70 411 123 32 22 13 4

80 273 45 15 4 0 0

90 199 17 4 0 0 0

100 214 22 0 0 0 0

110 11 0 0 0 0 0

120 2 0 0 0 0 0

130 2 0 0 0 0 0

140 0 0 0 0 0 0

150 0 0 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-14

Table J-14. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Fall River, 2013, 

children.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 38 101 132 176 239 294

10 173 273 403 494 592 671

20 419 644 819 1006 1089 1180

30 453 718 851 885 917 895

40 706 884 878 763 608 466

50 560 513 333 199 132 89

60 365 245 131 67 27 16

70 166 93 38 19 8 3

80 180 63 18 6 3 1

90 125 36 9 3 1 0

100 193 41 11 1 0 0

110 97 14 1 0 0 0

120 149 2 0 0 0 0

130 4 1 0 0 0 0

140 2 0 0 0 0 0

150 1 0 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-15

Table J-15. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Fall River, 2013, 

adults.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 1190 2540 3819 4922 5889 6649

10 3914 5240 5344 5184 4788 4422

20 3375 2914 2296 1685 1246 943

30 1597 948 508 314 206 134

40 1077 385 195 91 61 35

50 534 117 50 15 11 4

60 208 52 11 9 0 0

70 97 13 2 0 0 0

80 56 17 2 0 0 0

90 43 4 2 0 0 0

100 74 9 0 0 0 0

110 50 0 0 0 0 0

120 35 0 0 0 0 0

130 4 0 0 0 0 0

140 0 0 0 0 0 0

150 0 0 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-16

Table J-16. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Indianapolis, 2011, 

children.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 34 56 64 86 105 142

10 116 213 378 502 685 802

20 292 577 764 914 955 1082

30 401 678 794 985 1154 1240

40 667 1067 1446 1700 1985 2202

50 1015 1566 2030 2375 2487 2573

60 1258 1899 2015 1959 1850 1693

70 1345 1584 1472 1157 925 693

80 1708 1517 1105 794 483 292

90 1064 802 476 228 124 60

100 652 315 127 86 37 7

110 1337 434 124 7 7 4

120 558 79 15 11 0 0

130 142 19 0 0 0 0

140 60 11 0 0 0 0

150 7 0 0 0 0 0

170 64 7 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 22 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 56 0 0 0 0 0

400 34 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-17

Table J-17. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Indianapolis, 2011, 

adults.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 398 1014 1749 2701 3796 4860

10 3211 6683 9807 12203 13952 15240

20 5601 8600 9708 9764 9477 8911

30 6198 6939 6578 5675 4705 3802

40 6422 6067 4381 3479 2570 2184

50 4885 3267 2222 1431 996 647

60 3211 1624 902 454 268 149

70 1917 921 336 143 106 68

80 1767 573 218 100 44 37

90 915 143 68 25 6 0

100 467 87 25 0 0 0

110 697 100 12 0 0 0

120 149 12 0 0 0 0

130 100 19 0 0 0 0

140 37 0 0 0 0 0

150 25 0 0 0 0 0

170 25 0 0 0 0 0

190 6 0 0 0 0 0

200 0 0 0 0 0 0

210 12 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 6 0 0 0 0 0

400 25 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-18

Table J-18. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Indianapolis, 2012, 

children.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 15 56 67 112 124 135

10 127 292 449 622 779 910

20 296 509 757 888 981 1116

30 378 659 832 929 1127 1184

40 607 963 1150 1408 1543 1828

50 1101 1637 2109 2416 2659 2644

60 1626 2274 2453 2375 2210 1963

70 1723 1835 1558 1281 910 719

80 1277 1075 764 442 273 213

90 1258 697 408 247 161 71

100 779 461 165 67 37 19

110 461 202 82 30 4 0

120 607 127 22 4 4 4

130 127 7 4 4 0 0

140 109 19 4 0 0 0

150 337 15 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-19

Table J-19. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Indianapolis, 2012, 

adults.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 429 896 1593 2651 3672 4736

10 3460 7449 10784 13180 14991 16292

20 5881 8202 9154 9135 8805 8084

30 5489 6690 6030 5308 4524 3933

40 5986 5420 4232 3223 2365 1755

50 5196 3790 2732 1649 1108 803

60 4207 2122 915 516 361 199

70 2197 765 292 180 68 50

80 1058 311 137 93 56 37

90 821 218 100 44 6 6

100 554 118 31 6 6 6

110 380 44 12 12 6 0

120 236 37 6 0 0 0

130 56 0 0 0 0 0

140 50 0 0 0 0 0

150 93 0 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-20

Table J-20. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Indianapolis, 2013, 

children.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 30 79 94 127 139 161

10 146 228 401 581 742 903

20 243 536 738 895 981 1127

30 472 757 963 1015 1176 1169

40 712 1240 1412 1802 2041 2363

50 1401 2060 2614 2959 3083 2993

60 1749 2468 2382 1989 1704 1476

70 1940 1513 1247 925 689 461

80 1300 933 524 326 154 90

90 884 498 255 150 71 37

100 880 285 135 26 7 0

110 408 146 37 4 7 7

120 131 49 15 4 0 0

130 64 4 0 0 0 0

140 311 26 0 0 0 0

150 45 0 0 0 0 0

170 0 0 0 0 0 0

190 7 0 0 0 0 0

200 52 0 0 0 0 0

210 0 0 0 0 0 0

230 49 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-21

Table J-21. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Indianapolis, 2013, 

adults.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 454 1164 2159 3379 4499 5569

10 3522 7648 10884 13168 14661 15899

20 5924 8301 8830 8662 8507 7835

30 6254 7331 6590 5557 4536 3952

40 6932 5688 4269 3279 2464 1886

50 5345 3342 2116 1388 940 523

60 3105 1369 728 324 187 143

70 1774 616 249 131 87 50

80 1151 317 87 44 37 25

90 554 112 37 19 6 0

100 454 87 25 6 0 0

110 243 19 6 0 0 0

120 106 12 6 6 0 0

130 19 0 0 0 0 0

140 93 6 6 0 0 0

150 68 6 0 0 0 0

170 0 0 0 0 0 0

190 6 0 0 0 0 0

200 25 0 0 0 0 0

210 19 0 0 0 0 0

230 19 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-22

Table J-22. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Tulsa, 2011, children.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 224 460 724 930 1168 1397

10 1679 2616 3040 3251 3281 3218

20 1887 1570 1166 881 698 589

30 807 452 302 266 218 181

40 429 228 167 99 66 49

50 223 104 49 23 21 13

60 119 23 8 8 5 5

70 48 8 5 2 0 0

80 20 2 0 0 0 0

90 16 0 0 0 0 0

100 8 0 0 0 0 0

110 2 0 0 0 0 0

120 2 0 0 0 0 0

130 0 0 0 0 0 0

140 2 0 0 0 0 0

150 0 0 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-23

Table J-23. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Tulsa, 2011, adults.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 4898 7860 9613 10849 11728 12248

10 6176 5478 4411 3487 2783 2341

20 2272 1052 618 437 306 244

30 772 333 214 134 89 59

40 437 163 86 36 18 15

50 258 74 24 9 6 0

60 92 15 0 0 0 0

70 50 3 0 0 0 0

80 9 0 0 0 0 0

90 12 0 0 0 0 0

100 6 0 0 0 0 0

110 6 0 0 0 0 0

120 0 0 0 0 0 0

130 3 0 0 0 0 0

140 0 0 0 0 0 0

150 0 0 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-24

Table J-24. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Tulsa, 2012, children.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 203 437 670 882 1105 1285

10 967 1547 1940 2209 2352 2444

20 2397 2476 2133 1823 1555 1353

30 965 551 432 351 307 284

40 607 356 238 175 130 81

50 147 76 46 18 7 5

60 92 15 2 0 0 0

70 38 3 0 0 0 0

80 30 0 0 0 0 0

90 15 0 0 0 0 0

100 2 0 0 0 0 0

110 0 0 0 0 0 0

120 2 0 0 0 0 0

130 0 0 0 0 0 0

140 2 0 0 0 0 0

150 3 0 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-25

Table J-25. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Tulsa, 2012, adults.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 4474 7260 8998 10201 11057 11633

10 5228 5017 4331 3615 3027 2611

20 3571 2020 1251 867 659 496

30 957 413 258 172 119 107

40 496 214 92 53 33 21

50 140 30 9 6 0 0

60 65 9 0 0 0 0

70 21 0 0 0 0 0

80 18 0 0 0 0 0

90 6 0 0 0 0 0

100 0 0 0 0 0 0

110 0 0 0 0 0 0

120 0 0 0 0 0 0

130 3 0 0 0 0 0

140 3 0 0 0 0 0

150 0 0 0 0 0 0

170 3 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-26

Table J-26. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Tulsa, 2013, children.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 300 582 813 1034 1234 1417

10 815 1183 1465 1646 1823 1951

20 3009 2987 2710 2436 2146 1885

30 691 422 279 233 178 142

40 576 266 183 104 73 53

50 61 25 10 5 3 3

60 13 2 0 0 0 0

70 0 0 0 0 0 0

80 0 0 0 0 0 0

90 0 0 0 0 0 0

100 2 0 0 0 0 0

110 0 0 0 0 0 0

120 0 0 0 0 0 0

130 0 0 0 0 0 0

140 0 0 0 0 0 0

150 0 0 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-27

Table J-27. Estimated daily maximum SO2 exposures for air quality adjusted to just meet 

existing standard, while at elevated ventilation (binned): Tulsa, 2013, adults.

POPULATION ADJUSTED EXPOSURE TO BINS (NUMBER OF PEOPLE)

Level

At least 1 

Exposure

At least 2 

Exposures

At least 3 

Exposures

At least 4 

Exposures

At least 5 

Exposures

At least 6 

Exposures

0 5190 8264 9901 10997 11698 12251

10 4688 4108 3508 2956 2519 2127

20 4180 2326 1438 924 656 487

30 576 220 83 45 36 24

40 333 59 27 18 6 6

50 24 3 3 0 0 0

60 0 0 0 0 0 0

70 0 0 0 0 0 0

80 3 0 0 0 0 0

90 0 0 0 0 0 0

100 0 0 0 0 0 0

110 0 0 0 0 0 0

120 0 0 0 0 0 0

130 0 0 0 0 0 0

140 0 0 0 0 0 0

150 0 0 0 0 0 0

170 0 0 0 0 0 0

190 0 0 0 0 0 0

200 0 0 0 0 0 0

210 0 0 0 0 0 0

230 0 0 0 0 0 0

250 0 0 0 0 0 0

300 0 0 0 0 0 0

350 0 0 0 0 0 0

400 0 0 0 0 0 0

450 0 0 0 0 0 0

500 0 0 0 0 0 0

550 0 0 0 0 0 0

600 0 0 0 0 0 0
J-28

Figure J-1. Estimated percent of children with asthma expected to experience daily 

maximum 5-minute SO2 exposures at or above selected levels in Fall River 

study area, air quality adjusted to just meet the existing standard, 2011-2013 

(top to bottom panels).
J-29

Figure J-2. Estimated percent of adults with asthma expected to experience daily maximum 

5-minute SO2 exposures at or above selected levels in Fall River study area, air 

quality adjusted to just meet the existing standard, 2011-2013 (top to bottom 

panels).
J-30

Figure J-3. Estimated percent of children with asthma expected to experience daily 

maximum 5-minute SO2 exposures at or above selected levels in Indianapolis 

study area, air quality adjusted to just meet the existing standard, 2011-2013 

(top to bottom panels).
J-31

Figure J-4. Estimated percent of adults with asthma expected to experience daily maximum 

5-minute SO2 exposures at or above selected levels in Indianapolis study area, 

air quality adjusted to just meet the existing standard, 2011-2013 (top to bottom 

panels).
J-32

Figure J-5. Estimated percent of children with asthma expected to experience daily 

maximum 5-minute SO2 exposures at or above selected levels in Tulsa study 

area, air quality adjusted to just meet the existing standard, 2011-2013 (top to 

bottom panels).
J-33

Figure J-6. Estimated percent of adults with asthma expected to experience daily maximum 

5-minute SO2 exposures at or above selected levels in Tulsa study area, air 

quality adjusted to just meet the existing standard, 2011-2013 (top to bottom 

panels).
J-34

Table J-28. Exposure-Response Function for SO2-attributable increases (>100% and 

>200%) in sRaw: Mean, lower prediction interval and upper prediction 

interval. 

E-R sRaw 100% E-R sRaw 200%

exposure mean lower upper exposure mean lower upper

5 2.49E-07 2.87E-10 5.74E-05 5 5.77E-08 6.95E-12 6.09E-05

15 4.02E-05 6.70E-07 1.14E-03 15 8.64E-06 3.07E-08 7.35E-04

25 2.92E-04 1.33E-05 3.71E-03 25 6.38E-05 8.34E-07 2.04E-03

35 9.45E-04 7.74E-05 7.53E-03 35 2.13E-04 5.97E-06 3.81E-03

45 2.12E-03 2.58E-04 1.24E-02 45 4.93E-04 2.33E-05 5.93E-03

55 3.90E-03 6.33E-04 1.79E-02 55 9.32E-04 6.48E-05 8.32E-03

65 6.28E-03 1.28E-03 2.41E-02 65 1.55E-03 1.45E-04 1.09E-02

75 9.26E-03 2.25E-03 3.08E-02 75 2.34E-03 2.81E-04 1.37E-02

85 1.28E-02 3.61E-03 3.78E-02 85 3.33E-03 4.88E-04 1.66E-02

95 1.69E-02 5.40E-03 4.51E-02 95 4.50E-03 7.83E-04 1.96E-02

105 2.15E-02 7.64E-03 5.26E-02 105 5.86E-03 1.18E-03 2.27E-02

115 2.66E-02 1.03E-02 6.03E-02 115 7.40E-03 1.69E-03 2.59E-02

125 3.21E-02 1.35E-02 6.81E-02 125 9.11E-03 2.33E-03 2.92E-02

135 3.80E-02 1.71E-02 7.60E-02 135 1.10E-02 3.10E-03 3.25E-02

145 4.41E-02 2.12E-02 8.39E-02 145 1.30E-02 4.02E-03 3.58E-02

160 5.40E-02 2.81E-02 9.59E-02 160 1.63E-02 5.67E-03 4.09E-02

180 6.80E-02 3.87E-02 1.12E-01 180 2.13E-02 8.42E-03 4.79E-02

195 7.90E-02 4.76E-02 1.24E-01 195 2.53E-02 1.09E-02 5.31E-02

205 8.65E-02 5.39E-02 1.32E-01 205 2.81E-02 1.27E-02 5.66E-02

220 9.80E-02 6.38E-02 1.44E-01 220 3.25E-02 1.57E-02 6.19E-02

240 1.14E-01 7.79E-02 1.60E-01 240 3.87E-02 2.03E-02 6.91E-02

275 1.42E-01 1.04E-01 1.87E-01 275 5.04E-02 2.95E-02 8.17E-02

325 1.82E-01 1.44E-01 2.26E-01 325 6.83E-02 4.48E-02 1.00E-01

375 2.22E-01 1.83E-01 2.64E-01 375 8.72E-02 6.20E-02 1.20E-01

425 2.60E-01 2.20E-01 3.03E-01 425 1.07E-01 7.99E-02 1.40E-01

475 2.97E-01 2.55E-01 3.41E-01 475 1.27E-01 9.77E-02 1.61E-01

525 3.32E-01 2.87E-01 3.80E-01 525 1.47E-01 1.15E-01 1.84E-01

575 3.65E-01 3.15E-01 4.17E-01 575 1.67E-01 1.31E-01 2.08E-01
K-1

APPENDIX K

DAYTIME HOURLY CONCENTRATION ESTIMATES AND MEASUREMENTS BY 

SEASON

This appendix relates to the evaluation described in section 3.2.5. This evaluation is 

intended to inform the extent to which occurrences of the relatively higher daytime concentration 

events at ambient air monitors are reflected in the distribution of daytime model predicted 

concentrations. The following steps were performed to prepare the ambient air monitor and 

modeled concentration data sets for this evaluation.

(1) Selection of the datasets to best represent the distributions of ambient air monitor and 

model concentrations. For the monitor data, we used the reported unadjusted (as is) 

values with no augmentation for time points where missing. The monitor datasets used 

met completeness criteria (section 3.5.1) for each year used (2011-2013), although some 

seasons may be relatively more (or less) complete than others, even within the 3-year 

pooled dataset. The AERMOD estimates provide a complete time-series of hourly 

ambient air concentrations in each year. Similar to the Appendix D evaluation, this 

evaluation uses the AERMOD estimates (as is) for each monitor site. 

(2) Stratification of the monitor and model distributions of hourly concentrations by time of 

day and season. Time-of-day was split into two categories: daytime (hours most likely 

associated with population exposure) included the hours of 6AM to 8PM, and nighttime 

included all other hours. Seasons were stratified as winter (December, January, 

February), spring (March, April, May), summer (June, July, August), and fall 

(September, October, November). The result is 8 datasets of monitor concentrations and 

of model concentrations (winter daytime, winter nighttime, spring daytime, etc.) for each 

monitor location in each study area.

(3) Addressing missing monitor concentrations for some time points (while having complete 

model concentrations).

1 The model receptor concentration distribution was paired with 

the monitor concentration distribution based on percentile within each distribution.

The paired model and monitor concentration distributions were plotted for each of the 

two times-of-day and four seasons. Figure 3-1 presents the figures for the monitor location in 

each study area with the highest design value and for the daytime hours in the three warmer 

 

1 While the air quality model predicts values for every day and hour, an ambient air monitor typically does not 

measure for every hour in every year. Therefore, this distribution of values was calculated for each data set to 

have an equal reference point rather than compare only concentrations for reported measurements. To maximize 

the relative number of percentiles with respect to hours of data points in each season (which generally range from 

2,730 to 3,864), the 0 to 100 percentiles were calcuated using every 0.04 percentile, thus 2,500 values were 

generated for every season and time of day pair.
K-2

months. The complete set of daytime graphs for all monitor locations and seasons are provided 

here.

Figure K-1. Comparison of predicted and observed hourly SO2 concentrations in ambient 

air (2011-2013): Fall River monitor 250051004 having the highest design value

in study area.
K-3

 

Figure K-2. Comparison of predicted and observed hourly SO2 concentrations in ambient 

air (2011-2013): Indianapolis monitor 180970057 having the highest design 

value in study area.
K-4

Figure K-3. Comparison of predicted and observed hourly SO2 concentrations in ambient 

air (2011-2013): Indianapolis monitor 180970073.
K-5

Figure K-4. Comparison of predicted and observed hourly SO2 concentrations in ambient 

air (2011-2013): Indianapolis monitor 180970078.
K-6

Figure K-5. Comparison of predicted and observed hourly SO2 concentrations in ambient 

air (2011-2013): Tulsa monitor 401430175 having the highest design value in 

the study area.
K-7

 

Figure K-6. Comparison of predicted and observed hourly SO2 concentrations in ambient 

air (2011-2013): Tulsa monitor 401430235.
K-8

Figure K-7. Comparison of predicted and observed hourly SO2 concentrations in ambient 

air (2011-2013): Tulsa monitor 401431127.
United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, NC

Publication No. EPA-452/R-18-003

May 2018