October 2012

Prepared for

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

Research Triangle Park, NC

Neal Fann, Project Manager

Prepared by

Abt Associates Inc.

User's Manual
October 2012

2

BenMAP User's Manual

Table of Contents

1 Welcome to BenMAP 6

1.1 Overvi.e..w....o..f..B..e..n..M...A..P...&...B...e..n..e..f.i.t.s..A..s..s.e..s..s.m...e..n..t........................................................................ 8

1.2 How t.o...U...s.e...t.h..i.s...M..a..n..u..a..l....................................................................................................... 10

1.3 Comp..u..t.e..r..R..e..q..u..i.r.e..m...e..n..t.s...................................................................................................... 11

1.4 Instal.l.i.n..g...B..e..n..M...A..P................................................................................................................ 12

1.4.1 Upgrading.t..o...a...n..e...w....v..e..r..s..i.o...n...o..f...B..e..n...M...A...P....................................................................................................... 14

1.5 Unins.t.a..l.l.i.n..g...B..e..n..M...A..P............................................................................................................ 14

1.6 Conta..c.t.s...f.o..r..C..o..m...m...e..n..t.s..,..Q..u..e..s..t.i.o..n..s..&....B..u..g...R..e..p..o..r.t.i.n..g............................................................ 15

1.7 Sourc..e..s..f.o..r..M...o..r.e...I.n..f.o..r..m...a..t.i.o..n.............................................................................................. 16

1.8 Frequ..e..n..t.l.y..A...s.k..e..d...Q..u..e..s..t.i.o..n..s..(.G...e..n..e..r.a..l.)................................................................................ 17

2 Terminology and File Types 22

2.1 Comm...o..n...T..e..r.m...s................................................................................................................... 23

2.2 File T.y..p..e..s............................................................................................................................ 31

3 Overview of BenMAP Components 33

3.1 One-S..t.e..p...A..n..a..l.y..s..i.s............................................................................................................... 35

3.1.1 One-Step R..e..p...o..r..t.s.............................................................................................................................................. 36

3.1.2 Questions..R..e..g...a..r.d...i.n..g...t..h..e...O...n..e...-.S..t..e..p...A...n..a..l.y..s...i.s.............................................................................................. 39

3.2 Custo.m....A..n..a..l.y..s..i.s.................................................................................................................. 43

3.2.1 Create Air.Q...u..a..l.i.t..y...G..r..i.d..s.................................................................................................................................... 43

3.2.2 Create and...R..u..n....C..o...n..f.i.g...u..r..a..t.i.o..n......................................................................................................................... 44

3.2.3 Aggregatio..n..,..P...o..o..l.i.n...g..,..a..n..d....V..a..l.u...a..t.i.o..n.............................................................................................................. 46

3.2.4 Generate R..e..p...o..r..t.s.............................................................................................................................................. 49

3.3 Menu.s.................................................................................................................................. 52

3.3.1 Tools .......................................................................................................................................................... 52

4 Loading Data 55

4.1 Add, .M..o..d..i.f.y..,..a..n..d...D..e..l.e..t.e...S...e..t.u..p..s.......................................................................................... 56

4.1.1 Grid Defini.t.i.o..n...s.................................................................................................................................................. 58

Regular Gr.i.d....................................................................................................................................................... 60

Shapefile G..r..id..................................................................................................................................................... 62

4.1.2 Pollutants .......................................................................................................................................................... 63

Hourly Met.r.i.c..s.................................................................................................................................................... 66

Seasonal M..e..t.r..ic..s................................................................................................................................................ 70

Advanced.P..o..l.l.u..t.a..n..t..O..p..t.i.o..n..s............................................................................................................................... 72

4.1.3 Monitor Da.t..a....................................................................................................................................................... 73

Format Mon..i.t.o..r..D...a..t.a........................................................................................................................................... 76

4.1.4 Incidence/P...r.e...v..a..l.e..n..c..e....D..a..t.a............................................................................................................................... 79
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BenMAP User's Manual

Format Inci.d..e..n..c..e../.P..r.e..v..a..l.e..n..c..e...D..a..t.a..................................................................................................................... 82

4.1.5 Population..D..a..t..a.................................................................................................................................................. 83

Format Pop..u..la..t.i.o..n...D..a..t.a....................................................................................................................................... 90

4.1.6 Health Imp.a..c..t..F..u...n..c..t.i.o...n..s................................................................................................................................... 91

Format Hea..l.t.h...Im...p..a..c..t..F..u..n..c..t.i.o..n..s......................................................................................................................... 95

4.1.7 Variable Da..t.a....................................................................................................................................................... 97

Format Va..r..ia..b..l.e...D..a..t.a........................................................................................................................................ 102

4.1.8 Inflation D..a..t.a..................................................................................................................................................... 103

Format Inf..la..t.i.o..n...D..a..t.a......................................................................................................................................... 104

4.1.9 Valuation..D..a..t.a................................................................................................................................................... 105

Format Va..l.u..a..t.i.o..n...D..a..t.a...................................................................................................................................... 108

4.1.10 Income G.r..o..w...t..h...D..a..t..a....................................................................................................................................... 109

Format Inc..o..m...e...G...r.o..w...t.h...D...a..t.a............................................................................................................................ 113

4.1.11 QALY Data.......................................................................................................................................................... 114

Format QA...L..Y....D..a..t.a........................................................................................................................................... 116

4.2 Expo..r.t..a..n..d...I.m...p..o..r..t..S..e..t.u..p..s.................................................................................................. 117

4.2.1 Export Se.t.u...p..s................................................................................................................................................... 117

4.2.2 Import Se..t.u..p...s.................................................................................................................................................. 120

4.3 Que.s.t.i.o..n..s...R..e..g..a..r.d..i.n..g...L..o..a..d..i.n..g...D..a..t.a.................................................................................... 121

5 Air Quality Grid Creation 123

5.1 Mod.e..l..D..i.r.e..c..t...................................................................................................................... 125

5.2 Mon.i.t.o..r..D..i.r..e..c.t.................................................................................................................... 127

5.2.1 Closest M..o...n..i.t.o...r..f..o..r...M...o..n...i.t.o..r...D..i.r..e..c..t............................................................................................................. 129

5.2.2 Voronoi N..e..i.g..h...b..o..r...A...v..e..r..a..g..i.n..g....(.V...N..A...)..f.o...r..M....o..n..i.t..o..r...D..i.r..e..c..t........................................................................... 129

5.2.3 Fixed Rad.i.u..s....f.o..r...M...o...n..i.t.o...r..D...i.r.e...c..t................................................................................................................... 132

5.2.4 Custom M...o..n..i.t..o..r...F..i.l.t.e..r..i.n..g....f.o..r...E..P..A....S..t..a..n..d..a..r..d...M....o..n..i.t..o..r..s............................................................................. 132

5.3 Mon.i.t.o..r..a..n..d...M...o..d..e..l..R..e..l.a..t.i.v..e............................................................................................... 137

5.3.1 Spatial Sc.a..l.i.n..g................................................................................................................................................... 138

5.3.2 Temporal..S...c..a..l.i.n..g............................................................................................................................................. 139

5.3.3 Spatial an.d...T...e..m....p..o..r..a..l..S..c..a..l.i.n...g........................................................................................................................ 139

5.3.4 Examples.......................................................................................................................................................... 140

5.4 Mon.i.t.o..r..R..o..l.l.b..a..c..k................................................................................................................ 142

5.4.1 Example:.C...o..m....b..i.n..i.n...g...T..h...r.e...e...R..o...l.l.b..a..c..k...A...p..p...r.o...a..c..h..e..s....i.n...D..i.f..f.e..r..e..n...t..R..e...g..i.o..n...s.............................................. 146

5.5 Que.s.t.i.o..n..s...R..e..g..a..r.d..i.n..g...C..r..e..a..t.i.n..g...A..i.r..Q...u..a..l.i.t.y..G...r.i.d..s................................................................ 150

6 Incidence Estimation 152

6.1 Intro.d..u..c..t.i.o..n...t.o...E..s..t.i.m...a..t.i.n..g...H..e..a..l.t.h...I.n..c..i.d..e..n..c.e....................................................................... 153

6.2 Crea..t.e...H..e..a..l.t.h...I.m...p..a..c..t..C..o..n..f.i.g..u..r.a..t.i.o..n................................................................................... 154

6.2.1 Configura..t.i.o..n....S..e..t..t.i.n..g...s................................................................................................................................... 154

6.2.2 Select He.a..l.t.h...I.m....p..a..c..t...F..u..n..c..t..i.o..n..s..................................................................................................................... 156

6.3 Run.H...e..a..l.t.h...I.m...p..a..c.t..C...o..n..f.i.g..u..r.a..t.i.o..n....................................................................................... 159

6.4 Ope.n...&...M...o..d..i.f.y..E..x..i.s..t.i.n..g...H..e..a..l.t.h...I.m...p..a..c..t..C..o..n..f.i.g..u..r.a..t.i.o..n......................................................... 160

6.5 Que.s.t.i.o..n..s...R..e..g..a..r.d..i.n..g...H..e..a..l.t.h...I.m...p..a..c..t..C..o..n..f.i.g..u..r..a..t.i.o..n..s.......................................................... 160

7 Aggregation, Pooling, and Valuation 162
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7.1 Intro.d..u..c..t.i.o..n...t.o...V..a..l.u..a..t.i.o..n..,..D...i.s.c..o..u..n..t.i.n..g..,..a..n..d...P..o..o..l.i.n..g.......................................................... 163

7.1.1 Overview..o..f...V..a..l.u...a..t.i.o..n..................................................................................................................................... 163

Valuing R.e..d..u..c..t.i.o..n..s...in...P..r..e..m..a..t.u..r..e...M..o..r..t.a..li.t.y....................................................................................................... 165

7.1.2 Overview..o..f...D..i.s..c..o...u..n..t..i.n..g................................................................................................................................ 165

7.1.3 Overview..o..f...P..o..o...l.i.n..g........................................................................................................................................ 170

7.2 Crea..t.e...A..g..g..r.e..g..a..t.i.o..n..,..P..o..o..l.i.n..g..,..a..n..d...V...a..l.u..a..t.i.o..n...(.A..P..V..)..C...o..n..f.i.g..u..r.a..t.i.o..n..................................... 170

7.2.1 Pooling In..c..i.d..e..n...c..e...R..e...s..u..l.t..s............................................................................................................................. 171

Example: .S..i.m...p..le...S...o..r.t.i.n..g...&...P..o..o..l.in..g.................................................................................................................... 177

Example: .M...u..lt.i.p..l.e...P..o..o..li.n..g...W...i.n..d..o..w...s.................................................................................................................. 180

7.2.2 Valuing P.o..o..l.e...d...I.n..c..i.d...e..n..c..e....R..e..s...u..l.t.s................................................................................................................ 181

7.2.3 Using QAL...Y..s...W....e..i.g..h...t.s..................................................................................................................................... 186

7.2.4 APV Conf.i.g..u...r.a..t..i.o..n...A...d..v..a..n...c..e..d...S...e..t.t..i.n..g..s........................................................................................................ 190

Aggregat.io..n...&....P..o..o..li.n..g...................................................................................................................................... 191

Currency..&...I.n..c..o..m..e........................................................................................................................................... 193

7.3 Run.A...P..V...C..o..n..f.i.g..u..r..a..t.i.o..n...................................................................................................... 195

7.4 Ope.n...&...M...o..d..i.f.y..E..x..i.s..t.i.n..g...A..P..V....C..o..n..f.i.g..u..r.a..t.i.o..n........................................................................ 195

7.5 Que.s.t.i.o..n..s...R..e..g..a..r.d..i.n..g...A..P...V...C..o..n..f.i.g..u..r.a..t.i.o..n..s.......................................................................... 196

8 Reports 198

8.1 One.-.S..t.e..p...R..e..p..o..r.t.s............................................................................................................... 200

8.2 Incid..e..n..c..e..,..V..a..l.u..a..t.i.o..n..,..&...Q...A..L..Y...-..f.r.o..m....A..P...V..R...f.i.l.e.................................................................. 202

8.2.1 Incidence..R...e..s..u...l.t.s............................................................................................................................................ 204

8.2.2 Aggregat.e..d...I.n...c..i.d..e..n...c..e...R..e...s..u..l.t..s..................................................................................................................... 206

8.2.3 Pooled In.c..i.d..e..n...c..e...R..e...s..u..l.t..s.............................................................................................................................. 207

8.2.4 Valuation..R..e..s...u..l.t.s............................................................................................................................................. 208

Add Sums...B...u..t.t.o..n............................................................................................................................................. 209

8.2.5 Aggregat.e..d...V...a..l.u..a..t..i.o..n...R...e..s..u...l.t.s...................................................................................................................... 210

8.2.6 Pooled Va..l.u..a..t..i.o..n...R...e..s..u...l.t.s............................................................................................................................... 211

8.2.7 QALY Res..u..l.t.s.................................................................................................................................................... 212

8.2.8 Aggregat.e..d...Q...A...L..Y...R...e..s..u...l.t.s............................................................................................................................. 213

8.2.9 Pooled QA...L..Y...R...e..s..u...l.t.s...................................................................................................................................... 214

8.2.10 Export Inc..i.d..e..n...c..e..,..V...a..l.u..a..t.i.o...n..,..&....Q..A...L..Y....R..e..p...o..r..t.s............................................................................................ 215

8.3 Raw..I.n..c..i.d..e..n..c..e...-..f.r.o..m....C..F..G...R...f.i.l.e......................................................................................... 216

8.3.1 Export Ra.w....I.n..c..i.d...e..n..c..e...................................................................................................................................... 217

8.4 Audi.t..T..r..a..i.l..R..e..p..o..r.t.s...-..f.r.o..m....a..l.l..B..e..n..M...A..P...f.i.l.e..s........................................................................ 218

8.4.1 Export Au.d...i.t..T..r..a..i.l............................................................................................................................................ 219

8.5 Que.s.t.i.o..n..s...R..e..g..a..r.d..i.n..g...C..r..e..a..t.i.n..g...R..e..p..o..r.t.s.............................................................................. 220

9 GIS/Mapping 222

9.1 Over..v.i.e..w....o..f..M...a..p..p..i.n..g......................................................................................................... 223

9.1.1 Display Op...t.i.o..n...s................................................................................................................................................ 224

9.1.2 Taskbar B..u..t..t.o..n...s.............................................................................................................................................. 226

9.2 Map.p..i.n..g...f.r.o..m....t.h..e...T..o..o..l.s...M..e..n..u............................................................................................ 228

9.2.1 Air Quality...G...r..i.d..s............................................................................................................................................... 230

9.2.2 APV Conf.i.g..u...r.a..t..i.o..n...R...e..s..u...l.t.s............................................................................................................................ 232

9.2.3 Configura..t.i.o..n....R..e..s...u..l.t.s..................................................................................................................................... 236

9.2.4 Modeling..D..a..t.a................................................................................................................................................... 240
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9.2.5 Monitorin.g....D..a..t.a................................................................................................................................................ 241

9.2.6 Populatio.n...D..a..t..a................................................................................................................................................ 245

9.2.7 Mapping M....u..l.t.i.p...l.e...L..a..y..e...r.s....o..f..D...a..t.a.................................................................................................................. 246

9.3 View..i.n..g...M...a..p..s..i.n...a...B...e..n..M..A...P...A..n..a..l.y..s.i.s................................................................................. 248

9.3.1 Mapping A...i.r...Q..u...a..l.i.t.y...D..e...l.t.a..s............................................................................................................................. 249

9.3.2 Mapping M....o..n..i.t..o..r...D..i.r..e..c..t..A...i.r...Q..u...a..l.i.t.y...G...r.i.d...s................................................................................................... 250

9.3.3 Mapping A...i.r...Q..u...a..l.i.t.y...M...o...n..i.t.o...r.s....f.r..o..m.....t.h..e....A..d...v..a..n..c..e..d....M...o..n...i.t.o..r...F..i.l.t.e...r......................................................... 252

9.3.4 Mapping M....o..n..i.t..o..r...R..o..l.l.b...a..c..k...I.n..p...u..t.s....&...O...u..t..p..u..t..s............................................................................................. 255

9.4 Que.s.t.i.o..n..s...R..e..g..a..r.d..i.n..g...M...a..p..p..i.n..g........................................................................................... 257

10 Tools Menu 259

10.1 Air Q..u..a..l.i.t.y...G..r..i.d...A..g..g..r.e..g..a..t.o..r............................................................................................... 261

10.2 Mod.e..l..F..i.l.e...C..o..n..c..a..t.e..n..a..t.o..r................................................................................................... 263

10.3 Expo..r.t..A...i.r..Q..u..a..l.i.t.y...G...r.i.d....................................................................................................... 267

10.4 Neig.h..b..o..r...F..i.l.e...C..r.e..a..t.o..r........................................................................................................ 269

10.5 One.-.S..t.e..p...S..e..t.u..p.................................................................................................................. 271
Welcome to BenMAP

October 2012 6BenMAP User's Manual

CHAPTER 1

Welcome to

BenMAP

In this chapter...

Find an overview of the model.

Find installation instructions.

Find contacts, sources for more information and

answers to frequently asked general questions.
Welcome to BenMAP

October 2012 7BenMAP User's Manual

BenMAP is a powerful, yet easy-to-use program that helps you estimate human health benefits

resulting from changes in air quality. The U.S. Environmental Protection Agency (U.S. EPA)

originally developed this tool to analyze national-scale air quality regulations, including the

National Ambient Air Quality Standards for Particulate Matter (2006) and Ozone (2008) as well

as the Locomotive Marine Engine Rule (2008). To accommodate a wider range of potential

users interested in simplified analyses, local/regional analyses, or international analyses, EPA

developed a more flexible version of BenMAP that includes options for a one-step analysis or a

custom analysis.

A wide range of users, including scientists, policy analysts, and decision makers, can utilize

BenMAP to answer many policy questions, such as estimating the benefits from previous

reductions in air pollution emissions or the benefits of new requirements to control air pollution.
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October 2012 8BenMAP User's Manual

Advanced users can explore a wide range of options in the custom analysis, such as adding new

health impact and valuation functions, mapping results, and performing sensitivity analyses.

Beginning users can take advantage of EPA’s pre-programmed settings and reports in the onestep analysis.

1.1 Overview of BenMAP & Benefits Assessment

The BenMAP program estimates the human health impacts and economic value of air quality

changes; it is principally a tool that characterizes the benefits of changes in air quality policy. That

is — BenMAP relates air quality changes to human health benefits. Such analyses are a critical

component of air quality policy assessments, and a variety of Federal, State and Local air

pollution officials have used BenMAP to inform air quality management decisions.

BenMAP estimates benefits from improvements in human health, such as reductions in premature

mortality, heart attacks, chronic respiratory illnesses, and other adverse health effects. Other

benefits of reducing air pollution (i.e., visibility and ecosystem effects) are not quantified in the

current version of BenMAP. After estimating the reductions in adverse health effects, BenMAP

calculates the monetary benefits associated with those reductions.

How does BenMAP estimate human health effects?

First, BenMAP determines the change in ambient air pollution using user-specified air quality

data. Because BenMAP does not include an air quality model, this data must be input into

BenMAP as modeling data or generated from air pollution monitoring data pre-loaded in

BenMAP. Next, BenMAP calculates the relationship between the pollution and certain health

effects (also known as health endpoints). This step is often referred to as the health impact

function or the concentration-response (C-R) function in epidemiology studies. Finally, BenMAP

applies that relationship to the exposed population.

A simplified example is shown below.

Health Effect = Air Quality Change * Health Effect Estimate * Exposed Population * Health

Baseline Incidence

Air Quality Change. The air quality change is the difference between the starting air pollution

level, (i.e., the baseline), and the air pollution level after some change, such as a new regulation

(i.e., the control).

Health Effect Estimate. The health effect estimate is an estimate of the percentage change in an

adverse health effect due to a one unit change in ambient air pollution. Epidemiologicalstudies

provide a good source for effect estimates.

Exposed Population. The exposed population is the number of people affected by the air

pollution reduction. The government census office is a good source for this information. In

addition, private companies may collect this information and offer it for sale.
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October 2012 9BenMAP User's Manual

Health Baseline Incidence. The health incidence rate is an estimate of the average number of

people that die (or suffer from some adverse health effect) in a given population over a given

period of time. For example, the health incidence rate might be the probability that a person

will die in a given year. Health incidence rates and other health data are typically collected by

the government. In addition the World Health Organization is a good source for this. See

World Health Organization

How does BenMAP estimate the economic value of human health effects?

BenMAP also calculates the economic value of avoided health effects (see Section 7 for details).

After calculating the health changes, you can estimate the economic value by multiplying the

reduction of the health effect by an estimate of the economic value per case:

Economic Value = Health Effect * Value of Health Effect

There are several different ways of calculating the value of the health effect. For example, the

value of an avoided premature mortality is generally calculated using the Value of Statistical Life

(VSL). The value of a statistical life is the monetary value that people are willing to pay to slightly

reduce the risk of premature death. For other health effects, the medical costs of the illness may

be the only valuation data available. The BenMAP database includes several different functions

for VSL and valuation functions for other health effects for you to choose, or you can rely on

EPA’s preloaded selections.

Figure 1-1 summarizes the basic steps in BenMAP. This figure shows the types of choices that

you make regarding the modeling of population exposure, the types of health effects to model,

and how to place an economic value on these health effects. Please note that BenMAP does not

have air modeling capabilities, and instead relies on modeling and monitoring inputs.
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October 2012 10BenMAP User's Manual

Figure 1-1. BenMAP Flow Diagram

Census

Population

Data

Air Quality

Monitoring

Health

Functions

Valuation

Functions

Population

Projections

Air Quality

Modeling

Incidence and

Prevalence

Rates

Population

Estimates

Population

Exposure

Adverse

Health Effects

Economic

Costs

Black text represents user input

Blue text represents result from inputs

What else can BenMAP do?

BenMAP incorporates a Geographic Information System (GIS), allowing users to create, utilize,

and visualize maps of air pollution, population, incidence rates, incidence rate changes, economic

valuations, and other types of data.

BenMAP can thus be used for a variety of purposes, including:

Generating population/community level ambient pollution exposure maps;

Comparing benefits associated with various regulatory programs;

Characterizing the distribution of health impacts among population sub-groups;

Estimating health impacts and economic value of existing air pollution concentrations;

Estimating health benefits of alternative ambient air quality standards; and

Performing sensitivity analyses of health or valuation functions, or of other inputs.

1.2 How to Use this Manual

Chapters 2 through 9 of this manual provide step-by-step instructions on how to use BenMAP.

New users should start with Chapters 2 and 3, which are both very short, but provide a good
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October 2012 11BenMAP User's Manual

overview of the model and how it works, and explain some potentially confusing terminology.

Use the rest of the manual to answer any specific questions you may have, or to walk you stepby-step through the various modelfunctions. Chapter 4 discusses how to enter data into

BenMAP, Chapters 5 through 8 cover each of the four main buttons, and Chapter 9 covers

mapping.

Each chapter is introduced by a short section which describes what you can find within the

chapter, and provides an outline of the chapter’s contents. This is a good place to go if the Table

of Contents does not provide enough detailfor you to find the section you need. The end of

most chapters has a series of “Frequently Asked Questions,” which may also be helpful in

answering specific questions. In sections that provide instructions on navigating the model, the

following conventions are observed: menu items, buttons, and tab and selection box labels are in

bold type; prompts and messages are enclosed in quotation marks; and drop-down menu items,

options to click or check, and items that need to be filled in or selected by the user are italicized.

Throughout the chapters you will also see boxes presenting common mistakes and important

things to remember when working with BenMAP.

There is also a set of Technical Appendices to provide more detailed information on model

functions, data, and underlying assumptions.

Appendix A: Monitor Rollback Algorithms

Appendix B: Air Pollution Exposure Estimation Algorithms

Appendix C: Deriving Health Impact Functions

Appendix D: Health Incidence & Prevalence Data in U.S. Setup

Appendix E: Particulate Matter Health Impact Functions in U.S. Setup

Appendix F: Ozone Health Impact Functions in U.S. Setup

Appendix G: Nitrogen Dioxide Health Impact Functions in U.S. Setup

Appendix H: Sulfur Dioxide Health Impact Functions in U.S. Setup

Appendix I: Health Valuation Functions in U.S. Setup

Appendix J: Population & Other Data in U.S. Setup

Appendix K: Uncertainty & Pooling

Appendix L: Command Line BenMAP

Appendix M: Function Editor

References

1.3 Computer Requirements

The computer hardware requirements for BenMAP are typically modest, though this will again

vary depending on the complexity of the analysis. BenMAP requires a Windows platform, and

can be used on machines running Windows 2000, as well as more recent versions of Windows.

In particular, BenMAP requires a computer with:

Windows 2000 or greater.

Adobe Acrobat Reader
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October 2012 12BenMAP User's Manual

128 megabytes of RAM or greater. In general, the amount of RAM required depends on the

scope of the Grid Definitions defined for the model (specifically, the number of cells within the

Grid Definitions). If the largest grid contains hundreds of cells, 128 MB is required. If the

largest grid contains thousands of cells, 256-512 MB is required. If the largest grid contains tens

of thousands of cells, 1 GB is required.

Intel® or compatible processor, Pentium 166 MHz or higher. 1 GHz processor or greater

recommended for optimum performance.

A CD-ROM drive for CD based installation. Alternatively, an internet connection might be used

to download the (~350 MB) installer.

At least 3.5 GB free space is recommended for the installation of BenMAP U.S. version.

1.4 Installing BenMAP

The Installation of BenMAP 4.0 is very simple. Double click the Setup.exe in your installation

directory to bring up the setup wizard. Then follow the setup wizard by clicking 'Next' or 'OK'

to complete the installation.
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1.4.1 Upgrading to a new version of BenMAP

Periodically, new versions of BenMAP will be made available and posted to the BenMAP

website: http://www.epa.gov/air/benmap/. The upgrade just requires replacing the executable ("

BenMap 4.0.exe") located in the BenMAP directory, which is typically located at C:\Program

Files\BenMAP 4.0. The executable is a relatively smallfile, about 8 megabytes, so the upgrade

process is generally very fast.

For upgrades that require replacing or otherwise updating the underlying BenMAP databases,

then it might be necessary to completely uninstallBenMAP and then reinstallBenMAP with a

new installer package. A new installer package can be relatively large, about 400 megabytes for

the U.S. version. Luckily a complete reinstallation is generally not necessary. Simply replacing

the executable or importing new databases (discussed here in the Load Data chapter) is usually

all that is necessary.

1.5 Uninstalling BenMAP

To uninstallBenMAP, go to Control Panel, Add/Remove Programs and remove BenMAP

4.0. Note that uninstalling BenMAP does not also remove any files that you have created with

BenMAP.

When choosing to remove BenMAP 4.0, the Add or Remove Programs window should look

something like the following:
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1.6 Contacts for Comments, Questions & Bug Reporting

For comments and questions, please contact Neal Fann at the United States Environmental

Protection Agency.

Address: C539-07, U.S. EPA Mailroom, Research Triangle Park, NC 27711

Email: fann.neal@epa.gov

Telephone: 919-541-0209
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October 2012 16BenMAP User's Manual

Alternatively, you can send a message at the BenMAP website: http://www.epa.gov/air/benmap/

contact.html, or by simply emailing benmap@epa.gov.

To report programming bugs in BenMAP:

Community Modeling and Analysis System (CMAS) Center at the University of North

Carolina at Chapel Hill, BenMAP Bugzilla website. Available at: http://benmap-model.org/

1.7 Sources for More Information

For files that you can use in BenMAP:

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

(OAQPS), BenMAP website. Available at: http://www.epa.gov/air/benmap/

For more information on conducting benefit analysis, see the following documents:

U.S. EPA (1999). OAQPS Economic Analysis Resource Document. Office of Air Quality

Planning and Standards, Innovative Strategies and Economics Group. Available at: http://

www.epa.gov/ttn/ecas/analguid.html

U.S. EPA (various years). Costs and Benefits of the Clean Air Act. Available at: http://www.

epa.gov/oar/sect812/

U.S. EPA (2004). FinalRegulatory Analysis:Control of Emissions from Nonroad Diesel

Engines. EPA420-R-04-007. May. See:Chapter 9. Available at: http://www.epa.gov/

nonroad-diesel/2004fr/420r04007a.pdf

U.S. EPA (2006). FinalRegulatory Impact Analysis: 2006 National Ambient Air Quality

Standards for Particulate Matter. Office of Air Quality Planning and Standards. See:Chapter

5. Available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf

U.S. EPA (2008). Final Ozone NAAQS Regulatory Impact Analysis. Office of Air Quality

Planning and Standards. March. See: Chapter 6. Available at: http://www.epa.gov/ttn/ecas/

regdata/RIAs/6-ozoneriachapter6.pdf

U.S. EPA (2008). Regulatory Impact Analysis:Control of Emissions of Air Pollution from

Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per

Cylinder. Office of Transportation and Air Quality. EPA420-R-08-001a. May. See:Chapter

6. Available at: http://www.epa.gov/otaq/regs/nonroad/420r08001a.pdf

U.S. EPA (2010). FinalRegulatory Impact Analysis (RIA) for the SO2 National Ambient Air

Quality Standards (NAAQS). Office of Air Quality Planning and Standards. June. See:

Chapter 5. Available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602ch5.pdf

U.S. EPA (2010). FinalRegulatory Impact Analysis (RIA) for the NO2 National Ambient Air

Quality Standards (NAAQS). Office of Air Quality Planning and Standards. June. See:

Chapter 4. Available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIACh41-20-

10.pdf
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U.S. EPA (2010). Guidelines for Preparing Economic Analyses. Office of the Administrator,

NationalCenter for Environmental Economics. EPA 240-R-10-001. December. Available at:

http://yosemite.epa.gov/ee/epa/eed.nsf/pages/Guidelines.html

U.S. EPA (2011). Regulatory Impact Analysis (RIA) for the Final Transport Rule. Office of

Air and Radiation. June. See: Chapter 5. Available at: http://www.epa.gov/airtransport/pdfs/

FinalRIA.pdf

U.S. EPA (2011). Regulatory Impact Analysis for the Final Mercury and Air Toxics

Standards. Office of Air Quality Planning and Standards. December. See: Chapter 5.

Available at: http://www.epa.gov/ttnecas1/regdata/RIAs/matsriafinal.pdf

To get email updates about BenMAP:

U.S. EPA, Office of Air Quality Planning and Standards, BenMAP website - Get Email

Updates. Available at http://www.epa.gov/air/benmap/regis.html

1.8 Frequently Asked Questions (General)

Why do I get different results than someone else?

There are a lot of possible reasons why your results might differ from someone else's results.

One good place to start is the Audit Trailreporting option. With the Audit Trail you can

examine the assumptions that you have made to generate your results and compare your

assumptions with those made in another analysis.

How do I know which version of BenMAP I am using? How do I know if I have the

most current version of BenMAP? How do I get the most current version?

You can identify the version of BenMAP you are using by going to the Help menu and

choosing About. Here you willsee the version number and contact information. To determine

whether you have the most recent version of BenMAP, you can check the BenMAP website (

http://www.epa.gov/air/benmap/), which should have the latest version that is publicly

available. Alternatively, you can use the contact information to inquire about any upcoming

versions of the model.
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Why are my pop-up windows too small? Why are buttons missing?

You need to check the display properties for your computer. If you right-click on the desktop,

you will get the Display Properties window.
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At the Settings tab, click on the Advanced button and try a different DPI setting. If this does

not resolve the problem, try different Screen resolution and Color quality options back on the

Settings tab.
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What do I need to be aware of if I use BenMAP for a local scale analysis?

Perhaps the most important issue is to make sure that you have identified the resolution of your

analysis and have the appropriate grid definitions loaded into BenMAP. See here about loading

grid definitions. The next key step, which is closely connected to the grid definitions, is to

determine the data that you want to use. Data such as air quality modeling, incidence data, and

population data need to match the grid definitions that you are using. You also need to be careful

about the formatting of your data when loading it into BenMAP. The chapter describing how to

Load Data goes into this in detail.

How can I get training for BenMAP?

Contact benmap@epa.gov for the latest information on training options.
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Is BenMAP free? Is there a Terms of Use agreement? Are there any restrictions on

using BenMAP?

BenMAP is free. There is no Terms of Use agreement and there are no restrictions on using

BenMAP. Feelfree to share it with others.

Does BenMAP estimate effects of air pollution that are not related to human health (i.

e., ecological effects)?

No. BenMAP does not currently have impact functions to estimate other than human health

effects. In principle it would be possible to estimate ecological effects, as BenMAP is designed

to combine different types of geographically variable data. To do so, you would need to develop

and load data and impact functions appropriate to estimating ecological effects of interest.

Why don't my files created with an older version of BenMAP work with the newer

version of BenMAP?

Files created with an older version of BenMAP will not, in some cases, work with a newer

version of BenMAP because of changes to the program. For example, later versions of

BenMAP have the capability to handle population data differentiated by ethnicity. For this

reason, after completing an analysis with BenMAP, it is always good to archive the BenMAP

installer along with the files used in your analysis, so that you will always be able to reproduce

your work in the future.
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CHAPTER 2

Terminology and

File Types

In this chapter...

Find definitions for terms used in the model and in this

manual that may be confusing.

Find descriptions of the various types of files used in

BenMAP.
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October 2012 23BenMAP User's Manual

The first section of this chapter explains common terms used in this user’s manual and in the

model. The second section describes in detail the necessary format for externally-generated

model and monitor data files that can be read into BenMAP and used to generate the air quality

grid files.

2.1 Common Terms

Active Layer. In the GIS window, the active layer is the top-most data layer. All queries or

statistical analysis of the map will act upon this top-most layer.

Aggregation. The summing of grid cell level results to the county, state and national levels.

Aggregation, Pooling, and Valuation (APV) Configuration. APV Configurations store the

aggregation levels, pooling options, and valuation methods used in the analysis. In particular, you

may specify the aggregation level for incidence estimates, how to pool incidence estimates, the

valuation estimates to use, the aggregation level for the valuation estimates, and how to pool the

valuation estimates. APV Configurations are stored in files with an “.apv” file extension. The

results derived from an APV Configuration have an “.apvr” file extension. APV files are

typically stored in the Configurations folder, and APV Results files are typically stored in the

Configuration Results folder.

Air Quality Grid. An air quality grid contains air pollution data. BenMAP uses one air quality

grid for the baseline scenario and a second for the controlscenario, in order to estimate the

change in the number of adverse health effects between the two scenarios. Air Quality Grids

are stored in files with an “.aqg” file extension. AQG files are typically stored in the Air Quality

Grids folder.

Air Quality Metric. One of the measures typically used for air pollution. These are daily values

calculated directly from daily observations, or through various mathematical manipulations of

hourly observations. Typical ozone metrics include the highest hourly observations during the

course of each day, such as the average of all twenty four hourly observations. These daily

values may be summarized to characterize seasonal and annual values.

Air Quality Model. Air quality models are valuable air quality management tools. Models are

mathematical descriptions of pollution transport, dispersion, and related physical and chemical

processes in the atmosphere. Air quality models estimate the air pollutant concentration at many

locations, which are referred to as receptors, or over a spatial area that has been divided into

uniform grid squares. The number of receptors or grid-cells in a model far exceeds the number of

monitors one could typically afford to deploy in a monitoring study. Therefore, models provide a

cost-effective way to analyze pollutant impacts over a wide spatial area where factors such as

meteorology, topography, and emissions from both local and remote sources could be important.

Some examples include CMAQ and CAMx.
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October 2012 24BenMAP User's Manual

APV Configuration. See Aggregation, Pooling, and Valuation (APV) Configuration.

Attainment. The state of meeting the National Ambient Air Quality Standard (NAAQS) for a

pollutant. A geographical area that meets the NAAQS is called an “attainment area.”

Audit Trail. This is a report that contains a record of all the choices involved in creating a

particular file. Audit trails can be created for any file that BenMAP creates.

Background Concentration. The concentration of a pollutant irrespective of local,

anthropogenic sources. In other words, the background concentration is the level of the pollutant

due to long-range transport, naturalsources, and unidentified sources.

Background Incidence. The incidence of a given adverse effect due to all causes including air

pollution.

Baseline Scenario. The base air quality conditions where emissions are not adjusted or

controlled. The baseline scenario is usually considered to be the reference scenario against which

to compare a potential “controlscenario”, in which emissions levels are changed from the

baseline levels through the application of various control measures.

Beta. The coefficient for the health impact function. The value of beta (ß) typically represents

the percent change in a given adverse health impact per unit of pollution.

Binning. The process of summarizing data by sorting the data values from low to high, dividing

the sorted data into a pre-determined number of groups, and then choosing a representative value

for each group, by either averaging the values in each group, or picking the mid-point of each

group.

Closest Monitor. The procedure by which data from a nearest monitor are used to represent

air pollutant levels in a population grid cell. BenMAP can also scale the data from the nearest

monitor with air pollution modeling data. BenMAP includes two types of scaling – “temporal” and

“spatial”. See “Scaling” for additional explanation.

Community Multi-scale Air Quality (CMAQ) Model. The CMAQ modeling system is the

U.S. EPA 3rd generation air quality model that has been designed to approach air quality as a

whole by including state-of-the science capabilities for modeling multiple air quality issues,

including tropospheric ozone, fine particles, toxics, acid deposition, and visibility degradation.

Concentration-Response (C-R) Function. A C-R function estimates the relationship

between adverse health effects and ambient air pollution, and is used to derive health impact

functions (defined below). You will often see that the term C-R function and health impact

function are used interchangeably.

Configuration. A Configuration stores the health impact functions and model options used to

estimate adverse health effects. Configurations are stored in files with a “.cfg” file extension.

CFG files are typically stored in the Configurations folder. The results derived from a

Configuration have a “.cfgr” file extension. CFGR files are typically stored in the Configuration
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October 2012 25BenMAP User's Manual

Results folder.

Contingent valuation. A survey-based economic technique for the valuation of non-market

resources, such as environmental preservation or avoidance of air pollution health risk.

Control scenario. In a modeling study, this is a sensitivity scenario in which emissions from one

or more source sectors are changed (increased or decreased), from a given “baseline scenario”.

The subsequent modified emissions are then used within an air quality model to derive new

ambient concentrations. Controlscenarios are used to assess the efficacy of proposed emissions

controls for sources of pollution.

Cost of Illness (COI). The cost of illness includes the direct medical costs and lost earnings

associated with illness. These estimates generally understate the true value of reductions in risk

of a health effect, as they include just the direct expenditures related to treatment and lost

earnings, but not the value of avoided pain and suffering from the health effect.

C-R Function. See Concentration-Response (C-R) Function.

Currency year. The value of the dollar based on the year specified. Typically, you would adjust

the valuation estimate to a consistent currency year. For example, you might want to report the

valuation estimate in 2000 dollars to make it easier to compare with your cost analysis, which

uses that same currency year.

Custom analysis. This type of BenMAP analysis allows you to perform each part of an analysis

separately: air quality estimation; calculation of health impact functions; aggregation, pooling, and

valuation; and reporting.

Deltas. The difference between two pieces of data. As used in BenMAP, mapping the deltas

shows the reduction in air pollution from the baseline air quality grid to the control air quality grid.

Discount Rate. In this context, the discount rate is a quantitative method to account for the fact

that people generally value future benefits and costs less than current costs and benefits.

Typically, if a benefit occurs over multiple years, the benefit should be discounted.

Endpoint. An endpoint is a subset of an endpoint group, and represents a more specific class of

adverse health effects. For example, within the endpoint group Mortality, there might be the

endpoints Mortality, Long Term, All Cause and Mortality, Long Term, Cardiopulmonary.

Endpoint Group. A endpoint group represents a broad class of adverse health effects, such as

premature mortality, chronic bronchitis, and hospital admissions. BenMAP only allows pooling of

adverse health effects to occur within a given endpoint group, as it generally does not make sense

to sum together the number of cases of disparate health effects, such as premature mortality and

chronic bronchitis.

Epidemiology. The study of factors affecting the health and illness of populations.

Epidemiologicalstudies cannot prove that a specific risk factor actually causes the disease being

studied, and instead can only show that a risk factor is associated (correlated) with a higher
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October 2012 26BenMAP User's Manual

incidence of disease in the population exposed to that risk factor. The higher the correlation the

more certain the association, but it cannot prove the causation.

FIPS Code. Federal Information Processing Standard codes. Each state in the U.S. is assigned

a 2-digit code. For example, “01” refers to Alaska, “37” refers to North Carolina, and “56” refers

to Wyoming.

Fixed Effects Pooling. Fixed effects pooling is used to combine two or more distributions

(represented by Latin Hypercube points) into a single new distribution. Fixed effects pooling

assumes that there is a single true underlying relationship between these component distributions,

and that differences among estimated parameters are the result of sampling error. Weights for

the pooling are generated via inverse variance weighting, thus giving more weight to the input

distributions with lower variance and less weight to the input distributions with higher variance.

Fixed Radius. An air quality monitor interpolation option that uses all monitors within a fixed

radius (or distance) of a given point of interest. All monitors are used and weighted by their

relative distance.

GIS. Geographic Information System. A GIS is a system of hardware and software used for

storage, retrieval, mapping, and analysis of geographic data.

Grid Cell. One of the many geographic, or spatial components within Grid Definition.

Grid Definition. A BenMAP Grid Definition provides a method of breaking a geographic

region into areas of interest (Grid Cells) in conducting an analysis. This can be done in two ways

- by loading a Shapefile (a particular type of Geographic Information System file) or by

specifying a regularly shaped grid pattern. These are referred to as Shapefile Grid Definitions

and Regular Grid Definitions, respectively. Typically a Shapefile Grid Definition is used when

the areas of interest are political boundaries with irregularly shaped borders, while a Regular Grid

Definition is used when the areas of interest are uniformly shaped rectangles.

Growth Income Adjustment. See Income Growth Adjustment.

Health Impact Function. A health impact function calculates the change in adverse health

effects associated with a change in exposure to air pollution. Based on a C-R function, a typical

health impact function has inputs specifying the air quality metric and pollutant, the age, race and

ethnicity of the population affected, and the incidence rate of the adverse health effect.

Incidence. The total number of adverse health effects in a geographic region in a given unit

time. In BenMAP, this is the total number of adverse health effects avoided due to a change in

air pollution.

Incidence Rate. The background rate of a health effect per person in a given geographic region

(a.k.a. background incidence rate). The average number of adverse health effects per person

per unit of time, typically a day or a year.

Income Growth Adjustment. Adjusting certain valuation functions to reflect increases in real
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October 2012 27BenMAP User's Manual

income. Generally, an increase in real income implies an increase in the willingness to pay

(WTP).

Interpolation. The process of estimating the air quality level in an unmonitored area by using

one or more nearby air quality monitors. BenMAP uses two types of interpolation procedures:

one is to simply choose the closest monitor, the other is to use a technique called Voronoi

Neighbor Averaging. These interpolation methods are discussed in more detail in Appendix B.

Lat/Long. Latitude and longitude information to specify the geographic coordinates of a spatial

location. The CMAQ model data are usually provided for each grid cell identified by the latitude

and longitude of the grid cell’s center point. Latitude identifies the north-to-south location of a

point on the Earth. Latitude can be defined precisely as the angle between the vertical at a

location, and the equatorial plane of the Earth. Longitude identifies the east to west location of a

point on the Earth, by measuring the angular distance from the Greenwich meridian (or Prime

meridian, where longitude is 0), along the equator.

Latin Hypercube. A series of points generated by using specified percentiles in a given

distribution, such as that of a health impact coefficient. It is a short-cut method designed to

represent a distribution, while at the same time saving on computation time. For example, when

using 20 Latin Hypercube points, BenMAP would use the 2.5

th

, 7.5

th

, 12.5

th

, ..., and 97.5

th points

from the distribution. The Latin Hypercube points are used when combining the results of

different health impact functions (discussed in Chapter 7), and in presenting confidence intervals

for the incidence estimates (discussed in Chapter 8).

Layer. In geographic information systems (GIS), a layer represents a logicalseparation of

mapped data usually representing a theme, such as political boundaries, roads, ozone data,

number of mortalities avoided, etc. In BenMAP, “active layer” refers to the top-most layer with

which the user is currently working.

Layer Statistics. The summary statistics that correspond to the active layer in BenMAP. For

example, "mean", "standard deviation" or "max" of PM2.5air quality grid.

Micrograms per Cubic Meter (μg/m3). The unit of measure for particulate matter in the

NAAQS. This unit represents the mass of PM found in a cubic meter of air.

Model Data. Pollutant concentration data that are generated by running an air quality model

such as CMAQ. This is different from “monitor data,” which are based upon measurements.

Model Delta. Differences in pollutant concentrations from applying an air quality model (e.g.

CMAQ) for two different emissions scenarios – a baseline scenario and an emissions control

scenario.

Modeling. Estimating air pollution levels using air quality models. The EPA website discusses a

wide range of air quality models: http://www.epa.gov/scram001/aqmindex.htm and at http://www.

epa.gov/ttn/scram/.

Monetize. In the context of human health benefits assessment, this is the practice of expressing
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October 2012 28BenMAP User's Manual

society’s preferences for avoiding certain health effects as a dollar value. In BenMAP we

estimate monetized benefits by using either Willingness-to-Pay or Cost-of-Illness valuation

functions (see above and below).

Monitor Data. Pollutant concentration data that are based upon measurements. “Raw” monitor

data usually refers to data that are taken directly from measurement networks, with no additional

processing of the data. Monitor data are different from “model data,” which are based upon

numerical predictions.

Monitoring. Actual measurements of air pollution levels. The U.S. Environmental Protection

Agency has monitoring data, as well as other information related to monitoring, available through

its Air Quality System (AQS): http://www.epa.gov/air/data/aqsdb.html.

Morbidity. A measure of being diseased or afflicted by an illness (non-fatal).

Mortality. A measure of the number of deaths in a given population.

National Ambient Air Quality Standards (NAAQS). The U.S. EPA establishes levels for

pollutants that are considered harmful to public health and the environment. The Clean Air Act

established two types of national air quality standards. Primary standards set limits to protect

public health, including the health of "sensitive" populations such as asthmatics, children, and the

elderly. Secondary standards set limits to protect public welfare, including protection against

decreased visibility and against damage to animals, crops, vegetation, and buildings. The U.S.

EPA has set NAAQS for six principal pollutants, which are called "criteria" pollutants. (Carbon

Monoxide, Lead, Nitrogen Dioxide, Ozone, Particulate Matter (PM2.5, PM10), and Sulfur

Dioxide).

One-Step Analysis. An easier and shorter approach to performing analyses with BenMAP,

targeted for beginning users. This combines all three major steps of BenMAP, i.e., a) calculate

changes in pollutant concentrations, b) use concentration-response (C-R) function to estimate

health incidences, and c) calculate economic benefits due to changes in morbidity and mortality

into a single process. However, the choices of analyses are limited. For example, the health

incidences and valuation functions are predefined, and the user cannot change them.

Ordinality. In relation to AQ monitors, ordinality refers to the number of monitor values in the

season that can exceed your standard. For example, if we had set the ordinality to 4, then a

monitor can have as many as three daily averages (assuming that we are using the daily average

metric to define our standard) greater than your standard without violating the standard. In terms

of rollback, if it has more than 3 daily averages in exceedance of the standard, then the rollback

technique will be applied to that monitor.

Ozone (O3). A gas composed of three oxygen atoms. It is not usually emitted directly into the

air, but at ground-level is created by a chemical reaction between oxides of nitrogen (NOx) and

volatile organic compounds (VOC) in the presence of sunlight. Ozone has the same chemical

structure whether it occurs miles above the earth (in what is sometimes referred to as “the ozone

layer”) or at ground-level, and can be "good" or "bad," depending on its location in the

atmosphere. For the purposes of BenMAP, the focus is on ground-level ozone, which can be
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October 2012 29BenMAP User's Manual

breathed in. Breathing ozone can trigger a variety of health problems, including chest pain,

coughing, throat irritation, and congestion. It can worsen bronchitis, emphysema, and asthma.

Ground-level ozone also can reduce lung function and inflame the linings of the lungs. Repeated

exposure may permanently scar lung tissue.

Particulate Matter. "Particulate matter," also known as particle pollution or PM, is a complex

mixture of extremely small particles and liquid droplets. Particle pollution is made up of a number

of components, including acids (such as nitrates and sulfates), organic chemicals, metals, and soil

or dust particles. Once inhaled, these particles can affect the heart and lungs and cause serious

health effects. Includes PM2.5(particles less than 2.5 microns in aerodynamic diameter), PM10

(particles less than 10 microns in aerodynamic diameter), and PMC (particles between 2.5 and 10

microns in aerodynamic diameter).

Parts per Million (ppm). This unit represents the concentration of the pollutant in a million parts

of air. Carbon monoxide is often measured in units of ppm.

Parts per Billion (ppb). This unit represents the concentration of the pollutant in a billion parts

of air. Ozone concentrations in BenMAP are reported in units of ppb.

Pooling. The combining of different sets of data. BenMAP has several pooling methods,

including fixed effects, fixed/random effects, and subjective weighting. Appendix K discusses

the pooling approaches available in BenMAP.

Point Mode. When defining the configuration, you may choose to either estimate adverse

health effects in point mode or using a Latin Hypercube. The point mode simply means that

BenMAP will use the mean value of the coefficient in the health impact function.

Population Exposure versus Personal Exposure. Population (or ambient) exposure refers

to the average air pollution level measured in a grid cell. In contrast, personal exposure keeps

track over the course of a day the exposure individuals encounter in different microenvironments, such as the freeway, outdoors and indoors. BenMAP only keeps track of

population exposure.

Post-processing. Additional data modification steps that are undertaken to prepare either raw

monitor data or raw model data for subsequent import into BenMAP. Postprocessing may be as

simple as reformatting the data, or it may involve more complex operations such as performing

additional numerical calculations to aggregate or disaggregate data temporally or spatially.

Prevalence Rate. The percentage of individuals in a given population who already have a given

adverse health condition. Used to calculate changes in health conditions among those who

already have a health condition, such as asthmatics.

Quality Adjusted Life Years (QALYs). The number of "healthy" life years gained or lost due

to changes in air quality. The QALY weights each life year gained according to the severity of

the health effect.

Random Effects Pooling. Random effects pooling is an alternative to the fixed effects pooling
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October 2012 30BenMAP User's Manual

(see Fixed Effects Pooling, above), and allows the possibility that the estimated parameter from

different studies may in fact be estimates of different parameters, rather than just different

estimates of a single underlying parameter.

Regulatory Impact Analysis (RIA). A policy tool used to assess the likely effects of a

proposed regulation or regulatory change. It usually involves detailed analyses to quantify the

costs and benefits of the regulation.

Relative Risk. Relative risk typically is used as a measure of the change in risk of an adverse

health effect associated with an increase in air pollution levels. More specifically, it is the ratio of

the risk of illness with higher pollution to the risk of illness with a lower pollution level, where the

“risk” is defined as the probability that an individual will become ill.

Rollback. The process by which monitor data are reduced to a different level. BenMAP

facilitates three different types of monitor data rollback. Percentage rollback reduces all monitor

observations by the same percentage. Incremental rollback reduces all observations by the same

increment. Rollback to a standard reduces monitor observations so that they just meet a specified

standard.

Scaling. The process in which monitor data are used in conjunction with model data to improve

interpolation and/or forecast future air quality trends. BenMAP has 3 types of scaling – temporal

only, spatial only, temporal and spatialscaling.

Setup. A BenMAP Setup encapsulates all of the data needed to run analyses for a particular

geographic area - a city, an entire country, etc. These data consist of grid definitions, pollutants,

monitor data, incidence and prevalence rates, population data, health impact functions, variables,

inflation rates, and valuation functions.

Shapefile. A shapefile is a particular type of Geographic Information System (GIS) file, and has

a .shp extension. These files are accompanied by companion files with .shx and .dbf extensions,

and can be used to create Shapefile Grid Definitions. See http://www.esri.com/library/

whitepapers/pdfs/shapefile.pdf for more information.

Subjective Weighting Pooling. Subjective weights let you specify the weights that you want

to use when combining two or more distributions of results. The weights should sum to one. If

not, BenMAP normalizes the weights so that they do.

Sum Dependent Pooling. Summing two or more incidence or valuation results, assuming the

underlying functions are correlated.

Sum Independent Pooling. Summing two or more incidence or valuation results, assuming the

underlying functions are independent (uncorrelated).

Threshold. An air quality level below which benefits are not calculated. For example, if the

threshold is 10 μg/m3, then only areas with PM2.5concentrations equal to or greater than 10 μg/m3

will be included in estimating health incidence results.
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Unit Value. A unit value is the estimated mean value of avoiding a single case of a particular

health effect.

Valuation Function. Valuation functions are used by BenMAP to estimate the economic values

of changes in the incidence of health effects. These are selected within an Aggregation, Pooling,

and Valuation Configuration (APV Configuration).

VNA (Voronoi Neighbor Averaging). An algorithm used by BenMAP to interpolate air

quality monitoring data to an unmonitored location. BenMAP first identifies the set of monitors

that best “surround” the center of the population grid cell, and then takes an inverse-distance

weighted average of the monitoring values. This is discussed in detail in Appendix B.

WTP (Willingness to Pay). The willingness of individuals to pay for a good, such as a

reduction in the risk of illness. In general, economists tend to view an individual’s WTP for a

improvement in environmental quality as the appropriate measure of the value of a risk reduction.

An individual’s willingness-to-accept (WTA) compensation for not receiving an improvement is

also a valid measure. However, WTP is generally considered to be a more readily available and

conservative measure of benefits.

2.2 File Types

BenMAP has a number of file types that you can use to store the settings used in a BenMAP

analysis, the results of an analysis, as well as maps and reports. Table 2-1 presents the names of

the different file types, their functions, and their default folder locations.

Table 2-1. File Types Generated by BenMAP

File Extension Description Default Folder Location

*.aqg Air quality grid. Air Quality Grids

*.cfg Configuration specifying the health impact functions and

other options used to generate incidence estimates.

Configurations

*.cfgr Configuration results, containing incidence results at the

grid cell level.

Configuration Results

*.apv Aggregation, Pooling, and Valuation configuration

specifying the aggregation levels, pooling options, and

valuation methods used to generate aggregated incidence

estimates, pooled incidence estimates, valuation estimates,

aggregated valuation estimates, and pooled valuation

estimates.

Configurations
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File Extension Description Default Folder Location

*.apvr Aggregation, Pooling, and Valuation configuration results,

containing incidence results at the grid cell level, aggregated

incidence results, valuation results, aggregated valuation

results, and pooled valuation results.

Configuration Results

*.shp Shape files generated by BenMAP’s geographic information

system. These files can be viewed within BenMAP or within

shape file viewers, such as ArcView.

Maps

*.csv Reports are exported as *.csv files, which may be viewed in a

text editor, or in programs such as Excel.

Reports
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CHAPTER 3

Overview of

BenMAP

Components

In this chapter...

Learn about the One-Step Analysis.

Get an overview of the functions available with the

Custom Analysis.

Learn about the Tools and Help menu options.
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Upon starting BenMAP for the first time, you willsee the following main window.

The opening screen is broken into two sections. On the left hand slide is the One-Step Analysis

button. This feature enables you to perform a quick benefit analysis while selecting a minimum of

analysis options. On the right hand side is the Custom Analysis section, which contains four large

buttons. This feature allows you to perform a highly customized benefit analysis. The Tools menu

at the top of the screen is for less frequently used functions, such as importing and exporting data

and mapping.

The first section in this Chapter describes the One Step Analysis. The second section describes

the Custom Analysis. And the finalsection describes the functions found in the Tools and Help

menu options. All of these topics are covered in greater detail in subsequent chapters of this

manual.
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3.1 One-Step Analysis

The One-Step Analysis allows you to run a BenMAP analysis quickly. This option can be useful

when you are time constrained, when you don't have a strong preference regarding the benefit

analysis options, or if you are new to the program. This feature uses an EPA-developed benefit

analysis configuration to estimate the human health benefits of the air quality scenario that you

specify in the model.

The One-Step Analysis requires a minimal number of inputs. At minimum, you should:

Give your run a name. Very descriptive titles can help you remember more about your run

when you want to review your results in the future.

Choose an output directory. The output directory is the location on your computer in which

you willsave your results. (The default is to put the output in the Configuration Results folder.)

Select air quality grids. BenMAP estimates the benefits ofsome change in air quality for a

particular area of the country. Here, you're telling BenMAP which air quality files to use to

calculate benefits. You can either import an air quality file that has already been created, or you

can generate your own. If you want to create your own air quality file, you will want hit the

"create" button and follow the instructions under the custom analysis page below. For

additional details on air quality grid creation, see the chapter on Air Quality Grid Creation.

Choose your aggregation level. Select the geographic level at which you want to summarize

your results.
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After you have selected these options, just hit the "Go!" button, and BenMAP willrun the

analysis in a single step. The One-Step run time generally depends on the complexity of the air

quality data. More complex air quality data requires more time to run.

3.1.1 One-Step Reports

When BenMAP completes its run a new window will appear that gives you an array of graphical

and tabular reporting options. The top part of this window provides incidence report options, the

middle part of the window contains options for creating valuation reports and in the bottom part

of the window is the audit trailreport button. (For more details on One-Step reports, see the

chapter on Reports.)
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BenMAP willreport an "audit trail" that reports all of your data inputs and analysis options. This

report can be very useful when someone wants to replicate your analysis, or when you would

simply like to remember all of the data and options you used in your analysis. The audit trail is

discussed in further under "generate reports" in the Custom Analysis section below.
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3.1.2 Questions Regarding the One-Step Analysis

How do I know what currency year is used in the One-Step Analysis?

You can find the answer in the Audit trailfor the APVR file that you generated. Alternatively,

go to the Tools menu and choose One-Step Setup. Then choose a Pollutant (e.g., PM2.5) to

determine the currency year.
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How do I know what population year is used in the One-Step Analysis?

You can find this answer in the Audit Trail.
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How do I change the .cfg or .apv files used in the One-Step Analysis?

Go to the Tools menu and choose One-Step Setup. At the One Step Setup Parameters

window, you can browse for alternative .cfg and .apv files.

How do I find out which functions are used in the One-Step Analysis?

You can identify the functions used in the One-Step Analysis at least two ways. First, you can

use the Audit Trail option to identify the functions used in an existing set of One-Step results.

Alternatively, if you want to look at the functions prior to the analysis, then you might want to

look at the .cfg and .apv files using the Audit Trail available with the Report options under the

Custom Analysis.

How do I create the Air Quality Grids needed for the One-Step Analysis?

There are several ways to create Air Quality Grids needed for a One-Step Analysis, using

monitor data, model data, or both monitor and model data. These options are briefly

discussed in the Create Air Quality Grids section of this Overview chapter and in detail in the
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Air Quality Grid Creation chapter.

Why are the aggregation levels limited in the One-Step Analysis?

The aggregation levels are limited in the One-Step Analysis in order to simplify the analysis and

to make the results compatible with the pre-made tables and graphs.

3.2 Custom Analysis

Returning to the main BenMAP window, you can choose the Setup you want to use for an

analysis by selecting it from the Active Setup drop-down list. You are then ready to begin using

the functions available through the four main buttons.

These four buttons take you through the steps of an analysis. The first button allows you to

create air quality grids which contain estimates of population-level exposure to air pollution. The

second button lets you choose the air quality grids for a particular analysis, and then to choose

the health impact functions to estimate the incidence of adverse health effects. The third button

gives you different options for combining the health effects estimates and choosing economic

valuation functions. Using the fourth button, you can generate several different kinds of reports.

3.2.1 Create Air Quality Grids

BenMAP is not an air quality model, nor can it generate air quality data independently. Instead it

relies on the air quality inputs given to it. To estimate population exposure to air pollution,

BenMAP combines population data with an Air Quality Grid, which it generates using some

combination of air quality modeling and/or monitoring data. The Create Air Quality Grids

button allows you to put your air quality data into the format that is used by BenMAP, the first

step in conducting an analysis within BenMAP.

Grid Definitions

Air quality grids contain air pollution exposure estimates for a particular Grid Definition, as

defined in the Setup Manager window. Grid Definitions are typically comprised of either

regularly shaped rectangles covering the region of analysis, or irregularly shaped polygons

corresponding to political boundaries.

Modeling and Monitoring Data

To generate air quality grids, you can use air quality modeling data and air quality monitoring data

in three different ways, as discussed below. However, once generated, all air quality grids have

the same structure, and have the same .aqg extension that BenMAP uses to designate these file

types.

Model Direct. Model Direct grid creation simply takes raw model data and converts it into a

file that BenMAP recognizes as an air quality grid. This type of grid definition allows you to
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directly specify the air pollution values for each grid cell in a Grid Definition.

Monitor Direct. Monitor Direct grid creation uses air pollution monitoring data to estimate air

pollution levels for each grid cell in the selected Grid Definition. This may be done using one of

two interpolation procedures – Closest Monitor, or Voronoi Neighbor Averaging (VNA). With

closest monitor, BenMAP simply uses the data of the monitor closest to each grid cell's centroid.

With VNA, BenMAP first identifies the set of monitors that most closely “surround” each grid

cell, and then calculates an inverse-distance weighted average of the data from these neighboring

monitors.

Monitor and Model Relative. Monitor and Model Relative grid creation allows you to

combine information from both monitor and modeling data files. BenMAP uses the modeling data

to scale the monitoring data, to compensate for incomplete monitoring data coverage, and the

inability of monitor data to predict future conditions. Like the Monitor Direct approach, you

choose an interpolation method (Closest Monitor or VNA), but a scaling approach is also chosen,

either temporal, spatial, or both. This approach is described in more detail in the Air Quality Grid

Creation chapter, as well as in the appendix on Air Pollution Exposure Estimation Algorithms.

Monitor Rollback. Monitor Rollback grid creation allows you to reduce, or roll back, monitor

data using three methods: percentage rollback, incremental rollback, and rollback to a standard.

Percentage rollback reduces all monitor observations by the same percentage. Incremental

rollback reduces all observations by the same increment. Rollback to a standard reduces monitor

observations so that they just meet a specified standard. After the monitor data is rolled back, it

may be directly interpolated (as in Monitor Direct grid creation) or combined with modeling data

(as in Monitor and Model Relative grid creation). This approach is described in more detail in

the Air Quality Grid Creation chapter, as well as in the appendix on Monitor Rollback Algorithms.

3.2.2 Create and Run Configuration

The Create and Run Configuration button allows you to calculate the change in the incidence

of adverse health effects associated with changes in air quality. There are severalsteps in the

process.
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Step 1. Specify the baseline and control air quality grids that you created using the Create Air

Quality Grids button.

Step 2. Specify whether BenMAP should make a “point estimate”, or a set of “Latin

Hypercube” points. The point estimate is the change in incidence, generated using the mean

value of the coefficient in the health impact function. The Latin Hypercube points are a series of

points generated by using evenly spaced percentiles in the distribution of the health impact

coefficient – these points represent the distribution of incidence values. (The Latin Hypercube

points can later be used when combining the results of different health impact functions, and in

presenting confidence intervals for the incidence estimates.)

Step 3. Specify the threshold, or a lowest value for air quality data. Any observations which fall

below this threshold will be replaced with the threshold value in all calculations.
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Step 4. Choose the health impact functions that will be used in the estimation, and hit the Go!

button to start estimating the change in incidence.

BenMAP can store configuration choices in a user-named file with a .cfg extension, and can

store incidence change estimates in a user-named file with a .cfgr extension.

3.2.3 Aggregation, Pooling, and Valuation

The Aggregation, Pooling, and Valuation button allows you to specify an aggregation levelfor

previously calculated incidence estimates, pool these aggregated incidence estimates, place an

economic value on these pooled and aggregated incidence estimates, aggregate these economic

values, and finally pool these aggregated economic values. There are severalsteps in this

process.

Step 1. Choose whether to create a new .apv configuration or use an existing .apv

configuration. (For this description we will choose to create a new .apv configuration.)
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Step 2. Choose a set of incidence estimates to use. These will be loaded from a configuration

results file made with the Create and Run Configuration button, which is stored by BenMAP

with a .cfgr extension.

Step 3. Choose the desired pooling and aggregation options for the incidence results.
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Step 4. Choose the economic valuation functions to apply to the pooled and aggregated

incidence results.
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Step 5. Choose the desired pooling and aggregation options for the economic valuations, and hit

the Next button. If the Skip QALY Valuation box is checked, then BenMAP is ready to save

your APV Configuration and generate results.

Step 6. If desired, you can uncheck the Skip QALY Valuation box and proceed to choosing

your desired QALY functions. Why have Skip QALY Valuation box? It takes a bit of time for

the QALY functions to load, and since QALY functions are not always used, this box is intended

to speed up the time it takes to develop the APV Configuration.

BenMAP can store APV Configuration choices in a user-named file with an “.apv” extension, and

can store APV Configuration results in a user-named file with an “.apvr” extension. As needed,

you can access both files for later use.

3.2.4 Generate Reports

The Generate Reports button allows you to generate several types of reports.
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Incidence and Valuation Results reports use an Aggregation, Pooling, and Valuation

Results file (with the “.apvr” extension) to create reports for incidence, aggregated incidence,

pooled incidence, valuation, aggregated valuation, or pooled valuation results. These reports

are comma separated values (CSV) files (*.csv) which can be read using a text editor, or by

various spreadsheet and database programs, such as Microsoft Excel.

Raw Incidence Results use a Configuration Results file (with the “.cfgr” extension) to create

reports for incidence results. These reports are CSV files.
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Audit Trail Reports provide a summaries of the assumptions underlying each of five types of

files generated by BenMAP: Air Quality Grid (“.aqg”), Configuration (“.cfg”), Configuration

Results ( “.cfgr”), Aggregation, Pooling, and Valuation (“.apv”), and Aggregation,

Pooling, and Valuation Results (“.apvr”). These reports can be viewed within BenMAP in an

expandable tree structure, or can be exported to text files.
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3.3 Menus

There are two menu options found at the top of the main window:Tools and Help. The Tools

menu provides access to data import and export functionality and mapping. The Help menu

provides access to information about BenMAP - the version, contact information, and a

suggested citation.

3.3.1 Tools

The Tools menu has several options: Database Export, Database Import, GIS / Mapping,and

Modify Setup.
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Air Quality Grid Aggregation. Create a new air quality for a new grid definition (e.g., State)

from an existing air quality grid created with a different grid definition (e.g., County).

Model File Concatenator. Combine model files for use in the creation of air quality grids.

Database Export. Export all or part of BenMAP’s internal database to a file which can later be

used (on another computer, for example) with the Database Import tool. Manually loading data

into BenMAP can be time and labor intensive, so this tool can be quite useful in sharing data with

other users or computers.

Database Import. Import data exported using the Database Import tool.

Export Air Quality Grid. Generate a text file (.csv) with all of the data in air quality grid,

including summary statistics such as mean, median, minimum, and maximum.

GIS / Mapping. Generate a wide variety of maps, including maps of monitor data, air quality

grids, population data, incidence rates, and both incidence and valuation results. In addition, you
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can export the maps you have generated and view them in a shapefile viewer, such as ArcView.

Note that BenMAP also has context specific mapping capabilities in various parts of the

application. See the GIS/Mapping chapter for details on BenMAP's mapping capabilities.

Modify Setup. View, edit, add, and delete data from BenMAP’s internal database. For details

see the Load Data chapter.

Neighbor File Creator. Create a text file (.txt) identifying "neighbor" monitors and associated

interpolation weights for each grid cell in an air quality grid.

One-Step Setup. Specify the CFG and APV Configuration files and currency year for the OneStep Analysis.
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CHAPTER 4

Loading Data

In this chapter...

Create new data setups to load and store your input

data.

Find details on the file structures for data inputs.

Learn how to export and import setups.
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BenMAP encapsulates in a single dataset all of the data needed to run analyses for a particular

geographic area, such as a city, region, or nation. This dataset is called a “Setup” and consists

of grid definitions, pollutants, monitor data, incidence and prevalence rates, population data,

health impact functions, variables, inflation rates, valuation functions, income growth data, and

QALY data. Grouping the data in this way has a number of advantages. It makes it easy to

organize and view the data, export the data (either a whole setup or a portion of a setup), and

import setups generated by others.

In this Chapter we discuss how to add, modify, and delete setups, as well as how to export and

import existing setups. There are a number ofsteps involved in loading the data, particularly in

the formatting of the data, so it is important to carefully review the steps in this Chapter. Also, it

is important to keep in mind that if you delete one part of a setup, you may be affecting other

parts of the setup. We discuss this further below.

Finally, it's worth noting that many users will never need to modify the setup. If you are

performing an analysis with the U.S. setup, you may find that BenMAP contains all of the data

you need to perform your analysis, and no additional modifications are necessary.

4.1 Add, Modify, and Delete Setups

To add a new setup, modify an existing setup, or delete a setup, go to the Tools drop-down

menu, and choose the Modify Setup option. This will bring up the Manage Setups window. The

United States setup, which comes preinstalled with BenMAP comes with a variety of datasets

and might look something like this:
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Add a Setup. To add a setup, click the Add button. The New Setup window will appear,

where you can type the name of the setup.
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After naming a new setup, you can then define anew the elements that comprise a setup: Grid

Definitions, Pollutants, Monitor DataSets, Incidence and Prevalence DataSets,

Population DataSets, C-R Function DataSets, Variable DataSets, Inflation DataSets,

Valuation DataSets, Income Growth Adjustments, and QALY Distribution DataSets.

Some of the elements of a setup are fundamental and should be entered before the others,

namely Grid Definitions and Pollutants. The Incidence and Prevalence, Population, and

Variable DataSets depend on the Grid Definitions, and the Monitor and C-R Function

DataSets depend on the Pollutants that you have defined. As a result, it is best to start by

defining your Grid Definitions and Pollutants, and then defining the other elements of the setup.

Modify a Setup. To modify a setup, start by choosing the setup for modification from the dropdown menu in Available Setups. Click on the Edit button under one of the eleven components

comprising a setup. The sections below describe each of these components.

Delete a Setup. To delete a setup, choose the setup for deletion from the drop-down menu in

Available Setups. Click the Delete button. You will then be asked to confirm your decision.

4.1.1 Grid Definitions

A BenMAP Grid Definition provides a method of breaking a geographic region into areas of

interest (Grid Cells) in conducting an analysis. This can be done in two ways: by loading a

Shapefile (a particular type of Geographic Information System file) or by specifying a regularly

shaped grid pattern. These are referred to as Shapefile Grid Definitions and Regular Grid

Definitions, respectively. Typically a Shapefile Grid Definition is used when the areas of

interest are political boundaries with irregularly shaped borders, while a Regular Grid
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Definition is used when the areas of interest are uniformly shaped rectangles.

Ideally, one Shapefile Grid Definition will be created using a shapefile containing an outline of

the area of interest (a city boundary, for example). Additional grid definitions will then be created

for each subdivision of that area for which (a) data is available (see the Air Monitoring,

Population, Incidence and Prevalence, and Variables sections below), or (b) reports or maps are

desired.

For example, to conduct analyses for the United States, one might use the following grid

definitions:

Nation - this Shapefile Grid Definition contains an outline of the United States (just the lower

forty-eight states), defining an overall area of interest.

County - this Shapefile Grid Definition contains county borders, for use with county-based

population and incidence rate data.

CMAQ 12km Nation - this Shapefile Grid Definition contains grid cells that are roughly 12

kilometers on each side, for use with air quality modeling data.

State - this Shapefile Grid Definition contains state borders, for use in generating reports and

maps with results aggregated to the state level.

To start adding or modifying grid definitions, click on the Edit button below the Grid

Definitions box. The Manage Grid Definitions window will appear.

Click on the Add button below the Available Grid Definitions box,. The Grid Definition

window will appear, with two tabs showing the two main types of grid definitions that you may

use in BenMAP: Shapefile Grid or Regular Grid.
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4.1.1.1 Regular Grid

Regular Grid Definitions are defined by a lower left corner (specified as decimal degree

latitude and longitude, with West and South having negative values and East and North having

positive values), a total number of columns and rows, a number of columns per degree longitude,

and a number of rows per degree latitude. Individual cells within the resultant grid are numbered

in sequential order (columns from left to right, rows from bottom to top) starting at (1, 1). These

field values will be used to link the Regular Grid Definition with other sources of data, as

discussed in more detail below.

To define a Regular Grid, start by typing the name of the grid definition in the Grid ID box, and

then defining the number of Columns and Rows in the grid. To locate this grid geographically,

provide the decimal degree coordinates for the lower left-hand corner of the grid in the

Minimum Longitude and Minimum Latitude boxes.

To give the overall geographic size of the grid, provide the number of Columns Per Longitude

and Rows Per Latitude. For example, if there are 16 columns and 2 columns per degree

longitude, then the grid willspan 8 degrees longitude. And if there are 25 rows and 4 rows per

degree latitude, then the grid willspan 6.25 degrees of latitude.

Combining the numbers in this example, if the minimum longitude and latitude are -81 and 38 and

the grid spans 8 degrees longitude and 6 degrees latitude, then the grid willrun between -81 and

-73 degrees longitude and between 38 and 44.25 degrees latitude.

After defining the grid, click the Preview button to see what the grid looks like. You may change
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the parameters and click the Preview again to see how the grid changes. When you are satisfied

with the grid definition, click the OK button.

The name of your newly defined grid will then appear in the Manage Grid Definitions window.

You may click Edit to change the grid definition, Delete to permanently remove the grid that

you just defined, Add to define a new grid definition, or OK to get back to the Manage Setups

window.
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4.1.1.2 Shapefile Grid

Shapefiles used to create Shapefile Grid Definitions should be of the ESRI® Shapefile format.

Details on this format can be found at http://downloads.esri.com/support/whitepapers/other_/

shapefile.pdf. Shapefiles used must be unprojected - that is, they should store their geographic

information as decimal degree latitude and longitude values. If your shapefile is already projected,

you will want to use a program like ArcGIS® to reproject the shapefile using the NAD 1983

datum. Finally, any shapefiles used must contain integer fields named Column (or Col) and Row

, and each shape within the shapefile must contain a unique combination of values for these two

fields. These column and row values are used, just as the Column and Row field values in

Regular Grid Definitions, to link the Shapefile Grid Definition with other sources of data, as

discussed in more detail below.

To add a Shapefile Grid, click on the Add button in the Manage Grid Definitions window,

choose the Shapefile Grid tab, name the grid in the Grid ID, and browse for the correct

shapefile by clicking on the small open-file icon just to the right of the Load Shapefile box.

After locating the file in the Browse for a Shapefile window, click Open. This will choose the

file, and bring you back to the Grid Definition window. To view the shapefile, choose Preview.

When you are satisfied that the shapefile looks correct, click OK. This will bring you back to

Manage Grid Definitions window. Note that the Grid Type box displays the type of grid for

each of your grid definitions.

Click OK when you are finished loading grid definitions. The Manage Setups screen will now

list the Grid Definitions that you have just created. At any time, you may click the Edit button
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to add, modify, or delete grid definitions.

Note that if you delete a Grid Definition you will delete any data that is dependent on it, such as

any Incidence, Prevalence, Population, and Variable DataSets that use this particular Grid

Definition. As we discuss below each of these other elements of a setup, we will describe how

this might happen.

4.1.2 Pollutants

The Pollutants section of a setup specifies the pollutants that BenMAP will analyze and defines

the air quality metrics to be used by BenMAP. You are not importing air pollution data, but

rather naming your pollutants and defining the measures or metrics BenMAP will use when

performing a benefit analysis for each pollutant. You may include any pollutant, though typically

air pollutants such as particulate matter, ozone, sulfur dioxide, and carbon monoxide are used in a

BenMAP analysis.

A key concept for pollutants is the Metric. Air quality metrics are daily values calculated

directly from daily observations, or through various mathematical manipulations of hourly

observations. Typical ozone metrics include the highest hourly observations during the course of

each day, the average of all twenty four hourly observations, etc. In a benefit analysis, the air

quality change must be expressed in a metric that matches the metric used by the health impact

function; this concept is discussed further below.

In general, air pollution data in BenMAP is hierarchical – a pollutant can have multiple Metrics,

each of which has multiple Statistics (these are automatically calculated by BenMAP) and which

can have multiple Seasonal Metrics. Similarly, Seasonal Metrics have multiple Statistics.

Furthermore, air pollution data can be provided to BenMAP at any of these levels, in addition to

the daily/hourly observation level, as described in more detail in Section 4.3.

To start adding pollutant definitions to BenMAP, click on the Edit button below the Pollutants

box of the Manage Setups window. The Manage Pollutants window will appear. Here you

may click Add to add a new Pollutant, Delete to remove a previously defined pollutant, or Edit

to modify an existing pollutant.
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Add a Pollutant

Air pollution data in BenMAP is of two types - point source monitoring data, and Grid

Definition based modeling data. For both types, the data must be associated with a particular

pollutant. Table 4-1 describes these variables used to define a pollutant in BenMAP.

Table 4-1. BenMAP Pollutant Definitions

Pollutant FieldName Notes

Pollutant ID

Unique name for the pollutant which will be referenced in health impact functions,

associated with monitoring and modeling data, etc.

Observation Type

Pollutants may have hourly observations or daily observations. In the United

States, Ozone has hourly observations, while PM10and PM2.5have daily

observations.

Metrics

Daily values calculated directly fromdaily observations, or through various

mathematical manipulations of hourly observations. Typical ozone metrics include

the highest hourly observations during the course of each day, the mean of all

twenty four hourly observations, etc.

Seasonal Metrics

Seasonal values calculated frommetric values. In the United States, for example,

quarterly means are calculated for PM2.5fromdaily means.

To start defining a pollutant, click the Add button and the Pollutant Definition window will

appear. In the Pollutant ID box, you give a unique name for the pollutant (e.g., PM2.5), and then
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define the characteristics of this pollutant – the Observation Type and Metrics.

The Observation Type identifies whether a pollutant is measured hourly or daily. In the United

States, ozone, sulfur dioxide, carbon monoxide, and others have hourly observations, while

particulate matter has daily observations. Use the Observation Type drop-down menu to

choose either the Hourly or Daily observation type.

Next you need to define a pollutant’s Metrics. A pollutant has to have one or more metrics,

which are daily values calculated directly from daily observations, or through various

mathematical manipulations of hourly observations.

To add a Metric, click on the Add button below the Metrics box. A default name Metric 0

will appear in the box. Since the default name is not very descriptive of a metric, it is best to

change the name. Typical names used for metrics given in Table 4-2. These are provided just as

an example, and you may use any names that you like. However, keep in mind that the names

that you use for your metrics need to be consistent with the metric names that you include in your

air pollution monitoring and modeling data, as well as your health impact functions. (We will

discuss this further below.) Additionally, metric names are used to display pollutant

concentrations in BenMAP’s mapping window. As such, they must be consistent with GIS

naming conventions, meaning they must begin with a letter, and may only contain letters, numbers,

and underscores.
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Table 4-2. Examples of Metric Names

Name Description

D1HourMax Highest hourly value from12:00 A.M. through

11:59 P.M.

D24HourMean Average of hours from12:00 A.M. through

11:59 P.M.

D8HourMax Highest eight-hour average calculated between

12:00 A.M. and 11:59 P.M.

4.1.2.1 Hourly Metrics

Pollutants that are measured hourly (Observation Type = Hourly), such as ozone, sulfur

dioxide, carbon monoxide, and others, must be characterized by a daily metric, which

mathematically summarizes the hourly observations.

Table 4-2 lists some of the ways that metrics can be generated from hourly values. Note that

these metrics are not arbitrarily chosen, and instead match the metrics used in epidemiological

studies.

The Hourly Metric Generation portion of the Pollutant Definition window lets you define the

metrics that you want to use. There are three tabs that you may choose: Fixed Window,

Moving Window, and Custom.

The Fixed Window tab lets you define simple metrics which are calculated as statistics over a

fixed window of hours (Start Hour and End Hour) within each day. The Start Hour should be

less than or equal to the End Hour, and both can range from 0 to 23, where 0 stands for the

period 12:00 am to 12:59 am, and 23 stands for 11:00 pm to 11:59 pm. The Statistic includes

the Mean, Median, Max, Min, and Sum. Some examples follow:

D24HourMean: The mean of the observations from 12:00 am through 11:59 pm.

Start Hour = 0. End Hour = 23. Statistic = Mean.

D1HourMax: The highest hourly value of the observations from 12:00 am through

11:59 pm. Start Hour = 0. End Hour = 23. Statistic = Max.

D12HourMean: The mean of the daylight observations, defined as the period from

8:00 am through 7:59 pm. Start Hour = 8. End Hour = 19. Statistic = Mean.
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The Moving Window tab lets you consider metrics that are not based on the same set of hours

each day. The Window Size defines the number of hours that will be considered together. The

Window Statistic defines how the hours in the Window Size will be characterized. And the

Daily Statistic defines how BenMAP will use the statistics generated for each window.

For example, consider the highest eight-hour mean (D8HourMax) over the course of a day. You

would have the following settings:Window Size = 8. Window Statistic = Mean. Daily

Statistic = Max. BenMAP would calculate every possible eight-hour mean, starting with the

eight-hour mean from 12:00 am through 7:59 am, and ending with the eight-hour mean from 4:00

pm through 11:59 pm. This would generate 17 possible eight-hour means. BenMAP would then

choose the eight-hour mean that has the highest value.
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The Custom tab lets you define Metrics using customized functions. These functions can

include measures such as the sum of the number of hours of ozone exposure about 60 parts per

billion. The possibilities are quite diverse, as evidenced by the range of functions and variables

available for use in Table 4-3. However, the syntax for using these functions is somewhat

involved, so we have reserved discussion of this for the Function Editor appendix.
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Table 4-3. Available Functions and Variables for Custom Metrics

Available

Functions Description Available Variables Description

ABS(x) Returns the absolute value

of x.

Observations[i] All hourly observations for the

year (indexbegins at zero,

typically ranging to 8,760).

EXP(x) Returns e the power x, where

e is the base of the natural

logarithm.

DailyObservations[i] All hourly observations for the

day (indexed zero to twentythree).

IPower(x,y) Returns xto the power y (y

an integer value).

SortedObservations[i] All hourly observations for the

day, sorted fromlow to high

(indexed zero to twenty-three).

LN(x) Returns the natural logarithm

of x.

Day Indexof the day whose metric

value is being generated (index

begins at zero).

POWER(x,y) Returns xto the power y (y a

floating point value).

Mean Mean of the daily observations.
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Available

Functions Description Available Variables Description

SQR(x) Returns the square of x. Median Median of the daily

observations.

SQRT(x) Returns the positive square

root of x.

Min Minimumof the daily

observations.

Max Maximumof the daily

observations.

Sum Sumof the daily observations.

NoObservation Flag value indicating a missing

observation (-345)

4.1.2.2 Seasonal Metrics

Seasonal Metrics allow users to aggregate daily Metric values. This has a number of uses.

For example, if pollutant values vary greatly by season, you can calculate separate pollutant

measures for each season of interest. You might be interested in dry season versus wet season,

or differences between Winter, Spring, Summer, and Fall.

To add seasonal metrics, click on the Edit button below the Seasonal Metrics box. The

Manage Seasonal Metrics window will appear.

To add a Seasonal Metric, click on the Add button below the Seasonal Metrics box. A

default name Seasonal Metric will appear in the box. Since the default name is not very

descriptive, it is best to change the name to something more informative such as the

QuarterlyMean. As with the Metric names, keep in mind that the Seasonal Metric names

that you use need to be consistent with the metric names that you include in your air pollution

monitoring and modeling data, as well as your health impact functions.

The next step is to define the seasons that you want associated with your Seasonal Metric

name. For example, in the case of a Quarterly Mean, you would want to define four seasons.

To start this process, click on the Add button below the Seasons box. Then, in the far right side

of the window under Selected Season Details, give the Start Date and End Date for each

season.
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Next, you need to choose the Statistic tab or the Custom tab to determine how the daily

Metrics will be combined in each season. For example, you might choose the Mean from the

drop-down list on the Statistics tab. This would calculate the mean of the daily metrics in each

season. The Custom tab allows seasonal metric values to be calculated using customized

functions, similar to those used to calculate daily metric values from hourly observations. See the

Function Editor appendix for more detail on this topic.

Once you have finished defining the Seasonal Metrics, click OK to return to the Pollutant

Definition window.

Click OK after defining each Pollutant - this willreturn you to the Manage Pollutants window.
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4.1.2.3 Advanced Pollutant Options

The Advanced Pollutant Options button allows you to associate Seasons with a Pollutant.

These seasons differ somewhat from the Seasonal Metrics discussed above. They are used to

define:

The portion of the year for which benefits are calculated for a Pollutant. For

example, in the United States ozone benefits are only calculated for the ozone

season, which is assumed to be May 1 through September 30.

The portion(s) of the year for which missing pollutant concentrations are filled in by

BenMAP. That is, in order to calculate benefits, BenMAP in certain cases needs to

generate complete sets of metric values by estimating concentrations for those days

which have missing observations. This can be important if certain seasons tend to

have more missing values than others.

The portion(s) of the year for which monitor data is scaled by modeling data when

creating Monitor ModelRelative air quality grids (discussed in the Air Quality Grid

Creation chapter).

To define Seasons for a Pollutant, click the Advanced Pollutant Options button. This will

bring up the Define Seasons window. For each season desired, click the Add button, select

the appropriate Start Date and End Date, which define the days included in the season; the

appropriate Start Hour and End Hour, which define the hours included in Monitor Model

Relative scaling; and the appropriate Number of Bins, which define the number of bins used in

Monitor ModelRelative scaling. The advanced options for PM2.5look like the following:

Once you have finished defining the Seasons, click OK to return to the Pollutant Definition

window.

If you later wish to View or Edit a particular Pollutant definition, simply select the appropriate
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Pollutant within the Available Pollutants box and click the Edit button. When you are done,

click OK to return to the Manage Setups window.

After defining all of the pollutants that you want to consider, click OK. This willreturn you to

the Manage Setups window.

4.1.3 Monitor Data

The Monitor DataSets section of the Manage Setups window allows to you to add air

pollution monitoring data to your setup. Air pollution monitoring data may be used alone, or in

conjunction with air pollution modeling data, to estimate ambient pollution levels in each grid cell

defined by a Grid Definition. BenMAP uses a variety of procedures (such as Voronoi

Neighbor Averaging, discussed later) to interpolate the monitor data points across the area of

interest.

NOTE: Air pollution data in BenMAP is of two types - point source monitoring data, and Grid

Definition based modeling data. Both types of data must be associated with a particular

pollutant that you have defined. Only the point source monitoring data is stored in the setup

database. The modeling data are loaded into BenMAP as you need them for a particular

analysis.

To start, click on the Edit button below the Monitor DataSets box. The Manage Monitor

DataSets window will appear. From this window you may Add monitoring data, view and Edit

existing datasets, as well as Delete them. The section on the left under Available DataSets lists

the monitor datasets that are currently in the setup. The section on the right under the DataSet

Contents identifies the number of monitors in each dataset by pollutant and by year.

To start adding data, click the Add button. This will bring up the Monitor DataSet Definition

window. Give the dataset a name in the DataSet Name box, choose the appropriate pollutant

from the Pollutant drop-down menu, and then type the year of the data in the Year box.
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NOTE: The DataSet that you define can have one or more pollutants and multiple years of data.

If your data come from a single source, for simplicity it probably makes sense to combine these

data into a single DataSet.

Now choose one of the three tabs (Database, Columns; Database, Rows; or Text File) that

matches the format of your data. Monitor data may be formatted in three different ways:(1) in

two database files/tables, with monitor definition information in rows in one file/table and monitor

values in a single column in the other file/table (Database, Columns format), (2) in a database

file, with monitor definition information and monitor values in a single line (Database, Rows

format), and (3) in a text file, with monitor definition information and monitor values in a single line

(Text File format). There is no preferred approach, and instead use the approach with which you

are most comfortable. (We define these formats in detail below.)

Click on the Browse button next to the Monitor Data File box. This will bring up a window

from which you can browse the BenMAP Data directory to find the desired data file. The Files

of type drop-down menu allows you to choose from a variety of file types.

Click Open. This will choose the file, and bring you back to the Monitor DataSet Definition

window. If you are using the Database, Columns format, you will need to repeat this

procedure with the Monitor Definition File. To add monitors from the selected file(s), click

Load Monitor Data.
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Follow this same procedure to load all of your monitoring data. Select the pollutant from the

Pollutant drop-down menu, edit the Year box, choose the tab that matches the database format

that you are using, and select the appropriate file(s) by clicking the Browse button(s). When you

have clicked Load Monitor Data for your final dataset, click OK. This will bring you back to

the Manage Monitor DataSets window.

To see the years of data and the number of monitors each year, use the scrollbars on the bottom

and on the right of the DataSet Contents box.

If desired you can add additional monitor datasets (click the Add button), delete existing datasets

(select the dataset in the Available DataSets list and click the Delete button), or edit existing

datasets (select the dataset in the Available DataSets list and click the Edit button).
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When you have finished loading your monitor data, click OK in the Manage Monitor

DataSets window. This will take you back to the Manage Setups window, which willshow the

name of the DataSet(s) that you just entered.

4.1.3.1 Format Monitor Data

Monitor data may be formatted in three different ways. There is no preferred option. Simply use

whichever method seems most appropriate.

Database, Columns format. Two database files/tables, with monitor definition information in rows

in one file/table and monitor values in a single column in the other file/table.

Database, Rows format. One database file, with monitor definition information and monitor values

in a single line.

Text File format. One text file, with monitor definition information and monitor values in a single

line.

Tables 4-4a through 4-4d present the variables in each of the datasets when using the columns

format and a sample of what the files might look like. Tables 4-5a and 4-5b present the variables

in the dataset when using the rows format and a sample of what a data file might look like.

NOTE: The monitor data files do not specify the pollutant with which the data is associated - this

is specified by the user when loading the monitor data into BenMAP.

Table 4-4a. Column Format, Air Monitor Definition Variables

Variable Type Required Notes

Monitor

Name

text yes Unique name for the monitor.

Description text no Description of the Monitor.

Longitude numeric

(double)

yes Values should be in decimal degree format. Values in the eastern

hemisphere are positive, and those in the western hemisphere

are negative.

Latitude numeric

(double)

yes Values should be in decimal degree format. Values in the

northern hemisphere are positive, and those in the southern

hemisphere are negative.

Table 4-4b. Column Format, Sample Air Monitor Definition File

Monitor Name Description Latitude Longitude

Monitor1 City Park 40.107222 -74.882222

Monitor2 Industrial Site 39.835556 -75.3725
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Monitor Name Description Latitude Longitude

Monitor3 Suburban 40.112222 -75.309167

Monitor4 Rural 39.94 -75.6

Table 4-4c. Column Format, Air Monitoring Data Set Variables

Variable Type Required Notes

Metric text no This variable is either blank (signifying that the Values are

Observations, rather than Metric values), or must reference an

already defined Metric for the appropriate Pollutant.

Seasonal

Metric

text no This variable is either blank (signifying that the Values are not

Seasonal Metric values) or must reference an already defined

Seasonal Metric for the Metric.

Statistic text no This is an annual metric, which is either blank (signifying that the

values are not annualstatistics) or must be one of: None, Mean,

Median, Max, Min, Sum.

Monitor Name

(s)

numeric

(double)

yes Each of these field names should match up with a value in the

Monitor Name field in the Definitions file. The values will be

observations, metric, seasonal metric, or annual metric values, as

determined by the values of the other fields.

Table 4-4d. Column Format, Sample Air Monitoring Data File

Table 4-5a. Row Format, Air Monitoring Data File Variables

Variable Type Required Notes

Monitor Name text yes Unique name for each monitor in a particular location.

Description text no Description of the Monitor.

Longitude numeric

(double)

yes Values should be in decimal degree format. Values in the eastern

hemisphere are positive, and those in the western hemisphere are

negative.
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Variable Type Required Notes

Latitude numeric

(double)

yes Values should be in decimal degree format. Values in the northern

hemisphere are positive, and those in the southern hemisphere are

negative.

Metric text no This variable is either blank (signifying that the Values are

Observations, rather than Metric values), or must reference an

already defined Metric (e.g., 1-hour daily maximum) for the

appropriate Pollutant.

Seasonal

Metric

text no This variable is either blank (signifying that the Values are not

Seasonal Metric values) or must reference an already defined

Seasonal Metric for the Metric (e.g., mean of the 1-hour maximum

for the months of June through August).

Statistic text no This is an annual metric, which is either blank (signifying that the

values are not annualstatistics) or must be one of: None, Mean,

Median, Max, Min, Sum. (e.g., mean of the 1-hour maximumfor the

year)

Values comma

separated

values

(text)

yes If Metric is blank, either 365 or 8,760 comma separated

observations (depending on whether the Pollutant has daily or

hourly observations). If Metric is defined, but Seasonal Metric

and Statistic are blank, 365 metric values. If Seasonal Metric is

defined, but Statistic is blank, n seasonal metric values. If Statistic

is defined, one annualstatistic value (for either the Metric (if

Seasonal Metric is blank) or the Seasonal Metric. Missing values

are signified with a period ('.').

Table 4-5b. Row Format, Sample Air Monitoring Data File
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4.1.4 Incidence/Prevalence Data

Most health impact functions, such as those developed from log-linear or logistic health impact

functions, estimate the percent change in a health effect associated with a pollutant change. In

order to estimate the absolute change in incidence using these functions, the baseline incidence

rate (and in some cases the prevalence rate) of the adverse health effect are needed.

The incidence rate is the number of health effects per person in the population per unit of time,

and the prevalence rate is the percentage of people that suffer from a particular chronic illness.

For example, the incidence rate for asthma attacks may be 25 cases per asthmatic individual per

year, and the prevalence rate (measuring the percentage of the population that is asthmatic) might

be six percent.

NOTE: For both incidence and prevalence rates, BenMAP allows the user to have rates that

vary by race, ethnicity, gender, and age group. BenMAP can support multiple sets of incidence

and prevalence rates, if the rates differ by year or by grid definition.

To start adding incidence and prevalence data files, click on the Edit button below the

Incidence/Prevalence DataSets box. The Manage Incidence DataSets window will

appear.

In this window you may Add, Edit, and Delete datasets. The section on the left under

Available DataSets lists the incidence/prevalence datasets that are currently in the setup. The

section on the right under the DataSet Incidence Rates identifies the rates in the selected

dataset.

To add a dataset, click the Add button. This will bring up the Incidence DataSet Definition.

Give a name to the dataset that you are creating by typing a name in DataSet Name box.
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NOTE:If you have multiple incidence/prevalence datasets that vary by, say, year and grid

definition, then use the name to provide a reference to the year and grid definition.

Click the Load From Database button. In the Grid Definition drop-down list choose the grid

definition that matches the grid definition used to develop the incidence/prevalence dataset. Then

click on the Browse button, to browse for the dataset file. (The format for the dataset is detailed

in the next sub-section.)

After finding the file, click OK. The Incidence DataSet Definition window will appear,

displaying the rates in the data file that you just loaded.
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If the data look correct, click OK. This willreturn you to the Manage Incidence DataSets

window.

Follow the same procedure for any additional incidence/prevalence datasets that you want to add

to the setup database. When you have finished adding data, click OK in the Manage

Incidence DataSets window. The Incidence/Prevalence DataSets box in the Manage
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Setups window willshow the datasets that you have entered.

4.1.4.1 Format Incidence/Prevalence Data

Table 4-6a presents the variables that can be used in incidence and prevalence datasets, and

Table 4-6b presents a sample dataset that follows this format.

Table 4-6a. Health Incidence and Prevalence Data Set Variables

FieldName Type Required Notes

Endpoint Group text yes If this does not reference an already defined Endpoint Group,

one will be added.

Endpoint text yes If this does not reference an already defined Endpoint for the

Endpoint Group, one will be added.

Race text no Should either be blank (signifying All Races) or reference a

defined Race, such as "Black" (fromone or more Population

Configurations).

Ethnicity text no Should either be blank (signifying All Ethnicities) or reference

a defined Ethnicity, such as "Hispanic" (fromone or more

Population Configurations).

Gender text no Should either be blank (signifying All Genders) or reference a

defined Gender (fromone or more Population Configurations).

Start Age integer yes Specifies the low and high ages, inclusive. For example, Start

Age of “0" and End Age of “1" includes infants through the

End Ag first 12 months of life and all one-year old infants. e integer yes

Column integer yes

The column and the row link the incidence/prevalence data

with cells froma Grid Definition.

Row integer yes

Value numeric

(double)

yes The incidence/prevalence rate for the specified demographic

group for this location.

Type text no If value is a prevalence rate, then “Prevalence” should be

specified. Otherwise BenMAP assumes that the value is an

incidence rate.
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Table 4-6b. Sample Health Incidence and Prevalence Data Set

4.1.5 Population Data

The population data is used to estimate population exposure and in turn any adverse health

effects associated with a change in air pollution. BenMAP allows the user to specify race,

ethnicity, gender, and age of the population, as well as the year of the population estimate.

Population data loaded into BenMAP must be associated with a Population Configuration,

which defines the races, ethnicities, genders, and age ranges present in the data. Race, ethnicity,

and gender are unique text values representing population subgroups. Age ranges are defined by

integer values for starting age and ending age (inclusive), and a unique text value representing the

name of the age range. For example, “0TO1” might be used as a name for the age range defined

by a start age of zero and an end age of one, thus consisting of infants through the first twelve

months of life and all one-year old infants. The population data provided to BenMAP should

then contain population values for all combinations of race, ethnicity, gender, and age range. The

population values may be real numbers (e.g., non-integer values).

Population data must also be associated with a Grid Definition which specifies the geographic

areas for which the data is available (see for more details the section on Grid Definitions). If

population data is available for multiple grid definitions (cities and neighborhoods, for example),

users can have the option of using different sets of population data for different analyses.

BenMAP can also estimate populations for Grid Definitions for which no population data is

available by calculating spatial overlap percentages with Grid Definitions for which data is

available.

To add population data to BenMAP, click on the Edit button below the Population DataSets

box in the Manage Setups window. The Manage Population DataSets window will appear.
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Click on the Add button.

This brings up the Load Population DataSet window, which has a number of important

features. The Population DataSet Name box allows you to name the dataset.

The Population Configuration Definition window allows you to define the variables that are in

the population data file to be loaded into BenMAP. Use the drop-down list to choose an existing

population configuration and then view it by clicking the View button, or you may click the Add

button and define a new population configuration.

The Grid Definition drop-down list provides the list of existing grid definitions, and you need to

choose the grid definition that matches that in the population dataset. The Browse button to the

right of the Database box allows to find the datafile that you want to load into BenMAP.
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The Use Population Growth Weights checkbox should be checked when using population

data generated by the PopGrid software application. The population weights file assists in

forecasting population levels. This topic is fairly involved, so we have deferred discussion until

the appendix.

Defining a Population Configuration

The Population Configuration defines the age range, race, and gender of the variables in your

population database. It is critical that the definitions in the population configuration match those

used in the development of the database. If they do not match exactly, BenMAP willfail to load

the population data. (If this occurs, you need to go back and either develop a new population

configuration to match your data, or you need to revise your population database so that it

matches the population configuration.)

To add a population configuration, click the Add button in the Load Population DataSet

window.
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In the Population Configuration Name, replace "PopulationConfiguration 0" with a name of your

choosing. Under the Races box click on New and type in the name for any races present in your

population data. The names appear in both the Races list box and the Available Races list

box. (If you later create alternative population configurations, you can simply drag the relevant

names from the Available Races list box into the Races list box.) Similarly, under the

Available Genders and Available Ethnicity list boxes click on New and type in the name for

any ethnicity and gender identifiers present in your population data.

If you want to remove a category in Races, Genders, and Ethnicity list boxes, then highlight

the entry that you want to remove and click the Remove button. (It is not possible to remove

entries from either the Available Races, Available Genders, or Available Ethnicity list

boxes.)

The next step is to create the age groups that match the age groups in your population file. To
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start click on the Add button below the Age Ranges list box. The AgeRange Definition

window will appear. Type in the name of the age variable in the Age Range ID box and the

upper bound of the age range in the High Age box. (BenMAP automatically fills in the value for

the Low Age box.) For example, the age range names (with corresponding low and high ages)

might include the following: 0to0, 1to4, 5to9, 10to14, 15to19, 20to24, 25to29, 30to34,

35to39, 40to44, 45to49, 50to54, 55to59, 60to64, 65to69, 70to74, 75to79, 80to84, and

85up. The choice of the names is up to you. However, you must be sure that the names exactly

match those in your population datafile.

Click OK when you have defined the age range. If you make a mistake and want to delete an

age definition after you have entered it, click on the Delete button. This willremove the last age

group that you have entered. (Click on it twice if you want to remove the last two age groups

that you entered.) The population configurations can be quite detailed as in the case of the

United States Census population configuration that comes loaded with BenMAP.
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Or the population configuration can be somewhat simpler as in the case of the population

configuration for CityOne -- also pre-loaded with BenMAP:
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Click OK to return to the Load Population DataSet window. From the Grid Definition

drop-down list choose the grid definition (e.g., Tract). Then click on the Browse button to the

right of the Database box to choose the population data file to be loaded into BenMAP.

To finish loading the population datafile, click OK. The Manage Population DataSets

window will appear displaying the characteristics of the available population datafile that you just

loaded and any other datasets that have already been loaded into BenMAP.

Click OK. In the Population DataSets box of the Manage Setups window you should see an

entry for the population dataset that you just loaded.
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4.1.5.1 Format Population Data

Table 4-7 presents the variables that can be used in population datasets. Note that the names

used for the age ranges do not need to follow this same pattern, and instead should be based on

what seems most appropriate for you.

Table 4-7. Population Data Set Variables

Variable Type Required Notes

Age Range text yes References a defined age range in the associated

Population Configuration.

Column integer yes

The column and the row link the population data with cells

in a Grid Definition.

Row integer yes

Year integer yes The year of the data. Note that this may include historical

population estimates (such as froma census), as well as

population forecasts.

Population numeric (double) yes Population estimate. Note that the estimate is not

restricted to integers.

Race text yes References a defined race in the associated Population

Configuration.
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Variable Type Required Notes

Ethnicity text no References a defined ethnicity in the associated Population

Configuration.

Gender text yes References a defined gender in the associated Population

Configuration.

4.1.6 Health Impact Functions

Health impact functions calculate the change in the number of adverse health effects among a

certain population associated with a change in exposure to air pollution. A typical health impact

function has inputs specifying the pollutant; the metric (daily, seasonal, and/or annual); the age,

race, ethnicity, and gender of the population affected; and the incidence rate of the adverse health

effect.

Health impact functions are subdivided by user-specified types of adverse health effects. The

broadest category is the Endpoint Group, which represents a broad class of adverse health

effects, such as premature mortality, cardiovascular-related hospital admissions, and respiratoryrelated hospital admissions, among other categories. (BenMAP only allows pooling of adverse

health effects to occur within a given endpoint group, as it generally does not make sense to sum

or average together the number of cases of disparate health effects, such as premature mortality

and chronic bronchitis.) The Endpoint Group may then be subdivided by user-specified

Endpoints. For example, the respiratory-related hospital admission Endpoint Group, may have

separate Endpoints for Asthma-related hospital admissions and chronic bronchitis-related

hospital admissions.

There are a wide range of variables that can be included in a health impact function, to specify the

parameters of the function and to identify its source, such as the author, year, and location of the

study, as well as other pollutants used in the study. The bibliographic reference for the study may

be included, as well as any additional information needed to identify a particular impact function.

(The Reference and Qualifier variables are usefulfor this.) A number of health impact

functions have been developed based on epidemiologicalstudies in the United States and

Europe. However, researchers have conducted an increasing number of epidemiologicalstudies

in Asia and Latin America that can be used to develop more location-specific impact functions.

There are a number of issues that arise when deriving and choosing between health impact

functions that go well beyond this user manual. Hence, it is important to have a trained health

researcher assist in developing the impact function data file.

To add health impact functions to BenMAP, click on the Edit button below the C-R Functions

DataSets box in the Manage Setups window. The Manage C-R Functions DataSets

window will appear.
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In this window you may Add, Edit, and Delete datasets. The section on the left under

Available DataSets lists the health impact function datasets that are currently in the setup

database. The section on the right under the C-R Functions in DataSet lets you view the

functions in a selected dataset.

To add a new dataset, click the Add button. The C-R Function DataSet Definition window

will appear. Type the name that you want to use for the dataset in the C-R Function DataSet

Name box.

You may then enter functions into this dataset through an externally created database by clicking

the Load From Database button. Alternatively, you may Add, Delete, and Edit individuals

functions within BenMAP.

To add a database click the Load From Database button. In the Load a C-R Function

Database window, click the Browse button and then find and select the health impact function

database that you want to load into your setup.

The C-R Function DataSet Definition window willreappear, and you can then view the health

impact functions that you have loaded into your dataset. Note that the central boxed area in this

window has two parts. On the left is the Tree and on the right is the Data.
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The columns within each of the list boxes can be rearranged in order to provide the most useful

display. By clicking and holding the cursor on a column, you may move it within either the Tree

or Data boxes, or you may move columns between boxes. For example, by clicking and holding

on the Pollutant column and then dragging it to the far left of the tree, you can sort all of the

health impact functions by Pollutant. (Note rearranging the columns is only for display and has no

effect on the underlying health impact function database.)

Clicking OK brings you back to the Manage C-R Function DataSets window. Here you

have the opportunity to view the functions in your dataset. If you have more than one dataset,

you can choose the dataset that you want to view in the Available DataSets in the left-hand

section of the window. In the C-R Functions in DataSet in the right-hand section of the

window, you have the same Tree and Data structure that you have in the C-R Function

DataSet Definition window.
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You may edit this dataset by clicking the Edit button. This will bring up the C-R Function

DataSet Definition window. You may load an additional database to your dataset, or you

may Add, Edit, and Delete individualfunctions directly in BenMAP.

To edit an existing function, highlight a particular function in the C-R Function DataSet

Definition window, and then click the Edit button. The C-R Function Definition window

appears, and you may change any of the values in the boxes and the drop-down lists. When you

are finished click OK.
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From the C-R Function DataSet Definition window you can also add health impact functions

to the ones that are already in your dataset. Click the Add button and a blank C-R Function

Definition window will appear, and you can then create new health impact functions. (We will

discuss this in more detail in subsequent examples.)

After defining the new health impact function, click OK. This will take you back to the C-R

Function DataSet Definition window. When you are finished with any editing or adding of

health impact functions click OK. The Manage Setups window will appear. Here in the C-R

Function DataSets box you should see an entry for any health impact function datasets that you

have loaded.

4.1.6.1 Format Health Impact Functions

Table 4-8 presents the variables that can be used in health impact function datasets.

Table 4-8. Health Impact Function Data Set Variables

Variable Type Required Notes

Endpoint Group text yes If this does not reference an already defined Endpoint Group,

one will be added.
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Variable Type Required Notes

Endpoint text yes If this does not reference an already defined Endpoint for the

Endpoint Group, one will be added.

Pollutant text yes Should reference an already defined Pollutant.

Metric text yes Should reference an already defined Metric for the Pollutant.

Seasonal Metric text no Should either be blank (signifying no Seasonal Metric value)

or reference an already defined Seasonal Metric for the Metric.

Statistic text no Should either be blank (signifying no annual metric value) or

be one of: None, Mean, Median, Min, Max, Sum.

Race text no Should either be blank (signifying All Races) or reference a

defined Race.

Ethnicity text no Should either be blank (signifying All Ethnicities) or reference

a defined Ethnicity.

Gender text no Should either be blank (signifying All Genders) or reference a

defined Gender.

Start Age

End Age

integer yes Specifies the low and high ages, inclusive. For example, Start

Age of “0" and End Age of “1" includes infants through the

first 12 months of life and all one-year old infants

Author text no The author(s) of the study fromwhich the function is derived.

Year integer no The year of publication of the study.

Location text no The location of the study.

Qualifier text no Provides additional information to identify a particular health

impact function, such as when a particular study has multiple

functions.

Other

Pollutants

text no Identifies other pollutants that were included simultaneously

in the estimation equation for the pollutant of interest.

Reference text no Bibliographic reference, included to identify the source in the

health literature.

Function text yes The functional form, interpreted (executed) by BenMAP when

running an analysis to estimate air pollution-related health

impacts. For example, the log-linear formis as follows: “(1-(1/

EXP(Beta*DELTAQ)))*Incidence*POP”.

Baseline

Incidence

Function

text no The functional form, interpreted (executed) by BenMAP to

estimate health impacts due to all causes. This typically has

the form: "Incidence*POP".
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Variable Type Required Notes

Beta numeric

(double)

no Mean value of the Beta distribution.

Distribution

Beta

text no If the Beta has no distribution, any value is acceptable.

Otherwise, should be one of: Normal, Triangular, Poisson,

Binomial, LogNormal, Uniform, Exponential, Geometric,

Weibull, Gamma, Logistic, Beta, Pareto, Cauchy, Custom.

Parameter 1

Beta

numeric

(double)

no Parameter 1 of the Beta distribution (meaning depends on the

distribution - for Normal distributions this represents the

standard deviation).

Parameter 2

Beta

numeric

(double)

no Parameter 2 of the Beta distribution (meaning depends on the

distribution - for Normal distributions this is not required).

Name A text no Description of variable A.

A numeric

(double)

no A constant value which can be referenced by the Function

(see below).

Name B text no Description of variable B.

B numeric

(double)

no A constant value which can be referenced by the Function

(see below).

Name C text no Description of variable C.

C numeric

(double)

no A constant value which can be referenced by the Function.

Incidence

DataSet

text no Specifies the dataset fromwhich incidence data will be

derived. The user may choose frommultiple datasets.

(Initially this field may be left blank.)

Prevalence

DataSet

text no Specifies the dataset fromwhich prevalence data will be

derived. The user may choose frommultiple datasets.

(Initially this field may be left blank.)

Variable

DataSet

text no Specifies the dataset fromwhich "variable" data (e.g., income

data) will be derived. The user may choose frommultiple

datasets. (Initially this field may be left blank.)

4.1.7 Variable Data

Health Impact functions and/or Valuation functions may sometimes refer to variables other than

those for which BenMAP automatically calculates values. For example, some valuation functions

reference the median income within each area of analysis. To facilitate this, BenMAP allows you

to load datasets of variables, which may either be global values or may vary geographically

(meaning they are associated with a Grid Definition).
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To add variables to BenMAP (such as income and other miscellaneous variables that might be

needed in the analysis), click on the Edit button below the Variables DataSets box in the

Manage Setups window. The Manage Setup Variables DataSets window will appear.

In this window you may Add, Edit, and Delete datasets. The section on the left under

Available DataSets lists the variables datasets that are currently in the setup database. The

section on the right under the DataSet Variables lets you view the variables in a selected

dataset.

To add variables click the Add button. This will take you to the Setup Variable DataSet

Definition window. In this window you may add externally created variables through the Load

From Data button for any of your predefined grid definitions. Alternatively, by clicking the Add

button you may create global variables to your dataset that are not tied to a specific geographic

region.
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To start, type the name that you want to use for the dataset in the DataSet Name box. (This is

a name that is internal to BenMAP and used just for identification.)

To add an externally created variable database, click the Load From Database button. This

will bring up the Load Variable Database window choose. Here you need to choose the grid

definition from the Grid Definition drop-down list that matches the level of aggregation in your

variable database. Then you may use the Browse button to find and select the desired data file.

After choosing the database, click OK. This takes you back to the Setup Variable DataSet

Definition window. This allows you to see the variables in the dataset and their values.
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You may also add Global variables to the dataset by clicking on the Add button in the Setup

Variable DataSet Definition window. After clicking Add, type the name of the variable where

it says Setup Variable 0 in the DataSet Variables list box, and then in Value box type the

value of the global variable.
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When finished adding variables, click OK. This will take you to the Manage Setup Variable

DataSets window. In the Available DataSets list box there is an entry for the dataset that you

just added. And in the DataSet Variables list box are the variables in the highlighted dataset.
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At this point you may click Add to load an additional dataset, click Edit to edit the selected

dataset, click Delete to delete the selected dataset, or complete this variable management step

by clicking OK.

Clicking OK returns you to the Manage Setups window, where in the Variable DataSets

box, you should see an entry for the variable dataset that you just created.

4.1.7.1 Format Variable Data

Table 4-9a presents the variables that can be used in variable datasets, and Table 4-9b presents

a sample of what a dataset might look like.

Table 4-9a. Variable Data Set Variables

Variable Type Required Notes

Column integer yes

The column and the row link the population data with cells

in a Grid Definition.

Row integer yes

Variable Name numeric

(double)

no
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Table 4-9b. Sample Variable Data Set

COL ROW median_income

42 17 39871.19

42 29 43760.31

42 45 38186.77

42 91 42298.49

42 101 31261.07

4.1.8 Inflation Data

It may be desirable for the dollar values generated by Valuation functions to account for inflation,

and generate economic benefits for years other than the year initially specified by your valuation

data. To do this, you can load inflation data into BenMAP, and then include a reference to your

inflation data when developing valuation functions. (We give an example of this below.)

The valuation functions should have a consistent dollar year, and this dollar year has to be kept in

mind when developing the inflation datasets. That is, whichever dollar year is used for your

valuation functions, then the inflation values for that year should be set to 1. For example, in the

U.S. setup, the valuation functions have a dollar year of 2000, so the inflation datasets have a

value of 1 for the year 2000. (Values for years earlier than 2000 are less than 1, and values for

years after 2000 are greater than 1, because inflation has increased from one year to the next.)

The U.S. setup in BenMAP provides inflation factors for three different types of values:

All Goods Index can be used to adjust the value of generic goods.

Medical Cost Index can be used to adjust the value of medical expenses.

Wage Index can be used to adjust the value of wages.

If a valuation function includes an estimate of wage income, for example, this value could be

multiplied by the Wage Index adjustment factor to get the specified currency year. For

example, in valuing workloss days, the U.S. setup uses a function like the following:

DailyWage*WageIndex, where the DailyWage is specified in year 2000 dollars. The

WageIndex scales this DailyWage value up or down depending on the currency year you have

chosen. (The choice of currency year can be made with the One-Step Setup, discussed here,

and with the Advanced button when creating an APV file, discussed here.) If the currency year

is 2000, then the WageIndex has a value of 1 and no change is made to the DailyWage. If the

currency year is specified to, say, 2005, then the WageIndex will have a value greater than 1

because of the inflation that has occurred between 2000 and 2005.

To add inflation data to BenMAP, click on the Edit button below the Inflation DataSets box in

the Manage Setups window. The Inflation DataSet Manager window will appear. In this

window you may Add, Edit, and Delete datasets. The section on the left under Available

DataSets lists the inflation datasets that are currently in the setup database. The section on the

right presents the inflation factors in a selected dataset.
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Click on the Add button. In the Load Inflation DataSet window, type in the name of the

dataset in the Inflation DataSetName box, and then click on the Browse button to the right of

the Database box to choose the dataset that you want to import. Click OK. The Inflation

DataSet Manager window will appear. Here you may view the data that you just loaded.

At this point you may add more data by clicking Add, or you may complete this step by clicking

OK. Clicking OK takes you to the Manage Setups window, where in the Inflation DataSets

box, you should see an entry for the inflation dataset that you just loaded.

4.1.8.1 Format Inflation Data

Table 4-10a presents the variables that can be used in inflation datasets, and Table 4-10b

presents a sample of what a dataset might look like. Note that if you are loading your own

inflation data, you can use different names than the ones specified below. Instead ofspecifying

"AllGoodsIndex" you could have a variable called "GeneralIndex" -- this is fine so long as you

make sure that your valuation functions properly reference these inflation variables.

Table 4-10a. Inflation Data Set Variables in U.S. Setup

Variable Type Required Notes

Year integer yes The year of the data. Note that this will typically only

include historical estimates.

AllGoodsIndex integer no All goods inflation indexvalue.
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Variable Type Required Notes

MedicalCostIndex integer no Medical cost inflation indexvalue.

WageIndex integer no Wage inflation indexvalue.

< variable name > integer no Additional indices can be specified.

Table 4-10b. Sample Inflation Data Set

YEAR AllGoodsIndex MedicalCostIndex WageIndex

1995 0.75 0.62 0.71

1996 0.79 0.67 0.74

1997 0.81 0.72 0.76

1998 0.83 0.77 0.78

1999 0.86 0.8 0.81

2000 0.88 0.84 0.83

2001 0.91 0.87 0.86

2002 0.93 0.89 0.89

2003 0.94 0.92 0.92

2004 0.96 0.96 0.96

2005 1 1 1

2006 1.02 1.04 1.03

4.1.9 Valuation Data

BenMAP allows the valuation estimates to vary by Endpoint Group, Endpoint, and Age (note

that multiple estimates may be provided for a particular combination). BenMAP allows the

valuation function to be quite detailed, and allows an uncertain parameter (A) with a userspecified distribution. The user can modify the valuation function with a number of constant

values (B, C, and D) that might represent an adjustment for inflation, income growth, income

elasticity, or, say, purchasing power parity. Finally, BenMAP has two fields to more clearly

identify the valuation function (i.e., Qualifier and Reference).

When reviewing the economic literature to develop a valuation database or simply add additional

valuation functions to an existing database, it is important to have an economist assist. For an

overview of valuation, see the Overview of Valuation section in the chapter on Aggregation,

Pooling, and Valuation.

To add valuation functions to BenMAP, click on the Edit button below the Valuation DataSets

box in the Manage Setups window. The Manage Valuation Function DataSets window

will appear.
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In this window you may Add, Edit, and Delete datasets. The section on the left under

Available DataSets lists the valuation datasets that are currently in the setup database. The

section on the right under the Valuation Functions in DataSet lets you view the valuation

factors in a selected dataset.

To add a dataset click on Add. In the Valuation Function DataSet Name type the name of

the dataset that will be added.

You may load valuation data with an externally-created database, or you may add individual

valuation functions from within BenMAP. To load an externally-created valuation database, click

on the Load From Database button. This will bring you to the Load a Valuation Function

Database window.

In the Load a Valuation Function Database window use the File of Type drop-down list to

choose the type of file (e.g., MS Access Database). Choose the valuation database and click

Open. This will bring you back to the Valuation Function DataSet Definition window. Here

you can view the valuation functions that you have in your database.

Note that the large box is divided in two. On the left is the Tree and on the right is the Data.

The tree can be expanded by clicking on the “+” sign, or contracted by clicking on the “-“ sign.

The columns within each of the list boxes can be rearranged in order to provide the most useful

display. By clicking and holding the cursor on a column, you may move it within either the Tree

or Data boxes, or you may move columns between boxes. (Note that rearranging the columns is

only for display and has no effect on the underlying Valuation function dataset.)
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In the Valuation Function DataSet Definition window you can also edit the functions already

existing in your data set by highlighting a particular Valuation function and then clicking the Edit

button.

The Valuation Function Definition window allows you to change any of the values in the boxes

and the drop-down lists. When you are finished click OK.
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From the Valuation Function DataSet Definition window you can also add Valuation

functions to the ones that are already in your dataset. Click the Add button and a blank

Valuation Function Definition window will appear, and you can then create a new Valuation

function.

After defining the new Valuation function, click OK. This will take you back to the Valuation

Function DataSet Definition window. When you are finished with any editing or adding of

valuation functions click OK. The Manage Setups window will appear. Here in the Valuation

Function DataSets box you should see an entry for the valuation function dataset that you just

loaded.

4.1.9.1 Format Valuation Data

Table 4-11 presents the variables that can be used in valuation datasets.

Table 4-11. Valuation Data Set Variables

FieldName Type Required Notes

Endpoint Group text yes If this doesn't reference an already defined Endpoint Group, one

will be added.

Endpoint text yes If this doesn't reference an already defined Endpoint for the

Endpoint Group, one will be added.
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FieldName Type Required Notes

Qualifier text no Provides additional information to identify a particular valuation

function.

Reference text no Bibliographic reference, included to identify the source in the

economic literature.

Start Age integer no Specifies the low and high ages, inclusive. For example, Start

Age of “0" and End Age of “1" includes infants through the

End Age integer no first 12 months of life and all one-year old infants.

Point Estimate numeric

(double)

yes Central estimate of the unit value.

Function text yes The functional form, interpreted (executed) by BenMAP when

running an analysis. The BenMAP User Manual will contain

more information on the format of this field.

A Description text no Description of variable A.

A numeric

(double)

no Mean of the A distribution.

A Distribution text no If A has no distribution, any value is acceptable. Otherwise,

should be one of: Normal, Triangular, Poisson, Binomial,

LogNormal, Uniform, Exponential, Geometric, Weibull, Gamma,

Logistic, Beta, Pareto, Cauchy, Custom.

A Parameter 1 numeric

(double)

no Parameter 1 of the A distribution (meaning depends on the

distribution - for Normal distributions this represents the

standard deviation).

A Parameter 2 numeric

(double)

no Parameter 2 of the A distribution (meaning depends on the

distribution - for Normal distributions this is not required).

Constant

Description

text no Description of variables, B, C, and D.

Constant Value numeric

(double)

NO A constant value (denoted by B, C, and/or D) which can be

referenced by the Function.

4.1.10 Income Growth Data

As real income increases, willingness-to-pay (WTP) to avoid air-pollution related morbidity

effects and premature mortality should grow. BenMAP allows users to adjust the WTP estimates

to account for the growth in income over time. This adjustment is a combination of data on

income growth and estimated income elasticity of demand, which measures the responsiveness of

the quantity demanded of a good to the change in the income of the people demanding the good;

this is distinct from elasticity of demand, which quantifies the change in demand for goods and

services as a result of changes in price for those goods and services. This section describes how

to load the data adjusting for income growth and how EPA developed these adjustment factors.
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Note that the WTP estimates in the default valuation functions in the U.S. setup are assumed to

be based on 1990 income, so the income growth adjustments are allrelative to 1990. That is,

the income growth data has a value of 1 in 1990, and because income has generally increased

over time in the U.S., the income growth values are typically greater than 1 after 1990. (An

exception is 1991, when incomes declined slightly in the U.S.)

If you load in your own valuation functions and/or income growth adjustment factors, be sure that

you have carefully considered the income year. For example, if your valuation functions are

based on income in the year 2005, then the income growth adjustment should have a value of 1.0

in 2005, because no adjustment is necessary. As you forecast into the future, under the

assumption that incomes go up over time, then your income growth adjustment factors would

have values greater than 1.0 for years past 2005, and would have values less than 1.0 for years

prior to 2005.

To add income growth data to BenMAP, click on the Edit button below the Income Growth

Adjustments box in the Manage Setups window. The Income Growth Adjustment

DataSet Manager window will appear.

In this window you may Add and Delete datasets. The section on the left under Available

DataSets lists the income growth datasets that are currently in the setup database. The section

on the right presents the income growth adjustment factors in a selected dataset.

Click on the Add button. In the Load Income Growth Adjustment Factors DataSet

window, type in the name of the dataset in the Income Growth Adjustment DataSetName

box, and then click on the Browse button to the right of the Database box to choose the dataset

that you want to import.
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Click OK. The Income Growth Adjustment DataSet Manager window will appear. Here

you may view the data that you just loaded.

Developing Income Growth Adjustment Factors

Adjusting WTP to reflect growth in real income requires three steps:

1. Identify relevant income elasticity estimates from the peer-reviewed literature

2. Calculate changes in future income

3. Calculate adjustments to WTP based on changes in future income and income

elasticity estimates

1. Identifying income elasticity estimates

Income elasticity estimates relate changes in demand for goods to changes in income. Positive

income elasticity suggests that as income rises, demand for the good also rises. Negative income

elasticity suggests that as income rises, demand for the good falls. BenMAP does not adjust

Cost-of-Illness (COI) estimates according to changes in income elasticity due to the fact that

COI estimates the direct cost of a health outcome; instead we adjust this metric using inflation

factors described above. BenMAP includes income elasticity estimates specific to the type of

health endpoint associated with the WTP estimate. BenMAP contains elasticity estimates for

three types of health effects: minor, severe and premature mortality. Minor health effects are

those ofshort duration. Severe, or chronic, health effects are of longer duration. Consistent with

economic theory, the peer reviewed literature indicates that income elasticity varies according to

the severity of the health effect. Below we summarize the health endpoints considered minor and

severe within BenMAP.

Minor

Asthma exacerbation

Acute bronchitis

Acute respiratory symptoms (minor restricted activity days)

Lower respiratory symptoms

Upper respiratory symptoms

Severe

Chronic bronchitis

Chronic asthma

A review of the literature revealed a range of income elasticity estimates that varied across the

studies and according to the severity of health endpoint. Table 4-12 summarizes the income

elasticity estimates found in BenMAP to adjust minor health effects, severe health effects and

premature mortality. Here we have provided a lower-, upper- and central-estimate for each type

of health endpoint.
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Table 4-12. Income Elasticity Estimates

Health Endpoint Lower Bound Central Estimate Upper Bound

Minor Health Effect 0.04 0.15 0.30

Severe and Chronic

Health Effects

0.25 0.45 0.60

Premature Mortality 0.08 0.40 1.00

2. Calculating changes in future income

The next input to the WTP adjustment is annual changes in future income. To estimate changes in

future income, EPA used the Standard & Poor’s projections of future changes in Gross Domestic

Product (GDP) occurring after the year 2010. EPA then divided the projected change in GDP by

the Woods & Poole projected change in total US population to produce an estimate of the future

GDP per capita.

3. Calculating changes in WTP

The income elasticity estimates from Table 4-12 and the estimated changes in future income may

then be used to estimate changes in future WTP for each health endpoint. The adjustment formula

follows four steps:

Table 4-13 below summarizes the income-based WTP adjustments used within BenMAP for
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minor health endpoints, severe health endpoints, and premature mortality.

Table 4-13. Income-Based WTP Adjustments by Health Effect and Year

4.1.10.1Format Income Growth Data

Table 4-14a presents the variables that can be used in income growth adjustment datasets, and

Table 4-14b presents a sample of what a dataset might look like.
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Table 4-14a. Income Growth Data Set Variables

Variable Type Required Notes

Year integer yes The year of the data. Note that this will include

historical estimates as well as forecasts.

Mean integer yes Mean income growth adjustment factor.

EndPoint Group text yes

Table 4-14b. Sample Income Growth Data Set

Year Mean EndpointGroup

1990 1.0000 Acute Bronchitis

1991 0.9910 Acute Bronchitis

1992 0.9980 Acute Bronchitis

1993 1.0035 Acute Bronchitis

1994 1.0145 Acute Bronchitis

1995 1.0191 Acute Bronchitis

1996 1.0274 Acute Bronchitis

1997 1.0385 Acute Bronchitis

1998 1.0438 Acute Bronchitis

1999 1.0483 Acute Bronchitis

2000 1.0435 Acute Bronchitis

2001 1.0494 Acute Bronchitis

2002 1.0564 Acute Bronchitis

2003 1.0635 Acute Bronchitis

2004 1.0707 Acute Bronchitis

2005 1.0775 Acute Bronchitis

4.1.11 QALY Data

To add QALY data to BenMAP, click on the Edit button below the QALY Distribution

DataSets box in the Manage Setups window. The QALY DataSet Manager window will

appear.
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In this window you may Add and Delete datasets. The section on the left under Available

DataSets lists the QALY datasets that are currently in the setup database. The sections on

above and to the right describe the data and let you view the QALY factors in a selected

dataset.

To add a dataset click on Add. This will bring up the Load QALY Dataset window.
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In the QALY DataSet Name type the name of the dataset that will be added. Then click the

Browse button to find the QALY database that you want to load. This will bring up the Open

window. Use the File of Type drop-down list to choose the type of file (e.g., MS Access

Database). Choose the QALY database and click Open. This will bring you back to the Load

QALY DataSet window.

Each QALY dataset is assumed to be specific to an Endpoint Group and Endpoint, so use the

Endpoint Group and Endpoint drop-down menus to identify the appropriate selections.

Next use the Qualifier box to provide a brief description of the datafile. When creating an APV

Configuration, you willsee this Qualifier description, so choose a description that adequately

captures what you have in your QALY datafile. There can be multiple QALY datafiles, in which

case the Qualifier can be quite useful.

Finally, include a detailed description in the Description box. This is not required, but can be

useful when comparing different QALY datasets.

4.1.11.1Format QALY Data

Table 4-15a presents the variables that can be used in QALY datasets, and Table 4-15b

presents a sample of what a dataset might look like.
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Table 4-15a. QALY Data Set Variables

Variable Type Required Notes

Start Age

End Age

integer yes Specifies the low and high ages, inclusive. For

example, Start Age of “0" and End Age of “1" includes

infants through the first 12 months of life and all oneyear old infants

QALY integer yes QALYvalue

Table 4-15b. Sample QALY Data Set

StartAge EndAge QALY

18 24 5.666993

18 24 2.758569

18 24 5.485653

18 24 3.647018

18 24 4.196073

18 24 3.501245

18 24 6.018638

18 24 2.057168

4.2 Export and Import Setups

BenMAP allows you to export and import entire databases (all Setups), individual Setups, and

parts of individual Setups (e.g. all GridDefinitions, or individual health impact Function DataSets).

This functionality can be used to share data with other BenMAP users, as well as to allow you to

move databases between computers. In particular, all of the steps involved in creating a Setup

can be done just once, after which the data can be exported and then imported on other

computers.

4.2.1 Export Setups

To export part or all of an existing setup, go to the Tools drop-down menu, and choose the

Database Export option.
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This will bring up the DatabaseExportForm window. Initially, all of the setups are in a data

tree, which is initially collapsed into the dataset Available Setups. To view the available Setups,

click on the “+” sign to the left of Available Setups. This will expand the data tree. To contract

the tree simply click on the “-“ sign.

Choose an dataset to export by clicking on it. In the screenshot below, we are have chosen to

export EPA Standard Monitors.
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You may choose all available Setups, individual Setups, the various collections within a Setup (all

GridDefinitions, all Pollutants, all Population DataSets, etc.), and individual datasets within the

various collections within a Setup. Click OK when you have selected the dataset to export.

This will bring up the Export Database Objects To File window. From here you may name

the dataset and choose the folder into which to export it. Type the dataset name the Filename

box, and in the Save in drop-down menu locate the folder that you want to use.
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NOTE: Exported BenMAP database files have a .bdb extension, and are a binary format not

suitable for viewing in external applications.

4.2.2 Import Setups

To import part or all of an existing setup, go to the Tools drop-down menu, and choose the

Database Import option. This will bring up the Import Object Into Database window.
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The Database Object File box identifies the file that you want to import. Click on the Browse

button to locate the file to import. This will bring up the Import Database Object From File

window.

Select the file that you want to import, and then click Open. This will bring you back to the

Import Object Into Database window. Click OK to finish the import process.

If the file contains a sub-Setup item, such as a collection (e.g., a set of grid definitions) or an

individual item (e.g., a single grid definition from among many available), select the Setup into

which it should be imported. Select the Setup from the drop-down list below Target Setup.

Click OK to finish.

NOTE: Duplicates of datasets (typically identified by their names - e.g. “Population CityOne”)

will overwrite existing data in a Setup. New datasets (i.e., non-duplicated) will be added to the

Setup.

4.3 Questions Regarding Loading Data

I've loaded new baseline incidence data, but BenMAP won't let me select it in the

configuration stage

When formatting these data for importation to BenMAP, take special care to ensure that you
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have specified the health endpoints correctly. The baseline incidence rate must be associated

with a specific health endpoint and endpoint group in BenMAP. Be sure that you have

recorded the endpoint group and endpoint exactly as it is recorded in BenMAP. For example,

if the baseline incidence rate is for asthma-related hospital admissions, be sure you have

recorded the endpoint group as "Hospital Admissions, Asthma" and the endpoint as "HA,

Asthma".

I've loaded a new grid and new population data into BenMAP but I can't seem to use

these new data

Be sure to load the new grid definition first. Next, load the population dataset and be sure to

select your new grid definition.

How do I generate a population dataset for a new grid definition?

You can generate a population dataset using a variety of approaches. The key is that you

need to have a shapefile of your area of interest (e.g., census tracts in a city) and you have

census data matching your area of interest. One source for both a shapefile and the

associated population data is the U.S. Census Bureau. (A variety of other agencies have

census data, and you need to check around for your area of interest.) Another option for U.S.

population data is to use the PopGrid software application, described in appendix on

Population Data for U.S. Setup. Using PopGrid, you still need to have a shapefile for your

area of interest.

Can I edit a population configuration?

No, you cannot edit a population configuration. You can only view a population configuration.

If the population configuration does not match your data, then you need to either create a new

population configuration to match your data, or reshape your data so that it matches the

population configuration.
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CHAPTER 5

Air Quality Grid

Creation

In this chapter...

Create air quality grids using different methods.

Find details on the file structures for data inputs.

Interpolate with Closest Monitor or Voronoi Neighbor

Averaging.

Scale monitor data with modeling data.

Learn about the Monitor Rollback feature.
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BenMAP is not an air quality model, nor can it generate air quality data independently. Instead it

relies on the air quality inputs given to it. To estimate population exposure to air pollution,

BenMAP uses air quality grids that it generates from input air quality data (modeling and or

monitoring data).

BenMAP creates air quality grids to estimate the average exposure to ambient air pollution of

people living in some regularly-shaped area, or domain, such as that delineated by air quality

models, as well as more irregular shapes, such as cities or nations. However, BenMAP does not

estimate personal exposure. Instead, the air quality grids provide the average population

exposure for each grid cell that BenMAP can then use in health impact functions.

To create air quality grids, BenMAP uses a number of inputs, including modeling data, monitoring

data, or both. You may enter your own modeling and monitoring data, provided that the data are

in a format recognized by BenMAP.

To start the grid creation process, click on the Create Air Quality Grids button. BenMAP will

then ask which of the following types of air quality data you wish to use:

Model Direct. Choose this option if you have air quality modeling data that you wish to use

directly. Table 5-1 below describes the input format for modeling data.

Monitor Direct. Choose this option if you wish to use monitoring data, without additional

modeling data.

Monitor and ModelRelative. Choose this option if you wish to use a combination of monitor

and model data.

Monitor Rollback. Choose this option if you want to reduce monitor levels by a specified

amount.

Select your option and then click Go!. BenMAP will direct you through the necessary steps for

each option.
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5.1 Model Direct

After choosing the Model Direct option, you need to specify the grid definition to which the data

file corresponds (Grid Type), the pollutant that the data is modeling (Pollutant), and the location

of your data file (Model Database).

The Model File specifies the location of the air quality modelresults that you want to import.

Table 5-1 presents the structure that these files must have, and Table 5-2 presents a sample data

file with a variety of metrics. (For more information on air quality models, the EPA website has

detailed descriptions of a variety of models: http://www.epa.gov/ttn/scram/.)
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Table 5-1. Air Modeling Data File Variables

Variable Type Required Notes

Column integer yes The column and the row uniquely identify each set of

modeling values, and link the modeling data with cells in a

Row Grid Definition. integer yes

Metric text no This variable is either blank (signifying that the Values are

Observations, rather than Metric values), or must reference

an already defined Metric (e.g., 1-hour daily maximum) for

the appropriate Pollutant.

Seasonal Metric text no This variable is either blank (signifying that the Values are

not Seasonal Metric values) or must reference an already

defined Seasonal Metric for the Metric (e.g., mean of the 1-

hour maximumfor the months of June through August).

Statistic text no This variable is either blank (signifying that the values are

not an annual metric) or must be one of: None, Mean,

Median, Max, Min, Sum. (e.g., mean of the 1-hour maximum

for the year)

Values comma

separated

values (text)

yes If Metric is blank, either 365 or 8,760 comma separated

observations. If Metric is defined, but Seasonal Metric and

Statistic are blank, 365 metric values. If Seasonal Metric is

defined, but Statistic is blank, n seasonal metric values. If

Statistic is defined, one annual metric value (for either the

Metric (if Seasonal Metric is blank) or the Seasonal Metric.

Missing values are signified with a period ('.').
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Table 5-2. Sample Air Modeling Data File

5.2 Monitor Direct

Using the Monitor Direct grid creation option, you create an air quality grid directly from air

pollution monitoring data. At the top left of the Monitor Direct Settings window, you are

asked to select a previously defined grid definition from the Grid Type drop-down list, and a

previously-defined pollutant from the Pollutant drop-down list. And at the top right of the

Monitor Direct Settings window, you are asked to select an interpolation method. The

interpolation method is used to move from point-based monitor data to grid cell based air quality

data. That is, some grid cells will have many monitors in them, some will have just one, and some

will have none. BenMAP uses the interpolation methods to generate representative air quality

metric values for each grid cellfrom monitor data for all of these cases.
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BenMAP includes three interpolation methods. The Closest Monitor method simply uses the

monitor closest to a grid cell’s center as its representative value. The Voronoi Neighbor

Averaging method takes an inverse-distance weighted average of a set of the monitors

surrounding a grid cell’s center as its representative value. The Fixed Radius method averages all

of the monitors within a fixed radius. Each method is described below. For more detail, also

see the appendix on Air Pollution Exposure Estimation Algorithms.

In the middle of the Monitor Direct Settings window, you can choose the rest of the options to

create your air quality grid. You need to specify the Grid Type and the Pollutant. The choices

for Grid Type include only those that you have previously defined for the particular Setup in

which you are working. Similarly, the choices for Pollutant include only those that you have

already defined in your Setup. And you need to specify a source for your monitor data. In the

Library tab you may select data that you have already loaded into BenMAP.

You can also input your own monitor data file rather than use monitor data already existing in a

dataset, so long as it is in the format that BenMAP recognizes (see the Load Data chapter for

details on the format for monitor datasets). Click on the tab that matches one of the three

possible formats for your data:(1) in a database file, with monitor definition information and

monitor values in a single row (Database, Rows), (2) in a text file, with monitor definition

information and monitor values in a single line (Text File), and (3) in two database files/tables,

with monitor definition information in rows in one file/table and monitor values in a single column

in the other file/table (Database, Columns). There is no preferred approach; you should use the

approach with which you are most comfortable.
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5.2.1 Closest Monitor for Monitor Direct

If you choose the Closest Monitor option, BenMAP identifies the monitor closest to each grid

cell's center, and then assigns that monitor's data to the grid cell. Closest Monitor interpolation

can be modified by clicking on the Advanced button at the bottom of the window and typing in

a Maximum Neighbor Distance (in km).

The Maximum Neighbor Distance specifies the maximum distance (measured in kilometers) that a

monitor may be from a grid cell (distances are calculated using the grid cell centroid). Cells

without any monitors within this distance will not be included in the resultant air quality grid. The

default setting is infinite (i.e. no limit to the distance between a monitor and a grid cell).

Note: Both the Maximum Relative Neighbor Distance and the Weighting Approach options are

irrelevant, since BenMAP is only choosing a single monitor to assign to any given grid cell.

5.2.2 Voronoi Neighbor Averaging (VNA) for Monitor Direct

If you choose the VNA option, BenMAP first identifies the set of monitors that “surround” each

grid cell’s center (these monitors are referred to as the grid cell’s neighbors), and then BenMAP

calculates an inverse-distance weighted average of these neighboring monitors. In this section,

we provide some examples of the different ways that BenMAP calculates the average of the

neighbor monitors. See the appendix on Air Pollution Exposure Estimation Algorithms. for an

expanded discussion of VNA, including how the VNA algorithm actually chooses the neighbor

monitors, as well as the different ways that it may be used.

VNA interpolation has three advanced interpolation options, which can be modified by clicking

on the Advanced button at the bottom of the window:

Maximum Neighbor Distance (in km) specifies the maximum distance that a monitor may be

from a grid cell, and still be included in the set of neighbor monitors used to calculate air pollution

exposure at a particular grid cell. The default setting is infinite (i.e., no limit to the distance
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between a monitor and the grid cell).

Maximum Relative Neighbor Distance specifies the maximum ratio for the distance of each

monitor to the distance of the closest monitor. The default setting is infinite.

Weighting Approach specifies whether BenMAP should use inverse-distance weighting for the

monitors, or inverse-distance-squared weighting of the monitors. The default setting is inversedistance weighting.

The following examples illustrate how varying these options affects the final average concentration

estimate.

Example 1: Monitor Direct VNA method

Default options

Consider the following example at an hypotheticalrural grid cell, where there are relatively few

monitors, and where the distance from a monitor to the grid cell can be fairly large. With VNA,

BenMAP first identifies the set of “neighbor” monitors for each grid cell. The number of

neighbors is usually in the range of about three to eight. In this case, assume that there are five

monitors at distances of 25, 50, 100, 200, and 400 miles from the grid cell, with annual PM2.5

levels of 8, 13, 12, 18, and 15 μg/m3, respectively. BenMAP would calculate an inversedistance weighted average of the monitor values as follows:

PM average 2 5

1

25

8

1

50

13

1

100

12

1

200

18

1

400

15

1

25

1

50

1

100

1

200

1

400

. 10.68

Example 2: Monitor Direct VNA method

Maximum Neighbor Distance = 75

Using the same example that we used above, let us say you have specified a Maximum

Neighbor Distance of 75 miles, and left unchanged the default options (infinite value) for

Maximum Relative Neighbor Distance. BenMAP would only consider the first two

monitors, and would calculate an inverse-distance weighted average of the monitor values as

follows:
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PM average 2 5

1

25

8

1

50

13

1

25

1

50

. 9.67

Example 3: Monitor Direct VNA method

Maximum Relative Distance = 10

Alternatively, if you have specified that the Maximum Neighbor Distance is infinite, but the

Maximum Relative Neighbor Distance should have a value of, say, 10, then BenMAP would

calculate the ratio of the distance for each monitor to distance of the closest monitor. In this case,

the ratios would be 1 (=25/25), 2 (=50/25), 4 (=100/25), 8 (=200/25), and 16 (=400/25), and

BenMAP would drop the monitor with a ratio of 16. BenMAP would then calculate an inversedistance weighted average of the monitor values as follows:

PM average 2 5

1

25

8

1

50

13

1

100

12

1

200

18

1

25

1

50

1

100

1

200

. 10.53

Example 4: Monitor Direct VNA method

Inverse-distance squared neighbor scaling

In addition, you can specify the an inverse-distance-squared weighting of the monitors. Let us

say that you have left unchanged the defaults (infinite values) for Maximum Neighbor Distance

and Maximum Relative Neighbor Distance, and specified that the Weighting Approach is

inverse-distance-squared. BenMAP would then calculate an inverse-distance-squared weighted

average of the monitor values as follows:

PM average 2 5

1

625

8

1

2 500

13

1

10 000

12

1

40 000

18

1

160 000

15

1

625

1

2 500

1

10 000

1

40 000

1

160 000

. 9 26

, , , ,

, , , ,

.

Example 5: Monitor Direct VNA method

Maximum Neighbor Distance = 75

Maximum Relative Neighbor Distance = 10

Inverse-distance squared Weighting Approach
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Finally, you could specify changes to all three options: a Maximum Neighbor Distance of 75

miles, a Maximum Relative Neighbor Distance of 10, and a Weighting Approach of

inverse-distance-squared weighting. BenMAP would then calculate the following average:

PM average 2 5

1

625

8

1

2 500

13

1

625

1

2 500

. 9 00

,

,

.

5.2.3 Fixed Radius for Monitor Direct

The Fixed Radius method averages all of the monitors within a fixed radius (measured in

kilometers) that you specify. The the way that the monitors are averaged depends on the

Weighting Approach that you choose after clicking the Advanced button. You can choose

either Inverse Distance or Inverse Distance Squared weighting.

Note that the default option with the Fixed Radius approach is that BenMAP will not calculate an

average for a grid cell if there are no monitors within the fixed radius (distance) that you specify.

In the Advanced Options window, if you click Get Closest if None within Radius, then

BenMAP will calculate an average for those grid cells without any monitors within the fixed

radius, by reaching out and choosing the nearest monitor regardless of distance.

5.2.4 Custom Monitor Filtering for EPA Standard Monitors

The Custom Filtering option only applies to EPA Standard Monitors library. This option allows

you to filter, map, and export your monitor data. You can reach the Custom Filtering option by

first choosing your pollutant, data source (e.g., monitor library) and year.
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Click the Advanced button. This will take you to the Advanced Options window.

Click the Custom Monitor Filtering button. This will take you to the Filter Monitors

window. Note that the first six options are essentially the same for each pollutant, and the

seventh option depends on the pollutant.

1. Include specific monitors. Here you can specify particular monitor IDs that you want to

include in your analysis. If this is left blank, then BenMAP will include all monitors that meet the

rest of the selection criteria.
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2. Exclude specific monitors. Here you can exclude any particular monitor IDs from your

analysis. Here again, if this option is left blank then BenMAP will include all monitors that meet

the rest of the selection criteria.

3. Restrict to particular states and/or latitude/longitude. You can choose monitors to

include from particular states, by listing the two-character state abbreviation (e.g., CA =

California). You can also choose monitors within particular latitude and longitude ranges. The

default values for latitude (20 to 50) and longitude (-130 to -65) completely includes the

continental U.S. Here again, if this option is left blank then BenMAP will include all monitors that

meet the rest of the selection criteria.

4. POC code. The Maximum POC specifies the highest POC value allowed in the data. The

default is a value of 4. And to choose one monitor when more than one monitor is in the same

location, the POC Preference Order specifies the preferred ordering of POC codes.

5. Methods. The Method codes listed depend on the pollutant. In the case of O3and PM10, all

methods are checked by default. And in the case of PM2.5, only so-called federal-reference

methods (FRM) are chosen by default -- specifically numbers 116 through 120.

6. Monitor Objectives. The default is to choose all monitors regardless of monitor objective.

7. Parameters Specific to the Pollutant. The default options vary by pollutant. Below, we

have listed the windows that appear with PM2.5, PM10, and ozone.

PM10 Monitor Filter

The Number of Valid Observations Required Per Quarter specifies the number of days of

data needed. The default is to require 11 observation per quarter. The Data Types to Use

option specifies whether to use Local data (parameter code 85101), Standard data (parameter

code 81102), or Both. The default for PM10is to use both local and standard data. When

standard and local data are available at the same monitor location, the Preferred Type allows

you to choose which to use -- the default is Local. The Output Type option is designed to allow

you to make the data reasonably consistent when both local and standard data are used. The

default is to use the local output type, so standard data will converted to local.
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PM2.5 Monitor Filter

The default for PM2.5is to also require 11 observations per quarter. A key difference with 0, is

that only local PM2.5 data is used by default.
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Ozone Monitor Filter

The ozone specific options differ from PM10and PM2.5in large part because ozone is an hourly

pollutant. The Number of Valid Hours specifies the number of hours needed for a particular

day of monitor to be considered "valid."BenMAP counts the number of non-missing hourly

values from the Start Hour through the End Hour and compare this number with that specified

in the Number of Valid Hours.

The Percent of Valid Days specifies the percent of days between the Start Date and the End

Date that need to be valid for the monitor itself to be considered valid. The default is 50 percent

of the days between May 1 and September 30.
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5.3 Monitor and Model Relative

The Monitor and Model Relative option lets you scale interpolated monitor values with model

data. As with the Monitor Direct option, you can choose Closest Monitor or Voronoi

Neighbor Averaging (VNA). In addition, you can scale the monitor values with three different

approaches: Spatial Only, Temporal Only, and Spatial and Temporal. As discussed below,

these approaches let you combine the advantage of the actual monitor observations with the

information provided by the models.

Monitor and Model Relative air quality grid creation is exactly the same as Monitor Direct air

quality grid creation with the exception ofscaling. The concept ofscaling is to use modeling data

to improve interpolation and/or forecast future air quality trends.
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Identify and load your modeling data into BenMAP using the Base Year Model File box. If

temporalscaling is used, load your modeling forecasts using the Future Year Model File box.

The base year file should be created with modeling data which closely reflects the historical

conditions of the monitor data to be used. Typically multiple future year files will be used to

create multiple air quality grids for use in an analysis. That is, one future year file might represent

a future projection of current trends, while another might represent the results of implementing a

regulatory program.

5.3.1 Spatial Scaling

Spatialscaling involves only a Base Year Model File, and scales the concentrations of each
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neighboring monitor by the ratio of the modeled concentration at the grid cell to the modeled

concentration at the grid cell containing the monitor. This approach takes into account what the

air quality modeling reveals about spatial heterogeneity in pollution levels. For example, if the

monitors are in relatively polluted urban areas, and the grid cell is in a relatively unpolluted rural

area, then the scaling willresult in multiplying the monitor values with ratios less than one, and thus

produce lower values at the rural grid cell than would be estimated with interpolation of the

unscaled monitor data.

Spatialscaling, then, is useful because, while monitors provide invaluable information about

historical conditions, there are only a limited number of monitors. Many areas, particularly rural

areas, do not have close monitors. Model data can provide additional information that improves

the interpolated concentration estimates, and provides a more accurate picture of air quality.

5.3.2 Temporal Scaling

Temporalscaling involves both a Base Year Model File and a Future Year Model File, and

scales the concentrations of each neighboring monitor by the ratio of the modeled concentration

at the grid cell containing the monitor in the future year to the modeled concentration at the grid

cell containing the monitor in the base year. This approach takes into account what the air quality

modeling reveals about the changes in pollution levels over time at the monitor sites. For

example, if the modeling forecasts that in the future, pollution levels will decrease, then the scaling

willresult in multiplying the monitor values with a ratio less than one, and thus produce lower

forecasts at the grid cell than would be obtained with the unscaled monitor data.

Temporalscaling, then, is useful because monitors cannot provide any information about future

conditions. Model data can provide this information, which can then be used to project future

monitor concentrations.

5.3.3 Spatial and Temporal Scaling

Using both spatial and temporalscaling involves both a Base Year Model File and a Future

Year Model File, and is simply a combination ofspatialscaling and temporalscaling, except that

the future year data is used for the spatialscaling. That is, it scales the concentrations of each

neighboring monitor first by the ratio of the modeled concentration at the grid cell in the future

year to the modeled concentration at the grid cell containing the monitor in the future year (spatial

scaling), and then by the ratio of the modeled concentration at the grid cell containing the monitor

in the future year to the modeled concentration at the grid cell containing the monitor in the base

year (temporalscaling). Notice, however, that the two future year concentrations at the grid cell

containing the monitor cancel out, allowing the ratio used to be simply the modeled concentration

at the grid cell in the future year to the modeled concentration at the grid cell containing the

monitor in the base year.

Using both spatial and temporalscaling gives the benefits of both approaches - it both improves

the interpolated estimates of air quality, and provides a future forecast of air quality.
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5.3.4 Examples

The first example presented is a Monitor Direct example, which will provide a foundation for the

following Monitor and Model Relative examples. Recall that Monitor and Model Relative air

quality grid creation is exactly the same as Monitor Direct grid creation with the exception of

scaling. For additional examples with more detail, see Appendix B.

Example 1: Monitor Direct VNA Method

Default options

Consider the example at an hypotheticalrural grid cell, from Section 5.2.2, where there are five

monitors at a distance of 25, 50, 100, 200, and 400 miles from the grid cell. Further, let us say

that the monitors have annual PM2.5levels of 8, 13, 12, 18, and 15 μg/m3. Without any modelbased scaling, BenMAP would calculate an inverse-distance weighted average of the monitor

values as follows:

Example 2: Monitor and Model Relative VNA Method

Default options

Spatial Only scaling

Assume the same monitors and monitor concentrations as above. Additionally, assume that the

grid cell model value in the base-year is 6 μg/m3, and the model values at the monitors in the

base-year are: 10, 14, 11, 17, and 15 μg/m3. (This modeling suggests that the grid cell is a lesspolluted area than the area around the monitors.) Incorporating Spatial Only scaling, BenMAP

would calculate an inverse-distance weighted average of the monitor values using the same

approach as before, with the difference being that the monitor values are scaled with the modeling

values:
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Example 3: Monitor and Model Relative Closest Monitor Method

Default options

Spatial Only scaling

Assume the same monitors and monitor concentrations as above. Additionally, assume the same

model values as above. The Closest Monitor interpolation of these same values using Spatial

Only scaling would be calculated as follows:

Example 4: Monitor and Model Relative VNA Method

Default options

Temporal Only scaling

Again, assume the same monitors and monitor concentrations as above. Additionally, assume

that the model values at the monitors in the future-year are: 8, 11, 9, 14, and 11 μg/m3, and that

base-year model values at the monitors are: 10, 14, 11, 17, and 15 μg/m3. (This modeling

suggests that the future-year model values are generally lower.) Incorporating the Temporal

Only scaling, BenMAP would calculate an inverse-distance weighted average of the monitor

values as follows:

Example 5: Monitor and Model Relative VNA Method

Default options

Spatial and Temporal scaling

Again, assume the same monitors and monitor concentrations as above. Additionally, assume

that the future-year model value at the grid cell is 4 μg/m3, and that base-year model values at the

monitors are: 10, 14, 11, 17, and 15 μg/m3. (This modeling suggests that the future-year model

value at the grid cell is significantly lower than the base-year model values at the monitor.)

Incorporating the Spatial and Temporal scaling, BenMAP would calculate an inverse-distance

weighted average of the monitor values as follows:
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5.4 Monitor Rollback

The Monitor Rollback option allows you quickly test what the benefits would be from reducing

historical monitor levels. BenMAP has three methods to reduce, or “roll back,” monitor data:

percentage rollback, incrementalrollback, or rollback to a standard. Each of these methods are

depiocted below. Note that with each of these methods you can use the same two interpolation

algorithms (closest monitor, and VNA) as you can use with Monitor Direct and Monitor and

Model Relative.

Percentage rollback reduces all monitor observations by the same percentage.

Incrementalrollback reduces all observations by the same increment.

Rollback to a standard lets you choose a standard, and then reduces monitor observations so

that they just meet the standard.
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To apply a monitor rollback, first click the Create Air Quality Grids button. On the Air

Quality Grid Creation Method window, choose Monitor Rollback.

There are three steps to the Monitor Rollback method.

1. Monitor Rollback Settings: (1) Select Monitors. Choose the Pollutant for the monitor

data. If you use data from an existing dataset, then choose the Library tab, and from the dropdown list choose the Monitor DataSet and the Monitor Library Year.

If you want to use your own data, then choose the format tab that matches the data that you want

to load: Database, Columns; Database, Rows; or Text File. The file should have the monitor

data format specified in Load Data chapter.

Then choose the Rollback Grid Type from the drop-down list. This allows you to determine
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how detailed the rollback scenario may be. If the whole region (e.g., United States) will have the

same type of rollback then you may simply choose a grid outlining the area of interest. If you are

interested in different rollbacks within a region, then you should choose a more finely detailed grid

definition (e.g., states).

When you have finished making your choices, click Next.

2. Monitor Rollback Settings: Select Rollback Regions and Settings. In this section, you

can specify the type of the rollback method(s) that you would like to use, as well as the location

of the monitors that you want to rollback.

Choosing the Add Region button brings up the three rollback methods: Percentage Rollback,

Incremental Rollback, and Rollback to a Standard.
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After choosing the rollback type, you then need to specify the amount of the rollback and the

region to which you want to apply it. In this example, we specified a 10 percent reduction, a

background of 0 ppb, and applied it to all monitors in allstates by clicking the Select All. So,

BenMAP willreduce all observations for all monitors in allstates by 10 percent.

Note that just above the map of the states there are four buttons, typically seen in mapping

programs, that allow you to zoom in and zoom out, and to focus on the particular groups of grid

cells that interest you.

At any time you can change the grid cells that you have selected. This particular example is quite

simple, so we will use a more complicated example below. After choosing the counties where

you want to rollback monitors, click on the Next button. BenMAP will then perform the
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rollback you specified on the monitors in the grid cells that you have chosen.

3. Monitor Rollback Settings: Additional Grid Settings.

The third stage is similar to the Monitor Direct grid creation method. As in Monitor Direct, you

need to specify the Interpolation Method (Closest Monitor or VNA) and the Grid Type.

You may select a Scaling Method (None or Spatial Only). If you choose spatialscaling, it

works exactly as described in the Monitor and ModelRelative section, where the modeling data

is used to provide information in those areas that are unmonitored.

By checking the Make Baseline Grid (in addition to Control Grid) you may create a baseline

grid at the same time as the control grid. The baseline grid uses the same parameters as the

control grid, with the exception of the rollback. That is, the baseline uses the same monitor data,

interpolation method, scaling (if any), and the same grid type. The two resultant grids can then

serve as both baseline and controlscenarios in later stages of an analysis.

Note that there is an Advanced button that lets you select the Maximum Neighbor Distance,

Maximum Relative Neighbor Distance, and Weighting Approach. The specific availability of

advanced features depends on the interpolation method that you choose. You may also click the

Map button to view the inputs to the rollback grids that you are creating, as well as to view the

grids themselves. The chapter on GIS/Mapping discusses the mapping of grids in more detail.

5.4.1 Example: Combining Three Rollback Approaches in Different Regions

BenMAP allows you to have different rollback approaches in different regions. In this example,

we combine the three rollback types: Percentage Rollback, Incremental Rollback, and
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Rollback to a Standard. Start by clicking on the Create Air Quality Grids button, and

choosing Monitor Rollback. On the Monitor Rollback Settings: (1) Select Monitors

window, select your pollutant (Ozone) and year (2000), and click Next. On the next window,

click the Add Region button and enter 10 for the Percent. In the previous example, we used

the Select All button to include allstates in the rollback region. In this example we want to

create three regions instead, so just click on the three western-most states to add them to the

region. The states you have added to the region will turn yellow, as in the picture below.

To add states with a second type of rollback, click on the Add Region button, choose the

rollback type, and then click on the counties to include in this second region, which BenMAP

denotes as Region 2. In this example, we have chosen an Incremental Rollback of 5 and a

background of 20, and applied it to the 14 next western-most states.

The map now depicts two rollback regions. We can toggle back and forth between each region

by clicking on the legend on the left side of the map. Any counties that have not yet been

included in a region may be added to an existing region, or we may create one or more regions

for these remaining counties. Note that once counties have been included in a rollback region,

they cannot be included in a different rollback region. In our example, the three western-most

states are highlighted in gray.
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To add a third rollback type covering the rest of the counties, click again on the Add Region

button, and then choose the rollback type. However, instead of choosing individually the states,

simply click the Select All button. This willselect all of the states that are not yet included in a

region, and these remaining counties will now become Region 3.

In this third region, we have chosen a Rollback to a Standard, which involves two groups of

parameters - those associated with the Attainment Test, which determines whether a monitor is

in attainment (meets the standard), and those associated with the Rollback Methods, which are

used to bring out-of-attainment monitors into attainment.

The Attainment Test parameters are Metric, Seasonal Metric, Statistic, Ordinality, and

Standard. A monitor is considered in attainment if the n

th highest value of a daily metric

specified by Metric is at or below the value specified by Standard, where n is the value

specified by Ordinality. For example, if Metric is D8HourMax, Ordinality is 4, and

Standard is 85, a monitor will be considered in attainment if the fourth highest value of the eighthour daily maximum is at or below 85 ppb. In this step BenMAP calculates the metric to be

used to determine whether a monitor’s values must be rolled back and, ifso, how much (e.g., if

Metric is D8HourMax, BenMAP calculates the 8-hour daily maximum for each day at each

monitor).
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The Attainment Test can also be used for seasonal metrics (by choosing previously defined

seasonal metrics from the drop-down list below Seasonal Metric), as well as for annual metrics

(by using the drop-down list below Statistic). For example, if you want the annual mean ozone

level to go stay below 60 ppb, then you would choose the daily 24 hour mean (D24HourMean)

from the drop-down list below Metric, choose Mean from the drop-down list below Statistic,

and set the Standard to 60. (Note that in this case Ordinality cannot be chosen because there

is only a single annual value.)

The Rollback Method parameters are Interday Rollback Method, Interday Background

Level, Intraday Rollback Method, and Intraday Background Level. These four

parameters determine the rollback procedures used to simulate out-of-attainment monitors

coming into attainment. The Interday Rollback Method and Background Level are used to

generate target values for the metric specified by the Attainment Test. The Intraday Rollback

Method and Background Level are used to adjust hourly observations to meet the target

metric values generated in the previous step.

BenMAP provides several types ofInterday Rollback Methods (Percentage, Incremental,

Quadratic, and Peak Shaving) and several types ofIntraday Rollback Methods (

Percentage, Incremental, and Quadratic). The methods involved for each can be somewhat

complicated, so we have included a section in the Monitor Rollback Algorithms appendix goes

through several examples.
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5.5 Questions Regarding Creating Air Quality Grids

How can I generate a map of an air quality grid and export it?

You need to choose the GIS/Mapping option from the Tools drop-down menu available in the

upper left-hand corner of the main BenMAP window. The GIS/Mapping option, as described in

the GIS/Mapping chapter, provides the option to map and then save air quality grids.

For the Rollback to a Standard option, why are there Interday and Intraday rollback

options?

The Interday Rollback Method option identifies the approach (e.g., Percentage) to reduce

daily air pollution levels, in order to meet the specified standard. (In other words, there is more

than one way to reduce daily pollution levels so as to meet the standard you have chosen, and

BenMAP lets you choose from among several approaches.) The Background level associated

with the Interday Rollback specifies the bound, below which, BenMAP will not make

adjustments to daily levels.

The Intraday Rollback Method option is only relevant for hourly pollutants, like ozone. This

option specifies the approach (e.g., Percentage) used by BenMAP to reduce hourly air pollution

levels to reach is only relevant for hourly air pollution data. That is, once you have chosen the

approach to reduce daily air pollution levels, on any given day there is more than one way to

reduce the hourly air pollution values to meet the targeted pollution level for that day. The

Background level associated with the Intraday Rollback specifies the bound, below which,

BenMAP will not make adjustments to hourly levels.

The Interday and Intraday options are complicated. The appendix on Monitor Rollback

Algorithms explains these options in more detail and gives some numerical examples.

Can I use air quality grids based on different Grid Types in the baseline and control

scenarios?

No. In any given analysis, you need to use the same Grid Type in the baseline and control

scenarios.

Can I use air quality grids of the same Grid Type but based on different Grid Creation

Methods?

Yes. In any given analysis, you may use air quality grids made with different methods. Air
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quality grids made with Model Direct, Monitor Direct, and Monitor and Model Relative may

be used interchangeably, if desired. Similarly, air quality grids made with different interpolation

methods may be compared. However, it generally is not recommended to create grids with

different methods and use them in the same analysis.

Can I do an analysis with multiple pollutants?

You can only estimate impacts one pollutant at a time, however, BenMAP allows you to

aggregate the results of more than one pollutant. This is discussed in the chapter on

Aggregation, Pooling, and Valuation.

Why does it take so long to generate an Ozone Air Quality grid if there are a lot of grid

cells?

It can take a long time to create an air quality grid because the file being generated can be quite

large. In some cases, air quality grids can be several hundred megabytes in size. (One reason

the ozone files are large is that the definition of ozone has, by default, four metrics. If you do not

need all of the default metrics for the health impact functions in your database, then delete the

unneeded metrics and BenMAP will run faster and will generate smaller air quality grids. This is

an advanced step, so do not do it if you are unsure.)

How do I access data in an Air Quality grid?

You can access the data in an air quality grid by going to the Tools drop-down menu and

choosing the Export Air Quality Grid option. (The Tools drop-down menu is available in the

upper left-hand corner of the main BenMAP window.) You then locate the air quality grid

from which you want to export air quality data and then give a name to your exported file.

BenMAP will generate a text file that you can then examine. This is discussed in detail in the

Tools Menu chapter.

How do I create Air Quality grids for a One-Step Analysis?

Follow the same steps outlined.
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CHAPTER 6

Incidence

Estimation

In this chapter...

Get an overview of health incidence estimation.

Use the Create and Run Configuration button to specify

various options for calculating incidence results.

Learn about baseline and control scenarios.

Learn the difference between Point Mode and the Latin

Hypercube option.

Select health impact functions.

Run, save and reopen a configuration.
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A configuration is a record of the choices you make in estimating the change in adverse health

effects between a baseline and controlscenario. The choices include the following:

The air quality grids for the baseline and controlscenarios;

The year for the analysis;

The threshold for the analysis;

Whether the analysis will focus on a single “point” estimate (Point Mode), or a range of results

that mirror the variability in the inputs to the health impact functions (Latin Hypercube Points);

and,

The health impact functions to be used in estimating adverse health effects.

Once these choices are made, they can be saved in a configuration file for future reuse. BenMAP

gives you flexibility in the creation, editing, and saving of configuration files. You can open an

already existing configuration, and proceed directly to the estimation of incidence. Or, you can

create a new one, and proceed with the incidence estimation. In addition, you may save any

edits made to existing or new configuration files. BenMAP saves configuration files with a “.cfg”

extension. After calculating the change in adverse health effects, BenMAP saves the results in a

“configuration results” file with a “.cfgr” extension.

After clicking the Create and Run Configuration button, you will be asked if you want to

create a new configuration, or open an existing one, if you have already created one that you

would like to use again. Select the desired option and click Go!. Below, we discuss the

subsequent steps for each option.

6.1 Introduction to Estimating Health Incidence

Health impact functions relate a change in the concentration of a pollutant to a change in the

incidence of a health endpoint (i.e., premature mortality or work loss days). It is typically derived

from the estimated relationship between the concentration of a pollutant and the adverse health

effects suffered by a given population. This relationship is called the concentration-response (CR) function. For example, the concentration of the pollutant may be particulate matter (PM10

),

and the population response may be daily premature deaths. For the purposes of estimating

benefits, we are not interested in the C-R function itself, but rather the relationship between the

change in concentration of the pollutant, and the corresponding change in the population health
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response. We want to know, for example, if the concentration of PM10is reduced by 10

micrograms per meter cubed, how many premature deaths will be avoided?

To produce health incidence results, the first step is to calculate the change in pollution

concentrations that would be produced by the application of a given set of emissions controls.

The concentration change in a pollutant is the increment between the controlscenario and the

baseline scenario. This increment and a gridded population dataset are then used in health impact

functions to calculate the change in health incidence that would result from this change in pollution.

These functions are based on epidemiologicalstudies and can be selected by the user. Typically,

these health incidence results show the decrease in health incidence (e.g. the decrease in asthma,

bronchitis, mortality, etc) due to a decrease in pollution.

In BenMAP, the selected health impact functions are stored in configurations, which can be reused over and over again.

6.2 Create Health Impact Configuration

If you choose to create a new configuration, there are two steps. First, fill out the Configuration

Settings form with the following options: the air quality grids for the Baseline File and the

Control File, the Population DataSet, Population Year, Threshold, and whether BenMAP

willrun in Point Mode or use the Latin Hypercube Points option. In the second step, specify

the health impact functions that you want to use in your analysis.

6.2.1 Configuration Settings

The Configuration Settings window opens after you select Create New Configuration and

click Go!. In this window, you first specify the air quality grids for the Baseline File and

Control File. The baseline file contains the air quality metrics for the scenario assumed to occur

without any change in policy. The controlfile specifies the air quality metrics assuming that some

type of policy or change has been implemented. The air quality grids should be of the same

pollutant, and should also be based on the same grid-type. If you choose a particular grid type

(e.g., County) for the baseline file, then the same grid type must be used in the controlfile.

Conversely, it would not possible to use County grid-type in the baseline and a Tract grid type

in the controlfile.

You may choose existing air quality grids, by clicking the Open button, and selecting an air

quality grid, which is designated with an “.aqg” extension. You may also create a new air quality

grid by clicking the New button. This will take you to window for Air Quality Grid Creation

Method, where you follow the same steps outlined in the chapter on air quality grid creation.

The Pollutant specified in the air quality grids determines the suite of health impact functions

available for the configuration. Only functions associated with the specified pollutant will be

available for the configuration. Furthermore, if only certain Metrics associated with the pollutant

are present in one (or both) of the air quality grids (this see the information on monitor and model

data formats above for more information on how this can occur) only those health impact
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functions associated with those metrics will be available.

In choosing the Population DataSet and Population Year, you specify the population data that

will be used in the health impact function. The values in the menu for the Population Year

depend on the range of values in your Population DataSet.

The Threshold indicates the minimum air quality value that BenMAP will use to quantify health

impacts. That is, air quality metrics below the threshold will be replaced with the threshold value.

With a threshold of zero, there is no impact on the estimates generated by the health impact

functions. However, as the threshold increases, then it will have a progressively larger impact on

the incidence estimation. For most analyses, a threshold of zero is appropriate, as there is little

evidence suggesting the existence of a threshold. Nevertheless, the Threshold option allows you

to explore the impact of any given threshold on the incidence estimation. This is also usefulfor

scenarios where you might want to know the incidence associated with changes in air quality

occurring above a standard.

The Point Mode and Latin Hypercube Points options allow you to generate an average

incidence estimate, or a range of results that mirror the variability in the inputs to the health impact

functions. With the Point Mode option, BenMAP uses the mean values of the inputs to the

health impact functions, and generates a single “point estimate” of the change in adverse health
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effects.

With the Latin Hypercube Points option, you can generate a number of estimates that mirror

the variability in the inputs to the health impact functions. The Latin Hypercube Points option

allows you to generate specific percentiles along the estimated incidence distribution. For

example, if you specify 20 points, then BenMAP will generate estimates of the 2.5

th percentile,

7.5

th percentile, and so on, up through the 97.5th percentile. The number of points suggested in

the drop down menu varies between 10 and 100. In addition, you can simply type in the desired

number of points. The greater the number of chosen points, the greater the time needed by

BenMAP to process the results. The relationship between the number of points and time needed

is essentially linear, so a doubling of the number of points would double the processing time.

If Point Mode is chosen, the number of Latin Hypercube Points cannot be modified and will

be ignored (treated as zero). However, with the Latin Hypercube Points option, a point

estimate willstill be generated. As discussed in the chapter on Aggregation, Pooling, and

Valuation, by choosing the Point Mode, you have fewer pooling options. You cannot conduct

fixed/random effects pooling, nor any other procedure that depends on knowing the distribution,

or the range of variability of the incidence estimates.

6.2.2 Select Health Impact Functions

The second step in creating a configuration is to select the health impact functions. After filling in

both the Select Air Quality Grids and Settings of the Configuration Settings window, click

Next to select the health impact functions.
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The Configuration Settings window is split into two display boxes. The upper box presents

the Available C-R Functions which you may select and the lower box the Selected C-R

Functions that you have already selected. Both boxes have a tree structure and the ability to

change the order of the fields for easy viewing of the functions.

To add studies to your configuration, simply highlight the health impact functions of interest and

drag them over to the lower portion of the window. You can do this for blocks of health impact

functions by dragging over an endpoint group or an endpoint, or drill down and drag individual

functions.
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If you want to delete some of the health impact functions that you added to your configuration,

just highlight the studies of interest and hit the Delete key on your keyboard.

When you drag over a study, BenMAP displays the study’s information in two broad categories:

Function Identification and Function Parameters. The Function Identification includes

information such as the Endpoint Group, Endpoint, Metric, Location, and other variables.

This identification information is useful when distinguishing between multiple health impact

functions. The Function Parameters include those variables that you may directly edit: Race,

Ethnicity, Gender, Start Age, End Age, Incidence DataSet, Prevalence DataSet, and

Variable DataSet..

You can drag over the same study multiple times, and then make edits, in order to be able to

calculate the impact of changes in these variables. To edit Start Age and End Age, just highlight

the appropriate cell and type in the desired age values. Keep in mind that these age represent

inclusive age bounds, so if you type in 10 and 12 this will include all children ages ten, eleven, and

twelve years old. If you want just a single age year, then type the same year in both the Start

Age and the End Age. Note that the accuracy of the populations calculated for these age ranges

will depend on the specificity of the population data present in your selected population dataset.
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At the same time you can edit the Race, Ethnicity, and Gender variables, by clicking on the

appropriate cell, and then scrolling through the drop-down menu. Similarly, if you have multiple

datasets to choose from, you can change the source of the incidence and prevalence data that is

used for each function. Use the drop-down lists in the Incidence DataSet, Prevalence

DataSet, and Variable DataSet fields.

6.3 Run Health Impact Configuration

To begin the calculation of incidence for the health impact functions in the configuration, you click

the Run button on the bottom right-hand corner of the Configuration Window that has the list

of chosen health impact functions. After clicking Run, BenMAP allows you to save the

configuration, or begin the calculation. If you wish to save the configuration for future use, click

Save and specify a file with a “.cfg” extension.
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When ready to generate incidence estimates, click on the OK button. BenMAP then requires

that you specify a file in which to save the results, with a “.cfgr” extension.

6.4 Open & Modify Existing Health Impact Configuration

To use the same settings as a previous BenMAP run, you can choose to open an existing

configuration. After clicking the Create and Run Configuration button, you simply choose

Open Configuration and click the Go! button. Once an existing configuration is open,, you can

do all the things with it that you would do with a newly created configuration - modify settings,

add and delete health impact functions, save it to a configuration file, generate results, etc.

6.5 Questions Regarding Health Impact Configurations

I got an error message the "specified Baseline file does not exist." What does that

mean and how do I fix it?

The path and/or the file that you have specified do not exist. Go back and ensure that the path

and file are correctly identified.

How do I know which health impact functions to use? Which functions does EPA use?

One option regarding the choice of health impact functions is to work with someone, say
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another BenMAP user, who is familiar with the epidemiological literature and develop your

own set of health impact functions. Reviewing the epidemiological literature can be timeconsuming, though in some situations, this might be the best option, such as if you want to

estimate the health impacts of carbon monoxide exposure, for which BenMAP does not have

pre-installed health impact functions.

Another option is to use the ozone and PM2.5configurations used by EPA that come installed

with BenMAP. You willfind them in the Configurations folder. If desired you can edit this

configuration and then save it under a different file name -- it is always a good idea to keep the

original version, so you can go back to it if needed!

How do I edit or add other health impact functions?

To edit or add health impact functions you need to go to Modify Setup option in the Tools

drop-down menu available in the upper left-hand corner of the main BenMAP window. See

the health impact function section in the chapter on Loading Data for details on how to do this.

How do I learn more about the population data in BenMAP?

The appendix on Population Data for U.S. Setup describes the population data in detail.

Why did I not get results for a given geographic area that I wanted in my analysis?

Check to see if your air quality grids mapped properly.

Why does BenMAP give me a range check error when I try to run the configuration

step?

If you get a "range check" error, it usually means that you are using files that are incompatible

with your current version of BenMAP. Make sure that the files you are currently using were

generated with the version of BenMAP that you have installed. If the problem persists,

contact: benmap@epa.gov.
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CHAPTER 7

Aggregation,

Pooling, and

Valuation

In this chapter...

Get an overview of valuation, discounting, and pooling.

Use the Aggregation, Pooling, and Valuation (APV)

button to create an APV configuration.

Sort and pool incidence results.

Learn the differences between the pooling methods.

Assign economic values to incidence results.

Aggregate incidence results and valuations.

Run, save and re-open an APV configuration.
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Once you have created a configuration results file with incidence results based on your two air

quality grids (using the Create and Run Configuration button), you can use the Aggregation,

Pooling, and Valuation button to combine the incidence results and place an economic value

on the combined results. You have two options.

Create a New Configuration for Aggregation, Pooling, and Valuation. You can create a

new type of configuration, termed an Aggregation, Pooling, and Valuation (APV) Configuration.

This allows you to specify whether to aggregate incidence results at the county, state or national

level, or whether to leave them at the grid cell level. In addition, you can specify how you might

want to combine or “pool” the incidence results, using a variety of pooling options. Given the

aggregated and pooled incidence results, you then can specify how you might want to value them

– typically there are multiple valuations. These valuation results can then be further aggregated,

and these aggregated valuation results can be pooled. Having made all of your selections, you

may save this APV Configuration file (“.apv”) for future use, and then proceed to calculating the

results, which are stored in an APV Results file (“.apvr).

Open Existing Configuration for Aggregation, Pooling, and Valuation. You can load an

existing APV Configuration file, edit the configuration, save it with the same or a different name,

and then proceed to calculating the results.

7.1 Introduction to Valuation, Discounting, and Pooling

This section presents an introduction to valuation, discounting, and pooling. Valuation generally

refers to placing a dollar value on estimated health incidence. In BenMAP, you might also think

of valuation as including the assignment of quality adjust life year (QALY) weights, in the sense

that the placement of dollar values and QALY weights both occur in the APV configuration. We

discuss both dollar values and QALY weights here. In addition, we provide a brief introduction

to discounting, which has to do with placing less weight on things occurring in the future than we

do for things today. Finally, we discuss pooling, which has to do with combining comparable

results.

7.1.1 Overview of Valuation

Improvements in ambient air quality generally lower the risk of developing an adverse health

effect by a fairly small amount for a large population. A lower risk for everyone means that fewer

cases of the adverse health effect are expected, although we cannot predict which people would

be spared. Therefore, the health benefits conferred on individuals by a reduction in pollution are

actually reductions in the risk of having to endure certain health problems. Monetizing the

benefits of a reduction in air pollution involves estimating society’s willingness to pay (WTP) for

these reductions in risk and is typically referred to as valuation. BenMAP uses valuation

functions to estimate the benefits of reducing air pollution.

These benefits (reductions in risk) may not be the same for all individuals (and could be zero for

some individuals). Likewise, the WTP for a given benefit is likely to vary from one individual to
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another. In theory, the totalsocial value associated with the decrease in risk of a given health

problem resulting from a given reduction in pollution concentrations is generally taken to be the

sum of everyone’s WTP for the benefits they receive.

Epidemiologicalstudies allow us to estimate the number of cases of an adverse health effect that

would be avoided by a given reduction in pollutant concentrations. If we have an estimate of the

average individual’s WTP for the risk reduction conferred upon him, we can derive from that an

estimate of the value of a statistical case avoided. Suppose, for example, that a given reduction in

pollutant concentrations results in a decrease in mortality risk of 1/10,000. Then for every

10,000 individuals, one individual would be expected to die in the absence of the reduction in

pollutant concentrations (who would not be expected to die in the presence of the reduction in

pollutant concentrations). If the average individual’s WTP for this 1/10,000 decrease in mortality

risk is $100, then the value of a statistical life is 10,000 x $100, or $1 million. In general, the ex

ante WTP for a risk reduction of x can be converted into an ex post value of a statistical case

avoided by dividing the average individual’s WTP for the risk reduction of x by x (e.g.

$100/0.0001 = $1,000,000). The same type of calculation can produce values for statistical

incidences of other health endpoints.

Sometimes those values come from contingent valuation studies, in which study participants are

queried about their WTP to avoid a specific adverse health effect. When WTP estimates are not

available, it can be approximated by other measures, most notably cost of illness measures.

An individual’s WTP to avoid an adverse health effect will include, at a minimum, the amount of

money he or she would have to pay for medical expenses associated with the illness. Because

medical expenditures are to a significant extent shared by society, via medical insurance,

Medicare, etc., the medical expenditures actually incurred by the individual are likely to be less

than the total medical cost to society. The total value to society of an individual’s avoidance of an

adverse health effect, then, might be thought of as having two components:(1) the cost of the

illness (COI) to society, including the total value of the medicalresources used (some portion of

which will be paid by the individual), plus the value of the lost productivity, as well as (2) the

WTP of the individual, as well as that of others, to avoid the pain and suffering resulting from the

illness.

The COI approach attempts to estimate the total value of the medicalresources used up as well

as the value of the individual’s time lost as a result of the illness. Because this method does not

include the value of avoiding the pain and suffering resulting from the illness (a potentially large

component), it is generally believed to underestimate the total value of avoiding the illness,

perhaps substantially.

The contingent valuation method attempts to elicit from people what they would be willing to pay

to avoid the illness. Because of the distortion in the market for medical goods and services,

whereby individuals generally do not pay the full value of the medical care, this method too is

likely to understate the total value of avoiding the illness.

Although the COI and contingent valuation are the two most common methods, other methods

have been used in certain circumstances. The method the benefit analyst chooses to value a
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particular health endpoint will depend in part on what is available. The unit values currently

available for use in BenMAP are data or estimates that have been collected or generated by

researchers and can be readily obtained in publicly available databases or in the open literature.

When reviewing the economic literature to determine the appropriate valuation functions to use, it

is important to have an economist assist.

7.1.1.1 Valuing Reductions in Premature Mortality

The economics literature discussing the value of changes in fatality risks is extensive and provides

a basis for monetizing benefits when the number of deaths avoided as a result of a regulatory

action can be calculated, but the literature on certain issues regarding the appropriate method for

valuing reductions in premature mortality risk is still developing. Issues such as the appropriate

discount rate and whether there are factors, such as age or the quality of life, that should be taken

into consideration when estimating the value of avoided premature mortality are still under

discussion. BenMAP currently offers a variety of options reflecting the uncertainty surrounding

the unit value for premature mortality. See the appendix on Economic Value of Health Effects for

more detail on the valuation functions available in BenMAP.

Monetary estimates of changes in fatality risk are often expressed in terms of the Value of a

Statistical Life (VSL). This term is easily misinterpreted and should be carefully described when

used in benefit analysis. In particular, VSL refers to the WTP for reductions in the risk of

premature death aggregated over the population experiencing the risk reduction; that is, VSL

refers to the sum of many smallreductions in fatality risks. The basic assumption underlying the

VSL approach is that equal increments in fatality risks are valued equally. For similar reasons,

the VSL approach is only appropriate for marginal changes in the risk of death and should not be

used to value more significant changes. Because changes in individualfatality risks resulting from

environmentalregulation are typically very small, the VSL approach is usually acceptable for

these types of benefit analyses.

7.1.2 Overview of Discounting

What is discounting?

In general, people prefer current consumption to future consumption. In other words, a $1

today is worth more today than a $1 tomorrow is worth today, and that dollar continues to

decrease in value as you go further out into the future. (This concept is also referred to as the

socialrate of time preference or the time value of money. This is a different concept than

inflation, which is a general increase in the price level of goods and services.) Discounting is the

process of converting that future dollar into a value that can be compared to the value of a dollar

today. The discount rate expresses this process in quantitative terms. The higher the discount

rate, the faster value decreases over time. For example, $1 twenty years from now is worth

$0.55 today at a 3% annual discount rate, but worth only $0.26 at a 7% annual discount rate.

A basic discounting function is as follows:

Present Value = Future Value / (1+r)

t
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where r is the discount rate and t is the time period (usually years).

Example: $1 twenty years from now at a 3% annual discount rate is worth $0.55 today

Present Value = $1.00 / (1 + 0.03)

20 = 1 / (1.03)20 = 1 / 1.806111 = 0.553676 = $0.55

Why do we discount benefits?

The benefits of reductions in air pollution may need to be discounted for severalreasons.

1. Today’s society values benefits that occur today more than benefits in the future. Therefore,

we must discount in order to compare those future benefits with current benefits.

2. For a cost-benefit analysis, benefits estimates need to be comparable to the cost estimates.

Discounting can be used to compare the future streams of benefits and costs.

3. BenMAP only estimates changes in adverse health effects based on changes in air quality for

one specified analysis year, even though certain health benefits may occur after the analysis year.

Discounting can be used to compare the future benefits with benefits occurring during the analysis

year.

Under which scenarios would I need to discount benefits?

Health benefits may occur three different ways after the analysis year specified in BenMAP.

1. Certain health endpoints accrue medical expenses or lost earnings for multiple years. The

future medical expenses would need to be discounted to compare with expenses occurring in the

analysis year.

2. Pollution exposure and the resulting health effects do not occur within the same year (a.k.a. a

cession lag). The monetized benefits of future health effects would need to be discounted to

compare with the benefits of health effects that occur during the analysis year.

3. In some analyses, you may want to estimate a stream of benefits occurring over multiple

analysis years instead of just one analysis year. In this scenario, you would need to discount the

future benefits occurring in each year analyzed back to the present year in order to present the

cumulative total estimate of benefits (i.e., the net present value of a stream of benefits).

When would we not discount benefits?

In many instances, it is not necessary to discount the benefits estimates generated by BenMAP.

If the health effect and the monetized value of all the medical expenses, lost earnings, and

suffering occur entirely in the analysis year, then you may not need to discount your benefits. For

example, school loss days occur within the analysis year, and all monetized expenses occur within
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the analysis year. It is important that you understand the assumptions within the health and

valuation functions before you decide whether you need to discount. (If your analysis year for

your benefits estimates does not match the analysis year for your costs estimates, you may need

to discount in order to compare your benefits with your costs even if you meet the criteria listed

above.)

Which discount rate should I choose?

Selecting a discount rate is challenging and is one of the most contentious methodological issues

encountered in economic analyses of environmental policies. Because environmentalregulations

frequently have differing streams of costs and benefits over time, the selected discount rate may

determine whether the benefits of a regulatory action exceed the costs. In addition, selecting a

higher discount rate may result in a smaller benefits estimate because the future benefits are worth

much less than they would be if a lower discount rate was selected. For benefits that occur well

into the future, the issue of intergenerational equity further complicates the selection of the

discount rate. (In the context of environmental policy, intergenerational equity refers to the

fairness of the distribution of the costs and benefits of a long-lived policy when those costs and

benefits are borne by different generations. Most criteria pollutants are not considered to have

intergenerational equity issues, but the issue frequently arises in analyses of climate and mercury.)

There are various economic arguments in support of and in opposition to various discount rates.

(For more details see EPA [1999; 2000] listed in the section on Sources for More Information.)

To comply with OMB and EPA’s recommendations, EPA currently uses discount rates of 3%

and 7% for benefit analyses.

Which health endpoints accrue medical expenses or lost earnings for multiple years, and

how do I discount them?

BenMAP includes health and valuation functions for several chronic health effects, including PM2.5

–related chronic bronchitis and non-fatal acute myocardial infarctions (AMIs, or heart attacks).

Technically, AMIs are discrete, acute events, not chronic conditions. However, heart attacks

cause chronic follow-up health effects that accrue medical expenses over time, similar to chronic

conditions. You can discount the economic value of these chronic effects through the valuation

function in BenMAP. Chronic bronchitis is assumed to last from the initial onset of the illness

throughout the rest of the individual’s life. BenMAP currently includes one WTP function as well

as two COI functions representing the two discount rates for chronic bronchitis. AMIs are

assumed to accrue costs over five years. Although WTP functions for AMIs are not available,

BenMAP currently includes severalCOI functions that incorporate the direct medical costs and

the opportunity cost (lost earnings) for specific age groups at two discount rates. See Appendix I

for details on the discounting assumptions within the valuation functions.
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Should I discount the health incidence as well as the valuation?

You should not discount the health incidence for any of the scenarios mentioned above. Changes

in the lag assumptions do not change the total number of estimated deaths, for example, but

rather the timing of those deaths. If you discounted the health incidence along with valuation, you

would essentially be discounting twice. In contrast, the process for conducting cost-effectiveness

analyses and calculating QALYs involves discounting life years. See Appendix G of the PM RIA

for more information on cost-effectiveness analyses (http://www.epa.gov/ttn/ecas/regdata/RIAs/

Appendix%20G--Health%20Based%20Cost%20Effectiveness%20Analysis.pdf).

Which health endpoints do not occur in the same year as exposure?

In many cases, the health effect from exposure to air pollution occurs shortly after exposure, but

there can be a significant lag between exposure and the health effect. The cession lag can be a

matter of hours or days in duration, but some health effects may lag exposure by much longer. If

exposure and the health effect do not occur within the same year, it is necessary to discount those

benefits back to the analysis year. The only health function currently in BenMAP that falls into

this category is PM2.5–related premature mortality. Discounting PM-related premature mortality

is controversial because the lag structure is unknown, but scientific literature on similar adverse

health effects and new intervention studies suggest that premature mortality probably would not

occur in the same year as the exposure. (See:Roosli M, Kunzli N, Braun-Fahrlander C, Egger

M. 2005. “Years of life lost attributable to air pollution in Switzerland: dynamic exposureresponse model.” InternationalJournal of Epidemiology 34[5]:1029-35.)

EPA’s Science Advisory Board recommends future research to support the development of

defensible lag structures and provided a lag structure that could be assumed until additional

research has been completed. See Chapter 5 of the PM RIA for more detail on assumed lag

structures for PM2.5-related premature mortality (http://www.epa.gov/ttn/ecas/regdata/RIAs/

Chapter%205--Benefits.pdf). Some example lag structures from the PM RIA are shown in

Figures 6-1 and 6-2 below. Currently, BenMAP does not have the capability to do this type of

discounting, so you must discount outside of BenMAP.

Note: Discounting is not necessary for ozone-related premature mortality because it occurs

within the analysis year.
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Figure 6-1. Graphical representation of assumed lag structures analyzed in EPA’s PM

RIA as sensitivity analyses

Assumed Lag Structures for PM2.5 Premature Mortality

0%

10%

20%

30%

40%

1st year 2nd

year 3rdyear 4thyear 5thyear 6thyear 7thyear 8thyear 9thyear 10thyear 11thyear 12thyear 13thyear 14thyear 15thyear 16thyear 17thyear 18thyear 19thyear 20thyear Year Following Reduction in PM2.5

% Premature Mortality Occuring Within Year

SAB segmented lag OMB segmented lag Exponential decay model 5-year distributed lag

Figure 6-2. Graphical representation of cumulative assumed lag structures analyzed in

EPA’s PM RIA as sensitivity analyses

Assumed Lag Structures for PM2.5 Premature Mortality (Cumulative)

0%

25%

50%

75%

100%

1st year 2nd

year 3rdyear 4thyear 5thyear 6thyear 7thyear 8thyear 9thyear 10thyear 11thyear 12thyear 13thyear 14thyear 15thyear 16thyear 17thyear 18thyear 19thyear 20thyear Year Following Reduction in PM2.5

% Premature Mortality Occuring Within Year (Cumulative)

SAB segmented lag OMB segmented lag Exponential decay model 5-year distributed lag
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7.1.3 Overview of Pooling

For many of the health endpoints (e.g., mortality), BenMAP contains many different functions

from different studies that you could choose to include in your configuration. Combining data

from several comparable studies in order to analyze them together is often referred to as metaanalysis. For a number of reasons, it is often impractical or impossible to combine the original

data sets. Combining the results ofstudies provides a second-best way to synthesize

information. This is referred to as pooling.

BenMAP allows the pooling of incidence changes predicted by severalstudies for the same

pollutant-health endpoint group combination. It also allows the pooling of the corresponding

study-specific estimates of monetary benefits. See the appendix on Uncertainty & Pooling for

more detail on the pooling options available in BenMAP.

7.2 Create Aggregation, Pooling, and Valuation (APV) Configuration

To start you need to choose a configuration results file that contains incidence estimates at the

grid cell level(created by clicking the Create and Run Configuration button, see Chapter 6).

These incidence results are in files with a *.cfgr extension, and typically stored in the

Configuration Results folder.
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Once you open this file, you can begin creating your APV configuration. You willstart with

selecting and pooling your incidence results, then move on to valuation. These processes are

described in detail below. Advanced functions, including aggregation, are described below.

7.2.1 Pooling Incidence Results

After opening the configuration results file, you willfind a list of Available Incidence Results on

the left-hand side of the window. The results are represented by the health impact functions from

which they were created, and are displayed in a tree-structure with three levels, similar to the

tree-structure found in the Configuration Settings Window. Endpoint Groups occupy the

top-most level, followed by Endpoints, and then individual health impact Function identifiers.

You will also see a pooling window at the right, where you can select pooling options.

There are severalsteps to pooling your incidence results:

Step 1. Add incidence results to the pooling window

In the Incidence and Pooling window, you willsee all the incidence results generated from your

configuration in the left hand column. You can drag individual incidence results, or groups of
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results at any level of the tree and drag them over to the pooling window. Note that once you

drag a result or group of results into the pooling window it willstill be displayed on the left side.

You do not have to drag all of your incidence results over into the pooling window, but note that

only those results showing in the pooling window will be included in the pooled incidence or

valuation results.

Incidence results are displayed in the pooling window in a tree structure determined by (1) the

order of the columns, and (2) the values of the identifying variables of the health impact Functions

from which the incidence results were generated (Endpoint Group, Endpoint, etc.).

Each line in the pooling window represents a node in the tree structure, with each node

representing either an individual incidence result or a collection of incidence results which have

common values for their leftmost identifying variables. The tree structure is generated by

comparing the leftmost values of the incidence result’s identifying variables. High level nodes in

the tree are formed when results have common values for identifying variables, and branches in

the tree occur when the values differ.
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In the above example, four incidence results have been dragged into the pooling window. Each

of the four health impact Functions has Endpoint Group Asthma Exacerbation. Thus, the top

line, or root of the tree structure, represents allfour incidence results. A branch then occurs in

the tree structure, because two studies have Endpoint Asthma Exacerbation, Cough, while

two others have Endpoint Asthma Exacerbation, Wheeze and Endpoint Asthma

Exacerbation, Shortness of Breath. A further branch occurs within Endpoint Asthma

Exacerbation, Cough when Author of the two incidence results differs. Once a node has only

a single incidence result, no further branching can occur.

Step 2. Sort results

After dragging incidence results into the Pooling Window, you can rearrange the order of the

columns (variables), and thus change the tree structure. To do this, click on a column and hold

the button down as you drag it to its new location. Note that Endpoint Group is always the first

column, and Pooling Method is always the last column. All the other columns can be moved.

To see how the order of the columns in the pooling window affects the tree structure, consider

the following example:
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This example uses exactly the same incidence results as the previous example, but with the

Author column (variable) immediately after the Endpoint Group column.

Step 3. Select pooling methods

Once the tree structure is set up in the Pooling Window, you are ready to select your pooling

methods. Essentially each pooling method involves a different method of combining input

incidence results to generate new incidence results. Results can be pooled any time a branch

occurs in the tree structure - that is, any time two or more results share common values for their

leftmost variables. BenMAP helps you to identify these spots by inserting a value of None in the

Pooling Method column at each spot where pooling is possible.

Table 7-1 summarizes the different types of pooling approaches, and Appendix K provides a

detailed discussion of the approaches. Note that some pooling methods are only available in

Latin Hypercube mode. This is because these pooling methods attempt to combine distributions

of results into new distributions, and no distributional information is available in Point Mode. The

Pooling Method column will thus have different values in its drop down list depending on the

mode used to generate the incidence results being pooled.
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Table 7-1. Pooling Approaches for Incidence and Valuation Results

Pooling

Approach

Description of Pooling Approach

Availability

Point

Mode

Latin

Hypercube

None No pooling performed. Yes Yes

Sum

(Dependent)

Results are summed assuming they are perfectly correlated. In

Point Mode, this is just a simple sum. In Latin Hypercube mode,

BenMAP chooses the first point fromeach result in the pooling

and does a simple sumto generate the first point in the pooled

result, and so on for all of the points in the distribution of

results.

Yes Yes

Sum

(Independent)

Results are summed assuming that they are independent. A

Monte Carlo simulation is used. At each iteration, a random

point is chosen fromthe Latin Hypercube of each result, and the

sumof these values is put in a holding container. After some

number of iterations, the holding container is sorted low to high

and binned down to the appropriate number of Latin Hypercube

points.

No Yes

Subtraction

(Dependent)

Results are subtracted assuming they are perfectly correlated.

Allsubsequent results are subtracted fromthe first result (the

highest result in the display - to reorder results, simply click and

hold a result and then drag it to its new position). In Point

Mode, this is a simple subtraction. In Latin Hypercube mode,

BenMAP chooses the first point fromeach result in the pooling

and does a simple subtraction to generate the first point in the

pooled result, and so on for all of the points in the distribution

of results.

Yes Yes

Subtraction

(Independent)

Results are subtracted assuming that they are independent. A

Monte Carlo simulation is used. At each iteration, a random

point is chosen fromthe Latin Hypercube of the first result, and

then randompoints are chosen fromthe Latin Hypercube of

each subsequent result and subtracted fromthe first. The result

is put into a holding container. After some number of iterations,

the holding container is sorted low to high and binned down to

the appropriate number of Latin Hypercube points.

No Yes

Subjective

Weights

Weights are specified by the user (see Step 5, below). In Point

Mode, the new result is generated by a simple weighted sumof

the input results. In Latin Hypercube mode, the results are

combined using the user specified weights with the “Round

Weights to Two Digits” Advanced Pooling Method. Note that

the weights you enter need not add up to one - BenMAP will

normalize theminternally. Also note that BenMAP initializes all

the weights to 1/n, where n is the number of results being

pooled.

Yes Yes
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Pooling

Approach

Description of Pooling Approach

Availability

Point

Mode

Latin

Hypercube

Fixed Effects Pooling weights are generated automatically based on the

inverse variance of each input result, with the weights

normalized to sumto one. Results with a larger absolute

variance get smaller weights. Results are then combined

according to the chosen Advanced Pooling Method.

No Yes

Random/ Fixed

Effects

BenMAP first tests if randomweights should be used. If not,

BenMAP uses fixed effects weights. If yes, the weights take

into account both the variance within each set of results and the

variance between sets of results. Results are then combined

according to the chosen Advanced Pooling Method.

No Yes

Step 4. Create additional pooling windows if needed

Within a given pooling window, you can have only one ordering of the columns (variables). As

we have seen, however, the ordering of the columns determines the structure of the tree used to

poolresults. It may thus sometimes be necessary for analyses to have multiple tree structures to

handle the various pooling trees they require. To facilitate this, BenMAP allows additional

pooling windows to be added and deleted. To open a new pooling window, simply click on the

Add button. You may do this as many times as needed to accommodate different sort orders.

You can add the same incidence results to as many different pooling windows as you like.

As needed you can also delete a pooling window by using the “-- Window to Delete --” dropdown menu to identify the pooling window, and then hitting the Delete button.
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7.2.1.1 Example: Simple Sorting & Pooling

If you add a single incidence result to the right-hand window, you willsee just one line, and

therefore no opportunities to pool. This shown in the example below.
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If you add a second incidence result to the window whose health impact Function has the same

Endpoint Group, but a different Author, you will then have a tree with two items in it. The tree

branches at the point where the two health impact Functions vary - at the Author column.

Note that a pooling method can now be selected for the two incidence results, since a branch has

appeared. If we desired to pool these two incidence results, we would end up with a pooled

result representing two Hospital Admissions (HA), Respiratory incidence results.

If you now add four more incidence results to the window whose health impact Functions have
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the same Endpoint Group, you willsee the following:

Now you have many pooling options. Setting aside the issue of which pooling method to choose,

there are four different pooling options at this point (including doing nothing), since we have two

places where we can choose to pool or not to pool.

If you choose to pool at the spot corresponding to Endpoint (HA, Chronic Lung Disease) you

would end up with three results (one pooled and two unpooled) instead of four individual

incidence results.

If you choose to pool at the first place where the Pooling Method field says None, the spot

corresponding to the Endpoint Group (Hospital Admissions, Respiratory), you will end up with a

single result representing allfour of the original incidence results.
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If you pool at both spots:

First, the HA, Chronic Lung Disease results are pooled to a give a single HA, Chronic Lung

Disease result.

Next, the three separate Endpoint results are pooled to give a single Hospital Admissions,

Respiratory result.

These same principles apply no matter how many incidence results are being pooled, and

regardless of which pooling methods are selected.

7.2.1.2 Example: Multiple Pooling Windows

There are many different ways to pool your incidence results. Sometimes you may want to look

at the same results in different ways, or you may just have many results that need to be sorted by

different variables. In these cases, you can open up multiple pooling windows by clicking on the

Add button.
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For example, you might want to pool allresults of health impact Functions by a particular author,

rather than pooling allresults of health impact functions of a particular endpoint. The examples

below show the same set of incidence results, first sorted by Author, then sorted by Endpoint.

As you can see, the pooling options are very different.

If you use two different pooling windows, each sorted as shown above, you can create results

pooled by Author, and results pooled by Endpoint.

7.2.2 Valuing Pooled Incidence Results

After you have specified your incidence pooling options you can hit the Next button and select

valuations and valuation pooling options. The Select Valuation Methods, Pooling and

Aggregation window appears after you click Next on the Incidence Pooling and

Aggregation window. This window should look quite similar to the Incidence Pooling and

Aggregation window, with tree views on the left side representing the valuation databases
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available, and various pooling windows on the right side representing the selected valuations and

pooling options.

There will be one pooling window in the Select Valuation Methods, Pooling, and

Aggregation screen for each pooling window Incidence Pooling and Aggregation screen. In

each pooling window, there will be one result present for each incidence result left over after all

incidence pooling has occurred. Each of these results will be represented by a “-- Select --”

value in the Valuation Method column.

The columns present in the Select Valuation Methods, Pooling, and Aggregation window

are determined by the incidence results left after all incidence pooling has occurred. There will be

exactly enough columns in each pooling window to represent the “least” pooled incidence result.

That is, the columns will be in the same order they were in the Incidence Pooling and

Aggregation window, but the only columns present will be those up to the level of the pooled

incidence result with the most columns left over after all pooling has occurred. Here is an

example:

There are severalsteps to take in the Select Valuation Methods, Pooling, and Aggregation

window:
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Step 1. Select your valuation methods

Valuation methods are specific to endpoint groups, and sometimes to endpoints as well. The

only valuation methods which appear in the left side tree views are those which have the same

endpoint group values as the pooled incidence results which are available to be valued. To select

a valuation method, select it in the left side tree views and drag and drop it onto the appropriate

incidence result in the pooling window. Note that BenMAP will only allow you to drop valuation

methods onto incidence results which have the same endpoint group value. For example,

BenMAP will not allow you to drop a Mortality valuation on a Hospital Admissions incidence

result. Note also that you can only drag and drop individual valuation methods, not entire groups

of them. For explanations of the various valuations, see the appendix on the Economic Value of

Health Effects.

If you have added any of your own valuation methods, as described in the Valuation Data section

of the Loading Data chapter, you can drag and drop them in the same way as the EPA Standard

valuations.

When BenMAP runs the APV Configuration, it will generate a valuation result for each

valuation method you select by running the valuation method’s valuation functions on the

incidence results for which they were selected. You do not need to select valuation methods for

every incidence result - incidence results without any valuation methods willsimply be ignored

when valuation results are generated, aggregated, and pooled.

Because valuation functions have uncertainty associated with them, generating valuation results is

fairly complicated. The procedure used depends on whether the incidence results being used

were generated in Point Mode or with Latin Hypercube Points (see the chapter on Incidence
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Estimation for details on Point Mode and Latin Hypercube Points).

In Point Mode, BenMAP simply runs the valuation functions once using the point estimate of the

incidence result and the mean of the valuation function as inputs.

With Latin Hypercube Points, on the other hand, BenMAP generates one hundred percentile

points (from the 0.5th percentile to the 99.5th percentile) to represent the distribution of the

inputs to the valuation function. To get the value of the health incidence, BenMAP multiplies each

combination of values from the incidence result, Latin Hypercube, with each of the hundred

valuation points, and puts the results into a holding container. (For example, if the incidence

result has 10 Latin Hypercube Points and there are 100 valuation points, then the holding

container will have 1,000 values.) Finally, the holding container is sorted low to high and binned

back down to 100 Latin Hypercube points (representing the 0.5th percentile to the 99.5th

percentile of the economic value of the incidence).

Step 2. Sort results

Depending on how your incidence results were pooled, the columns in the valuation pooling

windows can be resorted in the same way as the incidence pooling window columns. This

resorting will have the same sort of impact on the tree structure of valuation results that it had on

the tree structure of incidence results. (See Step 2 in the section on Pooling Incidence Results.)

Step 3. Select pooling methods

The same pooling methods are available for valuation results which were available for incidence

results. (See Step 2 in the section on Pooling Incidence Results.) You should note that when

more than one valuation method is selected for a particular pooled incidence result, it is possible

to pool the generated valuation results.
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Step 4. Select Variable DataSet

In order to proceed to the next step, you must select a Variable DataSet from the drop-down

menu. The files in the Variable DataSet can include a variety of data, such as income and

poverty data that might be used in health or valuation functions. For the default EPA health and

valuation functions, you just need to select the EPA Standard Variables.

If you have developed your own setup, then you need to make sure that you also load a

Variable DataSet. This is necessary even if you do not need the extra variables that can

included in this dataset.
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7.2.3 Using QALYs Weights

After you have specified your incidence and valuation pooling options, you can select QALYs

and pooling options. By default, the Skip QALYWeights box is checked in the Select

Valuation Methods, Pooling, and Aggregation window. This is done to save time, as it takes

BenMAP a few moments to load the QALY datafiles. If you want to use QALY weights, then

uncheck this box, and then click Next.

The Select QALYMethods, Pooling and Aggregation window appears after you click Next

on the Select Valuation Methods, Pooling and Aggregation window. This window should

look quite similar to the Incidence Pooling and Aggregation and Select Valuation Methods,

Pooling and Aggregation windows, with tree views on the left side representing the valuation

databases available, and various pooling windows on the right side representing the selected

valuations and pooling options.

There will be one pooling window in the Select QALYMethods, Pooling, and Aggregation

screen for each pooling window Incidence Pooling and Aggregation screen. In each pooling

window, there will be one result present for each incidence result left over after all incidence

pooling has occurred. Each of these results will be represented by a “-- Select --” value in the

QALYMethod column.
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As with the Select Valuation Methods, Pooling, and Aggregation window, the columns

present in the Select QALYMethods, Pooling, and Aggregation window are determined by

the incidence results left after all incidence pooling has occurred. There will be exactly enough

columns in each pooling window to represent the “least” pooled incidence result. That is, the

columns will be in the same order they were in the Incidence Pooling and Aggregation

window, but the only columns present will be those up to the level of the pooled incidence result

with the most columns left over after all pooling has occurred. Here is an example:

There are severalsteps to take in the Select QALYMethods, Pooling, and Aggregation

window:

Step 1. Select your QALY methods

The QALY datasets that come installed with the U.S. setup have only a limited number of QALY

methods -- mortality, myocardial infarction (heart attack) and chronic bronchitis. And, as with

valuation methods, the QALY methods are specific to endpoint groups. The only QALY

methods which appear in the left side tree views are those which have the same endpoint group

values as the pooled incidence results which are available to be valued. For example, in the

standard APV Configuration for PM2.5 used by EPA, the available QALY methods appear in the

left side tree.
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On the other hand, in the case of the Endpoint Group Hospital Admissions, Respiratory, there

are no QALY methods from which to choose in the data that comes loaded with BenMAP, as

seen in the following screenshot:

To select a QALY method, select it in the left side tree views and drag and drop it onto the
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appropriate incidence result in the pooling window. Note that BenMAP will only allow you to

drop QALY methods onto incidence results which have the same endpoint group value. For

example, BenMAP will not allow you to drop a Mortality QALY method on a Acute

Myocardial Infarction incidence result. Note also that you can only drag and drop individual

QALY methods, not entire groups of them.

If you have added any of your own QALY methods, as described in the QALY Data section of

the Loading Data chapter, you can drag and drop them in the same way as the EPA Standard

valuations.

When BenMAP runs the APV Configuration, it will generate a QALY result for each QALY

method you select. You do not need to select QALY methods for every incidence result -

incidence results without any QALY methods willsimply be ignored when QALY results are

generated, aggregated, and pooled.

Because QALY method databases have uncertainty associated with them, generating QALY

results is fairly complicated. The procedure used depends on whether the incidence results being

used were generated in Point Mode or with Latin Hypercube Points (see the chapter on

Incidence Estimation for details on Point Mode and Latin Hypercube Points).

In Point Mode, BenMAP simply runs the QALY functions once using the point estimate of the

incidence result and the mean of the valuation function as inputs.

With Latin Hypercube Points, on the other hand, BenMAP uses a 5,000 draw Monte Carlo

approach to estimate QALYs. That is, BenMAP draws once from the QALY database and

once from the incidence Latin Hypercube, and multiplies the two draws together, repeating this

process 5,000 times and putting the results into a holding container. Finally, the holding container

is sorted low to high and binned back down to 100 Latin Hypercube points (representing the

0.5th percentile to the 99.5th percentile of the estimated QALY distribution).

Step 2. Sort results

Depending on how your incidence results were pooled, the columns in the QALY pooling

windows can be resorted in the same way as the incidence and valuation pooling window

columns. This resorting will have the same sort of impact on the tree structure of valuation results

that it had on the tree structure of incidence results. (See Step 2 in the section on Pooling

Incidence Results.)

Step 3. Select pooling methods

The same pooling methods are available for QALY results which were available for incidence

and valuation results. (See Step 2 in the section on Pooling Incidence Results.) You should note

that when more than one QALY method is selected for a particular pooled incidence result, it is

possible to pool the generated QALY results.
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7.2.4 APV Configuration Advanced Settings

At any point when specifying the incidence and valuation pooling options, you may click on the

Advanced button on the bottom-left of either the window for Incidence Pooling and

Aggregation, Select Valuation Methods, Pooling and Aggregation, or Select QALY

Methods, Pooling and Aggregation. This button will open the APV Configuration

Advanced Settings window.

The APV Configuration Advanced Settings window has three advanced option tabs for the

following:

Aggregation and Pooling. This lets you choose the level of aggregation for the incidence,

valuation, and QALY results. You can also specify details on the pooling procedure, such as

whether to use a Monte Carlo or Latin Hypercube approach.

Currency and Income. You can choose the currency year, how to adjust for inflation, and

how to adjust for income growth.
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7.2.4.1 Aggregation & Pooling

The Aggegation & Pooling tab lets you choose the level of aggregation for the incidence,

valuation, and QALY results. You can also specify details on the pooling procedure, such as

whether to use a Monte Carlo or Latin Hypercube approach.
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Default Advanced Pooling Method

The relative contribution of any one study in the pooling process depends on the weight assigned

to that study. A key component of the pooling process, then, is the determination of the weight

given to each study. BenMAP lets users assign "subjective" weights and it assign weights using a

fixed effects or a random effects approach. There are three options for using weights available in

the Default Advanced Pooling Method drop-down menu:

Round weights to two digits. BenMAP rounds each weight to two digits (e.g. 0.73), and

then multiplies these weights by 100 to get two digit integers. Each entire distribution (set of

Latin Hypercube points) is then put into a holding container an integral number of times,

according to its integral weight. This holding container is then sorted low to high and binned

down to the appropriate number of Latin Hypercube points.

Round weights to three digits. BenMAP rounds each weight to three digits (e.g. 0.732), and

then multiplies these weights by 1000 to get three digit integers. Each entire distribution (set of

Latin Hypercube points) is then put into a holding container an integral number of times,

according to its integral weight. This holding container is then sorted low to high and binned

down to the appropriate number of Latin Hypercube points.

Use exact weights for Monte Carlo. BenMAP uses exact weights and a Monte Carlo

simulation. On each iteration of the procedure, a particular result is chosen with a probability

equal to its weight. Once a result is chosen, one of its Latin Hypercube points is chosen at

random and put into a holding container. This is done some number of times (see Monte
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Carlo Iterations, below), and the holding container is then sorted low to high and binned down

to the appropriate number of Latin Hypercube points.

Default Monte Carlo Iterations

This drop down list is only enabled when Use exact weights for Monte Carlo is selected as the

Advanced Pooling Method. It specifies the number of iterations the Monte Carlo simulation

should be run (see above). Its initial value is set by the Default Monte Carlo Iterations value

from the APV Advanced Settings window (see Step 1, above).

Random Seed

The APV Configuration Advanced Settings window allows the specification of a Random

Seed. As mentioned at the beginning of this chapter (see Open an APV Results File, above)

many of the pooling methods require the generation ofsequences of random numbers (e.g.

choosing a random Latin Hypercube point during a Monte Carlo simulation). Providing a

specific Random Seed value allows you to ensure that the same sequence of random numbers is

generated as in a previous analysis, thus allowing exact results to be reproduced.

If you do not set the Random Seed for a particular run, one will be generated automatically from

the system clock (the number generated will depend on the date and time, and should change

every minute). Normally, you should not set the Random Seed value. If you need to reproduce

a specific set of results, however, the random seed used to generate previous APV

Configuration Results can be obtained from an APV Configuration Result file (*.apvr) Audit

TrailReport.

Sort Incidence Results

The Sort Incidence Results should generally be always checked. This setting ensures that the

incidence Latin Hypercube results are sorted low to high.

7.2.4.2 Currency & Income

The Currency & Income tab allows you to specify an Inflation DataSet and a Currency

Year, which in combination allow you to change the year of the valuation dollars. The Income

Growth Adjustment panel allows you to adjust the valuation estimates to account for the growth

in income over time.
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Inflation Adjustment

The inflation adjustment needs to be carefully considered in relation to the valuation database that

you are using. (This is discussed in detail in the section on loading inflation data.) The default

valuation database in the U.S. Setup has a currency year of 2000, so the inflation dataset has a

value of 1 for the year 2000.

Income Growth Adjustment

Willingness to pay (WTP) estimates are believed to be tied to the income of individuals. As

income rises over time, WTP estimates are likely to increase as well. The income growth

adjustment is designed to take this phenomenon into account, allowing you to account for income

growth between the time when WTP estimates were calculated and the year of your analysis.

As with the inflation adjustment, the income growth adjustment has a close connection to the

valuation estimates. For example, the valuation estimates in the default version of BenMAP are

assumed to be based on income levels from 1990, so the income growth adjustment database

has a value of 1 for the year 1990. (This is discussed in detail in the section on loading income

growth data.)

To use the income growth adjustment, you need to choose a dataset and then choose the income

year that you want to use. It is common to set the "Year" variable to the year of the population
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forecast in your analysis. Of course, you can only choose from the available data. If the income

growth adjustment data only goes to 2024 and the population data in your analysis are for 2030,

then there will be some, unavoidable mismatch.

7.3 Run APV Configuration

After having specified the various aggregation, pooling, and valuation/QALY options, you have

the opportunity to save your APV Configuration for future use. The file that you save has an

“apv” extension. The configuration that you have specified is similar in idea to the configuration

that you developed for choosing health impact functions. (That configuration has a “cfg”

extension.) Both files allow you to save choices that you have made, and re-run them at a later

time.

You can save your APV configuration when you have finished making your valuation pooling

choices. Click the Run button, and then choose Save.

You then need to name your configuration (*.apv) file. We suggest that you save this in the

Configurations folder. When ready to generate APV Configuration results, click the OK button.

BenMAP then requires that you specify a file in which to save the results, with an “.apvr”

extension.

7.4 Open & Modify Existing APV Configuration

If you have an existing configuration (*.apv) file, you can open, and then edit it.
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If you have only a few changes to make to an existing configuration, it is typically much quicker to

open the previous configuration, rather than entering all of your choices again. Note that the

various parts of an APV Configuration are quite interdependent, so modifying part of the

configuration may cause other parts to be reset. For example, modifying the tree structure for

incidence pooling will cause the valuation method selection and valuation pooling tree structure to

be cleared and reset. Changing the Configuration Results Filename in the Incidence Pooling and

Aggregation window will not reset the incidence or valuation pooling trees as long as the new file

contains incidence results generated from the same health impact Functions as the old file. This

can be quite helpfulfor generating new APV Configuration Results from several different

Configuration Results files which were generated from different baseline / controlscenarios, but

with the same set of health impact functions.

7.5 Questions Regarding APV Configurations

I am at the BenMAP valuation window and cannot proceed. What should I do?

In order to proceed to the next step, you must select a Variable DataSet from the dropdown menu in the Select Valuation Methods, Pooling, and Aggregation window. The

files in the Variable DataSet can include a variety of data, such as income and poverty data

that might be used in health or valuation functions. For the default EPA health and valuation

functions, you just need to select the EPA Standard Variables.

If you have developed your own setup, then you need to make sure that you also load a

Variable DataSet. This is necessary even if you do not need the extra variables that can be

included in this dataset.

How do I edit or add other valuation functions?

To edit or add valuation functions you need to go to Modify Setup option in the Tools dropdown menu available in the upper left-hand corner of the main BenMAP window. See the

valuation function section in the chapter on Loading Data for details on how to do this.
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How do I get to the QALY selection window?

By default, the Skip QALYWeights box is checked in the Select Valuation Methods,

Pooling, and Aggregation window. This is done to save time, as it takes BenMAP a few

moments to load the QALY datafiles. If you want to use QALY weights, then uncheck this

box, and then click Next.

The Select QALYMethods, Pooling and Aggregation window appears after you click

Next on the Select Valuation Methods, Pooling and Aggregation window.

How do I know what year dollars were used?

You can find the answer in the Audit trailfor the APVR file that you generated.

Do the currency year and year of the population data have to match?

No. The currency year and the year of the population data do not need to match.
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CHAPTER 8

Reports

In this chapter...

Use the Create Reports button to create incidence,

valuation, and QALY reports.

Find the file formats, including variable definitions and

program compatibility, for each report type.

Find out about using an Audit Trail report to keep track

of the options and assumptions underlying each

analysis.
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If you are interested in merely exporting results or merely viewing them, there are three types of

reports that you can access by clicking on the Create Reports button. You will be asked which

type of report you wish to create:

Incidence, Valuation, & QALY Results: Raw, Aggregated, and Pooled use an

Aggregation, Pooling, and Valuation Results file (with the “.apvr” extension) to create report for

incidence, aggregated incidence, pooled incidence, valuation, aggregated valuation, pooled

valuation, QALY, aggregated QALY, or pooled QALY results. These reports are comma

separated values (CSV) files (*.csv) which can be read into various spreadsheet and database

programs, such as Microsoft Excel.

Raw Incidence Results use a Configuration Results file (with the “.cfgr” extension) to create

reports for incidence results. These reports are CSV files.

Audit Trail Reports provide a summary of the assumptions underlying each of five types of

files generated by BenMAP: Air Quality Grids (with the “.aqg” extension), Incidence

Configurations (with the “.cfg” extension), Configuration Results (with the “.cfgr”

extension), Aggregation, Pooling, and Valuation Configurations (with the “.apv” extension),

and Aggregation, Pooling, and Valuation Results (with the “.apvr” extension). These

reports can be viewed within BenMAP in an expandable tree structure, or can be exported to

tab-delimited text files.
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8.1 One-Step Reports

trail report button.

The report module for one-step analysis is the same as custom analysis (see Section 8.2).

Alternatively, you can load in a data file and create reports at the One Step Analysis window.
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Click on the Open Results button. This will bring up the One Step Results window.
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Click on the Load APVR button, and browse for the APVR file that you want to load. Make

sure that the file you load has the results needed for the reports that you will be generating. If

you find that the tables, box plot, or bar charts look incomplete, misformatted, or otherwise odd,

check that the CFG and APV files are reasonably similar to the default files that come with

BenMAP. To the extent that there are divergences, then it is likely that these pre-defined reports

(which depend on the output looking a certain way) will not look right.

8.2 Incidence, Valuation, & QALY - from APVR file

Using the results in the Aggregation, Pooling, and Valuation Results file (“.apvr” extension), you

can create nine types of reports:raw incidence, aggregated incidence, pooled incidence, valuation

(of the pooled incidence), aggregated valuation, and pooled valuation. After you click on the

Create Reports button, you need to specify the APV Configuration Results File that you

want to use.
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After choosing your APVR file and clicking the Open button, you then need to choose a Result

Type.

Table 8-1 describes the results contained in each type of report.
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Table 8-1. Summary of the Reports Generated from APVR Files

Result Type Description

Incidence Results Incidence results for each health impact function at the grid-cell level or

aggregated at the county, state, or national level.

Aggregated Incidence

Results

Incidence results for each health impact function aggregated to the level you

specified in the Aggregation, Pooling, and Valuation configuration file.

Pooled Incidence Results Incidence results aggregated and pooled as you specified in the Aggregation,

Pooling, and Valuation configuration file.

Valuation Results Valuation results for the pooled and aggregated incidence results.

Aggregated Valuation

Results

Valuation results aggregated to the level you specified in the Aggregation,

Pooling, and Valuation configuration file.

Pooled Valuation Results Valuation results aggregated and pooled as you specified in the Aggregation,

Pooling, and Valuation configuration file.

QALYResults QALYresults for the pooled and aggregated incidence results.

Aggregated QALY

Results

QALYresults aggregated to the level you specified in the Aggregation,

Pooling, and Valuation configuration file.

Pooled QALYResults QALYresults aggregated and pooled as you specified in the Aggregation,

Pooling, and Valuation configuration file.

8.2.1 Incidence Results

The Incidence Results report gives you the opportunity to examine the results of each health

impact function at the grid-cell level, or aggregate them to, say, the state or national level.

Simply select the options that you desire from the four main sections of the Configuration

Results Report window: Column Selection (these include Grid Fields, C-R Function Fields

, and Result Fields), Grouping Options, Display Options, and Advanced Options. As you

modify your choices, the Preview section will be updated accordingly.

The Column Selection section allows you to choose the field names (and values) which will

appear in the report. The Grid Fields section allows the inclusion of Col and Row fields, which

can be helpful in identifying the grid-cell of a particular line in the report. These will not always be

necessary, however - for example, when results have been aggregated to the national level. The

C-R Function Fields section allows the inclusion of various fields which can be helpful in

identifying the health impact function of a particular line in the report. Almost all of the field
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names have appeared previously in the preparation of a Aggregation, Pooling, and Valuation

Results file. Finally, the Result Fields section allows the inclusion of various types of results.

In the Grouping Options section, you can change the sorting of the results, by clicking the radio

buttons Group by Gridcell, then by C-R function and Group by C-R function, then by

Gridcell.

In the Display Options section, you may set the number of digits that appear after the decimal

point, and you can set the number of rows that appear in the preview window.

In the Advanced Options, you can set the level of aggregation at the grid cell(none), county,

state, and national levels. You can also choose to generate Population Weighted Deltas, which

BenMAP calculates at the national levelfor each health impact function by weighting the change

in the pollution metric at each grid cell with the population of the grid cell. For example, if there

are large changes in highly populated urban grid cells and relatively small changes in lightly

population rural grid cells, then the population-weighted change would reflect the large urban

changes and be relatively large.

As noted above, most of these fields have been seen previously; Table 8-2 provides a summary

of the fields that are new to this report format. (See the section on Health Impact Functions data

format in the Loading Data chapter for a description of the C-R Function Fields).
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Table 8-2. Selected Variables in the Reports Based on the APVR file

Variable Description

Col The column of the grid cell of the result. For grid cell level results, this is the column

of the grid cell. For county and state level results, this is the state FIPS code. For

national results, this is always 1.

Row The row of the grid cell of the result. For grid cell level results, this is the row of the

grid cell. For county results, this is the county FIPS code. For state and national

results, this is always 1.

DataSet Specifies the dataset fromwhich a health impact function was chosen.

Population Population provides the number of persons used in the health impact function

calculation.

Delta The difference between the baseline and controlscenarios for the metric used in the

health impact function. Calculated by subtracting the metric value in the control

scenario fromthe metric value in the baseline scenario.

Point Estimate The point estimate for the result fromthe health impact function. The point estimate is

generally based on the mean estimate of the "Beta" fromthe health impact function.

Mean Mean of the points in the Latin Hypercube for this result. The mean is set to missing if

the Point Mode option is chosen.

Baseline Estimate based on the baseline function, which typically estimates health impacts due

to all causes (not just air pollution-related causes).

Percent of Baseline Estimates the percentage change in health impacts (e.g., hospital admissions) due to

the change in air quality fromthe baseline to the controlscenario. Calculated by

dividing the Point Estimate by the Baseline.

Std Dev Standard deviation calculated based on the points in the Latin Hypercube for this

result.

Latin Hypercube

Points

The number of percentiles depends on the number of points in the Latin Hypercube

for this result.

8.2.2 Aggregated Incidence Results

The Aggregated Incidence Results report presents the incidence results at the aggregation

level that you have previously specified in the Aggregation, Pooling, and Valuation Configuration

file. If you want to change the level of aggregation, you need to revise your choices in the APV

Configuration Advanced Settings window (see the section on advanced APV options).

The Column Selection section looks largely the same as in the Incidence Results Report.
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The Grouping Options section and Display Options section are exactly the same, as is the

Advanced Options section.

8.2.3 Pooled Incidence Results

The Pooled Incidence Results report provides results aggregated and pooled to the level that

you previously specified in the Aggregation, Pooling, and Valuation Configuration file. This

report has fewer health impact Function Fields than the Aggregated Incidence Results

Report, and values for others will be blank. This is because after pooling, only enough fields are

retained to uniquely identify individualresults. In addition, the Advanced Options section

(available for Incidence Results and Aggregated Incidence Results reports) no longer exists, as

the options in it do not apply to aggregated incidence results.
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8.2.4 Valuation Results

The Valuation Results report gives you the opportunity to examine the valuation results for the

pooled and aggregated incidence results. In the example below, the incidence results were

aggregated to the state level, so in the Column field, you can see the value changing from , so the

values in the Column field vary (e.g., 1=Alabama, 4=Arizona).
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8.2.4.1 Add Sums Button

To calculate the total value of different groups of effects, click on the Add Sums button in the

APV Configuration Results Report window. In the next window you can specify the effects

you want to sum and how you want to sum them. In the Include in Total field, simply check the

effects that you want to sum together, and then name the sum in the Valuation Sum Identifier

field. You also need to specify whether you are going to use the Dependent or Independent sum

approach. (See Chapter 7 for a discussion of these two summing approaches.) In the

Summation Type use the drop-down menu, choose the approach that you want to use. If you

choose the Independent sum, then you also need to specify the number of Monte Carlo draws

that you want BenMAP to use. The default is 5,000. You may repeat this procedure to

generate as many totals as you like.

Note that the Add Sums button is only enabled for reports involving monetary valuations, not

those involving incidence estimates. Typically, incidence estimates should not be summed across

endpoint groups (for example, Mortality and Hospital Admissions, Respiratory). Within endpoint

groups, incidence estimates can be summed - you may do this in Aggregation, Pooling and
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Valuation Configurations. Once results are in monetary values, however, summing across

endpoint groups can be useful in calculating aggregate benefits.

8.2.5 Aggregated Valuation Results

The Aggregated Valuation Results report presents valuation results aggregated to the level

you specified at the Advanced button when creating the APV configuration file. Note that the

aggregation levelspecified for the valuation does not need to be same as that for the incidence

aggregation. In the example below, the valuation results are aggregated to the three so-called

"report regions," so in the Row field, you can see the value changing from 1 (East), 2 (West

minus California), and 3 (California). (The underlying incidence data in this example were

aggregated to the state level.).

As with the Valuation Results Report, you can use the Add Sums button to create totals with

various valuation results.
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8.2.6 Pooled Valuation Results

The Pooled Valuation Results report presents valuation results aggregated and pooled to the

level you specified at the Advanced button when creating the APV configuration file. As with

the Pooled Incidence Results Report, fewer Pooled Valuation Method Fields are available,

because only enough fields are retained to uniquely identify individualresults. As with the

Valuation Results Report, you can use the Add Sums button to create totals with various

valuation results.
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8.2.7 QALY Results

The QALY Results report gives you the opportunity to examine the QALY results for the

pooled and aggregated incidence results. In the example below, the incidence results were

aggregated to the state level, so in the Column field, you can see the value changing from , so the

values in the Column field vary (e.g., 1=Alabama, 4=Arizona). You can use the Add Sums

button to create totals with various QALY results.
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8.2.8 Aggregated QALY Results

The Aggregated QALY Results report presents QALY results aggregated to the level you

specified at the Advanced button when creating the APV configuration file. (Recall that you can

aggregate incidence, valuation, and QALY results to different levels.) In the example below, the

QALY results are aggregated at the national level, so the values in the Column and Row fields

are set to 1. As with the Valuation Results Report, you can use the Add Sums button to

create totals with various QALY results.
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8.2.9 Pooled QALY Results

The Pooled QALY Results report presents QALY results aggregated and pooled to the level

you specified in the Aggregation, Pooling, and Valuation configuration file. As with the other

valuation reports, you can use the Add Sums button to create totals with various valuation

results.
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8.2.10 Export Incidence, Valuation, & QALY Reports

To export an Incidence, Valuation, & QALY Results report, first choose the Grouping

Options, Display Options, and Advanced Options that you want to see in your report. Then

choose the variables that you want to see in your report. Finally, choose the Save option from

the File drop-down menu.

This will bring up a window allowing you to name the file you want to save. Note that by default

BenMAP will export the file to the Reports folder. Carefully name the file that you are generating

so that you willrecognize it in the future!
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8.3 Raw Incidence - from CFGR file

The Raw Incidence Results report gives you the opportunity to examine the results of each

health impact function at the grid-cell level, county, state, or national level. It is based on the

Configuration Results file (with the *.cfgr extension), and is otherwise identical to the Incidence

Results Report generated from the Aggregation, Pooling, and Valuation results file (with the *.

apvr extension). BenMAP includes both versions to increase the reporting flexibility for users. (

See the section on Incidence Results reports for a description of the options available for this

report type.)
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8.3.1 Export Raw Incidence

To export the Raw Incidence Results report, first choose the Grouping Options, Display

Options, and Advanced Options that you want to see in your report. Then choose the variables

that you want to see in your report. Finally, choose the Save option from the File drop-down

menu.

This will bring up a window allowing you to name the file you want to save. Note that by default

BenMAP will export the file to the Reports folder. Carefully name the file that you are generating

so that you willrecognize it in the future!
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8.4 Audit Trail Reports - from all BenMAP files

The Audit Trail Reports provide a summary of the assumptions underlying the various parts of

the analysis. You may generate an audit trail with any of the file types used in BenMAP: Air

Quality Grids (with the “.aqg” extension), Incidence Configurations (with the “.cfg” extension),

Configuration Results (with the “.cfgr” extension), Aggregation, Pooling, and Valuation

Configurations (with the “.apv” extension), and Aggregation, Pooling, and Valuation Results (with

the “.apvr” extension). The report itself has a tree structure that lets you easily find the

information that you are seeking. Below is an example of an Audit TrailReport for a

Aggregation, Pooling, and Valuation Results file.

Note that each successive step in an analysis contains a summary of its assumptions, and those of

each previous step in the analysis. For example, in the below report the assumptions of the

Configuration Results file used to generate the APV Results are present in the Configuration

Results node. Similarly, the assumptions of both the baseline and control air quality grids are

present under the Configuration Results node.
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8.4.1 Export Audit Trail

Note that you can export Audit Trail as a text file . Each branch in the tree structure will be

converted to a tab in the exported file, allowing for easy viewing in Excel, WordPad, and a

variety of other programs. Simply click on the Export button, name the file, and click Save.

Note that by default BenMAP will export the file to the Reports folder. Carefully name the file

that you are generating so that you willrecognize it in the future!
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8.5 Questions Regarding Creating Reports

When creating reports from *.cfgr and *.apvr files, why do some of the variables that I

have checked appear as blanks?

When results are pooled together, some of the identifying information for individual health impact

functions gets lost. For example, when pooling together endpoints within the same endpoint

group, such as “HA, Pneumonia” and “HA, Chronic Lung Disease” (both within “Hospital

Admissions, Respiratory”), there is no longer a unique endpoint name for the pooled result. So,

BenMAP would leave the endpoint name blank.

How do I export my results?

Identify the type of report that you want to create, then refer to the sections in this chapter on

exporting reports.

How do I determine what the Column and Row refer to?

The Column and Row are variables designed to uniquely identify each grid cell in the grid

definition. In the case of the County grid definition, the Column refers to the state FIPS code
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and the row refers to the county FIPS code. One way to get a good sense of the Column and

Row variables is to create a map (discussed in the next chapter) and then view whether

particular Column and Row variables occur in the map.

In the report step, why is it that when I click the Done button the report is not saved?

You have to go to File menu and choose Save.
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CHAPTER 9

GIS/Mapping

In this chapter...

Learn about BenMAP’s mapping functions.

Map configuration inputs like air quality grids.

Map incidence and valuation results.

Map different variables and modify the map display.
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Mapping can be quite useful to check the quality of your work, and, of course, mapping provides

a useful platform for presenting your results. BenMAP features powerful, integrated mapping

capabilities which you can access at several points in the model. The main Mapping / GIS tool,

available via the Tools drop-down menu in the main window, allows you to map various types

of files and data associated with an analysis, including air quality grid files, monitor data,

population data, and valuation results.

In addition, at several points from within the program, you can view data being used in an

analysis. You can view maps when filtering monitor data, creating air quality grids, and when

creating configuration files (with the *.cfg extension). In particular, when creating a configuration

file, you have the option to map your baseline and control air quality grids, as well as to map the

difference between the two (the "delta"), which is used in health impact functions. The "delta" is

typically a key input to your calculations, and this is the only way to map it -- mapping of the

delta is not available from the Tools menu.

9.1 Overview of Mapping

You can access the some of the mapping capabilities in BenMAP by going to the Tools dropdown menu, and choosing the GIS/Mapping.
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In addition to the Tools menu, BenMAP provides several places where you can map the input

data that you are using for a particular analysis. Below, we provide examples of both types of

mapping. First, we discuss the display options and taskbar buttons which can be used in both

cases.

9.1.1 Display Options

After choosing the file or data type that you want to map, you must select the variable you want

to map, and how you want it displayed. Double-click on the layer name on the left-hand side of

the window. A small box will appear with the Display Options for the layer.
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This box allows you to set the following options:

Variable. The drop-down menu lists all of the variables contained in the layer you chose. Select

the variable that you want to view. Note that each layer can only show one variable at a time.

Start Color and End Color. These are the colors that represent the gradations in the selected

variable. BenMAP uses 10 equal-sized increments for the variable, with a gradual transition

between the Start Color and the End Color. To change either color, click on the colored

square and select a new color.

Default Color. This is the color is used for values that fall outside of the range of the Min

Value and the Max Value.

Min Value and the Max Value. These options define the range of the selected variable and are

automatically set to the minimum and maximum of the variable. However, you may wish to enter

other minimum and maximum values, such as in the case where there is one outlier that is

dominating the color scale. For example, some urban grid-cells such as those in Los Angeles

have extremely large values. Since BenMAP creates 10 equal-sized increments between the

minimum and the maximum, it is not uncommon to have most of the grid values in just a few of the

increments. If you change the minimum and/or maximum values to exclude outliers from the color

scale, the outliers will then appear in the Default color.

Decimal Digits. This specifies the number of digits used in displaying the results. The default is

two decimal points. However, for variables with values much smaller than one, you will want to

increase the number of digits that you display.

Grid Outline. This appears as a fine white line around the border of each grid cell. For some

grid types, this outline can make the map too complicated, and you may want to uncheck this

option. An alternative is to use the Reference Layer drop-down window (described in the

Taskbar Buttons section), which lets you add a blue outline for the nation, states, or counties.

Note that if you have specified point data, such as monitors, the Display Options window
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includes the ability to set the size of the points on the map. The Start Size, End Size, and

Default Size have a default value of 75, 150, and 120, respectively, which you can then edit, if

desired. For example, if you want smaller values to be all the same size, you might set all of the

values to 100.

9.1.2 Taskbar Buttons

There are a number ofstandard buttons used in most map viewing programs which you can use

to navigate and customize the map view.

Open a file. Use this to open maps for viewing.

Save active layer to file. Creates a shape file for use in other map-viewing

programs. The active layer is the topmost visible layer.

Zoom to full extent. Allows you to view the whole map that you are viewing.
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Increase zoom. Allows you to zoom in.

Decrease zoom. Allows you to zoom out.

Select a region for zooming. Allows you to select a region to view.

Drag mode. Allows you to manually move the map by clicking and dragging.

Click to display info for the cell under the mouse. Allows you to display

information (all the variable values) for individual cells or points by clicking on them.

Build Query. Allows you to view grid-cells that satisfy certain criteria. Hitting the

Execute button will produce a map of the cells that meet the criteria that you have

specified. Below is an example of how you might use this function.
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Layer Statistics. Provides information about the active layer. In the Fields section

simply choose the variable of interest, and BenMAP will display statistics and

sample values for that variable.

The drop-down menu provides alternative projections for displaying the data, with the default set

to Albers Equal Area Conic.

9.2 Mapping from the Tools Menu

To access the main mapping capabilities within BenMAP, go to the Tools drop-down menu, and

choose the GIS/Mapping option. A blank window will appear, with buttons at the top for
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managing files and navigating the map. To see the name of each button, simply hold the cursor

over it.

Use the Open a file button in the top-left corner to choose the file (or other type of data) that

you want to view. All of the options are straightforward, though some require a few more steps

than others. Below we give some step-by-step examples. See Table 9-1 for a brief description

of each type.

Table 9-1. Description of Mapping File Types

File Type Description Source File Used

Air Quality Grid Annualsummary values (e.g., daily average, daily

maximum, or other metric where available) within each

grid cell.

Air quality grids created by

BenMAP (*.aqg)

APV

Configuration

Results

Presents the full range of incidence, valuation, and

QALYresults. The results are available at the level

of aggregation of the air quality grids. In addition

aggregated results are available, as well as both

aggregated and pooled results.

Aggregation, Pooling and

Valuation Results files (*.apvr)
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File Type Description Source File Used

Configuration

Results

Presents the incidence estimate, change in air

pollution, and population used in the health impact

calculation.

Configuration Results files (*.

cfgr)

Modeling Data

Monitors Annualsummary values (average, median or other

metric where available) for each monitor.

Select monitors fromthe

BenMAP library or use your

own by loading a file.

Population Total population, and cross-tabulations of age and

gender with race/hispanic origin within each grid cell

(for the Year and GridType you specify).

Internal BenMAP population

files and projections, based on

Census block-level data.

Each map that you select and load into the GIS viewer will appear on the left-hand side of the

window as a layer. All of the options to create maps are straightforward, though some require a

few more steps than others. Below we give some step-by-step examples of each type.

9.2.1 Air Quality Grids

Air quality grid maps present annualsummary values of air pollution metrics (e.g., daily average,

daily maximum, or other metric where available) within each grid cell. After choosing the air

quality grid that you want to map, double-click on the layer name on the left-hand side of the

window. A small box will appear with the Display Options for the layer. Choose the metric

that you want to map. When mapping an air quality grid with county-level PM2.5 values, the

available default metrics are D24HourMean and QuarterlyMean.
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If you keep the default display options, the maps can often look a little complicated or busy. For

example, when mapping county-level level PM2.5levels with the default display options you might

get a map looking like the following:

Unchecking the Grid Outline box, reducing the Decimal Digits from 2 to 0, and using a State

Reference Layer can simplify the map, as follows:
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9.2.2 APV Configuration Results

When mapping APV Configuration results, you can generate nine different types of maps:(1)

Incidence, (2) Aggregated Incidence, (3) Pooled Incidence, (4) Valuation, (5) Aggregated

Valuation, (6) Pooled Valuation, and (7) QALY Results, (8) Aggregated QALY Results, and

(9) Pooled QALY Results. All of the options use the information contained in the Aggregation,

Pooling and Valuation Results files (*.apvr), which you can create using the Aggregation,

Pooling, and Valuation button on the main window.
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After you have chosen the results you want to map, a window will appear with identifiers for each

of the variables. On the right-hand side is the GIS Field Name. Mapping programs typically

have a default length of 10 characters for variable names, so it is necessary to specify your own

names or to use the default name given in the GIS Field Name column.
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Note that if you used more than one pooling window (described in Aggregation, Pooling, and

Valuation chapter), then BenMAP will create one layer for each pooling window. Also, note that

the "aggregated" results are aggregated to the levelspecified in the APVR file. If results were

aggregated to the national level, the US map will only show one color.

For each of the three valuation options (Valuation, Aggregated Valuation, and Pooled

Valuation) and for each of the three QALY options (QALY, Aggregated QALY, and Pooled

QALY), a second window appears that allows you to specify variables that you want to add

together. (If you have selected Incidence, Pooled Incidence, or Aggregated Incidence,

BenMAP will go directly to loading the new layer.)
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Click on the Add Sum button. Then in the far-right column, Include in Total, check the

variables that you want to include in the total. In the lower-left corner, give a name (no longer

than 10 characters) for the total in the Valuation Sum Identifier, and then choose whether to

use a Dependent or Independent sum. If you choose the latter, then you also need to choose

the number of Monte Carlo draws. To finish, click OK. You may repeat this step as many times

as desired.
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9.2.3 Configuration Results

The Configuration Results (*.cfgr file) are a subset of the APV Configuration Results (*.apvr file).

Specifically, they capture incidence results calculated at each grid cell. In those instances when

you have not yet generated an APV Configuration Results file, mapping your Configuration

Results file may be useful, particularly as a quick check that your results seem reasonable.

As in the case with mapping APV Configuration Results, after choosing the file you want to view,

you then need to name your variables. The layer will then be displayed, where you can then

choose particular variables to map by double-clicking on the layer name on the left-hand side of

the window. (It is not uncommon to have many variables to choose from, so you may need to

take some care in developing a variable naming convention that makes sense to you!)
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After choosing the rest of your display options, you can then view your map. Note that it is fairly

common for maps of Configuration Results to be dominated by a few regions, such as southern

California, with many other parts of the country indistinguishable from each other.

By changing the Max Value, End Color, and Default Color, as follows, you can develop a

map that provides more information regarding impacts in other parts of the country, aside from

those with the largest impacts.
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In this example, the areas with the largest impacts appear in bright red (i.e., southern California,

Phoenix, Houston, Chicago, and a handful of other locations). The areas with the next largest

impacts appear in dark green.

You might also use the Create a Query button to focus on your results of interest. For

example, if wanted to focus on those areas (in this instance counties) with at least one hundred

deaths as calculated with our "Laden" function, then we would first click the Create a Query
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button and build the following query.

Clicking OK will complete the query.

Running the query again on this hypothetical, we might to focus on just those areas with at least

200 deaths. The exact type of map that you want generate will of course depend on your

particular set of results and analytical goals. The point is that mapping capabilities in BenMAP

can provide some interesting, easy-to-use options.
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9.2.4 Modeling Data

You can map any modeling data that you planning to use to create an air quality grid (*.aqg file),

as long as you have a grid definition and a pollutant definition that matches your data. Choose

Modeling Data from the Open a file drop-down menu on the BenMAP GIS window. This

will bring up the Select Model Data to Map window. Click the Browse button to find the file

you want to map, and then choose the appropriate Grid Type and Pollutant from the dropdown menus.

Click OK, and then you will be ready to map. Double-click on the layer in the left panel, and this

will bring up the Display Options window, so you can choose the variable you want to map and

how you want it to look. In this example, the Grid Outline has been unchecked and the

Decimal Digits has been set to 0.

To add context to the map (because the Grid Outline is unchecked), you will likely want to use a

Reference Layer from the drop-down list.
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9.2.5 Monitoring Data

You can map monitor locations and concentration levels using the Monitors option. Click the

Open a file button and select Monitors from the drop-down menu. The Select Monitors

window will appear in which you select the Pollutant and then load monitor data for that

pollutant from either an existing library (in which case you choose the Library tab) or from a

dataset outside of BenMAP (in which case you would use either the Database Columns,

Database Rows, or Text File tabs, depending on the file format). The same monitor data can

be used many times, so it is generally a good idea to create a library -- see the Loading Data

chapter for how to do this.
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If you map data from the BenMAP library, you need to specify the Monitor DataSet and the

Monitor Library Year. In addition, you can change the default monitor filter options.

Generally speaking, this is not necessary. However, if desired, click the Advanced button. This

will bring up Filter Monitors window, where you can make whatever adjustments are deemed

necessary to the default options. (Note that the filtering is hardcoded into BenMAP, so whatever

filter options you choose will not persist to any future mapping or use of monitor data.)
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Click Go! after you have made your selections. This will bring you back to the Select Monitors

window. Click OK again, and BenMAP will generate a monitor layer that you can view.
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In this map, each red square is a monitor location. To see the monitor values displayed with

colors varying by the level of the monitor, double-click on the layer on the left side of the map,

and follow the steps outlined above for setting the display options. Values shown using the

Monitors mapping option are annual average values (μg/m3for PM and ppb for ozone).
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9.2.6 Population Data

You can map the population data that you have already loaded into BenMAP. Choose

Population from the Open a file drop-down menu on the BenMAP GIS window. This will

bring up the Select Population Data window. Choose the Population DataSet that you want

to map and the particular year from the Population Year from the drop-down menus.

Click OK, and then you will be ready to map. Double-click on the layer in the left panel, and this

will bring up the Display Options window, so you can choose the variable you want to map and

how you want it to look. In the example below, the Grid Outline has been unchecked and the

Decimal Digits has been set to 0.
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To add context to the map (because the Grid Outline is unchecked), you will likely want to use a

Reference Layer from the drop-down list.

9.2.7 Mapping Multiple Layers of Data

It is possible to map multiple layers simultaneously. The layer that you have opened most

recently appears on the top of the list, and in the map its values lie on top of the other layers. By
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right-clicking on any given layer, you can move its position within the list (select Move Up or

Move Down). For example in the following screenshot, we move the Control Grid layer to the

top by right-clicking on the Control Grid layer and choose Move Up (or you can right-click on

the Delta layer and choose Move Down).

By checking the box to the left of the layer name, you can turn a layer’s visibility off and on. For

instance, if you want to see the second layer in the list, simply un-check the box next to the first

layer in the list. The second layer will then be on top and be visible, as in the example below.
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9.3 Viewing Maps in a BenMAP Analysis

In addition to the Tools menu, BenMAP provides several places where you can map the input

data that you are using for a particular analysis. We provide examples of these mapping options

below.

Air Quality Deltas. When creating your Configuration (*.cfg file), you can map the baseline

minus the controlscenario to get a sense of where the major impacts are occurring in your

analysis.

Air Quality Grids. When creating an air quality grid (*.aqg file), you can map what the air

quality grid will look like. And in the case of air quality grids based on monitor data, you can

map the monitors as well.

Air Quality Monitors from the Advanced Monitor Filter. When filtering monitors, you can

map the monitors to see the impact of the filtering.

Monitor Rollback Inputs & Outputs. When creating a monitor rollback air quality grid (*.aqg

file), you can map both the baseline and control air quality grids and the monitor data used.
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9.3.1 Mapping Air Quality Deltas

When developing a Configuration file (with the *.cfg extension), you can map the baseline and

control air quality grids, as well as the difference between the two. In the Configuration Settings

window, after choosing the grids that you want to use, click on the Map Grids button.

This will generate layers with the delta (baseline minus control), baseline, and control values for

each grid cell. You can then double-click on the layers in the left panel to set your display

settings.
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9.3.2 Mapping Monitor Direct Air Quality Grids

When generating air quality grids there is an option to map the grid and the data used to create it.

For example, when generating a Monitor Direct grid, click on the Map button on the Monitor

Direct Settings page.
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BenMAP will then generate the grid, generate a map with both the monitors and the monitor data

interpolated to the grid cells. You can then use the display options to generate the map you

desire.
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9.3.3 Mapping Air Quality Monitors from the Advanced Monitor Filter

You can access mapping through the advanced monitor filter when generating air quality grids.

Click on the Create Air Quality Grids button and choose either Monitor Direct, Monitor and

Model Relative, or Monitor Rollback. On the Settings page, click on the Advanced button,

which brings up the Advanced Options page.
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Then click on the Custom Monitor Filtering button, which will bring up the Filter Monitors

window.

Choose your filtering options and click Go! Then click the Map button. An initial map appears
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with each monitor location identified by a red square. To immediately provide some context, you

may want to choose, say, the States reference layer.

Double-click on the layer to the left of the map. Choose the display options that you would like,

and then click OK.

A map with your display options will then appear. You may then use the various mapping

options available, such as zooming in to an area of interest, getting information on particular

monitors, querying the map to display monitors with certain characteristics, and saving the map as

a shapefile and viewing it in another map viewer.
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9.3.4 Mapping Monitor Rollback Inputs & Outputs

When using the Monitor Rollback option, you can generate two types of maps. First, you can

map the inputs to the rollback – the monitor data and any spatial adjustment file that you may

have used. Second, you can map the inputs and at the same time map the grid based on these

inputs. Start to create a monitor rollback grid, and when you get to the Monitor Rollback

Settings:(3) Additional Grid Settings, make your selections for the Select Interpolation Method,

Select Scaling Method, Grid Type, and whether you want to Make Baseline Grid (in addition

to Cotnrol Grid).
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In this example, BenMAP will produce a map with both the inputs, as well as the baseline and

control grids. (The baseline grid gets created because we checked the box for Make Baseline

Grid (in addition to Control Grid). Click on the Map button. This will bring up the BenMAP

GIS window.
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As before, you may use the various display options to generate the map that you desire. Recall

that the topmost layer lies on top of the other mapped layers. To change the ordering of the

layers, simply right-click on the layer that you want to move (up or down).

9.4 Questions Regarding Mapping

Why is the Open a File menu button disabled?

This happened because you did not use the Tools button and choose Mapping / GIS. When

viewing maps while generating air quality grids, filtering monitor data, and other activities within

BenMAP you do not have access to other types of maps. This is to avoid too many competing

activities.

All of the mapped values have the same color. How do I avoid this?

This can happen when the values are extremely small and you have not specified a sufficient

number of decimal points. Go to the Display Options window, and change the Decimal Points.

This can also happen when one of the grid cells is an outlier, with either a very low or very

small value. You can go to the Display Options window, and change either the Min Value or

the Max Value. Finally, this can happen if you have mapped national data. In this instance, you

should expect all areas to have the same color, since there is only a single national number for

display, such as when mapping national results, or mapping incidence rates that do not vary by

region (e.g., MRAD incidence rate).
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Can I map air quality for individual days?

No. BenMAP only maps annual averages. In the case of hourly metrics, such as the one-hour

daily maximum for ozone, BenMAP will map the average of the metric for the available days.

Can I print maps in BenMAP?

No. BenMAP does not currently allow printing directly from the program. However, you can

export shapefiles, and then read these shapefiles into a program that does support printing, such

as ArcView.

Why is my map all grey or all black?

You may need to select a variable or uncheck the grid outlines checkbox. You can do this by

double-clicking on the layer name.

How do I clear a query?

When using a query, you limit the areas that get displayed. That is, after using a query, some

areas will be white and other areas will have colors that you have specified. If you want to be

able to display all areas simply run a query that includes all areas. For example, if you run a

query such as choose all areas with population greater than or equal to zero, then you will get a

complete set of results.

What are the units?

The units that get displayed depend on the particular map that you are creating. For example, if

you are mapping PM2.5levels, then the units will be in micrograms per meter cubed. If you are

mapping ozone, the units will be in parts per billion. If you are mapping mortality or some other

type of health effect, then the units will be numbers of cases. If you are mapping valuation

results, then the results will be in dollars.

How do I change which layer is on top?

When mapping multiple layers, the top-most layer is the active layer. To change the position of a

layer, simply right-click on it, and you can move it up or down. This is discussed in the section

Mapping Multiple Layers of Data.
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CHAPTER 10

Tools Menu

In this chapter...

Learn about the options in the Tools menu.
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The Tools menu, available on the main BenMAP screen, provides access to a number of

specialized applications. We list them below and give a brief description. Note that other

sections of the manual have already covered several of them, such as GIS/Mapping, so we

merely list them here and point you to the appropriate section.

Air Quality Grid Aggregator. You can change an air quality grid based on one grid definition

to another grid definition, using a simple spatially-weighted average approach. This option is

described below.

Model File Concatenator. This is a highly specialized option that allows you to combine air

quality modeling data text files and produce a smaller, more efficient, binary file. This option is

described below.
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Database Export. Export entire databases (all Setups), individual Setups, and parts of individual

Setups (e.g. all GridDefinitions, or individual health impact Function DataSets). This functionality

can be used to share your data with other users, as well as to allow you to move databases

between computers. This is described in the Export Setups section of the Loading Data chapter.

Database Import. Import entire Setups or parts of individual Setups. This option is described

in the Import Setups section of the Loading Data chapter.

Export Air Quality Grid. Generate a data file with all of the data in an air quality grid. This

option is described below.

GIS/Mapping. Map various types of files and data associated with an analysis, including air

quality grid files, monitor data, population data, and valuation results. See the GIS/Mapping

chapter for details.

Modify Setup. BenMAP encapsulates in a single dataset, called a "Setup," all of the data

needed to run analyses for a particular geographic area. Using the Modify Setup option, you add

to, modify, and delete setups. See the Loading Data chapter for details.

Neighbor File Creator. Identify the monitors and weights used in the interpolation process

when creating air quality grids. This option is described below.

One-Step Setup. Allows you to set the CFG and APV files, as well as currency year, for the

One-Step analysis. This option is described below.

10.1 Air Quality Grid Aggregator

Using the Air Quality Grid Aggregator you can change an air quality grid based on one grid

definition to another grid definition, using a simple spatially-weighted average approach.

To start, choose Air Quality Grid Aggregator from the Tools drop-down menu. This will bring

up the Air Quality Grid Aggregator window. Click the Browse button to find the air quality

grid that you want to change and then use the drop-down menu in the Aggregation Grid panel to

identify the new grid definition that you want to use. (For example, you might want to aggregate a

county-level air quality gird to state-level.) Click OK when done.

This will bring up the Save Aggregated Air Quality Grid window, where you specify the name of
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the file you are creating and its location. After the file is created, BenMAP will take you back to

the main BenMAP screen. You can then use the new file just as you would any other air quality

grid.

Below is an example of going from a county-level PM2.5air quality grid to a state-level PM2.5air

quality grid. The county-levelfile looks as follows:

The state-levelfile looks as follows:
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The scale in the left panel is set to the same values, so you can compare the two maps more

easily. One key thing to note is that there is more variation in the county-levelfile. For example,

in California the county-levelfile has colors ranging from yellow to dark red, while in the statelevelfile, California is a solid orange color. This pattern is expected, because BenMAP is just

taking a simple spatially-weighted average of the data.

10.2 Model File Concatenator

The Model File Concatenator is a specialized tool that allows you to combine air quality

modeling data text files and produce a smaller, more efficient, binary file. For example, ozone

modeling files often are generated one day at a time and split between eastern and western

modeling domains, so the input modeling files to BenMAP might consist of dozens of files. The

Model File Concatenator combines these files into a single file that can then be easily used in

BenMAP.

You can use this tool if you have data for both a western and eastern domain. In case you want

to use this toolfor a single modeling domain, then just fill in the options for the western domain,

and ignore the eastern domain.

To start, choose Model File Concatenator from the Tools drop-down menu, the Model File

Concatenator window will appear. There are a number of options that need to be specified.

We describe each below.
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The Input Files options are Row/Column and Column/Row. The choice depends on how the

data files are formatted. Below is an example of what a typical datafile looks like. In this

example, the first column specifies the Row and the second column specified the Column. (In

other cases, this might be reversed.)
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The Column Delimiter options are White Space and Comma. In the example above, White

Space is the correct choice.

The BenMAP health impact functions for ozone generally assume that the data is in parts per

billion (ppb). If your model data happens to be in parts per million (ppm) then check the box

Convert model values from parts per million to parts per billion.

Click on the Select Western Domain Files button and choose the files that you want to load.

Note that each file represents a different day of modeling, and there can be any number of days.

After selecting the files for the western domain, then specify the number of Columns and Rows

in each file by filling in the Lower Bound and Upper Bound boxes. For example, if the file has

122 columns and 188 rows in the western domain, and 213 columns and 192 rows in the eastern

domain, then boxes would be filled out as follows:
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In this example, BenMAP willrenumber the eastern domain so that it follows the numbering in the

western domain. There will be 335 columns and 380 rows numbered consecutively.

When you have completed filling in the options, then click the Go! button. BenMAP will begin

processing the files. When the processing is complete, the Specify an Output File window will

appear. Provide a name for the file in the File Name box. Note that BenMAP automatically

uses a *.model extension for this type of file.
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After the file is saved, BenMAP brings you back to the Model File Concatenator window. You

can reuse the tool by choosing a new set of western and eastern domain modelfiles and making

the other appropriate selections. When you are done using this tool, you can just click the Done

button.

10.3 Export Air Quality Grid

The Export Air Quality Grid tool generates a data file (*.csv) with all of the data in an air

quality grid. After choosing Export Air Quality Grid from the Tools drop-down menu, the

Export Air Quality Grid window will appear. Click the Browse button to choose the air

quality grid that you want to examine.
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Click OK after you have selected your file. This will bring up the Reports window. Use the

Save in drop-down menu to choose the director where you want to save your file. And in the

File name box, type in the name of the file.

To help keep track of what you are doing, you might want to name the file the same thing as your

air quality grid, or something very similar. (If you name it the same as your air quality grid, you

can always distinguish the two files with the extension. An air quality grid has a *.aqg extension

and the file you are generating here has a *.csv extension.)

When done click the Save button. You can view the files you have created with any database

viewer. For each Metric and Seasonal Metric, you can see the actual values. In addition, you

can see the Statistics calculated for each. In the example below for a county-level PM2.5air
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quality grid, you can see in the first county (Column = 1, Row = 1) that the Mean of the

D24HourMean metric is 17.24, with a Median of 15.1, a Maximum of 52.1, a Minimum of

0.4, and finally the Sum of the D24HourMean values is 6292.

Note that the files may well be quite large, with more lines than can be completely read by a

program like Excel, which can read only the first 65,000, or so, lines of data. With large files,

you might want to use a database program like SAS. Alternatively, these files can also be read

by simple text editors, which do not have the 65,000 line limitation of Excel.

10.4 Neighbor File Creator

The Neighbor File Creator tool generates a file that specifies the monitors used to estimate air

quality in each grid cell of a Monitor Direct, Monitor and ModelRelative, or Monitor Rollback

air quality grid.

To start, choose Neighbor File Creator from the Tools drop-down menu. This will bring up
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the Create Neighbor File window. Click on the Browse button, and find the air quality you want

to analyze.

After finding the file, click the OK button. BenMAP will then ask you to provide a name for the

file you want to create. BenMAP will then save the file as a text file that can be easily viewed in a

variety of programs, including WordPad. An example is as follows:

The first two columns specify the Column and Row variables for each grid cell. In the example

above, you willsee that Column = 110 and Row = 60 are repeated seven times, indicating that

seven different monitors were used to estimate air quality at this grid cell. The third column gives

the monitor identifier. The fourth column the weight used in the air quality calculation (e.g.,

Voronoi Neighbor Averaging). And the fifth column gives the distance (in kilometers) from the

monitor to the center of the grid cell.

Note that if an air quality grid was created using the Closest Monitor option, then only a single

monitor is used for any given grid cell. As a result the resulting neighbor file only has three
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columns, the Column and Row identifier and the monitor used.

10.5 One-Step Setup

The One-Step Setup tool lets you specify the CFG and APV files (and currency year) for the

One-Step Analysis.

To start, choose One-Step Setup from the Tools drop-down menu. This brings up the OneStep Setup Parameters window. From the Pollutant drop-down menu, choose one of the

pollutants that you have already specified. The U.S. setup that comes with BenMAP already has

defaults for PM2.5and Ozone. If desired, you can change the defaults.
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